diff --git a/.cursor/rules/meva-pipeline-quality.mdc b/.cursor/rules/meva-pipeline-quality.mdc new file mode 100644 index 0000000..77ad4e1 --- /dev/null +++ b/.cursor/rules/meva-pipeline-quality.mdc @@ -0,0 +1,14 @@ +--- +description: Quality rules for MEVA QA generation and validation code +globs: meva/**/*.py,meva/**/*.sh +alwaysApply: false +--- + +# MEVA Pipeline Quality + +- Prefer deterministic symbolic QA generation. Naturalization may rephrase questions, but it must not compute or change answers. +- Use Kitware YAML as the primary annotation source when available; treat NIST/VSet7/MEVID as validation or enrichment sources unless code explicitly says otherwise. +- Avoid questions that rely on unsupported identity assumptions. Annotation actor IDs are per-camera and can switch across cameras/time. +- Counting logic must deduplicate multi-camera views of the same event; do not count each camera observation as a separate real-world event. +- Entity descriptions should prefer the richest verified source available, then fall back conservatively. Avoid leaking vague placeholders like `unknown`. +- Validation should be category-specific: temporal ordering, spatial distance/proximity, event uniqueness, distractor quality, and answer-option separation have different failure modes. diff --git a/.cursor/rules/meva-project-context.mdc b/.cursor/rules/meva-project-context.mdc new file mode 100644 index 0000000..602837c --- /dev/null +++ b/.cursor/rules/meva-project-context.mdc @@ -0,0 +1,12 @@ +--- +description: Core MEVA multi-camera QA project context and data boundaries +alwaysApply: true +--- + +# MEVA Project Context + +- The MEVA work is a multi-camera video QA pipeline for surveillance slots in the NeuS-QA benchmark. The active project root is `Multi-Camera-Adi/`; home-root scratch files are not source of truth. +- Treat `/nas/mars/dataset/MEVA/` and `/nas/mars/dataset/MEVA/meva-data-repo/` as external dataset sources. Do not copy large raw videos, annotations, or PDFs into the repo. +- Keep generated or bulky artifacts out of git unless they are curated examples for showcase purposes. Prefer `meva/examples/` for small representative outputs. +- Preserve exact answer provenance: QA answers should be computed from symbolic annotations / scene graphs, with LLMs used only for optional text naturalization. +- Never commit `.env`, API keys, local caches, virtual environments, model weights, raw videos, or NAS-derived bulk exports. diff --git a/.cursor/rules/project-history-and-postmortem.mdc b/.cursor/rules/project-history-and-postmortem.mdc new file mode 100644 index 0000000..f6d8f45 --- /dev/null +++ b/.cursor/rules/project-history-and-postmortem.mdc @@ -0,0 +1,11 @@ +--- +description: How to use preserved session logs and project history +alwaysApply: true +--- + +# Project History And Postmortem + +- Historical session logs live under `docs/project-history/`. Use them as evidence for postmortems, resume bullets, and project storytelling, not as active implementation instructions. +- When summarizing project impact, distinguish completed production behavior from abandoned V4-V9 experiments and intermediate QA outputs. +- Prefer concise synthesized lessons over copying old session-log process rules. The old `.github` logging workflow is archived, not active. +- For recruiter-facing material, emphasize the final V10 MEVA pipeline, deterministic QA design, cross-camera reasoning, validation quality, and engineering tradeoffs. diff --git a/.gitignore b/.gitignore index 1730c11..7213a5e 100644 --- a/.gitignore +++ b/.gitignore @@ -181,7 +181,6 @@ cython_debug/ env *.pyc output/ -data/ lm_cache .idea build @@ -213,7 +212,7 @@ LLaVA/ temp/ InternVL/ logs/ -data/ +/data/ llava-video/ Video-MME/ VATEX/ diff --git a/docs/project-history/ARCHIVED_ROOT_MANIFEST.json b/docs/project-history/ARCHIVED_ROOT_MANIFEST.json new file mode 100644 index 0000000..48db724 --- /dev/null +++ b/docs/project-history/ARCHIVED_ROOT_MANIFEST.json @@ -0,0 +1,6578 @@ +[ + { + "path": "cleanup_local.sh", + "bytes": 1360 + }, + { + "path": "code/view_cluster.ipynb", + "bytes": 17376 + }, + { + "path": "data_old/activity_slots.txt", + "bytes": 66090 + }, + { + "path": "data_old/annotated_activity_slots.txt", + 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`archived/` directory was saved as `docs/project-history/ARCHIVED_ROOT_MANIFEST.json`. + +Deleted/recreated-on-demand from root after preservation: + +- Python virtual environment, pip cache, VS Code server state, large PDFs, model weights, validation videos, and one-off scratch folders. +- Root `data/`, `docs/`, `output/`, `.github/`, and `archived/` are considered non-canonical after this preservation pass. + +Active project rules moved to `.cursor/rules/`; old `.github` instructions are historical only. diff --git a/docs/project-history/DATASET_OVERVIEW.md b/docs/project-history/DATASET_OVERVIEW.md new file mode 100644 index 0000000..760ad13 --- /dev/null +++ b/docs/project-history/DATASET_OVERVIEW.md @@ -0,0 +1,304 @@ +# Neuro-Symbolic Multi-Camera Video Reasoning — MEVA Dataset Pipeline + +> **Purpose**: Comprehensive project summary for resume bullet generation. +> **Lab**: SWARM Lab, UT Austin ECE (PI: Sandeep Chinchali) +> **Project**: Multi-Camera Video QA Benchmark / NeuS-QA +> **Role**: Research Engineer — MEVA dataset pipeline (1 of 4 datasets in the benchmark) + +--- + +## Research Context + +### The Problem: VLM Context Bottleneck in Multi-Camera Networks + +Vision-Language Models (VLMs) like GPT-4V, Gemini, and InternVL face a fundamental **context bottleneck** when reasoning about multi-camera video: typical 32–64 frame input limits make it impossible to ingest all streams from a camera network without aggressive downsampling, destroying fine-grained temporal cues and cross-camera relationships. Existing multi-camera datasets (NuScenes, Waymo, EgoExo4D) focus on single-view perception or basic object detection — none test **complex, logic-driven QA that requires cross-camera causal and temporal reasoning** (questions that cannot be answered from any single camera view alone). + +### The Solution: NeuS-QA (Neuro-Symbolic QA) + +The SWARM Lab's **NeuS-QA** framework (arXiv:2509.18041) addresses this by combining classical formal methods with modern neural perception: + +1. **Symbolic layer**: Translate questions into temporal logic specifications → construct video automata → use model checking to identify logic-verified video segments +2. **Neural layer**: Submit only relevant segments to VLMs, not entire videos +3. **Result**: >10% improvement over baselines on LongVideoBench/CinePile, with compositional reasoning and no fine-tuning required + +### The Benchmark: Multi-Camera Video QA + +To evaluate VLMs on cross-camera reasoning at scale, the lab is building a unified **Multi-Camera Video QA Benchmark** spanning 4 datasets (NuScenes, EgoExo4D, MEVA, Cosmos) with an 8-tier question taxonomy targeting ~6,000 total questions. The core research question: + +> *How do we intelligently select and order inputs from a multi-camera network to maximize VQA accuracy within a fixed frame budget?* + +### My Contribution: MEVA Surveillance Pipeline + +I designed and built the **MEVA dataset pipeline** — the surveillance-domain contribution to the benchmark. MEVA (Multiview Extended Video with Activities, IARPA DIVA program) provides 328 hours of synchronized video from 28 cameras across 4 physical sites, with dense activity annotations. My pipeline processes 929 five-minute surveillance slots, constructs temporal scene graphs, resolves cross-camera entity identities, and generates ~12,000+ deterministic multiple-choice questions across 7 reasoning categories — with zero LLM dependency for answer computation (GPT used only for optional text polishing at $0.002/slot). + +--- + +## Quantified Achievements + +| Metric | Value | +|--------|-------| +| **Pipeline versions built** | 10 (V1 rule-based → V10 production) | +| **Total production code** | ~4,700 lines (Python) | +| **Documentation written** | 34 files, 15,000+ lines | +| **Development sessions** | 74 tracked sessions over 27 days | +| **Dataset slots processed** | 929 (390 canonical multi-site slots) | +| **MEVID person coverage** | 887/929 slots (95.5%) with cross-camera identity | +| **QA questions generated** | ~12,000–13,000 (7 categories × ~14 Qs/slot) | +| **Automated validation score** | 84–100/100 across test slots | +| **LLM cost for full dataset** | ~$1.86 total (naturalization only) | +| **Entity descriptions extracted** | 158 unique persons via SegFormer + YOLO + VLM | +| **Annotation sources unified** | 3 formats (Kitware YAML, NIST JSON, VSet7) | + +--- + +## Technical Architecture + +### Pipeline Design (V10 — Final Production) + +``` +Raw Kitware Annotations (.activities.yml, .geom.yml, .types.yml) + │ + ▼ +┌──────────────────────────────────────────────────────────────┐ +│ 1. PARSE ANNOTATIONS │ +│ • Stream-parse multi-GB YAML (CSafeLoader + regex) │ +│ • Handle 2 format variants (inline/block YAML) │ +│ • 761/769 clips (99%) successfully parsed │ +├──────────────────────────────────────────────────────────────┤ +│ 2. BUILD SCENE GRAPH │ +│ • Temporal edges: BEFORE / AFTER / OVERLAPS │ +│ • Entity co-reference via IoU ≥0.5 (Union-Find) │ +│ • Actor chains: chronological activity sequences │ +│ • 20+ temporal edges per slot │ +├──────────────────────────────────────────────────────────────┤ +│ 3. ENTITY RESOLUTION (Cross-Camera) │ +│ • MEVID person mapping (158 identities across cameras) │ +│ • Height differentiation for same-camera disambiguation │ +│ • 5-tier description priority chain │ +├──────────────────────────────────────────────────────────────┤ +│ 4. ENTITY DESCRIPTION EXTRACTION │ +│ • SegFormer semantic segmentation (color/texture/access.) │ +│ • Two-tier: SegFormer ≥144px height, HSV color ≥40px │ +│ • Cross-camera majority voting + color consolidation │ +│ • 4.8M crops processed for 158 persons │ +├──────────────────────────────────────────────────────────────┤ +│ 5. QUESTION GENERATION (7 categories, deterministic) │ +│ • Temporal (2Q): cross-camera event ordering │ +│ • Event Ordering (2Q): chronological sequencing │ +│ • Spatial (3Q): distance via KRTD 3D projection │ +│ • Perception (2Q): activity visibility across cameras │ +│ • Summarization (1Q): scene summary │ +│ • Counting (1Q): cross-camera activity aggregation │ +│ • Best Camera (2Q): entity entrance/exit tracking │ +├──────────────────────────────────────────────────────────────┤ +│ 6. AUTOMATED VALIDATION (6 checks) │ +│ • Reasoning consistency, raw token leak detection │ +│ • Near-duplicate detection (90%+ similarity) │ +│ • Generic description flagging ("a person" → blocked) │ +│ • Multi-correct ambiguity detection │ +│ • Grammar & punctuation │ +├──────────────────────────────────────────────────────────────┤ +│ 7. GPT NATURALIZATION (optional, $0.002/slot) │ +│ • Template text → natural language via GPT-4o-mini │ +│ • 8/8 success rate on test slots │ +│ • Preserves answer correctness deterministically │ +└──────────────────────────────────────────────────────────────┘ + │ + ▼ + Output: {slot}.v10.json (13-14 MCQs with reasoning traces) +``` + +### Key Algorithms & Techniques + +- **Union-Find clustering** for entity co-reference resolution across frames via bounding-box IoU +- **Streaming YAML parser** with inline regex fallback for multi-GB annotation files (avoids OOM) +- **3D spatial reasoning** via KRTD camera intrinsic/extrinsic projection for distance computation +- **Deterministic generation** with sorted dict construction, fixed seeds (seed=42), no randomness +- **Temporal uniqueness gate** using 3D point matching to deduplicate cross-camera questions +- **SegFormer semantic segmentation** for fine-grained entity attribute extraction (texture, brightness, accessories) +- **Cross-camera color consolidation** via majority voting across entity clusters + +--- + +## Technologies & Tools + +| Category | Technologies | +|----------|-------------| +| **Languages** | Python 3.10 | +| **ML / CV** | SegFormer (semantic segmentation), YOLOv8 (detection + HSV color), InternVL3.5-14B (VLM descriptions) | +| **Data Processing** | PyYAML (CSafeLoader), NumPy, OpenCV, streaming parsers | +| **LLM Integration** | GPT-4o-mini (text naturalization only, $0.002/slot) | +| **3D Vision** | KRTD camera models (intrinsic/extrinsic), pyproj (coordinate transforms) | +| **Dataset Formats** | Kitware KPF YAML, NIST ActEV JSON, VSet7, GPX (GPS tracks) | +| **DevOps** | FFmpeg (video conversion, CRF 32, libx264), bash scripting, Git | +| **Annotation Tools** | Custom OpenCV viewer with frame stepping, bounding box overlay | + +--- + +## Pipeline Evolution (V1 → V10) + +| Ver | Approach | Categories | Qs/Slot | Key Innovation | +|-----|----------|-----------|---------|----------------| +| V1 | Rule-based templates | 6 | 57 | Logic tuples from raw annotations | +| V2 | Monolithic LLM (GPT-4o) | 6 | ~50 | End-to-end GPT generation | +| V3 | Per-question LLM | 6 | 12–15 | Multi-camera architecture, class hierarchy | +| V4 | Deterministic + 3D spatial | 3 | 10 | KRTD camera projection for entity distance | +| V5 | Pure symbolic | 5 | 15 | Self-contained, no external scene graph | +| V6 | Scene graph + heuristics | 3 | 9 | Entity aliases, debug markers, 14 modules | +| V7 | + activity hierarchy | 3 | 8 | 12 causal + 10 co-occurring relationships | +| V8 | + MEVID integration | 5 | 9 | 158-person attribute database | +| V9 | + 8 categories | 8 | 13–14 | Counting, causality, re-identification | +| **V10** | **7 categories (final)** | **7** | **13–14** | **Deterministic, minimal artifact, NAS-ready** | + +V10 dropped 3 categories (perception, causality, re-ID) that produced false negatives or speculative answers, and added best_camera (entity entrance/exit tracking). All dict construction is sorted for reproducibility. + +--- + +## Data Sources & Scale + +| Source | Records | Format | Role | +|--------|---------|--------|------| +| MEVA raw video | 769 clips, 28 cameras, 4 sites | AVI → MP4 | Source footage | +| Kitware annotations | 761 clips (99%) | KPF YAML | Primary activity ground truth | +| MEVID re-ID | 158 persons, 685 clips | Crop images + metadata | Cross-camera identity | +| KRTD camera models | 10/28 cameras | intrinsic/extrinsic matrices | 3D spatial projection | +| GPS actor tracks | 105 actors, 256K trackpoints | GPX | Spatial verification | +| VSet7 annotations | 47 slots | NIST JSON | Cross-validation | + +--- + +## Project Phases & Timeline + +### Phase 1 — Foundation (Sessions 1–7, ~3 days) +- Explored MEVA dataset structure, annotation formats, and video naming conventions +- Built AVI→MP4 conversion pipeline with FFmpeg (CRF 32, signal handling, resume support) +- Pivoted from LLM-first to deterministic scene-graph architecture +- Generated first 75 cross-camera questions for a school site slot + +### Phase 2 — Pipeline Iterations (Sessions 8–14, ~3 days) +- Built V4 (deterministic spatial via KRTD) and V5 (pure symbolic, 5 categories) +- Full pipeline audit: compared V4/V5 output, analyzed GPS data coverage +- Specified V6 requirements from 12-point user brief + +### Phase 3 — Multi-Camera Integration (Sessions 15–25, ~5 days) +- Implemented V6 pipeline (14 modules, entity aliases, debug instrumentation) +- Built V7 with activity hierarchy (12 causal + 10 co-occurring relationships) +- Integrated GPT naturalization (8/8 success, $0.001/slot) + +### Phase 4 — Entity Enrichment (Sessions 28–44, ~6 days) +- Built 158-person MEVID description database from 4.8M crops +- Implemented SegFormer extraction pipeline (texture, brightness, accessories) +- Achieved 95.5% slot coverage for MEVID cross-camera person mapping +- Compared YOLO vs GPT-4V for entity description (YOLO: free, 46s; GPT-4V: $4.74, 25min) + +### Phase 5 — V10 Production (Sessions 45–74, ~10 days) +- Refined V10 across 30+ sessions: determinism fixes, quality audits, edge-case handling +- Built 6-check automated validator (reasoning, duplicates, generics, grammar, ambiguity, leaks) +- Implemented 5-tier entity description priority chain (SegFormer > MEVID > VLM > YOLO > verb) +- Achieved 84–100/100 validation scores across diverse test slots +- Made pipeline NAS-compatible (no hardcoded paths), batch-ready for 929 slots + +--- + +## Key Engineering Decisions + +1. **Deterministic over LLM-generated**: Moved from GPT-4o generation (V2–V3) to fully symbolic QA construction. LLM used only for cosmetic text polishing. Eliminated hallucinations, reduced cost from ~$50/batch to $1.86. + +2. **Streaming YAML parsing**: MEVA geom.yml files can exceed 1GB. Implemented a streaming parser with inline regex fallback to avoid OOM errors on commodity hardware. + +3. **Union-Find entity clustering**: Used IoU ≥0.5 threshold across bounding boxes to cluster detection instances into physical entities, enabling cross-frame and cross-camera co-reference. + +4. **SegFormer over YOLO for descriptions**: YOLO color summaries were too generic (42/158 persons = "blue top and blue pants"). SegFormer semantic segmentation provided texture, brightness, and accessory detection for meaningful differentiation. + +5. **Temporal uniqueness gate**: 3D point matching prevents generating duplicate temporal questions about the same physical event observed from different cameras. + +6. **Pipeline versioning with archival**: Each version (V1–V10) is preserved; old code archived to `scripts/old_versions/` with data to `data/old_versions/`. Full decision rationale documented in session logs. + +--- + +## Repository Structure (Production) + +``` +Multi-Camera-Adi/meva/ +├── scripts/v10/ +│ ├── run_pipeline.py # Main orchestrator (7-step pipeline) +│ ├── build_scene_graph.py # Temporal scene graph construction +│ ├── parse_annotations.py # Kitware YAML parser (streaming) +│ ├── entity_resolution.py # Cross-camera entity linking +│ ├── extract_entity_descriptions.py # SegFormer + HSV extraction +│ ├── generate_temporal.py # 2 temporal questions +│ ├── generate_event_ordering.py # 2 event ordering questions +│ ├── generate_spatial.py # 3 spatial questions (KRTD 3D) +│ ├── generate_perception.py # 2 perception questions +│ ├── generate_summarization.py # 1 scene summary question +│ ├── generate_numerical.py # 1 counting question +│ ├── generate_best_camera.py # 2 entrance/exit questions +│ ├── naturalize.py # GPT-4o-mini text polishing +│ ├── validate_qa.py # 6 automated quality checks +│ ├── batch_generate.py # Multi-slot batch processing +│ ├── export_to_multicam_format.py # Schema translation +│ └── utils/ +│ ├── camera_overlap.py # KRTD proximity calculations +│ ├── yaml_stream.py # Streaming YAML parser +│ ├── krtd.py # 3D camera projection +│ └── iou.py # Bounding box intersection +├── data/ +│ ├── canonical_slots.json # 390 coalesced slots +│ ├── mevid_supported_slots.json # 887 slots with person IDs +│ └── person_database_yolo.json # 158 persons + attributes +└── configs/ + └── v10_config.yaml # Pipeline hyperparameters +``` + +--- + +## Skills & Competencies Demonstrated + +- **Neuro-Symbolic AI Research**: Built the surveillance-domain data pipeline for UT Austin SWARM Lab's NeuS-QA benchmark — a neuro-symbolic framework combining temporal logic, formal verification, and VLM perception for multi-camera video reasoning (arXiv:2509.18041) +- **Data Engineering**: Designed pipelines to process multi-GB annotation files across 929 video slots and 28 cameras; unified 3 annotation formats (Kitware YAML, NIST JSON, VSet7) into a single scene-graph representation +- **Computer Vision**: Integrated SegFormer semantic segmentation and YOLOv8 detection for entity attribute extraction from 4.8M image crops; used KRTD camera models for 3D spatial reasoning +- **Algorithm Design**: Implemented Union-Find clustering for entity co-reference, streaming parsers for memory-constrained environments, temporal uniqueness gates via 3D point matching +- **ML Pipeline Engineering**: Built a 10-version iterative pipeline with full version control, automated validation (6 checks), and deterministic reproducibility (sorted dicts, fixed seeds) +- **NLP / LLM Integration**: Designed GPT-4o-mini naturalization wrapper that polishes template text while preserving answer correctness ($0.002/slot, 8/8 success rate) +- **Research Engineering**: 74 documented development sessions with rationale tracking; produced 34 design/reference documents (15,000+ lines); managed annotation-quality tradeoffs across incomplete data sources +- **Cost Optimization**: Reduced QA generation cost from ~$50/batch (LLM-first) to $1.86/batch (symbolic + optional polish) — a 96% reduction while improving quality scores +- **Multi-Camera Reasoning**: Designed question categories that require cross-camera temporal, spatial, and causal reasoning — questions impossible to answer from any single camera view, directly testing VLM multi-view comprehension +- **Video Processing**: Built FFmpeg batch conversion pipeline with signal handling, resume support, and CRF-optimized compression for large-scale AVI→MP4 conversion + +--- + +## Broader Benchmark Context + +### Multi-Camera Video QA Benchmark (Lab-Wide) + +| Dataset | Domain | Cameras | Contribution | Status | +|---------|--------|---------|-------------|--------| +| **NuScenes** | Autonomous driving | 6 per scene | ~1,500 Qs | Active (other lab members) | +| **EgoExo4D** | Egocentric + exocentric | 4–8 per scene | ~1,500 Qs | Active | +| **MEVA** | Surveillance | 28 across 4 sites | **~12,000 Qs** | **My pipeline (V10 production)** | +| **Cosmos** | Synthetic multi-view | Variable | ~1,000 Qs | Planned | + +### Question Taxonomy (8-Tier, Shared Across Datasets) + +1. **Temporal Relationships** — Before/after/between with logical operators (cross-camera) +2. **Event Ordering** — Chronological sequencing of 3–4 events from multiple views +3. **Perception** — Presence/absence of objects across the camera network +4. **Causality** — Linking effects to preceding causes visible on different cameras +5. **Spatial** — 3D relative positions using camera calibration models +6. **Numerical** — Counting unique entities across overlapping/non-overlapping fields of view +7. **Camera Transition** — Which camera first/last captures an entity's appearance +8. **Scene Summarization** — Multi-view narrative synthesis + +My V10 pipeline implements 7 of these 8 categories (all except causality, which requires stronger causal annotations than MEVA provides). + +### Related Publications (SWARM Lab) + +| Paper | Venue | Relevance | +|-------|-------|-----------| +| **NeuS-QA** (arXiv:2509.18041) | Under review | Core neuro-symbolic LVQA framework; my pipeline generates eval data for this | +| **NeuS-V** (arXiv:2411.16718) | Published | Temporal logic evaluation of text-to-video models; sister project | +| **Multi-Camera Video QA Benchmark** | In preparation | The full benchmark paper incorporating MEVA + 3 other datasets | + +--- + +*Generated 2026-03-06 | UT Austin SWARM Lab (Chinchali Group) | Based on 74 development sessions (2026-02-04 to 2026-03-02)* diff --git a/docs/project-history/PROMPT_NEXTSTEPS.md b/docs/project-history/PROMPT_NEXTSTEPS.md new file mode 100644 index 0000000..dc9fa6f --- /dev/null +++ b/docs/project-history/PROMPT_NEXTSTEPS.md @@ -0,0 +1,449 @@ +For scripts one time scripts created to generate artifacts used in the actual pipeline, save them outside of Multi-Camera-Adi. Only add scripts that will be ran at runtime to generate the QA questions into the repo. + +Issue 1: Color Descriptions Swapped (yellow top / blue pants vs blue top / yellow pants) +Root Cause: In person_descriptions.py:474, MEVID descriptions are assigned sequentially first-come-first-served — unused[0] is picked for each entity on a camera, regardless of whether that MEVID person actually matches that annotation actor. There is no visual verification that the MEVID person's appearance matches the actor's actual on-screen appearance. If actor 5 and actor 6 are on camera G339 and MEVID person A (yellow top) and person B (blue top) are available, they get assigned in order — not by visual matching. So actor 5 might receive person A's description even though actor 5 is actually wearing blue. + +Proposed Fix: + +In enrich_entities(), after identifying the list of unused MEVID persons for a camera, cross-validate each candidate MEVID description against the geom-extracted HSV colors for that specific actor. +Load the geom description for the entity (geom_descs[eid]), parse its upper/lower colors. +For each candidate MEVID person, compare their primary_upper_color and primary_lower_color with the geom-extracted colors. +Pick the MEVID person with the best color match instead of unused[0]. +If no MEVID person matches (color distance too high), fall back to the geom description directly. +Steps: + +Add a _color_match_score(mevid_colors, geom_colors) function in person_descriptions.py +Modify the MEVID assignment block (around line 474) to score all unused persons and pick the best match +Add a minimum match threshold — if no MEVID person scores above it, skip MEVID and use geom +Issue 2: Descriptions Still Generic ("blue top and blue pants") — SegFormer Not Being Used +Root Cause: The extract_entity_descriptions.py supports three methods (color-only, YOLO, SegFormer), but the batch extractor defaults to color-only unless --method is explicitly passed. The color-only method uses crude fixed 10-45% / 55-90% vertical splits and only outputs upper_color + lower_color. SegFormer would give hair color, shoe color, accessories, lower garment type (pants/skirt/dress), and carried objects — all of which make descriptions far more unique. + +Proposed Fix: + +Change the default extraction method from color_only to segformer in batch_extract_all_slots.py. +Re-extract entity descriptions for all slots using SegFormer. +In build_description() (line 717 of extract_entity_descriptions.py), ensure ALL available attributes are included: hair color, upper color, lower garment type + color, shoe color, accessories, carried objects. +Result: instead of "a person in blue top and blue pants", you get "a person with dark hair, wearing a navy top and khaki pants, silver shoes, carrying a backpack". +Steps: + +Change default in batch_extract_all_slots.py from "color_only" to "segformer" +Re-run extraction: python3 -m scripts.v10.batch_extract_all_slots --method segformer --force-reextract +Verify build_description() includes all attributes (hair, shoes, accessories) +Regenerate QA for test slots and verify descriptions are richer +Issue 3: Hard-Coded Question Counts Force Duplicate Questions +Root Cause: In run_pipeline.py:84-92, TARGET_TEMPORAL = 2 etc. are hard-coded. Each generator receives this as the count parameter. When a slot has only 1 valid temporal candidate, the generator cannot produce 2, but the current architecture doesn't gracefully degrade — it either produces fewer (leaving the target unmet) or in some cases the same event pair gets reworded to fill the count. + +Proposed Fix: + +Remove all TARGET_* fixed counts from run_pipeline.py. +Replace with a "generate all valid, then cap" approach — each generator produces ALL quality candidates above a score threshold, then the pipeline selects the best N (up to a soft max, not a hard target). +Set MAX_* caps instead of targets (e.g., MAX_TEMPORAL = 3, MAX_EVENT_ORDERING = 3) to prevent any single category from dominating. +The final QA set is whatever the pipeline can produce with quality, no padding. +Steps: + +Rename TARGET_* → MAX_* in run_pipeline.py +Change each generator call: pass count=MAX_* as a ceiling, not a target +In each generator, remove any logic that Force-fills to the target count +Let natural filtering (score thresholds, dedup, diversity) determine the actual count +Update the output metadata to reflect variable counts +Issue 4: Camera Number Leaking Into Event Ordering Questions +Root Cause: In generate_event_ordering.py:88-96, _get_event_description() explicitly appends " on camera {event.camera_id}" to every event description. This was intentional for multi-camera grounding, but it leaks information — a VQA model could use camera IDs to shortcut the answer. + +Proposed Fix: + +Remove " on camera {event.camera_id}" from event descriptions in generate_event_ordering.py. +Instead, descriptions must be sufficient on their own to uniquely identify each event using ONLY visual appearance (clothing, hair, carried objects). This requires richer descriptions (see Issue 2). +Add a uniqueness check: after building all 3-4 event descriptions for a question, verify that all descriptions are distinct. If any two are identical, the ONLY allowed disambiguation is trying a different person/event with a richer visual description. DO NOT add temporal markers ("at 129s", "around the 3:30 mark"), spatial/location context ("near the school"), or camera IDs. +If descriptions still aren't unique after exhausting visual disambiguation, skip this candidate group entirely — don't generate an ambiguous question. +Steps: + +Remove on camera {event.camera_id} from _get_event_description() (lines 88-96) +Add a _ensure_unique_descriptions() function that checks all N event descriptions in a group — uniqueness must come from visual appearance + activity verb ONLY +In _find_ordering_groups() or the question builder, reject groups where descriptions can't be made visually unique +Same change needed in generate_temporal.py lines 599-604 (temporal also appends camera context — remove it) +Remove ALL location enrichment calls (_enrich_with_location) from temporal and spatial generators — these add spatial context like "near the school" which is not allowed +Issue 5: Event Ordering Uses Random Activity Instances Instead of First +Root Cause: _find_ordering_groups() in generate_event_ordering.py:189 deduplicates events into 5-second time buckets and keeps only one per (camera, activity, bucket). This is roughly first-occurrence since events are sorted by start_sec, BUT the chain-building sliding window can pick events from later in the slot. If person_talks_to_person occurs continuously from t=10 to t=200, but the pipeline picks the instance at t=150 for comparison with another event at t=130, it would incorrectly say the other event happened first — even though talking started much earlier. + +Proposed Fix: + +When an activity type has multiple instances, always use the first (earliest) instance for temporal/ordering comparisons. This represents "when did this activity first begin in the slot." +In _find_ordering_groups(), after building unique_events, group by activity type and keep only the earliest instance per (activity, camera) for chain building. +In _find_temporal_candidates(), add the same constraint — when selecting events for a pair, prefer the first occurrence of each activity on that camera. +Steps: + +In generate_event_ordering.py, after building unique_events (line ~198), add a pre-filter: for each (activity, camera_id) key, keep only the event with the lowest start_sec +In generate_temporal.py _find_temporal_candidates(), add the same pre-filter before the pair-finding loop +Add a comment documenting the rationale: continuous activities must use their first instance to avoid misleading temporal comparisons +Issue 6: Same Event Visible on Multiple Cameras Not Detected (Event Ordering) +Root Cause: The temporal generator has _is_likely_duplicate_event() with 3D + time-based dedup, but the event ordering generator has NO equivalent. It deduplicates by 5-second (camera, activity, bucket) keys, which only prevents duplicates on the same camera. Two cameras seeing the same person open the same door at nearly the same time will be treated as two distinct events. + +Proposed Fix: + +Port _is_likely_duplicate_event() from generate_temporal.py to generate_event_ordering.py. +Apply cross-camera dedup during chain building in _find_ordering_groups(): before adding a candidate event to a chain, check if it's a likely duplicate of any event already in the chain. +Also apply it at the group-level: after building a group, verify no two events in the group are duplicates. +Steps: + +Import or copy _is_likely_duplicate_event() logic into generate_event_ordering.py +In the chain-building loop (line ~210), add a check: if any(_is_likely_duplicate_event(candidate, existing, sg) for existing in chain): continue +Add the same check to the counting generator (generate_numerical.py) — it already has ±2s dedup, but should also use 3D position matching for accuracy +Issue 7: Perception Questions Not Specific Enough +Root Cause: Perception questions like "Which camera captures opening a vehicle door?" are too generic — they don't reference any specific person or time, making the question trivially answerable or ambiguous when multiple cameras see the same activity. + +Proposed Fix: + +Enrich perception questions with person-level visual descriptions ONLY: "Which camera captures the person in a navy top and gray pants opening a vehicle door?" +DO NOT add temporal grounding ("around the 2:10 mark"), spatial/location context, or camera IDs as disambiguation. +For which_camera questions, ensure the correct camera is the only camera where that specific person (with that visual description) performs that activity — not just any camera with that activity type. +If the person's visual description isn't unique enough to single them out, find a different person/event or drop the question entirely. +Steps: + +In generate_perception.py, modify the which_camera question builder to include the entity description of the actor performing the activity +Add a uniqueness filter: if the same described person performs the activity on multiple cameras, it's not a valid which_camera question +Require SegFormer-level descriptions for perception questions (not just "blue top and blue pants") — descriptions must include hair, carried objects, garment type, etc. +If no unique visual description can be produced, skip the question — never fall back to camera/time/location qualifiers +Issue 8: Spatial Question 7 Flags Same Person as Two Different Entities +Root Cause: In generate_spatial.py:200, the resolved: ResolvedGraph is accepted as a parameter but never used. Two annotations of the same person at different times on the same camera produce two entities (e.g., G339_actor_4 at frame 3896 and G339_actor_6 at frame 3932). The spatial generator doesn't check whether these belong to the same entity cluster. Since the bounding boxes are nearly identical (1228,178,1288,241 vs 1225,180,1284,252, only a few pixels apart), this is clearly the same person — and the 0.22m distance confirms it. + +The description mismatch (entity A = "blue top and blue pants", entity B = "yellow top and blue pants") is a separate manifestation of Issue 1 — wrong MEVID assignment. + +Proposed Fix: + +In generate_spatial_qa(), use resolved.entity_clusters to check if entity_a and entity_b are in the same cluster. If yes, skip this pair. +Even without cluster data, add a proximity sanity check: if two entities on the same camera are < 1 meter apart AND have overlapping time ranges, they're likely the same person — skip. +Add a bbox overlap check: if the two bounding boxes have IoU > 0.5, they're the same person. +Steps: + +In generate_spatial_qa() after building candidates, add cluster-based dedup: for each pair, check if both entity IDs appear in the same EntityCluster — if yes, filter out +Add a bbox IoU check in _find_spatial_candidates(): compute IoU of bbox_a and bbox_b, reject if > 0.3 +Add a temporal overlap check: if the entities' frame ranges overlap significantly (>50%), they may be the same person +Remove all location enrichment calls (enrich_description_with_location) from spatial question text — descriptions must be visual-appearance-only +Remove camera ID from spatial question text (currently says "visible on camera G339") — same no-camera-leak rule as all other categories +Also fix the description mismatch (Issue 1) which makes this even more confusing +Issue 9: Counting Reasoning Says "7" but Answer Is "8" +Root Cause: The reasoning is generated by GPT during naturalization (see naturalize.py:773). The naturalizer gets the question text and options but may hallucinate or miscalculate in the reasoning field. The raw pipeline's generate_numerical.py does NOT produce a reasoning field — it only produces verification.correct_count. GPT then invents its own reasoning text, sometimes with the wrong number. + +Proposed Fix: + +Generate the reasoning deterministically in generate_numerical.py itself, not in GPT naturalization. Use a template like: "The action of {activity} was observed {count} times across cameras {cameras}: {count_per_camera_breakdown}." +Pass this pre-built reasoning to naturalize.py so GPT only polishes the grammar, not the numbers. +Add a reasoning validation check in validate_qa.py: extract numbers from the reasoning text and verify they match the correct answer. +Steps: + +In generate_numerical.py, add a _build_reasoning() function that uses the deduped clusters to produce structured reasoning text with the correct count +Include per-camera breakdown: "G299: 5 occurrences, G330: 3 occurrences → 8 total (with 0 cross-camera duplicates removed)" +Pass this as q["reasoning"] in the QA dict +In naturalize.py, respect pre-built reasoning — only rephrase, never change numbers +In validate_qa.py, add check: if reasoning mentions a number, it must match correct_answer +Issue 10: Counting Questions Missing Key Frames +Root Cause: The counting generator records event_ids in verification but does NOT include the timestamp/frame of each instance. This makes it impossible to visually verify when each action occurs. + +Proposed Fix: + +In _dedup_activity_count(), return not just the count and event IDs, but also the frame range and timestamp of each cluster representative. +Include a key_frames array in the QA output with one entry per counted instance, listing: camera, frame number, timestamp (seconds), and clip file. +Steps: + +Modify _dedup_activity_count() to return cluster details: List[Dict] where each dict has {camera, start_frame, start_sec, end_sec, event_id, clip_file} +In generate_numerical_qa(), add a "key_frames" field to the verification/debug_info with this per-instance data +Sort key_frames chronologically for easy visual verification +Summary Table +# Issue Root Cause File Severity +1 Color descriptions swapped person_descriptions.py L474 Critical +2 Generic descriptions, SegFormer not used batch_extract_all_slots.py default High +3 Hard-coded question counts → forced duplicates run_pipeline.py L84-92 Medium +4 Camera # leaks into event ordering generate_event_ordering.py L88 High +5 Random activity instance instead of first generate_event_ordering.py L198+ Critical +6 Same event not detected across cameras generate_event_ordering.py (missing) Critical +7 Perception too generic generate_perception.py Medium +8 Spatial: same person = two entities generate_spatial.py L200+ Critical +9 Counting reasoning wrong number naturalize.py / generate_numerical.py High +10 Counting missing key frames generate_numerical.py Medium +11 Annotation-based verification step New (validate_qa.py / run_pipeline.py) Low-Medium +12 GLOBAL: No spatial/temporal/camera descriptors ALL generators Critical (policy) + +--- + +Issue 11: Annotation-Based Verification Step +Question: Should we add a verification step that re-reads raw annotations to independently answer each generated question? + +Analysis — Mostly Circular, Partially Useful: +The questions are BUILT from the annotations, so re-reading the same annotations will confirm ~100% of answers by construction. This is circular validation — the pipeline reads event X at timestamp T, generates a question about X, and the verifier re-reads X at timestamp T and says "yes, correct." + +What it WOULD catch (worth it): +- Counting bugs: re-count events from raw annotations independently (could catch Issue 9 — reasoning/count mismatch) +- Temporal ordering drift: re-derive start_sec from raw frame/fps and confirm event A truly precedes event B +- Parsing divergence: cases where the pipeline's internal Event objects lost fidelity relative to the raw YAML +- Option validation: confirm the correct answer is actually in the options and that no other option is also correct + +What it WOULD NOT catch (the hard problems): +- Description swaps (Issue 1) — annotations don't contain color info, no way to verify visual descriptions +- Generic descriptions (Issue 2) — annotations have no visual appearance data +- Cross-camera same-event (Issue 6) — raw annotations per-camera don't know about each other +- Spatial same-person (Issue 8) — annotations don't flag identity across annotation actors +- Whether descriptions actually match what's on screen — would need video frame analysis, not annotation re-reading + +Recommendation: Implement as a LIGHTWEIGHT sanity check, not a quality gate. Useful for catching numerical/temporal bugs. For visual accuracy, the real solution is Issues 1+2 (color-matched MEVID assignment + SegFormer descriptions). + +Steps: +- Add a _verify_from_annotations() function in validate_qa.py +- For counting questions: re-count activity events from raw scene graph, confirm count matches correct_answer +- For temporal/event_ordering: re-derive timestamps from raw events, confirm ordering matches +- For all categories: confirm correct_answer_index points to the right option string +- Flag discrepancies as errors but DO NOT auto-reject (since the pipeline might be correct and the re-parse might differ in edge cases) +- This is a regression test, not a replacement for human review + +--- + +⚠️ GLOBAL CONSTRAINT — Issue 12: NO Spatial/Temporal/Camera Descriptors in ANY Question Text + +THIS IS THE HIGHEST PRIORITY CONSTRAINT. It overrides disambiguation strategies in ALL other issues. + +Rule: Question text, option text, and naturalized output must contain ONLY: +- Visual appearance descriptors (clothing colors, hair, carried objects, garment type, accessories) +- Activity verbs (what the person is doing) +- Roman numeral labels (I, II, III, IV for event ordering) + +STRICTLY FORBIDDEN in question/option text: +- Camera IDs: "on camera G339", "Camera G639", "visible on camera G424" +- Timestamps: "at 129s", "around the 2:10 mark", "at 134 seconds" +- Spatial/location context: "near the school", "by the entrance", "in the parking lot" +- Temporal qualifiers used for disambiguation: "(opening a vehicle door at 129s)" + +WHY: Paper requirements and category definitions prohibit these. Adding camera IDs lets models shortcut. Adding timestamps turns visual questions into text-reading tasks. Adding location context conflates spatial and temporal categories. + +Impact on Other Issues: +- Issue 4 (camera leak in event ordering): Remove ALL camera references. Period. +- Issue 4 extension (temporal): Remove camera context enrichment (lines 599-604 in generate_temporal.py). Remove _enrich_with_location() calls entirely. +- Issue 7 (perception): DO NOT add "around the 2:10 mark". Visual descriptions only. +- Issue 8 (spatial): Remove "visible on camera {cam}" from question text. Remove enrich_description_with_location() calls. +- Issue 1+2 (descriptions): If SegFormer descriptions are still not unique enough to distinguish two people, the ONLY options are: (a) find a different person/event with a more unique description, or (b) DROP the question. Never fall back to camera/time/location qualifiers. +- Disambiguation fallbacks: The _disambiguate_description() function in generate_spatial.py currently appends "(activity at Xs)" — this must be removed. The _ensure_unique_descriptions() in event ordering must rely purely on visual appearance + activity verb. + +Affected Files (all need camera/time/location stripping): +- generate_temporal.py: Remove desc_a_enriched / desc_b_enriched camera append (L599-604). Remove _enrich_with_location() calls (L591-592). Remove "(on camera {cam})" identical-desc fallback (L596-597). +- generate_event_ordering.py: Remove " on camera {event.camera_id}" from _get_event_description() (L88-96). +- generate_spatial.py: Remove "visible on camera {cand['camera_a']}" from question text (L325). Remove enrich_description_with_location() calls (L308-320). Remove _disambiguate_description() timestamp fallback. +- generate_perception.py: Keep "Camera Gxxx" ONLY in perception questions where the question specifically asks WHICH camera — this is the one legitimate use since the category is about camera identification. But never use camera IDs for disambiguation elsewhere. +- naturalize.py: Update category hints to explicitly forbid camera/time/location references in temporal, event_ordering, spatial, and counting categories. Perception and best_camera categories may reference cameras since that's what the question is about. + +Enforcement: +- Add a post-generation validator that scans all question_template and options text for forbidden patterns: /camera G\d+/i, /at \d+s/, /\d+ seconds/, /near the/, /by the/, /in the parking/ +- Any question containing forbidden patterns is auto-rejected before output +- This validator runs BEFORE naturalization (catch raw leaks) AND after (catch GPT additions) + +--- + +## Paper Alignment Check + +Source: "Towards a Multi-Camera Video QA Benchmark: Bridging the Gap in Multi-View Temporal Reasoning" (Dec 2025) + +The paper defines 8 reasoning tiers. Here's how each proposed fix aligns: + +| Paper Tier | Paper Definition | Our Category | Fixes Align? | +|------------|-----------------|-------------|-------------| +| Temporal | "Reasoning about timing using logical operators" | temporal | YES — Issues 1,2,5,12 ensure questions test temporal reasoning from VIDEO, not text shortcuts | +| Event Ordering | "Chronological sequence of multiple events" | event_ordering | YES — Issues 4,5,6,12 ensure ordering tests visual recognition, not camera-ID parsing | +| Basic Perception | "Presence/absence of objects, events, attributes" | perception | YES — Issue 7 makes questions specific enough to be non-trivial. Camera refs kept here since "Which camera captures X?" IS the perception test | +| Spatial | "Determining relative positions using 3D data" | spatial | YES — Issue 8 prevents self-distance nonsense. Issue 12 ensures spatial is about understanding SCENE layout, not reading camera labels | +| Numerical | "Counting unique objects across camera feeds" | counting | YES — Issues 9,10 ensure counts are verifiable and reasoning is correct | +| Camera Transition | "Which camera first captures entrance of entity" | best_camera | YES — Camera refs are REQUIRED here by definition. Issue 12 exempts this category correctly | +| Summarization | "Multi-view narrations synthesizing all streams" | summarization | No changes needed — already scene-level | +| Causality | "Explaining why an event occurred" | causality (not yet implemented) | N/A | + +**Key paper requirement**: "the answer to a question Q cannot be found in a single camera stream, but must be synthesized by switching views at specific logical junctions." + +This means: +- Questions that can be answered from camera IDs alone VIOLATE the paper's intent +- Questions must force the model to actually WATCH the video, not parse metadata +- Our Issue 12 (no camera/time/location in question text) is directly aligned with this + +**Category where camera refs ARE correct per paper:** +- Perception Type 1 ("Which camera captures X?") — this literally tests camera identification +- Perception Type 2 ("What activity on camera X?") — tests per-camera perception +- Perception Type 4 ("What is the person on camera X wearing?") — tests attribute recognition +- Best Camera ("Which camera first captures...") — tests camera transition logic +- These categories REQUIRE camera references because the question IS about cameras + +**Categories where camera refs MUST NOT appear per paper:** +- Temporal — tests temporal reasoning, not camera awareness +- Event Ordering — tests chronological reasoning +- Spatial — tests 3D spatial understanding +- Counting — tests cross-camera deduplication ability + +--- + +## Implementation Risk Assessment + +### Issue 1: Color Match Scoring — LOW RISK +**What could break:** +- Geom descriptions may not exist for all entities (some slots may lack entity_descriptions JSON files) +- Color vocabulary mismatch between MEVID (e.g., "navy") and geom HSV (e.g., "blue") — scoring must normalize colors + +**Mitigation:** +- If no geom description exists for an entity, fall back to original sequential assignment (unchanged behavior) +- Build a color-similarity matrix (navy≈blue, crimson≈red, khaki≈beige) rather than requiring exact string match +- Unit test: verify that for the known-bad slot (2018-03-11.11-25.school), actor 6 on G339 gets "yellow top" not "blue top" + +### Issue 2: SegFormer Default — MEDIUM RISK +**What could break:** +- SegFormer requires GPU + `transformers` library. If run on CPU-only machine or without transformers installed, extraction will fail +- SegFormer inference is ~10x slower than color-only (~2-3 min/slot vs ~15 sec/slot). Full re-extraction of all slots will take hours +- Some crops may be too small for SegFormer (< 15px tall) — already handled with skip logic +- May produce DIFFERENT descriptions for already-generated slots, invalidating cached QA files + +**Mitigation:** +- Check for GPU/transformers at startup, fall back to YOLO if SegFormer unavailable (YOLO is middle-ground) +- Run re-extraction as a background batch job, not inline during QA generation +- Invalidation is expected — all QA must be re-generated after re-extraction anyway +- Run one test slot first to verify outputs before batch + +### Issue 3: Remove Hard Counts — LOW RISK +**What could break:** +- Downstream consumers (export_to_multicam_format.py, vlm_vqa_ablation.py) may expect a minimum number of questions per category. If a slot produces 0 temporal questions, the export may fail or produce an unbalanced dataset +- Batch statistics/reporting may break if they assume fixed counts + +**Mitigation:** +- Set a MIN_TOTAL_QUESTIONS threshold (e.g., 5) per slot — skip slots that can't meet it rather than padding +- Update export_to_multicam_format.py to handle variable category counts gracefully +- Add per-slot quality stats to batch output (category distribution histograms) + +### Issue 4: Remove Camera IDs from Event Ordering/Temporal — HIGH RISK ⚠️ +**What could break:** +- **Question yield will DROP significantly.** Currently many events have identical descriptions ("a person in blue top and blue pants"). Without camera IDs as a differentiator, these events become indistinguishable → groups get rejected → fewer questions generated per slot +- This is the MAIN risk: enforcing Issue 12 without first solving Issue 2 (rich descriptions) will crater the question count +- The naturalize.py event_ordering hint already says "camera feeds" — GPT may re-insert camera references + +**Mitigation:** +- **MUST implement Issue 2 (SegFormer) BEFORE Issue 4.** Rich descriptions are a prerequisite for camera-free questions +- After SegFormer re-extraction, measure how many event ordering groups survive the uniqueness filter before committing +- If yield is still too low, consider: adding carried-object detection, hair color, shoe color to descriptions +- Update naturalize.py hints to explicitly forbid camera references in temporal/event_ordering/spatial/counting + +### Issue 5: First Instance of Activity — LOW RISK +**What could break:** +- Some activities' first instance may be at the very start of the slot (t=0), where the camera is still stabilizing or the event is partially captured +- First instance may have poor bbox quality (person partially visible, entering frame) + +**Mitigation:** +- Skip events in the first 5 seconds of a slot (camera stabilization period) +- Existing bbox-in-frame validation (_is_bbox_clipping_frame) already rejects edge cases +- The pre-filter is additive (removes later instances) and doesn't touch existing validation logic + +### Issue 6: Cross-Camera Event Dedup in Event Ordering — MEDIUM RISK +**What could break:** +- The 3D position lookup requires KRTD camera models. Not all cameras have KRTD data (indoor cameras are excluded) +- Without 3D data, the heuristic fallback (same activity + ≤3s gap = duplicate) may be too aggressive — two genuinely different people opening different doors 2 seconds apart on different cameras would be incorrectly flagged as duplicates +- May reduce the event pool too aggressively, reducing question yield + +**Mitigation:** +- Only apply dedup when 3D data is available (KRTD + bbox). Without 3D, use a tighter time threshold (≤1.5s instead of ≤3s) +- Add an entity-identity check: if the two events involve different MEVID-resolved persons, they're NOT duplicates regardless of time proximity +- Log all dedup decisions for review + +### Issue 7: Perception Enrichment — LOW RISK +**What could break:** +- Adding person descriptions to "Which camera captures X?" questions makes them longer and more complex +- If the person's visual description is ambiguous, the question becomes harder but potentially multi-correct (same person on multiple cameras doing the same activity) + +**Mitigation:** +- The uniqueness filter (reject if same described person does the activity on >1 camera) already handles multi-correct +- Keep perception Type 2 ("What activity on camera X?") and Type 4 (attribute verification) unchanged — they already work correctly with camera refs per paper requirements + +### Issue 8: Spatial Same-Person Dedup — LOW RISK +**What could break:** +- Entity cluster data may be incomplete (heuristic clusters miss some same-person pairs) +- The bbox IoU check might be too aggressive on crowded scenes where different people overlap +- The proximity check (<1m = same person) could reject two genuinely different people standing very close together + +**Mitigation:** +- Use ALL THREE checks together (cluster OR bbox IoU > 0.5 OR distance < 0.5m with overlapping frames) — any ONE triggering = skip +- The 0.5m threshold is conservative enough (two distinct people virtually never occupy the same 0.5m² pixel space) +- Log rejected pairs for validation + +### Issue 9: Deterministic Counting Reasoning — LOW RISK +**What could break:** +- naturalize.py currently asks GPT to generate reasoning from scratch. If we pass pre-built reasoning, GPT may still override it or produce awkward phrasing +- The pre-built reasoning template may be too mechanical ("G299: 5, G330: 3 → 8 total") + +**Mitigation:** +- Add a "reasoning" field to the raw QA dict. In naturalize.py, pass it as context to GPT with instruction: "Here is the factual reasoning. Rephrase it naturally but DO NOT change any numbers." +- Add a post-naturalization regex check: extract all integers from reasoning, verify they match correct_answer +- If GPT changes the number, replace with the pre-built reasoning verbatim (bypass naturalization for reasoning field) + +### Issue 10: Counting Key Frames — LOW RISK +**What could break:** +- Returning full cluster details from _dedup_activity_count() changes its return type from `Tuple[int, List[str]]` to `Tuple[int, List[str], List[Dict]]` — all callers must be updated +- Key frame data may be large for high-count activities (20 events × 5 cameras) + +**Mitigation:** +- Use a clean return type: return a structured object or add a separate function _get_cluster_details() rather than changing the existing signature +- Cap key_frames to representative events only (1 per cluster, sorted chronologically) + +### Issue 11: Annotation Verification — LOW RISK +**What could break:** +- Re-parsing raw YAML annotations may be slow for slots with large geom files (some are 100MB+) +- Minor floating-point differences in timestamp calculation could produce false positives + +**Mitigation:** +- Only re-parse activities.yml (small), not geom.yml (large) +- Use a ±0.5s tolerance for timestamp comparisons +- Run as an optional --verify flag, not in the critical path + +### Issue 12: Global No-Camera/No-Time Policy — HIGH RISK ⚠️ +**What could break:** +- **This is the same risk as Issue 4 amplified.** Removing camera/time/location from ALL generators at once without rich descriptions in place will produce very few usable questions +- The _disambiguate_description() function in spatial currently uses "(activity at Xs)" — removing this without replacement means many spatial pairs become indistinguishable → question yield drops +- The temporal generator's identical-description fallback "(on camera {cam})" will be removed — questions with two identical descriptions will be dropped entirely + +**Mitigation:** +- **IMPLEMENTATION ORDER IS CRITICAL:** + 1. First: Issue 2 (SegFormer descriptions) + Issue 1 (color match validation) + 2. Second: Issue 8 (spatial dedup) + Issue 5 (first instance) + 3. Third: Issue 12 (remove camera/time/location) — ONLY after rich descriptions are confirmed working + 4. Last: Issue 4 (event ordering camera strip) + Issue 3 (variable counts) +- After step 1, run a diagnostic: "how many entity pairs would survive uniqueness filtering with SegFormer descriptions?" If < 30% pass, descriptions need further enrichment before proceeding +- Keep old camera-ref code behind a --legacy-camera-refs flag during transition for A/B testing + +--- + +## CRITICAL: Implementation Order + +DO NOT implement these in arbitrary order. The dependency chain is: + +``` +Issue 2 (SegFormer) ──→ Issue 1 (color match) ──→ Issue 12 (no camera/time) ──→ Issue 4 (event ordering strip) + │ │ + └── Issue 8 (spatial dedup) └── Issue 3 (variable counts) + Issue 5 (first instance) Issue 7 (perception enrich) + Issue 6 (cross-cam dedup) + Issue 10 (key frames) + Issue 9 (counting reasoning) + Issue 11 (annotation verify) +``` + +**Phase 1 — Foundation (must be first):** +- Issue 2: Switch to SegFormer, re-extract all slots +- Issue 1: Color-match MEVID assignment + +**Phase 2 — Bug Fixes (independent, can parallelize):** +- Issue 5: First instance of activity +- Issue 6: Cross-camera event dedup +- Issue 8: Spatial same-person dedup +- Issue 9: Deterministic counting reasoning +- Issue 10: Counting key frames +- Issue 11: Annotation verification (optional) + +**Phase 3 — Policy Enforcement (requires Phase 1 complete):** +- Issue 12: Remove all camera/time/location from non-perception/best_camera categories +- Issue 4: Strip camera IDs from event ordering + temporal +- Issue 3: Convert hard counts to soft caps +- Issue 7: Enrich perception with person descriptions + +**Validation Gate between phases:** +After Phase 1, run diagnostic on 5 test slots: +- Count how many temporal pairs have unique SegFormer descriptions +- Count how many event ordering groups pass uniqueness filter +- If < 50% pass → descriptions need more attributes before Phase 3 +- If > 70% pass → proceed to Phase 3 \ No newline at end of file diff --git a/docs/project-history/github-session-archive/2-17_todos.md b/docs/project-history/github-session-archive/2-17_todos.md new file mode 100644 index 0000000..f6ae764 --- /dev/null +++ b/docs/project-history/github-session-archive/2-17_todos.md @@ -0,0 +1,169 @@ +V9 Pipeline Analysis & Integration Report +1. Multi-Camera-Main vs Current V9: Critical Findings +CRITICAL BUG FOUND: Activity Counting Duplicates Multi-Camera Events +Both generate_counting.py (V6) and generate_numerical.py (V9) have the same bug: +# Both versions count ALL events without camera deduplication +activity_counts = Counter(e.activity for e in sg.events) +# ^^^ BUG: Same activity seen by 3 cameras = counted as 3 separate events + +Example Bug: + +One person opens a door at 10 seconds +Visible on cameras G299, G330, G336 +Current count: 3 events ❌ +Correct count: 1 event ✅ +Fix Strategy: Deduplicate by (activity, time_window, entity_cluster) + +## 2. MEVID Removal Plan → **REVISED: Batch YOLO on Geom Crops** + +### ✅ NEW APPROACH: Extract Crops from geom.yml + Run Batch YOLO + +**Key Insight**: Don't need MEVID tracklets - can extract crops directly from geom.yml bboxes! + +**Strategy**: +1. For each entity, select **best 1-2 frames** (largest bbox + mid-sequence) +2. Extract crops from raw AVIs using geom.yml coordinates +3. Save to disk: `~/data/yolo_crops/{slot}/{camera}_actor{id}_{idx}.jpg` +4. Run YOLO in batch on GPU: **~15K crops × 50ms = 12 min total** + +**Coverage**: +- Before (MEVID): 23 persons (<1% of entities) +- After (Geom+YOLO): **ALL annotated entities (100% coverage)** +- Quality: Same YOLO semantic richness, no MEVID dependency + +**Implementation**: +- Script: `scripts/batch_yolo_extraction.py` ✅ Created +- Test run: 24 crops from 1 slot ✅ Working + +### What Needs to Change: + +**Core File: `person_descriptions.py`** +- ❌ Remove: `load_person_database()`, `is_mevid_supported()`, `load_mevid_slots()` +- ✅ Replace with: `load_yolo_entity_descriptions()` (from batch extraction) +- ✅ Keep: HSV extraction as fallback for non-YOLO entities + +**New 2-Tier Description Priority**: +1. **YOLO-extracted** (from geom crops, ALL entities) - highest quality +2. **Activity Fallback** ("a person walking") - only for entities without geom bboxes + +**Impact**: +- Before: 887/929 slots (95.5%) with MEVID filter +- After: **929/929 slots (100%) processable** +- Before: 10% entity enrichment (MEVID only) +- After: **95%+ enrichment** (YOLO on all geom entities) + +**Batch Extraction Plan**: +```bash +# Step 1: Extract crops for all 929 slots (7-8 hours, run in tmux) +tmux new -s yolo_extract +cd /home/ah66742 && source venv/bin/activate +python3 scripts/batch_yolo_extraction.py --extract-crops --slots-file data/slot_index.json -v + +# Step 2: Run YOLO batch inference (12 minutes on GPU) +python3 scripts/batch_yolo_extraction.py --run-yolo -v +# Output: ~/data/yolo_entity_descriptions.json +``` +3. Files Requiring Edits +File Changes Priority +generate_numerical.py Add _deduplicate_events_by_cluster() P0 +person_descriptions.py Remove MEVID, rename functions P0 +run_pipeline.py Remove slot filtering P0 +generate_perception.py Make attribute_verification generic P1 +generate_reidentification.py Make optional (YOLO-only) P1 +generate_scene_summary.py Use entity counts P1 +4. External +Dataset Insights + +Ego-Exo4D Structure: +✅ Grounding metadata: activity + timestamp for every question +✅ Reasoning field: Explains why answer is correct +✅ Fully naturalized: No template markers +NuScenes Structure: +✅ Object-centric: Counts by class (human.pedestrian.construction_worker) +✅ Metadata preservation: Full object_counts for validation +❌ Awkward JSON: Question field is double-encoded JSON string +Recommendation: Add Ego-Exo4D's reasoning field to V9 output for transparency. + +## 5. Implementation Roadmap — **UPDATED WITH BATCH YOLO** + +### Phase 1: Batch YOLO Extraction (NEW — 8 hours total) + +**Step 1.1**: Extract crops for all 929 slots (**7-8 hours**, run in tmux) +```bash +tmux new -s yolo_extract +cd /home/ah66742 && source venv/bin/activate +python3 scripts/batch_yolo_extraction.py --extract-crops --slots-file data/slot_index.json -v +# Output: ~15K crops in ~/data/yolo_crops/{slot}/{camera}_actor{id}.jpg +``` + +**Step 1.2**: Run YOLO batch inference (**12 minutes on GPU**) +```bash +python3 scripts/batch_yolo_extraction.py --run-yolo -v +# Output: ~/data/yolo_entity_descriptions.json (entity_id → YOLO description) +``` + +**Step 1.3**: Integrate into pipeline (**30 min coding**) +- Update `person_descriptions.py` to load from `yolo_entity_descriptions.json` +- Remove MEVID references, use YOLO as primary source + +--- + +### Phase 2: Fix Numerical Deduplication (**1 hour**) + +```python +def _deduplicate_events_by_cluster(sg, resolved): + """Group events by (activity, time_window, entity_cluster).""" + TIME_WINDOW = 3.0 # seconds + + # Build entity→cluster lookup + entity_to_cluster = {} + for cluster in resolved.entity_clusters: + for eid in cluster.entities: + entity_to_cluster[eid] = cluster.cluster_id + + # Group and deduplicate... + return unique_activities # 1 per real-world activity instance +``` + +--- + +### Phase 3: Update Pipeline Files (**2 hours**) + +**refactor person_descriptions.py**: +```python +def load_yolo_descriptions() -> Dict[str, str]: + """Load YOLO descriptions from batch extraction.""" + with open("/home/ah66742/data/yolo_entity_descriptions.json") as f: + return json.load(f) + +def enrich_entities(sg, verbose=False): + """Priority: YOLO → Activity fallback.""" + yolo_descs = load_yolo_descriptions() + # Assign to entities... + return entity_descriptions, {"yolo": n_yolo, "fallback": n_fallback} +``` + +**Update run_pipeline.py**: +- Remove MEVID slot filtering +- Update stats reporting (yolo/fallback instead of mevid/hsv/fallback) + +**Modify perception/reidentification/scene_summary**: +- Make re-ID optional (only if entity has YOLO description) +- Use entity counts instead of MEVID person counts + +--- + +### Phase 4: Test & Batch (**4 hours**) + +1. Test 5 diverse slots with new YOLO descriptions +2. Batch 929 slots → ~13K questions +3. Validate counts (no multi-camera duplicates) + +--- + +**Total Timeline**: +- Phase 1: 8 hours (mostly autonomous in tmux) +- Phase 2-3: 3 hours coding +- Phase 4: 4 hours compute + +**Expected Result**: 95%+ entity enrichment with YOLO quality, 0% MEVID dependency \ No newline at end of file diff --git a/docs/project-history/github-session-archive/ANNOTATION_QUALITY_ANALYSIS.md b/docs/project-history/github-session-archive/ANNOTATION_QUALITY_ANALYSIS.md new file mode 100644 index 0000000..716b53b --- /dev/null +++ b/docs/project-history/github-session-archive/ANNOTATION_QUALITY_ANALYSIS.md @@ -0,0 +1,94 @@ +# MEVA Annotation Quality Analysis + +**Date Created:** February 6, 2026 +**Last Updated:** February 11, 2026 +**Status:** Documented — Mitigations Implemented in V5 Pipeline + +## Overview + +During comprehensive analysis of the MEVA dataset for multi-camera temporal question generation, significant annotation quality issues were identified. These are **by design** (activity-centric annotation protocol) and have been addressed through pipeline design choices in V5. + +## Core Annotation Characteristics + +### 1. **Activity-Centric Protocol (By Design)** +- From `MEVA-Annotation-Definitions.pdf`: Entities are ONLY tracked when performing one of 37 defined ActEV activities +- People visibly walking through a scene without performing a defined activity are NOT annotated +- This is systemic and applies to ALL annotation sources (Kitware, CMU, UMD, IBM, NIST) + +### 2. **Identity Switching** +- **Issue**: Objects/people receive different IDs across camera views and time segments +- **Impact**: Cannot track individuals across cameras using annotation IDs alone +- **V5 Mitigation**: Questions avoid referring to specific actor IDs; use activity descriptions instead + +### 3. **Temporal Annotation Density** +- **Issue**: Not all moments of activity are annotated (activity-centric, not continuous) +- **V5 Mitigation**: Questions only reference annotated events with verified timestamps from Kitware YAML + +### 4. **Coarse Activity Categories** +- **Issue**: Only 37 activity types for complex real-world scenarios +- **V5 Mitigation**: Questions use the 37 activities as-is; no finer distinctions required + +### 5. **Annotation Source Inconsistency** +- **Issue**: Kitware uses `person_opens_trunk`, NIST JSON uses `Open_Trunk` +- **V5 Mitigation**: V5 uses Kitware YAML exclusively (densest, most consistent source) +- **Priority order** (in `extract_logic_tuples.py`): `kitware > kitware-training > nist-json` + +## Annotation Source Inventory + +| Source | Clips | Activities | Quality | Used in V5? | +|--------|-------|------------|---------|-------------| +| **kitware** (eval) | 769 | 761 clips with acts (99%) | Gold standard | **Yes (primary)** | +| **kitware-meva-training** | 1,443 | 35,174 instances | Reduced audit | Fallback | +| **VSet7** (actev-data-repo) | 60 clips / 47 slots | 2,324 activities | NIST JSON | Comparison only | +| **CMU-v2** (contrib) | 1,111 | All 37 types | Old activity names | No | +| **UMD-IBM** (contrib) | 637 | All 37 types | Collaboration | No | +| **NIST JSON** (nist-kf1-json) | ~65 | Sparse subset | Legacy | No | + +## V5 QA Vulnerability Assessment + +| Category | Risk Level | Rationale | +|----------|------------|-----------| +| **temporal** | SAFE | Cross-camera event ordering uses verified Kitware timestamps | +| **event_ordering** | SAFE | Internal chronological consistency — all events are annotated | +| **perception** | SAFE | Positive-only "which camera shows X?" — eliminates false-negative risk | +| **spatial** | SAFE | KRTD camera-center positions — independent of annotations | +| **camera_transition** | SAFE | Entry/exit timing from Kitware YAML — factually correct within annotations | + +**Key V5 design decisions that eliminated vulnerabilities:** +- Dropped "No"/"doesn't show" perception questions (eliminated false-negative risk) +- Dropped causality category (required same-actor chains that annotations don't reliably provide) +- Spatial questions use camera positions (KRTD), not actor bounding boxes +- No GPT/LLM in answer computation — fully deterministic + +## GPS Data Assessment + +- **116 GPX files** with **continuous** actor GPS tracks (~10 sec intervals, ~28 pts/actor/slot) +- **105 unique actor IDs** (G517-G625), consistent within March session +- **No mapping exists** between GPS actor IDs and Kitware annotation actor IDs +- **Current use**: GPS parsed into scene graph for verification metadata only; NOT in question text +- **Potential**: Continuous tracks enable relative spatial queries between GPS actors + +## Recommendations + +### Implemented (V5) +1. Use Kitware-eval annotations exclusively (densest, highest quality) +2. Positive-only perception questions +3. Camera-center spatial instead of actor-bbox spatial (avoids OOM, annotation gaps) +4. Fully deterministic QA — no LLM-computed answers +5. Conservative event selection with verified timestamps + +### Future Improvements +1. **GPS-based spatial questions**: Continuous GPS tracks enable "which actors are closest" +2. **VSet7 cross-validation**: Use VSet7 annotations for QA validation on overlapping slots +3. **Numerical category**: Requires cross-camera person dedup to enable counting questions + +## Related Files + +| File | Purpose | +|------|---------| +| `scripts/generate_qa_v5.py` | V5 QA pipeline (current, 1525 lines) | +| `scripts/generate_qa_v4.py` | V4 pipeline (archived, entity-level spatial reference) | +| `scripts/extract_logic_tuples.py` | Slot index builder, annotation source priority | +| `scripts/integrate_vset7.py` | VSet7 annotation parser and exporter | +| `data/slot_index.json` | Machine-readable slot metadata (929 slots) | +| `data/qa_pairs/{slot}.v5.json` | V5 QA output | diff --git a/docs/project-history/github-session-archive/SESSION_LOG.md b/docs/project-history/github-session-archive/SESSION_LOG.md new file mode 100644 index 0000000..0d84b40 --- /dev/null +++ b/docs/project-history/github-session-archive/SESSION_LOG.md @@ -0,0 +1,1765 @@ +# Session Log + +This file tracks completed tasks and important decisions across sessions. + +--- + +## 2026-02-12 (Session 17): V7 Memory Optimization + V6 Pipeline Documentation + +### Context +User requested: (1) Confirm V7 has full GEOM loading (not sampled), and (2) comprehensive explanation of V6 pipeline from ground-up principles to low-level implementation. + +### Tasks Completed + +#### 1. Memory Optimization: Full GEOM Loading for V7 + +**Problem identified**: V6 used `sample_every=30` → only loads every 30th bbox frame +- Incomplete trajectories (miss activities between keyframes) +- Spatial questions use "closest frame" approximation (up to 15 frames = 0.5s off) +- Only ~300 bboxes per actor (vs 9000 for full clip) + +**Solution implemented**: Changed V7 to `sample_every=1` → loads ALL bboxes +- Modified `scripts/v7/build_scene_graph.py`: `sample_every=30` → `sample_every=1` +- Modified `scripts/v7/utils/yaml_stream.py`: Updated docstring, changed default to `sample_every=1` +- Updated `v7_todo.md` Section 6: Added comprehensive memory optimization explanation + +**Impact**: +- **Memory cost**: 20 actors × 9000 frames = 180K bboxes per camera (~7 MB) +- **Total for 8 cameras**: ~56 MB (0.04% of 128 GB RAM) — easily feasible +- **Benefits**: Complete trajectories, exact spatial positioning (no approximation), enables future trajectory-based questions + +**Rationale documented in v7_todo.md Section 6**: +- Temporal resolution: Activities can be 1-2 seconds (30-60 frames), sampling every 30 means only 2 samples +- Spatial accuracy: No more "closest frame" heuristic, get exact bbox at target frame +- Future-proof: Enables trajectory analysis ("Was person moving toward/away?") + +#### 2. Comprehensive V6 Pipeline Documentation + +Created `docs/V6_PIPELINE_EXPLAINED.md` (5000+ lines) with 8 major sections: + +**Section 1: Ground-Up Principles** +- Core problem: multi-camera coordination, entity ambiguity, scale, memory, quality +- Design philosophy: modular architecture, entity-centric model, ground truth + heuristics, streaming +- Key assumptions: 30 fps, ±1s camera sync, actor_id per-camera only, 37 activity taxonomy, 80% KRTD coverage + +**Section 2: Pipeline Architecture** +- High-level flow diagram (7 steps: parse → scene graph → resolution → temporal/spatial/perception QA → validation) +- Module structure (8 Python files + 4 utilities) +- Data flow diagram with arrows showing transformations + +**Section 3: Data Structures** +- Event (raw activity from YAML) +- Entity (tracked person/vehicle on one camera with bboxes) +- SceneGraph (slot-level knowledge: cameras + entities + events) +- ResolvedGraph (cross-camera links + clusters) +- Question output format (JSON schema with verification blocks) + +**Section 4: Step-by-Step Flow** +- **Step 1 (Parse)**: Load activities.yml, types.yml → List[Event] +- **Step 2 (Scene Graph)**: Load cameras, extract entities, stream geom.yml bboxes → SceneGraph +- **Step 3 (Resolution)**: MEVID camera-level lookup + temporal handoff heuristic → ResolvedGraph with Union-Find clustering +- **Step 4 (Temporal)**: Find cross-camera event pairs with 3-20s gaps → 3 temporal questions +- **Step 5 (Spatial)**: Project bboxes to 3D via KRTD, compute distances → 3 spatial questions (near/moderate/far) +- **Step 6 (Perception)**: Activity identification, camera selection, multi-camera confirmation → 3 perception questions +- **Step 7 (Validation)**: Category-specific deduplication + validation rules + +**Section 5: Low-Level Implementation Details** +- **5.1 YAML parsing**: Why CSafeLoader for small files, regex streaming for large (50+ MB) geom.yml + - Inline regex: `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` + - Performance: 1.5s for 100K lines vs 8s for full load +- **5.2 KRTD 3D projection**: 6-step pipeline (bbox → undistort → normalized coords → 3D ray → ground plane intersection → ENU position) + - Why feet not bbox center: feet on ground (Z=0), center is ~1m above +- **5.3 Why no MEVID links**: Can't map MEVID person_id → Kitware actor_id without image matching (10 hours for 47 slots) + - V7 adds camera-level validation flag (plausibility proxy) +- **5.4 Bug fixes explained**: Inline YAML regex (Bug 1), mega-cluster safeguards (Bug 2), category-specific dedup (Bug 3) + +**Section 6: Memory Management** +- V6 strategy: `sample_every=30` → 6000 bboxes per camera (240 KB), fast but incomplete +- V7 strategy: `sample_every=1` → 180K bboxes per camera (7 MB), complete trajectories +- HDF5 mmap future work: For 100+ slot batch processing, one-time indexing + constant RAM queries + +**Section 7: Question Generation Strategies** +- **7.1 Temporal**: Gap selection 3-20s (not trivial/ambiguous), distribute across ranges +- **7.2 Spatial**: Proximity buckets (near ≤5m, moderate 5-15m, far >15m), one per bucket +- **7.3 Perception**: Distractor bank with context/type/visual matching, 37 MEVA activities grouped + +**Section 8: Validation & Quality Control** +- Validation rules per category (cross-camera check, gap ranges, distance-proximity consistency) +- Success criteria (9 questions, 0 errors, <15s generation time) +- Output JSON schema with all required fields + +### Files Created/Modified + +**Created**: +- `docs/V6_PIPELINE_EXPLAINED.md` (comprehensive 5000+ line technical documentation) + +**Modified**: +- `scripts/v7/build_scene_graph.py` (changed `sample_every=30` → `sample_every=1`) +- `scripts/v7/utils/yaml_stream.py` (updated docstring for `sample_every=1` default) +- `v7_todo.md` (added Section 6: Scene Graph Memory Optimization with full loading rationale) +- `.github/SESSION_LOG.md` (this file) + +### Key Decisions + +**Memory optimization approach**: +- **V7 immediate**: Full GEOM loading (`sample_every=1`) — 56 MB for 8 cameras, trivial memory cost +- **Future batch processing**: HDF5 mmap indexing deferred until 100+ slot processing needed +- **Rationale**: Better data quality (complete trajectories, exact spatial) worth small memory increase + +**Documentation scope**: +- Ground-up principles explain WHY (multi-camera problem, scale challenges) +- Architecture explains WHAT (7 steps, modular design, data structures) +- Low-level details explain HOW (regex streaming, KRTD projection math, Union-Find clustering) +- Can now explain V6 to advisors, paper reviewers, or collaborators + +### Notes for Future Sessions + +**V7 implementation ready**: +- Full GEOM loading implemented ✅ +- Next: Entity aliasing (30 min), activity hierarchy (30 min), debug markers (1 hour) +- Follow v7_todo.md Section 9 (Implementation Plan) + +**To run V6 with full context**: +```bash +cd /home/ah66742 && source venv/bin/activate +python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v +``` + +**To read V6 documentation**: +```bash +cat docs/V6_PIPELINE_EXPLAINED.md +``` + +**Memory profile for V7 (expected)**: +- Scene graph build: ~100 MB (full GEOM for 8 cameras) +- Entity resolution: ~50 MB (link graph + clusters) +- Question generation: ~10 MB (candidate lists) +- **Total peak**: ~200 MB (0.15% of 128 GB RAM) + +--- + +## 2026-02-13 (Session 16): V7 Planning & MEVID Implementation Analysis + +### Context +User requested detailed explanations of V6 bugs, verification markers, and why MEVID links aren't used. Then requested V7 planning with specific implementation tasks and MEVID implementation evaluation. + +### Tasks Completed + +#### 1. V6 Bug Explanations +Documented three critical bug fixes from V6 development: + +**Bug 1: geom.yml Inline Format** +- Problem: Kitware uses inline `- { geom: {id1: X, ts0: Y, g0: ...} }` not block YAML +- Impact: 0 bboxes for 3/8 cameras (G299, G419, G423) +- Fix: Inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` +- Result: 516 actors found for G330 (was 0) + +**Bug 2: Entity Resolution Mega-Cluster** +- Problem: Too permissive linking → 33,592 links → 1 giant cluster (all 1,258 entities) +- Root causes: No gap limit, no link count limit, low confidence threshold (0.5), linked background actors +- Fix: MAX_HANDOFF_GAP=10s, MAX_LINKS_PER_ENTITY=2, threshold 0.7, only active entities (926 with events) +- Result: 1,330 links → 20 meaningful clusters + +**Bug 3: Deduplication `None == None`** +- Problem: Checked `event_a`/`event_b` for ALL categories, spatial/perception lack these → `None == None` → dropped all after first +- Impact: First run generated 5 questions (3 temporal + 1 spatial + 1 perception) instead of 9 +- Fix: Category-specific dedup (entity_a/b for spatial, question_type for perception) +- Result: Full 9 questions generated (3+3+3) + +#### 2. Verification Marker Documentation +Explained technical markers in V6 output: + +**entity_a/entity_b**: Kitware actor IDs like `G328_actor_1613855842107762240` (vague, needs human-readable aliases in V7) + +**world_pos_enu**: 3D world coordinates `[East, North, Up]` from KRTD camera calibration + bbox foot projection to ground plane (Z=0). Used to compute spatial distance questions. + +#### 3. MEVID Gap Analysis +Why V6 shows `"entity_link": "heuristic"` for all questions: + +**MEVID provides**: Camera-level person mapping: `Person 4: [G299, G328, G336, G419, G420, G421, G423]` + +**Missing link**: Can't map MEVID `person_id` → Kitware `actor_id` without: +1. Extracting MEVID tracklet image crops (500K-800K images) +2. Matching crops to Kitware geom.yml bboxes via image similarity (Person ReID model) +3. Spatial-temporal validation (frame overlap + IoU) +4. Compute-intensive: ~10 hours for 47 VSet7 slots, ~8 days for 929 slots + +**V6 current use**: MEVID used only for validation/stats (41 persons in slot) + +**V7 enhancement**: Camera-level MEVID validation — flag questions where both cameras covered by same person (plausibility proxy without full matching) + +#### 4. V7 Documentation +Created comprehensive V7 planning documents: + +**[v7_todo.md](v7_todo.md)** — Implementation plan with 8 phases: +1. ✅ Copy V6 → V7 +2. ⏳ Entity aliasing (30 min) +3. ⏳ Activity hierarchy (30 min) +4. ⏳ Update question generators (1 hour): aliases + debug_info + connected events +5. ⏳ MEVID validation logic (20 min) +6. ✅ Archive cleanup +7. ⏳ GPT wrapper (20 min) +8. ⏳ Testing (20 min) +9. Added memory optimizations section from v7_memory_optimizations.md + +**[V7_AUDIT.md](V7_AUDIT.md)** — Bug explanations, marker docs, MEVID analysis, pros/cons, next steps + +**[V6_V7_QUICK_REF.md](V6_V7_QUICK_REF.md)** — Quick reference guide for V6 bugs, V7 changes, run commands + +**[MEVID_IMPLEMENTATION_PLAN.md](MEVID_IMPLEMENTATION_PLAN.md)** — Detailed analysis of MEVID→Kitware mapping: +- 3 implementation options evaluated (ResNet embeddings, Person ReID, visual similarity) +- Recommended: Person ReID model (torchreid/OSNet) with spatial-temporal validation +- Complexity: 7 hours one-time MEVID feature extraction + 12 min/slot +- Total: 10 hours for 47 VSet7 slots, 8 days for 929 slots +- **Decision**: Defer to post-V7 — Complete V7 pipeline first with camera-level MEVID validation proxy + +#### 5. V7 Module Structure +Created `scripts/v7/` by copying V6, ready for enhancements: +- Entity aliasing: `make_alias()` method in build_scene_graph.py +- Activity hierarchy: New activity_hierarchy.py module +- Connected events: Temporal questions require scene graph connection +- Debug markers: Frame ranges, timestamps, clip files for manual verification +- MEVID validation: Camera-level flags for questions where both cameras covered by same person + +#### 6. Slot Viewer Tool +Created `scripts/view_slot.py` for visualizing all cameras at once (headless-compatible): +- **Frame grid mode**: Extract frames at timestamps → labeled PNG grid (1.5 MB, viewable in VS Code) +- **Filmstrip mode**: One row per camera, N columns across time +- **Mosaic video mode**: Tiled MP4 (using cv2.VideoWriter, no FFmpeg needed) +- **Annotation overlay**: Display active Kitware events on each camera +- Output: `output/slot_views/` + +### Files Created/Modified + +**Created**: +- `v7_todo.md` (updated with detailed implementation tasks + memory optimizations) +- `V7_AUDIT.md` (bug explanations, pros/cons, next steps) +- `V6_V7_QUICK_REF.md` (quick reference) +- `MEVID_IMPLEMENTATION_PLAN.md` (detailed Person ReID implementation plan) +- `scripts/v7/` (copied from V6, ready for enhancements) +- `scripts/view_slot.py` (headless slot viewer) +- `data/qa_pairs/2018-03-11.11-25-00.school.v6.json` (9 questions, 0 validation errors) + +**Modified**: +- `scripts/v6/utils/yaml_stream.py` (inline regex fix) +- `scripts/v6/entity_resolution.py` (tightened heuristic thresholds) +- `scripts/v6/run_pipeline.py` (category-specific dedup) +- `.github/SESSION_LOG.md` (this file) + +### Key Decisions + +**V7 Implementation Approach**: +- Refine V6 foundation (don't redesign) +- 4 core enhancements: entity aliases, connected events, debug markers, MEVID validation +- Total estimated time: ~3 hours +- Target same slot as V6 for direct comparison + +**MEVID Implementation Strategy**: +- **Best approach**: Person ReID model (torchreid/OSNet) + spatial validation +- **Complexity**: 10 hours for VSet7, 8 days for full dataset +- **Decision**: Defer to post-V7 — V7's camera-level validation sufficient as proxy +- **Rationale**: Complete V7 → batch VSet7 → evaluate quality → decide if full MEVID matching needed + +**Memory Optimization**: +- Documented HDF5 mmap approach for geom.yml (Section 13 in v7_todo.md) +- Decision: Keep streaming for V7, add mmap later for batch processing (929 slots) +- Justification: Streaming adequate for single-slot work, mmap overhead not amortized until 100+ slots + +### Notes for Future Sessions + +**Immediate next steps** (3 hours): +1. Add `Entity.make_alias()` to `scripts/v7/build_scene_graph.py` (30 min) +2. Create `scripts/v7/activity_hierarchy.py` with 10 relationships (30 min) +3. Update question generators with aliases + debug_info + connected events (1 hour) +4. Add MEVID camera-level validation (20 min) +5. Create `scripts/naturalize_v7_qa.py` GPT wrapper (20 min) +6. Test on `2018-03-11.11-25-00.school`, compare with V6 output (20 min) + +**After V7 completion**: +- Batch process 47 VSet7 slots +- VLM evaluation (GPT-4o/Gemini) +- Human validation (50 sample questions) +- If quality questioned → implement full MEVID→Kitware mapping + +**To view slot frames**: +```bash +python3 scripts/view_slot.py --slot 2018-03-11.11-25-00.school --time 60 120 --annotate -v +``` + +**To run V6 pipeline**: +```bash +cd /home/ah66742 && source venv/bin/activate +python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v +``` + +--- + +## 2026-02-13 (Session 15): V6 Pipeline Implementation — Full Execution + +### Context +Executed v6_todo.md end-to-end on a single MEVID-supported slot (`2018-03-11.11-25-00.school`). + +### Tasks Completed + +#### 1. V6 Pipeline Implementation (14 files) +Created complete modular pipeline in `scripts/v6/`: +- **Utilities** (4 files): `utils/yaml_stream.py` (geom.yml streaming parser), `utils/mevid.py` (MEVID person-camera mapping), `utils/krtd.py` (camera calibration + bbox→3D), `utils/iou.py` (IoU computation) +- **Pipeline steps** (5 files): `parse_annotations.py` (Kitware YAML → Events), `build_scene_graph.py` (entity-based scene graph), `entity_resolution.py` (MEVID + heuristic cross-camera linking), `distractor_bank.py` (wrong answer generation), `run_pipeline.py` (orchestrator) +- **QA generators** (3 files): `generate_temporal.py`, `generate_spatial.py`, `generate_perception.py` +- **Package files** (2): `__init__.py`, `utils/__init__.py` + +#### 2. Slot Selection +Chose `2018-03-11.11-25-00.school`: 9 cameras, 8/9 in MEVID, kitware annotation source, 41 MEVID cross-camera persons. + +#### 3. Bugs Fixed (3 iterations) +1. **geom.yml inline format**: Kitware uses single-line `- { geom: {id1: X, ts0: Y, g0: X1 Y1 X2 Y2} }`. Added inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` +2. **Entity resolution mega-cluster**: 33,592 heuristic links → 1 cluster. Fixed: MAX_HANDOFF_GAP=10s, MAX_LINKS_PER_ENTITY=2, only active entities, confidence threshold 0.7 → 1,330 links, 20 clusters +3. **Deduplication bug**: `is_duplicate_within_slot` used `None == None` comparisons for spatial/perception questions (no `event_a`/`event_b` fields). Rewrote with category-specific dedup logic. + +#### 4. Final Results +9 questions (3 temporal + 3 spatial + 3 perception), 0 validation issues, 7.9s runtime. +- **Temporal**: cross-camera event ordering, 3.03-3.04s gaps, entity-linked +- **Spatial**: near (2.87m), moderate (8.06m), far (98.22m) via KRTD bbox→3D on G328 +- **Perception**: which_camera, activity_identification, multi_camera_confirmation — all affirming + +### Files Created +- `scripts/v6/__init__.py` +- `scripts/v6/utils/__init__.py` +- `scripts/v6/utils/yaml_stream.py` +- `scripts/v6/utils/mevid.py` +- `scripts/v6/utils/krtd.py` +- `scripts/v6/utils/iou.py` +- `scripts/v6/parse_annotations.py` +- `scripts/v6/build_scene_graph.py` +- `scripts/v6/entity_resolution.py` +- `scripts/v6/distractor_bank.py` +- `scripts/v6/generate_temporal.py` +- `scripts/v6/generate_spatial.py` +- `scripts/v6/generate_perception.py` +- `scripts/v6/run_pipeline.py` +- `data/qa_pairs/2018-03-11.11-25-00.school.v6.json` + +### Key Decisions +- **Heuristic linking** used for temporal questions (MEVID provides camera-level mapping only, not MEVID person_id → Kitware actor_id) +- **Only G328 and G336** have KRTD models for this slot (G299, G330 excluded as indoor) — spatial questions limited to these cameras +- **3 geom.yml cameras returned 0 bboxes** (G299, G419, G423) — possibly different annotation sub-format or missing geom files +- Entity links labeled `heuristic` in output (not `mevid_ground_truth`) since we can't directly confirm same-person identity + +### Notes for Future Sessions +- geom.yml parsing still returns 0 for G299, G419, G423 — investigate if these lack geom.yml or use a different format +- KRTD coverage is sparse (2/8 cameras) — spatial questions come from G328 only +- Heuristic vs MEVID comparison report (Section 12.2 of v6_todo.md) documented but not implemented — future work +- To run: `cd /home/ah66742 && source venv/bin/activate && python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v` + +--- + +## 2026-02-12 (Session 14): V6 Plan Rewrite, Repo Cleanup Strategy + +### Context +User provided 12 specific V6 requirements and clarified local-vs-repo distinction for GitHub push. + +### Tasks Completed + +#### 1. Rewrote v6_todo.md with All 12 User Requirements +Complete rewrite incorporating: temporal gap 3-15s (fallback 20s), 2+ cameras for temporal only, geom.yml streaming prototype first, raw annotation text, simple spatial + 1 complex, easy difficulty default with classification system, distractor bank, V4/V5-style output format, within-slot deduplication, MEVID-only slots, start with 1 slot, validation plan (documented not implemented). + +Modular architecture: 9 files in `scripts/v6/` + 4 utility modules. Only 3 QA categories: temporal, spatial, perception. + +#### 2. MEVID Data Analysis +Decoded naming convention, found 158 persons / 8093 tracklets / 25 cameras. 98% of train persons on 2+ cameras. 900/929 MEVA slots overlap with MEVID cameras. + +#### 3. Created .gitignore + cleanup_local.sh +- `.gitignore`: excludes archived/, old outputs, large PDFs, personal dirs +- `cleanup_local.sh`: moves old outputs to `~/archived/` (no deletions) +- Replaced old `cleanup_for_push.sh` (which deleted files) + +### Files Created/Modified +- `v6_todo.md` — Complete rewrite (14 sections) +- `.gitignore` — New +- `cleanup_local.sh` — New (replaces cleanup_for_push.sh) + +### Key Decisions +- Scene graph is THE novelty — all cross-camera questions derive from it +- MEVID ground truth first, heuristic second (must prove heuristic works before expanding) +- 3 categories only: temporal, spatial, perception +- Raw annotation text in questions (GPT wrapper later) + +### Next Steps +1. Review v6_todo.md, run cleanup_local.sh, git init + push +2. Begin V6 Phase 1: prototype geom.yml streaming + +--- + +## 2026-02-11 (Session 13): Full Audit, Reference Cleanup, Progress Report + +### Context +Follow-up session after V5 pipeline was built in Session 12. Focus on documentation hygiene, deep GPS investigation, V4/V5 comparison, VSet7 analysis, and producing a finalized progress report for the team. + +### Tasks Completed + +#### 1. Updated ALL Outdated References +Rewrote 3 documentation files that referenced obsolete pipelines (V3, multicam_qa_pipeline, LLM-based approaches): +- `.github/ANNOTATION_QUALITY_ANALYSIS.md` — Rewrote with V5 vulnerability assessment (all 5 categories now SAFE); removed references to V3, MEVID integration, `_precompute_spatial_answer()` +- `.github/multicam-temporal-questions.md` — Complete rewrite: now documents V5 pipeline, 5 active categories, correct paths, pipeline history table +- `docs/README.md` — Replaced Multi-Camera integration guide with current V5 pipeline docs + +#### 2. V4 vs V5 Detailed Evaluation +Comprehensive comparison of both pipelines: +- **V5 wins**: Self-contained, 5 categories (vs 3), no OOM risk, no pre-build needed +- **V4 still valuable for**: Entity-level spatial (bbox→3D projection via KRTD) — more granular than V5's camera-center approach +- **V4 text quality better**: Has `ACTIVITY_DISPLAY` dict with 3 grammatical forms + `describe_by_action` priority scoring +- **V4 temporal harder**: Asks "what happens next?" (prediction) vs V5's "which occurred first?" (ordering) +- **Verdict**: V5 is production; V4 kept as spatial bbox reference + +#### 3. GPS Data Deep Investigation +**Key finding: GPS tracks ARE continuous** — not just start/end waypoints. +- Each GPX file has `` (waypoint summaries) AND `` (continuous tracks) +- **94 actors** per slot, **29-30 trackpoints each** at ~10 sec intervals +- Can compute pairwise distances between actors at any moment (e.g., G559-G525: 18.5m) +- Official GPS README confirms: "actors and personnel carried GPS loggers set to log their location once every 10 seconds" +- mevadata.org also confirms actor GPS logging + +**Decision**: GPS is NOT useless — it's rich continuous data. However, **GPS actor IDs (G517-G625) cannot be linked to annotation actor IDs** (no mapping exists), so GPS positions can't be used in video-answerable questions. GPS remains verification-only. Spatial questions use KRTD camera-center distances (video-grounded). + +Updated V5 `generate_spatial_qa()` docstring to document this decision. + +#### 4. Camera Transition Fix +Verified that camera transition options **already** come from same-slot cameras (using `sg.cameras.keys()`). Added minimum camera threshold: skip category if `len(sg.cameras) < 3` (trivial with only 2 cameras). + +#### 5. VSet7 vs Kitware — Definitive Comparison +Ran side-by-side comparison on `2018-03-11.16-20-00.school`: +- **Kitware**: 587 events, 6 cameras +- **VSet7**: 332 events, 3 cameras (G330, G423, G638) +- On overlapping cameras: annotations are **identical** (same activities, same frame ranges) +- VSet7 simply covers **fewer cameras** — it's a strict subset, NOT a supplement +- **VSet7 adds nothing that Kitware doesn't already have** +- Only value: cross-validation (confirms annotation quality) and NIST JSON format + +#### 6. Finalized Progress Report +Created `docs/PROGRESS_REPORT.md` — comprehensive team report covering: +- Executive summary, dataset overview, pipeline architecture +- 5 question categories with examples +- How we worked around annotation sparsity (positive-only perception, activity-based questions, KRTD spatial, timestamp pairing) +- Novel methods (cross-camera temporal pairing, camera-center spatial, event-level scene graph, vulnerability-aware categories) +- Pipeline evolution (V1→V5) +- Test results with sample questions +- Next steps and research opportunities + +### Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `.github/ANNOTATION_QUALITY_ANALYSIS.md` | **Rewritten** | Updated for V5, removed V3/MEVID references | +| `.github/multicam-temporal-questions.md` | **Rewritten** | Full V5 pipeline documentation | +| `docs/README.md` | **Rewritten** | Current V5 docs, removed Multi-Camera integration guide | +| `docs/PROGRESS_REPORT.md` | **Created** | Finalized team progress report | +| `scripts/generate_qa_v5.py` | **Modified** | Updated spatial docstring; added 3-camera min for camera_transition | +| `.github/SESSION_LOG.md` | **Updated** | This entry | + +### Key Decisions + +| Decision | Rationale | +|----------|-----------| +| GPS stays verification-only | Continuous tracks exist but no ID mapping to annotations | +| VSet7 = subset of Kitware | Identical annotations on overlapping cameras; fewer cameras | +| V5 is production pipeline | Self-contained, 5 categories, deterministic, no OOM | +| V4 kept for spatial reference | Entity-level bbox→3D projection code still valuable | +| Camera transition needs ≥3 cameras | Trivial with 2 cameras | + +### Next Session Priority +1. **Batch V5 across all 929 slots** — measure success/skip rates +2. **GPT naturalization** on validated slots +3. **Entity-level spatial** — cherry-pick V4's bbox projection for small geom files +4. **VLM evaluation** — feed generated QA to target model + +--- + +## 2026-02-11 (Session 12): V5 QA Pipeline — Build + Quality Fixes + +### Context +Fresh start for V5 QA pipeline following NuScenes approach: deterministic symbolic QA from scene graphs, GPT wrapper only at the end. All old QA pairs/scene graphs/logic tuples archived. Focus on getting 1-2 slots perfect before scaling. + +### Tasks Completed + +#### 1. Archive Old Outputs +- Moved all previous QA pairs (21 files: .v4, .symbolic, .crosscam, .natural) → `data/old_versions/qa_pairs/` +- Moved all scene graphs (V1 + V2) → `data/old_versions/scene_graphs/` +- Moved all logic tuples (18 files) → `data/old_versions/logic_tuples/` + +#### 2. VSet7 vs Kitware Comparison +**Key finding: VSet7 is a SUBSET of Kitware**, not a supplement. +- Slot `2018-03-11.16-20-00.school`: Kitware has 587 events (6 cameras), VSet7 has 332 events (3 cameras: G330, G423, G638) +- On overlapping cameras, annotations match exactly (same activities, same frame ranges) +- VSet7 simply covers fewer cameras + +#### 3. GPS Utility Analysis +- Parsed GPX for test slot: 88 actors, waypoints only (start/end positions), NOT continuous tracks +- GPS actor IDs (G517-G625) are NOT linked to annotation actor IDs → cannot use GPS actors directly in video QA questions +- Validated GPS→ENU conversion against 5 ground control points: ~18m systematic error with old geo_origin +- **Key insight**: GPS useful for validation/ground-truth distance, NOT for video-visible questions + +#### 4. Built `scripts/generate_qa_v5.py` (~800 lines) +Self-contained V5 pipeline: scene graph build + symbolic QA generation in one script. + +**5 active categories** (3 questions each): +| Category | Question Type | Data Source | +|----------|--------------|-------------| +| temporal | Cross-camera before/after | Kitware activities.yml timestamps | +| event_ordering | Chronological sequence of 4 events | Kitware activities across 3-4 cameras | +| perception | Which camera shows/doesn't show X? | Kitware activity→camera mapping | +| spatial | Camera position distances (KRTD) | KRTD camera center positions | +| camera_transition | First/last camera to capture event | Entry/exit events across cameras | + +**Architecture**: +- `_parse_activities_yml()` — Kitware YAML → Event dataclass (CSafeLoader for speed) +- `load_camera_info()` — Loads KRTD calibration per camera +- `build_scene_graph()` — Full SceneGraph with events, cameras, GPS, cross-cam pairs +- `_build_options()` — MCQ builder with MAX_MCQ_OPTIONS=5 cap +- 5 generator functions: `generate_{temporal,event_ordering,perception,spatial,camera_transition}_qa()` +- CLI: `--slot`, `--compare-vset7`, `--list-vset7-slots`, `--verbose`, `--output` +- Output: `data/qa_pairs/{slot}.v5.json` + +#### 5. Quality Fixes (Iteration 1) +**Fixed 3 issues found in initial run:** + +| Issue | Fix | +|-------|-----| +| Spatial: GPS actor IDs (G577, G599) unlinked to camera annotations | Replaced with KRTD camera-position-based questions | +| CamTrans Q3: absolute timestamp in question ("at 131.7 seconds") | Replaced with activity description ("after performing X on camera Y") | +| CamTrans: too many options (6-7 cameras) | Added `_build_options()` helper with MAX_MCQ_OPTIONS=5 cap | + +**New spatial question types** (KRTD-based, camera-grounded): +1. Cross-camera movement: exit cam A → enter cam B, distance from KRTD positions +2. Camera proximity: which pair of cameras positioned closest? +3. Camera distance: which pair monitors most distant locations? + +#### 6. Test Results (Both Slots Pass) +**Slot 1: `2018-03-11.16-20-00.school`** +- 587 Kitware events, 6 cameras (G299, G330, G421, G423, G638, G639) +- 88 GPS actors, 15 questions generated (3 per category) +- All 15 pass quality checks + +**Slot 2: `2018-03-11.11-30-00.school`** +- 527 Kitware events, 7 cameras (G299, G328, G330, G336, G419, G420, G423) +- 89 GPS actors, 14 questions (2 spatial — no cross-cam movement Q due to missing KRTD for G419/G420/G423) +- All 14 pass quality checks + +### Key Decisions +1. **GPS actors NOT used in QA questions** — IDs don't map to annotation actors +2. **KRTD camera positions for spatial** — only 4-6 cameras per slot have KRTD, but sufficient +3. **Indoor cameras** (G299, G330) noted but not excluded from spatial — they have KRTD positions +4. **MCQ cap at 5 options** for all camera-based questions +5. **MEVID skipped** — not needed for current QA categories + +### Camera KRTD Coverage (School Site) +| Camera | KRTD | Position (ENU) | +|--------|------|----------------| +| G299 | Yes | (-16, -12, 4) indoor | +| G328 | Yes | (62, 81, 6) | +| G330 | Yes | (-16, 12, 4) indoor | +| G336 | Yes | (6, 86, 9) | +| G419 | No | — | +| G420 | No | — | +| G421 | No | — | +| G423 | No | — | +| G638 | Yes | (10, 85, 8) | +| G639 | Yes | (48, 104, 11) | + +### Files Created/Modified +- **Created**: `scripts/generate_qa_v5.py` (~800 lines) +- **Created**: `data/qa_pairs/2018-03-11.16-20-00.school.v5.json` (15 QA pairs) +- **Created**: `data/qa_pairs/2018-03-11.11-30-00.school.v5.json` (14 QA pairs) +- **Archived**: `data/old_versions/{qa_pairs,scene_graphs,logic_tuples}/` (51 files total) +- **Installed**: pyproj 3.7.1 in venv + +### Next Steps +1. **Manual review** of generated questions by user — verify correctness against video +2. **GPT naturalization** — run `naturalize_qa.py` on V5 output to polish template text +3. **Expand to more slots** once quality is confirmed +4. **Numerical category** — still unsolved (cross-camera person dedup needed) +5. **Consider**: Loading small geom files (G421: 276KB, G638: 567KB) for bbox-based spatial questions + +--- + +## 2026-02-10 (Session 11 UPDATED): GPS Actors + MEVID Coverage Analysis + +### Context +Major discoveries about GPS data and MEVID coverage. GPS tracks **actors** (not cameras!), +and most MEVID clips already exist on disk. + +### Tasks Completed + +#### 1. GPS Data — ACTORS, NOT CAMERAS! +**Key discovery**: The GPX files contain **actor GPS tracks**, not camera positions. +- Each actor carried a GPS logger (ID format: Gnnn like G623) +- Loggers recorded position every 10 seconds +- 105 unique actors tracked across the KF1 collection +- Consistent actor IDs within each session (March and May) + +**Built `scripts/parse_gps_actors.py`** to extract and analyze actor tracks: +```bash +python3 scripts/parse_gps_actors.py --stats # Summary: 116 files, 105 actors, 256K trackpoints +python3 scripts/parse_gps_actors.py --slot "2018-03-07.17-05-00" -v # Single slot +python3 scripts/parse_gps_actors.py --all # Process all GPX files +``` + +| Metric | Value | +|--------|-------| +| GPX files | 116 (5-min slots) | +| Unique actor IDs | 105 (G517-G625 range) | +| Total actor-track instances | 9,070 | +| Total trackpoints | 256,456 | +| GPX slots with annotations | 87 of 116 (75%) | + +**Use case**: GPS tracks can validate cross-camera identity links — if two activities +share the same GPS logger position at similar times, they are the same actor. + +#### 2. MEVID Coverage — 70% ALREADY ON DISK! +Re-analyzed MEVID video URLs with correct directory structure (hour subdirs by END time): + +| Category | Clips | +|----------|-------| +| Total MEVID clips | 976 | +| **Already on disk** | **685 (70.2%)** | +| Missing (March) | 0 | +| Missing (May 05-14/16/18) | 291 | + +**All 685 March MEVID clips exist!** Only May dates (2018-05-14, 2018-05-16, 2018-05-18) are missing. + +**Download script created**: `scripts/download_mevid_may.sh` +```bash +./download_mevid_may.sh --dry-run # Preview download (~8.5-14.2 GB) +./download_mevid_may.sh # Download 291 May clips +``` + +#### 3. Extra Dates — Status Update +- **2018-03-08, 2018-03-10**: Not in MEVID, would need MANIFEST download +- **2018-05-14, 2018-05-16, 2018-05-18**: Available via MEVID URLs, download script ready + +#### 2. VSet7 Annotations — INTEGRATED +Built `scripts/integrate_vset7.py` to parse and export VSet7 NIST JSON annotations: + +| Metric | Value | +|--------|-------| +| Source file | `actev-data-repo/partitions/ActEV-Eval-CLI-Validation-Set7.activities.json` (144MB) | +| Total activities | 4,648 (2,324 after deduplicating SimSA5_ prefixes) | +| Activity types | 35 unique | +| Clips | 60 | +| Unique slots | 47 (all match our slot_index exactly) | +| Top activities | person_sits_down (273), person_exits_scene (253), person_talks_to_person (241) | + +**Usage**: +```bash +python3 scripts/integrate_vset7.py --stats # Show statistics +python3 scripts/integrate_vset7.py --list-slots # List 47 slots +python3 scripts/integrate_vset7.py # Export all to data/vset7_annotations/ +``` + +**Output**: `data/vset7_annotations/{slot}.json` — 47 per-slot JSON files in logic tuple format + +#### 3. GPS Data — NOT USEFUL FOR ACTORS +- Analyzed 116 GPX files in `gps-data/gps-for-released-meva-data/` +- **Finding**: GPX files contain **camera positions** (waypoints named "G559 start", "G559 end"), NOT actor GPS tracks +- 188 waypoints (camera start/end pairs) + 94 tracks of camera movement +- **Conclusion**: Cannot use for actor validation — GPS data is for camera geolocation only + +#### 4. MEVID Re-ID — DIFFERENT DATES +- Analyzed MEVID directory structure in `data/mevid/` +- **Finding**: MEVID uses dates **2018-05-16** and **2018-05-18** — which we don't have on disk +- Video URLs point to `drop-5-mevid` collection (separate from our main data) +- Tracklet format: numeric columns (frame ranges, person IDs, camera IDs encoded as numbers) +- **Conclusion**: Cannot integrate MEVID without downloading additional video data + +#### 5. V4 Pipeline Testing +Tested V4 on 2 existing slots with scene graphs: + +| Slot | Questions | Temporal | Spatial | Perception | +|------|-----------|----------|---------|------------| +| 2018-03-07.17-05-00.school | 10 | 3 | 3 | 4 | +| 2018-03-05.13-15-00.bus | 8 | 0 | 4 | 4 | + +Bus slot had fewer questions due to limited cross-camera temporal pairs. + +### Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/integrate_vset7.py` | Created | VSet7 JSON parser (405 lines) | +| `scripts/parse_gps_actors.py` | Created | GPS actor track parser (454 lines) | +| `scripts/download_mevid_may.sh` | Created | Download script for 291 May MEVID clips | +| `/nas/mars/dataset/MEVA/vset7_annotations/*.json` | Created | 47 per-slot VSet7 annotation files (moved from ah66742/data) | +| `/nas/mars/dataset/MEVA/gps_actors/*.json` | Created | 116 per-slot GPS actor files (moved from ah66742/data) | +| `/nas/mars/dataset/MEVA/mevid_data/` | Organized | MEVID re-ID data (moved from ah66742/data) | +| `data/qa_pairs/*.v4.json` | Created | V4 QA outputs for tested slots | +| `scripts/old_versions/` | Organized | Moved 6 old scripts (build_scene_graph.py, generate_qa_crosscam.py, etc.) | + +### Directory Reorganization +- **ah66742/data**: Now contains only QA pipeline data (logic_tuples, qa_pairs, scene_graphs, slot_index) +- **MEVA/**: Contains all dataset files (gps_actors, mevid_data, vset7_annotations) +- **Cleaned up**: Deleted old conversion logs, duplicate annotations folder, moved old scripts + +### Key Decisions + +| Decision | Rationale | +|----------|-----------| +| **GPS validation not feasible** | GPX files contain camera positions, not actor tracks | +| **MEVID integration skipped** | Requires downloading 2018-05-16/18 videos | +| **VSet7 is supplementary** | All 47 slots already in our index with Kitware annotations | +| **V4 pipeline validated** | Works correctly on tested slots | + +### Data Availability Summary + +| Data Source | Status | Notes | +|-------------|--------|-------| +| Kitware YAML (meva-data-repo) | ✅ Primary | 891 slots, comprehensive | +| VSet7 NIST JSON (actev-data-repo) | ✅ Supplementary | 47 slots, 2,324 activities | +| GPS actor tracks | ❌ N/A | Camera positions only | +| MEVID re-ID | ❌ Different dates | Would need video download | +| Extra dates (03-08, 03-10, 05-16) | ❌ Not on disk | In MANIFEST but not downloaded | + +### Next Session Priority +1. **Batch V4 across more slots** — Focus on slots with existing scene graphs +2. **Build scene graphs for VSet7 slots** — 47 slots with dense NIST annotations +3. **Merge VSet7 with Kitware** — Consider joint annotation source in extract_logic_tuples.py +4. **Quality analysis** — Compare V4 output diversity across slot types + +--- + +## 2026-02-10 (Session 10): V4 QA Generator + actev-data-repo Exploration + +### Context +Built V4 QA generator addressing all known quality issues from vulnerability analysis. Then explored +the newly cloned actev-data-repo from NIST/Kitware for additional data. + +### Tasks Completed + +#### V4 QA Generator (`scripts/generate_qa_v4.py`, ~935 lines) +- **3 categories only**: temporal (cross-cam), spatial (3D), perception (positive-only) +- **10 questions per slot**: 3 temporal + 3 spatial + 4 perception +- **Key design decisions**: + - **Dropped causality** — causal chains weren't ground truth, just heuristics + - **Spatial: 3D world coordinates** via KRTD back-projection instead of 2D left/right + - Projects bbox foot-point to ground plane intersection + - Distance in meters (Near ≤5m, Far ≥15m) + - Camera-invariant — same answer regardless of viewing angle + - **Temporal: cross-camera only** with 3-20s gap, different cameras, different activities + - **Perception: positive-only MCQ** — "Which activity occurs?" with 1 correct + 3 absent distractors + - Eliminates false-negative vulnerability entirely +- **Successfully tested**: school slot (2018-03-07.17-05-00.school) → 10 questions + - Temporal gaps: 4.9s, 10.3s, 11.5s + - Spatial distances: 1.4m, 32.5m, 36.2m + - All 4 perception questions verified correct + +#### actev-data-repo Exploration +- **Cloned**: `https://gitlab.kitware.com/actev/actev-data-repo.git` → `/nas/mars/dataset/MEVA/actev-data-repo/` +- **Git issue**: Remote pack file has corrupt blob (d71d3ea7...), checkout fails for corpora/partitions/scripts dirs. Used `git show HEAD:` to read all content. +- **Key findings**: + - **2,704 tracked files**: 666 annotations, 2,018 partitions, 6 corpora, 13 scripts + - **MEVA MANIFEST**: 4,159 R13 AVI files (610.5 GB), 737 MP4s, 1,575 SimSA clips + - Dates include 2018-03-08, 2018-03-10, 2018-05-16 (NOT in our avis/ directory) + - **MEVA KF1-examples**: 65 clips with KPF YAML annotations — **ALL already exist in meva-data-repo** + - **Validation Set 7**: 214 MEVA clips, 60 processed with **4,648 NIST JSON activity annotations** across **33 of our 891 slots**, 70 activity types (35 standard + 35 SimSA5) + - **HADCV22-Test**: 201 clips including dates we don't have (03-08, 03-10, 05-16) + - **VIRAT**: 118 clips (different dataset, same format) — not useful for MEVA work + - **SimUF-KA-SimSA5**: 154 activity exemplar clips with annotations — potential for VLM few-shot + - **kpf_to_json_py3.py**: Authoritative KPF→JSON converter + +#### Pipeline Explanation +- Provided comprehensive pipeline writeup for team presentation +- Covered: slots → scene graphs → QA generation → naturalization → evaluation flow + +### Files Created +- `scripts/generate_qa_v4.py` — V4 QA generator (3 categories, 10 questions) +- `data/qa_pairs/2018-03-07.17-05-00.school.v4.json` — Test output +- `docs/actev-data-repo-reference.md` — Comprehensive reference (11 sections) + +### Key Decisions +- **V4 is current best generator** — replaces symbolic + crosscam approaches +- **actev-data-repo adds no new annotations** for our pipeline (all 65 are duplicates) +- **VSet7 activities.json** is potentially useful — NIST JSON format with per-frame bboxes for 33 slots +- **HADCV22 dates** (03-08, 03-10, 05-16) could expand coverage if we find those videos on disk + +### Next Session Priority +1. **Batch V4** across all 891 slots — track success/failure rates +2. **GPS-based validation** — Parse 116 .gpx files for actor count confidence scores +3. **MEVID identity integration** — 175 overlapping slots with person re-ID tracklets +4. **VSet7 integration** — Consider importing NIST JSON annotations as additional source +5. **Check for extra dates** — Do we have 2018-03-08 and 2018-03-10 videos on disk? + +--- + +## 2026-02-08 (Session 7): Cross-Camera QA Generator + +### Context +Built on Session 6's V2 scene graph with cross-camera narrative chains. Created a new QA generator +specifically for questions that REQUIRE multiple camera views to answer — the "dream" questions for +multi-camera VLM evaluation. + +### Tasks Completed + +1. **Built `scripts/generate_qa_crosscam.py`** — 5 cross-camera question categories: + - **cross_camera_temporal** (15): "After the person who exits a vehicle opens a facility door, what do they do next in a different camera view?" + - **cross_camera_ordering** (15): Arrange 3-4 events from different cameras chronologically (gerund form in questions) + - **cross_camera_tracking** (15): "After [person] does X in camera G421, which camera view shows them next?" + - **cross_camera_gap** (15): "Between opening a facility door and dropping off a person, what does [person] do?" (middle event on possibly 3rd camera) + - **camera_identification** (6-15): "A person does X in one view. In which other view do they do Y?" (requires different activities on source/target cameras) + +2. **Quality fixes through 4 iterations:** + - **Entity type consistency**: `describe_by_action()` now takes `prefer_type` parameter so person questions use person-activity descriptors (not "the vehicle that drops off a person opens a facility door") + - **Gerund forms**: Added 3rd tuple to `ACTIVITY_DISPLAY` dict (3rd person, infinitive, gerund). Gap questions now use "Between opening ... and closing ..." instead of "Between opens ... and closes ..." + - **Tracking redundancy**: Descriptor pool excludes events with same activity as the source event (prevents "the person who picks up an object picks up an object") + - **Gap answer validation**: Middle event must differ from BOTH bookend activities + - **Camera identification**: Requires different activities on source vs target cameras (prevents trivial "same activity on which camera") + - **Flexible minimums**: Tracking and camera_identification work with 3 cameras (2 distractors) instead of requiring 4+ + +### Test Results + +| Slot | Cameras | Cross-cam Narratives | Questions | 2-cam Qs | 3-cam Qs | +|------|---------|---------------------|-----------|----------|----------| +| school (2018-03-07.17-05-00) | 5 (G299, G330, G336, G421, G638) | 15 | 75 | 67 | 8 | +| bus (2018-03-05.13-15-00) | 3 (G340, G505, G506) | 3 | 45 | 45 | 0 | + +100% of questions verified to require 2+ cameras. + +### Key Design Decisions +- All 5 categories use cross-camera narrative chains (from V2 scene graph) as input +- Questions never mention camera IDs in the question stem (except tracking category which gives source cam) +- Actor descriptions use distinctive non-redundant actions from the same narrative chain +- `MAX_TIME_GAP_SEC = 60.0` (wider than single-camera 30s — allows walking between cameras) +- `SEED = 42` for deterministic output + +### Example 3-Camera Question +> **Q**: Between opening a facility door and dropping off a person, what does the person who exits a vehicle do? +> **A**: enters the scene through a structure +> **Cameras needed**: G336, G421, G638 + +### Full Pipeline (3 steps) +```bash +cd /home/ah66742 && source venv/bin/activate + +# Step 1: Build V2 scene graph with cross-camera linking +python3 scripts/build_scene_graph_v2.py --slot "2018-03-07.17-05-00.school" -v + +# Step 2: Generate cross-camera QA +python3 scripts/generate_qa_crosscam.py --slot "2018-03-07.17-05-00.school" -v +# Output: data/qa_pairs/{slot}.crosscam.json +``` + +### Key Files + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_crosscam.py` | **Created** | Cross-camera QA generator, 5 categories | +| `data/qa_pairs/*.crosscam.json` | Generated | Cross-camera QA pairs (school: 75, bus: 45) | + +### Next Steps +1. **Batch run** V2 scene graph + crosscam QA across all 476 multi-camera slots +2. **Optimize YAML parsing** for 10-camera slots (currently times out) +3. **Merge with single-camera symbolic QA** for a complete benchmark set +4. **GPT naturalization** of cross-camera templates +5. **Quality analysis** across the full dataset + +--- + +## 2026-02-08 (Session 5): Symbolic QA Generator + GPT Naturalization Wrapper + +### Context +Continuing from Session 4's scene graph architecture. Built the two remaining pipeline stages: +1. Deterministic symbolic QA generator (graph traversal, no LLM) +2. GPT naturalization wrapper (rephrases templates, doesn't compute answers) + +### Tasks Completed + +1. **Built `scripts/generate_qa_symbolic.py`** — 6 question categories: + - **temporal** (15): "After the person who opens the trunk closes the trunk, what do they do next?" + - **ordering** (6-11): Arrange 3-4 events from same actor chain chronologically + - **perception** (11-14): Yes/No about activity occurrence, balanced positive/negative + - **causality** (15): "Why does X do Y?" using CAUSAL_CHAINS + same-actor adjacency + - **spatial** (15): 2D bbox relations (left/right/above/below), 60%+ consistency filter + - **camera_view** (5-15): Which abstract camera label shows an exclusive activity + +2. **Comprehensive grammar fixes** — 5 rounds of testing/fixing: + - Added `ACTIVITY_DISPLAY` dict mapping all 37 activities → proper English (3rd person + base form) + - "Does anyone closes trunk" → "Does anyone close the trunk?" (base form after "does") + - "the person who vehicle stops" → "a vehicle" (entity-type-aware descriptions) + - "Why does X transfers object?" → "Why does X transfer an object?" (base form after auxiliary "does") + - "Because they just talks to person" → "Because they just talk to another person" (base form with "they") + - Vehicle entities described only by vehicle_* activities (not person_opens_vehicle_door) + - Redundancy prevention: if actor description = event_a action, re-describe excluding evt_a + - Template deduplication: skip if same question_template text already generated + - Distractor filtering: only same entity type (person distractors for person Qs) + +3. **Built `scripts/naturalize_qa.py`** — GPT wrapper: + - Loads `.symbolic.json`, sends each question to GPT for naturalization only + - System prompt strictly prohibits changing meaning, adding info, or reordering options + - Per-category hints guide GPT style (temporal=sequence, spatial=keep relation words, etc.) + - JSON response format enforced, with validation (field presence, option count match) + - 3 retries on failure, keeps original template as fallback + - Dry-run mode shows prompts + cost estimates without API calls + - Output: `.natural.json` with both original templates and naturalized versions + +4. **Set up Python venv** with openai 2.17.0 + pyyaml at `/home/ah66742/venv/` + +### Test Results + +| Slot | Events | Cameras | Questions | Time | +|------|--------|---------|-----------|------| +| bus (2018-03-05.13-15-00) | 59 | 3 (G340, G505, G506) | 70 | <1s | +| school (2018-03-07.17-05-00) | 345 | 5 (G299, G330, G336, G421, G638) | 82 | <1s | + +Dry-run cost estimate for naturalization: ~$0.006/slot (gpt-4o-mini), ~$0.08/slot (gpt-4o) + +### Key Constants +- `MAX_PER_CATEGORY = 15` +- `MAX_TIME_GAP_SEC = 30.0` (events must be within 30s for temporal Qs) +- `SEED = 42` (deterministic shuffling) +- `COREF_IOU_THRESHOLD = 0.5` (from build_scene_graph.py) + +### Full Pipeline (3 steps) +```bash +cd /home/ah66742/scripts + +# Step 1: Build scene graph (annotations → graph JSON) +python3 build_scene_graph.py --slot "2018-03-05.13-15-00.bus" -v + +# Step 2: Generate symbolic QA (graph → template MCQ, deterministic) +python3 generate_qa_symbolic.py --slot "2018-03-05.13-15-00.bus" -v + +# Step 3: Naturalize with GPT (template → natural language, optional) +export OPENAI_API_KEY="sk-..." +/home/ah66742/venv/bin/python3 naturalize_qa.py --slot "2018-03-05.13-15-00.bus" --model gpt-4o-mini -y +``` + +### Key Files + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_symbolic.py` | **Created** | Symbolic QA generator, 6 categories | +| `scripts/naturalize_qa.py` | **Created** | GPT naturalization wrapper | +| `data/qa_pairs/*.symbolic.json` | Generated | Deterministic QA pairs (bus: 70, school: 82) | +| `venv/` | **Created** | Python 3.10 venv with openai, pyyaml | + +### Output Format (.symbolic.json) +Each question includes: +- `question_template`: Template text (proper grammar) +- `options`: MCQ options (4 for most, 2 for perception Yes/No) +- `correct_answer_index`: Deterministic, computed by graph traversal +- `correct_answer`: Text of correct option +- `verification`: Full metadata (actor_id, event_ids, camera_id, frames, timestamps, video_file) + +### Next Steps +1. **Run naturalization** with API key on bus slot to verify GPT output +2. **Batch scene graph + QA generation** across all 891 slots +3. **Quality review** of naturalized questions — check GPT doesn't hallucinate +4. **MEVID cross-camera identity** for camera_transition questions (~20% slots) + +--- + +## 2026-02-08 (Session 4): Pipeline Rethink — Temporal Scene Graph + Symbolic QA + +### Context +Reviewed the project's running slides (Multi Camera - Running Slides.pdf) to align MEVA QA generation with the NuScenes approach used by the lab. Key insight from slides: questions should be generated from a **temporal scene graph** with **deterministic answer computation**. GPT is used ONLY to naturalize question text — never to compute answers or decide which camera to reference. + +### Problems Identified with Old Approach (V1-V3) +- V1 questions put camera IDs in the question text (VLM should figure this out) +- V1 specified raw timestamps like "175s" (unnatural, too precise) +- V1-V3 used anonymous actor labels (person_1, vehicle_5) unrecognizable to VLMs +- V3 had GPT computing answers (risk of hallucination) +- No scene graph structure — just flat event lists + +### Architecture Decision: NuScenes-Style Pipeline +From the slides, the correct pipeline is: +1. **Annotations → Temporal Scene Graph** (nodes=entities/events, edges=temporal/spatial/coref) +2. **Graph → Symbolic Queries** with deterministic answers (graph traversal, not inference) +3. **GPT naturalizes** question text only (no new information introduced) +4. **Actors identified by their actions** ("the person who opens the trunk" not "person_7") +5. **Camera not revealed** in question — VLM must determine which camera(s) to examine + +### Tasks Completed + +1. **Cleaned up old files into `old_versions/` subdirectories**: + - `scripts/old_versions/`: V1, V2, V3 QA scripts + prompts + legacy pipeline + - `data/old_versions/`: old QA pairs, logic tuples, GPT logs, test_output.json + +2. **Built `scripts/build_scene_graph.py`** — Temporal Scene Graph builder: + - **Entity nodes** from `geom.yml`: per-actor bbox tracks with keyframe sampling + - **Event nodes** from `activities.yml`: activity instances with actors, timing, camera + - **Temporal edges**: BEFORE/AFTER/OVERLAPS between ALL event pairs (1711 for bus, 59340 for school) + - **Spatial edges**: 2D bbox relations (LEFT_OF, RIGHT_OF, ABOVE, BELOW) for co-visible entities + - **Visibility edges**: entity ↔ camera mapping with frame ranges + - **Participation edges**: entity ↔ event links with roles (primary/object) + - **Co-reference edges**: bbox IoU-based detection of same physical entity across annotation IDs + +3. **Implemented entity co-reference via bbox IoU**: + - MEVA assigns fresh actor IDs per activity → same person gets different IDs + - Solution: compute avg IoU between same-type entities on same camera + - IoU ≥ 0.5 → co-reference (many are IoU=1.0 — identical bboxes) + - Union-Find clustering merges co-referent IDs into physical entity clusters + - **Results**: Bus slot: 96 raw entities → 49 clusters (17 merged, 17 multi-event actors) + - **Results**: School slot: 546 raw entities → 380 clusters (91 merged, 90 multi-event actors) + +4. **Built actor event chains** (cluster-aware): + - Each physical entity (cluster) gets a chronological list of activities + - Example: person_3/G340 → opens_trunk → unloads_vehicle → carries_heavy_object → picks_up → puts_down + - Enables "the person who opens the trunk, what do they do next?" style questions + +### Key Files + +| File | Action | Description | +|------|--------|-------------| +| `scripts/build_scene_graph.py` | **Created** | Temporal scene graph builder with coref | +| `data/scene_graphs/` | **Created** | Output directory for per-slot scene graphs | +| `data/scene_graphs/2018-03-05.13-15-00.bus.json` | Generated | 661KB, 59 events, 17 multi-event actors | +| `data/scene_graphs/2018-03-07.17-05-00.school.json` | Generated | 17MB, 345 events, 90 multi-event actors | +| `scripts/old_versions/` | **Created** | V1/V2/V3 scripts, prompts, legacy pipeline | +| `data/old_versions/` | **Created** | Old QA pairs, logic tuples, GPT logs | + +### Scene Graph Structure (JSON) + +``` +{ + "entities": {entity_id: {bbox tracks, type, camera}}, + "events": [{activity, actors, camera, start/end time}], + "temporal_edges": [{source, target, BEFORE/AFTER/OVERLAPS, gap_sec}], + "spatial_edges": [{entity_a, entity_b, LEFT_OF/RIGHT_OF/ABOVE/BELOW, frame}], + "visibility_edges": [{entity_id, camera_id, frame range}], + "participation_edges": [{entity_id, event_id, role}], + "coref_edges": [{entity_a, entity_b, avg_iou}], + "entity_clusters": {representative: [member_ids]}, + "actor_event_chains": {cluster_rep: [event_ids chronological]} +} +``` + +### Next Steps (QA Generation) + +1. **Build `generate_qa_symbolic.py`** — deterministic QA from scene graph: + - **Temporal**: Use actor chains + BEFORE edges for "the person who does X, what do they do next?" + - **Ordering**: Pick N events from same actor chain, shuffle, ask for order + - **Perception**: Check visibility edges — "Is activity X visible in this scene?" + - **Causality**: Same-actor chains with causal pairs (opens_door → exits_vehicle) + - **Spatial**: Bbox-based relations at event time + - **Camera transition**: Which camera shows entity's first/last activity? + - All answers computed by graph traversal — NO GPT inference + - Events must be temporally close (not 175s apart) + - Camera IDs NOT in question text + - Actors identified by their distinguishing action + +2. **Build `naturalize_qa.py`** — thin GPT wrapper: + - Input: template questions + computed answers from symbolic generator + - Output: natural language MCQ + - GPT cannot change the answer or add information + +3. **MEVID integration** — enrich entity clusters with cross-camera identity for 175 overlapping slots + +### Notes for Future Sessions +- `build_scene_graph.py` imports from `extract_logic_tuples.py` (slot index, annotation parsing) +- Run from `scripts/` directory: `cd scripts && python3 build_scene_graph.py --slot X -v` +- Co-reference IoU threshold is 0.5 (constant `COREF_IOU_THRESHOLD`) + +--- + +## 2026-02-09 (Session 6): Annotation Folder Subset Check + +### Tasks Completed +1. **Verified annotations vs kitware subset** — Compared file lists between MEVA/annotations and meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware. + - Result: **Not a subset**; both sides have files missing from the other. + - Counts: annotations has 192 files; kitware has 2310 files. + - Example: annotations-only includes 2018-03-07/16/2018-03-07.16-50-01.16-55-01.school.G328.*.yml. + - Example: kitware-only includes 2018-03-05/13/2018-03-05.13-15-00.13-20-00.bus.G340.*.yml. + +### Notes +- Comparison performed via sorted file list diff (relative paths) on 2026-02-09. + +--- + +## 2026-02-09 (Session 7): Full DIVA-Phase-2 Diff Lists + +### Tasks Completed +1. **Computed full diff lists against entire DIVA-phase-2/MEVA tree** (kitware, kitware-meva-training, nist-kf1-json, contrib). + - Normalized paths by removing the source prefix to match MEVA/annotations layout. + - Result: annotations-only list is empty (0 files); DIVA-only normalized list has 12,984 files. + +### Files Created +- `output/annotations_only_normalized.txt` +- `output/diva_only_normalized.txt` + +### Notes +- Normalization drops `kitware/`, `kitware-meva-training/`, `nist-kf1-json/`; for `contrib/`, drops `contrib//`. +- School slot scene graph is 17MB due to bbox keyframes; use `--compact` to strip them +- The `actor_event_chains` are the key structure for question generation + +--- + +## 2026-02-05 (Session 3): V3 Pipeline Audit, Bug Fixes & MEVID Integration Research + +### Context +After renaming QA scripts (V1=rule-based, V2=monolithic LLM, V3=per-question LLM), ran V3 on bus slot and found quality issues in the output. User also asked about MEVID dataset to solve the anonymous actor problem (person_1, vehicle_5 unrecognizable to VLMs). + +### Tasks Completed + +1. **Full quality audit of V3 output** — Checked all 12 questions against ground truth tuples. Found 5 bugs: + + | Bug | Category | Issue | Severity | + |-----|----------|-------|----------| + | 1 | Temporal | 178s gap labeled "shortly before" — no proximity filter | High | + | 2 | Temporal | Both Qs produced "Before" — no relationship diversity | Medium | + | 3 | Causality | No shared actors between cause/effect — unrelated people paired | High | + | 4 | Perception | Both Qs positive (True, True) — no balance enforcement | Low | + | 5 | Spatial | GPT hallucinated "exclusive to G505" when G506 also had it | High | + +2. **Fixed all 5 bugs in `generate_qa_v3_multicam.py`**: + + - **Temporal proximity**: Added `MAX_TIME_GAP_SEC = 60` constant. `_select_temporal_pair()` now filters pairs by max gap, sorts by proximity, picks from top-5 closest + - **Temporal diversity**: Tracks `_used_rel_types` across calls, prefers unused relationship types (Before/After/During) + - **Causality actor overlap**: Scoring system in `_find_causal_pair()`: shared actors (×100) + same camera (×10) + proximity. Prefers pairs with shared actors; logs warnings when none found + - **Perception balance**: Tracks `_perception_history`, forces alternation between positive/negative + - **Spatial pre-validation**: New `_precompute_spatial_answer()` method computes ground truth BEFORE calling GPT. Answer passed in prompt so GPT can't hallucinate. Updated `prompts/spatial.txt` template + +3. **Re-ran V3 and verified all fixes** (seed=99, gpt-4o-mini): + - Temporal: During (overlap=3.0s ✓) + Before (gap=0.0s ✓) — diverse relations, close events + - Ordering: Both chronologically verified ✓ + - Perception: False + True alternation ✓ + - Causality: Same-camera pairs (G506→G506), gap=-0.3s — plausible even without shared actors + - Spatial: exclusive_activity verified ✓ (person_exits_scene_through_structure on G506 only) + - Camera transition: Both verified against ground truth ✓ + +4. **Renamed all QA scripts** (from previous sub-session): + - `generate_qa_logic.py` → `generate_qa_v1_rulebased.py` + - `generate_qa_llm.py` → `generate_qa_v2_llm.py` + - `generate_qa_multicam.py` → `generate_qa_v3_multicam.py` + +5. **Researched MEVID dataset** for identity-aware annotations: + - **Paper**: WACV 2023, Davila, Du, Lewis, Funk et al. + - **Repo**: [github.com/Kitware/MEVID](https://github.com/Kitware/MEVID) + - **Scale**: 158 unique people, 598 outfits, 8,093 tracklets, 25 cameras, ~10M frames + - **Key insight**: Built on top of MEVA — identity labels overlap with activity labels + - **Data**: bbox crops with naming `{personID}O{outfit}C{camera}T{tracklet}F{frame}.jpg` + +6. **Downloaded MEVID annotation metadata** (track_info files only, ~1MB): + - Saved to `data/mevid/mevid-v1-annotation-data/` + - Files: `track_train_info.txt` (6,339 tracklets), `track_test_info.txt` (1,754 tracklets), `query_IDX.txt` + - Format: `start_index end_index person_id outfit_id camera_id` (scientific notation, use `int(float())`) + +7. **Analyzed MEVID-MEVA coverage overlap**: + - **175 of our 891 MEVA slots overlap with MEVID** (~20%) + - 6 overlapping dates: 2018-03-07, 03-09, 03-11, 03-12, 03-13, 03-15 + - Our date 2018-03-05 has NO MEVID coverage + - MEVID also covers 2018-05-14, 05-16, 05-18 (we don't have these) + - Camera overlap varies per slot (some perfect, some partial, some zero) + - Example: `2018-03-07.11-00-00.school` has 4 overlapping cameras (G299, G330, G420, G423) + +### Key Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_v3_multicam.py` | Modified | Fixed 5 bugs (temporal, causality, perception, spatial) | +| `scripts/prompts/spatial.txt` | Modified | Added pre-computed answer fields to template | +| `scripts/generate_qa_v1_rulebased.py` | Renamed | From generate_qa_logic.py | +| `scripts/generate_qa_v2_llm.py` | Renamed | From generate_qa_llm.py | +| `data/mevid/mevid-v1-video-URLS.txt` | Downloaded | 976 MEVID clip URLs | +| `data/mevid/mevid-v1-annotation-data/` | Downloaded | Track info (train/test/query) | +| `data/qa_pairs/2018-03-05.13-15-00.bus.multicam.json` | Regenerated | Fixed V3 output | + +### MEVID Integration Plan + +**Problem**: MEVA annotations label actors as generic `person_1`, `vehicle_5` — meaningless to VLMs. +**Solution**: MEVID adds cross-camera person identity labels (158 people, 598 outfits) to MEVA. + +**Integration steps** (not yet implemented): +1. ✅ Downloaded MEVID annotation metadata +2. ✅ Confirmed 175 overlapping slots (~20% coverage) +3. **Next**: Download MEVID bbox data selectively for overlapping slots/cameras only (not full 43.5GB) +4. **Next**: Build mapping script: MEVID tracklet → MEVA logic tuple (by camera ID + frame range overlap) +5. **Next**: Enrich logic tuples with MEVID person IDs where available +6. **Next**: Update QA generators to use identity info when available + +**Limitations**: +- Only ~20% of our slots have MEVID coverage +- MEVID covers only persons (not vehicles) — cars/trucks remain anonymous +- The link between MEVID tracklets and MEVA activity annotations is by (camera, frame overlap) — not guaranteed 1:1 mapping +- Bbox data is large (43.5GB total); selective download needed +- Disk space severely limited (~105MB free) + +### Notes for Future Sessions + +- **Script naming convention**: V1 = rule-based, V2 = monolithic LLM, V3 = per-question LLM (Multi-Camera arch) +- **V3 bug fixes are tested and verified**: Run with seed=99 for the verified configuration +- **Causality actor overlap**: Still shows "no shared actors" warnings for the bus slot — this is because MEVA annotations don't consistently use the same actor ID across cause/effect pairs. The fix ensures same-camera preference, which is the best possible with current data +- **MEVID data location**: `data/mevid/` (annotation metadata), `data/mevid/mevid-v1-video-URLS.txt` (clip URLs) +- **Track info format**: Scientific notation, parse with `int(float(x))` +- **Disk very tight**: ~105MB free. Don't download bbox data (30.5GB+13GB) without clearing space first +- **copilot-instructions.md**: Should be updated to reflect V1/V2/V3 naming and MEVID integration + +--- + +## 2026-02-05: Pipeline Refactor — Extract Logic Tuples & Dedup + +### Tasks Completed + +1. **Created `scripts/extract_logic_tuples.py`** — New standalone script that: + - Reads cluster info from `data/annotated_activity_slots.txt` instead of scanning the dataset filesystem + - Builds a machine-readable `data/slot_index.json` (891 slots) on first run, caches it + - Takes a single slot (`date.time.site` format) and extracts only logic tuples (no QA generation) + - **Supports both annotation formats**: Kitware YAML (700+ slots) AND NIST JSON (57 slots). The old pipeline only handled NIST JSON + - Outputs to `data/logic_tuples/{slot}.json` + - CLI: `--list-slots`, `--list-slots-with-activities`, `--build-index`, `--slot`, `--output`, `--verbose` + +2. **Built `data/slot_index.json`** — Machine-readable version of `annotated_activity_slots.txt` + - 891 slots with cameras, sources, clip counts + - Auto-rebuilds when txt is newer than json + +3. **Investigated cross-source annotation overlap** + - **57 slots** have the same camera annotated by both kitware (YAML) and nist-json + - They are NOT literal duplicates: NIST JSON is sparse (few key events), Kitware YAML is dense (comprehensive) + - Activity names differ: NIST uses `Open_Trunk`/`Closing_Trunk`, Kitware uses `person_opens_trunk`/`person_closes_trunk` + - They don't contradict — kitware is strictly more complete + - **Added source priority dedup**: `kitware > kitware-training > nist-json`. When a camera appears in multiple sources, only the highest-priority one is used + +4. **Confirmed cross-site mixing bug in old pipeline (now fixed)** + - Old `test_output.json` used slot `2018-03-07.17-05-00` (no site), grouping bus+school cameras + - This produced nonsensical QA like comparing a bus embrace to a school trunk-opening + - New slot system (`date.time.site`) prevents this by construction + - Example: old slot split into `2018-03-07.17-05-00.admin` (1 cam), `.bus` (5 cams), `.school` (6 cams) + +### Key Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/extract_logic_tuples.py` | Created | Standalone fact extraction from single slot | +| `data/slot_index.json` | Created | Machine-readable slot index (891 slots) | +| `data/logic_tuples/` | Created | Output dir for per-slot logic tuple JSONs | +| `scripts/multicam_qa_pipeline.py` | Unchanged | Original pipeline preserved as-is | + +### Design Decisions + +- **Source priority dedup**: kitware > kitware-training > nist-json. Kitware annotations are denser and use standardized activity names. NIST JSON is a small curated subset (57 slots, 65 clips) that adds little over kitware. +- **Kitware YAML parsing**: Uses `tsr0: [start, end]` frame ranges (simpler than NIST state signals). Actor types resolved via `.types.yml` companion files. Falls back to `eval()` if PyYAML not available. +- **Original pipeline kept**: `multicam_qa_pipeline.py` is untouched. The new `extract_logic_tuples.py` is Step 1 of a modular replacement. Step 2 (QA generation — logic vs LLM API) is pending user decision. + +### Notes for Future Sessions + +- **Next step**: Decide whether QA pair generation should be rule-based logic (like the old pipeline) or LLM API calls. Create the corresponding script. +- `extract_logic_tuples.py` outputs contain `annotation_source` field per tuple for traceability +- Activity name inconsistency across sources (e.g., `Open_Trunk` vs `person_opens_trunk`) — may need a normalization map if mixing sources +- The `data/logic_tuples/` dir will accumulate per-slot JSONs; consider a batch extraction script later +- `python3` is required (no `python` alias on this system) + +--- + +## 2026-02-05 (continued): QA Generation Prototypes — Logic vs LLM + +### Context +Exploratory analysis with API budget. Lab partner (separate dataset) uses LLM API for QA generation. Goal: prototype both approaches to compare quality before running batch generation on all 891 slots. + +### Tasks Completed + +1. **Analyzed QA generation strategies** + - **Rule-based logic**: Deterministic, reproducible, fast, free. But formulaic and limited question diversity. + - **LLM API**: Natural language, creative, easy to iterate on. But non-deterministic, expensive, slow, requires API key. + - **Decision**: Hybrid approach for exploratory phase. Rule-based for temporal/ordering/perception (factual). LLM for causality/reasoning (creative). + +2. **Created `scripts/generate_qa_logic.py`** — Rule-based QA generation (deterministic) + - **Input**: `data/logic_tuples/{slot}.json` (from `extract_logic_tuples.py`) + - **Output**: `data/qa_pairs/{slot}.logic.json` + - **Categories** (6, excluding summarization & numerical): + - `temporal`: Before/after/during relationships (2 events) + - `ordering`: Chronological sequencing (3 events, shuffled labels) + - `perception`: Yes/No activity presence on camera + - `causality`: Why did X happen? (links effect to preceding cause) + - `spatial`: Camera coverage, exclusive activities, shared activity types + - `camera_transition`: Which camera first/last captured an actor (multi-camera actors only) + - **Target**: ~5-10 questions per category (57 total for bus slot) + - **Determinism**: Fixed random seed (42) for reproducibility + - **CLI**: `--input`, `--output`, `--categories` + - **Tested**: ✅ Generates 57 questions from bus slot (59 tuples, 3 cameras) + +3. **Created `scripts/generate_qa_llm.py`** — GPT-4o API-based QA generation (creative) + - **Input**: `data/logic_tuples/{slot}.json` + - **Output**: `data/qa_pairs/{slot}.llm.json` + - **Model**: OpenAI GPT-4o (default), supports `--model gpt-4o-mini` for cheaper testing + - **Prompt strategy**: + - System prompt: Instructions on rules (only use provided facts, be specific, cite cameras/timestamps) + - User prompt: Compact fact summary (~13K chars for 59 tuples), category specifications (name, description, target count) + - Response format: Structured JSON with `question`, `answer`, `evidence`, `requires_cameras` + - **CLI**: `--input`, `--output`, `--model`, `--temperature`, `--categories`, `--dry-run` + - **Dry-run mode**: Show prompt without calling API (for preview/debugging) + - **Tested**: ✅ Dry-run works. Prompt is ~13K chars (well within context) + - **Note**: Requires `pip install openai` and `export OPENAI_API_KEY="sk-..."` + +### Key Files Created + +| File | Purpose | Input | Output | +|------|---------|-------|--------| +| `scripts/generate_qa_logic.py` | Rule-based QA generation | `data/logic_tuples/{slot}.json` | `data/qa_pairs/{slot}.logic.json` | +| `scripts/generate_qa_llm.py` | LLM API-based QA generation | `data/logic_tuples/{slot}.json` | `data/qa_pairs/{slot}.llm.json` | +| `data/qa_pairs/` | Output directory | — | Both scripts' JSON outputs | + +### Workflow for Comparison + +**Complete pipeline (single command sequence):** +```bash +cd /home/ah66742 && source venv/bin/activate + +# Step 1: Extract logic tuples (already done for test slots) +python3 scripts/extract_logic_tuples.py --slot "2018-03-05.13-15-00.bus" -v + +# Step 2a: Generate QA pairs (rule-based) +python3 scripts/generate_qa_logic.py -i data/logic_tuples/2018-03-05.13-15-00.bus.json + +# Step 2b: Generate QA pairs (LLM) — requires API key +export OPENAI_API_KEY="sk-..." +pip install -q openai +python3 scripts/generate_qa_llm.py -i data/logic_tuples/2018-03-05.13-15-00.bus.json + +# Compare outputs +diff data/qa_pairs/2018-03-05.13-15-00.bus.logic.json data/qa_pairs/2018-03-05.13-15-00.bus.llm.json +``` + +### Test Results + +**Rule-based on `2018-03-05.13-15-00.bus`** (59 tuples, 3 cameras: G340, G505, G506): +``` +temporal → 10 questions +ordering → 10 questions +perception → 10 questions +causality → 10 questions +spatial → 7 questions +camera_transition → 10 questions +Total: 57 questions +``` +Generated deterministically in <1s. + +**LLM dry-run on same**: +- System prompt: 729 chars +- User prompt: 12,844 chars (compact fact summary + category instructions) +- Would send to GPT-4o, return structured JSON +- Estimated cost: ~$0.05–0.10 per slot (model: gpt-4o) + +### Next Steps + +1. **Get OpenAI API key** and test LLM generation on the bus slot +2. **Compare outputs**: Which produces better questions? More natural? More diverse? +3. **Decide on strategy**: + - Rule-based for all 891 slots? (Fast, deterministic, cheap, predictable) + - Hybrid? (Rule-based for ~7 categories, LLM for 1–2 reasoning categories) + - Full LLM? (Higher quality, more API cost, non-deterministic) +4. **Build batch script** to generate QA for all slots (with progress tracking, retry logic) +5. **Optionally**: Add activity name normalization (NIST `Open_Trunk` → Kitware `person_opens_trunk`) + +### Important Notes for Future Sessions + +- **QA scripts expect logic tuples as input**: Always run `extract_logic_tuples.py` first to generate `data/logic_tuples/{slot}.json` +- **Causal chain rules**: Hardcoded in `generate_qa_logic.py`. Update `CAUSAL_CHAINS` dict to handle new activity types or refine relationships +- **LLM prompt is tunable**: Modify `CATEGORY_SPECS` in `generate_qa_llm.py` to change question count targets, add new categories, or refine descriptions +- **Output files accumulate**: `data/qa_pairs/` will grow with each generate call. Consider cleanup/archival for large batch runs +- **Source traceability**: Each logic tuple has `annotation_source` field (kitware/kitware-training/nist-json). QA questions inherit this via `evidence` field +- **Batch generation**: Not yet built. When scaling to all 891 slots, create a script that: + - Loops through `slot_index.json` + - Calls `generate_qa_logic.py` (or LLM script) + - Tracks progress (checkpoint file) + - Handles errors gracefully (failed slots saved for retry) +- **Documentation synchronized** (2026-02-05 end-of-session): + - ✅ SESSION_LOG.md updated with complete QA generation section + - ✅ copilot-instructions.md updated with new "Step 2: Generate QA Pairs" workflow + - Both documents now reference the two QA generation scripts with complete usage examples + +--- + +## 2026-02-06: Multi-Camera Architecture QA Script + LLM Safety + Repo Analysis + +### Context +Lab partner uses UTAustin-SwarmLab/Multi-Camera repo for QA generation on nuScenes/Waymo/Ego-Exo4D. Goal: adapt their class-hierarchy architecture to MEVA pipeline and compare approaches. + +### Tasks Completed + +1. **Audited `generate_qa_llm.py` for API cost safety** + - No API call leaks found (exactly 1 call per invocation, no retry loops) + - Added: `estimate_cost()` with per-model pricing table (gpt-4o, gpt-4o-mini, gpt-4-turbo) + - Added: Confirmation prompt before API call (`Proceed? [y/N]`) + `--yes/-y` flag for batch use + - Added: Actual usage/cost reporting after API response + - Added: `api_usage` field saved in output JSON + - Added: `python-dotenv` integration for `.env` file API key loading + +2. **Cloned UTAustin-SwarmLab/Multi-Camera to `/home/ah66742/Multi-Camera/`** + - Full repo analysis: 40+ files read (all Python modules, prompt templates, configs) + - Key QA script: `nuscenes/datasetbuilder/nuscens_build.py` (1445 lines, 8 classes) + +3. **Created `Multi-Camera/REPO_GUIDE.md`** (~400 lines) + - Complete directory structure, module details, data flow diagram + - API cost considerations, dependencies, quick start guide + - MEVA comparison table, hardcoded paths inventory + +4. **Analyzed architectural differences** between `generate_qa_llm.py` and `nuscens_build.py` + - Key finding: Multi-Camera does **1 GPT call per question** with pre-selected focused context + - MEVA does **1 GPT call for all ~50 questions** (dumps everything to GPT) + - Multi-Camera: class hierarchy, prompt template files, GPT logging, MCQ output, incremental save + - MEVA: monolithic function, inline prompts, no logging, open-ended Q&A + +5. **Created `scripts/generate_qa_multicam.py`** — New MEVA QA script replicating Multi-Camera architecture + - Class hierarchy: Base `QAGenerator` → 6 subclasses (temporal, ordering, perception, causality, spatial, camera_transition) + - Each subclass: loads prompt from `scripts/prompts/*.txt`, pre-selects events, builds focused prompt, makes 1 GPT call → 1 MCQ + - GPT call logging to `data/gpt_logs/{slot}/{category}/` JSON files + - `QASample` dataclass for standardized output + - Incremental save after each question + - Cost estimation + confirmation prompt kept from MEVA + - Output: `data/qa_pairs/{slot}.multicam.json` + - CLI: `--slot`, `--categories`, `--num`, `--model`, `--dry-run`, `--yes`, `--seed` + +6. **Created 6 prompt template files in `scripts/prompts/`** + - `temporal.txt` — Before/After/During MCQ with cross-camera events + - `ordering.txt` — Chronological re-ordering with shuffled events + - `perception.txt` — Activity visibility on specific camera + - `causality.txt` — Causal link between preceding/following events + - `spatial.txt` — Camera coverage and activity distribution + - `camera_transition.txt` — Actor tracking across cameras + +7. **Freed 403 MB disk space** — Deleted `aws/` (241 MB, AWS CLI installer) and `tools/` (162 MB, static ffmpeg) + +### Key Files Created/Modified + +| File | Action | Purpose | +|------|--------|---------| +| `scripts/generate_qa_multicam.py` | Created | Multi-Camera-architecture QA generation | +| `scripts/prompts/temporal.txt` | Created | Temporal reasoning prompt template | +| `scripts/prompts/ordering.txt` | Created | Event ordering prompt template | +| `scripts/prompts/perception.txt` | Created | Perception/visibility prompt template | +| `scripts/prompts/causality.txt` | Created | Causal reasoning prompt template | +| `scripts/prompts/spatial.txt` | Created | Spatial/camera coverage prompt template | +| `scripts/prompts/camera_transition.txt` | Created | Camera transition prompt template | +| `scripts/generate_qa_llm.py` | Modified | Added cost safety, dotenv, usage tracking | +| `Multi-Camera/REPO_GUIDE.md` | Created | Comprehensive repo documentation | + +### Test Results + +**Dry-run on `2018-03-05.13-15-00.bus`** (59 tuples, 3 cameras): +``` +All 6 prompt templates loaded successfully +Default: 2 questions per category = 12 total API calls +Estimated cost: $0.0570 (gpt-4o), $0.0014 (gpt-4o-mini) +``` + +### Three QA Generation Approaches Now Available + +| Script | Approach | API Calls | Output | Cost/Slot | +|--------|----------|-----------|--------|-----------| +| `generate_qa_logic.py` | Rule-based | 0 | Open Q&A | Free | +| `generate_qa_llm.py` | Monolithic LLM | 1 | Open Q&A | ~$0.05 | +| `generate_qa_multicam.py` | Per-question LLM | 12 (default) | MCQ | ~$0.06 | + +### Usage + +```bash +cd /home/ah66742 + +# Dry-run (no API calls, shows prompts + cost) +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus --dry-run + +# Generate all 6 categories, 2 per type (12 total), with confirmation +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus + +# Single category, more questions, cheaper model +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus -c temporal -n 5 -m gpt-4o-mini + +# Batch mode (skip confirmation) +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus -y +``` + +### Notes for Future Sessions + +- **Venv activation issue**: `source venv/bin/activate` doesn't properly set `python3` path in this shell. Use full path: `/home/ah66742/venv/bin/python3` +- **aws/ and tools/ deleted** to free disk space. If ffmpeg is needed again, install via system package manager +- **Multi-Camera repo** at `/home/ah66742/Multi-Camera/` has full REPO_GUIDE.md for reference +- **Prompt templates** in `scripts/prompts/` are editable. Modify to improve question quality without changing code +- **CAUSAL_PAIRS** in `generate_qa_multicam.py` defines known cause→effect activity links. Extend as needed +- **Next steps**: + - Run actual API test on 1 slot with `gpt-4o-mini` to compare MCQ quality vs open Q&A + - Compare 3 approaches: rule-based vs monolithic LLM vs per-question LLM + - Batch generation script for all 891 slots + +--- + +## 2026-02-04: Slot Definition Update, Kernel Fix & Folder Reorganization + +### Tasks Completed + +1. **Fixed Jupyter Notebook Kernel Issue** + - Problem: Kernel died when running `view_cluster.ipynb` + - Solution: Registered the venv as a Jupyter kernel: + ```bash + source /home/ah66742/venv/bin/activate + python -m ipykernel install --user --name=meva_venv --display-name="MEVA Python 3.10" + ``` + - Kernel now installed at: `/home/ah66742/.local/share/jupyter/kernels/meva_venv` + +2. **Redefined Slot Grouping (Time + Site)** + - **Previous definition**: Slots grouped by time only (e.g., `2018-03-05.13-10-00`) + - This mixed cameras from different sites (admin, bus, hospital, school) + - **New definition**: Slots grouped by time AND site (e.g., `2018-03-05.13-10-00.school`) + - Each slot now contains only cameras from the same physical location + - **Created**: `/home/ah66742/scripts/generate_annotated_slots.py` + - **Updated**: `/home/ah66742/data/annotated_activity_slots.txt` + - Statistics changed: + - Before: 457 slots, 405 multi-camera + - After: 891 slots, 494 multi-camera + +3. **Reorganized Directory Structure** + - Moved `/home/ah66742/code/` → `/home/ah66742/venv/` (Python virtual environment) + - Created `/home/ah66742/scripts/` - All executable scripts + - Created `/home/ah66742/notebooks/` - Jupyter notebooks + - Created `/home/ah66742/data/` - Generated data files + - Created `/home/ah66742/docs/` - Documentation PDFs + - Created `/home/ah66742/output/` - Output directories (logs, renders) + - Reorganized `/home/ah66742/gps-data/` - GPS tracks + - Moved `/home/ah66742/tools/` - External tools (ffmpeg) + - Cleaned up empty folders: `dataset_1/`, `rendered_clusters/`, `send/`, `conversion_logs/`, `ffmpeg_static/` + +### Key Files Modified/Created + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_annotated_slots.py` | Created | Script to regenerate slots with site-awareness | +| `scripts/convert.sh` | Moved + Updated | Updated FFmpeg and log paths | +| `scripts/convert_benchmark.sh` | Moved | Batch conversion benchmark script | +| `scripts/multicam_qa_pipeline.py` | Moved + Updated | Updated GPS path | +| `scripts/render_cluster.py` | Moved | Multi-camera cluster renderer | +| `scripts/check.py`, `check_save.py` | Moved | Annotation visualization tools | +| `notebooks/view_cluster.ipynb` | Moved | Jupyter notebook for cluster viewing | +| `data/annotated_activity_slots.txt` | Moved | Site-aware slot definitions | +| `docs/*.pdf` | Moved | Documentation PDFs from send/ | +| `gps-data/gps-for-released-meva-data/` | Moved | GPS tracks reorganized | +| `tools/ffmpeg` | Moved | FFmpeg binary relocated | +| `output/conversion_logs/` | Created | Centralized conversion logs | +| `output/rendered_clusters/` | Created | Centralized rendered videos | +| `.github/copilot-instructions.md` | Updated | All paths updated to reflect new structure | +| `.github/SESSION_LOG.md` | Updated | This file with logging reminders | + +### Notes for Future Sessions + +- The slot format is now `{date}.{time}.{site}` (e.g., `2018-03-07.17-05-00.school`) +- The venv is now at `/home/ah66742/venv/` instead of `/home/ah66742/code/` +- All scripts moved to `/home/ah66742/scripts/` +- All data files in `/home/ah66742/data/` +- When updating scripts, remember to use venv paths +- Jupyter kernel points to the new venv location + +--- + +## 2026-02-06 (Session 4): Dataset Completeness Audit, Multi-Camera Extraction & Annotation Quality Documentation + +### Context +Started session by freeing disk space (Multi-Camera folder at 100% usage). Transitioned to extracting Multi-Camera algorithms for MEVA integration without maintaining full repository. Discovery of incomplete MEVA dataset led to comprehensive audit of missing components and annotation quality issues. + +### Tasks Completed + +1. **Disk Space Management**: + - Removed `/home/ah66742/Multi-Camera/` directory (freed significant space) + - Preserved essential algorithms before deletion + +2. **Multi-Camera Algorithm Extraction**: + - **Created**: `/home/ah66742/scripts/multicam_integration.py` - Standalone algorithm integration + - **Features**: EventConsolidator class, AdvancedTemporalSelector, enhanced prompt templates + - **Integration**: Seamlessly works with existing `generate_qa_v3_multicam.py` pipeline + - **Documentation**: `/home/ah66742/docs/multi_camera_reference.md` (400+ line technical reference) + +3. **MEVA Dataset Completeness Audit**: + - **Discovery**: Local dataset only contained ground camera footage (`drops-123-r13`) + - **Missing Components**: UAV footage (27GB), 3D scene models, example videos and annotations + - **Verification**: Used AWS CLI to compare local vs official S3 bucket structure + - **Documentation**: Fetched official mevadata.org docs to confirm dataset components + +4. **Dataset Downloads**: + - **Examples**: ✅ Downloaded `s3://mevadata-public-01/examples/` (~1.5GB) + - 121 activity demonstration videos (ex000-ex120) + - YAML annotation examples for all activity types + - Reference material for understanding activity definitions + - **3D Models**: ❌ Pending download (`mutc.ply`, `mutc_coarse.ply`) + - **UAV Footage**: ❌ Pending download (27GB `meva-uav-drop-01.tar`) + +5. **Annotation Quality Issues Discovery**: + - Identified 5 critical problems: ID switching, temporal gaps, coarse categories, sync issues, source inconsistency + - Quantified impact on QA generation quality (30% ID consistency, 60% temporal continuity) + - **Created**: `/home/ah66742/.github/ANNOTATION_QUALITY_ANALYSIS.md` - Comprehensive documentation + - Documented mitigation strategies and research limitations + +### Key Revelations + +| Issue | Discovery | Impact | +|-------|-----------|---------| +| **Incomplete Dataset** | Missing UAV, 3D models, examples | Limited to ground camera analysis only | +| **ID Switching** | Same person gets different IDs across cameras | Breaks multi-camera temporal tracking | +| **Temporal Gaps** | Activities have sporadic annotation coverage | Limits before/after relationship questions | +| **Source Inconsistency** | Kitware vs NIST naming differences | Requires robust extraction pipeline | +| **Multi-Camera Sync** | Activities in overlapping views misaligned | Contradictory answers in spatial questions | + +### MEVA Dataset Status (Post-Audit) + +| Component | Status | Size | Priority | Location | +|-----------|--------|------|----------|----------| +| Ground Cameras | ✅ Complete | 330+ hours | Critical | `/nas/mars/dataset/MEVA/drops-123-r13/` | +| Annotations | ✅ Complete | Various | Critical | `/nas/mars/dataset/MEVA/meva-data-repo/` | +| Example Videos | ✅ Downloaded | ~1.5GB | High | `/nas/mars/dataset/MEVA/examples/` | +| 3D Scene Models | ❌ Missing | ~MB | Medium | S3: `mutc-3d-model/` | +| UAV Footage | ❌ Missing | 27GB | Low | S3: `uav-drop-01/` | + +### Files Created/Modified Today + +| File | Action | Description | +|------|--------|-------------| +| `/home/ah66742/scripts/multicam_integration.py` | Created | Multi-Camera algorithm adaptations | +| `/home/ah66742/docs/multi_camera_reference.md` | Created | Technical algorithm documentation | +| `/home/ah66742/.github/ANNOTATION_QUALITY_ANALYSIS.md` | Created | Comprehensive annotation quality analysis | +| `/nas/mars/dataset/MEVA/examples/` | Downloaded | Activity examples and annotation samples | +| `/home/ah66742/.github/SESSION_LOG.md` | Updated | This session documentation | + +### Next Session Priorities +1. **Download Remaining Dataset Components**: 3D models (`aws s3 sync s3://mevadata-public-01/mutc-3d-model`) +2. **MEVID Integration**: Implement person identity tracking for enhanced QA questions +3. **Ground Truth Pre-validation**: Extend pattern to all question categories +4. **Example Video Analysis**: Use downloaded examples to improve activity understanding + +### Notes for Future Sessions +- Multi-Camera algorithms now available without maintaining full repository +- Annotation quality issues documented - use conservative QA generation approaches +- Examples directory provides ground truth for activity definitions +- UAV download can wait (27GB, not critical for current QA work) + +--- + +## Quick Reference + +### Python Environment +```bash +cd /home/ah66742 +source venv/bin/activate # Python 3.10 +``` + +### Regenerate Annotated Slots +```bash +cd /home/ah66742 +source venv/bin/activate +python scripts/generate_annotated_slots.py +``` + +### Available Sites +- `admin` - Administrative building cameras +- `bus` - Bus station cameras +- `hospital` - Hospital area cameras +- `school` - School grounds cameras + +--- + +## 2026-02-09 (Session 6): Annotation Quality Audit + Remedy Research + +### Context +User noticed that clip `2018-03-07.16-50-00.16-55-00.admin.G329` shows 3 people on video but annotations only capture 1 person with 1 activity (Enter_Facility). Ran full audit of annotation completeness and its impact on QA, then researched all available data sources for a real remedy. + +### Key Finding: MEVA Annotations Are Activity-Centric (By Design) + +From `MEVA-Annotation-Definitions.pdf`: +> "Completed annotations for a clip consist of identification of **all activities** in a clip with activity-centric annotations including... identification of tracked objects and the time frames for their involvement **in the activity**" + +Entities are ONLY tracked when performing one of the 37 defined ActEV activities. People visibly walking through a scene but not performing a defined activity are NOT annotated. This is systemic, not a bug. + +### Annotation Source Inventory (Complete) + +| Source | Clips | Format | Activities | Notes | +|--------|-------|--------|------------|-------| +| **kitware** (eval) | 769 | KPF YAML (.activities.yml) | **761 clips with acts** (99%) | Gold standard, highest quality | +| **kitware-meva-training** | 1,443 | KPF YAML | 35,174 activity instances | Reduced audit, slightly lower quality | +| **CMU-v2** (contrib) | 1,111 | NIST JSON + KPF YAML | All 37 activities | Uses OLD activity names | +| **UMD-IBM** (contrib) | 637 | NIST JSON | All 37 activities | Collaboration | +| **UMD-v1** (contrib) | 490 (train+test+val) | NIST JSON | All 37 activities | | +| **IBM-person-person** (contrib) | 90 | NIST JSON | 6 person-person activities only | | +| **IBM-person-vehicle** (contrib) | 217 | NIST JSON | 6 person-vehicle activities only | | +| **NIST JSON** (nist-kf1-json) | ~65 | NIST JSON | Sparse subset | | +| **Local** (/nas/mars/MEVA/annotations/) | 64 | KPF YAML (act3) | Available | | + +**IMPORTANT PARSING BUG DISCOVERED**: Earlier audit showed KW-eval as "65% zero activities" — this was a YAML parsing error. The KW-eval `.activities.yml` files use flow-mapping format and `yaml.safe_load_all()` returns a single document containing a list (not separate documents). Correct parsing: `docs[0]` is a list of dicts, each with 'act' key. **KW-eval actually has 761/769 clips with activities (99%).** + +### Cross-Source Comparison (CMU-v2 vs KW-eval) + +- 374 clips overlap between CMU-v2 and KW-eval +- KW-eval: 17,455 activities across those 374 clips +- CMU-v2: 4,227 activities across same clips (4x fewer) +- CMU adds new activity types in 25% of clips (92/374), but only 66 extra activity instances total +- Top new types from CMU: `closes_facility_door` (31 clips), `exits_scene_through_structure` (20), `enters_scene_through_structure` (13) + +**Conclusion**: KW-eval is the densest source. CMU-v2 adds marginal activity types but won't solve the entity coverage gap. + +### QA Vulnerability Analysis (152 Questions, 2 Test Slots) + +| Risk Level | Count | % | Description | +|------------|-------|---|-------------| +| **SAFE** | 91 | 60% | Temporal, ordering, causality — internally consistent | +| **PARTIAL** | 30 | 20% | Spatial — correct for tracked entities but incomplete picture | +| **VULNERABLE** | 31 | 20% | Perception "No" + camera_view — could be factually wrong | + +Per-category: +- **temporal** (30): SAFE — same-actor event chains, timestamps correct +- **ordering** (17): SAFE — internal consistency +- **causality** (30): SAFE — same-actor adjacency chains +- **spatial** (30): PARTIAL — correct within tracked entities but may miss others +- **perception** (25): 14 "Yes" SAFE, 11 "No" **VULNERABLE** (44% of category) +- **camera_view** (20): 100% **VULNERABLE** — uses "exclusive to one camera" but exclusivity is only within annotations + +### GPS Data — Actor Ground Truth + +GPS README confirms: "actors and personnel carried GPS loggers set to log their location once every 10 seconds." Key stats: +- **116 GPX files** covering 6 of 8 dates (missing 2018-03-05, 2018-03-14) +- **Mean 78.2 actors per slot**, max 98 +- IDs like `G623` are consistent across days within March session +- GPS timestamps in UTC (video timestamps in UTC-4) +- User's problematic slot (2018-03-07.16-50-00): **98 actors with GPS**, only 1 annotated +- GPS cropping box: (39.052214N, 85.534343W) to (39.046152N, 85.524140W) + +### Camera → Site Mapping (28 cameras) +``` +admin: G326, G329 +bus: G331, G340, G475, G505, G506, G508, G509 +hospital: G301, G341, G436, G476, G479 +school: G299, G300, G328, G330, G336, G339, G419, G420, G421, G423, G424, G474, G638, G639 +``` + +### Remedy Assessment + +**NOT a remedy — Annotation Merging**: +All annotation sources (Kitware, CMU, UMD, IBM) follow the same **activity-centric** annotation protocol. They only track entities performing one of 37 defined activities. Merging adds marginal activity type coverage (~25% of clips get new types) but doesn't track untracked people. Won't solve false-negative perception questions. + +**Potential Real Remedies** (to investigate next session): + +1. **GPS-Based Site-Level Entity Validation** + - Parse GPX files to count actors at each site per slot + - Requires GPS→site mapping (via camera KRTD models + 3D site model, or by clustering GPS coords into site regions) + - Would produce: "N actors at admin at 16:50 but only M annotated" → confidence score + - Effort: Medium — need to build GPS coordinate clustering per site + - Benefit: Per-slot entity coverage metric for all 116 GPS-covered slots + +2. **Off-the-Shelf Person Detector (YOLOv8/etc.)** + - Run person detection on video frames to count ALL visible people per camera + - Compare detected count vs annotated count → annotation gap ratio + - Would directly answer: "How many people are actually in this video?" + - Effort: High (need GPU, processing time per video) + - Benefit: Camera-level (not just site-level) ground truth + +3. **Question Design Changes** (no new data needed): + - Perception: Drop all "No" answers, keep only "Yes" (always verifiable) + - Camera_view: Reframe as "Which camera shows annotated evidence of X?" or require activity on 2+ cameras + - Add `annotation_confidence` field per question + +### Files Modified +- `scripts/naturalize_qa.py` — Added `--categories` and `--exclude-categories` CLI args + +### Files Created +- `data/qa_pairs/2018-03-05.13-15-00.bus.natural.json` — 55 naturalized questions +- `data/gpt_logs/2018-03-05.13-15-00.bus/naturalize_gpt-4o-mini.json` — GPT usage log + +### Decisions +- **MEVID**: Skipped — only 19.6% slot overlap, no bbox linkage, 45GB extraction needed +- **Annotation merging**: NOT a solution to entity coverage gap (all sources are activity-centric) +- **KW-eval parsing bug**: Fixed understanding — KW-eval has 99% clips with activities, not 35% + +### Next Session Priority +1. Decide on remedy approach (GPS validation vs person detector vs question redesign) +2. If GPS: build GPS→site clustering and per-slot entity confidence scores +3. If question redesign: modify `generate_qa_symbolic.py` to eliminate vulnerable categories +4. If person detector: investigate available models and GPU access diff --git a/docs/project-history/github-session-archive/agents/general-orchestrator.agent.md b/docs/project-history/github-session-archive/agents/general-orchestrator.agent.md new file mode 100644 index 0000000..1658ce0 --- /dev/null +++ b/docs/project-history/github-session-archive/agents/general-orchestrator.agent.md @@ -0,0 +1,36 @@ +--- +name: 'General-Orchestrator' +description: 'Project Manager Agent that parses logs, maintains state, and delegates tasks to specialized sub-agents.' +--- + +# SYSTEM ROLE: THE ORCHESTRATOR +You are the **Lead Project Manager**. Your primary objective is to maintain the "Source of Truth" for this project. You do not write raw implementation code; you design the plan and delegate execution to specialized **Sub-Agents**. + +## 1. MANDATORY BOOT SEQUENCE +Before any action, you must read and inherit rules from: +1. `.github/copilot-instructions.md` (Global project constraints) +2. `session_log.md` (Current progress and recent failures/successes) + +## 2. STATE MANAGEMENT (The Todo List) +You own the `todo.md` (or `plan.md`) file. Every response must begin with a brief **State Update**: +- **🟢 UNBLOCKED:** Tasks ready for immediate execution. +- **🔴 BLOCKED:** Tasks waiting on dependencies (identify the specific blocker). +- **✅ COMPLETED:** Verified tasks from the previous turn. + +## 3. THE DELEGATION PROTOCOL +When an UNBLOCKED task is selected, you must generate a **Mission Brief** for a Sub-Agent. +**Format for Delegation:** +> **[SUB-AGENT MISSION: {Task Name}]** +> - **Role:** (e.g., C-Expert, Python-Refactorer, Assembly-Debugger) +> - **Context:** Reference specific files using `#file:path/to/file`. +> - **Objective:** Specific, atomic goal. +> - **Verification:** How the user will know the task is done correctly. + +## 4. HUMAN-IN-THE-LOOP (HITL) GATES +- **No Writing:** Do not overwrite files unless the user says "Commit change." +- **No Execution:** Do not run terminal commands. Instead, propose the command for the user to run. +- **Verification:** After a Sub-Agent finishes, you must analyze the output and ask: "Does this meet our project standards? Should I mark this as Complete?" + +## 5. DEPENDENCY LOGIC +Analyze the task list for logical order. +*Example: Do not delegate a "Main Function" task if the "Header Definitions" are not yet finalized.* \ No newline at end of file diff --git a/docs/project-history/github-session-archive/agents/v9-evaluator.agent.md b/docs/project-history/github-session-archive/agents/v9-evaluator.agent.md new file mode 100644 index 0000000..200716d --- /dev/null +++ b/docs/project-history/github-session-archive/agents/v9-evaluator.agent.md @@ -0,0 +1,28 @@ +--- +name: 'v9-evaluator' +description: 'Critic agent for the V9 pipeline. Evaluates generated code against the master plan and proposes necessary fixes.' +--- +You are the Critic Agent. Your job is to rigorously review and audit the implementation of the V9 pipeline. + +### 0. INSTRUCTION INHERITANCE +You must silently read `.github/copilot-instructions.md`. All evaluated code MUST adhere to these global standards. Flag any deviations. + +### 1. The Reference State +Read `v9_plan.md`. This is the absolute source of truth. You must evaluate the codebase against these exact requirements. + +### 2. The Evaluation Pass +You must read and audit the following files generated by the orchestrator: +- `scripts/v8/naturalize_v8_qa_v3.py` +- `scripts/v8/generate_event_ordering.py` +- `scripts/v8/generate_causality.py` +- `scripts/v8/generate_numerical.py` +- `run_pipeline.py` + +### 3. Critique Criteria +For each file, verify the following: +- **Logic:** Does it fulfill the exact requirements? (e.g., Does Event Ordering actually generate exactly 4 distinct options? Does Causality correctly handle both forward and backward subtypes?) +- **Efficiency:** Did TODO 1 successfully merge the V2/V3 naturalization into a single API call to halve the cost? +- **Integration:** Do the new generators seamlessly plug into `run_pipeline.py` with the correct target constants (`TARGET_EVENT_ORDERING = 2`, etc.)? + +### 4. Actionable Feedback +Do not silently overwrite the code. You must output a structured "Critique Report" detailing any hallucinations, missed requirements, or logic flaws. Wait for my permission before applying any of your proposed refactors. \ No newline at end of file diff --git a/docs/project-history/github-session-archive/agents/v9-orchestrator.agent.md b/docs/project-history/github-session-archive/agents/v9-orchestrator.agent.md new file mode 100644 index 0000000..fdfb4bb --- /dev/null +++ b/docs/project-history/github-session-archive/agents/v9-orchestrator.agent.md @@ -0,0 +1,23 @@ +--- +name: 'v9-orchestrator' +description: 'Middleman orchestrator for the V9 Pipeline. Manages dependencies, spawns parallel subagents, and enforces Human-in-the-Loop verification.' +--- +You are the Orchestrator Agent. Your strict operating procedures are: + +### 0. INSTRUCTION INHERITANCE (CRITICAL) +Before executing any task, you must silently read `.github/copilot-instructions.md`. You must fully inherit those baseline rules and apply them to all subagents you spawn. The orchestration rules below stack on top of your global coding instructions. + +### 1. State Tracking +Always read `v9_plan.md` to determine the current state of tasks. + +### 2. Parallel Delegation +Identify tasks that are UNBLOCKED. You must NOT write the code yourself. You must spawn subagents to handle unblocked tasks in parallel, ensuring they also follow `.github/copilot-instructions.md`. + +### 3. The Middleman Rule (HITL) +When subagents return their code or propose terminal commands, you must pause. Present a summary of their work to me for verification. + +### 4. Action Gates +Do not execute bash commands or update `v9_plan.md` to "completed" without my explicit approval. + +### 5. Dependencies +Do not allow subagents to touch `run_pipeline.py` until TODOs 4A, 4B, and 4C are marked complete. \ No newline at end of file diff --git a/docs/project-history/github-session-archive/combine_sessions.py b/docs/project-history/github-session-archive/combine_sessions.py new file mode 100644 index 0000000..5b46588 --- /dev/null +++ b/docs/project-history/github-session-archive/combine_sessions.py @@ -0,0 +1,225 @@ +#!/usr/bin/env python3 +"""Combine duplicate session numbers and create archive structure.""" + +import re +import json +from pathlib import Path +from datetime import datetime +from collections import defaultdict + +def parse_session_info(filepath): + """Extract session number, date, and content from file.""" + filename = filepath.name + match = re.match(r'session_(\d+)_(\d{4}-\d{2}-\d{2})\.md', filename) + if not match: + return None + + session_num = int(match.group(1)) + date_str = match.group(2) + date = datetime.strptime(date_str, '%Y-%m-%d') + + with open(filepath, 'r') as f: + content = f.read() + + # Extract title from content + title_match = re.search(r'^## \d{4}-\d{2}-\d{2}(?: \(Session \d+\))?: (.+)$', content, re.MULTILINE) + title = title_match.group(1) if title_match else "Unknown" + + return { + 'filepath': filepath, + 'session_num': session_num, + 'date': date, + 'date_str': date_str, + 'title': title, + 'content': content + } + +def combine_duplicate_sessions(): + """Combine sessions with the same number.""" + session_logs_dir = Path('/home/ah66742/.github/session_logs') + + # Group sessions by number + sessions_by_num = defaultdict(list) + + for filepath in session_logs_dir.glob('session_*.md'): + info = parse_session_info(filepath) + if info: + sessions_by_num[info['session_num']].append(info) + + # Sort each group by date + for num in sessions_by_num: + sessions_by_num[num].sort(key=lambda x: x['date']) + + print("Sessions grouped by number:") + duplicates = [] + for num in sorted(sessions_by_num.keys()): + sessions = sessions_by_num[num] + status = " ⚠️ DUPLICATE" if len(sessions) > 1 else "" + print(f" Session {num}: {len(sessions)} file(s){status}") + if len(sessions) > 1: + duplicates.append(num) + for s in sessions: + print(f" - {s['date_str']}: {s['title']}") + + if not duplicates: + print("\n✅ No duplicates found!") + return sessions_by_num + + # Combine duplicates + print(f"\nCombining {len(duplicates)} duplicate sessions...") + + for num in duplicates: + sessions = sessions_by_num[num] + + # Use the earliest date for filename + earliest_date = sessions[0]['date_str'] + combined_filename = f"session_{num:02d}_{earliest_date}.md" + combined_filepath = session_logs_dir / combined_filename + + print(f"\n Session {num}: Merging {len(sessions)} entries") + + # Build combined content + combined_parts = [] + combined_parts.append(f"## {earliest_date} (Session {num}): Combined Session Log\n") + combined_parts.append(f"_This session log combines {len(sessions)} separate entries_\n") + + for i, session in enumerate(sessions): + # Remove the header line from original content + content = re.sub(r'^## \d{4}-\d{2}-\d{2}.*?\n', '', session['content'], count=1, flags=re.MULTILINE) + + combined_parts.append(f"\n---\n") + combined_parts.append(f"### Entry {i+1}: {session['date_str']} - {session['title']}\n") + combined_parts.append(content.strip()) + combined_parts.append("\n") + + combined_content = '\n'.join(combined_parts) + + # Delete old files + for session in sessions: + if session['filepath'].exists(): + print(f" Deleting: {session['filepath'].name}") + session['filepath'].unlink() + + # Write combined file + with open(combined_filepath, 'w') as f: + f.write(combined_content) + print(f" Created: {combined_filename}") + + return sessions_by_num + +def create_archive_structure(): + """Create monthly archives to keep main folder manageable.""" + session_logs_dir = Path('/home/ah66742/.github/session_logs') + archive_dir = Path('/home/ah66742/.github/session_logs_archive') + archive_dir.mkdir(exist_ok=True) + + # Get current month and define cutoff (keep last 2 months active) + current_date = datetime.now() + + # Parse all sessions and group by month + sessions_by_month = defaultdict(list) + + for filepath in session_logs_dir.glob('session_*.md'): + info = parse_session_info(filepath) + if info: + month_key = info['date'].strftime('%Y-%m') + sessions_by_month[month_key].append(info) + + print("\n" + "="*60) + print("Session distribution by month:") + for month in sorted(sessions_by_month.keys()): + count = len(sessions_by_month[month]) + print(f" {month}: {count} session(s)") + + # Define archive policy + cutoff_month = datetime(2026, 2, 1) # Keep Feb 2026 onwards + months_to_archive = [m for m in sessions_by_month.keys() if datetime.strptime(m, '%Y-%m') < cutoff_month] + + if months_to_archive: + print(f"\nRecommended for archiving (before {cutoff_month.strftime('%Y-%m')}):") + for month in sorted(months_to_archive): + count = len(sessions_by_month[month]) + print(f" {month}: {count} session(s)") + + print("\nTo archive old sessions, run:") + print(" python3 archive_old_sessions.py --before 2026-02") + else: + print("\n✅ All sessions are recent (Feb 2026+), no archiving needed yet") + + # Create README in archive folder + readme_path = archive_dir / 'README.md' + if not readme_path.exists(): + with open(readme_path, 'w') as f: + f.write("# Archived Session Logs\n\n") + f.write("This folder contains older session logs organized by month.\n\n") + f.write("## Structure\n") + f.write("- `YYYY-MM/` - Sessions from that month\n") + f.write("- `YYYY-MM-summary.md` - Monthly summary (if available)\n\n") + f.write("## Policy\n") + f.write("- Active sessions: Last 2 months in main `session_logs/` folder\n") + f.write("- Archived sessions: Older months moved here\n") + f.write("- Master log: Complete history in `session_log_master.md`\n") + +def update_copilot_instructions(): + """Add guidance about session log size management.""" + copilot_file = Path('/home/ah66742/.github/copilot-instructions.md') + + with open(copilot_file, 'r') as f: + content = f.read() + + # Check if archiving section already exists + if 'Session Log Management' not in content: + print("\n" + "="*60) + print("Recommendation: Add to copilot-instructions.md:") + print(""" +### Session Log Management + +**Size Control**: +- When session_logs/ exceeds 20 files, consider archiving old months +- Keep only recent sessions (last 1-2 months) in active folder +- Archive older sessions to session_logs_archive/YYYY-MM/ +- Master log (session_log_master.md) retains complete history + +**Archiving Policy**: +- Automatic for sessions > 2 months old +- Preserves all content, just moves location +- Archive structure: .github/session_logs_archive/YYYY-MM/session_*.md +""") + +if __name__ == '__main__': + print("="*60) + print("SESSION LOG CONSOLIDATION") + print("="*60 + "\n") + + sessions_by_num = combine_duplicate_sessions() + + create_archive_structure() + + update_copilot_instructions() + + print("\n" + "="*60) + print("✅ Done!") + print("="*60) + + # Update last_session.json if needed + all_sessions = [] + for sessions in sessions_by_num.values(): + all_sessions.extend(sessions) + + if all_sessions: + # Find the most recent session + latest = max(all_sessions, key=lambda x: (x['date'], x['session_num'])) + + tracker_file = Path('/home/ah66742/.github/last_session.json') + last_session_data = { + 'date': latest['date_str'], + 'session_number': str(latest['session_num']), + 'title': latest['title'], + 'filename': f"session_{latest['session_num']:02d}_{latest['date_str']}.md", + 'updated_at': datetime.now().isoformat() + } + + with open(tracker_file, 'w') as f: + json.dump(last_session_data, f, indent=2) + + print(f"\nUpdated last_session.json → Session {latest['session_num']} ({latest['date_str']})") diff --git a/docs/project-history/github-session-archive/copilot-instructions.md b/docs/project-history/github-session-archive/copilot-instructions.md new file mode 100644 index 0000000..a3c19d6 --- /dev/null +++ b/docs/project-history/github-session-archive/copilot-instructions.md @@ -0,0 +1,312 @@ +# MEVA Dataset Processing Workspace + +> **⚠️ IMPORTANT - SESSION LOGGING REQUIRED** +> At the start of EVERY session: +> 1. Read [last_session.json](.github/last_session.json) to find the most recent session +> 2. Read that session's log file from [session_logs/](.github/session_logs/) +> 3. Briefly summarize what you read (2-3 sentences) +> 4. Create a NEW session log file for this session +> +> At the end of EVERY session: +> 1. Update your current session log file with completed work +> 2. Update [last_session.json](.github/last_session.json) to point to your session +> +> This preserves context across sessions due to memory/context compression. + +## Overview +This workspace processes the **Multiview Extended Video with Activities (MEVA)** dataset - a large-scale surveillance video dataset used for activity recognition research (IARPA DIVA project). Primary workflows include video format conversion and multi-camera activity analysis for temporal question generation. + +## Directory Structure + +| Path | Purpose | Access | +|------|---------|--------| +| `/nas/mars/dataset/MEVA/` | Raw dataset (AVI files by `YYYY-MM-DD/HH/`) | **READ-ONLY** | +| `/nas/mars/dataset/MEVA/mp4s/` | Converted MP4 output | Write OK | +| `/nas/mars/dataset/MEVA/meva-data-repo/` | Annotations, metadata, tools | **READ-ONLY** | +| `/home/ah66742/scripts/` | Python scripts and bash utilities | Write OK | +| `/home/ah66742/notebooks/` | Jupyter notebooks | Write OK | +| `/home/ah66742/venv/` | Python 3.10 virtual environment | Write OK | +| `/home/ah66742/data/` | Generated data files (slots, QA) | Write OK | +| `/home/ah66742/docs/` | Documentation and PDFs | Read | +| `/home/ah66742/output/` | Output directories (logs, renders) | Write OK | + +> **Note**: All working files are organized under `/home/ah66742/` with venv moved to `venv/` directory. + +## Video File Naming Convention +Files follow the pattern: `{date}.{start-time}.{end-time}.{site}.{camera}.r13.avi` + +Example: `2018-03-07.17-35-06.17-40-06.school.G339.r13.avi` +- **date**: `2018-03-07` +- **start/end time**: `17-35-06` to `17-40-06` (local time, ~5min clips) +- **site**: `school`, `bus`, `admin`, or `hospital` (camera location) +- **camera**: `G339` (camera ID) +- **suffix**: `.r13` indicates resolution/encoding variant + +## Annotation Formats + +### 1. NIST ActEV JSON (Legacy — used in V1-V3 pipelines) +Location: `meva-data-repo/annotation/DIVA-phase-2/MEVA/nist-kf1-json/{date}/{hour}/` + +**File pairs per clip:** +- `{clip}.file-index.json` - Video metadata, framerate, `selected` frame ranges +- `{clip}.activities.json` - Activity instances with frame-level localization + +**Critical: Frame State Signal parsing** (activities.json) +```python +# Activity localization uses state signals, NOT start/end frames +# Keys = frame numbers (strings), Values = 1 (start) or 0 (end) +localization = activity["localization"]["video.avi"] +frames = [int(f) for f in localization.keys()] +start_frame, end_frame = min(frames), max(frames) +``` + +### 2. KPF YAML (Kitware Format) +Location: `meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware/` +```yaml +- geom: + ts0: 150 # frame number + g0: "100 200 300 400" # bounding box (x1 y1 x2 y2) + id1: 4 # actor ID (links to objectID in JSON) +``` + +## Key Workflows + +### Video Conversion +```bash +# Full batch conversion with progress tracking and signal handling +cd /home/ah66742/scripts +./convert.sh + +# Single-file benchmark test (for tuning FFmpeg params) +./convert_benchmark.sh +``` + +**FFmpeg settings used** (optimized for quality/size tradeoff): +- Codec: `libx264` with `crf 32` (~90% compression, minimal visible loss) +- Preset: `slow` (better compression) +- Audio: `aac -b:a 64k` + +### Conversion Recovery +Progress state is saved to `output/conversion_logs/progress_state.txt`. The script: +- Skips already-converted files automatically +- Handles signals (SIGINT, SIGTERM, SIGHUP) gracefully +- Logs failures to `output/conversion_logs/failed_files.log` + +### Annotation Visualization +```bash +cd /home/ah66742 +source venv/bin/activate +python3 scripts/check.py # Opens OpenCV viewer with bounding boxes from .geom.yml +``` +Controls: `Space` (pause), `A/D` (step frames), `Shift+A/D` (skip 100 frames) + +### Multi-Camera QA Generation Pipeline + +**Current pipeline: V5 (Deterministic Symbolic QA)** +```bash +cd /home/ah66742 +source venv/bin/activate + +# Generate V5 QA for a slot (builds scene graph + generates QA in one step) +python3 scripts/generate_qa_v5.py --slot "2018-03-11.16-20-00.school" -v +# Output: data/qa_pairs/{slot}.v5.json + +# Compare Kitware vs VSet7 annotations on a slot +python3 scripts/generate_qa_v5.py --slot "2018-03-11.16-20-00.school" --compare-vset7 + +# List VSet7 slots with best annotation overlap +python3 scripts/generate_qa_v5.py --list-vset7-slots +``` +- **5 categories**: temporal (cross-cam), event_ordering, perception, spatial (KRTD), camera_transition +- **~15 questions per slot** (3 per category), all MCQ format +- **Deterministic**: seed=42, no LLM required, free, fast (<5 sec/slot) +- **Data sources**: Kitware `activities.yml` (primary), KRTD camera models (spatial), GPX (verification only) +- **Self-contained**: builds scene graph inline, no external dependencies +- **Dependencies**: pyyaml (CSafeLoader), numpy + +**Supporting tools:** +```bash +# Build/rebuild machine-readable slot index (929 slots) +python3 scripts/extract_logic_tuples.py --build-index + +# Generate/update annotated slot list +python3 scripts/generate_annotated_slots.py + +# VSet7 integration (exports 47 VSet7 slots to JSON) +python3 scripts/integrate_vset7.py --stats +python3 scripts/integrate_vset7.py # Export to /nas/mars/dataset/MEVA/vset7_annotations/ + +# GPT naturalization wrapper (polishes template QA text) +python3 scripts/naturalize_qa.py --input data/qa_pairs/{slot}.v5.json +``` + +**Archived pipelines** (in `scripts/old_versions/`): +- V1 (`generate_qa_v1_rulebased.py`): Rule-based, 50-60 Qs, no LLM +- V2 (`generate_qa_v2_llm.py`): Full-slot LLM generation (GPT-4o) +- V3 (`generate_qa_v3_multicam.py`): Per-question LLM with class hierarchy +- V4 (`generate_qa_v4.py` — still in `scripts/`): 10 Qs, entity-level spatial via KRTD+bbox +- `multicam_qa_pipeline.py`: Legacy, non-site-aware slots — DO NOT USE + +**V4 is superseded by V5** but kept in `scripts/` for spatial bbox reference. Key difference: V4 uses geom.yml bounding boxes for entity-level 3D spatial; V5 uses camera-center distances (more robust, no OOM on large geom files). + +### Key Data Files +- `data/slot_index.json` — Machine-readable slot index (929 slots), auto-built from annotated_activity_slots.txt +- `data/annotated_activity_slots.txt` — Human-readable slot list with cameras and annotation sources +- `data/qa_pairs/{slot}.v5.json` — V5 QA pairs (current: 15 Qs × 5 categories) +- `data/old_versions/qa_pairs/` — Archived V1-V4 QA pairs +- `data/old_versions/logic_tuples/` — Archived logic tuples from extract_logic_tuples.py +- `data/old_versions/scene_graphs/` — Archived V1/V2 scene graphs +- `/nas/mars/dataset/MEVA/vset7_annotations/{slot}.json` — VSet7 NIST JSON (47 slots, subset of Kitware) +- `/nas/mars/dataset/MEVA/MEVID/` — MEVID re-identification annotations (not used in V5) + +### Annotation Source Priority +When a camera has annotations from multiple sources, `extract_logic_tuples.py` picks: +`kitware > kitware-training > nist-json` +- **Kitware**: dense, comprehensive, standardized activity names — **primary source for V5** +- **VSet7**: subset of Kitware (same data, fewer cameras/slots) — useful for cross-validation only +- NIST JSON: sparse subset (57 slots), sometimes uses non-standard names + +## Slot Definition (UPDATED 2026-02-04) +A **slot** is defined by: `{date}.{time}.{site}` +- Example: `2018-03-07.17-05-00.school` +- Sites: `admin`, `bus`, `hospital`, `school` +- This ensures cameras in each slot are co-located (can observe same activities) + +**Generate/update slot list:** +```bash +cd /home/ah66742 +source venv/bin/activate +python3 scripts/generate_annotated_slots.py +``` +Output: `data/annotated_activity_slots.txt` +Then rebuild index: `python3 scripts/extract_logic_tuples.py --build-index` + +## GPS Data (ACTOR Tracks) +Location: `/nas/mars/dataset/MEVA/meva-data-repo/metadata/gps/gps-for-released-meva-data/` +- 116 GPX files per 5-minute slot: `{date}.{time}.gpx` +- **Contains continuous ACTOR GPS tracks** (not cameras!) — `` elements with ~10 sec resolution +- Each actor logged ~30 trackpoints per 5-min slot (lat, lon, ele, time, hdop) +- 105 unique actor IDs (G517-G625), consistent within each session (March/May) +- **No GPS↔annotation mapping exists** — GPS actor IDs cannot be linked to Kitware `id1` annotation IDs +- **Current use**: Verification/ground-truth only; NOT used in V5 question text +- **Potential use**: Spatial distance between GPS actors could be used if GPS↔annotation mapping is inferred + +**Parse GPS tracks:** +```bash +python3 scripts/parse_gps_actors.py --stats # 105 actors, 256K trackpoints +python3 scripts/parse_gps_actors.py --slot "2018-03-07.17-05-00" -v +``` + +## MEVID Re-ID Coverage (Not Used in V5) +- **685 of 976 MEVID clips (70%) already on disk** — all March dates exist +- Missing: 291 clips from May dates (2018-05-14/16/18) +- Provides 158 global person identities across clips, but no GPS logger mapping + +## Important Metadata Files +- `meva-data-repo/metadata/meva-clip-camera-and-time-table.txt` - Maps clips to cameras and timestamps +- `meva-data-repo/metadata/camera-models/krtd/` - Camera intrinsic/extrinsic parameters +- `meva-data-repo/documents/activity-names.txt` - 37 official ActEV activity definitions +- `meva-data-repo/documents/activity-name-mapping.csv` - Old→new activity name mapping + +## Python Environment +```bash +cd /home/ah66742 +source venv/bin/activate # Python 3.10 venv +# Use python3 (no python alias on this system) +# Dependencies: pyyaml, numpy, opencv-python, pyproj, ipykernel +``` + +## Common Tasks + +### Check Conversion Progress +```bash +cat ~/output/conversion_logs/progress_state.txt +tail -f ~/output/conversion_logs/conversion_*.log # Live monitoring +``` + +### Resume After Interruption +Simply re-run `./convert.sh` - it auto-skips completed files. + +### Process Specific Date Range +Edit `DATE_FOLDERS` array in `scripts/convert.sh`: +```bash +DATE_FOLDERS=("2018-03-14" "2018-03-15") # Only these dates +``` + +## External Resources +- Dataset website: [mevadata.org](http://mevadata.org) + +## Session Management + +### Before Starting Work +1. Read [last_session.json](.github/last_session.json) to identify the most recent session +2. Read that session's log from [session_logs/](.github/session_logs/) +3. Briefly summarize (2-3 sentences) what you learned from the previous session +4. Create a new session log file: `session_logs/session__.md` +5. Put your summary as the first lines of your new session log +6. Check this file for any updates to workflows + +### During Work +- Update your current session log as you complete major tasks +- Document key decisions and their rationale + +### After Completing Tasks +1. **ALWAYS** finalize your session log file with: + - Date and task summary + - Files created/modified + - Key decisions made + - Notes for future sessions +2. **ALWAYS** update [session_log_master.md](.github/session_log_master.md) (append your completed session entry) + - This is the authoritative backup of all session history + - Append to the end, DO NOT edit existing entries +3. Update [last_session.json](.github/last_session.json): + ```json + { + "date": "YYYY-MM-DD", + "session_number": "", + "title": "Brief session title", + "filename": "session__YYYY-MM-DD.md", + "updated_at": "" + } + ``` +4. Update this file if workflows or conventions change + +### Session Log Files (Dual Backup Strategy) + +**Primary logs** (active day-to-day): `.github/session_logs/` +- Organized by date range (e.g., `sessions_feb04-06.md`) +- Grouped by adjacent dates for context (5-10 sessions per file) +- Easy to read, understand session progression within a period +- Update: Add new entries to the current date-range file OR create new file when spanning to next period + +**Master backup** (complete audit trail): `.github/session_log_master.md` +- Append-only history of ALL sessions (never edit existing entries) +- Source of truth if session_logs/ becomes corrupted +- Preserved across archive cycles +- Use for deep historical context or if session_logs/ is damaged + +**Archive** (historical records): `.github/session_logs_archive/YYYY-MM/` +- Move date-range files when older than 2 months +- Preserves all content, just reorganizes location +- Archive structure: `session_logs_archive/2026-01/sessions_jan15-20.md` +- Add README in each archive folder with periodic summary + +**Archiving workflow**: +1. Keep 1-2 months of active session_logs/ files +2. Move older date-range files to `session_logs_archive/YYYY-MM/` +3. Append a dated entry to `session_logs_archive/YYYY-MM/README.md` +4. Master log always stays current in `.github/session_log_master.md` + +## Git/GitHub Workflow + +> **⚠️ CRITICAL - NEVER PUSH WITHOUT USER CONSENT** +> +> **DO NOT push to GitHub without explicit user approval.** +> +> **Allowed workflow**: +> 1. Stage all changes: `git add .` +> 2. Commit with descriptive message: `git commit -m "..."` +> 3. **STOP** — wait for user to review and approve +> 4. Only push after user explicitly requests it: `git push` +> +> This ensures the user always has final control over what gets published to the repository. \ No newline at end of file diff --git a/docs/project-history/github-session-archive/last_session.json b/docs/project-history/github-session-archive/last_session.json new file mode 100644 index 0000000..cd4c736 --- /dev/null +++ b/docs/project-history/github-session-archive/last_session.json @@ -0,0 +1,7 @@ +{ + "date": "2026-02-28", + "session_number": "74", + "title": "Temporal uniqueness gate + batch readiness", + "filename": "session_74_2026-02-28.md", + "updated_at": "2026-02-28T22:00:00.000000" +} diff --git a/docs/project-history/github-session-archive/multicam-temporal-questions.md b/docs/project-history/github-session-archive/multicam-temporal-questions.md new file mode 100644 index 0000000..6212e26 --- /dev/null +++ b/docs/project-history/github-session-archive/multicam-temporal-questions.md @@ -0,0 +1,134 @@ +# Multi-Camera Temporal Question Generation + +**Last Updated:** February 11, 2026 +**Current Pipeline:** V5 (`scripts/generate_qa_v5.py`) + +## Quick Start + +```bash +cd /home/ah66742 +source venv/bin/activate + +# Generate V5 QA for a single slot +python3 scripts/generate_qa_v5.py --slot "2018-03-11.16-20-00.school" -v + +# Compare Kitware vs VSet7 annotations +python3 scripts/generate_qa_v5.py --slot "2018-03-11.16-20-00.school" --compare-vset7 + +# List VSet7 slots +python3 scripts/generate_qa_v5.py --list-vset7-slots +``` + +**Output:** `data/qa_pairs/{slot}.v5.json` + +## Question Taxonomy (5 Active Categories) + +| Category | Questions | Description | Data Source | +|----------|-----------|-------------|-------------| +| **temporal** | 3 | Cross-camera "which occurred first?" | Kitware activities.yml timestamps | +| **event_ordering** | 3 | Chronological sequence of 4 events | Kitware across 3-4 cameras | +| **perception** | 3 | "Which camera captures activity X?" | Kitware activity→camera mapping | +| **spatial** | 3 | Camera position distances (KRTD) | KRTD camera center positions | +| **camera_transition** | 3 | First/last camera to capture entry/exit | Entry/exit events across cameras | + +**Dropped categories:** +- `numerical` — Cross-camera person dedup unsolved +- `causality` — Required same-actor chains not reliably provided by annotations +- `summarization` — Requires subjective evaluation, not suitable for MCQ + +**Total:** ~15 questions per slot (3 per category) + +## Pipeline Architecture (V5 — Self-Contained) + +``` +User Input: slot = "2018-03-11.16-20-00.school" + │ + ▼ +┌───────────────────────────────────────────────────────────┐ +│ Step 1: LOAD SLOT INDEX │ +│ Read data/slot_index.json (929 slots) │ +│ Find all clips for this slot │ +└───────────────────────────────────────────────────────────┘ + │ + ▼ +┌───────────────────────────────────────────────────────────┐ +│ Step 2: BUILD SCENE GRAPH (inline) │ +│ Parse Kitware activities.yml + types.yml (CSafeLoader) │ +│ Load KRTD camera calibrations │ +│ Parse GPX for GPS actor verification data │ +│ Build cross-camera event pairs │ +│ Output: SceneGraph dataclass │ +└───────────────────────────────────────────────────────────┘ + │ + ▼ +┌───────────────────────────────────────────────────────────┐ +│ Step 3: GENERATE SYMBOLIC QA (deterministic, seed=42) │ +│ 5 category-specific generators │ +│ No LLM — pure graph traversal │ +│ Output: data/qa_pairs/{slot}.v5.json │ +└───────────────────────────────────────────────────────────┘ + │ + ▼ +┌───────────────────────────────────────────────────────────┐ +│ Step 4 (optional): GPT NATURALIZATION │ +│ python3 scripts/naturalize_qa.py --input {v5.json} │ +│ Rephrases templates only — does NOT compute answers │ +└───────────────────────────────────────────────────────────┘ +``` + +## Slot Format + +Slots follow: `{date}.{time}.{site}` (e.g., `2018-03-11.16-20-00.school`) + +Sites: `admin`, `bus`, `hospital`, `school` + +## Annotation Source + +V5 uses **Kitware YAML** exclusively (`.activities.yml` + `.types.yml`): +- Location: `meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware/` +- 769 clips, 761 with activities (99%) +- Standardized activity names, dense coverage + +## V5 Output Format + +```json +{ + "slot": "2018-03-11.16-20-00.school", + "version": "v5", + "annotation_source": "kitware", + "generator": "symbolic_deterministic", + "seed": 42, + "cameras": ["G299", "G330", "G421", "G423", "G638", "G639"], + "total_events": 587, + "total_questions": 15, + "category_counts": {"temporal": 3, "event_ordering": 3, "perception": 3, "spatial": 3, "camera_transition": 3}, + "questions": [ + { + "id": "temporal_001", + "category": "temporal", + "question": "A 'person sits down' event was observed on camera G330...", + "options": ["...", "...", "...", "..."], + "correct_answer": 0, + "correct_option_text": "...", + "cameras_required": ["G330", "G299"], + "verification": { "event_a": {...}, "event_b": {...}, "gap_sec": 57.66 } + } + ] +} +``` + +## Activity Types (37 ActEV Activities) + +**Person activities:** `person_enters_vehicle`, `person_exits_vehicle`, `person_opens_trunk`, `person_closes_trunk`, `person_opens_vehicle_door`, `person_closes_vehicle_door`, `person_picks_up_object`, `person_puts_down_object`, `person_carries_heavy_object`, `person_transfers_object`, `person_loads_vehicle`, `person_unloads_vehicle`, `person_talks_to_person`, `person_embraces_person`, `person_talks_on_phone`, `person_texts_on_phone`, `person_reads_document`, `person_interacts_with_laptop`, `person_rides_bicycle`, `person_sits_down`, `person_stands_up`, `person_enters_scene_through_structure`, `person_exits_scene_through_structure`, `person_opens_facility_door`, `person_closes_facility_door`, `person_purchases`, `person_steals_object`, `person_abandons_package`, `hand_interacts_with_person` + +**Vehicle activities:** `vehicle_starts`, `vehicle_stops`, `vehicle_turns_left`, `vehicle_turns_right`, `vehicle_makes_u_turn`, `vehicle_reverses`, `vehicle_picks_up_person`, `vehicle_drops_off_person` + +## Pipeline History + +| Version | Approach | Status | +|---------|----------|--------| +| V1 (`generate_qa_v1_rulebased.py`) | Rule-based, 50-60 Qs, no LLM | Archived in `scripts/old_versions/` | +| V2 (`generate_qa_v2_llm.py`) | Full-slot LLM generation (GPT-4o) | Archived | +| V3 (`generate_qa_v3_multicam.py`) | Per-question LLM with class hierarchy | Archived | +| V4 (`generate_qa_v4.py`) | 10 Qs, entity-level spatial via KRTD+bbox | In `scripts/`, kept for spatial reference | +| **V5** (`generate_qa_v5.py`) | **15 Qs, deterministic, self-contained** | **Current** | diff --git a/docs/project-history/github-session-archive/renumber_sessions.py b/docs/project-history/github-session-archive/renumber_sessions.py new file mode 100644 index 0000000..5fad023 --- /dev/null +++ b/docs/project-history/github-session-archive/renumber_sessions.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +"""Renumber sessions chronologically and create archive structure.""" + +import re +import json +import shutil +from pathlib import Path +from datetime import datetime + +def parse_session_file(filepath): + """Extract date and title from session file.""" + with open(filepath, 'r') as f: + content = f.read() + + # Find the first header line + match = re.search(r'^## (\d{4}-\d{2}-\d{2})(?: \(Session \d+\))?: (.+)$', content, re.MULTILINE) + if match: + date_str = match.group(1) + title = match.group(2) + date = datetime.strptime(date_str, '%Y-%m-%d') + return { + 'date': date, + 'date_str': date_str, + 'title': title, + 'filepath': filepath, + 'content': content + } + return None + +def renumber_sessions(): + """Renumber all sessions chronologically.""" + session_logs_dir = Path('/home/ah66742/.github/session_logs') + + # Parse all existing session files + sessions = [] + for filepath in session_logs_dir.glob('session_*.md'): + session_data = parse_session_file(filepath) + if session_data: + sessions.append(session_data) + + # Sort by date + sessions.sort(key=lambda x: x['date']) + + print(f"Found {len(sessions)} sessions") + print("\nChronological order:") + for i, session in enumerate(sessions): + print(f" {i+1}. {session['date_str']} - {session['title']}") + + # Renumber and rewrite files + print("\nRenumbering sessions...") + for i, session in enumerate(sessions): + new_number = i + 1 + new_filename = f"session_{new_number:02d}_{session['date_str']}.md" + new_filepath = session_logs_dir / new_filename + + # Update header in content + old_header_pattern = r'^## (\d{4}-\d{2}-\d{2})(?: \(Session \d+\))?: (.+)$' + new_header = f"## {session['date_str']} (Session {new_number}): {session['title']}" + new_content = re.sub(old_header_pattern, new_header, session['content'], count=1, flags=re.MULTILINE) + + # Delete old file if different name + if session['filepath'] != new_filepath: + session['filepath'].unlink() + + # Write new file + with open(new_filepath, 'w') as f: + f.write(new_content) + + print(f" {session['filepath'].name} → {new_filename}") + + # Update last_session.json + last_session = sessions[-1] + last_number = len(sessions) + last_session_data = { + 'date': last_session['date_str'], + 'session_number': str(last_number), + 'title': last_session['title'], + 'filename': f"session_{last_number:02d}_{last_session['date_str']}.md", + 'updated_at': datetime.now().isoformat() + } + + tracker_file = Path('/home/ah66742/.github/last_session.json') + with open(tracker_file, 'w') as f: + json.dump(last_session_data, f, indent=2) + + print(f"\nUpdated last_session.json to Session {last_number}") + + return sessions + +def create_archive_structure(): + """Create archive structure for old sessions (optional).""" + session_logs_dir = Path('/home/ah66742/.github/session_logs') + archive_dir = Path('/home/ah66742/.github/session_logs_archive') + archive_dir.mkdir(exist_ok=True) + + # Count sessions by month + sessions_by_month = {} + for filepath in session_logs_dir.glob('session_*.md'): + match = re.search(r'session_\d+_(\d{4}-\d{2})-\d{2}\.md', filepath.name) + if match: + month = match.group(1) + sessions_by_month.setdefault(month, []).append(filepath) + + print("\nSessions by month:") + for month, files in sorted(sessions_by_month.items()): + print(f" {month}: {len(files)} sessions") + + # Optionally archive old months (keeping last 2 months active) + current_date = datetime.now() + cutoff_date = datetime(current_date.year, current_date.month - 1, 1) if current_date.month > 1 else datetime(current_date.year - 1, 12, 1) + cutoff_month = cutoff_date.strftime('%Y-%m') + + print(f"\nKeeping sessions from {cutoff_month} onwards in active logs") + print("Older sessions can be archived if needed (not done automatically)") + +if __name__ == '__main__': + sessions = renumber_sessions() + print("\n" + "="*60) + create_archive_structure() + print("\n" + "="*60) + print(f"\n✅ Done! {len(sessions)} sessions renumbered sequentially.") diff --git a/docs/project-history/github-session-archive/session_log_master.md b/docs/project-history/github-session-archive/session_log_master.md new file mode 100644 index 0000000..dbcbcfd --- /dev/null +++ b/docs/project-history/github-session-archive/session_log_master.md @@ -0,0 +1,2213 @@ +# Session Log + +This file tracks completed tasks and important decisions across sessions. + +--- + +## 2026-02-12 (Session 17): V7 Memory Optimization + V6 Pipeline Documentation + +### Context +User requested: (1) Confirm V7 has full GEOM loading (not sampled), and (2) comprehensive explanation of V6 pipeline from ground-up principles to low-level implementation. + +### Tasks Completed + +#### 1. Memory Optimization: Full GEOM Loading for V7 + +**Problem identified**: V6 used `sample_every=30` → only loads every 30th bbox frame +- Incomplete trajectories (miss activities between keyframes) +- Spatial questions use "closest frame" approximation (up to 15 frames = 0.5s off) +- Only ~300 bboxes per actor (vs 9000 for full clip) + +**Solution implemented**: Changed V7 to `sample_every=1` → loads ALL bboxes +- Modified `scripts/v7/build_scene_graph.py`: `sample_every=30` → `sample_every=1` +- Modified `scripts/v7/utils/yaml_stream.py`: Updated docstring, changed default to `sample_every=1` +- Updated `v7_todo.md` Section 6: Added comprehensive memory optimization explanation + +**Impact**: +- **Memory cost**: 20 actors × 9000 frames = 180K bboxes per camera (~7 MB) +- **Total for 8 cameras**: ~56 MB (0.04% of 128 GB RAM) — easily feasible +- **Benefits**: Complete trajectories, exact spatial positioning (no approximation), enables future trajectory-based questions + +**Rationale documented in v7_todo.md Section 6**: +- Temporal resolution: Activities can be 1-2 seconds (30-60 frames), sampling every 30 means only 2 samples +- Spatial accuracy: No more "closest frame" heuristic, get exact bbox at target frame +- Future-proof: Enables trajectory analysis ("Was person moving toward/away?") + +#### 2. Comprehensive V6 Pipeline Documentation + +Created `docs/V6_PIPELINE_EXPLAINED.md` (5000+ lines) with 8 major sections: + +**Section 1: Ground-Up Principles** +- Core problem: multi-camera coordination, entity ambiguity, scale, memory, quality +- Design philosophy: modular architecture, entity-centric model, ground truth + heuristics, streaming +- Key assumptions: 30 fps, ±1s camera sync, actor_id per-camera only, 37 activity taxonomy, 80% KRTD coverage + +**Section 2: Pipeline Architecture** +- High-level flow diagram (7 steps: parse → scene graph → resolution → temporal/spatial/perception QA → validation) +- Module structure (8 Python files + 4 utilities) +- Data flow diagram with arrows showing transformations + +**Section 3: Data Structures** +- Event (raw activity from YAML) +- Entity (tracked person/vehicle on one camera with bboxes) +- SceneGraph (slot-level knowledge: cameras + entities + events) +- ResolvedGraph (cross-camera links + clusters) +- Question output format (JSON schema with verification blocks) + +**Section 4: Step-by-Step Flow** +- **Step 1 (Parse)**: Load activities.yml, types.yml → List[Event] +- **Step 2 (Scene Graph)**: Load cameras, extract entities, stream geom.yml bboxes → SceneGraph +- **Step 3 (Resolution)**: MEVID camera-level lookup + temporal handoff heuristic → ResolvedGraph with Union-Find clustering +- **Step 4 (Temporal)**: Find cross-camera event pairs with 3-20s gaps → 3 temporal questions +- **Step 5 (Spatial)**: Project bboxes to 3D via KRTD, compute distances → 3 spatial questions (near/moderate/far) +- **Step 6 (Perception)**: Activity identification, camera selection, multi-camera confirmation → 3 perception questions +- **Step 7 (Validation)**: Category-specific deduplication + validation rules + +**Section 5: Low-Level Implementation Details** +- **5.1 YAML parsing**: Why CSafeLoader for small files, regex streaming for large (50+ MB) geom.yml + - Inline regex: `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` + - Performance: 1.5s for 100K lines vs 8s for full load +- **5.2 KRTD 3D projection**: 6-step pipeline (bbox → undistort → normalized coords → 3D ray → ground plane intersection → ENU position) + - Why feet not bbox center: feet on ground (Z=0), center is ~1m above +- **5.3 Why no MEVID links**: Can't map MEVID person_id → Kitware actor_id without image matching (10 hours for 47 slots) + - V7 adds camera-level validation flag (plausibility proxy) +- **5.4 Bug fixes explained**: Inline YAML regex (Bug 1), mega-cluster safeguards (Bug 2), category-specific dedup (Bug 3) + +**Section 6: Memory Management** +- V6 strategy: `sample_every=30` → 6000 bboxes per camera (240 KB), fast but incomplete +- V7 strategy: `sample_every=1` → 180K bboxes per camera (7 MB), complete trajectories +- HDF5 mmap future work: For 100+ slot batch processing, one-time indexing + constant RAM queries + +**Section 7: Question Generation Strategies** +- **7.1 Temporal**: Gap selection 3-20s (not trivial/ambiguous), distribute across ranges +- **7.2 Spatial**: Proximity buckets (near ≤5m, moderate 5-15m, far >15m), one per bucket +- **7.3 Perception**: Distractor bank with context/type/visual matching, 37 MEVA activities grouped + +**Section 8: Validation & Quality Control** +- Validation rules per category (cross-camera check, gap ranges, distance-proximity consistency) +- Success criteria (9 questions, 0 errors, <15s generation time) +- Output JSON schema with all required fields + +### Files Created/Modified + +**Created**: +- `docs/V6_PIPELINE_EXPLAINED.md` (comprehensive 5000+ line technical documentation) + +**Modified**: +- `scripts/v7/build_scene_graph.py` (changed `sample_every=30` → `sample_every=1`) +- `scripts/v7/utils/yaml_stream.py` (updated docstring for `sample_every=1` default) +- `v7_todo.md` (added Section 6: Scene Graph Memory Optimization with full loading rationale) +- `.github/SESSION_LOG.md` (this file) + +### Key Decisions + +**Memory optimization approach**: +- **V7 immediate**: Full GEOM loading (`sample_every=1`) — 56 MB for 8 cameras, trivial memory cost +- **Future batch processing**: HDF5 mmap indexing deferred until 100+ slot processing needed +- **Rationale**: Better data quality (complete trajectories, exact spatial) worth small memory increase + +**Documentation scope**: +- Ground-up principles explain WHY (multi-camera problem, scale challenges) +- Architecture explains WHAT (7 steps, modular design, data structures) +- Low-level details explain HOW (regex streaming, KRTD projection math, Union-Find clustering) +- Can now explain V6 to advisors, paper reviewers, or collaborators + +### Notes for Future Sessions + +**V7 implementation ready**: +- Full GEOM loading implemented ✅ +- Next: Entity aliasing (30 min), activity hierarchy (30 min), debug markers (1 hour) +- Follow v7_todo.md Section 9 (Implementation Plan) + +**To run V6 with full context**: +```bash +cd /home/ah66742 && source venv/bin/activate +python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v +``` + +**To read V6 documentation**: +```bash +cat docs/V6_PIPELINE_EXPLAINED.md +``` + +**Memory profile for V7 (expected)**: +- Scene graph build: ~100 MB (full GEOM for 8 cameras) +- Entity resolution: ~50 MB (link graph + clusters) +- Question generation: ~10 MB (candidate lists) +- **Total peak**: ~200 MB (0.15% of 128 GB RAM) + +--- + +## 2026-02-13 (Session 16): V7 Planning & MEVID Implementation Analysis + +### Context +User requested detailed explanations of V6 bugs, verification markers, and why MEVID links aren't used. Then requested V7 planning with specific implementation tasks and MEVID implementation evaluation. + +### Tasks Completed + +#### 1. V6 Bug Explanations +Documented three critical bug fixes from V6 development: + +**Bug 1: geom.yml Inline Format** +- Problem: Kitware uses inline `- { geom: {id1: X, ts0: Y, g0: ...} }` not block YAML +- Impact: 0 bboxes for 3/8 cameras (G299, G419, G423) +- Fix: Inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` +- Result: 516 actors found for G330 (was 0) + +**Bug 2: Entity Resolution Mega-Cluster** +- Problem: Too permissive linking → 33,592 links → 1 giant cluster (all 1,258 entities) +- Root causes: No gap limit, no link count limit, low confidence threshold (0.5), linked background actors +- Fix: MAX_HANDOFF_GAP=10s, MAX_LINKS_PER_ENTITY=2, threshold 0.7, only active entities (926 with events) +- Result: 1,330 links → 20 meaningful clusters + +**Bug 3: Deduplication `None == None`** +- Problem: Checked `event_a`/`event_b` for ALL categories, spatial/perception lack these → `None == None` → dropped all after first +- Impact: First run generated 5 questions (3 temporal + 1 spatial + 1 perception) instead of 9 +- Fix: Category-specific dedup (entity_a/b for spatial, question_type for perception) +- Result: Full 9 questions generated (3+3+3) + +#### 2. Verification Marker Documentation +Explained technical markers in V6 output: + +**entity_a/entity_b**: Kitware actor IDs like `G328_actor_1613855842107762240` (vague, needs human-readable aliases in V7) + +**world_pos_enu**: 3D world coordinates `[East, North, Up]` from KRTD camera calibration + bbox foot projection to ground plane (Z=0). Used to compute spatial distance questions. + +#### 3. MEVID Gap Analysis +Why V6 shows `"entity_link": "heuristic"` for all questions: + +**MEVID provides**: Camera-level person mapping: `Person 4: [G299, G328, G336, G419, G420, G421, G423]` + +**Missing link**: Can't map MEVID `person_id` → Kitware `actor_id` without: +1. Extracting MEVID tracklet image crops (500K-800K images) +2. Matching crops to Kitware geom.yml bboxes via image similarity (Person ReID model) +3. Spatial-temporal validation (frame overlap + IoU) +4. Compute-intensive: ~10 hours for 47 VSet7 slots, ~8 days for 929 slots + +**V6 current use**: MEVID used only for validation/stats (41 persons in slot) + +**V7 enhancement**: Camera-level MEVID validation — flag questions where both cameras covered by same person (plausibility proxy without full matching) + +#### 4. V7 Documentation +Created comprehensive V7 planning documents: + +**[v7_todo.md](v7_todo.md)** — Implementation plan with 8 phases: +1. ✅ Copy V6 → V7 +2. ⏳ Entity aliasing (30 min) +3. ⏳ Activity hierarchy (30 min) +4. ⏳ Update question generators (1 hour): aliases + debug_info + connected events +5. ⏳ MEVID validation logic (20 min) +6. ✅ Archive cleanup +7. ⏳ GPT wrapper (20 min) +8. ⏳ Testing (20 min) +9. Added memory optimizations section from v7_memory_optimizations.md + +**[V7_AUDIT.md](V7_AUDIT.md)** — Bug explanations, marker docs, MEVID analysis, pros/cons, next steps + +**[V6_V7_QUICK_REF.md](V6_V7_QUICK_REF.md)** — Quick reference guide for V6 bugs, V7 changes, run commands + +**[MEVID_IMPLEMENTATION_PLAN.md](MEVID_IMPLEMENTATION_PLAN.md)** — Detailed analysis of MEVID→Kitware mapping: +- 3 implementation options evaluated (ResNet embeddings, Person ReID, visual similarity) +- Recommended: Person ReID model (torchreid/OSNet) with spatial-temporal validation +- Complexity: 7 hours one-time MEVID feature extraction + 12 min/slot +- Total: 10 hours for 47 VSet7 slots, 8 days for 929 slots +- **Decision**: Defer to post-V7 — Complete V7 pipeline first with camera-level MEVID validation proxy + +#### 5. V7 Module Structure +Created `scripts/v7/` by copying V6, ready for enhancements: +- Entity aliasing: `make_alias()` method in build_scene_graph.py +- Activity hierarchy: New activity_hierarchy.py module +- Connected events: Temporal questions require scene graph connection +- Debug markers: Frame ranges, timestamps, clip files for manual verification +- MEVID validation: Camera-level flags for questions where both cameras covered by same person + +#### 6. Slot Viewer Tool +Created `scripts/view_slot.py` for visualizing all cameras at once (headless-compatible): +- **Frame grid mode**: Extract frames at timestamps → labeled PNG grid (1.5 MB, viewable in VS Code) +- **Filmstrip mode**: One row per camera, N columns across time +- **Mosaic video mode**: Tiled MP4 (using cv2.VideoWriter, no FFmpeg needed) +- **Annotation overlay**: Display active Kitware events on each camera +- Output: `output/slot_views/` + +### Files Created/Modified + +**Created**: +- `v7_todo.md` (updated with detailed implementation tasks + memory optimizations) +- `V7_AUDIT.md` (bug explanations, pros/cons, next steps) +- `V6_V7_QUICK_REF.md` (quick reference) +- `MEVID_IMPLEMENTATION_PLAN.md` (detailed Person ReID implementation plan) +- `scripts/v7/` (copied from V6, ready for enhancements) +- `scripts/view_slot.py` (headless slot viewer) +- `data/qa_pairs/2018-03-11.11-25-00.school.v6.json` (9 questions, 0 validation errors) + +**Modified**: +- `scripts/v6/utils/yaml_stream.py` (inline regex fix) +- `scripts/v6/entity_resolution.py` (tightened heuristic thresholds) +- `scripts/v6/run_pipeline.py` (category-specific dedup) +- `.github/SESSION_LOG.md` (this file) + +### Key Decisions + +**V7 Implementation Approach**: +- Refine V6 foundation (don't redesign) +- 4 core enhancements: entity aliases, connected events, debug markers, MEVID validation +- Total estimated time: ~3 hours +- Target same slot as V6 for direct comparison + +**MEVID Implementation Strategy**: +- **Best approach**: Person ReID model (torchreid/OSNet) + spatial validation +- **Complexity**: 10 hours for VSet7, 8 days for full dataset +- **Decision**: Defer to post-V7 — V7's camera-level validation sufficient as proxy +- **Rationale**: Complete V7 → batch VSet7 → evaluate quality → decide if full MEVID matching needed + +**Memory Optimization**: +- Documented HDF5 mmap approach for geom.yml (Section 13 in v7_todo.md) +- Decision: Keep streaming for V7, add mmap later for batch processing (929 slots) +- Justification: Streaming adequate for single-slot work, mmap overhead not amortized until 100+ slots + +### Notes for Future Sessions + +**Immediate next steps** (3 hours): +1. Add `Entity.make_alias()` to `scripts/v7/build_scene_graph.py` (30 min) +2. Create `scripts/v7/activity_hierarchy.py` with 10 relationships (30 min) +3. Update question generators with aliases + debug_info + connected events (1 hour) +4. Add MEVID camera-level validation (20 min) +5. Create `scripts/naturalize_v7_qa.py` GPT wrapper (20 min) +6. Test on `2018-03-11.11-25-00.school`, compare with V6 output (20 min) + +**After V7 completion**: +- Batch process 47 VSet7 slots +- VLM evaluation (GPT-4o/Gemini) +- Human validation (50 sample questions) +- If quality questioned → implement full MEVID→Kitware mapping + +**To view slot frames**: +```bash +python3 scripts/view_slot.py --slot 2018-03-11.11-25-00.school --time 60 120 --annotate -v +``` + +**To run V6 pipeline**: +```bash +cd /home/ah66742 && source venv/bin/activate +python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v +``` + +--- + +## 2026-02-13 (Session 15): V6 Pipeline Implementation — Full Execution + +### Context +Executed v6_todo.md end-to-end on a single MEVID-supported slot (`2018-03-11.11-25-00.school`). + +### Tasks Completed + +#### 1. V6 Pipeline Implementation (14 files) +Created complete modular pipeline in `scripts/v6/`: +- **Utilities** (4 files): `utils/yaml_stream.py` (geom.yml streaming parser), `utils/mevid.py` (MEVID person-camera mapping), `utils/krtd.py` (camera calibration + bbox→3D), `utils/iou.py` (IoU computation) +- **Pipeline steps** (5 files): `parse_annotations.py` (Kitware YAML → Events), `build_scene_graph.py` (entity-based scene graph), `entity_resolution.py` (MEVID + heuristic cross-camera linking), `distractor_bank.py` (wrong answer generation), `run_pipeline.py` (orchestrator) +- **QA generators** (3 files): `generate_temporal.py`, `generate_spatial.py`, `generate_perception.py` +- **Package files** (2): `__init__.py`, `utils/__init__.py` + +#### 2. Slot Selection +Chose `2018-03-11.11-25-00.school`: 9 cameras, 8/9 in MEVID, kitware annotation source, 41 MEVID cross-camera persons. + +#### 3. Bugs Fixed (3 iterations) +1. **geom.yml inline format**: Kitware uses single-line `- { geom: {id1: X, ts0: Y, g0: X1 Y1 X2 Y2} }`. Added inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` +2. **Entity resolution mega-cluster**: 33,592 heuristic links → 1 cluster. Fixed: MAX_HANDOFF_GAP=10s, MAX_LINKS_PER_ENTITY=2, only active entities, confidence threshold 0.7 → 1,330 links, 20 clusters +3. **Deduplication bug**: `is_duplicate_within_slot` used `None == None` comparisons for spatial/perception questions (no `event_a`/`event_b` fields). Rewrote with category-specific dedup logic. + +#### 4. Final Results +9 questions (3 temporal + 3 spatial + 3 perception), 0 validation issues, 7.9s runtime. +- **Temporal**: cross-camera event ordering, 3.03-3.04s gaps, entity-linked +- **Spatial**: near (2.87m), moderate (8.06m), far (98.22m) via KRTD bbox→3D on G328 +- **Perception**: which_camera, activity_identification, multi_camera_confirmation — all affirming + +### Files Created +- `scripts/v6/__init__.py` +- `scripts/v6/utils/__init__.py` +- `scripts/v6/utils/yaml_stream.py` +- `scripts/v6/utils/mevid.py` +- `scripts/v6/utils/krtd.py` +- `scripts/v6/utils/iou.py` +- `scripts/v6/parse_annotations.py` +- `scripts/v6/build_scene_graph.py` +- `scripts/v6/entity_resolution.py` +- `scripts/v6/distractor_bank.py` +- `scripts/v6/generate_temporal.py` +- `scripts/v6/generate_spatial.py` +- `scripts/v6/generate_perception.py` +- `scripts/v6/run_pipeline.py` +- `data/qa_pairs/2018-03-11.11-25-00.school.v6.json` + +### Key Decisions +- **Heuristic linking** used for temporal questions (MEVID provides camera-level mapping only, not MEVID person_id → Kitware actor_id) +- **Only G328 and G336** have KRTD models for this slot (G299, G330 excluded as indoor) — spatial questions limited to these cameras +- **3 geom.yml cameras returned 0 bboxes** (G299, G419, G423) — possibly different annotation sub-format or missing geom files +- Entity links labeled `heuristic` in output (not `mevid_ground_truth`) since we can't directly confirm same-person identity + +### Notes for Future Sessions +- geom.yml parsing still returns 0 for G299, G419, G423 — investigate if these lack geom.yml or use a different format +- KRTD coverage is sparse (2/8 cameras) — spatial questions come from G328 only +- Heuristic vs MEVID comparison report (Section 12.2 of v6_todo.md) documented but not implemented — future work +- To run: `cd /home/ah66742 && source venv/bin/activate && python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v` + +--- + +## 2026-02-12 (Session 14): V6 Plan Rewrite, Repo Cleanup Strategy + +### Context +User provided 12 specific V6 requirements and clarified local-vs-repo distinction for GitHub push. + +### Tasks Completed + +#### 1. Rewrote v6_todo.md with All 12 User Requirements +Complete rewrite incorporating: temporal gap 3-15s (fallback 20s), 2+ cameras for temporal only, geom.yml streaming prototype first, raw annotation text, simple spatial + 1 complex, easy difficulty default with classification system, distractor bank, V4/V5-style output format, within-slot deduplication, MEVID-only slots, start with 1 slot, validation plan (documented not implemented). + +Modular architecture: 9 files in `scripts/v6/` + 4 utility modules. Only 3 QA categories: temporal, spatial, perception. + +#### 2. MEVID Data Analysis +Decoded naming convention, found 158 persons / 8093 tracklets / 25 cameras. 98% of train persons on 2+ cameras. 900/929 MEVA slots overlap with MEVID cameras. + +#### 3. Created .gitignore + cleanup_local.sh +- `.gitignore`: excludes archived/, old outputs, large PDFs, personal dirs +- `cleanup_local.sh`: moves old outputs to `~/archived/` (no deletions) +- Replaced old `cleanup_for_push.sh` (which deleted files) + +### Files Created/Modified +- `v6_todo.md` — Complete rewrite (14 sections) +- `.gitignore` — New +- `cleanup_local.sh` — New (replaces cleanup_for_push.sh) + +### Key Decisions +- Scene graph is THE novelty — all cross-camera questions derive from it +- MEVID ground truth first, heuristic second (must prove heuristic works before expanding) +- 3 categories only: temporal, spatial, perception +- Raw annotation text in questions (GPT wrapper later) + +### Next Steps +1. Review v6_todo.md, run cleanup_local.sh, git init + push +2. Begin V6 Phase 1: prototype geom.yml streaming + +--- + +## 2026-02-11 (Session 13): Full Audit, Reference Cleanup, Progress Report + +### Context +Follow-up session after V5 pipeline was built in Session 12. Focus on documentation hygiene, deep GPS investigation, V4/V5 comparison, VSet7 analysis, and producing a finalized progress report for the team. + +### Tasks Completed + +#### 1. Updated ALL Outdated References +Rewrote 3 documentation files that referenced obsolete pipelines (V3, multicam_qa_pipeline, LLM-based approaches): +- `.github/ANNOTATION_QUALITY_ANALYSIS.md` — Rewrote with V5 vulnerability assessment (all 5 categories now SAFE); removed references to V3, MEVID integration, `_precompute_spatial_answer()` +- `.github/multicam-temporal-questions.md` — Complete rewrite: now documents V5 pipeline, 5 active categories, correct paths, pipeline history table +- `docs/README.md` — Replaced Multi-Camera integration guide with current V5 pipeline docs + +#### 2. V4 vs V5 Detailed Evaluation +Comprehensive comparison of both pipelines: +- **V5 wins**: Self-contained, 5 categories (vs 3), no OOM risk, no pre-build needed +- **V4 still valuable for**: Entity-level spatial (bbox→3D projection via KRTD) — more granular than V5's camera-center approach +- **V4 text quality better**: Has `ACTIVITY_DISPLAY` dict with 3 grammatical forms + `describe_by_action` priority scoring +- **V4 temporal harder**: Asks "what happens next?" (prediction) vs V5's "which occurred first?" (ordering) +- **Verdict**: V5 is production; V4 kept as spatial bbox reference + +#### 3. GPS Data Deep Investigation +**Key finding: GPS tracks ARE continuous** — not just start/end waypoints. +- Each GPX file has `` (waypoint summaries) AND `` (continuous tracks) +- **94 actors** per slot, **29-30 trackpoints each** at ~10 sec intervals +- Can compute pairwise distances between actors at any moment (e.g., G559-G525: 18.5m) +- Official GPS README confirms: "actors and personnel carried GPS loggers set to log their location once every 10 seconds" +- mevadata.org also confirms actor GPS logging + +**Decision**: GPS is NOT useless — it's rich continuous data. However, **GPS actor IDs (G517-G625) cannot be linked to annotation actor IDs** (no mapping exists), so GPS positions can't be used in video-answerable questions. GPS remains verification-only. Spatial questions use KRTD camera-center distances (video-grounded). + +Updated V5 `generate_spatial_qa()` docstring to document this decision. + +#### 4. Camera Transition Fix +Verified that camera transition options **already** come from same-slot cameras (using `sg.cameras.keys()`). Added minimum camera threshold: skip category if `len(sg.cameras) < 3` (trivial with only 2 cameras). + +#### 5. VSet7 vs Kitware — Definitive Comparison +Ran side-by-side comparison on `2018-03-11.16-20-00.school`: +- **Kitware**: 587 events, 6 cameras +- **VSet7**: 332 events, 3 cameras (G330, G423, G638) +- On overlapping cameras: annotations are **identical** (same activities, same frame ranges) +- VSet7 simply covers **fewer cameras** — it's a strict subset, NOT a supplement +- **VSet7 adds nothing that Kitware doesn't already have** +- Only value: cross-validation (confirms annotation quality) and NIST JSON format + +#### 6. Finalized Progress Report +Created `docs/PROGRESS_REPORT.md` — comprehensive team report covering: +- Executive summary, dataset overview, pipeline architecture +- 5 question categories with examples +- How we worked around annotation sparsity (positive-only perception, activity-based questions, KRTD spatial, timestamp pairing) +- Novel methods (cross-camera temporal pairing, camera-center spatial, event-level scene graph, vulnerability-aware categories) +- Pipeline evolution (V1→V5) +- Test results with sample questions +- Next steps and research opportunities + +### Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `.github/ANNOTATION_QUALITY_ANALYSIS.md` | **Rewritten** | Updated for V5, removed V3/MEVID references | +| `.github/multicam-temporal-questions.md` | **Rewritten** | Full V5 pipeline documentation | +| `docs/README.md` | **Rewritten** | Current V5 docs, removed Multi-Camera integration guide | +| `docs/PROGRESS_REPORT.md` | **Created** | Finalized team progress report | +| `scripts/generate_qa_v5.py` | **Modified** | Updated spatial docstring; added 3-camera min for camera_transition | +| `.github/SESSION_LOG.md` | **Updated** | This entry | + +### Key Decisions + +| Decision | Rationale | +|----------|-----------| +| GPS stays verification-only | Continuous tracks exist but no ID mapping to annotations | +| VSet7 = subset of Kitware | Identical annotations on overlapping cameras; fewer cameras | +| V5 is production pipeline | Self-contained, 5 categories, deterministic, no OOM | +| V4 kept for spatial reference | Entity-level bbox→3D projection code still valuable | +| Camera transition needs ≥3 cameras | Trivial with 2 cameras | + +### Next Session Priority +1. **Batch V5 across all 929 slots** — measure success/skip rates +2. **GPT naturalization** on validated slots +3. **Entity-level spatial** — cherry-pick V4's bbox projection for small geom files +4. **VLM evaluation** — feed generated QA to target model + +--- + +## 2026-02-11 (Session 12): V5 QA Pipeline — Build + Quality Fixes + +### Context +Fresh start for V5 QA pipeline following NuScenes approach: deterministic symbolic QA from scene graphs, GPT wrapper only at the end. All old QA pairs/scene graphs/logic tuples archived. Focus on getting 1-2 slots perfect before scaling. + +### Tasks Completed + +#### 1. Archive Old Outputs +- Moved all previous QA pairs (21 files: .v4, .symbolic, .crosscam, .natural) → `data/old_versions/qa_pairs/` +- Moved all scene graphs (V1 + V2) → `data/old_versions/scene_graphs/` +- Moved all logic tuples (18 files) → `data/old_versions/logic_tuples/` + +#### 2. VSet7 vs Kitware Comparison +**Key finding: VSet7 is a SUBSET of Kitware**, not a supplement. +- Slot `2018-03-11.16-20-00.school`: Kitware has 587 events (6 cameras), VSet7 has 332 events (3 cameras: G330, G423, G638) +- On overlapping cameras, annotations match exactly (same activities, same frame ranges) +- VSet7 simply covers fewer cameras + +#### 3. GPS Utility Analysis +- Parsed GPX for test slot: 88 actors, waypoints only (start/end positions), NOT continuous tracks +- GPS actor IDs (G517-G625) are NOT linked to annotation actor IDs → cannot use GPS actors directly in video QA questions +- Validated GPS→ENU conversion against 5 ground control points: ~18m systematic error with old geo_origin +- **Key insight**: GPS useful for validation/ground-truth distance, NOT for video-visible questions + +#### 4. Built `scripts/generate_qa_v5.py` (~800 lines) +Self-contained V5 pipeline: scene graph build + symbolic QA generation in one script. + +**5 active categories** (3 questions each): +| Category | Question Type | Data Source | +|----------|--------------|-------------| +| temporal | Cross-camera before/after | Kitware activities.yml timestamps | +| event_ordering | Chronological sequence of 4 events | Kitware activities across 3-4 cameras | +| perception | Which camera shows/doesn't show X? | Kitware activity→camera mapping | +| spatial | Camera position distances (KRTD) | KRTD camera center positions | +| camera_transition | First/last camera to capture event | Entry/exit events across cameras | + +**Architecture**: +- `_parse_activities_yml()` — Kitware YAML → Event dataclass (CSafeLoader for speed) +- `load_camera_info()` — Loads KRTD calibration per camera +- `build_scene_graph()` — Full SceneGraph with events, cameras, GPS, cross-cam pairs +- `_build_options()` — MCQ builder with MAX_MCQ_OPTIONS=5 cap +- 5 generator functions: `generate_{temporal,event_ordering,perception,spatial,camera_transition}_qa()` +- CLI: `--slot`, `--compare-vset7`, `--list-vset7-slots`, `--verbose`, `--output` +- Output: `data/qa_pairs/{slot}.v5.json` + +#### 5. Quality Fixes (Iteration 1) +**Fixed 3 issues found in initial run:** + +| Issue | Fix | +|-------|-----| +| Spatial: GPS actor IDs (G577, G599) unlinked to camera annotations | Replaced with KRTD camera-position-based questions | +| CamTrans Q3: absolute timestamp in question ("at 131.7 seconds") | Replaced with activity description ("after performing X on camera Y") | +| CamTrans: too many options (6-7 cameras) | Added `_build_options()` helper with MAX_MCQ_OPTIONS=5 cap | + +**New spatial question types** (KRTD-based, camera-grounded): +1. Cross-camera movement: exit cam A → enter cam B, distance from KRTD positions +2. Camera proximity: which pair of cameras positioned closest? +3. Camera distance: which pair monitors most distant locations? + +#### 6. Test Results (Both Slots Pass) +**Slot 1: `2018-03-11.16-20-00.school`** +- 587 Kitware events, 6 cameras (G299, G330, G421, G423, G638, G639) +- 88 GPS actors, 15 questions generated (3 per category) +- All 15 pass quality checks + +**Slot 2: `2018-03-11.11-30-00.school`** +- 527 Kitware events, 7 cameras (G299, G328, G330, G336, G419, G420, G423) +- 89 GPS actors, 14 questions (2 spatial — no cross-cam movement Q due to missing KRTD for G419/G420/G423) +- All 14 pass quality checks + +### Key Decisions +1. **GPS actors NOT used in QA questions** — IDs don't map to annotation actors +2. **KRTD camera positions for spatial** — only 4-6 cameras per slot have KRTD, but sufficient +3. **Indoor cameras** (G299, G330) noted but not excluded from spatial — they have KRTD positions +4. **MCQ cap at 5 options** for all camera-based questions +5. **MEVID skipped** — not needed for current QA categories + +### Camera KRTD Coverage (School Site) +| Camera | KRTD | Position (ENU) | +|--------|------|----------------| +| G299 | Yes | (-16, -12, 4) indoor | +| G328 | Yes | (62, 81, 6) | +| G330 | Yes | (-16, 12, 4) indoor | +| G336 | Yes | (6, 86, 9) | +| G419 | No | — | +| G420 | No | — | +| G421 | No | — | +| G423 | No | — | +| G638 | Yes | (10, 85, 8) | +| G639 | Yes | (48, 104, 11) | + +### Files Created/Modified +- **Created**: `scripts/generate_qa_v5.py` (~800 lines) +- **Created**: `data/qa_pairs/2018-03-11.16-20-00.school.v5.json` (15 QA pairs) +- **Created**: `data/qa_pairs/2018-03-11.11-30-00.school.v5.json` (14 QA pairs) +- **Archived**: `data/old_versions/{qa_pairs,scene_graphs,logic_tuples}/` (51 files total) +- **Installed**: pyproj 3.7.1 in venv + +### Next Steps +1. **Manual review** of generated questions by user — verify correctness against video +2. **GPT naturalization** — run `naturalize_qa.py` on V5 output to polish template text +3. **Expand to more slots** once quality is confirmed +4. **Numerical category** — still unsolved (cross-camera person dedup needed) +5. **Consider**: Loading small geom files (G421: 276KB, G638: 567KB) for bbox-based spatial questions + +--- + +## 2026-02-10 (Session 11 UPDATED): GPS Actors + MEVID Coverage Analysis + +### Context +Major discoveries about GPS data and MEVID coverage. GPS tracks **actors** (not cameras!), +and most MEVID clips already exist on disk. + +### Tasks Completed + +#### 1. GPS Data — ACTORS, NOT CAMERAS! +**Key discovery**: The GPX files contain **actor GPS tracks**, not camera positions. +- Each actor carried a GPS logger (ID format: Gnnn like G623) +- Loggers recorded position every 10 seconds +- 105 unique actors tracked across the KF1 collection +- Consistent actor IDs within each session (March and May) + +**Built `scripts/parse_gps_actors.py`** to extract and analyze actor tracks: +```bash +python3 scripts/parse_gps_actors.py --stats # Summary: 116 files, 105 actors, 256K trackpoints +python3 scripts/parse_gps_actors.py --slot "2018-03-07.17-05-00" -v # Single slot +python3 scripts/parse_gps_actors.py --all # Process all GPX files +``` + +| Metric | Value | +|--------|-------| +| GPX files | 116 (5-min slots) | +| Unique actor IDs | 105 (G517-G625 range) | +| Total actor-track instances | 9,070 | +| Total trackpoints | 256,456 | +| GPX slots with annotations | 87 of 116 (75%) | + +**Use case**: GPS tracks can validate cross-camera identity links — if two activities +share the same GPS logger position at similar times, they are the same actor. + +#### 2. MEVID Coverage — 70% ALREADY ON DISK! +Re-analyzed MEVID video URLs with correct directory structure (hour subdirs by END time): + +| Category | Clips | +|----------|-------| +| Total MEVID clips | 976 | +| **Already on disk** | **685 (70.2%)** | +| Missing (March) | 0 | +| Missing (May 05-14/16/18) | 291 | + +**All 685 March MEVID clips exist!** Only May dates (2018-05-14, 2018-05-16, 2018-05-18) are missing. + +**Download script created**: `scripts/download_mevid_may.sh` +```bash +./download_mevid_may.sh --dry-run # Preview download (~8.5-14.2 GB) +./download_mevid_may.sh # Download 291 May clips +``` + +#### 3. Extra Dates — Status Update +- **2018-03-08, 2018-03-10**: Not in MEVID, would need MANIFEST download +- **2018-05-14, 2018-05-16, 2018-05-18**: Available via MEVID URLs, download script ready + +#### 2. VSet7 Annotations — INTEGRATED +Built `scripts/integrate_vset7.py` to parse and export VSet7 NIST JSON annotations: + +| Metric | Value | +|--------|-------| +| Source file | `actev-data-repo/partitions/ActEV-Eval-CLI-Validation-Set7.activities.json` (144MB) | +| Total activities | 4,648 (2,324 after deduplicating SimSA5_ prefixes) | +| Activity types | 35 unique | +| Clips | 60 | +| Unique slots | 47 (all match our slot_index exactly) | +| Top activities | person_sits_down (273), person_exits_scene (253), person_talks_to_person (241) | + +**Usage**: +```bash +python3 scripts/integrate_vset7.py --stats # Show statistics +python3 scripts/integrate_vset7.py --list-slots # List 47 slots +python3 scripts/integrate_vset7.py # Export all to data/vset7_annotations/ +``` + +**Output**: `data/vset7_annotations/{slot}.json` — 47 per-slot JSON files in logic tuple format + +#### 3. GPS Data — NOT USEFUL FOR ACTORS +- Analyzed 116 GPX files in `gps-data/gps-for-released-meva-data/` +- **Finding**: GPX files contain **camera positions** (waypoints named "G559 start", "G559 end"), NOT actor GPS tracks +- 188 waypoints (camera start/end pairs) + 94 tracks of camera movement +- **Conclusion**: Cannot use for actor validation — GPS data is for camera geolocation only + +#### 4. MEVID Re-ID — DIFFERENT DATES +- Analyzed MEVID directory structure in `data/mevid/` +- **Finding**: MEVID uses dates **2018-05-16** and **2018-05-18** — which we don't have on disk +- Video URLs point to `drop-5-mevid` collection (separate from our main data) +- Tracklet format: numeric columns (frame ranges, person IDs, camera IDs encoded as numbers) +- **Conclusion**: Cannot integrate MEVID without downloading additional video data + +#### 5. V4 Pipeline Testing +Tested V4 on 2 existing slots with scene graphs: + +| Slot | Questions | Temporal | Spatial | Perception | +|------|-----------|----------|---------|------------| +| 2018-03-07.17-05-00.school | 10 | 3 | 3 | 4 | +| 2018-03-05.13-15-00.bus | 8 | 0 | 4 | 4 | + +Bus slot had fewer questions due to limited cross-camera temporal pairs. + +### Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/integrate_vset7.py` | Created | VSet7 JSON parser (405 lines) | +| `scripts/parse_gps_actors.py` | Created | GPS actor track parser (454 lines) | +| `scripts/download_mevid_may.sh` | Created | Download script for 291 May MEVID clips | +| `/nas/mars/dataset/MEVA/vset7_annotations/*.json` | Created | 47 per-slot VSet7 annotation files (moved from ah66742/data) | +| `/nas/mars/dataset/MEVA/gps_actors/*.json` | Created | 116 per-slot GPS actor files (moved from ah66742/data) | +| `/nas/mars/dataset/MEVA/mevid_data/` | Organized | MEVID re-ID data (moved from ah66742/data) | +| `data/qa_pairs/*.v4.json` | Created | V4 QA outputs for tested slots | +| `scripts/old_versions/` | Organized | Moved 6 old scripts (build_scene_graph.py, generate_qa_crosscam.py, etc.) | + +### Directory Reorganization +- **ah66742/data**: Now contains only QA pipeline data (logic_tuples, qa_pairs, scene_graphs, slot_index) +- **MEVA/**: Contains all dataset files (gps_actors, mevid_data, vset7_annotations) +- **Cleaned up**: Deleted old conversion logs, duplicate annotations folder, moved old scripts + +### Key Decisions + +| Decision | Rationale | +|----------|-----------| +| **GPS validation not feasible** | GPX files contain camera positions, not actor tracks | +| **MEVID integration skipped** | Requires downloading 2018-05-16/18 videos | +| **VSet7 is supplementary** | All 47 slots already in our index with Kitware annotations | +| **V4 pipeline validated** | Works correctly on tested slots | + +### Data Availability Summary + +| Data Source | Status | Notes | +|-------------|--------|-------| +| Kitware YAML (meva-data-repo) | ✅ Primary | 891 slots, comprehensive | +| VSet7 NIST JSON (actev-data-repo) | ✅ Supplementary | 47 slots, 2,324 activities | +| GPS actor tracks | ❌ N/A | Camera positions only | +| MEVID re-ID | ❌ Different dates | Would need video download | +| Extra dates (03-08, 03-10, 05-16) | ❌ Not on disk | In MANIFEST but not downloaded | + +### Next Session Priority +1. **Batch V4 across more slots** — Focus on slots with existing scene graphs +2. **Build scene graphs for VSet7 slots** — 47 slots with dense NIST annotations +3. **Merge VSet7 with Kitware** — Consider joint annotation source in extract_logic_tuples.py +4. **Quality analysis** — Compare V4 output diversity across slot types + +--- + +## 2026-02-10 (Session 10): V4 QA Generator + actev-data-repo Exploration + +### Context +Built V4 QA generator addressing all known quality issues from vulnerability analysis. Then explored +the newly cloned actev-data-repo from NIST/Kitware for additional data. + +### Tasks Completed + +#### V4 QA Generator (`scripts/generate_qa_v4.py`, ~935 lines) +- **3 categories only**: temporal (cross-cam), spatial (3D), perception (positive-only) +- **10 questions per slot**: 3 temporal + 3 spatial + 4 perception +- **Key design decisions**: + - **Dropped causality** — causal chains weren't ground truth, just heuristics + - **Spatial: 3D world coordinates** via KRTD back-projection instead of 2D left/right + - Projects bbox foot-point to ground plane intersection + - Distance in meters (Near ≤5m, Far ≥15m) + - Camera-invariant — same answer regardless of viewing angle + - **Temporal: cross-camera only** with 3-20s gap, different cameras, different activities + - **Perception: positive-only MCQ** — "Which activity occurs?" with 1 correct + 3 absent distractors + - Eliminates false-negative vulnerability entirely +- **Successfully tested**: school slot (2018-03-07.17-05-00.school) → 10 questions + - Temporal gaps: 4.9s, 10.3s, 11.5s + - Spatial distances: 1.4m, 32.5m, 36.2m + - All 4 perception questions verified correct + +#### actev-data-repo Exploration +- **Cloned**: `https://gitlab.kitware.com/actev/actev-data-repo.git` → `/nas/mars/dataset/MEVA/actev-data-repo/` +- **Git issue**: Remote pack file has corrupt blob (d71d3ea7...), checkout fails for corpora/partitions/scripts dirs. Used `git show HEAD:` to read all content. +- **Key findings**: + - **2,704 tracked files**: 666 annotations, 2,018 partitions, 6 corpora, 13 scripts + - **MEVA MANIFEST**: 4,159 R13 AVI files (610.5 GB), 737 MP4s, 1,575 SimSA clips + - Dates include 2018-03-08, 2018-03-10, 2018-05-16 (NOT in our avis/ directory) + - **MEVA KF1-examples**: 65 clips with KPF YAML annotations — **ALL already exist in meva-data-repo** + - **Validation Set 7**: 214 MEVA clips, 60 processed with **4,648 NIST JSON activity annotations** across **33 of our 891 slots**, 70 activity types (35 standard + 35 SimSA5) + - **HADCV22-Test**: 201 clips including dates we don't have (03-08, 03-10, 05-16) + - **VIRAT**: 118 clips (different dataset, same format) — not useful for MEVA work + - **SimUF-KA-SimSA5**: 154 activity exemplar clips with annotations — potential for VLM few-shot + - **kpf_to_json_py3.py**: Authoritative KPF→JSON converter + +#### Pipeline Explanation +- Provided comprehensive pipeline writeup for team presentation +- Covered: slots → scene graphs → QA generation → naturalization → evaluation flow + +### Files Created +- `scripts/generate_qa_v4.py` — V4 QA generator (3 categories, 10 questions) +- `data/qa_pairs/2018-03-07.17-05-00.school.v4.json` — Test output +- `docs/actev-data-repo-reference.md` — Comprehensive reference (11 sections) + +### Key Decisions +- **V4 is current best generator** — replaces symbolic + crosscam approaches +- **actev-data-repo adds no new annotations** for our pipeline (all 65 are duplicates) +- **VSet7 activities.json** is potentially useful — NIST JSON format with per-frame bboxes for 33 slots +- **HADCV22 dates** (03-08, 03-10, 05-16) could expand coverage if we find those videos on disk + +### Next Session Priority +1. **Batch V4** across all 891 slots — track success/failure rates +2. **GPS-based validation** — Parse 116 .gpx files for actor count confidence scores +3. **MEVID identity integration** — 175 overlapping slots with person re-ID tracklets +4. **VSet7 integration** — Consider importing NIST JSON annotations as additional source +5. **Check for extra dates** — Do we have 2018-03-08 and 2018-03-10 videos on disk? + +--- + +## 2026-02-08 (Session 7): Cross-Camera QA Generator + +### Context +Built on Session 6's V2 scene graph with cross-camera narrative chains. Created a new QA generator +specifically for questions that REQUIRE multiple camera views to answer — the "dream" questions for +multi-camera VLM evaluation. + +### Tasks Completed + +1. **Built `scripts/generate_qa_crosscam.py`** — 5 cross-camera question categories: + - **cross_camera_temporal** (15): "After the person who exits a vehicle opens a facility door, what do they do next in a different camera view?" + - **cross_camera_ordering** (15): Arrange 3-4 events from different cameras chronologically (gerund form in questions) + - **cross_camera_tracking** (15): "After [person] does X in camera G421, which camera view shows them next?" + - **cross_camera_gap** (15): "Between opening a facility door and dropping off a person, what does [person] do?" (middle event on possibly 3rd camera) + - **camera_identification** (6-15): "A person does X in one view. In which other view do they do Y?" (requires different activities on source/target cameras) + +2. **Quality fixes through 4 iterations:** + - **Entity type consistency**: `describe_by_action()` now takes `prefer_type` parameter so person questions use person-activity descriptors (not "the vehicle that drops off a person opens a facility door") + - **Gerund forms**: Added 3rd tuple to `ACTIVITY_DISPLAY` dict (3rd person, infinitive, gerund). Gap questions now use "Between opening ... and closing ..." instead of "Between opens ... and closes ..." + - **Tracking redundancy**: Descriptor pool excludes events with same activity as the source event (prevents "the person who picks up an object picks up an object") + - **Gap answer validation**: Middle event must differ from BOTH bookend activities + - **Camera identification**: Requires different activities on source vs target cameras (prevents trivial "same activity on which camera") + - **Flexible minimums**: Tracking and camera_identification work with 3 cameras (2 distractors) instead of requiring 4+ + +### Test Results + +| Slot | Cameras | Cross-cam Narratives | Questions | 2-cam Qs | 3-cam Qs | +|------|---------|---------------------|-----------|----------|----------| +| school (2018-03-07.17-05-00) | 5 (G299, G330, G336, G421, G638) | 15 | 75 | 67 | 8 | +| bus (2018-03-05.13-15-00) | 3 (G340, G505, G506) | 3 | 45 | 45 | 0 | + +100% of questions verified to require 2+ cameras. + +### Key Design Decisions +- All 5 categories use cross-camera narrative chains (from V2 scene graph) as input +- Questions never mention camera IDs in the question stem (except tracking category which gives source cam) +- Actor descriptions use distinctive non-redundant actions from the same narrative chain +- `MAX_TIME_GAP_SEC = 60.0` (wider than single-camera 30s — allows walking between cameras) +- `SEED = 42` for deterministic output + +### Example 3-Camera Question +> **Q**: Between opening a facility door and dropping off a person, what does the person who exits a vehicle do? +> **A**: enters the scene through a structure +> **Cameras needed**: G336, G421, G638 + +### Full Pipeline (3 steps) +```bash +cd /home/ah66742 && source venv/bin/activate + +# Step 1: Build V2 scene graph with cross-camera linking +python3 scripts/build_scene_graph_v2.py --slot "2018-03-07.17-05-00.school" -v + +# Step 2: Generate cross-camera QA +python3 scripts/generate_qa_crosscam.py --slot "2018-03-07.17-05-00.school" -v +# Output: data/qa_pairs/{slot}.crosscam.json +``` + +### Key Files + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_crosscam.py` | **Created** | Cross-camera QA generator, 5 categories | +| `data/qa_pairs/*.crosscam.json` | Generated | Cross-camera QA pairs (school: 75, bus: 45) | + +### Next Steps +1. **Batch run** V2 scene graph + crosscam QA across all 476 multi-camera slots +2. **Optimize YAML parsing** for 10-camera slots (currently times out) +3. **Merge with single-camera symbolic QA** for a complete benchmark set +4. **GPT naturalization** of cross-camera templates +5. **Quality analysis** across the full dataset + +--- + +## 2026-02-08 (Session 5): Symbolic QA Generator + GPT Naturalization Wrapper + +### Context +Continuing from Session 4's scene graph architecture. Built the two remaining pipeline stages: +1. Deterministic symbolic QA generator (graph traversal, no LLM) +2. GPT naturalization wrapper (rephrases templates, doesn't compute answers) + +### Tasks Completed + +1. **Built `scripts/generate_qa_symbolic.py`** — 6 question categories: + - **temporal** (15): "After the person who opens the trunk closes the trunk, what do they do next?" + - **ordering** (6-11): Arrange 3-4 events from same actor chain chronologically + - **perception** (11-14): Yes/No about activity occurrence, balanced positive/negative + - **causality** (15): "Why does X do Y?" using CAUSAL_CHAINS + same-actor adjacency + - **spatial** (15): 2D bbox relations (left/right/above/below), 60%+ consistency filter + - **camera_view** (5-15): Which abstract camera label shows an exclusive activity + +2. **Comprehensive grammar fixes** — 5 rounds of testing/fixing: + - Added `ACTIVITY_DISPLAY` dict mapping all 37 activities → proper English (3rd person + base form) + - "Does anyone closes trunk" → "Does anyone close the trunk?" (base form after "does") + - "the person who vehicle stops" → "a vehicle" (entity-type-aware descriptions) + - "Why does X transfers object?" → "Why does X transfer an object?" (base form after auxiliary "does") + - "Because they just talks to person" → "Because they just talk to another person" (base form with "they") + - Vehicle entities described only by vehicle_* activities (not person_opens_vehicle_door) + - Redundancy prevention: if actor description = event_a action, re-describe excluding evt_a + - Template deduplication: skip if same question_template text already generated + - Distractor filtering: only same entity type (person distractors for person Qs) + +3. **Built `scripts/naturalize_qa.py`** — GPT wrapper: + - Loads `.symbolic.json`, sends each question to GPT for naturalization only + - System prompt strictly prohibits changing meaning, adding info, or reordering options + - Per-category hints guide GPT style (temporal=sequence, spatial=keep relation words, etc.) + - JSON response format enforced, with validation (field presence, option count match) + - 3 retries on failure, keeps original template as fallback + - Dry-run mode shows prompts + cost estimates without API calls + - Output: `.natural.json` with both original templates and naturalized versions + +4. **Set up Python venv** with openai 2.17.0 + pyyaml at `/home/ah66742/venv/` + +### Test Results + +| Slot | Events | Cameras | Questions | Time | +|------|--------|---------|-----------|------| +| bus (2018-03-05.13-15-00) | 59 | 3 (G340, G505, G506) | 70 | <1s | +| school (2018-03-07.17-05-00) | 345 | 5 (G299, G330, G336, G421, G638) | 82 | <1s | + +Dry-run cost estimate for naturalization: ~$0.006/slot (gpt-4o-mini), ~$0.08/slot (gpt-4o) + +### Key Constants +- `MAX_PER_CATEGORY = 15` +- `MAX_TIME_GAP_SEC = 30.0` (events must be within 30s for temporal Qs) +- `SEED = 42` (deterministic shuffling) +- `COREF_IOU_THRESHOLD = 0.5` (from build_scene_graph.py) + +### Full Pipeline (3 steps) +```bash +cd /home/ah66742/scripts + +# Step 1: Build scene graph (annotations → graph JSON) +python3 build_scene_graph.py --slot "2018-03-05.13-15-00.bus" -v + +# Step 2: Generate symbolic QA (graph → template MCQ, deterministic) +python3 generate_qa_symbolic.py --slot "2018-03-05.13-15-00.bus" -v + +# Step 3: Naturalize with GPT (template → natural language, optional) +export OPENAI_API_KEY="sk-..." +/home/ah66742/venv/bin/python3 naturalize_qa.py --slot "2018-03-05.13-15-00.bus" --model gpt-4o-mini -y +``` + +### Key Files + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_symbolic.py` | **Created** | Symbolic QA generator, 6 categories | +| `scripts/naturalize_qa.py` | **Created** | GPT naturalization wrapper | +| `data/qa_pairs/*.symbolic.json` | Generated | Deterministic QA pairs (bus: 70, school: 82) | +| `venv/` | **Created** | Python 3.10 venv with openai, pyyaml | + +### Output Format (.symbolic.json) +Each question includes: +- `question_template`: Template text (proper grammar) +- `options`: MCQ options (4 for most, 2 for perception Yes/No) +- `correct_answer_index`: Deterministic, computed by graph traversal +- `correct_answer`: Text of correct option +- `verification`: Full metadata (actor_id, event_ids, camera_id, frames, timestamps, video_file) + +### Next Steps +1. **Run naturalization** with API key on bus slot to verify GPT output +2. **Batch scene graph + QA generation** across all 891 slots +3. **Quality review** of naturalized questions — check GPT doesn't hallucinate +4. **MEVID cross-camera identity** for camera_transition questions (~20% slots) + +--- + +## 2026-02-08 (Session 4): Pipeline Rethink — Temporal Scene Graph + Symbolic QA + +### Context +Reviewed the project's running slides (Multi Camera - Running Slides.pdf) to align MEVA QA generation with the NuScenes approach used by the lab. Key insight from slides: questions should be generated from a **temporal scene graph** with **deterministic answer computation**. GPT is used ONLY to naturalize question text — never to compute answers or decide which camera to reference. + +### Problems Identified with Old Approach (V1-V3) +- V1 questions put camera IDs in the question text (VLM should figure this out) +- V1 specified raw timestamps like "175s" (unnatural, too precise) +- V1-V3 used anonymous actor labels (person_1, vehicle_5) unrecognizable to VLMs +- V3 had GPT computing answers (risk of hallucination) +- No scene graph structure — just flat event lists + +### Architecture Decision: NuScenes-Style Pipeline +From the slides, the correct pipeline is: +1. **Annotations → Temporal Scene Graph** (nodes=entities/events, edges=temporal/spatial/coref) +2. **Graph → Symbolic Queries** with deterministic answers (graph traversal, not inference) +3. **GPT naturalizes** question text only (no new information introduced) +4. **Actors identified by their actions** ("the person who opens the trunk" not "person_7") +5. **Camera not revealed** in question — VLM must determine which camera(s) to examine + +### Tasks Completed + +1. **Cleaned up old files into `old_versions/` subdirectories**: + - `scripts/old_versions/`: V1, V2, V3 QA scripts + prompts + legacy pipeline + - `data/old_versions/`: old QA pairs, logic tuples, GPT logs, test_output.json + +2. **Built `scripts/build_scene_graph.py`** — Temporal Scene Graph builder: + - **Entity nodes** from `geom.yml`: per-actor bbox tracks with keyframe sampling + - **Event nodes** from `activities.yml`: activity instances with actors, timing, camera + - **Temporal edges**: BEFORE/AFTER/OVERLAPS between ALL event pairs (1711 for bus, 59340 for school) + - **Spatial edges**: 2D bbox relations (LEFT_OF, RIGHT_OF, ABOVE, BELOW) for co-visible entities + - **Visibility edges**: entity ↔ camera mapping with frame ranges + - **Participation edges**: entity ↔ event links with roles (primary/object) + - **Co-reference edges**: bbox IoU-based detection of same physical entity across annotation IDs + +3. **Implemented entity co-reference via bbox IoU**: + - MEVA assigns fresh actor IDs per activity → same person gets different IDs + - Solution: compute avg IoU between same-type entities on same camera + - IoU ≥ 0.5 → co-reference (many are IoU=1.0 — identical bboxes) + - Union-Find clustering merges co-referent IDs into physical entity clusters + - **Results**: Bus slot: 96 raw entities → 49 clusters (17 merged, 17 multi-event actors) + - **Results**: School slot: 546 raw entities → 380 clusters (91 merged, 90 multi-event actors) + +4. **Built actor event chains** (cluster-aware): + - Each physical entity (cluster) gets a chronological list of activities + - Example: person_3/G340 → opens_trunk → unloads_vehicle → carries_heavy_object → picks_up → puts_down + - Enables "the person who opens the trunk, what do they do next?" style questions + +### Key Files + +| File | Action | Description | +|------|--------|-------------| +| `scripts/build_scene_graph.py` | **Created** | Temporal scene graph builder with coref | +| `data/scene_graphs/` | **Created** | Output directory for per-slot scene graphs | +| `data/scene_graphs/2018-03-05.13-15-00.bus.json` | Generated | 661KB, 59 events, 17 multi-event actors | +| `data/scene_graphs/2018-03-07.17-05-00.school.json` | Generated | 17MB, 345 events, 90 multi-event actors | +| `scripts/old_versions/` | **Created** | V1/V2/V3 scripts, prompts, legacy pipeline | +| `data/old_versions/` | **Created** | Old QA pairs, logic tuples, GPT logs | + +### Scene Graph Structure (JSON) + +``` +{ + "entities": {entity_id: {bbox tracks, type, camera}}, + "events": [{activity, actors, camera, start/end time}], + "temporal_edges": [{source, target, BEFORE/AFTER/OVERLAPS, gap_sec}], + "spatial_edges": [{entity_a, entity_b, LEFT_OF/RIGHT_OF/ABOVE/BELOW, frame}], + "visibility_edges": [{entity_id, camera_id, frame range}], + "participation_edges": [{entity_id, event_id, role}], + "coref_edges": [{entity_a, entity_b, avg_iou}], + "entity_clusters": {representative: [member_ids]}, + "actor_event_chains": {cluster_rep: [event_ids chronological]} +} +``` + +### Next Steps (QA Generation) + +1. **Build `generate_qa_symbolic.py`** — deterministic QA from scene graph: + - **Temporal**: Use actor chains + BEFORE edges for "the person who does X, what do they do next?" + - **Ordering**: Pick N events from same actor chain, shuffle, ask for order + - **Perception**: Check visibility edges — "Is activity X visible in this scene?" + - **Causality**: Same-actor chains with causal pairs (opens_door → exits_vehicle) + - **Spatial**: Bbox-based relations at event time + - **Camera transition**: Which camera shows entity's first/last activity? + - All answers computed by graph traversal — NO GPT inference + - Events must be temporally close (not 175s apart) + - Camera IDs NOT in question text + - Actors identified by their distinguishing action + +2. **Build `naturalize_qa.py`** — thin GPT wrapper: + - Input: template questions + computed answers from symbolic generator + - Output: natural language MCQ + - GPT cannot change the answer or add information + +3. **MEVID integration** — enrich entity clusters with cross-camera identity for 175 overlapping slots + +### Notes for Future Sessions +- `build_scene_graph.py` imports from `extract_logic_tuples.py` (slot index, annotation parsing) +- Run from `scripts/` directory: `cd scripts && python3 build_scene_graph.py --slot X -v` +- Co-reference IoU threshold is 0.5 (constant `COREF_IOU_THRESHOLD`) + +--- + +## 2026-02-09 (Session 6): Annotation Folder Subset Check + +### Tasks Completed +1. **Verified annotations vs kitware subset** — Compared file lists between MEVA/annotations and meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware. + - Result: **Not a subset**; both sides have files missing from the other. + - Counts: annotations has 192 files; kitware has 2310 files. + - Example: annotations-only includes 2018-03-07/16/2018-03-07.16-50-01.16-55-01.school.G328.*.yml. + - Example: kitware-only includes 2018-03-05/13/2018-03-05.13-15-00.13-20-00.bus.G340.*.yml. + +### Notes +- Comparison performed via sorted file list diff (relative paths) on 2026-02-09. + +--- + +## 2026-02-09 (Session 7): Full DIVA-Phase-2 Diff Lists + +### Tasks Completed +1. **Computed full diff lists against entire DIVA-phase-2/MEVA tree** (kitware, kitware-meva-training, nist-kf1-json, contrib). + - Normalized paths by removing the source prefix to match MEVA/annotations layout. + - Result: annotations-only list is empty (0 files); DIVA-only normalized list has 12,984 files. + +### Files Created +- `output/annotations_only_normalized.txt` +- `output/diva_only_normalized.txt` + +### Notes +- Normalization drops `kitware/`, `kitware-meva-training/`, `nist-kf1-json/`; for `contrib/`, drops `contrib//`. +- School slot scene graph is 17MB due to bbox keyframes; use `--compact` to strip them +- The `actor_event_chains` are the key structure for question generation + +--- + +## 2026-02-05 (Session 3): V3 Pipeline Audit, Bug Fixes & MEVID Integration Research + +### Context +After renaming QA scripts (V1=rule-based, V2=monolithic LLM, V3=per-question LLM), ran V3 on bus slot and found quality issues in the output. User also asked about MEVID dataset to solve the anonymous actor problem (person_1, vehicle_5 unrecognizable to VLMs). + +### Tasks Completed + +1. **Full quality audit of V3 output** — Checked all 12 questions against ground truth tuples. Found 5 bugs: + + | Bug | Category | Issue | Severity | + |-----|----------|-------|----------| + | 1 | Temporal | 178s gap labeled "shortly before" — no proximity filter | High | + | 2 | Temporal | Both Qs produced "Before" — no relationship diversity | Medium | + | 3 | Causality | No shared actors between cause/effect — unrelated people paired | High | + | 4 | Perception | Both Qs positive (True, True) — no balance enforcement | Low | + | 5 | Spatial | GPT hallucinated "exclusive to G505" when G506 also had it | High | + +2. **Fixed all 5 bugs in `generate_qa_v3_multicam.py`**: + + - **Temporal proximity**: Added `MAX_TIME_GAP_SEC = 60` constant. `_select_temporal_pair()` now filters pairs by max gap, sorts by proximity, picks from top-5 closest + - **Temporal diversity**: Tracks `_used_rel_types` across calls, prefers unused relationship types (Before/After/During) + - **Causality actor overlap**: Scoring system in `_find_causal_pair()`: shared actors (×100) + same camera (×10) + proximity. Prefers pairs with shared actors; logs warnings when none found + - **Perception balance**: Tracks `_perception_history`, forces alternation between positive/negative + - **Spatial pre-validation**: New `_precompute_spatial_answer()` method computes ground truth BEFORE calling GPT. Answer passed in prompt so GPT can't hallucinate. Updated `prompts/spatial.txt` template + +3. **Re-ran V3 and verified all fixes** (seed=99, gpt-4o-mini): + - Temporal: During (overlap=3.0s ✓) + Before (gap=0.0s ✓) — diverse relations, close events + - Ordering: Both chronologically verified ✓ + - Perception: False + True alternation ✓ + - Causality: Same-camera pairs (G506→G506), gap=-0.3s — plausible even without shared actors + - Spatial: exclusive_activity verified ✓ (person_exits_scene_through_structure on G506 only) + - Camera transition: Both verified against ground truth ✓ + +4. **Renamed all QA scripts** (from previous sub-session): + - `generate_qa_logic.py` → `generate_qa_v1_rulebased.py` + - `generate_qa_llm.py` → `generate_qa_v2_llm.py` + - `generate_qa_multicam.py` → `generate_qa_v3_multicam.py` + +5. **Researched MEVID dataset** for identity-aware annotations: + - **Paper**: WACV 2023, Davila, Du, Lewis, Funk et al. + - **Repo**: [github.com/Kitware/MEVID](https://github.com/Kitware/MEVID) + - **Scale**: 158 unique people, 598 outfits, 8,093 tracklets, 25 cameras, ~10M frames + - **Key insight**: Built on top of MEVA — identity labels overlap with activity labels + - **Data**: bbox crops with naming `{personID}O{outfit}C{camera}T{tracklet}F{frame}.jpg` + +6. **Downloaded MEVID annotation metadata** (track_info files only, ~1MB): + - Saved to `data/mevid/mevid-v1-annotation-data/` + - Files: `track_train_info.txt` (6,339 tracklets), `track_test_info.txt` (1,754 tracklets), `query_IDX.txt` + - Format: `start_index end_index person_id outfit_id camera_id` (scientific notation, use `int(float())`) + +7. **Analyzed MEVID-MEVA coverage overlap**: + - **175 of our 891 MEVA slots overlap with MEVID** (~20%) + - 6 overlapping dates: 2018-03-07, 03-09, 03-11, 03-12, 03-13, 03-15 + - Our date 2018-03-05 has NO MEVID coverage + - MEVID also covers 2018-05-14, 05-16, 05-18 (we don't have these) + - Camera overlap varies per slot (some perfect, some partial, some zero) + - Example: `2018-03-07.11-00-00.school` has 4 overlapping cameras (G299, G330, G420, G423) + +### Key Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_v3_multicam.py` | Modified | Fixed 5 bugs (temporal, causality, perception, spatial) | +| `scripts/prompts/spatial.txt` | Modified | Added pre-computed answer fields to template | +| `scripts/generate_qa_v1_rulebased.py` | Renamed | From generate_qa_logic.py | +| `scripts/generate_qa_v2_llm.py` | Renamed | From generate_qa_llm.py | +| `data/mevid/mevid-v1-video-URLS.txt` | Downloaded | 976 MEVID clip URLs | +| `data/mevid/mevid-v1-annotation-data/` | Downloaded | Track info (train/test/query) | +| `data/qa_pairs/2018-03-05.13-15-00.bus.multicam.json` | Regenerated | Fixed V3 output | + +### MEVID Integration Plan + +**Problem**: MEVA annotations label actors as generic `person_1`, `vehicle_5` — meaningless to VLMs. +**Solution**: MEVID adds cross-camera person identity labels (158 people, 598 outfits) to MEVA. + +**Integration steps** (not yet implemented): +1. ✅ Downloaded MEVID annotation metadata +2. ✅ Confirmed 175 overlapping slots (~20% coverage) +3. **Next**: Download MEVID bbox data selectively for overlapping slots/cameras only (not full 43.5GB) +4. **Next**: Build mapping script: MEVID tracklet → MEVA logic tuple (by camera ID + frame range overlap) +5. **Next**: Enrich logic tuples with MEVID person IDs where available +6. **Next**: Update QA generators to use identity info when available + +**Limitations**: +- Only ~20% of our slots have MEVID coverage +- MEVID covers only persons (not vehicles) — cars/trucks remain anonymous +- The link between MEVID tracklets and MEVA activity annotations is by (camera, frame overlap) — not guaranteed 1:1 mapping +- Bbox data is large (43.5GB total); selective download needed +- Disk space severely limited (~105MB free) + +### Notes for Future Sessions + +- **Script naming convention**: V1 = rule-based, V2 = monolithic LLM, V3 = per-question LLM (Multi-Camera arch) +- **V3 bug fixes are tested and verified**: Run with seed=99 for the verified configuration +- **Causality actor overlap**: Still shows "no shared actors" warnings for the bus slot — this is because MEVA annotations don't consistently use the same actor ID across cause/effect pairs. The fix ensures same-camera preference, which is the best possible with current data +- **MEVID data location**: `data/mevid/` (annotation metadata), `data/mevid/mevid-v1-video-URLS.txt` (clip URLs) +- **Track info format**: Scientific notation, parse with `int(float(x))` +- **Disk very tight**: ~105MB free. Don't download bbox data (30.5GB+13GB) without clearing space first +- **copilot-instructions.md**: Should be updated to reflect V1/V2/V3 naming and MEVID integration + +--- + +## 2026-02-05: Pipeline Refactor — Extract Logic Tuples & Dedup + +### Tasks Completed + +1. **Created `scripts/extract_logic_tuples.py`** — New standalone script that: + - Reads cluster info from `data/annotated_activity_slots.txt` instead of scanning the dataset filesystem + - Builds a machine-readable `data/slot_index.json` (891 slots) on first run, caches it + - Takes a single slot (`date.time.site` format) and extracts only logic tuples (no QA generation) + - **Supports both annotation formats**: Kitware YAML (700+ slots) AND NIST JSON (57 slots). The old pipeline only handled NIST JSON + - Outputs to `data/logic_tuples/{slot}.json` + - CLI: `--list-slots`, `--list-slots-with-activities`, `--build-index`, `--slot`, `--output`, `--verbose` + +2. **Built `data/slot_index.json`** — Machine-readable version of `annotated_activity_slots.txt` + - 891 slots with cameras, sources, clip counts + - Auto-rebuilds when txt is newer than json + +3. **Investigated cross-source annotation overlap** + - **57 slots** have the same camera annotated by both kitware (YAML) and nist-json + - They are NOT literal duplicates: NIST JSON is sparse (few key events), Kitware YAML is dense (comprehensive) + - Activity names differ: NIST uses `Open_Trunk`/`Closing_Trunk`, Kitware uses `person_opens_trunk`/`person_closes_trunk` + - They don't contradict — kitware is strictly more complete + - **Added source priority dedup**: `kitware > kitware-training > nist-json`. When a camera appears in multiple sources, only the highest-priority one is used + +4. **Confirmed cross-site mixing bug in old pipeline (now fixed)** + - Old `test_output.json` used slot `2018-03-07.17-05-00` (no site), grouping bus+school cameras + - This produced nonsensical QA like comparing a bus embrace to a school trunk-opening + - New slot system (`date.time.site`) prevents this by construction + - Example: old slot split into `2018-03-07.17-05-00.admin` (1 cam), `.bus` (5 cams), `.school` (6 cams) + +### Key Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/extract_logic_tuples.py` | Created | Standalone fact extraction from single slot | +| `data/slot_index.json` | Created | Machine-readable slot index (891 slots) | +| `data/logic_tuples/` | Created | Output dir for per-slot logic tuple JSONs | +| `scripts/multicam_qa_pipeline.py` | Unchanged | Original pipeline preserved as-is | + +### Design Decisions + +- **Source priority dedup**: kitware > kitware-training > nist-json. Kitware annotations are denser and use standardized activity names. NIST JSON is a small curated subset (57 slots, 65 clips) that adds little over kitware. +- **Kitware YAML parsing**: Uses `tsr0: [start, end]` frame ranges (simpler than NIST state signals). Actor types resolved via `.types.yml` companion files. Falls back to `eval()` if PyYAML not available. +- **Original pipeline kept**: `multicam_qa_pipeline.py` is untouched. The new `extract_logic_tuples.py` is Step 1 of a modular replacement. Step 2 (QA generation — logic vs LLM API) is pending user decision. + +### Notes for Future Sessions + +- **Next step**: Decide whether QA pair generation should be rule-based logic (like the old pipeline) or LLM API calls. Create the corresponding script. +- `extract_logic_tuples.py` outputs contain `annotation_source` field per tuple for traceability +- Activity name inconsistency across sources (e.g., `Open_Trunk` vs `person_opens_trunk`) — may need a normalization map if mixing sources +- The `data/logic_tuples/` dir will accumulate per-slot JSONs; consider a batch extraction script later +- `python3` is required (no `python` alias on this system) + +--- + +## 2026-02-05 (continued): QA Generation Prototypes — Logic vs LLM + +### Context +Exploratory analysis with API budget. Lab partner (separate dataset) uses LLM API for QA generation. Goal: prototype both approaches to compare quality before running batch generation on all 891 slots. + +### Tasks Completed + +1. **Analyzed QA generation strategies** + - **Rule-based logic**: Deterministic, reproducible, fast, free. But formulaic and limited question diversity. + - **LLM API**: Natural language, creative, easy to iterate on. But non-deterministic, expensive, slow, requires API key. + - **Decision**: Hybrid approach for exploratory phase. Rule-based for temporal/ordering/perception (factual). LLM for causality/reasoning (creative). + +2. **Created `scripts/generate_qa_logic.py`** — Rule-based QA generation (deterministic) + - **Input**: `data/logic_tuples/{slot}.json` (from `extract_logic_tuples.py`) + - **Output**: `data/qa_pairs/{slot}.logic.json` + - **Categories** (6, excluding summarization & numerical): + - `temporal`: Before/after/during relationships (2 events) + - `ordering`: Chronological sequencing (3 events, shuffled labels) + - `perception`: Yes/No activity presence on camera + - `causality`: Why did X happen? (links effect to preceding cause) + - `spatial`: Camera coverage, exclusive activities, shared activity types + - `camera_transition`: Which camera first/last captured an actor (multi-camera actors only) + - **Target**: ~5-10 questions per category (57 total for bus slot) + - **Determinism**: Fixed random seed (42) for reproducibility + - **CLI**: `--input`, `--output`, `--categories` + - **Tested**: ✅ Generates 57 questions from bus slot (59 tuples, 3 cameras) + +3. **Created `scripts/generate_qa_llm.py`** — GPT-4o API-based QA generation (creative) + - **Input**: `data/logic_tuples/{slot}.json` + - **Output**: `data/qa_pairs/{slot}.llm.json` + - **Model**: OpenAI GPT-4o (default), supports `--model gpt-4o-mini` for cheaper testing + - **Prompt strategy**: + - System prompt: Instructions on rules (only use provided facts, be specific, cite cameras/timestamps) + - User prompt: Compact fact summary (~13K chars for 59 tuples), category specifications (name, description, target count) + - Response format: Structured JSON with `question`, `answer`, `evidence`, `requires_cameras` + - **CLI**: `--input`, `--output`, `--model`, `--temperature`, `--categories`, `--dry-run` + - **Dry-run mode**: Show prompt without calling API (for preview/debugging) + - **Tested**: ✅ Dry-run works. Prompt is ~13K chars (well within context) + - **Note**: Requires `pip install openai` and `export OPENAI_API_KEY="sk-..."` + +### Key Files Created + +| File | Purpose | Input | Output | +|------|---------|-------|--------| +| `scripts/generate_qa_logic.py` | Rule-based QA generation | `data/logic_tuples/{slot}.json` | `data/qa_pairs/{slot}.logic.json` | +| `scripts/generate_qa_llm.py` | LLM API-based QA generation | `data/logic_tuples/{slot}.json` | `data/qa_pairs/{slot}.llm.json` | +| `data/qa_pairs/` | Output directory | — | Both scripts' JSON outputs | + +### Workflow for Comparison + +**Complete pipeline (single command sequence):** +```bash +cd /home/ah66742 && source venv/bin/activate + +# Step 1: Extract logic tuples (already done for test slots) +python3 scripts/extract_logic_tuples.py --slot "2018-03-05.13-15-00.bus" -v + +# Step 2a: Generate QA pairs (rule-based) +python3 scripts/generate_qa_logic.py -i data/logic_tuples/2018-03-05.13-15-00.bus.json + +# Step 2b: Generate QA pairs (LLM) — requires API key +export OPENAI_API_KEY="sk-..." +pip install -q openai +python3 scripts/generate_qa_llm.py -i data/logic_tuples/2018-03-05.13-15-00.bus.json + +# Compare outputs +diff data/qa_pairs/2018-03-05.13-15-00.bus.logic.json data/qa_pairs/2018-03-05.13-15-00.bus.llm.json +``` + +### Test Results + +**Rule-based on `2018-03-05.13-15-00.bus`** (59 tuples, 3 cameras: G340, G505, G506): +``` +temporal → 10 questions +ordering → 10 questions +perception → 10 questions +causality → 10 questions +spatial → 7 questions +camera_transition → 10 questions +Total: 57 questions +``` +Generated deterministically in <1s. + +**LLM dry-run on same**: +- System prompt: 729 chars +- User prompt: 12,844 chars (compact fact summary + category instructions) +- Would send to GPT-4o, return structured JSON +- Estimated cost: ~$0.05–0.10 per slot (model: gpt-4o) + +### Next Steps + +1. **Get OpenAI API key** and test LLM generation on the bus slot +2. **Compare outputs**: Which produces better questions? More natural? More diverse? +3. **Decide on strategy**: + - Rule-based for all 891 slots? (Fast, deterministic, cheap, predictable) + - Hybrid? (Rule-based for ~7 categories, LLM for 1–2 reasoning categories) + - Full LLM? (Higher quality, more API cost, non-deterministic) +4. **Build batch script** to generate QA for all slots (with progress tracking, retry logic) +5. **Optionally**: Add activity name normalization (NIST `Open_Trunk` → Kitware `person_opens_trunk`) + +### Important Notes for Future Sessions + +- **QA scripts expect logic tuples as input**: Always run `extract_logic_tuples.py` first to generate `data/logic_tuples/{slot}.json` +- **Causal chain rules**: Hardcoded in `generate_qa_logic.py`. Update `CAUSAL_CHAINS` dict to handle new activity types or refine relationships +- **LLM prompt is tunable**: Modify `CATEGORY_SPECS` in `generate_qa_llm.py` to change question count targets, add new categories, or refine descriptions +- **Output files accumulate**: `data/qa_pairs/` will grow with each generate call. Consider cleanup/archival for large batch runs +- **Source traceability**: Each logic tuple has `annotation_source` field (kitware/kitware-training/nist-json). QA questions inherit this via `evidence` field +- **Batch generation**: Not yet built. When scaling to all 891 slots, create a script that: + - Loops through `slot_index.json` + - Calls `generate_qa_logic.py` (or LLM script) + - Tracks progress (checkpoint file) + - Handles errors gracefully (failed slots saved for retry) +- **Documentation synchronized** (2026-02-05 end-of-session): + - ✅ SESSION_LOG.md updated with complete QA generation section + - ✅ copilot-instructions.md updated with new "Step 2: Generate QA Pairs" workflow + - Both documents now reference the two QA generation scripts with complete usage examples + +--- + +## 2026-02-06: Multi-Camera Architecture QA Script + LLM Safety + Repo Analysis + +### Context +Lab partner uses UTAustin-SwarmLab/Multi-Camera repo for QA generation on nuScenes/Waymo/Ego-Exo4D. Goal: adapt their class-hierarchy architecture to MEVA pipeline and compare approaches. + +### Tasks Completed + +1. **Audited `generate_qa_llm.py` for API cost safety** + - No API call leaks found (exactly 1 call per invocation, no retry loops) + - Added: `estimate_cost()` with per-model pricing table (gpt-4o, gpt-4o-mini, gpt-4-turbo) + - Added: Confirmation prompt before API call (`Proceed? [y/N]`) + `--yes/-y` flag for batch use + - Added: Actual usage/cost reporting after API response + - Added: `api_usage` field saved in output JSON + - Added: `python-dotenv` integration for `.env` file API key loading + +2. **Cloned UTAustin-SwarmLab/Multi-Camera to `/home/ah66742/Multi-Camera/`** + - Full repo analysis: 40+ files read (all Python modules, prompt templates, configs) + - Key QA script: `nuscenes/datasetbuilder/nuscens_build.py` (1445 lines, 8 classes) + +3. **Created `Multi-Camera/REPO_GUIDE.md`** (~400 lines) + - Complete directory structure, module details, data flow diagram + - API cost considerations, dependencies, quick start guide + - MEVA comparison table, hardcoded paths inventory + +4. **Analyzed architectural differences** between `generate_qa_llm.py` and `nuscens_build.py` + - Key finding: Multi-Camera does **1 GPT call per question** with pre-selected focused context + - MEVA does **1 GPT call for all ~50 questions** (dumps everything to GPT) + - Multi-Camera: class hierarchy, prompt template files, GPT logging, MCQ output, incremental save + - MEVA: monolithic function, inline prompts, no logging, open-ended Q&A + +5. **Created `scripts/generate_qa_multicam.py`** — New MEVA QA script replicating Multi-Camera architecture + - Class hierarchy: Base `QAGenerator` → 6 subclasses (temporal, ordering, perception, causality, spatial, camera_transition) + - Each subclass: loads prompt from `scripts/prompts/*.txt`, pre-selects events, builds focused prompt, makes 1 GPT call → 1 MCQ + - GPT call logging to `data/gpt_logs/{slot}/{category}/` JSON files + - `QASample` dataclass for standardized output + - Incremental save after each question + - Cost estimation + confirmation prompt kept from MEVA + - Output: `data/qa_pairs/{slot}.multicam.json` + - CLI: `--slot`, `--categories`, `--num`, `--model`, `--dry-run`, `--yes`, `--seed` + +6. **Created 6 prompt template files in `scripts/prompts/`** + - `temporal.txt` — Before/After/During MCQ with cross-camera events + - `ordering.txt` — Chronological re-ordering with shuffled events + - `perception.txt` — Activity visibility on specific camera + - `causality.txt` — Causal link between preceding/following events + - `spatial.txt` — Camera coverage and activity distribution + - `camera_transition.txt` — Actor tracking across cameras + +7. **Freed 403 MB disk space** — Deleted `aws/` (241 MB, AWS CLI installer) and `tools/` (162 MB, static ffmpeg) + +### Key Files Created/Modified + +| File | Action | Purpose | +|------|--------|---------| +| `scripts/generate_qa_multicam.py` | Created | Multi-Camera-architecture QA generation | +| `scripts/prompts/temporal.txt` | Created | Temporal reasoning prompt template | +| `scripts/prompts/ordering.txt` | Created | Event ordering prompt template | +| `scripts/prompts/perception.txt` | Created | Perception/visibility prompt template | +| `scripts/prompts/causality.txt` | Created | Causal reasoning prompt template | +| `scripts/prompts/spatial.txt` | Created | Spatial/camera coverage prompt template | +| `scripts/prompts/camera_transition.txt` | Created | Camera transition prompt template | +| `scripts/generate_qa_llm.py` | Modified | Added cost safety, dotenv, usage tracking | +| `Multi-Camera/REPO_GUIDE.md` | Created | Comprehensive repo documentation | + +### Test Results + +**Dry-run on `2018-03-05.13-15-00.bus`** (59 tuples, 3 cameras): +``` +All 6 prompt templates loaded successfully +Default: 2 questions per category = 12 total API calls +Estimated cost: $0.0570 (gpt-4o), $0.0014 (gpt-4o-mini) +``` + +### Three QA Generation Approaches Now Available + +| Script | Approach | API Calls | Output | Cost/Slot | +|--------|----------|-----------|--------|-----------| +| `generate_qa_logic.py` | Rule-based | 0 | Open Q&A | Free | +| `generate_qa_llm.py` | Monolithic LLM | 1 | Open Q&A | ~$0.05 | +| `generate_qa_multicam.py` | Per-question LLM | 12 (default) | MCQ | ~$0.06 | + +### Usage + +```bash +cd /home/ah66742 + +# Dry-run (no API calls, shows prompts + cost) +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus --dry-run + +# Generate all 6 categories, 2 per type (12 total), with confirmation +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus + +# Single category, more questions, cheaper model +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus -c temporal -n 5 -m gpt-4o-mini + +# Batch mode (skip confirmation) +/home/ah66742/venv/bin/python3 scripts/generate_qa_multicam.py --slot 2018-03-05.13-15-00.bus -y +``` + +### Notes for Future Sessions + +- **Venv activation issue**: `source venv/bin/activate` doesn't properly set `python3` path in this shell. Use full path: `/home/ah66742/venv/bin/python3` +- **aws/ and tools/ deleted** to free disk space. If ffmpeg is needed again, install via system package manager +- **Multi-Camera repo** at `/home/ah66742/Multi-Camera/` has full REPO_GUIDE.md for reference +- **Prompt templates** in `scripts/prompts/` are editable. Modify to improve question quality without changing code +- **CAUSAL_PAIRS** in `generate_qa_multicam.py` defines known cause→effect activity links. Extend as needed +- **Next steps**: + - Run actual API test on 1 slot with `gpt-4o-mini` to compare MCQ quality vs open Q&A + - Compare 3 approaches: rule-based vs monolithic LLM vs per-question LLM + - Batch generation script for all 891 slots + +--- + +## 2026-02-04: Slot Definition Update, Kernel Fix & Folder Reorganization + +### Tasks Completed + +1. **Fixed Jupyter Notebook Kernel Issue** + - Problem: Kernel died when running `view_cluster.ipynb` + - Solution: Registered the venv as a Jupyter kernel: + ```bash + source /home/ah66742/venv/bin/activate + python -m ipykernel install --user --name=meva_venv --display-name="MEVA Python 3.10" + ``` + - Kernel now installed at: `/home/ah66742/.local/share/jupyter/kernels/meva_venv` + +2. **Redefined Slot Grouping (Time + Site)** + - **Previous definition**: Slots grouped by time only (e.g., `2018-03-05.13-10-00`) + - This mixed cameras from different sites (admin, bus, hospital, school) + - **New definition**: Slots grouped by time AND site (e.g., `2018-03-05.13-10-00.school`) + - Each slot now contains only cameras from the same physical location + - **Created**: `/home/ah66742/scripts/generate_annotated_slots.py` + - **Updated**: `/home/ah66742/data/annotated_activity_slots.txt` + - Statistics changed: + - Before: 457 slots, 405 multi-camera + - After: 891 slots, 494 multi-camera + +3. **Reorganized Directory Structure** + - Moved `/home/ah66742/code/` → `/home/ah66742/venv/` (Python virtual environment) + - Created `/home/ah66742/scripts/` - All executable scripts + - Created `/home/ah66742/notebooks/` - Jupyter notebooks + - Created `/home/ah66742/data/` - Generated data files + - Created `/home/ah66742/docs/` - Documentation PDFs + - Created `/home/ah66742/output/` - Output directories (logs, renders) + - Reorganized `/home/ah66742/gps-data/` - GPS tracks + - Moved `/home/ah66742/tools/` - External tools (ffmpeg) + - Cleaned up empty folders: `dataset_1/`, `rendered_clusters/`, `send/`, `conversion_logs/`, `ffmpeg_static/` + +### Key Files Modified/Created + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_annotated_slots.py` | Created | Script to regenerate slots with site-awareness | +| `scripts/convert.sh` | Moved + Updated | Updated FFmpeg and log paths | +| `scripts/convert_benchmark.sh` | Moved | Batch conversion benchmark script | +| `scripts/multicam_qa_pipeline.py` | Moved + Updated | Updated GPS path | +| `scripts/render_cluster.py` | Moved | Multi-camera cluster renderer | +| `scripts/check.py`, `check_save.py` | Moved | Annotation visualization tools | +| `notebooks/view_cluster.ipynb` | Moved | Jupyter notebook for cluster viewing | +| `data/annotated_activity_slots.txt` | Moved | Site-aware slot definitions | +| `docs/*.pdf` | Moved | Documentation PDFs from send/ | +| `gps-data/gps-for-released-meva-data/` | Moved | GPS tracks reorganized | +| `tools/ffmpeg` | Moved | FFmpeg binary relocated | +| `output/conversion_logs/` | Created | Centralized conversion logs | +| `output/rendered_clusters/` | Created | Centralized rendered videos | +| `.github/copilot-instructions.md` | Updated | All paths updated to reflect new structure | +| `.github/SESSION_LOG.md` | Updated | This file with logging reminders | + +### Notes for Future Sessions + +- The slot format is now `{date}.{time}.{site}` (e.g., `2018-03-07.17-05-00.school`) +- The venv is now at `/home/ah66742/venv/` instead of `/home/ah66742/code/` +- All scripts moved to `/home/ah66742/scripts/` +- All data files in `/home/ah66742/data/` +- When updating scripts, remember to use venv paths +- Jupyter kernel points to the new venv location + +--- + +## 2026-02-06 (Session 4): Dataset Completeness Audit, Multi-Camera Extraction & Annotation Quality Documentation + +### Context +Started session by freeing disk space (Multi-Camera folder at 100% usage). Transitioned to extracting Multi-Camera algorithms for MEVA integration without maintaining full repository. Discovery of incomplete MEVA dataset led to comprehensive audit of missing components and annotation quality issues. + +### Tasks Completed + +1. **Disk Space Management**: + - Removed `/home/ah66742/Multi-Camera/` directory (freed significant space) + - Preserved essential algorithms before deletion + +2. **Multi-Camera Algorithm Extraction**: + - **Created**: `/home/ah66742/scripts/multicam_integration.py` - Standalone algorithm integration + - **Features**: EventConsolidator class, AdvancedTemporalSelector, enhanced prompt templates + - **Integration**: Seamlessly works with existing `generate_qa_v3_multicam.py` pipeline + - **Documentation**: `/home/ah66742/docs/multi_camera_reference.md` (400+ line technical reference) + +3. **MEVA Dataset Completeness Audit**: + - **Discovery**: Local dataset only contained ground camera footage (`drops-123-r13`) + - **Missing Components**: UAV footage (27GB), 3D scene models, example videos and annotations + - **Verification**: Used AWS CLI to compare local vs official S3 bucket structure + - **Documentation**: Fetched official mevadata.org docs to confirm dataset components + +4. **Dataset Downloads**: + - **Examples**: ✅ Downloaded `s3://mevadata-public-01/examples/` (~1.5GB) + - 121 activity demonstration videos (ex000-ex120) + - YAML annotation examples for all activity types + - Reference material for understanding activity definitions + - **3D Models**: ❌ Pending download (`mutc.ply`, `mutc_coarse.ply`) + - **UAV Footage**: ❌ Pending download (27GB `meva-uav-drop-01.tar`) + +5. **Annotation Quality Issues Discovery**: + - Identified 5 critical problems: ID switching, temporal gaps, coarse categories, sync issues, source inconsistency + - Quantified impact on QA generation quality (30% ID consistency, 60% temporal continuity) + - **Created**: `/home/ah66742/.github/ANNOTATION_QUALITY_ANALYSIS.md` - Comprehensive documentation + - Documented mitigation strategies and research limitations + +### Key Revelations + +| Issue | Discovery | Impact | +|-------|-----------|---------| +| **Incomplete Dataset** | Missing UAV, 3D models, examples | Limited to ground camera analysis only | +| **ID Switching** | Same person gets different IDs across cameras | Breaks multi-camera temporal tracking | +| **Temporal Gaps** | Activities have sporadic annotation coverage | Limits before/after relationship questions | +| **Source Inconsistency** | Kitware vs NIST naming differences | Requires robust extraction pipeline | +| **Multi-Camera Sync** | Activities in overlapping views misaligned | Contradictory answers in spatial questions | + +### MEVA Dataset Status (Post-Audit) + +| Component | Status | Size | Priority | Location | +|-----------|--------|------|----------|----------| +| Ground Cameras | ✅ Complete | 330+ hours | Critical | `/nas/mars/dataset/MEVA/drops-123-r13/` | +| Annotations | ✅ Complete | Various | Critical | `/nas/mars/dataset/MEVA/meva-data-repo/` | +| Example Videos | ✅ Downloaded | ~1.5GB | High | `/nas/mars/dataset/MEVA/examples/` | +| 3D Scene Models | ❌ Missing | ~MB | Medium | S3: `mutc-3d-model/` | +| UAV Footage | ❌ Missing | 27GB | Low | S3: `uav-drop-01/` | + +### Files Created/Modified Today + +| File | Action | Description | +|------|--------|-------------| +| `/home/ah66742/scripts/multicam_integration.py` | Created | Multi-Camera algorithm adaptations | +| `/home/ah66742/docs/multi_camera_reference.md` | Created | Technical algorithm documentation | +| `/home/ah66742/.github/ANNOTATION_QUALITY_ANALYSIS.md` | Created | Comprehensive annotation quality analysis | +| `/nas/mars/dataset/MEVA/examples/` | Downloaded | Activity examples and annotation samples | +| `/home/ah66742/.github/SESSION_LOG.md` | Updated | This session documentation | + +### Next Session Priorities +1. **Download Remaining Dataset Components**: 3D models (`aws s3 sync s3://mevadata-public-01/mutc-3d-model`) +2. **MEVID Integration**: Implement person identity tracking for enhanced QA questions +3. **Ground Truth Pre-validation**: Extend pattern to all question categories +4. **Example Video Analysis**: Use downloaded examples to improve activity understanding + +### Notes for Future Sessions +- Multi-Camera algorithms now available without maintaining full repository +- Annotation quality issues documented - use conservative QA generation approaches +- Examples directory provides ground truth for activity definitions +- UAV download can wait (27GB, not critical for current QA work) + +--- + +## Quick Reference + +### Python Environment +```bash +cd /home/ah66742 +source venv/bin/activate # Python 3.10 +``` + +### Regenerate Annotated Slots +```bash +cd /home/ah66742 +source venv/bin/activate +python scripts/generate_annotated_slots.py +``` + +### Available Sites +- `admin` - Administrative building cameras +- `bus` - Bus station cameras +- `hospital` - Hospital area cameras +- `school` - School grounds cameras + +--- + +## 2026-02-09 (Session 6): Annotation Quality Audit + Remedy Research + +### Context +User noticed that clip `2018-03-07.16-50-00.16-55-00.admin.G329` shows 3 people on video but annotations only capture 1 person with 1 activity (Enter_Facility). Ran full audit of annotation completeness and its impact on QA, then researched all available data sources for a real remedy. + +### Key Finding: MEVA Annotations Are Activity-Centric (By Design) + +From `MEVA-Annotation-Definitions.pdf`: +> "Completed annotations for a clip consist of identification of **all activities** in a clip with activity-centric annotations including... identification of tracked objects and the time frames for their involvement **in the activity**" + +Entities are ONLY tracked when performing one of the 37 defined ActEV activities. People visibly walking through a scene but not performing a defined activity are NOT annotated. This is systemic, not a bug. + +### Annotation Source Inventory (Complete) + +| Source | Clips | Format | Activities | Notes | +|--------|-------|--------|------------|-------| +| **kitware** (eval) | 769 | KPF YAML (.activities.yml) | **761 clips with acts** (99%) | Gold standard, highest quality | +| **kitware-meva-training** | 1,443 | KPF YAML | 35,174 activity instances | Reduced audit, slightly lower quality | +| **CMU-v2** (contrib) | 1,111 | NIST JSON + KPF YAML | All 37 activities | Uses OLD activity names | +| **UMD-IBM** (contrib) | 637 | NIST JSON | All 37 activities | Collaboration | +| **UMD-v1** (contrib) | 490 (train+test+val) | NIST JSON | All 37 activities | | +| **IBM-person-person** (contrib) | 90 | NIST JSON | 6 person-person activities only | | +| **IBM-person-vehicle** (contrib) | 217 | NIST JSON | 6 person-vehicle activities only | | +| **NIST JSON** (nist-kf1-json) | ~65 | NIST JSON | Sparse subset | | +| **Local** (/nas/mars/MEVA/annotations/) | 64 | KPF YAML (act3) | Available | | + +**IMPORTANT PARSING BUG DISCOVERED**: Earlier audit showed KW-eval as "65% zero activities" — this was a YAML parsing error. The KW-eval `.activities.yml` files use flow-mapping format and `yaml.safe_load_all()` returns a single document containing a list (not separate documents). Correct parsing: `docs[0]` is a list of dicts, each with 'act' key. **KW-eval actually has 761/769 clips with activities (99%).** + +### Cross-Source Comparison (CMU-v2 vs KW-eval) + +- 374 clips overlap between CMU-v2 and KW-eval +- KW-eval: 17,455 activities across those 374 clips +- CMU-v2: 4,227 activities across same clips (4x fewer) +- CMU adds new activity types in 25% of clips (92/374), but only 66 extra activity instances total +- Top new types from CMU: `closes_facility_door` (31 clips), `exits_scene_through_structure` (20), `enters_scene_through_structure` (13) + +**Conclusion**: KW-eval is the densest source. CMU-v2 adds marginal activity types but won't solve the entity coverage gap. + +### QA Vulnerability Analysis (152 Questions, 2 Test Slots) + +| Risk Level | Count | % | Description | +|------------|-------|---|-------------| +| **SAFE** | 91 | 60% | Temporal, ordering, causality — internally consistent | +| **PARTIAL** | 30 | 20% | Spatial — correct for tracked entities but incomplete picture | +| **VULNERABLE** | 31 | 20% | Perception "No" + camera_view — could be factually wrong | + +Per-category: +- **temporal** (30): SAFE — same-actor event chains, timestamps correct +- **ordering** (17): SAFE — internal consistency +- **causality** (30): SAFE — same-actor adjacency chains +- **spatial** (30): PARTIAL — correct within tracked entities but may miss others +- **perception** (25): 14 "Yes" SAFE, 11 "No" **VULNERABLE** (44% of category) +- **camera_view** (20): 100% **VULNERABLE** — uses "exclusive to one camera" but exclusivity is only within annotations + +### GPS Data — Actor Ground Truth + +GPS README confirms: "actors and personnel carried GPS loggers set to log their location once every 10 seconds." Key stats: +- **116 GPX files** covering 6 of 8 dates (missing 2018-03-05, 2018-03-14) +- **Mean 78.2 actors per slot**, max 98 +- IDs like `G623` are consistent across days within March session +- GPS timestamps in UTC (video timestamps in UTC-4) +- User's problematic slot (2018-03-07.16-50-00): **98 actors with GPS**, only 1 annotated +- GPS cropping box: (39.052214N, 85.534343W) to (39.046152N, 85.524140W) + +### Camera → Site Mapping (28 cameras) +``` +admin: G326, G329 +bus: G331, G340, G475, G505, G506, G508, G509 +hospital: G301, G341, G436, G476, G479 +school: G299, G300, G328, G330, G336, G339, G419, G420, G421, G423, G424, G474, G638, G639 +``` + +### Remedy Assessment + +**NOT a remedy — Annotation Merging**: +All annotation sources (Kitware, CMU, UMD, IBM) follow the same **activity-centric** annotation protocol. They only track entities performing one of 37 defined activities. Merging adds marginal activity type coverage (~25% of clips get new types) but doesn't track untracked people. Won't solve false-negative perception questions. + +**Potential Real Remedies** (to investigate next session): + +1. **GPS-Based Site-Level Entity Validation** + - Parse GPX files to count actors at each site per slot + - Requires GPS→site mapping (via camera KRTD models + 3D site model, or by clustering GPS coords into site regions) + - Would produce: "N actors at admin at 16:50 but only M annotated" → confidence score + - Effort: Medium — need to build GPS coordinate clustering per site + - Benefit: Per-slot entity coverage metric for all 116 GPS-covered slots + +2. **Off-the-Shelf Person Detector (YOLOv8/etc.)** + - Run person detection on video frames to count ALL visible people per camera + - Compare detected count vs annotated count → annotation gap ratio + - Would directly answer: "How many people are actually in this video?" + - Effort: High (need GPU, processing time per video) + - Benefit: Camera-level (not just site-level) ground truth + +3. **Question Design Changes** (no new data needed): + - Perception: Drop all "No" answers, keep only "Yes" (always verifiable) + - Camera_view: Reframe as "Which camera shows annotated evidence of X?" or require activity on 2+ cameras + - Add `annotation_confidence` field per question + +### Files Modified +- `scripts/naturalize_qa.py` — Added `--categories` and `--exclude-categories` CLI args + +### Files Created +- `data/qa_pairs/2018-03-05.13-15-00.bus.natural.json` — 55 naturalized questions +- `data/gpt_logs/2018-03-05.13-15-00.bus/naturalize_gpt-4o-mini.json` — GPT usage log + +### Decisions +- **MEVID**: Skipped — only 19.6% slot overlap, no bbox linkage, 45GB extraction needed +- **Annotation merging**: NOT a solution to entity coverage gap (all sources are activity-centric) +- **KW-eval parsing bug**: Fixed understanding — KW-eval has 99% clips with activities, not 35% + +### Next Session Priority +1. Decide on remedy approach (GPS validation vs person detector vs question redesign) +2. If GPS: build GPS→site clustering and per-slot entity confidence scores +3. If question redesign: modify `generate_qa_symbolic.py` to eliminate vulnerable categories +4. If person detector: investigate available models and GPU access + +--- + +## 2026-02-13 (Session 19): MEVID Matching Pipeline — Full Implementation + +### Tasks Completed +- Replaced all MEVID_matching stubs with production code (9 Python files, all compile clean) +- Config rewrite with verified filesystem paths (bbox_train, Kitware, video) +- 3 utility modules: reid_model.py (ResNet50 2048-dim), video_crop.py (OpenCV), spatial_validation.py (IoU/cosine) +- 4 main scripts: extract_mevid_features.py, extract_kitware_features.py, compute_matches.py, evaluate_mapping.py +- validate_setup.py: pre-flight checks (2 failures: torch not installed, bbox_train incomplete) +- EXECUTION_GUIDE.md: step-by-step tmux commands for user to run long-running scripts + +### Key Decisions +- torchvision ResNet50 over torchreid (reliable, sufficient for same-camera matching) +- Visual similarity only (no frame overlap — MEVID frames are tracklet-relative) +- Keyframe-only bboxes from geom.yml + +### Remaining User Action +1. `tar -xzf mevid-v1-bbox-train.tgz` (~30 min, 31 GB) +2. `pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124` + +--- + +## 2026-02-13 (Sessions 20-23): Kitware IDs, GPT Prompts, V7 Planning, Counting Investigation + +### Summary +- **Session 20**: Kitware id1/id2 analysis—two format types (janitor hash vs good sequential), no cross-camera linking +- **Session 21**: GPT prompt template research—read all ego-exo4d (5) and nuscenes (7) prompt files and output formats +- **Session 22**: V7 implementation plan—full V6 code review, confirmed V7 is 99% identical to V6, created V7_IMPLEMENTATION_PLAN.md +- **Session 23**: Counting question bug—104 events is ~68 unique (cross-camera double-counting), counting category already deleted from V6 + +--- + +## 2026-02-14 (Session 24): V7 Full Implementation — All Features + +### Tasks Completed +Full V7 pipeline implementation: 2 new files + 6 rewritten files across 16 total V7 modules. + +#### New Files +- `scripts/v7/activity_hierarchy.py` — CAUSAL_RELATIONSHIPS (12 parent activities), CO_OCCURRING (10 pairs), `humanize_activity()`, relationship scoring +- `scripts/v7/naturalize_v7_qa.py` — GPT wrapper with category-specific few-shot examples, dry_run, strip_debug_markers + +#### Rewritten Files +- `generate_temporal.py` — Connected events via `_score_connection()` (same_entity_cluster=3.0, related=×2.0, MEVID=1.5), debug_info +- `generate_spatial.py` — Entity aliases in questions, debug_info blocks +- `generate_perception.py` — humanize_activity() for readable names, debug_info +- `run_pipeline.py` — V7 version, v7_stats block, fixed import paths + +#### Modified Files +- `build_scene_graph.py` — Entity.make_alias() method, alias field +- `entity_resolution.py` — mevid_person_cameras field in ResolvedGraph + +### Test Results +- **16/16 files compile clean** +- Test slot `2018-03-11.11-25-00.school`: 8 Qs (2T+3S+3P), 12.52s +- 2 MEVID-validated, 2 connected, 1258 entities with aliases, 0 validation issues +- V6→V7: Better link quality (score 5.0 vs heuristic), entity aliases, debug markers + +### Output +- `data/qa_pairs/2018-03-11.11-25-00.school.v7.json` + +--- + +## 2026-02-13 (Session 25): Cleanup, Perception Fix, GPT Naturalization Test + +### Tasks Completed +- **Root cleanup**: Moved 19 loose files — 3 deleted (duplicates), 11 to docs/, 2 to MEVID/, 2 to MEVID_matching/scripts/, 1 to archived/ +- **Perception gerund fix**: Added `humanize_activity_gerund()` with 30-verb gerund map, "Opening a facility door is occurring" instead of "opens facility door is occurring" +- **Archived scripts**: Moved `analyze_kitware_ids.py`, `generate_qa_v4.py`, `generate_qa_v5.py`, `naturalize_qa.py` to scripts/archived/ +- **GPT naturalization test**: 8/8 questions naturalized with gpt-4o-mini, 0 failures, 5220 tokens, ~$0.001 +- **MEVID extraction**: tar still running (PID 2055125), 10/69 folders extracted + +### Output +- `data/qa_pairs/2018-03-11.11-25-00.school.v7.natural.json` — Naturalized V7 QA +- `data/gpt_logs/2018-03-11.11-25-00.school/naturalize_v7_gpt-4o-mini.json` + +--- + +## 2026-02-15 (Session 28): MEVID Person Attribute Database Investigation + +### Tasks Completed +- **MEVID data analysis**: 158 persons, 4.8M frames, 154 multi-camera, 140 multi-outfit, 22 cameras. Crop quality: 150-400px (close cams) to 25-50px (far cams). +- **JPG vs Kitware comparison**: MEVID JPGs preferred — same quality plus built-in cross-camera identity and outfit tracking. Kitware bboxes offer no advantage. +- **Person database prototype**: `build_person_db.py` — parses metadata, extracts HSV color attributes from crops. Tested on 5 persons with local crops. Color extraction viable even on small crops. +- **Database output**: `data/person_database.json` — 158 persons, 277 KB, schema includes cameras, outfits, per-outfit attributes, global attributes. +- **NAS workaround**: Background script copies best crops locally (PID 2289750). Only 10/69 persons extracted from tarball. + +### Files Created +- `scripts/build_person_db.py` — Person attribute database builder +- `scripts/mevid_person_survey.py` — Quick person inventory +- `scripts/copy_best_mevid_crops.sh` — Background NAS crop copy +- `data/person_database.json` — Person database + +--- + +## 2026-02-13 (Session 29): MEVID Continuation — Tarball Corruption & Documentation + +### Tasks Completed +- **Tar diagnosis**: Both NAS tarballs (`mevid-v1-bbox-train.tgz`, `mevid-v1-bbox-test.tgz`) are corrupted — `invalid compressed data--format violated`. Not a process issue. +- **Local extraction completed**: 14 train + 9 test = 23 persons extracted locally (8.9 GB on `~/MEVID/`). Local disk has 857 GB free. +- **Database updated**: `build_person_db.py` now searches 4 paths (local train → local test → samples → NAS). Rebuilt: 23 persons with attributes. +- **Next steps documented**: Created `MEVID_NEXT_STEPS.md` — tarball workarounds, GPT-4V plan (hold off), V7 integration path, architecture diagram. +- **Design principle established**: No ground truth needed — attributes are visual descriptors for QA question accuracy only. + +### Files Created/Modified +- `MEVID_NEXT_STEPS.md` — NEW: comprehensive status + next steps + integration plan +- `scripts/build_person_db.py` — MODIFIED: added LOCAL_TEST, multi-source search +- `data/person_database.json` — REBUILT: 23 persons with attributes (298 KB) + +--- + +## Session 30: 2026-02-17 — Slot Aggregation & YOLO Attribute Detection Setup + +**Objective**: Clarify 3-tier data architecture, aggregate all supported slots, choose attribute detection method (YOLO vs GPT-4V) + +**Key Tasks**: +1. Established three-tier MEVID infrastructure (DATA/IMAGES/ATTRIBUTES with clear purposes) +2. Created slot aggregation tool: 887/929 slots (95.5%) have MEVID person coverage +3. Comprehensive YOLO vs GPT-4V comparison: YOLO recommended (10-100× faster, free, reproducible) +4. Created YOLO skeleton ready for Session 31 implementation + +**Key Deliverables**: +- `data/mevid_supported_slots.json` — 887 slots with full MEVID metadata (595 KB) +- `scripts/aggregate_mevid_slots.py` — Slot aggregation tool (complete, tested) +- `scripts/extract_attributes_yolo.py` — YOLO skeleton ready for Session 31 +- `YOLO_vs_GPT4V_COMPARISON.md` — Detailed comparison with cost analysis +- `MEVID_NEXT_STEPS.md` — Updated with 3-tier architecture and YOLO recommendation + +**Key Findings**: +- **95.5% Coverage**: 887/929 MEVA slots have MEVID persons; only 42 slots lack coverage +- **Top 6 Benchmark Slots**: All have perfect coverage (10 MEVID cameras + 23 persons) at school site (2018-03-15) +- **YOLO Wins**: 10-100× faster than GPT-4V, zero cost, 100% locally reproducible +- **Cost Comparison**: YOLO = $0 for full dataset; GPT-4V = $30-150 depending on sampling + +**Architecture Clarity**: +- **Tier 1 (DATA)**: `mevid_supported_slots.json` — metadata for heuristic validation +- **Tier 2 (IMAGES)**: 761K local crops for 23 persons (8.9 GB total) +- **Tier 3 (ATTRIBUTES)**: YOLO-extracted descriptors for V7 naturalization + +**Next Steps** (Session 31): +- Run full YOLO extraction: `python3 scripts/extract_attributes_yolo.py --build --with-crops -v` +- Generate `data/person_database_yolo.json` with 23 persons +- Test on benchmark slot: `2018-03-15.14-50-00.school` +- Integrate YOLO attributes into V7 naturalization + +**Status**: Ready to proceed. All prerequisites complete except YOLO extraction impl. + +--- + +## Session 32: 2026-02-13 — V8 Pipeline Implementation (MEVID Description Integration) + +**Summary**: Implemented complete V8 QA pipeline integrating MEVID person descriptions into question generation. 5 categories (temporal, spatial, perception, re-identification, scene summary), ~9 Qs/slot. Tested on sample slot with 0 validation issues. + +**Key Files Created**: +- `~/scripts/v8/` — Full V8 pipeline (11 modules + utils/) +- `~/V8_DESIGN.md` — V8 design document with V7 weakness analysis (W1-W8) +- `~/data/qa_pairs/2018-03-11.11-25-00.school.v8.json` — Test output + +**V8 New Modules**: person_descriptions.py (MEVID loader + enrichment), generate_reidentification.py, generate_scene_summary.py, naturalize_v8_qa.py, run_pipeline.py + +**V8 Updated Modules**: generate_temporal.py, generate_spatial.py, generate_perception.py (+ attribute_verification) + +**Test Results**: 9 Qs in 8.15s, 132/1258 entities with MEVID descriptions, 21 MEVID persons, 0 validation issues. + +**Bugs Fixed**: Description doubling, function signature mismatches, validator key name. + +**Next**: Run naturalization, batch across MEVID-supported slots. + + +## Session 33: 2026-02-13 — V8 Naturalization V2 + Quality Fixes + +**Summary**: Created naturalize_v8_qa_v2.py with a FREE pre-processing layer that fixes V8 output quality issues: description simplification ("upper body garment" → "top"), activity doubling removal, grammar/capitalization fixes, camera reference management. Initially added temporal anchors for disambiguation but removed them after user identified they leak the answer. Camera references serve as the correct disambiguator. Drafted Kitware MEVID corruption email. + +**Key Files Created**: +- `~/scripts/v8/naturalize_v8_qa_v2.py` — V2 naturalization with pre-processing layer (3 modes) +- `~/data/kitware_mevid_email_draft.txt` — Email about corrupt mevid-v1-bbox-train.tgz + +**Key Files Modified**: +- `~/scripts/v8/person_descriptions.py` — Added simplify_description(), garment substitutions, strip patterns + +**Key Decision**: Temporal anchors ("at the very start", "6 seconds in") leak answers → removed. Camera refs kept for temporal/perception/re-ID (necessary for VLM to locate events), removed from spatial only. + +**Test Results**: 7/9 questions cleaned by pre-processing (free, instant). Descriptions simplified, camera refs properly managed, no timestamp leaks. + +**Next**: Run V2 GPT naturalization, batch V8 across MEVID slots, send Kitware email. + +--- + +## Session 34: 2026-02-13 — Entity Description Extractor + V8 Integration + +**Summary**: Built extract_entity_descriptions.py to extract color descriptions from raw AVI videos + geom.yml bounding boxes. Optimized video reading with adaptive strategy (sequential grab for dense, random seek for sparse). Improved entity enrichment from 10% (MEVID-only) to 38% (MEVID + geom-color). Integrated into V8 pipeline with 3-tier priority: MEVID → geom-color → activity-verb fallback. + +**Key Files Created**: +- `~/scripts/v8/extract_entity_descriptions.py` — 690 lines: geom parsing, AVI crop extraction, HSV color analysis + +**Key Insights**: AVI preferred over MP4 for color fidelity. Sequential video reading for dense cameras (G330: 517 actors) dropped from minutes to 22.8s. 578/578 actors got descriptions. + +**Test Results**: 9 questions, 0 validation issues. Descriptions: 132 MEVID + 352 geom + 442 fallback. + +--- + +## Session 35: 2026-02-13 — 2-Pass Naturalization, V3 Variety Mode, Repo Push + +**Summary**: Added grammar checker pass (temp=0.3) to naturalize_v8_qa_v2.py for 2-pass naturalization. Created V3 mode (temp=0.95) for aggressive question variation with camera refs stripped from temporal Qs (descriptions serve as disambiguators). Archived old V5/V6/V7 output. Pushed secure, minimal V8 to Multi-Camera/meva/ (16 modules + data, no docs/secrets). Security scan: CLEAN. + +**Key Achievements**: +- GRAMMAR_CHECKER_PROMPT: Conservative copy editor, fixes grammar/punctuation without changing meaning +- V3 system prompt: Varied sentence structures, requests "different person wrote it" phrasing +- V3 temporal Qs: Stripped camera refs from questions (descriptions disambiguate), preserved in answers +- Full pipeline test: 46s end-to-end (22.8s extraction + 8.2s V8 gen + 15s 2-pass naturalization) +- All 9 questions: 0 failures on both naturalizer and grammar checker passes +- Repo: Minimal (scripts + required data), archived V6/V7, removed all agentic documentation + +**Cost Analysis**: 2-pass naturalization $0.002/slot (9 Qs × 2 calls × ~400 tokens × $0.15/1M gpt-4o-mini). + +**Git Status**: Commit 4d86937 successful (66 files, 181K insertions). Push blocked: 403 error (adihebbalae lacks write access). Remedies: request collaborator status, fork+PR, or SSH if available. + +**Next**: Manual push, batch 929 slots (est. $1.86 GPT + 9 hours compute). + +--- + +## 2026-02-16 (Session 40): V9 Pipeline Complete — Bugs Fixed, Plan Closed + +**Summary**: Fixed causality template duplication bug (fallback entity labels containing activity name caused doubled text). Updated SYSTEM_PROMPT_UNIFIED with camera-ID ban (Rule 3), ontology translation (Rule 7), and human-sounding phrasing examples. Re-verified pipeline: 13 questions, 0 validation issues, ~13s. Marked v9_plan.md as complete. + +**Files Modified**: +- `scripts/v8/generate_causality.py` — Template duplication fix (forward + backward) +- `scripts/v8/naturalize_v8_qa_v3.py` — SYSTEM_PROMPT_UNIFIED updated +- `v9_plan.md` — Marked complete with summary + +**V9 Pipeline Status**: All 8 categories operational (temporal:2, spatial:2, perception:2, re_id:2, scene_summary:1, event_ordering:2, causality:1, numerical:1). Ready for batch generation across 929 slots. + +**Next**: Execute MEVID download, batch V9 pipeline, naturalization pass, Multi-Camera repo push. + + +--- + +## 2026-02-16 (Session 41): V9 Critique Report + Fixes + Archival + +**Summary**: Evaluation pass on all V9 scripts found 6 issues (1 bug, 3 logic, 2 quality gaps). Applied all fixes: event ordering description duplication guard, causality forward/backward randomization, naturalizer few-shot examples for 3 new categories, activity diversity filter, rotate-by-1 distractor strategy, stale V8→V9 argparse text. Re-ran pipeline: 13 questions, 0 validation issues, 11.39s. Archived V1-V8 outputs. MEVID extraction actively running (6/104 folders, 125K JPGs so far from 31GB tarball). Combined Feb 16 session logs (sessions 36-41 → sessions_feb16.md). + +**Files Modified**: +- `scripts/v8/generate_event_ordering.py` — 3 fixes (description duplication, activity diversity, rotate distractor) +- `scripts/v8/generate_causality.py` — Randomized forward/backward subtype selection +- `scripts/v8/naturalize_v8_qa_v2.py` — Added CATEGORY_EXAMPLES_V3 entries for event_ordering, causality, numerical +- `scripts/v8/run_pipeline.py` — Argparse V8→V9 text + +**V9 Pipeline**: All 8 categories, all bugs fixed, ready for batch generation (929 slots × ~14 Qs = ~13K questions). + +--- + +## 2026-02-17 (Session 43): MEVID Fresh Download + Full Extraction — COMPLETE + +Root cause of MEVID extraction failure confirmed: 2022 download was corrupted during transfer. Kitware's S3 originals were always valid. Fresh download of both tarballs (train 31GB, test 13GB), SHA256 verified (train matches Kitware ref), both pass `gunzip -t`, both extract fully with zero errors. Results: Train 104/104 folders, 3,609,156 JPGs (exact match); Test 54/54 folders, 1,186,251 JPGs. Total 158 persons, 4.8M crops, 55GB extracted. Scripts: `mevid_full_pipeline.sh`, `mevid_extract_robust.py`. + +--- + +## 2026-02-17 (Session 44): Full MEVID YOLO+HSV Extraction (158 Persons) + +Updated all MEVID path references to fresh extraction, deleted old partial data + tarballs (53GB recovered), ran YOLO+HSV on all 158 persons (5,676 crops, 46s, $0). Output: `data/person_database_yolo.json` with 158 persons. Color stats: blue dominates train split (surveillance clothing), test split more diverse. Carried objects detected: suitcase(9), backpack(5), handbag(4), cell phone(3), bottle(3). + +## 2026-02-20 (Session 45): Multi-Cam-Dataset Format Alignment & Export + +Executed 2-20 checklist: (1) Removed camera IDs from temporal Q&A options, replaced with description-based labels using entity appearances. Fixed duplication bug in activity-verb fallback. (2) Added AVI video paths via `_build_video_paths()` helper in run_pipeline.py. (3) Rewrote naturalizer system prompt to output only `{question, reasoning}`, frozen options. (4) Created `export_to_multicam_format.py` — transforms V9 naturalized output to agibot/ego-exo4d schema (dict options, letter answers, camera_names, grounding). (5) Created `question_format.md` documenting all 4 datasets' schemas. Verified on 2 slots: school (11 Qs) and hospital (8 Qs), ALL CHECKS PASSED. Known issue: MEVID slot matching broken (missing `slots` key in person DB). + +## 2026-02-20 (Session 46): Fix MEVID Matching + Entity Descriptions + Video Paths + Perception Format + +Fixed 4 issues found after Session 45 review: (1) MEVID slot matching — rewrote `get_mevid_persons_for_slot()` and `get_mevid_persons_with_cameras()` in person_descriptions.py to read from `mevid_supported_slots.json` instead of broken person DB `slots` key lookup. School: 21 persons found, 66 entities enriched. (2) Entity descriptions auto-extraction — added Step 3.5 in run_pipeline.py that runs `extract_entity_descriptions.py` (YOLO+HSV, $0) on first pipeline run per slot, caches result. Eliminates "someone" fallback. (3) Video path bloat — rewrote `_build_video_paths()` to check `events` key (event_ordering), `camera_N_event` (perception multi-cam), run all checks unconditionally. Event ordering: 32→4 paths. (4) Perception format — added naturalizer instruction + example to preserve "What activity…" / "Which camera…" wording. All tests passed, 0 "someone" occurrences, re-ID questions now generated. + +## 2026-02-20 (Session 47): FINAL Pipeline — Slot Audit + 7-Category Pipeline + +Slot audit: 929 raw slots → 390 canonical slots (cameras at same site/time with `:01` offsets merged to `:00`). Created `data/canonical_slots.json`. MEVID coverage confirmed: 158 persons (all YOLO), 887 supported slots. Created `scripts/final/` pipeline with exactly 7 categories matching paper taxonomy: temporal(2), event_ordering(2), perception(2), spatial(2), summarization(1), counting(1), best_camera(2). REMOVED re_identification + causality. NEW `generate_best_camera.py` — Camera Transition Logic ("Which camera first captures the entrance of {entity}?") using `person_enters_scene_through_structure` events, grouped by resolved entity cluster, visual descriptions only. Renamed scene_summary→summarization, numerical→counting. Tested on 3 slots (school x2, hospital): 9-10 Qs each, 0 validation issues, no banned categories. + +## 2026-02-20 (Session 48): FINAL Pipeline Production Audit — 8 Bugs Fixed + +Full production audit of FINAL pipeline targeting 2,750 questions across 7 categories. Found and fixed 8 bugs: (1) canonical slot resolution — `find_clips_for_slot` couldn't handle canonical names, added `_resolve_to_raw_slots()`. (2) Geom parser format — only handled kitware format, missed kitware-meva-training quoted-key format, spatial yield jumped 10%→70%. (3) Per-slot targets too low — increased spatial 2→3, counting 1→2, best_camera 2→3. (4) Camera ID leaks in fallback descriptions. (5) Missing gerund mappings (transfers, reverses). (6) Too-few-options on 3-camera slots — global MEVA camera pool fallback. (7) Article agreement "a orange"→"an orange" — `fix_articles()` in run_pipeline + preprocessor. (8) "1 cameras" grammar + identical spatial descriptions filtered. Final results: 40-slot sample → projected 3,217 vs 2,750 target (+467 surplus), 0 validation issues, 0 <4 option questions. Export chain verified: pipeline→naturalizer→export all working. Pipeline ready for batch execution. + +--- + +## Session 50 — 2026-02-22: MP4 Reorganization into Slot Subdirectories + +- Created `scripts/reorganize_mp4s.py` +- Moved all 3,956 MP4 files from flat `{date}/{hour}/` into `{date}/{hour}/{slot}/` subdirs +- 2,544 canonical + 1,412 non-canonical (irregular start times), 0 errors +- Supports `--dry-run` and `--undo` + +--- + +## Session 51 — 2026-02-23: Research — Connection Scoring Logic + +- Research-only session documenting `connection_strength`, `connection_score`, and `group_score` scoring logic in temporal and event_ordering generators +- No code changes + +--- + +## Session 52 — 2026-02-24: Video Renderer Overhaul + QA Quality Audit (8 Issues) + +**Part 1 — Renderer Rewrite**: Rewrote `render_question_validation.py` with blue bbox highlighting for question-relevant actors, full word-wrapped question text (never truncated), all options with correct answer in green, 5-second context before/after activities, activity labels during events, timeline progress bar, and blue cell borders during active events. Tested on all 14 Qs for school-1 naturalized — 14/14 OK. + +**Part 2 — Manual QA Audit**: Reviewed rendered videos, found 8 quality issues: +1. **Counting entity**: Drop entity-counting (undercounts — only cross-camera-linked clusters counted) +2. **Counting activity**: Add cross-camera temporal dedup (same activity ±2s = 1 instance) +3. **Bbox filter**: Add 2% screen area min (41,472 px²) at scene graph level — filters tiny distant actors from ALL categories +4. **Temporal reuse**: Add `used_event_ids` + `used_activity_names` to prevent same event/activity in multiple temporal Qs +5. **Enter/exit wording**: Change "enters scene through structure" → "enters the camera's view through a doorway/gate" +6. **Event ordering gaps**: MIN_GAP 3→2s, add MAX_GAP=10s +7. **Spatial same-cam**: Restrict to same-camera pairs only (keep KRTD distance math) +8. **Color verification**: Inherent dataset limitation, no immediate fix + +Files modified: `render_question_validation.py` (rewrite). 7 other files identified for next session fixes. Total Qs per slot: 14 (was 15, counting reduced 2→1). + +--- + +## Session 54 — 2026-02-24: Implement All 8 QA Quality Fixes + +All 8 fixes from Session 52 audit implemented: +1. `build_scene_graph.py`: MIN_BBOX_AREA=41472 with median area filter +2. `generate_numerical.py`: Entity-counting removed; activity-counting with ±2s cross-camera dedup +3. `generate_temporal.py`: `used_event_ids` + `used_activity_names` across all 4 selection passes +4. `generate_event_ordering.py`: MIN_GAP=2.0s, MAX_GAP=10.0s +5. `generate_spatial.py`: Same-camera-only filter +6. `activity_hierarchy.py`: "enters/leaves the camera's view through a doorway" +7. `run_pipeline.py`: TARGET_COUNTING=1 +8. `extract_entity_descriptions.py`: MIN_BBOX_HEIGHT/WIDTH=144 + +Verified on `2018-03-07.17-05.school`: 9 Qs, all 8 fixes confirmed active. + +--- + +## Session 55 — 2026-02-25: Run Pipeline on New MEVID Slot + Fix best_camera Schema + +Ran pipeline on `2018-03-11.16-20.school` (7 cameras, MEVID-supported). Generated 8 questions: temporal=2, perception=2, summarization=1, counting=1, best_camera=2. Event ordering and spatial yielded 0 (fallback-only candidates filtered, no KRTD-matched entities). + +**Bug fix**: `generate_best_camera.py` was missing `correct_answer` string field — only had `correct_answer_index`. Added `"correct_answer": options[correct_idx]` to both first_entrance and last_entrance question dicts. + +Rendered 8 validation videos to `data/qa_pairs/2018-03-11.16-20.school/validation_videos/` (8.2 MB total). All rendered successfully. + +--- + +## Session 56 — 2026-02-25: Code Review & Optimize naturalize.py (V10) + +Senior code review of `meva/scripts/v10/naturalize.py` (1437 → 1428 lines). Addressed colleague's V3 prompt analysis and found 10 issues: + +**Fixes applied:** +1. Removed dead code: unused `_strip_letter_prefixes`, `DEFAULT_TEMPERATURE_TWO`, dead `simplified` assignment in `_preprocess_perception` +2. Replaced 12-entry `_ACTIVITY_REWRITES` dict (case variants) with 2 case-insensitive regex patterns via module-level `_apply_ontology_rewrites()` +3. Eliminated duplicate `CATEGORY_EXAMPLES` entries (`summarization`=`scene_summary`, `counting`=`numerical`) — now uses `_CAT_ALIASES` dict +4. Fixed double article-fix (`a` → `an`) in `_preprocess_temporal` + `preprocess_all` — removed from temporal, kept global +5. Merged two post-processing loops (ontology rewrites + article fixes) into one +6. **Prompt rewrite** (key fix): Replaced 12 conflicting numbered rules with ranked priority system (4 priorities + 2 constraint sections). Removed "preserve this exact phrasing structure" vs "VARY your phrasing" conflict. Reduced DO-NOT constraints from 6 to 1. Added bad→good transformation anchor example per colleague's recommendation. +7. Fixed `_short_activity_label` to avoid wasteful capitalize-then-lowercase (was calling `_humanize_gerund().lower()`, now computes directly) +8. Fixed `_preprocess_perception` dead `simplified` variable — now actually substitutes simplified description into template + +### Session 56 Continuation — V2 Colleague Feedback (Architectural Refactor) + +Implemented V2 colleague feedback. Major architectural cleanup: removed two-pass mode entirely, restructured GPT input from JSON to plaintext, separated concerns (Python=deterministic transforms, GPT=language rewriting). 1428 → 1136 lines (-292 lines / -20%). + +**Key changes:** +- Deleted: `_naturalize_two_pass()`, `_grammar_check_one()`, `SYSTEM_PROMPT_NATURALIZE`, `GRAMMAR_CHECKER_PROMPT`, `--two-pass` CLI arg, dual temperature constants +- `_naturalize_single_pass()` → `_naturalize_question()` with labeled plaintext input (CATEGORY/QUESTION/OPTIONS/CONTEXT sections) +- Article agreement (`a→an`) removed from Python — delegated to GPT +- `SYSTEM_PROMPT_UNIFIED` → `SYSTEM_PROMPT` (simplified, removed ontology section GPT doesn't need) +- `_short_activity_label` deduplicated (25 lines → 3-line wrapper around `_humanize_gerund`) +- Temperature: 0.8 → 0.4 (single constant) + +### Session 56 Continuation 2 — HoE Review + Pipeline Test Run + +HoE code review: agreed on explicit DO NOT constraints (added 4) and temp 0.3 (lowered from 0.4). Disagreed on JSON→plaintext output — `response_format=json_object` is constrained decoding, safer than text splitting. + +**Bug fix**: `mevid_supported_slots.json` uses HH-MM-SS but pipeline passes HH-MM — all MEVID lookups silently returned empty. Fixed `_resolve_all_mevid_slots()` in `person_descriptions.py` to bridge both formats. + +**Full pipeline test**: `2018-03-07.11-00.school` (11 cameras, 23 MEVID persons) → 12 questions generated, 12/12 naturalized (0 failures, 10572 tokens), 12 validation videos rendered. Output in `data/qa_pairs/2018-03-07.11-00.school/`. Previous QA pairs archived to `data/qa_pairs/archived/`. + +--- + +## 2026-02-26 (Session 58): Full Path Cleanup + NAS Pipeline Output + +**STANDARD PIPELINE COMMANDS** (update for all future sessions): +```bash +cd /home/ah66742/Multi-Camera-Adi/meva && source /home/ah66742/venv/bin/activate +source /home/ah66742/.env && export MEVA_OUTPUT_DIR=/nas/neurosymbolic/multi-cam-dataset/meva/data +export PYTHONPATH=$PYTHONPATH:$(pwd) +SLOT="2018-03-07.11-00.school" +python3 -m scripts.v10.run_pipeline --slot "$SLOT" -v +python3 -m scripts.v10.naturalize --input $MEVA_OUTPUT_DIR/qa_pairs/$SLOT.final.raw.json -v --yes +python3 -m scripts.v10.export_to_multicam_format --slot "$SLOT" -v +``` + +**Root cause of 0 temporal/spatial questions**: `entity_descriptions/` moved to NAS but `_GEOM_DESC_DIR` still pointed to `_OUTPUT/entity_descriptions`. Fixed with `MEVA_ENTITY_DESC_DIR` env var (default: `/nas/mars/dataset/MEVA/entity_descriptions`). + +**Full path audit**: Eliminated all remaining hardcoded `/home/ah66742/` paths in `batch_extract_all_slots.py`, `build_geom_slot_index.py`, `review_qa.py`. Added `MEVA_MULTICAM_OUT` env var to `export_to_multicam_format.py` (default: `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs`). Fixed `run.sh` RAW_JSON path bug (spurious `$SLOT/` subdir). Verified with strace: zero runtime `/home/ah66742/data` reads. + +**Pipeline verified** on two slots writing exclusively to NAS: +- `2018-03-07.11-00.school` → 14 Qs, 3 steps ✓ +- `2018-03-11.11-25.school` → 10 Qs, 3 steps ✓ + +Commits pushed: `27d5d7f`, `f62b6d1`, `0db3a04`, `172672f` + + +--- + +## 2026-02-26 (Session 59): Determinism Verification + +Pulled coworker commit `6a1c27e "Make runs deterministic"`. Fixes non-determinism at source: +- `person_descriptions.py`: `for pid in sorted(mevid_persons)` → dict built in stable order +- `build_scene_graph.py`: sorted `entity_actor_ids.keys()` and each `actor_ids` list +- `parse_annotations.py`: sorted `info["cameras"]` and glob results + +Verified: ran `run.sh 2018-03-11.11-25.school` twice, `diff` shows only `generation_time_sec` differs. All questions/answers/cameras byte-for-byte identical. Pipeline is fully deterministic as of `6a1c27e`. + +## Session 59 (continued) — 2026-02-26: MEVID Rebuild + Path Cleanup + +Diagnosed 0 spatial questions (KRTD coverage gap — persons on G419/G420 which have no KRTD files). Audited MEVID utilization: only 23/158 persons used. Created `build_mevid_slots.py` standalone script to rebuild `mevid_supported_slots.json` from authoritative MEVID annotation sources. Result: **80 cross-camera persons across 168 slots** (3.5× improvement). Added `MEVA_MEVID_DATA_DIR`/`MEVA_MEVID_URLS` env var overrides. Removed all `/home/ah66742` references from live code. Pushed as commit `c234283`. No collision with Agent 2 (entity descriptions work). + +## Session 63 — 2026-02-26: Home Directory Full Cleanup + +Audited V10 pipeline (3 entry points, 13 modules, 4 utils, 6 standalone tools). Executed full home directory cleanup: deleted 8 folders (~65.5 GB — MEVID bbox crops, tmp_* test artifacts, stale Multi-Camera-Main clone, empty dirs), 9 one-off scripts, 11 obsolete .md files, 5 misc files. Merged 3 V10 docs into `docs/V10_REFERENCE.md`. Moved old `data/` and `scripts/` to `archived/`. Moved 5 reference docs to `docs/`. Root now clean: Multi-Camera-Adi, venv, docs, archived, output, scholarship_app, snap + dotfiles. Zero pipeline code changes. + +## Session 64 — 2026-02-27: V10 QA Quality Fixes + +User identified 10 problems with QA output for `2018-03-11.16-40.school`. Root cause analysis found 5 systemic issues: coarse HSV descriptions (264 actors, 80 unique), no cross-camera event dedup in temporal, "Someone" fallback leak, no bbox-in-frame validation, best_camera dedup by cluster_id only. Fixed across 7 files: person_descriptions.py (vehicle reclassification), generate_temporal.py (3D dedup, bbox validation, camera enrichment), generate_event_ordering.py (exhaustive fallback chain), generate_spatial.py (bbox validation, description disambiguation, question dedup), generate_perception.py ("someone"→"a person", contextual identification), generate_best_camera.py (visual desc prioritization), run_pipeline.py (entity_description dedup). Tested on 2 slots, 9/10 issues resolved (1 is annotation completeness limitation). + +## Session 65 — 2026-02-27: Automated QA Validator + Rich Descriptions + 3D Context + Iterative Loop + +Built 4-phase self-correcting system. Phase 1: `validate_qa.py` — 6 automated checks (reasoning consistency, raw token leaks, duplicates, generic descriptions, multi-correct ambiguity, grammar), scoring 0-100. Phase 2: Expanded `_hsv_to_color()` from 11→25 CSS colors, added height/position to `build_description()`, added `--method`/`--force-reextract` to batch extractor. Phase 3: Created `scene_context.py` (KRTD camera models + PLY building meshes, near/moderate proximity labels, 3D matching scores), enhanced `entity_resolution.py` with 3D+description matching, added location enrichment to `generate_temporal.py` and `generate_spatial.py`. Phase 4: Created `batch_generate.py` — iterative generate→validate→fix→regenerate loop with auto-fix (grammar, raw leaks, duplicate removal), tested 5 rounds on `2018-03-11.11-25.school`: scores [44, 59, 64, 59, 44], best=64/100 (round 3). 3 new files, 5 modified (+320 lines). Remaining quality barrier: generic descriptions ("a person") — generators need deeper entity description integration. +## Session 66 — 2026-02-27: Implement 12 QA Pipeline Issues (Phase 1+2) +Implemented 8 of 12 issues from prompt_nextsteps.md audit. Phase 1: Issue 1 (color-match MEVID assignment via scoring in person_descriptions.py), Issue 2 (SegFormer default in batch_extract_all_slots.py). Phase 2: Issue 5 (first-instance pre-filter in temporal + event ordering), Issue 6 (cross-camera dedup imported into event ordering + 3D matching in numerical), Issue 8 (same-person spatial filter via cluster/IoU/proximity), Issue 9 (deterministic _build_reasoning() for counting), Issue 10 (key frames in counting output), Issue 11 (annotation verification check in validate_qa.py — function added, not yet wired into checks list). 7 files modified. Remaining: Issue 11 wiring, Phase 3 Issues 12 (no-camera policy), 4 (camera leak), 3 (soft caps), 7 (perception enrichment). + +## Session 67 — 2026-02-27: Two-tier extraction, grammar/perception fixes, multi-slot testing +Fixed three bugs and tested pipeline across 4 diverse slots. (1) Grammar: `humanize_activity_gerund()` now uses "an" before vowels ("a object"→"an object"). (2) Perception: exclude ALL cameras with same activity from distractors, not just correct camera — eliminates multi_correct_ambiguity errors. (3) Two-tier extraction committed (from prev conversation): SegFormer ≥144px, HSV color-only ≥40px. Re-extracted admin (24 actors), hospital (243 actors), school (1261 actors). Test scores: admin=100, hospital=85, school-1=89, school-2=51. School-2 lower due to many small-bbox entities filtered by scene graph MIN_BBOX_AREA. Committed fac33b6 (not pushed). Outputs synced to /nas/neurosymbolic/. 4 files modified: activity_hierarchy.py, extract_entity_descriptions.py, generate_perception.py, validate_qa.py. + +## Session 68 — 2026-02-28: Kill perception, new spatial closest-approach, fix counting reasoning, fix validator FPs +Killed perception category (commented out, not deleted). Rewrote `generate_spatial.py` entirely: new closest-approach distance metric sampling bboxes at every 10th frame across overlapping entity time windows, 3D projection via KRTD, categories near/moderate/far/cross_paths. Fixed counting reasoning: GPT naturalization was corrupting numbers — added explicit count preservation hints + post-validation fallback to raw reasoning. Fixed validator false positives: raised near-dup threshold 80→90%, added structural template exclusion for same-category questions, improved generic_description regex with clothing-context negative lookahead. Test scores: admin=100, hospital=92, school-10-15=84, school-11-25=86 (up from 51!). SegFormer batch at 82/351 slots (23%). Committed 07bb83c (not pushed). 5 files modified: run_pipeline.py, generate_spatial.py, naturalize.py, validate_qa.py, batch_extract_all_slots.py. + +## Session 68b — 2026-02-27: Texture/pattern detection + confidence filtering for entity descriptions +Added _detect_texture() analyzing SegFormer mask regions for solid/patterned/striped via HSV variance + directional gradients. Light/dark brightness qualifiers for chromatic colors (skip inherently dark/light colors like black/white). _majority_vote_with_confidence() tracks per-attribute agreement 0-1, drops low-confidence (<40%) colors from descriptions. Crop selection now prefers middle-of-track (inner 80%) for stable pose/lighting. Test scores: admin=100, hospital=92, school-10-15=90(+6), school-11-25=88(+2). Found coworker's VLM code at waymo/captions/vlm_annotator_v2.py (InternVL3.5-14B). SegFormer batch 120/814 ETA ~3:30AM. Committed 7fba6c6 (not pushed). 1 file modified: extract_entity_descriptions.py. + +## Session 69 — 2026-02-28: Cross-camera entity clustering + height differentiation +Implemented `merge_cross_camera_descriptions()` in person_descriptions.py: uses entity_resolution clusters to merge SegFormer attributes across cameras via majority voting + color consolidation + accessory union. Added `differentiate_within_camera()`: adds tall/short prefix based on avg_crop_height when meaningful spread exists. Wired into pipeline as Step 4b. Test results on 5 school slots: uniqueness 37-67%, cross-camera clustering improves +2-5 unique descriptions. Root cause: limited attribute palette (only upper+lower color differentiate, 15 HSV colors), batch ran with pre-68b code (no texture/brightness). Worst case: 42 entities sharing "a person wearing navy top + black pants". Next: re-extract with 68b code for texture/brightness fields. 2 files modified: person_descriptions.py (+250 lines), run_pipeline.py. +## Session 69b — 2026-02-28: VLM integration + per-question ambiguity + representative QA batches +Integrated VLM descriptions into person_descriptions.py as Priority 2 (MEVID > VLM > SegFormer > fallback). Updated `_build_description()` to include 68b fields (texture: striped/patterned, brightness: dark/light). Added `_compute_question_ambiguity()` to run_pipeline.py (Step 13): per-question metric counting same-camera entities sharing descriptions with referenced entities. Fixed import bug (merge/differentiate functions missing from primary import block). Generated 4 representative QA batches to meva/data/qa_pairs/: admin (3q, 100% unique), bus (3q, 67% unique), school (10q, 100% unique), hospital (8q, 100% unique). SegFormer re-extraction: 215/354 slots done with 68b fields (texture/brightness/confidence). VLM batch script ready but not yet launched. 3 files modified: person_descriptions.py, run_pipeline.py, vlm_describe_entities.py (new). 4 QA files created. + +## Session 70 — 2026-03-01: QA quality audit — 7 fixes, regenerate + naturalize all slots +User reviewed naturalized output and identified 7 systematic issues. All diagnosed and fixed: (1) vehicle-as-person bug — generate_temporal.py uses `get_activity_entity_type()` fallback; (2) "unknown" texture leak — added to exclusion set in `_build_description()`; (3) vague activity terms — 28 special overrides in `humanize_activity()` + `_GERUND_SPECIAL` dict; (4) camera overlap dedup — NEW utils/camera_overlap.py module (KRTD proximity + viewing direction), integrated into generate_numerical.py (8s window) and generate_temporal.py; (5) article agreement — computed a/an in `_build_description()` + `_clean_geom_description()`; (6) color consolidation — initially consolidated but user objected, reverted: specific colors (indigo/navy/teal) kept for display, consolidation only for matching; (7) description matching gap — investigated, 270/270 match (100%), 28 entities lack extraction (G505 missing), not a pipeline bug. Regenerated + naturalized all 4 test slots: admin 3q, bus 3q, hospital 8q, school 10q — **ZERO quality issues detected**. 5 files modified, 1 new file created. + +## Session 71 — 2026-03-02: Fix entity description priority chain (SegFormer > MEVID YOLO) +Diagnosed and fixed why character descriptions "kept disappearing" — MEVID YOLO colors (Priority 1) were overriding rich SegFormer descriptions. Root cause: all 158 MEVID persons had `gpt_description: null`, so code fell to YOLO color summary; 129/158 (82%) had `primary_upper_color: blue` → everything became "blue top and blue pants". Rewrote `enrich_entities()` in person_descriptions.py with 5-tier priority: (1) MEVID GPT [if populated] → (2) SegFormer/geom → (3) VLM → (4) MEVID YOLO → (5) activity-verb fallback. Updated run_pipeline.py stats/verbose output. Also re-ran SegFormer extraction for 92 empty/missing slots (3 gained actors, rest genuinely empty). Verified fix on all 4 test slots: hospital descriptions changed from "blue top and blue pants"×11 to diverse colors (indigo, teal, navy, dark gray, plum). School best_camera questions now show "indigo hair, dark indigo top, plum shoes, bag, hat, scarf". 2 files modified: person_descriptions.py, run_pipeline.py. 4 QA files regenerated. diff --git a/docs/project-history/github-session-archive/session_records/session_19_2026-02-13.md b/docs/project-history/github-session-archive/session_records/session_19_2026-02-13.md new file mode 100644 index 0000000..fcccc90 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_19_2026-02-13.md @@ -0,0 +1,126 @@ +# Session 19 - 2026-02-13 + +Summary of previous session(s): The recent sessions implemented the V6 pipeline end-to-end, documented V6/V7 architecture and bugs, and created the V7 planning documents. The latest update reorganized MEVID docs and split session logs into date-grouped files, with V7 memory optimizations applied. + +## Current Session +- Date: 2026-02-13 +- Goal 1: Fix divergent branches for Multi-Camera repo ✓ +- Goal 2: Set up MEVID implementation infrastructure (no implementation yet) ✓ + +## Work Log + +### Part 1: Multi-Camera Git Fix +- Checked divergence: local `e6b38b1` vs remote `a04c9a1` (ahead 1, behind 1) +- User chose: reset to remote (discard local commit) +- Executed: `git reset --hard origin/main` ✓ +- Result: Local main now synced with remote + +### Part 2: MEVID Implementation Setup (Infrastructure Only) +Complete setup of MEVID person-to-actor mapping pipeline without starting implementation. + +**Directories Created**: +- `scripts/mevid_matching/` — Main module +- `scripts/mevid_matching/utils/` — Utility functions +- `data/mevid_features/` — Feature cache output +- `data/mevid_matches/` — Matching results output +- `output/mevid_logs/` — Logging + +**Core Files Created**: +1. `config.py` — Centralized configuration (paths, hyperparameters, thresholds) +2. Placeholder scripts (Step 1-5 + evaluation): + - `extract_mevid_features.py` — Extract ReID embeddings from MEVID crops (8h runtime) + - `extract_kitware_features.py` — Extract embeddings from video bboxes (30m/slot) + - `compute_matches.py` — Match via similarity + spatial validation (5m/slot) + - `evaluate_mapping.py` — Compare heuristic vs ground-truth accuracy +3. Utility stubs: + - `utils/reid_model.py` — Load torchreid ReID model, extract features + - `utils/video_crop.py` — Extract bbox crops from MP4 videos + - `utils/spatial_validation.py` — IoU, frame overlap, confidence scoring +4. Supporting files: + - `requirements_mevid.txt` — Dependencies (torch, torchreid, opencv, etc.) + - `validate_setup.py` — Pre-flight validation script + - `README_MEVID_SETUP.md` — **Comprehensive setup guide with tmux instructions** (5000+ lines) + - `SETUP_CHECKLIST.md` — Checklist of all setup tasks completed + +**Configuration & Documentation**: +- `config.py`: 100+ lines, all settings centralized (ReID model, GPU, thresholds, batch sizes) +- `README_MEVID_SETUP.md`: Complete guide including: + - Overview of MEVID→Kitware mapping goal + - Directory structure + - Prerequisites (data access, environment) + - Setup validation step-by-step + - **Full tmux workflow for 5-step pipeline** (background processing) + - Detailed tmux examples for Steps 1-4 + - Batch processing patterns (parallel slots) + - Full pipeline example + - Tmux quick reference + - Troubleshooting guide + - Estimated times (8-10h total with MEVID extraction) + +**What's NOT Implemented Yet**: +- ReID feature extraction logic +- Video bbox crop extraction +- IoU/frame overlap computation +- Matching algorithm +- Evaluation script + +All placeholder scripts have `TODO` comments + detailed docstrings describing implementation tasks. + +## Decisions +- User requested NO implementation yet, just setup + tmux instructions +- Centralized config for easy tuning before running +- Comprehensive README with real tmux examples (so agent doesn't keep polling during 8-hour runs) +- Step functions are stubs — ready for implementation when needed + +## Files Touched +- `/home/ah66742/Multi-Camera`: Git reset ✓ +- `scripts/mevid_matching/` — 8 new files ✓ +- `scripts/mevid_matching/utils/` — 3 new files ✓ +- `data/mevid_features/` — created ✓ +- `data/mevid_matches/` — created ✓ +- `output/mevid_logs/` — created ✓ + +Total: 12 new Python files + 4 dirs + 1 requirements file + 2 docs + +### Part 3: Directory Reorganization +Restructured MEVID and scripts directories for clarity: + +**Created**: +- `~/MEVID_matching/` — New top-level folder for MEVID pipeline +- `~/MEVID_matching/docs/` — Documentation (3 markdown files) +- `~/MEVID_matching/scripts/mevid_matching/` — MEVID implementation code +- `~/scripts/archived/` — Archive for old/obsolete scripts + +**Moved to docs**: +- `MEVID_SETUP_READY.md` +- `README_MEVID_SETUP.md` +- `SETUP_CHECKLIST.md` + +**Moved to scripts/mevid_matching**: +- All MEVID Python files (config.py, extract_*.py, compute_matches.py, etc.) +- utils/ folder with 3 modules +- requirements_mevid.txt +- validate_setup.py + +**Moved to scripts/archived**: +- build_scene_graph_v2.py +- check_id1_consistency.py +- check_save.py +- download_mevid_may.sh +- render_cluster.py +- old_versions/ folder + +**Active scripts remaining in ~/scripts/**: +- v6/, v7/ — Active pipelines (kept) +- check.py, extract_logic_tuples.py, generate_qa_v5.py, etc. (kept - active) +- convert.sh, convert_benchmark.sh (kept - active) + +## Notes for Next Session +- MEVID setup and reorganization complete ✓ +- Clean directory structure: ~/MEVID_matching/{docs,scripts/mevid_matching} +- Old scripts archived to ~/scripts/archived/ (not deleted, just organized) +- All utility stubs have docstrings explaining what to implement +- Validation script (`validate_setup.py`) at ~/MEVID_matching/scripts/mevid_matching/ +- README has full tmux workflows: ~/MEVID_matching/docs/README_MEVID_SETUP.md +- Next: User implements the 7 placeholder functions in utils/ + 4 main scripts +- Estimated implementation time: 4-6 hours coding, 2 hours testing = 8h + 10h runtime = total ~18h wall-clock diff --git a/docs/project-history/github-session-archive/session_records/session_49_2026-02-21.md b/docs/project-history/github-session-archive/session_records/session_49_2026-02-21.md new file mode 100644 index 0000000..7d0c5f2 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_49_2026-02-21.md @@ -0,0 +1,103 @@ +# Sessions: February 21, 2026 (Session 49) + +> Eliminate fallback activity-verb entity descriptions from all questions + +--- + +## Session 49: Fallback Description Elimination + +**Previous**: Session 48 fixed 8 bugs and declared pipeline production-ready with 3,217 projected questions (40-slot audit). User identified remaining quality issue: activity-verb fallback descriptions ("a person opens facility door") still appear in questions. + +### Problem +When entities lack MEVID or geom-color descriptions, `enrich_entities()` falls back to describing them by their activity (e.g., "a person opens facility door", "a person enters scene through structure"). These appear in temporal, event_ordering, and spatial question text, producing bad questions like: +- "a person opens facility door and a person in blue top picks up object — which occurred first?" + +User directive: "I dont want any questions that is 'does person do Y' because thats just bad question writing. Just ditch the question entirely." + +### Changes Made + +1. **`person_descriptions.py`**: Added `_VISUAL_KEYWORDS` frozenset and `is_visual_description(desc)` helper. Updated `enrich_entities()` to return 3-tuple: `(entity_descriptions, desc_counts, fallback_eids)`. + +2. **`run_pipeline.py`**: Updated to unpack 3-tuple and pass `fallback_eids` to temporal, event_ordering, and spatial generators. + +3. **`generate_temporal.py`**: Added `fallback_eids` parameter, updated `_get_event_description()` to skip fallback entities, filters candidate pairs requiring BOTH events to have visual descriptions. + +4. **`generate_event_ordering.py`**: Added `fallback_eids` parameter, updated description/scoring to skip fallbacks, filters groups requiring ALL events to have visual descriptions. + +5. **`generate_spatial.py`**: Added `fallback_eids` parameter, filters candidate pairs where EITHER entity has a fallback description. + +### Yield Impact (40-slot audit) +- **Before (Session 48)**: 3,217 projected +- **After**: 1,818 projected (~43% reduction) +- event_ordering hit hardest (2/40 slots); temporal reduced but still viable; spatial slight reduction +- perception, counting, summarization, best_camera: unchanged + +### Quality Verified +All questions now use visual clothing descriptions only — no more "a person opens facility door" entity identifiers. + +### Files Modified +- `scripts/final/person_descriptions.py` +- `scripts/final/run_pipeline.py` +- `scripts/final/generate_temporal.py` +- `scripts/final/generate_event_ordering.py` +- `scripts/final/generate_spatial.py` + +--- + +## Session 49b: Batch Extraction Preparation + +**Problem Discovery**: User identified that geom-color extraction hasn't been run. The `/home/ah66742/data/entity_descriptions/` directory has 139 empty JSON files. Pipeline loads them but gets 0 descriptions, causing 93% fallback rate. + +**Root Cause**: Extraction script exists ([scripts/final/extract_entity_descriptions.py](scripts/final/extract_entity_descriptions.py)) but hasn't been executed for the full dataset. + +### Batch Extraction System Created + +1. **[scripts/final/batch_extract_all_slots.py](scripts/final/batch_extract_all_slots.py)**: Main batch processor + - Processes all 929 slots from slot_index.json + - Resume capability (saves progress every 5 slots) + - 10-minute timeout per slot + - Full logging and error tracking + +2. **[start_overnight_extraction.sh](start_overnight_extraction.sh)**: Quick-start wrapper + - Creates tmux session automatically + - Validates environment + - Shows monitoring commands + +3. **[OVERNIGHT_EXTRACTION_INSTRUCTIONS.md](OVERNIGHT_EXTRACTION_INSTRUCTIONS.md)**: Complete execution guide + - Step-by-step tmux/screen usage + - Monitoring and verification + - Troubleshooting + - Expected timeline + +### Validation Results + +**Dry-run**: 929/929 slots passed validation + +**Single extraction test** (2018-03-11.11-25-00.school): +- 578 actors processed +- 577 descriptions extracted (99.8% success) +- Duration: 51 seconds +- Output: "a person in blue top and black pants", "a person in green top and gray pants", etc. + +**Projected overnight run**: +- Duration: ~13 hours (929 slots × 51 sec) +- Total entities: ~500,000 +- Visual coverage: 7% → 95%+ +- Expected yield: 1,818 → 3,500+ questions + +### Next Steps + +User will execute overnight batch extraction using: +```bash +./start_overnight_extraction.sh +``` + +After completion: +1. Verify ~500K entities extracted +2. Re-run 40-slot audit to measure yield recovery +3. Temporal/event_ordering should jump from 18/2 to hundreds + +### Files Created +- `scripts/final/batch_extract_all_slots.py` — Batch processor +- `start_overnight_extraction.sh` — Quick-start wrapper +- `OVERNIGHT_EXTRACTION_INSTRUCTIONS.md` — Complete guide diff --git a/docs/project-history/github-session-archive/session_records/session_50_2026-02-20.md b/docs/project-history/github-session-archive/session_records/session_50_2026-02-20.md new file mode 100644 index 0000000..4985c02 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_50_2026-02-20.md @@ -0,0 +1,9 @@ +# Session 50: February 20, 2026 — Pipeline Annotation & Geom Investigation + +> **Previous (Session 49)**: Eliminated fallback activity-verb entity descriptions from all questions. Updated person_descriptions.py, run_pipeline.py, and all three question generators (temporal, event_ordering, spatial) to skip entities without visual descriptions. + +## Task +Investigate geom.yml loading flow and annotation source coverage in the MEVA pipeline. + +## Findings +See detailed report delivered in conversation. diff --git a/docs/project-history/github-session-archive/session_records/session_50_2026-02-22.md b/docs/project-history/github-session-archive/session_records/session_50_2026-02-22.md new file mode 100644 index 0000000..367065a --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_50_2026-02-22.md @@ -0,0 +1,50 @@ +# Session 50: MP4 Reorganization into Slot Subdirectories + +**Date**: 2026-02-22 +**Duration**: ~10 minutes +**Previous session summary**: Session 49 fixed fallback description filtering, discovered geom extraction was never run, fixed a critical regex bug in kitware-training geom format (field order `g0→id1→ts0` vs `id1→ts0→g0`), completed full batch extraction (89,739 entities, 814 slots), and validated the V10 pipeline on 2 random slots (11 questions each, 2.7s each). + +--- + +## Task: Organize MP4 files into slot-named subdirectories + +### Problem +3,956 MP4 files were flat inside `mp4s/{date}/{hour}/` directories, making it hard to navigate by slot. + +### Solution +Created `scripts/reorganize_mp4s.py` which: +- Derives slot name from filename: `{date}.{start}.{end}.{site}.{camera}.r13.mp4` → `{date}.{start}.{site}` +- Moves each file to `mp4s/{date}/{hour}/{slot}/filename` +- Supports `--dry-run` (preview) and `--undo` (flatten back) +- Moves ALL files, including non-canonical slots (irregular start times like 09-49-37) + +### Result +``` +Total MP4s (flat): 3956 +Moved: 3956 + Canonical slots: 2544 + Non-canonical slots: 1412 +Errors: 0 +``` + +### New structure +``` +mp4s/2018-03-07/17/ + 2018-03-07.17-05-00.school/ + 2018-03-07.17-05-00.17-10-00.school.G299.r13.mp4 + 2018-03-07.17-05-00.17-10-00.school.G330.r13.mp4 + ... + 2018-03-07.17-05-00.bus/ + ... +``` + +### Files Created +- `scripts/reorganize_mp4s.py` — MP4 reorganization script + +--- + +## Notes for Next Session +- Full QA generation (390 slots) still not started — ready to run +- Command: `python3 -m scripts.final.run_pipeline --all -v` +- Geom extraction complete (89,739 entities, 0 fallback descriptions) +- Pipeline tested: 11 questions/slot in ~2.7s diff --git a/docs/project-history/github-session-archive/session_records/session_51_2026-02-23.md b/docs/project-history/github-session-archive/session_records/session_51_2026-02-23.md new file mode 100644 index 0000000..94b533a --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_51_2026-02-23.md @@ -0,0 +1,24 @@ +# Session 51: Research — Connection Scoring Logic in V10/Final Pipeline + +**Date**: 2026-02-23 +**Previous session summary**: Session 50 reorganized 3,956 MP4 files into slot-named subdirectories under `mp4s/{date}/{hour}/{slot}/`, with `--dry-run` and `--undo` support. Session 49 had fixed geom extraction and validated the V10 pipeline. + +--- + +## Task: Document connection_strength, connection_score, and group_score + +Research-only session — read relevant code in `scripts/final/` to understand scoring logic for temporal QA generation. + +### Files Examined +- `scripts/final/generate_temporal.py` (L1-408) +- `scripts/final/generate_event_ordering.py` (L110-200, L560-608) +- `scripts/final/entity_resolution.py` (L1-299) +- `scripts/final/activity_hierarchy.py` (L80-160) +- `scripts/final/run_pipeline.py` (L485-500) + +### Key Findings +See user-facing response for complete details on: +- `connection_strength`: categorical label (strong/medium/weak) +- `connection_score`: numeric float from `_score_connection()` +- `group_score`: float from `_score_group()` (event_ordering only) +- Temporal candidate ranking: sort by (-score, gap_sec), MEVID-first selection diff --git a/docs/project-history/github-session-archive/session_records/session_52_2026-02-24.md b/docs/project-history/github-session-archive/session_records/session_52_2026-02-24.md new file mode 100644 index 0000000..3137f24 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_52_2026-02-24.md @@ -0,0 +1,114 @@ +# Session 52: Video Renderer Overhaul + QA Quality Audit (8 Issues Found) + +**Date**: 2026-02-24 +**Previous session summary**: Session 51 was a research-only session documenting `connection_strength`, `connection_score`, and `group_score` scoring logic in the V10/Final temporal and event_ordering generators. Session 50 reorganized 3,956 MP4s into slot subdirectories. + +--- + +## Part 1: Video Renderer Rewrite + +### Motivation +The existing `render_question_validation.py` produced basic multi-camera grids with green bounding boxes, truncated question text (120 chars), minimal context (1s padding), and no way to tell which actors were relevant to the question being verified. + +### Changes Made +Rewrote `scripts/final/render_question_validation.py` (679 → ~620 lines) with these features: + +1. **Blue bounding box highlighting**: During event frames, actors involved in the question's activity are drawn with thick BLUE boxes (`COLOR_BOX_ACTIVE = (255, 180, 0)` in BGR). Other actors get thin green boxes. During context frames, all boxes are green/thin. + +2. **Full question text display**: Dynamic word-wrapped header showing the complete question (never truncated), all A/B/C/D options, and the correct answer highlighted in green with `<< CORRECT` marker. Header height computed dynamically based on text length. + +3. **5-second context padding**: Changed from 1s to 5s (`CONTEXT_PADDING_SEC = 5.0`) before first activity and after last activity. `MAX_RENDER_FRAMES` increased from 300 to 9000 (5 min at 30fps). + +4. **Activity labels**: During event frames, camera cells show `>> person_texts_on_phone` label at the bottom. During context frames, shows `[CONTEXT]`. Blue border drawn around the camera cell during active events. + +5. **Timeline progress bar**: Bottom bar showing event regions (blue rectangles), red playback cursor, and `ACTIVITY`/`CONTEXT` status label. + +6. **Enhanced camera labels**: Now include frame counter AND timestamp in seconds. + +### Test Results +- Rendered all 14 questions for `2018-03-07.17-05.school` (naturalized) — 14/14 OK +- File sizes: 224KB (best_camera, short) to 5.7MB (event_ordering, 55s) +- All categories render correctly: temporal, event_ordering, perception, spatial, summarization, counting, best_camera + +--- + +## Part 2: Manual QA Quality Audit — 8 Issues Found + +Reviewed rendered validation videos for slot `2018-03-07.17-05.school`. Found 8 quality issues across the pipeline: + +### Issue 1: Counting — Entity Counting Fundamentally Broken +**Problem**: "How many unique individuals?" counts only cross-camera-linked entity clusters (via Union-Find in `entity_resolution.py`). Single-camera entities excluded. Annotations are sparse — many visible people have no annotations at all. Answer was "5" but visually there are clearly more people. +**Root cause**: `_entity_counting_candidates()` in `generate_numerical.py` line 125 counts `resolved.entity_clusters` which requires `len(members) >= 2` AND `len(cameras) >= 2`. +**Decision**: Drop entity-counting subtype entirely. Keep activity-counting with cross-camera temporal dedup. + +### Issue 2: Tiny/Distant Actors in Questions +**Problem**: Many questions reference people with bounding boxes that are tiny pixel sizes — far away from the camera, impossible to verify visually. No minimum bbox area filter exists in the scene graph builder. +**Root cause**: `build_scene_graph.py` adds ALL entities from annotations regardless of bbox size. The only size filter (`MIN_BBOX_HEIGHT=25, MIN_BBOX_WIDTH=10`) is in `extract_entity_descriptions.py` for crop quality only. +**Decision**: Add global min bbox area filter of 2% of screen (41,472 px² ≈ 200×208 px) at scene graph construction level. Entities below threshold excluded from ALL question categories. + +### Issue 3: Temporal Questions Reuse Same Activity +**Problem**: Q0 and Q1 both reference the same activity (e.g., `person_texts_on_phone` on G421). The `seen` set in `_find_temporal_candidates()` only prevents duplicate `(actA, camA, actB, camB)` tuples, but the same event can appear in multiple selected questions. +**Root cause**: No `used_event_ids` or `used_activity_names` set during selection loop (lines 300-340 of `generate_temporal.py`). +**Decision**: Add `used_event_ids` and `used_activity_names` tracking to prevent any event or activity string from appearing in more than one temporal question. + +### Issue 4: "Enters Scene Through Structure" Confusion +**Problem**: Activity says "entering through a structure" but visually the person appears to be exiting a building. Confusing for manual verification. +**Root cause**: MEVA definition means entering the CAMERA's field of view through a doorway/gate — not entering the building. Someone walking out of a building toward the camera IS "entering the scene through a structure." +**Decision**: Update humanized form to "enters the camera's view through a doorway/gate" to clarify the MEVA-specific meaning. + +### Issue 5: Event Ordering — Events Too Close Together +**Problem**: Event ordering sequences contain events within ~1 second of each other, making ordering ambiguous. +**Root cause**: `MIN_GAP_SEC = 3.0` in `generate_event_ordering.py` but no MAX_GAP, and the gap may not be enough. +**Decision**: Change to `MIN_GAP_SEC = 2.0`, add `MAX_GAP_SEC = 10.0`. Events must be 2-10 seconds apart. + +### Issue 6: Spatial Questions Use Cross-Camera Without 3D Ground Truth +**Problem**: Spatial questions compare people on different cameras using KRTD projection, but without point cloud data, cross-camera distance estimates are unreliable and unverifiable. +**Decision**: Restrict spatial questions to same-camera pairs only. Keep KRTD bbox-foot-to-world distance math (still works for same-camera entities). + +### Issue 7: Activity Counting — Cross-Camera Duplication +**Problem**: "How many instances of closing a trunk?" counts 5, but two cameras likely see the same physical trunk-closing event. Activity counting uses raw `Counter(e.activity for e in sg.events)` with no cross-camera dedup. +**Root cause**: `_activity_counting_candidates()` in `generate_numerical.py` line 97 — no temporal overlap check. +**Decision**: Add temporal dedup: events with same activity overlapping within ±2 seconds across cameras count as 1 instance. Use entity cluster membership when available. + +### Issue 8: Color Descriptions Unverifiable on Dark Footage +**Problem**: Questions describe people by clothing color ("blue top and gray pants") but surveillance footage is dark — everything looks black/dark gray. Hard to manually verify. +**Root cause**: Inherent dataset limitation. HSV color extraction works on pixel values but human perception on compressed dark footage differs. +**Recommendation**: No immediate code fix. Could merge "dark gray" and "black" in `_hsv_to_color()` in future pass. The bbox area filter (Issue 2) will help by ensuring only reasonably-close actors with better-visible colors are referenced. + +--- + +## Decisions Summary + +| Issue | Decision | Target File | +|-------|----------|-------------| +| Counting entity | Drop entity-counting | `generate_numerical.py` | +| Counting activity | Add temporal dedup | `generate_numerical.py` | +| Bbox filter | 2% screen area (41,472 px²) | `build_scene_graph.py` | +| Temporal reuse | Track used_event_ids + used_activity_names | `generate_temporal.py` | +| Enter/exit wording | "camera's view through doorway/gate" | `activity_hierarchy.py` | +| Event ordering gaps | MIN=2s, MAX=10s | `generate_event_ordering.py` | +| Spatial same-cam | Filter to same-camera pairs | `generate_spatial.py` | +| Counting target | Reduce TARGET_COUNTING 2→1 | `run_pipeline.py` | + +**Total questions per slot**: 14 (was 15) + +--- + +## Files Modified This Session +- `scripts/final/render_question_validation.py` — Complete rewrite with activity highlighting, full question display, 5s padding, timeline bar + +## Files NOT Yet Modified (planned for next session) +- `scripts/final/build_scene_graph.py` — Add MIN_BBOX_AREA filter +- `scripts/final/generate_numerical.py` — Drop entity counting, add temporal dedup +- `scripts/final/generate_temporal.py` — Prevent event/activity reuse +- `scripts/final/generate_event_ordering.py` — MIN_GAP 2s, MAX_GAP 10s +- `scripts/final/generate_spatial.py` — Same-camera only filter +- `scripts/final/activity_hierarchy.py` — Clarify enter/exit wording +- `scripts/final/run_pipeline.py` — TARGET_COUNTING 2→1 + +## Notes for Next Session +- Implement all 8 fixes listed above +- Re-run pipeline on test slot after changes +- Re-render validation videos to verify fixes +- Run Phase 1 + Phase 2 on corrected output +- Consider running on school-2 and school-3 for broader validation diff --git a/docs/project-history/github-session-archive/session_records/session_53_2026-02-24.md b/docs/project-history/github-session-archive/session_records/session_53_2026-02-24.md new file mode 100644 index 0000000..307c83e --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_53_2026-02-24.md @@ -0,0 +1,27 @@ +# Session 53: Remove entity_counting from generate_numerical.py + +**Date**: 2026-02-24 +**Previous session summary**: Session 52 rewrote the video renderer with blue bounding boxes, full question text display, 5s context padding, and timeline progress bars. Also ran a QA quality audit finding 8 issues. + +--- + +## Changes Made + +Modified `/home/ah66742/scripts/final/generate_numerical.py` (402 → 381 lines): + +1. **Module docstring**: Removed entity_counting subtype references. Now describes only activity_counting with cross-camera temporal deduplication. + +2. **Added `_dedup_activity_count()` helper** (module-level): Clusters events of the same activity across different cameras within ±2 seconds into single instances. Same-camera events are always counted separately. + +3. **Modified `_activity_counting_candidates()`**: Now groups events by activity and calls `_dedup_activity_count()` instead of using raw `Counter()`. Returns deduped counts. + +4. **Deleted `_entity_counting_candidates()`** entirely (was ~30 lines counting person entity clusters from ResolvedGraph). + +5. **Simplified `_score_candidate()`**: Removed the `entity_counting` bonus branch. Now just adds flat +0.5 for the only subtype. + +6. **Simplified `_make_question_text()`**: Removed the `entity_counting` template branch. + +7. **Simplified `generate_numerical_qa()`**: Removed the call to `_entity_counting_candidates(sg, resolved)`. Updated verbose print. Kept `resolved` parameter in signature for API compatibility. + +## Files Modified +- `scripts/final/generate_numerical.py` diff --git a/docs/project-history/github-session-archive/session_records/session_54_2026-02-24.md b/docs/project-history/github-session-archive/session_records/session_54_2026-02-24.md new file mode 100644 index 0000000..a26f674 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_54_2026-02-24.md @@ -0,0 +1,60 @@ +# Session 54: Implement All 8 QA Quality Fixes in V10 Pipeline + +**Date**: 2026-02-24 +**Previous session summary**: Session 52 rewrote the video renderer and identified 8 QA quality issues. Session 53 began partial fixes. This session implements all 8 fixes comprehensively. + +--- + +## Changes Implemented + +### Fix 1: Global Minimum Bounding Box Area Filter +**File**: `scripts/final/build_scene_graph.py` +- Added `MIN_BBOX_AREA = 41472` constant (2% of 1920×1080) +- Entity construction computes median bbox area; entities below threshold excluded +- Also updated `extract_entity_descriptions.py`: `MIN_BBOX_HEIGHT/WIDTH = 144` + +### Fix 2: Drop Entity-Counting, Add Temporal Dedup +**File**: `scripts/final/generate_numerical.py` +- Removed `_entity_counting_candidates()` entirely +- Added `_dedup_activity_count()` for cross-camera temporal dedup (±2s overlap → 1 instance) +- Cleaned up scoring, text templates, and main generator + +### Fix 3: Prevent Temporal Event/Activity Reuse +**File**: `scripts/final/generate_temporal.py` +- Added `used_event_ids` and `used_activity_names` sets across all 4 selection passes + +### Fix 4: Tighten Event Ordering Gaps +**File**: `scripts/final/generate_event_ordering.py` +- `MIN_GAP_SEC = 2.0`, added `MAX_GAP_SEC = 10.0` + +### Fix 5: Same-Camera Spatial Filter +**File**: `scripts/final/generate_spatial.py` +- Only same-camera entity pairs; simplified question text; `requires_multi_camera = False` + +### Fix 6: Clarify Enter/Exit Wording +**File**: `scripts/final/activity_hierarchy.py` +- Override: "enters/leaves the camera's view through a doorway/gate" +- Added "leaves" → "leaving" to gerund map + +### Fix 7: Reallocate Question Slots +**File**: `scripts/final/run_pipeline.py` +- `TARGET_COUNTING = 1` (was 2), total ~14 per slot + +--- + +## Verification: `2018-03-07.17-05.school` +- 9 questions (temporal=2, perception=2, summarization=1, counting=1, best_camera=3) +- Bbox filter excluded ~600+ tiny actors ✓ +- Temporal Q1/Q2 have zero event/activity overlap ✓ +- Counting is activity-only with dedup ✓ +- All 8 files compile cleanly ✓ + +## Files Modified (8 total) +- `scripts/final/build_scene_graph.py`, `generate_numerical.py`, `generate_temporal.py` +- `scripts/final/generate_event_ordering.py`, `generate_spatial.py`, `activity_hierarchy.py` +- `scripts/final/run_pipeline.py`, `extract_entity_descriptions.py` + +## Notes for Next Session +- Test on MEVID-supported slots for spatial/event_ordering yield +- Consider lowering MIN_BBOX_AREA if spatial yield drops too much +- Render validation videos to visually confirm diff --git a/docs/project-history/github-session-archive/session_records/session_55_2026-02-25.md b/docs/project-history/github-session-archive/session_records/session_55_2026-02-25.md new file mode 100644 index 0000000..31ea75c --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_55_2026-02-25.md @@ -0,0 +1,43 @@ +# Session 55: Run Pipeline on New MEVID Slot + Fix best_camera Schema + +**Date**: 2026-02-25 +**Previous session summary**: Session 54 implemented all 8 QA quality fixes (bbox filtering, temporal dedup, event ordering gaps, same-camera spatial, enter/exit wording, counting allocation). Verified on `2018-03-07.17-05.school`. + +--- + +## Work Completed + +### 1. Pipeline Run on New Slot +- **Slot**: `2018-03-11.16-20.school` (7 cameras, MEVID-supported) +- **Result**: 8 questions generated in 7.0s + - temporal: 2, perception: 2, summarization: 1, counting: 1, best_camera: 2 + - event_ordering: 0 (all 6 candidates were fallback-only, filtered out) + - spatial: 0 (0 entities with 3D positions from KRTD) +- Output: `data/qa_pairs/2018-03-11.16-20.school.final.raw.json` + +### 2. Bug Fix: Missing `correct_answer` in best_camera Generator +- **File**: `scripts/final/generate_best_camera.py` +- Both `first_entrance` and `last_entrance` question dicts were missing the `correct_answer` key +- Had only `correct_answer_index` — renderer and downstream tools expect `correct_answer` string +- Added `"correct_answer": options[correct_idx]` to both question types + +### 3. Validation Video Rendering +- All 8 videos rendered successfully (8.2 MB total) +- Output: `data/qa_pairs/2018-03-11.16-20.school/validation_videos/` +- Videos use per-slot subdirectory structure (updated in prior session) + +### 4. Video Output Path Update (from prior session continuation) +- Renderer updated to use `_get_video_output_dir(slot)` → `data/qa_pairs/{slot}/validation_videos/` +- Enter/exit wording: `humanize_activity()` updated with `_SPECIAL` dict + +## Files Modified +- `scripts/final/generate_best_camera.py` — added `correct_answer` field (bug fix) + +## Files Created +- `data/qa_pairs/2018-03-11.16-20.school.final.raw.json` — 8 QA pairs +- `data/qa_pairs/2018-03-11.16-20.school/validation_videos/` — 8 MP4 validation videos + +## Notes for Next Session +- Spatial and event_ordering yielded 0 questions on this slot — consider testing slots with more diverse activity and KRTD-matched entities +- 0 MEVID persons matched — MEVID IDs exist (21 in slot) but no annotations mapped to them; investigate MEVID matching pipeline +- Consider running on a slot with known strong event_ordering results for full category coverage diff --git a/docs/project-history/github-session-archive/session_records/session_56_2026-02-25.md b/docs/project-history/github-session-archive/session_records/session_56_2026-02-25.md new file mode 100644 index 0000000..fe3f5c6 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_56_2026-02-25.md @@ -0,0 +1,135 @@ +# Session 56: Code Review & Optimize naturalize.py (V10) + +**Date**: 2026-02-25 +**Previous session summary**: Session 55 ran the pipeline on `2018-03-11.16-20.school` (8 Qs), fixed missing `correct_answer` in `generate_best_camera.py`, and rendered 8 validation videos. + +--- + +## Work Completed + +### Senior Code Review of `meva/scripts/v10/naturalize.py` + +Reviewed the 1437-line file created by a subagent, incorporating colleague's V3 prompt analysis. Found and fixed 10 issues across 4 categories. + +#### Dead Code Removal +- `_strip_letter_prefixes()` — defined but never called +- `DEFAULT_TEMPERATURE_TWO = 0.7` — never referenced +- `simplified` variable in `_preprocess_perception` — assigned but never used (now actually applies the simplification to the template) + +#### Redundancy Consolidation +- **`_ACTIVITY_REWRITES`**: Was a 12-entry dict (2 base rewrites × 6 case/article variants). Replaced with 2 case-insensitive regex patterns in module-level `_apply_ontology_rewrites()` — cleaner, smaller, handles any case variant automatically +- **`CATEGORY_EXAMPLES`**: `summarization` was a verbatim copy of `scene_summary`, `counting` was a copy of `numerical`. Removed duplicates, added `_CAT_ALIASES` dict for lookup +- **`_short_activity_label`**: Was calling `_humanize_gerund(activity).lower()` — wasteful capitalize-then-lowercase. Now inlines the gerund logic directly (skipping the capitalize step) + +#### Double-Fix Bug +- `_preprocess_temporal` applied `a → an` article fix, then `preprocess_all` applied the same fix again globally. Removed from temporal, kept the single global pass. + +#### Post-processing Loop Merge +- Two separate loops over all preprocessed QAs (one for ontology rewrites, one for article fixes) merged into a single loop. + +#### Prompt Rewrite (Key Fix — addresses colleague's V3 analysis) +The `SYSTEM_PROMPT_UNIFIED` had the exact issues diagnosed in the V3 review: +- **Conflicting instructions**: Rule 1 "VARY your phrasing" vs Rule 6 "preserve this exact phrasing structure" +- **Too many DO-NOT constraints**: Rules 3, 4, 5, 6 all negatives → makes GPT rigid/stiff +- **12 numbered rules with conceptual overlap** → weakened instruction hierarchy + +**Fix**: Replaced with ranked priority system: +1. Preserve factual meaning exactly +2. Ensure flawless grammar and natural phrasing +3. Apply creative stylistic variation +4. Add one concise reasoning sentence + +Plus 2 compact constraint sections (Constraints, Ontology Translation, Grammar & Polish). Added bad→good transformation anchor example per colleague's recommendation. Rule 6 softened from "preserve this exact phrasing structure" to "maintain the direct question form but you may vary surrounding wording naturally." + +## Files Modified +- `meva/scripts/v10/naturalize.py` — all changes above (1437 → 1428 lines) + +## Verification +- `ast.parse()` — syntax valid +- Module import test — all exports work +- Ontology rewrite test — all 6 case variants produce correct output +- CATEGORY_EXAMPLES keys: 9 (no duplicates), aliases resolve correctly +- Prompt has 4 numbered rules (no duplicates) + +## Notes for Next Session +- Consider adding a `--validate` flag that runs `preprocess_all` on real data and checks for regressions + +--- + +## Continuation: V2 Colleague Feedback (Architectural Refactor) + +Implemented colleague's V2 feedback — major architectural cleanup removing two-pass mode and separating concerns between Python (deterministic transforms) and GPT (language rewriting). + +### Changes Applied (1428 → 1136 lines, -292 lines / -20%) + +#### Two-Pass Mode Removed Entirely +- **Deleted functions**: `_naturalize_two_pass()`, `_grammar_check_one()` +- **Deleted prompts**: `SYSTEM_PROMPT_NATURALIZE`, `GRAMMAR_CHECKER_PROMPT` +- **Deleted constants**: `DEFAULT_TEMPERATURE_V3`, `DEFAULT_TEMPERATURE_SINGLE` → single `DEFAULT_TEMPERATURE = 0.4` +- **Removed**: `--two-pass` CLI arg, `two_pass` parameter from `naturalize_batch()` and `dry_run()`, all branching logic, `mode_label`, `calls_per_q` + +#### GPT Input Restructured (JSON → Plaintext) +- `_naturalize_single_pass()` → `_naturalize_question()` — rewritten with labeled plaintext sections: + - `CATEGORY:`, `QUESTION TO REWRITE:`, `OPTIONS (context only):`, `CONTEXT:` +- JSON output format kept (`response_format=json_object`) — 2-token overhead, reliable 2-field parsing + +#### Separation of Concerns +- **Python handles**: Vocabulary normalization (ontology rewrites), structural preprocessing (template expansion, option formatting) +- **GPT handles**: ALL language rewriting, grammar, article agreement, style variation +- Removed `a → an` article fix from `_preprocess_spatial()` and `preprocess_all()` global loop — delegated to GPT + +#### `SYSTEM_PROMPT_UNIFIED` → `SYSTEM_PROMPT` +- Simplified: removed ontology translation section (Python pre-handles it) +- Kept 4-priority ranking system from V3 review round + +#### `_short_activity_label` Deduplicated +- Was 25-line inline copy of `_humanize_gerund` logic. Now 3 lines: calls `_humanize_gerund()` and lowercases first char. + +### Verification +- `ast.parse()` — syntax valid (1136 lines) +- 21 functions defined, no dead references +- No remaining references to: `two_pass`, `_naturalize_single_pass`, `_naturalize_two_pass`, `_grammar_check_one`, `GRAMMAR_CHECKER`, `SYSTEM_PROMPT_NATURALIZE`, `DEFAULT_TEMPERATURE_V3`, `DEFAULT_TEMPERATURE_SINGLE`, `mode_label`, `calls_per_q` + +### Files Modified +- `meva/scripts/v10/naturalize.py` — all changes above (1428 → 1136 lines) + +--- + +## Continuation 2: HoE Code Review + Full Pipeline Test Run + +### HoE Feedback Assessment +1. **JSON → plaintext output**: Disagreed. `response_format=json_object` is API-level constrained decoding — guarantees valid JSON. Plaintext splitting (`split("REASONING:")`) is fragile if output contains delimiter strings. Kept JSON output. +2. **Add explicit DO NOT constraints**: Agreed. Added 4 negative constraints to prevent semantic drift. +3. **Temperature 0.3**: Agreed. Lowered from 0.4 → 0.3. + +### Changes Applied +- `DEFAULT_TEMPERATURE`: 0.4 → 0.3 +- `SYSTEM_PROMPT` constraints section expanded with 4 explicit negative rules: + - Do NOT change meaning + - Do NOT add new facts + - Do NOT remove constraints/simplify logical requirement + - Do NOT alter person descriptions + +### Bug Fix: MEVID Slot Format Mismatch +Discovered `mevid_supported_slots.json` uses `HH-MM-SS` format but pipeline passes `HH-MM`. All 3 MEVID lookup functions returned empty/false for valid MEVID slots. + +Fixed in `person_descriptions.py`: +- Added `_resolve_mevid_slot()` and `_resolve_all_mevid_slots()` — try both `HH-MM` and `HH-MM-SS` formats, merge across all matching raw slots +- Updated `is_mevid_supported()`, `get_mevid_persons_for_slot()`, `get_mevid_persons_with_cameras()` to use the new resolvers + +### Full Pipeline Test Run +- **Slot**: `2018-03-07.11-00.school` (11 cameras, 23 MEVID persons) +- **Archived**: All previous QA pairs moved to `data/qa_pairs/archived/` +- **Raw generation**: 12 questions (2 temporal, 2 perception, 3 spatial, 1 summarization, 1 counting, 3 best_camera), 27 MEVID entities resolved, 6.5s +- **Naturalization**: 12/12 succeeded, 0 failures, 10572 tokens, temp=0.3, gpt-4o-mini +- **Validation videos**: 12/12 rendered successfully (16.6 MB total) + +### Output Files +- `data/qa_pairs/2018-03-07.11-00.school/2018-03-07.11-00.school.final.raw.json` +- `data/qa_pairs/2018-03-07.11-00.school/2018-03-07.11-00.school.final.naturalized.json` +- `data/qa_pairs/2018-03-07.11-00.school/validation_videos/` (12 MP4s) +- `data/gpt_logs/2018-03-07.11-00.school/naturalize_gpt-4o-mini.json` + +### Files Modified +- `meva/scripts/v10/naturalize.py` — temperature 0.4→0.3, expanded DO NOT constraints +- `meva/scripts/v10/person_descriptions.py` — MEVID slot format normalization fix diff --git a/docs/project-history/github-session-archive/session_records/session_57_2026-02-25.md b/docs/project-history/github-session-archive/session_records/session_57_2026-02-25.md new file mode 100644 index 0000000..4cb7f13 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_57_2026-02-25.md @@ -0,0 +1,14 @@ +# Session 57: Pipeline Research & Documentation + +**Date**: 2026-02-25 +**Previous session summary**: Session 56 performed a senior code review of `naturalize.py` (v10), fixing 10 issues: dead code removal, redundancy consolidation, a double-fix article bug, loop merging, and a critical system prompt rewrite to address conflicting GPT instructions. + +--- + +## Work Completed + +### Full V10 Pipeline Research +- Listed all 22 Python scripts in `meva/scripts/v10/` with descriptions +- Documented full pipeline flow: run_pipeline → naturalize → render_question_validation → export +- Identified 887 MEVID-supported slots from `data/mevid_supported_slots.json` +- Provided 5 sample MEVID slots and exact commands for each pipeline stage diff --git a/docs/project-history/github-session-archive/session_records/session_57_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_57_2026-02-26.md new file mode 100644 index 0000000..5e8bbe2 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_57_2026-02-26.md @@ -0,0 +1,54 @@ +# Session 57: Collaborator Onboarding — Fix Hardcoded Paths + QUICKSTART + +**Date**: 2026-02-26 +**Previous session summary**: Session 56 refactored `naturalize.py` (v10), removing two-pass mode and dead code (~300 lines removed). Fixed a MEVID slot format mismatch bug in `person_descriptions.py`. Ran full pipeline test on `2018-03-07.11-00.school` — 12 Qs generated + naturalized (12/12), 12 validation videos rendered. + +--- + +## Work Completed + +### Root Cause: Hardcoded `/home/ah66742/` Paths + +Collaborators on the PR couldn't run the pipeline because all data paths were hardcoded to `/home/ah66742/data/` — both for: +- **Read-only data files** that actually live in the repo at `meva/data/` (canonical_slots.json, slot_index.json, person_database*.json, mevid_supported_slots.json) +- **User output dirs** (qa_pairs, gpt_logs, entity_descriptions) which need to be per-user configurable + +Also: invocation `python -m meva.scripts.v10.run_pipeline` doesn't work because there's no `meva/__init__.py`. Must use `python3 -m scripts.v10.run_pipeline` from within `meva/`. + +### Fix Strategy + +- `_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data"` — resolves to `meva/data/` in the repo regardless of clone location +- `_OUTPUT = Path(os.environ.get("MEVA_OUTPUT_DIR", str(Path.home() / "data")))` — configurable per user, defaults to `~/data` (backward compat) + +### Files Modified + +| File | What changed | +|------|-------------| +| `scripts/v10/run_pipeline.py` | `OUTPUT_DIR`, `CANONICAL_SLOTS_PATH` → repo-relative + env var; updated usage docstring | +| `scripts/v10/parse_annotations.py` | `SLOT_INDEX_PATH`, `CANONICAL_SLOTS_PATH` → repo-relative; added `import os` | +| `scripts/v10/person_descriptions.py` | `PERSON_DB_PATH`, `PERSON_DB_ORIG_PATH`, `MEVID_SLOTS_PATH` → repo-relative; `_GEOM_DESC_DIR` → env var; added `import os` | +| `scripts/v10/naturalize.py` | `QA_DIR`, `LOG_DIR` → env var | +| `scripts/v10/export_to_multicam_format.py` | `INPUT_DIR` → env var; added `import os` | +| `scripts/v10/render_question_validation.py` | `QA_OUTPUT_DIR` → env var; added `import os` | +| `scripts/v10/extract_entity_descriptions.py` | `OUTPUT_DIR` → env var | +| `run.sh` | Full 3-step pipeline script with setup comments | +| `QUICKSTART.md` | New — collaborator setup + usage guide | + +### Verification +- All 7 Python files: `ast.parse()` — syntax valid +- `_REPO_DATA` resolves to `/home/ah66742/Multi-Camera-Adi/meva/data/` ✓ +- All 5 data files confirmed present ✓ + +## Answer to Collaborator + +From the `meva/` directory: +```bash +cd /path/to/repo/meva +export PYTHONPATH=$PYTHONPATH:$(pwd) +export MEVA_OUTPUT_DIR=~/data # optional, defaults to ~/data +export OPENAI_API_KEY=sk-... # only for naturalization + +python3 -m scripts.v10.run_pipeline --slot "2018-03-15.15-00.school" -v +``` +NOT: `python -m meva.scripts.v10.run_pipeline` (no `__init__.py` at meva/ level) + diff --git a/docs/project-history/github-session-archive/session_records/session_58_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_58_2026-02-26.md new file mode 100644 index 0000000..1f55322 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_58_2026-02-26.md @@ -0,0 +1,119 @@ +# Session 58: Path Fixes, NAS Output, Full Pipeline Runs + +**Date**: 2026-02-26 +**Previous session summary**: Session 57 fixed all hardcoded `/home/ah66742/` paths across 7 Python files for collaborator compatibility, added `QUICKSTART.md`, and verified all data files resolve via `_REPO_DATA`. One hardcoded path was missed: `desc_path` in `run_pipeline.py` line 484. + +--- + +## STANDARD PIPELINE COMMANDS (run these every session) + +```bash +# Setup (once per terminal session) +cd /home/ah66742/Multi-Camera-Adi/meva +source /home/ah66742/venv/bin/activate +source /home/ah66742/.env # loads OPENAI_API_KEY +export MEVA_OUTPUT_DIR=/nas/neurosymbolic/multi-cam-dataset/meva/data +export PYTHONPATH=$PYTHONPATH:$(pwd) + +SLOT="2018-03-07.11-00.school" # ← change this + +# Step 1 — generate raw QA (~7s, free) +python3 -m scripts.v10.run_pipeline --slot "$SLOT" -v + +# Step 2 — naturalize with GPT (~$0.002/slot) +python3 -m scripts.v10.naturalize \ + --input $MEVA_OUTPUT_DIR/qa_pairs/$SLOT.final.raw.json \ + -v --yes + +# Step 3 — export to multi-cam-dataset format +python3 -m scripts.v10.export_to_multicam_format --slot "$SLOT" -v +``` + +Output dir: `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs/` +All-in-one: `bash run.sh "$SLOT"` (after setting env vars above) + +--- + +## Work Completed + +### 1. Ran full pipeline + naturalization on default slot `2018-03-07.11-00.school` + +Results: +- **12 questions generated** (raw), 6.56s runtime +- **12/12 naturalized** via GPT-4o-mini, 10586 tokens +- Categories: `temporal×2, perception×2, spatial×3, summarization×1, counting×1, best_camera×3` +- Output: `/home/ah66742/data/qa_pairs/2018-03-07.11-00.school.final.naturalized.json` +- Entity coverage: 27 MEVID + 171 geom-color + 0 fallback (of 210 entities) +- Temporal: 5279 candidate pairs, filtered to 84 visual, yielded 2 MEVID-validated Qs + +### 2. Fixed missed hardcoded path + +| File | Line | Before | After | +|------|------|--------|-------| +| `scripts/v10/run_pipeline.py` | 484 | `Path("/home/ah66742/data/entity_descriptions")` | `_OUTPUT / "entity_descriptions"` | + +This was the one path missed in Session 57's path-fixing sweep. + +### 3. Temporal question diagnosis + +**Q: "Why is there no temporal questions? Did we make a change that limited this?"** + +**A: Temporal works correctly on this slot. The filter that can cause 0 temporal questions is intentional.** + +The `fallback_eids` visual description requirement in `generate_temporal.py` (lines ~275-293) filters out candidate pairs where EITHER event only has a fallback ("someone walking") description: + +```python +if fallback_eids: + visual_candidates = [ + c for c in candidates + if _event_has_visual_desc(c["event_a"], sg, fallback_eids) + and _event_has_visual_desc(c["event_b"], sg, fallback_eids) + ] +``` + +**When this causes 0 temporal questions:** +- Slot has no MEVID persons (not in `mevid_supported_slots.json`) +- AND all geom actors fail the bbox area threshold (`< 41472 px²` — actors too far/small) +- Result: all 210 entities land in `fallback_eids` → ALL temporal candidates filtered → 0 questions + +For `2018-03-07.11-00.school`: 27 MEVID + 171 geom visual → only 0 fallback → 84/5279 candidates survive → 2 temporal Qs ✓ + +**This was added intentionally** as a quality gate (temporal Qs with "someone" descriptions aren't meaningful cross-camera). But it silently returns 0 for slots without visual coverage. + +**Suggestion for future**: Add `--verbose` warning when temporal returns 0 due to fallback filtering (distinct from 0 candidates). + +--- + +## Session 58 Work Completed + +### 1. Diagnosed entity_descriptions path breakage (0 temporal/spatial/ordering questions) +`MEVA_ENTITY_DESC_DIR` env var added to `person_descriptions.py` and `run_pipeline.py`. Default: `/nas/mars/dataset/MEVA/entity_descriptions`. Without this, all 2910 candidates were "fallback-only" → 0 temporal questions. + +### 2. Full `/home/ah66742/data` path audit across all v10 scripts +Verified with `strace` + `grep`. Eliminated ALL runtime hardcoded paths. Changes: +- `batch_extract_all_slots.py` — `_REPO_DATA`, `MEVA_ENTITY_DESC_DIR`, `_OUTPUT` +- `build_geom_slot_index.py` — `_REPO_DATA` relative +- `review_qa.py` — `_OUTPUT` / `MEVA_OUTPUT_DIR` +- `export_to_multicam_format.py` — `MEVA_MULTICAM_OUT` env var (default: `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs`) +- `run.sh` — fix RAW_JSON path (had spurious extra `$SLOT/` subdirectory) + +### 3. Canonical output location set to NAS +`MEVA_OUTPUT_DIR=/nas/neurosymbolic/multi-cam-dataset/meva/data` +All 3 pipeline steps write to NAS; verified `~/data/qa_pairs` stays empty. + +### 4. Full pipeline runs completed +- `2018-03-07.11-00.school` → 14 Qs (27 MEVID + 171 geom), all 3 steps ✓ +- `2018-03-11.11-25.school` → 10 Qs (19 MEVID + 19 geom), all 3 steps ✓ + +### 5. Commits pushed (4 total this session) +- `27d5d7f` — MEVA_ENTITY_DESC_DIR + session 57 path fixes +- `f62b6d1` — utility scripts data_back placeholder (superseded) +- `0db3a04` — properly fixed utility scripts with env vars +- `172672f` — export OUTPUT_DIR + run.sh RAW_JSON bug + +## Env Vars Reference +| Var | Default | Purpose | +|-----|---------|---------| +| `MEVA_OUTPUT_DIR` | `~/data` | QA output, logs | +| `MEVA_ENTITY_DESC_DIR` | `/nas/mars/dataset/MEVA/entity_descriptions` | Geom-color descriptions | +| `MEVA_MULTICAM_OUT` | `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs` | Final export destination | diff --git a/docs/project-history/github-session-archive/session_records/session_59_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_59_2026-02-26.md new file mode 100644 index 0000000..2d03706 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_59_2026-02-26.md @@ -0,0 +1,97 @@ +# Session 59 — 2026-02-26: Determinism Verification + +## Summary of Session 58 (from last_session.json) +Session 58 eliminated all hardcoded `/home/ah66742/data` paths across v10 scripts, added `MEVA_ENTITY_DESC_DIR` and `MEVA_MULTICAM_OUT` env vars, fixed `run.sh` RAW_JSON path bug, and verified the full 3-step pipeline writing exclusively to NAS on two slots. Non-determinism was diagnosed (Q5 camera selection — `attr_candidates` built from unordered `mevid_persons` set iteration) but fix was deferred to coworker. + +## Session 59 Work + +### 1. Pulled coworker's determinism fix +Coworker pushed commit `6a1c27e "Make runs deterministic"` which fixes non-determinism at the source: +- `person_descriptions.py` line 370: `for pid in sorted(mevid_persons)` — dict now built in deterministic order +- `build_scene_graph.py`: sorted `entity_actor_ids.keys()` and each `actor_ids` list +- `parse_annotations.py`: sorted `info["cameras"]` iteration and glob match results + +Note: `generate_perception.py` was NOT changed — not needed because `person_cameras.items()` now has deterministic order from the fix above. + +### 2. Verified determinism +Ran `run.sh 2018-03-11.11-25.school` twice consecutively. Diff showed only `generation_time_sec` differs (wall-clock, expected). All questions, answers, distractors, camera references — byte-for-byte identical. + +## Files Modified This Session +- None (pulled coworker's changes only, no local commits) + +## Commands +```bash +# Reset, pull, run twice, verify determinism: +cd /home/ah66742/Multi-Camera-Adi && git add . && git reset --hard HEAD && git pull +cd meva && bash run.sh 2018-03-11.11-25.school +cp /nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs/2018-03-11.11-25.school.final.raw.json /tmp/run1.json +bash run.sh 2018-03-11.11-25.school +diff /tmp/run1.json /nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs/2018-03-11.11-25.school.final.raw.json +# → Only generation_time_sec differs → DETERMINISTIC ✓ +``` + +## Standard Pipeline Commands (for future sessions) +```bash +cd /home/ah66742/Multi-Camera-Adi/meva +source /home/ah66742/venv/bin/activate +source /home/ah66742/.env +export MEVA_OUTPUT_DIR=/nas/neurosymbolic/multi-cam-dataset/meva/data +export PYTHONPATH=$PYTHONPATH:$(pwd) +SLOT="2018-03-11.11-25.school" + +# Step 1: Raw QA generation +python3 -m scripts.v10.run_pipeline --slot "$SLOT" -v + +# Step 2: Naturalization (requires OPENAI_API_KEY) +python3 -m scripts.v10.naturalize --input $MEVA_OUTPUT_DIR/qa_pairs/$SLOT.final.raw.json -v --yes + +# Step 3: Export to multi-cam-dataset format +python3 -m scripts.v10.export_to_multicam_format --slot "$SLOT" -v + +# Or just use run.sh (Step 1 only by default, Steps 2+3 commented out): +bash run.sh "$SLOT" +``` + +### 3. Diagnosed 0 spatial questions +Investigated why slot `2018-03-11.11-25.school` produces 0 spatial questions. Root cause: KRTD camera models exist for 7 cameras, but ALL 38 person entities are on G419/G420 (no KRTD files). 5 entities on KRTD cameras are vehicles. Dataset coverage gap, not code bug. + +### 4. MEVID utilization audit & rebuild +Discovered only 23 of 158 MEVID persons were utilized. Root causes: +- `mevid_supported_slots.json` built from stale/limited source +- Only persons with **2+ cameras within the same slot** are useful + +Created `build_mevid_slots.py` — standalone script that: +- Parses authoritative MEVID annotation data (`train_name.txt` + `test_name.txt`) +- Cross-references with video URLs to determine slot assignments +- Filters to persons with 2+ cameras per slot (cross-camera requirement) +- Result: **80 persons across 168 slots** (was 23/887) +- 13 "lost" persons were never truly cross-camera in annotations — correct to exclude + +### 5. Path cleanup + env vars +- Added `MEVA_MEVID_DATA_DIR` / `MEVA_MEVID_URLS` env var overrides to `build_mevid_slots.py` and `utils/mevid.py` +- Removed all `/home/ah66742` references from live Python/shell files (docstrings in `export_to_multicam_format.py`, `batch_extract_all_slots.py`, `run.sh`) +- Verified zero remaining `/home/ah66742` references in live code + +### 6. Pushed commit +Commit `c234283`: "v10: rebuild MEVID slots index (23→80 persons) + env var paths" +- 6 files changed: `build_mevid_slots.py` (new), `mevid_supported_slots.json` (rebuilt), `utils/mevid.py`, `batch_extract_all_slots.py`, `export_to_multicam_format.py`, `run.sh` +- No collision with Agent 2 (entity descriptions work is separate from MEVID slot mapping) + +## Files Modified This Session +- `meva/scripts/v10/build_mevid_slots.py` — NEW: standalone MEVID slot index builder +- `meva/data/mevid_supported_slots.json` — REBUILT: 80 persons, 168 slots (was 23/887) +- `meva/scripts/v10/utils/mevid.py` — Added `MEVA_MEVID_DATA_DIR`/`MEVA_MEVID_URLS` env vars +- `meva/scripts/v10/batch_extract_all_slots.py` — Docstring path cleanup +- `meva/scripts/v10/export_to_multicam_format.py` — Docstring path cleanup +- `meva/run.sh` — Removed `/home/ah66742` from comment + +## Commits +- `c234283` — v10: rebuild MEVID slots index (23→80 persons) + env var paths + +## Notes for Next Session +- `MEVA_ROOT` env var for `parse_annotations.py`, `krtd.py` still deferred (hardcoded `/nas/mars/` — low priority, NAS required regardless) +- Pipeline is fully deterministic as of commit `6a1c27e` +- All output goes to `/nas/neurosymbolic/multi-cam-dataset/meva/data/` +- MEVID now covers 80 persons (3.5× improvement); 13 excluded persons are correct (single-camera only) +- Agent 2 is working on entity descriptions — avoid touching `person_descriptions.py` +- `.bak` file (`render_question_validation.py.bak`) still has hardcoded paths — low priority, backup file diff --git a/docs/project-history/github-session-archive/session_records/session_60_2026-02-27.md b/docs/project-history/github-session-archive/session_records/session_60_2026-02-27.md new file mode 100644 index 0000000..b9553c0 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_60_2026-02-27.md @@ -0,0 +1,45 @@ +# Session 60 — 2026-02-27: SegFormer Human Parsing Upgrade + +## Summary of Session 59 +Session 59 verified determinism (commit 6a1c27e), diagnosed 0 spatial questions (KRTD gap), rebuilt MEVID slots (23→80 persons), and cleaned up hardcoded paths. Pipeline is fully deterministic and production-ready. + +## Session 60 Work + +### 1. Spatial question fix (from earlier in conversation) +- Root cause: `MIN_BBOX_AREA = 41472` too high for wide-field KRTD cameras (median bbox ~6k-11k px²) +- Fix: Added `MIN_BBOX_AREA_KRTD = 2048` in `build_scene_graph.py` for KRTD cameras +- Commit `6cb4993`: verified 2018-03-13.16-20.school went from 0 → 3 spatial questions + +### 2. SegFormer human parsing upgrade to entity descriptions +Replaced crude fixed vertical splits (10-45% upper, 55-90% lower) with SegFormer semantic body-part segmentation (`mattmdjaga/segformer_b2_clothes`). + +**Model details:** +- 18 semantic classes: Background, Hat, Hair, Sunglasses, Upper-clothes, Skirt, Pants, Dress, Belt, Left-shoe, Right-shoe, Face, Left-leg, Right-leg, Left-arm, Right-arm, Bag, Scarf +- 109MB model, loads in ~1s, inference on GPU (RTX A5000) + +**Changes to `extract_entity_descriptions.py`:** +- Added `analyze_crops_segformer()` — lazy-loads model, segments each crop, extracts per-region HSV color +- Updated `build_description()` — richer output: "a person with black hair, wearing a blue top and black pants, black shoes, carrying a bag" +- Added `--method` CLI flag: segformer (default), yolo, color-only +- Fixed `OUTPUT_DIR` to match `person_descriptions.py` reader default (`/nas/mars/dataset/MEVA/entity_descriptions`) +- Backward compatible: still outputs `upper_color`, `lower_color`, `description` keys + +**Results on `2018-03-11.11-25.school`:** +- OLD: 48 unique descriptions across 1301 actors (3.7% uniqueness) +- NEW: 44 unique descriptions across 70 actors (62.9% uniqueness) +- Processing time: ~50s for 13 cameras, 70 actors on GPU +- Descriptions now include: hair color, shoe color, garment type (dress/skirt/pants), accessories (hat, bag, scarf, sunglasses) + +## Files Modified +- `meva/scripts/v10/extract_entity_descriptions.py` — Major upgrade (+257/-46 lines) + +## Commits +- `6cb4993` — fix spatial: lower MIN_BBOX_AREA for KRTD cameras +- `d03ae37` — upgrade entity descriptions: SegFormer human parsing replaces fixed vertical splits + +## Notes for Next Session +- Need to re-extract entity descriptions for all 355 slots using new segformer method +- `transformers` package must be installed in venv (`pip install transformers`) +- Old descriptions at `/nas/mars/dataset/MEVA/entity_descriptions/` will be overwritten +- Consider lowering `MIN_BBOX_HEIGHT`/`MIN_BBOX_WIDTH` from 144 to capture more actors +- Some color labels may be inaccurate for distant/low-res surveillance crops (e.g., "blue hair" = dark hair under blue-tinted lighting) diff --git a/docs/project-history/github-session-archive/session_records/session_61_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_61_2026-02-26.md new file mode 100644 index 0000000..e8392d4 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_61_2026-02-26.md @@ -0,0 +1,18 @@ +# Session 61 — 2026-02-26: V10 Pipeline Dependency Graph Analysis + +## Summary of Session 60 +Session 60 fixed spatial questions (lowered MIN_BBOX_AREA for KRTD cameras) and upgraded entity descriptions to use SegFormer human parsing (18 body-part classes), improving description uniqueness from 3.7% to 62.9%. + +## Session 61 Work + +### Task: Complete Import/Dependency Graph of V10 Pipeline + +Traced all imports for: +1. `scripts.v10.run_pipeline` — main QA generation orchestrator +2. `scripts.v10.naturalize` — GPT-powered question naturalization +3. `scripts.v10.export_to_multicam_format` — export to multi-cam-dataset format + +Identified 15 pipeline files (directly/transitively imported by entry points) and 6 standalone tools. + +**Files created**: None (analysis only) +**Files modified**: session logs diff --git a/docs/project-history/github-session-archive/session_records/session_62_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_62_2026-02-26.md new file mode 100644 index 0000000..dc3db20 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_62_2026-02-26.md @@ -0,0 +1,14 @@ +# Session 62 — 2026-02-26: V10 Pipeline Complete Import Trace + +## Summary of Session 61 +Session 61 began a dependency graph analysis of the V10 pipeline, tracing imports for 3 entry points (run_pipeline, naturalize, export_to_multicam_format). Identified 15 pipeline files and 6 standalone tools. + +## Session 62 Work + +### Task: Complete line-by-line import trace of all 20 pipeline files + 6 standalone files + +Read all 20 pipeline files entirely and extracted every import statement (top-level and inside functions), path references, os.environ calls, and data file loads. + +**Files read**: All 20 pipeline files + 6 standalone file headers +**Files created**: This session log +**Files modified**: last_session.json diff --git a/docs/project-history/github-session-archive/session_records/session_63_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_63_2026-02-26.md new file mode 100644 index 0000000..c613512 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_63_2026-02-26.md @@ -0,0 +1,42 @@ +# Session 63 — 2026-02-26: Home Directory Full Cleanup + +## Summary of Session 62 +Session 62 completed a line-by-line import trace of all 20 V10 pipeline files + 6 standalone tools, documenting every import, path reference, and data file load. + +## Session 63 Work + +### 1. Pipeline Audit (Multi-Camera-Adi/meva) +Traced complete import graph for all 3 pipeline steps: +- Step 1 (`run_pipeline.py`): imports 13 modules + 4 utils +- Step 2 (`naturalize.py`): self-contained +- Step 3 (`export_to_multicam_format.py`): self-contained +- 6 standalone tools (batch_extract, build_geom, build_mevid, extract_entity, render_validation, review_qa) +- Flagged dead import: `run_pipeline.py:491` → `scripts.final` (doesn't exist, try/except) + +### 2. Home Directory Cleanup (~65.5 GB reclaimed) + +**Deleted folders (8):** tmp_local_test/ (6.3 GB), tmp_python_test/ (4.2 GB), tmp_test_test/, MEVID/ (55 GB), Multi-Camera-Main/ (35 MB), MEVID_matching/ (240 KB), receive/, send/ + +**Deleted scripts (9):** audit_40_slots_new.py, auto_start_extraction.sh, estimate_post_extraction_yields.py, extract_with_progress.sh, start_overnight_extraction.sh, test_extract.py, test_extract_robust.py, test_extract_streaming.py, verify_extraction_results.sh + +**Deleted misc (5):** fresh_extract.log, nohup.out, python_extract.log, streaming_extract.log, cleanup_done.txt + +**Deleted .md files (11):** __next.md, 2-20-todo.md, CONSOLIDATION_COMPLETE.md, DUAL_COPY_ISSUE_AND_REAL_CHANGES.md, MEVID_NEXT_STEPS.md, MEVID_SLOTS_QUICK_REFERENCE.md, MEVID_SUPPORTED_SLOTS_REPORT.md, OVERNIGHT_EXTRACTION_INSTRUCTIONS.md, PRE_PUSH_CHECKLIST.md, READY_TO_RUN.md, v9_plan.md + +**Merged V10 docs** (3→1): V10_CHANGES_SUMMARY + V10_QUICK_REFERENCE + V10_PUSH_CHECKLIST → `docs/V10_REFERENCE.md` + +**Moved to archived/:** data/ → archived/data_old/, scripts/ → archived/scripts_old/, MEVID/*.md → archived/mevid_docs/ + +**Moved to docs/:** V10_TODOS.md, V9_PIPELINE_EXPLANATION.md, V8_DESIGN.md, DATA_AUDIT_REPORT_2026-02-16.md, YOLO_vs_GPT4V_COMPARISON.md + +### 3. Final Root State +Multi-Camera-Adi (61 MB), venv (6 GB), docs (61 MB), archived (230 MB), output (25 MB), scholarship_app, snap, .github, cleanup_local.sh, .env, yolov8n*.pt + +## Files Created +- `docs/V10_REFERENCE.md` — merged V10 reference doc + +## Notes for Next Session +- Pipeline fully functional, zero code changes +- `render_question_validation.py.bak` still in repo (not touched per user request) +- Dead import `scripts.final` in run_pipeline.py:491 (fails gracefully) +- `archived/` can be further trimmed (scene_graphs/ is 122 MB) diff --git a/docs/project-history/github-session-archive/session_records/session_64_2026-02-26.md b/docs/project-history/github-session-archive/session_records/session_64_2026-02-26.md new file mode 100644 index 0000000..ab5e9b2 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_64_2026-02-26.md @@ -0,0 +1,18 @@ +# Session 64 — 2026-02-26: V10 Pipeline Deep Research + +## Summary of Session 63 +Session 63 completed home directory cleanup (~65 GB reclaimed), traced all import graphs for the V10 pipeline, and flagged a dead import in run_pipeline.py:491 (scripts.final). + +## Session 64 Work + +### Research of V10 QA Generation Pipeline +Read all 20+ Python files in `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/` to produce a comprehensive pipeline architecture report covering all 7 question categories, entity description flow, cross-camera deduplication, and identified gaps/bugs. + +## Files Modified +- None (read-only research session) + +## Files Created +- This session log + +## Notes for Next Session +- See pipeline research output in conversation for detailed findings diff --git a/docs/project-history/github-session-archive/session_records/session_64_2026-02-27.md b/docs/project-history/github-session-archive/session_records/session_64_2026-02-27.md new file mode 100644 index 0000000..fa3c084 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_64_2026-02-27.md @@ -0,0 +1,49 @@ +# Session 64 (continued) — 2026-02-27 + +## Summary of Session 63 +Session 63 cleaned up the home directory (~65 GB reclaimed), merged V10 reference docs into one file, moved old data/scripts to `archived/`, and traced the complete import graph for all pipeline files. No code changes were made. + +## Session 64 Work — QA Quality Fixes + +User identified 10 specific problems with QA output for slot `2018-03-11.16-40.school`. Root cause analysis identified 5 systemic issues mapped to all 10 complaints. Implemented fixes across 7 files. + +### Root Causes Identified +1. **Coarse/duplicate HSV descriptions** — 264 actors, only 80 unique descriptions; "black top, black pants" appears 66 times +2. **No cross-camera event dedup** in temporal questions — same real-world event from 2 cameras treated as distinct +3. **"Someone" fallback leak** — generic descriptor when no visual description available +4. **No bbox-in-frame validation** — entities with frame-edge bboxes used in questions +5. **Best_camera dedup by cluster_id only** — standalone clusters with identical text pass dedup + +### Fixes Implemented + +| File | Changes | +|------|---------| +| `person_descriptions.py` | Vehicle→person reclassification when all activities start with "person_" | +| `generate_temporal.py` | Cross-camera event dedup (3D position + time), bbox-in-frame validation, "Someone"→"A person" fallback, cross-category enrichment (camera IDs) | +| `generate_event_ordering.py` | Exhaustive fallback chain: fallback_desc → entity_descs lookup → "A person {activity} on camera {cam}" | +| `generate_spatial.py` | Bbox-clipping validation, description disambiguation (activity/temporal context), dedup by (desc_a, desc_b, camera) tuple, temporal context enrichment | +| `generate_perception.py` | "someone" → "a person" in which_camera questions, contextual person identification for attribute verification | +| `generate_best_camera.py` | Visual description prioritization (entities with clothing descriptions sorted first) | +| `run_pipeline.py` | Best_camera dedup enhanced: entity_description match in addition to cluster_id check | + +### Testing +- Tested on `2018-03-11.16-40.school` (14 questions) and `2018-03-11.16-20.school` (13 questions) +- All 10 reported issues resolved (except #5: annotation completeness — not fixable without denser annotations) +- Spatial disambiguation working: same-description pairs get activity/timestamp context +- Best camera: now 3 distinct questions instead of 2 duplicates + 1 generic +- No "Someone", no "a vehicle opens door", no duplicate questions + +## Files Modified +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/person_descriptions.py` +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/generate_temporal.py` +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/generate_event_ordering.py` +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/generate_spatial.py` +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/generate_perception.py` +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/generate_best_camera.py` +- `/home/ah66742/Multi-Camera-Adi/meva/scripts/v10/run_pipeline.py` + +## Notes for Next Session +- All fixes are code-level only (no data regeneration needed) +- The counting question still uses "someone" — could be changed to "a person" for consistency +- Event ordering items II/III sometimes lack visual descriptions when geom.yml doesn't have matching actors — this is a data limitation, not a code bug +- Consider running batch test across all MEVID-supported slots to verify no regressions diff --git a/docs/project-history/github-session-archive/session_records/session_65_2026-02-27.md b/docs/project-history/github-session-archive/session_records/session_65_2026-02-27.md new file mode 100644 index 0000000..0f3963e --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_65_2026-02-27.md @@ -0,0 +1,49 @@ +# Session 65 — 2026-02-27 + +## Summary of Session 64 +Session 64 fixed 10 QA quality issues across 7 pipeline files for slot `2018-03-11.16-40.school`. Root causes: coarse/duplicate HSV descriptions, no cross-camera event dedup in temporal, "someone" fallback leak, no bbox validation, and best_camera dedup only by cluster_id. All fixes were code-level. + +## Session 65 Work — Automated QA Validator + Rich Descriptions + 3D Context + Iterative Loop + +### Goal +Build a 4-phase system that transforms the MEVA QA pipeline from one-shot to iterative self-correcting: +- Phase 1: validate_qa.py — 6 automated checks (reasoning consistency, raw leak, duplicates, generic desc, multi-correct ambiguity, grammar) +- Phase 2: Rich entity descriptions (expanded ~25-color CSS vocabulary) +- Phase 3: Camera-aware spatial context (scene_context.py using KRTD + PLY models) +- Phase 4: batch_generate.py — generate→validate→fix→regenerate loop (5 iterations) + +### Files Created +1. **`meva/scripts/v10/validate_qa.py`** — 6-check QA validator (reasoning, raw leaks, duplicates, generic desc, multi-correct, grammar). Scoring 0-100 (error=-10, warning=-5, info=-1). CLI: `python3 -m scripts.v10.validate_qa --input path.json -v` +2. **`meva/scripts/v10/scene_context.py`** — Camera-aware spatial context using KRTD camera models + PLY building meshes. `SceneContext` class with building proximity, cardinal directions, `enrich_description_with_location()`, `compute_3d_matching_score()`. Thresholds: NEAR=25m, MODERATE=60m. +3. **`meva/scripts/v10/batch_generate.py`** — Iterative generate→validate→fix→regenerate orchestrator. Auto-fixes: grammar, raw leaks, duplicate removal. Tracks best score across rounds. CLI: `python3 -m scripts.v10.batch_generate --slot SLOT --rounds 5 -v` + +### Files Modified (5 files, +320 lines) +1. **`meva/scripts/v10/extract_entity_descriptions.py`** — `_hsv_to_color()` expanded from 11→25 CSS colors (black, charcoal, dark gray, gray, silver, ivory, white, maroon, crimson, red, rust, orange, khaki, gold, yellow, olive, green, teal, navy, blue, indigo, plum, purple, mauve, pink). `build_description()` added `include_position`, `height_category`, `frame_position` support. +2. **`meva/scripts/v10/batch_extract_all_slots.py`** — Added `--method` flag (yolo/segformer/color_only), `--force-reextract` flag. +3. **`meva/scripts/v10/entity_resolution.py`** — Added 3D+description matching: `_enhance_links_with_3d()`, `_description_overlap()`, `_normalize_desc_tokens()`. `resolve_entities()` now accepts optional `entity_descs` parameter for cross-validation. +4. **`meva/scripts/v10/generate_temporal.py`** — Added `scene_context` import (graceful degradation), `_enrich_with_location()` for projecting entity bboxes to 3D and getting location labels. +5. **`meva/scripts/v10/generate_spatial.py`** — Added `scene_context` import (graceful degradation), location enrichment for entity descriptions using `enrich_description_with_location()`. + +### Files Created Outside Repo (Restore Point) +- `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs/batch_logs/2018-03-11.11-25.school.log.json` — batch iteration log +- `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs/raw/2018-03-11.11-25.school.raw.json` — best QA output from 5-round test (overwritten existing) + +### Test Results (5-Round Iteration) +- Slot: `2018-03-11.11-25.school` +- Scores across rounds: [44, 59, 64, 59, 44] +- Best: round 3 → 64/100 +- Auto-fix improvement: +10-12 points per round (grammar, duplicate removal) +- Remaining issues: generic descriptions ("a person") and multi-correct ambiguity — these are structural, requiring generator-level enrichment to fully resolve +- Pipeline runs ~5 sec/round after first round (MEVID cache warm) + +### Key Decisions +- Proximity thresholds set to 25/60m (not 10/30m) because building PLY centroids are center-of-mass of large meshes, not building entrances +- `validate_qa.py` uses conservative scoring: error=-10 (wrong answer), warning=-5 (quality issue), info=-1 (cosmetic) +- `batch_generate.py` varies seed per round (42, 43, 44...) for diversity in stochastic generators +- `scene_context.py` import is always guarded with try/except so pipeline works without trimesh installed + +### Notes for Future Sessions +- Generic description warnings (9 per slot) are the biggest quality barrier — generators need to pull entity descriptions from `/nas/mars/dataset/MEVA/entity_descriptions/` more aggressively +- Entity description files exist for all sites (actors dict with upper_color, lower_color, carried_objects per actor) +- Multi-correct ambiguity needs generator-level fix: check options against ground truth before finalizing +- Consider lowering `min_score` default from 90 to 70 for realistic batch runs until generator enrichment is complete diff --git a/docs/project-history/github-session-archive/session_records/session_66_2026-02-27.md b/docs/project-history/github-session-archive/session_records/session_66_2026-02-27.md new file mode 100644 index 0000000..df68e0f --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_66_2026-02-27.md @@ -0,0 +1,59 @@ +# Session 66 — 2026-02-27 + +## Summary of Session 65 +Session 65 built a 4-phase iterative QA system with `validate_qa.py` (6 automated checks), expanded HSV to 25 colors, KRTD+PLY camera-aware spatial context, and `batch_generate.py` for iterative generate→validate→fix loops. Test on 2018-03-11.11-25.school scored 64/100. + +## Session 66 Work — Implement 12 QA Pipeline Issues (Phase 1+2 complete) + +### Goal +Implement all 12 issues from prompt_nextsteps.md following prescribed phase order. + +### Completed + +**Issue 1 — Color Match MEVID** (`person_descriptions.py`): Added `_COLOR_GROUPS`, `_normalize_color()`, `_colors_similar()`, `_color_match_score()`. Modified `enrich_entities()` to score all unused MEVID against geom HSV, pick best match (threshold -0.5). + +**Issue 2 — SegFormer Default** (`batch_extract_all_slots.py`): Changed `--method` default from "yolo" to "segformer". + +**Issue 5 — First Instance** (`generate_event_ordering.py`, `generate_temporal.py`): Added first-instance pre-filter per (activity, camera), skip first 5s. + +**Issue 6 — Cross-Camera Dedup** (`generate_event_ordering.py`, `generate_numerical.py`): Imported `_is_likely_duplicate_event` into event ordering. Enhanced numerical with 3D position matching. + +**Issue 8 — Spatial Same-Person** (`generate_spatial.py`): Added `_compute_bbox_iou()`, `_is_likely_same_person()` with cluster/IoU/proximity checks. + +**Issue 9 — Deterministic Reasoning** (`generate_numerical.py`): Added `_build_reasoning()` for per-camera breakdown. ⚠️ NEEDS VERIFICATION that function was fully added. + +**Issue 10 — Key Frames** (`generate_numerical.py`): Changed `_dedup_activity_count()` to return 3-tuple with `cluster_details`. + +**Issue 11 — Annotation Verification** (`validate_qa.py`): Added `check_annotation_verification()` as Check 8. Wired into checks list. + +### Phase 3 — Policy Enforcement (ALL COMPLETE) + +**Issue 12 — No-Camera/Time Policy**: Removed camera IDs from event ordering (7 return paths), temporal (skip identical-desc pairs), spatial (activity-only disambiguation, no timestamps). Added `check_forbidden_patterns()` as Check 7 (7 regex patterns). Updated 4 naturalize.py category hints. + +**Issue 4 — Camera Leak**: Covered by Issue 12 implementation. + +**Issue 3 — Soft Caps** (`run_pipeline.py`): Renamed `TARGET_*` → `MAX_*`. + +**Issue 7 — Perception Enrichment** (`generate_perception.py`): Type 1 "which_camera" finds entity descriptions unique to one camera, enriching questions with visual appearance. + +### Integration Test +- Slot: `2018-03-11.11-25.school` — 13 questions generated in 12.4s +- `forbidden_pattern`: PASS, `annotation_verify`: PASS, `reasoning_consistency`: PASS +- Score: 27/100 (generic descriptions from cached geom-HSV — need SegFormer re-extraction) + +### Still TODO +- Re-extract entity descriptions with SegFormer to improve generic description warnings +- Test on more slots +- Run batch generation across all slots + +### Files Modified +1. `meva/scripts/v10/person_descriptions.py` — Issue 1 +2. `meva/scripts/v10/batch_extract_all_slots.py` — Issue 2 +3. `meva/scripts/v10/generate_event_ordering.py` — Issues 4, 5, 6, 12 +4. `meva/scripts/v10/generate_temporal.py` — Issues 5, 12 +5. `meva/scripts/v10/generate_numerical.py` — Issues 6, 9, 10 +6. `meva/scripts/v10/generate_spatial.py` — Issues 8, 12 +7. `meva/scripts/v10/validate_qa.py` — Issues 11, 12 +8. `meva/scripts/v10/run_pipeline.py` — Issue 3 +9. `meva/scripts/v10/generate_perception.py` — Issue 7 +10. `meva/scripts/v10/naturalize.py` — Issue 12 diff --git a/docs/project-history/github-session-archive/session_records/session_67_2026-02-28.md b/docs/project-history/github-session-archive/session_records/session_67_2026-02-28.md new file mode 100644 index 0000000..bc94957 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_67_2026-02-28.md @@ -0,0 +1,51 @@ +# Session 67 — 2026-02-28 + +## Summary of Session 66 +Session 66 implemented all 12 QA pipeline issues from prompt_nextsteps.md (Phases 1-3). Test scored 27/100 initially due to cached generic descriptions, then 79/100 after SegFormer re-extraction on 2018-03-09.10-15.school. Committed as b62bae6. + +## Previous Conversation Context +Diagnosed missing entity descriptions — MIN_BBOX_HEIGHT=144 was filtering out small actors. Implemented two-tier extraction (SegFormer ≥144px, HSV color-only ≥40px) in extract_entity_descriptions.py. Re-extracted 2018-03-09.10-15.school (674→3,256 actors). Pipeline now shows 235 geom descriptions (up from 135), 2 fallback (down from 102). + +## Session 67 Work + +### Goals +- Run validator on improved output +- Test more slots (2-3 diverse) +- Re-extract 2018-03-11.11-25.school with two-tier approach +- Commit two-tier extraction fix +- Copy outputs to /nas/ directory + +### Completed + +**Grammar fix** (`activity_hierarchy.py`): Fixed `humanize_activity_gerund()` to use "an" before vowels ("a object" → "an object", "a area" → "an area"). + +**Perception multi-correct fix** (`generate_perception.py`): Exclude ALL cameras with the same activity from distractor options, not just the correct camera. Prevents multi_correct_ambiguity errors. + +**Entity re-extraction**: Re-extracted 3 slots with two-tier SegFormer: +- `2018-03-11.16-20.admin`: 24 actors (all segformer) +- `2018-03-13.16-00.hospital`: 243 actors (9 segformer, 234 color-fallback) +- `2018-03-11.11-25.school`: 1261 actors (394 segformer, 867 color-fallback) + +**Pipeline tested on 4 slots**: +| Slot | Questions | Score | Notes | +|------|-----------|-------|-------| +| 2018-03-11.16-20.admin | 6 | 100/100 | 2 cameras, MEVID | +| 2018-03-13.16-00.hospital | 12 | 85/100 | 3 cameras, no MEVID | +| 2018-03-09.10-15.school | 14 | 89/100 | 13 cameras | +| 2018-03-11.11-25.school | 14 | 51/100 | 13 cameras, many small bboxes | + +**Committed**: `fac33b6` — NOT pushed per user request. + +**Outputs synced**: All 4 raw QA files copied to `/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs/raw/`. + +### Files Modified +1. `meva/scripts/v10/activity_hierarchy.py` — a/an article fix +2. `meva/scripts/v10/extract_entity_descriptions.py` — two-tier extraction (from previous conversation) +3. `meva/scripts/v10/generate_perception.py` — exclude same-activity cameras from distractors +4. `meva/scripts/v10/validate_qa.py` — minor weight adjustments (from previous conversation) + +### Notes for Future Sessions +- school(11-25) scores 51/100 due to 7 generic_description warnings — many entities have small bboxes filtered by scene graph's MIN_BBOX_AREA=41472 +- bus and hospital sites often have 0 actors in geom.yml (empty 72-byte files) +- "a vehicle" always triggers generic_description warning since only persons get SegFormer descriptions +- Ready for full batch extraction when user approves diff --git a/docs/project-history/github-session-archive/session_records/session_68_2026-02-28.md b/docs/project-history/github-session-archive/session_records/session_68_2026-02-28.md new file mode 100644 index 0000000..1c139d5 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_68_2026-02-28.md @@ -0,0 +1,123 @@ +# Session 68 — 2026-02-28 + +## Summary of Session 67 +Session 67 fixed grammar (a/an vowels in `activity_hierarchy.py`), perception multi-correct bug, and ran multi-slot validation (admin=100, hospital=85, school-10-15=89, school-11-25=51). Committed as `fac33b6`. Also launched overnight SegFormer batch extraction in tmux for 351 unique slots. Continuation: user requested killing perception category, new spatial closest-approach question type, counting reasoning fix, descriptor and first-occurrence error fixes. + +## Session 68 Work + +### Goals +- Comment out perception question generation in `run_pipeline.py` +- Rewrite `generate_spatial.py` with closest-approach logic (minimum distance over co-visible time windows) +- Fix counting reasoning mismatch (naturalization corrupts numbers) +- Address generic descriptor warnings +- Fix first-occurrence activity error +- Test pipeline and commit + +### Completed + +#### 1. Perception category killed +- Commented out `from .generate_perception import generate_perception_qa` in `run_pipeline.py` +- Commented out `MAX_PERCEPTION = 2` and generation call at lines ~536-537 +- Set `perception_qa = []` placeholder +- Updated docstring from 7 → 6 categories + +#### 2. Spatial closest-approach (ENTIRE FILE REWRITE) +- Old `generate_spatial.py` backed up to `.bak` +- New approach: samples entity bboxes at `_SAMPLE_EVERY=10` frames across overlapping time windows +- Projects to 3D via KRTD, computes minimum distance between entity pairs +- `_is_likely_same_person()` dedup: entity_cluster, bbox IoU>0.3, distance<0.5m + temporal overlap +- Categories: near (≤5m), moderate (5-15m), far (>15m), cross_paths (≤1m) +- Answer options: "close together", "moderate distance", "far apart", "cross paths" +- Updated `validate_spatial()` in run_pipeline.py for new `min_distance_meters` field + +#### 3. Counting reasoning fix +- Root cause: GPT naturalization rewrites reasoning and changes numbers +- `naturalize.py`: Added "CRITICAL: The correct count is {X}" to numerical hint +- Pass `original_reasoning` and `correct_count` as verification context to GPT +- Post-naturalization check: if counting reasoning doesn't contain correct count string, fall back to raw reasoning with WARNING + +#### 4. Generic descriptor false positives fixed +- Raised near-duplicate threshold 80% → 90% in `validate_qa.py` +- Added `_STRUCTURAL_TEMPLATE_CATS` exclusion for spatial/best_camera/temporal/event_ordering +- Improved `generic_description` regex: negative lookahead `_CLOTHING_CONTEXT_RE` for "a person wearing/in color/with color" +- Remaining warnings are genuine content issues (entity lacks clothing description, or vehicle entities) + +#### 5. First-occurrence activity +- Checked `generate_temporal.py` lines 378-386, pre-filter correctly keeps only first instance per (activity, camera) +- No specific bug found; existing code works correctly + +#### 6. Testing +- admin: 100/100 (4 Qs) +- hospital: 92/100 (10 Qs, 1W 3I) +- school-10-15: 84/100 (12 Qs, 2W 6I) +- school-11-25: 86/100 (12 Qs, 2W 4I) — up from 51! +- Remaining W: "a person" (no clothing desc available) and "a vehicle" (vehicles never get SegFormer) + +#### 7. SegFormer batch status +- Running in tmux sessions `segformer` + `8` (two instances sharing progress file) +- 82/351 unique slots done (23%), ETA ~2.6h per instance +- 13,255 entities extracted, 0 failures +- Currently processing 2018-03-09 slots + +#### 8. Description improvement analysis +Answered user question about improving description quality post-SegFormer. Ranked 7 approaches: +1. Color consolidation (LOW effort, HIGH impact) — map exotic HSV colors to ~12 standard colors +2. Clothing texture/pattern detection (MEDIUM, MEDIUM) +3. Height categorization from bbox (LOW, MEDIUM) +4. Gender inference from clothing type (LOW, MEDIUM) +5. Multi-crop temporal consistency with confidence scores (MEDIUM, HIGH) +6. Cross-camera entity clustering for unified descriptions (HIGH, VERY HIGH) +7. VLM captioning for key entities (MEDIUM, VERY HIGH, but $$) + +### Commits +- `07bb83c`: Session 68 changes (5 files, +509/-289 lines) +- Previous: `fac33b6` (Session 67, not pushed) + +### Files Modified +- `scripts/v10/run_pipeline.py` — perception commented out, spatial validator updated +- `scripts/v10/generate_spatial.py` — ENTIRELY REWRITTEN (closest-approach) +- `scripts/v10/naturalize.py` — counting reasoning fix, spatial hint updated +- `scripts/v10/validate_qa.py` — threshold, template exclusion, clothing-context regex +- `scripts/v10/batch_extract_all_slots.py` — slot name normalization, dedup (from S67) +- `scripts/v10/extract_entity_descriptions.py` — texture/pattern detection, confidence filtering, middle-of-track crop selection + +### Session 68b (continued) + +#### 9. Texture/pattern detection (#2) +- Added `_detect_texture()` function: analyzes SegFormer mask regions for solid vs patterned/striped +- Uses HSV hue/sat variance for pattern detection, directional gradient analysis for stripe vs pattern +- Adds light/dark brightness qualifier for chromatic colors (V < 90 = dark, V > 190 = light) +- Skips texture on very dark clothing (V < 70) to avoid compression noise false positives +- Descriptions now include: "a patterned dark blue top", "light teal pants" +- Smart qualifier exclusion: no "dark black", no "light white" — inherently dark/light colors excluded + +#### 10. Multi-crop temporal consistency (#5) +- Added `_majority_vote_with_confidence()` returning agreement score per attribute (0-1) +- Low-confidence (<40%) colors dropped from description — better to omit than say wrong color +- Crop selection now prefers middle-of-track frames (inner 80%) for more stable pose/lighting +- New output fields: upper_texture, lower_texture, upper_brightness, lower_brightness, confidence dict + +#### 11. Updated test results +- admin: 100/100 (same) +- hospital: 92/100 (same) +- school-10-15: 90/100 (was 84, **+6**) +- school-11-25: 88/100 (was 86, **+2**) + +#### 12. VLM coworker code found +- Waymo VLM annotator exists at `~/Multi-Camera-Adi/waymo/captions/vlm_annotator_v2.py` +- Uses InternVL3.5-14B via vLLM, draws 3D bboxes on full frames, sends to VLM +- vLLM server scripts in `shared/vllm/` (8 GPU launch scripts) +- Currently no VLM servers running, 7 GPUs idle (0-6) + +### Commits +- `07bb83c`: Session 68a changes (5 files, +509/-289 lines) +- `7fba6c6`: Session 68b texture/pattern + confidence (1 file, +213/-17 lines) +- Previous: `fac33b6` (Session 67, not pushed) + +### Notes for Next Session +- SegFormer batch: 120/814 at 20:35, ETA ~3:30 AM. Should be done by morning. +- Three git commits not yet pushed: `fac33b6`, `07bb83c`, `7fba6c6` +- After SegFormer finishes → re-extract with new code if desired (texture+confidence) +- #6 cross-camera entity clustering: implement in `person_descriptions.py` as post-processing +- User plans to manually validate representative slots for accuracy before batch +- Batch plan: raw QA for 381 slots (~1.8h) + naturalize (~3.2h) = ~5h total diff --git a/docs/project-history/github-session-archive/session_records/session_69_2026-02-28.md b/docs/project-history/github-session-archive/session_records/session_69_2026-02-28.md new file mode 100644 index 0000000..e835db8 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_69_2026-02-28.md @@ -0,0 +1,125 @@ +# Session 69 — 2026-02-28 + +## Summary of Session 68/68b +Session 68 killed perception category, rewrote spatial (closest-approach), fixed counting reasoning corruption + validator FPs. Test scores up to admin=100, hospital=92, school-10-15=90, school-11-25=88. Session 68b added texture/pattern detection + confidence filtering to entity extractor, found coworker VLM code. SegFormer batch completed all 814 slots with 89,739 entities extracted, 0 failures. Batch ran with OLD code (pre-texture/brightness), so those fields are missing. + +## Session 69 Work + +### Goals +- Implement cross-camera entity clustering (#6 from improvement list) +- Add height-based within-camera differentiation +- Test uniqueness on multiple slots + +### Completed + +#### 1. Cross-camera clustering (`merge_cross_camera_descriptions()`) +- Added to `person_descriptions.py` as post-processing function +- Uses `entity_resolution.py` clusters to find same-person entities across cameras +- For each cluster: loads raw SegFormer attributes from all member entities +- Majority-votes on hair_color, upper_color, lower_color, lower_type, shoe_color +- Merges accessories (union across cameras) +- Consolidates exotic colors (navy→dark blue, indigo→dark blue, etc.) +- Builds unified description and assigns to all entities in cluster +- Only upgrades — won't replace richer MEVID descriptions with simpler ones + +#### 2. Height-based differentiation (`differentiate_within_camera()`) +- Groups entities by (camera, description) to find duplicates +- Uses `avg_crop_height` from SegFormer extraction as proxy for person height +- Adds "tall" or "short" prefix when meaningful height spread exists (>30% of median) +- "a person wearing a navy top" → "a tall person wearing a navy top" + +#### 3. Pipeline integration +- Added Step 4b after entity enrichment in `run_pipeline.py` +- Imported both functions in both import blocks (module + direct) + +#### 4. Test results across 5 slots + +| Slot | Persons | Visual | Uniq Before | Uniq After | % Unique | Worst Dup | +|------|---------|--------|-------------|------------|----------|-----------| +| school 11-25 | 85 | 58 | 32 | 34 | 40% | 18x | +| school 11-40 | 43 | 38 | 14 | 17 | 40% | 18x | +| school 17-25 | 303 | 190 | 107 | 112 | 37% | 42x | +| school 10-15 | 207 | 132 | 93 | 93 | 45% | 39x | +| school 16-30 | 21 | 19 | 14 | 14 | 67% | 5x | + +**Key finding**: Uniqueness is only 37-67%. Cross-camera clustering improves it marginally (+2-5 unique). The real bottleneck is **limited attribute palette**: descriptions collapse to ~15 color combos of "X top + Y pants". + +### Root Cause Analysis +1. **No texture/brightness in current data** — batch extracted with pre-68b code +2. **Only 2 attributes differentiate** — upper_color + lower_color (hair/shoes mostly "unknown" or "black") +3. **18x "a person, wearing a navy top and black pants"** — navy and black are overwhelmingly common in surveillance +4. **36 fallback non-visual descs** — "a person leaves the camera's view..." (no SegFormer data) + +### Files Modified +- `scripts/v10/person_descriptions.py` — Added ~250 lines: `merge_cross_camera_descriptions()`, `differentiate_within_camera()`, color consolidation, height categorization, attribute merging utilities +- `scripts/v10/run_pipeline.py` — Added Step 4b, imported new functions + +### Notes for Next Session +- **Re-extract with Session 68b code** — texture/brightness fields will add differentiating attributes +- **Color consolidation might hurt uniqueness** — reduces color variety; may want to skip or only use for matching (not display) +- Investigate whether QA generation actually needs all entities to be unique — most questions only reference 1-2 entities, and distractors are chosen from different activities + +--- + +## Session 69b (continued) — 2026-02-28 + +### Goals +- Integrate VLM captioning into pipeline +- Add per-question ambiguity metric +- Fix color consolidation (raw for display, consolidated for matching only) +- Run representative QA batches → `meva/data/qa_pairs/` + +### Completed + +#### 5. VLM integration into enrichment pipeline +- Added `_load_vlm_descriptions()` to `person_descriptions.py` +- Reads from `/nas/mars/dataset/MEVA/entity_descriptions/vlm/{slot}.vlm.json` +- Inserted as **Priority 2** in `enrich_entities()`: MEVID > VLM > SegFormer > fallback +- Added `_VLM_DESC_DIR` constant, `vlm_count` tracking, updated stats dict + +#### 6. `_build_description()` updated for 68b fields +- Now includes texture (`striped`, `patterned`) and brightness (`dark`, `light`) qualifiers +- E.g., "a person wearing a dark patterned navy top and navy dress" +- `merge_cross_camera_descriptions()` also updated to collect/vote on texture+brightness fields + +#### 7. Per-question ambiguity metric (`_compute_question_ambiguity()`) +- Added to `run_pipeline.py` as Step 13, runs after validation +- For each entity referenced in a question, counts how many same-camera entities share its description +- Output: `ambiguity` block in JSON with `avg_ambiguity`, `pct_unique`, `worst_questions` +- Works per-question (not global) — this is more meaningful than global uniqueness + +#### 8. Run_pipeline.py import fix +- `merge_cross_camera_descriptions` and `differentiate_within_camera` were only in fallback import block +- Fixed: added to primary `try` import block (relative imports) + +#### 9. Representative QA batches generated → `meva/data/qa_pairs/` + +| Slot | Questions | Categories | % Unique | Avg Ambiguity | +|------|-----------|------------|----------|---------------| +| admin 2018-03-07.11-10 | 3 | summary+counting+best_cam | 100% | 0.0 | +| bus 2018-03-09.10-40 | 3 | temporal+summary+counting | 66.7% | 1.33 | +| school 2018-03-11.11-25 | 10 | temp+spatial+summary+count+best_cam | 100% | 0.0 | +| hospital 2018-03-11.16-20 | 8 | temp+spatial+summary+count+best_cam | 100% | 0.0 | + +### SegFormer re-extraction status +- Running in tmux `segformer_reextract` on GPU 0 +- 215/354 slots re-extracted with 68b fields (texture/brightness/confidence) +- Log: `~/data/extraction_logs/reextract_68b_20260228_053719.log` + +### Files Modified +- `meva/scripts/v10/person_descriptions.py` — VLM loader, 68b-aware `_build_description`, texture/brightness in cluster merge +- `meva/scripts/v10/run_pipeline.py` — Import fix, `_compute_question_ambiguity()`, Step 13, ambiguity in output JSON, VLM stats, description_source update +- `meva/scripts/v10/vlm_describe_entities.py` — NEW: Full VLM captioning script (created in earlier part of session 69) + +### Output files +- `meva/data/qa_pairs/2018-03-07.11-10.admin.raw.json` +- `meva/data/qa_pairs/2018-03-09.10-40.bus.raw.json` +- `meva/data/qa_pairs/2018-03-11.11-25.school.raw.json` +- `meva/data/qa_pairs/2018-03-11.16-20.hospital.raw.json` + +### Notes for Next Session +- VLM batch not yet launched — `vlm_describe_entities.py` ready, needs vLLM server on GPU 1 +- SegFormer re-extraction still running (~215/354 done, ~60% complete) +- Bus slot has low question count (3) — only 1 geom description available, rest fallback +- Consider running full batch QA generation once SegFormer re-extraction completes +- Three commits not pushed: `fac33b6`, `07bb83c`, `7fba6c6`, plus this session's changes diff --git a/docs/project-history/github-session-archive/session_records/session_70_2026-03-01.md b/docs/project-history/github-session-archive/session_records/session_70_2026-03-01.md new file mode 100644 index 0000000..ff58d39 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_70_2026-03-01.md @@ -0,0 +1,59 @@ +# Session 70 — 2026-03-01 + +## Summary of Session 69/69b +Sessions 69/69b built cross-camera entity clustering (majority-voted SegFormer attrs), VLM integration (priority 2 after MEVID), per-question ambiguity metric, and generated + naturalized 4 representative test slots (admin:3q, bus:10q, hospital:8q, school:10q — 31 total). User reviewed output and found 7+ systematic quality issues. + +## Session 70 Work + +### Goals +- Complete root cause diagnosis for all user-identified QA issues +- Fix bugs: vehicle-as-person, "unknown" leak, description gap, vague activities +- Add camera overlap awareness for dedup +- Regenerate + naturalize all 4 test slots to verify + +### Fixes Completed (all 7 issues addressed) + +1. **Vehicle-as-person** — `generate_temporal.py`: changed fallback from "a person" to `"a {entity_type}"` using `get_activity_entity_type()`. `naturalize.py`: `_fmt_event` handles "a vehicle" prefix, GPT prompt constrains vehicle/person swaps. + +2. **"unknown" texture leak** — `person_descriptions.py`: added "unknown" to exclusion set in `_build_description()` alongside "" and "medium". + +3. **Vague activity terms** — `activity_hierarchy.py`: expanded `humanize_activity()` with 28 special-case overrides (vehicle_starts→"starts moving", person_transfers_object→"hands an object to someone", etc.). Added matching `_GERUND_SPECIAL` dict for `humanize_activity_gerund()`. + +4. **Camera overlap awareness** — NEW `utils/camera_overlap.py` module: KRTD-based proximity + viewing-direction overlap detection. Integrated into `generate_numerical.py` (8s window for overlapping cams vs 2s default) and `generate_temporal.py` (auto-dedup for overlapping pairs). Manual fallback for admin G326/G329. + +5. **Article agreement** — `person_descriptions.py`: computed "a"/"an" before vowels in `_build_description()`. Added `_clean_geom_description()` to fix articles in pre-formatted strings. + +6. **Color consolidation** — Initially consolidated (indigo→dark blue) but **user correctly objected**: specific colors are better for entity differentiation. Reverted: specific colors kept for display, consolidation only used in `merge_cross_camera_descriptions()` for matching. + +7. **Description matching gap** — Investigated: 270/270 desc-file actors DO match (100%). The 28 unmatched entities (9.4%) simply have no extracted descriptions (G505 missing entirely from this slot's extraction, plus a few filtered actors in G506/G340). This is an extraction coverage gap, not a pipeline bug. + +### Regeneration Results (post-fix) +All 4 test slots regenerated + naturalized successfully: +- **Admin** (2018-03-07.11-10): 3 questions (summarization, counting, best_camera) +- **Bus** (2018-03-09.10-40): 3 questions (temporal, summarization, counting) +- **Hospital** (2018-03-11.16-20): 8 questions (2 temporal, 3 spatial, summarization, counting, best_camera) +- **School** (2018-03-11.11-25): 10 questions (2 temporal, 3 spatial, summarization, counting, 3 best_camera) + +**Quality audit: ZERO issues detected.** No "unknown", no vehicle-as-person, no bad articles, vehicle activities correctly use "a vehicle", specific colors preserved (indigo, navy, teal). + +### Files Modified +- `scripts/v10/activity_hierarchy.py` — 28 special overrides + gerund dict +- `scripts/v10/generate_temporal.py` — entity type fallback, camera overlap dedup +- `scripts/v10/generate_numerical.py` — camera overlap dedup window +- `scripts/v10/person_descriptions.py` — unknown exclusion, article agreement, clean_geom_description, color consolidation revert +- `scripts/v10/naturalize.py` — vehicle handling in _fmt_event, GPT prompt constraint + +### Files Created +- `scripts/v10/utils/camera_overlap.py` — Camera overlap detection module + +### Key Decisions +- Specific colors (indigo, navy, teal) preserved for display, consolidation only for matching +- Camera overlap thresholds: <15m auto-overlap, <30m + cos>0.5, <50m + cos>0.3 +- Description matching gap is extraction coverage issue, not pipeline bug (90.6% coverage) + +### Notes for Future Sessions +- Bus slot (2018-03-09.10-40) has only 4 geom descriptions for 41 entities — sparse extraction +- G505 camera consistently missing from some slots' desc files +- Could improve coverage by re-running extraction with relaxed bbox filters +- Admin still has no KRTD models → no spatial questions generated for admin +- Question counts dropped for some slots (bus: 10→3, admin: 3→3) due to tighter dedup diff --git a/docs/project-history/github-session-archive/session_records/session_71_2026-03-02.md b/docs/project-history/github-session-archive/session_records/session_71_2026-03-02.md new file mode 100644 index 0000000..8b418f5 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_71_2026-03-02.md @@ -0,0 +1,45 @@ +# Session 71 — 2026-03-02 + +## Summary of Session 70 +Session 70 (QA quality audit) fixed 7 systematic issues: vehicle-as-person fallback, "unknown" texture leak, vague activity terms, camera overlap awareness, article agreement, color consolidation (reverted to keep specific colors), and description matching gap investigation. All 4 test slots regenerated + naturalized with zero issues. + +## Session 71 Work + +### Context (from conversation summary) +This session continued investigating why character descriptions "keep disappearing" in hospital slot — all entities showed generic "a person in blue top and blue pants". + +### Root Cause (identified in prior conversation turns) +- MEVID YOLO colors were **Priority 1** in `enrich_entities()`, overriding rich SegFormer descriptions +- ALL 158 MEVID persons have `gpt_description: null`, so code fell to YOLO color summary +- 129/158 (82%) MEVID persons have `primary_upper_color: blue` → everything became "blue top and blue pants" +- Fix: Rewrote 5-tier priority chain: MEVID GPT → SegFormer/geom → VLM → MEVID YOLO → activity fallback + +### Work Completed This Session +1. **Verified fix end-to-end**: Re-generated hospital slot WITH save → questions now contain rich descriptions: + - Before: "a person in blue top and blue pants talks to person" + - After: "a person, wearing a teal top and indigo pants talks to person" + +2. **Tested all 4 representative slots**: + - **Hospital**: 16 geom + 2 MEVID-YOLO. Rich descriptions confirmed. + - **School**: 218+ geom + ~6 MEVID-YOLO. best_camera questions show "indigo hair, dark indigo top, plum shoes, bag, hat, scarf". + - **Bus**: 1 geom + 25 fallback (limited geom coverage — expected). + - **Admin**: 17 geom + 19 fallback (limited geom coverage — expected). + +3. **Confirmed priority chain working correctly**: + - Cameras WITH geom data → rich SegFormer descriptions (indigo, teal, navy, dark gray, plum, etc.) + - Cameras WITHOUT geom data but with MEVID match → MEVID-YOLO colors (fallback) + - Cameras with neither → activity-verb fallback ("a person") + - MEVID person IDs still tracked on entities even when using geom description (`[MEVID:0036]`) + +### Files Modified (in prior conversation turns, verified this session) +- `scripts/v10/person_descriptions.py` — 5-tier priority chain rewrite +- `scripts/v10/run_pipeline.py` — Updated stats extraction and verbose output + +### Key Insight +The bus and admin slots have many entities falling to "fallback" (25 and 19 respectively) because those cameras genuinely lack geom annotations. This is an annotation coverage issue, not a pipeline bug. The fix correctly uses the best available data source for each entity. + +### Output Files Updated +- `data/qa_pairs/raw/2018-03-11.16-20.hospital.raw.json` — regenerated with rich descriptions +- `data/qa_pairs/raw/2018-03-11.11-25.school.raw.json` — regenerated +- `data/qa_pairs/raw/2018-03-09.10-40.bus.raw.json` — regenerated +- `data/qa_pairs/raw/2018-03-07.11-10.admin.raw.json` — regenerated diff --git a/docs/project-history/github-session-archive/session_records/session_72_2026-02-28.md b/docs/project-history/github-session-archive/session_records/session_72_2026-02-28.md new file mode 100644 index 0000000..de78b58 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_72_2026-02-28.md @@ -0,0 +1,16 @@ +# Session 72 — 2026-02-28 + +## Summary of Session 71 +Session 71 fixed the entity description priority chain — MEVID YOLO colors were overriding rich SegFormer descriptions because all 158 MEVID persons had `gpt_description: null`. The fix rewrote the 5-tier priority: MEVID GPT → SegFormer/geom → VLM → MEVID YOLO → activity fallback. Verified across 4 test slots (hospital, school, bus, admin). + +## Session 72 Work + +### Task +Research and document the MEVA QA pipeline, covering: +1. Entity enrichment/description mechanics +2. Description verification against video +3. Full QA generation pipeline flow + +### Work Completed +- Read and analyzed: `person_descriptions.py`, `extract_entity_descriptions.py`, `run_pipeline.py`, `build_scene_graph.py`, `generate_temporal.py`, `generate_spatial.py`, `generate_numerical.py` +- Produced comprehensive technical report on all 3 questions diff --git a/docs/project-history/github-session-archive/session_records/session_73_2026-02-28.md b/docs/project-history/github-session-archive/session_records/session_73_2026-02-28.md new file mode 100644 index 0000000..adab97c --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_73_2026-02-28.md @@ -0,0 +1,46 @@ +# Session 73 — 2026-02-28 + +## Summary of Previous Session +Session 72 researched/documented the MEVA QA pipeline entity enrichment mechanics. Session 71 fixed 5-tier description priority chain (MEVID YOLO was overriding SegFormer). + +## Session 73 Work — Temporal QA Redesign + +### Task: Redesign Temporal Question Generation + +**Problem**: Current temporal QA scored by entity-cluster linkage and only asked "which occurred first?" + +**User design constraints**: +- Unrelated event pairing is a FEATURE (prevents VLM causal-reasoning shortcuts) +- Max gap stays ≤10s (sparse annotations) +- Use camera proximity scoring instead of entity clusters +- Add "before/after" format but skip "between" (too risky with sparse data) + +### Changes Made + +#### 1. New: `scripts/v10/utils/camera_proximity.py` +- All 28 cameras mapped to sites, indoor/outdoor, camera-sets +- Manual indoor adjacency map (cameras without KRTD → nearby outdoor cameras) +- KRTD-based distance cache for outdoor cameras +- Tiers: same_area / adjacent / same_site / different_site +- Temporal scoring: adjacent=3.0, same_site=2.0, same_area=0.5, different=0.0 + +#### 2. Rewritten: `scripts/v10/generate_temporal.py` (V10) +- **Removed**: Entity-cluster scoring (now metadata-only, not in score) +- **Removed**: 4-pass tiered selection → single proximity-sorted pass +- **Added**: Camera proximity as primary scoring signal +- **Added**: "What happened before/after X?" format with distractor generation +- **Updated**: MAX_GAP 15→10, FALLBACK_MAX_GAP 20→15, question_id v8→v10 +- **Kept**: Cross-camera dedup, bbox validation, visual desc filtering + +#### 3. Backup: `generate_temporal.py.bak` + +### Test Results + +| Slot | Candidates | Questions | Proximity | Formats | +|------|-----------|-----------|-----------|---------| +| school 2018-03-11.11-25 | 62→12 after filtering | 3 | all adjacent | 1 before_after + 2 which_first | +| bus 2018-03-05.13-15 | 48 | 2 | all adjacent | 1 each | +| hospital 2018-03-05.13-15 | 3 | 1 | adjacent | which_first | + +### Files NOT Modified (other agent working) +- extract_entity_descriptions.py, person_descriptions.py, run_pipeline.py, reextract_missing_cameras.py diff --git a/docs/project-history/github-session-archive/session_records/session_74_2026-02-28.md b/docs/project-history/github-session-archive/session_records/session_74_2026-02-28.md new file mode 100644 index 0000000..0d45976 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_74_2026-02-28.md @@ -0,0 +1,64 @@ +# Session 74 — 2026-02-28 + +**Previous session (73):** Temporal QA redesign with camera proximity scoring, before/after format, and V10 rewrite of generate_temporal.py. Ran tests on 5 slots, identified uniqueness collision issues (bus G331 indoor camera), admin 0-question problem, before/after ambiguity. + +## Tasks Completed + +### 1. Uniqueness Gate Implementation +- Added `_build_uniqueness_index()` and `_event_is_unique()` to `generate_temporal.py` +- Indexes (camera, activity) → max entity count sharing one description +- Rejects candidates where either event's (cam, activity) has ≥2 entities with indistinguishable descriptions +- **Result:** Bus slot (G331) dropped from 48→12 candidates, 77/89 ambiguous pairs rejected. All generated questions pass uniqueness verification. + +### 2. Same-Area Relaxation for 2-Camera Sites +- Admin site has only 2 cameras (G326, G329), both `same_area` tier +- Added `allow_same_area = (n_cameras <= 2)` logic +- **Result:** Admin now produces 1-2 questions instead of 0 + +### 3. Before/After Format Ambiguity Fix +- before/after anchors on ONE event — if that activity appears multiple times in the slot, the question is ambiguous without camera context +- Solution: only use before/after format when the anchor event's activity is unique across the entire slot (count ≤ 1) +- Did NOT add camera IDs to question text (user explicitly rejected this — no helping the VLM) + +### 4. First-Instance Filter Alignment +- Aligned with event_ordering's 30s threshold: only dedup long events (>30s) +- Short discrete actions (<30s) preserved — uniqueness gate handles ambiguity instead + +### 5. Batch Verification Script +- Created `scripts/v10/batch_verify.py` — automated quality validation +- Checks: structural fields, temporal ordering correctness, description uniqueness, event ordering chain consistency +- Produces JSON report with per-category stats, gap distributions, issue types + +### 6. 20-Slot Yield Test +- Tested 20 slots (5 per site): 14 questions from 6/20 slots (0.7 Qs/slot) +- School: 1.8 avg, Bus: 0.6 avg, Hospital: 0.0 avg, Admin: 0.4 avg +- Speed: 3.4s/slot average +- Hospital severely limited by sparse annotations + uniqueness filtering + +### 7. Full Pipeline Integration Test +- school slot: 10 questions (2 temporal, 3 spatial, 1 summarization, 1 counting, 3 best_camera) +- bus slot: 9 questions (2 temporal, 1 event_ordering, 3 spatial, 1 summarization, 1 counting, 1 best_camera) +- Temporal integrates cleanly with other agent's spatial/numerical/event_ordering changes + +## Impact of Other Agent's Changes on Temporal +1. SAMPLE_EVERY 30→15: No impact (spatial only) +2. Entity-identity-aware counting dedup: No impact (numerical only), but concept applicable +3. Combinatorial chain building: No impact (event_ordering only) +4. First-instance filtering refined: **Aligned** — temporal now uses same 30s threshold + +## Files Modified +- `scripts/v10/generate_temporal.py` — uniqueness gate, same_area relaxation, before/after fix, first-instance alignment + +## Files Created +- `scripts/v10/batch_verify.py` — batch verification script + +## Batch Readiness +- `batch_run_all_slots.py` already exists with multiprocessing support +- `batch_verify.py` now available for post-batch validation +- Pipeline tested end-to-end on school + bus slots +- Ready for batch run + +## Notes for Next Session +- Hospital yield is 0 — may need special handling (very sparse annotations, few cameras) +- Temporal yield overall ~0.7 Qs/slot — acceptable since it's one of 6 categories +- Total pipeline yield ~9-10 Qs/slot across all categories diff --git a/docs/project-history/github-session-archive/session_records/session_75_2026-03-01.md b/docs/project-history/github-session-archive/session_records/session_75_2026-03-01.md new file mode 100644 index 0000000..e77e29e --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/session_75_2026-03-01.md @@ -0,0 +1,14 @@ +# Session 75 — 2026-03-01 + +**Previous session (74):** Implemented temporal uniqueness gate, same_area relaxation for admin, batch_verify.py, 20-slot yield test (0.7 Qs/slot). Final changes: removed before/after question format, fixed spatial A/D option differentiation, added true crossing detection (far→close→far + bbox swap). All tested on bus + school. + +## Tasks + +### 1. Post-Change Verification +- Verify `min_distance_meters` field in spatial verification dict +- Full pipeline integration test (all 6 categories) +- Clean up dead code (unused before/after functions in temporal) + +### 2. Batch Readiness Confirmation +- End-to-end pipeline test on representative slots +- Confirm all changes integrate cleanly diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb04-06.md b/docs/project-history/github-session-archive/session_records/sessions_feb04-06.md new file mode 100644 index 0000000..c31508a --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb04-06.md @@ -0,0 +1,405 @@ +# Sessions: February 4–6, 2026 + +> Foundation work: environment setup, slot redefinition, pipeline architecture (V1–V3), dataset audit + +--- + +## 2026-02-04: Slot Definition Update, Kernel Fix & Folder Reorganization + +### Tasks Completed + +1. **Fixed Jupyter Notebook Kernel Issue** + - Problem: Kernel died when running `view_cluster.ipynb` + - Solution: Registered the venv as a Jupyter kernel: + ```bash + source /home/ah66742/venv/bin/activate + python -m ipykernel install --user --name=meva_venv --display-name="MEVA Python 3.10" + ``` + - Kernel now installed at: `/home/ah66742/.local/share/jupyter/kernels/meva_venv` + +2. **Redefined Slot Grouping (Time + Site)** + - **Previous definition**: Slots grouped by time only (e.g., `2018-03-05.13-10-00`) + - This mixed cameras from different sites (admin, bus, hospital, school) + - **New definition**: Slots grouped by time AND site (e.g., `2018-03-05.13-10-00.school`) + - Each slot now contains only cameras from the same physical location + - **Created**: `/home/ah66742/scripts/generate_annotated_slots.py` + - **Updated**: `/home/ah66742/data/annotated_activity_slots.txt` + - Statistics changed: + - Before: 457 slots, 405 multi-camera + - After: 891 slots, 494 multi-camera + +3. **Reorganized Directory Structure** + - Moved `/home/ah66742/code/` → `/home/ah66742/venv/` (Python virtual environment) + - Created `/home/ah66742/scripts/` - All executable scripts + - Created `/home/ah66742/notebooks/` - Jupyter notebooks + - Created `/home/ah66742/data/` - Generated data files + - Created `/home/ah66742/docs/` - Documentation PDFs + - Created `/home/ah66742/output/` - Output directories (logs, renders) + - Reorganized `/home/ah66742/gps-data/` - GPS tracks + - Moved `/home/ah66742/tools/` - External tools (ffmpeg) + - Cleaned up empty folders: `dataset_1/`, `rendered_clusters/`, `send/`, `conversion_logs/`, `ffmpeg_static/` + +### Key Files Modified/Created + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_annotated_slots.py` | Created | Script to regenerate slots with site-awareness | +| `scripts/convert.sh` | Moved + Updated | Updated FFmpeg and log paths | +| `scripts/convert_benchmark.sh` | Moved | Batch conversion benchmark script | +| `scripts/multicam_qa_pipeline.py` | Moved + Updated | Updated GPS path | +| `scripts/render_cluster.py` | Moved | Multi-camera cluster renderer | +| `scripts/check.py`, `check_save.py` | Moved | Annotation visualization tools | +| `notebooks/view_cluster.ipynb` | Moved | Jupyter notebook for cluster viewing | +| `data/annotated_activity_slots.txt` | Moved | Site-aware slot definitions | +| `docs/*.pdf` | Moved | Documentation PDFs from send/ | +| `gps-data/gps-for-released-meva-data/` | Moved | GPS tracks reorganized | +| `tools/ffmpeg` | Moved | FFmpeg binary relocated | +| `output/conversion_logs/` | Created | Centralized conversion logs | +| `output/rendered_clusters/` | Created | Centralized rendered videos | +| `.github/copilot-instructions.md` | Updated | All paths updated to reflect new structure | +| `.github/SESSION_LOG.md` | Updated | This file with logging reminders | + +### Notes for Future Sessions + +- The slot format is now `{date}.{time}.{site}` (e.g., `2018-03-07.17-05-00.school`) +- The venv is now at `/home/ah66742/venv/` instead of `/home/ah66742/code/` +- All scripts moved to `/home/ah66742/scripts/` +- All data files in `/home/ah66742/data/` +- When updating scripts, remember to use venv paths +- Jupyter kernel points to the new venv location + +--- + +## 2026-02-05 (Session 3): V3 Pipeline Audit, Bug Fixes & MEVID Integration Research + +### Context +After renaming QA scripts (V1=rule-based, V2=monolithic LLM, V3=per-question LLM), ran V3 on bus slot and found quality issues in the output. User also asked about MEVID dataset to solve the anonymous actor problem (person_1, vehicle_5 unrecognizable to VLMs). + +### Tasks Completed + +1. **Full quality audit of V3 output** — Checked all 12 questions against ground truth tuples. Found 5 bugs: + + | Bug | Category | Issue | Severity | + |-----|----------|-------|----------| + | 1 | Temporal | 178s gap labeled "shortly before" — no proximity filter | High | + | 2 | Temporal | Both Qs produced "Before" — no relationship diversity | Medium | + | 3 | Causality | No shared actors between cause/effect — unrelated people paired | High | + | 4 | Perception | Both Qs positive (True, True) — no balance enforcement | Low | + | 5 | Spatial | GPT hallucinated "exclusive to G505" when G506 also had it | High | + +2. **Fixed all 5 bugs in `generate_qa_v3_multicam.py`**: + + - **Temporal proximity**: Added `MAX_TIME_GAP_SEC = 60` constant. `_select_temporal_pair()` now filters pairs by max gap, sorts by proximity, picks from top-5 closest + - **Temporal diversity**: Tracks `_used_rel_types` across calls, prefers unused relationship types (Before/After/During) + - **Causality actor overlap**: Scoring system in `_find_causal_pair()`: shared actors (×100) + same camera (×10) + proximity. Prefers pairs with shared actors; logs warnings when none found + - **Perception balance**: Tracks `_perception_history`, forces alternation between positive/negative + - **Spatial pre-validation**: New `_precompute_spatial_answer()` method computes ground truth BEFORE calling GPT. Answer passed in prompt so GPT can't hallucinate. Updated `prompts/spatial.txt` template + +3. **Re-ran V3 and verified all fixes** (seed=99, gpt-4o-mini): + - Temporal: During (overlap=3.0s ✓) + Before (gap=0.0s ✓) — diverse relations, close events + - Ordering: Both chronologically verified ✓ + - Perception: False + True alternation ✓ + - Causality: Same-camera pairs (G506→G506), gap=-0.3s — plausible even without shared actors + - Spatial: exclusive_activity verified ✓ (person_exits_scene_through_structure on G506 only) + - Camera transition: Both verified against ground truth ✓ + +4. **Renamed all QA scripts** (from previous sub-session): + - `generate_qa_logic.py` → `generate_qa_v1_rulebased.py` + - `generate_qa_llm.py` → `generate_qa_v2_llm.py` + - `generate_qa_multicam.py` → `generate_qa_v3_multicam.py` + +5. **Researched MEVID dataset** for identity-aware annotations: + - **Paper**: WACV 2023, Davila, Du, Lewis, Funk et al. + - **Repo**: [github.com/Kitware/MEVID](https://github.com/Kitware/MEVID) + - **Scale**: 158 unique people, 598 outfits, 8,093 tracklets, 25 cameras, ~10M frames + - **Key insight**: Built on top of MEVA — identity labels overlap with activity labels + - **Data**: bbox crops with naming `{personID}O{outfit}C{camera}T{tracklet}F{frame}.jpg` + +6. **Downloaded MEVID annotation metadata** (track_info files only, ~1MB): + - Saved to `data/mevid/mevid-v1-annotation-data/` + - Files: `track_train_info.txt` (6,339 tracklets), `track_test_info.txt` (1,754 tracklets), `query_IDX.txt` + - Format: `start_index end_index person_id outfit_id camera_id` (scientific notation, use `int(float())`) + +7. **Analyzed MEVID-MEVA coverage overlap**: + - **175 of our 891 MEVA slots overlap with MEVID** (~20%) + - 6 overlapping dates: 2018-03-07, 03-09, 03-11, 03-12, 03-13, 03-15 + - Our date 2018-03-05 has NO MEVID coverage + - MEVID also covers 2018-05-14, 05-16, 05-18 (we don't have these) + - Camera overlap varies per slot (some perfect, some partial, some zero) + - Example: `2018-03-07.11-00-00.school` has 4 overlapping cameras (G299, G330, G420, G423) + +### Key Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/generate_qa_v3_multicam.py` | Modified | Fixed 5 bugs (temporal, causality, perception, spatial) | +| `scripts/prompts/spatial.txt` | Modified | Added pre-computed answer fields to template | +| `scripts/generate_qa_v1_rulebased.py` | Renamed | From generate_qa_logic.py | +| `scripts/generate_qa_v2_llm.py` | Renamed | From generate_qa_llm.py | +| `data/mevid/mevid-v1-video-URLS.txt` | Downloaded | 976 MEVID clip URLs | +| `data/mevid/mevid-v1-annotation-data/` | Downloaded | Track info (train/test/query) | +| `data/qa_pairs/2018-03-05.13-15-00.bus.multicam.json` | Regenerated | Fixed V3 output | + +### MEVID Integration Plan + +**Problem**: MEVA annotations label actors as generic `person_1`, `vehicle_5` — meaningless to VLMs. +**Solution**: MEVID adds cross-camera person identity labels (158 people, 598 outfits) to MEVA. + +**Integration steps** (not yet implemented): +1. ✅ Downloaded MEVID annotation metadata +2. ✅ Confirmed 175 overlapping slots (~20% coverage) +3. **Next**: Download MEVID bbox data selectively for overlapping slots/cameras only (not full 43.5GB) +4. **Next**: Build mapping script: MEVID tracklet → MEVA logic tuple (by camera ID + frame range overlap) +5. **Next**: Enrich logic tuples with MEVID person IDs where available +6. **Next**: Update QA generators to use identity info when available + +**Limitations**: +- Only ~20% of our slots have MEVID coverage +- MEVID covers only persons (not vehicles) — cars/trucks remain anonymous +- The link between MEVID tracklets and MEVA activity annotations is by (camera, frame overlap) — not guaranteed 1:1 mapping +- Bbox data is large (43.5GB total); selective download needed +- Disk space severely limited (~105MB free) + +### Notes for Future Sessions + +- **Script naming convention**: V1 = rule-based, V2 = monolithic LLM, V3 = per-question LLM (Multi-Camera arch) +- **V3 bug fixes are tested and verified**: Run with seed=99 for the verified configuration +- **Causality actor overlap**: Still shows "no shared actors" warnings for the bus slot — this is because MEVA annotations don't consistently use the same actor ID across cause/effect pairs. The fix ensures same-camera preference, which is the best possible with current data +- **MEVID data location**: `data/mevid/` (annotation metadata), `data/mevid/mevid-v1-video-URLS.txt` (clip URLs) +- **Track info format**: Scientific notation, parse with `int(float(x))` +- **Disk very tight**: ~105MB free. Don't download bbox data (30.5GB+13GB) without clearing space first +- **copilot-instructions.md**: Should be updated to reflect V1/V2/V3 naming and MEVID integration + +--- + +## 2026-02-05: Pipeline Refactor — Extract Logic Tuples & Dedup + +### Tasks Completed + +1. **Created `scripts/extract_logic_tuples.py`** — New standalone script that: + - Reads cluster info from `data/annotated_activity_slots.txt` instead of scanning the dataset filesystem + - Builds a machine-readable `data/slot_index.json` (891 slots) on first run, caches it + - Takes a single slot (`date.time.site` format) and extracts only logic tuples (no QA generation) + - **Supports both annotation formats**: Kitware YAML (700+ slots) AND NIST JSON (57 slots). The old pipeline only handled NIST JSON + - Outputs to `data/logic_tuples/{slot}.json` + - CLI: `--list-slots`, `--list-slots-with-activities`, `--build-index`, `--slot`, `--output`, `--verbose` + +2. **Built `data/slot_index.json`** — Machine-readable version of `annotated_activity_slots.txt` + - 891 slots with cameras, sources, clip counts + - Auto-rebuilds when txt is newer than json + +3. **Investigated cross-source annotation overlap** + - **57 slots** have the same camera annotated by both kitware (YAML) and nist-json + - They are NOT literal duplicates: NIST JSON is sparse (few key events), Kitware YAML is dense (comprehensive) + - Activity names differ: NIST uses `Open_Trunk`/`Closing_Trunk`, Kitware uses `person_opens_trunk`/`person_closes_trunk` + - They don't contradict — kitware is strictly more complete + - **Added source priority dedup**: `kitware > kitware-training > nist-json`. When a camera appears in multiple sources, only the highest-priority one is used + +4. **Confirmed cross-site mixing bug in old pipeline (now fixed)** + - Old `test_output.json` used slot `2018-03-07.17-05-00` (no site), grouping bus+school cameras + - This produced nonsensical QA like comparing a bus embrace to a school trunk-opening + - New slot system (`date.time.site`) prevents this by construction + - Example: old slot split into `2018-03-07.17-05-00.admin` (1 cam), `.bus` (5 cams), `.school` (6 cams) + +### Key Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `scripts/extract_logic_tuples.py` | Created | Standalone fact extraction from single slot | +| `data/slot_index.json` | Created | Machine-readable slot index (891 slots) | +| `data/logic_tuples/` | Created | Output dir for per-slot logic tuple JSONs | +| `scripts/multicam_qa_pipeline.py` | Unchanged | Original pipeline preserved as-is | + +### Design Decisions + +- **Source priority dedup**: kitware > kitware-training > nist-json. Kitware annotations are denser and use standardized activity names. NIST JSON is a small curated subset (57 slots, 65 clips) that adds little over kitware. +- **Kitware YAML parsing**: Uses `tsr0: [start, end]` frame ranges (simpler than NIST state signals). Actor types resolved via `.types.yml` companion files. Falls back to `eval()` if PyYAML not available. +- **Original pipeline kept**: `multicam_qa_pipeline.py` is untouched. The new `extract_logic_tuples.py` is Step 1 of a modular replacement. Step 2 (QA generation — logic vs LLM API) is pending user decision. + +### Notes for Future Sessions + +- **Next step**: Decide whether QA pair generation should be rule-based logic (like the old pipeline) or LLM API calls. Create the corresponding script. +- `extract_logic_tuples.py` outputs contain `annotation_source` field per tuple for traceability +- Activity name inconsistency across sources (e.g., `Open_Trunk` vs `person_opens_trunk`) — may need a normalization map if mixing sources +- The `data/logic_tuples/` dir will accumulate per-slot JSONs; consider a batch extraction script later +- `python3` is required (no `python` alias on this system) + +--- + +## 2026-02-05 (continued): QA Generation Prototypes — Logic vs LLM + +### Context +Exploratory analysis with API budget. Lab partner (separate dataset) uses LLM API for QA generation. Goal: prototype both approaches to compare quality before running batch generation on all 891 slots. + +### Tasks Completed + +1. **Analyzed QA generation strategies** + - **Rule-based logic**: Deterministic, reproducible, fast, free. But formulaic and limited question diversity. + - **LLM API**: Natural language, creative, easy to iterate on. But non-deterministic, expensive, slow, requires API key. + - **Decision**: Hybrid approach for exploratory phase. Rule-based for temporal/ordering/perception (factual). LLM for causality/reasoning (creative). + +2. **Created `scripts/generate_qa_logic.py`** — Rule-based QA generation (deterministic) + - **Input**: `data/logic_tuples/{slot}.json` (from `extract_logic_tuples.py`) + - **Output**: `data/qa_pairs/{slot}.logic.json` + - **Categories** (6, excluding summarization & numerical): + - `temporal`: Before/after/during relationships (2 events) + - `ordering`: Chronological sequencing (3 events, shuffled labels) + - `perception`: Yes/No activity presence on camera + - `causality`: Why did X happen? (links effect to preceding cause) + - `spatial`: Camera coverage, exclusive activities, shared activity types + - `camera_transition`: Which camera first/last captured an actor (multi-camera actors only) + - **Target**: ~5-10 questions per category (57 total for bus slot) + - **Determinism**: Fixed random seed (42) for reproducibility + - **CLI**: `--input`, `--output`, `--categories` + - **Tested**: ✅ Generates 57 questions from bus slot (59 tuples, 3 cameras) + +3. **Created `scripts/generate_qa_llm.py`** — GPT-4o API-based QA generation (creative) + - **Input**: `data/logic_tuples/{slot}.json` + - **Output**: `data/qa_pairs/{slot}.llm.json` + - **Model**: OpenAI GPT-4o (default), supports `--model gpt-4o-mini` for cheaper testing + - **Prompt strategy**: + - System prompt: Instructions on rules (only use provided facts, be specific, cite cameras/timestamps) + - User prompt: Compact fact summary (~13K chars for 59 tuples), category specifications (name, description, target count) + - Response format: Structured JSON with `question`, `answer`, `evidence`, `requires_cameras` + - **CLI**: `--input`, `--output`, `--model`, `--temperature`, `--categories`, `--dry-run` + - **Dry-run mode**: Show prompt without calling API (for preview/debugging) + - **Tested**: ✅ Dry-run works. Prompt is ~13K chars (well within context) + - **Note**: Requires `pip install openai` and `export OPENAI_API_KEY="sk-..."` + +### Key Files Created + +| File | Purpose | Input | Output | +|------|---------|-------|--------| +| `scripts/generate_qa_logic.py` | Rule-based QA generation | `data/logic_tuples/{slot}.json` | `data/qa_pairs/{slot}.logic.json` | +| `scripts/generate_qa_llm.py` | LLM API-based QA generation | `data/logic_tuples/{slot}.json` | `data/qa_pairs/{slot}.llm.json` | +| `data/qa_pairs/` | Output directory | — | Both scripts' JSON outputs | + +### Test Results + +**Rule-based on `2018-03-05.13-15-00.bus`** (59 tuples, 3 cameras: G340, G505, G506): +``` +temporal → 10 questions +ordering → 10 questions +perception → 10 questions +causality → 10 questions +spatial → 7 questions +camera_transition → 10 questions +Total: 57 questions +``` +Generated deterministically in <1s. + +**LLM dry-run on same**: +- System prompt: 729 chars +- User prompt: 12,844 chars (compact fact summary + category instructions) +- Would send to GPT-4o, return structured JSON +- Estimated cost: ~$0.05–0.10 per slot (model: gpt-4o) + +--- + +## 2026-02-06: Multi-Camera Architecture QA Script + LLM Safety + Repo Analysis + +### Context +Lab partner uses UTAustin-SwarmLab/Multi-Camera repo for QA generation on nuScenes/Waymo/Ego-Exo4D. Goal: adapt their class-hierarchy architecture to MEVA pipeline and compare approaches. + +### Tasks Completed + +1. **Audited `generate_qa_llm.py` for API cost safety** + - No API call leaks found (exactly 1 call per invocation, no retry loops) + - Added: `estimate_cost()` with per-model pricing table (gpt-4o, gpt-4o-mini, gpt-4-turbo) + - Added: Confirmation prompt before API call (`Proceed? [y/N]`) + `--yes/-y` flag for batch use + - Added: Actual usage/cost reporting after API response + - Added: `api_usage` field saved in output JSON + - Added: `python-dotenv` integration for `.env` file API key loading + +2. **Cloned UTAustin-SwarmLab/Multi-Camera to `/home/ah66742/Multi-Camera/`** + - Full repo analysis: 40+ files read (all Python modules, prompt templates, configs) + - Key QA script: `nuscenes/datasetbuilder/nuscens_build.py` (1445 lines, 8 classes) + +3. **Created `Multi-Camera/REPO_GUIDE.md`** (~400 lines) + - Complete directory structure, module details, data flow diagram + - API cost considerations, dependencies, quick start guide + - MEVA comparison table, hardcoded paths inventory + +4. **Analyzed architectural differences** between `generate_qa_llm.py` and `nuscens_build.py` + - Key finding: Multi-Camera does **1 GPT call per question** with pre-selected focused context + - MEVA does **1 GPT call for all ~50 questions** (dumps everything to GPT) + - Multi-Camera: class hierarchy, prompt template files, GPT logging, MCQ output, incremental save + - MEVA: monolithic function, inline prompts, no logging, open-ended Q&A + +5. **Created `scripts/generate_qa_multicam.py`** — New MEVA QA script replicating Multi-Camera architecture + - Class hierarchy: Base `QAGenerator` → 6 subclasses (temporal, ordering, perception, causality, spatial, camera_transition) + - Each subclass: loads prompt from `scripts/prompts/*.txt`, pre-selects events, builds focused prompt, makes 1 GPT call → 1 MCQ + - GPT call logging to `data/gpt_logs/{slot}/{category}/` JSON files + - `QASample` dataclass for standardized output + - Incremental save after each question + - Cost estimation + confirmation prompt kept from MEVA + - Output: `data/qa_pairs/{slot}.multicam.json` + - CLI: `--slot`, `--categories`, `--num`, `--model`, `--dry-run`, `--yes`, `--seed` + +6. **Created 6 prompt template files in `scripts/prompts/`** + +7. **Freed 403 MB disk space** — Deleted `aws/` (241 MB, AWS CLI installer) and `tools/` (162 MB, static ffmpeg) + +### Three QA Generation Approaches Now Available + +| Script | Approach | API Calls | Output | Cost/Slot | +|--------|----------|-----------|--------|-----------| +| `generate_qa_logic.py` | Rule-based | 0 | Open Q&A | Free | +| `generate_qa_llm.py` | Monolithic LLM | 1 | Open Q&A | ~$0.05 | +| `generate_qa_multicam.py` | Per-question LLM | 12 (default) | MCQ | ~$0.06 | + +--- + +## 2026-02-06 (Session 4): Dataset Completeness Audit, Multi-Camera Extraction & Annotation Quality Documentation + +### Context +Started session by freeing disk space (Multi-Camera folder at 100% usage). Transitioned to extracting Multi-Camera algorithms for MEVA integration without maintaining full repository. Discovery of incomplete MEVA dataset led to comprehensive audit of missing components and annotation quality issues. + +### Tasks Completed + +1. **Disk Space Management**: + - Removed `/home/ah66742/Multi-Camera/` directory (freed significant space) + - Preserved essential algorithms before deletion + +2. **Multi-Camera Algorithm Extraction**: + - **Created**: `/home/ah66742/scripts/multicam_integration.py` - Standalone algorithm integration + - **Features**: EventConsolidator class, AdvancedTemporalSelector, enhanced prompt templates + - **Integration**: Seamlessly works with existing `generate_qa_v3_multicam.py` pipeline + - **Documentation**: `/home/ah66742/docs/multi_camera_reference.md` (400+ line technical reference) + +3. **MEVA Dataset Completeness Audit**: + - **Discovery**: Local dataset only contained ground camera footage (`drops-123-r13`) + - **Missing Components**: UAV footage (27GB), 3D scene models, example videos and annotations + - **Verification**: Used AWS CLI to compare local vs official S3 bucket structure + +4. **Dataset Downloads**: + - **Examples**: ✅ Downloaded `s3://mevadata-public-01/examples/` (~1.5GB) + - **3D Models**: ❌ Pending + - **UAV Footage**: ❌ Pending (27GB) + +5. **Annotation Quality Issues Discovery**: + - Identified 5 critical problems: ID switching, temporal gaps, coarse categories, sync issues, source inconsistency + - Quantified impact on QA generation quality (30% ID consistency, 60% temporal continuity) + - **Created**: `/home/ah66742/.github/ANNOTATION_QUALITY_ANALYSIS.md` + - Documented mitigation strategies and research limitations + +### Key Revelations + +| Issue | Discovery | Impact | +|-------|-----------|--------| +| **Incomplete Dataset** | Missing UAV, 3D models, examples | Limited to ground camera analysis only | +| **ID Switching** | Same person gets different IDs across cameras | Breaks multi-camera temporal tracking | +| **Temporal Gaps** | Activities have sporadic annotation coverage | Limits before/after relationship questions | +| **Source Inconsistency** | Kitware vs NIST naming differences | Requires robust extraction pipeline | +| **Multi-Camera Sync** | Activities in overlapping views misaligned | Contradictory answers in spatial questions | + +### Files Created/Modified Today + +| File | Action | Description | +|------|--------|-------------| +| `/home/ah66742/scripts/multicam_integration.py` | Created | Multi-Camera algorithm adaptations | +| `/home/ah66742/docs/multi_camera_reference.md` | Created | Technical algorithm documentation | +| `/home/ah66742/.github/ANNOTATION_QUALITY_ANALYSIS.md` | Created | Comprehensive annotation quality analysis | +| `/nas/mars/dataset/MEVA/examples/` | Downloaded | Activity examples and annotation samples | + +--- diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb08-09.md b/docs/project-history/github-session-archive/session_records/sessions_feb08-09.md new file mode 100644 index 0000000..ce535f6 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb08-09.md @@ -0,0 +1,240 @@ +# Sessions: February 8–9, 2026 + +> Scene graph era: temporal scene graph architecture, symbolic QA, cross-camera QA, annotation quality audit + +--- + +## 2026-02-08 (Session 4): Pipeline Rethink — Temporal Scene Graph + Symbolic QA + +### Context +Reviewed the project's running slides (Multi Camera - Running Slides.pdf) to align MEVA QA generation with the NuScenes approach used by the lab. Key insight from slides: questions should be generated from a **temporal scene graph** with **deterministic answer computation**. GPT is used ONLY to naturalize question text — never to compute answers or decide which camera to reference. + +### Problems Identified with Old Approach (V1-V3) +- V1 questions put camera IDs in the question text (VLM should figure this out) +- V1 specified raw timestamps like "175s" (unnatural, too precise) +- V1-V3 used anonymous actor labels (person_1, vehicle_5) unrecognizable to VLMs +- V3 had GPT computing answers (risk of hallucination) +- No scene graph structure — just flat event lists + +### Architecture Decision: NuScenes-Style Pipeline +From the slides, the correct pipeline is: +1. **Annotations → Temporal Scene Graph** (nodes=entities/events, edges=temporal/spatial/coref) +2. **Graph → Symbolic Queries** with deterministic answers (graph traversal, not inference) +3. **GPT naturalizes** question text only (no new information introduced) +4. **Actors identified by their actions** ("the person who opens the trunk" not "person_7") +5. **Camera not revealed** in question — VLM must determine which camera(s) to examine + +### Tasks Completed + +1. **Cleaned up old files into `old_versions/` subdirectories**: + - `scripts/old_versions/`: V1, V2, V3 QA scripts + prompts + legacy pipeline + - `data/old_versions/`: old QA pairs, logic tuples, GPT logs, test_output.json + +2. **Built `scripts/build_scene_graph.py`** — Temporal Scene Graph builder: + - **Entity nodes** from `geom.yml`: per-actor bbox tracks with keyframe sampling + - **Event nodes** from `activities.yml`: activity instances with actors, timing, camera + - **Temporal edges**: BEFORE/AFTER/OVERLAPS between ALL event pairs (1711 for bus, 59340 for school) + - **Spatial edges**: 2D bbox relations (LEFT_OF, RIGHT_OF, ABOVE, BELOW) for co-visible entities + - **Visibility edges**: entity ↔ camera mapping with frame ranges + - **Participation edges**: entity ↔ event links with roles (primary/object) + - **Co-reference edges**: bbox IoU-based detection of same physical entity across annotation IDs + +3. **Implemented entity co-reference via bbox IoU**: + - MEVA assigns fresh actor IDs per activity → same person gets different IDs + - Solution: compute avg IoU between same-type entities on same camera + - IoU ≥ 0.5 → co-reference (many are IoU=1.0 — identical bboxes) + - Union-Find clustering merges co-referent IDs into physical entity clusters + - **Results**: Bus slot: 96 raw entities → 49 clusters (17 merged, 17 multi-event actors) + - **Results**: School slot: 546 raw entities → 380 clusters (91 merged, 90 multi-event actors) + +4. **Built actor event chains** (cluster-aware): + - Each physical entity (cluster) gets a chronological list of activities + - Example: person_3/G340 → opens_trunk → unloads_vehicle → carries_heavy_object → picks_up → puts_down + - Enables "the person who opens the trunk, what do they do next?" style questions + +### Scene Graph Structure (JSON) + +``` +{ + "entities": {entity_id: {bbox tracks, type, camera}}, + "events": [{activity, actors, camera, start/end time}], + "temporal_edges": [{source, target, BEFORE/AFTER/OVERLAPS, gap_sec}], + "spatial_edges": [{entity_a, entity_b, LEFT_OF/RIGHT_OF/ABOVE/BELOW, frame}], + "visibility_edges": [{entity_id, camera_id, frame range}], + "participation_edges": [{entity_id, event_id, role}], + "coref_edges": [{entity_a, entity_b, avg_iou}], + "entity_clusters": {representative: [member_ids]}, + "actor_event_chains": {cluster_rep: [event_ids chronological]} +} +``` + +--- + +## 2026-02-08 (Session 5): Symbolic QA Generator + GPT Naturalization Wrapper + +### Context +Continuing from Session 4's scene graph architecture. Built the two remaining pipeline stages: +1. Deterministic symbolic QA generator (graph traversal, no LLM) +2. GPT naturalization wrapper (rephrases templates, doesn't compute answers) + +### Tasks Completed + +1. **Built `scripts/generate_qa_symbolic.py`** — 6 question categories: + - **temporal** (15): "After the person who opens the trunk closes the trunk, what do they do next?" + - **ordering** (6-11): Arrange 3-4 events from same actor chain chronologically + - **perception** (11-14): Yes/No about activity occurrence, balanced positive/negative + - **causality** (15): "Why does X do Y?" using CAUSAL_CHAINS + same-actor adjacency + - **spatial** (15): 2D bbox relations (left/right/above/below), 60%+ consistency filter + - **camera_view** (5-15): Which abstract camera label shows an exclusive activity + +2. **Comprehensive grammar fixes** — 5 rounds of testing/fixing: + - Added `ACTIVITY_DISPLAY` dict mapping all 37 activities → proper English (3rd person + base form) + - Entity-type-aware descriptions, redundancy prevention, template deduplication + - Distractor filtering: only same entity type + +3. **Built `scripts/naturalize_qa.py`** — GPT wrapper: + - Loads `.symbolic.json`, sends each question to GPT for naturalization only + - System prompt strictly prohibits changing meaning, adding info, or reordering options + - 3 retries on failure, keeps original template as fallback + - Dry-run mode shows prompts + cost estimates without API calls + +4. **Set up Python venv** with openai 2.17.0 + pyyaml at `/home/ah66742/venv/` + +### Test Results + +| Slot | Events | Cameras | Questions | Time | +|------|--------|---------|-----------|------| +| bus (2018-03-05.13-15-00) | 59 | 3 (G340, G505, G506) | 70 | <1s | +| school (2018-03-07.17-05-00) | 345 | 5 (G299, G330, G336, G421, G638) | 82 | <1s | + +### Full Pipeline (3 steps) +```bash +cd /home/ah66742/scripts +python3 build_scene_graph.py --slot "2018-03-05.13-15-00.bus" -v +python3 generate_qa_symbolic.py --slot "2018-03-05.13-15-00.bus" -v +/home/ah66742/venv/bin/python3 naturalize_qa.py --slot "2018-03-05.13-15-00.bus" --model gpt-4o-mini -y +``` + +--- + +## 2026-02-08 (Session 7): Cross-Camera QA Generator + +### Context +Built on Session 6's V2 scene graph with cross-camera narrative chains. Created a new QA generator +specifically for questions that REQUIRE multiple camera views to answer — the "dream" questions for +multi-camera VLM evaluation. + +### Tasks Completed + +1. **Built `scripts/generate_qa_crosscam.py`** — 5 cross-camera question categories: + - **cross_camera_temporal** (15): "After the person who exits a vehicle opens a facility door, what do they do next in a different camera view?" + - **cross_camera_ordering** (15): Arrange 3-4 events from different cameras chronologically + - **cross_camera_tracking** (15): "After [person] does X in camera G421, which camera view shows them next?" + - **cross_camera_gap** (15): "Between opening a facility door and dropping off a person, what does [person] do?" + - **camera_identification** (6-15): "A person does X in one view. In which other view do they do Y?" + +2. **Quality fixes through 4 iterations:** + - Entity type consistency, gerund forms, tracking redundancy prevention + - Gap answer validation, camera identification cross-activity requirement + - Flexible minimums: works with 3 cameras (2 distractors) + +### Test Results + +| Slot | Cameras | Cross-cam Narratives | Questions | 2-cam Qs | 3-cam Qs | +|------|---------|---------------------|-----------|----------|----------| +| school (2018-03-07.17-05-00) | 5 | 15 | 75 | 67 | 8 | +| bus (2018-03-05.13-15-00) | 3 | 3 | 45 | 45 | 0 | + +100% of questions verified to require 2+ cameras. + +### Example 3-Camera Question +> **Q**: Between opening a facility door and dropping off a person, what does the person who exits a vehicle do? +> **A**: enters the scene through a structure +> **Cameras needed**: G336, G421, G638 + +--- + +## 2026-02-09 (Session 6): Annotation Folder Subset Check + +### Tasks Completed +1. **Verified annotations vs kitware subset** — Compared file lists between MEVA/annotations and meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware. + - Result: **Not a subset**; both sides have files missing from the other. + - Counts: annotations has 192 files; kitware has 2310 files. + +--- + +## 2026-02-09 (Session 7): Full DIVA-Phase-2 Diff Lists + +### Tasks Completed +1. **Computed full diff lists against entire DIVA-phase-2/MEVA tree** (kitware, kitware-meva-training, nist-kf1-json, contrib). + - Normalized paths by removing the source prefix to match MEVA/annotations layout. + - Result: annotations-only list is empty (0 files); DIVA-only normalized list has 12,984 files. + +### Files Created +- `output/annotations_only_normalized.txt` +- `output/diva_only_normalized.txt` + +--- + +## 2026-02-09 (Session 6): Annotation Quality Audit + Remedy Research + +### Context +User noticed that clip `2018-03-07.16-50-00.16-55-00.admin.G329` shows 3 people on video but annotations only capture 1 person with 1 activity (Enter_Facility). Ran full audit of annotation completeness and its impact on QA, then researched all available data sources for a real remedy. + +### Key Finding: MEVA Annotations Are Activity-Centric (By Design) + +From `MEVA-Annotation-Definitions.pdf`: +> "Completed annotations for a clip consist of identification of **all activities** in a clip with activity-centric annotations including... identification of tracked objects and the time frames for their involvement **in the activity**" + +Entities are ONLY tracked when performing one of the 37 defined ActEV activities. People visibly walking through a scene but not performing a defined activity are NOT annotated. This is systemic, not a bug. + +### Annotation Source Inventory (Complete) + +| Source | Clips | Format | Activities | Notes | +|--------|-------|--------|------------|-------| +| **kitware** (eval) | 769 | KPF YAML | **761 clips with acts** (99%) | Gold standard | +| **kitware-meva-training** | 1,443 | KPF YAML | 35,174 instances | Slightly lower quality | +| **CMU-v2** (contrib) | 1,111 | NIST JSON + KPF YAML | All 37 activities | OLD activity names | +| **UMD-IBM** (contrib) | 637 | NIST JSON | All 37 activities | | +| **UMD-v1** (contrib) | 490 | NIST JSON | All 37 activities | | +| **IBM-person-person** | 90 | NIST JSON | 6 activities | | +| **IBM-person-vehicle** | 217 | NIST JSON | 6 activities | | +| **NIST JSON** | ~65 | NIST JSON | Sparse subset | | +| **Local** (/nas/mars/) | 64 | KPF YAML | Available | | + +**IMPORTANT PARSING BUG DISCOVERED**: KW-eval `.activities.yml` files use flow-mapping format — `yaml.safe_load_all()` returns a single document containing a list (not separate documents). **KW-eval actually has 761/769 clips with activities (99%).** + +### QA Vulnerability Analysis (152 Questions, 2 Test Slots) + +| Risk Level | Count | % | Description | +|------------|-------|---|-------------| +| **SAFE** | 91 | 60% | Temporal, ordering, causality — internally consistent | +| **PARTIAL** | 30 | 20% | Spatial — correct for tracked entities but incomplete picture | +| **VULNERABLE** | 31 | 20% | Perception "No" + camera_view — could be factually wrong | + +### GPS Data — Actor Ground Truth + +GPS README confirms: "actors and personnel carried GPS loggers set to log their location once every 10 seconds." Key stats: +- **116 GPX files** covering 6 of 8 dates +- **Mean 78.2 actors per slot**, max 98 +- IDs like `G623` are consistent across days within March session +- GPS timestamps in UTC (video timestamps in UTC-4) + +### Camera → Site Mapping (28 cameras) +``` +admin: G326, G329 +bus: G331, G340, G475, G505, G506, G508, G509 +hospital: G301, G341, G436, G476, G479 +school: G299, G300, G328, G330, G336, G339, G419, G420, G421, G423, G424, G474, G638, G639 +``` + +### Remedy Assessment + +**NOT a remedy — Annotation Merging**: All annotation sources follow the same activity-centric protocol. Merging adds marginal activity types (~25% of clips) but doesn't solve false-negative perception questions. + +**Potential Real Remedies**: +1. **GPS-Based Site-Level Entity Validation** — Parse GPX to count actors per site per slot +2. **Off-the-Shelf Person Detector** — Run YOLO on video frames to count ALL visible people +3. **Question Design Changes** — Drop "No" perception answers, add annotation_confidence field + +--- diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb10-11.md b/docs/project-history/github-session-archive/session_records/sessions_feb10-11.md new file mode 100644 index 0000000..06b6d51 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb10-11.md @@ -0,0 +1,240 @@ +# Sessions: February 10–11, 2026 + +> V4–V5 pipeline development: V4 QA generator, GPS/MEVID analysis, V5 deterministic pipeline, full audit + +--- + +## 2026-02-10 (Session 10): V4 QA Generator + actev-data-repo Exploration + +### Context +Built V4 QA generator addressing all known quality issues from vulnerability analysis. Then explored +the newly cloned actev-data-repo from NIST/Kitware for additional data. + +### Tasks Completed + +#### V4 QA Generator (`scripts/generate_qa_v4.py`, ~935 lines) +- **3 categories only**: temporal (cross-cam), spatial (3D), perception (positive-only) +- **10 questions per slot**: 3 temporal + 3 spatial + 4 perception +- **Key design decisions**: + - **Dropped causality** — causal chains weren't ground truth, just heuristics + - **Spatial: 3D world coordinates** via KRTD back-projection instead of 2D left/right + - Projects bbox foot-point to ground plane intersection + - Distance in meters (Near ≤5m, Far ≥15m) + - Camera-invariant — same answer regardless of viewing angle + - **Temporal: cross-camera only** with 3-20s gap, different cameras, different activities + - **Perception: positive-only MCQ** — "Which activity occurs?" with 1 correct + 3 absent distractors + - Eliminates false-negative vulnerability entirely +- **Successfully tested**: school slot → 10 questions (temporal gaps: 4.9s, 10.3s, 11.5s; spatial distances: 1.4m, 32.5m, 36.2m) + +#### actev-data-repo Exploration +- **Cloned**: `https://gitlab.kitware.com/actev/actev-data-repo.git` → `/nas/mars/dataset/MEVA/actev-data-repo/` +- **Key findings**: + - **MEVA MANIFEST**: 4,159 R13 AVI files (610.5 GB), including dates 2018-03-08, 2018-03-10, 2018-05-16 (NOT on our disk) + - **MEVA KF1-examples**: 65 clips — **ALL already exist in meva-data-repo** + - **Validation Set 7**: 214 MEVA clips, 60 processed with 4,648 NIST JSON activity annotations + - **HADCV22-Test**: 201 clips including dates we don't have + - **SimUF-KA-SimSA5**: 154 activity exemplar clips (potential for VLM few-shot) + +### Files Created +- `scripts/generate_qa_v4.py` — V4 QA generator (3 categories, 10 questions) +- `data/qa_pairs/2018-03-07.17-05-00.school.v4.json` — Test output +- `docs/actev-data-repo-reference.md` — Comprehensive reference (11 sections) + +### Key Decisions +- **V4 is current best generator** — replaces symbolic + crosscam approaches +- **actev-data-repo adds no new annotations** for our pipeline (all 65 are duplicates) +- **VSet7 activities.json** is potentially useful — NIST JSON format with per-frame bboxes for 33 slots + +--- + +## 2026-02-10 (Session 11 UPDATED): GPS Actors + MEVID Coverage Analysis + +### Context +Major discoveries about GPS data and MEVID coverage. + +### Tasks Completed + +#### 1. GPS Data — ACTORS, NOT CAMERAS! +**Key discovery**: The GPX files contain **actor GPS tracks**, not camera positions. +- Each actor carried a GPS logger (ID format: Gnnn like G623) +- Loggers recorded position every 10 seconds +- 105 unique actors tracked across the KF1 collection +- Consistent actor IDs within each session (March and May) + +**Built `scripts/parse_gps_actors.py`**: +```bash +python3 scripts/parse_gps_actors.py --stats # 116 files, 105 actors, 256K trackpoints +python3 scripts/parse_gps_actors.py --slot "2018-03-07.17-05-00" -v +``` + +| Metric | Value | +|--------|-------| +| GPX files | 116 (5-min slots) | +| Unique actor IDs | 105 (G517-G625 range) | +| Total trackpoints | 256,456 | +| GPX slots with annotations | 87 of 116 (75%) | + +#### 2. MEVID Coverage — 70% ALREADY ON DISK! +| Category | Clips | +|----------|-------| +| Total MEVID clips | 976 | +| **Already on disk** | **685 (70.2%)** | +| Missing (May 05-14/16/18) | 291 | + +**All 685 March MEVID clips exist!** Download script created: `scripts/download_mevid_may.sh` + +#### 3. VSet7 Annotations — INTEGRATED +Built `scripts/integrate_vset7.py`: + +| Metric | Value | +|--------|-------| +| Total activities | 2,324 (after deduplicating SimSA5 prefixes) | +| Clips | 60 | +| Unique slots | 47 | + +**Output**: `/nas/mars/dataset/MEVA/vset7_annotations/{slot}.json` — 47 per-slot JSON files + +#### 4. V4 Pipeline Testing +| Slot | Questions | Temporal | Spatial | Perception | +|------|-----------|----------|---------|------------| +| school (2018-03-07.17-05-00) | 10 | 3 | 3 | 4 | +| bus (2018-03-05.13-15-00) | 8 | 0 | 4 | 4 | + +### Files Created/Modified +- `scripts/integrate_vset7.py` — VSet7 JSON parser (405 lines) +- `scripts/parse_gps_actors.py` — GPS actor track parser (454 lines) +- `scripts/download_mevid_may.sh` — Download script for 291 May MEVID clips +- `/nas/mars/dataset/MEVA/vset7_annotations/*.json` — 47 per-slot VSet7 files +- `/nas/mars/dataset/MEVA/gps_actors/*.json` — 116 per-slot GPS actor files + +### Data Availability Summary + +| Data Source | Status | Notes | +|-------------|--------|-------| +| Kitware YAML | ✅ Primary | 891 slots, comprehensive | +| VSet7 NIST JSON | ✅ Supplementary | 47 slots, 2,324 activities | +| GPS actor tracks | ✅ Available | 105 actors, no ID mapping to annotations | +| MEVID re-ID | ⚠️ 70% on disk | 685/976 clips, missing May dates | + +--- + +## 2026-02-11 (Session 12): V5 QA Pipeline — Build + Quality Fixes + +### Context +Fresh start for V5 QA pipeline following NuScenes approach: deterministic symbolic QA from scene graphs, GPT wrapper only at the end. All old QA pairs/scene graphs/logic tuples archived. + +### Tasks Completed + +#### 1. Archive Old Outputs +- Moved all previous QA pairs (21 files) → `data/old_versions/qa_pairs/` +- Moved all scene graphs → `data/old_versions/scene_graphs/` +- Moved all logic tuples (18 files) → `data/old_versions/logic_tuples/` + +#### 2. VSet7 vs Kitware Comparison +**Key finding: VSet7 is a SUBSET of Kitware**, not a supplement. +- On overlapping cameras, annotations match exactly (same activities, same frame ranges) +- VSet7 simply covers fewer cameras + +#### 3. GPS Utility Analysis +- GPS actor IDs (G517-G625) are NOT linked to annotation actor IDs → cannot use in video QA +- **Key insight**: GPS useful for validation/ground-truth only, NOT for video-visible questions + +#### 4. Built `scripts/generate_qa_v5.py` (~800 lines) +Self-contained V5 pipeline: scene graph build + symbolic QA generation in one script. + +**5 active categories** (3 questions each): +| Category | Question Type | Data Source | +|----------|--------------|-------------| +| temporal | Cross-camera before/after | Kitware activities.yml timestamps | +| event_ordering | Chronological sequence of 4 events | Kitware across 3-4 cameras | +| perception | Which camera shows/doesn't show X? | Activity→camera mapping | +| spatial | Camera position distances (KRTD) | KRTD camera center positions | +| camera_transition | First/last camera to capture event | Entry/exit events across cameras | + +**Architecture**: CSafeLoader for speed, `_build_options()` MCQ builder with MAX_MCQ_OPTIONS=5, deterministic seed=42. + +#### 5. Quality Fixes +| Issue | Fix | +|-------|-----| +| Spatial: GPS actor IDs unlinked to annotations | Replaced with KRTD camera-position-based questions | +| CamTrans: absolute timestamps in questions | Replaced with activity descriptions | +| CamTrans: too many options | MAX_MCQ_OPTIONS=5 cap | + +#### 6. Test Results (Both Slots Pass) +| Slot | Events | Cameras | Questions | +|------|--------|---------|-----------| +| 2018-03-11.16-20-00.school | 587 | 6 | 15 (3 per category) | +| 2018-03-11.11-30-00.school | 527 | 7 | 14 (2 spatial — missing KRTD) | + +### Key Decisions +1. **GPS actors NOT used in QA questions** — IDs don't map to annotation actors +2. **KRTD camera positions for spatial** — only 4-6 cameras per slot have KRTD, but sufficient +3. **MCQ cap at 5 options** for all camera-based questions +4. **MEVID skipped** — not needed for current QA categories + +### Files Created/Modified +- `scripts/generate_qa_v5.py` (~800 lines) +- `data/qa_pairs/2018-03-11.16-20-00.school.v5.json` (15 QA pairs) +- `data/qa_pairs/2018-03-11.11-30-00.school.v5.json` (14 QA pairs) +- Archived 51 files to `data/old_versions/` + +--- + +## 2026-02-11 (Session 13): Full Audit, Reference Cleanup, Progress Report + +### Context +Follow-up session after V5 pipeline was built. Focus on documentation hygiene, deep GPS investigation, V4/V5 comparison, VSet7 analysis, and producing a finalized progress report. + +### Tasks Completed + +#### 1. Updated ALL Outdated References +Rewrote 3 documentation files that referenced obsolete pipelines: +- `.github/ANNOTATION_QUALITY_ANALYSIS.md` — Rewrote with V5 vulnerability assessment (all 5 categories now SAFE) +- `.github/multicam-temporal-questions.md` — Complete rewrite: V5 pipeline, 5 categories, correct paths +- `docs/README.md` — Replaced Multi-Camera integration guide with current V5 pipeline docs + +#### 2. V4 vs V5 Detailed Evaluation +- **V5 wins**: Self-contained, 5 categories (vs 3), no OOM risk, no pre-build needed +- **V4 still valuable for**: Entity-level spatial (bbox→3D projection via KRTD) — more granular +- **V4 text quality better**: Has `ACTIVITY_DISPLAY` dict with 3 grammatical forms + `describe_by_action` +- **Verdict**: V5 is production; V4 kept as spatial bbox reference + +#### 3. GPS Data Deep Investigation +**Key finding: GPS tracks ARE continuous** — not just start/end waypoints. +- Each GPX file has `` (summaries) AND `` (continuous tracks) +- **94 actors** per slot, **29-30 trackpoints each** at ~10 sec intervals +- **Decision**: GPS stays verification-only — no GPS↔annotation ID mapping exists + +#### 4. Camera Transition Fix +Added minimum camera threshold: skip category if `len(sg.cameras) < 3` + +#### 5. VSet7 vs Kitware — Definitive Comparison +On `2018-03-11.16-20-00.school`: +- **Kitware**: 587 events, 6 cameras +- **VSet7**: 332 events, 3 cameras (G330, G423, G638) +- On overlapping cameras: annotations are **identical** +- **VSet7 adds nothing that Kitware doesn't already have** + +#### 6. Finalized Progress Report +Created `docs/PROGRESS_REPORT.md` — comprehensive team report covering pipeline architecture, 5 question categories, workarounds for annotation sparsity, pipeline evolution V1→V5, sample questions. + +### Files Created/Modified + +| File | Action | Description | +|------|--------|-------------| +| `.github/ANNOTATION_QUALITY_ANALYSIS.md` | Rewritten | Updated for V5 | +| `.github/multicam-temporal-questions.md` | Rewritten | Full V5 documentation | +| `docs/README.md` | Rewritten | Current V5 docs | +| `docs/PROGRESS_REPORT.md` | Created | Team progress report | +| `scripts/generate_qa_v5.py` | Modified | Updated spatial docstring; 3-camera min | + +### Key Decisions + +| Decision | Rationale | +|----------|-----------| +| GPS stays verification-only | Continuous tracks exist but no ID mapping to annotations | +| VSet7 = subset of Kitware | Identical annotations on overlapping cameras; fewer cameras | +| V5 is production pipeline | Self-contained, 5 categories, deterministic, no OOM | +| V4 kept for spatial reference | Entity-level bbox→3D projection code still valuable | + +--- diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb12-13.md b/docs/project-history/github-session-archive/session_records/sessions_feb12-13.md new file mode 100644 index 0000000..f44ff24 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb12-13.md @@ -0,0 +1,236 @@ +# Sessions: February 12–13, 2026 + +> V6–V7 era: V6 modular pipeline implementation, V7 planning, memory optimization, MEVID implementation analysis + +--- + +## 2026-02-12 (Session 14): V6 Plan Rewrite, Repo Cleanup Strategy + +### Context +User provided 12 specific V6 requirements and clarified local-vs-repo distinction for GitHub push. + +### Tasks Completed + +#### 1. Rewrote v6_todo.md with All 12 User Requirements +Complete rewrite incorporating: temporal gap 3-15s (fallback 20s), 2+ cameras for temporal only, geom.yml streaming prototype first, raw annotation text, simple spatial + 1 complex, easy difficulty default with classification system, distractor bank, V4/V5-style output format, within-slot deduplication, MEVID-only slots, start with 1 slot, validation plan (documented not implemented). + +Modular architecture: 9 files in `scripts/v6/` + 4 utility modules. Only 3 QA categories: temporal, spatial, perception. + +#### 2. MEVID Data Analysis +Decoded naming convention, found 158 persons / 8093 tracklets / 25 cameras. 98% of train persons on 2+ cameras. 900/929 MEVA slots overlap with MEVID cameras. + +#### 3. Created .gitignore + cleanup_local.sh +- `.gitignore`: excludes archived/, old outputs, large PDFs, personal dirs +- `cleanup_local.sh`: moves old outputs to `~/archived/` (no deletions) +- Replaced old `cleanup_for_push.sh` (which deleted files) + +### Files Created/Modified +- `v6_todo.md` — Complete rewrite (14 sections) +- `.gitignore` — New +- `cleanup_local.sh` — New + +### Key Decisions +- Scene graph is THE novelty — all cross-camera questions derive from it +- MEVID ground truth first, heuristic second +- 3 categories only: temporal, spatial, perception +- Raw annotation text in questions (GPT wrapper later) + +--- + +## 2026-02-12 (Session 17): V7 Memory Optimization + V6 Pipeline Documentation + +### Context +User requested: (1) Confirm V7 has full GEOM loading (not sampled), and (2) comprehensive explanation of V6 pipeline from ground-up principles to low-level implementation. + +### Tasks Completed + +#### 1. Memory Optimization: Full GEOM Loading for V7 + +**Problem identified**: V6 used `sample_every=30` → only loads every 30th bbox frame +- Incomplete trajectories (miss activities between keyframes) +- Spatial questions use "closest frame" approximation (up to 15 frames = 0.5s off) + +**Solution implemented**: Changed V7 to `sample_every=1` → loads ALL bboxes +- Modified `scripts/v7/build_scene_graph.py`: `sample_every=30` → `sample_every=1` +- Modified `scripts/v7/utils/yaml_stream.py`: Updated default to `sample_every=1` + +**Impact**: +- Memory cost: 20 actors × 9000 frames = 180K bboxes per camera (~7 MB) +- Total for 8 cameras: ~56 MB (0.04% of 128 GB RAM) — easily feasible +- Benefits: Complete trajectories, exact spatial positioning + +#### 2. Comprehensive V6 Pipeline Documentation + +Created `docs/V6_PIPELINE_EXPLAINED.md` (5000+ lines) with 8 major sections: + +1. **Ground-Up Principles**: Core problem, design philosophy, key assumptions +2. **Pipeline Architecture**: 7-step flow diagram, module structure, data flow +3. **Data Structures**: Event, Entity, SceneGraph, ResolvedGraph, Question output +4. **Step-by-Step Flow**: Parse → Scene Graph → Resolution → Temporal/Spatial/Perception QA → Validation +5. **Low-Level Details**: YAML parsing (CSafeLoader vs regex streaming), KRTD 3D projection (6-step pipeline), MEVID gap analysis, bug fixes +6. **Memory Management**: V6 sampling vs V7 full loading vs future HDF5 mmap +7. **Question Generation Strategies**: Gap selection, proximity buckets, distractor bank +8. **Validation & Quality Control**: Rules, success criteria, output JSON schema + +### Files Created/Modified +- `docs/V6_PIPELINE_EXPLAINED.md` (5000+ line documentation) +- `scripts/v7/build_scene_graph.py` (`sample_every=30` → `sample_every=1`) +- `scripts/v7/utils/yaml_stream.py` (updated default) +- `v7_todo.md` (added Section 6: memory optimization rationale) + +--- + +## 2026-02-13 (Session 15): V6 Pipeline Implementation — Full Execution + +### Context +Executed v6_todo.md end-to-end on a single MEVID-supported slot (`2018-03-11.11-25-00.school`). + +### Tasks Completed + +#### 1. V6 Pipeline Implementation (14 files) +Created complete modular pipeline in `scripts/v6/`: +- **Utilities** (4): `utils/yaml_stream.py` (geom.yml streaming), `utils/mevid.py` (MEVID mapping), `utils/krtd.py` (calibration + bbox→3D), `utils/iou.py` (IoU computation) +- **Pipeline steps** (5): `parse_annotations.py`, `build_scene_graph.py`, `entity_resolution.py`, `distractor_bank.py`, `run_pipeline.py` +- **QA generators** (3): `generate_temporal.py`, `generate_spatial.py`, `generate_perception.py` +- **Package files** (2): `__init__.py`, `utils/__init__.py` + +#### 2. Slot Selection +Chose `2018-03-11.11-25-00.school`: 9 cameras, 8/9 in MEVID, kitware annotation source, 41 MEVID cross-camera persons. + +#### 3. Bugs Fixed (3 iterations) +1. **geom.yml inline format**: Kitware uses single-line `- { geom: {...} }`. Added inline regex +2. **Entity resolution mega-cluster**: 33,592 links → 1 cluster. Fixed: MAX_HANDOFF_GAP=10s, MAX_LINKS_PER_ENTITY=2, only active entities, confidence 0.7 → 1,330 links, 20 clusters +3. **Deduplication bug**: `is_duplicate_within_slot` used `None == None` for spatial/perception. Rewrote with category-specific dedup logic. + +#### 4. Final Results +9 questions (3 temporal + 3 spatial + 3 perception), 0 validation issues, 7.9s runtime. +- **Temporal**: cross-camera event ordering, 3.03-3.04s gaps, entity-linked +- **Spatial**: near (2.87m), moderate (8.06m), far (98.22m) via KRTD bbox→3D +- **Perception**: which_camera, activity_identification, multi_camera_confirmation + +### Files Created +- 14 files in `scripts/v6/` (see above) +- `data/qa_pairs/2018-03-11.11-25-00.school.v6.json` (9 questions) + +### Key Decisions +- **Heuristic linking** used for temporal (MEVID provides camera-level only, not person→actor mapping) +- **Only G328 and G336** have KRTD models for this slot — spatial questions from G328 only +- Entity links labeled `heuristic` in output (not `mevid_ground_truth`) + +--- + +## 2026-02-13 (Session 16): V7 Planning & MEVID Implementation Analysis + +### Context +User requested detailed explanations of V6 bugs, verification markers, and why MEVID links aren't used. Then requested V7 planning and MEVID implementation evaluation. + +### Tasks Completed + +#### 1. V6 Bug Explanations + +**Bug 1: geom.yml Inline Format** +- Problem: Kitware uses inline `- { geom: {id1: X, ts0: Y, g0: ...} }` not block YAML +- Impact: 0 bboxes for 3/8 cameras +- Fix: Inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` + +**Bug 2: Entity Resolution Mega-Cluster** +- Problem: Too permissive linking → 33,592 links → 1 giant cluster (all 1,258 entities) +- Fix: MAX_HANDOFF_GAP=10s, MAX_LINKS=2, threshold 0.7, only active entities → 1,330 links, 20 clusters + +**Bug 3: Deduplication `None == None`** +- Problem: Checked `event_a`/`event_b` for ALL categories, spatial/perception lack these → `None == None` → dropped all after first +- Fix: Category-specific dedup → full 9 questions (3+3+3) + +#### 2. MEVID Gap Analysis +- MEVID provides: Camera-level person mapping: `Person 4: [G299, G328, G336, G419, G420, G421, G423]` +- Missing link: Can't map MEVID `person_id` → Kitware `actor_id` without ReID model +- Estimated cost: ~10 hours for 47 VSet7 slots, ~8 days for 929 slots +- **Decision**: Defer to post-V7 + +#### 3. V7 Documentation +- **v7_todo.md** — 8-phase implementation plan (entity aliasing, activity hierarchy, MEVID validation, debug markers) +- **V7_AUDIT.md** — Bug explanations, marker docs, MEVID analysis, pros/cons +- **V6_V7_QUICK_REF.md** — Quick reference guide +- **MEVID_IMPLEMENTATION_PLAN.md** — Detailed Person ReID evaluation (3 options, recommended torchreid/OSNet) + +#### 4. V7 Module Structure +Created `scripts/v7/` by copying V6, ready for: +- Entity aliasing: `make_alias()` method +- Activity hierarchy: New `activity_hierarchy.py` module +- Connected events, debug markers, MEVID camera-level validation + +#### 5. Slot Viewer Tool +Created `scripts/view_slot.py`: frame grid mode, filmstrip mode, mosaic video mode, annotation overlay. + +### Files Created/Modified +- `v7_todo.md` (detailed implementation tasks + memory optimizations) +- `V7_AUDIT.md` (bug explanations, pros/cons) +- `V6_V7_QUICK_REF.md` (quick reference) +- `MEVID_IMPLEMENTATION_PLAN.md` (Person ReID implementation plan) +- `scripts/v7/` (copied from V6) +- `scripts/view_slot.py` (headless slot viewer) + +### Key Decisions + +**V7 Implementation Approach**: +- Refine V6 foundation (don't redesign) +- 4 core enhancements: entity aliases, connected events, debug markers, MEVID validation +- Total estimated time: ~3 hours + +**MEVID Strategy**: +- Best approach: Person ReID model (torchreid/OSNet) + spatial validation +- Complexity: 10 hours for VSet7, 8 days for full dataset +- **Decision**: Defer to post-V7 — camera-level MEVID validation sufficient as proxy + +--- + +## 2026-02-13 (Session 19): MEVID Matching Pipeline — Full Implementation + +### Context +User requested full implementation of MEVID matching pipeline. Previous sessions had created infrastructure (directories, stubs, docs) but no working code. This session replaced all stubs with production-ready Python scripts. + +### Tasks Completed + +#### 1. Filesystem Discovery & Config Fix +- Verified actual data paths on disk (bbox_train, metadata, Kitware, videos) +- Found only 2/69 person folders extracted from 31GB bbox_train.tgz +- Rewrote `config.py` with verified paths, MEVID filename regex, Kitware geom regex + +#### 2. Utility Module Implementation (3 files) +- **`utils/reid_model.py`**: torchvision ResNet50 backbone (2048-dim features), lazy loading, batch extraction +- **`utils/video_crop.py`**: OpenCV-based video crop extraction, single-actor and multi-actor single-pass modes +- **`utils/spatial_validation.py`**: IoU, cosine similarity, vectorized N×M similarity matrix + +#### 3. Main Pipeline Scripts (4 files) +- **`extract_mevid_features.py`** (Step 1): Scans bbox_train dirs, groups by (person, camera), batched GPU feature extraction → `mevid_embeddings.pkl` +- **`extract_kitware_features.py`** (Step 2): Parses geom.yml inline regex, extracts video bbox crops, computes actor features → `kitware_{slot}.pkl` +- **`compute_matches.py`** (Step 3): Loads both caches, cosine similarity matrix per camera, ranked matches with high/medium/low confidence → `{slot}.matches.json` +- **`evaluate_mapping.py`** (Step 4): Diagnostic analysis — similarity distribution, conflict detection, coverage stats + +#### 4. Validation & Documentation +- **`validate_setup.py`**: Pre-flight checks for paths, deps, GPU, extraction status +- **`docs/EXECUTION_GUIDE.md`**: Step-by-step tmux instructions for all long-running scripts +- All 9 Python files pass `py_compile` with no errors + +### Files Created/Modified +- `config.py` — Complete rewrite (verified paths, regex, thresholds) +- `utils/reid_model.py` — Full implementation +- `utils/video_crop.py` — Full implementation +- `utils/spatial_validation.py` — Full implementation +- `extract_mevid_features.py` — Full implementation +- `extract_kitware_features.py` — Full implementation +- `compute_matches.py` — Full implementation (NEW) +- `evaluate_mapping.py` — Full implementation (NEW) +- `validate_setup.py` — Complete rewrite (NEW) +- `docs/EXECUTION_GUIDE.md` — User execution guide (NEW) + +### Key Decisions +- **torchvision ResNet50** over torchreid/OSNet: reliable install, sufficient for same-camera matching +- **No frame overlap validation**: MEVID frame IDs are tracklet-relative, not absolute → visual similarity only +- **Keyframe-only bboxes** from geom.yml (more reliable annotations) + +### Remaining Prerequisites (User Action) +1. Extract bbox_train.tgz: `cd /nas/mars/dataset/MEVA/MEVID && tar -xzf mevid-v1-bbox-train.tgz` (~30 min) +2. Install torch: `pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124` + +--- diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb13-14.md b/docs/project-history/github-session-archive/session_records/sessions_feb13-14.md new file mode 100644 index 0000000..8f0d3d6 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb13-14.md @@ -0,0 +1,339 @@ +# Sessions: February 13–14, 2026 + +> Continuation of MEVA annotation research + +--- + +## 2026-02-13 (Session 20): Kitware id1/id2 Research + +### Context +Previous session (19) implemented MEVID matching pipeline. This session investigates id1 (geom) and id2 (activities) values in Kitware annotations — uniqueness, ranges, relationships. + +### Tasks Completed + +#### Kitware id1/id2 Analysis +Comprehensive analysis of 769 activities.yml + 769 geom.yml files across all Kitware annotations. + +**Key findings:** +- Two distinct annotation formats: "janitor" (YAML, hash IDs) vs "good" (Python dict, sequential IDs) +- Hash IDs are globally unique per camera; sequential IDs collide across cameras +- id1 (geom actor) and id2 (activity) are different ID spaces but linked within activities.yml +- No cross-camera identity linking exists in either format +- 2018-03-11 is heavily weighted toward hash format (hours 11,12,16) + +### Files Created +- `scripts/analyze_kitware_ids.py` — Analysis script +--- + +## 2026-02-13 (Session 21): GPT Prompt Template Research for V7 Naturalization + +### Context +Previous session (20) analyzed Kitware id1/id2 annotation formats. This session researches GPT prompt templates and output formats used in ego-exo4d and nuscenes multi-camera QA pipelines to inform MEVA V7 naturalization wrapper design. + +### Tasks Completed + +#### Full Research of GPT Prompt Templates +- Read all 5 ego-exo4d prompt templates (camera, causal, ordering, summarization, temporal) +- Read all 7 nuscenes prompt templates (counting, ordering, spatial, spatio_temporal v1+v2, summarization, temporal) +- Read output JSON schemas for both datasets (all output files) +- Read GPT client code: ego_exo4d/llm.py, ego_exo4d/question_generator.py, nuscenes/nuscens_build.py +- Read existing MEVA naturalize_qa.py (434 lines, already functional) +- Produced detailed comparison and recommendations + +### Key Findings +- ego-exo4d: GPT-4o via simple LLM wrapper, prompt = entire user message (no system prompt), generates QA from annotations +- nuscenes: GPT-4 via QAGenerator class, user prompt only, gpt_logs_dir logging +- Both pipelines use GPT for GENERATION (full QA from raw data), not just naturalization +- MEVA naturalize_qa.py already follows best practices: system+user prompt, JSON mode, retry, token tracking +- MEVA approach is more efficient: deterministic generation + GPT polish only + +--- + +## 2026-02-13 (Session 22 — late): V7 Implementation Plan Research + +### Context +Previous sessions (20-21) analyzed Kitware annotations and GPT prompt templates. This session performs a complete code review of V6 pipeline and all V7 planning documents to produce a comprehensive implementation plan. + +### Tasks Completed + +#### Full V6 Code Review +- Read all 14 V6 source files (2,350 LOC total across 9 modules + 5 utils) +- Read V6 output JSON (9 questions for school slot: 3 temporal, 3 spatial, 3 perception) + +#### V7 Planning Document Review +- Read 7 V7 planning/reference documents (v7_todo.md, V7_AUDIT.md, V7_FAQ.md, v7_additions.md, v7_memory_optimizations.md, V4_V5_V6_COMPARISON.md, V6_V7_QUICK_REF.md) + +#### V6 vs V7 Code Diff +- Only 2 files differ: build_scene_graph.py (sample_every=30→1) and yaml_stream.py (default param change) +- All other 12 V7 files are byte-identical copies of V6 +- V7 is currently 99% identical to V6 + +#### Comprehensive Implementation Plan +- Created `/home/ah66742/V7_IMPLEMENTATION_PLAN.md` — 11 sections, ~300 lines +- 12 specific tasks organized in 5 phases +- File-by-file change map for all 14 files +- Identified 6 issues/blockers (import paths, version strings, memory, etc.) +- Estimated 2.5 hours total implementation time + +### Files Created +- `V7_IMPLEMENTATION_PLAN.md` — Comprehensive V7 implementation plan + +### Key Finding +V7 has extensive documentation but virtually no implementation beyond the V6 copy. All features (entity aliases, connected events, activity hierarchy, debug markers, MEVID validation) remain unimplemented. + +--- + +## 2026-02-13 (Session 23): V6 Counting Question Investigation — person_picks_up_object + +### Context +Previous sessions (20-22) analyzed id1/id2 formats, GPT prompts, and V7 plans. This session investigates a suspicious activity count in the V6 QA output for slot 2018-03-11.11-25-00.school. + +### Tasks Completed + +#### Investigation: V6 counting_003 — "How many times does person picks up object occur?" +- Found the question in the **multi-cam-dataset copy** of V6 JSON (12 questions including counting category) +- The home copy (data/qa_pairs/) has only 9 questions (no counting) — the counting module (`generate_counting.py`) exists only in the Multi-Camera repo and was since deleted + +#### Raw Annotation Analysis +- **G299**: 52 entries, sequential IDs ("good" format), very short events (12-28 frames, 0.4-0.9s) +- **G330**: 52 entries, hash IDs ("janitor" format), longer events (30-85 frames, 1.0-2.8s) +- Both cameras' entries have unique id2 values — all 52 per camera are distinct annotation entries +- G330 meta header declares "person_picks_up_object 52 instances" + +#### Cross-Camera Duplicate Analysis +- Matched G299 vs G330 timestamps (≤60 frame combined distance threshold) +- **36 out of 52 events match across cameras** — same physical pick-up annotated independently on both views +- 16 events unique to G299, 16 unique to G330 +- **True unique physical events: ~68**, not 104 + +#### Root Cause +`generate_counting.py` counts raw `sg.events` without cross-camera deduplication: +```python +for evt in sg.events: + activity_counts[evt.activity] += 1 +``` +Each annotation entry = 1 event, regardless of camera. Same physical event annotated on N cameras = counted N times. + +#### Also Affects Other Counting Questions in This Slot +- counting_001: person_puts_down_object = 115 (G299:57 + G330:58) — likely ~75 unique +- counting_002: person_enters_scene_through_structure = 108 (7 cameras) — likely ~40-60 unique + +### Key Findings +1. The 52 events per camera ARE legitimate annotations (not frame-level artifacts) +2. The double-counting is a **pipeline bug**: naively summing across cameras +3. Question asks about "this scene" (ambiguous — per-camera or physical world?) +4. If "scene" = physical world, answer should be ~68 not 104 +5. If "scene" = all camera feeds combined, 104 is technically correct but misleading + +### Suggested Fix +Option A: Change question semantics to per-camera ("How many times on camera G330?") +Option B: Deduplicate cross-camera events using timestamp proximity matching before counting +Option C: Remove counting category entirely (V6 core doesn't include it; only the deleted Multi-Camera add-on had it) + +--- + +## 2026-02-14 (Session 24): V7 Full Implementation — All Features + +### Context +Previous sessions (20-22) analyzed Kitware IDs, GPT prompts, and created a comprehensive V7 implementation plan. Session 23 investigated counting question double-counting. This session implements ALL V7 features: entity aliases, activity hierarchy, connected temporal events, MEVID validation, debug markers, and GPT naturalization wrapper. + +### Tasks Completed + +#### 1. V6 Codebase Review +- Read all 14 V6 source files (~2,350 LOC across 9 modules + 5 utils) +- Confirmed V7 was 99% identical to V6 (only `build_scene_graph.py` sample_every=1 and `yaml_stream.py` docstring differed) +- Read V7 planning docs: `v7_todo.md` (734 lines), `V7_AUDIT.md` (384 lines), `V6_V7_QUICK_REF.md` +- Read existing `naturalize_qa.py` (434 lines) for GPT wrapper reference + +#### 2. Created `activity_hierarchy.py` (NEW — 148 lines) +- `CAUSAL_RELATIONSHIPS` dict: 12 parent activities with sequential/causal children +- `CO_OCCURRING` list: 10 pairs of typically simultaneous activities +- Precomputed `_CAUSAL_PAIRS` and `_CO_OCCURRING_PAIRS` frozensets +- Functions: `are_related()`, `get_relationship()`, `get_relationship_strength()`, `humanize_activity()` +- `humanize_activity()` strips `person_`/`vehicle_` prefix, replaces underscores → used throughout V7 + +#### 3. Updated `build_scene_graph.py` — Entity Aliases +- Added `alias: str = ""` field to `Entity` dataclass +- Added `make_alias(event_list)` method using humanize_activity() + actor_id + camera + timestamp +- Hash IDs (>10000) get "Person {activity} (on {camera} @ {t}s)" format +- Sequential IDs get "(#{actor_id} on {camera} @ {t}s)" format +- Added step 5 in `build()` to populate all entity aliases +- Updated `to_dict()` to include alias field + +#### 4. Rewrote `generate_temporal.py` — Connected Events + MEVID Validation +- New `_score_connection()` function: Scores event pairs by connection quality + - same_entity_cluster: +3.0 (entities in same resolved cluster) + - related_activities: +strength×2.0 (from activity_hierarchy) + - MEVID validation: +1.5 (person seen on both cameras) + - different_activities: +0.5 (fallback) +- `_find_temporal_candidates()`: Cross-camera pairs within 3-15s gap, sorted by (-score, gap) +- `_build_debug_info()`: frame_range, timestamp, fps, clip_file, entity_alias +- `_get_event_alias()`: Entity alias if available, else humanized activity + camera +- Output includes `verification` and `debug_info` blocks per question + +#### 5. Rewrote `generate_spatial.py` — Entity Aliases + Debug Info +- Question text uses entity aliases instead of raw activity names +- `debug_info` blocks include: bbox coordinates, frame number, timestamp, world_pos per entity +- `verification` includes `entity_a_alias` and `entity_b_alias` fields + +#### 6. Rewrote `generate_perception.py` — Humanized Activities + Debug Info +- Uses `humanize_activity()` for question text AND option text +- Activity identification options: "opens facility door" instead of "person_opens_facility_door" +- `debug_info` with representative_event per camera + +#### 7. Updated `entity_resolution.py` — MEVID Camera Mapping +- Added `mevid_person_cameras: Dict[int, Set[str]]` field to `ResolvedGraph` dataclass +- Updated `to_dict()` to serialize the mapping +- Updated `resolve_entities()` to pass `mevid_persons` as `mevid_person_cameras` + +#### 8. Rewrote `run_pipeline.py` — V7 Version + Stats +- Version: "v7", generator: "v7_pipeline" +- Fixed import fallback paths: `scripts.v7.*` (was `scripts.v6.*`) +- Added `v7_stats` block: questions_with_mevid_validation, questions_with_connections, questions_with_debug_info, entities_with_aliases +- Verbose output shows V7-specific statistics + +#### 9. Created `naturalize_v7_qa.py` (NEW — ~350 lines) +- GPT naturalization wrapper modeled after ego-exo4d/nuscenes prompts +- System prompt with rules: rephrase naturally, remove debug markers, generate reasoning +- Category-specific few-shot examples (temporal, spatial, perception) +- `strip_debug_markers()`: Offline debug removal without API calls +- `dry_run()`: Cost estimation before running +- Output schema matches nuscenes/ego-exo4d format +- CLI: `--dry-run`, `--strip-debug`, `--model`, `--temperature`, `--verbose` + +#### 10. Compile Testing — All 16 V7 Files Pass +``` +scripts/v7/__init__.py OK +scripts/v7/activity_hierarchy.py OK (NEW) +scripts/v7/build_scene_graph.py OK (MODIFIED) +scripts/v7/distractor_bank.py OK +scripts/v7/entity_resolution.py OK (MODIFIED) +scripts/v7/generate_perception.py OK (REWRITTEN) +scripts/v7/generate_spatial.py OK (REWRITTEN) +scripts/v7/generate_temporal.py OK (REWRITTEN) +scripts/v7/naturalize_v7_qa.py OK (NEW) +scripts/v7/parse_annotations.py OK +scripts/v7/run_pipeline.py OK (REWRITTEN) +scripts/v7/utils/__init__.py OK +scripts/v7/utils/iou.py OK +scripts/v7/utils/krtd.py OK +scripts/v7/utils/mevid.py OK +scripts/v7/utils/yaml_stream.py OK +``` + +#### 11. Pipeline Test — 2018-03-11.11-25-00.school +- **Result**: 8 questions generated in 12.52 seconds + - 2 temporal (target 3, dedup removed 1 duplicate activity pair) + - 3 spatial + - 3 perception +- **All questions passed validation** (0 issues) +- **V7-specific stats**: 2 MEVID-validated, 2 connected, 8 with debug_info, 1258 entities with aliases +- **Temporal Q1**: same_entity_cluster connection (score 5.0), MEVID-validated (person 138) +- **Temporal Q2**: related_activities_causal connection (score 4.0), MEVID-validated (person 138) + +#### 12. V6 vs V7 Comparison +| Metric | V6 | V7 | +|--------|----|----| +| Questions | 9 | 8 | +| Temporal Qs | 3 | 2 | +| MEVID-validated | 0 | 2 | +| Connected events | 0 | 2 | +| Entity aliases | No | Yes (1258) | +| Debug info | No | Yes | +| Generation time | 7.9s | 12.5s | +| Temporal linking | Heuristic only | Score-based + validated | + +### Files Created +- `scripts/v7/activity_hierarchy.py` — Activity relationship hierarchy +- `scripts/v7/naturalize_v7_qa.py` — GPT naturalization wrapper + +### Files Modified +- `scripts/v7/build_scene_graph.py` — Entity alias field + make_alias() +- `scripts/v7/entity_resolution.py` — mevid_person_cameras field +- `scripts/v7/generate_temporal.py` — Full rewrite with connected events +- `scripts/v7/generate_spatial.py` — Full rewrite with aliases + debug +- `scripts/v7/generate_perception.py` — Full rewrite with humanized activities +- `scripts/v7/run_pipeline.py` — Full rewrite for V7 version + +### Output Files +- `data/qa_pairs/2018-03-11.11-25-00.school.v7.json` — V7 test output (8 Qs) + +### Decisions Made +- **Connection scoring weights**: same_entity_cluster (3.0) > related_activities (×strength×2.0) > MEVID (1.5) > different (0.5) +- **Dedup is correct**: 3→2 temporal Qs after removing duplicate activity pair is quality improvement +- **Hash ID aliases**: "Person {activity} (on {camera} @ {t}s)" — avoids exposing meaningless 64-bit IDs +- **60% slower is acceptable**: 12.5s vs 7.9s for significantly better link quality + +### Known Issues +- Only 2/3 temporal Qs generated (dedup removed duplicate pattern) — could relax dedup or expand connection types +- Perception Q3 template: "opens facility door is occurring" should capitalize/use gerund ("Opening a facility door") +- GPT wrapper not tested with API (needs OPENAI_API_KEY) + +### Next Session Priority +1. Test GPT naturalization on V7 output (set OPENAI_API_KEY, dry-run first) +2. Run V7 on all 47 VSet7 slots (batch runner) +3. Fix perception question template capitalization +4. Check MEVID bbox_train extraction progress (tar PID 2055125) + +--- + +## 2026-02-13 (Session 25): Cleanup, Perception Fix, GPT Naturalization Test + +### Context +Continuation of session 24 (V7 full implementation). This session performs workspace cleanup, fixes the perception gerund template, archives old scripts, and successfully tests GPT naturalization. + +### Tasks Completed + +#### 1. Root Folder Cleanup +Moved all 19 loose files from `~/` to appropriate directories: +- **Deleted** (3 duplicates already in docs/): `V6_PIPELINE_EXPLAINED.md`, `V7_FAQ.md`, `multi_camera_reference.md` +- **Moved to docs/** (11 files): `V4_V5_V6_COMPARISON.md`, `V6_V7_QUICK_REF.md`, `V7_AUDIT.md`, `V7_IMPLEMENTATION_PLAN.md`, `v7_additions.md`, `v7_memory_optimizations.md`, `CLEANUP_PLAN.md`, `PRE_PUSH_CHECKLIST.md`, `SUMMARY.md`, `v6_todo.md`, `v7_todo.md` +- **Moved to MEVID/** (2): `MEVID_IMPLEMENTATION_PLAN.md`, `MEVID_IMPLEMENTATION_PLAN_2.md` +- **Moved to MEVID_matching/scripts/** (2): `extract_mevid_metadata.py`, `link_mevid_to_kitware.py` +- **Moved to archived/** (1): `cleanup_local.sh` + +Root now contains only directories: `MEVID/`, `MEVID_matching/`, `Multi-Camera/`, `archived/`, `data/`, `docs/`, `output/`, `receive/`, `scholarship_app/`, `scripts/`, `send/`, `snap/`, `venv/` + +#### 2. Perception Template Gerund Fix +- Added `humanize_activity_gerund()` to `activity_hierarchy.py` +- Added `_GERUND_MAP` dict (30 verb→gerund mappings) +- Smart article insertion: skips prepositions/adverbs/particles (up, through, to, right, etc.) +- Updated `generate_perception.py` Type 3 template to use gerund form +- Before: "opens facility door is occurring" → After: "Opening a facility door is occurring" +- Tested on 10 activity names — all grammatically correct + +#### 3. MEVID Extraction Status +- tar PID 2055125 still running (state D+ = I/O wait on NAS) +- 10 of ~69 person folders extracted so far +- Tarball: 31 GB on NAS, extraction continues in background + +#### 4. Archived Old Scripts +Moved 4 superseded scripts from `scripts/` to `scripts/archived/`: +- `analyze_kitware_ids.py` — One-off Session 20 analysis +- `generate_qa_v4.py` — Superseded by V5/V6/V7 +- `generate_qa_v5.py` — Superseded by V6/V7 +- `naturalize_qa.py` — Replaced by V7 `naturalize_v7_qa.py` + +Remaining in scripts/: `check.py`, `convert.sh`, `convert_benchmark.sh`, `extract_logic_tuples.py`, `generate_annotated_slots.py`, `integrate_vset7.py`, `parse_gps_actors.py`, `view_slot.py`, `v6/`, `v7/` + +#### 5. GPT Naturalization Test — SUCCESS +- Dry run: 8 questions, ~2400 tokens, est. $0.001 (gpt-4o-mini) +- Full run: 8/8 naturalized, 0 failures, 5220 tokens +- Model: gpt-4o-mini, temperature: 0.7 +- Output: `data/qa_pairs/2018-03-11.11-25-00.school.v7.natural.json` +- GPT log: `data/gpt_logs/2018-03-11.11-25-00.school/naturalize_v7_gpt-4o-mini.json` +- Quality: Debug markers stripped, natural English, reasoning added +- Cost projection: ~$0.89 for all 929 slots + +### Files Modified +- `scripts/v7/activity_hierarchy.py` — Added `_GERUND_MAP`, `humanize_activity_gerund()` +- `scripts/v7/generate_perception.py` — Type 3 template uses gerund form + +### Files Created +- `data/qa_pairs/2018-03-11.11-25-00.school.v7.natural.json` — Naturalized output +- `data/gpt_logs/2018-03-11.11-25-00.school/naturalize_v7_gpt-4o-mini.json` — Token usage log + +### Next Session Priority +1. Run V7 batch on all 47 VSet7 slots +2. Run naturalization on batch output +3. Wait for MEVID extraction to complete, then run MEVID matching pipeline \ No newline at end of file diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb13.md b/docs/project-history/github-session-archive/session_records/sessions_feb13.md new file mode 100644 index 0000000..06bfd99 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb13.md @@ -0,0 +1,237 @@ +# Session 32: 2026-02-13 — V8 Pipeline Implementation + MEVID Identity Verification + +## Previous Session Summary +Session 31 completed YOLO+GPT extraction (23 persons, 77 outfits, $0.01). Built person_database_yolo.json with slot linkage (10,157 person-slot links). CROPS_PER_OUTFIT was 5, could re-run with 15. Analyzed confidence (12% zero-detect from small crops). + +## Tasks +1. Analyze MEVID identity verification feasibility (raw AVI + geom crops) +2. Deep V7 pipeline analysis → improvement tips for V8 +3. Design and implement V8 QA generator with MEVID descriptions +4. New categories: attribute verification, re-identification, scene summary +5. Documentation + +## Work Log + +### MEVID Identity Verification Analysis +- Analyzed track_train_info.txt: 6339 tracklets, columns = start_frame, end_frame, person_id, outfit_id, camera_id +- MEVID crop frame numbers are sequential indices (NOT AVI frame numbers) +- ~196 Kitware actors per camera per slot — direct actor_id mapping is complex +- **Verdict**: Feasible via color histogram matching but NOT blocking V8 +- Documented in V8_DESIGN.md as separate utility tool + +### V7 Pipeline Analysis (all 10+ modules, ~2819 lines) +- 8 weaknesses identified (W1-W8): generic entity descriptions, no person identity, no attribute questions, all "easy" difficulty, no scene summary, debug markers in text, no smart slot filtering, no carried-object questions + +### V8 Implementation — COMPLETE +Created full V8 pipeline in `~/scripts/v8/`: + +**New V8 modules:** +- `person_descriptions.py` (304 lines) — MEVID description loader + entity enrichment, 3-tier priority (GPT→YOLO→activity-verb) +- `generate_reidentification.py` (216 lines) — 2 question types: which_camera_reid, same_person_confirmation +- `generate_scene_summary.py` (270 lines) — scene characterization + busiest camera +- `naturalize_v8_qa.py` — V8 naturalization with re-ID + scene summary few-shot examples +- `run_pipeline.py` — V8 orchestrator: 5 categories, ~9 Qs/slot, MEVID-aware + +**Updated V8 modules (from V7):** +- `generate_temporal.py` — entity_descs parameter, MEVID-prioritized selection +- `generate_spatial.py` — entity_descs parameter, MEVID descriptions in question text +- `generate_perception.py` — NEW attribute_verification subtype (clothing color MCQ) + +**Copied from V7 (unchanged):** +- parse_annotations.py, build_scene_graph.py, entity_resolution.py, activity_hierarchy.py, distractor_bank.py, utils/* + +### Test Results — 2018-03-11.11-25-00.school +- 9 questions generated in 8.15s +- 2 temporal + 2 spatial + 2 perception (1 attr verification) + 2 re-ID + 1 scene summary +- 132/1258 entities got MEVID descriptions (21 MEVID persons in slot) +- 0 validation issues +- Naturalization dry-run: ~$0.001 per slot (gpt-4o-mini) +- Output: `data/qa_pairs/2018-03-11.11-25-00.school.v8.json` + +### Bugs Fixed During Testing +1. Description doubling: "the person The person is wearing..." → fixed GPT prefix stripping in person_descriptions.py +2. Function signature mismatch: generate_reidentification_qa/generate_scene_summary_qa missing entity_descs param +3. Validation check: re-ID verification uses "all_person_cameras" not "cameras" + +## Files Created/Modified +- `/home/ah66742/V8_DESIGN.md` — Comprehensive V8 design document +- `/home/ah66742/scripts/v8/` — Full V8 pipeline (11 modules + utils/) +- `/home/ah66742/data/qa_pairs/2018-03-11.11-25-00.school.v8.json` — Test output + +## Key Decisions +- V8 targets ~9 Qs/slot (2T + 2S + 2P + 2R + 1SS) vs V7's 9 (3T + 3S + 3P) +- MEVID identity verification deferred (not blocking V8 QA generation) +- Person descriptions assigned at camera level (no actor_id→person_id mapping) +- Attribute verification uses YOLO primary colors (not outfit-specific) + +## Notes for Next Session +- Run V8 naturalization on sample slot (costs ~$0.001) +- Batch V8 across MEVID-supported slots +- Consider V8.1 improvements: outfit-specific colors, carried object questions +- Identity verification tool could be built as standalone utility + +--- + +# Session 33: 2026-02-13 — V8 Naturalization V2 + Quality Fixes + +## Previous Session Summary +Session 32 implemented the complete V8 pipeline (9 Qs, 5 categories, MEVID descriptions) and tested on 2018-03-11.11-25-00.school successfully. Description doubling and signature mismatches were fixed during testing. + +## Tasks +1. Fix V8 output quality issues (descriptions, grammar, camera refs, temporal ambiguity) +2. Create naturalize_v8_qa_v2.py with pre-processing layer +3. Draft Kitware MEVID corruption email + +## Work Log + +### V8 Output Quality Issues Identified +User reviewed v8.json output and identified: +- "upper body garment" / "lower body clothing" too verbose → should be "top" / "pants" +- Trailing periods embedded mid-sentence in question text +- Camera refs in temporal questions (partially debated — see below) +- "interacts with person interacts with person" — activity name doubling +- No capitalization of question starts +- Temporal ambiguity: activities repeat 40-115× per slot + +### Description Simplification (person_descriptions.py) +- Added `simplify_description()` with _GARMENT_SUBS (7 rules) and _STRIP_PATTERNS (6 regex) +- "upper body garment" → "top", "lower body clothing" → "pants", "hooded jacket" → "hoodie" +- Strips posture/action context: "sitting on a chair", "with their back turned" +- Updated `get_person_description()`: "the person" → "a person", calls simplify, strips trailing periods + +### naturalize_v8_qa_v2.py — NEW (Pre-process + GPT Naturalization) +Created standalone V2 script with 3 modes: +- `--preprocess-only`: FREE, instant — cleans templates without API calls +- `--dry-run`: Shows what GPT would receive +- Default: Pre-process + GPT naturalization + +**Pre-processing layer (free) handles:** +- Description simplification (same _GARMENT_SUBS rules) +- Activity doubling fix (reconstructs from structured verification data) +- Capitalization +- Camera reference management (kept for temporal/perception/re-ID, removed for spatial) +- Grammar cleanup (missing articles: "through structure" → "through a structure") + +**Temporal anchor debate:** +- Initially added temporal anchors ("at the very start", "about 6 seconds in") to disambiguate events +- User correctly identified this LEAKS the answer — VLM can solve without watching video +- **Fix**: Removed temporal anchors, restored camera references for temporal questions +- Camera refs are the correct disambiguator — they tell VLM WHERE to look without revealing WHEN + +### Test Results — Pre-process Only +7/9 questions modified (2 temporal, 2 spatial, 1 perception, 2 re-ID): +- temporal: camera refs + clean descriptions, no timestamp leaks +- spatial: "blue upper body garment" → "blue top", camera refs removed +- perception: "carries heavy object event" → "carrying a heavy object" +- re-ID: "The person" → "A person", garment simplification + +### Kitware Email Draft +Drafted email about mevid-v1-bbox-train.tgz corruption: +- 31 GB tarball corrupted at source (S3 bucket) +- Only 10/~104 person folders extract before gzip error +- Fresh S3 download has identical corruption → source issue +- bbox-test.tgz extracts cleanly (controls rules out transfer issues) +- Saved to: data/kitware_mevid_email_draft.txt + +## Files Created/Modified +- `/home/ah66742/scripts/v8/naturalize_v8_qa_v2.py` — NEW: V2 pre-process + GPT naturalization +- `/home/ah66742/scripts/v8/person_descriptions.py` — Added simplify_description(), _GARMENT_SUBS, _STRIP_PATTERNS +- `/home/ah66742/data/qa_pairs/2018-03-11.11-25-00.school.v8.preprocessed.json` — Preprocessed test output +- `/home/ah66742/data/kitware_mevid_email_draft.txt` — Email draft + +## Key Decisions +- Temporal anchors (timestamps) LEAK answers → removed, camera refs serve as disambiguator +- Camera refs kept in temporal/perception/re-ID (necessary), removed from spatial only +- Pre-processing layer is free and instant; GPT naturalization is optional polish +- Description simplification done both at source (person_descriptions.py) AND in post-processing (V2 script handles raw V8 output) + +## Notes for Next Session +- Run V2 naturalization with GPT on sample slot +- Batch V8 generation + V2 pre-processing across MEVID-supported slots +- Send Kitware email (fill in university/name/email) +- Consider: should person_descriptions.py simplification also apply at V8 generation time (currently only V2 post-processes) + +--- + +# Session 34: 2026-02-13 — Entity Description Extractor (Geom + Video → Color Descriptions) + +## Previous Session Summary +Session 33 created naturalize_v8_qa_v2.py (free pre-processing + GPT naturalization), fixed temporal anchor leaks, and drafted Kitware MEVID email. Only 10% of entities had visual descriptions (MEVID-limited). + +## Tasks +1. Build prototype to extract person descriptions from raw AVI + geom.yml bounding boxes +2. Optimize video reading for dense-frame cameras (G330: 517 actors) +3. Integrate geom-extracted descriptions into V8 pipeline +4. Test end-to-end + +## Work Log + +### extract_entity_descriptions.py — NEW +Created self-contained script (`scripts/v8/extract_entity_descriptions.py`, ~690 lines): +- Parses geom.yml → per-actor bounding boxes (regex-based, no YAML load) +- Opens raw AVI video → extracts 5 representative crops per actor +- HSV color analysis: upper body (10-45% height) and lower body (55-90%) +- Majority vote across 5 crops per actor +- Template description: "a person in a [color] top and [color] pants" +- Optional YOLO mode adds carried object detection (backpack, bottle, etc.) + +### Video Read Optimization +Initial approach (random seek via `cap.set(CAP_PROP_POS_FRAMES)`) choked on G330 (517 actors, 176K bbox frames): +- H.264 random seek decodes from nearest keyframe each time = very slow +- **Fix**: Adaptive strategy selection + - Dense cameras (>150 targets or >2% of span): sequential `cap.grab()` (no decode) + `cap.read()` only on target frames + - Sparse cameras (<150 targets): random seek (faster for sparse access) +- G330 went from minutes (choking) to ~15s + +### AVI vs MP4 Switch +- User correctly noted MP4s are CRF 32 re-encodes with degraded colors +- Updated `find_slot_files()` to prefer raw AVI over MP4, with MP4 fallback +- AVI path: `/nas/mars/dataset/MEVA/avis/{date}/{hour}/` with `.r13.avi` suffix +- Result: Slightly faster (22.8s vs 26.6s) due to no H.264 decode overhead on AVI + +### Extraction Results — 2018-03-11.11-25-00.school +- 578 actors extracted across 5 cameras (G299/G423 have 0 geom annotations) +- 578/578 (100%) got color descriptions +- 27 unique description combos (8-color HSV bucketing) +- Top: "blue top + blue pants" (141x), "blue top + green pants" (85x) +- Processing time: 22.8s (color-only mode, no YOLO) +- Output: `data/entity_descriptions/2018-03-11.11-25-00.school.json` + +### V8 Pipeline Integration +- Added `_load_geom_descriptions()` to `person_descriptions.py`: loads pre-extracted JSON +- Updated `enrich_entities()` with 3-tier priority: + 1. MEVID descriptions (GPT/YOLO from MEVID crops — richest) + 2. Geom-extracted descriptions (HSV color from raw AVI + geom bbox) + 3. Activity-verb fallback ("a person walking") +- `enrich_entities()` now returns (descriptions, counts) tuple +- Updated `run_pipeline.py` to track all 3 tiers in stats + +### End-to-End Test Results +``` +Entity enrichment: 132 MEVID + 352 geom-color + 442 fallback (1258 total) +9 questions, 0 validation issues, 8.29s +``` +- Before: 132/1258 (10%) entities had visual descriptions +- After: 484/1258 (38%) entities have visual descriptions (132 MEVID + 352 geom) +- Temporal questions now use color descriptions: "a person in a blue top and green pants interacts with person on G330" + +## Files Created/Modified +- `/home/ah66742/scripts/v8/extract_entity_descriptions.py` — NEW: Geom + AVI → color descriptions +- `/home/ah66742/scripts/v8/person_descriptions.py` — Added _load_geom_descriptions(), 3-tier enrich_entities() +- `/home/ah66742/scripts/v8/run_pipeline.py` — Updated Step 4 stats (MEVID + geom + fallback counts) +- `/home/ah66742/data/entity_descriptions/2018-03-11.11-25-00.school.json` — Extracted descriptions +- `/home/ah66742/data/qa_pairs/2018-03-11.11-25-00.school.v8.json` — Updated V8 output + +## Key Decisions +- HSV color-only mode is sufficient (no YOLO needed for basic disambiguation) +- 8-color vocabulary (black/white/gray/red/orange/yellow/green/blue) gives 27 unique combos — enough for per-question disambiguation +- Raw AVI preferred over MP4 for color fidelity +- Sequential video read for dense cameras, random seek for sparse +- Geom descriptions stored as pre-computed JSON, loaded at pipeline time (not re-computed) + +## Notes for Next Session +- Run extraction on all annotated slots (929 slots × ~22s = ~5.7 hours) +- Consider batch script with tmux + progress tracking +- YOLO mode could add carried objects for even more unique descriptions +- G336 has only 1 unique description (all 8 actors look the same) — might need finer color bucketing +- 442 fallback entities have no geom annotations — some cameras lack geom.yml entirely diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb13_session35.md b/docs/project-history/github-session-archive/session_records/sessions_feb13_session35.md new file mode 100644 index 0000000..7b2af7c --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb13_session35.md @@ -0,0 +1,153 @@ +# Session 35: 2026-02-13 — 2-Pass Naturalization, V3 Variety Mode, Repo Push + +## Previous Session Summary +Session 34 built extract_entity_descriptions.py (geom + AVI color analysis) and integrated it into V8, boosting visual description coverage from 10% to 38%. Entity enrichment now uses 3-tier priority (MEVID → geom-color → fallback). + +## Tasks +1. Add 2-pass naturalization (naturalizer + grammar checker) +2. Create V3 mode for question variety (high temp, stripped camera refs) +3. Test end-to-end pipeline on sample slot +4. Archive old V5/V6/V7 output files +5. Push V8 to Multi-Camera repo (no docs, security-clean) + +## Work Log + +### 2-Pass Naturalization (naturalize_v8_qa_v2.py) +Updated naturalize_v8_qa_v2.py to add Pass 2: +- **Pass 1**: SYSTEM_PROMPT_V2 (conservatively rephrase while preserving all details) +- **Pass 2**: NEW GRAMMAR_CHECKER_PROMPT (copy editor persona, temperature=0.3) + - Function: `_grammar_check_one()` sends naturalized output back to GPT + - Fixes grammar, punctuation, awkward phrasing without changing meaning + - Conservative: only fixes clear errors, preserves camera IDs and descriptions exactly + - Low temp (0.3) ensures fidelity +- Output: `naturalized_question`, `naturalized_options`, `reasoning`, `grammar_checked` flag per Q +- All 9 questions passed both passes (0 failures) + +### V3 Variety Mode (NEW) +Added aggressive question variety mode to naturalize_v8_qa_v2.py: +- **V2 mode** (default): Conservative, formulaic — all questions start with "In this multi-camera scene, we have two events..." +- **V3 mode** (--v3 flag): Creative variation at temperature=0.95 + - NEW SYSTEM_PROMPT_V3: "rewrite as if a DIFFERENT person wrote it" + - NEW CATEGORY_EXAMPLES_V3: Diverse sentence structures, varied openings + - Examples V3 produces: + - Temporal: "Can you identify which event took place first..." + - Spatial: "What would you say is the distance between..." + - Perception: "Can you tell me which camera shows..." + - Scene: "How would you summarize the events captured by all 8 cameras..." +- **Key insight**: Temporal Qs in V3 strip camera refs from QUESTION text + - Person descriptions (clothing/objects) serve as primary disambiguator + - Camera IDs preserved in ANSWER options + - Fixes duplication: no more "on camera G421" in Q + A of same event + - User explicitly requested this: "character descriptors, bypasses the duplication problem" + +### Updated Run Pipeline (End-to-End Test) +- `--v3` flag added to CLI (auto selects --v3 system prompts + examples) +- temp defaults: 0.7 (V2) or 0.95 (V3) +- `-m/--model`, `-t/--temperature` allow override +- Output naming: `.v8.natural.v2.json` or `.v8.natural.v3.json` + +### Test Results — Full Pipeline: 2018-03-11.11-25-00.school +``` +Step 1: Extract descriptions (22.8s) + 578 actors, all with color descriptions + +Step 2: Generate V8 QA (8.2s) + 9 questions, 0 validation issues + +Step 3: Naturalize V2 (2-pass, 15s) + 10,104 tokens, $0.002 + 9/9 passed naturalizer, 9/9 passed grammar checker + +Step 3-alt: Naturalize V3 (2-pass, 15s) + 10,558 tokens, $0.002 + Outputs: .v8.natural.v2.json, .v8.natural.v3.json +``` + +### V2 vs V3 Comparison +- **V2 Temporal Q1**: "In this multi-camera scene, we have two events: (1) A person dressed in a gray top and green pants, carrying a black backpack, enters through a structure on camera G421..." +- **V3 Temporal Q1**: "Can you identify which event took place first: the individual in gray who had a black backpack enter the scene through a structure..." +- **V2 Spatial Q1**: "In the scene, how far apart are the person in a blue top and blue pants with a black graphic hoodie and the person also in a blue top and blue pants?" +- **V3 Spatial Q1**: "Can you estimate the distance between the individual dressed in a blue top and blue pants with a black graphic hoodie and the person also in a blue top and blue pants?" + +**Key difference**: V2 temporal KEEPS camera refs in question, V3 STRIPS them: +- V2: "on camera G421" and "on camera G330" in question text +- V3: Person descriptions alone (no camera refs in question, preserved in options) + +### Archive Old Output Files +Moved old V5/V6/V7 QA output to `~/archived/qa_pairs/`: +```bash +mv data/qa_pairs/*.v5.json ./archived/ +mv data/qa_pairs/*.v5.natural.json ./archived/ +mv data/qa_pairs/*.v6.json ./archived/ +mv data/qa_pairs/*.v6.natural.json ./archived/ +mv data/qa_pairs/*.v7.json ./archived/ +mv data/qa_pairs/*.v7.natural.json ./archived/ +``` +Only V8 and V8.natural files remain in `data/qa_pairs/` (ready for batch processing). + +### Security Scan (Pre-Repo Push) +Ran comprehensive security scan on all V8 scripts: +- ✅ No hardcoded secrets/API keys +- ✅ No os.environ[] direct access (only getters with OpenAI check) +- ✅ No subprocess/eval/exec +- ✅ No network library imports (OpenAI SDK lazy-imported) +- ✅ No pickle/marshal deserialization attacks +- ⚠️ 15 hardcoded absolute paths (`/home/ah66742/` and `/nas/mars/`) — acceptable for pipeline (dataset paths are repo-specific) +- Line counts: 1-1176 lines per file (naturalize_v8_qa_v2.py largest) + +### Repo Push (Multi-Camera/meva/) +Pushed updated V8 to Multi-Camera repo (commit 4d86937): +``` +Archive tree: +- meva/scripts/archived/v6/ (previous V6 scripts, already there) +- meva/scripts/archived/v7/ (previous V8 scripts → renamed v7) +- meva/scripts/v8/ (new updated V8: all 16 modules + 5 utils) + +Data files: +- meva/data/slot_index.json (304K) +- meva/data/person_database_yolo.json (2.6M) +- meva/data/mevid_supported_slots.json (595K) +- meva/data/examples/ (sample V8 + V3 naturalized output) + +Removed: +- meva/docs/ (V6_PIPELINE_EXPLAINED, V7_FAQ, multi_camera_reference) — no agentic docs +- All .env, passwords, secret files (CLEAN) + +.gitignore: +- Root: added negation rules for meva/data/*.json and meva/data/examples/ +- meva/: updated to allow data/ + examples/ +- Force-added data files with git add -f to bypass parent data/ ignore rule +``` + +**Commit details**: +- 66 files changed, 181K insertions, 2.3K deletions +- V7 → renamed as archived/v7/ (preservation) +- V8 complete: 3.6K lines of Python (core) + 3.7M data +- All secrets verified clean (security scan passed) + +**Push result**: +- ❌ Push failed with 403: account `adihebbalae` has no write access to UTAustin-SwarmLab/Multi-Camera +- ✅ Local commit successful (4d86937) +- Remedies: (1) Get added as collaborator, (2) Fork + PR, (3) SSH if you have access elsewhere + +## Files Created/Modified +- `/home/ah66742/scripts/v8/naturalize_v8_qa_v2.py` — Added GRAMMAR_CHECKER_PROMPT, _grammar_check_one(), V3 mode, 2-pass loop +- `/home/ah66742/data/qa_pairs/2018-03-11.11-25-00.school.v8.natural.v2.json` — V2 output (10.1K tokens) +- `/home/ah66742/data/qa_pairs/2018-03-11.11-25-00.school.v8.natural.v3.json` — V3 output (10.6K tokens) +- `/home/ah66742/Multi-Camera/meva/scripts/v8/` — Updated repo copy (all 16 modules) +- `/home/ah66742/Multi-Camera/meva/data/` — Repo data files (slot_index, person_database, etc.) +- `/home/ah66742/Multi-Camera/meva/scripts/archived/v7/` — Previous V8 as v7 for preservation + +## Key Decisions +- V3 camera ref stripping in temporal Qs solved duplication: natural variation + better VLM prompting +- 2-pass naturalization cost: ~$0.002/slot (9 Qs × 2 calls × 400 tokens × $0.15/1M gpt-4o-mini) +- Archive v5/v6/v7 locally to keep working dir lean (only v8 active) +- Repo: minimal footprint (scripts + data), no documentation or agentic artifacts +- Force-add data files to work around parent .gitignore (standard practice) + +## Notes for Next Session +- Manual git push needed (account access issue) +- Batch V8 extraction + naturalization (2-pass) across all 929 slots +- Estimate: 929 slots × 23s extraction × 2 = ~9 hours, 929 × $0.002 naturalization = ~$1.86 total +- Consider: parameterized paths for portability (currently hardcoded /home/ah66742/) +- YOLO mode still optional but could boost description coverage for carried objects diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb15-17.md b/docs/project-history/github-session-archive/session_records/sessions_feb15-17.md new file mode 100644 index 0000000..0d950cc --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb15-17.md @@ -0,0 +1,372 @@ +# Sessions: February 15–17, 2026 + +> MEVID person attribute database investigation and implementation + +--- + +## 2026-02-15 (Session 28): MEVID Person Attribute Database Investigation + +### Context +Previous sessions (24-26) completed V7 pipeline implementation, naturalization fix, and workspace cleanup. Session 27 began MEVID investigation but NAS was too slow. This session completes the investigation and builds a working prototype. + +### Previous Session Summary +Session 26 fixed naturalization by stripping camera/time/debug info from GPT output (V1 version saved as `naturalize_v7_qa_v1.py`). Session 27 started exploring MEVID bbox_train data — found 10/69 person folders extracted, decoded naming convention. NAS I/O was blocking all interactive exploration. + +### Tasks Completed + +#### 1. MEVID Data Investigation — Complete Analysis + +**MEVID Overview:** +- 158 unique persons (104 train, 54 test) +- 4,795,407 total frames of person crops +- 154/158 persons appear on multiple cameras (avg 9.5 cameras/person) +- 140/158 persons have multiple outfits (avg 3.8 outfits/person) +- 22 distinct cameras across all persons + +**Naming Convention Decoded:** +`{PersonID}O{OutfitID}C{CameraID}T{TrackletID}F{FrameNumber}.jpg` +- Example: `0008O009C299T020F00000.jpg` = Person 8, Outfit 9, Camera G299, Tracklet 20, Frame 0 + +**Crop Quality Analysis (22 local samples):** +| Camera Type | Width (px) | Height (px) | Quality | +|-------------|-----------|-------------|---------| +| Close (G420, G330) | 150-293 | 211-408 | SUFFICIENT for attributes | +| Medium (G328, G331) | 37-52 | 86-103 | MARGINAL | +| Far (G299) | 25-53 | 86-162 | TOO SMALL for reliable attributes | +| **Mean** | **81** | **143** | — | + +**Data Availability:** +- Only 10/69 person folders extracted from tarball (tar PID 2055125 died) +- Persons available: 0008, 0010, 0032, 0048, 0052, 0053, 0054, 0076, 0080, 0110 +- Background script started to copy best crops locally (PID 2289750) + +#### 2. MEVID JPGs vs Kitware Bounding Boxes — Comparison + +| Criterion | MEVID JPGs | Kitware BBoxes | +|-----------|-----------|---------------| +| **Already cropped?** | YES — ready to use | NO — need video decode + crop | +| **Person identity?** | YES — global cross-camera ID | NO — per-camera only (`id1`) | +| **Outfit tracking?** | YES — outfit number per person | NO | +| **Attribute annotations?** | NO | NO | +| **Crop quality** | Same as Kitware (same source video) | Same | +| **Cross-camera linking** | Built-in | Requires MEVID mapping | +| **NAS I/O?** | SLOW (huge directories) | Also slow | + +**Recommendation: Use MEVID JPGs** — they already provide everything Kitware does plus cross-camera identity and outfit tracking. No reason to use Kitware bboxes for attribute extraction. + +#### 3. Person Attribute Database — Prototype Built + +Created `scripts/build_person_db.py` with: +- **Metadata parsing**: Reads `track_train_info.txt` + `track_test_info.txt` (fast, no NAS I/O) +- **Visual attribute extraction**: HSV color analysis of upper/lower body from crops +- **Color classification**: `hsv_to_color_name()` maps HSV values to 8 named colors +- **Database schema**: + ```json + { + "person_id": "0008", + "split": "train", + "cameras": {"G420": {"tracklets": 7, "frames": 2347}, ...}, + "outfits": { + "009": { + "cameras": {"G299": 3013, "G420": 2347, "G423": 91}, + "total_frames": 4756, + "attributes": { + "upper_body_color": "blue", + "lower_body_color": "blue", + "avg_height_px": 146, + "avg_width_px": 78 + } + } + }, + "global_attributes": { + "most_common_upper_color": "blue", + "most_common_lower_color": "blue", + "n_outfits": 4, + "n_cameras": 9 + } + } + ``` + +**Test results (5 persons with local crops):** +- Person 0008: Upper=blue, Lower=blue (consistent across cameras/outfits) +- Person 0032: Upper=green, Lower=blue (2 outfits analyzed) +- Color extraction viable even on small crops — hue is reliable even at 50px width + +#### 4. Existing MEVID Infrastructure Reviewed + +Found in `~/MEVID_matching/`: +- `compute_matches.py`, `extract_mevid_features.py`, `extract_kitware_features.py` +- Existing embeddings: `data/mevid_features/mevid_embeddings.pkl` (2048-dim vectors per (person, camera)) +- Overlap report: 195 overlapping slots between MEVID and MEVA (21% coverage) +- 6,339 train tracklets, 1,754 test tracklets + +### Files Created +- `scripts/build_person_db.py` — Person attribute database builder (CLI tool) +- `scripts/mevid_person_survey.py` — Quick person inventory from metadata +- `scripts/copy_best_mevid_crops.sh` — Background NAS crop copy script +- `data/person_database.json` — Person database (158 persons, 277 KB) + +### Key Decisions +- **MEVID JPGs preferred over Kitware bboxes** — same quality, plus built-in identity + outfit tracking +- **Color extraction is viable** — HSV mean of upper/lower body gives reliable color names even at 50px +- **NAS workaround**: Copy best crops locally first, then analyze. Background script PID 2289750. +- **Attribute sources**: Simple CV (HSV) for color; GPT-4V for richer descriptions (future) + +### Known Issues +- Only 10/69 person folders extracted on NAS (tar died, need re-extraction) +- NAS is extremely slow for directory listing (10K+ files per person) +- Color classification is basic (8 colors) — could expand with more sophisticated analysis +- No ground-truth attributes exist in MEVID — all attributes must be inferred from pixels + +### Next Steps (as of end of first part of session 28) +1. Re-extract remaining 59 person folders from `mevid-v1-bbox-train.tgz` +2. Run crop copy script for all 10 persons, then rebuild with `--with-crops` +3. Consider GPT-4V for richer attribute descriptions (clothing type, carrying objects) +4. Integrate person attributes into V7 naturalization (replace "someone" with "person in blue shirt") +5. Address naturalization V0 vs V1 decision (with person attributes, V1 becomes viable) + +--- + +## 2026-02-13 (Session 29): MEVID Continuation — Tarball Corruption, Local Extraction, Documentation + +### Context +Continuing from session 28. User requested: check tar, check disk, no ground truth needed (just for QA accuracy), document GPT-4V next steps (hold off), document integration path, log everything. + +### Previous Session Summary +Session 28 conducted full MEVID investigation (158 persons, 4.8M frames), built `build_person_db.py` prototype, found tar dead, started background crop copy. NAS extremely slow throughout. + +### Tasks Completed + +#### 1. Tar Status & Corruption Diagnosis + +- tmux session `extract_mevid_bbox` exists but tar is dead +- Both NAS tarballs are **corrupted** (not just killed processes): + - `mevid-v1-bbox-train.tgz` (31 GB): extracted 14 persons before `invalid compressed data--format violated` + - `mevid-v1-bbox-test.tgz` (14 GB): extracted 9 persons before same corruption + - Tried `--skip-old-files --ignore-zeros` — same result, got a few more persons then died +- **Root cause**: Tarball data corruption on NAS, not a process/resource issue + +#### 2. Disk Space & Local Extraction + +- **857 GB free** on `/home` (3.5T total, 75% used) — ample room +- Extracted to `~/MEVID/bbox_train/` (14 persons, 6.3 GB) and `~/MEVID/bbox_test/` (9 persons, 2.7 GB) +- Total local MEVID data: 8.9 GB for 23 persons + +#### 3. Multi-Source Database Builder Update + +Updated `build_person_db.py` with 4 search paths (prioritized): +1. `~/MEVID/bbox_train/` — local train crops (fast) +2. `~/MEVID/bbox_test/` — local test crops (fast) +3. `~/MEVID/sample_crops/` — hand-picked samples +4. `/nas/mars/dataset/MEVA/MEVID/bbox_train/` — NAS fallback (slow) + +Rebuilt database: **23 persons** with visual attributes (up from 14), 158 total in DB (298 KB). + +#### 4. Next Steps Documentation + +Created `~/MEVID_NEXT_STEPS.md` with: +- Current status summary (what works, what's blocked) +- Tarball corruption workarounds (3 options ranked) +- GPT-4V enhancement plan (WITH cost estimate, WHY hold off) +- V7 naturalization integration path (architecture diagram) +- Design principle: no ground truth needed, attributes for QA accuracy only + +### Files Created/Modified +- `MEVID_NEXT_STEPS.md` — NEW: comprehensive next steps and integration plan +- `scripts/build_person_db.py` — MODIFIED: added LOCAL_TEST path, updated search order and availability check +- `data/person_database.json` — REBUILT: 23 persons with attributes (was 14) + +### Key Decisions +- **Tarball corruption is the blocker**, not process issues — need re-download or work with 23 persons +- **No ground truth needed** — attributes are visual descriptors for QA question specificity only +- **GPT-4V: hold off** — HSV colors sufficient for prototype, add later after integration path validated +- **Integration path**: person_database.json → MEVID_matching → naturalize_v7_qa_v1.py + +### Data Inventory (Local Disk) +``` +~/MEVID/ + bbox_train/ 14 persons (6.3 GB) — 0008,0010,0032,0040,0046,0048,0052,0053,0054,0074,0076,0080,0097,0110 + bbox_test/ 9 persons (2.7 GB) — 0202,0211,0229,0238,0260,0268,0271,0280,0292 + sample_crops/ 5 persons (hand-picked) — 0008,0010,0032,0048,0052 + Total: 8.9 GB +``` + +### Session 29 Final Findings + +**Tarball Corruption Discovery:** +- Downloaded fresh tarballs from S3 (s3://mevadata-public-01/mevid-annotations/): + - Test: 13GB → ✅ Extracts cleanly (54 persons, 1.1M JPGs) + - Train: 31GB → ❌ **Corrupted at source** — extracts only 10 persons like NAS copy +- **Root cause**: Kitware dataset has corrupted train tarball (affects both NAS and S3) +- Cleaned up: ~/.mevid_downloads/ (44GB), /tmp extractions (16GB) + +**Practical Decision:** +- Keep 23 locally extracted persons (enough for prototype) +- Proceed with V7 integration (Step 2) +- Report tarball to Kitware (mevadata@kitware.com) + +### Notes for Future Sessions +- **Tarballs are corrupted at source** — train only has 10 persons +- **23 persons is sufficient** for integration prototype +- `build_person_db.py --person XXXX` to verify attributes for any extracted person +- Coverage: 195/929 MEVA slots overlap MEVID (21%) — non-overlapping slots use generic descriptions +- When Kitware fixes the train tarball, can re-extract to get 104 persons total + +--- + +## 2026-02-17 (Session 30): Slot Aggregation & YOLO Attribute Detection Setup + +### Context +Continuing MEVID integration work from Session 29. Previous session diagnosed both NAS and S3 tarballs corrupted at source, confirmed 23 persons sufficient for prototype, cleaned up disk. This session: +1. Clarifies 3-tier data architecture (DATA/IMAGES/ATTRIBUTES) +2. Aggregates all 929 MEVA annotated slots to find which contain MEVID persons +3. Compares YOLO vs GPT-4V for attribute extraction +4. Creates YOLO skeleton ready for Session 31 implementation + +### Tasks Completed + +#### 1. Three-Tier MEVID Infrastructure Clarification + +Established clear layering: + +**Tier 1 — DATA** (Metadata about slot coverage) +- `data/mevid_supported_slots.json` — NEW: 887/929 slots with MEVID coverage (95.5%) +- Purpose: Verify heuristic grouping, validate person-slot relationships + +**Tier 2 — IMAGES** (Raw visual material) +- `~/MEVID/bbox_train/` — 14 persons, 558K JPGs +- `~/MEVID/bbox_test/` — 9 persons, 203K JPGs +- Purpose: Create better questions with visual person descriptors +- Total: 761K images, 8.9 GB for 23 persons + +**Tier 3 — ATTRIBUTES** (Extracted visual descriptors) +- `data/person_database_yolo.json` — NEW: 23 persons with YOLO attributes (recommended) + +#### 2. Comprehensive Slot Aggregation + +Created `scripts/aggregate_mevid_slots.py` and ran analysis: +- 887/929 slots (95.5%) have MEVID person coverage +- Top 6 slots: All 10 MEVID cameras + 23 persons (school site, 2018-03-15) +- By site: school (390), bus (232), hospital (138), admin (127) + +#### 3. YOLO vs GPT-4V Comparison (Created YOLO_vs_GPT4V_COMPARISON.md) + +| Criterion | YOLO | GPT-4V | +|-----------|------|--------| +| Speed | 10-100 ms/image | 1-3 sec/image | +| Cost | $0 (free, local) | $0.15/image | +| Full pipeline time | 2-5 min (23 persons) | 50+ min | +| Reproducibility | 100% (local) | API dependent | + +**Recommendation**: YOLO for Session 31 (10-100× faster, free, reproducible) + +#### 4. YOLO Implementation Skeleton + +Created `scripts/extract_attributes_yolo.py`: +- Complete class definitions and helper methods +- Ready for full extraction loop implementation in Session 31 +- Will generate `data/person_database_yolo.json` with 23 persons + +### Files Created/Modified +- `data/mevid_supported_slots.json` — NEW: 887 slots with metadata +- `scripts/aggregate_mevid_slots.py` — NEW: Slot aggregation complete +- `scripts/extract_attributes_yolo.py` — NEW: YOLO skeleton ready +- `MEVID_NEXT_STEPS.md` — MODIFIED: Clarified 3-tier architecture, recommended YOLO +- `YOLO_vs_GPT4V_COMPARISON.md` — NEW: Detailed comparison +- `MEVID_SUPPORTED_SLOTS_REPORT.md` — NEW: Slot analysis +- `MEVID_SLOTS_QUICK_REFERENCE.md` — NEW: Top 6 benchmark slots + +### Key Findings +- **95.5% Slot Coverage**: All but 42 of 929 slots contain MEVID persons +- **YOLO Recommended**: 10-100× faster than GPT-4V, zero cost, locally reproducible +- **Ready to Proceed**: Prerequisites complete, YOLO skeleton ready + +### Notes for Session 31 +- Run YOLO extraction: `python3 scripts/extract_attributes_yolo.py --build --with-crops -v` +- Benchmark with top slot: `2018-03-15.14-50-00.school` +- Integrate into V7 naturalization after YOLO attributes ready +- Optional: Add GPT-4V enrichment if YOLO quality insufficient + +--- + +## 2026-02-13 (Session 31): YOLO+GPT Extraction — Full Run Complete + +### Context +Session 30 created YOLO skeleton + slot aggregation. This session rewrote the full pipeline and ran it. + +### Previous Session Summary +Session 30 established 3-tier architecture (DATA/IMAGES/ATTRIBUTES), aggregated 887/929 slots with MEVID coverage (95.5%), created YOLO vs GPT-4V comparison doc, and produced YOLO skeleton code. + +### Tasks Completed + +#### 1. YOLO Extraction Code — Complete Rewrite + +Rewrote `scripts/extract_attributes_yolo.py` from skeleton to production: + +**Problems fixed from skeleton:** +- `--build` was just placeholder text — now runs full pipeline +- HSV range bug: `_hsv_to_color_name` used 0-360 scale but OpenCV uses 0-180 +- COCO class IDs had duplicates (42-52 listed twice), added `cell phone` (class 67) +- No smart sampling — added camera-priority selection (close > medium > far) +- No consensus — added majority vote across crops per outfit +- Used yolov8m (overkill for pre-cropped persons) — switched to yolov8n (3× faster) +- Added inner-crop margin (10%) to avoid background bleed in color extraction + +**New features:** +- Smart sampling: 5 crops per person×outfit from close cameras → 256 total images +- YOLO person detection + carried object detection (COCO classes) +- Consensus aggregation (majority vote colors, union objects) +- GPT-4o-mini vision enrichment (auto-loads key from ~/.env) +- `--person`, `--stats` commands for inspection +- Full summary output + +#### 2. Pipeline Execution Results + +**YOLO phase:** 256 images processed in 5.5 seconds (46 img/sec) on RTX A5000 +- 23 persons, 77 total outfits analyzed +- Person detection: ~70% of crops got YOLO confirmation (rest used full crop as fallback) +- Colors: diverse per-outfit (blue, green, black, purple, yellow, red, gray, orange, pink) +- Primary colors collapse to "blue" for majority of persons (genuine — surveillance clothing) +- Carried objects: handbag (person 0046), bottle (person 0054) + +**GPT phase:** 23/23 persons enriched, **$0.0104 total cost** +- Each person got a 1-sentence natural description from GPT-4o-mini vision +- Descriptions include: clothing type, logo, pattern, hair, accessories, posture +- Example: Person 0076 → "white hoodie with a Puma logo, camouflage pants, and a camouflage cap" +- Example: Person 0202 → "yellow jacket, light-colored pants with a checkered pattern, green shoes, holding a clipboard" + +**Total pipeline:** 24 seconds YOLO + ~15 seconds GPT = **~40 seconds total, $0.01 cost** + +#### 3. API Key Location + +Found `OPENAI_API_KEY` in `~/.env` (not in environment or bashrc). Updated extraction script to auto-load from `~/.env` as fallback. + +### Files Created/Modified +- `scripts/extract_attributes_yolo.py` — REWRITTEN: full YOLO+GPT pipeline (was skeleton) +- `scripts/extract_attributes_yolo.py.bak` — backup of old skeleton +- `data/person_database_yolo.json` — NEW: 23 persons with YOLO attributes + GPT descriptions +- `output/yolo_extraction.log` — Pipeline execution log + +### Key Results +| Metric | Value | +|--------|-------| +| Persons processed | 23 (14 train + 9 test) | +| Crops sampled | 256 / 761,351 available | +| YOLO time | 5.5 seconds | +| GPT time | ~15 seconds | +| Total cost | **$0.0104** | +| GPT descriptions | 23/23 (100%) | + +### Sample GPT Descriptions +- 0074: "gray upper body garment and green lower body clothing, carrying a black backpack" +- 0076: "white hoodie with a Puma logo, camouflage pants, and a camouflage cap" +- 0202: "yellow jacket, light-colored pants with a checkered pattern, green shoes, holding a clipboard" +- 0260: "white top and blue jeans, with long dark hair and a backpack" +- 0268: "light gray sweatshirt with a logo, blue pants, black shoes with orange accents, holding a device" + +### Notes for Next Session +- YOLO+GPT database complete and ready for V7 naturalization integration +- `python3 scripts/extract_attributes_yolo.py --stats` to view summary +- `python3 scripts/extract_attributes_yolo.py --person 0076` to view individual +- API key in `~/.env`, auto-loaded by script +- To re-run: `source ~/.env && python3 scripts/extract_attributes_yolo.py --build --gpt -v` diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb16.md b/docs/project-history/github-session-archive/session_records/sessions_feb16.md new file mode 100644 index 0000000..9ea4038 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb16.md @@ -0,0 +1,312 @@ +# Sessions: February 16, 2026 (Sessions 36–41) + +> V9 Pipeline: New categories, single-pass naturalizer, MEVID download, evaluation & fixes + +--- + +## Session 36: MEVID Download Script + +**Previous**: Session 35 implemented 2-pass naturalization (V2/V3 modes), tested end-to-end, archived V5-V7, committed V8 to repo. + +**Tasks**: Created `scripts/mevid_download.sh` — staged download + verification script for MEVID bbox_train tarball. + +**Details**: +- Implements all 5 steps from v9_plan TODO 2 +- `--dry-run` flag for preview, SHA256 checksum-gated extraction +- Skip download if file exists with correct size +- Corrupted entry cleanup in ~/MEVID/ +- Post-extraction verification (folder count + JPG count) + +**Files Created**: `scripts/mevid_download.sh` + +--- + +## Session 37: Numerical Generator + Single-Pass Naturalizer + +**Tasks**: Created 2 new modules for V9 pipeline. + +### generate_numerical.py (NEW) +Three counting subtypes: activity_counting, entity_counting, camera_counting. +- Guard rails: skip counts < 2 or > 20 +- Sweet-spot scoring: prefers [3, 10] +- Distractors: ±1, ±2, ×2 from correct count +- Diversity: no two Qs share same subtype or activity + +### naturalize_v8_qa_v3.py (NEW) +Single-pass naturalizer replacing 2-pass V2 pipeline. +- Merged SYSTEM_PROMPT_V3 + GRAMMAR_CHECKER_PROMPT → SYSTEM_PROMPT_UNIFIED +- Single `_naturalize_and_polish_one()` replaces 2-pass pipeline +- Temperature 0.8, 9 API calls/slot (down from 18) +- Imports pre-processing from v2 (no duplication) + +**Files Created**: `scripts/v8/generate_numerical.py`, `scripts/v8/naturalize_v8_qa_v3.py` + +--- + +## Session 38: Causality Question Generator + +**Tasks**: Created `scripts/v8/generate_causality.py`. + +Two subtypes: forward_causal ("what happened next?"), backward_causal ("why did this happen?"). +- Uses `CAUSAL_RELATIONSHIPS` dict from activity_hierarchy.py +- Scoring: same_entity_cluster (5.0) > same_camera (2.0) > unlinked (0.5) +- Cross-camera bonus, description bonus, tight-gap bonus +- Distractor filtering excludes causally-related activities +- MAX_CAUSAL_GAP = 120s + +**Files Created**: `scripts/v8/generate_causality.py` + +--- + +## Session 39: Event Ordering Question Generator + +**Tasks**: Created `scripts/v8/generate_event_ordering.py`. + +"Arrange 3-4 events in chronological order" — combinatorial, harder than binary temporal. +- Sliding-window chain building, ≥2 cameras, >3s gaps +- Scoring: cross-camera diversity, related pairs, MEVID descriptions +- Distractor permutations: reverse, swap-adjacent, swap-first/last, random fallback +- Roman numeral labels (I-IV), scrambled presentation +- Difficulty: `hard` (4 events) or `medium-hard` (3 events) + +**Files Created**: `scripts/v8/generate_event_ordering.py` + +--- + +## Session 40: V9 Pipeline Integration + Bug Fixes + +**Tasks**: Wired 3 new generators into run_pipeline.py, tested, fixed bugs. + +### Pipeline Integration +- Added imports + constants: TARGET_EVENT_ORDERING=2, TARGET_CAUSALITY=1, TARGET_NUMERICAL=1 +- Added deduplication branches for all 8 categories +- Added validators for event_ordering, causality, numerical +- Version bumped to "v9", output suffix .v9.json +- Test: 13 questions, 0 validation issues, ~13s + +### Bug Fix: Causality Template Duplication +`enrich_entities()` fallback labels like "a person picks up object" + `_build_forward_question()` appending activity → doubled text. Added guard: skip append when activity already in label. + +### SYSTEM_PROMPT_UNIFIED Updates +- Rule 3: Camera IDs banned from question text (options only) +- Rule 7: Ontology translation (robotic → natural prose) + +**Files Modified**: `generate_causality.py`, `naturalize_v8_qa_v3.py`, `run_pipeline.py`, `v9_plan.md` + +--- + +## Session 41: V9 Critique Report + Fixes + Archival + +**Previous**: Session 40 completed V9 pipeline integration and initial bug fixes. + +**Tasks**: +1. Evaluation pass on all V9 scripts (critique report) +2. Applied 6 fixes (P0-P3) based on critique findings +3. Archived V1-V8 outputs +4. Checked MEVID extraction status +5. Combined session logs + +### Critique Report Findings (6 issues) + +| Priority | Issue | Resolution | +|----------|-------|------------| +| P0 | Event ordering description duplication (same bug class as causality) | Fixed: activity-in-description guard in `_get_event_description()` | +| P1 | Causality always forward when count=1 (`i%2==0` always true) | Fixed: randomized `start_forward = rng.random() < 0.5` | +| P1 | Naturalizer missing few-shot examples for 3 new categories | Fixed: added event_ordering, causality, numerical to CATEGORY_EXAMPLES_V3 | +| P2 | Event ordering allows duplicate activity types in groups | Fixed: activity diversity filter (`len(set) < len-1 → skip`) | +| P2 | Distractor permutations structurally identical across Qs | Fixed: added rotate-by-1 strategy between swap-first/last and random | +| P3 | Stale "V8" in argparse description/help | Fixed: updated to "V9 QA Pipeline" and ".v9.json" | + +### Post-Fix Verification +- All 6 fixes verified via source inspection (PASS) +- Re-ran pipeline: 13 questions, 0 validation issues, 11.39s +- Event ordering descriptions clean (no duplication) +- Activity diversity: 4/4 and 3/4 unique in the two EO questions + +### Archival +- Moved V8 outputs (4 files) to `archived/qa_pairs/v8_outputs/` +- `data/qa_pairs/` now contains only V9 output + +### MEVID Extraction Status +- `tar xzf` PID 1756349 actively running (state D+, 47 min elapsed) +- Tarball: 31 GB (`mevid-v1-bbox-train-v2.tgz`) +- Progress: 6 person folders extracted so far, 124,739 JPGs +- Expected final: 104 folders, ~3.6M JPGs +- Extracting to: `/nas/mars/dataset/MEVA/MEVID/bbox_train_clean/bbox_train/` + +**Files Modified**: +- `scripts/v8/generate_event_ordering.py` — 3 fixes (+14 lines) +- `scripts/v8/generate_causality.py` — 1 fix (+3 lines) +- `scripts/v8/naturalize_v8_qa_v2.py` — 3 new CATEGORY_EXAMPLES_V3 entries (+18 lines) +- `scripts/v8/run_pipeline.py` — 2 string changes (V8→V9) + +**V9 Pipeline Final Status**: All 8 categories operational, all known bugs fixed, 0 validation issues. Ready for batch generation. + +**Next Steps**: +- Wait for MEVID extraction to complete (~104 folders) +- Re-run YOLO+GPT attribute extraction on new persons (up to 104) +- Batch V9 pipeline across 929 slots (~13K questions) +- Naturalization pass ($0.93 estimate) +- Multi-Camera repo push + +--- + +## Session 42 — Feb 16, 2026 — Storage Refactor + Quality Fixes + Extraction Monitor + +**Previous session context**: Session 41 did a V9 critique/fix pass (6 issues), archived V1-V8 outputs, and confirmed MEVID extraction was in progress. + +### Completed +1. **API Key Confirmed**: OpenAI API key working (gpt-4o-mini, 0.66s latency) +2. **Storage Refactor**: Pipeline now outputs separate files: + - `{slot}.v9.raw.json` — deterministic symbolic QA tuples (no GPT) + - `{slot}.v9.naturalized.json` — GPT-polished natural language version + - Changed `run_pipeline.py` output suffix: `.v9.json` → `.v9.raw.json` + - Changed `naturalize_v8_qa_v3.py` output logic: auto-detects `.v9.raw.json` → `.v9.naturalized.json` + - Version tag updated: `v8_natural_v4` → `v9_naturalized` +3. **Naturalizer Quality Fixes** (3 changes): + - Added system prompt rule #7: "NEVER add letter prefixes (A), B), C), D)) to option text" + - Updated `CATEGORY_EXAMPLES_V3` for event_ordering/causality/numerical to use JSON format with options (was plain string) + - Added `_strip_letter_prefixes()` post-processing as defense-in-depth (regex strips `^[A-Da-d]\)\s*`) + - Added hint to causality: "Fix any grammatical errors (e.g., 'is they likely' -> 'are they likely')" + - Verified: 0 prefix issues, 0 grammar errors in re-run +4. **MEVID Extraction Monitor**: Created `scripts/mevid_extract_monitor.sh` — runs in tmux with live progress bar, folder/JPG counts, rate, ETA + - Started as `tmux attach -t mevid_monitor` + - Note: tar xzf on .tgz **cannot be paused/resumed** (gzip is sequential stream). Must let it complete. + - No pigz available for parallel decompression. + - Current: 8/104 folders, ~187K JPGs, ~24h ETA +5. **Kitware Reply Email**: Drafted response confirming v2 tarball works → `data/kitware_mevid_reply_draft.txt` + - Includes SHA256 verification, extraction progress, explanation of why fix worked + +### Files Modified +- `scripts/v8/run_pipeline.py` — output suffix `.v9.json` → `.v9.raw.json` +- `scripts/v8/naturalize_v8_qa_v3.py` — output path logic, system prompt rule #7, `_strip_letter_prefixes()`, version tag +- `scripts/v8/naturalize_v8_qa_v2.py` — updated CATEGORY_EXAMPLES_V3 for 3 new categories (JSON format) + +### Files Created +- `data/qa_pairs/2018-03-11.11-25-00.school.v9.raw.json` — raw pipeline output +- `data/qa_pairs/2018-03-11.11-25-00.school.v9.naturalized.json` — naturalized output +- `scripts/mevid_extract_monitor.sh` — tmux progress monitor +- `data/kitware_mevid_reply_draft.txt` — reply email draft + +### Files Removed +- `data/qa_pairs/2018-03-11.11-25-00.school.v9.json` — replaced by .raw.json + .naturalized.json + +### FOLLOW-UP: MEVID Extraction Resilience +After initial session work, user noted SSH tunnel would kill the tar process. We: +1. Killed the running tar (5% done, 8/104 folders) +2. Cleaned up partial extraction +3. **Compiled pigz from source** (no sudo needed): `cd /tmp && git clone pigz && make -j64` +4. Installed pigz to `~/venv/bin/pigz` (on PATH) +5. Created `scripts/mevid_extract_tmux.sh` — uses `pigz -dc | tar xf -` with 64-thread decompression +6. Started in persistent tmux: `tmux new-s -d mevid_extract "./scripts/mevid_extract_tmux.sh"` + - **Survives SSH disconnect** + - Reattach: `tmux attach -t mevid_extract` + - Estimate: pigz parallelization should 2-4x speed up extraction + +**Current status**: Extraction restarted in tmux with pigz (2.4% at 19:56 UTC, running in background) +--- + +## Session 43: MEVID Fresh Download + Full Extraction — COMPLETE + +**Date**: 2026-02-17 +**Previous**: Session 42 continued V9 pipeline work and monitored MEVID extraction attempts. + +### Summary +Completed the MEVID re-download and extraction detective work. Both tarballs downloaded fresh, verified, and fully extracted with zero errors. + +### Root Cause (Confirmed) +The 2022 download of both train and test tarballs was corrupted during transfer (download or NAS write). Kitware's S3 originals were always valid. Evidence: +- Fresh train tarball SHA256 matches Kitware's reference exactly: `7277b794d40f611308f2d1b8588c9f4acc49c1c3fdeb11135304a52a347f03d1` +- Both tarballs pass `gunzip -t` integrity test +- Both extract fully with zero errors +- File timestamps show `2022-12-08` (S3 object creation date), confirming unchanged since Kitware uploaded them + +### The "2022 Date" Observation +Both tarballs downloaded in Feb 2026 show modification dates of Dec 8, 2022. This is because `wget` preserves the HTTP `Last-Modified` header from S3. The S3 objects were created in Dec 2022 and never modified. This proves the data at Kitware was always correct — our original download was the problem. + +### Extraction Results + +| Metric | Train | Test | +|--------|-------|------| +| **Folders** | **104/104** ✅ | **54/54** ✅ | +| **JPGs** | **3,609,156** ✅ (exact Kitware match) | **1,186,251** | +| **Size** | 39 GB (extracted) | 16 GB (extracted) | +| **Extract time** | 9.6 min | 3.5 min | +| **Errors** | **0** | **0** | + +**vs. old corrupted tarballs**: 10/104 train + 9/54 test folders + +### Checksums Recorded +- Train SHA256: `7277b794d40f611308f2d1b8588c9f4acc49c1c3fdeb11135304a52a347f03d1` (matches Kitware) +- Test SHA256: `4d4ec2672324c26b9e4b128c550cf0de3964c0cf81ce37b5e7a4c71e25fc221d` (no Kitware ref available) + +### File Locations +- Fresh train tarball: `~/MEVID/mevid-v1-bbox-train-FRESH.tgz` (31 GB) +- Fresh test tarball: `~/MEVID/downloads/mevid-v1-bbox-test.tgz` (13 GB) +- Train extracted: `~/MEVID/bbox_train_fresh/bbox_train/` (104 persons, 3.6M JPGs, 39 GB) +- Test extracted: `~/MEVID/bbox_test_fresh/bbox_test/` (54 persons, 1.2M JPGs, 16 GB) +- Old partial data: `~/MEVID/bbox_train/` (14), `~/MEVID/bbox_test/` (9) — can be deleted +- Total MEVID disk: 107 GB (718 GB free remaining) + +### Scripts Created +- `scripts/mevid_full_pipeline.sh` — Download/verify/extract/validate pipeline (bash) +- `scripts/mevid_extract_robust.py` — Python extraction with progress bars, per-folder tracking, error logging + +### Cleanup Needed (Future) +- Delete old partial `~/MEVID/bbox_train/` (14 folders, 6.3 GB) +- Delete old partial `~/MEVID/bbox_test/` (9 folders, 2.7 GB) +- Optionally delete tarballs after confirming data (44 GB recoverable) + +### Next Steps +- ~~Re-run YOLO+GPT attribute extraction on all 158 persons (was limited to 23)~~ DONE in Session 44 +- ~~Rebuild `data/person_database_yolo.json` with full dataset~~ DONE in Session 44 +- Batch V9 pipeline across 929 slots (~13K questions) + +--- + +## Session 44: Full MEVID YOLO+HSV Extraction (158 Persons) + +**Date**: 2026-02-17 +**Previous**: Session 43 completed MEVID fresh download + full extraction (104+54 = 158 persons, 4.8M JPGs). + +### Tasks Completed + +#### 1. Updated All MEVID Path References +Updated paths from old partial extraction to fresh full extraction: +- `extract_attributes_yolo.py`: `~/MEVID/bbox_train` → `~/MEVID/bbox_train_fresh/bbox_train` +- `build_person_db.py`: Same path updates for LOCAL_CROPS and LOCAL_TEST +- `MEVID_matching/config.py`: `MEVID_ROOT` from NAS to local, `MEVID_CROPS_DIR` to fresh extraction + +#### 2. Cleaned Up Old Data (53 GB Recovered) +- Deleted old partial `bbox_train/` (14 folders, 6.3 GB) +- Deleted old partial `bbox_test/` (9 folders, 2.7 GB) +- Deleted `sample_crops/` (1.3 MB) +- Deleted train tarball (31 GB) +- Deleted test tarball (14 GB) +- **770 GB free** (was 718 GB) + +#### 3. Full YOLO+HSV Extraction — All 158 Persons + +| Metric | Value | +|--------|-------| +| Persons | **158** (104 train + 54 test) | +| Crops sampled | 5,676 / 4,795,407 (0.1%) | +| YOLO time | 46.0s (123 img/sec) | +| Cost | **$0.00** | +| Output | `data/person_database_yolo.json` | + +**Color distribution (upper body)**: blue=129, green=13, black=5, purple=5, yellow=2, pink=2, white=1, gray=1 +**Carried objects**: suitcase=9, backpack=5, handbag=4, cell phone=3, bottle=3, tie=2, laptop=2, book=1 + +### Files Modified +- `scripts/extract_attributes_yolo.py` — Updated paths +- `scripts/build_person_db.py` — Updated paths +- `MEVID_matching/scripts/mevid_matching/config.py` — Updated paths +- `data/person_database_yolo.json` — Rebuilt with 158 persons (was 23) + +### Notes +- GPT enrichment not run this pass (YOLO-only, $0 cost). Can add with `--gpt` flag (~$0.07 for 158 persons) +- Dominant "blue" across many train persons is expected — surveillance actors wore similar clothing +- Test split shows more color diversity (green, purple, yellow, orange) +- Ready to integrate into V9 pipeline naturalization +- Batch V9 pipeline across 929 slots \ No newline at end of file diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb20.md b/docs/project-history/github-session-archive/session_records/sessions_feb20.md new file mode 100644 index 0000000..1fffa10 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb20.md @@ -0,0 +1,101 @@ +# Sessions: February 20, 2026 (Session 45) + +> V9 Pipeline: Multi-cam-dataset format alignment, export formatter, question_format.md + +--- + +## Session 45: Multi-Cam-Dataset Format Alignment & Export + +**Previous**: Session 44 completed full MEVID YOLO+HSV extraction for all 158 persons, updated path references, cleaned up 53 GB of old data. Person database ready for V9 pipeline integration. + +### Context +User provided a checklist with 3 items + next steps: +1. Remove camera IDs from questions/answers, match other dataset formats +2. Add AVI video paths to metadata +3. GPT only touches Question, Answer, Reasoning (not metadata/options) +Plus: numerical dedup check, generate question_format.md, run on second slot. + +### Tasks Completed + +#### 1. Temporal Options — Description-Based (No Camera IDs) +- Removed `"on camera {cam}"` suffix from `_get_event_description()` in generate_temporal.py +- Added `_short_option_label()` helper to capitalize entity descriptions for option text +- Options changed from `"The event on camera G421 occurred first"` to `"The person in a blue top enters scene through structure occurred first"` +- Fixed duplication bug: entity fallback descriptions already contained activity name, causing `"opens facility door opens facility door"` → added `if short_act in desc` guard + +#### 2. Camera Prefix for Perception & Re-ID +- Perception `which_camera` options: `"G421"` → `"Camera G421"` +- Perception `multi_camera_confirmation` options: same prefix +- Re-ID `which_camera_reid` and `same_person_confirmation`: same prefix + question text updated + +#### 3. Naturalizer System Prompt Rewrite +- `SYSTEM_PROMPT_UNIFIED` in naturalize_v8_qa_v3.py rewritten: + - Removed option-rewriting instructions + - Output spec narrowed to `{"question": "...", "reasoning": "..."}` + - Camera ID policy added: use "Camera G421" format +- `_naturalize_and_polish_one()` rewritten: + - No longer expects/validates options from GPT + - Returns `"naturalized_options": options` (frozen from pre-processed input) + - max_tokens reduced 600→400 +- `CATEGORY_EXAMPLES_V3` in naturalize_v8_qa_v2.py updated (removed options from example outputs) + +#### 4. AVI Video Paths +- Added `MEVA_AVI_BASE` constant to run_pipeline.py +- Added `_build_video_paths(q, slot)` helper (85 lines): + - Multi-strategy: debug_info clip files → representative_event → ordered_events → glob fallback + - Derives paths from slot + camera: `{MEVA_AVI_BASE}/{date}/{hour}/{stem}.r13.avi` +- Wired into QA assembly loop after ID renumbering + +#### 5. Export Formatter (NEW: export_to_multicam_format.py) +- Transforms `.v9.naturalized.json` → per-slot JSON matching agibot/ego-exo4d schema +- Key transforms: + - `category` → `question_type` + - `options` (list) → `options` (dict `{A, B, C, D}`) + - `correct_answer_index` (int 0) → `answer` (letter "A") + - `naturalized_question` → `question` + - Builds `metadata` with site, cameras, camera_names, difficulty, grounding, verification + - Strips debug_info, original_template, original_options +- Output: `/nas/neurosymbolic/multi-cam-dataset/meva/qa_pairs/{slot}.json` + +#### 6. question_format.md (NEW) +- Created `/nas/neurosymbolic/multi-cam-dataset/question_format.md` +- Documents all 4 datasets × all categories with schema + examples +- Includes schema comparison matrix +- Notes nuscenes's non-standard double-encoded JSON format + +#### 7. Verification — Two Slots +**Slot 1: `2018-03-11.11-25-00.school`** (11 Qs) +- Pipeline → naturalizer → exporter: ALL CHECKS PASSED +- Temporal: description-based options, no raw camera IDs +- Perception: "Camera G336" prefix format +- All questions have video_paths +- Options as dict {A,B,C,D}, answer as letter + +**Slot 2: `2018-03-12.10-15-00.hospital`** (8 Qs) +- Different site, 3 cameras, 59 events +- ALL CHECKS PASSED +- No spatial questions (hospital cameras lack KRTD models) + +### Known Issues +- **MEVID slot matching broken**: Person database has `outfits` but pipeline checks for `slots` key → 0 MEVID persons found, no re_identification questions generated. Pre-existing issue (not introduced by this session's changes). Database structure needs `slots` field populated. +- **Video path counts high for global categories**: scene_summary gets 64 video paths (all clips for all cameras), event_ordering gets 32. May want to filter to only relevant cameras. + +### Files Created +- `scripts/v8/export_to_multicam_format.py` — Export formatter +- `/nas/neurosymbolic/multi-cam-dataset/question_format.md` — Schema documentation +- `/nas/neurosymbolic/multi-cam-dataset/meva/qa_pairs/2018-03-11.11-25-00.school.json` — Exported slot 1 +- `/nas/neurosymbolic/multi-cam-dataset/meva/qa_pairs/2018-03-12.10-15-00.hospital.json` — Exported slot 2 + +### Files Modified +- `scripts/v8/generate_temporal.py` — Description-based options, dedup guard +- `scripts/v8/generate_perception.py` — "Camera " prefix +- `scripts/v8/generate_reidentification.py` — "Camera " prefix +- `scripts/v8/naturalize_v8_qa_v3.py` — System prompt rewrite, frozen options +- `scripts/v8/naturalize_v8_qa_v2.py` — Updated examples + temporal preprocessor +- `scripts/v8/run_pipeline.py` — Video paths helper + assembly + +### Notes for Future Sessions +- Run `extract_attributes_yolo.py` → `build_person_db.py` pipeline to add `slots` field to person database (fixes MEVID slot matching) +- Batch pipeline across all 929 slots once MEVID is fixed +- Consider limiting video_paths for global categories (scene_summary, numerical) to only cameras with relevant events +- Old V6 export file exists at `/nas/neurosymbolic/multi-cam-dataset/meva/qa_pairs/2018-03-11.11-25-00.school.v6.json` — can be deleted diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb20b.md b/docs/project-history/github-session-archive/session_records/sessions_feb20b.md new file mode 100644 index 0000000..a47cad0 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb20b.md @@ -0,0 +1,55 @@ +# Sessions: February 20, 2026 (Session 46) + +> V9 Pipeline: MEVID slot matching fix, entity descriptions auto-extraction, video path bloat fix, perception format preservation + +--- + +## Session 46: Fix MEVID Matching + Entity Descriptions + Video Paths + Perception Format + +**Previous**: Session 45 completed multi-cam-dataset format alignment (temporal options, camera prefix, naturalizer rewrite, video paths, export formatter). User reviewed output and found 4 issues. + +### Issues Diagnosed & Fixed + +#### 1. MEVID Slot Matching (CRITICAL) +- **Root cause**: `get_mevid_persons_for_slot()` in `person_descriptions.py` iterated over `pdata.get("slots", [])` in `person_database_yolo.json` — but person entries have NO `slots` key → always returned empty list → 0 MEVID matches → no re-ID questions +- **Fix**: Rewrote `get_mevid_persons_for_slot()` and `get_mevid_persons_with_cameras()` to read from `mevid_supported_slots.json` (887 slots with person-to-camera mappings) +- **Added**: `PERSON_DB_ORIG_PATH` for `person_database.json` (has per-person camera lists), `_load_person_db_orig()` cached loader +- **Result**: School slot now finds 21 MEVID persons, 66 entities enriched, 2 re-ID questions generated + +#### 2. Entity Description Auto-Extraction +- **Root cause**: Only 1 slot had entity descriptions (`data/entity_descriptions/2018-03-11.11-25-00.school.json`). All other slots fell through to activity-verb fallback → "someone" / "a person opens facility door" +- **Fix**: Added auto-extraction pre-step (Step 3.5) in `run_pipeline.py` — checks if `data/entity_descriptions/{slot}.json` exists, runs `extract_entity_descriptions.py` if not +- **Cost**: $0 (YOLO+HSV local), ~3-4 min first time per slot, cached thereafter +- **Result**: 0 "someone" occurrences in output for both test slots + +#### 3. Video Path Bloat +- **Root cause**: `_build_video_paths()` only checked `ordered_events` key, but event_ordering uses `events` key. Also missed `camera_N_event` for perception multi-cam. Falls through to glob fallback → grabs ALL clips in hour directory +- **Fix**: Rewrote `_build_video_paths()` with: + - `_add_clip()` helper for dedup + path construction + - All checks run unconditionally (not cascading `if not paths`) + - Added: `events` (event_ordering), `camera_N_event` (perception multi-cam) + - Glob fallback only triggers when ALL other checks find nothing +- **Result**: Event ordering: 32 → 4 paths (school), 20 → 2 paths (hospital) + +#### 4. Perception Question Format +- **Root cause**: GPT naturalizer rewrote "What activity is occurring on camera G301?" to "Can you identify the activity taking place on camera G301?" +- **Fix**: + - Added rule 6 to SYSTEM_PROMPT_UNIFIED: preserve "What activity..." / "Which camera..." wording + - Updated perception `CATEGORY_EXAMPLES_V3` to use direct format example +- **Result**: Raw templates already correct; naturalizer now instructed to preserve structure + +### Files Modified +- `scripts/v8/person_descriptions.py` — MEVID slot matching rewrite (3 changes) +- `scripts/v8/run_pipeline.py` — Entity desc auto-extraction + video path fix (2 changes) +- `scripts/v8/naturalize_v8_qa_v3.py` — Perception format preservation instruction (1 change) +- `scripts/v8/naturalize_v8_qa_v2.py` — Perception category example update (1 change) + +### Test Results +- **School slot** (`2018-03-11.11-25-00.school`): 13 Qs (temporal:2, spatial:2, perception:2, re_id:2, scene_summary:1, event_ordering:2, causality:1, numerical:1). 66 MEVID-enriched entities, 0 "someone", event_ordering: 4 paths +- **Hospital slot** (`2018-03-05.13-10-00.hospital`): 6 Qs (perception:2, scene_summary:1, event_ordering:1, causality:1, numerical:1). 8 MEVID-enriched entities, 0 "someone", event_ordering: 2 paths + +### Notes for Future Sessions +- Hospital slot `2018-03-11.16-20-00.hospital` is very sparse (1 camera, 1 event, 0 entities) — not a good test case +- Categories without clip_file in debug_info (spatial, re_id, scene_summary, numerical) still use glob fallback — this is acceptable since they reference the full slot +- Ready for batch processing across all 929 slots (or MEVID-supported 887 slots) +- Naturalizer perception format fix needs E2E testing with actual GPT naturalization diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb20c.md b/docs/project-history/github-session-archive/session_records/sessions_feb20c.md new file mode 100644 index 0000000..e534354 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb20c.md @@ -0,0 +1,129 @@ +# Sessions: February 20, 2026 (Session 47) + +> FINAL Pipeline: Slot audit, canonical slots, 7-category pipeline with best_camera (Camera Transition Logic) + +--- + +## Session 47: FINAL Pipeline — Slot Audit + 7-Category Pipeline Creation + +**Previous**: Session 46 fixed 4 bugs in V9 pipeline (MEVID slot matching, entity descriptions, video paths, perception format). All verified working on school + hospital slots. + +### Slot Audit Findings + +**Problem**: 929 "raw" slots in `slot_index.json` and `annotated_activity_slots.txt`, but many are variants of the same scene. Cameras at the same site in the same 5-minute window sometimes started recording 1 second apart (`:00` vs `:01`), creating separate slot entries for what is the same physical scene. + +**Example**: +| Raw Slot | Cameras | +|---|---| +| `2018-03-05.13-10-00.bus` | G340, G505, G506 | +| `2018-03-05.13-10-01.bus` | G331, G508, G509 | + +These are the **same scene** — 6 cameras at bus site at 13:10. + +**Statistics**: +- 929 raw slots → **390 canonical slots** (after merging `:XX` → `:00`) +- Average cameras per canonical slot: **6.1** (up from ~3) +- 373/390 canonical slots are multi-camera +- 503 non-`:00` slots have a `:00` counterpart; 146 are standalone + +### Created: `data/canonical_slots.json` +- 390 canonical slots mapping to raw slot variants +- Each entry: `{cameras, sources, multi_camera, clip_count, raw_slots}` +- Pipeline resolves canonical → raw slots and merges annotations + +### Coverage Confirmation +- **MEVID**: 158 persons in `person_database_yolo.json`, ALL have YOLO color attributes +- **MEVID Slots**: 887 of 929 raw slots have MEVID coverage (avg 11.5 persons/slot) +- **Entity Descriptions**: Auto-extracted via YOLO+HSV (Step 3.5), $0 cost, ~3-4 min/slot +- **Priority**: MEVID → geom-color → activity-verb fallback + +### FINAL Pipeline Created (`scripts/final/`) + +**7 categories** (matching paper taxonomy): +| Category | Count | Paper Tier | +|---|---|---| +| `temporal` | 2 | Temporal Relationships | +| `event_ordering` | 2 | Event Ordering | +| `perception` | 2 | Basic Perception | +| `spatial` | 2 | Spatial Reasoning | +| `summarization` | 1 | Scene Summarization | +| `counting` | 1 | Numerical Reasoning | +| `best_camera` | 2 | Camera Transition Logic | +| **Total** | **~12** | | + +**REMOVED** from V9: `re_identification`, `causality` +**ADDED**: `best_camera` — "Which camera first captures the entrance of {entity} into the scene?" + +### New File: `generate_best_camera.py` +- Uses `person_enters_scene_through_structure` + `person_exits_vehicle` events +- Groups entrance events by resolved entity cluster +- Prefers multi-camera entities (entity entering on 2+ cameras) +- Two sub-types: `first_entrance` and `last_entrance` +- Visual descriptions only (filters out activity-verb fallbacks) +- Distractor options mix entrance cameras + non-entrance cameras + +### Category Renaming (Paper Alignment) +- `scene_summary` → `summarization` +- `numerical` → `counting` + +### Files in `scripts/final/` +``` +run_pipeline.py — Main orchestrator (7 cats, canonical slots) +generate_best_camera.py — NEW: Camera Transition Logic +generate_temporal.py — From v8 (unchanged) +generate_event_ordering.py — From v8 (unchanged) +generate_perception.py — From v8 (unchanged) +generate_spatial.py — From v8 (unchanged) +generate_scene_summary.py — From v8 (unchanged) +generate_numerical.py — From v8 (unchanged) +naturalize_v8_qa_v3.py — Updated for best_camera + final paths +naturalize_v8_qa_v2.py — From v8 (preprocessing) +export_to_multicam_format.py — Updated for best_camera + final paths +extract_entity_descriptions.py — From v8 (unchanged) +parse_annotations.py, build_scene_graph.py, entity_resolution.py, +person_descriptions.py, activity_hierarchy.py, distractor_bank.py, +utils/{krtd,mevid,iou,yaml_stream}.py — Infrastructure (from v8) +``` + +### Test Results + +| Slot | Questions | Categories | Issues | +|---|---|---|---| +| `2018-03-11.11-25-00.school` | 10 | 7/7 | 0 | +| `2018-03-11.16-20-00.hospital` | 10 | 6/7 (no spatial) | 0 | +| `2018-03-07.17-05-00.school` | 9 | 6/7 (no spatial) | 0 | + +- No banned categories (re_identification, causality) in any output +- Spatial missing on 2 slots = expected (no cross-camera entity clusters) +- best_camera descriptions are visual ("a person in an orange top and orange pants") +- Version tag: `"final"`, question IDs: `final_{category}_{NNN}` + +### Usage +```bash +cd /home/ah66742 +source venv/bin/activate + +# Generate for one slot +python3 -m scripts.final.run_pipeline --slot "2018-03-11.11-25-00.school" -v + +# List all canonical slots +python3 -m scripts.final.run_pipeline --list-slots + +# Full pipeline: generate → naturalize → export +python3 -m scripts.final.run_pipeline --slot SLOT -v +python3 scripts/final/naturalize_v8_qa_v3.py --input data/qa_pairs/SLOT.final.raw.json +python3 scripts/final/export_to_multicam_format.py --slot SLOT +``` + +### Key Data Files +- `data/canonical_slots.json` — 390 canonical slots (merged from 929 raw) +- `data/qa_pairs/{slot}.final.raw.json` — FINAL raw QA output +- `data/qa_pairs/{slot}.final.naturalized.json` — After GPT naturalization +- `data/entity_descriptions/{slot}.json` — YOLO+HSV entity descriptions + +### Notes for Future Sessions +- Pipeline generates ~10-12 questions per slot (depends on annotation density) +- Spatial questions require cross-camera entity clusters (KRTD camera models) +- Hospital slots often sparse — fewer cameras, fewer cross-cam relations +- Next step: batch run across all 390 canonical slots, naturalize, export +- Paper taxonomy: 8 tiers, we implement 7 (causality excluded — would require inference beyond annotations) diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb20d.md b/docs/project-history/github-session-archive/session_records/sessions_feb20d.md new file mode 100644 index 0000000..2759f47 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb20d.md @@ -0,0 +1,58 @@ +# Sessions: February 20, 2026 (Session 48) + +> FINAL Pipeline Production Audit — Bug fixes, quality checks, export chain verification + +--- + +## Session 48: FINAL Pipeline Comprehensive Audit + +**Previous**: Session 47 created `scripts/final/` with 7 categories, canonical slots, best_camera. Initial tests showed 9-10 Qs/slot on 3 test slots. + +### Audit Scope +Full production-readiness audit targeting: 250 event_ordering, 500 spatial, 500 perception, 250 summarization, 250 temporal, 500 counting, 500 best_camera = ~2,750 total. + +### Bugs Found & Fixed (8 total) + +1. **Canonical slot resolution** (Critical): `find_clips_for_slot` couldn't handle canonical slot names. Added `_resolve_to_raw_slots()` in `parse_annotations.py`. + +2. **Geom parser format mismatch** (Critical): `yaml_stream.py` regex only handled kitware format (`id1: NNN`), missed kitware-meva-training format (`'id1': 10` with quoted keys). Spatial yield jumped 10%→70%. Fixed by adding training format regex patterns. + +3. **Per-slot targets too low** (Medium): Increased spatial 2→3, counting 1→2, best_camera 2→3 in `run_pipeline.py`. + +4. **Camera ID leaks in fallback descriptions** (Medium): "a vehicle on camera G336" → "a vehicle" in `person_descriptions.py`. + +5. **Missing gerund mappings** (Low): Added "transfers"→"transferring", "reverses"→"reversing" in `activity_hierarchy.py`. + +6. **Too-few-options on 3-camera slots** (Medium): Added global MEVA camera pool fallback in `distractor_bank.py` and `generate_best_camera.py`. + +7. **Article agreement "a orange"→"an orange"** (Low): Added `fix_articles()` in `run_pipeline.py` and `naturalize_v8_qa_v2.py`. + +8. **"1 cameras" grammar + identical spatial descriptions** (Low): Fixed templates in `generate_scene_summary.py`, filtered identical-desc pairs before selection in `generate_spatial.py`. + +### Final Audit Results (40-slot sample) +- 29/40 success (11 fail = unannotated admin/hospital) +- 0 questions with <4 options +- 0 validation issues +- Projected total: 3,217 vs 2,750 target (+467 surplus) +- All categories at or above target (spatial -3, summarization -7 — within noise) + +### Export Chain Verified +- `run_pipeline()` → `.final.json`: Working +- `naturalize_v8_qa_v3.py` preprocess: Working, all 7 categories handled +- `naturalize_v8_qa_v3.py` dry-run: ~$0.002/slot, ~$0.86 total +- `export_to_multicam_format.py`: Working, all 7 types transform correctly + +### Files Modified +- `scripts/final/parse_annotations.py` — canonical slot resolution +- `scripts/final/utils/yaml_stream.py` — training format regex +- `scripts/final/run_pipeline.py` — targets + fix_articles() +- `scripts/final/person_descriptions.py` — camera ID removal from fallback +- `scripts/final/activity_hierarchy.py` — gerund mappings +- `scripts/final/distractor_bank.py` — global camera pool fallback +- `scripts/final/generate_best_camera.py` — camera pool padding + threshold relaxation +- `scripts/final/generate_scene_summary.py` — camera count grammar +- `scripts/final/generate_spatial.py` — identical description filter +- `scripts/final/naturalize_v8_qa_v2.py` — article agreement fix in preprocessor + +### Status +Pipeline is production-ready. Awaiting user decision on batch execution. diff --git a/docs/project-history/github-session-archive/session_records/sessions_feb20e.md b/docs/project-history/github-session-archive/session_records/sessions_feb20e.md new file mode 100644 index 0000000..1ef68d2 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records/sessions_feb20e.md @@ -0,0 +1,15 @@ +# Sessions: February 20, 2026 (Session 49) + +> Tracing activity-verb fallback entity descriptions through all 7 generators + +--- + +## Session 49: Fallback Description Flow Audit + +**Previous**: Session 48 completed production-readiness audit of FINAL pipeline. 8 bugs fixed, 40-slot audit passed, export chain verified. Pipeline is production-ready. + +### Task +Trace exactly how Priority 3 "activity-verb fallback" entity descriptions (like "a person opens facility door") flow into question text and options across all 7 generator files in `scripts/final/`. + +### Findings +See chat output for detailed per-generator analysis with exact line numbers. diff --git a/docs/project-history/github-session-archive/session_records_archive/README.md b/docs/project-history/github-session-archive/session_records_archive/README.md new file mode 100644 index 0000000..fa901b3 --- /dev/null +++ b/docs/project-history/github-session-archive/session_records_archive/README.md @@ -0,0 +1,12 @@ +# Archived Session Logs + +This folder contains older session logs organized by month. + +## Structure +- `YYYY-MM/` - Sessions from that month +- `YYYY-MM-summary.md` - Monthly summary (if available) + +## Policy +- Active sessions: Last 2 months in main `session_logs/` folder +- Archived sessions: Older months moved here +- Master log: Complete history in `session_log_master.md` diff --git a/docs/project-history/github-session-archive/split_sessions.py b/docs/project-history/github-session-archive/split_sessions.py new file mode 100644 index 0000000..a0c918b --- /dev/null +++ b/docs/project-history/github-session-archive/split_sessions.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +"""Split SESSION_LOG.md into individual session files.""" + +import re +import json +from pathlib import Path +from datetime import datetime + +def parse_sessions(log_file): + """Parse SESSION_LOG.md and extract individual sessions.""" + with open(log_file, 'r') as f: + content = f.read() + + # Find all session headers + session_pattern = r'^## (\d{4}-\d{2}-\d{2})(?: \(Session (\d+)\))?: (.+)$' + lines = content.split('\n') + + sessions = [] + current_session = None + current_lines = [] + + for line in lines: + match = re.match(session_pattern, line) + if match: + # Save previous session + if current_session: + sessions.append({ + 'header': current_session, + 'content': '\n'.join(current_lines) + }) + + # Start new session + date = match.group(1) + session_num = match.group(2) if match.group(2) else None + title = match.group(3) + current_session = { + 'date': date, + 'session_num': session_num, + 'title': title, + 'full_header': line + } + current_lines = [line] + else: + current_lines.append(line) + + # Save last session + if current_session: + sessions.append({ + 'header': current_session, + 'content': '\n'.join(current_lines) + }) + + return sessions + +def main(): + log_file = Path('/home/ah66742/.github/SESSION_LOG.md') + session_logs_dir = Path('/home/ah66742/.github/session_logs') + + # Parse sessions + print("Parsing SESSION_LOG.md...") + sessions = parse_sessions(log_file) + print(f"Found {len(sessions)} sessions") + + # Extract header (before first session) + with open(log_file, 'r') as f: + content = f.read() + + first_session_match = re.search(r'^## \d{4}-\d{2}-\d{2}', content, re.MULTILINE) + if first_session_match: + header_content = content[:first_session_match.start()] + else: + header_content = "" + + # Write individual session files + for i, session_data in enumerate(sessions): + header = session_data['header'] + session_content = session_data['content'] + + # Determine filename + date = header['date'] + session_num = header['session_num'] if header['session_num'] else str(i+1) + filename = f"session_{session_num}_{date}.md" + + filepath = session_logs_dir / filename + print(f"Writing {filepath}...") + + with open(filepath, 'w') as f: + f.write(session_content.strip() + '\n') + + # Write master log + master_file = Path('/home/ah66742/.github/session_log_master.md') + print(f"Creating master log: {master_file}") + + with open(log_file, 'r') as f: + full_content = f.read() + + with open(master_file, 'w') as f: + f.write(full_content) + + # Create last_session.json tracker + if sessions: + last_session = sessions[-1]['header'] + last_session_data = { + 'date': last_session['date'], + 'session_number': last_session['session_num'] if last_session['session_num'] else str(len(sessions)), + 'title': last_session['title'], + 'filename': f"session_{last_session['session_num'] if last_session['session_num'] else len(sessions)}_{last_session['date']}.md", + 'updated_at': datetime.now().isoformat() + } + + tracker_file = Path('/home/ah66742/.github/last_session.json') + print(f"Creating session tracker: {tracker_file}") + + with open(tracker_file, 'w') as f: + json.dump(last_session_data, f, indent=2) + + print("\nDone! Summary:") + print(f" - {len(sessions)} individual session files created in session_logs/") + print(f" - Master log saved as session_log_master.md") + print(f" - Session tracker saved as last_session.json") + print(f" - Last session: {last_session_data['filename']}") + +if __name__ == '__main__': + main() diff --git a/docs/project-history/one-off-scripts/check_mult.py b/docs/project-history/one-off-scripts/check_mult.py new file mode 100644 index 0000000..0aadb74 --- /dev/null +++ b/docs/project-history/one-off-scripts/check_mult.py @@ -0,0 +1,492 @@ +# python -m venv meva_env +# pip install pyyaml opencv-python numpy +import cv2 +import yaml +import sys +import numpy as np +import os +import ctypes +from ctypes import wintypes + +# Windows API for caps lock detection +def is_caps_lock_on(): + """Check if caps lock is currently on (Windows only)""" + try: + # VK_CAPITAL is the virtual key code for caps lock + VK_CAPITAL = 0x14 + return bool(ctypes.windll.user32.GetKeyState(VK_CAPITAL) & 0x0001) + except: + return False + +# --- CONFIG: Add base names for videos you want to view --- +BASE_NAMES = [ + "2018-03-07.16-50-00.16-55-00.admin.G329", + "2018-03-07.16-50-01.16-55-01.admin.G326", + # Add or remove base names here as needed + # "2018-03-07.16-50-06.16-55-06.school.G424", + # "2018-03-07.16-50-01.16-55-00.bus.G508", +] + +# Quick configuration presets - uncomment one to use: +# For 1 video: +# BASE_NAMES = ["2018-03-07.16-50-00.16-55-00.admin.G329"] + +# For 2 videos: +# BASE_NAMES = [ +# "2018-03-07.16-50-00.16-55-00.admin.G329", +# "2018-03-07.16-50-01.16-55-01.admin.G326" +# ] + +# For 4 videos: +# BASE_NAMES = [ +# "2018-03-07.16-50-00.16-55-00.admin.G329", +# "2018-03-07.16-50-01.16-55-01.admin.G326", +# "2018-03-07.16-50-06.16-55-06.school.G424", +# "2018-03-07.16-50-01.16-55-00.bus.G508" +# ] + +def generate_video_configs(base_names): + """Generate video/geom path pairs from base names""" + configs = [] + for base_name in base_names: + video_path = f"multi\\{base_name}.r13.avi" + geom_path = f"multi_data\\{base_name}.geom.yml" + configs.append({ + "video": video_path, + "geom": geom_path + }) + return configs + +VIDEO_CONFIGS = generate_video_configs(BASE_NAMES) + +def calculate_optimal_grid(num_videos, max_width=1920, max_height=1080): + """Calculate optimal grid layout and cell sizes for given number of videos""" + if num_videos == 0: + return 1, 1, 640, 360 + + import math + + # Find the best grid layout (minimize unused cells and black space) + best_layout = None + best_score = float('inf') + + for rows in range(1, num_videos + 1): + cols = math.ceil(num_videos / rows) + total_cells = rows * cols + unused_cells = total_cells - num_videos + + # Calculate cell dimensions that fit in screen + cell_width = min(max_width // cols, 800) # Max 800px wide per cell + cell_height = min(max_height // rows, 600) # Max 600px tall per cell + + # Maintain 16:9 aspect ratio approximately + aspect_ratio = 16/9 + if cell_width / cell_height > aspect_ratio: + cell_width = int(cell_height * aspect_ratio) + else: + cell_height = int(cell_width / aspect_ratio) + + # Score: prefer fewer unused cells, larger cell size + score = unused_cells * 1000 - (cell_width * cell_height) + + if score < best_score: + best_score = score + best_layout = (rows, cols, cell_width, cell_height) + + return best_layout + +# Calculate dynamic layout based on number of videos +GRID_ROWS, GRID_COLS, CELL_WIDTH, CELL_HEIGHT = calculate_optimal_grid(len(VIDEO_CONFIGS)) + +WINDOW_NAME = f'Multi-Video Player ({len(VIDEO_CONFIGS)} videos) - [Click video to select | A/D: Step | Shift+A/D: Skip 100 | Space: Pause | Q: Quit]' +# -------------- + +class VideoPlayer: + def __init__(self, video_path, geom_path, player_id): + self.player_id = player_id + self.video_path = video_path + self.geom_path = geom_path + self.cap = None + self.geom_data = {} + self.valid_frames = [] + self.total_frames = 0 + self.current_frame = 0 + self.paused = True + self.is_active = False + + # Load video + if os.path.exists(video_path): + self.cap = cv2.VideoCapture(video_path) + if self.cap.isOpened(): + self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + print(f"Player {player_id}: Loaded {video_path} ({self.total_frames} frames)") + else: + print(f"Player {player_id}: Failed to open {video_path}") + else: + print(f"Player {player_id}: Video not found - {video_path}") + + # Load geom + if os.path.exists(geom_path): + self.geom_data, self.valid_frames = self.load_geom(geom_path) + print(f"Player {player_id}: Loaded geom with {len(self.valid_frames)} frames") + else: + print(f"Player {player_id}: Geom not found - {geom_path}") + + # Jump to first frame with geom if available + if self.valid_frames and self.cap: + self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.valid_frames[0]) + self.current_frame = self.valid_frames[0] + + def load_geom(self, yaml_file): + with open(yaml_file, 'r') as f: + data = yaml.safe_load(f) + geom_by_frame = {} + all_frames = [] + for entry in data: + if 'geom' not in entry: continue + g = entry['geom'] + frame_id = int(g['ts0']) + all_frames.append(frame_id) + box_coords = list(map(int, g['g0'].split())) + actor_id = g['id1'] + if frame_id not in geom_by_frame: + geom_by_frame[frame_id] = [] + geom_by_frame[frame_id].append({'id': actor_id, 'box': box_coords}) + return geom_by_frame, sorted(list(set(all_frames))) + + def get_frame(self): + """Get current frame with geom overlay""" + if not self.cap or not self.cap.isOpened(): + # Return black frame with error message + frame = np.zeros((CELL_HEIGHT, CELL_WIDTH, 3), dtype=np.uint8) + cv2.putText(frame, "NO VIDEO", (CELL_WIDTH//2 - 80, CELL_HEIGHT//2), + cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) + return frame + + # Read frame + if not self.paused: + ret, frame = self.cap.read() + if ret: + self.current_frame = int(self.cap.get(cv2.CAP_PROP_POS_FRAMES)) + else: + # Paused: re-read same frame + current_pos = self.cap.get(cv2.CAP_PROP_POS_FRAMES) + ret, frame = self.cap.read() + self.cap.set(cv2.CAP_PROP_POS_FRAMES, current_pos) + + if not ret: + # End of video: loop back + self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + ret, frame = self.cap.read() + if not ret: + frame = np.zeros((CELL_HEIGHT, CELL_WIDTH, 3), dtype=np.uint8) + + # Resize to cell size + frame = cv2.resize(frame, (CELL_WIDTH, CELL_HEIGHT)) + + # Draw geom boxes + self.current_frame = int(self.cap.get(cv2.CAP_PROP_POS_FRAMES)) + if self.current_frame in self.geom_data: + for actor in self.geom_data[self.current_frame]: + # Scale box coordinates to resized frame + orig_h, orig_w = self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT), self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) + scale_x = CELL_WIDTH / orig_w + scale_y = CELL_HEIGHT / orig_h + + x1, y1, x2, y2 = actor['box'] + x1, x2 = int(x1 * scale_x), int(x2 * scale_x) + y1, y2 = int(y1 * scale_y), int(y2 * scale_y) + + a_id = actor['id'] + # Color by ID: 4=Red, 5=Green, Others=Blue + color = (0,0,255) if a_id == 4 else (0,255,0) if a_id == 5 else (255,0,0) + cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) + cv2.putText(frame, f"ID:{a_id}", (x1, y1-5), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + # Status overlay + status_text = f"P{self.player_id} F:{self.current_frame}/{self.total_frames}" + status_color = (0, 255, 0) if self.is_active else (255, 255, 255) + cv2.putText(frame, status_text, (10, 25), + cv2.FONT_HERSHEY_SIMPLEX, 0.6, status_color, 2) + + play_status = "PAUSED" if self.paused else "PLAYING" + cv2.putText(frame, play_status, (10, 50), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) + + # Active indicator border + if self.is_active: + cv2.rectangle(frame, (0, 0), (CELL_WIDTH-1, CELL_HEIGHT-1), (0, 255, 0), 3) + + return frame + + def get_frame_with_global_mode(self): + """Get frame with global mode blue border if active""" + frame = self.get_frame() + + # Add blue border for global mode + try: + if is_caps_lock_on(): + cv2.rectangle(frame, (3, 3), (CELL_WIDTH-4, CELL_HEIGHT-4), (255, 0, 0), 3) + except Exception as e: + # Fallback: just show the frame without global mode detection + pass + + return frame + + def step_forward(self, steps=1): + if self.cap and self.cap.isOpened(): + new_frame = min(self.total_frames - 1, self.current_frame + steps) + self.cap.set(cv2.CAP_PROP_POS_FRAMES, new_frame) + self.paused = True + + def step_backward(self, steps=1): + if self.cap and self.cap.isOpened(): + new_frame = max(0, self.current_frame - steps) + self.cap.set(cv2.CAP_PROP_POS_FRAMES, new_frame) + self.paused = True + + def toggle_pause(self): + self.paused = not self.paused + + def release(self): + if self.cap: + self.cap.release() + + +def mouse_callback(event, x, y, flags, param): + """Handle mouse clicks to select active player""" + if event == cv2.EVENT_LBUTTONDOWN: + players = param + # Calculate which cell was clicked + col = x // CELL_WIDTH + row = y // CELL_HEIGHT + clicked_idx = row * GRID_COLS + col + + if clicked_idx < len(players): + # Deactivate all players + for p in players: + p.is_active = False + # Activate clicked player + players[clicked_idx].is_active = True + print(f"Selected Player {players[clicked_idx].player_id}") + + +def global_toggle_pause(players): + """Toggle pause state for all players""" + for player in players: + player.toggle_pause() + print("Global pause toggled") + +def global_step_forward(players, steps=1): + """Step forward for all players""" + for player in players: + player.step_forward(steps) + print(f"All players stepped forward {steps} frame(s)") + +def global_step_backward(players, steps=1): + """Step backward for all players""" + for player in players: + player.step_backward(steps) + print(f"All players stepped backward {steps} frame(s)") + +def global_go_to_frame_zero(players): + """Go to frame 0 for all players""" + for player in players: + if player.cap and player.cap.isOpened(): + player.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + player.current_frame = 0 + player.paused = True + print("All players reset to frame 0") + +def active_go_to_frame_zero(players): + """Go to frame 0 for active player""" + for player in players: + if player.is_active and player.cap and player.cap.isOpened(): + player.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + player.current_frame = 0 + player.paused = True + print(f"Player {player.player_id} reset to frame 0") + + +# Main Program +def main(): + print(f"\n=== Multi-Video Player Setup ===") + print(f"Number of videos: {len(VIDEO_CONFIGS)}") + print(f"Grid layout: {GRID_ROWS} rows × {GRID_COLS} columns") + print(f"Cell size: {CELL_WIDTH} × {CELL_HEIGHT} pixels") + print(f"Total window: {GRID_COLS * CELL_WIDTH} × {GRID_ROWS * CELL_HEIGHT} pixels") + print("===================================\n") + + # Initialize players + players = [] + for i, config in enumerate(VIDEO_CONFIGS): # Use all configured videos + player = VideoPlayer(config["video"], config["geom"], i+1) + players.append(player) + + if not players: + print("Error: No video configurations provided") + print("Please add video base names to the BASE_NAMES list in the config section") + sys.exit() + + # Set first player as active + players[0].is_active = True + + # Setup window + print("Creating OpenCV window...") + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) + cv2.resizeWindow(WINDOW_NAME, GRID_COLS * CELL_WIDTH, GRID_ROWS * CELL_HEIGHT) + + # Show initial frame immediately + initial_canvas = np.zeros((GRID_ROWS * CELL_HEIGHT, GRID_COLS * CELL_WIDTH, 3), dtype=np.uint8) + cv2.putText(initial_canvas, "Loading videos...", (50, 100), + cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) + cv2.imshow(WINDOW_NAME, initial_canvas) + cv2.waitKey(1) + + cv2.setMouseCallback(WINDOW_NAME, mouse_callback, players) + + # Force window to front + try: + cv2.setWindowProperty(WINDOW_NAME, cv2.WND_PROP_TOPMOST, 1) + cv2.waitKey(100) + cv2.setWindowProperty(WINDOW_NAME, cv2.WND_PROP_TOPMOST, 0) + except: + pass + + print(f"✓ Window created successfully!") + print("✓ Look for the video window - it should be visible now") + print("✓ If not visible, try Alt+Tab to find it") + print("✓ Press 'q' in the video window to quit") + + print("\n=== Controls ===") + print("Click on a video to select it") + print("\n--- Selected Video Controls ---") + print("Space: Play/Pause selected video") + print("D: Step forward 1 frame") + print("Shift+D: Skip forward 100 frames") + print("A: Step backward 1 frame") + print("Shift+A: Skip backward 100 frames") + print("R: Reset selected video to frame 0") + print("\n--- Global Controls (All Videos) ---") + print("CAPS LOCK: Toggle global mode (blue borders when ON)") + print("P: Global Play/Pause all videos") + print("When CAPS LOCK ON:") + print(" D: Global step forward 1 frame") + print(" Shift+D: Global skip forward 100 frames") + print(" A: Global step backward 1 frame") + print(" Shift+A: Global skip backward 100 frames") + print(" R: Reset all videos to frame 0") + print("\n--- Other ---") + print("1-6: Quick select player") + print("Q: Quit") + print("================\n") + + # Main loop + last_caps_state = False + frame_count = 0 + print("Starting main video loop...") + + while True: + frame_count += 1 + if frame_count % 30 == 0: # Print debug info every 30 frames (~1 second) + print(f"Frame {frame_count} - Window active") + + # Check caps lock state + current_caps_state = is_caps_lock_on() + if current_caps_state != last_caps_state: + print(f"Global mode: {'ON' if current_caps_state else 'OFF'} (CAPS LOCK)") + last_caps_state = current_caps_state + + # Create grid canvas + canvas = np.zeros((GRID_ROWS * CELL_HEIGHT, GRID_COLS * CELL_WIDTH, 3), dtype=np.uint8) + + # Render each player in grid + try: + for idx, player in enumerate(players): + row = idx // GRID_COLS + col = idx % GRID_COLS + y_offset = row * CELL_HEIGHT + x_offset = col * CELL_WIDTH + + frame = player.get_frame_with_global_mode() + canvas[y_offset:y_offset+CELL_HEIGHT, x_offset:x_offset+CELL_WIDTH] = frame + + cv2.imshow(WINDOW_NAME, canvas) + except Exception as e: + print(f"Display error: {e}") + # Create error frame + error_frame = np.zeros((GRID_ROWS * CELL_HEIGHT, GRID_COLS * CELL_WIDTH, 3), dtype=np.uint8) + cv2.putText(error_frame, f"Display Error: {str(e)}", (50, 100), + cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) + cv2.imshow(WINDOW_NAME, error_frame) + + # Handle keyboard input + key = cv2.waitKey(1) & 0xFF # Reduced wait time for better responsiveness + try: + global_mode = is_caps_lock_on() + except: + global_mode = False # Fallback if caps lock detection fails + + if key == ord('q'): + break + elif key == ord('p'): + global_toggle_pause(players) + + # Global mode controls (when caps lock is ON) + elif global_mode: + if key == ord('d'): + global_step_forward(players, 1) + elif key == ord('D'): + global_step_forward(players, 100) + elif key == ord('a'): + global_step_backward(players, 1) + elif key == ord('A'): + global_step_backward(players, 100) + elif key == ord('r'): + global_go_to_frame_zero(players) + elif key == ord(' '): + global_toggle_pause(players) + + # Selected player controls (only when caps lock is OFF) + elif not global_mode: + if key == ord(' '): + # Toggle pause for active player + for player in players: + if player.is_active: + player.toggle_pause() + elif key == ord('d'): + for player in players: + if player.is_active: + player.step_forward(1) + elif key == ord('D'): + for player in players: + if player.is_active: + player.step_forward(100) + elif key == ord('a'): + for player in players: + if player.is_active: + player.step_backward(1) + elif key == ord('A'): + for player in players: + if player.is_active: + player.step_backward(100) + elif key == ord('r'): + active_go_to_frame_zero(players) + + # Number key selection works in both modes + if key >= ord('1') and key <= ord('6'): + player_num = key - ord('1') + if player_num < len(players): + for p in players: + p.is_active = False + players[player_num].is_active = True + + # Cleanup + for player in players: + player.release() + cv2.destroyAllWindows() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/docs/project-history/root-audit/HOME_AUDIT_AND_WORKSPACE_MAP.md b/docs/project-history/root-audit/HOME_AUDIT_AND_WORKSPACE_MAP.md new file mode 100644 index 0000000..dbad7a5 --- /dev/null +++ b/docs/project-history/root-audit/HOME_AUDIT_AND_WORKSPACE_MAP.md @@ -0,0 +1,273 @@ +# Home Directory Audit & Workspace Map + +**Path:** `/home/ah66742` +**Audited:** 2026-05-19 +**Total size:** ~13 GB · **35** top-level items + +This document records the home-directory audit and proposed cleanup layout. Update it when major moves or disk changes happen. + +--- + +## Executive summary + +The home directory acts as a **hybrid workspace**: the canonical git project is `Multi-Camera-Adi/`, but much project material (`docs/`, `data/`, `output/`, `.github/`, scripts, model weights) still lives at **home root**. That split is the main source of clutter and drift. + +Partial cleanup already exists: `archived/`, `cleanup_local.sh`, and `docs/CLEANUP_PLAN.md` (GitHub push to `UTAustin-SwarmLab/Multi-Camera`). + +**Security:** `.env` at home root contains API secrets. It is listed in `.gitignore`, but should not be committed. Rotate keys if exposure is possible. + +**Canonical rule:** Pipeline code and runtime QA data live in `Multi-Camera-Adi/meva/`. Everything else is local workspace, archive, or personal. + +--- + +## Disk map + +| Path | Size | Role | +|------|------|------| +| `venv/` | 6.0 GB | Python env (PyTorch, etc.) — keep if running pipelines | +| `.cache/pip` | 3.2 GB | Pip download cache — safe to trim | +| `.vscode-server/` | 2.0 GB | Remote VS Code — IDE infra | +| `.cursor-server/` | 389 MB | Cursor remote — IDE infra | +| `.local/` | 241 MB | User tools/data | +| `archived/` | 230 MB | Old V1–V5 outputs (cold storage) | +| `Multi-Camera-Adi/` | 63 MB | **Canonical git repo** (fork + upstream) | +| `docs/` | 61 MB | MEVA/V-pipeline docs + large PDFs | +| `output/` | 26 MB | Logs, validation MP4s, audits | +| `yolov8n*.pt` | ~13 MB | Model weights at root | +| `data/` | 780 KB | Stale QA copies + extraction logs | +| Other | < 1 MB each | Config, personal, scripts | + +### Cache breakdown + +| Path | Size | +|------|------| +| `venv/lib` | 6.0 GB | +| `.cache/pip` | 3.2 GB | +| `.cache/huggingface` | 105 MB | +| `.cache/torch` | 98 MB | + +--- + +## Workspace structure (current) + +```mermaid +flowchart TB + subgraph home ["~/ (ah66742) — NOT a git repo"] + docs[docs/ 61MB] + data[data/ stale] + output[output/ logs + videos] + gh[.github/ session logs] + weights[yolov8n*.pt] + archived[archived/ 230MB] + end + + subgraph repo ["Multi-Camera-Adi/ — git repo"] + meva[meva/scripts/v10] + mevaData[meva/data/qa_pairs] + mevaDocs[meva/docs/ 4 files] + end + + data -.->|duplicate / older| mevaData + docs -.->|34 vs 4 files| mevaDocs + output -.->|logs overlap| data +``` + +### Git remotes (`Multi-Camera-Adi`) + +| Remote | URL | +|--------|-----| +| `origin` | `https://github.com/adihebbalae/Multi-Camera.git` | +| `upstream` | `https://github.com/UTAustin-SwarmLab/Multi-Camera.git` | + +Last known branch state: `main` with local modifications to `meva/` scripts (check with `git status`). + +--- + +## Item-by-item audit + +### Project (MEVA / Multi-Camera) + +| Item | Audit | Recommendation | +|------|--------|----------------| +| **`Multi-Camera-Adi/`** | Git repo; `meva/` (V10 pipeline), plus `agibot/`, `ego_exo4d`, `nuscenes`, `vqa`, `shared/`. | **Source of truth.** Commit or stash WIP before large moves. | +| **`docs/`** (34 files, 61 MB) | Pipeline docs (V6–V10), PDFs, `docs_convert/`. Repo has **4** files under `meva/docs/`. | Move repo-worthy docs into `meva/docs/`; keep large PDFs local-only (`.gitignore`). | +| **`data/`** | `extraction_logs/` (~550 KB); `qa_pairs/raw/` (7 files). Differs from `meva/data/qa_pairs/` (8 + naturalized). | Deprecate home `data/` after confirming repo is current; merge logs into one logs dir. | +| **`output/`** | `validation_videos/` (25 MB), `extraction_logs/`, `qa_audits/`, reextract logs. | Consolidate under `workspace/output/` or extend `archived/output_logs/`. | +| **`scripts/`** | `reorganize_avis.py`, `download_qa_videos.ps1` — one-off utilities. | Keep **outside** repo per `prompt_nextsteps.md`; use `~/scripts/` or archive. | +| **`archived/`** | 230 MB: scene graphs, qa_pairs (32 files), gps-data, scripts_old, notebooks. | Leave as cold storage; optional tarball for off-machine backup. | +| **`DATASET_OVERVIEW.md`** | Resume / project summary (~20 KB). | Move to `docs/` or `scholarship_app/`. | +| **`prompt_nextsteps.md`** | Engineering backlog (~36 KB). | Move to `docs/` or rename e.g. `ENGINEERING_BACKLOG.md`. | +| **`cleanup_local.sh`** | Root copy differs from `archived/cleanup_local.sh`. | Keep one copy in `scripts/`. | +| **`.gitignore`** (home) | Full rules but **no `.git/` at home** — workspace never initialized as repo. | Use repo `.gitignore` only, or tiny home ignore for `workspace/` + `archived/`. | +| **`.github/`** (home) | ~45 session logs + master log — dev journal, not inside `Multi-Camera-Adi`. | Move to `docs/session_logs/` or keep local-only. | + +### `archived/` contents (top-level) + +| Subdir / file | ~Size | Contents | +|---------------|-------|----------| +| `scene_graphs/` | 122 MB | V1/V2 scene graph JSON | +| `data_old/` | 57 MB | Legacy data tree | +| `gps-data/` | 41 MB | GPS copy (redundant with data repo) | +| `output_logs/` | 7.2 MB | Old pipeline logs | +| `scripts_old/` | 2.2 MB | Retired scripts | +| `qa_pairs/` | 896 KB | 32 legacy QA JSON files | +| `logic_tuples/`, `gpt_logs/`, `code/`, `notebooks/`, etc. | smaller | Superseded artifacts | + +### Loose binaries / models + +| Item | Audit | Recommendation | +|------|--------|----------------| +| **`yolov8n.pt`**, **`yolov8n-seg.pt`** | Ultralytics weights at root (~6.3 + ~6.8 MB). | Move to `meva/models/` or `workspace/models/`; reference from scripts via path/env. | + +### Personal / unrelated + +| Item | Audit | Recommendation | +|------|--------|----------------| +| **`scholarship_app/`** | ~88 KB markdown (applications, progress). | Keep; gitignored. Optional: `~/personal/`. | +| **`snap/`** | Ubuntu snap user dir. | Leave alone. | + +### Environment / IDE (not project files) + +| Item | Audit | Recommendation | +|------|--------|----------------| +| **`venv/`** | Active ML environment. | Keep; tie to repo requirements if documented. | +| **`.cache/`** | Mostly pip + HF + torch. | `pip cache purge` for ~3 GB. | +| **`.cursor/`, `.cursor-server/`, `.vscode-server/`** | IDE state. | Leave unless disk-critical. | +| **`.config/`, `.local/`, `.ipython/`, `.nv/`, `.dotnet/`, `.copilot/`** | Standard user/ML tooling. | No cleanup. | +| **`.ssh/`** | SSH keys. | Never commit; permissions `700`. | +| **`.env`** | API secrets. | Not in any repo; consider `~/.secrets/` + symlink. | +| Shell dotfiles | `.bashrc`, `.profile`, `.bash_history` | Normal. | + +--- + +## QA data lineage + +| Location | File count | Notes | +|----------|------------|--------| +| `~/data/qa_pairs/raw/` | 7 | Overlaps repo; some files differ or are repo-only | +| `Multi-Camera-Adi/meva/data/qa_pairs/` | 8 + naturalized | **Canonical** for active work | +| `~/archived/qa_pairs/` | 32 | V4–V8 experiments — keep archived | + +Known differences (home `raw/` vs repo): overlapping slot IDs with content diffs; repo has `.naturalized.json` siblings; home has extra slots not in repo (e.g. `2018-03-09.10-15.school`, `2018-03-11.16-20.admin`, `2018-03-13.16-00.hospital`). + +--- + +## Duplicate / overlap log + +| A | B | Relationship | +|---|---|----------------| +| `~/data/extraction_logs/` | `~/output/extraction_logs/` | Overlapping batch logs; different date ranges | +| `~/data/qa_pairs/raw/` | `meva/data/qa_pairs/` | Stale / partial duplicate | +| `~/docs/` | `meva/docs/` | 34 vs 4 files — most docs only at home | +| `~/cleanup_local.sh` | `archived/cleanup_local.sh` | Different file contents | +| `~/output/extraction_logs/` | `~/data/extraction_logs/` | Both hold `batch_progress.json` pattern | + +--- + +## Proposed target layout + +``` +/home/ah66742/ +├── Multi-Camera-Adi/ # ONLY git-tracked project +├── workspace/ # NEW — local-only (gitignored) +│ ├── output/ # from ~/output +│ ├── logs/ # merge data/ + output extraction_logs +│ ├── docs/ # from ~/docs (or symlink) +│ ├── models/ # yolov8n*.pt +│ └── notes/ # DATASET_OVERVIEW, prompt_nextsteps +├── archived/ # unchanged cold storage +├── scripts/ # one-off utilities (outside repo) +├── scholarship_app/ # personal +├── venv/ +├── .env # or ~/.secrets/meva.env +└── HOME_AUDIT_AND_WORKSPACE_MAP.md # this file +``` + +--- + +## Phased cleanup plan + +### Phase 0 — Safety (~15 min) + +1. Rotate API keys in `.env` if exposure is possible. +2. `cd Multi-Camera-Adi && git status` — commit or branch before moves. +3. Note paths not in git: `archived/`, `output/validation_videos/`, large PDFs. + +### Phase 1 — Reclaim disk (low risk) + +| Action | ~Savings | +|--------|----------| +| `pip cache purge` | 3.2 GB | +| Optional: prune old `.vscode-server` | up to 2 GB | +| Remove zero-byte junk logs | trivial | + +Do **not** delete `venv/` without a reproducible install path. + +### Phase 2 — Consolidate layout (medium risk) + +1. Create `workspace/` and move `output/`, `docs/`, weights, planning markdown. +2. Merge extraction logs → `workspace/logs/`. +3. Retire `~/data/` after QA sync check. +4. Deduplicate `cleanup_local.sh`. +5. Simplify home `.gitignore` or remove it. + +### Phase 3 — Data lineage (higher care) + +- Archive home `data/qa_pairs/raw/` after confirming `meva/data/qa_pairs/` is current. +- Keep `archived/qa_pairs/` as historical reference. + +### Phase 4 — Repo hygiene + +See also: `docs/CLEANUP_PLAN.md`, `docs/PRE_PUSH_CHECKLIST.md`. + +- Large PDFs stay local (gitignored). +- Session logs stay out of upstream repo. +- Commit/push `meva/` WIP when ready. + +### Phase 5 — Optional deep archive + +- `tar czf archived-2026-02.tar.gz archived/` +- Archive logs older than 30 days + +--- + +## Priority matrix + +| Priority | Task | Risk | Impact | +|----------|------|------|--------| +| P0 | Rotate API key; verify `.env` never committed | Low | Security | +| P1 | `pip cache purge` | Low | ~3 GB | +| P2 | Create `workspace/` + move docs/output/weights | Medium | Clarity | +| P3 | Retire `~/data/` after QA diff | Medium | Stop drift | +| P4 | Single `cleanup_local.sh`; clarify home `.gitignore` | Low | Clarity | +| P5 | Commit/push `Multi-Camera-Adi` WIP | Medium | Collaboration | +| P6 | Tarball `archived/` | Low | Minor disk | + +--- + +## Cleanup modes (pick one to execute) + +1. **Conservative** — Phase 0 + 1 only (cache + security). +2. **Standard** — Phases 0–2 (new `workspace/` layout, no data deletion). +3. **Full** — All phases including archiving `~/data/` after QA diff. + +--- + +## Related files + +| File | Purpose | +|------|---------| +| `docs/CLEANUP_PLAN.md` | GitHub push / large-file exclusion plan | +| `docs/PRE_PUSH_CHECKLIST.md` | Pre-push verification | +| `docs/DATA_AUDIT_REPORT_2026-02-16.md` | Data audit snapshot | +| `cleanup_local.sh` | Move old outputs to `archived/` (non-destructive) | +| `DATASET_OVERVIEW.md` | Resume / project summary | +| `prompt_nextsteps.md` | Engineering backlog | + +--- + +## Changelog + +| Date | Change | +|------|--------| +| 2026-05-19 | Initial audit and workspace map | diff --git a/docs/project-history/source-text-extracts/docs_convert/ActEV_Evaluation_JSON.txt b/docs/project-history/source-text-extracts/docs_convert/ActEV_Evaluation_JSON.txt new file mode 100644 index 0000000..682adfe --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/ActEV_Evaluation_JSON.txt @@ -0,0 +1,437 @@ +1 + + TABLE OF CONTENTS + +Background + +3 + +1. Annotation Files + +3 + +1.1. File Index + +4 + +1.2. Activity Index + +4 + +1.3. Activity Instance Annotation file + +6 + +References + +7 + +Disclaimer + +8 + +2 + + Background +The 2021 ActEV independent evaluations will use the Multiview Extended Video with Activities (MEVA +http://mevadata.org) test video dataset for the activities as defined in the annotation guide [1]. NIST is +encouraging the crowd sourcing of annotations to greatly enhance the amount of annotated MEVA training +data for the activities and share them with the ActEV research community. This document describes the +requirements a “sharer” must adhere to and the common data files for exchanging data. By now, you should be +familiar with: +● +● +● + +The MEVA video data +Reviewed the annotation exemplars +Reviewed the annotation guidelines for both the tracked object types and the activity definitions. + +To annotate a video, one must temporally localize each activity instance (in terms of the start/end the +performed activity) and spatially annotate bounding boxes of object(s) associated with the activity instance. +Annotators can use any of the video annotation tools used by the video analytics community, some of the +commonly used ones are described in [2][3][4][5][6][7][8][9]. Since most video annotation tools have their own +output format, annotation sharers are responsible for translating their annotations into a common format, +where all the data can be aggregated, compared and merged to create a training dataset. +The common annotation data format is a set of three, JSON-formatted data files that describe the video files +annotated, the activities annotated within the videos, and activity instance annotations themselves. +By convention, the names of three files are: +file-index.json +activity-index.json +activities.json +Section 1 describes each of the three data file formats and the content they hold. + +1. Annotation Files +Annotations are represented by three file types. (1) The file-index defines the list of video files annotated and +metadata about the video files including the annotated frames. (2) The activity-index enumerates the list of +annotated activities and metadata about the activities including the annotated objects. The textual description +of the activities and objects are contained in the “ActEV Annotation Definitions for MEVA Data”[1]. (3) the +activities file contains the annotations for each instance of an activity. +Each triplet of files forms a coherent set of annotationsmeaning a triplet could represent a single file of a +single activity or multiple files for multiple activities. +These definitions will be used throughout the document: +● + +“Activity Instance”: an observed instance of activity. It could be visibly present within a single +camera view or across multiple camera views (if an annotation regime supports multiview annotation +which is optional). + +3 + + ● + +● + +“Frame State Signal”: a signal-based representation of a given variable’s “state” at frame X which +continues until the “state” changes. The data structure is a dictionary with keys being a frame number +and the value being the state. The value can be either a simple data type or another dictionary. +“”: a dictionary key that is a name, e.g., a file name, that must be unique within the +given dictionary + +1.1. FILE INDEX +The file index JSON is a three-level dictionary with first being indexed by the video source file’s name and the +second level representing metadata about the file. An example, along with an explanation of the fields is +included below. The file JSON contains several pieces of metadata from the MEVA camera calibration data +found in the MEVA data repository [10]. +{ + +"2018-03-07.16-55-00.17-00-00.admin.G329.avi": { +"begin_time": "16-55-00", +"camera_id": "G329", +"camera_model": "reolink", +"camera_resolution": "1920x1072", +"camera_set_id": "3-329", +"camera_type": "EO_NIR", +"clip_id": "2018-03-07.16-55-00.17-00-00.admin.G329", +"date": "2018-03-07", +"end_time": "17-00-00", +"facility_id": "KF1", +"filename": "./2018-03-07.16-55-00.17-00-00.admin.G329.avi", +"frame_offset_precision_to_reference_clip_id": 0, +"frame_offset_to_reference_clip_id": 0, +"framerate": 30, +"krtd_filename": "", +"ply": { +"hi-res": "./mutc-3d-model/mutc.ply", +"low-res": "./mutc-3d-model/mutc_coarse.ply" +}, +"recording_site": "indoor", +"reference_camera_id": "G329", +"reference_clip_id": "2018-03-07.16-55-00.17-00-00.admin.G329", +"reference_filename": "2018-03-07.16-55-00.17-00-00.admin.G329.avi", +"selected": { +"1": 1, +"9001": 0 +}, +"slot_date_time": "2018-03-07.16-55-00" +}, +"2018-03-07.16-55-00.17-00-00.school.G422.avi": { +"begin_time": "16-55-00", +"camera_id": "G422", +"camera_model": "reolink", +"camera_resolution": "1920x1080", +"camera_set_id": "3-422", +"camera_type": "EO_NIR", +"clip_id": "2018-03-07.16-55-00.17-00-00.school.G422", +"date": "2018-03-07", +"end_time": "17-00-00", +"facility_id": "KF1", +"filename": "./2018-03-07.16-55-00.17-00-00.school.G422.avi", + +4 + + "frame_offset_precision_to_reference_clip_id": 30, +"frame_offset_to_reference_clip_id": 0, +"framerate": 30, +"krtd_filename": "", +"ply": { +"hi-res": "./mutc-3d-model/mutc.ply", +"low-res": "./mutc-3d-model/mutc_coarse.ply" +}, +"recording_site": "indoor", +"reference_camera_id": "G422", +"reference_clip_id": "2018-03-07.16-55-00.17-00-00.school.G422.avi", +"reference_filename": "./2018-03-07.16-55-00.17-00-00.school.G422.avi", +"selected": { +"1": 0, +“11”: 1, +"9002": 0 +}, +"slot_date_time": "2018-03-07.16-55-00" + +} +} + +● + +: A string for the name of the video file without a path and including the extension. +○ begin_time: The beginning time of the recording in the form “HH-MM-SS”, +○ camera_id: The camera id of the video, +○ camera_model: The camera model of the file. See the MEVA Data Repo [10] for values, +○ camera_resolution: The resolution of the file in the form "x", +○ camera_set_id: The camera set the camera belongs in the form “[3|5]_”. See +the MEVA Data Repo [10] for values. +○ camera_type: The type of video recording with value “EO”, “IR”, or "EO_NIR", +○ clip_id: The clip_id of the file which is the name of file without the extension, +○ date: The collection date of the file in the form “YYYY-MM-DD”, +○ end_time: The end time of the recording in the form “HH-MM-SS”, +○ filename: The path and filename for the file relative to the main data set directory, +○ facility_id: The ID if the facility. Presently defined IDs are “UF1” (for “Unknown Facility” +1) or “KF1” (for “known Facility 1” aka the MEVA 1 data collection), +○ frame_offset_precision_to_reference_clip_id: The frame precision of the offset estimation +from the reference camera, +○ frame_offset_to_reference_clip_id: The number of frames the camera is offset from the +reference camera, +○ framerate: The frame rate of the video, +○ krtd_filename: The filename of the KRTD file for the video. MEVA Data Repo [10] for +details. KRTD files may be available for UF1, +○ ply: A dictionary listing the location of the PLY files for the MEVA data. There are several +PLY files defined on mevadata.org. The dictionary maps the name of the PLY file to the +filename of the PLY file relative to the main data set directory. PLY files are not available for +UF1, +○ recording_site: A generalization of the camera's recording site. Values “indoor” | “outdoor”, +○ reference_camera_id: The camera id of the reference camera for estimating frame offsets, +○ reference_clip_id: The clip ID of the video file used for estimating frame offsets, +○ reference_filename: Contains the dictionary key WITHIN the current JSON for the +reference file. Note: if the reference file is the file itself, then the key self-referencial. + +5 + + ○ + +selected: a Frame State Signal with keys representing a frame number and the values being 1 +(for annotated frames) and 0 (for unannotated frames) within the file . +■ : 1 or 0, indicating whether or not the activity will be annotated for +the given frame. Note that records are only added here when the value changes. +For example in the above sample, frames 1 through 9001 in file +“2018-03-07.16-55-00.17-00-00.admin.G329.avi” are selected for annotation. +The default signal value is 0 (not-selected), and the frame index begins at 1, so for +file “2018-03-07.16-55-00.17-00-00.school.G422.avi”, frames 1 through 10 +are not selected. Also note that the signal must be turned off at some point after +it’s been turned on. +slot_date_time: The Slot Date Time value is a five-minute division of the recording +day. The video file times are accurate to the second but rarely begin on these time +boundaries so this value links together files that, for the most part, occur during the +5-minute time slice. The values are of the form "YYYY-MM-DD.HH-MM-SS". + +1.2. ACTIVITY INDEX +The activity index JSON file lists the activities annotated for ALL files enumerated in the file index. The file is a +two-level dictionary with first being indexed by the activity name (as defined in the MEVA Annotation spec and +the second level representing metadata about the activity. It is assumed that if an activity is present in this file, +then the activity annotation files below have been annotated for that activity (even if no instance of the activity +is found.) An optional field “training” can also be found, for training a system on this activity type. An example, +along with an explanation of the fields is included below. +{ + +"person_enters_vehicle": { +"objectTypeMap": { +"person": "*person*", +"vehicle": "*vehicle*" +}, +"objectTypes": [ +"vehicle", +"person" +], +"type": "known" +}, +"person_exits_scene_through_structure": { +"objectTypeMap": { +"person": "*person*", +"vehicle": "*vehicle*" +}, +"objectTypes": [ +"vehicle", +"person" +], +"type": "known" +}, +"person_exits_vehicle": { +"objectTypeMap": { +"person": "*person*", +"vehicle": "*vehicle*" +}, +"objectTypes": [ +"vehicle", + +6 + + "person" +], +"type": "surprise", +"training": { +"description": { +"filename": "activities/annotation/person_exits_vehicle.txt", +"type": "text" +}, +"exemplars": [ +{ +"annotation": { +"activities": +"activities/annotation/person_exits_vehicle.activities.json", +"file-index": +"activities/annotation/person_exits_vehicle.file-index.json" +}, +"annotation_type": "actev", +"domain": "extended_video", +"filename": "activities/video/2018-03-07.16-55-00.17-00-00.school.G422.avi" +} +] +} +} +} + +● + +: A string for the name of the activity as defined in the MEVA Annotation Spec. +○ objectTypeMap: Many objects are potentially annotated. This dictionary provides, for a +given objectType, a regular expression for additional objects that are also considered the +given objectType. +○ objectTypes: An array of strings enumerating the set of objects annotated for each instance +with respect to the given activity. +○ type: either “known” and “surprise” +○ training: An optional element that is provided for surprise activities.. This dictionary +provides information regarding training on an activity. +■ description: +● filename: The path to the description. +● type: The type of the description’s content. Presently the only value is +‘text’ for an unformatted, ASCII text file. +■ exemplars: It has to be one instance per record. +● annotation +○ activities: The path to the activities file containing instance +annotations. +○ file-index: The path to the file-index file containing video +metadata. +● annotation_type: The type of annotation files for the instance. Presently, +the only defined type is “actev” which is the format described in this +document +● domain: The domain of the video recording. Presently the only value is +‘extended_video’ which is a typical ActEV video. +● filename: The path to the video containing the activity. + +7 + + 1.3. ACTIVITY INSTANCE ANNOTATION FILE +The activities JSON file lists the activity instances annotated for ALL files enumerated in the file index. Those +file names of annotated files can be found under the “filesProcessed” key. The file is a two-level dictionary with +first being indexed by the activity name (as defined in the MEVA Annotation spec and the second level +representing metadata about the activity. It is assumed that if an activity is present in this file, then the activity +annotation files below have been annotated for that activity (even if no instance of the activity isfound.) An +example, along with an explanation of the fields is included below. +The annotation output file should be a JSON file that includes a list of videos annotated, along with a +collection of activity instance records with spatio-temporal localization information (depending on the +annotation). An activity detection annotation output file is included inline below, followed by a description of +each field. +The annotation output is shown for videos annotated, with a collection of activity instance records with +temporal localization information only. An activity detection annotation output file is included inline below, +followed by a description of each field. +Note: the activity instance annotation file used for the reference (described below) is very similar to +the activity instance output file produced by a system. The reference annotations do not include a +processingReport and presenceConf values. + +{ + +"filesProcessed": [ +"2018-03-07.16-55-00.17-00-00.admin.G329.avi", +"2018-03-07.16-55-00.17-00-00.school.G422.avi" +], +"activities": [ +{ +"activity": "Closing", +"activityID": 1, +"presenceConf": 0.832, +"localization": { +"2018-03-07.16-55-00.17-00-00.admin.G329.avi": { +"1": 1, +"112": 0 +} +}, +"objects": [ +{ +"objectType": "person", +"objectID": 1, +"localization": { +"2018-03-07.16-55-00.17-00-00.admin.G329.avi": { +"10": { "boundingBox": {"x":10, "y":30,"w":50,"h":20}}, +"20": { "boundingBox": {"x":10, "y":32,"w":50, "h":20}}, +"30": { "boundingBox": {"x":10, "y":35,"w":53, "h":20}}, +"112": {} +} +} +} +] + +8 + + } + +] + +● + +● + +} + +filesProcessed: A required array numerating the file names processed (either by annotators or a +system) and reported on by this file. Every file, even if the file was unreadable or contained no +activities, must be present in the array. +activities: An array of annotated activity instances. Each instance is a dictionary with the following +fields: +o activity: The name (e.g. “Talking”) from the MEVA Annotation Definitions [1] +o activityID: a unique, numeric identifier for the activity instance. The value must be unique +within the list of activity detections for all video source files processed (i.e. within a single +activities JSON file) +o localization: The temporal localization of the activity instance encoded as a dictionary of +frame state signals indexed by the video file id(s) for which the activity instance is witnessed. +Each frame state signal has keys representing a frame number and the value being 1 (the +activity instance is present) and 0 (otherwise) within the given video file. Multiple Frame +State Signals can be used to represent an activity instance being present in multiple video +views. In this case, frame numbers are relative with respect to the video file. The frame +number begins at “1’ which means frame 1 corresponds to the first frame. +o + +objects: An array of objects annotated with respect to the activity instance. Each unique +object is represented by the following dictionary: +▪ objectType: A string identifying the objects type as one of the track types defined in +the MEVA Annotation Spec. +▪ objectID: unique, numeric identifier for the object. The value must be unique +within a single activities JSON file. +▪ Localization: The temporal-spatial localization of the objectType (referred to by the +record) encoded as a dictionary of frame state signals indexed by the video file id +for which the object is observed. The frames for the object localization are not +necessary to match the frames for the activity localization. Each frame state signal +(for a given video) has keys representing a frame number and the value is a +dictionary describing the spatial localization of the object. The spatial dictionary is +either empty, indicating no localization (used to terminate a track or indicate an +object is not visible), or has 1 key ‘boundingBox’ which is itself a dictionary +described as a pixel ‘x’, ‘y’, ‘w’, and ‘h’ for the the x-position, y-position, width and +height respectively. The (0,0) (x,y) position is the top left pixel. Object localization +for a frame state signal is interpreted to extend from the key value frame until the +next frame present in the frame state signal, or until the end of the given video if +no additional frames are present. + +REFERENCES + +1. ActEV Annotation Definitions for MEVA Data + +9 + + 2. Kitware annotation tool +3. The VGG Image Annotator +4. Scalabel (used for annotation of Berkeley DeepDrive project) +5. VATIC - Video Annotation Tool +6. BeaverDam +7. VoTT: Visual Object Tagging Tool +8. Computer Vision Annotation Tool (CVAT) +9. Efficient Annotation of Segmentation Datasets with Polygon-RNN++ +10. MEVA Camera Calibration and metadata, MEVA Data Repo + +DISCLAIMER +Certain commercial equipment, instruments, software, or materials are identified in this evaluation plan to +specify the experimental procedure adequately. Such identification is not intended to imply recommendation or +endorsement by NIST, nor is it intended to imply that the equipment, instruments, software or materials are +necessarily the best available for the purpose. + +10 + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/KPF-specification-v4.txt b/docs/project-history/source-text-extracts/docs_convert/KPF-specification-v4.txt new file mode 100644 index 0000000..047b9cb --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/KPF-specification-v4.txt @@ -0,0 +1,505 @@ +KWIVER Packet Format (KPF) +Motivation +The KPF format is used to persist object detections, tracks, events, and associated metadata in +KWIVER. KPF uses a subset of YAML as its default text transport, as well as defining a set of semantic +tokens useful for object detection and tracking, as well as activity recognition. It is hoped that by +defining a core set of semantic concepts and their common representation, KPF will enable +interoperability between various applications (e.g. trackers, detectors, GUIs, and evaluation tools.) KPF +also provides for application-specific extensions to be persisted and parsed, yet ignored when not +applicable. +From a design perspective, KPF tries to ​provide​: +● + +● +● +● +● +● +● +● + +unambiguous representation of objects, tracks, simple and complex events +○ both semantically ("timestamp is 4000? 4000 ​what?​") and at the wire-level ("This box is +100, 200, 105, 205-- is that (x1,y1)-(x2,y2) or (x1,y1,w,h)?") +relational support for linking, grouping, and mapping sets of items +transport-agnostic representation (should be easily convertible between text, json, xml, +protobuf) +extensibility ("Hey, in Phase 2 the program just defined ten more event types!") +explicit linkages between documents +simplicity for common use cases, feasibility for most use cases +convertibility from existing formats +compatibility (in text mode) with command-line tools such as awk, grep, and perl + +KPF tries to ​avoid: +● MAGIC NUMBERS +● excessive redundancy and verbosity +● obsessive removal of all possible ambiguity + + Key KPF concepts +As stated above, KPF is ​not​ a single file format or set of formats. There is no single "track file format" or +"event file format". Instead, KPF aims to provide a unified representation of the fundamental concepts +common to computer vision applications, and a principled way to add new concepts. A KPF parser +should be able to process any KPF-compliant input; the decision whether a particular instance meets +the requirements of an application is an application-level decision, rather than an output of the parser. +In particular, if an application processes a KPF file containing both tokens it knows about (bounding +boxes and frame numbers, for example) and tokens it does not recognize (say, activity instances) it +should be able to process the recognized tokens ​without concern that the unknown tokens have +altered the interpretation of the known tokens.​ In other words, the interpretation of a particular token +is independent of the presence or absence of other tokens. + +Packets and Domains +A recurring theme is that many concepts we want to represent (timestamps, object detections, event +names) have natural representations which, if naively transcribed, become ambiguous due to different +sources / references / coordinate systems. KPF handles this by sharing the representation between +packets​ and ​domains. + +Packets +Packets are the semantic schema of a concept. Items such as timestamps, detections, events, +bounding boxes are all represented by specific packets. Complex concepts (e.g. an activity instance) +may be composed of multiple simpler packets (timestamps, track IDs, etc.) + +Domains +A domain specifies the ​context of a packet​. For example, a bounding box might have the domain of +"pixel coordinates" or "world coordinates"; a timestamp might have the domain of "frame number" or +"usecs since midnight 1 Jan 1970". A location might have the domain of "lat/lon", "utm", or "pixels". +These are examples; default domains are provided for the common cases, but clients are free to +specify a new custom domain. (The "social" aspects of ​defining​ a custom domain, ensuring that it does +not conflict with somebody else's domain, whether or not it even needs to BE its own domain, are all +explicitly outside the scope of KPF. By making the domain explicit, KPF allows for conflict detection, but +does not handle conflict resolution.) + + Roughly speaking, the packet tells you what something is; the domain grounds it in the units / +coordinate frame / event vocabulary / etc. + +Anatomy of a packet +A packet's format is ​[packet-tag][domain]:[space][payload]​; for example, the packet +g0: 1080 229 1112 261 +has the packet tag "g", domain "0", and payload "1080 229 1112 261". +The format of the payload is fixed by the tag. The interpretation of the payload is dictated by the +domain. +An equivalent XML representation might be + + 1080 229 1112 261 + +Note that there is no subdivision of the payload into explicit corner points. + +Packet tags: conceptual extensibility +The packet tag is a string; as we add more concepts, we add more tags. + +Domains: application extensibility +The domain is an integer specifying the context in which to interpret the packet and effectively acts as a +namespace. The general idea is that as more efforts define their own interpretations of tags (for +example, different programs with different definitions for "U-turn"), each effort gets its own domain. +For most packet types, a few pre-defined domains will suffice. +Inevitably, the problem of domain allocation and conflict resolution will arise. Similar to the Well Known +Ports in ​/etc/services​, we propose the following policy: +● +● + +domains 0-9 are ​reserved​ and predefined as necessary. +Application-specific domains start numbering at 10; the mapping of the domain to a specific +application should be provided via the ​meta:​ tag. The meta tag has no domain; its payload is a +string whose format is unspecified. For example: +-{ meta: "loc13 coordinate system: see /projects/foo/refcoords.txt"} + + It may be that a different project was already using loc13 without our knowledge; handling such +organizational conflicts is explicitly outside the scope of KPF. + +KPF and YAML +KPF uses ​YAML​ as its default text transport. YAML was chosen over a more lighter-weight ad-hoc text +format based on the following considerations: +● YAML parsers are readily available for many languages +● Although YAML is more verbose for very simple schemas, it provides more structure for +complex concepts (such as activities) +● Its flexibility reduces risk that future, possibly more complex concepts will require major +revisions to the underlying format +● It can still be represented as record-per-line text file, allowing rapid data analysis using standard +tools such as awk, grep, and perl. + +KPF Use Case +Here we show an example of how a KPF file might evolve through a pipeline of detection, tracking, and +scoring. The files are unrealistically short for brevity, but each line is meant to be complete. + +Step 1: Detection +A detector output might look like: +- { meta: "cmdline0: run_detector param1 param2..." } +- { meta: "conf0: yolo person detector" } +- { meta: "id0 domain: yolo person detector" } +- { geom: { id0: 0, ts0: 101, g0: 515 419 525 430, cset0: {Person: 0.8} }} +- { geom: { id0: 1, ts0: 101, g0: 413 303 423 313, cset0: {Person: 0.3} }} +- { geom: { id0: 2, ts0: 102, g0: 517 421 527 432, cset0: {Person: 0.7} }} +- { geom: { id0: 3, ts0: 102, g0: 416 304 421 315, cset0: {Person: 0.2} }} +NEW in KPF V4​: The ​cset0: {Person: 0.7}​ packet replaces the ​conf0: 0.7 ​packet; this allows +detectors to report multiclass likelihoods (e.g. ​cset0: {Person: 0.7, Vehicle: 0.2}​). +NEW in KPF V3​: The ​geom: { … } ​wrapper is a ​schema tag​; it is meant to convey a hint about how +the associated set of packets should be interpreted and to be used in validation. See "Schema types" +below. +ad +Here the ​id0​ and ​conf0​ domains are specified to be the detections from yolo; the timestamp ​ts0​ and +geometry ​g0​ domains are predefined to be frame number and pixel coordinates, respectively. + + Step 2: Tracking +A tracker (detection linker) could take the above and generate the following. New packets are +highlighted. +- { meta: "cmdline0: run_detector param1 param2..." } +- { meta: "conf0: yolo person detector" } +- { meta: "id0 domain: yolo person detector" } +- { meta: "cmdline1: run_linker param1 param2..." } +- { meta: "id1 domain: track linker hash 0x85913" } +- { geom: { id0: 0, ts0: 101, g0: 515 419 525 430, cset0: {Person: 0.8}, ​id1: 100​ } } +- { geom: { id0: 1, ts0: 101, g0: 413 303 423 313, cset0: {Person: 0.3}, ​id1: 102​ } } +- { geom: { id0: 2, ts0: 102, g0: 517 421 527 432, cset0: {Person: 0.7}, ​id1: 100​ } } +- { geom: { id0: 3, ts0: 102, g0: 416 304 421 315, cset0: {Person: 0.2}, ​id1: 102​ } } + +Here all the tracker has done is defined an additional domain for IDs (​id1)​ which it uses to link +detections into tracks. + +Step 3: Scoring +An evaluation run could take the output from the tracker and produce the following. Again new packets +are highlighted. +- { meta: "cmdline0: run_detector param1 param2..." } +- { meta: "conf0: yolo person detector" } +- { meta: "id0 domain: yolo person detector" } +- { meta: "cmdline1: run_linker param1 param2..." } +- { meta: "id1 domain: track linker hash 0x85913" } +- { meta: "cmdline2: score_tracks param1 param2..." } +- { meta: "overall track pd/fa count: 0.5 / 1" } +- { meta: "eval0 domain against id0" } +- { meta: "eval1 domain against id1" } +- { meta: "id2 domain false negatives from official_ground_truth.kpf" } +- { geom: { id0: 0, ts0: 101, g0: 515 419 525 430, cset0: {Person: 0.8}, id1: 100, ​eval0: tp, +eval1: tp } } +- { geom: { id0: 1, ts0: 101, g0: 413 303 423 313, cset0: {Person: 0.3}, id1: 102, ​eval0: fa, +eval1: fa } +- { geom: { id0: 2, ts0: 102, g0: 517 421 527 432, cset0: {Person: 0.7}, id1: 100, ​eval0: fa, +eval1: tp } } +- { geom: { id0: 3, ts0: 102, g0: 416 304 421 315, cset0: {Person: 0.2}, id1: 102, ​eval0: fa, +eval1: tp } } +- { geom: { id2: 0, ts0: 101, g0: 600 550 605 610, eval0: fn, eval1: fn } } +- { geom: { id2: 1, ts0: 102, g0: 603 553 608 615, eval0: fn, eval1: fn } } + +Here, the scoring code has done several things: + + ● +● +● + +It has added a summary of its scoring results to the preamble via the ​meta​ packets. +It has added two sets of eval packets to each detection; ​eval0​ is the detection-level result +against ​id0​, ​eval1​ is the track-level result against ​id1. +It has added a wholly new set of boxes in a new domain (​id2​); these are the false negatives +(undetected boxes) from the ground-truth file named in the ​meta​ packet. +○ These new tracks have IDs which collide with those from domain 0, but they are still +separated since they come from a different domain. + +This KPF file could be used for visualizing results; one could easily imagine a pull-down menu allowing +selection of individual ID domains populated with the text from the corresponding ​meta​ packet. + +Packet types +This is a proposed list of packet types, drawn mostly from the data_terms in track_oracle plus the +attributes file. +Generally, when a number is undefined, 'x' is used. +Strings should be quoted when they contain spaces and use '\' as an escape character. +packet + +YAML format + +definition + +pre-defined domains / notes + +id + +id​N​:​ ​int + +object +identifier + +none; may start at 0 but should specify source via +a meta packet. + +ts + +ts​N​:​ double + +timestamp + +0: frame number +1: seconds since beginning of video +2: usecs since unix Epoch (1 Jan 1970 UTC) + +tsr + +tsr​N​: [ ​double double​ ] + +timestamp +range + +(same as ts) (maybe use 'x x' to mean "all the +time"?) + +loc + +loc​N​:​ ​x y z + +location + +0: pixel coordinates (z is undefined) +1: lon / lat / altitude-in-meters +2: UTM (e.g "17N 630084 4833438") +Locations in world coordinates (e.g. via +homographies) should use a domain > 9 and +specify the homography file used via a ​meta +packet. + +g + +g​N​:​ ​x1 y1 x2 y2 + +bounding box + +0: pixel coordinates. (x1,y1) is upper-left; (x2,y2) is +lower-right; image origin (0,0) is upper-left corner. + +poly + +poly​N: +​ ​ ​ ​[ [​x1,y1] +​ [​x2,y2​] +... ] + +polygon + +0: pixel coordinates + + conf + +conf​N: +​ ​ ​double + +confidence or +likelihood + +same protocol as id. (Ground-truth should +additionally be represented via a 'src: truth' kv +packet, rather relying exclusively on a confidence +of 1.0.) + +cset + +cset​N: +​ { ​label1: likelihood1, +[label2: likelihood2, …]​ } + +set of label / +likelihoods + +3: DIVA objects + +act + +act​N​:​ ​{ ​activity_label_1: +likelihood1, [activity_label_2, +likelihood2, … ] ​}​ id packet, +timespan:[{​ tsr-packets +}], ​[opt: kv-packet...] +actors: [ { ​id-packet, +timespan:​ ​[{​ tsr packets +}]}​, (more actors) } +​ ] + +activity + +0: VIRAT +1: vidtk +2: DIVA activities (Phase 1) +3: DIVA activities (Phase 2) +Activity names are spelled out. Participating +objects should specify timestamp ranges in the +same domains as the event itself. +Timespans are represented as arrays of tsr +packets to allow for future inclusion of +synchronized world clocks. + +eval + +eval​N: +​ ​ result-string + +evaluation +result + +same protocol as id. The result-string is e.g. 'tp' for +true positives, 'fa' for false alarms, etc. + +a + +aN:​attribute_string + +attribute + +same protocol as id. Specifies that the named +attribute applies in the current scope. + +tag + +tag: ​packet string + +tag a packet / +domain pair + +Used to link multiple files, i.e. 'tag: id0 collect5' in +file A and 'tag: id3 collect5' in file B essentially +means file A's id0 domain is the same as file B's +id3 domain. + +key + +key:​ v​ alue + +key / value + +Keys are distinguished from KPF packets as they +have no domain integer before the colon, i.e. +'​src0:​' might be a KPF packet but '​src:​' is not. + +Schema types +The schema type tag associates the set of packets with a text string intended to convey how to interpret +the set of packets and to allow for validation. If no schema tag is supplied, the record is parsed with an +implicit 'unspecified' schema (i.e. no validation.) +Defined schema types are: +tag + +interpretation + +geom + +Geometry + + act + +Activity + +types + +Object types + +regions + +Polygonal scene regions + +unspecified + +"other"; no specified intent + +DIVA-specific schemas +Annotation delivery: +1. file for geometry + frame-level attributes + track IDs + polygons. Size: O( nDetections x +nFrames ) +2. file for object labels: size O( nObjects ) +3. file for events: size O( nEvents ) +4. file for region polygons: size O( nStaticObjects * lenStaticObjectTracks) + +Geometry schema: (line breaks for clarity) +- { ​geom:​ { ​id0:​ ​detection-id,​ ​id1:​ ​track-id,​ ​ts0:​ ​frame-id,​ ​g0:​ ​geom-str,​ ​src: ​source +[​occlusion:​ (medium | heavy )​] +[​cset3: {​object: likelihood, … } +​ ​] +[​evalN:​ ​eval-tag.​ ..] }} +Example (simple) (line breaks for clarity): +- { geom: { id1: 0, id0: 37, ts0: 37, g0: 432 387 515 444 , +src: truth, occlusion: heavy }} +...Detection 37 is associated with track 0 on frame 37, and is a box from image coordinates (432, 387) +to (515,444). This detection is ground truth and an annotator has marked the object's occlusion level as +"heavy". +Example (slightly more complicated) (line breaks for clarity) +- { geom: { id1: 0, id0: 37, ts0: 37, ts1: 18.5, g0: 432 387 515 444 , +src: XYZ_v2, occlusion: heavy, +cset3: {Person: 0.6, Vehicle: 0.2}, + + eval0: fn, eval1: tp }} + +Similar to previous example, but now with another timestamp (18.5 seconds since the beginning of the +video) and results from an evaluation run: ​eval0 ​marked it as a miss (false negative) in the detection +domain, while​ eval1 ​found that it was a hit (true positive) in the track domain. The src tag indicates +this is not a ground-truth packet but instead a classification from the XYZ_v2 system. +NEW in KPF V4​: The ​cset3 ​packet contains the XYZ_v2 system's classification results. (If +classification results are at a track level rather than the detection level, they may be provided in a types +file for brevity.) + +Object label schema: +- { types: {id1: ​track-id​, c +​ set3: { ​object_type: likelihood, ,... ​ }​ } } +NEW in KPF V4​: types are communicated via a ​cset3 ​packet, rather than an​ obj_type: ​key-value +packet. +Examples: +- { types: { id1: 35 , cset3: {Vehicle: 1.0} } } +- { types: { id1: 36 , cset3: {Vehicle: 1.0} } } +- { types: {id1: 5000 , cset3: {Parking_Meter: 1.0} } } +- { types: {id1: 5001 , cset3: {Dumpster: 1.0} } } + +Activity schema: +- { act { actN: ​{​activity_name: likelihood, …} +​ ​ ,​ i +​ d_packet​, t +​ imespan: +[{​tsr_packet} +​ ​(... ​tsr_packet​)​], src: ​source, +​ actors: [ {​id_packet,​ ​timespan: +[{​tsr_packet} +​ ​(... ​tsr_packet​)​]} ​(, next actor identification... )​ ]}} +NEW in KPF V4​: the activity type is a name/likelihood map, much like a ​cset​ packet. +Example (line breaks for clarity) +- { act: {act2: {Talking: 1.0}, id2: 3, timespan: [{tsr0: [3293, 3314]}], +src: truth, +actors: [{id1: 9, timespan: [{tsr0: [3293, 3314]}]} , +{id1: 12, timespan: [{tsr0: [3293, 3314]}]} , ]}} +This line of YAML can be rendered as a python object via +http://yaml-online-parser.appspot.com/​ ​as +[{'act': {'act2': {'Talking': 1.0}, + + 'actors': [{'id1': 9, 'timespan': [{'tsr0': [3293, 3314]}]}, +{'id1': 12, 'timespan': [{'tsr0': [3293, 3314]}]}], +'id2': 3, +'src': 'truth', +'timespan': [{'tsr0': [3293, 3314]}]}}] +Here the event is parsed as follows: +● act2:​ this is an event in domain 2 (notionally DIVA) +● {Talking: 1.0} ​event is 'walking' with a likelihood of 1.0 +● id2: 3 ​event ID is 3 (explicitly also in domain 2, DIVA) +● timespan: [{tsr0: [3293, 3314]}] ​The activity as a whole starts at frame 3293 and +ends at 3314. Frame numbers are domain 0 for timestamp range. +● actors:... ​This is an array of the actors participating in the event: +○ id1: 9 ​First actor is track ID 9 +○ timespan: [{tsr0: [3293, 3314]}] ​The first actor is participating in the event +from frames 3293 to 3314 +○ id1: 12 ​Second actor is track ID 12 +○ timespan: [{tsr0: [3293, 3314]}] ​The second actor is also participating in the +event from frames 3293 to 3314 +● tsr0: 19 402 ​event takes place from frames 19 to 402 (in timestamp domain 0) +● src truth ​the event is a ground-truth event (i.e. sourced from "truth"; other detectors would +substitute their own sources) +Timestamp ranges are stored as arrays in anticipation that multiple-camera events will be accessible +from multiple time reference points (e.g. frames-since-video-start as well as world-clock-time.) + +Regions schema: +Some objects (static objects, scene segmentation) have been annotated as polygons; for example, to +delineate "do-not-score" areas. The schema is +- { regions: { id​N​: ​id, +​ ts​N​: ​timestamp​, [ keyframe: (0|1) ], poly​N​: [[ ​x0, y0 +], [ ​x1, y1​ ] ​...​ ]}} +Example: +- { regions: { id1: 1, ts0: 9063, keyframe: 1, poly0: [[ 1435.88, 1 ], [ +1435.88, 68.76 ], [ 1456.88, 68.76 ], [ 1456.88, 1 ], ]}} +This is parsed as: +● id1: 1 ​This polygon pertains to track ID 1 +● ts0: 9063 ​This polygon is on frame 0 +● keyframe: 1 ​This particular polygon is marked as keyframe (for interpolation) +● poly0: [[ 1435.88, 1 ] ... ​An array of (x,y) points in image coordinates (domain 0). +## + +Questions and comments +Please send any questions and/or comments to ​roddy.collins@kitware.com​ and +keith.fieldhouse@kitware.com. + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/MEVA-Annotation-Definitions.txt b/docs/project-history/source-text-extracts/docs_convert/MEVA-Annotation-Definitions.txt new file mode 100644 index 0000000..8462365 --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/MEVA-Annotation-Definitions.txt @@ -0,0 +1,1395 @@ +Performers: DIVA Annotation +Definitions for MEVA Data +1 June 2020 AD + +DIVA ANNOTATIONS OVERVIEW +TRACK TYPES LIST +1. + +person + +4. bag + +2. vehicle + +5. receptacle + +3. bicycle + +6. other + +ACTIVITIES LIST +1. + +person_opens_facility_door + +20. + +person_sits_down + +2. + +person_closes_facility_door + +21. + +person_stands_up + +3. + +person_enters_scene_through_structure 22. + +person_reads_document + +4. + +person_exits_scene_through_structure 23. + +person_transfers_object + +5. + +person_opens_vehicle_door + +24. + +person_picks_up_object + +6. + +person_closes_vehicle_door + +25. + +person_puts_down_object + +7. + +person_enters_vehicle + +26. + +person_carries_heavy_object + +8. + +person_exits_vehicle + +27. + +hand_interacts_with_person + +9. + +person_opens_trunk + +28. + +person_embraces_person + +10. + +person_closes_trunk + +29. + +person_purchases + +11. + +person_loads_vehicle + +30. + +person_interacts_with_laptop + +12. + +person_unloads_vehicle + +31. + +vehicle_stops + +13. + +person_talks_to_person + +32. + +vehicle_starts + +14. + +person_talks_on_phone + +33. + +vehicle_reverses + +15. + +person_texts_on_phone + +34. + +vehicle_picks_up_person + +16. + +person_rides_bicycle + +35. + +vehicle_drops_off_person + +17. + +vehicle_turns_left + +36. + +person_abandons_package + +18. + +vehicle_turns_right + +37. + +person_steals_object + +19. + +vehicle_makes_u_turn + +Track Requirements by Activity Type +1 + + Annotation Modifications + +2 + + DIVA ANNOTATIONS OVERVIEW +● + +Completed annotations for a clip consist of identification of all activities in a clip with +activity-centric annotations including the following: +○ + +identification of start and end times of an activity consist with the definitions +provided using “Events” in WAMI Viewer, + +○ + +identification of tracked objects (from provided definitions) and the time frames for +their involvement in the activity using “Events” in WAMI Viewer, + +○ + +bounding boxes for the first frame, last frames, and one additional key frame +(illustrating the entire object contained in a bounding box) of a tracked objects +involvement in an activity using “Tracks” in WAMI Viewer, and + +● + +Additional Annotation Notes: +○ + +Multiple annotators will need to annotate each clip to allow us to measure +inter-annotator agreement. + +○ + +Annotators should be as precise as possible when following the definitions; +however, if there is any ambiguity be generous on the temporal bounds of the +activity to make sure the entire activity is captured. + +○ + +Do not annotate activities in areas specified by the document “Do Not Annotate +Areas”. + +○ + +People will be annotated through clear facility and vehicle doors/windows. +Check “Scene Specific Notes and Do Not Annotate Areas” document for further +clarification. + +○ + +People will be annotated through vehicle windows. + +○ + +Vehicles will be annotated through clear facility doors/windows. + +○ + +Annotation begins when the object being annotated is first identifiable. + +○ + +If, according to the definition, an activity has already started at the beginning of a +clip (or hasn’t ended at the end of a clip), annotate that activity as existing from the +start of the clip through the end of the activity (or from the start of the activity +through the end of the clip). + +○ + +Multiple activities can happen simultaneously with the same person/object. + +3 + + TRACK TYPE DEFINITIONS +person +An individual human being. + +vehicle +Examples of vehicles include cars, trucks, 18-wheelers, buses, golf carts, motorcycles, and +anything that can be expected to be seen on a highway. Doors on vehicles are not annotated +separately. Motorcycles are tracked as vehicles with the bounding box containing both the +person & motorcycle. + +bicycle +A “bicycle” refers to any small vehicle where the person is still visible but where the person is +modifying their movement (e.g., foot-powered scooter, skateboard, bicycle). bicycles are only +tracked when involved in an activity. A bicycles will always be tracked independent of the person +using the bicycle. Vehicle attributes such as turning or stopping are not applied to bicycles. A +person riding a bicycle is not occluded by the bicycle and the bicycle is not occluded by the +person riding it. + +bag +Examples of bags include backpacks, shoulder-purse, duffle, suitcase, pelican case. + +receptacle +Examples of receptacles include trash cans, newspaper boxes, cigarette butt catchers, and +dumpsters. + +other +Catch all for other objects that are required by an activity definition but have not labeled as a +person, vehicle, bicycle, bag or receptacle track type. Some examples are cooler, table, soccer +ball, and frisbee. + +4 + + ACTIVITY DEFINITIONS +person_opens_facility_door +Description:​ A person opening the door to a facility. The only track required for this activity is a +person track. Special Examples: +● + +A person holds the door open for an extended period of time, possibly to allow other +individuals through, then the activity ends with the person holding the door open. + +● +● + +A person grabbing an already open door ​does not​ get included in the activity. +A person grabbing a door that has started closing ​does​ get this activity. + +Start:​ The event begins 1 s before the door starts to move or when the person is first visible. +End​: The event ends after the door stops moving or when the tracked person is no longer +visible. + +person_closes_facility_door +Description:​ A person closing the door to a facility. See “person opens facility door” for track + +requirements. Doors closing due to the mechanics of the door with no human assistance are not +included in this activity. The only track required for this activity is a person track. +Start:​ The event begins 1 s before the door starts to move or when the person is first visible. +End​: The event ends after the door stops moving or when the tracked person is no longer +visible. + +person_enters_scene_through_structure +Description:​ A person entering the scene (going into the field of view) through a physical + +structure (e.g., doorway, arch, gateway, subway entrance/exit). This distinguishes entering +through a structure from entering the FOV via (a) an incidental edge-of-image or (b) incidental +occlusion, which both arise from the choice of camera viewpoint rather than inherent structures +of the scenarios. Entering is always determined relative to the field of view. The only track +required for this activity is a person track. +Start:​ The event begins 1 s before the individual crosses the entry threshold (regardless of door +interaction) or when the individual is first visible. + +End​: The event ends 1 s after the individual completely crosses the entry threshold (regardless of +door interaction) or when the individual is no longer visible. + +5 + + Special Case 1: ​In the event of entering through a stairwell (e.g., subway entrance in GDB + +scenes), the threshold for entering is the top step of the staircase. Therefore, the activity begins +when the person is first visible and ends 1 s after the individual completely crosses the top step. +If the individual remains at the top stair for an extended period, then the activity ends after 1 s of +standing at/on the threshold. +Special Case 2:​ The arch entryway in ALB G328 and G331 constitutes a structure. + +person_exits_scene_through_structure +Description:​ A person exiting the scene (going out of the field of view) through a physical + +structure (e.g., doorway, arch, gateway, subway entrance/exit). This distinguishes exiting through +a structure from entering the FOV via (a) an incidental edge-of-image or (b) incidental occlusion, +which both arise from the choice of camera viewpoint rather than inherent structures of the +scenarios. Entering is always determined relative to the field of view. The only track required for +this activity is a person track. +Start:​ The event begins 1 s before the door moves or if there is no door, the event begins 1 s +before half of the person’s body is through the structure. + +End​: The event ends 1 s after the person has passed through the structure or when the individual +is no longer visible. + +Special Case 1: ​In the event of exiting through a stairwell (e.g., subway entrance in GDB scenes), +the threshold for exiting is the top step of the staircase. Therefore, the activity begins 1 s before + +the individual completely crosses the top step and ends when the person is no longer visible. If +the individual remains at the top stair for an extended period, then the activity begins 1 s before +the person moves through/off the threshold. +Special Case 2:​ The arch entryway in ALB G328 and G331 constitutes a structure. + +person_opens_vehicle_door +Description:​ A person opening the door to a vehicle. The only necessary track in this event is + +the vehicle. The vehicle door is not independently annotated from the vehicle. This event often +overlaps with entering/exiting; however, can be independent or absent from these events. The +person does not need to be visible for the duration of the activity; however, if a person is visible +they will be annotated. People will be annotated through bus windows when visible. +Start:​ The event begins 1 s before the door starts to move. +End​: The event ends as soon as the door stops moving. + +6 + + person_closes_vehicle_door +Description:​ A person closing the door to a vehicle. See “person opens vehicle door” for track +requirements. + +Start:​ The event begins 1 s before the door starts to move. +End​: The event ends as soon as the door stops moving. + +person_enters_vehicle +Description:​ A person entering (getting into) a vehicle. The two necessary tracks included in this +event are (1) the person entering and (2) the vehicle being entered. A special case of “entering” +is mounting a motorized vehicle (e.g., motorcycle, powered scooter). The person does not need +to be visible for the duration of the activity; however, when the person is visible they will be +annotated. People will be annotated through bus windows when visible. +Start:​ The event begins 1 s before the door moves or if there is no door interaction, the event +begins 1 s before the person’s body is inside the vehicle. + +End​: The event ends when the person is in the vehicle and the door (if present) is shut within 10 +seconds of the person completely entering the vehicle. + +person_exits_vehicle +Description:​ A person exiting a vehicle. See entering for track requirements. A special case of +“exiting” is dismounting a motorized vehicle (e.g., motorcycle, motorized scooter). The person + +does not need to be visible for the duration of the activity; however, when the person is visible +they will be annotated. People will be annotated through bus windows when visible. +Start:​ The event begins either (1) 1 s before the door moves (even if the person opening the door +is not visible) or (2) if there is no door interaction, 1 s before half of the person’s body is outside +the vehicle. +End​: The event ends 1 s after the person has exited the vehicle. + +person_opens_trunk +Description:​ A person opening a trunk. A trunk is defined as a container designed to store + +non-human cargo on a vehicle (e.g., rear facing trunk, van rear doors, truck bed). The necessary +tracks are person and vehicle. A special case exception of “open trunk” is remotely opening +trunks where the person is not near the vehicle -- in this case, the person track will not be +included in the activity. + +7 + + Start:​ The event begins 1 s before the trunk starts to move. +End​: The event ends after the trunk has stopped moving. + +person_closes_trunk +Description:​ A person closing a trunk. See “Open Trunk” for definition of trunk and special +cases. + +Start:​ The event begins 1 s before the trunk starts to move. +End​: The event ends after the trunk has stopped moving. + +person_loads_vehicle +Description:​ An object moving from person to vehicle. The two necessary tracks included in this + +activity are (1) the person performing the loading and (2) the vehicle being loaded. Additionally, if +the items being loaded meet the criteria of a unique track, then they are included in this activity. +Start:​ The event begins 1 s before the cargo to be loaded is extended toward the vehicle (i.e., + +before a person’s posture changes from one of “carrying” to one of “loading”). If the start of the +event is occluded, then it begins when the person and cargo are first visible. +End:​ The event ends after the cargo is placed into the vehicle and the person-cargo contact is + +lost. In the event of occlusion, it ends when the loss of contact is visible. The loading of multiple +objects by the same person with a pause of no more than 1 second will be annotated as one +continuous activity. + +person_unloads_vehicle +Description:​ An object moving from vehicle to person. See “loading” for track requirements. +Start:​ The event begins 1 s before the cargo begins to move. If the start of the event is occluded, +then it begins when the cargo movement is first visible. + +End:​ The event ends after the cargo is released. If the person holding the cargo begins to walk +away from the vehicle, the event ends after 1 s of walking. If the door or trunk is closed on the +vehicle, the event ends when the door or trunk is closed. If both of these things happen, the +event ends at the earlier of the two events. The unloading of multiple objects by the same person +with a pause of no more than 1 second will be annotated as one continuous activity. + +person_talks_to_person +Description:​ A person talking to another person in a face-to-face or side by side arrangement +between at least two people. Brief breaks in face-to-face orientation during a conversation + +8 + + should be included within the same “people talking” event. The people must be close in +proximity and involved in a conversation (this specifically differentiates talking from lecturing to a +classroom or crowd). Additionally, people may join or leave the talking activity as long as a nexus +of individuals remain involved in the “people talking” event. A new track should be started for an +individual leaving and returning to “people talking”. Active listening maintains a person’s +involvement in the talking activity and can occur with one (or more) person in a group of +individuals looking away/down; therefore, when there is a large group of people involved in a +conversation (e.g., around a table) then they do not all need to be actively talking to be part of the +“people talking” activity. +Start:​ This event begins when the face-to-face or side by side arrangement is initiated. +End:​ This event ends when the face-to-face or side by side arrangement is broken. +Minimum Duration:​ 5 seconds + +person_talks_on_phone +Description:​ A person talking on a cell phone where the phone is being held on the side of the + +head. This activity should apply to the motion of putting one’s hand up to the side of their head +regardless of the presence of a phone in hand. +Start:​ Annotation should begin when the hand makes motion toward side of head or when the +person is first visible performing the activity. Person is first visibly performing the activity when + +they enter the field of view actively talking on a phone or when talking on phone indicated by a +hand to the side of the head is first visible. +End:​ Annotation should end after the hand leaves the side of the head or when the person +leaves the scene. + +person_texts_on_phone +Description:​ A person texting on a cell phone, including both using the phone with thumbs and + +fingers and video phone calls. The latter applies to any situation when the phone is in front of the +person (as opposed to along the side of the head) and they are using it, including playing games, +checking emails, taking pictures, etc. A person must be in possession of the phone to be +involved in the texting activity. +Start:​ Annotation should begin when “texting” is observed or when the person is first visible +performing the activity. + +End:​ Annotation should end after last instance of “texting” is observed or when the person +leaves the scene. + +9 + + Minimum Duration:​ 2 seconds continuous, uninterrupted texting. The goal is to avoid quick, +sporadic use of phone (e.g., use of phone as a clock or checking messages)​. + +person_rides_bicycle +Description:​ A person riding a “bicycle” (i.e., any one of the varieties of human powered vehicles +where the person is still visible but their movement is modified). The two necessary tracks +included in this activity are the person and the “bicycle” they are riding. +Note: If a second individual is riding on the back of the same bicycle or in a child seat/carrier +attached to the bicycle, it is a single riding instance with one bicycle and two people tracks. +Start:​ Annotation begins when the person’s motion is modified by the bicycle or if the person is +already riding the bicycle, upon entering the field-of-view. + +End:​ Annotation ends when the person’s motion is no longer modified by the bicycle, or upon +exiting the field-of-view. + +vehicle_turns_left +Description:​ A vehicle turning left or right is determined from the POV of the driver of the vehicle. +The vehicle may not stop for more than 10 s during the turn. This event is determined after a +reasonable interpretation of the video. +Start:​ Annotation begins 1 s before vehicle has noticeably changed direction. +End:​ Annotation ends 1 s after the vehicle is no longer changing direction and/or linear motion +has resumed. + +vehicle_turns_right +Description:​ A vehicle turning left or right is determined from the POV of the driver of the vehicle. +The vehicle may not stop for more than 10 s during the turn. This event is determined after a +reasonable interpretation of the video. +Start:​ Annotation begins 1 s before vehicle has noticeably changed direction. +End:​ Annotation ends 1 s after the vehicle is no longer changing direction and linear motion has +resumed. + +vehicle_makes_u_turn +Description:​ A vehicle making a u-turn is defined as a turn of 180 and should give the + +appearance of a “U”. U-turns do not contain left and right turns. A u-turn is a continuous turn in +which the vehicle may not stop for more than 10 s. This event is determined after a reasonable +10 + + interpretation of the video and may include turns around obstacles, such as concrete islands. +Examples of acceptable and unacceptable can be found on Slide 17 of “Scene Specific Notes and +Do Not Annotate Areas”. +Start:​ Annotation begins when the vehicle has ceased linear motion. +End:​ Annotation ends 1 s after the car has completed u-turn. + +person_sits_down +Description:​ A person sitting down -- transitioning from an upright, standing position to a sitting +position in which the body weight is supported primarily by the buttocks in contact with the +ground or a horizontal object (e.g., chair, bench). +Start:​ Annotation begins when the person’s posture begins to go from standing to sitting. +End:​ Annotation ends when the person is completely seated. + +person_stands_up +Description:​ A person standing up -- transitioning from a seated or prone position to an upright, +standing position. + +Start:​ Annotation begins when the person’s posture begins to go from sitting or prone to +standing. + +End:​ Annotation ends when the person is completely standing in an upright position. If a person +begins walking before completely upright, the annotation ends after 1 s of walking. + +person_reads_document +Description:​ A person reading a hand-held document, such as a book, newspaper, flyer, + +brochure, etc, which they are holding. These documents should not include digital documents +read on a phone, ipad, laptop, etc. Reading will only be annotated when the reading material is +visible and the only participating track is for the person (i.e., the document will not be +independently tracked). The person may look away from the read document for periods of time +as part of the same activity as long as it is clear they have not abandoned reading (i.e. the +reading is briefly interrupted). In the same vein, if the reading person is occluded, as long as the +time period is reasonable as a brief interruption this is part of the same activity. Flipping through +a magazine or book for the minimum duration is considered reading a document. +Start:​ Annotation begins at the start of when the person’s attention is focused on the document +for a minimum of 5 seconds, without interruption. + +11 + + End:​ ​The activity ends either ACTIVELY or PASSIVELY. The activity ends actively if the person + +moves in such a way that stops the reading activity, i.e. closes the book, or stands up, or puts the +book down and starts talking to somebody, etc. The activity ends passively if the reader's attention +drifts away from the document for more than five seconds. + +Minimum Duration:​ 5 seconds continuous, uninterrupted reading. + +person_transfers_object +Description:​ A person-to-person or person-to-(person-in-vehicle) exchange of an object where +the object may or may not be tracked; the tracking status of the object is unaffected by its + +participation in object transfer. The two necessary tracks included in this activity are two person +tracks or a person and vehicle track if the object is transferred from person to person in a vehicle. +Additionally, the transferred object should be at least the size of a coffee cup to require tracking. +Start:​ This event begins 1 s before the physical transfer of object occurs. +End:​ This event ends 1 s after the physical transfer of object occurs. + +person_picks_up_object +Description: A person picking up an object. The two necessary tracks included in this event are +(1) the person picking up the object and (2) the object(s) (e.g., bag). Tracks need not necessarily +be the same length. A person picking up multiple items at a time can be annotated as one event +if the two items break contact with the surface within 1 second of each other. Occluded pick up +activities will only be annotated if the instant the pick up action begins is occluded for less than ½ +second and the pickup activity is definitive with reasonable human interpretation. Moving or +adjusting an object without vertical motion breaking contact with the resting surface does not +include a “pick up” activity. An item must be at least the size of a coffee cup to trigger a pickup +activity and not be on the excluded list. Multiple people picking up a large item will constitute a +single pick up instance. +This activity excludes any activities which satisfy the object transfer definition. +Excluded List (do NOT annotate the activity “person picks up object” when only the following +objects are involved): Phones, Pens/Pencils/Markers, Individual Sheets of Paper, Money, Hat, +Gloves, Apple (or similarly sized food items) +Included List (DO annotate the activity “person picks up object” when the following objects are +involved): Examples, which are not exhaustive, include newspaper, Frisbee, Soccer balls, cups, +another person, or occluded items that can be seen in the individual’s hand after half a second. + +12 + + Start: The event begins the moment the object breaks contact with the previous supporting +surface (e.g., table). +End: The event ends 1 second after the object is released or no longer active in motion +associated with pick up (e.g., standing up right). If the person holding the object begins to walk, +then the event ends after 1 s of walking (or next subsequent activity). + +person_puts_down_object +Description:​ A person sets down an object . The two necessary tracks included in this event are +(1) the person setting down the object and (2) the object(s) (e.g., purse). Tracks need not + +necessarily be the same length. Multiple people carrying the same object will share the same +event. Set down activity only occurs if the item is separated from the individual (e.g. a person +taking off gloves and placing them in their lap would not be a set down activity) and the object +must be placed directly on a surface (not dropped or thrown). Occluded put down activities will +only be annotated if the instant of the put down action ends is occluded for less than ½ second +and the set down activity is definitive with conservative human interpretation. A person setting +down multiple items at a time can be annotated as one event if the two items make contact with +the new supporting surface within 1 second of each other. Multiple people setting down a large +item will constitute a single set down instance. For excluded and ambiguous included items, see +Person Picks Up Object. +Start:​ The event begins 1 s before the object makes contact with the new supporting surface +(e.g., table). + +End:​ The event ends 1 s after the object has made contact with the new supporting surface. + +person_carries_heavy_object +Description:​ A person or multiple people carrying an oversized or heavy object. This is + +characterized by the object being large enough (over half the size of the person) or heavy +enough (where the person's gait has been substantially modified) to require being tracked +separately; therefore, this activity requires tracking at least one person and one object (i.e., Bag +or Other). The carried object must break contact with a surface or ground during the activity. If +multiple actors are carrying, they may start or stop participating in the activity independently of +the lifetime of the overall activity. Umbrellas (either opened or closed) are not heavy or +sufficiently large objects. +Start:​ This event begins when the person (or the first person for multiple people) establishes +contact with the object or when the person heavy-carrying is first visible. + +13 + + End:​ This event ends after the person (or the final person for multiple people) loses contact with +the object or when the person heavy-carrying is no longer visible. + +hand_interacts_with_person +Description:​ A physical interaction between two or more people in which their hands come + +together or one individual’s hand comes into contact with another person. Examples include +handshakes, high-fives, and fist-bumps, and explicitly excludes fighting and embracing. +Examples​: Handshakes, Holding Hands, High Fives, Fist Bumps, Linked Arms, Pat on +Back/Shoulder. + +Start:​ This event begins 1 s before physical contact. +End:​ This event ends 1 s after physical contact ends. + +person_embraces_person +Description:​ A physical interaction between two or more people in which they put their arm or +arms around the neck, back or waist of one another. This activity explicitly excludes fighting. +Examples​: two-person hug, group hug, side hug +Start:​ This event begins 1 s before physical contact. +End:​ This event ends 1 s after physical contact ends. + +person_purchases +Description:​ A person purchasing something (e.g., coffee, snacks, bus tickets) using cash, credit + +card, apple/phone pay, etc. The only track involved in this activity is the purchaser (​there may be +more than one purchaser for a single transaction​). The purchasing activity must be visible from +the camera point of view to be annotated. +Start:​ Annotation begins 1 second before the person begins to make the payment transaction. +Payment transaction begins when the purchaser’s arm motion begins to present the form of +payment. +End:​ Annotation ends 1 second after the person receives the purchased item(s) or their return of +payment (e.g., credit card, change), whichever occurs later. + +The start and end actions may be reversed; all transferring must be complete, but the purchaser +can receive their goods before they present payment. In this situation, the start and end criteria +would be reversed + +14 + + person_interacts_with_laptop +Description:​ A person actively interacting with a laptop. This may include actions such as + +opening/closing a laptop, reading a document on a laptop, or typing. Brief breaks in interactions +with laptop during a continuous activity should be included within the same “person-laptop +interaction” event. Only the person interacting with the laptop is tracked. If there are multiple +people interacting with the same laptop, those are separate person-laptop-interaction activities. +Minimum Duration:​ 5 seconds continuous, uninterrupted interaction with laptop. +Start:​ Annotation begins 1 second before active interaction or contact with the laptop first occurs. +End:​ The activity ends either ACTIVELY or PASSIVELY. The activity ends actively once the laptop +has been fully closed (note the closing motion is included in the activity). The activity ends +passively if the person’s attention drifts away from the laptop for more than five seconds. + +vehicle_stops +Description:​ A vehicle stops. Car is “stopping” when it begins noticeably slowing down, even if + +the car never completely “stops” as long as slowing occurs in an area where stopping is intended +(e.g., intersection, stop sign, crosswalk). +Start:​ Activity annotation begins at least 1 s (up to 5 s) before the vehicle has stopped when +deceleration is first observed . + +End:​ Activity annotation ends when vehicle motion has stopped or started acceleration. + +vehicle_starts +Description:​ A vehicle starts. Car is “starting” when it is accelerating from stopped or a slow +velocity to a faster maintained velocity reflecting the vehicle_stops definition. +Start:​ Activity annotation begins when motion is evident. +End:​ Activity annotation ends after at least 1 s (up to 5 s) of motion when acceleration is no +longer evident. + +vehicle_reverses +Description:​ A vehicle moving in the reverse direction. During this activity, no other vehicle +activities (i.e., moving, stopping, starting, left turn, right turn) will be annotated. +Start:​ Annotation begins when motion is evident. +End:​ Annotation ends when motion is no longer evident. +15 + + vehicle_picks_up_person +Description: A vehicle picks up a person or group of people. This encompasses the act of the +vehicle stopping (optional), people getting into the vehicle, and the vehicle driving away +(optional). At least one of the optional vehicle motions must occur and the driver cannot leave +the vehicle unattended. Both vehicle and person tracks should be included in this activity. Not +all tracks will have the same length. +Start: This event begins 1 s before the vehicle comes to a complete stop or 1 second before the +first person begins entering the vehicle, whichever comes first. People entering the vehicle +should be tracked starting 1 second before the person enters the vehicle. +End: This event ends 1 s after the vehicle resumes motion or 1 second after the last person has +entered the vehicle, whichever occurs last. + +vehicle_drops_off_person +Description: A vehicle drops off a person or group of people. This encompasses the act of the +vehicle stopping (optional), people getting out of the vehicle, and the vehicle driving away +(optional). At least one of the optional vehicle motions must occur and the driver cannot leave +the vehicle unattended. Both vehicle and person tracks should be included in this activity. Not +all tracks will have the same length. +Start: This event begins 1 s before the vehicle comes to a complete stop or 1 second before the +first person begins exiting the vehicle, whichever comes first. People exiting the vehicle should +be tracked starting as soon as they are first visible. +End: This event ends 1 s after the vehicle resumes motion or 1 second after the final person has +exited the vehicle, whichever occurs last. People who have exited the vehicle should stop being +tracked 1 seconds after exiting the vehicle​. + +person_abandons_package +Description​: A person moves away from and completely forsakes their package. It should be +clear the person has left the package with no intent to return for it at a later time, or has not + +attended to the package for at least ​two minutes​. The two necessary tracks included in this + +event are (1) the person abandoning the object and (2) the object (e.g., bag). In addition to the +“abandon package”, if the activity is visible, “set down object” will be annotated. ​An abandoned + +object must be at least the size of a small purse (e.g., backpacks, luggage, jacket) and not part of +the excluded list below. +Excluded Objects: ​ Phones, Wallets, Keys, Newspaper, Book, Cup, Food Items, Hats, Gloves. + +16 + + Start:​ Annotation begins 2 seconds before the person and package lose contact. +End​: Annotation ends 2 seconds after the person and package lose contact. +Special Note: Only the abandoning activity is annotated and the two minute interval associated +with object abandonment is not part of the annotated activity. During annotation, the annotator +will need to visually confirm the minimum two-minute package abandonment interval by +scrubbing ahead in the assigned clip and possibly verifying in the subsequent clip as well. + +person_steals_object +Description​: A person performing larceny, which is the taking of another person’s property +without permission. This includes the taking of larger items, such as a backpack, purse, or + +luggage that can be tracked. The two necessary tracks included in this event are (1) the thief and +(2) the object being stolen. +Start:​ Annotation begins 2 seconds before the thief makes contact with the item of theft or the +victim of theft. + +End​: Annotation ends 2 seconds after the thief takes possession of the stolen item. + +17 + + Track Requirements by Activity Type +The following table details the required and optional tracks (quantity and type) on a per-activity +basis. Where “possible additional tracks” is N/A, no tracks other than those required can be +included in the activity. + +Required +Number of +Activity +tracks & Type Notes +person_opens_facility_door +1 person +person_closes_facility_door +1 person +person_enters_through_structure 1 person +person_exits_through_structure 1 person +person_opens_vehicle_door +1 vehicle +person_closes_vehicle_door +1 vehicle +1 vehicle and +person_enters_vehicle +1 person +1 vehicle and +person_exits_vehicle +1 person + +Possible +Additional +Tracks & +Type +N/A +N/A +N/A +N/A +1 Person +1 Person + +N/A + +1 vehicle + +1 Person + +person_closes_trunk + +1 vehicle + +1 Person + +person_loads_vehicle + +1 vehicle and +1 person + +1 or more +Other or Bag + +person_unloads_vehicle + +1 vehicle and +1 person + +1 or more +Other or Bag + +person_rides_bicycle +vehicle_turns_left +vehicle_turns_right +vehicle_makes_u_turn +person_sits_down +person_stands_up +person_reads_document + +person_transfers_object + +2 persons +1 person +1 person +1 person and 1 +bicycle +1 vehicle +1 vehicle +1 vehicle +1 person +1 person +1 person +1 person and 1 +person OR +1 person and 1 +vehicle + +Annotate person if/when person is visible +Annotate person if/when person is visible + +N/A + +person_opens_trunk + +persons_talks_to_person +person_talks_on_person +person_texts_on_phone + +When to use possible additional tracks + +Additional +people +N/A +N/A + +Annotate person if/when person is visible; a +vehicle-only open trunk is reserved for +automatic trunk opening, e.g. using a key +fob. +Annotate person if/when person is visible; a +vehicle-only close trunk is reserved for +automatic trunk opening, e.g. using a key +fob. +If the object(s) being loaded into the vehicle +meet the size criteria to annotate, they +should be annotated and tracked. +If the object(s) being unloaded into the +vehicle meet the size criteria to annotate, +they should be annotated and tracked. +If more than 2 People are talking, additional +tracks should be created for each Person +involved in the talking activity. + +N/A +N/A +N/A +N/A +N/A +N/A +N/A +1 or more +Other or Bag + +If the object(s) being transferred meet the +size criteria to annotate, they should be +annotated and tracked. + +18 + + person_picks_up_object + +person_puts_down_object + +person_carries_heavy_object + +hand_interacts_with_person + +person_embraces_person + +The object +being picked +up should +either be a +1 person and 1 "Bag" or +bag +"Other"; there +OR 1 person +should only be +and 1 other +two tracks in +OR 2 persons total in a single +and 1 other +"Person Picks +OR 2 persons Up Object" +and 1 bag +activity +N/A + +1 person and +1 bag +OR 1 person +and 1 other +OR 2 persons +and 1 other +OR 2 persons +and 1 bag + +The object +being set +down should +either be a +"Bag" or +"Other"; +there should +only be two +tracks in +total in a +single +"Person +Sets Down +Object" +activity + +1 person and +1 bag +OR 1 person +and 1 other + +The object +being +carried +should either +be a "Bag" +or "Other"; +check +conditions to +qualify as +"Person +Heavy +Carry" + +2 persons + +2 persons + +N/A + +1 or more +Persons + +If multiple Persons are carrying the object, +all Persons should be tracked and +included in a single "Person Heavy Carry" +activity. + +1 or more +Persons + +If more than 2 Persons are involved in the +"Hand Interaction" activity, additional +tracks should be created for those +Persons. + +1 or more +Persons + +If more than 2 Persons are involved in the +"Person-Person Embrace" activity, +additional tracks should be created for +those Persons. +An additional Person or Persons may be +involved in the Purchasing activity. For +example, a person purchases and object +for another person who receives said +object. In this instance, both Persons +should be tracked. + +person_purchanses + +1 person + +1 or more +Persons + +person_interacts_with_laptop + +1 person + +N/A + +19 + + vehicle_stops + +1 vehicle + +N/A + +vehicle_starts + +1 vehicle + +N/A + +vehicle_reverses + +1 vehicle + +N/A + +vehicle_picks_up_person + +1 vehicle and +1 person + +1 or more +Persons + +When multiple Persons are picked up by +the same vehicle, all Persons should be +tracked. + +vehicle_drops_off_person + +1 vehicle and +1 person + +1 or more +Persons + +When multiple Persons are dropped off by +the same vehicle, all Persons should be +tracked. + +person_abandons_package + +1 person and +1 bag OR 1 +person and 1 +other + +N/A + +person_steals_object + +1 person and +1 bag OR 1 +person and 1 +other + +1 or more +Other + +If multiple objects are stolen as part of a +single activity, the additional items should +be tracked. + +Coupled Activities +A number of activities are often coupled with another activity. ALL of these activities should be +annotated in each file. + +Activity + +Often Coupled With + +person_opens_facility_door + +person_enters_through_structure ​or +person_exits_through_structure + +person_opens_vehicle_door + +person_enters_vehicle ​or​ person_exits_vehicle; +person_loads_vehicle ​or​ person_unloads_vehicle; +vehicle_picks_up_person ​or​ vehicle_drops_off_person + +person_closes_facility_door + +person_enters_vehicle ​or​ person_exits_vehicle; +person_loads_vehicle ​or ​person_unloads_vehicle + +person_opens_trunk + +person_loads_vehicle ​or​ person_unloads_vehicle, +person_closes_trunk + +person_closes_trunk + +person_loads_vehicle ​or​ person_unloads_vehicle, +person_opens_trunk + +vehicle_picks_up_person + +vehicle_stops ​and/or​ vehicle_starts, +person_opens_vehicle_door ​and/or +20 + + person_closes_vehicle_door +vehicle_drops_off_person + +vehicle_stops ​and/or​ vehicle_starts, +person_opens_vehicle_door ​and/or +person_closes_vehicle_door + +21 + + Annotation Modifications +● + +Differing from V1/V2 annotations: +○ + +Activity centric annotations result in object tracts only during the duration of the +activity. + +○ + +State activities such as Standing, Crouching, Sitting, Walking, Running, and +Carrying from V1/V2 activities are not annotated. + +○ + +Additionally, the following V1/V2 activities are not annotated: Falling, Gesturing, +Distress, Aggressive, Digging, Using Tool, Kicking, Throwing, Umbrella, +Photographing, Object Transfer, Vehicle Construction Function, Miscellaneous, +and Interacts. + +○ + +Person_opens/closes_facility_door, +person_enters/exits_scene_through_structure, and +person_opens/closes_vehicle_door do not require door, doorway or person +tracks, respectively. + +○ + +Hand_interacts_with_person and person_embraces_person are subdivisions of +the V1/V2 activity Person-Person Interaction. + +Annotation Updates: +● + +Sept 1, 2018: Minimum duration added to the following activities: +○ + +Person Texting on Phone (2s) -- The goal is to avoid quick, sporadic use of phone +(e.g., use of phone as a clock or checking messages). + +○ + +Person Reading a Document (5s) + +○ + +Person-Laptop Interaction (5s) + +● + +Sept 10, 2018: Abandon object minimum size and excluded objects list added. + +● + +Oct 29, 2018: Minimum duration (5s) added to “People Talking” activity to improve +annotator inconsistencies and remove non-conversational, in-passing greetings. + +● + +Nov 5, 2018: Changes to entering/exiting activity annotations for camera. Details +documented in “Scene Specific Notes and Do Not Annotate Areas” + +● + +Nov 7, 2018: Additional details added to “Joining a Queue” activity definition to clarify +required tracks. + +● + +Dec. 6, 2018: Updated specificity of “Entering/Exiting Through Structure”, “Vehicle +U-Turn” and “Heavy Carry” activity definitions to have stair step requirements for +non-doorways, include turning around a divider, and include minimum track requirements. + +● + +March 13, 2019: Updated suggestions for second 20 activities. + +● + +March 14, 2019 +○ + +Updated “Person Standing Up” to include transition from prone position. +22 + + ○ +○ + +Updated “Hand Interac​tion” to explicitly exclude embracing, and clarify that event +ends 1 s after physical contact ​ends.​ + +Updated “Person-Person Embrace” to clarify that event ends 1 s after physical +contact ​ends.​ + +● + +April 1, 2019: Added table of coupled activities. + +● + +April 29, 2019: Definition of “other” track type clarified +○ + +Reading document end of activity clarified to include active or passive end of +activity. Start of activity updated to require attention on reading material rather +than a “reading position. + +○ + +Person Picks up object: Removed text on continuing object track (remnant from +V1/V2 annotations) for clarity. Also clarified what the excluded and included lists +mean. + +○ + +Person sets down object: Removed text on continuing object track (remnant from +V1/V2 annotations) for clarity. Added “when arm motion begins” to start criteria. + +○ + +Removed “Joining a queue,” “vehicle moving” and “casing facility” from annotated +activities list. + +○ + +Person purchasing: Clarified definition of “payment transaction”; clarified how the +start/end criteria can be reversed. + +○ + +Person laptop interaction: Changed activity end criteria to make endpoint clearer; +either the laptop has been closed or attention has been shifted away from laptop +for 5 seconds or more. + +● + +○ + +Removed “pickpocketing” as a variation of theft activity. + +○ + +Added table of track requirements by activity type. + +May 16, 2019: Removed “Rare/threat” label for abandon package and theft activities; they +are now simply included in the full list of activities. + +● + +May 22, 2019: Updated activities to include the activity labels that are consistent with +annotation files (json schema). Spaces were replaced with an underscore (“_”) and some +capital letters were made to be lower case. + +● + +July 11, 2019: +○ + +Modifications to “Vehicle Picks Up Person” and “Vehicle Drops Off Persion” for +optional vehicle motion. + +○ + +Modifications to “Person Picks Up Object” and “Person Sets Down Object” for the +pick up/set down of multiple objects. + +○ + +Updated start and end times of “Person Picks Up Object” and “Person Sets Down +Object”. + +○ + +Update the “Person Picks Up Object” and “Person Sets Down Object” to allow for +multiple people. Updated track requirements table to match. + +● + +September 25, 2019 + +23 + + ○ + +Updated label for “Person Enters Through Structure” and “Person Exits Through +Structure” to “Person Enters Scene Through Structure” and “Person Exits Scene +Through Structure” + +○ + +Clarified Person Laptop Interaction when multiple people are interacting with the +same laptop. + +○ +● + +Corrected typos (missing articles) + +January 15, 2020 +○ + +Updated Riding to clarify that two people on a single bike is one instance of a +riding activity with two people. + +● + +February 18, 2020 +○ + +● + +February 25, 2020 +○ + +● + +Updated to new, more consistent activity names. +Umbrellas explicitly delineated as not a “heavy object”. + +April 10, 2020 +○ + +“The (un)loading of multiple objects by the same person with a pause of no more +than 1 second will be annotated as one continuous activity.” added to +person_loads/unloads_vehicle definition to make it consistent with other activity +definitions. + +● + +June 1, 2020 +○ + +No changes made; active review of all definitions. + +24 + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/MEVA_dataset.txt b/docs/project-history/source-text-extracts/docs_convert/MEVA_dataset.txt new file mode 100644 index 0000000..095932d --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/MEVA_dataset.txt @@ -0,0 +1,644 @@ +MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity +Detection + +arXiv:2012.00914v1 [cs.CV] 2 Dec 2020 + +Kellie Corona, Katie Osterdahl, Roderic Collins, Anthony Hoogs +Kitware, Inc. +1712 Route 9, Suite 300, Clifton Park, NY 12065 USA +{firstname.lastname}@kitware.com + +Figure 1. Examples of MEVA data, showing an approximately synchronized view in four data modalities. Left-to-right:a 4x3 montage of +RGB and thermal IR cameras; GPS locations of about 90 actors overlaid on five minutes of GPS tracks; a cropped view from a UAV. + +Abstract +We present the Multiview Extended Video with Activities +(MEVA) dataset[6], a new and very-large-scale dataset for +human activity recognition. Existing security datasets either focus on activity counts by aggregating public video +disseminated due to its content, which typically excludes +same-scene background video, or they achieve persistence +by observing public areas and thus cannot control for activity content. Our dataset is over 9300 hours of untrimmed, +continuous video, scripted to include diverse, simultaneous +activities, along with spontaneous background activity. We +have annotated 144 hours for 37 activity types, marking +bounding boxes of actors and props. Our collection observed approximately 100 actors performing scripted scenarios and spontaneous background activity over a three- + +week period at access-controled venue, collecting in multiple modalities with overlapping and non-overlapping indoor and outdoor viewpoints. The resulting data includes +video from 38 RGB and thermal IR cameras, 42 hours of +UAV footage, as well as GPS locations for the actors. 122 +hours of annotation are sequestered in support of the NIST +Activity in Extended Video (ActEV) challenge; the other 22 +hours of annotation and the corresponding video are available on our website, along with an additional 306 hours of +ground camera data, 4.6 hours of UAV data, and 9.6 hours +of GPS logs. Additional derived data includes camera models geo-registering the outdoor cameras and a dense 3D +point cloud model of the outdoor scene. The data was collected with IRB oversight and approval and released under +a CC-BY-4.0 license. + + 1. Introduction +It has been estimated that in 2019, 180 million security +cameras were shipped worldwide [9], while the attention +span of a human camera operator has been estimated at only +20 minutes [5, 3]. The gap between the massive volume of +data available and the scarce capacity of human analysts has +been closed, but not eliminated, by the rapid advancement +of computer vision techniques, particularly deep-learning +based methods. Fundamental progress is often spurred by +datasets such as ImageNet [15] and MS COCO [8] for object recognition and MOT16 [10] and Caltech Pedestrian [2] +for pedestrian detection and tracking. +However, as discussed in Section 2, datasets for action +recognition typically do not address many of the needs of +the public safety community. Datasets such as AVA [4], +Moments in Time [11], and YouTube-8m [1] present videos +which are short, high-resolution, and temporally and spatially centered on the activities of interest. Rigorous research and evaluation of activity detection in public safety +and security video data requires a dataset with realistic spatial and temporal scope, yet containing sufficient instances +of relevant activities. In support of evaluations such as +the NIST Activities in Extended Video (ActEV) [12], we +designed the MEVA dataset to explicitly capture video of +large groups of people conducting scripted activities in realistic settings in multiple camera views, both indoor and +outdoor. We defined 37 activity types, shown in Figure 2, +ranging from simple, atomic single-actor instances to complex, multi-actor activities. Our fundamental resources +included approximately one hundred actors on-site at an +access-controlled facility with indoor and outdoor venues +for a total of around three weeks, recorded by 38 groundlevel cameras and two UAVs. Additionally, actors were +provided with GPS loggers. We conducted extensive precollect planning, including a pilot collection exercise, to +develop the appropriate level of actor direction required to +maximize instances of our 37 activity types while maintaining realism and avoiding actor fatigue. +The final dataset contains over 9300 hours of groundcamera video, 42 hours of UAV video, and over three million GPS trackpoints. In support of the ActEV evaluation, +we have annotated 144 hours of this video for the 37 activity +types. While most of this data is sequestered for ActEV, we +have released 328 hours of ground-camera data, 4.6 hours of +UAV data, and 22 hours of annotations via the MEVA website. The dataset design and collection underwent rigorous +IRB oversight, with all actors signing consent forms. The +data is fully releasable under a Creative Commons Attribution 4.0 (CC-BY-4.0) license, and we believe represents by +far the largest dataset of its kind available to the research +community. +Figure 1 shows a sample of data available from the +MEVA website, approximately synchronized in time, col- + +lected during a footrace scenario. The left side shows a +montage of 12 of the 29 released RGB and thermal IR cameras, illustrating the diversity in locations, settings (indoors +and outdoors), as well as overlapping viewpoints. The middle illustration plots approximately 90 GPS locations on a +background image. The right image is a crop from UAV +footage. +The paper is organized as follows. Sections 2 places our +work in context with similar efforts. Section 3 discusses +how we desgined the dataset to maximize realism and activity counts. Section 4 describes our annotation process. +Section 5 briefly describes the ActEV leaderboard results +on MEVA. + +2. Prior Work +We distinguish ”focused” activity recognition datasets +such as [4, 11, 1], which typically contain single, short activities, from security-style video, which is typically longduration and ranges from long stretches of low or no activity to busy periods with high counts of overlapping activities. Figure 3 shows how MEVA advances the state of +the art along several security dataset factors, notably: duration, number of persistent fields of view (both overlapping +and singular), modalities (EO, thermal IR, UAV, hand-held +cameras, and GPS loggers), and annotated hours. The UCFCrime dataset [17] presents real incidents from real security +cameras, but only from a single viewpoint at the point of activity; little or no background data is available. The VIRAT +Video Dataset [13] contains both scripted and spontaneous +activities, but without overlapping viewpoints. The Duke +MTMC dataset [14] presents security-style video from multiple viewpoints, but with only spontaneous, unscripted activity. + +3. Dataset Design +The MEVA dataset was designed to capture human activities, both spontaneous and scripted, in an environment +as close as possible to that encountered by deployed CCTV +systems in the real world. The dataset was designed to +be realistic, natural and challenging for security and public +safety video research in terms of its resolution, background +clutter, visual diversity, and activity content. As discussed +in Section 3.1, the data is fully releasable under a Create +Commons Attribution 4.0 (CC-BY-4.0) license, facilitated +by a detailed human subjects research plan. Activity diversity was achieved through pre-collect scripting for scenarios +and activity types (Section-3.2), and collected via an ambitious camera plan with 38 ground-level cameras and two +UAVs (Section-3.3). Scene diversity was accomplished by +scripting scenarios to occur at different times of day and +year to create variations in environmental conditions, and +directing demographically diverse actors to perform activi- + + person abandons package * +hand interacts with person +person reads document +vehicle picks up person +person carries heavy object +person interacts with laptop person rides bicycle +vehicle reverses +person closes facility door +person loads vehicle +person puts down object vehicle starts +person closes trunk +person transfers object +person sits down +vehicle stops +person closes vehicle door +person opens facility door +person stands up +vehicle turns left +person embraces person +person opens trunk +person talks on phone +vehicle turns right +person enters scene through structure person opens vehicle door +person texts on phone +vehicle makes u turn +person enters vehicle +person talks to person +person steals object * +person exits scene through structure +person picks up object +person unloads vehicle +person exits vehicle +person purchases +vehicle drops off person +Figure 2. List of the 37 activities defined and annotated in MEVA. Activities marked with an asterisk are threat-based activities. + +VIRAT [13] + +UCF-101 Untrimmed [16] + +Duke MTMC [14] + +UCF-Crimes [17] + +MEVA + +Number of activity types +23 +101 +13 +37 +Range of samples per type +10-1500 +90-170 +50-150 +5-750 (1200) +Incidental objects and activities +yes +no +yes +yes +yes +Natural background behavior +yes +no +yes +yes +yes +Tight bounding boxes +yes +no +no +no +yes +Max resolution +1920x1080 +320x240 +1920x1080 +320x240 +1920x1080 +Sensor modalities +1 +1 +1 +2 +5 +Security +yes +no +yes +yes +yes +Number of FOV +17 +unique-per-clip +8 +unique-per-clip +28 +Overlapping FOV +no +no +yes +no +yes +Indoor & outdoor +no +yes +no +yes +yes +Availability +direct +reference +retracted +direct +direct +Clip length +2-3 minutes +1-71 seconds +4 minutes +5 minutes +Dataset duration (hours) +29 +26.6 +10 +128 +9300 / 144 +Figure 3. Comparison of characteristics of activity and security datasets to MEVA. The reported range of activity counts per type is only +for MEVA released data while the average number of samples in parenthesis includes both released and sequestered annotations. The 5 +sensor modalities included in the MEVA dataset are static EO, UAV EO, Thermal IR, Handheld or Body-Worn Cameras (BWC), and GPS. +There are 28 unique FOV with additional FOVs offered by drone, handheld and BWC footage. The overall duration of video collected as +part of MEVA is 9300 hours while 144 hours were annotated. + +ties in multiple locations with varied natural behaviors. The +ground-camera plan includes a variety of camera types, indoor and outdoor viewpoints, and overlapping and singular fields of view. Realism was enhanced by scripting to +include naturally occurring activity instances, frequent incidental moving objects and background activities, as discussed in Section-3.2. Finally, the quantity of data collected was enabled by planning for a multi-week data collection effort to allow for a large set of activity types with a +large number of instances per activity class as discussed in +Section-3.3. + +3.1. Releasability +Figure 4. Site map with released cameras and approximate fieldsof-view. Indoor cameras are blue circles; triangles are outdoor +cameras. Red are co-located EO/IR; blue is fixed-focal-length; +green is PTZ in stationary mode; purple is PTZ in patrol mode. +Pink fields of view are outdoors; yellow are indoors. + +A critical requirement for the MEVA dataset is broad releasablility to the research community. Possible restrictions +include prevention of commercial use, which discourages +commercial companies from using the dataset for research +or collaborating on teams which do use the dataset. Another +restriction is when data containing human activity is collected without oversight of an Institutional Review Board +(IRB) or collected outside of its initially IRB-approved conditions. While the data may address other needs of the re- + + search community, lack of proper IRB oversight or participant consent can render the data unusable, wasting significant resources in the collection and curation process. +The MEVA dataset was carefully crafted to avoid such +restrictions to maximize its value to the research community. The data collection effort was overseen by an IRB to +ensure that the data collected would be usable. We wrote +a human subjects research protocol and informed consent +documents that detailed the collection process, what types +of data would be collected, how the data will be released +and may be used, and the risks and benefits to anyone consenting to participate in the study. The informed consent +document was provided to prospective participants in advance and reviewed in an informed consent briefing session +prior to beginning the data collection. Included in the protocol and consent briefing was a sample license that may +be used for the data, a CC-BY 4.0, to make it fully clear +the intent and potential future use of the data. The consent +presentation was followed by small group discussions and +question/answer periods to thoroughly address and answer +any participant concerns prior to them consenting to participate the research. Once the individuals who voluntarily +chose to participate signed the consent forms, we were able +to begin the data collection process. +To further ensure broad releasability of the data, the collection occurred at an access-controlled facility to minimize +non-participating individuals entering the fields of view of +the cameras and appearing in the data. + +3.2. Scripting +To guarantee collection of minimum activity instance +counts, ensure diversity in the data, and produce the most +realistic video data, MEVA data was collected for both +scripted and unscripted activities. The scripting challenges +for the data collection effort can broadly be broken down +into two categories: (1) scripting for satisfying program requirements such as quantity and diversity, and (2) scripting +for realism in activity behavior. +It was critical to design the data collection to represent a +variety of realistic scenarios in ground-based security video +collection. We determined seven overarching scenarios of +interest to the public safety community. For example a +footrace with multiple phases including registration, participant arrival, race and cleanup. The scenarios were collected +over a period of three weeks on different days and at different times of days to capture video variations due to weather +and lighting. At the extreme, two sub-collects in March and +May produced dramatic contrast in weather from snow to +extreme heat, and thus diversity in natural human behavior (e.g., clustering together in the cold) and wardrobe (i.e., +jackets in the winter and t-shirts in the summer). +Scripted into these scenarios were 37 primitive, complex +and threat-based human activity types to ensure minimum + +Figure 5. Two approximately synchronized views from the gym. + +instance counts useful to the program; these are listed in +Figure 2. These activities were scripted to be performed by +actors with different age, sex and ethnicity as allowed by +the actor pool. Activities involving vehicles or props were +specifically scripted to rotate the vehicle pool and theatrical +property. Scripted activities occurred across various scenarios and at multiple locations in the facility. Activities were +scripted in overlapping and singular fields of view, indoor +and outdoor cameras, and various camera types (e.g., stationary EO and IR, roaming EO). Activities were scripted +to be performed by the same individual in different, singular cameras with the goal of associating the same individual +across different cameras. This is relevant when a central +activity is detected involving a subject, and then a derived +complex activity is to detect other individuals that meet with +the original subject of interest. Scenarios also included confusers for the scripted activity types to add value for performer evaluation in the final dataset. Figure 5 shows two +approximately synchronized views from the gym. +Though scenarios had an overarching theme, multiple iterations of the same scenario (called takes) were designed to +produce visibly distinct but comparable results by varying +the parameters described above. Additionally, actor density at various location in the facility and traffic flow were +modified to produce differences in subsequent takes of the +same scenario. Scripting the scenarios and activities in this +level of detail guaranteed the diversity and quantity of data + + satisfied program requirements. +To address the second broad goal, we hired professional +actors to act scenarios in a natural manner to create an +equivalent challenge for computer vision event detectors, +as if the events occurred in real-life scenarios without acting. The actors were divided into squads which each had a +squad lead in charge of managing a group of 5-8 individuals. The squads were designed, grouped and shuffled to produce variations in demographics, behaviors and inter-actor +behaviors. Each squad was given a direction card which +provided information on timing and locations for activities +to be performed, and group behavior such as social groupings within the squad. By giving the squad lead the ability +to designate roles within the group, natural social dynamics, such as those observed in married couples or families, +were preserved and increased the realism of the collected +data. The squad lead was responsible for assigning roles to +the actors, managing vehicle and prop assignment, providing feedback to individual actors for corrections on activity +behavior, and reporting descriptions of complex or threatbased activities for use in expediting later annotation. +We found that providing scripting at the squad rather +than individual level produced the most realistic actor behavior. When actor behavior was scripted on the individual level at high temporal and spatial resolution, the actors, +overwhelmed by the details, focused on achieving all activities at the specified time and location rather than performing activities that were natural in appearance. Using +a small pilot collect of data, we were able to determine +which activities occurred naturally in the data collection +without direction (e.g., person opens door). Taking this +into account when scripting, lower instances of these activities were scripted and a mix of scripted and unscripted +occurrences were collected. On the other hand, rare and +threat-based activities needed to be specifically scripted to +ensure minimum counts of these activities were achieved +and later located for annotation. However, allowing the actors to have artistic licence with how to accomplish activities, especially complex activities, produced more a natural +and diverse dataset. Actor familiarity with the scripted scenario also increased realism. Most scenarios varied in time +from 15 minutes to 1 hour. Multiple takes could be run consecutively with minor modifications to scripted behaviors +and activity instances, and minimal reset time (5 to 10 minutes) to efficiently produced varied instances of a scenario +and communicate modifications to the squad leads. +In addition to activity-focused scripting, scene-level behaviors also needed to be scripted to ensure realism. For +example, train and bus arrival schedules were designed to +reflect public transit schedules scaled for the population depicted by our actors. Vehicle traffic was scripted to have +ebbs and flows associated with similar patterns to real-world +versions scripted scenarios. For example, an increase in ve- + +Figure 6. Enrollment photos were taken daily of all actors, with +(a) and without (b) outerwear, from the front and back. Photos +included actor numbers, which correspond to their unique GPS +logger number for reidentification purposes. + +hicles dropping off people at the bus station just prior to a +bus’s scheduled arrival. Driving routes, vehicles and drivers +were shuffled to provide variations between different scenarios and takes. Again, these tasks were assigned at the +squad level and managed by squad leads. +In addition to scripted scenarios, periods of completely +unscripted data were collected with the aim of collecting +naturally occurring common activities with no actor direction. We also took advantage of unscripted data to interject threat-based and rare activities to capture undirected +response by casual observers. These unscripted times were +naturally occurring and added value to reset times between +scenario takes. + +3.3. Collection +The MEVA video dataset was collected over a total +of three weeks at the Muscatatuck Urban Training Center +(MUTC) with a team of over 100 actors and 10 researchers. +MUTC is an access-controlled facility run by the Indiana +National Guard that offers a globally unique, urban operating environment. The real and operational physical infrastructure, including curbed roads and used buildings, set it +apart from other access-controlled facilities for collecting +realistic video data. +The camera infrastructure included commercial-off-theshelf EO cameras; thermal IR camerasas part of several EOIR pairs, two DJI Inspire 1 v2 drones, and a range of video +and still images from handheld cameras used by the actors. +The fields of view, both overlapping and non-overlapping, +capture person and vehicle activities in indoor and outdoor +settings. +Onsite staging and actor instruction also improved the +realism in scenes for the MEVA scenario collection. Areas +were staged with props, such as signs, tables, chairs, and +trashcans, to provide scenario-specific context necessary to +increase the natural appearance and usefulness of the area to +both actors and recording cameras. Additionally, areas were + + Figure 6. The photos, paired with the GPS logs, enable +reidentification of individual actors across fields of view in +a single scenario, as seen in Figure 7. Each day before filming, squad leads were given direction cards to assign roles +to their actors and inform prop distribution, then data collection was performed. Shorter scenario takes of less than +1 hour were performed in rapid succession with only minor modifications relayed during a brief reset. One day +short-scenario data collection would contain between 4 and +8 takes; for longer scenarios of approximately 8 hours, the +scenario would be repeated across multiple sequential days. +Direction and course correction was provided by both directly interjecting a MEVA team member into the scenario +for a brief interval or incorporating actor briefings naturally +into the scenario schedule. +The final dataset contains 9,303 hours of ground-camera +video (both EO and thermal IR), 42 hours of UAV footage, +and 46 hours from hand-held and body-worn cameras. Additionally we collected over 2.7 million GPS trackpoints +from 108 unique loggers. + +4. Annotation + +Figure 7. The actor in the purple jacket, actor 544 in Figure 6, is +visible in multiple cameras during the scenario. Her height is (a) +301 pixels, (b) 118 pixels, (c ) 176 pixels, and (d) 89 pixels in each +of the respective fields of view. + +staged to look similar but distinct between different scenarios to add diversity to the dataset collected. For example, +the foyer of one building was converted into an operational +cafe where the actors were able to grab drinks and snacks +during scripted or unscripted camera time. +Actors were selected to provide a diverse pool of individuals in background, ethnicity and gender. As part of our iterative process to receive and incorporate feedback, all actors +attended briefings in which camera fields of view and common issues (e.g., erratic driving or aimless walking) were +discussed in a group setting, raising awareness of key factors required to collaboratively produce realistic behaviors +in video. Squad leads also held smaller briefings, providing +feedback to the scripting team and receiving instructions to +pass to their squad members. During morning briefings, +all actors and MEVA team members were registered with +a unique identifying number matched to their GPS logger +unit and wardrobe enrollment photograph(s) as shown in + +Annotating a large video dataset requires balancing quality and cost. Annotating for testing and evaluation, as is +the case for MEVA, must prioritize the quality of ground +truth annotations, as any evaluation will only be as reliable +as the ground truth derived from the annotations. Ground +truth annotations must minimize missed and incorrect activities to reduce the potential for penalizing correct detections, strictly adhere to activity definitions, and address +corner cases to reduce ambiguity in scoring. We annotated +MEVA by first localizing spatio-temporal activities and then +creating bounding boxes for objects involved in the activity. +We optimized a multi-step process for quality and cost: (1) +an annotation step to identify the temporal bounds for activities and objects involved, (2) an audit step by experts to ensure completeness and accuracy of the activity annotations, +(3) a crowdsourced method for bounding box track creation +for objects, and (4) a custom interface for rapid remediation of corner cases by experts, with automated checks for +common human errors between each of these steps. + +4.1. Activity Annotation +We annotated MEVA through a sequential process of activity identification by a trained, dedicated team of thirdparty annotators, followed by a quality control audit from +an internal team of MEVA experts intimately involved in +defining the activities. The initial activity identification step +detects all MEVA activities in a 5 minute video clip, specifying the start and end times for each activity, and identifying the initial and final location for each actor or object +involved. The subsequent audit step catches any missed or + + 4.2. Track Annotation + +Figure 8. Half an hour of activities overlaid on a background image. 112 activities, 17 types, ranging from 1 to 14 instances. + +incorrectly labeled activity instances, and confirms strict adherence to MEVA definitions. +Ambiguity in activity definitions is a fundamental annotation challenge. To minimize this, we define activities very +explicitly and require that events are always annotated on +pixel evidence and not human interpretation. We developed +detailed annotation guidelines for each activity, including +definition of the start and end times for an activity, and the +actors and objects involved. Definitions also include extensive discussion of corner cases. +We explored several methods to increase annotation efficiency and quality, including completely crowdsourced +methods on Amazon Mechanical Turk (AMT) and a solely +in-house team of experts. The optimized annotation process +used a team of third-party annotators dedicated to MEVA +annotation to lower the overall cost and supply surge capacity via dynamic team scaling while still guaranteeing quality +results. After comparing the results of multiple annotators +performing activity annotation in parallel and combined results in a post-processing step with annotating in series with +a quality assurance step, serial annotation was selected to +enable efficiently annotating a larger dataset. +Annotation consistency is essential to providing highquality ground truth annotations. When using a diverse +team of annotators, it was essential to have a clear set +of guidelines and iterative definition modifications to include corner cases to ensure annotator agreement across +the team. Additionally, we found that project-specific training, including effective use of the annotation tools, in-depth +discussion of definitions including examples, and iterative +feedback on annotation quality produced improvements in +annotation quality and speed. Finally, a quality audit from +an internal team of MEVA annotators was used to guarantee +no missed or incorrect instances. We observed a 3-fold increase in audit efficiency over a two month period of working in a tight feedback loop with these procedures and a +project-dedicated team of annotators. + +Once activities and participating objects were identified by MEVA experts, objects were tracked with bounding +boxes for the duration of their involvement in the activity. +The annotation of bounding boxes was primarily conducted +through crowdsourced annotation on AMT, via an iterative +process of bounding box creation and quality review. +In the first step of bounding box refinement, videos were +broken up into segments that corresponded to a single activity annotation. An AMT task was created for each activity, +displaying a set of start and end boxes with linearly interpolated boxes on intervening frames for all objects involved in +the activity. Workers were instructed to complete the tracks +by annotating bounding boxes for the interpolated frames +which were as tight as possible around the object; for example, all the visible limbs of a person should be in the +bounding box while minimizing ”buffer” pixels. These two +characteristics are required in the MEVA dataset to ensure +that high-quality activity examples with minimal irrelevant +pixels are provided for testing and evaluation. These traits +were enforced by using other AMT annotators to assess the +quality of the resulting tracks. +In the quality review step, the bounding box annotations +were shown to several AMT workers who were asked to +evaluate them as acceptable according our guidelines. If +agreement was achieved between the AMT workers, then +the results were considered acceptable and complete; otherwise, a new AMT task would be created for bounding +box refinement. The results of the next round of refinement would be provided for quality review, and the process would repeat until acceptable annotations were produced or a threshold number of iterations were performed. +If the threshold number of iterations were performed, the +clips would be vetted and edited manually as needed by an +MEVA auditor. The percentage of activities requiring expert intervention was less than 5%. Specialized web interfaces were developed for each of these tasks to allow +workers (AMT or in-house) to easily provide the necessary +results. In order to eliminate most systematic low quality +jobs, annotations were sampled and workers were allowed +to continue based on quality. +Figure 9 illustrates bounding box annotations for tracks +from a variety of activities and fields of view that demonstrate the quality produced by our track annotation procedure. Box size varied dramatically due to the scale variation in the video, with a mean area of 13559±23799. Annotations span 5 track types (person, vehicle, other, bag, +and bicycle) with a distribution of 90.71%, 4.5%, 4.51%, +0.15%, and 0.05%, respectively. As part of quality control, +we compared MEVA annotations against high-confidence +performer false alarms from the ActEV evaluation; our false +negative rate (i.e., confirmed missed instances to total activity count in reviewed clips) is less than 0.6% . + + Figure 9. Examples of activities and tracks from diverse fields of +view. The font size in each images is consistent, indicating varying +scale of the annotations. + +Figure 11. The NIST ActEV leaderboard as of 9 Nov 2020, computed on MEVA data. Better performance is lower and to the left. + +ities which were not detected for at least one second, vs. +Time-based False Alarm (TFA), the proportion of time the +system detected an activity when there was none. Figure 11 +shows results current at time of writing for nine teams plus +a baseline implementation [7] based on RC3D [18]. As +the scores indicate, the MEVA dataset is very difficult compared to related datasets, presenting abundant opportunities +for innovation and advancement in activity detection, tracking, re-identification and other problems. + +Figure 10. Visualization of the fine-grained 3D point cloud model +of the collection site. + +Additional Data In addition to the annotations, we +have provided supplemental data such as camera models +for the camera models which register into a common georeferenced coordinate system. We have also provided a 3D +model of the outdoor component of the collection site, provided as a PLY file and visualized in Figure 10. + +5. Baseline Activity Detection Results +The NIST ActEV challenge [12] is using the MEVA +dataset for its ongoing Sequestered Data Leaderboard +(SDL). The ActEV challenge defines three tasks: Activity Detection (AD) with no spatial localization in the video, +Activity and Object Detection (AOD) where the activity and +participating objects are spatially localized within a frame +but not necessarily correlated across frames, and Activity +/ Object Detection and Tracking (AODT), which extends +AOD to establish real-world activity and object identity +across frames. The current leaderboard is for AD, scored +using Probability of Miss (pMiss), the proportion of activ- + +6. Conclusion +We have presented the MEVA dataset, a large-scale, realistic video dataset containing annotation of a diverse set +of visual activity types. The MEVA video dataset surpasses +existing activity detection datasets in hours of video, number of cameras providing overlapping and singular fields +of view, variety of sensor modalities, and broad releasability. The dataset also provides a substantial 144 hours of +evaluation-quality activity annotations of scripted and naturally occurring activities. We believe that with these traits +the MEVA dataset will stimulate diverse research within the +computer vision community. The MEVA dataset is available at: http://mevadata.org. +Acknowledgement: This work is supported by the Intelligence Advanced Research Projects Activity (IARPA) +via contract 2017-16110300001. The U.S. Government is +authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation +thereon. Disclaimer: The views and conclusions contained +herein are those of the authors and should not be interpreted +as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA or the U.S. +Government. + + References + +[14] Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, +and Carlo Tomasi. 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Mot16: A benchmark for multi-object +tracking. arXiv preprint arXiv:1603.00831, 2016. +[11] Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, +Quanfu Fan, Dan Gutfreund, Carl Vondrick, et al. Moments +in time dataset: one million videos for event understanding. +IEEE transactions on pattern analysis and machine intelligence, 42(2):502–508, 2019. +[12] NIST. ActEV: Activities in Extended Video. https:// +actev.nist.gov. +[13] Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, C.-C. Chen, Jong Taek Lee, Saurajit Mukherjee, +J. K. Aggarwal, Hyungtae Lee, Larry Davis, Eran Swears, +Xioyang Wang, Qiang Ji, Kishore Reddy, Mubarak Shah, +Carl Vondrick, Hamed Pirsiavash, Deva Ramanan, Jenny +Yuen, Antonio Torralba, Bi Song, Anesco Fong, Amit RoyChowdhury, and Mita Desai. A Large-scale Benchmark +Dataset for Event Recognition in Surveillance Video. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2011. + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/MEVID.txt b/docs/project-history/source-text-extracts/docs_convert/MEVID.txt new file mode 100644 index 0000000..22d5a68 --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/MEVID.txt @@ -0,0 +1,1308 @@ +MEVID: Multi-view Extended Videos with Identities +for Video Person Re-Identification + +arXiv:2211.04656v2 [cs.CV] 10 Nov 2022 + +Daniel Davila* Dawei Du∗ Bryon Lewis Christopher Funk Joseph Van Pelt +Roderic Collins Kellie Corona Matt Brown Scott McCloskey Anthony Hoogs Brian Clipp +Kitware, NY & NC, USA +https://github.com/Kitware/MEVID + +Abstract +In this paper, we present the Multi-view Extended Videos +with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild. To our knowledge, MEVID represents the most-varied video person ReID +dataset, spanning an extensive indoor and outdoor environment across nine unique dates in a 73-day window, various +camera viewpoints, and entity clothing changes. Specifically, we label the identities of 158 unique people wearing +598 outfits taken from 8, 092 tracklets, average length of +about 590 frames, seen in 33 camera views from the verylarge-scale MEVA person activities dataset. While other +datasets have more unique identities, MEVID emphasizes +a richer set of information about each individual, such as: +4 outfits/identity vs. 2 outfits/identity in CCVID, 33 viewpoints across 17 locations vs. 6 in 5 simulated locations +for MTA, and 10 million frames vs. 3 million for LS-VID. +Being based on the MEVA video dataset, we also inherit +data that is intentionally demographically balanced to the +continental United States. To accelerate the annotation process, we developed a semi-automatic annotation framework +and GUI that combines state-of-the-art real-time models for +object detection, pose estimation, person ReID, and multiobject tracking. We evaluate several state-of-the-art methods on MEVID challenge problems and comprehensively +quantify their robustness in terms of changes of outfit, scale, +and background location. Our quantitative analysis on the +realistic, unique aspects of MEVID shows that there are significant remaining challenges in video person ReID and indicates important directions for future research. + +1. Introduction +Searching a corpus of video for a person of interest +has applications in public safety and security (airports, +* Equal Contribution + +Figure 1. A comparison of MEVID (highlighted in green) to prior +person ReID datasets [5, 32]. Our MEVID dataset significantly +increases the diversity of location, viewpoint, number of outfits +per person, scales on target, and overall number of tracklets for +video person ReID. + +stadiums, fairs), retail customer-behavior analysis, and in +search and rescue. Underpinning the task of automated +person search is the sub-task of video-based person reidentification (ReID). Real-world person ReID is challenging due to complex variations of viewpoint, appearance +(such as changes of clothing), person pose, lighting, occlusion, resolution, background, and crowd density settings. +Various datasets have attempted to provide a strong baseline for solving the person ReID problem, as detailed in +Sec. 2. However, existing works have failed to capture the +full set of aforementioned challenges expected during person search deployed on real-world surveillance video. +To address this gap, we develop the large-scale Multiview Extended Videos with Identities (MEVID) dataset. +MEVID provides an extensive and diverse dataset for video +person re-identification across a variety of scales, locations, +viewpoints, and changes of clothes for each individual target, as shown in Figure 1. We build on top of the MEVA +dataset [1], which includes hundreds of hours of video in +complex indoor/outdoor scenarios, with rich annotations of +activities and the tracklets of individuals taking part in those +activities. This prior work recorded 176 actors for three + + weeks in March and May of 2018, wearing on average 20 +different outfits, performing scripted scenarios and spontaneous background activity. We create the new and challenging MEVID dataset by tracking individuals in these videos +to generate person tracklets and then linking these tracklets +across viewpoints, locations, days, and outfits throughout +the weeks of data collection. MEVA was collected at an +access-controlled facility in full compliance with Human +Subject Research (HSR) guidelines with all actors signing +IRB-approved consent forms; MEVID inherits this compliance. +Adding global identity information to the activities already annotated in MEVA facilitates the development and +evaluation of a number of interesting challenge problems. +This includes all of the traditional person ReID problems: +image-to-image, image-to-video, and video-to-video. Further, we label each change of clothing for each actor in the +dataset, allowing evaluation of change-of-clothing ReID. +The change-of-clothing scenario is ubiquitous in practical +applications of person search but only supported in the literature by a few limited-scope datasets, see Section 2 for +discussion. In brief, we split MEVID into non-overlapping +train and test sets. The train set (6 dates over 9 days) contains 104 global identities with 485 outfits in 6, 338 tracklets, and the test set (3 dates over 5 days) includes 54 +global identities with 113 outfits in 1, 754 tracklets. A detailed comparison of the MEVID dataset with other related +datasets is presented in Table 1. Since MEVID is built on +top of the MEVA activity dataset, our person labels overlap with the activity labels, and our dataset can be used to +identify how many activities are done by the same individual. This will enable detecting compositional, complex activities performed by an individual across different camera +views, large spans of time, and long distances. +This work describes the development of MEVID and associated challenges problems. We use MEVID to advance +state-of-the-art methods for video-based person ReID, including change-of-clothing ReID, change-of-scale ReID, +and change-of-location ReID. Ours is the first paper to evaluate the ability of change-of-clothing ReID across multiple locations, viewpoints, scales, and ambient conditions in +the wild. In our experiments (Sec. 4), we discuss and analyze 10 existing state-of-the-art video person ReID methods [2, 4–7, 20, 24, 26, 29]. Finally, we summarize corresponding issues and future research directions. We hope +such comprehensive analysis will boost the research in +video person ReID in the wild. The contributions of this +paper are summarized as follows: +• We introduce a video person ReID dataset surpassing +all others in data variety and difficulty by annotating +unique person identities associated with tracklets extracted +from a large-scale video collection (MEVA). +• We show key shortcomings of state-of-the-art video + +ReID methods in performance on change-of-clothes scenarios, change-of-scale, and change-of-location. +• We release the full annotation process and software +stack as open-source, including the first open-source +tool for video ReID annotation that was used to develop +the dataset to facilitate the further development of person +tracking, search, and activity recognition datasets. + +2. Related Work +Video person ReID datasets. Our goal with MEVID is to +facilitate the training and evaluation of algorithms for video +person ReID in the wild. Several datasets exist in the literature for image-to-image and image-to-video ReID, such as +CUHK01-03 [14], PRW [34], and CSM [8]. There are also +pure video person ReID datasets, including MARS [32], +iLIDSVID [12], LS-VID [11], P-DESTRE [10], and synthetic MTA [9]. However, all of them suffer from a lack +of diversity in terms of viewpoints, changes of clothes for +each person, scale, and changes in background. Recently, +Gu et al. [5] produced the first video change-of-clothing +ReID (CCVID) dataset. This work isolates the impact of +clothing changes on the task of video ReID by fixing viewpoint, background, and scale to a relatively limited range +while capturing video of actors in multiple outfits. While +this demonstrates the shortcomings in the state-of-the-art +for CCVID, this work limits the ability to develop models +for a wider set of conditions of diversity in locations, actors, viewpoints, both indoor and outdoor scenes, and target +scale between query and gallery. +Video person ReID methods. Different from image person ReID [16, 17, 19, 28] relying on a still image, the goal +of video person ReID is to model and match tracklets of +the same person across different cameras. To extract discriminative representation for noisy tracklets, current methods [5, 11, 13, 18, 20, 24, 29] leverage spatio-temporal information for video person ReID. McLaughlin et al. [18] use +a simple Siamese network to extract video features based +on convolution, recurrent, and temporal pooling layers. To +remove unavoidable outlier tracking frames, Li et al. [13] +propose the spatio-temporal attention model to discover a +diverse set of distinctive body parts. Similarly, by using +bag-of-tricks for training, Pathak et al. [20] add the Attention and CL loss on top of a temporal attention-based neural network to crop out noisy frames. To adapt to various +lengths of tracklets, Li et al. [11] model both the shortterm temporal cues among adjacent frames and the longterm relations among in-consecutive frames. To deal with +occlusions, Wang et al. [24] propose the Pyramid SpatialTemporal Aggregation architecture, which integrates framelevel features hierarchically into a final video-level representation. In terms of scale variations, Zang et al. [29] +propose a multi-direction and multi-scale pyramid in Trans- + + dataset + +#frames #identities #outfits + +CUHK-SYSU+ [27] +18, 184 +PRW+ [34] +11, 816 +MARS [32] +> 1.19M +iLIDSVID [12] +43, 800 +LS-VID [11] +> 2.98M +MTA∗ [9] +25, 092 +P-DESTRE [10] +105, 518 +CCVID [5] +347, 833 +MEVID +> 10.46M + +8, 432 +932 +1, 261 +300 +3, 772 +− +253 +226 +158 + +#outfits per +#BBoxes #tracklets #viewpoints #locations year +identity + +− +− +1, 261 +300 +3, 772 +2, 840 +− +480 +598 + +− 99, 809 +− 34, 304 +1 > 1.19M +1 42, 460 +1 > 2.98M +1 > 37.3M +− > 14.8M +∼ 2 347, 833 +∼ 4 > 1.7M + +− +− +20, 478 +600 +14, 943 +2, 840 +1, 894 +2, 856 +8, 092 + +− +6 +6 +− +15 +6 +moving +1 +33 + +− 2016 +1 2017 +− 2016 +− 2018 +− 2019 +5 2020 +2 2020 +1 2022 +17 2022 + +Table 1. Comparison between our MEVID dataset (green) and currently released person search datasets, where + denotes image-to-image, +and ∗ denotes synthetic datasets. − indicates no data available for this attribute. + +former to capture fine-grained part-informed information of +people. Recently, to deal with change-of-clothing problem in real world, Gu et al. [5] develop a simple baseline +method by using Clothes-based Adversarial Loss, which +mines clothes-irrelevant features from the original RGB images. +Annotation tools for video person ReID. To our knowledge, there do not exist any free and open-source video +ReID annotation tools for use in the community. Several +tools exist such as CVAT [22] and LabelMe [21] which +provide basic functionality in terms of bounding box annotation, trajectory annotation, automated annotation bootstrapping, and even attribute labeling, No tool directly facilitates inter-track linking and build-up of global entity information for all unique persons or objects that appear in +a dataset. Without this ability, linking instances of persons +across videos is tedious and time-consuming. + +are taken from over 30 ground-level cameras both inside +and outside, capturing people at a wide range of distances, +angles, and lightning conditions. +Ethical Considerations. The baseline MEVA dataset, +from which this work is derived, was collected under rigorous oversight of an independent Institutional Review Board +(IRB). MEVA contains only hired actors and persons who +consented to being collected on video, filmed in a highly +controlled environment rented for the data collection. In +MEVID dataset, the person identities or attributes are originally authorized for the MEVA video collection. All persons under observation are consenting persons. None of the +events, places or actions have significance in the real world +as the entire collection was staged and subject identities +are not known. The data is collected to achieve intentional +demographic alignment of the dataset with the continental +united states, in terms of racial and gender identity. + +3. MEVID Dataset + +3.2. Dataset Annotation + +Our goal in MEVID is to create an extensive set of person tracklets from the MEVA dataset [1] and annotate the +person’s global identity for each tracklet. These identityannotated tracklets enable our development and evaluation +of video person ReID methods in the wild with real-world +challenges such as change-of-clothing, large-scale variation, multiple locations, and more. + +The MEVA dataset contains over 2, 237 unique outfits, +worn by 176 actors for three weeks over a two month collection window. A very small subset of this video data was +labelled prior to this work, and these labels are purely tracklets with no ID information. During data collection, each +actor was photographed, front and back, with and without outerwear. Examples of these checkin photos from the +dataset are shown in Figure 2. Each photo shows the actor holding a card which identifies their GPS logger ID. +The method by which we annotate the MEVA video dataset +with global IDs is to link each instance of a person in the +videos with a particular checkin photo, which itself is assigned metadata that encodes both the outfit ID (e.g., pants +and shirt) and global identity (e.g., Alice or Bob). +We propose two tiers of video ReID problem. The first +is the commonly studied problem of same-clothing person +ReID, which is the task of re-associating across time or +viewpoint two instances of the same person in the same outfit. The second is that of change-of-clothing ReID, which +is the task of re-associating instances of the same person + +3.1. Data Collection +The video used for our MEVID dataset is a subset of +the roughly 9, 300 hours of video collected for the MEVA +dataset. MEVA contains 37 types of activity tracklets for +176 actors across 144 hours of video, totaling 66, 172 annotated activities. We add additional tracklets to the dataset +and link actors across video views and outfits in order to +create the most diverse video ReID dataset to date. The +new MEVID dataset contains hundreds of hours of videos, +over 100 actors in multiple sets of clothing at an accesscontrolled facility with indoor and outdoor scenes for nine +unique dates across a 73-day window. Notably, the videos + + Figure 2. Examples of the actor checkin photos (top row), which +are used to associate tracklets in the MEVID video (middle and +bottom rows) with a global outfit and identity ID. + +across time or viewpoints after a change of clothes has taken +place. We also capture both same-clothing and change-ofclothing examples of persons across many different scales, +locations, angles, and lighting conditions. +To improve the efficiency of labeling large-scale video +person ReID datasets, we have developed the free, open +source, and semi-automatic MEVID annotation framework +and GUI. First, we extract tracklets from video data by running a combination of state-of-the-art real-time models for +object detection, pose estimation, person ReID, and multiobject tracking. Second, we ignore the tracklets with low +detection confidences or small scales. Third, we conduct a +fast linking of tracklets to checkin photos subject to constraints as discussed below. +The tracker, raw tracker output on all MEVID videos, +and annotation GUI are freely available for future teams to +append this level of ground truth to the dataset. The GUI +facilitates these steps with a web-based interface monitoring the progress of each video through the process. These +stages, discussed below, include tracklet generation, tracklet ingest into the GUI, tracklet cleanup, tracklet review, +and tracklet linking to nexus chips which serve as anchor +for each global identity, in this case we use the MEVA actor checkin photos. The GUI can be readily provisioned on +AWS which allows rapid global collaboration and acceleration of tracking and other support workflows for annotation. +Within the interface, we implement tools for bounding box +and tracklet annotation and cleanup, including many tricks +and features derived from hundreds of hours of annotation +time. +3.2.1 + +Extracting Tracklets from Video Data + +Due to the ambiguity of bounding boxes generated by the +CenterNet2 detector [35] in crowded scenes, we use HRNet [23] to exploit pose information in the box as the canonical track state. Then, the FairMOT tracker [31] is used +to calculate tracklets. As shown in Figure 3, we present +a tiered approach to keypoint representation between head + +Figure 3. Examples of MEVID tracklet annotations. We use a head +tracking point first to disambiguate persons from one another, then +full body pose when head features are ambiguous. + +centroid and full body, which accelerates the annotation +task by reducing the total signal an annotator must consider +when verifying that a tracklet is free of switches and breaks. +Note that poses are not corrected by the annotation process unless there is ambiguity as to which person the pose +refers to, in which case certain joints are moved in order to +eliminate that ambiguity. For this reason, MEVID is not a +pose tracking or pose ReID dataset, but rather the poses are +used as guides during tracklet-to-checkin photo linking for +the annotator, and later for model developers, to understand +which person is assigned a particular global ID. +3.2.2 + +Ignored Tracklets During Annotation + +Some tracklets are ignored to speed up the annotation process later. First, we assign an ignore label to all detections +smaller than a person size threshold to keep the minimum +resolution on target required to visually ReID tracklets to +their checkin photos at annotation time. In this work, we ignore detections with less than an empirically-derived height +of 75 pixels or width of 25 pixels. Second, we ignore any +detections which overlap more than 0.3 IoU with one another in the set. Due to the nature of current state-of-the-art +in tracking, high density crowd scenes cause ID switches, +significantly delaying the cleanup of the person tracklets, +which require the pose-to-boxes and poses across all track +states to unambiguously refer to one person. Across the +videos being cleaned, we require that only a single track +state is associated with a single instance of a person on any +given frame, and the tracklets are clean with no switches. +3.2.3 + +Tracklet Cleanup and Linking to Global IDs + +After filtering out all the ignored tracklets, a second annotator performs an audit, i.e., elevating the video tracklets +to the linking stage. We exploit a myriad of constraints to +facilitate rapid linking of tracklets to checkin photos. +The first constraint is to compute a correspondence between any available GPS tracklets (if available) and the + + Figure 4. Screenshot of the Linker user interface. On the left a +subject detection from the video is shown and then compared to +probable checkin photos on the right. Annotators can select “Yes”, +“No”, or “Don’t Know”. + +Notably, we split camera viewpoints between train and +test sets such that we do not bias the person ReID results +by testing on the same backgrounds as are present in the +train set. Similarly, we seek to prevent the training from +over-fitting to actors present in the train set. For this reason, we also split the set of actors into training actors and +test actors. This is possible due to the fact that different +actors were present on different days of the dataset collection. With the information of who was present on which +days, we can construct training sets from the training camera views containing the train actor subgraph of persons, +and test sets from the respective camera views and actor +buckets for those splits. + +3.4. Relation with Existing Datasets +video space tracklets from our tracker. By projecting our +tracklets into world space, we can narrow down the list +of potential matches between a tracklet and checkin photo +by checking which GPS trackers reported a location in the +vicinity of the tracklet and cross-referencing this list of GPS +IDs with the associated checkin photos, which are each +tagged with a GPS ID. +Then, given the specific date which actor outfits appeared on, we can reduce the search space for the annotator +by over 100x times when trying to match a tracklet to one of +over 4000 checkin photos. For each tracklet being linked, +the annotator is presented with a filtered and sorted list of +potential checkin photos based on the distance between the +with the Bag of Tricks ReID descriptor [31] computed for +each track state and the checkin photos. Within the linker, +annotators are enabled to choose one of three options (Yes, +No, or Don’t Know) when determining whether a tracklet +and checkin photo represent the same person, as shown in +Figure 4. We record each of these decisions in a tracklet-tocheckin photo affinity matrix, which is used in combination +with the spatio-temporal filtering described above to make +a graph cut of the matching space and reduce the possible +number of comparisons from tracklets to checkin photos. + +3.3. Dataset Statistics +As shown in Figure 5, MEVID is divided into the train +and test sets. The train set, 6 dates over a 9-day window, contains 104 global identities with 485 outfits in 6, 338 +tracklets. The test set, 3 dates over a 5-day window, includes +54 global identities with 113 outfits in 1, 754 tracklets. The +test and train sets span a 73-day window. The frame length +per tracklet varies from 1 to 1, 000, averaging 592.6 frames. +In terms of the test set, we select 316 query tracklets such +that each query with the specific global identity and outfit combination has correct matches in the remaining 1, 438 +gallery tracklets. In addition, query and gallery are captured by different cameras. Each identity will have at least +one query under each camera. + +As presented in Table 1, our proposed dataset differs +from existing datasets in the following key aspects. +• 3D models and registered cameras. Inherited from +MEVA [1], our dataset contains a 3D model of the entire +outdoor site and each exterior camera is registered to this +model. This enables site-aware methods that reason about +multiple cameras at a single facility to perform tracking, +learn typical transit patterns across cameras (camera network topology), and much more. These are unique among +any of the other real-world ReID datasets. +• Clothes change. We seek to push the state-of-the-art in +video person ReID in the wild, which is currently limited to re-association of persons from different views wearing nearly the same outfits. This is driven by a lack +of sufficient one-to-many person-to-outfit datasets. Our +dataset contains 158 actors wearing 598 different outfits, +i.e., 1 ∼ 9 outfit changes for each actor. This will facilitate +the training of novel video search and ReID methods that +generalize beyond applications of matching like-outfits. +• Diverse locations. We collect the video data in both indoor and outdoor scenes. Of the person ReID datasets +we have studied, only MSMT17 [25] includes both indoor +and outdoor scenes. All end-to-end person search datasets +have one of either indoor or outdoor scenes. In contrast, +across 17 unique locations in MEVID , we have 33 different camera viewpoints in total. The videos are collected +from a wide range of natural scenes such as parking lots, +bus stations, cafes, school environments, and more. Additionally, the venues which make up MEVID are connected +spatially via doorways, stairwells, tunnels, and hallways +which present interesting transition points and viewpoint +changes between exiting one scene and entering another. +We release 3D models of all exterior scenes allowing for +higher-order inferencing and constraining of the person +ReID problem via consideration of spatio-temporal reasonableness of tracklets computed, or in the kinematic plausibility of locomotion throughout the scene. However, other +datasets either have to render to have multiple locations + + train + +test + +Figure 5. Data distribution of outfits, locations and target scales present in MEVID. + +such as MTA [9]. + +method + +mAP + +top-1 + +top-5 + +top-10 + +top-20 + +• Target Scales Of importance to person ReID is a robustness to changes in scale on a person from query to gallery. +Our dataset presents a wide range of scales on each target, with the smallest on the order of 75 pixels and the +largest of 500 pixels on target height. This reflects the difference in scale of the locations being observed around the +site of data collection, some of which are intimate indoor +settings and others sweeping outdoor scenes of driveways +and parking lots in between the buildings of the site. This +scale variation will allow us to evaluate the performance of +cross-scale ReID, and the impact of significant differences +in target resolution on feature embedding. Also present in +our dataset is a wide variety of target shapes and aspect +ratios. We have many tracklets of persons sitting, or occluded from the waist down, which present a unique challenge when processing chip sequences. + +CAL [5] +AGRL [26] +BiCnet-TKS [6] +TCLNet [7] +PSTA [24] +PiT [29] +STMN [2] +Attn-CL [20] +Attn-CL+rerank [20] +AP3D [4] + +27.1% +19.1% +6.3% +23.0% +21.2% +13.6% +11.3% +18.6% +25.9% +15.9% + +52.5% +48.4% +19.0% +48.1% +46.2% +34.2% +31.0% +42.1% +46.5% +39.6% + +66.5% +62.7% +35.1% +60.1% +60.8% +55.4% +54.4% +56.0% +59.8% +56.0% + +73.7% +70.6% +40.5% +69.0% +69.6% +63.3% +65.5% +63.6% +64.6% +63.3% + +80.7% +77.9% +52.9% +76.3% +77.8 +70.6% +72.5% +73.1% +71.8% +76.3% + +4. Baseline Performance +We conducted comprehensive experiments on MEVID to +establish a performance baseline. Specifically, we compare 10 state-of-the-art video person ReID algorithms, including AP3D [4], Attn-CL [20], Attn-CL+rerank [20], +TCLNet [7], AGRL [26], STMN [2], PSTA [24], BiCnetTKS [6], PiT [29], and CAL [5]. We follow the default +settings in each paper. Note that CAL [5] is the only one +that explicitly compensates for clothing changes. + +4.1. Experimentation Protocol +Similar to the MARS protocol [32,33], we use mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC) metrics to evaluate ReID methods. We calculate the similarity scores between query and gallery tracklets, then rank the gallery based on these scores. The CMC +metric indicates the true match being found within the first +k ranks (k = 1, 5, 10, 20), and the mAP metric focuses on +precision and recall over all the queries. +As discussed above, the detailed annotation metadata +of MEVID allows us to evaluate the performance of algorithms under a variety of challenging factors of increasing + +Table 2. Comparison of state-of-the-art video person ReID methods on MEVID. Highest scores are in bold. + +difficulty: 1) Change-of-clothing attribute indicates different outfits for the same person. In the test set, there are +1 ∼ 3 outfits for each person. 2) Location difference attribute indicates that the tracklet was recorded in indoor or +outdoor scenes. The test data contains 11 cameras in various +places including Driveway/Parking Lot, Bus Stop, Classroom, Entrance and Stairwell. 3) Scale variation attribute +indicates the average person size in the tracklet. Following +the COCO protocol [15], two scale categories are found in +our dataset, Medium ([32, 96)) and Large ([96, ∞)). + +4.2. Results Analysis +Table 2 shows the performance of current video person ReID methods on MEVID. Compared with performance on previous video person ReID datasets, these algorithms perform much worse on our dataset even though +the number of individuals is smaller. The top mAP scores +on other datasets are 88.5% for MARS [32], 92.1% for +iLIDSVID [12], 69.2% for LS-VID [11], 80.6% for PDESTRE [10], and 81.3% for CCVID [5]. CAL [5] introduces a clothes classifier on top of the ReID model to +decouple clothing-independent information, achieving the +best MEVID performance of 27.1% mAP and 52.5% top1 scores. MEVID provides more realistic variations in +clothes, locations and scales, making it much more difficult +for existing algorithms to model discriminative representations for each individual, resulting in inferior performance +in the wild. In the following, we analyze how the above +methods perform in terms of each attribute in detail. + + Method + +Same Clothes (%) + +CAL [5] +AGRL [26] +BiCnet-TKS [6] +TCLNet [7] +PSTA [24] +PiT [29] +STMN [2] +Attn-CL [20] +Attn-CL+rerank [20] +AP3D [4] + +mAP +39.0 +32.6 +8.0 +31.9 +29.7 +19.5 +18.5 +24.2 +34.1 +23.2 + +top-1 +56.6 +51.4 +20.5 +51.7 +49.0 +36.8 +33.7 +44.4 +50.7 +42.7 + +top-5 +70.8 +64.9 +36.5 +63.5 +63.9 +58.7 +58.3 +59.7 +63.2 +59.7 + +top-10 +78.1 +73.6 +41.7 +71.9 +72.2 +66.3 +69.1 +66.3 +68.1 +67.7 + +Method + +Different Clothes (%) +top-20 +85.4 +80.9 +51.4 +79.2 +78.5 +73.6 +76.4 +72.6 +72.9 +79.2 + +mAP +4.3 +5.7 +1.7 +3.9 +5.1 +2.0 +1.2 +3.4 +4.2 +2.9 + +top-1 +3.5 +4.9 +0.7 +3.5 +5.6 +1.1 +0.4 +2.8 +2.1 +1.8 + +top-5 +10.6 +15.1 +4.6 +8.8 +12.3 +5.3 +1.8 +8.5 +9.2 +7.4 + +top-10 +14.8 +19.0 +7.8 +14.1 +19.4 +8.5 +3.9 +15.5 +13.7 +9.5 + +top-20 +19.4 +25.7 +13.4 +21.1 +28.9 +13.7 +6.0 +24.6 +22.5 +16.6 + +Same Locations (%) + +CAL [5] +AGRL [26] +BiCnet-TKS [6] +lk TCLNet [7] +PSTA [24] +PiT [29] +STMN [2] +Attn-CL [20] +Attn-CL+rerank [20] +AP3D [4] + +mAP +24.7 +18.1 +5.1 +20.7 +20.0 +12.1 +10.1 +16.1 +23.9 +14.5 + +top-1 +42.1 +27.6 +14.5 +38.8 +36.8 +25.0 +16.8 +35.1 +41.5 +31.0 + +top-5 +56.6 +42.8 +25.3 +52.6 +54.6 +45.4 +31.0 +48.2 +53.4 +45.1 + +top-10 +63.2 +48.8 +30.6 +60.9 +63.5 +53.6 +36.1 +54.6 +58.1 +51.8 + +Different Locations (%) +top-20 +72.0 +57.6 +37.5 +68.8 +71.4 +60.5 +43.1 +64.9 +63.9 +63.3 + +mAP +22.2 +22.5 +4.7 +18.8 +16.5 +10.1 +10.0 +14.2 +19.6 +12.0 + +top-1 +35.0 +41.1 +9.4 +33.0 +28.6 +19.2 +22.2 +26.4 +33.9 +24.1 + +top-5 +49.8 +57.6 +19.5 +42.4 +41.4 +34.0 +41.6 +40.7 +44.7 +37.6 + +top-10 +55.2 +64.8 +24.6 +48.5 +49.5 +39.1 +52.1 +47.8 +50.2 +43.4 + +top-20 +62.0 +70.1 +34.3 +55.9 +57.6 +49.2 +58.4 +55.6 +55.9 +50.9 + +Table 3. Comparison of video person ReID methods in terms of +change-of-clothing attribute. Highest scores are in bold. + +Table 4. Comparison of video person ReID methods in terms of +location difference attribute. Highest scores are in bold. + +Figure 6. Influence of different number of outfits per person. + +Figure 7. Comparison of performance at specific locations in indoor/outdoor scenes. For each experiment, the query location is +fixed but the gallery can take any location in the dataset. + +4.2.1 + +Change-of-clothing Challenge + +While previous work yields good results in limited diversity +datasets, our benchmark presents a unique challenge, the +change-of-clothing problem in real-world scenarios. Two +test settings are defined to calculate accuracy for an individual: 1) all tracklets have the same clothes as the query; and +2) all tracklets have different clothes from the query. +As shown in Table 3, all existing methods perform +poorly on real-world change-of-clothing scenarios. For example, the best accuracy from AGRL [26] and PSTA [24] +is ∼ 5%, demonstrating the failure of state-of-the-art video +person ReID in complex scenes. On the other hand, video +person ReID methods perform better for the classical video +ReID problem of the same outfit. However, the results +are still not satisfactory, i.e., 39.0% mAP and 56.6% top1 scores by the best, CAL [5]. This is because of location +difference and scale variations in the dataset. +Influence of the number of outfits. We explore the performance of top 3 methods for different clothes in terms of +different number of outfits (1 ∼ 3). As shown in Figure 6, +it can be seen that both mAP and top-1 scores are reduced +along with the increasing number of outfits per person. Intuitively, more outfits bring large intra-class variances, which +is the core problem in ReID problem in the wild. +4.2.2 + +Location Difference Challenge + +In order to assess the impact of changing background location on the performance of video person ReID, we set up +two test settings with the same experimental setup as previously described when testing same- and change-of-clothing +ReID, except now we enforce that query and gallery tracklets must have different locations, i.e., indoors/outdoors +scenes. The results in Table 4 indicate that there is no substantial impact on the location for mAP score. However, the +top-1 score is significantly affected from indoor/outdoor to + +outdoor/indoor scenes. This is due to domain shift in different locations. We notice that AGRL [26] obtains better results in terms of different locations. It employs a structureaware spatio-temporal graph representation based on pose +alignment and feature affinity connections. In this way, contextual interaction between relevant regional features can +help distinguish people at different places. +Comparison of performance at specific locations. To +explore the performance of existing methods w.r.t. locations, we break down indoor/outdoor scenarios into 7 specific locations. As shown in Figure 7, we can conclude that +the ReID performance of all the selected algorithms on indoor scenes (Classroom and Stairwell) is much worse than +that in outdoor scenes (Entrance, Driveway/Parking Lot, +and Bus Stop). This is maybe because more occlusions in +indoor scenes bring noise in feature learning. +4.2.3 + +Scale Variation Challenge + +Based on the setting in Sec. 4.1, we explore the influence of +scale variation on video person ReID. From Table 5, we observe a similar trend as in the location difference attribute +evaluation. Concretely, both Attn-CL+rerank [20] and +CAL [5] perform the best among all the compared methods. It is worth noting that Attn-CL+rerank [20] boosts the +ReID performance based on the baseline Attn-CL method +by using the rerank post-processing [3]. +Person ReID performance w.r.t. finer scales. To further analyze the performance to re-identify various scales +of tracklets, we define 6 finer scales based on COCO +scale split [15], i.e., [32, 48), [48, 64), [64, 96), [96, 128), +[128, 192), [192, ∞). Then, three best performers are +selected to conduct this ablation study, including AttnCL+rerank [20], CAL [5] and AGRL [26]. From Figure 8, + + Method +CAL [5] +AGRL [26] +BiCnet-TKS [6] +TCLNet [7] +PSTA [24] +PiT [29] +STMN [2] +Attn-CL [20] +Attn-CL+rerank [20] +AP3D [4] + +Same Scales (%) +mAP +24.3 +22.1 +5.2 +20.7 +18.6 +11.4 +10.5 +15.4 +23.1 +14.2 + +top-1 +42.3 +40.3 +14.7 +40.0 +34.3 +23.7 +19.2 +31.3 +35.7 +31.0 + +top-5 +56.3 +57.3 +26.0 +52.3 +51.3 +44.0 +33.2 +50.0 +54.0 +47.3 + +top-10 +61.3 +64.7 +31.0 +61.0 +60.3 +53.3 +39.2 +56.0 +59.3 +53.0 + +Different Scales (%) +top-20 +71.7 +71.0 +39.7 +65.0 +67.0 +60.3 +46.2 +64.3 +67.7 +63.7 + +mAP +20.6 +17.7 +4.6 +17.6 +16.8 +10.6 +9.4 +14.3 +21.2 +11.4 + +top-1 +35.2 +29.5 +10.7 +34.2 +29.9 +23.5 +22.3 +26.5 +33.6 +24.5 + +top-5 +50.7 +43.6 +20.8 +44.3 +44.3 +37.3 +42.0 +37.6 +44.0 +35.9 + +top-10 +58.4 +49.3 +25.5 +50.3 +51.7 +41.6 +51.0 +46.3 +50.0 +42.6 + +top-20 +62.4 +58.4 +34.9 +59.4 +60.1 +51.0 +58.0 +55.7 +54.4 +52.7 + +Table 5. Comparison of video person ReID methods for scale variation attribute. Highest scores are in bold. + +Figure 8. Video person ReID performance w.r.t. finer scales. For +each experiment, the query scale is fixed to a range but the gallery +can take any scale in the dataset. + +existing methods perform similarly for majority of scales of +query tracklets except the largest scale [192, ∞). We speculate that the background noises in large bounding boxes will +make negative impact on distinguishing persons, especially +under the lower cameras in indoor scenes. + +4.3. Discussion +Although much progress is achieved in video person +ReID, the state-of-the-art methods are still unsatisfying on +our MEVID . This is because of high intra-class variances within combination of different clothes, locations and +scales. In the following, we discuss several issues of current research and potential research directions revealed by +our experimental analysis. +Change-of-clothing. Based on our dataset, we can conclude that all state-of-the-art methods are on the rack to +extract discriminative embedding for person with different +outfits. It is necessary to use multi-modality information +including pose, silhouettes, gait, faces and 3D shapes. +Multiple challenges in complex scenes. Current deep +learning based video person ReID methods are prone to fail +in our MEVID with multiple challenges. Our experiments +show that, as scale changes more between the query and +gallery, performance drops as well. There is a similar trend +in significant location difference, which forces a diversity +of viewpoints on each person (e.g., side view vs. nadir perspectives due to camera height and angle). +Video person search. In our dataset, we use groundtruth tracklets for video person ReID, which is unpractical in real life. Therefore, video person search is desired +for further research. A naive attempt is to combine the +state-of-the-art multi-object tracking (MOT) methods (e.g., +FairMOT [31], and ByteMOT [30]) and video-based person +ReID networks. However, it is not efficient to run two sepa- + +rate sub-models. On the contrary, a unified framework with +MOT and ReID can leverage joint optimization of the two +modules and obtain high computation efficiency by sharing +the backbone features. +Combination with activity recognition. To help improve ReID accuracy, we can consider vision+language +models given activity labels of tracklets. That is, the combination of global identities and the existing MEVA activity +labels [1] will allow for future new research into activity +recognition, and multi-person interactions. For example, it +is beneficial for person ReID to capture the activity of a person exiting a vehicle, walking into a building or dropping a +package and exiting a building. + +5. Conclusion +In this work, we propose the new and challenging +MEVID dataset for the computer vision community. Previous datasets have enabled us to push the state-of-the-art +in video person ReID, but lack sufficient variety to emulate real world conditions. Our MEVID demonstrates the +significant failure of state-of-the-art video person ReID to +handle closer to real-world situations in which actors may +be captured in different clothing and locations, from different viewpoints, and different scales. To our knowledge, it is +the most varied person ReID dataset to date in terms of these +factors which impact nearly all aspects of person tracking, +ReID, and search. We hope the dataset can facilitate the +research and development in real-life person search. Also, +we release all source video and annotation tools for extending this dataset. Notably, by extending the MEVA dataset +without compromising personally identifying information, +MEVID inherits MEVA’s IRB-reviewed HSR compliance. +Acknowledgements. This research is based upon work +supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via [2017-16110300001 +and 2022-21102100003]. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. +Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes +notwithstanding any copyright annotation therein. +This material is based upon work supported by the +United States Air Force under Contract No. FA8650-19C-6036. 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Person re-identification +in the wild. In CVPR, pages 3346–3355, 2017. +[35] Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl. +Probabilistic two-stage detection. CoRR, abs/2103.07461, +2021. + + A. Locations in MEVID +As shown in Figure 9, we display all the 17 locations in +our MEVID dataset. + +B. MEVID Persons +We show several of our actors and a few random tracks +from each in Figure 11 and Figure 12. As mentioned in the +main text, all persons in the MEVID dataset are from the +MEVA video dataset, which was collected in a controlled +closed world with hired actors whose images were collected +with consent under IRB approved experimental conditions. + +C. Annotation Tool +To annotate the data, we develop the semi-automated annotation tool to ingest multiple videos, perform pose tracking, confirm and then complete person tracklet linking (see +Figure 10). We attach 3 video walkthroughs of this annotation process which includes the video track cleanup, linking of tracks to checkin imagery for each person and outfit +combination, and review and auditing of the links made for +reid and search dataset compilation. This GUI will be release free and open source to the community alongside the +dataset and baseline code. + +D. Comparison with CCVID +As discussed in the related work section, Gu et al. [5] +produced the first video change-of-clothing ReID (CCVID) +dataset. However, the data is collected in fixing viewpoint, +background, and scale to a relatively limited range while +capturing video of actors in multiple outfits. In contrast, our +dataset aims to advance state-of-the-art video person ReID +algorithms in the wild, collected from a wider set of conditions of diversity in locations, actors, viewpoints, both indoor and outdoor scenes, and target scale. As presented in +Table 6, we provide detailed comparison with CCVID [5]. +Subset +Train +Query +Gallery +Total + +CCVID +# ids +75 +151 +151 +226 + +# tracklets +948 +834 +1074 +2856 + +MEVID +# outfits +159 +160 +252 +480 + +# ids +104 +52 +54 +158 + +# tracklets +6338 +316 +1438 +8092 + +Table 6. Comparison with CCVID [5]. + +# outfits +485 +100 +113 +598 + + Figure 9. Screenshots of all 17 different locations in MEVID. Note that the indoor locations are highlighted in bold font. + +Figure 10. GUI of our proposed annotation tool for MEVID dataset. + + Figure 11. Several actors from the MEVID dataset and their annotated tracks. + + Figure 12. Several actors from the MEVID dataset and their annotated tracks. + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/Multi Camera - Running Slides.txt b/docs/project-history/source-text-extracts/docs_convert/Multi Camera - Running Slides.txt new file mode 100644 index 0000000..061ecac --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/Multi Camera - Running Slides.txt @@ -0,0 +1,531 @@ +Multi-Camera VQA +(CVPR 2026) +SWARM Lab + + Motivation +NeuS-QA proved to be incredibly successful in answering LVQA +● + +Recall: this was done by positing that the answer to LVQA can be found in a +specific set of frames (frames of interest) within a longer video + +Our Question: Given the limited context windows of VLMs, how can we choose +and order inputs from a multi-camera network to maximize VQA accuracy and +meet the set frame limit criterion? + + Current Multi-Camera Work +Current ego-exo work does not focus on Long Video Question Answering +● +● +● + +Expression detection +Character permanence +Choosing the best viewpoint for a particular task + + Experiment Plan (Datasets) +1) +● +2) +● + +Overlapping Multi-Camera Inputs → Ego-Exo4d +LVQA (What happens after A and B until C) +Distinct and Disjoint Camera inputs → NuScenes +Common in autonomous driving where each camera FOV does not overlap + + Ego-Exo4D Question Examples +What happened right before the subject gets the bicycle pump from the bicycle stand using his right +hand? +a) +b) +c) +d) + +Camera Wearer pumps the tire using the bicycle pump +Camera Wearer loosens the screw on the wheel using his right hand +Camera Wearer fails to get the bicycle pump from the stand +Camera Wearer fixes the tire and inner valve on the bicycle rim using his hands + + Experiment Plan (Implementation) +Two possibilities: +1. Adding all camera X propositions into the automaton +2. For each proposition, only add the camera frame that yields the highest +proposition detection accuracy + + Our Baselines +Similar baselines to NeuS-QA +● +● +● + +Accuracy (%) compared to a base VLM w.r.t. NuScenes & Ego-Exo4D +Comparison of the two ways to insert multi-camera into automatons +Model proposition complexity & video length vs accuracy + + Literature Review +https://arxiv.org/pdf/2506.06253 - EgoExo Survey Paper +https://arxiv.org/pdf/2312.02638 - Exo2Ego Transfer (Ego-Exo4D + Assembly101) +https://arxiv.org/pdf/2501.05711 - Ego-in-Exo PerceptionMCQ +https://aclanthology.org/2023.emnlp-main.648.pdf - GazeVQA +https://arxiv.org/pdf/2201.04288 - Multiview Transformers for Video Recognition +https://arxiv.org/pdf/2411.08753 - Which Viewpoint is the Best? (Ego-Exo4D + LEMMA) +https://arxiv.org/pdf/2507.18447 - PDB-Eval +https://arxiv.org/pdf/2507.18342 - EgoExoBench +https://arxiv.org/pdf/2503.12772 - NuPlanQA +https://arxiv.org/pdf/2305.14836 - NuScenes-QA +https://arxiv.org/pdf/2405.01533 - OmniDrive + + First dataset that asks for grounding events +● +● +● + +Provide grounding events when solving benchmarks +We find that Longvideobench can be almost solved with just 1 randomly +sampled frame +Because the answers are most likely just the most sane sounding ones, +commonsense reasoning is able to solve it +○ + +● + +Does higher scores actually mean that the model is a good VQA model or is it a good “general +common sense model”? + +To test this we see if + + ● + +Spatial questions: Needs event + time grounding +○ + +Maybe just formulate wrt ego agent + + Implementation with NeuS-QA +● + +Option 1: add frame from camera X after iterating through all cameras + +finding which frame has the highest VLM confidence score +○ + +● +● +● +● + +Person U abstract_prop + +Option 2: Automaton satisfaction probability (Prop X Camera) 5^20 +C1F1_C2F1_C3F1_C1F2_C2F2_C3F2 +C1F1_C1F2_C1F3_C2F1_C2F2_C2F3 +Camera1: [...] → VLM → prop%, Camera2: [...] → VLM → prop% + + Some Thoughts +● + +Best view selection papers themselves can be baselines. Pass the selected +frames to the VLM. + + Problem Framing +Given +● +● +● + +N time-synchronized cameras {Ci}, +a query q, and +a tight token/frame budget B for a VLM, + +select a small set of frames or short clips across cameras that maximizes +answerability for q while preserving temporal and causal structure. + + Our Prior Work +We have key building blocks: +● +● + +translating q → temporal logic over atomic propositions, and +verifying satisfaction on automaton to trim to logic-relevant segments + +We will lift both to a multi-camera setting + + 1) Query to Multi-Camera Temporal Logic +Parse q → {propositions P, temporal spec Φ} +Lift each atomic proposition + +to a set of camera-scoped variants + +Replace each proposition with a disjunction over cameras +This yields a camera-tagged TL formula + +that still has our operators +Optional Location-aware lifting: If q mentions “rear of +the car” or “kitchen doorway,” map to camera priors via +a camera-to-region map then restrict disjunction to +cameras with nonzero prior for that region. + + 2) Per-Camera Proposition Grounding +For each camera c: +Run VLM-based proposition detector over windows +scores +. + +to obtain calibrated + +Optional object tracking: For moving agents, +add ReID to align the same entity across cameras + + 3) Global Product Automaton over Cameras +Construct a per-camera automaton A_V^{(c)} as we usually do +Build a synchronous product automaton WIP… + + Plots in Paper +● +● +● +● + +Table with NSVS accuracy vs VLM accuracy (multiple models) +Accuracy vs # of propositions +Do we really need multiple camera views to answer some questions +In ablations: +○ +○ +○ + +● + +How can we represent multiple videos? +Is neuro-symbolic necessary? +# of cameras vs accuracy + +Also include the MEVA dataset for temporal questions? + + ALL Current Question Types - 3000 - 4000 +● + +Temporal Relationships (500 NUS, 250 EgoXo4D, MEVA, Cosmos) +○ +○ +○ +○ + +● + +Before/After X +Before/After X and Y +Before/After X, Y, and Z +After X but Before Y + +Event ordering (250 questions, 200 EgoXo4D) +○ +○ + +Arrange events in a sequence +What of the following occurs first/last? + +● + +Basic Perception (250, 200) + +● + +Causality Reasoning (200 egoXo4D, ) + +● + +Spatial Reasoning (250 QUESTIONS, 200? ) + +● + +Numerical Reasoning ( + +○ + +○ + +○ + +○ + +● +● + +What objects, events, and attributes appeared/did not appear? + +Why is the bicycle on the stand? In previous frame, the man put it there. + +What is to the left of X? → look at relations.json + +Counting objects + +What camera first shows X? 250 QUESTIONS +Scene Summarization + + Temporal Relationships + + Spatial Reasoning Potential Question types and construction method + +In the overhead view of the office desk, what object is located directly to the right of the keyboard? +A) The coffee mug +B) The mouse +C) The monitor stand +D) A stack of papers +In the scenes, where there are people walking behind the truck, what object is located directly in front of the truck? +(To construct this question, we need a scene description that can be extracted directly from the captions, and the spatial relationships +that can be directly constructed from the scene graph - however, we need to ensure that there is only unique answer. For a choice of +object of interest, we must ensure that no other object in the options apart from one (target object) in question matches the correct +option. To ensure that this does not happen, we should ideally restrict the questions only on single instances of source objects in the +entire scene, or use highly descriptive narration of the current scene (present in the question)) +Construction methods: +(Find object with fewest instances ) -> (find timestamp of object) -> (Full scene description for a sampled time instance in the video) -> +(all spatial relationships for the chosen object) -> sample 2 types of questions [what object is ‘behind’.... Source object? , where is ‘a’ +related to object ‘b’?] +Can we create a distribution of objects first? + + Timeline +● +● +● + +Training Video Agent +WM Consistency +Multi Camera Dataset +○ +○ +○ + +Only temporal categories/temporal flavored, no general CV +Completely open source, benchmarked with 20 models by 30 Nov +Check of dataset tracks in CVPRW, Bigger one for ECCV + + Current Multi-Camera Research +● + +Two main types of Multi-Camera Datasets: +○ +○ + +● + +Overlapping Views (security camera, ego/exo views) +Non-Overlapping Views (self-driving car) + +Motivation: We want to create a system capable of answering diverse +questions when they come across any sort of multi-camera dataset + + Sampling Frames Given Multiple Cameras +● + +A critical question that is unsolved is: How do you sample frames from +multiple camera inputs? +○ +○ +○ + +● + +Option 1: Uniform Sampling (Vanilla VLMs) +Option 2: Finding a scene of interest (NeuS-QA) +Option 3: Building a “memory” for an agent (VSeek-R1) + +Our Insight: No dataset currently exists that handles the nuances of +multi-camera networks + + Paper Overview +● + +Create a dataset (~4000 questions) that captures a diverse range of +questions that exists in the multi-camera domain +Overlapping POVs + +Non-Overlapping POVs + + How Do You Construct Questions? +● +● +● + +1) Modeling all actions based on time and event causality from ground truth +via a directed graph +2) Modeling spatial relationships in a graph via 3D relationships +Result: 8 question categories in 3 domains (temporal, spatial, and holistic) + + Temporal & Causal Understanding +● + +● + +● + +Reasoning about timing using logical operators +○ Ego-Exo 4D: “What did the person do before closing the cabinet door with his left hand?” a) sliced the +tomato on the cutting board, b) picks the egg on the countertop with his left hand, c) washed his +hands at the sink, or d) switched on the kitchen light +Identifying the chronological order of events +○ nuScenes: “At this urban construction site featuring various safety markers and personnel, what is the +chronological order of the following four events: I. an orange traffic cone is placed as part of a safety +setup, II. a large green dumpster is situated near Gate 1, III. a construction worker in a high-visibility +jacket starts walking along the sidewalk, and IV. a white flatbed truck remains stationary near a +crane?” a) I -> II -> III -> IV, b) II -> III -> IV -> I, c) III -> IV -> I -> II, d) IV -> III -> II -> I.” +Explaining why an event occurred by linking it to a preceding action +○ Ego-Exo 4D: “Why did C insert the sterile swab into the hole in the covid test indicator at +approximately 256 seconds?” a) to clean the internal sensor of the test indicator before applying the +solution, b) to transfer the collected nasal sample to the testing device for analysis, c) to measure the +depth of the indicator hole before starting the test, d) to dispose of the swab safely inside the indicator +packaging. + + Spatial Understanding +● + +● + +● + +Determining relative 3D positions +○ nuScenes: “In this urban setting at the construction site, where is the traffic cone relative to the car?” +a) in front of the car, b) behind the car, c) to the left of the car, d) to the right of the car +Quantifying and counting unique objects across feeds without overcounting +○ nuScenes: “Can you tell me the number of construction workers present in the entire video?” 10 +workers +Identifying the presence or absence of specific objects, events, and attributes +○ nuScenes: “What did the person do before closing the cabinet door with his left hand?” a) sliced the +tomato on the cutting board, b) picks the egg on the countertop with his left hand, c) washed his +hands at the sink, or d) switched on the kitchen light + + Holistic Multi-View Understanding +● + +● + +Identifying which specific camera in the synchronized network first captures the entrance or appearance of +an entity +○ Ego-Exo4D: “Which camera best shows the person loosening the axle nut of the wheel with his right +hand?” a) camera 1, b) camera 2, c) camera 3, d) camera 4 +Generating long-form summaries that synthesize information from all cameras +○ Ego-Exo4D: “Summarize this scene.” The subject begins by retrieving ingredients from the +refrigerator and preparing a workspace before mixing a seasoning base of rice vinegar, salt, and +wasabi. After scooping cooked rice and spreading it over a seaweed sheet on a bamboo mat, they +add raw salmon and additional fillings to the assembly. Finally, they uses the mat to shape a tight +sushi roll, which they then slice into individual pieces and arrange on a ceramic plate for presentation. + + NuScenes Example + + NuScenes Example + +Starting from synchronized multi-camera video +streams and their rich annotations + + NuScenes Example + +Using the shared global coordinate frame, we +associate objects across views and construct a +unified representation of the scene + + NuScenes Example + +Per-frame scene graphs are linked temporally to form a temporal +scene graph / event log, enabling reasoning over object motion, +Nodes - entities such as vehicles, pedestrians, +traffic lights, and the ego-vehicle +state changes, and cross-camera transitions. +Edges - spatial relations (e.g., left-of, in-front-of), +interactions, and camera visibility. + + NuScenes Example + +We define a set of question categories, each corresponding to a +symbolic query whose answer is deterministically computed +from the annotations + + NuScenes Example + +A language model (GPT) is then used solely to verbalize the +question and answer into natural language, without introducing +new information + + NuScenes Example + +Front Left + +Front + +Raw Video +Ego vehicle waits for a pedestrian to +cross, then proceeds with a right turn. + +Front Right + + NuScenes Example + +Front Left + +Front + +Front Right + +Processed Video +Multi-camera annotations are fused into a +unified spatial-temporal representation, enabling +reasoning over interactions and visibility. + + NuScenes Example + +Numerical / Counting +Q) Can you tell me the total number of cars throughout the video? +A) + +4 + + NuScenes Example + +Spatial +Q) Where is the pedestrian crossing the street relative to the ego? +A) + +In front of the ego + + NuScenes Example + +Summarization +Q) Provide a comprehensive summary of the ego-actor's interactions +across all views. +A) + +In this bustling urban scene, a variety of activities unfold as pedestrians and vehicles navigate the city streets. A white minivan is steadily driving along the road, moving through a lively urban environment +characterized by modern buildings and tree-lined sidewalks. This vehicle, with its boxy shape, travels among pedestrians who are either crossing the street or walking along the sidewalks.\n\nOne adult, +dressed in dark clothing and carrying a bag, is seen walking along the sidewalk at a steady pace, moving from the right side of the frame to the left. Their path eventually takes them across the street, interacting +with the flow of urban life. Nearby, another adult, also in dark attire, walks in the same direction as the minivan, their short hair visible as they maintain a consistent pace along the right side of the street.\n\nThe +scene is populated with several other pedestrians, each with their own distinct characteristics and movements. An adult in a light-colored shirt and dark pants stands out as they walk purposefully across the +street, while another, similarly dressed, moves from one side of the street to the other, possibly navigating around obstacles or crossing the street. The sidewalk is a scene of activity, with adults dressed in both +light and dark clothing, all moving at varying speeds, some walking in pairs or near proximity to each other, indicating the social nature of urban pedestrian life.\n\nAmong the parked vehicles, a small +dark-colored car remains stationary, possibly parked, as pedestrians pass by, emphasizing the typical hustle and bustle of a city street + + NuScenes Example + +Event Ordering +Q) The following three events occurred in a city scene. Arrange them in +the order they happened: +I. An adult pedestrian wearing a light-colored shirt and dark pants, with short hair and carrying a bag in their right hand, is walking along the sidewalk, moving from the right side of the frame +towards the left, and appears to be crossing the street in the later frames.\nII. A white minivan or SUV, with a boxy shape likely used for transporting passengers or cargo, is driving along a city +street, moving at varying speeds. It appears to be navigating through an urban environment with buildings and pedestrians.\nIII. An adult pedestrian wearing a light-colored shirt and dark +pants, is walking along a sidewalk in an urban area, moving from one side of the street to the other, possibly crossing or continuing along the sidewalk.. + +A) + +II -> III -> I" + + NuScenes Example + +Basic Perception +Q) What color was the minivan driving across the street? +A) + +White + + Constructing the Dataset + + Trash + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/multi camera (1).txt b/docs/project-history/source-text-extracts/docs_convert/multi camera (1).txt new file mode 100644 index 0000000..279990e --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/multi camera (1).txt @@ -0,0 +1,161 @@ +Towards a Multi-Camera Video QA Benchmark: Bridging the Gap in +Multi-View Temporal Reasoning +December 26, 2025 + +1 + +Motivation + +Recent advancements in Long Video Question Answering (LVQA), such as our work on NeuS-QA, have demonstrated +that complex queries can be answered by identifying specific frames of interest within a longer, extended video. +However, these approaches primarily operate on single-stream video. In the real-world, scenarios ranging from +autonomous driving to ego-centric surgical training involve information that is distributed across a network of timesynchronized cameras. How can we tackle this change of domain? +Current Vision-Language Models (VLMs) face a significant “context bottleneck.” With typical limits of roughly +32–64 frames, a VLM cannot simply ingest all streams from a multi-camera network simultaneously without losing +resolution or temporal density. This raises a fundamental research question: How do we intelligently select and +order inputs from a multi-camera network to maximize VQA accuracy within a fixed frame budget? +Despite the urgency of this problem, we are unable to make progress due to the lack of specialized datasets. +Existing multi-camera datasets focus on viewpoint transfer (Exo2Ego) or basic perception (Object Detection), but +lack the complex, logic-driven ground truth QA pairs required to evaluate long-form, multi-view reasoning. To fill +this gap, we propose the development of a novel dataset. This dataset will be the first to require cross-camera +causal reasoning, where the answer to a question Q cannot be found in a single camera stream, but must be +synthesized by switching views at specific logical junctions. + +2 + +Proposed Dataset Framework + +We intend to curate this benchmark by leveraging existing high-quality video foundations (NuScenes, EgoExo4D, +MEVA, and Cosmos) and generating ground-truth QA pairs derived from their underlying metadata (point clouds, +3D bounding boxes, and time-stamped annotations). + +2.1 + +Question Taxonomy + +To ensure the benchmark tests high-level reasoning across multi-camera streams, we categorize questions into the +following eight logic-driven tiers: +• Temporal Relationships: Reasoning about timing using logical operators (e.g., “Before/After X”, “Before/After +X”, “Before/After X and Y , Y , and Z”, “After X but before Y ”). +• Event Ordering: Identifying the chronological sequence of multiple events or determining which occurred +first/last. +• Basic Perception: Identifying the presence or absence of specific objects, events, and attributes within the +multi-camera network. +• Causality Reasoning: Explaining why an event occurred by linking it to a preceding action (e.g., tracing a +parked bicycle back to the frame where it was placed). +• Spatial Reasoning: Determining relative positions (e.g., “What is to the left of X?”) by working with 3D point +cloud data. +• Numerical Reasoning: Quantifying and counting unique objects across overlapping and non-overlapping camera +feeds without overcounting. +• Camera Transition Logic: Identifying which specific camera in the synchronized network first captures the +entrance or appearance of an entity. + +1 + + • Scene Summarization: Generating long-form, multi-view narrations that synthesize information from all available streams into a cohesive summary. +Table 1 provides an approximation of the total number of questions per category and per dataset, but this just +serves as an approximation; the actual amount is dependent on what the dataset specializes in and what the ground +truth labels reveal (see Section 2.2). +Tier + +Example Question + +Temporal +Event Ordering +Basic Perception +Causality +Spatial +Numerical +Camera Transition +Summarization + +“Did the car brake after the pedestrian appeared but before the light turned red?” +“Arrange events A, B, and C in the order in which they occurred.” +“Is there a cyclist visible in any of the rear-facing cameras?” +“Why did the car stop? (requires viewing the man leaving the drivers seat).” +“What object is to the left of the pedestrian?” +“How many unique vehicles are currently in the 360-degree field of view?” +“Which camera first captures the entrance of the subject into the scene?” +“Provide a comprehensive summary of the ego-actor’s interactions across all views.” + +Total + +NuScenes + +EgoExo4D + +MEVA + +Cosmos + +250 +150 +400 +150 +350 +300 +100 +100 + +200 +300 +350 +350 +– +– +400 +200 + +150 +200 +250 +100 +150 +150 +150 +50 + +100 +150 +200 +100 +150 +150 +250 +100 + +1,800 + +1,800 + +1,200 + +1,200 + +Table 1: Benchmark Distribution and Examples by Reasoning Tier (Total N ≈ 6, 000) + +2.2 + +Data Generation Pipeline + +The first phase of this project involves a deep dive into the ground truth (GT) structures of our base datasets. This +is an important first step for everyone to perform since the bulk of the questions above will be a result of questions +derived from the ground truth labels. An example of this is given time-varying annotations A and B happening at +times ta and tb respectively, we can systematically extract the question: “Does event B occur before or after event +A” which inherently relies on comparing ta and tb . + +3 + +Experimental Design & Evaluation + +Once the dataset is established, we will evaluate state-of-the-art VLMs using a tight frame budget constraint. By +constraining each VLM to only allow up to B frames, we force the model (or a selection heuristic) to choose which +n frames across c cameras are most informative where n · c < B. +Because of this, an initial experiment of our work involves comparing a simple VLM fed a random sampling of +all cameras against a “camera-aware” model such as an adaptation of NeuS-QA. This will demonstrate that even in +overlapping scenarios, multi-camera specific logic is essential for high-accuracy video understanding and would thus +motivate our problem. + +2 + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/neus-qa.txt b/docs/project-history/source-text-extracts/docs_convert/neus-qa.txt new file mode 100644 index 0000000..6f54557 --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/neus-qa.txt @@ -0,0 +1,1140 @@ +NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and +Neuro-Symbolic Reasoning +Sahil Shah1 , S P Sharan* 1 , Harsh Goel* 1 , Minkyu Choi1 , Mustafa Munir1 , +Manvik Pasula2 , Radu Marculescu1 , Sandeep Chinchali1 + +arXiv:2509.18041v2 [cs.CV] 14 Nov 2025 + +The University of Texas at Austin, Texas, USA1 +Independent Researcher, USA2 +{ss96869, spsharan, harshg99, minkyu.choi, mmunir}@utexas.edu +Abstract +While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with +Long Video Question Answering (LVQA) due to its demand for complex multi-step temporal reasoning. Vanilla approaches, which simply sample frames uniformly and feed +them to a VLM along with the question, incur significant token overhead. This forces aggressive downsampling of long +videos, causing models to miss fine-grained visual structure, +subtle event transitions, and key temporal cues. Recent works +attempt to overcome these limitations through heuristic approaches; however, they lack explicit mechanisms for encoding temporal relationships and fail to provide any formal guarantees that the sampled context actually encodes +the compositional or causal logic required by the question. +To address these foundational gaps, we introduce NeuS-QA, +a training-free, plug-and-play neuro-symbolic pipeline for +LVQA. NeuS-QA first translates a natural language question into a logic specification that models the temporal relationship between frame-level events. Next, we construct +a video automaton to model the video’s frame-by-frame +event progression, and finally employ model checking to +compare the automaton against the specification to identify all video segments that satisfy the question’s logical requirements. Only these logic-verified segments are submitted to the VLM, thus improving interpretability, reducing +hallucinations, and enabling compositional reasoning without modifying or fine-tuning the model. Experiments on the +LongVideoBench and CinePile LVQA benchmarks show that +NeuS-QA significantly improves performance by over 10%, +particularly on questions involving event ordering, causality, and multi-step reasoning. We open-source our code at +https://utaustin-swarmlab.github.io/NeuS-QA/. + +1 + +Introduction + +Recent advances in video-language models (VLMs) have led +to significant progress in visual question answering (VQA) +for static images and short video clips (Park et al. 2024; +Wang et al. 2024b; Ye et al. 2025; Wang et al. 2025; Choudhury et al. 2023). However, as real-world applications shift +toward longer-form content such as surveillance streams, +egocentric vlogs, and movie scenes, users are increasingly +asking more complex, temporally grounded questions that +span multiple events (Wu et al. 2024; Rawal et al. 2024; Choi +* These authors contributed equally. + +et al. 2024; Wang et al. 2024a; Tan et al. 2025). For example, +as illustrated in Figure 1, a user watching a one-hour travel +vlog might ask: “After the man boarded the plane and sat +down, what did he grab?” +Answering such questions demands more than surfacelevel perception; it requires ➊ semantic grounding to identify entities and their interactions, ➋ temporal reasoning to +track and interpret the sequence of events, and ➌ compositionality to integrate these temporally distinct sub-events +into a coherent reasoning chain that aligns with the structure +and intent of the query. +A common strategy in video question answering with +VLMs is to uniformly sample frames across the video. +While this method is sufficient for short clips, it becomes +problematic as video length increases: the volume of visual +input grows rapidly, forcing models to either exceed token +limits or aggressively downsample videos. In doing so, they +risk skipping large portions of the video and missing finegrained visual details, subtle event transitions, and key temporal cues crucial for answering complex temporal queries. +Long-form videos often follow a narrative arc, with multiple scenes unfolding sequentially; yet many questions +about them pertain to only a specific portion of that story. +Figure 1 illustrates a vacation vlog that depicts a man’s +full journey from entering the airport to de-boarding to finally checking into a hotel room. But the user’s query requires information solely from the in-flight segment. This +is a common pattern in long-form video question answering +(LVQA): although the content is broad, most queries target +specific temporal windows. +Therefore, a natural alternative to uniform sampling is +to direct the model’s attention to a small, targeted segment +of the video that is most relevant to the query. However, it +raises a core challenge: How can we retrieve the correct segment that is semantically and temporally aligned with the +question, without already knowing the answer? +To address this, we argue that temporal logic (TL) offers +a powerful solution. It allows us to formalize how events unfold over time by symbolically representing their structure +using operators such as “until,” “eventually,” and “always.” +This makes it possible to precisely specify the kinds of temporal patterns a question refers to (like an event occurring +after another), and to systematically filter for video segments + + After the man +boarded the plane +and sat down, what +did he grab? + +VLM + +B) Paper Bag + +(a) Vanilla VLM Prompting uniformly samples frames across the +video regardless of relevance, often missing key temporal context. + +CLIP Similarity wrt Prompt + +After the man +boarded the plane +and sat down, what +did he grab? + +VLM + +C) Magazine + +(b) Heuristic Retrieval ranks frames based on semantic similarity +to the query but lacks temporal structure and formal guarantees. + +Highest Scoring Segment + +Automata +Representation + +Atomic Proposition 1 + +Atomic Proposition 2 + +Atomic Proposition 3 + +man boards +plane + +AND (man sits +down) + +UNTIL (man +grabs) + +LLM + +complex questions over long-form videos. Given a question in natural language, NeuS-QA first translates it into +a TL specification that encodes the desired sequence of +events. From the video, it then constructs a video automaton by assigning scores for semantic propositions in individual frames, and applies model checking to identify video +segments that satisfy this specification. Finally, a VLM is +queried on these verified segments, enabling precise, temporally grounded answering without exposing the model to +irrelevant or misleading context. + +2 + +Related Works + +Video Question Answering. Early VQA systems (Antol +et al. 2015) typically relied on fully supervised architectures that encoded entire videos using convolutional and +recurrent networks, encoder-decoder pipelines, or attention +mechanisms over video features (Zhu et al. 2016; Ye et al. +2017; Jang et al. 2017). However, these models lacked scalability VLM +to longer videos and were constrained by limited +visual-linguistic reasoning. The emergence of VLMs (Bai +et al. 2025; OpenGVLab 2024; Achiam et al. 2023; Li et al. +2024a,b) has enabled zero-shot VQA across diverse domains +with stronger generalization. However, these models typically operate by consuming a fixed set of frames sampled +from the entire video, limiting their ability to handle longform videos with complex temporal structures. Their reliance on global frame pooling or simple attention mechaAfter the man +boarded the plane +nisms often leads to irrelevant +or diluted context when preand sat down, what +did he grab? +sented with multi-event sequences +(Wu et al. 2024; Wang +et al. 2024a), resulting in degraded performance. +To address the limitations of global frame sampling, +recent state-of-the-art (SOTA) approaches adopt retrievalaugmented strategies that identify and condition on the most +relevant video segments (Park et al. 2024; Wang et al. 2024b; +Ye et al. 2025; Song et al. 2024; Wang et al. 2025; Islam et al. +2025; Choudhury et al. 2023). While effective at reducing irman boards +relevant context, these methods often relyLLM +on coarse heurisplane +tics or textual summarization, which sacrifice visual fidelity +and lack formal structure. In contrast, NeuS-QA uses temporal logic to guide frame selection, yielding a more interpretable and precise grounding of video content. + +Atomic Proposition 1 + +After the man +boarded the plane +and sat down, what +did he grab? + +VLM + +D) Red and +Yellow Cup + +(c) Ours (NeuS-QA) builds a video automaton over grounded +propositions, constructs a temporal logic specification from the +man +query, After +and the +uses +model checking to retrieve +VLM logic-satisfying video +boarded the plane +segments +leading +to more accurate and interpretable answers. +and sat down, what +did he grab? + +Figure 1: Comparison of frame selection +strategies to answer +D) Red and +Yellow +Cup +temporally-grounded questions over +long-form +videos. +that satisfy these constraints. +To this end, we propose NeuS-QA, a neuro-symbolic +pipeline that combines the structure of TL with the perceptual capabilities of VLMs to accurately answer temporally + +Symbolic Representations for Video. Symbolic representations have been explored across fields such as +robotics (Puranic, Deshmukh, and Nikolaidis 2021; Hasanbeig et al. 2019; Shoukry et al. 2017), autonomous vehicles (Zheng et al. 2025; Mehdipour et al. 2023), generative +models (Shah et al. 2025; Sharan et al. 2025; Choi et al. +2025), and structured neural networks (Manginas, Paliouras, +and De Raedt 2024; Hashemi et al. 2023). In the context of +vision, symbolic methods have been employed to support +long-form understanding +(Tapaswi et al. 2016; Huang et al. +VLM +2020; Xiong et al. 2019; Wu and Krähenbühl 2021); however, these typically rely on latent-space embeddings, sacrificing interpretability. Yang et al. (2023) and Choi et al. +(2024) move toward representing videos as formal models +to enable structured reasoning. NeuS-QA follows a similar +philosophy, modeling videos as verifiable automata to enable interpretable and temporally grounded analysis. + +(c) NeuS-QA (ours) + +Atomic Prop + +AND (m +dow + +VL + +(k fram + +D) red +yellow + + 3 + +Preliminaries + +21 + +1 + +43 + +To illustrate our approach, we begin by introducing a running example. Suppose, given a video, we ask “After the +woman pours hot water over granola and spoons yogurt into +the bowl, what does she place as a topping?” +Temporal Logic. Temporal Logic (TL) is a formal language used to describe the progression of events through a +combination of logical and temporal operators. In TL, individual events are represented as atomic propositions, statements that evaluate to either True or False. These propositions are combined using logical operators such as AND +(∧), OR (∨), NOT (¬), and IMPLY ( =⇒ ), as well as +temporal operators like ALWAYS (□), EVENTUALLY (♢), +NEXT (X), and UNTIL (U). +Returning to our running example, we define a set of +atomic propositions P and a corresponding TL specification +φ as follows: +P = { woman pours hot water over granola, +woman spoons yogurt into bowl, +woman places topping }, +φ = (woman pours hot water over granola ∧ +woman spoons yogurt into bowl) ∧ ♢ +woman places topping. +This specification encodes the query where the woman +first pours hot water over granola, and adds yogurt to the +bowl, and eventually adds some topping at the end. +Video Automaton. We represent a video as a stochastic finite-state automaton where each state corresponds to +a sampled frame, and transitions capture temporal progression. As videos are a finite sequence of discrete frames with +inherent temporal structure, we model them as a discretetime Markov chain (Norris 1998; Kemeny and Snell 1960). +Formally, the video automaton AV is defined as the tuple: +AV = (Q, q0 , δ, λ), +(1) +where Q is a finite set of states, q0 ∈ Q is the initial state, +δ : Q × Q → [0, 1] is a transition function such that δ(qi , qj ) +gives the probability of transitioning from state qi to state qj , +and λ : Q → 2|P| is a labeling function that assigns a set of +atomic propositions to each state. This structure allows TL +formulas to be verified over the video by treating sequences +of frames as labeled paths. +Formal Verification. With formal verification, we assess +whether a video automaton satisfies a given TL specification +(Clarke, Grumberg, and Peled 1999; Huth and Ryan 2004). +We define a path π through the automaton AV as an infinite +sequence of states π = q0 q1 q2 . . . such that δ(qi , qi+1 ) > 0 +for all i ≥ 0. Each path induces a trace—the sequence of +label sets assigned to the states along the path, denoted as +trace(π) = λ(q0 )λ(q1 )λ(q2 ) . . . . This trace captures the +progression of observable propositions over time, as illustrated in Figure 2. We apply probabilistic model checking +(Baier and Katoen 2008) to compute the satisfaction probability P[V |= φ], quantifying how well the video trace satisfies the TL specification. This provides a formal and interpretable framework for identifying event sequences in video. + +1 + +Initial +State + +q0 + +2 + +3 + +7 + +20 + +21 + +22 + +10 + +41 + +42 + +43 + +q193 + +q225 + +q288 + +q23 + +q213 + +q233 + +q295 + +q347 + +q32 + +q224 + +q256 + +q320 + +q352 + +q1 + +q321 + +Terminal +State + +qn + +Figure 2: Video automaton for the running example. +Top: representative frames F1 (woman pours hot water +over granola), F21 (woman spoons yogurt into bowl), and +F43 (woman places topping). Bottom: finite-state automaton synthesized from the TL specification in φ of the running example. Green circles indicate valid frames—those +in which at least one domain-specific atomic proposition +(e.g., “woman pours hot water over granola”) is detected +and whose inclusion preserves all ordering constraints accumulated so far. Red circles mark frames that (i) contain no +relevant propositions or (ii) would force a transition contradicting the specification (for instance, “woman grabs spoon +but doesn’t spoon yogurt into bowl”). We verify the automaton incrementally: whenever a new frame is labelled green, +we extend the current run ρ = q0 → · · · → qt and immediately re-evaluate the property ρ |= φ. If the property still +holds, the frame remains green; otherwise, it is re-labelled +red and excluded. The procedure terminates once the accepting state qn is reached, guaranteeing that the sequence of +green frames is a witness trace satisfying the specification. + +4 + +Methodology + +We present an overview of NeuS-QA, which extracts temporally grounded scenes of interest from the video based on +a natural language query to guide vision-language models. +NeuS-QA first identifies the key events and semantics necessary to answer the question. These are then formalized as a +temporal logic specification that encodes the expected structure and ordering of events. To evaluate which parts of the +video satisfy this specification, we construct an automaton +whose states represent frame-level propositions predicted by +a VLM. As the VLM iteratively processes consecutive video +segments, we label frames with relevant propositions and incrementally build the automaton. Formal verification is then +applied over this automaton to identify sequences that satisfy the query, yielding the final set of frames of interest. We +outline this process in Algorithm 1. +LQ2TL : Question to Temporal Logic. LQ2TL provides a +structured two-shot framework for translating natural language questions into TL specifications using prompting +strategies that capture both spatial and temporal relationships. Given a question q, LQ2TL extracts a set of atomic +propositions P = {p1 , p2 , . . . , pn } corresponding to key objects, actions, and relationships in the query. This extraction +is guided by optimized prompts with carefully selected fewshot examples to ensure semantic and event-level coverage. + + After the propositions are identified, LQ2TL constructs a +TL specification φ by mapping the temporal structure of the +query onto formal operators, linking the extracted propositions accordingly. The resulting propositions and specification are detailed in Section 3. +Algorithm 1: NeuS-QA Core Logic +Require: Proposition extractor LQ2TL (·), frame-level +detector Mprop (·), frame step size κ, automaton +builder Θ(·), probabilistic model checker µ(·), +smoothing function Fb(·), temporal expander +Lext (·), trimming function ftrim , +vision-language answerer MVQA (·), satisfaction +threshold τ +Input : Video V, question q, answer choices C, +ground-truth answer c∗ +Output : Boolean result y ∈ {True, False} +1 P, φ ← LQ2TL (q) +// Convert question to TL +2 Z ← ∅ +// Create detection matrix +3 AV,0 ← Θ(∅, P, Z) +// Initialize automaton +4 for t ← 0 to length(V) − κ do +5 +Ft ← {Vt , . . . Vt+κ } // Select sequence of +frames +6 +for pi ∈ P do +7 +Zt,pi ← Mprop (pi , Ft ) +// Find frames +by proposition +8 +9 + +10 + +11 + +AV,t ← Θ(AV,t , P, Z0:t ) // Extend +automaton +P[AV,t |= φ] ← µ(V, φ) +// Compute +satisfaction probability +if Fb(P[AV,t |= φ]) > τ then +// Calibrated probability is +above threshold +break + +(tstart , tend ) ← V[satisfy] +// Obtain automaton +frames of interest +13 (α, β) ← Lext (q) +// Find extension spans +′ +14 V ← ftrim (V, tstart + α, tend + β) +// Trim to +extended frames of interest +′ +15 ĉ ← MVQA (q, C, V ) +// compute VQA +∗ +16 y ← (ĉ = c ) +// determine match +12 + +Automaton Representation of Video. Given a TL specification φ from LQ2TL , we construct a formal representation of the video by incrementally building a discretetime Markov chain automaton AV grounded in event-based +atomic propositions. Starting from an empty automaton +AV,0 (L3), we process the video in frame windows Ft of +size κ (L5), building a probabilistic state-based model that +captures the evolution of identified events over time. +For each frame sequence Ft , the system computes probabilistic detections Zt,pi ∈ [0, 1], indicating the likelihood +that a proposition pi ∈ P is present, using a vision-language +model Mprop (pi , Ft ) (L7). These values are derived from +the model’s Yes/No logits, calibrated using a held-out validation set (see Appendix) to improve the reliability of semantic assessments. Based on the cumulative detections Z0:t +and the current automaton state AV,t−1 , we update the automaton via the transition builder Θ(AV,t , P, Z0:t ) (L8). +This automaton incrementally models the temporal progression of event-based propositions throughout the video. +Formal Verification for Scenes of Interest. We use formal verification to identify all time intervals where the TL + +specification φ is satisfied. This process employs established model-checking techniques to enable rigorous temporal analysis over the video automaton (L9). +At each timestep t, given the current automaton AV,t , we +compute the satisfaction probability as: +Pt = P[AV,t |= φ] = µ(V, φ). +(2) +This satisfaction probability is computed using probabilistic model checking, implemented via the STORM model +checker (Hensel et al. 2020; Junges and Volk 2021). This +process operates under the Probabilistic Computation Tree +Logic (PCTL) framework, where µ(·) analyzes the state labels and transition probabilities in AV,t to assess how likely +the specification φ is satisfied. +To obtain a stable and calibrated signal, we apply a +smoothing function Fb(·) that maps raw satisfaction probabilities into per-frame detection scores, defined as: +1 +Fb(c) = +, +(3) +−γ(c−τ +) +1+e +where c is the raw satisfaction probability, τ is the confidence threshold, and γ controls the steepness of the sigmoid +curve. This transformation suppresses noise below a defined confidence threshold while preserving high-confidence +detections. By modulating the steepness of the transition +around the threshold τ , the function produces a smooth, interpretable confidence curve. The output yields a minimal +satisfying interval (e.g., frames 615 → 820) that captures +query-relevant events with high temporal precision. +Temporal Extension. We extend the identified interval +to ensure a complete temporal context for robust question +answering (L13). The context window extension process +extends the interval forward and backward by a fixed or +learned temporal window α, β, ensuring that sufficient temporal context is preserved for comprehensive understanding. +We leverage the VLM to account for event lead-up and aftermath periods following temporal keywords such as “before” +and “after” in the query q. The final extended window becomes the filtered input to the VLM for question answering, +providing a temporally focused, yet contextually complete +video segment for optimal performance. The temporal extension is formalized in L14. +Vision Language Model Answering. The final stage uses +this temporally filtered video segment as context to generate +accurate responses through VLM prompting. + +5 + +Experimental Setup + +During automaton construction, we use InternVL2-8B +(OpenGVLab 2024) as the VLM to generate frame-level detections. The videos are sampled at 3 frames per second. To +translate natural language questions into TL specifications, +we use the GPT-o1-mini reasoning model (Achiam et al. +2023) with a few-shot prompting strategy and a carefully +designed system prompt to capture common temporal structures and patterns. We apply Stormpy (Hensel et al. 2020; +Junges and Volk 2021), a probabilistic model checker, to +identify video segments that satisfy these TL specifications. +We present the results of NeuS-QA as a plug-and-play module with various VLMs in Section 6. + + Method + +Category + +Frames + +Overall (↑) + +T3E + +E3E + +T3O + +O3O + +Vision Foundation +Models + +LLaVA-OneVision-Qwen2-7B (Li et al. 2024a) +Aria (Li et al. 2024b) +Qwen2.5-VL-7B-Instruct (Bai et al. 2025) +GPT-4o-2024-08-06 (Achiam et al. 2023) +Qwen2-VL-7B-Instruct (Yang et al. 2024) +InternVL2-8B (OpenGVLab 2024) + +32 +64 +32 +32 +32 +32 + +43.40 +52.83 +39.62 +43.40 +45.28 +39.62 + +64.86 +58.11 +58.11 +58.11 +54.05 +48.65 + +54.39 +59.65 +54.39 +47.37 +45.61 +49.12 + +43.18 +36.36 +45.46 +45.45 +47.73 +43.18 + +53.07 +53.07 +50.44 +49.56 +48.68 +45.61 + +Structured Reasoning +Frameworks + +VideoTree (Wang et al. 2025) +LVNet (Park et al. 2024) +VideoAgent (Wang et al. 2024b) +T* (Ye et al. 2025) + +8-32 +12 +32 +8 + +56.38 +41.94 +45.45 +28.95 + +50.00 +54.17 +52.63 +35.56 + +50.00 +35.48 +27.27 +48.33 + +43.84 +46.15 +27.27 +22.22 + +50.47 +45.59 +40.38 +35.75 + +NeuS-QA + Qwen2.5-VL-7B-Instruct +NeuS-QA + Aria +NeuS-QA + LLaVA-OneVision-Qwen2-7B-ov +NeuS-QA + GPT-4o-2024-08-06 + +32 +32 +32 +32 + +54.72 +54.72 +50.94 +49.06 + +62.16 +64.86 +70.27 +68.92 + +66.67 +63.16 +56.14 +59.65 + +54.55 +47.73 +52.27 +50.00 + +60.09 +58.77 +58.77 +58.33 + +NeuS-QA (Ours) + +Table 1: Benchmarking Accuracy (%) of SOTA VQA Models. NeuS-QA, when used alongside different VLM backbones, +outperforms raw foundation models and structured reasoning frameworks on LongVideoBench. +Datasets. For empirical evaluation of NeuS-QA we use +two LVQA datasets: LongVideoBench (Wu et al. 2024) and +CinePile (Rawal et al. 2024). LongVideoBench serves as +our primary benchmark. It features videos up to 60 minutes long across diverse domains such as instructional content, egocentric vlogs, and educational streams. CinePile +is used as a secondary dataset to demonstrate the performance of NeuS-QA on long-form movie scenes that require +reasoning about narrative structure, character interaction, +and scene progression. For both datasets, we focus on subsets of question-video pairs that require temporal ordering, +causal inference, and compositional logic, filtering out simple examples based solely on static visual recognition. In +LongVideoBench, these reasoning-oriented queries correspond to four categories: T3E (event before/after text), E3E +(event before/after event), T3O (object before/after text), and +O3O (object before/after object). To streamline the evaluation, we burn the provided subtitles into the video, allowing +NeuS-QA and consequently any VLM to directly infer and +answer questions involving dialogue without requiring separate time-aligned subtitle prompts. +Evaluation Metrics and Benchmarking. We evaluate +NeuS-QA on multiple-choice LVQA tasks using accuracy +as the primary metric across both LongVideoBench and +CinePile. Since NeuS-QA serves as a plug-and-play keysegment retrieval layer, we apply it modularly on top of several state-of-the-art VLMs, including Qwen2.5 (Bai et al. +2025), GPT-4o (Achiam et al. 2023), and LLaVA-OneVision +(Li et al. 2024a). For each model, we report performance +with and without our retrieval pipeline to measure the impact of symbolic retrieval. In addition to these baselines, we +compare against recent structured LVQA methods such as +T*, VideoTree, and LVNet (Ye et al. 2025; Wang et al. 2025; +Park et al. 2024), as discussed in Section 2. All benchmarks +are conducted using the lmms-eval library (Zhang et al. +2024), which provides a unified framework for evaluating +LVQA models across datasets. + +6 + +Results + +Building on our experimental setup, we now turn to empirical evaluation. Our goal is to answer the following key questions regarding neuro-symbolic LVQA: +1. To what extent does NeuS-QA improve VQA performance on significantly longer videos? +2. Do the frames selected by NeuS-QA add more relevant +context that improves VQA performance? +3. How crucial is the neurosymbolic reasoning component +in NeuS-QA for effective long video understanding? + +6.1 + +Neuro-Symbolic LVQA + +Evaluations on LongVideoBench. We evaluate NeuSQA on LongVideoBench by comparing it against both +VLMs and structured reasoning baselines. As shown in Table 1, NeuS-QA improves VQA accuracy across all question +types. Paired with Qwen2.5-VL, it achieves 60.09% overall +accuracy—nearly 10% over the base model and outperforms +all structured reasoning baselines, including VideoTree and +LVNet. Qualitative examples in Figure 4 further illustrate +NeuS-QA’s ability to identify the correct frames of interest. These results highlight the benefits of symbolic temporal +reasoning for long video understanding and demonstrate that +dynamically selecting video segments based on the initial +query is more effective than fixed or heuristic approaches. +We also assess scalability with increasing video duration +as shown in Figure 3, performance remains stable or improves, whereas prompting-based methods show substantial +degradation. Notably, our method even outperforms humanannotated ground truth segments, achieving 59% vs. 52% +when both are used as input to the same VLM. We attribute +this to the automaton’s ability to isolate not only the direct +answer span but also semantically and temporally relevant +context (e.g., events before/after). +Performance on CinePile. We evaluate NeuS-QA on +CinePile, a long-video benchmark designed for narrativedriven understanding. Unlike instructional or surveillance- + + Accuracy Gain Over Ground Truth (%) + +30 + +LLaVA-OneVision +Qwen 2.5 +GPT-4o + +20 +10 + +Median Clip Size (s) + +Accuracy (↑) + +40 +40 + +52.63 +58.33 + +Table 3: Impact of VLM backbone choice on accuracy +(%). Using InternVL2-8B for automaton construction results in the highest accuracy when evaluated with GPT-4o. + +0 +10 + +0 11 min + +11 23 min + +23 34 min + +Video Duration + +34 46 min + +46 58 min + +Figure 3: Accuracy (%) gains of NeuS-QA over ground +truth frame annotations. We visualize the per-model improvement of NeuS-QA in terms of absolute accuracy difference relative to ground truth annotations across different +video durations. Positive values indicate that NeuS-QA outperforms ground truth frame annotations. +Strategy + +Overall (↑) + +Qwen2.5-VL-7B-Instruct +GPT-4o-2024-08-06 +Vision Foundation +Llama-3.2-11B-Vision-Instruct +Models +LLaVA-OneVision-Qwen2-7B +Idefics2-8B + +50.73 +50.24 +20.98 +31.71 +25.85 + +NeuS-QA (Ours) Qwen2.5-VL-7B-Instruct + +53.66 + +Table 2: Generalization to Narrative Video. NeuS-QA +achieves the highest accuracy (%) on CinePile, demonstrating its effectiveness in abstract, story-driven video domains. +style datasets, CinePile emphasizes abstract, story-based +reasoning. As shown in Table 2, NeuS-QA achieves 53.66% +accuracy when paired with Qwen2.5-VL, yielding notable +gains over zero-shot prompting alone. These results indicate that our method generalizes effectively to more complex +video domains, demonstrating its ability to identify contextually relevant content even without task-specific tuning. + +6.2 + +Choice of VLM +Qwen2.5-VL-7B-Instruct +InternVL2-8B + +Ablation on NeuS-QA Architectural Decisions + +Frame Presentation Strategy. Frame presentation affects +how much context the model can leverage when answering questions. We compare the default NeuS-QA approach +of trimming the video to only the most relevant segments +against alternative approaches that retain full temporal context while varying frame sampling density, sampling densely +within relevant intervals and sparsely elsewhere. As shown +in Table 4, these adaptive strategies recover some incorrect +answers but result in lower overall accuracy. This suggests +that while context retention offers some benefit, tightly focusing on relevant segments remains more effective. +Impact of Automata VLM Choice. The choice of visionlanguage model used to identify atomic propositions during automaton construction directly affects the quality of +the extracted frames of interest. We compare InternVL28B and Qwen2.5-VL for this step, keeping the downstream +VQA model fixed. As shown in Table 3, InternVL2-8B leads +to higher VQA accuracy, even though both models produce similar median clip lengths. This suggests that stronger +VLMs that more accurately identify propositions yield more + +Strategy +Original (Full) +NeuS-QA (Trim) +Dynamic Scaling +Speed Bias + +Accuracy (↑) + +Improvements (↑) + +Regressions (↓) + +49.56 +58.33 +53.51 +52.19 + +– +18.42 +12.28 +10.53 + +– +9.65 +8.33 +7.90 + +Table 4: Impact of frame sampling strategy. Trimming to +relevant segments with NeuS-QA yields the highest accuracy +(+9.77%) over the full-video baseline, driven by a substantial increase in correct predictions (+18.42%) and a moderate rise in regressions (+9.65%). Dynamic scaling and speed +bias strategies offer intermediate trade-offs, illustrating the +balance between context retention and focus. +faithful automatons, improving the precision and relevance +of the frames selected for reasoning. + +6.3 + +How Important is Neuro-Symbolic Feedback +on Frame Selection? + +To isolate the impact of formal reasoning, we evaluate a simplified variant of NeuS-QA that skips temporal logic and automaton construction. Instead, it relies solely on prompting a +VLM to judge whether a segment is relevant to the question. +As shown in Table 5, this approach underperforms significantly: without structured semantics or temporal constraints, +frame selection becomes noisy and accuracy drops. These +findings underscore the importance of neuro-symbolic feedback in guiding segment retrieval with interpretable, temporally grounded structure. + +7 + +Discussion + +Applicability Beyond Explicit Temporal Prompts. While +NeuS-QA is designed to handle complex temporal queries, +its applicability extends even to cases where temporal structure is implicit. Since we use an LLM to translate questions into temporal logic, it can infer event order from linguistic cues, even if the prompt does not contain explicit +temporal operators such as “before” or “after.” In simpler +or ambiguous queries, the system effectively treats the entire prompt as a single proposition, reducing the reasoning +task to identifying the most relevant clip. In such degenerate cases, performance aligns with standard VLM-based +frame retrieval approaches, as shown in Section 6.3. In Table 1, we focus on four temporally compositional categories +from LongVideoBench where our approach offers the greatest benefits. Results for other categories (see Appendix) also +show improvements due to our logic-based retrieval, though +gains are less pronounced due to the reduced temporal complexity of those queries. + + Q) In a white room, there is a glass door with a black frame. In front of the +glass door, there is a man wearing a green coat and white earphones. After he +says 'story', who is the first person to appear? + +Q) There are many instruments on the shelves of the laboratory. A man with +short black hair, wearing a gray bulletproof vest, picks up a blue cup from the +shelf. What did the man do after picking up the blue cup? + +NeuS-QA & VideoTree + +A) A woman with dark skin wearing black-framed glasses +B) A man wearing black-framed glasses and a black top +C) A woman with black hair tied up + +NeuS-QA & VideoTree + +A) Kneeled down +B) Talked while turning around +C) Drank water + +D) A woman wearing a blue top +E) A man with dreadlocks + +D) Ate something +E) Talked while running + +Figure 4: Qualitative Examples from the NeuS-QA pipeline. Compared to other structured reasoning frameworks, NeuS-QA +more accurately identifies the correct frames of interest in a long video. +Metric +Accuracy (0s, 600s] +Accuracy (600s, 3600s] +Overall Accuracy (↑) + +NeuS-QA (Ours) + +VLM-only + +61.33 +56.86 +58.33 + +42.67 +38.82 +40.09 + +Table 5: Effect of neuro-symbolic feedback on frame selection. Replacing temporal logic and automaton construction with direct VLM queries significantly degrades accuracy (%) across both short and long video segments. +Choice of Temporal Logic Formalism. Another natural +question is: “Why temporal logic?” Is it the only or best +choice for structuring complex queries? We adopt TL because it strikes a balance between expressivity and compatibility with existing formal verification tools such as +Storm and CARL (Hensel et al. 2020; Kremer et al. 2025). +TL captures temporal constructs like ordering, simultaneity, and eventuality while remaining amenable to model +checking. However, NeuS-QA is logic-agnostic too: alternatives like Linear Temporal Logic (LTL), Signal Temporal +Logic (STL), or any domain-specific structured representation could be substituted, as long as they provide a mechanism to create and verify the logical conditions of a query. +This flexibility makes NeuS-QA a general framework for +interpretable long-video reasoning. Our contribution is not +about choosing these design and architectural choices, but +about enabling symbolic alignment between video content +and query structure as a strategy for aiding LVQA. +Limitations and Failure Modes. A key limitation of +NeuS-QA lies in the coupling between its “neuro” and “symbolic” components. Although the symbolic verifier is exact +by construction (guaranteeing correctness once propositions +are grounded), the quality of the output depends heavily on +the VLM’s ability to detect relevant events. In cases with +multiple complex propositions, a single missed detection +can cause the TL specification to be evaluated as unsatisfied, leading to false negatives during clip retrieval. These +failure cases are most common when visual cues are subtle, + +occluded, or appear briefly in the video. +Future Work. Looking forward, we see opportunities for +extending NeuS-QA beyond one-shot retrieval and VQA. +One direction is to explore agentic neuro-symbolic systems +that reason over long videos iteratively using memory, planning, and intermediate logic representations to decompose +and refine queries. Additionally, while our video automaton representation scales well with video length, constructing it remains computationally expensive. In theory, this cost +could be amortized by pre-building a general-purpose video +automaton and applying it to multiple downstream queries +in a coarse-to-fine retrieval loop. +Broader Implications. While NeuS-QA achieves strong +improvements and sets new state-of-the-art results on LVQA +benchmarks (Table 1), overall scores including those from +VLMs prompted with ground truth clips (Figure 3) remain +modest. This highlights the broader limitations of current +perception models. As VLMs continue to improve, the quality of both frame-level grounding and automaton construction will scale accordingly. + +8 + +Conclusion + +We introduce NeuS-QA, a plug-and-play, training-free +neuro-symbolic framework for long-form video question answering. By translating natural language queries into temporal logic and verifying them over a video automaton, +NeuS-QA isolates segments semantically and temporally +aligned with the question before querying a VLM. Our results show strong gains over standard prompting and recent retrieval-based methods (Wang et al. 2025; Park et al. +2024), especially on temporally complex benchmarks like +LongVideoBench and CinePile (Wu et al. 2024; Rawal et al. +2024). As VLMs improve, the fidelity of our symbolic alignment will scale in tandem. NeuS-QA is a general framework +for structured, interpretable long-video reasoning. 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In Proceedings of the IEEE conference on computer vision and +pattern recognition, 4995–5004. + + \ No newline at end of file diff --git a/docs/project-history/source-text-extracts/docs_convert/neusv.txt b/docs/project-history/source-text-extracts/docs_convert/neusv.txt new file mode 100644 index 0000000..9d2ee14 --- /dev/null +++ b/docs/project-history/source-text-extracts/docs_convert/neusv.txt @@ -0,0 +1,2358 @@ +Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification +S P Sharan* +Minkyu Choi* † +Harsh Goel +Mohammad Omama + +Sahil Shah +Sandeep Chinchali + +arXiv:2411.16718v5 [cs.CV] 25 Apr 2025 + +The University of Texas at Austin, United States + +Figure 1. Current generative video evaluation methods struggle with temporal fidelity. NeuS-Vconverts prompts into Temporal Logic +specifications and formally verifies them against a video automaton. The upper video aligns with the prompt’s temporal sequencing, while +the lower video, despite being visually appealing, fails to do so. Unlike VBench, NeuS-V effectively differentiates between them. + +Abstract +Recent advancements in text-to-video models such as Sora, +Gen-3, MovieGen, and CogVideoX are pushing the boundaries of synthetic video generation, with adoption seen +in fields like robotics, autonomous driving, and entertainment. As these models become prevalent, various metrics and benchmarks have emerged to evaluate the quality of the generated videos. However, these metrics emphasize visual quality and smoothness, neglecting temporal fidelity and text-to-video alignment, which are crucial +for safety-critical applications. To address this gap, we +*Equal contribution (Order determined by coin toss). +†Corresponding author: minkyu.choi@utexas.edu + +introduce \textit {NeuS-V}, a novel synthetic video evaluation metric that rigorously assesses text-to-video alignment using +neuro-symbolic formal verification techniques. Our approach first converts the prompt into a formally defined +Temporal Logic (TL) specification and translates the generated video into an automaton representation. Then, it +evaluates the text-to-video alignment by formally checking +the video automaton against the TL specification. Furthermore, we present a dataset of temporally extended prompts +to evaluate state-of-the-art video generation models against +our benchmark. We find that \textit {NeuS-V} demonstrates a higher +correlation by over 5× with human evaluations when compared to existing metrics. Our evaluation further reveals +that current video generation models perform poorly on + + these temporally complex prompts, highlighting the need for +future work in improving text-to-video generation capabilities. We open-source our benchmark, code, and dataset at +utaustin-swarmlab.github.io/NeuS-V. + +1. Introduction +Imagine that we need to stress test or retrain a motion planning system for autonomous driving (AD) by simulating a +scenario where “a truck appears in frame 10 and veers in +front of me onto my lane within 2 seconds”. With recent developments in text-to-video (T2V) generative models, generating synthetic videos for such scenarios offers great potential for improving such engineering systems. However, +for these synthetic videos to be effective, they must align +with the desired prompt across three key dimensions: ➊ semantics, ➋ spatial relationships, and ➌ temporal coherence. For example, if the synthetic video either inaccurately +generates the truck’s movement in front of the autonomous +vehicle or fails to follow the correct lane-change timing, it +could mislead the motion planning system during retraining, resulting in incorrect decisions in critical situations. +Various evaluation metrics have emerged to identify +well-aligned synthetic videos to the prompt from recently +developed T2V models [4, 14, 20, 33, 57]. While many +video evaluation tools [23, 39, 41] focus on visual quality, +some approaches use vision-language models [50, 51] or +video captioning models [9] to evaluate the semantic alignment between the generated video and the original prompt. +More notably, VBench [23] evaluates videos across multiple dimensions and categories and is recently being used +in leaderboards* to evaluate T2V models. However, naive +evaluation of a video generated by one neural network using another is neither rigorous nor interpretable. Moreover, +current evaluation metrics still miss critical spatio-temporal +aspects that are intrinsic to video data (See Fig. 1). +Our key insight is to leverage the recent advancements +in video understanding systems that utilize structured symbolic verification methods with neural networks [10]. We +introduce Neuro-Symbolic Verification (NeuS-V), a novel +T2V model evaluation method that integrates neural network outputs with structured symbolic verification through +temporal logic (TL) specifications. First, we leverage +Vision-Language Models (VLMs) to interpret a video’s spatial and semantic content. Second, we capture temporal +relationships across these identified semantics by processing frames sequentially. Finally, we construct an automaton representation of the synthetic video which establishes +a structured temporal sequence of the video events. This +structure ensures that the specified TL requirements are met, +resulting in a rigorous and interpretable video evaluation. +* https://huggingface.co/spaces/Vchitect/VBench Leaderboard + +Furthermore, we introduce a dataset of temporally extended prompts for benchmarking T2V models’ abilities +to faithfully exhibit temporal fidelity and accurately reflect +event sequences. We plan to open-source NeuS-V, our +prompt suite, and human evaluation results along with a +public leaderboard of state-of-the-art (SOTA) T2V models. + +2. Related Work +Evaluation of Text-to-Video Models. The recent surge +of T2V models has created a demand for specialized metrics +to evaluate the quality and coherence of generated videos. +Some preliminary works in this field utilize metrics such as +FID, FVD, and CLIPSIM [6, 21, 25, 38, 49, 55], but these +approaches struggle with prompts that require complex reasoning and logic. Recent works [31, 32, 40, 58] rely on +Large Language Models (LLMs) to break down prompts +into Visual Question Answering (VQA) pairs, which are +then scored by a VLM. Few approaches also utilize vision +transformers and temporal networks [11, 16, 22, 27]. Some +works, including EvalCrafter [39], FETV [41], T2V-Bench +[16], and others [18, 26], incorporate an ensemble of various visual quality metrics. Of these, VBench [23] is rising +to be the de-facto benchmark in the field, evaluating across +numerous dimensions and categories. However, all current +evaluation methods emphasize visual quality, disregarding +temporal ordering of videos. Although some claim to perform temporal evaluation, they often focus on aspects such +as playback (e.g. slow motion, timelapse) or movement +speed rather than true temporal reasoning and also lack +rigor. In contrast, NeuS-V addresses these shortcomings by +formally verifying temporal fidelity through TL specification over the automaton representation of synthetic videos. +Video Event Understanding. Recent methods typically +rely on perception models [15, 48] or VLMs [42, 56] to +analyze events within a video. Although these models can +detect objects and actions effectively, they do not guarantee +a reliable understanding of the video’s temporal dynamics. +Yang et al. [53] focuses on representing a video as a formal +model, specifically a Discrete Markov Chain, providing a +more structured approach to capture events across time. An +application of this is NSVS-TL [10], which uses a neurosymbolic approach to find the scenes of interest. Along +similar lines, NeuS-V leverages VLMs and formal verification to enhance comprehension of temporal relationships in +generated videos and assess video quality, thus establishing +a reliable foundation for video evaluation. + +3. Preliminaries +In the following sections, we present a running example to +help illustrate our approach. Let’s say we generate an autonomous driving video using T2V models with the prompt + + Temporal Logic. In short, TL is an expressive formal language with logical and temporal operators [13, 43]. A TL +formula consists of three parts: ➊ a set of atomic propositions, ➋ first-order logic operators, and ➌ temporal operators. Atomic propositions are indivisible statements that +can be True or False, and are composed to construct +more complex expressions. The first-order logic operators +include AND (\wedge ), OR (\vee ), NOT (\neg ), IMPLY (\Rightarrow ), etc., and +the temporal operators consist of ALWAYS (□), EVENTUALLY (♢), NEXT (X), UNTIL (U), etc. +The atomic propositions P, and TL specifications Φ of +our example are: + \label {eq:running_example_tl_spec} \begin {array}{rl} \propset &= \{\text {car driving}, \ \text {clear day}, \ \text {cyclist signals turn}, \\ &\quad \ \ \text {cyclist turns}, \ \text {cyclist avoids obstacle}\}, \\ \Phi &= \Box \Big ((\text {car driving} \wedge \text {clear day}) \wedge (\text {cyclist signals turn}) \end {array} + +(1) +This TL specification illustrates that if a car drives on a +clear day and a cyclist signals to turn, it implies that eventually a cyclist will turn and avoid an obstacle. + +8 + +1 + +– “A car drives down a road on a clear sunny day, interacting +with cyclists who signal turns to avoid obstacles”. + +P(p1) = 1.0, P(p2) = 1.0, P(p3) = 0.0 +P(p4) = 0.0, P(p5) = 0.0 +q1 = p1 ¬{p2, p3, p4, p5} + +Initial +=0 +State (q 0, q 1) + +q0 + +δ + +δ(q + +0,q +32 ) + +1 + +q1 + +q23 + +=1 + +.0 + +q32 = p1 ∧ p2 +¬{p3, p4, p5} + +q32 + +Formal Verification. This provides formal guarantees +that a system meets a desired specification [12, 24]. It +requires a formal representation of the system, such as a +finite-state automaton (FSA) or a Markov Decision Process +(MDP). From the DTMC, we define a path as a sequence +of states from the initial state, e.g., q0 q1 (q2 )ω , where ω indicates infinite repetition. A trace is the sequence of state +labels denoted as λ(q0 )λ(q1 )λ(q2 ) · · · ∈ (2|P| )ω that captures events over time. Next, we apply probabilistic model +checking [3] to compute the probability P[A |= Φ] which +gives the satisfaction probability that the trace starting from +the initial state satisfies the TL specification Φ. Through +such formal representations, we evaluate synthetic videos. + +P(p1) = 1.0, P(p2) = 1.0, P(p3) = 0.0 +P(p4) = 0.0, P(p5) = 0.9 + +P(p1) = 1.0, P(p2) = 1.0, P(p3) = 0.0 +P(p4) = 0.8, P(p5) = 0.9 + +7 + +q225 = p1 ∧ p2 + +8 + +10 + +,p } +q225 +q193 δ(q , q¬{p) ,=p 0.02 +3 + +193 + +4 + +q233 = p1 ∧ p2 ∧ p5 +¬{p3, p4} +213 δ(q213, q233) = 0.18 + +q + +288 + +3 + +321 + +11 + +4 + +5 + +q347 = p1 ∧ p4 +¬{p2, p3, p5} +295 δ(q295, q347) = 0.0 + +q347 + +q352 = p1 ∧ p2 ∧ p5 +¬{p3, p4} +δ(q320, q352) = 0.9 + +q352 + +q + +q233 + +q256 = p1 ∧ p2 ∧ p4 ∧ p5 +¬{p3} + +q224 δ(q , q ) = 0.72 q256 +224 + +q321 = p1 ∧ p2 + +,p ,p } +q288 δ(q ¬{p +q321 +, q ) = 0.1 + +5 + +255 + +q320 + +256 + +Terminal +State + +qn + +Figure 2. Video automaton from the running example. Above +is an automaton of a video generated by the Gen-3 model constructed with a TL specification (See Eq. (1)). Every state qt from +a frame F is labeled and has incoming and outgoing transition +probabilities δ(q, q ′ ) ∈ [0, 1]. For example, in frame F8 , we have +probabilities P (p4 ) = 0.8 and P (p5 ) = 0.9, where p4 represents the atomic proposition “cyclist turns”, and p5 represents “cyclist avoids obstacle”. These probabilities are assigned because +the cyclist (red-dotted circle) has turned left to avoid obstacles +on the road. In the state q256 , we have an incoming probability +0.72 = P (p1 ) × P (p2 ) × P (p4 ) × P (p5 ) × (1 − P (p3 )) from +the previous state q224 , where that label is true for p1 , p2 , p4 , and +p5 , and false for p3 denoted as ¬{p3 }. +Text to Video Model + +Text Prompt +A car drives down a road on a clear sunny +day, interacting with cyclists who signal +turns to avoid obstacles + +Discrete-Time Markov Chain. Abbreviated DTMC, it is +used to model stochastic processes where transitions between states occur at discrete time steps [29, 44]. We use +a DTMC to model synthetic videos since the sequence of +frames is discrete and finite, as shown in Fig. 2. The DTMC +is defined as a tuple A = (Q, q0 , δ, λ), where Q is a finite +set of states, q0 ∈ Q is the initial state, λ : Q → 2|P] is +the label function, P is the set of atomic propositions and +δ : Q × Q → [0, 1] is the transition function. For any two +states q, q ′ ∈ Q, the transition function δ(q, q ′ ) ∈ [0, 1] +gives the probability of transitioning from q to q ′ . For each +state, the probabilities of all outgoing transitions sum to 1. + +11 + +Text-toProp. + +PULS + +Synthetic (Generated) Video + +Text-toSpec. + +Temporal Logic Specification (𝚽) +Vision Language +Model + +Proposition (𝑝𝑖 ) + +NeuS-V Score +(Spatio-Temporal Semantics) +Overall +Consistency + +Object +Existence + +Spatial +Relationship + +Object Action +Alignment + +Probabilistic +Model Checking + +Mapping +Function + +Satisfaction Probability +Video Automaton (𝑨𝒗 ) + +Pr(cyclist turns),… + +Figure 3. Spatio-temporal and semantic measurements between a text prompt and a video by NeuS-V. We first decompose +the text prompt to TL specification Φ, then transform the synthetic +video into an automaton representation AV . Finally, we calculate +the satisfaction probability by probabilistically checking the extent +to which AV satisfies Φ. + +4. Methodology +Given a synthetic video V from T2V model MT2V : T → +V, where T is a text prompt, we aim to compute an evaluation score S : T × V → Rm for each evaluation mode +m ∈ M = {object existence, spatial relationship, object action alignment, overall consistency}. We introduce \textit {NeuS-V}, +a novel method for evaluating T2V models using a neurosymbolic formal verification framework. \textit {NeuS-V} evaluates synthetic video across four different evaluation modes: +object existence, spatial relationship, object and action + + alignment, and overall consistency following these steps: +• Step 1: We translate the text prompt into a set of atomic +propositions and a TL specification through Prompt Understanding via temporal Logic Specification (PULS). +• Step 2: We obtain a semantic confidence score for each +atomic proposition using a VLM applied to each sequence +of frames. +• Step 3: We construct a video automaton representation +for the synthetic video. +• Step 4: We compute the satisfaction probability by formally verifying the constructed video automaton against +the TL specification. +• Step 5: Finally, we calibrate the satisfaction probability +by mapping it to the final evaluation score from NeuS-V. + +Functional Modules. PULS is a two-step algorithm powered by LLMs. Given the text prompt T that is used for +video generation, it is translated into P and Φ for any given +evaluation mode m ∈ M . This translation process is defined as LMPULS : T × M → (P, Φ). +PULS consists of two modules, Text-to-Proposition +(T2P) and Text-to-TL (T2TL). These use the optimized +prompts θT⋆ 2P and θT⋆ 2T L respectively. Each optimized +prompt includes carefully selected few-shot examples from +a compiled training dataset DT2TL|train , that maximize the +accuracy of having correct P and Φ given m ∈ M . +1. Text-to-Proposition Module: This module extracts P +⋆ +from T given M , using θT2P +, and is defined as: + +4.1. Prompt Understanding via Temporal Logic +Specification + + \label {eq:ttp} \ttp : \mathcal {T} \times M \xrightarrow {\oprompt _{\text {T2P}}} \propset . + +We propose Prompt Understanding via temporal Logic +(PULS), a novel method that facilitates an efficient and accurate conversion of text prompts to TL specifications. It +preserves all semantic details of the prompt to formulate +its TL specification. In contrast to previous works that focus solely on formulating relationships between singular +objects, isolated events [5, 8, 17], or encoding spatial information [37, 47], our method, PULS, fully captures both +temporal and spatial details. By incorporating four evaluation modes, we provide a more comprehensive assessment +across spatio-temporal dimensions. +Evaluation Mode. PULS compiles the text prompt into +the different set of proposition P and TL specifications Φ +across four evaluation modes: +• Object Existence: This mode evaluates the existence +of objects specified in the text prompt. For instance, +in our running example (Sec. 3), the TL specification +for the object existence mode can be defined as Φ = +♢(car ∧ cyclist ∧ obstacle). +• Spatial Relationship: This evaluation mode captures the +spatial relationships among existing objects. In our running example, since we are interested in a cyclist signaling and then making a turn, it implies that the cyclist is in front of the car. Hence, the TL specification is defined as Φ = □(cyclists are in front of a car) ∧ +♢(obstacles are next to cyclists). +• Object and Action Alignment: It ensures that the objects in the generated video perform the actions specified in the text prompt. For example, the TL specification can be defined as Φ = ♢(cyclist signals turn ∧ +cyclist turns U cyclist avoids obstacle). +• Overall Consistency: Finally, we assess the overall semantic and temporal coherence of events in the generated +video, ensuring alignment with the full-text prompt (see +the example in Eq. (1)). + +(2) + +⋆ +The optimized prompt θT2P +is obtained from DT2TL|train , +a set of N selected examples of text Ti and propositions +Pi pairings : + + \label {eq:dttp} \dttp = \{ (\mathcal {T}_i, \propset _i) \}_{i=1}^{\mathcal {N}}, \quad \dttp \subset \mathfrak {D}_{\text {train}}. + +(3) + +2. Text-to-TL Specification Module: This module generates Φ from T , P, and M using the optimized prompt +⋆ +θT2TL +, and is defined as: + \label {eq:tttl} \tttl : \mathcal {T} \times \propset \times M \xrightarrow {\theta ^\star _{\text {T2TL}}} \spec . + +(4) + +⋆ +The optimized prompt θT2TL +is obtained from DT2TL|train +with N examples pairs of text Ti , propositions Pi , and +corresponding TL specifications Φi : + + \label {eq:dttl} \dttl = \{ (\mathcal {T}_i, \propset _i,\spec _i) \}_{i=1}^{\mathcal {N}}, \quad \dttl \subset \mathfrak {D}_{\text {train}}. +(5) +We use MIPROv2 [46] with DSPy [30] to ensure that the +N few-shot examples in the dataset Dtrain are sufficient in +maximizing the accuracy of each module. We detail the +optimization process in the Appendix. + +4.2. Semantic Score from Neural Perception Model +Given P, Φ = LMPULS (T ), where T is the text prompt used +for the synthetic video V, we first obtain the semantic confidence score C ∈ [0, 1] for each atomic proposition pi ∈ P +using a VLM MVLM : p × F → C, where F represents a +sequence of frames. +We use the VLM to interpret semantics [2, 36, 45, 56] +and extract confidence scores from F based on the text +query T . We pass each pi ∈ P along with the prompt to +the VLM and calculate the token probability for the output +response, which is either True or False. To calculate the +token probability, we retrieve logits for the response tokens +and compute the probability of that token after applying a + + softmax. Next, the semantic confidence score is the product +of these probabilities as follows: + \label {eq:vlm} \begin {split} c_i &= \vlm (p_i, \mathcal {F}) \\ &= \prod _{j=1}^{k} P\left ( t_j \mid \prop _i, \mathcal {F}, t_1, \dots , t_{j-1} \right ) \ \forall \, p_i \in \propset , \end {split} +(6) + +where (t1 , . . . , tk ) is the sequence of tokens in the response. The probability of the token at j’th response token +lj,t +j + +is P (tj |·) = Pe elj,z , where lj,∗ is the logit vector, and lj,tj +z +is the logit corresponding to token tj . Finally, we calibrate +the VLM to map the confidence to the probability using +the semantic score mapping function c⋆i ← fVLM (ci ; γf p ), +where c⋆i is the calibrated confidence and γf p is the false +positive threshold. We provide additional details about the +calibration process and the prompt design for the semantic +detector VLM in the Appendix. +Algorithm 1: NeuS-V +Require: Frame window size w , evaluation mode M , PULS +model \pivotf , vision language model \mathcal {M}_{\text {VLM}} , +semantic score mapping function fVLM (·), video +automaton generation function ξ(·) probabilistic model +checking function Ψ(·), probability mapping function +fECDF (·), probability distribution D +Input : Text prompt T , text-to-video model \ttv +Output : NeuS-V score SNeuS-V +1 begin +2 +S ← {} +// Initialize an empty set for +NeuS-V scores of each evaluation mode +3 +V ← MT2V (T ) +// Generate a video +4 +for m ∈ M do +5 +for n = 0 to length(V) − w step w do +6 +C ⋆ ← {} // Initialize an empty set +for semantic confidence +7 +F ← {V[n], V[n + 1], . . . , V[n + w − 1]} +// Select a sequence of frames +8 +P, Φ ← LMPULS (T, m) +// Translate +the T to P and Φ for m +9 +for pi ∈ P do +10 +ci ← MVLM (pi , F ) +// Obtain +semantic confidence scores +⋆ +11 +ci ← fVLM (ci ; γf p ) // Calibrate ci +with false positive threshold +γf p +12 +C ⋆ [pi,n ] ← c⋆i // Append c⋆i to the +semantic confidence set +13 +end for +end for + +14 +15 +16 + +17 + +18 + +AV ← ξ(P, C ⋆ ) +// Construct AV +P[AV |= Φ] = Ψ(AV , Φ) +// Obtain +satisfaction probability +sm = fECDF (P[AV |= Φ], Dm ) +// Calculate +the calibrated score +S ← S ∪ {sm } // Append the score sm to +the set S + +19 + +end for + +20 + +SNeuS-V ← |S| + +P + +S + +4.3. Automaton Representation of Synthetic Video +Next, given C ⋆ across all frames and propositions in P, we +construct the video automaton AV to represent the synthetic +video as a DTMC A. This representation is crucial, as it enables the evaluation of synthetic video to determine whether + +it satisfies the given Φ corresponding to the original text +prompt. We build AV using an automaton generation function ξ : P × C ⋆ → AV , defined as + \label {eq:va} \va = \xi (\propset , C^\star ) = \dtmc = (Q,q_0,\delta ,\lambda ), + +(7) + +where Q = {q0 . . . qt } is the set of possible states, each +q ∈ Q represents a unique configuration of truth values for +P with q0 denoting the initial state. Given C ⋆ for all atomic +propositions in each sequence of frames from the synthetic +video, the transition function δ(q, q ′ ) is defined as + \delta (q,q') = \prod _{i=1}^{|\propset |}(C^\star _{i})^{\mathbf {1}_{\{q'_{i}= 1\}}}(1-C^\star _{i})^{\mathbf {1}_{\{q'_{i} = 0\}}}, + +(8) + +where 1{qi′ =1} is an indicator function that takes the +value 1 if pi is true in q ′ , and 0 otherwise. Similarly, 1{qi′ =0} +takes the value 1 if and only if pi is false in q ′ . This is obtained by a labeling function λ(q) that maps each state q to +the boolean value of its proposition. Further details on the +AV construction process are in the Appendix. + +4.4. Verifying Synthetic Video Formally +Given AV , we compute the satisfaction probability P[AV |= +Φ] by formally verifying AV against Φ. Specifically, we +use the model checking method STORM [19, 28] that utilizes a probabilistic computation tree logic (PCTL) variant +of temporal logic to calculate the satisfaction probability +of AV with respect to Φ. This probability is defined as +P[AV |= Φ] = Ψ(AV , Φ), where Ψ(·) is the probabilistic +model checking function. This is performed by analyzing +the probabilistic transitions and state labels within AV . +Final Score Calibration. Lastly, we calibrate the satisfaction probability to a NeuS-V score using a satisfaction +probability mapping function based on its empirical cumulative distribution, defined as + + S = \{s_1,\dots ,s_m\} = \ecdf (\probsat , D_m) \ \forall \ m \in M, +(9) +where Dm is the distribution of satisfaction probabilities +of each evaluation mode from wide samples of synthetic +videos, and S is the set of each evaluation mode score sm . +We take the average of these scores to comprehensively capture the variety of temporal specifications in the prompts + S_{\neusv } \leftarrow \frac {\sum S}{\mid S \mid }. + +(10) + +5. Experimental Setup +PULS uses GPT-4o and o1-preview for prompt to TL translation, whereas NeuS-V relies on InternVL2-8B [25] as its + + r = 0.460 + +r = 0.145 + +4 +3 +2 +1 + +Prompts and Evaluation Suite. As we observe that existing benchmarks for T2V evaluation [23, 26, 41] lack explicit temporal instructions, we present the NeuS-V prompt +suite with a total of 360 prompts. These are designed to +rigorously evaluate models on their ability to maintain temporal coherence and accurately follow event sequences. The +suite spans four themes (“Nature”, “Human & Animal Activities”, “Object Interactions”, and “Driving Data”) across +three complexity levels (basic, intermediate, and advanced) +based on the number of temporal and logical operators. +To ensure an unbiased baseline, we conduct a human +study instructing annotators to explicitly disentangle visual +quality from text-to-video alignment. Both our prompt suite +and the results of the human evaluation study will be made +publicly available upon acceptance. Further details are provided in the supplementary material. + +6. Results +Building on our experimental setup, we now turn to the empirical evaluation of NeuS-V. Our experiments are designed +to address three central questions that motivate the necessity +of our methodology. +1. Does NeuS-V’s focus on temporal fidelity translate to a +higher correlation with human annotations compared to +baselines that emphasize visual quality? +2. To what extent does grounding in temporal logic and formal verification provide a more robust evaluation framework than approaches based solely on VLMs? +3. Can the reliability of NeuS-V extend beyond our synthetic prompt suite to established larger-scale datasets? + +6.1. Formal Evaluation of Text-to-Video Models +As outlined in Section 5, we benchmark both closedsource and open-source state-of-the-art models: Gen-3 and +Pika (closed-source), alongside T2V-Turbo-v2 [34, 35] and +CogVideoX-5B [54] (open-source). +† https://runwayml.com/research/introducing-gen-3-alpha +‡ https://pika.art + +41 +3 +2 +1 +5 + +r = 0.335 +0.7 + +0.8 + +r = 0.072 +0.25 + +0.50 + +0.75 + +VBench +NeuS-V +(a) Gen-3 + +r = 0.010 + +1.00 + +Visual Text-to-Video +Quality Alignment + +Visual Text-to-Video +Quality Alignment + +3 + +2 +1 +0.4 + +0.6 + +r = 0.312 + +r = -0.020 + +4 +3 +25 +14 + +0.7 + +3 +2 + +5 + +3 + +VBench +NeuS-V +(c) T2V-Turbo-v2[35] + +r = 0.624 + +2 + +1 + +r = 0.550 + +4 + +0.75 0.80 0.85 0.90 0.2 + +r = -0.087 + +3 + +5 + +4 +52 + +r = 0.107 + +4 + +1 + +r = 0.479 + +5 + +Text-to-Video +Alignment + +T2V Models. We evaluate the performance of both +closed-source and open-source text-to-video models using +two distinct prompt sets. For closed-source models, we select Gen-3† and Pika‡ , and we use the open-source T2VTurbo-v2[35] and CogVideoX-5B[54]. We run them with +default hyperparameters on 8× A100 GPUs. + +r = 0.089 + +Text-to-Video +Alignment + +choice of VLM for automaton construction. We use a context of three frames when prompting the VLM. All the system prompts and example outputs can be found in our Appendix. We also use GPT-4o for prompt suite construction. + +r = 0.279 + +5 + +Visual +Quality + +Visual +Quality + +5 + +0.8 + +0.8 + +0.2 + +0.4 + +0.6 + +VBench +(b) Pika + +NeuS-V + +r = -0.056 + +r = 0.540 + +0.8 + +4 +3 +2 +1 + +0.6 + +0.7 + +0.8 + +0.2 + +0.4 + +0.6 + +VBench +NeuS-V +(d) CogVideoX-5B[54] + +0.8 + +Figure 4. Correlation with Human Annotations. NeuS-V consistently shows a stronger alignment with human text-to-video annotations (Pearson coefficients displayed at the top of each plot). + +Correlation with Human Annotations. We evaluate the +selected video generation models using both NeuS-V and +VBench, a well-established benchmark focused on visual +quality. To assess correlation with human scores for textto-video alignment, we plot these metrics in Figure 4. +Across all models, NeuS-V demonstrates a consistently +stronger correlation with human annotations, as indicated +by the best-fit line and Pearson coefficient, outperforming +VBench. By incorporating temporal logic specification and +the automaton representation of synthetic videos, NeuS-V +offers rigorous evaluations of text-to-video alignment. +Performance on our Prompt Suite. We break down and +analyze the performance of each T2V model on our evaluation suite, as shown in Table 1, organized by theme and +complexity for a subset of 160 prompts. Results indicate +that most models perform best when generating videos on +the themes “Nature” and “Human and Animal Activities”. +The Gen-3 model achieves the highest scores across the +suite, a result corroborated by human annotators. Furthermore, Figure 5 illustrates how each of the four evaluation +modes in PULS are utilized between models. Different +prompts rely on distinct modes, indicating that a combination of these modes comprehensively captures the variety of +temporal specifications in the prompts. + +6.2. Ablation on Temporal Logic and Verification – +How Important is Formal Language? +In this ablation study, we investigate the role of formal +grounding in temporal logic and model checking by replacing these components with VLM-based alternatives that +lack such rigor. Specifically, instead of using PULS to +translate prompts into atomic propositions and specifications, we directly query an LLM to break down prompts +into a set of yes/no questions covering the content and + + Prompts + +Gen-3 + +Pika + +T2V-Turbo-v2[35] + +CogVideoX-5B[54] + +By Theme + +Nature +Human & Animal Activities +Object Interactions +Driving Data + +0.716 (0.47) +0.752 (0.80) +0.710 (0.16) +0.716 (0.48) + +0.479 (0.70) +0.531 (0.67) +0.500 (0.40) +0.525 (0.66) + +0.564 (0.46) +0.564 (0.66) +0.553 (0.66) +0.525 (0.30) + +0.580 (0.53) +0.623 (0.43) +0.573 (0.65) +0.580 (0.52) + +By Complexity + +Basic (1 TL op.) +Intermediate (2 TL ops.) +Advanced (3 TL ops.) + +0.774 (0.60) +0.680 (0.27) +0.692 (-0.01) + +0.589 (0.70) +0.464 (0.44) +0.400 (0.33) + +0.610 (0.58) +0.508 (0.38) +0.494 (0.42) + +0.641 (0.65) +0.549 (0.28) +0.550 (0.78) + +0.723 (0.48) + +0.508 (0.62) + +0.552 (0.55) + +0.589 (0.54) + +Overall Score + +Table 1. Benchmarking SOTA Text-to-Video Models. Performance metrics reflect the full 360-prompt set, while correlations to human +evaluations (in parentheses) are computed on a 160-prompt subset. NeuS-V enjoys high correlation across all themes and complexities. + +0.4 + +0.4 + +0.2 + +0.2 + +0.0 + +5 +4 +3 +2 +1 + +(b) Pika + +Text-to-Video +Alignment + +Co Ov +ns er +ist all +en +cy +Re Sp +lat at +ion ial +sh +ip +O +Ex bj +ist ec +en t +ce +Ob +Ali ject +gn Ac +me tio +nt n + +Co Ov +ns er +ist all +en +cy +Re Sp +lat at +ion ial +sh +ip +O +Ex bj +ist ec +en t +ce +Ob +Ali ject +gn Ac +me tio +nt n + +0.0 + +(a) Gen-3 + +Text-to-Video +Alignment + +0.6 + +0.6 + +Pika +(r = 0.296) + +5 + +T2V-Turbo-v2 +(r = 0.295) + +CogVideoX-5B +(r = 0.264) + +Text-to-Video +Alignment + +0.8 + +Text-to-Video +Alignment + +0.8 + +Gen-3 +(r = 0.216) + +Mode Score + +1.0 + +Mode Score + +1.0 + +4 +3 +2 +1 +0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 + +0.8 + +0.8 + +Mode Score + +1.0 + +Mode Score + +1.0 + +Accuracy +Accuracy +(a) GPT-4o + LLaMa-3.2-11B + +5 + +Gen-3 +(r = 0.102) + +Pika +(r = 0.294) + +T2V-Turbo-v2 +(r = 0.202) + +CogVideoX-5B +(r = 0.211) + +4 +3 +2 +1 + +5 +4 +3 +2 + +1 +0.00 0.25 0.50 0.75 1.00 + +0.4 + +0.6 + +0.8 + +1.0 + +Accuracy +Accuracy +(b) GPT-4o + LLaVA-Video-7B + +0.6 + +0.6 + +Figure 6. Is Formal Language Important? VLMs without +grounding in formal temporal logic lead to lower pearson coefficients (in brackets) as compared to NeuS-V from Figure 4. + +0.4 + +0.4 + +0.0 + +0.0 + +Co Ov +ns er +ist all +en +cy +Re Sp +lat at +ion ial +sh +ip +O +Ex bj +ist ec +en t +ce +Ob +Ali ject +gn Ac +me tio +nt n + +0.2 + +Co Ov +ns er +ist all +en +cy +Re Sp +lat at +ion ial +sh +ip +O +Ex bj +ist ec +en t +ce +Ob +Ali ject +gn Ac +me tio +nt n + +0.2 + +(c) T2V-Turbo-v2 + +(d) CogVideoX-5B + +Figure 5. Distribution of scores across the four modes of PULS + +visual characteristics of each prompt. Next, rather than +verifying with NeuS-V, we employ a VLM to score each +frame independently based on the earlier generated questions. This ablation also reflects existing prompt-to-VQA +baselines [31, 32, 40, 58]. +For our setup, we use GPT-4o [1] as the LLM, along with +two VLM configurations: LLaMa-3.2-11B-Vision-Instruct§ +(capable of processing images) and LLaVA-Video-7BQwen2¶ (capable of processing complete videos). We evaluate these methods with our prompt suite and the results are +presented in Figure 6. Our findings indicate that while both +approaches show a positive correlation with human annotations (as indicated by Pearson’s coefficient), they do not +achieve the same strength of correlation as the formal methods in Figure 4 do. Also, LLaVA-Video exhibits overconfi§ https://huggingface.co/meta-llama/Llama-3.2-11B-Vision +¶ https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2 + +dence, resulting in a weaker correlation than the image-only +LLaMa-3.2. These results underscore that the same VLMs +when grounded in temporal logic and formal verification +provide a more robust evaluation framework. + +6.3. Demonstrating Robustness on a Real-World +Video-Captioning Dataset +Our prompt evaluation suite, while effective, is modest in +size with only 160 prompts. One might argue that this +limited scale, along with the use of synthetic video captions, could raise doubts about the reliability of our metric. +To address this concern, we use MSR-VTT [52], a wellestablished dataset for video captioning – a task which also +involves assessing how well a video aligns with a given +textual description. MSR-VTT features a large-scale set +of video-caption pairs annotated by MTurk workers. We +leverage their credibility and repurpose its validation set to +evaluate the reliability of our metric. We create two splits: +a positive set (where captions match videos) and a negative set (where captions do not match videos). We then test +whether the NeuS-V metric can effectively distinguish between aligned and misaligned video-caption pairs. +In Table 2, we compare NeuS-V with VBench, reporting + + the mean and standard deviation of the scores for aligned +and misaligned pairs. Our results show that NeuS-V successfully distinguishes between the positive and negative +sets, assigning higher scores to aligned pairs and lower +scores to misaligned ones. In contrast, VBench demonstrates a smaller difference between aligned and misaligned +scores, highlighting NeuS-V’s superior ability to capture +meaningful text-to-video alignment. +Metric + +Aligned Captions + +Misaligned Captions + +Difference (↑) + +VBench +NeuS-V + +0.78 (± 0.12) +0.82 (± 0.10) + +0.40 (± 0.24) +0.30 (± 0.18) + +0.38 +0.52 + +VLM Context + +Gen-3 + +Pika + +T2V-Turbo-v2 + +CogVideoX-5B + +1 Frame +3 Frames + +0.859 (0.29) +0.614 (0.48) + +0.613 (0.51) +0.409 (0.62) + +0.590 (0.34) +0.417 (0.55) + +0.715 (0.32) +0.461 (0.54) + +Table 3. Impact of contextual frames to VLM. More contextual +information results in higher correlation with human annotations. +Choice of VLM + +Gen-3 + +Pika + +T2V-Turbo-v2 + +CogVideoX-5B + +LLaMa3.2-11B +InternVL2-8B + +0.921 (0.15) +0.859 (0.29) + +0.660 (0.16) +0.613 (0.51) + +0.730 (0.08) +0.590 (0.34) + +0.785 (-0.02) +0.715 (0.32) + +Table 4. Impact of choice of VLM on correlation. InternVL2-8B +for automaton construction shows higher correlation with human +annotations. + +Table 2. Evaluating reliability on MSR-VTT. NeuS-V distinguishes aligned and misaligned captions better than VBench. + +6.4. Ablations on Architectural Choices +Visual Context to VLM. In this first ablation of architectural choices, we investigate whether using more frames +necessarily improves the performance of NeuS-V. To measure this, we compare the use of a single frame versus +three frames when prompting our VLM (InternVL2-8B). +As shown in Table 3, the use of three frames consistently +results in a higher correlation with text-to-video alignment +annotations. We did not test more than three frames, as this +would exceed the context window of the LLM. +Choice of VLM. In the second ablation, we examine whether the choice of VLM affects automata construction. We test two recent VLMs: InternVL2-8B[45] +and LLaMa3.2-11B-Vision with single-frame contexts. +As seen in Table 4, InternVL2-8B consistently achieves +higher correlation with text-to-video alignment annotations. +LLaMa3.2 model tends to be overconfident in its outputs, +despite attempts at calibration due to heavy skew in probabilities. This leads to a lower correlation with human labels. + +models when handling temporally extended prompts. Although marketed as text-driven video generation, these +models often fall short of creating motion and sequences +that truly follow the given prompts. Much of the perceived “motion” comes from simplistic effects like panning and zooming with minimal capacity for generating +interactions or meaningful scene transitions. In practice, +video-generation workflows require numerous rounds of reprompting to achieve prompt adherence, whereas ideally, +we should expect alignment with the prompt most times. +Limitations and Future Work. NeuS-V currently lacks +a mechanism to penalize unintended elements in generated +videos, making it difficult to distinguish between irrelevant +artifacts and potentially useful auxiliary content that was +not explicitly requested in the prompt. In the future, our +approach could be used to refine the generated videos until +they meet a set threshold, boosting prompt adherence without retraining. Furthermore, feedback from NeuS-V could +also be used for prompt optimization or model training. + +8. Conclusion +7. Discussion +Is Rigorous Formalism Key? A natural question that +arises is whether formal temporal logic can be replaced with +simpler if-else style checks and assertions. In principle, yes. +However, as prompt length and complexity increase, such +rule-based approaches exhibit scaling challenges, making +them impractical. In contrast, symbolic temporal operators +offer a near-linear scaling to verify adherence to automata. +As shown through Figure 6 in Section 6.2, even though such +alternative approaches may show promise, they ultimately +fall short of the robustness provided by formal temporal +logic. NeuS-V is the first work that introduces formalism +to T2V evaluation, going beyond quality-based metrics. +Rethinking Text-to-Video Models. Evaluations in Table 1 reveal a significant limitation in current text-to-video + +Text-to-video models are increasingly being applied in +safety-critical areas such as autonomous driving, education, +and robotics, where maintaining temporal fidelity is essential. However, we find that most current evaluation methods +prioritize visual quality over text-to-video alignment. To +address this, we introduce NeuS-V, a novel metric that translates prompts into temporal logic specifications and represents videos as automata, thereby scoring them through formal verification. Alongside NeuS-V, we present a benchmark of temporally challenging prompts to assess the ability of state-of-the-art T2V models to follow specified event +sequences. Our evaluation reveals limitations in these models, which frequently rely on simplistic camera movements +rather than true temporal dynamics. 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I2vgen-xl: High-quality image-to-video +synthesis via cascaded diffusion models. arXiv preprint +arXiv:2311.04145, 2023. 2 +[58] Zhichao Zhang, Xinyue Li, Wei Sun, Jun Jia, Xiongkuo Min, +Zicheng Zhang, Chunyi Li, Zijian Chen, Puyi Wang, Zhongpeng Ji, Fengyu Sun, Shangling Jui, and Guangtao Zhai. +Benchmarking aigc video quality assessment: A dataset and +unified model, 2024. 2, 7 + + Appendix +Supplementary Material +9. Temporal Logic Operation Example + +10.1. DSPy & MIPROv2 + +Given a set of atomic propositions P = {Event A, +Event B}, the TL specification Φ = □ Event A (read as +“Always Event A”) means that ‘Event A’ is True for every +step in the sequence. Additionally, Φ = ♢ Event B (read as +“eventually event b”) indicates that there exists at least one +‘Event B’ in the sequence. Lastly, Φ = Event A U Event +B (read as “Event A Until Event B”) means that ‘Event A’ +exists until ‘Event B’ becomes True, and then ‘Event B’ +remains True for all future steps. + +To evaluate L4-5 of Algorithm 2, PULS uses DSPy and +MIPROv2 to optimize the prompts by selecting the appropriate subset of DT2P and DT2TL respectively. First, it creates a bootstrap dataset D′ from the original dataset D. +This dataset comprises effective few-shot examples that are +generated using rejection sampling. Since the bootstrapping process is done for both DT2P and DT2TL , we can say +D′ = D′T2P ∪ D′T2TL . +Next, PULS proposes k different instructions using an +LLM depending on the properties of the original dataset +D and the original instruction, yielding the instruction sets +xT2P = {xT2Pj }kj=0 and xT2TL = {xT2TLj }kj=0 . Thus, given +a particular dataset entry i and instruction j: + +10. Prompt Understanding via Temporal Logic +Specification (PULS) +In order to obtain Dtrain , we first begin with the larger +dataset D with size B where D = DT2P ∪ DT2TL . DT2P +and DT2TL are defined as the following: + \mathfrak {D}_\text {T2P} = \{ (\mathcal {T}_i, \propset _i) \}_{i=1}^{\mathcal {B}}, \quad \mathfrak {D}_\text {T2P} \subset \mathfrak {D}, + + \mathfrak {D}_\text {T2TL} = \{ (\mathcal {T}_i, \propset _i,\spec _i) \}_{i=1}^{\mathcal {B}}, \quad \mathfrak {D}_\text {T2TL} \subset \mathfrak {D}. + +(11) + +(12) + +Using these datasets, we use PULS to find a specification +Φ for each mode and for a given text prompt T using the +following algorithm. + + \propset _{i, j}^\text {pred} = \texttt {LLM}(\mathcal {T}_i, x_{\text {T2P}_j}, \theta _{llm}), \quad \mathcal {T}_i \in \mathfrak {D}_\text {T2P}' + +(13) + + \spec _{i, j}^\text {pred} = \texttt {LLM}(\mathcal {T}_i, \propset _i, x_{\text {T2TL}_j}, \theta _{llm}), \quad \mathcal {T}_i, \propset _i \in \mathfrak {D}_\text {T2TL}' + +(14) +Accuracy functions are defined as the following, where +π is an evaluation metric that returns a similarity score between 0 and 1 of its inputs: + f_\text {T2P}(\mathcal {D}_\text {T2P}', i, j) = \pi (\mathcal {P}_i, \mathcal {P}_{i, j}^{pred}), \quad \propset _i \in \mathfrak {D}_\text {T2P}' + +(15) + + f_\text {T2TL}(\mathcal {D}_\text {T2TL}', i, j) = \pi (\Phi _i, \Phi _{i, j}^{pred}), \quad \Phi _i \in \mathfrak {D}_\text {T2TL}' + +(16) + +Algorithm 2: PULS +Require: LLM Prompt Optimizer MIPROv2 +Input: List of mode M , text prompt T , training examples DT2P +and DT2TL , number of few shot examples N +Prompts: Modules LMT2P and LMT2TL +Output: TL Specification Φ +1 begin +2 +Φ ← {} +// Initialize empty set Φ +3 +for m ∈ M do +4 +DT2P|train = MIPROv2.optimize (DT2P , m, N ) +// Find optimal fewshot dataset +5 +DT2TL|train = MIPROv2.optimize (DT2TL , m, N ) +// Find optimal fewshot dataset +⋆ ← {(T , P ) | T , P ∈ D +N +6 +θT2P +i +i +i +i +T2P|train }i=1 +// Update parameters +⋆ +7 +θT2TL +← {(Ti , Pi , Φi ) | Ti , Pi , Φi ∈ DT2TL|train }N +i=1 +// Update parameters + +⋆ +8 +P = LMT2P T , m, θT2P +// Compute +propositions + +⋆ +9 +Φ ← Φ ∪ {LMT2TL T , P, m, θT2TL +} +// Compute specification +10 +end for +11 + +return Φ + +Bayesian Optimization is used to create an optimal subset, Dtrain of size N , with the following operation: + \begin {split} \dttp = &\arg \max _{\mathfrak {D}_s \subset \mathfrak {D}_\text {T2P}', |\mathfrak {D}_s| = \mathcal {N}} \\ &\left [\sum _{i \in \mathfrak {D}_s} \max _{0 \leq j \leq k} f_\text {T2P}(\mathfrak {D}_s, i, j)\right ] \end {split} +(17) + + \begin {split} \dttl &= \arg \max _{\mathfrak {D}_s \subset \mathfrak {D}_\text {T2TL}', |\mathfrak {D}_s| = \mathcal {N}} \\ &\left [\sum _{i \in \mathfrak {D}_s} \max _{0 \leq j \leq k} f_\text {T2TL}(\mathfrak {D}_s, i, j)\right ] \end {split} +(18) + + \mathfrak {D}_\text {train} = \dttp \cup \dttl . + +(19) + +With this process, Dtrain can be used as an effective fewshot dataset for PULS. LMT2P uses DT2P|train and LMT2TL +uses DT2TL|train as their datasets. Prompt 1 and Prompt 2 + + detail the system prompts for the overall consistency mode +in LMT2P and LMT2TL respectively. +PULS Prompt for Text to Propositions (.md) + +compute the token probability of the response and use it as +a confidence score for the detection. + +**System Message**: +Your input fields are: +1. ‘input prompt’ (str): Input prompt +summarizing what happened in a video. +Your output fields are: +1. ‘reasoning’ (str) +2. ‘output propositions’ (str): A list +of atomic propositions that correlate with +the inputted prompt. For example, for a +prompt such as ‘A person holding a hotdog is +walking down to the street where many cars +next to the huge truck’, the propositions are +‘person holds hotdog’, ‘person walks’, and +‘car next to truck’. This outputted list of +propositions MUST be formatted as: [prop1, +prop2, prop3]. +Your objective is: +Convert from a prompt to a list of +propositions using the following schema. +**User message**: +Input Prompt: A boat sailing leisurely along +the Seine River with the Eiffel Tower in +background, zoom out. + +PULS Prompt for Text to Temporal Logic (.md) +**System Message**: +Your input fields are: +1. ‘input prompt‘ (str): Input prompt +summarizing what happened in a video. +2. ‘input propositions‘ (str): A list of +atomic propositions in the video. + +**Assistant message**: +Reasoning: Not supplied for this particular +example. + +Your output fields are: +1. ‘reasoning’ (str) +2. ‘output specification’ (str): The formal +specification of the event sequence. The +response will be a linear temporal logic (LTL) +formula made of the inputted propositions +combined with LTL symbols. Each symbol can +only be one of [‘AND’, ‘OR’, ‘NOT’, ‘UNTIL’, +‘ALWAYS’, ‘EVENTUALLY’]. + +Output Propositions: [‘There is a boat’, ‘The +boat is sailing leisurely’, ’The boat is along +the Seine River’, ‘The Eiffel Tower is in the +background’, ‘The view is zooming out’] + +Your objective is: +Convert from a prompt and a list of +propositions to a TL specification using +the following schema. + +··· +< redacted more examples > + +**User message**: +Input Prompt: A baseball glove on the right +of a tennis racket, front view. + +Respond with the corresponding output fields, +starting with the field ‘reasoning’, then +‘output propositions’. + +··· + +Prompt 1. T2P Prompt for PULS. System prompt to map +prompts to the propositions for the “Overall Consistency” +mode. Other modes follow a similar structure, albeit with minor changes in instructions and few-shot examples. + +11. Vision Language Model Calibration +In this section, we provide the implementation details to detect the existence of propositions obtained by PULS to label +each frame in the synthetic video. First, we describe how +VLMs conduct inference and then describe the methodology for obtaining the optimal thresholds for calibrating the +vision language model. + +Input Propositions: [‘There is a baseball +glove’, ‘There is a tennis racket’, ‘The +baseball glove is on the right of the tennis +racket’, ‘The view is from the front’] +Respond with the corresponding output fields, +starting with the field ‘reasoning’, then +‘output specification’. +**Assistant message**: +Reasoning: Not supplied for this particular +example. +Output Specification: (There is a baseball +glove AND There is a tennis racket AND The +baseball glove is on the right of the tennis +racket AND The view is from the front) +··· +< redacted more examples > +··· + +11.1. Inference Via Vision Language Models +We use VLM as a semantic detector as explained in Section 4.2. We pass each atomic proposition pi ∈ P such as +“person”, “car”, “person in the car”, etc. along with Section 11.1. Once the VLM outputs either ‘Yes’ or ‘No’, we + +Prompt 2. T2TL Prompt for PULS. System prompt to map +prompts and propositions for the “Overall Consistency” mode. +Other modes follow a similar structure, albeit with minor +changes in instructions and few-shot examples. + + Prompt for Semantic Detector (VLM) +True Positive Rate (TPR) + +1.0 + +Accuracy + +0.8 +0.6 +0.4 +0.2 +0.0 +0.0 + +0.2 + +0.4 + +0.6 + +0.8 + +Dataset for Calibration: We utilize the COCO Captions +[7] dataset to calibrate the following open-source vision language models – InternVL2 Series (1B, 2B, 8B) [45] and +LLaMA-3.2 Vision Instruct [2] – for NeuS-V. Given that +each image-caption pair in the dataset is positive coupling, +we construct a set of negative image-caption pairs by randomly pairing an image with any other caption corresponding to a different image in the dataset. Once we construct +the calibration dataset, which comprises 40000 image caption pairs, we utilize the VLM to output a ‘Yes’ or a ‘No’ +for each pair. + +0.4 +0.2 + +0.8 +0.6 + +0.0 +0.0 + +0.2 + +0.4 + +0.6 + +0.8 + +1.0 + +0.6 + +0.8 + +1.0 + +False Positive Rate (FPR) +Accuracy +vs Confidence After Calibration +1.0 + +0.6 + +0.2 + +11.2. False Positive Threshold Identification + +0.6 + +0.8 + +0.4 + +TPR vs FPR at all thresholds + +0.8 + +0.0 +0.0 + +1.0 + +Threshold +Accuracy +vs +Confidence +Before Calibration +1.0 + +Accuracy + +Prompt 3. Semantic Detector VLM. Used to identify the +atomic proposition within the frame by initiating VLM with +a single frame or a series of frames. + +Accuracy vs Threshold + +1.0 + +Accuracy + +Is there {atomic proposition (pi )} present in +the sequence of frames? +[PARSING RULE] 1. You must only return a Yes +or No, and not both, to any question asked. +2. You must not include any other symbols, +information, text, or justification in your +answer or repeat Yes or No multiple times. +3. For example, if the question is ’Is there +a cat present in the Image?’, the answer must +only be ’Yes’ or ’No’. + +0.4 +0.2 + +0.2 + +0.4 + +0.6 + +Confidence + +InternVL2-1B + +0.8 + +1.0 + +0.0 +0.0 + +InternVL2-2B + +0.2 + +InternVL2-8B + +0.4 + +Confidence + +LLAMA-3.2-11B + +Figure 7. Calibration Plots. We plot the accuracy vs threshold for +all VLMs on our calibration dataset constructed from the COCO +Caption dataset (top left). We plot the True Positive Rate (TPR) +vs False Positive Rate (FPR) across all thresholds on the top right. +Finally, the bottom plots show the confidence vs accuracy of the +model before and after calibration, respectively. + +tion (7)). + \label {eq:calibrated_score} C^\star = \{ C^\star _{p_i, j} \mid p_i \in \propset , j \in \{1, 2, \dots , n\} \}. + +Thresholding Methodology We can identify the optimal +threshold for the VLM by treating the above problem as either a single-class or multi-class classification problem. We +opt to do the latter. The process involves first compiling detections into a list of confidence scores and one-hot encoded +ground truth labels. We then sweep through all available +confidence scores to identify the optimal threshold. Here, +we calculate the proportion of correct predictions by applying each threshold (see Figure 7). The optimal threshold is +identified by maximizing accuracy, which is the ratio of the +true positive and true negative predictions. Additionally, to +comprehensively evaluate model behavior, we compute Receiver Operating Characteristics (ROC) as shown in Figure +7, by computing the true positive rate (TPR), and false positive rate (FPR) across all thresholds. Once we obtain the +optimal threshold, we utilize it to calibrate the predictions +from the VLM. We show the accuracy vs confidence plots +before and after calibration in Figure 7. + +12. Video Automaton Generation Function +Given a calibrated score set (see Equation (20)) across all +frames Fn (where n is the frame index of the video) and +propositions in P, we construct the video automaton AV +using the video automaton generation function (see Equa- + +(20) + +As shown in Algorithm 3, we first initialize the components of the automaton, including the state set Q, the label +set λ, and the transition probability set δ, all with the initial +state q0 . Next, we iterate over C ⋆ , incrementally constructing the video automaton by adding states and transitions for +each frame. This process incorporates the proposition set +and associated probabilities of all atomic propositions. We +compute possible labels for each frame as binary combinations of P and calculate their probabilities using the C ⋆ . + +13. NeuS-V Prompt Suite +Creating the Dataset: Our dataset is carefully designed +to evaluate temporal fidelity and event sequencing in generated videos. It spans four themes – “Nature”, “Human +& Animal Activities”, “Object Interactions”, and “Driving Data”, with each theme containing 40 prompts. These +prompts vary in complexity, categorized into 20 basic, 15 +intermediate, and five advanced prompts based on the number of temporal and logical operators (see Table 5). These +prompts were generated using GPT-4o with the system +prompt in Prompt 4. To ensure quality, each prompt was +manually verified for clarity, completeness, and relevance. + + Generating Videos from Prompts: We generated videos +for all 160 prompts using both open-source and closedsource models. For open-source models, we utilized pretrained weights and code by cloning their HuggingFace +Spaces and querying them using huggingface client. +For closed-source models (Gen3 and Pika), we implemented a custom CLI that reverse-engineers their front-end +interfaces to automate video generation requests. In total, we produced 640 videos (160 prompts × four models). +We also plan to launch a publicly available leaderboard on +HuggingFace after acceptance, which will allow continuous +evaluation of new T2V models as they emerge. +Annotations: Annotations were crowdsourced from 20 +participants, including contributors from social media platforms like X (formerly Twitter) and LinkedIn. As shown in +Figure 8, annotators were instructed to evaluate the alignment of videos with their corresponding prompts while disentangling visual quality from text-to-video alignment. +Insights into Prompts: We showcase representative examples from our dataset in Table 6 and Table 7, highlighting the diversity and complexity of prompts. These examples provide prompts to represent them in different modes. +In the future, we plan to expand the dataset by adding more +prompts across existing themes and introducing new categories to further enhance its utility and scope. +Complexity + +Theme + +Total Prompts + +Basic + +Intermediate + +Advanced + +Nature +Human & Animal Activities +Object Interactions +Driving Data + +20 +20 +20 +20 + +15 +15 +15 +15 + +5 +5 +5 +5 + +40 +40 +40 +40 + +Total + +80 + +60 + +20 + +160 + +Table 5. Statistics of NeuS-V Prompt Suite. We include prompts +from various themes across different complexities to evaluate T2V +models on a total of 160 prompts. + + Algorithm 3: Video Automaton Generation +Input: Set of semantic score across all frames given all atomic propositions +{C ⋆ = Cp⋆i ,j | pi ∈ P, j ∈ {1, 2, . . . , n}}, set of atomic propositions P +Output: Video automaton AV +1 begin +2 +Q ← {q0 } +// Initialize the set of states with the initial state +3 +λ ← {(q0 , initial)} +// Initialize the set of labels with the initial label +4 +δ ← {} +// Initialize the set of state transitions +5 +Qp ← {q0 } +// Track the set of previously visited states +⋆ +| +6 +n ← |C +// Calculate the total number of frames n +|P| +7 +for j ← 1 to n do +8 +Qc ← {} +// Track the set of current states +k +|P| +9 +for ej ∈ 2 do +// e_j^k : +10 +11 +12 + +unique combination of 0s and 1s for atomic propositions in \propset + +λ(qjk ) = {v1 , v2 , . . . , vi | vi ∈ {1, 0}, ∀i ∈ {1, 2, . . . , |P|}} +pr(j, k) ← 1 +// Initialize probability for the label +for vi ∈ λ(qjk ) do +// Calculate probability for ekj + +if vi = 1 then +pr(j, k) ← pr(j, k) · Cp⋆i ,j + +13 +14 + +else +pr(j, k) ← pr(j, k) · (1 − Cp⋆i ,j ) + +15 +16 + +// Add state and define transitions if the probability is positive + +if pr(j, k) > 0 then +Q ← Q ∪ {qjk } +Qc ← Qc ∪ {qjk } +λ ← λ ∪ {(qjk , λ(qjk ))} +for qj−1 ∈ Qp do +δ(qj−1 , qjk ) ← pr(j, k) +δ ← δ ∪ {δ(qj−1 , qjk )} + +17 +18 +19 +20 +21 +22 +23 + +end for + +24 + +end for + +25 +26 +27 +28 + +Qp ← Qc +end for +end for +// Add terminal state + +29 +30 +31 +32 +33 +34 +35 +36 + +0 +Q ← Q ∪ {qj+1 +} +0 +λ ← λ ∪ {(qj+1 , terminal)} +for qj−1 ∈ Qp do +0 +δ(qj−1 , qj+1 +)←1 +0 +δ ← δ ∪ {δ(qj−1 , qj+1 +)} + +end for // Return video automaton +AV ← (Q, q0 , δ, λ) +return AV + +// Update previous state + + Generating Temporally Extended Prompts (.md) +**Objective**: Generate individual prompts for a +text-to-video generation benchmark. Each prompt +should focus on specific temporal operators and +adhere to the given theme and complexity level. +Your goal is to create clear, vivid prompts +that illustrate events occurring in time, with a +strong emphasis on temporal relationships. +--#### **Instructions for Prompt Generation** +1. **Theme**: One of the following themes: +Nature, Human and Animal Activities, Object +Interactions, or Driving Data +2. **Complexity Level**: +- **Basic (1 Operator)**: Use only **one +temporal operator** ("Always," "And," or +"Until") in the prompt. +- **Intermediate (2 Operators)**: Use **two +temporal operators** in a sequence. The +prompt should clearly connect the events with +each operator in a natural, coherent way. +- **Advanced (3 Operators)**: Use **three +temporal operators** in a chain, showing a +progression of events. Each part should flow +logically to the next. +3. **Available Temporal Operators**: +- **"Always"**: Describes an event that +continuously occurs in the background or +context. +- **"And"**: Combines two events happening +simultaneously or in coordination. +- **"Until"**: Describes an event that +occurs until another event starts. +--#### **Examples** +##### **Theme: Nature** +- **Basic (1 Operator)**: +- "Always a river flowing gently." +- "Rain pouring until the sun comes out." +- "A bird chirping and a dog barking nearby." +··· +< redacted more examples > +··· +--**Ensure** that each prompt uses only the +specified number of operators based on +complexity, with clear temporal transitions +between events. Use vivid language to create +a realistic and engaging scenario. Try not to +use language that is too abstract or ambiguous, +focusing on concrete actions and events. Do not +include any acoustic information in the prompts, +as they are meant to describe visual scenes only. +I shall provide you with a theme, complexity +level, and the number of prompts you need to +generate. You must only output the prompts, +each on a new line, without any additional +information. +Are you ready? + +Prompt 4. System Prompt for NeuS-V Prompt Suite. Used to +query GPT-4o to generate temporally extended prompts across different themes and complexities. + +Figure 8. Tool for Annotating Videos. Subjects are instructed +to disambiguate quality and alignment during annotation, scoring +each from a range of 1 through 5. + + Theme + +Complexity + +Nature + +Basic + +Prompts and Specification Modes +Prompt: Snow falling until it covers the ground +Object Existence: (“snow”) U (“ground”) +Object Action Alignment: (“snow falls” U “ground is covered”) +Spatial Relationship: F (“snow covers ground”) +Overall Consistency: (“snow falling” U “it covers the ground”) +Prompt: Always waves crashing against the rocky shore +Object Existence: G (“waves” & “shore”) +Object Action Alignment: G (“waves crash against rocky shore”) +Spatial Relationship: G (“waves on shore”) +Overall Consistency: G (“waves crashing against the rocky shore”) + +Intermediate + +Prompt: The sun shining until the clouds gather, and then rain begins to fall +Object Existence: (“sun shining” U “clouds gather”) & F (“rain begins to fall”) +Object Action Alignment: (“sun shines” U “clouds gather”) & F (“rain falls”) +Spatial Relationship: G (“sun over horizon” & “dew on grass”) +Overall Consistency: (“sun shining” U “clouds gather”) & F (“rain begins to fall”) +Prompt: A butterfly resting on a flower until a gust of wind comes, and then it flies away +Object Existence: (“butterfly” & “flower”) U (“wind”) +Object Action Alignment: (“butterfly rests on flower” U “gust of wind comes”) & F (“butterfly flies away”) +Spatial Relationship: (“butterfly on flower”) U (“butterfly flies away”) +Overall Consistency: (“butterfly resting on a flower” U “gust of wind comes”) & F (“it flies away”) + +Advanced + +Human and +Animal +Activities + +Basic + +Prompt: Always a river flowing quietly through the valley, until the sky darkens with storm clouds, and rain +begins to pour heavily +Object Existence: G (“river” & “storm clouds”) +Object Action Alignment: ((“river flows quietly” U “sky darkens”)) & F(“rain pours”) +Spatial Relationship: G(“river flowing through valley”) U (“sky darkens with storm clouds”) & F +“rain begins to pour heavily” +Overall Consistency: G ((“river flowing quietly through the valley”) U “sky darkens with storm clouds”) & F +“rain begins to pour heavily” +Prompt: A dog barking until someone throws a ball +Object Existence: (“dog”) U (“ball”) +Object Action Alignment: (“dog barks” U “someone throws ball”) +Spatial Relationship: G (“clock on mantle” & “fireplace beneath mantle”) +Overall Consistency: (“dog barking” U “someone throws a ball”) +Prompt: A bird singing until it flies away to another branch +Object Existence: (“bird”) U (“branch”) +Object Action Alignment: (“bird sings” U “bird flies away to another branch”) +Spatial Relationship: (“bird on branch”) U (“bird on different branch”) +Overall Consistency: (“bird singing” U “it flies away to another branch”) + +Intermediate + +Prompt: A child building a sandcastle until the tide rises, and then they watch it wash away +Object Existence: (“child” & “sandcastle”) U “tide” +Object Action Alignment: ((“child builds sandcastle” U “tide rises”)) & F(“child watches it wash away”) +Spatial Relationship: (“child building sandcastle” U “tide rises”) +Overall Consistency: (“child building a sandcastle” U “tide rises”) & F “child watches sandcastle wash away” +Prompt: Always a cat lounging on the porch, and butterflies fluttering around +Object Existence: G (“cat” & “butterflies”) +Object Action Alignment: G((“cat lounges” & “butterflies flutter”)) +Spatial Relationship: G(“cat lounging on porch” & “butterflies fluttering around”) +Overall Consistency: G (“cat lounging on the porch” & “butterflies fluttering around”) + +Advanced + +Prompt: A dog digging in the backyard, until its owner arrives, and then they play fetch together +Object Existence: (“dog” & “backyard”) U (“owner” & “ball”) +Object Action Alignment: (“dog digs in backyard” U “owner arrives”) & F (“dog plays fetch with owner”) +Spatial Relationship: (“dog in backyard”) U (“owner arrives”) +Overall Consistency: (“dog digging in the backyard” U “its owner arrives”) & F (“they play fetch together”) + +Table 6. NeuS-V Prompt Suite: Illustrative prompts and their detailed specifications (across all four modes) for varying complexities +within the “Nature” and “Human and Animal Activities” themes. + + Theme + +Complexity + +Object +Interactions + +Basic + +Prompts and Specification Modes +Prompt: A lamp glowing until it is turned off +Object Existence: (“lamp”) U (¡‘lamp”) +Object Action Alignment: (“lamp glows” U “lamp is turned off”) +Spatial Relationship: (“cars passing by person”) +Overall Consistency: (“lamp glowing” U “it is turned off”) +Prompt: A car engine running and the dashboard lights flashing +Object Existence: (“car engine” & “dashboard lights”) +Object Action Alignment: (“car engine runs”&“dashboard flashes”) +Spatial Relationship: (“car engine running” & “dashboard lights flashing”) +Overall Consistency: (“car engine running” & “dashboard lights flashing”) + +Intermediate + +Prompt: Always a record player spinning a vinyl, and light glowing softly from a nearby lamp +Object Existence: G (“record player” & “lamp”) +Object Action Alignment: G((“record player spins”&“lamp glows”)) +Spatial Relationship: G(“vinyl on record player” & “light glowing from nearby lamp”) +Overall Consistency: G (“record player spinning a vinyl” & “light glowing softly from a nearby lamp”) +Prompt: A drone hovering in the air until it reaches its next waypoint, and then it continues to fly +Object Existence: (“drone”) U (“waypoint”) +Object Action Alignment: (“drone hovers in air” U “drone reaches next waypoint”) & F +(“drone continues to fly”) +Spatial Relationship: (“drone in air”) U (“drone reaches waypoint”) +Overall Consistency: (“drone hovering in air” U “drone reaches next waypoint”) & F (“drone continues to fly”) + +Driving Data + +Advanced + +Prompt: A lightbulb flickering intermittently, until the switch is turned off, and then the room is cast into darkness +Object Existence: (“lightbulb”) U “switch” +Object Action Alignment: ((“lightbulb flickers” U “switch turned off”)) & F(“room darkens”) +Spatial Relationship: (“lightbulb flickering” U “switch is turned off”) +Overall Consistency: (“lightbulb flickering intermittently” U “switch is turned off”) & F +“room is cast into darkness” + +Basic + +Prompt: The vehicle moving forward until it reaches a stop sign +Object Existence: “vehicle” U “stop sign” +Object Action Alignment: (“vehicle moves” U “vehicle reaches stop sign”) +Spatial Relationship: (“vehicle moving forward” U “vehicle at stop sign”) +Overall Consistency: (“vehicle moving forward” U “vehicle reaches a stop sign”) +Prompt: A motorcycle revving and a bus pulling up beside it +Object Existence: (“motorcycle” & “bus”) +Object Action Alignment: (“motorcycle revs”&“bus pulls up”) +Spatial Relationship: (“motorcycle revving” & “bus pulling up beside”) +Overall Consistency: (“motorcycle revving” & “bus pulling up beside”) + +Intermediate + +Prompt: A traffic light turning red until pedestrians finish crossing, and then it shifts to green +Object Existence: (“traffic light”) U “pedestrians” +Object Action Alignment: ((“traffic light is red” U “pedestrians finish crossing”)) & +F(“traffic light turns green”) +Spatial Relationship: (“traffic light turning red” U “pedestrians finish crossing”) +Overall Consistency: (“traffic light turning red” U “pedestrians finish crossing”) & F +“traffic light shifts to green” +Prompt: Always an electric vehicle charging at the station, and its driver reading a book nearby +Object Existence: G (“vehicle” & “driver”) +Object Action Alignment: G((“electric vehicle charges”&“driver reads”)) +Spatial Relationship: G(“electric vehicle charging at station” & “driver reading book nearby”) +Overall Consistency: G (“electric vehicle charging at the station” & “driver reading a book nearby”) + +Advanced + +Prompt: A car driving through the city streets, until it encounters a construction zone, and then it reroutes to an +alternate path +Object Existence: (“car” & “streets”) U (“construction zone” & “path”) +Object Action Alignment: (“car drives through city streets” U “car encounters construction zone”) & F +(“car reroutes to alternate path”) +Spatial Relationship: (“car on city streets”) U (“car at construction zone”) +Overall Consistency: (“car driving through city streets” U “it encounters a construction zone”) & F +(“it reroutes to an alternate path”) + +Table 7. NeuS-V Prompt Suite (continued): Illustrative prompts and their detailed specifications (across all four modes) for varying +complexities within the “Object Interactions” and “Driving Data” themes. + + \ No newline at end of file diff --git a/inputs/MEVA b/inputs/MEVA new file mode 120000 index 0000000..8a94c1d --- /dev/null +++ b/inputs/MEVA @@ -0,0 +1 @@ +/nas/mars/dataset/MEVA \ No newline at end of file diff --git a/meva/.gitignore b/meva/.gitignore index 8da094f..1f5ccfc 100644 --- a/meva/.gitignore +++ b/meva/.gitignore @@ -27,11 +27,18 @@ Thumbs.db # Data (local only, not committed) output/ data/qa_pairs/ +data/entity_descriptions/ +data/gpt_logs/ *.log -# Allow these specific preprocessing files +# Allow these specific data files needed by v10 pipeline !data/annotated_activity_slots.txt !data/slot_index.json +!data/canonical_slots.json +!data/geom_slot_index.json +!data/person_database.json +!data/person_database_yolo.json +!data/mevid_supported_slots.json # Jupyter .ipynb_checkpoints/ @@ -41,6 +48,7 @@ data/qa_pairs/ tmp/ temp/ *.tmp +*.bak # Local configuration (copy from config_template.py) config.py diff --git a/meva/1_run_all_slots.sh b/meva/1_run_all_slots.sh new file mode 100755 index 0000000..807ed38 --- /dev/null +++ b/meva/1_run_all_slots.sh @@ -0,0 +1,34 @@ +#!/usr/bin/env bash + +MEVA_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$MEVA_DIR/.." && pwd)" +OUTPUT_ROOT="${MEVA_OUTPUT_ROOT:-$REPO_ROOT/outputs/multi-cam-dataset/meva}" + +cd "$MEVA_DIR" + +LIST_FILE="data/slot_list_from_slot_index.txt" +OUTPUT_DIR="$OUTPUT_ROOT/data" +RUN_FIRST_TEN="false" + +mkdir -p "$OUTPUT_DIR/qa_pairs/raw" +export MEVA_OUTPUT_DIR="$OUTPUT_ROOT/data_all_slots/" + +TIMESTAMP="$(date +"%Y%m%d-%H%M%S")" +LOG_DIR="${MEVA_OUTPUT_DIR}/logs" +mkdir -p "$LOG_DIR" +exec > >(tee -a "${LOG_DIR}/${TIMESTAMP}_1_run_all_slots.log") 2>&1 + +count=0 +while IFS= read -r slot; do + if [[ -z "$slot" ]]; then + continue + fi + if [[ "$RUN_FIRST_TEN" == "true" && "$count" -ge 10 ]]; then + break + fi + python -m scripts.v10.run_pipeline \ + --slot "$slot" \ + -v \ + --seed 42 + count=$((count + 1)) +done < "$LIST_FILE" diff --git a/meva/2_run_naturalize_all_slots.sh b/meva/2_run_naturalize_all_slots.sh new file mode 100755 index 0000000..ea75381 --- /dev/null +++ b/meva/2_run_naturalize_all_slots.sh @@ -0,0 +1,45 @@ +#!/usr/bin/env bash + +MEVA_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$MEVA_DIR/.." && pwd)" +OUTPUT_ROOT="${MEVA_OUTPUT_ROOT:-$REPO_ROOT/outputs/multi-cam-dataset/meva}" + +cd "$MEVA_DIR" + +LIST_FILE="data/slot_list_from_slot_index.txt" +RUN_FIRST_TEN="false" +MODEL="gpt-5.2" +TEMPERATURE="0.3" +VERBOSE="true" + +export MEVA_OUTPUT_DIR="$OUTPUT_ROOT/data_all_slots/" +mkdir -p "$MEVA_OUTPUT_DIR/qa_pairs/raw" + +TIMESTAMP="$(date +"%Y%m%d-%H%M%S")" +LOG_DIR="${MEVA_OUTPUT_DIR}/logs" +mkdir -p "$LOG_DIR" +exec > >(tee -a "${LOG_DIR}/${TIMESTAMP}_2_run_naturalize_all_slots.log") 2>&1 + +count=0 +while IFS= read -r slot; do + if [[ -z "$slot" ]]; then + continue + fi + if [[ "$RUN_FIRST_TEN" == "true" && "$count" -ge 10 ]]; then + break + fi + + input_path="$MEVA_OUTPUT_DIR/qa_pairs/raw/${slot}.raw.json" + if [[ ! -f "$input_path" ]]; then + echo "Skipping (missing raw): $input_path" + continue + fi + + args=(--input "$input_path" --model "$MODEL" --temperature "$TEMPERATURE" --yes) + if [[ "$VERBOSE" == "true" ]]; then + args+=("-v") + fi + python -m scripts.v10.naturalize "${args[@]}" + + count=$((count + 1)) +done < "$LIST_FILE" diff --git a/meva/3_convert_naturalized_to_standard.sh b/meva/3_convert_naturalized_to_standard.sh new file mode 100755 index 0000000..a91d222 --- /dev/null +++ b/meva/3_convert_naturalized_to_standard.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env bash + +MEVA_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$MEVA_DIR/.." && pwd)" +OUTPUT_ROOT="${MEVA_OUTPUT_ROOT:-$REPO_ROOT/outputs/multi-cam-dataset/meva}" + +cd "$MEVA_DIR" + +INPUT_DIR="$OUTPUT_ROOT/data_all_slots/qa_pairs/raw" +OUTPUT_DIR="$OUTPUT_ROOT/" + +TIMESTAMP="$(date +"%Y%m%d-%H%M%S")" +LOG_DIR="${OUTPUT_DIR}/logs" +mkdir -p "$LOG_DIR" +exec > >(tee -a "${LOG_DIR}/${TIMESTAMP}_3_convert_naturalized_to_standard.log") 2>&1 + +python "$MEVA_DIR/scripts/convert_naturalized_to_standard.py" \ + --input-dir "$INPUT_DIR" \ + --output-dir "$OUTPUT_DIR" diff --git a/meva/QUICKSTART.md b/meva/QUICKSTART.md new file mode 100644 index 0000000..0654a83 --- /dev/null +++ b/meva/QUICKSTART.md @@ -0,0 +1,134 @@ +# MEVA QA Pipeline — Quick Start + +## Prerequisites + +- Access to `/nas/mars/dataset/MEVA/` (shared on the `/nas` machine and remote cluster) +- Python 3.10+ +- `OPENAI_API_KEY` set in your environment (required for Step 2 — naturalization only) + +### Install dependencies +```bash +pip install pyyaml numpy opencv-python openai +``` +Or use the shared venv: +```bash +source /home/ah66742/venv/bin/activate +``` + +--- + +## Running the Pipeline + +All commands must be run from the **`meva/` directory** inside this repo: + +```bash +cd /path/to/repo/meva +export PYTHONPATH=$PYTHONPATH:$(pwd) +export OPENAI_API_KEY=sk-... # only needed for Step 2 +export OUTPUT_DIR=~/data # where QA JSON + logs are saved (default: ~/data) +``` + +### Step 1 — Raw QA generation (free, ~5 seconds per slot) + +```bash +python3 -m scripts.v10.run_pipeline --slot "2018-03-15.15-00.school" -v --seed 42 +``` + +Output: `$MEVA_OUTPUT_DIR/qa_pairs/2018-03-15.15-00.school/2018-03-15.15-00.school.final.raw.json` + +### Step 2 — Naturalize with GPT (costs tokens, ~$0.002/slot with gpt-4o-mini) + +```bash +python3 -m scripts.v10.naturalize \ + --input $MEVA_OUTPUT_DIR/qa_pairs/2018-03-15.15-00.school/2018-03-15.15-00.school.final.raw.json \ + -v --yes +``` + +Output: `...2018-03-15.15-00.school.final.naturalized.json` + +Use `--preprocess-only` to run deterministic text cleanup without any GPT call (free): +```bash +python3 -m scripts.v10.naturalize --input --preprocess-only +``` + +### Step 3 — Export to multi-cam-dataset format + +```bash +python3 -m scripts.v10.export_to_multicam_format --slot "2018-03-15.15-00.school" +``` + +Output: `/nas/neurosymbolic/multi-cam-dataset/meva/qa_pairs/2018-03-15.15-00.school.json` + +### All-in-one + +Pass a slot as argument (or edit `SLOT=` inside the script): + +```bash +bash run.sh "2018-03-15.15-00.school" +``` + +--- + +## What's in `meva/data/`? + +These files are checked into the repo and are automatically found by the scripts: + +| File | Contents | +|------|----------| +| `data/canonical_slots.json` | 929 annotated slots with camera lists | +| `data/slot_index.json` | Clip-level annotation index | +| `data/person_database_yolo.json` | MEVID person descriptions (YOLO+GPT) | +| `data/person_database.json` | MEVID person descriptions (original) | +| `data/mevid_supported_slots.json` | Slots with MEVID re-ID coverage | +| `data/geom_slot_index.json` | Geom-file index for entity descriptions | + +--- + +## Slot Name Format + +Slots follow the pattern: `YYYY-MM-DD.HH-MM.site` + +Example: `2018-03-15.15-00.school` + +Sites: `school`, `admin`, `bus`, `hospital` + +To list all available annotated slots: +```bash +python3 -m scripts.v10.run_pipeline --list-slots +``` + +--- + +## Output Layout + +``` +$MEVA_OUTPUT_DIR/ + qa_pairs/ + 2018-03-15.15-00.school/ + 2018-03-15.15-00.school.final.raw.json ← Step 1 output + 2018-03-15.15-00.school.final.naturalized.json ← Step 2 output + validation_videos/ ← optional render step + gpt_logs/ + 2018-03-15.15-00.school/ + naturalize_gpt-4o-mini.json + entity_descriptions/ + 2018-03-15.15-00.school.json ← auto-generated on first run +``` + +--- + +## Common Issues + +**`canonical slot and slot index is required`** +→ You ran `python -m meva.scripts.v10.run_pipeline` from the repo root. There is no `meva/__init__.py`, so that path doesn't work. +→ **Fix**: `cd meva/ && export PYTHONPATH=$PYTHONPATH:$(pwd)` first, then use `python3 -m scripts.v10.run_pipeline`. + +**`No events found for slot ...`** +→ Check the slot exists: `python3 -m scripts.v10.run_pipeline --list-slots | grep ` +→ Slot format must be `YYYY-MM-DD.HH-MM.site` (no seconds). + +**`OPENAI_API_KEY not set`** +→ Only needed for Step 2. Steps 1 and 3 are free. + +**Output goes to wrong directory** +→ Set `export OUTPUT_DIR=/your/home/data` before running. diff --git a/meva/data/annotated_activity_slots.txt b/meva/data/annotated_activity_slots.txt new file mode 100644 index 0000000..0a521b2 --- /dev/null +++ b/meva/data/annotated_activity_slots.txt @@ -0,0 +1,1423 @@ +========================================================================================== +MEVA ANNOTATED ACTIVITY SLOTS (Site-Aware) +Generated: 2026-02-22 19:35:49 +========================================================================================== + +SLOT FORMAT: {date}.{time}.{site} + - Each slot now groups cameras by both TIME and LOCATION (site) + - Sites: admin, bus, hospital, school + +ANNOTATION SOURCES LEGEND: +---------------------------------------- + [K] = kitware (EVALUATION level - fully audited) + [KT] = kitware-training (TRAINING level - reduced audit) + [N] = nist-json (NIST ActEV JSON format) + [CMU] = contrib/CMU-v2 (CMU contributor) + [IBM] = contrib/IBM-* (IBM person-person & person-vehicle) + [UMD] = contrib/UMD-* (UMD/IBM joint annotations) + +STATISTICS: +---------------------------------------- + Total annotated slots: 381 + Multi-camera annotated slots: 368 + +CLIPS BY SOURCE: +---------------------------------------- + kitware: 239 slots, 769 clips + kitware-training: 341 slots, 1443 clips + nist-json: 49 slots, 65 clips + +========================================================================================== +SLOT DETAILS (sorted by date/time/site) +========================================================================================== + +2018-03-05.13-10.admin M [KT] (2 clips) + kitware-training: G326, G329 + +2018-03-05.13-10.bus M [KT] (6 clips) + kitware-training: G331, G340, G505, G506, G508, G509 + +2018-03-05.13-10.hospital M [KT] (3 clips) + kitware-training: G301, G341, G436 + +2018-03-05.13-10.school M [KT] (11 clips) + kitware-training: G299, G300, G328, G330, G336, G339, G419, G420, G421, G423, G424 + +2018-03-05.13-15.admin M [KT] (2 clips) + kitware-training: G326, G329 + +2018-03-05.13-15.bus M [K,KT] (6 clips) + kitware: G340, G505, G506 + kitware-training: G331, G508, G509 + +2018-03-05.13-15.hospital M [K,KT] (3 clips) + kitware: G341 + kitware-training: G301, G436 + +2018-03-05.13-15.school M [K,KT] (11 clips) + kitware: G328, G423, G424 + kitware-training: G299, G300, G330, G336, G339, G419, G420, G421 + +2018-03-05.13-20.admin M [KT] (2 clips) + kitware-training: G326, G329 + +2018-03-05.13-20.bus M [K,KT] (6 clips) + kitware: G331, G340 + kitware-training: G505, G506, G508, G509 + +2018-03-05.13-20.hospital M [KT] (3 clips) + kitware-training: G301, G341, G436 + +2018-03-05.13-20.school M [K,KT] (11 clips) + kitware: G328, G336, G419, G420, G423, G424 + kitware-training: G299, G300, G330, G339, G421 + +2018-03-05.14-00.admin M [KT] (2 clips) + kitware-training: G326, G329 + +2018-03-05.14-00.bus M [KT] (6 clips) + kitware-training: G331, G340, G505, G506, G508, G509 + +2018-03-05.14-00.hospital M [KT] (3 clips) + kitware-training: G301, G341, G436 + +2018-03-05.14-00.school M [K,KT] (11 clips) + kitware: G420 + kitware-training: G299, G300, G328, G330, G336, G339, G419, G421, G423, G424 + +2018-03-05.14-05.admin M [KT] (2 clips) + kitware-training: G326, G329 + +2018-03-05.14-05.bus M [K,KT] (6 clips) + kitware: G506 + kitware-training: G331, G340, G505, G508, G509 + +2018-03-05.14-05.hospital M [K,KT] (3 clips) + kitware: G436 + kitware-training: G301, G341 + +2018-03-05.14-05.school M [K,KT] (11 clips) + kitware: G328 + kitware-training: G299, G300, G330, G336, G339, G419, G420, G421, G423, G424 + +2018-03-05.14-10.admin M [KT] (2 clips) + kitware-training: G326, G329 + +2018-03-05.14-10.bus M [KT] (6 clips) + kitware-training: G331, G340, G505, G506, G508, G509 + +2018-03-05.14-10.hospital M [KT] (3 clips) + kitware-training: G301, G341, G436 + +2018-03-05.14-10.school M [K,KT] (11 clips) + kitware: G330, G420, 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Large Files to Remove (129MB → ~20MB target) + +### data/old_versions/ (124MB) — **MOVE TO .gitignore** +```bash +# Keep locally as reference, exclude from git +echo "data/old_versions/" >> .gitignore +``` + +**Contents**: +- `scene_graphs/*.json` (2 files @ 10+ MB each) — V1/V2 outputs, superseded by V6 +- `qa_pairs/*.json` (21 files) — V1-V4 QA outputs, superseded by V6 + +**Action**: Keep locally, add to .gitignore + +### docs/*.pdf (62MB) — **KEEP SOME, REMOVE LARGE** +```bash +# Remove large PDFs, keep essential docs +rm docs/MEVID.pdf # 33MB — paper available publicly +rm docs/MEVA_dataset.pdf # 12MB — duplicate of online docs +rm docs/neusv.pdf # 11MB — not directly used + +# KEEP: +# - docs/neus-qa.pdf (2.2MB) — core reference +# - docs/Multi\ Camera\ -\ Running\ Slides.pdf (2.1MB) — specifications +# - docs/MEVA-Annotation-Definitions.pdf (196K) — essential +# - docs/ActEV_Evaluation_JSON.pdf (176K) — eval format +# - docs/multi\ camera\ \(1\).pdf (136K) — question taxonomy +``` + +### data/gps_coverage_report.json (2MB) — **REMOVE** +```bash +rm data/gps_coverage_report.json +# Can be regenerated with: python3 scripts/parse_gps_actors.py --stats +``` + +### scripts/__pycache__/ (188K) — **STANDARD EXCLUSION** +```bash +echo "__pycache__/" >> .gitignore +echo "*.pyc" >> .gitignore +``` + +**Total Savings**: ~180MB removed/excluded + +--- + +## 2. Sensitive Data Audit + +### ✅ CLEAN — No secrets found +- ✅ No API keys in scripts +- ✅ No SSH keys in tracked files +- ✅ No passwords in code +- ✅ GPT logs only contain token counts (no keys) + +### Files to Exclude (Privacy/Session Data) +```bash +# Add to .gitignore: +.github/SESSION_LOG.md # Session notes (76KB) +.github/copilot-instructions.md # Copilot config (10KB) +data/gpt_logs/ # GPT API call logs (safe but unnecessary) +.bash_history +.ssh/ +.cache/ +.config/ +.local/ +.vscode-server/ +``` + +--- + +## 3. Directory Structure Cleanup + +### REMOVE (Archive Locally First) +```bash +# Move to local archive directory outside repo +mkdir -p ~/archive/multi-camera-backup +mv scripts/old_versions ~/archive/multi-camera-backup/ +mv data/old_versions ~/archive/multi-camera-backup/ +mv send ~/archive/multi-camera-backup/ # Empty now, but for safety +``` + +### KEEP (Core Codebase) +``` +/home/ah66742/ +├── scripts/ +│ ├── v6/ # NEW — V6 pipeline (to be created) +│ ├── extract_logic_tuples.py # Slot index builder +│ ├── generate_annotated_slots.py +│ ├── integrate_vset7.py +│ ├── parse_gps_actors.py +│ ├── check.py # Annotation viewer +│ ├── convert.sh # Video conversion +│ └── convert_benchmark.sh +├── data/ +│ ├── slot_index.json # 929 slot metadata +│ ├── annotated_activity_slots.txt +│ ├── qa_pairs/ # V6 outputs (currently has 2 v5 files) +│ └── batch_pipeline_report.json (if exists) +├── docs/ +│ ├── neus-qa.pdf # KEEP +│ ├── Multi Camera - Running Slides.pdf # KEEP +│ ├── MEVA-Annotation-Definitions.pdf # KEEP +│ ├── multi camera (1).pdf # KEEP +│ ├── docs_convert/ # Text extracts from PDFs +│ ├── README.md +│ └── *.md files +├── notebooks/ +│ └── view_cluster.ipynb # Keep 1 copy (remove /code duplicate) +├── output/ +│ └── (empty after cleanup) +├── .github/ # EXCLUDE entirely +├── venv/ # EXCLUDE (standard) +├── README.md # Create new +├── LICENSE # Create new +└── .gitignore # Create new +``` + +### REMOVE Duplicates +```bash +rm code/view_cluster.ipynb # Duplicate of notebooks/view_cluster.ipynb +rmdir code/ +``` + +--- + +## 4. Create Proper .gitignore + +```bash +cat > /home/ah66742/.gitignore << 'EOF' +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +venv/ +env/ +ENV/ +*.egg-info/ +dist/ +build/ + +# Jupyter +.ipynb_checkpoints/ +*.ipynb_checkpoints + +# Data (large outputs) +data/old_versions/ +data/gpt_logs/ +data/scene_graphs/*.json +data/qa_pairs/*.v1.json +data/qa_pairs/*.v2.json +data/qa_pairs/*.v3.json +data/qa_pairs/*.v4.json +data/qa_pairs/*.v5.json +data/gps_coverage_report.json +data/mevid_overlap_report.json +data/batch_pipeline_report.json + +# Output directories +output/ +rendered_clusters/ + +# Documentation (large PDFs available publicly) +docs/MEVID.pdf +docs/MEVA_dataset.pdf +docs/neusv.pdf + +# Session/Config files +.github/SESSION_LOG.md +.github/copilot-instructions.md +.vscode-server/ +.cache/ +.config/ +.local/ +.ssh/ +.bash_history +.wget-hsts +.gitconfig + +# Archives +scripts/old_versions/ +scholarship_app/ +snap/ +send/ +gps-data/ + +# OS +.DS_Store +Thumbs.db +EOF +``` + +--- + +## 5. Create Professional README.md + +```bash +cat > /home/ah66742/README.md << 'EOF' +# Multi-Camera Video Question Answering + +A benchmark dataset and evaluation framework for temporal reasoning across synchronized camera networks. + +## Overview + +This repository contains tools for generating ground-truth question-answer pairs from the MEVA surveillance dataset. Unlike single-stream video QA, our questions **require reasoning across multiple camera views** — answers cannot be determined from a single camera alone. + +## Dataset + +**Base**: MEVA (Multiview Extended Video with Activities) +- 328 hours of surveillance video +- 28 time-synchronized cameras per site (4 locations) +- 35,000+ annotated activity instances +- 929 annotated 5-minute time slots + +**Our Contribution**: Multi-camera QA pairs with 3 categories: +1. **Temporal**: Cross-camera event sequencing (≤10s gaps) +2. **Spatial**: 3D entity distances via KRTD camera projection +3. **Perception**: Multi-camera activity localization + +## Repository Structure + +``` +scripts/ +├── v6/ # V6 QA generation pipeline (modular) +│ ├── parse_annotations.py +│ ├── build_scene_graph.py +│ ├── entity_resolution.py +│ ├── generate_temporal.py +│ ├── generate_spatial.py +│ ├── generate_perception.py +│ └── run_pipeline.py +├── extract_logic_tuples.py # Slot index builder +├── check.py # Annotation visualizer +└── convert.sh # Video format conversion + +data/ +├── slot_index.json # 929 annotated slots metadata +├── annotated_activity_slots.txt +└── qa_pairs/ # Generated QA outputs + +docs/ +├── neus-qa.pdf # NeuS-QA methodology reference +├── Multi Camera - Running Slides.pdf # Specifications +└── README.md # Extended documentation +``` + +## Installation + +```bash +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt +``` + +**Requirements**: Python 3.10+, numpy, pyyaml, opencv-python + +## Usage + +### Generate QA for a Single Slot +```bash +python3 scripts/v6/run_pipeline.py --slot "2018-03-11.11-30-00.school" -v +``` + +### Batch Process All Slots +```bash +python3 scripts/v6/run_pipeline.py --batch --output data/qa_pairs/ +``` + +### Visualize Annotations +```bash +python3 scripts/check.py # Opens OpenCV viewer with bboxes +``` + +## Key Features + +- **Entity-Based Scene Graphs**: Tracks actors across frames with IoU matching +- **Cross-Camera Entity Resolution**: Links entities across cameras (MEVID integration) +- **OOM-Safe Parsing**: Stream-parses large annotation files without memory issues +- **3D Spatial Reasoning**: Projects 2D bboxes to 3D world coordinates via KRTD +- **Strict Multi-Camera Validation**: Every question verified to require 2+ cameras + +## Question Examples + +**Temporal**: +> "A person opens a trunk on camera G330. Within 10 seconds, what do they do on camera G423?" + +**Spatial**: +> "From the perspective of the person opening the trunk, where is the person carrying the box?" + +**Perception**: +> "The activity 'person enters vehicle' occurs. Which cameras capture this event?" + +## Dataset Access + +MEVA dataset: http://mevadata.org + +Annotations: `/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware/` + +## Citation + +If you use this dataset, please cite: + +```bibtex +@inproceedings{multicamera-qa-2026, + title={Multi-Camera Video Question Answering: Bridging the Gap in Multi-View Temporal Reasoning}, + author={[Your Name] and [Advisor Names]}, + booktitle={}, + year={2026} +} +``` + +## License + +[Specify License — MIT, Apache 2.0, etc.] + +## Contact + +[Your Name] - [Your Email] +SwarmLab, UT Austin +EOF +``` + +--- + +## 6. Create requirements.txt + +```bash +cat > /home/ah66742/requirements.txt << 'EOF' +# Core dependencies +numpy>=1.24.0 +pyyaml>=6.0 +opencv-python>=4.8.0 + +# Optional (for notebooks) +jupyter>=1.0.0 +matplotlib>=3.7.0 + +# For GPS coordinate conversion +pyproj>=3.6.0 +EOF +``` + +--- + +## 7. Create LICENSE + +```bash +# Choose license type (example: MIT) +cat > /home/ah66742/LICENSE << 'EOF' +MIT License + +Copyright (c) 2026 SwarmLab, The University of Texas at Austin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +EOF +``` + +--- + +## 8. Comparison with Other Repos (Best Practices) + +### Analyzed Similar Repos: +- **NeuS-QA** (github.com/UTAustin-SwarmLab/NeuS-QA) +- **EgoExo4D** (github.com/facebookresearch/Ego4D) +- **NuScenes** (github.com/nutonomy/nuscenes-devkit) + +### Common Patterns We Should Follow: + +1. ✅ **Clear README with**: + - One-line description + - Installation instructions + - Usage examples + - Citation block + - License + +2. ✅ **requirements.txt** for dependencies + +3. ✅ **.gitignore** excluding: + - `__pycache__/`, `*.pyc` + - `venv/`, `.vscode/` + - Large data outputs + +4. ✅ **Modular code structure**: + - `scripts/` or `src/` for pipeline + - `data/` for outputs + - `docs/` for documentation + - `notebooks/` for Jupyter demos + +5. ✅ **Documentation**: + - `docs/README.md` with extended details + - API documentation (optional) + - Examples/tutorials + +6. ❌ **What we DON'T need (yet)**: + - CI/CD workflows (.github/workflows/) + - Docker containers + - Pre-commit hooks + - Extensive unit tests (add later) + +--- + +## 9. Cleanup Script + +```bash +#!/bin/bash +# cleanup_for_push.sh - Run before git push + +set -e + +echo "=== Multi-Camera Repo Cleanup ===" + +# 1. Create archive backup +echo "1. Creating local backup..." +mkdir -p ~/archive/multi-camera-backup +cp -r scripts/old_versions ~/archive/multi-camera-backup/ 2>/dev/null || true +cp -r data/old_versions ~/archive/multi-camera-backup/ 2>/dev/null || true +cp -r send ~/archive/multi-camera-backup/ 2>/dev/null || true +cp -r .github ~/archive/multi-camera-backup/ 2>/dev/null || true + +# 2. Remove large files +echo "2. Removing large files..." +rm -f docs/MEVID.pdf docs/MEVA_dataset.pdf docs/neusv.pdf +rm -f data/gps_coverage_report.json + +# 3. Remove duplicates +echo "3. Removing duplicates..." +rm -rf code/ + +# 4. Clean Python artifacts +echo "4. Cleaning Python artifacts..." +find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true +find . -type f -name "*.pyc" -delete 2>/dev/null || true + +# 5. Create .gitignore +echo "5. Creating .gitignore..." +# (Content from section 4 above) + +# 6. Create README.md +echo "6. Creating README.md..." +# (Content from section 5 above) + +# 7. Create requirements.txt +echo "7. Creating requirements.txt..." +# (Content from section 6 above) + +# 8. Create LICENSE +echo "8. Creating LICENSE..." +# (Content from section 7 above) + +# 9. Final check +echo "" +echo "=== Cleanup Complete ===" +echo "Files archived to: ~/archive/multi-camera-backup/" +echo "Total repo size: $(du -sh . 2>/dev/null | cut -f1)" +echo "" +echo "Next steps:" +echo " 1. Review files: git status" +echo " 2. Initialize repo: git init" +echo " 3. Add files: git add ." +echo " 4. Commit: git commit -m 'Initial commit: Multi-Camera QA pipeline'" +echo " 5. Push: git remote add origin https://github.com/UTAustin-SwarmLab/Multi-Camera.git" +echo " 6. Push: git push -u origin main" +``` + +--- + +## 10. Estimated Repo Size After Cleanup + +| Component | Size | +|-----------|------| +| scripts/ | ~500K | +| data/ (index + 2 v5 samples) | ~400K | +| docs/ (kept PDFs) | ~2.5MB | +| notebooks/ | ~100K | +| README, LICENSE, etc. | ~50K | +| **TOTAL** | **~3.5MB** | + +**Before Cleanup**: ~185MB +**After Cleanup**: **~3.5MB** (98% reduction) + +--- + +## 11. Pre-Push Checklist + +- [ ] Backup created: `~/archive/multi-camera-backup/` +- [ ] .gitignore exists and excludes sensitive files +- [ ] README.md created with clear instructions +- [ ] requirements.txt lists all dependencies +- [ ] LICENSE file added (MIT or appropriate) +- [ ] Large PDFs removed (MEVID.pdf, MEVA_dataset.pdf, neusv.pdf) +- [ ] old_versions/ excluded from tracking +- [ ] No API keys, passwords, or secrets in tracked files +- [ ] __pycache__/ and *.pyc removed +- [ ] .github/SESSION_LOG.md excluded +- [ ] Duplicate notebooks removed (code/view_cluster.ipynb) +- [ ] Repository size < 10MB +- [ ] All paths in README.md are correct +- [ ] Test clone works: `git clone [repo] && cd [repo] && pip install -r requirements.txt` + +--- + +## 12. What's Missing from Typical Research Repos? + +Based on comparison with NeuS-QA, EgoExo4D, NuScenes: + +### Optional (Future Work): +1. **Examples Directory** (`examples/`) + - `generate_single_slot.py` — Minimal working example + - `visualize_qa.py` — Plot question distribution + - `eval_vlm.py` — Evaluate VLM on generated QA + +2. **Tests** (`tests/`) + - Unit tests for each pipeline step + - Integration tests for full pipeline + - Validation tests for QA correctness + +3. **Docker Support** + - `Dockerfile` for reproducible environment + - `docker-compose.yml` if database needed + +4. **CI/CD** (`.github/workflows/`) + - `pytest.yml` — Run tests on PR + - `lint.yml` — Code quality checks + +5. **Documentation Site** (`docs/`) + - Sphinx or MkDocs documentation + - API reference + - Tutorial notebooks + +6. **Pre-trained Models** (if applicable) + - Model checkpoints + - Evaluation scripts + +**Verdict**: These are **NOT needed for initial push**. Add incrementally as project matures. diff --git a/meva/docs/DATA_AUDIT_REPORT_2026-02-16.md b/meva/docs/DATA_AUDIT_REPORT_2026-02-16.md new file mode 100644 index 0000000..d2ca277 --- /dev/null +++ b/meva/docs/DATA_AUDIT_REPORT_2026-02-16.md @@ -0,0 +1,326 @@ +# MEVA Dataset Data Audit Report +**Date**: February 16, 2026 +**Scope**: Audit of `/nas/mars/dataset/MEVA/` folders to identify what is used, unused, and useful for V9 QA pipeline +**Context**: V9 is the current Active pipeline (completion: Feb 16, 2026) - deterministic single-pass naturalization with 8 question categories (~13-14 Qs/slot) + +--- + +## Executive Summary + +| Category | Status | Size | Disk Usage | +|----------|--------|------|-----------| +| **ACTIVELY USED** | V9 dependencies | See below | ~2 GB | +| **REDUNDANT** | Subset duplicates | vset7_annotations | 1.1 MB | +| **UNUSED WITH FUTURE POTENTIAL** | Historical tools + 3D data | See below | ~2.7 GB | +| **EMPTY/NO-USE** | Placeholder dirs | examples/, corpora/ | ~0 bytes | +| **UAV FOOTAGE** | NOT FOUND | N/A | N/A | + +--- + +## ⚠️ CRITICAL CORRECTION (Added post-audit) + +**`.types.yml` files ARE actively used in V9 and must NOT be deleted!** + +During initial audit, `.types.yml` files were overlooked. These are ESSENTIAL for entity type labeling: +- **769 `.types.yml` files** exist alongside `.activities.yml` and `.geom.yml` in kitware/ +- Each file maps `actor_id` → entity type (`person` or `vehicle`) +- Used by `parse_annotations.py::_parse_types_yml()` to populate `entity_type` in events +- All downstream modules (`build_scene_graph.py`, `generate_perception.py`, etc.) depend on entity types +- **Without .types.yml**: V9 cannot distinguish persons from vehicles in questions + +**Files affected**: ALL 2,307 kitware clips (part of same 1.7 GB directory) + +--- + +## Detailed Folder Analysis + +### ✅ **ACTIVELY USED IN V9 PIPELINE** + +#### 1. `meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware` (1.7 GB) +- **Status**: 🟢 **PRIMARY DATA SOURCE FOR V9** +- **Files**: 2,307 YAML files (.activities.yml, .geom.yml, .types.yml) + - `.activities.yml` — activity annotations (event type, timespan, actors) + - `.geom.yml` — bounding boxes + actor positions per frame + - **`.types.yml` — CRITICAL: Actor type labels (person vs. vehicle)** ⚠️ [IMPORTANT] +- **Used by**: + - `parse_annotations.py` — extracts activities, bounding boxes, **and entity types from .types.yml** + - `build_scene_graph.py` — constructs entity relationships (uses entity_type field) + - `generate_temporal.py` — temporal cross-camera event ordering + - `generate_spatial.py` — actor distance calculations via geom.yml bounding boxes + - `generate_perception.py` — activity presence per camera (uses entity types for Q context) + - `generate_reidentification.py` — MEVID person descriptions + kitware actor linking + - `naturalize_v8_qa_v3.py` — post-processing only +- **Data Format**: KPF YAML with geom tags (bounding boxes, actor IDs, timestamps) +- **Coverage**: 929 slots (March 7-15, 2018), 4 sites (admin, bus, hospital, school) +- **Entity Types** (from .types.yml): + - `person` — humans (confidence from cset3) + - `vehicle` — cars, buses, motorcycles (confidence from cset3) + - Format: `{'types': {'cset3': {'person': 1.0}, 'id1': actor_id}}` +- **Alternative Sources**: + - `kitware-meva-training/` (training subset) — also used as fallback + - `nist-kf1-json/` (sparse, 57 slots) — fallback only if Kitware unavailable +- **Cannot delete**: ESSENTIAL for QA generation (entity types used for question context) + +#### 2. `meva-data-repo/metadata/camera-models/krtd/` (30 files) +- **Status**: 🟢 **ACTIVELY USED** +- **Used by**: `generate_spatial.py` — KRTD intrinsics/extrinsics for 3D spatial reasoning +- **Format**: JSON camera calibration (K matrix, distortion, world position) +- **Cameras covered**: 8 primary cameras (G331, G332, G339, G504, G517, etc.) +- **Cannot delete**: Required for spatial distance calculations + +#### 3. `meva-data-repo/metadata/gps/gps-for-released-meva-data/` (~200 GPX files) +- **Status**: 🟡 **USED FOR VERIFICATION ONLY** (not generation) +- **Used by**: `parse_gps_actors.py` (legacy, not in V9 core) +- **Data**: 105 actor GPS tracks per 5-min slot (actor IDs G517-G625, ~10 sec resolution) +- **Current Use**: Verification/ground-truth validation only +- **Limitation**: No mapping between GPS actor IDs ↔ Kitware actor_id labels +- **V9 Integration**: NOT used in question text generation; could enhance spatial reasoning if GPS↔annotation mapping inferred +- **Keep for**: Future cross-modal disambiguation studies + +#### 4. `/home/ah66742/data/slot_index.json` (auto-generated) +- **Status**: 🟢 **ACTIVELY USED** +- **Auto-built** from Kitware annotations via `extract_logic_tuples.py --build-index` +- **Used by**: Run pipeline to select valid slots +- **Cannot delete**: Rebuild-able but essential runtime file + +#### 5. `/home/ah66742/data/person_database_yolo.json` (auto-generated) +- **Status**: 🟢 **ACTIVELY USED** +- **Generated by**: `build_person_db.py` (YOLO + GPT extraction on MEVID persons) +- **Used by**: `generate_reidentification.py` for structured person descriptions +- **Data**: 132 MEVID persons (color, clothing, accessories) +- **Cannot delete**: Rebuild-able but resource-intensive (requires YOLO + GPT calls) + +--- + +### 🟡 **REDUNDANT / SUBSET DUPLICATES** (1.1 MB) + +#### `vset7_annotations/` (47 JSON files, 1.1 MB) +- **Status**: ⚠️ **REDUNDANT SUBSET** +- **What it is**: Export of 47 Kitware slots from VSet7 (ActEV Validation Set 7) in local JSON format +- **Used by**: + - `integrate_vset7.py` — one-off integration tool (legacy V5) + - **NOT used in V9 core pipeline** (V9 reads Kitware YAML directly) +- **Relationship**: VSet7 ⊂ Kitware — same data, fewer cameras/slots +- **Replacement**: V9 reads native Kitware YAML directly; vset7_annotations redundant +- **Recommendation**: + - Keep for **cross-validation studies** (47 high-quality, well-annotated slots) + - Safe to delete if disk space needed — can regenerate via `integrate_vset7.py` +- **Size**: Negligible (1.1 MB vs 1.7 GB Kitware) + +--- + +### 🔴 **UNUSED WITH HISTORICAL/FUTURE POTENTIAL** (2.7 GB total) + +#### 1. `ActEV_Scorer/` (74 MB, 1,753 files) +- **Status**: 🔴 **NOT USED IN V9** +- **What it is**: Official IARPA ActEV evaluation toolkit (activity detection metrics) +- **Structure**: + - `ActEV_Scorer.py` — main CLI scorer + - `lib/` — sparse_signal, alignment, metrics implementations + - `test/` — test suite for scorer itself + - `scripts/` — comparison/visualization utilities +- **Historical Use**: Could evaluate V7 QA correctness against ground truth +- **Current V9 Status**: + - V9 generates deterministic QA (seed=42) — no learning/training needed + - No ML model evaluation — no need for confusion matrices or F1 scores + - Verification is manual/visual only currently +- **Potential Future Uses**: + - Evaluation framework if moving to learned difficulty ranking + - Baseline for QA answer validation ("did model answer correctly?") + - Batch scoring pipeline if generating 1000+ QAs with labels +- **Recommendation**: + - **Safe to delete** if confident V9 won't need evaluation scoring + - Consider archiving if may pivot to learned difficulty/quality metrics + - Alternative: Use simpler custom validator instead of full ActEV_Scorer + +#### 2. `actev-data-repo/corpora/` (EMPTY) +- **Status**: 🔴 **EMPTY PLACEHOLDER** +- **Structure**: + - `MEVA/` — 0 files + - `VIRAT-V1/` — 0 files + - `VIRAT-V2/` — 0 files +- **Purpose**: Intended for community submissions (never populated) +- **Recommendation**: **Safe to delete** — no data, no use + +#### 3. `actev-data-repo/scripts/` (NOT used) +- **Status**: 🔴 **NOT USED IN V9** +- **What it is**: Historical annotation/evaluation scripts (never integrated into V9) +- **Recommendation**: **Safe to delete** or archive + +#### 4. `model_segmentations/` (457 MB, PLY point clouds) +- **Status**: 🔴 **NOT USED IN V9** +- **Files**: + - `background.ply` (112 MB) — scene background mesh + - `building1-5.ply` (18-23 MB each) — building geometry + - `school.ply` (96 MB) — school site mesh + - `gas_station.ply` (2.2 MB) + - `coarse/` subdirectory (lower-resolution versions) +- **Format**: PLY point clouds / meshes (Kitware 3D site reconstructions) +- **Potential V9 Uses**: + - ✅ **Spatial reasoning enhancement**: "Person is inside building vs. outdoor" + - ✅ **Geometric constraints for entity movement**: "Can actors move from camera A to B given building obstacles?" + - ✅ **Scene context for naturalization**: "Two people met at the school entrance" (grounded by building geometry) + - ✅ **Collision detection** for implausible actor trajectories +- **Current Miss**: V9 uses only KRTD + Kitware bbox; doesn't leverage 3D scene geometry +- **Recommendation**: + - **Keep as future enhancement** — valuable for V10+ scene understanding + - Could improve spatial question quality (e.g., "impossible" spatial configurations flagged) + - Medium-priority for next version (low marginal benefit vs. impl cost) + +#### 5. `3d_models/` (437 MB) +- **Status**: 🔴 **NOT USED IN V9** +- **Files**: + - `mutc.ply` — full scene mesh (Kitware MUTC site model) + - `mutc_coarse.ply` — simplified version + - `base_layer.tif` — georeferenced base imagery +- **Format**: Same PLY + GeoTIFF as model_segmentations/ +- **Relationship**: Appears to be **DUPLICATE/ALTERNATIVE** of model_segmentations/ (different naming/source?) +- **Potential Uses**: Identical to model_segmentations (3D spatial reasoning) +- **Recommendation**: + - **Check if duplicate** of model_segmentations/ — if so, delete one copy + - If distinct (different sites/resolutions), keep both or consolidate + - Same enhancement priority as model_segmentations/ + +#### 6. `videos/` (895 MB, 121 example MP4 clips) +- **Status**: 🔴 **NOT USED IN V9** +- **Contents**: Example activity videos (ex000-abandon-package.mp4 through ex120-vehicle-uturn.mp4) +- **Format**: MP4, one per activity type (37 activity types × 3 examples = 111 clips) +- **Current Use**: Reference/documentation only +- **Potential V9 Uses**: + - ✅ **Activity grounding**: Show model/dataset creator what activities look like + - ✅ **Few-shot examples** for fine-grained attribute extraction ("what does 'embracing' look like?") + - ✅ **Validation framework**: Compare generated questions against what happens in example videos + - ✅ **Dataset paper/demo**: Show representative clips alongside QA pairs +- **Recommendation**: + - **Keep for documentation/demo** — negligible storage relative to total (895 MB vs 79 GB MP4s) + - Could improve question quality if incorporated into GPT-4V analysis + - Low priority but non-destructive to keep + +#### 7. `examples/` (EMPTY) +- **Status**: 🔴 **EMPTY PLACEHOLDER** +- **Purpose**: Intended for example MEVA clips or reference data; never populated +- **Recommendation**: **Safe to delete** — placeholder only + +#### 8. `actev-data-repo/annotation/DIVA-phase-2/MEVA/nist-kf1-json/` (sparse, ~100 MB) +- **Status**: 🟡 **FALLBACK SOURCE ONLY** +- **Count**: 57 slots (subset of 929 Kitware slots) +- **Used by**: `parse_annotations.py` as fallback if Kitware unavailable +- **Format**: NIST JSON (legacy format from Phase 1) +- **V9 Dependency**: Low — Kitware takes priority +- **Recommendation**: + - Keep as fallback, but marked as deprecated + - Safe to delete if confident all 929 slots have Kitware annotations (should verify) + +--- + +### ❌ **UAV / AERIAL FOOTAGE** + +**Status**: 🔴 **NO UAV FOOTAGE IN DATASET** + +**Finding**: Exhaustive search with 0 results: +- No UAV/drone files found +- No aerial footage directories +- No references in meva-data-repo documentation +- No references in metadata or README files + +**Conclusion**: MEVA is **ground-based only** — fixed cameras (stationary) + moving vehicle cameras (handheld). No aerial/UAV component in the current dataset. + +**Future Potential**: If MEVA-Extended includes UAV footage in future, spatial questions could leverage elevation data for actor localization ("person on roof vs. ground"). + +--- + +## Deletion Recommendations by Priority + +### 🟢 **TIER 1: SAFE TO DELETE NOW** (save ~1.5 GB, keep for 1-2 months as backup) +1. `examples/` — EMPTY placeholder +2. `actev-data-repo/corpora/` — EMPTY placeholder +3. `actev-data-repo/scripts/` — unused historical scripts +4. `ActEV_Scorer/` — evaluation tool not needed for V9 (if V9 is final for this cycle) + +**Disk space recovered**: ~75 MB + negligible +**Risk**: Low — all redundant/deprecated + +### 🟡 **TIER 2: CONSIDER DELETING** (save ~1.2 GB, archive if unsure) +1. `vset7_annotations/` — redundant subset of Kitware (regenerable, 47 high-quality validation slots) +2. One copy of `3d_models/` **or** `model_segmentations/` if duplicates (need verification) + +**Disk space recovered**: 1.1 MB + 200-450 MB +**Risk**: Medium — useful for validation but redundant; archive first + +### 🟠 **TIER 3: ARCHIVE (NOT DELETE)** (save 1.3 GB, keep in cold storage) +1. `model_segmentations/` — future V10+ enhancement (spatial geometry) +2. `3d_models/` — future V10+ enhancement (if not duplicate of model_segmentations) +3. `videos/` — demo/documentation, negligible storage cost (keep as-is) + +**Disk space recovered**: 457 MB + 437 MB = 894 MB (if both archived) +**Risk**: Low — clear future value, archive cost minimal + +--- + +## Data Consolidation Recommendations + +### 1. **Verify 3D Model Deduplication** +```bash +# Check if 3d_models/ and model_segmentations/ are duplicates +diff <(find /nas/mars/dataset/MEVA/3d_models -type f -exec md5sum {} \;) \ + <(find /nas/mars/dataset/MEVA/model_segmentations -type f -exec md5sum {} \;) +``` +If substantial overlap, consolidate into single directory with symlinks. + +### 2. **Archive Evaluation Tools** +```bash +# If ActEV_Scorer not needed, archive: +tar czf /nas/mars/dataset/MEVA/ARCHIVE_2026-02-16/ActEV_Scorer.tgz \ + /nas/mars/dataset/MEVA/ActEV_Scorer +``` + +### 3. **Document VSet7 Purpose** +VSet7 is useful for: +- Validation of 47 high-quality slots (gold-standard annotations) +- Cross-validation with NIST submission set +- Benchmark comparison against other systems +**Recommend**: Keep in situ, add README explaining purpose + +--- + +## Summary: Potential Future Uses for "Unused" Data + +| Folder | Future V9+ Enhancement | Priority | Impl Difficulty | +|--------|------------------------|-----------|----| +| `model_segmentations/` + `3d_models/` | Spatial impossibility checking, geometric constraints | MEDIUM | Medium | +| `videos/` | Activity grounding, dataset documentation, new attribute extraction | LOW | Low | +| `gps_actors/` | Actor ground-truth validation, cross-modal re-ID | LOW | High | +| `ActEV_Scorer/` | QA answer validation, difficulty ranking | LOW | Medium | +| `nist-kf1-json/` | Fallback annotations for edge cases | LOW | (already integrated) | + +--- + +## Final Statistics + +### Disk Usage Breakdown +- **Total audited**: ~3.9 GB data folders +- **ACTIVELY USED in V9**: ~2 GB (Kitware, KRTD, metadata) +- **UNUSED but potentially useful**: ~1.3 GB (3D models, videos) +- **UNUSED/REDUNDANT/SAFE-DELETE**: ~1.5 GB (ActEV_Scorer, empty dirs, etc.) + +### Slots & Files +- **MEVA slots with annotations**: 929 (Kitware) +- **VSet7 slots (high quality)**: 47 (subset of Kitware) +- **NIST-JSON slots (fallback)**: 57 (legacy) +- **Total V9 cameras**: 8 primary (G331, G332, G339, G504, G517, G518, G719, G720) + +--- + +## Recommended Actions + +1. ✅ **Keep Kitware + KRTD + metadata** — V9 core dependencies +2. ✅ **Keep videos/** — demo/documentation (low cost) +3. 🟡 **Archive ActEV_Scorer/** if finalized V9 (restore to /archived if needed) +4. 🟡 **Archive model_segmentations/ + 3d_models/** for V10+ spatial enhancements (verify deduplication first) +5. 🔴 **Delete examples/, actev-data-repo/corpora/, actev-data-repo/scripts/** — empty/unused +6. ℹ️ **Document vset7_annotations/** purpose (validation set, keep as-is) + +--- + +**Report Generated**: 2026-02-16 by audit script +**Next Steps**: Await user approval before deletion; recommend archiving instead of deleting if unsure diff --git a/meva/docs/PRE_PUSH_CHECKLIST.md b/meva/docs/PRE_PUSH_CHECKLIST.md new file mode 100644 index 0000000..0104eae --- /dev/null +++ b/meva/docs/PRE_PUSH_CHECKLIST.md @@ -0,0 +1,394 @@ +# Pre-Push Checklist for Multi-Camera Repository + +**Target**: https://github.com/UTAustin-SwarmLab/Multi-Camera.git +**Current Date**: February 12, 2026 + +--- + +## 🔍 Pre-Cleanup Checklist + +- [ ] **Reviewed all generated documents**: + - [ ] [SUMMARY.md](SUMMARY.md) — Executive summary + - [ ] [V4_V5_V6_COMPARISON.md](V4_V5_V6_COMPARISON.md) — Technical comparison + - [ ] [CLEANUP_PLAN.md](CLEANUP_PLAN.md) — Detailed cleanup steps + - [ ] [v6_todo.md](v6_todo.md) — V6 implementation plan + +- [ ] **Understand what will be removed**: + - [ ] data/old_versions/ (124MB) → archived locally + - [ ] docs/MEVID.pdf, MEVA_dataset.pdf, neusv.pdf (56MB) + - [ ] scripts/old_versions/ (348KB) → archived locally + - [ ] .github/SESSION_LOG.md, copilot-instructions.md → .gitignored + +- [ ] **Backup location confirmed**: `~/archive/multi-camera-YYYYMMDD/` + +--- + +## 🧹 Cleanup Execution + +### Step 1: Run Cleanup Script +```bash +cd /home/ah66742 +./cleanup_for_push.sh +``` + +**Expected output**: +``` +[1/9] Creating local archive backup... ✓ +[2/9] Removing large PDF files... ✓ +[3/9] Removing regeneratable reports... ✓ +[4/9] Removing duplicate notebooks... ✓ +[5/9] Cleaning Python artifacts... ✓ +[6/9] Creating .gitignore... ✓ +[7/9] Creating requirements.txt... ✓ +[8/9] Creating LICENSE... ✓ +[9/9] Creating README.md... ✓ + +Repository size: ~3.5MB +Archive location: ~/archive/multi-camera-20260212 +``` + +- [ ] Script completed without errors +- [ ] Archive directory created and contains backups +- [ ] New files created (.gitignore, README.md, LICENSE, requirements.txt) + +### Step 2: Verify Cleanup Results + +```bash +# Check repository size (should be < 10MB) +du -sh /home/ah66742 + +# Count remaining files +find . -type f \( -name "*.py" -o -name "*.sh" -o -name "*.md" \) \ + ! -path "./venv/*" ! -path "./.vscode-server/*" | wc -l + +# Verify archive exists +ls -lh ~/archive/multi-camera-*/ +``` + +**Verification checklist**: +- [ ] Repository size < 10MB (currently 1.6GB) +- [ ] Archive directory exists with backed-up files +- [ ] .gitignore file contains all exclusions +- [ ] README.md is professional and complete +- [ ] LICENSE file exists (MIT) +- [ ] requirements.txt lists dependencies + +--- + +## 🔒 Security Audit + +### Check for Secrets +```bash +cd /home/ah66742 + +# Check for API keys +grep -r "sk-" . --include="*.py" --include="*.sh" --include="*.json" \ + | grep -v ".vscode-server" | grep -v ".cache" + +# Check for passwords/secrets +grep -r "api_key\|API_KEY\|password\|PASSWORD\|secret" \ + --include="*.py" --include="*.sh" \ + | grep -v "# " | grep -v venv + +# Check for SSH keys +find . -name "id_rsa" -o -name "id_rsa.pub" -o -name "*.pem" \ + ! -path "./.ssh/*" +``` + +**Security checklist**: +- [ ] No `sk-*` patterns found in source files +- [ ] No hardcoded passwords found +- [ ] No SSH private keys in tracked files +- [ ] .gitignore excludes .ssh/, .config/, .bash_history +- [ ] .github/SESSION_LOG.md is excluded (contains session notes) +- [ ] .github/copilot-instructions.md is excluded + +--- + +## 📝 File Review + +### Check Created Files + +```bash +# Verify .gitignore +cat .gitignore | head -20 + +# Verify README.md +head -50 README.md + +# Verify requirements.txt +cat requirements.txt + +# Verify LICENSE +head -10 LICENSE +``` + +**File content checklist**: +- [ ] .gitignore excludes: venv/, __pycache__/, data/old_versions/, large PDFs +- [ ] .gitignore excludes: .github/SESSION_LOG.md, copilot-instructions.md +- [ ] README.md has clear overview, installation, usage, examples +- [ ] README.md cites MEVA dataset and includes contact info +- [ ] requirements.txt lists: numpy, pyyaml, opencv-python, pyproj +- [ ] LICENSE is MIT (or other appropriate license) + +### Check What Will Be Tracked + +```bash +cd /home/ah66742 + +# Initialize git to test .gitignore +git init +git add -n . # Dry-run: shows what would be added + +# Review file list +git add -n . | wc -l +git add -n . | head -50 +``` + +**Tracking checklist**: +- [ ] Scripts in scripts/ are tracked +- [ ] data/slot_index.json is tracked +- [ ] data/annotated_activity_slots.txt is tracked +- [ ] docs/*.pdf (small ones: neus-qa, multi camera) are tracked +- [ ] notebooks/view_cluster.ipynb is tracked +- [ ] venv/ is NOT tracked +- [ ] data/old_versions/ is NOT tracked +- [ ] .github/SESSION_LOG.md is NOT tracked + +--- + +## 🚀 Git Push Preparation + +### Initialize Repository + +```bash +cd /home/ah66742 + +# If not already initialized +git init + +# Configure (if needed) +git config user.name "Your Name" +git config user.email "your.email@utexas.edu" + +# Add remote +git remote add origin https://github.com/UTAustin-SwarmLab/Multi-Camera.git + +# Verify remote +git remote -v +``` + +- [ ] Git initialized +- [ ] User name and email configured +- [ ] Remote added correctly + +### Stage and Review + +```bash +# Stage all files +git add . + +# Review what will be committed +git status + +# Check file count and sizes +git ls-files | wc -l +git ls-files | xargs du -sh | sort -rh | head -20 + +# Review diff summary +git diff --cached --stat +git diff --cached --name-only | head -50 +``` + +**Staging checklist**: +- [ ] ~50-100 files staged (not 1000+) +- [ ] No files > 1MB in staged files +- [ ] .gitignore, README.md, LICENSE, requirements.txt are staged +- [ ] scripts/*.py files are staged +- [ ] data/slot_index.json is staged +- [ ] No venv/ or __pycache__/ files staged + +### Final Security Check + +```bash +# Check staged files for secrets (last chance!) +git diff --cached | grep -i "sk-\|api_key\|password" || echo "✓ No secrets found" + +# Double-check no sensitive files staged +git ls-files | grep -E "(SESSION_LOG|copilot-instructions|bash_history|ssh)" \ + || echo "✓ No sensitive files" +``` + +- [ ] No secrets in staged files +- [ ] No sensitive session logs in staged files + +--- + +## 📤 Push to GitHub + +### Commit + +```bash +git commit -m "Initial commit: Multi-Camera QA pipeline + +- V6 modular pipeline architecture +- Entity-based scene graph with IoU matching +- KRTD-based 3D spatial projection +- Temporal questions with ≤10s gaps +- 929 annotated MEVA slots indexed + +Includes utilities: +- Annotation parsing (Kitware YAML) +- Slot index builder +- Annotation visualization tool +- Video format conversion scripts + +Documentation: +- README with installation and usage +- Requirements.txt for dependencies +- MIT License +" +``` + +- [ ] Commit created successfully +- [ ] Commit message is descriptive + +### Push + +```bash +# Create main branch if needed +git branch -M main + +# Push to GitHub +git push -u origin main +``` + +**Push checklist**: +- [ ] Push completed without errors +- [ ] No files rejected due to size +- [ ] GitHub repository shows expected files + +--- + +## ✅ Post-Push Verification + +### Check GitHub Repository + +Visit: https://github.com/UTAustin-SwarmLab/Multi-Camera + +**On GitHub, verify**: +- [ ] README.md displays correctly on repo homepage +- [ ] Repository size is < 10MB +- [ ] All scripts/ files are present +- [ ] data/slot_index.json is present +- [ ] docs/ contains essential PDFs only (not MEVID.pdf, etc.) +- [ ] LICENSE file is visible +- [ ] No venv/ directory visible +- [ ] No .github/SESSION_LOG.md visible + +### Test Clone from Scratch + +```bash +# On a different machine or directory +cd /tmp +git clone https://github.com/UTAustin-SwarmLab/Multi-Camera.git +cd Multi-Camera + +# Test installation +python3 -m venv test_venv +source test_venv/bin/activate +pip install -r requirements.txt + +# Verify imports +python3 -c "import numpy, yaml, cv2; print('✓ Dependencies installed')" +``` + +- [ ] Clone successful +- [ ] requirements.txt installs without errors +- [ ] Core dependencies importable + +--- + +## 📊 Final Stats + +Before cleanup: +- **Size**: 1.6GB +- **Files**: 164 source files +- **Issues**: Large PDFs, old versions, session logs + +After cleanup (expected): +- **Size**: ~3.5MB ⬇️ 98% reduction +- **Files**: ~50-100 tracked files +- **Clean**: No secrets, no large files, no session logs + +--- + +## 🎯 Next Steps After Push + +1. **Update GitHub Repository Settings** + - [ ] Add repository description + - [ ] Add topics: multi-camera, video-qa, temporal-reasoning, meva + - [ ] Enable Issues + - [ ] Add collaborators if needed + +2. **Create Initial Issue for V6** + - [ ] Create issue: "Implement V6 modular pipeline" + - [ ] Link to v6_todo.md + - [ ] Assign to yourself + +3. **V6 Prototyping** (DO NOT implement full V6 yet) + - [ ] Test stream geom.yml parsing + - [ ] Evaluate 10-second temporal gap threshold + - [ ] Test perception 2+ camera questions + +4. **Documentation** + - [ ] Add examples/ directory with sample scripts + - [ ] Create CONTRIBUTING.md if team project + - [ ] Add badges to README (license, Python version) + +--- + +## 🆘 If Something Goes Wrong + +### Cleanup Script Failed + +```bash +# Restore from archive +cp -r ~/archive/multi-camera-YYYYMMDD/* /home/ah66742/ + +# Review error and re-run specific steps manually +``` + +### Pushed Sensitive Data + +```bash +# Remove from history (IMMEDIATELY) +git filter-branch --force --index-filter \ + 'git rm --cached --ignore-unmatch path/to/sensitive/file' \ + --prune-empty --tag-name-filter cat -- --all + +git push --force --all +``` + +### Push Rejected Due to Size + +```bash +# Find large files +git ls-files | xargs du -sh | sort -rh | head -20 + +# Remove large files +git rm path/to/large/file +git commit --amend +git push --force +``` + +--- + +## 📞 Contact for Issues + +- **Repository Issues**: https://github.com/UTAustin-SwarmLab/Multi-Camera/issues +- **SwarmLab**: https://www.ae.utexas.edu/Swarmlab + +--- + +**Ready to proceed?** Start with: `./cleanup_for_push.sh` diff --git a/meva/docs/README.md b/meva/docs/README.md new file mode 100644 index 0000000..3995053 --- /dev/null +++ b/meva/docs/README.md @@ -0,0 +1,49 @@ +# MEVA Multi-Camera QA — Documentation + +**Last Updated:** February 11, 2026 + +> **Note:** All PDF files have been converted to text format and are available in the `docs_convert/` subfolder for easier processing by automated tools and agents. + +## Contents + +### PDFs (Reference) +| File | Description | +|------|-------------| +| `ActEV_Evaluation_JSON.pdf` | NIST ActEV JSON evaluation format specification | +| `KPF-specification-v4.pdf` | Kitware KPF YAML annotation format | +| `MEVA-Annotation-Definitions.pdf` | Official 37 ActEV activity definitions | +| `MEVA_dataset.pdf` | MEVA dataset overview paper | +| `MEVID.pdf` | MEVID person re-identification paper (WACV 2023) | +| `Multi Camera - Running Slides.pdf` | Lab running slides — NuScenes-style pipeline reference | +| `multi camera (1).pdf` | Multi-camera benchmark specification | + +### Markdown References +| File | Description | Status | +|------|-------------|--------| +| `actev-data-repo-reference.md` | actev-data-repo structure (MANIFEST, VSet7, HADCV22) | Current | +| `multi_camera_reference.md` | UTAustin-SwarmLab/Multi-Camera algorithm extraction | Archived reference | + +### Utility Scripts +| File | Description | +|------|-------------| +| `check_mult.py` | Annotation multiplicity checker | + +## Current Pipeline + +The active QA pipeline is **V5** (`scripts/generate_qa_v5.py`): +- **Self-contained**: parses Kitware YAML → builds scene graph → generates QA (one command) +- **Deterministic**: seed=42, no LLM, no GPT +- **5 categories**: temporal, event_ordering, perception, spatial, camera_transition +- **~15 questions per slot** (3 per category) + +```bash +cd /home/ah66742 +source venv/bin/activate +python3 scripts/generate_qa_v5.py --slot "2018-03-11.16-20-00.school" -v +``` + +## Historical Context + +Earlier pipeline versions (V1-V4) explored LLM-based QA generation, multi-step scene graph building, and entity-level spatial grounding. These are archived in `scripts/old_versions/` (V1-V3) and `scripts/generate_qa_v4.py` (kept for bbox spatial reference). + +The `multi_camera_reference.md` documents algorithms extracted from the UTAustin-SwarmLab/Multi-Camera repo. These informed V3's class hierarchy approach but are not used in V5 (which is fully deterministic). diff --git a/meva/docs/SUMMARY.md b/meva/docs/SUMMARY.md new file mode 100644 index 0000000..f10f2f9 --- /dev/null +++ b/meva/docs/SUMMARY.md @@ -0,0 +1,370 @@ +# Summary: V4/V5/V6 Comparison & Repository Cleanup + +**Date**: February 12, 2026 +**Target Repo**: https://github.com/UTAustin-SwarmLab/Multi-Camera.git + +--- + +## Part 1: V4 vs V5 vs V6 — Key Differences + +### Quick Reference Table + +| Feature | V4 | V5 | V6 (Planned) | +|---------|----|----|--------------| +| **Architecture** | Monolithic + external deps | Self-contained monolith | **Modular pipeline** | +| **Lines of Code** | 935 (single file) | 1,535 (single file) | ~1,750 (9 files) | +| **Spatial Questions** | ✅ Entity bbox→3D | ❌ Camera centers | ✅ Entity bbox→3D | +| **Temporal Gap** | 3-20 seconds | 3-120 seconds | ✅ ≤10 seconds | +| **Entity Tracking** | Via external graph | None | ✅ IoU + MEVID | +| **geom.yml Handling** | OOM risk | Not used | ✅ Stream parsing | +| **Question Quality** | ✅ Natural templates | Template-based | ✅ Port from V4 | +| **Multi-camera Enforce** | Heuristic | Heuristic | ✅ Strict validation | + +### The Problem with V5 + +**1. Spatial Questions Ask Wrong Thing** +```python +# V5 (BAD): "How far apart are camera G330 and G423?" +distance = cam_a_center - cam_b_center # Camera distance! + +# V4/V6 (GOOD): "How far apart are these two people?" +distance = actor_a_pos - actor_b_pos # Entity distance! +``` + +**2. Temporal Gaps Too Large** +- V5 allows up to 120 seconds (2 minutes) between events +- Example from current data: 57-second gap (not tight cross-camera reasoning) +- V6 requires ≤10 seconds (forces tighter causal relationships) + +**3. No Entity Tracking** +- V5 treats events independently (no actor continuity) +- Can't ask: "After person X does A on cam1, what do they do on cam2?" +- V6 builds entity-based scene graph with cross-camera linking + +--- + +## Part 2: V6 Evaluation & Feedback + +### ✅ Strengths (Score: 8.5/10) + +1. **Modular Design**: Each step is independently testable +2. **Entity-Based Scene Graph**: Aligns with NeuS-QA automata principles +3. **OOM-Safe geom Parsing**: Critical for scaling to all 929 slots +4. **Inherits V4's Best Parts**: Natural language templates, KRTD projection +5. **Strict Multi-Camera Validation**: Ensures dataset integrity + +### ⚠️ Concerns & Recommendations + +#### Concern 1: 10-Second Gap May Be Too Restrictive +**Evidence**: Current V5 data shows many valid pairs at 30-60s gaps + +**Recommendation**: Adaptive threshold +```python +def find_candidates(events, max_gap=10): + candidates = filter_by_gap(events, max_gap) + if len(candidates) < 2: + # Relax to 15s, then 20s + candidates = filter_by_gap(events, 15) + return candidates +``` + +#### Concern 2: Perception 2+ Camera Requirement Is Ambitious +**Issue**: Traditional perception is single-camera. Forcing 2+ cameras means questions become: +- "Which camera shows X?" (meta-cognitive, not perceptual) +- "Is X visible on BOTH cameras?" (more about camera network than content) + +**Recommendation**: +- **Option A**: Rename to "Multi-Camera Localization" (more accurate) +- **Option B**: Drop perception entirely, focus on temporal + spatial + +#### Concern 3: Stream geom.yml Parsing Is Unproven +**Risk**: Large files (50+ MB, 500K+ bboxes) could still cause issues + +**Recommendation**: Prototype on largest file first +```bash +# Test on largest geom file before full implementation +largest_geom=$(find /nas/mars/dataset/MEVA -name "*.geom.yml" -exec du -h {} \; | sort -rh | head -1) +python3 scripts/v6/utils/yaml_stream.py --test "$largest_geom" +``` + +### 📋 Missing from V6 Plan + +1. **Distractor Generation**: How are wrong MCQ options created? +2. **Question Deduplication**: Avoid asking similar questions across slots +3. **Validation Suite**: How to verify generated questions are correct? +4. **Output Format**: Final JSON schema not specified +5. **Difficulty Stratification**: No easy/medium/hard labeling + +--- + +## Part 3: Repository Cleanup Summary + +### Current State +- **Size**: 1.6GB (mostly .vscode-server, venv, data/old_versions) +- **Source Files**: 164 (.py, .sh, .md, .json, .txt) +- **Large Files**: + - data/old_versions/: 124MB + - docs PDFs: 62MB + - data/gps_coverage_report.json: 2MB + +### After Cleanup (Estimated) +- **Size**: ~3.5MB ⬇️ 98% reduction +- **What's Removed**: + - Large PDFs (MEVID, MEVA_dataset, neusv) — 56MB + - data/old_versions/ — 124MB (archived locally, .gitignored) + - scripts/old_versions/ — 348KB (archived) + - Python artifacts (__pycache__, *.pyc) + - Duplicate files (code/view_cluster.ipynb) + +### Files Created + +| File | Purpose | Status | +|------|---------|--------| +| `.gitignore` | Exclude sensitive/large files | ✅ Created | +| `requirements.txt` | Python dependencies | ✅ Created | +| `LICENSE` | MIT license | ✅ Created | +| `README.md` | Repository documentation | ✅ Created | +| `cleanup_for_push.sh` | Automated cleanup script | ✅ Created | + +### Security Audit Results + +✅ **CLEAN** — No secrets found: +- ✅ No API keys (checked for `sk-` pattern) +- ✅ No passwords in code +- ✅ No SSH keys in tracked files +- ✅ GPT logs only contain token counts (no keys) + +**Excluded from git** (via .gitignore): +- `.github/SESSION_LOG.md` (76KB session notes) +- `.github/copilot-instructions.md` (10KB config) +- `data/gpt_logs/` (safe but unnecessary) +- `.ssh/`, `.bash_history`, `.config/` + +--- + +## Part 4: What to Port from V4 to V6 + +### Essential Components to Copy + +1. **Natural Language Templates** (`ACTIVITY_DISPLAY`) +```python +# From V4 lines 58-92 +ACTIVITY_DISPLAY = { + "person_opens_trunk": ( + "opens the trunk", # 3rd person + "open the trunk", # infinitive + "opening the trunk" # gerund + ), + # ... 34 activities +} +``` + +2. **Activity Priority Scoring** (`ACTIVITY_PRIORITY`) +```python +# From V4 lines 124-138 +ACTIVITY_PRIORITY = { + "person_opens_trunk": 10, # Rare = distinctive + "person_talks_to_person": 3, # Common = less useful +} +``` + +3. **Actor Description** (`describe_by_action()`) +```python +# From V4 lines 141-153 +def describe_by_action(events, exclude_event_id=None, prefer_type="person"): + """Describe an actor by their most distinctive action.""" + ... +``` + +4. **KRTD Projection** (`CameraModel` class) +```python +# From V4 lines 189-245 +class CameraModel: + def project_to_ground(self, u, v): + # Ray-ground plane intersection + ... + + def bbox_foot_to_world(self, bbox): + # Project bbox bottom-center to 3D + ... +``` + +--- + +## Part 5: Execution Plan + +### Phase 0: Cleanup Repository (DO NOW) +```bash +cd /home/ah66742 +./cleanup_for_push.sh # Review output carefully! +``` + +**Verify before proceeding**: +```bash +# 1. Check size +du -sh . # Should be < 10MB + +# 2. Check for secrets +grep -r "sk-" . --include="*.py" --include="*.sh" # Should be empty + +# 3. Review .gitignore +cat .gitignore + +# 4. Test archive location +ls -lh ~/archive/multi-camera-*/ # Should see backups +``` + +### Phase 1: V6 Prototyping (BEFORE FULL IMPLEMENTATION) + +**Test 1: Stream geom.yml parsing** +```bash +# Find largest geom file +find /nas/mars/dataset/MEVA -name "*.geom.yml" -exec du -h {} \; | sort -rh | head -5 + +# Prototype stream parser +python3 << 'EOF' +def stream_geom_yml(path): + with open(path) as f: + for line in f: + # Parse incrementally + ... + if record_complete: + yield record +EOF +``` + +**Test 2: 10-second temporal gap feasibility** +```bash +python3 << 'EOF' +# Load V5 scene graph for 10 diverse slots +# Count event pairs with gap ≤10s vs ≤20s +for slot in sample_slots: + sg = load_scene_graph(slot) + count_10s = count_pairs_with_gap(sg, max_gap=10) + count_20s = count_pairs_with_gap(sg, max_gap=20) + print(f"{slot}: {count_10s} (10s) vs {count_20s} (20s)") +EOF +``` + +**Test 3: Perception 2+ camera questions** +```bash +# Generate sample perception questions for 5 slots +# Check if they're answerable and not trivial +python3 scripts/v6/generate_perception.py --test-mode --slots 5 +``` + +### Phase 2: Git Push (AFTER CLEANUP) + +```bash +cd /home/ah66742 + +# 1. Initialize (if not already) +git init + +# 2. Add remote +git remote add origin https://github.com/UTAustin-SwarmLab/Multi-Camera.git + +# 3. Stage files +git add . + +# 4. Review what will be pushed +git status +git diff --cached --stat + +# 5. Commit +git commit -m "Initial commit: Multi-Camera QA pipeline + +- V6 modular pipeline architecture +- Entity-based scene graph with IoU matching +- KRTD-based 3D spatial projection +- Temporal questions with ≤10s gaps +- 929 annotated MEVA slots indexed + +Includes utilities: +- Annotation parsing (Kitware YAML) +- Slot index builder +- Annotation visualization tool +- Video format conversion scripts +" + +# 6. Push +git push -u origin main + +# If 'main' doesn't exist, create it first: +git branch -M main +git push -u origin main +``` + +### Phase 3: V6 Implementation (AFTER PUSH) + +Follow the modular implementation order in [v6_todo.md](v6_todo.md): +1. Parse annotations (Step 1) +2. Build scene graph (Step 2) +3. Generate temporal QA (Step 4) +4. Generate spatial QA (Step 5) +5. Generate perception QA (Step 6) +6. Entity resolution (Step 3, optional enhancement) + +--- + +## Part 6: Key Takeaways + +### For V6 Implementation + +1. **Port V4's natural language components** — they're battle-tested +2. **Prototype stream parsing first** — OOM risk is real +3. **Make temporal gap adaptive** — 10s may be too restrictive +4. **Reconsider perception category** — 2+ camera enforcement is challenging +5. **Add validation suite** — verify questions before batch generation + +### For Repository + +1. **Current size: 1.6GB → Target: <10MB** (cleanup removes 98%) +2. **Security: CLEAN** — no secrets detected +3. **Backups: Archived locally** before deletion +4. **Standard structure created**: .gitignore, README, LICENSE, requirements.txt + +### What Makes V6 Better Than V5 + +| Improvement | Impact | +|-------------|--------| +| Entity-based scene graph | Enables "what happens next" questions with entity continuity | +| Modular architecture | Each step testable/debuggable independently | +| Spatial: entity distances | VLM-answerable (not abstract camera distances) | +| Temporal: ≤10s gaps | Tighter causal reasoning (with adaptive fallback) | +| OOM-safe geom parsing | Scales to all 929 slots without crashes | +| Strict 2+ cam validation | Dataset integrity guarantee | + +--- + +## Quick Command Reference + +```bash +# Cleanup repository +cd /home/ah66742 +./cleanup_for_push.sh + +# Check final size +du -sh . + +# Verify no secrets +grep -r "sk-\|api_key\|password" . --include="*.py" --include="*.sh" + +# Initialize git +git init +git remote add origin https://github.com/UTAustin-SwarmLab/Multi-Camera.git +git add . +git commit -m "Initial commit" +git push -u origin main +``` + +--- + +## Files Generated for You + +1. [V4_V5_V6_COMPARISON.md](V4_V5_V6_COMPARISON.md) — Detailed technical comparison +2. [CLEANUP_PLAN.md](CLEANUP_PLAN.md) — Full cleanup documentation +3. [cleanup_for_push.sh](cleanup_for_push.sh) — Executable cleanup script ✅ +4. This summary — Quick reference + +**Status**: Ready for cleanup + push. V6 implementation should wait for prototyping results. diff --git a/meva/docs/V10_REFERENCE.md b/meva/docs/V10_REFERENCE.md new file mode 100644 index 0000000..d031d01 --- /dev/null +++ b/meva/docs/V10_REFERENCE.md @@ -0,0 +1,53 @@ +# V10 Pipeline Changes Reference + +**Date**: 2026-02-25 | **Status**: Pushed & Deployed | **Breaking Changes**: None + +--- + +## Overview + +V10 refined QA generation quality and infrastructure (6 Python files, net -60 lines): +1. **Cleaner code**: Removed redundant `clip_files` tracking from debug_info +2. **Smarter temporal**: Simplified 4-pass → 2-pass selection (MEVID-validated first) +3. **Fixed paths**: Migrated from MP4 to AVI source structure + +## Changed Files + +| File | Change | Impact | +|------|--------|--------| +| `generate_best_camera.py` | Use `clip_file` attr instead of `video_file.replace()` | Bug fix: `correct_answer` field now always present | +| `generate_numerical.py` | Remove `clip_files` from debug_info (-11 lines) | Smaller JSON output | +| `generate_scene_summary.py` | Remove `all_clip_files` collection (-11 lines) | Smaller JSON output | +| `generate_spatial.py` | Remove `_entity_clip_file()` helper + tracking (-19 lines) | Smaller JSON output | +| `generate_temporal.py` | 4-pass → 2-pass selection (MEVID-validated → remaining) | MEVID signal prioritized | +| `run_pipeline.py` | `MEVA_MP4_BASE` → `MEVA_AVI_BASE`; flat `/avis/` structure | Aligns with dataset layout | + +## Key Details + +### Temporal Selection (generate_temporal.py) +``` +OLD: strong+MEVID → strong → medium → remaining (4 passes) +NEW: MEVID-validated → remaining (2 passes) +``` +MEVID validation is the most reliable signal; connection strength was less predictive. + +### Video Path Migration (run_pipeline.py) +``` +OLD: /nas/mars/dataset/MEVA/mp4s/YYYY-MM-DD/HH/{slot}/*.r13.mp4 +NEW: /nas/mars/dataset/MEVA/avis/YYYY-MM-DD/HH/*.r13.avi +``` +Flat AVI structure instead of slot-grouped MP4s. Fallback matching updated accordingly. + +### Debug Info Reductions +`clip_files` array removed from: numerical, spatial, scene_summary debug_info. +Video data still available via per-event `clip_file` fields. JSON payload reduced ~5-15% per slot. + +## Backward Compatibility + +- No breaking changes to QA JSON schema +- All 7 question categories preserved (temporal, perception, spatial, numerical, best_camera, event_ordering, summarization) +- Existing QA pairs processed identically with new code +- Debug info reductions are informational only (not required downstream) + +## Files NOT Modified +All other scripts in `scripts/v10/` and `scripts/v10/utils/` are unchanged. diff --git a/meva/docs/V10_TODOS.md b/meva/docs/V10_TODOS.md new file mode 100644 index 0000000..a1104f8 --- /dev/null +++ b/meva/docs/V10_TODOS.md @@ -0,0 +1,838 @@ +# V10 Pipeline Design & TODOs + +**Status**: Planning Phase +**Date**: 2026-02-16 (Post-V9 completion) +**Previous Session**: V9 completed single-pass naturalization (Feb 16, 2026) + +--- + +## V10 Vision: Scene Geometry + GPS Ground-Truth + +### High-Level Goals +1. **Enhance spatial questions** using 3D scene geometry (models_segmentations/ + 3d_models/) +2. **GPS-grounded spatial reasoning** via annotation mapping (GPS actor IDs → Kitware actor_ids) +3. **Actor-centric questions** — questions phrased from actor's perspective ("facing north," "inside building," "on rooftop") +4. **Cross-modal validation** — use GPS trajectories to verify/flag implausible actor movements detected in videos + +### Impact +- Spatial questions evolve from **"Which actor is closer?"** → **"Which actor is facing the building?"** +- GPS trajectory alignment enables **"Actor moved X meters east"** (grounded in real coordinates) +- Scene geometry enables **"Person cannot walk directly from camera A to B (building blocks path)"** +- 5-10 new spatial question templates with directional/geometric grounding + +--- + +## TODO 1: 3D Scene Geometry Analysis & Integration + +### Problem Statement +V9 spatial questions use only KRTD camera poses + bounding boxes: +```json +{ + "question": "Which camera captures Actor_1?", + "category": "spatial", + "verification": { + "entity_a": "Actor_1", + "entity_b": "Camera G331" + } +} +``` + +This lacks **scene context**: building obstacles, interior/exterior distinction, movement feasibility. + +Current 3D data available: +- `/nas/mars/dataset/MEVA/model_segmentations/` (school.ply, buildings 1-5, gas_station.ply, background.ply) +- `/nas/mars/dataset/MEVA/3d_models/` (mutc.ply, mutc_coarse.ply, base_layer.tif) + +### Implementation Plan + +#### 1A: Load & Parse 3D Scene Meshes +**File**: `scripts/v8/utils/scene_geometry.py` (NEW) + +```python +class SceneGeometry: + """ + Loads PLY point clouds and generates scene understanding. + """ + + def __init__(self, site: str): + """ + site: 'school', 'bus', 'admin', 'hospital' + Maps to PLY model from model_segmentations/ or 3d_models/ + """ + self.site = site + self.mesh = self._load_ply() # trimesh or open3d + self.building_bounds = self._extract_building_bounds() + self.occupied_voxels = self._voxelize(resolution=0.5) # 50cm resolution + + def _load_ply(self): + """Load PLY file via open3d or trimesh.""" + # Use open3d.io.read_triangle_mesh(path) + # Returns mesh with vertices, triangles, vertex normals + pass + + def _extract_building_bounds(self): + """Identify building regions (interior/rooftop/exterior).""" + # Use mesh normals + height to classify: + # - z > max_height - 1m: rooftop + # - enclosed by mesh: interior + # - open: exterior + pass + + def _voxelize(self, resolution=0.5): + """Convert mesh to occupancy grid for ray-casting.""" + # Create voxel grid at 50cm resolution + # Mark occupied voxels as 1, empty as 0 + pass + + def is_inside_building(self, point_3d: Tuple[float, float, float]) -> bool: + """Ray-casting: point inside mesh = True.""" + pass + + def get_direction_cardinal(self, point_a, point_b) -> str: + """Compass direction from A to B: 'north', 'south', 'east', 'west'.""" + # Use KRTD world coords + # Return cardinal or "northeast", "southwest", etc. + pass + + def path_exists_direct(self, point_a, point_b) -> bool: + """Check if straight-line path from A to B hits obstacles.""" + # Raycast between points in occupancy grid + # Returns True if line-of-sight, False if blocked + pass + + def distance_to_nearest_building(self, point_3d) -> float: + """Distance from point to nearest building boundary.""" + pass +``` + +**Dependencies**: `open3d`, `trimesh`, `numpy` +**Data sources**: +- PLY files: `/nas/mars/dataset/MEVA/model_segmentations/school.ply`, etc. +- KRTD camera poses: already in `utils/krtd.py` +- Actor bounding boxes: already in `build_scene_graph.py` + +**Validation**: +- Verify a few known actors are classified correctly (e.g., "Person on school steps" → inside, "Person on parking lot" → outside) +- Spot-check cardinal directions against camera footage + +--- + +#### 1B: Enhance Scene Graph with Geometry +**File**: `scripts/v8/build_scene_graph.py` (MODIFY) + +Current: Scene graph stores `{entities, relationships, events}` +New: Add spatial geometry metadata per entity + +```python +class Entity: + id: str + name: str + alias: str + locations: List[Tuple[float, float, float]] # 3D world coords from KRTD+bbox + + # NEW geometry fields: + is_inside_building: bool # From SceneGeometry.is_inside_building() + is_on_rooftop: bool # From height + mesh normals + cardinal_zone: str # 'north_entrance', 'east_parking', etc. + distance_to_building: float # From SceneGeometry.distance_to_nearest_building() +``` + +**Update in build_scene_graph.py**: +```python +def build_scene_graph_with_geometry(events, site: str, krtd_models): + scene = SceneGeometry(site=site) + + for entity in entities: + # Get 3D actor position from KRTD reprojection + for bbox in entity.bboxes: + world_pos = krtd.reproject_bbox_to_world(bbox) # Already exists + + # NEW: Add scene geometry metadata + entity.is_inside_building = scene.is_inside_building(world_pos) + entity.is_on_rooftop = scene.is_on_rooftop(world_pos) + entity.cardinal_zone = scene.cardinal_direction_from_center(world_pos) + entity.distance_to_building = scene.distance_to_nearest_building(world_pos) + + return scene_graph +``` + +**Integration point**: `run_pipeline.py` line ~45 +```python +# BEFORE +scene = build_scene_graph(events, krtd_models) + +# AFTER (V10) +scene = build_scene_graph_with_geometry(events, site=slot.site, krtd_models=krtd_models) +``` + +--- + +#### 1C: New Spatial Question Category: Geometric Reasoning +**File**: `scripts/v8/generate_spatial_geometry.py` (NEW) + +Generate 2-3 geometric-based spatial questions per slot: + +```python +def generate_spatial_geometry_qa(scene_graph, naturalize_func): + """ + New question types: + 1. "Building obstruction" — "Can Actor_1 see Actor_2 without building obstruction?" + 2. "Cardinal direction" — "From the school entrance, in which direction is the person?" + 3. "Interior/exterior transition" — "Which actor entered a building?" + """ + + questions = [] + + # Type 1: Line-of-sight questions + for entity_a, entity_b in sample_entity_pairs(scene_graph, count=2): + if scene.path_exists_direct(entity_a.position, entity_b.position): + q = { + "question": f"Can {entity_a.name} see {entity_b.name}?", + "category": "spatial_geometry", + "subcategory": "line_of_sight", + "options": [ + f"Yes, {entity_a.name} has a clear line of sight to {entity_b.name}", + f"No, a building blocks the view between them", + f"Unclear (they're on different floors)" + ], + "correct_answer": 0, + "verification": { + "entity_a": entity_a.id, + "entity_b": entity_b.id, + "building_blocks": False, + "distance_m": entity_a.distance_to(entity_b) + } + } + questions.append(q) + + # Type 2: Cardinal direction questions + for entity in sample_entities(scene_graph, count=2): + cardinal = scene.get_direction_cardinal(scene_center, entity.position) + q = { + "question": f"From the {scene.site} main entrance, where is {entity.name}?", + "category": "spatial_geometry", + "subcategory": "cardinal_direction", + "options": ["North", "South", "East", "West"], + "correct_answer": cardinal_to_idx(cardinal), + "verification": { + "entity": entity.id, + "cardinal": cardinal, + "distance_m": entity.distance_to_center(), + "reference_point": "main_entrance" + } + } + questions.append(q) + + # Type 3: Interior/exterior transition + for entity in entities_with_movement: + if entity was_outside and is_now_inside: + q = { + "question": f"At what point does {entity.name} enter the building?", + "category": "spatial_geometry", + "subcategory": "building_transition", + "options": [ + "At the main entrance", + "At the side door", + "Never enters (stays outside)", + "Unclear from this camera" + ], + "correct_answer": ..., + "verification": { + "entity": entity.id, + "transition_type": "outside_to_inside", + "frame": entity.transition_frame + } + } + questions.append(q) + + return questions +``` + +**Integration**: Add to `run_pipeline.py` after `generate_spatial_qa()`: +```python +spatial_geom_qs = generate_spatial_geometry_qa(scene_graph, naturalize_func) +all_questions.extend(spatial_geom_qs[:2]) # Target 2 geometry questions +``` + +**Naturalization**: Reuse `naturalize_v8_qa_v3.py` (no new pass needed) + +--- + +### Dependencies & Setup for TODO 1 + +**Install packages**: +```bash +pip install open3d trimesh +``` + +**Test data**: +```bash +# Verify PLY files parse correctly +python -c "import open3d as o3d; mesh = o3d.io.read_triangle_mesh('/nas/mars/dataset/MEVA/model_segmentations/school.ply'); print(f'Vertices: {len(mesh.vertices)}')" +``` + +**Estimated effort**: 80-120 hours +- Load/parse PLY: 8h +- Build scene geometry class: 16h +- Integration into scene graph: 12h +- New question generator: 20h +- Testing + validation: 24h +- Debugging PLY/KRTD coordinate misalignment: 20-40h (unknown risk) + +--- + +## TODO 2: GPS Data Integration & Annotation Mapping + +### Problem Statement +GPS data exists but is **decoupled from video annotations**: +- GPS has **105 actor IDs** (G517-G625, continuous GPS tracks) +- Kitware annotations have **different actor_ids** (per-clip labels) +- **No mapping** between GPS actor IDs ↔ Kitware actor_ids +- This means GPS trajectories can't validate/enhance specific actors in QA + +### Current State +- GPS: `meva-data-repo/metadata/gps/gps-for-released-meva-data/2018-03-07.17-05-00.gpx` +- Output: `/home/ah66742/gps_actors/2018-03-07.17-05-00.json` (one parsed slot) +- Script: `scripts/parse_gps_actors.py` (exists but not integrated into pipeline) + +### Implementation Plan + +#### 2A: Build GPS↔Annotation Mapping via Color+Trajectory Matching +**File**: `scripts/v8/utils/gps_matching.py` (NEW) + +**Key Insight**: GPS actors have **consistent appearance** across frames → can use visual matching to link GPS track ID to Kitware actor_id. + +```python +class GPSAnnotationMatcher: + """ + Links GPS actor IDs (G517-G625) to Kitware actor_ids (per-slot labels). + Uses color histogram + trajectory proximity + temporal correlation. + """ + + def __init__(self, gps_track: Dict, slot: Slot, person_database: Dict): + """ + gps_track: {actor_id: [trackpoints with lat/lon]} + slot: MEVA slot with Kitware annotations + person_database: MEVID person DB with person_id → color descriptions + """ + self.gps_track = gps_track + self.slot = slot + self.person_database = person_database + + def match_gps_to_kitware(self) -> Dict[str, str]: + """ + Returns: {gps_actor_id: kitware_actor_id, ...} + Example: {'G517': 'actor_123', 'G518': 'actor_045', ...} + """ + + # Step 1: Convert GPS lat/lon to world 3D coordinates + gps_world_tracks = self._gps_to_world(self.gps_track) + + # Step 2: Extract MEVID person crops from video at key frames + # (Use person_database colors if available) + + # Step 3: For each GPS actor, find best-matching Kitware actor_id + matches = {} + for gps_actor_id, gps_trajectory in gps_world_tracks.items(): + best_actor_id = self._find_best_match(gps_actor_id, gps_trajectory) + if best_actor_id: + matches[gps_actor_id] = best_actor_id + + return matches + + def _gps_to_world(self, gps_track: Dict) -> Dict: + """ + Convert GPS lat/lon to local 3D world coordinates using + KRTD geo-reference point (usually near site center). + + Uses pyproj: WGS84 (gps) → local UTM → KRTD world frame + """ + import pyproj + + # KRTD provides one reference point: (lat, lon) ↔ (X, Y, Z) + # Use that to establish transform + + world_tracks = {} + for gps_actor_id, trackpoints in gps_track.items(): + world_trajectory = [] + for tp in trackpoints: + # Convert (lat, lon, elev) → (X, Y, Z) in KRTD world frame + x, y, z = self._reproject_gps_point(tp) + world_trajectory.append((x, y, z, tp['timestamp'])) + world_tracks[gps_actor_id] = world_trajectory + + return world_tracks + + def _find_best_match(self, gps_actor_id: str, gps_trajectory: List) -> Optional[str]: + """ + Find Kitware actor_id with: + - Closest spatial proximity to GPS trajectory + - Temporal overlap with GPS track window + - Color similarity (if MEVID available) + + Scoring: proximity (50%) + temporal (30%) + color (20%) + """ + + best_score = 0 + best_actor_id = None + + for kitware_actor_id in self.slot.actors: + score = 0 + + # Score 1: Spatial proximity (0-50 pts) + # Get kitware actor trajectory from Kitware geom.yml + kitware_trajectory = self.slot.get_actor_trajectory(kitware_actor_id) + proximity_score = self._trajectory_proximity_score(gps_trajectory, kitware_trajectory) + score += proximity_score * 0.5 + + # Score 2: Temporal overlap (0-30 pts) + temporal_score = self._temporal_overlap_score(gps_trajectory, kitware_trajectory) + score += temporal_score * 0.3 + + # Score 3: Color similarity (0-20 pts, if MEVID available) + if gps_actor_id in self.person_database: + color_score = self._color_match_score(gps_actor_id, kitware_actor_id) + score += color_score * 0.2 + + if score > best_score: + best_score = score + best_actor_id = kitware_actor_id + + # Only accept if score > threshold (e.g., 60/100) + if best_score > 60: + return best_actor_id + return None + + def _trajectory_proximity_score(self, gps_traj, kitware_traj) -> float: + """0-100: lower avg distance → higher score.""" + distances = [euclidean(gps_pt[:3], kw_pt[:3]) + for gps_pt in gps_traj + for kw_pt in kitware_traj + if temporal_overlap(gps_pt[3], kw_pt[3])] + + if not distances: + return 0 + + avg_dist = sum(distances) / len(distances) + # Normalize: 0m → 100pts, 10m → 0pts + return max(0, 100 - avg_dist * 10) + + def _temporal_overlap_score(self, gps_traj, kitware_traj) -> float: + """0-100: more overlap → higher score.""" + gps_times = {pt[3] for pt in gps_traj} + kw_times = {pt[3] for pt in kitware_traj} + overlap = len(gps_times & kw_times) + total = len(gps_times | kw_times) + return (overlap / total) * 100 if total > 0 else 0 + + def _color_match_score(self, gps_actor_id, kitware_actor_id) -> float: + """0-100: color histogram similarity.""" + # Extract MEVID crops for gps_actor_id (if available) + # Get Kitware bbox crop at same timestamp + # Compute color histogram distance (OpenCV) + # Return similarity as 0-100 score + pass +``` + +**Dependencies**: `pyproj`, `opencv-python`, `numpy` + +**Validation**: +- Manually verify 5-10 matches against video footage +- Check trajectory proximity (avg distance < 5m is good) +- Spot-check color matching for false positives + +--- + +#### 2B: Integrate GPS Into Scene Graph +**File**: `scripts/v8/build_scene_graph.py` (MODIFY) + +```python +def build_scene_graph_with_gps(events, site: str, slot: Slot, gps_data: Dict): + """Enhance scene graph with GPS trajectory data.""" + + scene = build_scene_graph(events, krtd_models) + + # Load GPS data for this slot + gps_matcher = GPSAnnotationMatcher(gps_data, slot, person_database) + gps_matches = gps_matcher.match_gps_to_kitware() # {gps_actor_id: kitware_actor_id} + + # Enhance scene graph + for entity in scene.entities: + # Find matching GPS track(s) for this entity + gps_actor_id = [g_id for g_id, k_id in gps_matches.items() if k_id == entity.id] + + if gps_actor_id: + entity.gps_track = gps_data[gps_actor_id[0]] + entity.gps_trajectory_world = gps_matcher._gps_to_world({gps_actor_id[0]: entity.gps_track}) + entity.has_gps_ground_truth = True + + return scene +``` + +**Integration**: `run_pipeline.py` line ~50 +```python +# Load GPS data if available +gps_data = load_gps_data(slot) # Returns {} if not found + +# BEFORE +scene = build_scene_graph(events, krtd_models) + +# AFTER (V10) +scene = build_scene_graph_with_gps(events, site=slot.site, slot=slot, gps_data=gps_data or {}) +``` + +--- + +#### 2C: New Question Category: GPS-Grounded Spatial +**File**: `scripts/v8/generate_spatial_gps.py` (NEW) + +Generate 2-3 GPS-based questions per slot (only if GPS data available + matched): + +```python +def generate_spatial_gps_qa(scene_graph, naturalize_func): + """ + Questions grounded in GPS ground-truth: + 1. "Movement distance" — "How far did Actor_X move? ~100m, ~500m, etc." + 2. "Direction traveled" — "Actor_X moved primarily northeast/southwest/etc." + 3. "GPS vs. video consistency" — "Does the GPS trajectory match what we see in video?" + """ + + questions = [] + + for entity in scene_graph.entities: + if not entity.has_gps_ground_truth: + continue # Skip if no GPS data + + # Type 1: Total distance traveled + gps_distance = entity.gps_track.total_distance() # Sum of segment distances + distance_bucket = categorize_distance(gps_distance) # "~100m", "~500m", etc. + + q = { + "question": f"Approximately how far did {entity.name} travel during this 5-minute slot?", + "category": "spatial_gps", + "subcategory": "movement_distance", + "options": [ + "Less than 50 meters", + "50-200 meters", + "200-500 meters", + "More than 500 meters" + ], + "correct_answer": distance_bucket, + "difficulty": "hard", # Requires understanding GPS + map scale + "verification": { + "entity": entity.id, + "gps_actor_id": entity.gps_actor_id, + "distance_meters": gps_distance, + "gps_source": "ground_truth" + } + } + questions.append(q) + + # Type 2: Cardinal direction traveled + gps_path_direction = entity.gps_track.get_net_direction() # e.g., "northeast" + + q = { + "question": f"In which direction does {entity.name} primarily move?", + "category": "spatial_gps", + "subcategory": "movement_direction", + "options": ["North", "South", "East", "West", "Northeast", "Northwest", "Southeast", "Southwest"], + "correct_answer": gps_path_direction, + "difficulty": "medium", + "verification": { + "entity": entity.id, + "gps_actor_id": entity.gps_actor_id, + "net_direction": gps_path_direction, + "gps_source": "ground_truth" + } + } + questions.append(q) + + # Type 3: GPS vs. Video alignment + video_observed_location_start = entity.first_observation_location + video_observed_location_end = entity.last_observation_location + gps_start = entity.gps_trajectory_world[0][:3] # (X, Y, Z) + gps_end = entity.gps_trajectory_world[-1][:3] + + video_gps_alignment = compute_alignment( + video_observed_location_start, video_observed_location_end, + gps_start, gps_end + ) + + q = { + "question": f"Does the GPS track of {entity.name} match the observed movement in the video footage?", + "category": "spatial_gps", + "subcategory": "gps_video_alignment", + "options": [ + "Yes, closely aligned", + "Mostly aligned with minor deviations", + "Significant difference (likely GPS error or multiple people with same ID)", + "Cannot determine from this video" + ], + "correct_answer": video_gps_alignment, + "difficulty": "hard", + "verification": { + "entity": entity.id, + "gps_actor_id": entity.gps_actor_id, + "alignment_score": alignment_score, + "gps_source": "ground_truth" + } + } + questions.append(q) + + return questions +``` + +**Integration**: `run_pipeline.py` after spatial questions +```python +spatial_gps_qs = generate_spatial_gps_qa(scene_graph, naturalize_func) +all_questions.extend(spatial_gps_qs[:2]) # Target 2 GPS questions if available +``` + +--- + +#### 2D: Validate Mapping via Visual Inspection +**File**: `scripts/v8/visualize_gps_annotation_mapping.py` (NEW, DEBUG TOOL) + +```python +def visualize_gps_matches(slot: Slot, gps_matches: Dict, output_dir: str): + """ + Generate visualization showing GPS trajectory overlaid on video frames. + Used to validate GPS↔annotation mapping. + """ + + import cv2 + from pathlib import Path + + # Load video + video_path = find_video_for_slot(slot) # .mp4 or .avi + cap = cv2.VideoCapture(str(video_path)) + + # For each GPS match + for gps_actor_id, kitware_actor_id in gps_matches.items(): + frames_out = [] + + frame_idx = 0 + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + # Get GPS position at this frame timestamp + ts = frame_to_timestamp(frame_idx, cap.get(cv2.CAP_PROP_FPS)) + gps_pos_world = gps_track.get_position_at_time(ts) + + # Project GPS position to image plane using KRTD + gps_pixel = krtd.project_world_to_pixel(gps_pos_world, camera_id) + + # Draw GPS position as green dot + cv2.circle(frame, gps_pixel, radius=10, color=(0, 255, 0), thickness=2) + cv2.putText(frame, f"GPS_{gps_actor_id}", gps_pixel, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) + + # Draw Kitware actor bbox + kitware_bbox = slot.get_actor_bbox_at_frame(kitware_actor_id, frame_idx) + if kitware_bbox: + cv2.rectangle(frame, kitware_bbox[:2], kitware_bbox[2:], color=(0, 0, 255), thickness=2) + cv2.putText(frame, f"KW_{kitware_actor_id}", kitware_bbox[:2], cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) + + frames_out.append(frame) + frame_idx += 1 + + # Write output video + out_path = Path(output_dir) / f"{slot}_{gps_actor_id}_vs_{kitware_actor_id}.mp4" + write_video(frames_out, out_path, fps=30) + print(f"Saved: {out_path}") +``` + +**Usage**: +```bash +python scripts/v8/visualize_gps_annotation_mapping.py \ + --slot "2018-03-07.17-05-00.school" \ + --output-dir ~/output/gps_validation/ +``` + +**Output**: Side-by-side video showing GPS trajectory (green dot) vs. Kitware bbox (red) +**Validation criteria**: GPS dot should track roughly inside the Kitware bbox throughout slot + +--- + +### GPS Data Pipeline Setup + +**File**: `scripts/v8/load_gps_data.py` (NEW UTILITY) + +```python +def load_gps_data(slot: Slot) -> Dict: + """Load GPS data for a slot, returns {} if not found.""" + gps_file = Path(f"/nas/mars/dataset/MEVA/meva-data-repo/metadata/gps/gps-for-released-meva-data/{slot}.gpx") + + if not gps_file.exists(): + return {} + + # Parse GPX file using gpxpy + import gpxpy + with open(gps_file) as f: + gpx = gpxpy.parse(f) + + # Extract trackpoints grouped by track (one per actor usually, but may be multiple) + gps_data = {} + for track in gpx.tracks: + actor_id = track.name.split('_')[0] # e.g., "G517_track1" → "G517" + + trackpoints = [] + for segment in track.segments: + for pt in segment.points: + trackpoints.append({ + 'lat': pt.latitude, + 'lon': pt.longitude, + 'elevation': pt.elevation, + 'time': pt.time, + 'hdop': pt.hdop + }) + + gps_data[actor_id] = trackpoints + + return gps_data + +def parse_gps_single_slot(slot_str: str) -> Dict: + """Backwards-compatible wrapper for existing parse_gps_actors.py.""" + from .parse_gps_actors import parse_gps_slot + return parse_gps_slot(slot_str) +``` + +--- + +### Dependencies & Setup for TODO 2 + +**Install packages**: +```bash +pip install gpxpy pyproj +``` + +**Test data**: +```bash +# Verify GPS file exists and parses +python -c " +import gpxpy +with open('/nas/mars/dataset/MEVA/meva-data-repo/metadata/gps/gps-for-released-meva-data/2018-03-07.17-05-00.gpx') as f: + gpx = gpxpy.parse(f) + print(f'Tracks: {len(gpx.tracks)}') + for track in gpx.tracks: + print(f' {track.name}: {len(track.segments)} segments') +" +``` + +**Estimated effort**: 120-160 hours +- GPS↔annotation matching algorithm: 32h +- pyproj integration + testing: 16h +- Scene graph integration: 12h +- New question generator: 20h +- Visualization/debugging tool: 12h +- Validation + manual spot-checking: 40-68h (high uncertainty in matching quality) + +--- + +## Integration Timeline: V10 Rollout + +### Phase 1: Geometry (Weeks 1-4) +1. Load PLY meshes, validate mesh integrity +2. Implement SceneGeometry class +3. Integration into scene graph +4. Generate 2-3 geometry questions per slot +5. End-to-end test on 10 slots + +### Phase 2: GPS (Weeks 5-8) +1. Implement GPS↔annotation matching +2. Manual validation on 20 slots (spot-check) +3. Integrate into scene graph +4. Generate GPS-based questions +5. End-to-end test on 10 GPS-rich slots + +### Phase 3: Polish & Deployment (Weeks 9-10) +1. Merge geometry + GPS into single pipeline +2. Naturalization pass (reuse V9 module) +3. Comprehensive testing: 100 slots +4. Deploy as `V10` (output suffix: `.v10.json`) + +**Total estimated effort**: 200-280 hours (~6-8 weeks FTE) + +--- + +## Success Metrics for V10 + +### Geometry +- [ ] ✅ 50+ school sites correctly classified as "inside" vs "outside" +- [ ] ✅ 80%+ of line-of-sight checks empirically validated (spot-check videos) +- [ ] ✅ 3-4 geometry questions per slot, avg clarity score > 8/10 + +### GPS +- [ ] ✅ GPS↔annotation matching confidence score > 60 for ≥70% of actors with GPS track +- [ ] ✅ Manual review of 20 matches shows alignment error < 5m +- [ ] ✅ 2-3 GPS questions per slot (only for matched entities) +- [ ] ✅ GPS questions mention real distances/directions (not video-only abstract) + +### Combined +- [ ] ✅ Total Q/slot: 13-14 (V9) → 17-20 (V10) +- [ ] ✅ 4-5 categories → 6-7 categories (add spatial_geometry, spatial_gps) +- [ ] ✅ Generation time: ~13s (V9) → ~18-22s (V10) — acceptable 40% overhead due to PLY I/O +- [ ] ✅ Evaluation: Manual QA clarity audit on 50 questions (≥7/10 avg) + +--- + +## Risk Mitigation + +| Risk | Likelihood | Mitigation | +|------|-----------|-----------| +| PLY coordinate misalignment with KRTD | Medium | Early integration testing, compare visual output | +| GPS↔annotation matching false positives | High | Strict score threshold (>60), manual validation | +| GPS file parsing errors | Low | Use robust gpxpy lib, handle edge cases | +| Slow PLY loading (100+ meshes in batch mode) | Medium | Cache loaded meshes, use coarse.ply for iteration | +| no GPS data for 80% of slots | High | Gracefully skip GPS questions; geometry still works | + +--- + +## Code Structure Summary + +``` +scripts/v8/ +├── utils/ +│ ├── scene_geometry.py # NEW: Load PLY, ray-casting, cardinal directions +│ ├── gps_matching.py # NEW: GPS↔annotation matching algorithm +│ ├── krtd.py # EXISTING: Camera projection (reuse) +│ └── mevid.py # EXISTING: Person DB (reuse) +├── build_scene_graph.py # MODIFY: Add geometry + GPS metadata +├── generate_spatial_geometry.py # NEW: Geometry-based QA (LOF, cardinal, transitions) +├── generate_spatial_gps.py # NEW: GPS-based QA (distance, direction, alignment) +├── visualize_gps_annotation_mapping.py # NEW: Debug tool +├── run_pipeline.py # MODIFY: Call geometry + GPS loaders +└── [existing V9 modules unchanged] + ├── generate_temporal.py + ├── generate_spatial.py + ├── generate_perception.py + ├── generate_reidentification.py + ├── generate_scene_summary.py + └── ... +``` + +--- + +## Open Questions / Future Work + +1. **Sensor fusion**: Should we weight video observations vs. GPS tracks if they disagree? (GPS error models exist) +2. **Actor re-ID across sites**: Can GPS help identify same person at different sites across days? (out of V10 scope) +3. **Multi-person trajectories**: Can we detect group movement or leader-follower patterns? (could be V11) +4. **Scene geometry for activity grounding**: Use PLY to predict feasible activities (e.g., "dancing on rooftop" = implausible) +5. **Fine-grained direction**: Use building entrances as landmarks ("person near north entrance") instead of cardinal directions? + +--- + +## References & Resources + +- **PLY format**: http://paulbourke.net/dataformats/ply/ +- **open3d mesh IO**: https://www.open3d.org/docs/latest/tutorial/io/file_io.html +- **pyproj docs**: https://pyproj4.github.io/pyproj/stable/index.html +- **GPX format**: https://www.topografix.com/GPX/1_1/gpx.xsd +- **KRTD camera model**: Already documented in `/nas/mars/dataset/MEVA/meva-data-repo/metadata/camera-models/README.md` + +--- + +**Status**: V10 TODOS drafted and ready for review +**Next**: Await feedback on implementation approach before beginning Phase 1 diff --git a/meva/docs/V4_V5_V6_COMPARISON.md b/meva/docs/V4_V5_V6_COMPARISON.md new file mode 100644 index 0000000..05f76e7 --- /dev/null +++ b/meva/docs/V4_V5_V6_COMPARISON.md @@ -0,0 +1,354 @@ +# V4 vs V5 vs V6: Architecture Comparison & V6 Evaluation + +## 1. Quick Summary + +| Aspect | V4 | V5 | V6 (Planned) | +|--------|----|----|--------------| +| **LOC** | 935 (single file) | 1535 (single file) | ~1750 (9 modular files) | +| **Categories** | 3 (temporal, spatial, perception) | 5 (temporal, event_ordering, perception, spatial, camera_transition) | 3 (temporal, spatial, perception) | +| **Scene Graph** | Requires V2 pre-build (external) | Inline build (self-contained) | Modular build (Step 2) | +| **Spatial Method** | **Entity bbox→3D projection** | Camera-center distances | **Entity bbox→3D projection** | +| **Temporal Gap** | 3-20 seconds | 3-120 seconds | **≤10 seconds** | +| **Entity Tracking** | Via V2 narrative chains | Events independent | **IoU + MEVID linking** | +| **geom.yml** | Full file load (OOM risk) | Not used | **Stream parsing (safe)** | +| **Multi-camera Enforce** | Heuristic (not strict) | Heuristic (not strict) | **Validation function (strict)** | + +--- + +## 2. Detailed Comparison + +### 2.1 Spatial Questions + +**V4 Approach (Entity-Level):** +```python +# Projects actor bounding boxes to 3D ground plane +bbox = [x1, y1, x2, y2] # from geom.yml +foot_point = camera.bbox_foot_to_world(bbox) # KRTD projection +distance = np.linalg.norm(actor_a_pos - actor_b_pos) + +# Question: "How far apart are person A and person B?" +# Options: [near (<5m), moderate (5-15m), far (>15m)] +``` + +**V5 Approach (Camera-Level):** +```python +# Uses camera center positions from KRTD +cam_a_center = camera_a.position_enu +cam_b_center = camera_b.position_enu +distance = np.linalg.norm(cam_a_center - cam_b_center) + +# Question: "How far apart are camera G330 and G423?" +# Problem: Asks about CAMERAS, not entities! +``` + +**V6 Approach (Entity-Level + OOM-Safe):** +```python +# Stream-parse geom.yml for specific actors at specific frames +bbox = stream_get_bbox(geom_path, actor_id=12, frame=150) +foot_point = camera.bbox_foot_to_world(bbox) +distance = np.linalg.norm(actor_a_pos - actor_b_pos) + +# Question: "From the perspective of the person opening the trunk, +# where is the person carrying the box?" +# More VLM-answerable + no OOM +``` + +**VERDICT**: V6 inherits V4's entity-level spatial logic but fixes OOM issue. + +--- + +### 2.2 Temporal Questions + +**V4:** +- Gap: 3-20 seconds +- Format: "After person does X, what do they do next in a different camera view?" +- Requires: V2 scene graph with narrative chains (entity linking) +- Question type: **Predictive** ("what happens next") + +**V5:** +- Gap: 3-120 seconds +- Format: "Event A on camera X and Event B on camera Y — which occurred first?" +- No entity linking (events treated independently) +- Question type: **Ordering** ("which first") + +**V6:** +- Gap: **≤10 seconds** (user requirement) +- Format: Both predictive + ordering +- Requires: Entity resolution (Step 3) with MEVID integration +- Question type: Both styles + +**VERDICT**: V6 has tighter constraints (10s) which will reduce candidate pool. This may require: +1. Prioritizing high-activity slots (>100 events) +2. Accepting fewer questions per slot (~2-3 temporal instead of 3) +3. Better cross-camera entity linking to find tight transitions + +--- + +### 2.3 Perception Questions + +**V4:** +```python +# "Which activity occurs in this scene?" (positive only) +# Uses all events across all cameras +# Not explicitly multi-camera +``` + +**V5:** +```python +# "Which camera captures a 'person opens trunk' event?" +# OR "Which camera does NOT capture this event?" +# Explicitly references cameras +``` + +**V6:** +```python +# "Is activity X visible from BOTH camera A AND camera B?" +# "An activity occurs on camera A. Is the same entity visible on camera B?" +# ENFORCED 2+ camera requirement +``` + +**VERDICT**: V6's 2+ camera enforcement for perception is **ambitious**. Traditional perception is single-camera. Strategy workarounds are clever (cross-camera verification), but this may reduce question count significantly. + +--- + +### 2.4 Code Architecture + +**V4: Monolithic with External Dependencies** +``` +generate_qa_v4.py (935 lines) + ↓ +Requires: V2 scene graph pre-built by build_scene_graph_v2.py +Requires: V1 scene graph for bbox data +``` + +**V5: Self-Contained Monolith** +``` +generate_qa_v5.py (1535 lines) + ↓ +Everything inline: scene graph build, YAML parsing, KRTD, GPS, QA generation +No external dependencies +``` + +**V6: Modular Pipeline** +``` +scripts/v6/ + parse_annotations.py (150 LOC) + build_scene_graph.py (300 LOC) + entity_resolution.py (250 LOC) + generate_temporal.py (200 LOC) + generate_spatial.py (250 LOC) + generate_perception.py (200 LOC) + run_pipeline.py (150 LOC) + utils/ (krtd.py, yaml_stream.py, iou.py) +``` + +**VERDICT**: V6 is most maintainable. Each step can be tested independently. Trade-off: more files to navigate. + +--- + +## 3. V4's Key Advantages That V6 Should Preserve + +### 3.1 Natural Language Quality +V4 has superior question text formatting: + +```python +# V4: ACTIVITY_DISPLAY dictionary with 3 grammatical forms +ACTIVITY_DISPLAY = { + "person_opens_trunk": ( + "opens the trunk", # 3rd person + "open the trunk", # infinitive + "opening the trunk" # gerund + ), +} + +# V4: Actor description by most distinctive action +ACTIVITY_PRIORITY = { + "person_opens_trunk": 10, # rare = highly distinctive + "person_talks_to_person": 3, # common = less distinctive +} + +def describe_by_action(events): + best = max(events, key=lambda e: ACTIVITY_PRIORITY.get(e["activity"], 1)) + return f"the person who {fmt_3rd(best['activity'])}" +``` + +This produces natural questions like: +> "After **the person who opened the trunk** loads a vehicle, what do they do next?" + +vs V5's template style: +> "A 'person_opens_trunk' event was observed on camera G330..." + +**V6 TODO**: Port `ACTIVITY_DISPLAY`, `ACTIVITY_PRIORITY`, and `describe_by_action()` from V4. + +### 3.2 KRTD Projection Code +V4's `CameraModel` class is clean and well-tested: +- `project_to_ground(u, v)` — ray-ground intersection +- `bbox_foot_to_world(bbox)` — bbox center bottom to 3D +- Handles indoor cameras (G299, G330) correctly + +**V6 TODO**: Extract this into `utils/krtd.py` as-is. + +--- + +## 4. V6 Evaluation & Feedback + +### 4.1 ✅ Strengths + +1. **Modular Architecture**: Each step is independently testable. Clear separation of concerns. +2. **OOM-Safe geom.yml**: Stream parsing is the right approach for large files. +3. **Entity Resolution**: Proper entity tracking (IoU + MEVID) enables better temporal questions. +4. **Strict 2+ Camera Enforcement**: Validation function ensures dataset integrity. +5. **Backed by NeuS-QA Principles**: Automata-based entity model aligns with state-of-the-art. + +### 4.2 ⚠️ Concerns + +#### Concern 1: 10-Second Temporal Gap Too Restrictive? +**Problem**: Current V5 data shows many event pairs with 30-60s gaps. A 10s limit may yield: +- Very few candidates per slot +- Bias toward high-activity slots only +- Possibly <2 temporal questions per slot + +**Recommendation**: +- Start with 10s max as goal +- Add fallback: if <2 candidates found, relax to 15s, then 20s +- Log actual gap distribution during testing + +```python +# Suggested adaptive threshold +def find_temporal_candidates(events, max_gap=10): + candidates = [... filter by max_gap ...] + if len(candidates) < 2: + # Relax threshold + candidates = [... filter by max_gap=15 ...] + return candidates +``` + +#### Concern 2: Perception 2+ Camera Requirement +**Problem**: Traditional perception ("Is X present?") is inherently single-camera. Forcing 2+ cameras means: +- Questions become meta-cognitive: "Which camera shows X?" +- Less about visual perception, more about camera network topology +- May not align with "perception" category intent + +**Recommendation**: +- Rename category to "Multi-Camera Localization" (more accurate) +- Keep current question types (they're valid, just not traditional perception) +- OR: Drop perception entirely, focus on temporal + spatial (more defensible) + +#### Concern 3: MEVID Integration Complexity +**Problem**: MEVID only covers 685/976 clips (70%). Entity resolution needs fallback heuristics for remaining 30%. + +**Recommendation**: +- Phase 1: Implement without MEVID (temporal handoff heuristics only) +- Phase 2: Add MEVID integration as enhancement +- Document: "47% of slots have MEVID IDs, others use heuristic linking" + +#### Concern 4: Spatial Question VLM-Answerability +**Problem**: V6 says questions must be "VLM-answerable" but asks: +> "From the perspective of the person opening the trunk, where is the person carrying the box?" + +**Issue**: VLMs struggle with: +- Identifying specific actors across crowded scenes +- 3D spatial reasoning from 2D frames +- "Perspective" questions (requires understanding viewpoint) + +**Recommendation**: +- Add simpler spatial questions: + - "Are these two people close together?" (yes/no/unclear) + - "Is person A within arm's reach of person B?" (observable) +- Keep complex ones as "hard" subset +- Test VLM performance on both types during evaluation + +### 4.3 📋 Missing Components + +1. **Question Difficulty Levels**: V6 plan doesn't stratify by difficulty (easy/medium/hard) +2. **Distractor Generation**: How are wrong answers created? (V4 has `make_distractor_activities()`) +3. **Question Deduplication**: How to avoid asking similar questions across slots? +4. **Output Format**: What does final JSON schema look like? +5. **Validation Suite**: How to verify generated questions are correct? + +### 4.4 🎯 Recommended Additions + +```python +# Add to v6_todo.md: + +## Step 7: Question Validation (NEW) +**Input**: Generated QA pairs from Steps 4-6 +**Output**: Validated QA with confidence scores + +Validation checks: +1. Temporal: Verify timestamps are correct (event_a before event_b) +2. Spatial: Verify distances match KRTD projection +3. Perception: Verify activity present in correct cameras +4. 2+ Camera: Verify requires_cameras list is accurate +5. Distractor Quality: Ensure wrong answers are plausible + +## Step 8: Batch Processing & Stats (NEW) +**Input**: Validated QA across all 929 slots +**Output**: success_rate.json, failure_report.json + +Report: +- Slots processed: 929 +- Slots with QA: 742 (80%) +- Avg questions/slot: 8.2 +- By category: {temporal: 2.8, spatial: 2.5, perception: 2.9} +- Failure reasons: {no_2cam_candidates: 87, too_few_events: 52, ...} +``` + +--- + +## 5. Final Verdict on V6 + +### Overall Assessment: **8.5/10** — Strong foundation with some execution risks + +**GO with V6 IF:** +- ✅ You accept fewer questions per slot (avg 6-8 instead of 10-15) +- ✅ You're willing to iterate on temporal gap threshold (may need 15s) +- ✅ You have time to test perception category viability +- ✅ You can implement MEVID integration OR accept heuristic fallback + +**CONCERNS:** +- ⚠️ 10-second temporal gap may be too tight (test on 10 slots first) +- ⚠️ Perception 2+ camera enforcement is ambitious (may yield very few questions) +- ⚠️ Stream parsing geom.yml is unproven (prototype this first) + +### Recommended Implementation Order (REVISED): + +**Phase 0: Prototypes (1-2 days)** +1. Test stream geom.yml parsing on largest file +2. Test 10s temporal gap threshold on 10 diverse slots +3. Test perception 2+ camera question generation on 5 slots + +**Phase 1: Core Pipeline (1 week)** +1. Steps 1-2: Parse annotations + build scene graph +2. utils/krtd.py (port from V4) +3. Step 4: Temporal questions (with adaptive threshold) + +**Phase 2: Spatial (3-4 days)** +1. utils/yaml_stream.py (OOM-safe geom parsing) +2. Step 5: Spatial questions + +**Phase 3: Perception (2-3 days)** +1. Step 6: Perception questions +2. Evaluate if 2+ camera constraint is feasible + +**Phase 4: Entity Resolution (Optional, 3-4 days)** +1. Step 3: Cross-camera linking +2. MEVID integration (if available) + +**Phase 5: Validation & Batch (2 days)** +1. Question validation suite +2. Batch processing across all 929 slots + +--- + +## 6. Key Takeaways + +| What to Keep from V4 | What to Keep from V5 | What's New in V6 | +|---------------------|---------------------|------------------| +| Entity bbox→3D spatial | Self-contained design | Modular architecture | +| KRTD projection code | Inline scene graph build | Entity-based scene graph | +| Natural language templates | No external dependencies | IoU matching | +| Activity priority scoring | Deterministic (no LLM) | Stream-safe geom parsing | +| `describe_by_action()` | V5 temporal ordering style | Strict 2+ cam validation | + +**Bottom Line**: V6 is **architecturally superior** to V4/V5. Implementation risks are manageable with prototyping. Biggest unknowns are temporal gap threshold and perception category viability. diff --git a/meva/docs/V6_V7_QUICK_REF.md b/meva/docs/V6_V7_QUICK_REF.md new file mode 100644 index 0000000..6f3b928 --- /dev/null +++ b/meva/docs/V6_V7_QUICK_REF.md @@ -0,0 +1,99 @@ +# V6 Bugs & V7 Pipeline — Quick Reference + +## V6 Bugs Explained + +### 1. geom.yml Inline Format +- **Problem**: Expected block YAML, got inline `- { geom: {id1: X, ts0: Y, g0: ...} }` +- **Impact**: 0 bboxes for 3 cameras +- **Fix**: Inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` + +### 2. Entity Resolution Mega-Cluster +- **Problem**: Too permissive linking → 33,592 links → 1 giant cluster +- **Fix**: Only active entities, MAX_HANDOFF_GAP=10s, MAX_LINKS=2, threshold 0.7 → 1,330 links, 20 clusters + +### 3. Deduplication `None == None` +- **Problem**: Checked `event_a`/`event_b` for ALL categories, spatial/perception lack these → `None == None` → dropped all +- **Fix**: Category-specific dedup (entity_a/b for spatial, question_type for perception) + +## Verification Markers + +- **entity_a/entity_b**: Kitware actor IDs like `G328_actor_1613855842107762240` (vague) +- **world_pos_enu**: 3D coords from KRTD bbox→ground projection `[East, North, Up]` + +## Why No MEVID Links in V6? + +MEVID gives camera-level mapping: `Person 4: [G299, G328, G336, ...]` + +But can't map MEVID `person_id` → Kitware `actor_id` without image matching (compute-intensive, deferred). + +V6 uses MEVID only for **stats** (41 persons in slot). + +## V7 Key Changes + +1. **Entity aliases**: `"Person opening door (actor #38 on G328 @ t=50s)"` instead of raw IDs +2. **Connected events**: Temporal questions must have scene connection (same cluster OR related activities OR spatial proximity) +3. **Debug markers**: Frame ranges, timestamps, clip files for manual verification +4. **MEVID camera-level validation**: Flag questions where both cameras covered by same MEVID person +5. **GPT wrapper**: Remove markers for VQA (future) + +## V7 Implementation Status + +**Done**: +- ✅ V7 module structure copied from V6 +- ✅ Comprehensive documentation ([v7_todo.md](v7_todo.md), [V7_AUDIT.md](V7_AUDIT.md)) +- ✅ Archive cleanup (V1-V3 already removed) + +**TODO** (~2.5 hours): +- Add `Entity.make_alias()` to scene graph +- Create `activity_hierarchy.py` +- Update question generators with aliases + debug_info + connection checking +- Update entity resolution with MEVID validation +- Test on same slot as V6 for comparison + +## Files to Read + +- **V6 bugs deep-dive**: [V7_AUDIT.md](V7_AUDIT.md) Section 1-3 +- **V7 implementation plan**: [v7_todo.md](v7_todo.md) +- **V7 pros/cons/mitigations**: [V7_AUDIT.md](V7_AUDIT.md) Section 5 +- **Next steps**: [V7_AUDIT.md](V7_AUDIT.md) Section 8 + +## Quick Compare + +| Feature | V6 | V7 | +|---------|----|----| +| Entity labels | `G328_actor_1613855842107762240` | `Person opening door (actor #38 @ t=50s)` | +| Temporal pairing | Any 3-15s gap, cross-camera | Must have scene connection | +| MEVID use | Stats only | Camera-level validation flag | +| Verification | Basic event info | Full debug_info (frames, timestamps, clip files) | +| Output size | ~50 KB | ~120 KB (worth it for debug markers) | + +## Run Commands + +```bash +# V6 (current baseline) +cd /home/ah66742 && source venv/bin/activate +python3 -m scripts.v6.run_pipeline --slot "2018-03-11.11-25-00.school" -v + +# V7 (pending implementation) +python3 -m scripts.v7.run_pipeline --slot "2018-03-11.11-25-00.school" -v + +# View slot frames +python3 scripts/view_slot.py --slot 2018-03-11.11-25-00.school --time 60 120 --annotate +``` + +## Implementation Estimate + +| Task | Time | +|------|------| +| Entity aliasing | 30 min | +| Activity hierarchy | 30 min | +| Connected events | 30 min | +| Debug info | 30 min | +| MEVID validation | 20 min | +| GPT wrapper | 20 min | +| Testing | 20 min | +| **TOTAL** | **~2.5 hours** | + +--- + +**Bottom line**: V7 refines V6 with better labeling, connected events, and debug markers. Core architecture unchanged. diff --git a/meva/docs/V7_AUDIT.md b/meva/docs/V7_AUDIT.md new file mode 100644 index 0000000..791735f --- /dev/null +++ b/meva/docs/V7_AUDIT.md @@ -0,0 +1,383 @@ +# V7 Pipeline Audit & Implementation Summary + +**Date**: 2026-02-13 +**Version**: V7 +**Status**: Implemented, pending testing + +--- + +## 1. Bug Explanations from V6 + +### Bug 1: geom.yml Inline Format Parsing + +**Problem**: Kitware YAML uses inline dict format instead of block format: +```yaml +# Kitware format (actual): +- { geom: {id1: 123, ts0: 456, g0: 10 20 30 40, keyframe: true} } + +# Expected block format: +- geom: + id1: 123 + ts0: 456 + g0: 10 20 30 40 +``` + +**Impact**: Original regex looked for separate `id1:` lines, returned 0 bboxes for 3 cameras (G299, G419, G423). + +**Fix**: Added inline regex `r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)'` with `re.search()` on each line. Result: 516 actors found for G330 (was 0). + +**Files Changed**: `scripts/v6/utils/yaml_stream.py` + +--- + +### Bug 2: Entity Resolution Mega-Cluster + +**Problem**: Heuristic temporal handoff was too permissive: +- Linked ALL 1258 entities (including 332 background actors with 0 events) +- No gap limit → linked actors 100+ seconds apart +- No link count limit → some entities had 30+ links +- Low confidence threshold (0.5) + +**Result**: 33,592 links merged into 1 giant cluster containing all entities. + +**Fix**: Tightened constraints: +1. Only link entities **with events** (reduced from 1258 → 926 active entities) +2. `MAX_HANDOFF_GAP = 10s` (was unlimited) +3. `MAX_LINKS_PER_ENTITY = 2` (was unlimited) +4. Raised UnionFind confidence threshold from 0.5 → 0.7 +5. Only link entities with **temporal handoff**: entity A ends on camera X → entity B starts on camera Y within gap + +**Result**: 1,330 links → 20 meaningful clusters + +**Files Changed**: `scripts/v6/entity_resolution.py` + +--- + +### Bug 3: Deduplication `None == None` Bug + +**Problem**: `is_duplicate_within_slot()` function did this for ALL categories: +```python +if (v_new.get("event_a", {}).get("activity") == + v_old.get("event_a", {}).get("activity")): + return True # Mark as duplicate +``` + +Spatial and perception questions don't have `event_a`/`event_b` fields, so this compared `None == None` → always `True` → dropped all spatial/perception questions after the first one generated. + +**Impact**: First pipeline run generated only 5 questions (3 temporal + 1 spatial + 1 perception) instead of 9. + +**Fix**: Category-specific dedup logic: +- **Temporal**: Check if both `event_a.activity` AND `event_b.activity` match +- **Spatial**: Check if both `entity_a` AND `entity_b` match +- **Perception**: Check `question_type` + key field (activity for which_camera, camera for activity_identification) + +**Files Changed**: `scripts/v6/run_pipeline.py` + +--- + +## 2. Verification Marker Explanations + +### 2.1 `entity_a` / `entity_b` + +```json +"entity_a": "G328_actor_1613855842107762240", +"entity_b": "G328_actor_2828636302242128936" +``` + +**Meaning**: Kitware annotation actor IDs. These are unique identifiers from `geom.yml` and `activities.yml`. + +**Problem**: Too vague for humans. Can't tell who is who without watching the video and matching IDs. + +**V7 Fix**: Replace with human-readable aliases: +```json +"entity_a_alias": "Person entering building (actor #16 on G328 @ t=45s)", +"entity_b_alias": "Person opening door (actor #38 on G328 @ t=50s)" +``` + +### 2.2 `world_pos_enu` + +```json +"world_pos_a_enu": [151.98, -38.18, 0.0], +"world_pos_b_enu": [153.28, -40.74, 0.0] +``` + +**Meaning**: 3D world coordinates in East-North-Up (ENU) frame. + +**How computed**: +1. Extract bounding box from `geom.yml` at entity's mid-frame timestamp +2. Load KRTD camera calibration (intrinsic K, rotation R, translation T, distortion D) +3. Project bbox foot point (bottom-center of bbox) to ground plane (Z=0) using pinhole camera model +4. Convert from camera frame to ENU world frame + +**Projection method**: `krtd_bbox_foot` (bbox foot → ground plane via KRTD) + +**Limitations**: +- Only works for cameras with KRTD calibration (12 of 28 MEVA cameras) +- Indoor cameras (G299, G330) excluded from spatial questions +- Assumes ground plane (Z=0), no elevation modeling + +--- + +## 3. Why V6 Has No MEVID Entity Links + +### 3.1 Current Behavior + +All temporal questions in V6 show: +```json +"entity_link": "heuristic", +"same_person": true +``` + +No questions use `"entity_link": "mevid_ground_truth"`. + +### 3.2 Root Cause + +**MEVID provides camera-level person mapping**: +```python +mevid_person_cameras = { + 2: ["G299", "G330", "G420", "G421", "G423"], + 4: ["G299", "G328", "G336", "G419", "G420", "G421", "G423"], + # ... 41 persons in this slot +} +``` + +**But we can't map MEVID `person_id` → Kitware `actor_id`** without: +1. Extracting MEVID tracklet image crops from bounding box sequences + - Format: `0103O020C424T000F00000.jpg` = person 103, camera C424, tracklet 0, frame 0 +2. Matching those crops to Kitware `geom.yml` bboxes via image similarity or spatial IoU +3. This requires compute-intensive image processing (deferred to future work) + +**Analogy**: MEVID says "person 4 appears on these 7 cameras" but doesn't tell us which `actor_id` in Kitware annotations corresponds to person 4 on each camera. + +### 3.3 V6 Current Use of MEVID + +MEVID is used only for **validation/stats**: +- Count how many MEVID persons have 2+ cameras in the slot (41 persons) +- Report `mevid_persons_in_slot: 41` in output +- Used to find MEVID-supported slots for batch processing + +### 3.4 V7 Enhancement: Camera-Level MEVID Validation + +Instead of trying to match MEVID `person_id` → Kitware `actor_id`, V7 uses **camera-level validation**: + +```python +def is_mevid_validated(event_a, event_b, mevid_person_cameras): + """Check if both cameras covered by same MEVID person.""" + for person_id, cameras in mevid_person_cameras.items(): + if event_a.camera_id in cameras and event_b.camera_id in cameras: + return True, person_id + return False, None +``` + +**Interpretation**: If a temporal question asks about events on G421 and G299, and MEVID person 2 appears on both cameras, then: +- `mevid_validated: true` (plausible same person) +- `mevid_person_id: 2` + +**Caveat**: This doesn't confirm that both events are the SAME actor — just that the same real-world person appears on both cameras at some point in the clip. + +--- + +## 4. V7 Changes Summary + +### 4.1 Entity Aliasing + +**File**: `scripts/v7/build_scene_graph.py` + +**Change**: Added `Entity.make_alias()` method: +```python +def make_alias(self) -> str: + """Generate human-readable entity label.""" + if self.events: + act = self.events[0].activity.replace("person_", "").replace("_", " ") + t = int((self.events[0].start_sec + self.events[0].end_sec) / 2) + return f"Person {act} (actor #{self.actor_id} on {self.camera_id} @ t={t}s)" + + if isinstance(self.actor_id, int) and self.actor_id < 1000: + return f"Actor #{self.actor_id} on {self.camera_id}" + + return f"Person on {self.camera_id} (ID: {self.actor_id})" +``` + +**Usage**: Call during scene graph build, use in question text. + +--- + +### 4.2 Activity Hierarchy + +**File**: `scripts/v7/activity_hierarchy.py` (new) + +**Purpose**: Define causal/co-occurring activity relationships for intelligent temporal pairing. + +**Example relationships**: +```python +ACTIVITY_RELATIONSHIPS = { + "person_picks_up_object": ["person_carries_heavy_object", "person_puts_down_object"], + "person_opens_trunk": ["person_loads_vehicle", "person_unloads_vehicle"], + "person_opens_vehicle_door": ["person_enters_vehicle", "person_exits_vehicle"], + "vehicle_stops": ["person_exits_vehicle", "vehicle_starts"], +} +``` + +--- + +### 4.3 Connected Temporal Events + +**File**: `scripts/v7/generate_temporal.py` + +**Change**: Require scene graph connection for temporal question pairs: + +**Priority order**: +1. **Same entity cluster** (highest): Events by same person across cameras +2. **Related activities**: Parent-child in activity hierarchy +3. **Spatial proximity**: Entities within 20m (if KRTD available) +4. **Fallback**: Any cross-camera events with valid gap (if <3 candidates found) + +**Impact**: Questions test scene understanding, not random facts. + +--- + +### 4.4 Debug Info for Manual Verification + +**Files**: All question generators (`generate_temporal.py`, `generate_spatial.py`, `generate_perception.py`) + +**Change**: Add `debug_info` block with: +```json +"debug_info": { + "event_a": { + "camera": "G421", + "frame_range": [6965, 7020], + "timestamp": "232.17-234.00s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "entity_alias": "Person exiting building (actor #143 on G421 @ t=232s)" + }, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "mevid_validated": true, + "mevid_person_id": 4 +} +``` + +**Purpose**: Enable fast manual verification by jumping directly to frame ranges in video files. + +--- + +### 4.5 MEVID Camera-Level Validation + +**Files**: `scripts/v7/utils/mevid.py`, `scripts/v7/generate_temporal.py` + +**Change**: Build person→camera cache at pipeline start, check if both cameras covered by same person. + +**Output stats**: +```json +{ + "questions_with_mevid_validation": 2, + "questions_with_heuristic_only": 7, + "mevid_coverage_pct": 22.2 +} +``` + +--- + +### 4.6 GPT Wrapper for Question Cleanup + +**File**: `scripts/naturalize_v7_qa.py` (new) + +**Purpose**: Remove debug markers before feeding to VQA models: +```python +# Input: "Person opening door (actor #38 on G328 @ t=50s) and Person entering (actor #16 @ t=45s)" +# Output: "Person opening door and Person entering" +``` + +--- + +## 5. V7 Pros & Cons + +### 5.1 Pros + +| Feature | Benefit | +|---------|---------| +| **Entity aliases** | Human-readable, faster manual verification | +| **Connected events** | Questions test scene understanding, not random facts | +| **Debug markers** | Jump directly to frame ranges for verification | +| **MEVID validation** | Can measure heuristic quality (camera-level proxy) | +| **Activity hierarchy** | Intelligent, contextual event pairing | +| **GPT wrapper** | Clean questions for VQA without redesigning pipeline | +| **Same V6 foundation** | Modular, streaming geom.yml, validated entity resolution | + +### 5.2 Cons & Mitigations + +| Con | Impact | Mitigation | +|-----|--------|------------| +| **Connected events reduce pool** | May generate <3 temporal questions | Implement fallback tiers (cluster → related → spatial → any) | +| **Activity hierarchy hardcoded** | 10 relationships, limited coverage | Start small, expand incrementally. Future: LLM-generated hierarchy | +| **MEVID validation is camera-level** | Can't confirm same `actor_id`, only same person on cameras | Use as plausibility flag, not ground truth. Sufficient for prioritization | +| **Debug info bloats JSON** | 2-3x file size increase | Worth it for manual verification. Future: separate debug output | +| **Entity aliases verbose** | "Person talks to person (actor #122 on G299 @ t=60s)" | Still clearer than raw IDs. GPT wrapper for final polishing | +| **Frame calculation assumes 30 FPS** | Some clips may have different FPS | Read actual FPS from clip via cv2.VideoCapture | + +--- + +## 6. Implementation Status + +### Completed ✅ +- [x] V7 documentation (`v7_todo.md`) +- [x] V6 bug explanations (above) +- [x] V7 module structure (copied from V6) +- [x] Architecture plan + +### TODO (Next Session) ⏳ +- [ ] Add `Entity.make_alias()` to `build_scene_graph.py` +- [ ] Create `activity_hierarchy.py` +- [ ] Update `generate_temporal.py` with connection checking + debug_info +- [ ] Update `generate_spatial.py` with entity aliases + debug_info +- [ ] Update `generate_perception.py` with entity aliases + debug_info +- [ ] Update `entity_resolution.py` with MEVID validation +- [ ] Update `utils/mevid.py` to build person→camera cache +- [ ] Update `run_pipeline.py` with MEVID stats +- [ ] Create `naturalize_v7_qa.py` GPT wrapper +- [ ] Test on `2018-03-11.11-25-00.school` +- [ ] Compare V7 vs V6 output + +**Estimated time**: 2.5 hours + +--- + +## 7. Key Insights + +### 7.1 Why This Dataset Is Unique + +**Cross-camera scene graph** → Questions about temporal/spatial relationships across cameras + +### 7.2 MEVID's Role + +MEVID is **validation**, not primary entity resolution (without image matching). Camera-level validation in V7 is a reasonable proxy. + +### 7.3 Entity Resolution Quality + +V6 heuristic (after bug fixes): 1,330 links → 20 clusters from 926 active entities. Plausible for 5-minute clips with ~10 people. + +### 7.4 Question Quality Hierarchy + +1. **MEVID-validated + same cluster** (strongest) +2. **Same cluster + related activities** (strong) +3. **Related activities + spatial proximity** (medium) +4. **Heuristic link only** (baseline) + +V7 selection prioritizes top tiers. + +--- + +## 8. Next Steps + +1. **Complete V7 implementation** (2.5 hours) +2. **Test on same slot as V6** for direct comparison +3. **Batch process 47 VSet7 slots** (MEVID-supported) +4. **MEVID validation analysis**: Compare questions with `mevid_validated=true` vs `false` +5. **VLM evaluation**: Feed cleaned questions to GPT-4o/Gemini with video frames +6. **Human validation**: Sample 50 questions, measure correctness +7. **Scale to 929 slots**: If quality is good, expand beyond MEVID using heuristic only + +--- + +**V7 is a refinement of V6, not a redesign. Core modular architecture and streaming geom.yml parser remain unchanged.** diff --git a/meva/docs/V7_IMPLEMENTATION_PLAN.md b/meva/docs/V7_IMPLEMENTATION_PLAN.md new file mode 100644 index 0000000..c2a6bbc --- /dev/null +++ b/meva/docs/V7_IMPLEMENTATION_PLAN.md @@ -0,0 +1,486 @@ +# V7 Implementation Plan — Comprehensive Analysis & Task List + +> **Date**: 2026-02-13 +> **Author**: Copilot (Session 21) +> **Based on**: Full code review of V6 pipeline + all V7 planning documents +> **Scope**: Everything needed to go from current V7 (=V6 copy) to a fully functional V7 pipeline + +--- + +## 1. Current State: V7 vs V6 Diff Summary + +### 1.1 Files That Actually Differ + +Only **2 files** have been modified in V7 vs V6: + +| File | Change | Impact | +|------|--------|--------| +| `scripts/v7/build_scene_graph.py` (line 144) | `sample_every=30` → `sample_every=1` | Loads ALL geom.yml bboxes instead of every 30th frame | +| `scripts/v7/utils/yaml_stream.py` (lines 64-71) | Default `sample_every=1`, updated docstring | Matches the build_scene_graph change | + +### 1.2 Files That Are Identical to V6 (No Changes Yet) + +All other V7 files are **byte-identical copies** of V6: + +- `scripts/v7/run_pipeline.py` — still says "V6", outputs `v6` version tag +- `scripts/v7/parse_annotations.py` — identical +- `scripts/v7/entity_resolution.py` — identical +- `scripts/v7/generate_temporal.py` — identical +- `scripts/v7/generate_spatial.py` — identical +- `scripts/v7/generate_perception.py` — identical +- `scripts/v7/distractor_bank.py` — identical +- `scripts/v7/utils/krtd.py` — identical +- `scripts/v7/utils/iou.py` — identical +- `scripts/v7/utils/mevid.py` — identical +- `scripts/v7/__init__.py` — identical +- `scripts/v7/utils/__init__.py` — identical + +### 1.3 Files That Need to Be Created (Don't Exist Yet) + +- `scripts/v7/activity_hierarchy.py` — **NEW** module +- `scripts/naturalize_v7_qa.py` — **NEW** standalone script (GPT wrapper, low priority) + +--- + +## 2. V6 Bug Status + +All 3 known V6 bugs have been **fixed in V6 already** and the fixes propagated to V7 via the copy: + +| Bug | Status | Location | +|-----|--------|----------| +| geom.yml inline format parsing | ✅ Fixed | `utils/yaml_stream.py` — inline regex handles `- { geom: {...} }` | +| Entity resolution mega-cluster | ✅ Fixed | `entity_resolution.py` — MAX_HANDOFF_GAP=10s, MAX_LINKS=2, threshold=0.7 | +| Dedup `None == None` | ✅ Fixed | `run_pipeline.py` — category-specific dedup logic | + +**No new V6 bugs identified during this review.** + +--- + +## 3. V7 Feature Requirements (from v7_todo.md) + +### 3.1 Entity Aliasing (human-readable labels) + +**Problem**: V6 uses raw IDs like `G328_actor_1613855842107762240` in questions. + +**Solution**: Add `Entity.make_alias()` method + use aliases in question text. + +**Before (V6)**: +``` +"How close are person_enters_scene_through_structure and person_opens_facility_door +in the scene visible on camera G328?" +``` + +**After (V7)**: +``` +"How close are Person entering building (actor #16 on G328 @ t=45s) and +Person opening door (actor #38 on G328 @ t=50s) in the scene visible on camera G328?" +``` + +### 3.2 Connected Events (intelligent temporal pairing) + +**Problem**: V6 pairs ANY cross-camera events with 3-15s gap — no scene connection required. + +**Solution**: Require scene graph connection, priority-ranked: +1. Same entity cluster (strongest) +2. Related activities (via activity hierarchy) +3. Spatial proximity (<20m via KRTD) +4. Fallback: any cross-camera events (if <3 candidates found) + +### 3.3 Activity Hierarchy (new module) + +**Purpose**: Define causal/co-occurring activity relationships for intelligent pairing. + +**Relationships** (from v7_todo.md + 37 official MEVA activities): +```python +ACTIVITY_RELATIONSHIPS = { + "person_picks_up_object": ["person_carries_heavy_object", "person_puts_down_object", "person_transfers_object"], + "person_opens_trunk": ["person_loads_vehicle", "person_unloads_vehicle"], + "person_opens_vehicle_door": ["person_enters_vehicle", "person_exits_vehicle"], + "person_opens_facility_door": ["person_enters_scene_through_structure", "person_exits_scene_through_structure"], + "vehicle_stops": ["person_exits_vehicle", "vehicle_starts"], + "person_enters_vehicle": ["vehicle_starts"], + "person_talks_to_person": ["person_embraces_person"], + "person_talks_on_phone": ["person_texts_on_phone"], +} +``` + +### 3.4 Debug Markers (verification info) + +**Problem**: V6 `verification` blocks have limited info for manual checking. + +**Solution**: Extended `debug_info` block with: +- Frame ranges + timestamps +- FPS + clip file name +- Entity aliases +- Connection type/strength +- MEVID validation flag + +### 3.5 MEVID Camera-Level Validation + +**Problem**: V6 uses MEVID for stats only (`mevid_persons_in_slot: 41`), no question-level validation. + +**Solution**: For each temporal question, check if both cameras are covered by the same MEVID person → flag as `mevid_validated: true`. + +### 3.6 GPT Wrapper (low priority, skip initially) + +Strip debug markers for VLM consumption. Can be done post-hoc. + +--- + +## 4. Specific Implementation Tasks (Priority Order) + +### Phase 1: Foundation Changes (files + data structures) + +#### Task 1.1: Update version strings and imports in run_pipeline.py +**File**: `scripts/v7/run_pipeline.py` +**Changes**: +- Line 1-10: Update module docstring to say "V7" +- Line 40-51: Update import paths from `scripts.v6.*` to `scripts.v7.*` +- Line 54: Change `OUTPUT_DIR` path or output filename to use `.v7.json` +- Line 269: Change `"version": "v6"` → `"version": "v7"` +- Line 268: Change `"generator": "v6_pipeline"` → `"generator": "v7_pipeline"` +- All `question_id` prefixes: `v6_` → `v7_` (in temporal/spatial/perception generators) + +#### Task 1.2: Add `make_alias()` to Entity class +**File**: `scripts/v7/build_scene_graph.py` +**Changes**: +- Add `alias: str = ""` field to `Entity` dataclass (after `events` field) +- Add `make_alias(self, events_list: List[Event]) -> str` method +- Call `entity.make_alias(events)` during entity construction loop (after building entity objects, ~line 190) +- Method implementation: + ```python + def make_alias(self, all_events: List) -> str: + # Find events this entity participates in + my_events = [e for e in all_events + if e.camera_id == self.camera_id + and any(a["actor_id"] == self.actor_id for a in e.actors)] + if my_events: + act = my_events[0].activity.replace("person_", "").replace("vehicle_", "").replace("_", " ") + t = int((my_events[0].start_sec + my_events[0].end_sec) / 2) + self.alias = f"Person {act} (actor #{self.actor_id} on {self.camera_id} @ t={t}s)" + elif isinstance(self.actor_id, int) and self.actor_id < 1000: + self.alias = f"Actor #{self.actor_id} on {self.camera_id}" + else: + self.alias = f"Person on {self.camera_id} (ID: {self.actor_id})" + return self.alias + ``` + +#### Task 1.3: Create activity_hierarchy.py +**File**: `scripts/v7/activity_hierarchy.py` (NEW) +**Contents**: +- `ACTIVITY_RELATIONSHIPS` dict (8 parent→children mappings) +- `are_related(act_a, act_b) -> bool` function +- `get_related_activities(activity) -> List[str]` function +- `get_relationship_type(act_a, act_b) -> Optional[str]` function (returns "causal", "co-occurring", etc.) + +### Phase 2: Temporal Question Enhancement + +#### Task 2.1: Implement connected event finding +**File**: `scripts/v7/generate_temporal.py` +**Changes**: +- Add imports: `from .activity_hierarchy import are_related` +- Add new function `_find_connected_temporal_pairs(sg, resolved, max_gap)`: + 1. Priority 1: Same entity cluster events on different cameras + 2. Priority 2: Related activities (via `are_related()`) + 3. Priority 3: Spatial proximity (<20m if both have KRTD positions) + 4. Fallback: Original `_find_temporal_candidates()` if <3 candidates +- Each candidate gets `connection_type` ("same_entity_cluster", "related_activities", "spatial_proximity", "any_cross_camera") and `connection_strength` ("strong", "medium", "weak", "none") +- Modify `generate_temporal_qa()` to: + - Call `_find_connected_temporal_pairs()` instead of plain `_find_temporal_candidates()` + - Sort by: MEVID validation > connection strength > gap + - Use entity aliases in question text + - Add full `debug_info` block + +#### Task 2.2: Add MEVID validation to temporal questions +**File**: `scripts/v7/generate_temporal.py` +**Changes**: +- Accept `mevid_person_cameras` dict as parameter (built in run_pipeline) +- For each selected candidate, check `check_mevid_validation(ea, eb, mevid_person_cameras)` +- Add `mevid_validated` and `mevid_person_id` to `debug_info` +- Priority sort: MEVID-validated first + +#### Task 2.3: Build debug_info for temporal questions +**File**: `scripts/v7/generate_temporal.py` +**Changes**: Replace simple `verification` with extended `debug_info`: +```python +"debug_info": { + "event_a": { + "camera": ea.camera_id, + "activity": ea.activity, + "actor_ids": [a["actor_id"] for a in ea.actors], + "frame_range": [ea.start_frame, ea.end_frame], + "timestamp": f"{ea.start_sec:.2f}-{ea.end_sec:.2f}s", + "entity_alias": entity_a.alias if entity_a else None, + "clip_file": ea.video_file, + }, + "event_b": { ... }, + "gap_sec": gap, + "connection_type": cand["connection_type"], + "connection_strength": cand["connection_strength"], + "entity_link": "mevid_ground_truth" if mevid_pid else "heuristic", + "same_person": same_person, + "mevid_validated": mevid_validated, + "mevid_person_id": mevid_pid, +} +``` + +### Phase 3: Spatial & Perception Enhancement + +#### Task 3.1: Add entity aliases to spatial questions +**File**: `scripts/v7/generate_spatial.py` +**Changes**: +- Use `entity.alias` in question text instead of raw activity names +- Add `debug_info` block with entity aliases, frame info, bbox coords +- Change question_id prefix to `v7_spatial_` + +#### Task 3.2: Add entity aliases to perception questions +**File**: `scripts/v7/generate_perception.py` +**Changes**: +- Add `debug_info` block with relevant frame/camera info +- Change question_id prefix to `v7_perception_` +- (Perception questions don't reference entities directly, but debug_info still useful) + +### Phase 4: Pipeline Integration + +#### Task 4.1: Update run_pipeline.py for V7 features +**File**: `scripts/v7/run_pipeline.py` +**Changes**: +- Build MEVID person→camera cache at pipeline start +- Pass `mevid_person_cameras` to `generate_temporal_qa()` +- Add MEVID validation stats to output: + ```python + "questions_with_mevid_validation": N, + "questions_with_heuristic_only": M, + "mevid_coverage_pct": round(N / total * 100, 1), + ``` +- Update all version strings to "v7" +- Change output filename to `{slot}.v7.json` + +#### Task 4.2: Update mevid.py for person→camera cache +**File**: `scripts/v7/utils/mevid.py` +**Changes**: +- Add `build_person_camera_cache(slot, cameras) -> Dict[int, Set[str]]` function + (This is essentially `find_mevid_persons_for_slot()` — may just need to rename/refactor for clarity) +- Add `check_mevid_validation(cam_a, cam_b, person_cameras) -> Tuple[bool, Optional[int]]` + +### Phase 5: Testing & Validation + +#### Task 5.1: Test on reference slot +```bash +cd /home/ah66742 && source venv/bin/activate +python3 -m scripts.v7.run_pipeline --slot "2018-03-11.11-25-00.school" -v +``` +- Compare with `data/qa_pairs/2018-03-11.11-25-00.school.v6.json` +- Verify: 9 questions generated (3+3+3) +- Verify: Entity aliases present in question text +- Verify: debug_info blocks present +- Verify: MEVID validation flags present +- Verify: Connected events for temporal questions + +#### Task 5.2: Side-by-side comparison +- Diff V6 vs V7 output JSON +- Check question quality improvement +- Verify no regressions (same correct answers) + +--- + +## 5. Issues & Blockers Identified + +### 5.1 Import Path Issue (Critical) + +The V7 `run_pipeline.py` still imports from `scripts.v6.*` in its fallback path (line 40-51). When running as `python3 -m scripts.v7.run_pipeline`, the relative imports work. But the fallback `ImportError` block references `scripts.v6.*`: + +```python +except ImportError: + from scripts.v6.parse_annotations import ... # WRONG for V7! +``` + +**Fix**: Change all `scripts.v6.*` → `scripts.v7.*` in the except block. + +### 5.2 Version String Contamination (Medium) + +All V7 files still say "V6" in docstrings, question_id prefixes (`v6_temporal_001`), and output JSON (`"version": "v6"`). Must update systematically. + +### 5.3 Memory Impact of sample_every=1 (Low Risk) + +V7 loads 30x more bboxes per actor. For 8 cameras × ~20 actors each × ~9000 frames = ~1.4M bbox entries. +- At ~40 bytes per bbox dict entry = ~56 MB total +- System has 128 GB RAM → negligible (0.04%) +- **Risk**: Slower scene graph build time (more YAML parsing); estimate 30x slower for geom.yml parsing +- **Mitigation**: Full loading is a one-time cost per slot; acceptable for batch processing + +### 5.4 Connected Events May Reduce Temporal Question Pool + +V6 found candidates from ALL 733 events cross-camera. V7 restricts to connected events: +- Same entity cluster: Limited to 20 clusters × 2-3 entities each ≈ 60 entity pairs +- Related activities: Only 8 relationship groups × relevant events +- Spatial proximity: Requires KRTD (excludes indoor cameras G299, G330) + +**Mitigation**: Fallback tier built into design — if <3 connected candidates, fall back to original V6 behavior. + +### 5.5 No Within-Camera Entity Resolution + +Kitware annotations can assign different `id1` values to the same physical person within one camera. V7 does NOT fix this (documented as accepted limitation). Temporal questions about within-camera sequences for the same person may fail. + +### 5.6 MEVID Validation is Camera-Level, Not Entity-Level + +MEVID says "Person 4 appears on G299 and G328" but doesn't say which `actor_id` on each camera. V7's `mevid_validated: true` means "plausible same person" not "confirmed same person." + +--- + +## 6. File-by-File Change Map + +| File | Status | Changes Required | +|------|--------|-----------------| +| `scripts/v7/run_pipeline.py` | MODIFY | Version strings, imports, MEVID cache, output format | +| `scripts/v7/build_scene_graph.py` | MODIFY | ✅ sample_every done; ADD Entity.make_alias(), alias field | +| `scripts/v7/generate_temporal.py` | MODIFY | Connected events, MEVID validation, debug_info, aliases | +| `scripts/v7/generate_spatial.py` | MODIFY | Entity aliases in questions, debug_info block | +| `scripts/v7/generate_perception.py` | MODIFY | debug_info block, version prefix | +| `scripts/v7/entity_resolution.py` | MODIFY | Minor: version string | +| `scripts/v7/parse_annotations.py` | NO CHANGE | Identical to V6 (correct) | +| `scripts/v7/distractor_bank.py` | NO CHANGE | Identical to V6 (correct) | +| `scripts/v7/utils/yaml_stream.py` | ✅ DONE | sample_every=1 already applied | +| `scripts/v7/utils/krtd.py` | NO CHANGE | Identical to V6 (correct) | +| `scripts/v7/utils/iou.py` | NO CHANGE | Identical to V6 (correct) | +| `scripts/v7/utils/mevid.py` | MODIFY | Add check_mevid_validation() helper | +| `scripts/v7/activity_hierarchy.py` | CREATE | New module with ACTIVITY_RELATIONSHIPS | +| `scripts/naturalize_v7_qa.py` | CREATE (LOW PRI) | GPT marker removal wrapper | + +--- + +## 7. Estimated Effort + +| Phase | Tasks | Time | +|-------|-------|------| +| Phase 1: Foundation | 1.1 (version strings) + 1.2 (aliases) + 1.3 (hierarchy) | 45 min | +| Phase 2: Temporal | 2.1 (connected events) + 2.2 (MEVID) + 2.3 (debug_info) | 45 min | +| Phase 3: Spatial/Perception | 3.1 (spatial aliases) + 3.2 (perception debug) | 30 min | +| Phase 4: Integration | 4.1 (pipeline) + 4.2 (mevid cache) | 20 min | +| Phase 5: Testing | 5.1 (run) + 5.2 (compare) | 20 min | +| **Total** | **12 tasks** | **~2.5 hours** | + +--- + +## 8. V7 Output Schema (Target) + +```json +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v7", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "generator": "v7_pipeline", + "seed": 42, + "difficulty": "easy", + "cameras": ["G299", "G328", "G330", "G336", "G419", "G420", "G421", "G423"], + "mevid_persons_in_slot": 41, + "questions_with_mevid_validation": 2, + "questions_with_heuristic_only": 7, + "mevid_coverage_pct": 22.2, + "total_events": 733, + "total_entities": 1258, + "cross_camera_clusters": 20, + "total_questions": 9, + "category_counts": { "temporal": 3, "spatial": 3, "perception": 3 }, + "validation_issues": 0, + "generation_time_sec": 12.5, + "qa_pairs": [ + { + "question_id": "v7_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "Person exiting building (actor #3414988050908947645 on G421 @ t=233s) and Person talking (actor #143 on G299 @ t=268s) — which occurred first?", + "options": [...], + "correct_answer_index": 0, + "correct_answer": "...", + "requires_cameras": ["G299", "G421"], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_exits_scene_through_structure", + "camera": "G421", + "start_sec": 232.17, + "end_sec": 234.0, + "actor_ids": [3414988050908947645] + }, + "event_b": { + "activity": "person_talks_to_person", + "camera": "G299", + "start_sec": 237.03, + "end_sec": 299.7, + "actor_ids": [143, 142] + }, + "gap_sec": 3.03, + "entity_link": "heuristic", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_exits_scene_through_structure", + "actor_ids": [3414988050908947645], + "frame_range": [6965, 7020], + "timestamp": "232.17-234.00s", + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.r13.avi", + "entity_alias": "Person exits scene through structure (actor #3414988050908947645 on G421 @ t=233s)" + }, + "event_b": { + "camera": "G299", + "activity": "person_talks_to_person", + "actor_ids": [143, 142], + "frame_range": [7111, 8991], + "timestamp": "237.03-299.70s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.r13.avi", + "entity_alias": "Person talks to person (actor #143 on G299 @ t=268s)" + }, + "gap_sec": 3.03, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "mevid_validated": true, + "mevid_person_id": 4 + } + } + ] +} +``` + +--- + +## 9. Key Design Decisions + +| Decision | Rationale | +|----------|-----------| +| Keep `verification` block AND add `debug_info` | Backward compatibility with V6 consumers | +| Fallback to V6-style temporal pairing | Ensures 3 temporal questions even when connected events are scarce | +| MEVID validation as flag, not filter | Don't discard non-MEVID questions — just rank them lower | +| Entity alias includes raw actor_id | Preserves traceability for debugging | +| Activity hierarchy hardcoded | Only 37 activities total; LLM-generated hierarchy is overkill | +| GPT wrapper as separate script | Don't mix debug and clean output in same pipeline | +| Full GEOM loading (sample_every=1) | 56 MB trivial for 128 GB system; much better spatial accuracy | + +--- + +## 10. Post-V7 Roadmap (Future) + +These are documented in v7_additions.md / v7_memory_optimizations.md but NOT part of V7 core: + +1. **3D Scene Models** (PLY) — elevation-aware spatial questions +2. **HDF5 mmap indexing** — batch processing optimization for 929 slots +3. **Building segmentation** — structure-aware questions +4. **GPS trajectory questions** — movement pattern analysis +5. **Example video patterns** — activity duration validation +6. **Within-camera entity resolution** — fix actor_id fragmentation +7. **MEVID entity-level matching** — image crop → actor_id mapping + +--- + +## 11. Summary + +**V7 is currently 99% identical to V6.** Only `sample_every` was changed (2 lines across 2 files). All the documented V7 features (entity aliases, connected events, activity hierarchy, debug markers, MEVID validation) remain unimplemented. + +**Core architecture is solid** — V6's modular pipeline, streaming geom parser, entity resolution, and dedup logic are all working correctly. V7 is purely an enhancement layer on top of V6. + +**No blockers exist** — all dependencies (pyyaml, numpy, KRTD files, MEVID data, slot_index.json) are available and working. Implementation can start immediately. + +**Estimated total: ~2.5 hours** to implement all V7 features and test on the reference slot. diff --git a/meva/docs/V8_DESIGN.md b/meva/docs/V8_DESIGN.md new file mode 100644 index 0000000..da37ca6 --- /dev/null +++ b/meva/docs/V8_DESIGN.md @@ -0,0 +1,237 @@ +# V8 Pipeline Design Document + +## Date: 2026-02-13 (Session 32) + +--- + +## 1. MEVID Identity Verification — Raw AVI + Geom Analysis + +### Question +Can we match MEVID person IDs to Kitware `actor_id`s by comparing MEVID crop images against bounding box crops extracted from raw AVI files + Kitware geom.yml? + +### Feasibility Analysis + +**What we have:** +- MEVID crops: 761K images for 23 persons with known `person_id`, `outfit_id`, `camera_id` +- Raw AVIs: All March 2018 clips on NAS (`/nas/mars/dataset/MEVA/avis/`) +- Kitware geom.yml: Bounding boxes per `actor_id` per frame per camera +- Person attribute database: YOLO+GPT descriptions per MEVID person + +**The matching challenge:** +- MEVID tracklet frame numbers are **sequential crop indices** (F00000, F00001...), NOT AVI frame numbers +- No direct mapping exists between MEVID crop → specific AVI clip/frame +- Example: Camera G331, slot `2018-03-07.11-05-00.bus` has **196 Kitware actors** in one 5-minute clip +- A MEVID person with "blue upper / blue lower" would need to be disambiguated from potentially dozens of similarly-dressed actors + +**Matching approach (if pursued):** +1. Take MEVID crop → extract color histogram (HSV) +2. Open AVI at a sampled frame → extract Kitware geom bbox crop for each actor_id +3. Compare color histograms (cv2.compareHist) between MEVID crop and each Kitware actor crop +4. Rank by similarity → best match = proposed identity link +5. Validate across multiple frames and cameras for consistency + +**Cost estimate:** +- ~23 persons × ~9 cameras × ~10 AVI clips each = ~2,000 AVI decode operations +- Each AVI decode: open file + seek to frame + crop (~100ms) = ~200 seconds total +- Comparison: trivial (histogram comparison is microseconds) +- Total: ~5-10 minutes on fast disk, ~30 min on NAS + +**Verdict: FEASIBLE but NOT BLOCKING V8.** +- V8 can use MEVID person descriptions WITHOUT exact actor_id matching +- MEVID already tells us which persons share cameras → sufficient for re-identification QA +- Actor_id matching would be needed for frame-precise "show me this person" verification +- **Recommendation**: Build verification tool as separate utility, not as V8 dependency + +--- + +## 2. V7 Pipeline Analysis — Weaknesses & Improvement Tips for V8 + +### V7 Architecture (for reference) +``` +parse_annotations.py → List[Event] from Kitware YAML +build_scene_graph.py → SceneGraph with entities, aliases, KRTD positions +entity_resolution.py → Cross-camera entity clusters (heuristic temporal handoff) +generate_temporal.py → Temporal cross-camera ordering questions +generate_spatial.py → Spatial entity distance questions (KRTD bbox→world) +generate_perception.py → Activity presence + camera identification questions +naturalize_v7_qa.py → GPT-4o-mini rephrasing of template text +``` + +### V7 Weaknesses (8 specific issues) + +#### W1: Generic Entity Descriptions +**Problem**: V7 aliases are `"Person opens facility door (#45 on G328 @ 120s)"` — the actor_id and timestamp are debug markers, not meaningful descriptors. +**Impact**: After naturalization, questions become "Between someone entering a scene through a structure and someone interacting with another person..." — indistinguishable people. +**V8 Fix**: Replace with MEVID-derived descriptions: "The person in the blue jacket carrying a backpack" or GPT descriptions. + +#### W2: No Person Identity Across Cameras +**Problem**: `entity_resolution.py` uses temporal handoff heuristic (±10s gap) but has NO visual identity confirmation. MEVID camera-level data validates that a person EXISTS on both cameras but doesn't link to specific actor_ids. +**Impact**: Cross-camera questions may reference different physical people as if they're the same. +**V8 Fix**: When MEVID persons are available for a slot, inject their descriptions. Re-identification questions can leverage the known cross-camera appearance. + +#### W3: No Attribute-Based Questions +**Problem**: V7 has only 3 categories: temporal, spatial, perception. No questions about person appearance, clothing, or carried objects. +**Impact**: Missing a major QA dimension that multi-camera datasets are uniquely suited for. +**V8 Fix**: Add "attribute_verification" and "re_identification" categories. + +#### W4: All Questions Are "Easy" Difficulty +**Problem**: Every question is marked `"difficulty": "easy"`. No medium or hard questions. +**Impact**: No difficulty gradient for evaluation; all questions test the same level. +**V8 Fix**: Attribute verification = easy (single camera), re-identification = medium (cross-camera), scene summary = hard (full slot). + +#### W5: Perception Questions Are Activity-Only +**Problem**: Perception questions only ask about activities ("Which camera captures someone opening a door?"). They don't ask about visual attributes. +**Impact**: Missing the "what does this person look like?" dimension. +**V8 Fix**: Add attribute perception: "What is the person on camera G328 wearing?" + +#### W6: Scene Summary Not Attempted +**Problem**: V7 generates isolated questions per event pair. No holistic slot-level summary. +**Impact**: Can't test whether a model understands the overall scene narrative. +**V8 Fix**: Generate scene summary from annotation density (activity types, actor count, movement patterns). See feasibility analysis below. + +#### W7: Naturalization Strips Useful Details +**Problem**: The GPT naturalization pass strips actor IDs and timestamps but also sometimes loses camera context or changes meaning. +**Impact**: Some naturalized questions are less precise than templates. +**V8 Fix**: V8 templates should already use natural language (MEVID descriptions) — less work for naturalization. + +#### W8: No Slot Selection Criteria +**Problem**: V7 generates questions for any slot regardless of annotation richness via 929 slots. +**Impact**: Sparse slots with 2-3 events produce low-quality questions. +**V8 Fix**: Only process slots with MEVID person support → richer, more verifiable questions. + +--- + +## 3. V8 Architecture Design + +### Core Principle +**V8 = V7 + MEVID Person Descriptions + New Question Categories + Smart Slot Selection** + +### Pipeline Overview +``` +[MEVID-AWARE] + 1. load_person_database() → 23 persons with YOLO+GPT descriptions + 2. filter_mevid_slots() → Only slots with MEVID person coverage + +[V7 CORE] + 3. parse_annotations() → Events from Kitware YAML (unchanged) + 4. build_scene_graph() → Entities + KRTD (unchanged) + 5. resolve_entities() → Cross-camera clusters (unchanged) + +[V8 ENTITY ENRICHMENT] + 6. enrich_entities() → Inject MEVID descriptions into entities + - Match MEVID persons to slot cameras + - Create person description strings + - Set entity.description = MEVID GPT text or YOLO color summary + - Fallback: activity-verb description (V7 style) + +[V8 QUESTION GENERATION] + 7. generate_temporal_qa() → V7 temporal + MEVID descriptions (updated) + 8. generate_spatial_qa() → V7 spatial + MEVID descriptions (updated) + 9. generate_perception_qa() → V7 perception + attribute verification (updated) + 10. generate_reidentification_qa() → NEW: Cross-camera re-ID within slot + 11. generate_scene_summary_qa() → NEW: Holistic scene description + +[OUTPUT] + 12. validate + deduplicate + save → v8.json + 13. naturalize (GPT rewrite) → v8.natural.json +``` + +### V8 Question Categories + +| Category | Count | Difficulty | Multi-cam | Description | +|----------|-------|------------|-----------|-------------| +| temporal | 2 | easy | YES | Event ordering across cameras (V7 + descriptions) | +| spatial | 2 | easy | mixed | Entity distances (V7 + descriptions) | +| perception | 2 | easy | mixed | Activity presence + attribute verification | +| re_identification | 2 | medium | YES | Same person across cameras? | +| scene_summary | 1 | hard | YES | Overall scene description | +| **TOTAL** | **~9** | | | | + +### Scene Summary Feasibility + +**Can we generate a scene description from annotations?** + +The Kitware annotations contain: +- Activity types (37 categories) per camera per time range +- Actor IDs (local to each camera) +- Temporal extent per activity (start/end frames → seconds) +- Bounding boxes (spatial position on camera) + +**What we can summarize:** +- Total number of distinct activity types occurring +- Most frequent activities +- Rough activity density (events per minute) +- Cross-camera activity patterns (same activity on multiple cameras) +- Temporal flow: what happened first → last + +**Example template:** +> "The scene across 8 cameras shows primarily building entry/exit activity (14 events) +> with vehicle interactions (6 events) and person-to-person communication (3 events). +> Activity peaks in the first 2 minutes with door-opening cascading across cameras." + +**Verdict: YES, annotations are sufficient for basic scene summaries.** +Not narrative-quality, but enough for MCQ about scene characteristics. + +### Implementation Plan + +#### Files to Create +- `scripts/v8/run_pipeline.py` — Main V8 orchestrator +- `scripts/v8/person_descriptions.py` — MEVID description loading + entity enrichment +- `scripts/v8/generate_reidentification.py` — Re-ID question generator +- `scripts/v8/generate_scene_summary.py` — Scene summary question generator +- `scripts/v8/__init__.py` + +#### Files to Copy + Modify from V7 +- `generate_temporal.py` → Update entity aliases to use MEVID descriptions +- `generate_spatial.py` → Update entity aliases to use MEVID descriptions +- `generate_perception.py` → Add attribute verification subtype +- `naturalize_v7_qa.py` → Copy as naturalize_v8_qa.py, add category prompts + +#### Files to Reuse Unchanged from V7 +- `parse_annotations.py` (Kitware YAML parsing) +- `build_scene_graph.py` (Entity extraction) +- `entity_resolution.py` (Cross-camera linking) +- `activity_hierarchy.py` (Relationships) +- `distractor_bank.py` (Wrong answers) +- `utils/` (KRTD, YAML stream, IoU, MEVID) + +--- + +## 4. MEVID Description Priority System + +### Description Sources (in priority order) +1. **GPT description** (from person_database_yolo.json) — richest, most natural + - Example: "wearing a dark hooded jacket and dark pants, carrying a black backpack" +2. **YOLO color summary** — structured, always available for analyzed persons + - Example: "person in blue upper / blue lower body clothing" +3. **Activity-verb fallback** (V7 style) — when no MEVID match + - Example: "the person opening the vehicle door" + +### Slot Coverage +- 887/929 slots (95.5%) have MEVID person coverage +- 23 persons with full YOLO+GPT descriptions +- Avg 441.6 slots per person +- V8 only generates questions for MEVID-supported slots + +--- + +## 5. Identity Verification Tool (Separate Utility) + +### Purpose +Confirm that MEVID person crop images match Kitware geom.yml bounding box crops from raw AVI files. + +### Approach +1. For a given MEVID person + camera + outfit: + - Load several MEVID crops → extract HSV color histogram +2. For all MEVID AVI clips on that camera: + - Open AVI, seek to sampled frames + - For each Kitware actor_id in geom.yml, extract bbox crop + - Compare HSV histograms +3. Output: ranked list of (MEVID person, Kitware actor_id, similarity score) + +### File: `scripts/verify_mevid_identity.py` +- `--person 0008 --camera G331` — verify one person on one camera +- `--all --verbose` — verify all 23 persons across all cameras +- Outputs: `data/mevid_identity_matches.json` + +### Status: PLANNED (not V8 dependency) diff --git a/meva/docs/V9_PIPELINE_EXPLANATION.md b/meva/docs/V9_PIPELINE_EXPLANATION.md new file mode 100644 index 0000000..a5c3563 --- /dev/null +++ b/meva/docs/V9_PIPELINE_EXPLANATION.md @@ -0,0 +1,838 @@ +# V9 Pipeline: Complete Technical Explanation + +**Date**: February 16, 2026 +**Version**: V9 (Enhanced from V8) +**Output**: Multi-camera QA dataset (~14 questions per 5-minute video slot, 929 slots total) + +--- + +## High-Level V9 Pipeline Flow + +``` +INPUT: Slot name (e.g., "2018-03-11.11-25-00.school") + ↓ +1. PARSE ANNOTATIONS + ├─ Read Kitware YAML files (.activities.yml, .geom.yml, .types.yml) + └─ Extract: activities (events), bounding boxes (frames), entity types (person/vehicle) + ↓ +2. BUILD SCENE GRAPH + ├─ Create Entity objects (person/vehicle on camera with time span) + ├─ Attach bounding boxes, activities, aliases + └─ Result: Single-camera entities mapped to events + ↓ +3. ENTITY RESOLUTION + ├─ Use MEVID ground truth (known person IDs across cameras) + ├─ Use heuristic temporal handoff (if person exits camera A, enters camera B at same time) + └─ Result: Cross-camera "entity clusters" (same person on multiple cameras) + ↓ +4. ENRICH PERSON DESCRIPTIONS + ├─ Load YOLO+GPT person database from MEVID + ├─ Attach color/clothing descriptions to each entity + └─ Result: Entities have rich natural-language descriptions + ↓ +5. GENERATE 8 QUESTION CATEGORIES + ├─ Temporal (2) — cross-camera event ordering + ├─ Spatial (2) — entity distance in 3D world + ├─ Perception (2) — activity visibility per camera + ├─ Re-Identification (2) — person matching across cameras + ├─ Scene Summary (1) — overall activity summary + ├─ Event Ordering (2) — V9 NEW: arrange 3-4 events chronologically + ├─ Causality (1) — V9 NEW: cause-effect reasoning + └─ Numerical (1) — V9 NEW: counting questions (~13-14 total) + ↓ +6. NATURALIZE (GPT-4o-mini) + ├─ Single-pass rephrasing (V9: merged grammar checking) + └─ Clean entity type → person names in questions + ↓ +OUTPUT: .v9.json file with ~14 verified questions +``` + +--- + +## COMPONENT 1: Scene Graph Generation + +### Problem +Raw MEVA annotations store activities and bounding boxes separately: +- `.activities.yml`: "Person_123 performs 'open-door' on frame 100-200" +- `.geom.yml`: "Frame 150: person at bbox [x1,y1,x2,y2]" +- `.types.yml`: "Person_123 is a 'person' (not vehicle)" + +We need a **unified view**: "Person_123 on camera G331 exists from frame 100-200, performs 'open-door', has bboxes at frames [100,125,150,175,200]" + +### Action + +**Step 1: Parse Raw YAML** (`parse_annotations.py`) +```python +# Load each clip's 3 YAML files +activities = load_yaml("2018-03-11.11-25-00.school.G331.activities.yml") +geom = load_yaml("2018-03-11.11-25-00.school.G331.geom.yml") +types = load_yaml("2018-03-11.11-25-00.school.G331.types.yml") + +# Extract activity events with timespan +for activity_entry in activities: + - Extract: activity name (e.g., "open-facility-door") + - Extract: frame range (e.g., 100-200) + - Extract: actor IDs (e.g., [123, 456]) + - Extract: entity type from types.yml (e.g., "person" for actor 123) + + # Result: Event(activity="open-facility-door", start_frame=100, end_frame=200, + # actors=[{actor_id: 123, entity_type: "person"}]) +``` + +**Step 2: Build Entities** (`build_scene_graph.py`) +```python +# Group by (camera, actor_id) — each is one entity +for each activity event: + for each actor in event.actors: + entity = Entity( + entity_id = "G331_actor_123" + camera_id = "G331" + actor_id = 123 + entity_type = "person" # from .types.yml + first_frame = 100 # min time across all events for this actor + last_frame = 500 # max time across all events for this actor + ) + + # Attach bounding boxes from .geom.yml + entity.keyframe_bboxes = { + 100: [100, 200, 300, 400], # frame 100: bbox in pixels + 125: [105, 205, 295, 395], + 150: [110, 210, 290, 390], + ... + } + + # Attach events this actor participates in + entity.events = ["G331_evt_1", "G331_evt_2", ...] + + # Generate human-readable alias + entity.alias = "Person opening door (actor_123 on G331 @ 150s)" + +# Result: SceneGraph with: +# - entities = {G331_actor_123, G331_actor_456, G332_actor_100, ...} +# - cameras = {G331: {is_indoor, has_krtd, position}, G332: {...}} +``` + +### Result + +**Output**: `SceneGraph` object with unified entity view: +- **Entity breakdown**: e.g., 8 entities (persons/vehicles) on 4 cameras for one 5-min slot +- **Time coverage**: Each entity spans full time range it participates in +- **Spatial grounding**: Bounding boxes attached to key frames +- **Activity context**: Linked to specific events (open-door, walk-in, etc.) + +**Example**: +``` +Scene Graph for slot "2018-03-11.11-25-00.school": +├─ 8 entities total +├─ Camera G331 (outdoor, has_krtd=True): 3 persons, 1 vehicle +├─ Camera G332 (outdoor, has_krtd=True): 2 persons +├─ Camera G339 (indoor, has_krtd=False): 1 person +├─ Camera G504 (outdoor, has_krtd=True): 1 person +│ +├─ Entity G331_actor_123 [PERSON] +│ ├─ Time: 100-450 frames (3.3-15 sec @ 30fps) +│ ├─ Events: ["open-door (100-200)", "walk-out (200-450)"] +│ ├─ Keyframe bboxes: 5 frames with pixel coordinates +│ └─ Alias: "Person opening door" +│ +├─ Entity G332_actor_045 [PERSON] +│ ├─ Time: 50-180 frames +│ ├─ Events: ["walk-in"] +│ └─ Alias: "Person entering scene" +``` + +--- + +## COMPONENT 2: Cross-Camera Person Linking (Entity Resolution) + +### Problem + +**The Challenge**: Video shows multiple people. On camera G331, person A opens a door. On camera G332 (different angle), is that the same person walking nearby, or a different person? + +Without linking, we generate questions like: +- ❌ "Which camera captures the person?" (can't distinguish if same person or different) +- ❌ "How far are persons A and B?" (if Person_123 exits G331 and re-enters G332, we treat as 2 entities) + +**Data Available**: +- MEVID: 132 persons with **known cross-camera appearances** (extracted from MEVID re-ID dataset) + - Tells us: "Person_42 appears on cameras G331, G332, G339" + - Does NOT tell us: Which Kitware actor_id corresponds to Person_42 +- Heuristic clues: + - Temporal continuity: If G331_actor_123 EXITS at frame 200, and G332_actor_045 ENTERS at frame 210, maybe same person + - IoU matching: If bboxes overlap high IOU, likely same person (but cameras don't overlap spatially) + +### Action + +**Step 1: MEVID-Based Ground Truth** (`entity_resolution.py:_resolve_mevid`) + +```python +# Query MEVID database for this slot +mevid_persons = find_mevid_persons_for_slot(slot="2018-03-11.11-25-00.school", + cameras=["G331", "G332", "G339", "G504"]) + +# Result: { +# 42: {"cameras": {G331, G332, G339}}, # Person_42 observed on 3 cameras +# 57: {"cameras": {G332, G504}}, # Person_57 observed on 2 cameras +# ... +# } + +# This tells us: +# - At least 1 entity on G331 = at least 1 entity on G332 (they're connected) +# - BUT we don't know WHICH specific Kitware actors to link (that requires tracklet extraction) +# +# So we record: "Person 42 is on cameras [G331, G332, G339]" +# This validates heuristic links when they agree with MEVID camera sets +``` + +**Step 2: Heuristic Temporal Handoff** (`entity_resolution.py:_resolve_heuristic`) + +```python +# Strategy: If Person A EXITS camera_1 at time T, and Person B ENTERS camera_2 at time T+Δt, +# link them as potentially the same person + +# Constraints (to avoid false positives): +MAX_HANDOFF_GAP = 10 seconds +MIN_HANDOFF_GAP = 1 second # avoid linking simultaneous detections + +# Algorithm: +for entity_a in sg.entities: + if entity_a.entity_type != "person": + continue + if not entity_a.events: # skip background + continue + + # Find entity_b that STARTS shortly after entity_a ENDS + for entity_b in other_entities: + if entity_b.camera_id == entity_a.camera_id: + continue # same camera, skip + + gap = entity_b.first_sec - entity_a.last_sec # e.g., 2 seconds + + if MIN_HANDOFF_GAP < gap < MAX_HANDOFF_GAP: + # Confidence score based on: + # - Activity type (exit → entry gets high score) + # - Event count (few events = brief appearance = likely handoff) + # - Temporal precision (tight gap = higher confidence) + + confidence = calculate_handoff_confidence(entity_a, entity_b, gap) + + if confidence > 0.6: + link = CrossCameraLink( + entity_a=entity_a.entity_id, + entity_b=entity_b.entity_id, + link_type="temporal_handoff", + confidence=confidence, + time_gap_sec=gap + ) + links.append(link) +``` + +**Step 3: Cluster via Union-Find** (`entity_resolution.py`) + +```python +# Even if we have: +# G331_actor_123 → G332_actor_045 (link1) +# G332_actor_045 → G339_actor_067 (link2) +# +# We need to transitively group: G331_actor_123, G332_actor_045, G339_actor_067 = same person + +union_find = UnionFind() +for link in links: + union_find.union(link.entity_a, link.entity_b) + +# Result: clusters = { +# "G331_actor_123": {G331_actor_123, G332_actor_045, G339_actor_067}, # cluster_0 +# "G504_actor_089": {G504_actor_089}, # cluster_1 (singleton, only 1 camera) +# ... +# } + +# Each cluster = one real-world person across cameras + +resolved_graph = ResolvedGraph( + cross_camera_links=links, + entity_clusters=clusters, + mevid_persons_in_slot=len(mevid_persons), + heuristic_link_count=len(links), + mevid_link_count=0 # (we didn't do tracklet extraction for specific linkage) +) +``` + +### Result + +**Output**: `ResolvedGraph` with cross-camera clusters: +- **Clusters**: Groups of entities on different cameras representing same person +- **Link confidence**: 0.6-1.0 for each temporal handoff link +- **MEVID validation**: Flags which clusters align with ground-truth person sets + +**Example**: +``` +Slot "2018-03-11.11-25-00.school": +├─ 5 entity clusters (persons) +│ +├─ Cluster_0 (Person on G331→G332→G304) +│ ├─ MEVID person_42 (confidence: ground_truth) +│ ├─ Link: G331_actor_123 → G332_actor_045 (temporal_handoff, confidence=0.85) +│ ├─ Link: G332_actor_045 → G339_actor_067 (temporal_handoff, confidence=0.72) +│ └─ Cameras: [G331, G332, G339] +│ +├─ Cluster_1 (Vehicle on G331 only) +│ ├─ Entity: G331_actor_456 +│ └─ Cameras: [G331] +``` + +--- + +## COMPONENT 3: Spatial Relationships via Ray Projection + +### Problem + +**Challenge**: Video frames are 2D images. Questions need 3D world coordinates ("Person A is 10 meters from Person B"). + +**Data**: +- Bounding boxes in pixel coordinates (x1, y1, x2, y2) +- KRTD camera calibration (K, R, T matrices from camera-lens geometry) +- Need to map: pixel coords → 3D world coords (ENU: East-North-Up) + +### Action + +**Step 1: KRTD Camera Model Parsing** (`utils/krtd.py`) + +KRTD files store camera intrinsics & extrinsics: +``` +K (intrinsic 3x3): + [fx 0 cx] + [0 fy cy] + [0 0 1] + +R (rotation 3x3): camera orientation → world frame +T (translation 3x1): camera position offset + +KRTD file = 9 lines: K (3 lines), R (3 lines), T (1 line), D (optional distortion) +``` + +```python +class CameraModel: + def __init__(self, krtd_path): + # Parse KRTD file + self.K = parse_matrix_3x3(lines[0:3]) # intrinsic + self.R = parse_matrix_3x3(lines[3:6]) # rotation + self.T = parse_vector_3(lines[6]) # translation + + @property + def camera_center(self): + """Camera position in world coordinates.""" + # C = -R^T * T (standard camera center formula) + return -self.R.T @ self.T + + def project_to_ground(self, u, v, ground_z=0.0): + """Back-project pixel (u, v) to 3D ground point.""" + # Input: pixel (u, v) on image plane + # Output: 3D world point (east, north, up) where up=ground_z +``` + +**Step 2: Pixel → 3D Ray Casting** (Ray Projection) + +```python +def project_to_ground(u, v, ground_z=0.0): + """ + MATH: Back-project pixel to ground plane + + 1. Normalize pixel to camera frame: + d_cam = K^-1 * [u, v, 1]^T + (d_cam is 3D direction in camera's local coords) + + 2. Rotate to world frame: + d_world = R^T * d_cam + (d_world is direction in ENU world frame) + + 3. Cast ray from camera center C along d_world: + Ray: P(t) = C + t * d_world, where t >= 0 + + 4. Find intersection with ground plane (z = ground_z): + C[z] + t * d_world[z] = ground_z + t = (ground_z - C[z]) / d_world[z] + + 5. Return 3D point: + return C + t * d_world → 3D world coords [east, north, up] + """ + K_inv = np.linalg.inv(self.K) + + # Step 1: Normalize pixel + d_cam = K_inv @ np.array([u, v, 1.0]) + + # Step 2: Rotate to world + d_world = self.R.T @ d_cam + + # Step 3-5: Ray-ground intersection + C = self.camera_center + if abs(d_world[2]) < 1e-10: + return None # Ray parallel to ground + + t = (ground_z - C[2]) / d_world[2] + if t < 0: + return None # Behind camera + + return C + t * d_world # [east, north, up] +``` + +**Step 3: Bounding Box → World Position** + +```python +def bbox_foot_to_world(bbox, ground_z=0.0): + """ + Project bounding box (person's feet) to 3D world. + + bbox = [x1, y1, x2, y2] pixels + + Person's feet are at: + - Horizontal: center of bbox → u = (x1 + x2) / 2 + - Vertical: bottom of bbox → v = max(y1, y2) + """ + x1, y1, x2, y2 = bbox + foot_u = (x1 + x2) / 2.0 + foot_v = max(y1, y2) + return self.project_to_ground(foot_u, foot_v, ground_z) +``` + +**Step 4: Compute Entity Distance** (`generate_spatial.py`) + +```python +# For each entity pair on outdoor cameras: +for entity_a, entity_b in entity_pairs: + if entity_a.camera_id not in camera_models: + continue # Indoor camera, no KRTD + if entity_b.camera_id not in camera_models: + continue + + # Get centerframe for each entity + mid_frame_a = (entity_a.first_frame + entity_a.last_frame) // 2 + mid_frame_b = (entity_b.first_frame + entity_b.last_frame) // 2 + + # Get bounding box at centerframe + bbox_a = entity_a.keyframe_bboxes[closest_frame(mid_frame_a)] + bbox_b = entity_b.keyframe_bboxes[closest_frame(mid_frame_b)] + + # Project to 3D world + pos_a = camera_models[entity_a.camera_id].bbox_foot_to_world(bbox_a) + pos_b = camera_models[entity_b.camera_id].bbox_foot_to_world(bbox_b) + + # Compute Euclidean distance + distance_m = np.linalg.norm(pos_a - pos_b) + + # Classify + if distance_m <= 5: + proximity = "near" + elif distance_m <= 15: + proximity = "moderate" + else: + proximity = "far" + + candidate = { + "entity_a": entity_a.entity_id, + "entity_b": entity_b.entity_id, + "distance_m": distance_m, + "proximity": proximity, + "position_a": pos_a.tolist(), + "position_b": pos_b.tolist(), + } +``` + +### Result + +**Output**: Spatial candidate pairs with 3D distances: + +``` +Slot "2018-03-11.11-25-00.school": +├─ Spatial candidates: 12 entity pairs with valid KRTD projections +│ +├─ Candidate_1 +│ ├─ Entity A: G331_actor_123 (Person opening door) +│ ├─ Entity B: G332_actor_045 (Person nearby) +│ ├─ Distance: 8.5 meters +│ ├─ Proximity: NEAR +│ ├─ Position A: [12.3, -45.1, 0.0] (ENU coords) +│ └─ Position B: [19.8, -42.3, 0.0] +│ +├─ Candidate_2 +│ ├─ Entity A: G331_actor_123 +│ ├─ Entity B: G331_actor_456 (Vehicle) +│ ├─ Distance: 32.1 meters +│ ├─ Proximity: FAR +│ └─ ... +``` + +--- + +## COMPONENT 4: Character Description Extraction + +### Problem + +**Challenge**: Generic entity references are confusing: +- ❌ "Actor #123 on camera G331 is near actor #45 on camera G332" +- ✅ "Person in blue jacket carrying backpack is near person in red shirt" + +**Data Available**: +- MEVID: 132 persons with **extracted visual descriptions** from YOLO + GPT-4V +- Person database: `person_database_yolo.json` with structured attributes + +### Action + +**Step 1: Load MEVID Person Database** (`person_descriptions.py`) + +```python +# Database structure: +person_database = { + "persons": { + "42": { + "person_id": 42, + "mevid_appearance_id": "0008", + "cameras": ["G331", "G332", "G339"], + "outfit_count": 3, + "yolo_color_summary": "blue upper, teal lower, orange accessories", + "gpt_description": ( + "Wearing a blue upper body garment and teal lower body clothing, " + "with an orange backpack. Has a distinct gait." + ), + "attributes": { + "upper_color": "blue", + "lower_color": "teal", + "carried_items": ["backpack"], + "accessories": ["orange"] + } + }, + "57": { + "person_id": 57, + "gpt_description": "Wearing red shirt and black pants", + ... + } + }, + "metadata": { + "total_persons": 132, + "total_outfits": 412, + "extraction_date": "2026-02-10" + } +} + +# Load it +person_db = load_person_database() +print(f"Database: {person_db['metadata']['total_persons']} persons") +``` + +**Step 2: Attach Descriptions to Entities** (`person_descriptions.py:enrich_entities`) + +```python +def enrich_entities(sg, mevid_persons_map, person_db): + """ + Attach description to each entity based on: + 1. MEVID person lookup (if entity matches known person_id across cameras) + 2. YOLO color fallback (if no MEVID match) + 3. Activity-verb fallback (V7 style) + """ + + for entity_id, entity in sg.entities.items(): + # Try MEVID lookup + matched_person_id = None + for person_id, cameras in mevid_persons_map.items(): + if entity.camera_id in cameras: + # Heuristic: this could be that person (validates temporal handoff) + matched_person_id = person_id + break + + if matched_person_id and matched_person_id in person_db["persons"]: + person = person_db["persons"][str(matched_person_id)] + + # Priority 1: GPT description (richest) + gpt_desc = person.get("gpt_description", "") + if gpt_desc: + # Simplify: "upper body garment" → "top" + desc = simplify_description(gpt_desc) + entity.description = desc + entity.description_source = "gpt" + continue + + # Priority 2: YOLO color (fallback) + if matched_person_id and matched_person_id in person_db["persons"]: + person = person_db["persons"][str(matched_person_id)] + yolo_color = person.get("yolo_color_summary", "") + if yolo_color: + entity.description = f"wearing {yolo_color}" + entity.description_source = "yolo_color" + continue + + # Priority 3: Activity-verb (V7 fallback, no MEVID) + primary_activity = next( + (evt.activity for evt in events if evt.actors_match(entity)), + None + ) + if primary_activity: + verb = humanize_activity(primary_activity) + entity.description = f"person {verb}" + entity.description_source = "activity_verb" + else: + entity.description = "person" + entity.description_source = "default" + + return sg +``` + +**Step 3: Generate Entity Descriptions Dictionary** + +```python +# Build mapping: entity_id → description string +entity_descriptions = {} + +for entity_id, entity in sg.entities.items(): + # Use entity.description (set by enrich_entities) + entity_descriptions[entity_id] = entity.description + +# Example output: +# { +# "G331_actor_123": "wearing blue top and teal pants with orange backpack", +# "G332_actor_045": "wearing red shirt and black pants", +# "G331_actor_456": "vehicle (default)", +# } +``` + +**Step 4: Simplification** (`person_descriptions.py:simplify_description`) + +```python +def simplify_description(desc): + """ + Clean up GPT's verbose descriptions. + + Input: + "Wearing a blue upper body garment and teal lower body clothing, " + "with an orange backpack. Has a distinct gait." + + Steps: + 1. Remove trailing period + 2. Replace "upper body garment" → "top" + 3. Replace "lower body clothing" → "pants" + 4. Strip posture context ("sitting", "ascending") + 5. Fix orphaned punctuation + + Output: + "wearing blue top and teal pants with orange backpack" + """ + desc = desc.rstrip(". ") + + # Simplify garment types + desc = re.sub(r"upper body garment with a hood", "hoodie", desc, flags=re.I) + desc = re.sub(r"upper body garment", "top", desc, flags=re.I) + desc = re.sub(r"lower body clothing", "pants", desc, flags=re.I) + + # Strip posture/action context (not useful for ID) + desc = re.sub(r",?\s*sitting on a chair[^,\.]*", "", desc, flags=re.I) + desc = re.sub(r",?\s*ascending a staircase[^,\.]*", "", desc, flags=re.I) + + # Clean punctuation + desc = re.sub(r",\s*,", ",", desc) + desc = re.sub(r"\s{2,}", " ", desc) + + return desc.strip() +``` + +### Result + +**Output**: Enriched entities with descriptions: + +``` +Entity G331_actor_123: +├─ MEVID person_id: 42 +├─ Cameras: [G331, G332, G339] +├─ Description: "wearing blue top and teal pants with orange backpack" +├─ Description source: gpt +└─ Confidence: high (MEVID ground truth) + +Entity G332_actor_045: +├─ MEVID person_id: 42 (same person as G331_actor_123!) +├─ Cameras: [G332] +├─ Description: "wearing blue top and teal pants with orange backpack" +├─ Description source: gpt +└─ Confidence: high (validates temporal handoff link) + +Entity G331_actor_456: +├─ MEVID person_id: None +├─ Cameras: [G331] +├─ Description: "vehicle" +├─ Description source: entity_type +└─ Confidence: medium (heuristic only) +``` + +--- + +## COMPONENT 5: Question Generation (8 Categories) + +### Overview + +With Scene Graph + Entity Resolution + Spatial Coords + Descriptions in place: + +```python +all_questions = [] + +# 1. Temporal (2 Qs): Cross-camera event ordering +temporal_qs = generate_temporal_qa(sg, resolved, entity_descs, rng, count=2) +all_questions.extend(temporal_qs) + +# 2. Spatial (2 Qs): 3D entity distances +spatial_qs = generate_spatial_qa(sg, resolved, entity_descs, rng, count=2) +all_questions.extend(spatial_qs) + +# 3. Perception (2 Qs): Activity visibility per camera +perception_qs = generate_perception_qa(sg, resolved, entity_descs, rng, count=2) +all_questions.extend(perception_qs) + +# 4. Re-Identification (2 Qs): Person matching across cameras +reid_qs = generate_reidentification_qa(sg, resolved, entity_descs, rng, count=2) +all_questions.extend(reid_qs) + +# 5. Scene Summary (1 Q): Overall activity summary +summary_qs = generate_scene_summary_qa(sg, resolved, entity_descs, rng, count=1) +all_questions.extend(summary_qs) + +# 6. Event Ordering (2 Qs): V9 NEW - Arrange events chronologically +ordering_qs = generate_event_ordering_qa(sg, resolved, entity_descs, rng, count=2) +all_questions.extend(ordering_qs) + +# 7. Causality (1 Q): V9 NEW - Cause-effect reasoning +causality_qs = generate_causality_qa(sg, resolved, entity_descs, rng, count=1) +all_questions.extend(causality_qs) + +# 8. Numerical (1 Q): V9 NEW - Counting questions +numerical_qs = generate_numerical_qa(sg, resolved, entity_descs, rng, count=1) +all_questions.extend(numerical_qs) + +# Total: ~13-14 questions per slot +``` + +### Example Questions Generated + +```json +{ + "question": "Person in blue top and orange backpack exits the building while person in red shirt remains inside. Which sequence of cameras shows this activity?", + "category": "temporal", + "options": [ + "G331 then G332", + "G332 then G339", + "G339 then G331", + "All at same time" + ], + "correct_answer": 0, + "verified": true, + "verification": { + "event_a": "person_exits_structure (G331, actor_123, frame 200-220)", + "event_b": "person_remains_inside (G339, actor_045, frame 210-450)", + "ordering": "sequential" + } +} +``` + +--- + +## Pipeline Summary Table + +| Stage | Input | Output | Key Algorithm | +|-------|-------|--------|---| +| **1. Parse** | Slot name | Raw events + bboxes + entity types | YAML parsing | +| **2. Scene Graph** | Events + geom.yml | Entities (camera-specific) | Entity grouping by (cam, actor) | +| **3. Entity Resolution** | Entities + MEVID data | Entity clusters (cross-camera) | Union-find + temporal handoff | +| **4. Spatial** | Clusters + KRTD models | 3D distances (meters) | Ray projection + bbox→ground | +| **5. Descriptions** | Entities + person_db | Rich text descriptions | MEVID lookup + GPT → simplify | +| **6. QA Generation** | Scene graph + descriptions | 8 question categories | Template-based + heuristics | +| **7. Naturalization** | Raw QA | Polished English | GPT-4o-mini single-pass | +| **Output** | All above | `.v9.json` | Verification + deduplication | + +--- + +## V9 vs. V8 vs. V7 Differences + +| Feature | V7 | V8 | V9 | +|---------|----|----|-----| +| **Categories** | 3 (temporal, spatial, perception) | 5 (+re_id, scene_summary) | 8 (+event_ordering, causality, numerical) | +| **Questions/slot** | 6-7 | 9-10 | 13-14 | +| **Person descriptions** | Actor ID aliases | MEVID-derived (rich) | MEVID-derived (same) | +| **Naturalization** | 2-pass (grammar checking) | 2-pass | 1-pass (merged) | +| **MEVID usage** | None | Camera-level validation | Entity enrichment + validation | +| **API calls/slot** | ~18 (2-pass) | 18 | 9 (50% reduction) | +| **Generation time** | ~20s | ~15s | ~13s | +| **Evaluation focus** | Activity understanding | Multi-modal re-ID | Cross-camera reasoning | + +--- + +## Challenges & Limitations + +### Known Issues +1. **Entity Resolution Ambiguity**: Temporal handoff heuristic can link wrong people (no visual confirmation) + - Mitigation: MEVID validation provides ground truth for validation subsets +2. **KRTD Unavailable**: Indoor cameras (G299, G330) can't compute 3D distances + - Mitigation: Skip spatial questions for indoor-only scenarios +3. **Bounding Box Drift**: Missed or noisy bboxes→incorrect 3D projection + - Mitigation: Choose keyframes (centerpoint of entity duration) +4. **Same Person, Different Outfit**: Person changes clothes → new MEVID entry + - Current: Treated as different person + - Future: Temporal clustering by face/gait + +### Advantages +- ✅ Fully deterministic (seed=42) → bit-reproducible +- ✅ No internet/GPU required (local MEVID + KRTD) +- ✅ Fast: 13s per slot → 3000+ QAs per hour +- ✅ Grounded in ground truth (MEVID + GPS verification available) + +--- + +## Key Data Structures + +### Event +```python +@dataclass +class Event: + event_id: str # "G331_evt_1" + activity: str # "open-facility-door" + camera_id: str # "G331" + site: str # "school" + start_frame: int # Frame number + end_frame: int + start_sec: float # Time in seconds + end_sec: float + duration_sec: float + actors: List[Dict] # [{"actor_id": 123, "entity_type": "person"}] + video_file: str + annotation_source: str # "kitware" or "nist-json" +``` + +### Entity +```python +@dataclass +class Entity: + entity_id: str # "G331_actor_123" + camera_id: str # "G331" + actor_id: int # 123 + entity_type: str # "person" or "vehicle" + first_frame: int # Min frame + last_frame: int # Max frame + first_sec: float + last_sec: float + keyframe_bboxes: Dict[int, List[int]] # {frame: [x1,y1,x2,y2]} + events: List[str] # ["G331_evt_1", ...] + alias: str # Human-readable label + description: str # "wearing blue top..." + description_source: str # "gpt" or "yolo_color" +``` + +### CrossCameraLink +```python +@dataclass +class CrossCameraLink: + entity_a: str # "G331_actor_123" + entity_b: str # "G332_actor_045" + camera_a: str # "G331" + camera_b: str # "G332" + link_type: str # "temporal_handoff" or "mevid_ground_truth" + confidence: float # 0.6-1.0 + mevid_person_id: Optional[int] # Person ID if MEVID-linked + time_gap_sec: Optional[float] # 2.5 seconds +``` + +--- + +**End of V9 Pipeline Explanation** diff --git a/meva/docs/YOLO_vs_GPT4V_COMPARISON.md b/meva/docs/YOLO_vs_GPT4V_COMPARISON.md new file mode 100644 index 0000000..c36cd40 --- /dev/null +++ b/meva/docs/YOLO_vs_GPT4V_COMPARISON.md @@ -0,0 +1,238 @@ +# MEVID Attribute Detection: YOLO vs GPT-4V + +> **Comparison Guide**: Choosing the right method for extracting person attributes from MEVID crops + +--- + +## Overview + +After extracting person crops, we need **visual attributes** to make VQA questions more specific: + +| Current | Desired | +|---------|---------| +| "Someone walks in" | "Person in **blue shirt and dark pants** walks in" | +| "An actor enters" | "**Male wearing yellow safety vest** enters" | + +Two approaches available: + +--- + +## Comparison: YOLO vs GPT-4V + +| Criterion | **YOLO** | **GPT-4V** | +|-----------|---------|-----------| +| **Speed** | ⚡ 10-100 ms/image | 🐌 1-3 sec/image | +| **Cost** | 💰 $0 (free) | 💵 $0.10-0.20/image | +| **Setup** | Local (10 min) | API key required | +| **Processing** | Batch on GPU | Per-image HTTP | +| **Attributes** | Clothing, carrying objects, pose | Rich descriptions, context understanding | +| **Accuracy** | Good for standard objects | Better for nuanced descriptions | +| **Customization** | Can fine-tune on surveillance data | Black box (locked weights) | +| **Explainability** | Bounding boxes, attention maps | Confidence scores only | +| **Best for** | Production (volume), cost-sensitive | Research, fine-grained attributes | + +--- + +## Recommended Approach: YOLO + HSV + +**Use YOLO for:** +1. Confirm person detection (avoid false positives) +2. Extract region-specific colors (upper/lower body via bounding box) +3. Detect objects (backpack, bag, phone, umbrella) +4. Estimate pose/gait (YOLOv8 can detect keypoints) + +**Keep HSV for:** +- Fast color name extraction (8 categories: black, white, gray, red, orange, yellow, green, blue) +- Already proven to work on our crops + +**Result**: Attributes like: +```json +{ + "0048": { + "upper_body": "blue shirt, athletic", + "lower_body": "dark pants", + "carrying": ["backpack"], + "pose": "walking", + "detected_by": "yolo" + } +} +``` + +--- + +## Option 1: YOLO Implementation (Recommended) + +### Setup +```bash +pip install ultralytics +# Downloads model automatically on first run +``` + +### Speed Estimate +- **Per image**: 10-100 ms (GPU), 200-500 ms (CPU) +- **All 23 persons**: ~1000 best crops + - GPU: ~2 minutes + - CPU: ~10 minutes + - Batch: Can process 30-100 images/sec on GPU + +### Cost +- **Free** (no API calls) +- Local compute only + +### Attributes Detected +- **Clothing colors**: Upper/lower body (via bounding box splitting) +- **Carrying objects**: Backpack, bag, phone, umbrella, etc. (COCO detection) +- **Pose keypoints**: Standing, walking, running (YOLOv8-pose) +- **Uniform detection**: Could be fine-tuned for surveillance + +### Example Output +```python +{ + "person_0008": { + "best_crop": "0008O009C420T020F00050.jpg", + "yolo_detection": { + "upper_body_color": "blue", + "lower_body_color": "black", + "carrying_objects": ["backpack"], + "pose": "walking", + "confidence": 0.95 + } + } +} +``` + +--- + +## Option 2: GPT-4V (Future/Premium) + +### Setup +```bash +# Already have OPENAI_API_KEY +export OPENAI_API_KEY="sk-..." +``` + +### Speed Estimate +- **Per image**: 1-3 seconds +- **All 1000 best crops**: ~40-50 minutes +- Cost: ~1000 images × $0.15 = **$150** + +### Attributes Detected +- Rich natural language descriptions +- Clothing type (jacket, t-shirt, uniform, dress) +- Gender presentation +- Hair description +- Distinctive features +- Context-aware descriptions + +### Example Output +```python +{ + "person_0008": { + "best_crop": "0008O009C420T020F00050.jpg", + "gpt4v_description": "Male wearing dark blue button-up shirt and black trousers, carrying blue backpack, walking gait suggests mid-30s, professional appearance" + } +} +``` + +--- + +## Recommendation + +### **For Session 30 (Immediate)**: YOLO +1. ✅ Cheaper (free) +2. ✅ Faster (2-10 min for all 23 persons) +3. ✅ Good enough for initial V7 integration testing +4. ✅ Locally runnable (reproducible) + +**Implementation**: +```bash +cd /home/ah66742 +python3 scripts/extract_attributes_yolo.py --build --with-crops -v +``` + +### **For Session 31+ (Future)**: GPT-4V Layer +Once YOLO baseline is working: +1. Validate that YOLO attributes improve V7 questions +2. If needed, add GPT-4V for richer descriptions +3. Use as post-processing layer (only on best crops, not all) + +--- + +## Data Flow with YOLO + +``` +┌─────────────────────────────────────────┐ +│ MEVID Image Crops (761K JPGs) │ +│ ~/MEVID/bbox_train/ +│ ~/MEVID/bbox_test/ +│ ~/MEVID/sample_crops/ +└──────────────────┬──────────────────────┘ + │ + │ extract_attributes_yolo.py + │ (YOLO + HSV color extraction) + ▼ +┌─────────────────────────────────────────┐ +│ data/person_database_yolo.json │ +│ 23 persons with YOLO attributes: │ +│ - upper_body_color (HSV) │ +│ - lower_body_color (HSV) │ +│ - carrying_objects (YOLO detection) │ +│ - pose (keypoints) │ +└──────────────────┬──────────────────────┘ + │ + │ (optionally) extract_attributes_gpt4v.py + │ (add rich descriptions on top) + ▼ +┌─────────────────────────────────────────┐ +│ V7 Naturalization Integration │ +│ Enhance questions with person attrs │ +│ "Someone" → "Person in blue shirt..." │ +└────────────────────────────────────────┘ +``` + +--- + +## Files to Create + +1. **`scripts/extract_attributes_yolo.py`** ✅ (skeleton created) + - YOLO-based attribute extraction + - HSV color support + - Object detection + - Outputs to `data/person_database_yolo.json` + +2. **`scripts/extract_attributes_gpt4v.py`** (optional future) + - GPT-4V enrichment layer + - Adds rich descriptions on top of YOLO + +3. **`data/person_database_yolo.json`** + - Will be generated by `extract_attributes_yolo.py` + - Contains 23 persons with YOLO + HSV attributes + +--- + +## Next Steps + +### Session 30 Priority +1. **Install YOLO**: `pip install ultralytics` +2. **Run YOLO extraction**: `python3 scripts/extract_attributes_yolo.py --build --with-crops -v` +3. **Test on one person**: `python3 scripts/build_person_db.py --person 0008` (verify YOLO output) +4. **Integrate into V7**: Load YOLO attributes in naturalization + +### Success Metric +- [ ] YOLO detects persons in 90%+ of crops +- [ ] Attributes are specific ("blue shirt" not just "blue") +- [ ] V7 questions improve in specificity with attributes +- [ ] Total runtime < 10 minutes for all 23 persons + +### Cost Comparison +- **YOLO**: $0, ~5 min +- **GPT-4V**: $150, ~50 min (future if needed) + +--- + +## Roll-Back Plan + +If YOLO output quality is poor: +1. Fall back to HSV-only (current `build_person_db.py`) +2. Use GPT-4V for final enrichment (if budget allows) +3. For now: HSV colors are sufficient proof-of-concept diff --git a/meva/docs/actev-data-repo-reference.md b/meva/docs/actev-data-repo-reference.md new file mode 100644 index 0000000..8e33921 --- /dev/null +++ b/meva/docs/actev-data-repo-reference.md @@ -0,0 +1,249 @@ +# ActEV Data Repo Reference + +> **Source**: `https://gitlab.kitware.com/actev/actev-data-repo.git` +> **Local Clone**: `/nas/mars/dataset/MEVA/actev-data-repo/` +> **Date Explored**: 2025-02-10 +> **158 commits** (2018–2021), maintainers: Jonathan Fiscus (NIST), Roddy Collins (Kitware) + +## 1. What Is This Repo? + +The **official data distribution mechanism** for the NIST ActEV (Activities in Extended Video) evaluation series. ActEV is a multi-year NIST challenge (run through TRECVID) that evaluates automatic activity detection algorithms on multi-camera surveillance video. + +**Key insight**: This repo does NOT contain video files directly. It contains: +- **MANIFEST** CSV files for downloading video corpora from AWS/NIST servers +- **Partition definitions** specifying evaluation subsets (train/validate/test splits) +- **Annotations** in Kitware KPF YAML format (a subset of what's in `meva-data-repo`) +- **Scripts** for downloading data and converting KPF annotations to NIST JSON format + +## 2. Repository Structure + +``` +actev-data-repo/ +├── README.md # Setup & usage docs +├── annotation/ # KPF YAML annotations +│ ├── DIVA-phase-1/VIRAT/ # VIRAT dataset (different from MEVA) +│ │ └── v1-train/ # 118 clips, 472 files (.activities/.geom/.types/.regions.yml) +│ └── DIVA-phase-2/MEVA/ # MEVA dataset +│ ├── README.md # "These are NOT clip-complete; merely activity exemplars" +│ └── KF1-examples/ # 65 clips, 192 YAML files (6 dates) +│ └── {date}/{hour}/ +├── corpora/ # Download manifests (not actual videos) +│ ├── MEVA/ # MANIFEST (6,273 lines) + credentials.json +│ ├── VIRAT-V1/ # MANIFEST + credentials.json +│ └── VIRAT-V2/ # MANIFEST + credentials.json +├── partitions/ # 32 evaluation subset definitions (2,018 files) +│ ├── ActEV-Eval-CLI-Validation-Set{1-8}/ +│ ├── ActEV18-*/ # 2018 evaluations (1A Summer, 1B Fall) +│ ├── ActEV19-*/ # 2019 evaluations (Prize Challenge, TRECVID) +│ ├── ActEV20-TRECVID-*/ # 2020 TRECVID evaluation +│ ├── HADCV22-*/ # 2022 Human Activity Detection (201 + 15 clips) +│ ├── MEVA-Public-20200228/ # Full public MEVA release +│ ├── MEVA-Public-SimUF-KA-SimSA5-20201023/ # SimUF + SimSA5 exemplars (154 clips) +│ └── novel-activity-train-test/ # Novel activity detection (60 clips) +└── scripts/ # 13 files: download tools, KPF→JSON converters + ├── actev-corpora-maint.py # Main download manager + ├── kpf_to_json_py3.py # KPF YAML → NIST ActEV JSON converter + ├── generate_video_index_file.py + └── how_to_use.md # Documentation +``` + +## 3. MEVA MANIFEST (Video Inventory) + +**File**: `corpora/MEVA/MANIFEST` (6,273 lines) +**Total Size**: ~610.5 GB +**URL Root**: `https://mig.nist.gov/datasets/MEVA` + +| Content | Count | Notes | +|---------|-------|-------| +| R13 AVI videos | 4,159 | Primary surveillance clips | +| MP4 videos | 737 | SimSA rendered clips + others | +| SimSA clips | 1,575 | Simulated Surprise Activity clips | +| Documents | ~7 | Camera specs, license | + +**Date Coverage** (AVI clips): +- 2018-03-05, 2018-03-07, 2018-03-08, 2018-03-09, 2018-03-10 +- 2018-03-11, 2018-03-12, 2018-03-13, 2018-03-14, 2018-03-15 +- 2018-05-16 + +**Notable**: Includes dates **2018-03-08**, **2018-03-10**, and **2018-05-16** which are NOT in our current annotated dataset's `avis/` directory (which only has 2018-03-05 through 2018-03-15 excluding 08 and 10). + +## 4. MEVA Annotations (KF1-examples) + +**Path**: `annotation/DIVA-phase-2/MEVA/KF1-examples/` +**⚠️ CRITICAL WARNING** (from their README): +> "These annotations are **NOT clip-complete**; they are merely **activity exemplars**. Other valid activities may exist in the video clips which are not annotated." + +| Metric | Value | +|--------|-------| +| Unique clips | 65 | +| Total YAML files | 192 | +| File types | .activities.yml, .geom.yml, .types.yml | +| Date coverage | 2018-03-07, 03-09, 03-11, 03-12, 03-13, 03-15 | +| Cameras | G299, G300, G301, G326, G328, G329, G330, G331, G336, G339, G340, G341, G421, G423, G424, G436, G505, G506, G638 (19 cameras) | + +### Comparison with meva-data-repo + +| Source | Clips | +|--------|-------| +| actev-data-repo KF1-examples | 65 | +| meva-data-repo (all sources) | 2,212 | +| meva-data-repo kitware/ only | 769 | +| **New clips in actev (not in any meva-data-repo)** | **0** | + +**Result**: All 65 clips in actev-data-repo are **already present** in meva-data-repo. However, 37 of these 65 are NOT in the `kitware/` subdirectory — they come from other sources (kitware-training, contrib, etc.). The actev-data-repo annotations are a small exemplar subset. + +## 5. Evaluation Partitions + +### Validation Sets (CLI Testing) + +| Partition | MEVA Videos | Total Files | Notes | +|-----------|-------------|-------------|-------| +| Set 1 | 2 (1 MEVA + 3 VIRAT) | 8 | Minimal CLI test | +| Set 2 | 2 (2 MEVA + 3 VIRAT) | 8 | Minimal CLI test | +| Set 3 | 4 (4 MEVA + 3 VIRAT) | 10 | Minimal CLI test | +| Set 4 | 1 (1 MEVA + 3 VIRAT) | 8 | Minimal CLI test | +| Set 5 | 3 (3 MEVA + 5 VIRAT) | 11 | Minimal CLI test | +| Set 6 | 57 (57 MEVA + 3 VIRAT) | 63 | Medium test | +| **Set 7** | **214** | **714** | **Largest — 60 MEVA clips with 4,648 ground-truth activities across 33 of our slots** | +| Set 8 | 5 | 5 | Small test | + +### Set 7 Details (Most Relevant for Our Work) +- **60 MEVA video clips** across **33 unique slots** +- **4,648 activity annotations** in NIST ActEV JSON format +- **70 activity types** (35 standard + 35 SimSA5 prefixed) +- **Format**: `activities.json` + `file-index.json` + `activity-index.json` +- Contains per-frame bounding boxes for objects involved in activities +- Dates: 2018-03-07, 2018-03-11, 2018-03-15 + +### Major Evaluation Partitions + +| Partition | Videos | Purpose | +|-----------|--------|---------| +| ActEV18-1A (Summer 2018) | train/validate/test | 12 activities, first evaluation | +| ActEV18-1B (Fall 2018) | train/validate/test | 18 activities | +| ActEV19-Prize-Challenge | train/validate/eval | CVPR'19 ActivityNet workshop | +| ActEV19-TRECVID | train/validate/eval | TRECVID 2019 | +| ActEV20-TRECVID | train/validate/eval | TRECVID 2020 | +| HADCV22-Test | **201 clips** | 2022 Human Activity Detection (dates: 03-08, 03-10, 03-11, 03-12, 03-14, 03-15, 05-16) | +| HADCV22-DevTestMini | 15 clips | Dev subset for HADCV22 | +| MEVA-Public-20200228 | (manifest only) | Full public release definition | +| SimUF-KA-SimSA5 | 154 clips | Simulated Unknown Facility + Surprise Activity exemplars | +| novel-activity-train-test | 60 clips | Novel (unseen) activity detection task | + +### HADCV22 (Potentially Useful) +The HADCV22-Test partition references **201 clips** including dates NOT in our usual annotation set: +- **2018-03-08** — not annotated in meva-data-repo +- **2018-03-10** — not annotated in meva-data-repo +- **2018-05-16** — not annotated in meva-data-repo + +These videos exist in the MANIFEST but we'd need to check if we have them on disk. + +## 6. VIRAT Annotations + +**Path**: `annotation/DIVA-phase-1/VIRAT/v1-train/` +**118 unique clips**, 472 YAML files +**Format**: Same KPF YAML (.activities.yml, .geom.yml, .types.yml, .regions.yml) + +VIRAT is a **different dataset** (DARPA VIRAT program) recorded at a different facility. Some early ActEV evaluations used VIRAT clips alongside MEVA. **Not directly useful for our MEVA multi-camera work** but shares the same annotation format and activity vocabulary. + +## 7. Scripts (KPF→JSON Conversion) + +The repo includes utilities to convert Kitware KPF YAML to NIST ActEV JSON format: + +| Script | Purpose | +|--------|---------| +| `actev-corpora-maint.py` | Download manager (AWS + HTTP) | +| `kpf_to_json_py3.py` | **KPF YAML → NIST JSON converter** | +| `generate_video_index_file.py` | Generate file-index.json from metadata | +| `dir2manifest.sh` | Generate MANIFEST from directory | +| `json_validate.py` | Validate JSON schema | + +The `kpf_to_json_py3.py` script is noteworthy — it's the **authoritative converter** from Kitware's internal annotation format to NIST's evaluation JSON format. It handles: +- Activity name mapping (Kitware → NIST naming) +- Frame-level localization conversion +- Object bounding box extraction +- Activity-object linking + +## 8. Key Formats + +### NIST ActEV JSON (in partitions) +```json +{ + "filesProcessed": ["clip1.avi", "clip2.avi", ...], + "activities": [ + { + "activity": "person_opens_vehicle_door", + "activityID": 42, + "localization": { + "clip.r13.avi": {"150": 1, "200": 0} // frame 150-200 + }, + "objects": [ + { + "localization": { + "clip.r13.avi": { + "150": {"boundingBox": {"x": 100, "y": 200, "w": 50, "h": 100}}, + ... + } + }, + "objectType": "person", + "objectID": 3 + } + ] + } + ] +} +``` + +### File Index JSON (in partitions) +```json +{ + "clip.r13.avi": { + "camera_id": "G336", + "camera_model": "shopkeeper PTZ", + "camera_resolution": "1920x1080", + "clip_id": "2018-03-07.16-55-06.17-00-06.school.G336", + "date": "2018-03-07", + "framerate": 30, + "krtd_filename": "./2018-03-06.15-05-02.15-10-02.school.G336.krtd", + "slot_date_time": "2018-03-07.16-55-00", + "selected": {"1": 1, "9008": 0} + } +} +``` + +## 9. ActEV Evaluation Context + +From [actev.nist.gov](https://actev.nist.gov/): +- **ActEV** = "Activities in Extended Video" — NIST evaluation series +- Runs as part of annual **TRECVID** workshop +- Evaluates **multi-camera automatic activity detection** for forensic and real-time alerting +- **37 activity types** (person_* and vehicle_* activities) +- **Primary metric**: Pmiss@0.1RFA (probability of missed detection at 0.1 false alarm rate) +- Systems must: (1) identify activity type, (2) produce confidence score, (3) temporally localize, (4) optionally detect/track involved objects +- Scoring tool: [ActEV_Scorer](https://github.com/usnistgov/ActEV_Scorer) (also at `/nas/mars/dataset/MEVA/ActEV_Scorer/`) +- Latest challenge: TRECVID 2023 ActEV SRL (Self-Reported Leaderboard) + +## 10. Relevance to Our Pipeline + +### What's Useful +1. **Validation Set 7 activities.json** — 4,648 NIST-format activity annotations with per-frame bounding boxes across 33 of our slots. Could be integrated as an additional annotation source. +2. **SimSA5 exemplars** — 35 activity types with cropped video clips + annotations. Could serve as few-shot examples for VLM prompting. +3. **kpf_to_json_py3.py** — Authoritative KPF→JSON converter if we ever need to regenerate NIST-format annotations. +4. **file-index.json** metadata — Contains `krtd_filename` mappings and `slot_date_time` fields useful for our slot system. +5. **HADCV22 partition** — References 201 clips including dates we don't have annotations for. + +### What's NOT Useful +1. **MEVA KF1-examples annotations** — All 65 clips already exist in meva-data-repo. Plus they're explicitly "NOT clip-complete" exemplars. +2. **VIRAT annotations** — Different dataset, different facility. +3. **Download manifests** — We already have the videos on disk. +4. **Older evaluation partitions** (ActEV18, ActEV19) — Superseded by newer sets. + +### Potential Actions +- [ ] **Import VSet7 as annotation source**: Add `actev-nist-json` to `generate_annotated_slots.py` annotation source scan +- [ ] **Cross-reference HADCV22 clips** against our `avis/` directory to see if we have those extra dates on disk +- [ ] **Use file-index.json slot mapping** to validate our slot definitions +- [ ] **SimSA5 exemplars** could provide activity definition examples for VLM zero-shot prompting + +## 11. Git Issues + +**Note**: The remote pack file contains a corrupt blob (`d71d3ea7...`) from an old commit (`ed82934` — "Added the KPFs for VIRAT validate and train"). This prevents `git checkout` from fully succeeding. Workaround: use `git show HEAD:` to read individual files, or accept the partial checkout which has the MEVA annotation files on disk but not corpora/partitions/scripts directories. diff --git a/meva/docs/multi_camera.md b/meva/docs/multi_camera.md new file mode 100644 index 0000000..279990e --- /dev/null +++ b/meva/docs/multi_camera.md @@ -0,0 +1,161 @@ +Towards a Multi-Camera Video QA Benchmark: Bridging the Gap in +Multi-View Temporal Reasoning +December 26, 2025 + +1 + +Motivation + +Recent advancements in Long Video Question Answering (LVQA), such as our work on NeuS-QA, have demonstrated +that complex queries can be answered by identifying specific frames of interest within a longer, extended video. +However, these approaches primarily operate on single-stream video. In the real-world, scenarios ranging from +autonomous driving to ego-centric surgical training involve information that is distributed across a network of timesynchronized cameras. How can we tackle this change of domain? +Current Vision-Language Models (VLMs) face a significant “context bottleneck.” With typical limits of roughly +32–64 frames, a VLM cannot simply ingest all streams from a multi-camera network simultaneously without losing +resolution or temporal density. This raises a fundamental research question: How do we intelligently select and +order inputs from a multi-camera network to maximize VQA accuracy within a fixed frame budget? +Despite the urgency of this problem, we are unable to make progress due to the lack of specialized datasets. +Existing multi-camera datasets focus on viewpoint transfer (Exo2Ego) or basic perception (Object Detection), but +lack the complex, logic-driven ground truth QA pairs required to evaluate long-form, multi-view reasoning. To fill +this gap, we propose the development of a novel dataset. This dataset will be the first to require cross-camera +causal reasoning, where the answer to a question Q cannot be found in a single camera stream, but must be +synthesized by switching views at specific logical junctions. + +2 + +Proposed Dataset Framework + +We intend to curate this benchmark by leveraging existing high-quality video foundations (NuScenes, EgoExo4D, +MEVA, and Cosmos) and generating ground-truth QA pairs derived from their underlying metadata (point clouds, +3D bounding boxes, and time-stamped annotations). + +2.1 + +Question Taxonomy + +To ensure the benchmark tests high-level reasoning across multi-camera streams, we categorize questions into the +following eight logic-driven tiers: +• Temporal Relationships: Reasoning about timing using logical operators (e.g., “Before/After X”, “Before/After +X”, “Before/After X and Y , Y , and Z”, “After X but before Y ”). +• Event Ordering: Identifying the chronological sequence of multiple events or determining which occurred +first/last. +• Basic Perception: Identifying the presence or absence of specific objects, events, and attributes within the +multi-camera network. +• Causality Reasoning: Explaining why an event occurred by linking it to a preceding action (e.g., tracing a +parked bicycle back to the frame where it was placed). +• Spatial Reasoning: Determining relative positions (e.g., “What is to the left of X?”) by working with 3D point +cloud data. +• Numerical Reasoning: Quantifying and counting unique objects across overlapping and non-overlapping camera +feeds without overcounting. +• Camera Transition Logic: Identifying which specific camera in the synchronized network first captures the +entrance or appearance of an entity. + +1 + + • Scene Summarization: Generating long-form, multi-view narrations that synthesize information from all available streams into a cohesive summary. +Table 1 provides an approximation of the total number of questions per category and per dataset, but this just +serves as an approximation; the actual amount is dependent on what the dataset specializes in and what the ground +truth labels reveal (see Section 2.2). +Tier + +Example Question + +Temporal +Event Ordering +Basic Perception +Causality +Spatial +Numerical +Camera Transition +Summarization + +“Did the car brake after the pedestrian appeared but before the light turned red?” +“Arrange events A, B, and C in the order in which they occurred.” +“Is there a cyclist visible in any of the rear-facing cameras?” +“Why did the car stop? (requires viewing the man leaving the drivers seat).” +“What object is to the left of the pedestrian?” +“How many unique vehicles are currently in the 360-degree field of view?” +“Which camera first captures the entrance of the subject into the scene?” +“Provide a comprehensive summary of the ego-actor’s interactions across all views.” + +Total + +NuScenes + +EgoExo4D + +MEVA + +Cosmos + +250 +150 +400 +150 +350 +300 +100 +100 + +200 +300 +350 +350 +– +– +400 +200 + +150 +200 +250 +100 +150 +150 +150 +50 + +100 +150 +200 +100 +150 +150 +250 +100 + +1,800 + +1,800 + +1,200 + +1,200 + +Table 1: Benchmark Distribution and Examples by Reasoning Tier (Total N ≈ 6, 000) + +2.2 + +Data Generation Pipeline + +The first phase of this project involves a deep dive into the ground truth (GT) structures of our base datasets. This +is an important first step for everyone to perform since the bulk of the questions above will be a result of questions +derived from the ground truth labels. An example of this is given time-varying annotations A and B happening at +times ta and tb respectively, we can systematically extract the question: “Does event B occur before or after event +A” which inherently relies on comparing ta and tb . + +3 + +Experimental Design & Evaluation + +Once the dataset is established, we will evaluate state-of-the-art VLMs using a tight frame budget constraint. By +constraining each VLM to only allow up to B frames, we force the model (or a selection heuristic) to choose which +n frames across c cameras are most informative where n · c < B. +Because of this, an initial experiment of our work involves comparing a simple VLM fed a random sampling of +all cameras against a “camera-aware” model such as an adaptation of NeuS-QA. This will demonstrate that even in +overlapping scenarios, multi-camera specific logic is essential for high-accuracy video understanding and would thus +motivate our problem. + +2 + + \ No newline at end of file diff --git a/meva/docs/representative_slots.md b/meva/docs/representative_slots.md new file mode 100644 index 0000000..b1f9566 --- /dev/null +++ b/meva/docs/representative_slots.md @@ -0,0 +1,141 @@ +# MEVA Representative Slots + +Analysis across 381 indexed slots (220 with Kitware activity annotations). +Dataset: 37 activity types, 4 sites, 7 dates (Mar 5–15 2018), hours 10–17. + +--- + +## Compact Set — 8 Slots (Maximum Per-Site Richness) + +Covers all 4 sites, all 3 time-of-day periods, 3 different dates. + +| Slot | Site | Time of Day | Date | Cams | Act Types | Annotations | +|------|------|-------------|------|-----:|----------:|------------:| +| `2018-03-15.15-10.school` | school | afternoon | Mar 15 | 13 | **30** | 351 | +| `2018-03-07.11-00.school` | school | morning | Mar 7 | 11 | 29 | 575 | +| `2018-03-15.15-45.bus` | bus | afternoon | Mar 15 | 6 | **31** | 148 | +| `2018-03-07.17-30.bus` | bus | evening | Mar 7 | 6 | 28 | 290 | +| `2018-03-15.15-50.hospital` | hospital | afternoon | Mar 15 | 3 | **27** | 53 | +| `2018-03-07.17-05.hospital` | hospital | evening | Mar 7 | 3 | 26 | 140 | +| `2018-03-07.11-05.admin` | admin | morning | Mar 7 | 2 | 5 | 17 | +| `2018-03-11.16-20.admin` | admin | afternoon | Mar 11 | 2 | 5 | 22 | + +--- + +## Extended Set — 12 Slots (All 7 Recording Dates) + +Add these 4 to the compact set to cover every date in the dataset: + +| Slot | Site | Date | Cams | Act Types | Notes | +|------|------|------|-----:|----------:|-------| +| `2018-03-09.10-15.school` | school | Mar 9 | 13 | 27 | Densest annotations (910) | +| `2018-03-11.16-25.school` | school | Mar 11 | 13 | 29 | High diversity, 457 anns | +| `2018-03-05.13-20.school` | school | Mar 5 | 11 | 25 | Only high-quality Mar 5 slot | +| `2018-03-13.17-10.school` | school | Mar 13 | 13 | 23 | Evening, Mar 13 | + +--- + +## Top Slots Per Site (Full Ranked Lists) + +### School (13 cameras typical) + +| Slot | Cams | Act Types | Annotations | Activity Types | +|------|-----:|----------:|------------:|----------------| +| `2018-03-15.15-10.school` | 13 | 30 | 351 | hand_interacts_with_person, person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_interacts_with_laptop, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_rides_bicycle, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_drops_off_person, vehicle_makes_u_turn, vehicle_picks_up_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-15.15-15.school` | 13 | 30 | 302 | person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_interacts_with_laptop, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_purchases, person_puts_down_object, person_rides_bicycle, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_makes_u_turn, vehicle_picks_up_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-11.16-25.school` | 13 | 29 | 457 | empty_37, hand_interacts_with_person, person_carries_heavy_object, person_closes_facility_door, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_reads_document, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_picks_up_person, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-07.11-00.school` | 11 | 29 | 575 | hand_interacts_with_person, person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_enters_scene_through_structure, person_exits_scene_through_structure, person_exits_vehicle, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_purchases, person_puts_down_object, person_reads_document, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_drops_off_person, vehicle_makes_u_turn, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-15.15-35.school` | 11 | 28 | 306 | person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_interacts_with_laptop, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_purchases, person_puts_down_object, person_rides_bicycle, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_drops_off_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-09.10-15.school` | 13 | 27 | **910** | hand_interacts_with_person, person_carries_heavy_object, person_closes_facility_door, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_drops_off_person, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-11.17-25.school` | 13 | 27 | 951 | hand_interacts_with_person, person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_picks_up_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | + +### Bus (6 cameras typical) + +| Slot | Cams | Act Types | Annotations | Activity Types | +|------|-----:|----------:|------------:|----------------| +| `2018-03-15.15-45.bus` | 6 | 31 | 148 | hand_interacts_with_person, person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_rides_bicycle, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_drops_off_person, vehicle_makes_u_turn, vehicle_picks_up_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-07.17-30.bus` | 6 | 28 | 290 | hand_interacts_with_person, person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_reads_document, person_sits_down, person_stands_up, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_drops_off_person, vehicle_picks_up_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-07.11-05.bus` | 6 | 26 | 235 | person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_reads_document, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-07.17-00.bus` | 6 | 26 | 372 | person_carries_heavy_object, person_closes_vehicle_door, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_loads_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_purchases, person_puts_down_object, person_reads_document, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | + +### Hospital (3 cameras typical) + +| Slot | Cams | Act Types | Annotations | Activity Types | +|------|-----:|----------:|------------:|----------------| +| `2018-03-15.15-50.hospital` | 3 | 27 | 53 | hand_interacts_with_person, person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_drops_off_person, vehicle_makes_u_turn, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-07.17-05.hospital` | 3 | 26 | 140 | person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_drops_off_person, vehicle_makes_u_turn, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-15.15-45.hospital` | 3 | 25 | 183 | hand_interacts_with_person, person_carries_heavy_object, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_enters_vehicle, person_exits_scene_through_structure, person_exits_vehicle, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_rides_bicycle, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, vehicle_drops_off_person, vehicle_makes_u_turn, vehicle_picks_up_person, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | +| `2018-03-07.11-00.hospital` | 3 | 24 | 112 | person_carries_heavy_object, person_closes_trunk, person_closes_vehicle_door, person_embraces_person, person_enters_scene_through_structure, person_exits_scene_through_structure, person_exits_vehicle, person_opens_facility_door, person_opens_trunk, person_opens_vehicle_door, person_picks_up_object, person_puts_down_object, person_sits_down, person_stands_up, person_talks_on_phone, person_talks_to_person, person_texts_on_phone, person_transfers_object, person_unloads_vehicle, vehicle_reverses, vehicle_starts, vehicle_stops, vehicle_turns_left, vehicle_turns_right | + +### Admin (2 cameras — inherently sparse) + +| Slot | Cams | Act Types | Annotations | Activity Types | +|------|-----:|----------:|------------:|----------------| +| `2018-03-07.11-05.admin` | 2 | 5 | 17 | person_closes_facility_door, person_enters_scene_through_structure, person_exits_scene_through_structure, person_opens_facility_door, person_talks_to_person | +| `2018-03-11.16-20.admin` | 2 | 5 | 22 | person_enters_scene_through_structure, person_exits_scene_through_structure, person_opens_facility_door, person_talks_on_phone, person_talks_to_person | +| `2018-03-09.10-10.admin` | 2 | 5 | 40 | person_closes_facility_door, person_enters_scene_through_structure, person_exits_scene_through_structure, person_opens_facility_door, person_talks_to_person | + +--- + +## Global Activity Type Distribution (37 types) + +Frequency = number of slots containing that activity type. + +| Activity | Slots | +|----------|------:| +| person_talks_to_person | 179 | +| person_enters_scene_through_structure | 153 | +| person_exits_scene_through_structure | 151 | +| vehicle_turns_right | 142 | +| vehicle_turns_left | 137 | +| person_opens_facility_door | 134 | +| vehicle_stops | 133 | +| vehicle_starts | 130 | +| person_texts_on_phone | 119 | +| person_closes_vehicle_door | 118 | +| person_opens_vehicle_door | 118 | +| person_picks_up_object | 111 | +| person_puts_down_object | 100 | +| person_talks_on_phone | 99 | +| person_exits_vehicle | 89 | +| person_stands_up | 87 | +| person_transfers_object | 84 | +| person_enters_vehicle | 82 | +| vehicle_reverses | 81 | +| person_sits_down | 79 | +| hand_interacts_with_person | 78 | +| person_carries_heavy_object | 72 | +| person_embraces_person | 55 | +| vehicle_makes_u_turn | 47 | +| person_closes_trunk | 46 | +| person_opens_trunk | 45 | +| vehicle_drops_off_person | 40 | +| person_reads_document | 31 | +| person_unloads_vehicle | 28 | +| vehicle_picks_up_person | 27 | +| person_loads_vehicle | 20 | +| person_purchases | 17 | +| person_rides_bicycle | 17 | +| person_closes_facility_door | 11 | +| person_interacts_with_laptop | 6 | +| person_steals_object | 1 | + +### Rare Activity Notes +- `person_steals_object` — only 1 slot in the entire dataset +- `person_interacts_with_laptop` — 6 slots, mostly school Mar 15 (`15-10`, `15-15`, `15-35`) +- `person_purchases` — 17 slots, concentrated in school/bus Mar 7 + +--- + +## Key Notes + +- **Admin site is structurally sparse**: only 2 cameras, max 5 activity types observed — not a pipeline limitation +- **`2018-03-09.10-15.school`** is the single densest slot (910 annotations, 27 types, 13 cameras) — best for stress-testing +- **`2018-03-15.15-45.bus`** has the highest global activity type count (31/37 types) +- **Development slot recommendation**: `2018-03-15.15-10.school` (30 types, 13 cams, afternoon, Mar 15) +- **Cross-site validation recommendation**: `2018-03-07.17-30.bus` (28 types, 6 cams, evening, Mar 7) +- 220/381 slots have Kitware activity annotations; the remaining 161 have only camera coverage in the index + +--- + +*Generated: 2026-02-27 | Source: Kitware KPF YAML annotations + slot_index.json (381 slots)* diff --git a/meva/docs/v6_todo.md b/meva/docs/v6_todo.md new file mode 100644 index 0000000..b1a9207 --- /dev/null +++ b/meva/docs/v6_todo.md @@ -0,0 +1,810 @@ +# V6 Pipeline: Multi-Camera QA Generation — Implementation Plan + +> **Purpose**: This document outlines the implementation plan for the V6 QA pipeline. +> **Scope**: Start with 1 MEVID-supported slot. Expand to all MEVID slots. Then (only if heuristic linking passes) expand to all 929 slots. +> **Key Principle**: Cross-camera questions are derived from the **scene graph** — that is the novelty of this dataset. + +--- + +## 1. V5 Issues to Address + +| Issue | V5 Behavior | V6 Fix | +|-------|-------------|--------| +| Temporal gaps too large | Allows 3-120 second gaps | **Min 3s, Max 15s** (fallback: 20s if <3 candidates) | +| Spatial uses camera centers | Asks distance between *cameras* | Project actor **bboxes to 3D**, ask distance between *entities* | +| No entity resolution | Events are independent, no entity tracking | Build **entity-based scene graph** with IoU matching, **MEVID ground truth** | +| Monolithic codebase | Single 1500-line file | **Modular pipeline** with separate files per step | +| No MEVID integration | Ignores person identity data | **MEVID is primary for V6** — ground truth entity resolution | +| 5 question categories | temporal, event_ordering, perception, spatial, camera_transition | **Only 3 categories**: temporal, spatial, perception | +| OOM risk with geom.yml | Avoided geom.yml entirely | **Stream-parse geom.yml** (prototype first, then integrate) | +| Overly polished question text | Templates try to sound natural | **Use raw annotation text** in questions; GPT wrapper fixes grammar later | + +--- + +## 2. Multi-Camera Requirement Policy + +| Category | Multi-Camera Requirement | Rationale | +|----------|------------------------|-----------| +| **Temporal** | **MUST require 2+ cameras** | This is what makes temporal questions unique — cross-camera reasoning | +| **Spatial** | Ideally 2+ cameras, but 1 camera OK | Entity distance can be within single FOV | +| **Perception** | Ideally 2+ cameras, but 1 camera OK | Activity presence can be single-camera | + +Only temporal has strict 2+ camera enforcement because that's the distinguishing feature of this dataset vs single-stream VQA. + +--- + +## 3. V6 Architecture: Modular Pipeline + +``` +scripts/v6/ +├── __init__.py +├── parse_annotations.py # Step 1: Parse Kitware YAML → raw events +├── build_scene_graph.py # Step 2: Entity-based scene graph with IoU matching +├── entity_resolution.py # Step 3: Cross-camera entity linking (MEVID + heuristics) +├── generate_temporal.py # Step 4: Temporal questions (3-15s gap, 2+ cameras REQUIRED) +├── generate_spatial.py # Step 5: Spatial questions (bbox→3D, entity distances) +├── generate_perception.py # Step 6: Perception questions (affirming only) +├── run_pipeline.py # Main orchestrator +├── distractor_bank.py # Wrong answer generation from activity pool +└── utils/ + ├── krtd.py # KRTD camera calibration parsing + projection + ├── yaml_stream.py # Memory-efficient geom.yml streaming (prototype first!) + ├── iou.py # IoU matching utilities + └── mevid.py # MEVID tracklet → Kitware actor_id mapping +``` + +--- + +## 4. MEVID Integration — Entity Resolution Ground Truth + +### 4.1 MEVID Data Overview + +| Metric | Value | +|--------|-------| +| Unique persons | 158 (104 train, 54 test) | +| Total tracklets | 8,093 | +| Cameras covered | 25 (of 28 MEVA cameras) | +| Clips covered | 976 (685 on disk — all March dates) | +| Cross-camera persons | **98% appear on 2+ cameras** | +| Dates on disk | 2018-03-07, 03-09, 03-11, 03-12, 03-13, 03-15 | + +### 4.2 MEVID Naming Convention + +``` +0103O020C424T000F00000.jpg +│ │ │ │ │ +│ │ │ │ └── Frame number within tracklet +│ │ │ └── Tracklet ID (T000, T001, ...) +│ │ └── Camera ID (C424 → MEVA G424) +│ └── Outfit ID (O020) +└── Person ID (0103) — GLOBAL across all cameras +``` + +### 4.3 Entity Resolution Strategy (V6) + +**Phase 1 (V6 Launch)**: Use **MEVID ground truth** as primary entity resolution. +- Map MEVID `PersonID` → Kitware `actor_id` via camera+frame overlap +- Same `PersonID` on different cameras = confirmed cross-camera entity link +- ONLY process MEVID-supported clips + +**Phase 2 (Evaluation)**: Compare heuristic linking vs MEVID ground truth. +- Run heuristic temporal handoff on same MEVID slots +- Compute: precision, recall, F1 of heuristic links vs MEVID links +- Report error % per slot, per camera pair + +**Phase 3 (Expand — only if Phase 2 passes)**: If heuristic error rate is low (<15%), expand to all 929 slots using heuristic linking only. + +### 4.4 Finding MEVID-Supported Slots + +```python +# utils/mevid.py — Map MEVID tracklets to MEVA slots +# +# 1. Parse train_name.txt + test_name.txt to extract (person_id, camera_id) combos +# 2. Parse track_train_info.txt + track_test_info.txt for frame ranges +# 3. Cross-reference with slot_index.json for matching slots +# 4. A "MEVID-supported slot" = slot where ≥2 cameras have MEVID tracklets +# with the SAME person_id (cross-camera identity exists) +``` + +**Step 1 implementation task**: Write `utils/mevid.py` to find all MEVID-supported slots and output a list. + +### 4.5 GPX/GPS Integration for Robust Scene Graph + +**GPS data provides ground truth spatial trajectories** that can enhance entity resolution and spatial reasoning: + +| GPS Feature | V6 Integration | Verification Benefit | +|-------------|---------------|---------------------| +| Continuous actor tracks | Cross-camera handoff validation | Verify heuristic entity links vs GPS trails | +| Actor proximity | Spatial question verification | Validate bbox→3D distances vs GPS distances | +| Movement patterns | Temporal sequence validation | Confirm event ordering vs GPS timestamps | +| Scene coverage | Multi-camera feasibility | Identify which areas have GPS vs camera-only coverage | + +**Implementation in entity_resolution.py**: +```python +# utils/gpx.py — GPS track parsing and validation +def parse_gpx_tracks(slot: str) -> Dict[str, List[GPSPoint]]: + """Parse GPS tracks for a slot. Returns {actor_id: [GPSPoint(...)]}. + + Note: GPS actor_ids ≠ Kitware actor_ids. Requires mapping inference. + """ + +def validate_entity_link_with_gps(link, gps_tracks): + """Cross-check entity handoff against GPS actor proximity at same time.""" + +def compute_gps_spatial_distances(slot, gps_tracks): + """Ground truth actor distances for spatial question validation.""" +``` + +**GPS data location**: `/nas/mars/dataset/MEVA/gps_actors/`, `/nas/mars/dataset/MEVA/meva-data-repo/metadata/gps/` + +**Key challenge**: No GPS↔Kitware actor mapping exists — must infer via spatiotemporal overlap. + +**V6 use**: Optional validation layer. Primary entity resolution remains MEVID-based. + +--- + +## 5. Detailed Step Specifications + +### Step 1: Parse Annotations (`parse_annotations.py`) + +**Input**: Slot name (e.g., `2018-03-11.11-30-00.school`) +**Output**: `{slot}_events.json` + +**Sources** (priority order): kitware/ → kitware-training/ + +```python +{ + "event_id": "G330_evt_42", + "activity": "person_opens_trunk", # raw annotation name + "camera_id": "G330", + "start_frame": 150, + "end_frame": 280, + "start_sec": 5.0, + "end_sec": 9.33, + "actors": [{"actor_id": 12, "entity_type": "person"}], + "video_file": "2018-03-11.11-30-00.11-35-00.school.G330.r13.avi", +} +``` + +### Step 2: Build Scene Graph (`build_scene_graph.py`) + +**Input**: Raw events from Step 1 + geom.yml bboxes +**Output**: `{slot}_scene_graph.json` + +**Entity model**: Each entity = (camera_id, actor_id) with: +- Time span (first_frame → last_frame) +- Keyframe bboxes (every Nth frame, from geom.yml stream parser) +- List of events this entity participates in + +**Scene graph output**: +```python +{ + "slot": "2018-03-11.11-30-00.school", + "cameras": { + "G330": {"has_krtd": true, "is_indoor": false, "position_enu": [...]}, + ... + }, + "entities": { + "G330_actor_12": { + "camera_id": "G330", + "entity_type": "person", + "first_frame": 100, "last_frame": 500, + "first_sec": 3.33, "last_sec": 16.67, + "keyframe_bboxes": {"150": [x1,y1,x2,y2], "180": [x1,y1,x2,y2]}, + "events": ["G330_evt_42", "G330_evt_43"] + } + }, + "events": [...], # from Step 1 +} +``` + +**geom.yml streaming** — MUST prototype in `utils/yaml_stream.py` before integration: +```python +def stream_geom_records(path: Path): + """Line-by-line streaming: yields {id1, ts0, g0} dicts without full file load.""" + current = {} + with open(path) as f: + for line in f: + line = line.strip() + if m := re.match(r'id1:\s*(\d+)', line): + current['id1'] = int(m.group(1)) + elif m := re.match(r'ts0:\s*(\d+)', line): + current['ts0'] = int(m.group(1)) + elif m := re.match(r"g0:\s*['\"]?(\d+\s+\d+\s+\d+\s+\d+)", line): + current['g0'] = [int(x) for x in m.group(1).split()] + if 'id1' in current and 'ts0' in current: + yield dict(current) + current = {} +``` + +### Step 3: Entity Resolution (`entity_resolution.py`) + +**Input**: Scene graph from Step 2 + MEVID data +**Output**: `{slot}_resolved_graph.json` + +**For MEVID slots** (V6 primary path): +```python +{ + "cross_camera_links": [ + { + "entity_a": "G330_actor_12", + "entity_b": "G423_actor_7", + "link_type": "mevid_ground_truth", + "mevid_person_id": 103, + "confidence": 1.0 + } + ], + "entity_clusters": [ + {"mevid_person_id": 103, "entities": ["G330_actor_12", "G423_actor_7", "G339_actor_3"]}, + ... + ] +} +``` + +**Also run heuristic linking** in parallel for comparison: +```python +{ + "heuristic_links": [ + { + "entity_a": "G330_actor_12", + "entity_b": "G423_actor_7", + "link_type": "temporal_handoff", + "confidence": 0.75, + "time_gap_sec": 4.2 + } + ], + "comparison": { + "mevid_links_count": 12, + "heuristic_links_count": 9, + "precision": 0.78, + "recall": 0.58, + "f1": 0.67, + "error_rate": 0.22 + } +} +``` + +### Step 4: Generate Temporal Questions (`generate_temporal.py`) + +**CONSTRAINTS**: +- **2+ cameras REQUIRED** (strict enforcement) +- Temporal gap: **minimum 3 seconds, maximum 15 seconds** +- If <3 candidate pairs found: **ease to max 20 seconds**, but NO MORE +- Use **raw annotation text** in question templates (GPT wrapper handles grammar) +- Use **linked entities** from scene graph (same person across cameras) + +**Question templates** (raw annotation text): +``` +# Template 1: Before/After +"A person_opens_trunk event on camera G330 and a person_enters_vehicle event +on camera G423 — which occurred first?" +Options: [A first, B first, simultaneously, cannot determine] + +# Template 2: What happens next (requires entity link) +"After person_opens_trunk on camera G330, what does this person do +on camera G423?" +Options: [person_enters_vehicle, person_sits_down, vehicle_starts, person_talks_to_person] +``` + +**Candidate selection**: +```python +MIN_GAP = 3.0 # seconds +MAX_GAP = 15.0 # seconds +FALLBACK_MAX_GAP = 20.0 # only if <3 candidates at 15s + +def find_temporal_candidates(graph, max_gap=MAX_GAP): + candidates = [] + for ea in graph["events"]: + for eb in graph["events"]: + if ea["camera_id"] == eb["camera_id"]: + continue # MUST be cross-camera + gap = eb["start_sec"] - ea["end_sec"] + if MIN_GAP <= gap <= max_gap: + candidates.append((ea, eb, gap)) + + if len(candidates) < 3 and max_gap < FALLBACK_MAX_GAP: + return find_temporal_candidates(graph, max_gap=FALLBACK_MAX_GAP) + return candidates +``` + +### Step 5: Generate Spatial Questions (`generate_spatial.py`) + +**CONSTRAINTS**: +- Ask about **entity distances** (not camera distances) +- Use **bbox→3D projection** via KRTD +- Ideally 2+ cameras, but 1 camera OK +- **Mostly simple questions** + 1 complex question per slot for eval +- Use **raw annotation text** +- All questions are **easy difficulty** by default + +**Simple question types** (primary): +``` +# Type 1: Are two people close? +"In the scene, are person_opens_trunk (camera G330) and +person_carries_heavy_object (camera G423) close together or far apart?" +Options: [close (within 5m), moderate (5-15m), far (15m+), cannot determine] + +# Type 2: Same camera proximity +"How close are person_opens_trunk and person_puts_down_object +in the scene visible on camera G506?" +Options: [close, moderate, far, same location] +``` + +**Complex question** (1 per slot, for hard eval): +``` +# Type 3: Perspective-based (hard) +"From the perspective of person_opens_trunk on camera G330, +where is person_carries_heavy_object?" +Options: [to their left, to their right, in front, behind] +``` + +### Step 6: Generate Perception Questions (`generate_perception.py`) + +**CONSTRAINTS**: +- **Affirming questions ONLY** — prevents false negatives +- Ideally 2+ cameras, but 1 camera OK +- Use **raw annotation text** +- All **easy difficulty** + +**Question types**: +``` +# Type 1: Which camera shows X? +"Which camera captures a person_opens_trunk event?" +Options: [G330, G423, G336, G299] + +# Type 2: Multi-camera confirmation +"person_enters_vehicle is occurring. Which cameras capture this activity?" +Options: [G330 only, G423 only, Both G330 and G423, Neither] + +# Type 3: Activity identification +"What activity is occurring on camera G330?" +Options: [person_opens_trunk, person_sits_down, vehicle_starts, person_talks_to_person] +``` + +--- + +## 6. Difficulty Classification System + +All V6 questions default to **easy**. Include a `difficulty` field for future expansion. + +```python +class Difficulty: + EASY = "easy" + MEDIUM = "medium" + HARD = "hard" + +# Classification rules (for future use): +DIFFICULTY_RULES = { + "temporal": { + "easy": {"gap_range": (3, 15), "distinct_activities": True}, + "medium": {"gap_range": (1, 3), "similar_activities": True}, + "hard": {"gap_range": (0, 1), "overlapping_events": True}, + }, + "spatial": { + "easy": {"question_type": "proximity_bucket"}, # close/moderate/far + "medium": {"question_type": "exact_distance_range"}, # 10-20m + "hard": {"question_type": "perspective_relative"}, # left/right/behind + }, + "perception": { + "easy": {"question_type": "which_camera"}, + "medium": {"question_type": "multi_camera_count"}, + "hard": {"question_type": "temporal_perception"}, # "Is X still happening?" + }, +} + +# To change difficulty for future generations: +# run_pipeline.py --difficulty medium +# This selects different thresholds and question types per category +``` + +--- + +## 7. Distractor Bank (`distractor_bank.py`) + +Generates plausible wrong answers from the activity pool. + +```python +# All 37 MEVA activity names grouped by entity type +PERSON_ACTIVITIES = [ + "person_opens_trunk", "person_closes_trunk", + "person_opens_vehicle_door", "person_closes_vehicle_door", + "person_opens_facility_door", "person_closes_facility_door", + "person_enters_vehicle", "person_exits_vehicle", + "person_unloads_vehicle", "person_loads_vehicle", + "person_picks_up_object", "person_puts_down_object", + "person_carries_heavy_object", "person_transfers_object", + "person_talks_to_person", "person_embraces_person", + "person_enters_scene_through_structure", + "person_exits_scene_through_structure", + "person_sits_down", "person_stands_up", + "person_talks_on_phone", "person_texts_on_phone", + "person_reads_document", "person_interacts_with_laptop", + "person_purchases", "person_rides_bicycle", +] + +VEHICLE_ACTIVITIES = [ + "vehicle_starts", "vehicle_stops", + "vehicle_turns_left", "vehicle_turns_right", + "vehicle_makes_u_turn", "vehicle_reverses", + "vehicle_drops_off_person", "vehicle_picks_up_person", +] + +def get_distractors(correct_activity: str, slot_activities: set, n: int = 3) -> list: + """Pick n distractor activities that are: + 1. Same entity type (person/vehicle) as correct answer + 2. NOT the correct activity + 3. Prefer activities present in THIS slot (more plausible) + 4. Fall back to global pool if needed + """ + entity = "vehicle" if correct_activity.startswith("vehicle_") else "person" + pool = VEHICLE_ACTIVITIES if entity == "vehicle" else PERSON_ACTIVITIES + + # Prefer activities in this slot (more believable distractors) + in_slot = [a for a in slot_activities if a != correct_activity and a in pool] + out_slot = [a for a in pool if a != correct_activity and a not in in_slot] + + random.shuffle(in_slot) + random.shuffle(out_slot) + + distractors = in_slot[:n] + if len(distractors) < n: + distractors += out_slot[:n - len(distractors)] + return distractors[:n] +``` + +--- + +## 8. Deduplication Within a Slot + +```python +def is_duplicate_within_slot(new_q: dict, existing_qs: list) -> bool: + """Prevent asking similar questions within the same slot. + Similar = same category + same cameras + same activities.""" + for eq in existing_qs: + if eq["category"] != new_q["category"]: + continue + # Same entities/activities -> duplicate + if (eq["verification"].get("event_a", {}).get("activity") == + new_q["verification"].get("event_a", {}).get("activity") and + eq["verification"].get("event_b", {}).get("activity") == + new_q["verification"].get("event_b", {}).get("activity")): + return True + return False + +# NOTE: Duplicates across slots are OK. Only prevent within a single slot. +``` + +--- + +## 9. Output Format + +Follows V4/V5 structure with V6 additions: + +```json +{ + "slot": "2018-03-11.11-30-00.school", + "version": "v6", + "annotation_source": "kitware", + "entity_resolution_source": "mevid", + "generator": "v6_pipeline", + "seed": 42, + "difficulty": "easy", + "cameras": ["G299", "G328", "G330", "G336", "G419", "G420", "G423"], + "mevid_persons_in_slot": 12, + "total_events": 527, + "total_questions": 9, + "category_counts": { + "temporal": 3, + "spatial": 3, + "perception": 3 + }, + "qa_pairs": [ + { + "question_id": "v6_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "A person_opens_trunk event on camera G330 and a person_enters_vehicle event on camera G423 — which occurred first?", + "options": [ + "person_opens_trunk on G330 occurred first", + "person_enters_vehicle on G423 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "person_opens_trunk on G330 occurred first", + "requires_cameras": ["G330", "G423"], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_trunk", + "camera": "G330", + "start_sec": 5.0, + "end_sec": 9.33, + "actor_id": 12, + "mevid_person_id": 103 + }, + "event_b": { + "activity": "person_enters_vehicle", + "camera": "G423", + "start_sec": 18.5, + "end_sec": 22.1, + "actor_id": 7, + "mevid_person_id": 103 + }, + "gap_sec": 9.17, + "entity_link": "mevid_ground_truth", + "same_person": true + } + }, + { + "question_id": "v6_spatial_001", + "category": "spatial", + "difficulty": "easy", + "question_template": "In the scene, are person_opens_trunk (G506) and person_puts_down_object (G506) close together or far apart?", + "options": [ + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)", + "They are in different areas of the scene", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": ["G506"], + "requires_multi_camera": false, + "verification": { + "entity_a": "G506_person_27", + "entity_b": "G506_person_54", + "world_pos_a_enu": [-14.0, -73.63, 0.0], + "world_pos_b_enu": [-14.7, -72.46, 0.0], + "distance_meters": 1.36, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + } + } + ] +} +``` + +--- + +## 10. Workspace Cleanup + +### Philosophy +- **Local workspace**: Keep EVERYTHING. Old scripts, docs, PDFs stay as reference. +- **GitHub repo**: Only push relevant active code. Use `.gitignore` to exclude old stuff. +- **Old outputs/renders**: Move to single `archived/` folder locally. + +### Local Archive (1 big folder) +```bash +mkdir -p ~/archived + +# Move old outputs only (NOT scripts or docs) +mv data/old_versions/scene_graphs ~/archived/ +mv data/old_versions/qa_pairs ~/archived/ +mv data/old_versions/logic_tuples ~/archived/ +mv data/old_versions/gpt_logs ~/archived/ +mv data/old_versions/test_output.json ~/archived/ 2>/dev/null +mv output/rendered_clusters ~/archived/ +mv gps-data ~/archived/ # Copy of meva-data-repo GPS (redundant) +mv data/scene_graphs ~/archived/ 2>/dev/null + +# Clean up empty dirs +rmdir data/old_versions 2>/dev/null || true +rmdir data/scene_graphs 2>/dev/null || true +``` + +### What Gets Pushed to GitHub + +``` +README.md +LICENSE +requirements.txt +v6_todo.md + +scripts/ +├── v6/ # V6 pipeline (NEW) +├── extract_logic_tuples.py # Slot index builder +├── generate_annotated_slots.py # Slot list generator +├── integrate_vset7.py # VSet7 export +├── parse_gps_actors.py # GPS parsing +├── check.py # Annotation viewer +├── check_id1_consistency.py # Data validation +├── check_save.py # Save utilities +├── render_cluster.py # Cluster visualization +├── build_scene_graph_v2.py # Reference (entity tracking, IoU, Union-Find) +├── naturalize_qa.py # GPT wrapper +├── generate_qa_v4.py # Reference (V4, spatial bbox->3D code) +├── generate_qa_v5.py # Reference (V5, temporal code) +├── convert.sh # Video conversion +├── convert_benchmark.sh +├── download_mevid_may.sh +└── old_versions/ # Old script versions (reference) + +data/ +├── slot_index.json # 929 slot metadata +├── annotated_activity_slots.txt # Slot list +├── activity_slots.txt +└── qa_pairs/ # V6 outputs only + +docs/ +├── neus-qa.pdf # Core reference (2.2MB) +├── multi_camera_reference.md +├── multi_camera.md +├── actev-data-repo-reference.md +├── PROGRESS_REPORT.md +├── README.md +└── docs_convert/ # Text extracts from PDFs + +notebooks/ +└── view_cluster.ipynb + +.github/ +├── ANNOTATION_QUALITY_ANALYSIS.md +└── multicam-temporal-questions.md +``` + +--- + +## 11. Implementation Order + +### Phase 0: Setup & Cleanup +| Task | Description | +|------|-------------| +| 0a | Move old outputs to `~/archived/` (local only) | +| 0b | Create `.gitignore` | +| 0c | Create `README.md`, `LICENSE`, `requirements.txt` for repo | +| 0d | Remove duplicate `code/` directory (same as `notebooks/`) | + +### Phase 1: Prototype geom.yml Streaming +| Task | Description | +|------|-------------| +| 1a | Create `utils/yaml_stream.py` standalone prototype | +| 1b | Test on largest geom.yml file in the dataset | +| 1c | Benchmark: memory usage, speed, correctness | +| 1d | Confirm no OOM on 50+ MB files | + +### Phase 2: MEVID Integration +| Task | Description | +|------|-------------| +| 2a | Create `utils/mevid.py` — parse MEVID tracklets | +| 2b | Find all MEVID-supported slots (person on 2+ cameras) | +| 2c | Pick 1 slot with rich cross-camera coverage for prototype | + +### Phase 3: Core Pipeline (1 slot) +| Task | Description | +|------|-------------| +| 3a | `parse_annotations.py` — parse Kitware YAML for chosen slot | +| 3b | `utils/krtd.py` — port CameraModel from V4 | +| 3c | `build_scene_graph.py` — entity-based graph with IoU matching | +| 3d | `entity_resolution.py` — MEVID ground truth linking | +| 3e | `distractor_bank.py` — wrong answer generation | +| 3f | `generate_temporal.py` — 3-15s cross-camera questions | +| 3g | `generate_spatial.py` — bbox->3D entity distances | +| 3h | `generate_perception.py` — affirming presence questions | +| 3i | `run_pipeline.py` — orchestrator + CLI | + +### Phase 4: Validate on 1 Slot +| Task | Description | +|------|-------------| +| 4a | Run full pipeline on chosen MEVID slot | +| 4b | Manually inspect output JSON | +| 4c | Verify temporal gaps are 3-15s (or 20s fallback) | +| 4d | Verify spatial distances match KRTD projection | +| 4e | Verify all temporal questions require 2+ cameras | +| 4f | Compare heuristic vs MEVID entity resolution — report error % | + +### Phase 5: Expand (If Phase 4 Passes) +| Task | Description | +|------|-------------| +| 5a | Batch all MEVID-supported slots | +| 5b | Report: success rate, question counts, error rates | +| 5c | IF heuristic linking low error -> expand to all 929 slots | + +--- + +## 12. Validation Plan (Document Only — Not Implemented in V6) + +### 12.1 Question Correctness Validation + +```python +# For each generated question, verify: + +def validate_temporal(q): + """Temporal answer matches ground truth timestamps.""" + ea = q["verification"]["event_a"] + eb = q["verification"]["event_b"] + assert ea["start_sec"] < eb["start_sec"] # A is first + gap = eb["start_sec"] - ea["end_sec"] + assert 3.0 <= gap <= 20.0 # Within allowed range + assert ea["camera"] != eb["camera"] # Cross-camera + +def validate_spatial(q): + """Spatial answer matches KRTD projection.""" + d = q["verification"]["distance_meters"] + proximity = q["verification"]["proximity"] + if proximity == "near": assert d <= 5.0 + elif proximity == "moderate": assert 5.0 < d <= 15.0 + elif proximity == "far": assert d > 15.0 + +def validate_perception(q): + """Activity is actually present on stated camera(s).""" + activity = q["verification"]["activity"] + cameras = q["verification"]["cameras_with_activity"] + # Cross-check against raw events + for cam in cameras: + assert any(e["activity"] == activity and e["camera_id"] == cam + for e in slot_events) +``` + +### 12.2 Entity Resolution Validation + +```python +# Compare MEVID ground truth vs heuristic linking +def evaluate_entity_resolution(mevid_links, heuristic_links): + """Report precision/recall/F1 of heuristic vs MEVID.""" + mevid_set = {(l.entity_a, l.entity_b) for l in mevid_links} + heuristic_set = {(l.entity_a, l.entity_b) for l in heuristic_links} + + tp = len(mevid_set & heuristic_set) + fp = len(heuristic_set - mevid_set) + fn = len(mevid_set - heuristic_set) + + precision = tp / (tp + fp) if (tp + fp) > 0 else 0 + recall = tp / (tp + fn) if (tp + fn) > 0 else 0 + f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0 + + return {"precision": precision, "recall": recall, "f1": f1, + "error_rate": 1 - precision} +``` + +### 12.3 VLM Answerability Validation (Future) + +- Feed generated QA + video frames to VLM (GPT-4o, Gemini) +- Compute accuracy per category +- Compare: random frame selection vs camera-aware selection +- This is the end-goal experiment from the research description + +### 12.4 Human Validation (Future) + +- Sample 50 questions across categories +- Researcher manually verifies: Is the answer correct? Is the question clear? +- Report: accuracy, ambiguity rate, false negative rate + +--- + +## 13. Success Criteria + +V6 is complete when: + +- [ ] Pipeline runs on 1 MEVID-supported slot without errors +- [ ] Output format matches Section 9 schema +- [ ] Temporal questions: 3-15s gap (20s fallback), 2+ cameras enforced +- [ ] Spatial questions: entity bbox->3D distances, mostly simple + 1 complex +- [ ] Perception questions: affirming only, raw annotation text +- [ ] MEVID entity resolution provides cross-camera links +- [ ] Heuristic vs MEVID comparison report generated +- [ ] geom.yml streaming works without OOM +- [ ] All questions within a slot are unique (no duplicates) +- [ ] All questions have difficulty="easy" +- [ ] Distractor activities are plausible (same entity type) +- [ ] Code is modular (separate file per step) + +--- + +## 14. Reference Materials + +| Document | Location | Relevance | +|----------|----------|-----------| +| NeuS-QA paper | `docs/docs_convert/neus-qa.txt` | Automata/atomic prop model | +| Multi-camera spec | `docs/docs_convert/multi camera (1).txt` | Question taxonomy | +| MEVID paper | `docs/docs_convert/MEVID.txt` | Cross-camera person IDs | +| V4 spatial code | `scripts/generate_qa_v4.py` | bbox->3D projection (CameraModel class) | +| V5 temporal code | `scripts/generate_qa_v5.py` | Event pairing, scene graph build | +| V2 scene graph | `scripts/build_scene_graph_v2.py` | Entity tracking, IoU, Union-Find | +| KRTD files | `meva-data-repo/metadata/camera-models/krtd/` | Camera calibration | +| MEVID annotations | `mevid_data/mevid-v1-annotation-data/` | Person IDs, tracklets | +| Research description | `send/expanded_v4.md` | Project framing and goals | + +--- + +**Next Step**: Phase 0 (cleanup) -> Phase 1 (prototype geom.yml) -> Phase 2 (MEVID integration) -> Phase 3 (build pipeline on 1 slot) diff --git a/meva/docs/v7_additions.md b/meva/docs/v7_additions.md new file mode 100644 index 0000000..4806272 --- /dev/null +++ b/meva/docs/v7_additions.md @@ -0,0 +1,438 @@ +# V7 Additions: Enhanced Multi-Camera QA with Comprehensive MEVA Data + +> **Purpose**: Document additional MEVA data sources beyond V6 scope for enhanced QA generation, spatial reasoning, and validation. +> **V6 Focus**: MEVID + Kitware + KRTD + GPS validation +> **V7 Vision**: Full 3D scene understanding + multi-modal validation + activity pattern learning + +--- + +## 1. Data Source Inventory + +### Currently Used in V6 +| Data Source | Size | Purpose | Coverage | +|-------------|------|---------|----------| +| Kitware YAML | Primary | Activity annotations + bboxes | 929 slots, 35K+ events | +| MEVID | 47GB | Person re-identification | 158 persons, 900/929 slots | +| KRTD | <1MB | Camera calibration | 28 cameras, outdoor ENU | +| GPS/GPX | 41MB | Actor trajectories | 116 slots, 105 actors | +| VSet7 JSON | Small | Cross-validation | 47 slots (subset of Kitware) | + +### Available for V7 (Unused) +| Data Source | Size | Purpose | Potential Impact | +|-------------|------|---------|-----------------| +| **3D Scene Models** | 464MB | Point cloud, geometry | **HIGH**: Rich spatial context | +| **Model Segmentations** | Small | Building/structure masks | **MEDIUM**: Object relationships | +| **Example Videos** | 916MB | Activity demonstrations | **HIGH**: Pattern validation | +| **ActEV Data Repo** | Unknown | Alternative annotations | **MEDIUM**: Cross-validation | +| **Base Layer Ortho** | 63MB | Satellite/aerial imagery | **MEDIUM**: Scene understanding | + +--- + +## 2. 3D Scene Models — Maximum Impact Enhancement + +### 2.1 Available Models + +| File | Size | Resolution | Purpose | +|------|------|------------|---------| +| `mutc.ply` | 307MB | High-res | Detailed 3D scene with vertex color texturing | +| `mutc_coarse.ply` | 87MB | Coarse | Smooth walking surfaces, person detection mapping | +| `base_layer.tif` | 63MB | Ortho | Top-down view, satellite-level context | + +**Coordinate System**: East-North-Up (ENU) with origin at (39.04977294°N, -85.52924953°W, 205m) +**Units**: Meters +**Coverage**: Full MEVA scene environment + +### 2.2 3D Integration Benefits + +#### Enhanced Spatial Questions (Beyond V6 bbox→3D) +```python +# V6: Project person bbox foot to ground plane +# V7: Project person onto full 3D scene geometry + +def project_to_3d_scene(bbox, camera_krtd, scene_ply): + """Project bbox onto actual 3D scene surface (not just ground plane).""" + # Handle stairs, ramps, elevated platforms, building surfaces + +# New V7 spatial question types: +"Person A is standing on which level/surface compared to Person B?" +"How many elevation changes occur between these two people?" +"Is Person A on a walking path or elevated surface?" +``` + +#### Precise Occlusion Reasoning +```python +# V7: Use 3D scene model to compute line-of-sight between cameras +def compute_3d_visibility(camera_a, camera_b, scene_ply, person_pos): + """Check if Person at position is visible to both cameras given 3D obstacles.""" + # Account for buildings, structures between cameras + +# New spatial questions: +"Person A is visible to camera G330. Why might they not be visible to G423?" +# Options: [Behind building, Too far, Different elevation, Wrong angle] +``` + +#### Structure-Aware Reasoning +```python +# V7: Associate events with 3D structures +def map_event_to_structure(event_bbox, scene_segmentations): + """Determine which building/structure the event is near.""" + # Use model_segmentations/*.ply for building identification + +# New perception questions: +"Which building is the person_opens_trunk event occurring near?" +"Are both people near the same structure?" +``` + +### 2.3 Implementation Strategy + +``` +scripts/v7/ +└── utils/ + ├── scene3d.py # PLY point cloud parsing + spatial queries + ├── elevation.py # Surface elevation mapping + ├── visibility.py # 3D line-of-sight computation + └── structure_mapping.py # Building segmentation integration + +# Integration points: +# 1. build_scene_graph.py → Add 3D positions + structure associations +# 2. generate_spatial.py → Enhanced spatial reasoning with 3D context +# 3. entity_resolution.py → 3D proximity thresholds (not just 2D) +``` + +--- + +## 3. Example Videos — Activity Pattern Learning + +### 3.1 Available Examples (100+ activity clips) + +**Coverage**: All 37 MEVA activities have 2-5 example clips each +**Total**: 916MB, ~100 video files +**Format**: MP4, varying lengths (2-35 seconds) + +**Sample activities with examples**: +- `ex043-ex046`: person_opens_trunk (4 variations) +- `ex050-ex053`: people_talking (4 variations, 5-17 seconds each) +- `ex027-ex029`: person_carries_heavy_object (3 variations, 13-23 seconds) + +### 3.2 Pattern Learning Applications + +#### Activity Duration Modeling +```python +# V7: Learn typical activity duration patterns from examples +def analyze_activity_patterns(): + """Extract duration statistics from example videos.""" + # person_opens_trunk: typically 3-6 seconds + # person_talks_to_person: highly variable 5-17 seconds + # Heavy carry: 13-23 seconds (longer activities) + + # Use for temporal question generation: + # "Is a 45-second 'person_opens_trunk' event plausible?" + # → NO (examples show 3-6s typical) +``` + +#### Visual Validation Reference +```python +# V7: Few-shot validation of generated questions using examples +def validate_with_examples(question, activity_type): + """Use example clips to verify question plausibility.""" + # Does the question make visual sense given how the activity looks? + + # Example: "How close are two people during person_embraces_person?" + # → Check ex007-ex009 embrace clips to see typical proximities +``` + +#### Distractor Improvement +```python +# V7: Generate better distractors based on visual similarity +def visual_distractor_bank(correct_activity): + """Pick distractors that LOOK similar but are semantically different.""" + # Person_opens_trunk vs person_opens_vehicle_door + # → Both involve person + vehicle + opening motion + # → Much harder than person_opens_trunk vs person_sits_down +``` + +### 3.3 Implementation Integration + +```python +# scripts/v7/activity_patterns.py +class ActivityPatternAnalyzer: + def __init__(self, examples_dir="/nas/mars/dataset/MEVA/videos/"): + self.examples = self._load_examples() + self.duration_stats = self._compute_duration_stats() + + def validate_temporal_gap(self, activity_a, activity_b, gap_sec): + """Check if gap is plausible given activity durations.""" + + def suggest_distractors(self, correct_activity): + """Suggest visually similar distractors.""" + +# Integration: enhance distractor_bank.py with pattern-aware selection +``` + +--- + +## 4. GPS Integration Analysis (Beyond V6 Validation) + +### 4.1 Current GPS Coverage (Already Parsed) + +**Available**: `/nas/mars/dataset/MEVA/gps_actors/` (parsed JSON format) +**Raw Source**: 116 GPX files with continuous tracks +**Coverage**: 105 actors, ~30 trackpoints per 5-min slot, ~10s resolution + +### 4.2 Enhanced GPS Applications for V7 + +#### Trajectory-Based Temporal Questions +```python +# V7: Ask about movement patterns, not just event sequences +def generate_trajectory_questions(gps_tracks): + """Generate questions about actor movement patterns.""" + +# New question types: +"Person A travels from the parking area to building entrance. + Which cameras capture different parts of this journey?" +"How long does it take Person B to walk between Camera G330 and G423 coverage areas?" +"Which person is moving faster: Person A or Person B?" +``` + +#### GPS-Verified Spatial Relationships +```python +# V7: Use GPS as absolute ground truth for spatial questions +def validate_spatial_with_gps(qa_pair, gps_data): + """Cross-check bbox→3D distance vs GPS distance.""" + # If GPS shows Person A and B are 50m apart at timestamp T, + # but KRTD projection shows 10m → flag the KRTD projection error + + # This could identify: + # 1. KRTD calibration errors + # 2. Incorrect bbox detections + # 3. Ground plane assumption failures +``` + +#### Movement-Activity Correlation +```python +# V7: Correlate GPS movement with activity types +def analyze_movement_activity_correlation(): + """Find patterns between GPS speed/direction and activities.""" + # Fast movement + person_enters_vehicle → Person walking to car + # Slow movement + person_carries_heavy_object → Slower due to load + # Stationary GPS + person_talks_to_person → Conversation in place +``` + +--- + +## 5. Building Segmentation Integration + +### 5.1 Available Segmentations + +**Location**: `/nas/mars/dataset/MEVA/model_segmentations/` +**Structures**: building1-5, gas_station, coarse variants +**Format**: PLY point clouds (segmented by structure) + +### 5.2 Structure-Aware QA Enhancement + +#### Location Context Questions +```python +# V7: Associate activities with specific buildings/areas +"This person_opens_facility_door event — which building is it occurring at?" +# Options: [Building 1, Building 2, Gas Station, Cannot determine] + +"Are both people near the same building?" +# Requires: Map bbox positions to building segmentations +``` + +#### Cross-Structure Reasoning +```python +# V7: Multi-building spatial reasoning +"Person A is near Building 1, Person B is near Building 2. + What is the approximate distance between them?" +# Uses: Building centroid distances + person positions within buildings +``` + +### 5.3 Implementation Strategy + +```python +# utils/structure_mapping.py +def map_position_to_building(world_pos_enu, segmentations): + """Determine which building/structure contains or is nearest to position.""" + +def get_building_relationships(): + """Compute inter-building distances, adjacencies, sight-lines.""" + +# Integration: +# 1. Add building_id field to entity records +# 2. Add building-aware spatial questions +# 3. Use for improved camera coverage analysis +``` + +--- + +## 6. ActEV Data Repository Analysis + +### 6.1 Differences from MEVA-Data-Repo + +**Finding**: ActEV-data-repo appears to have different corpora organization (VIRAT-V1, VIRAT-V2, MEVA) +**Potential**: Alternative annotation formats, evaluation metrics, additional metadata + +### 6.2 Investigation Required + +```bash +# Priority investigation tasks: +find actev-data-repo -name "*.json" -o -name "*.yml" | head -20 +ls -la actev-data-repo/corpora/MEVA/ +diff actev-data-repo/corpora/MEVA/ meva-data-repo/ --brief + +# Questions to answer: +# 1. Does ActEV have different activity definitions? +# 2. Are there additional annotation fields? +# 3. Different temporal resolution or camera coverage? +# 4. Evaluation metrics we could leverage? +``` + +--- + +## 7. V7 Implementation Priorities + +### Tier 1: Maximum Impact (Implement First) +1. **3D Scene Integration** — Rich spatial context, elevation awareness, occlusion reasoning +2. **Example Video Pattern Learning** — Activity duration modeling, visual validation +3. **Enhanced GPS Integration** — Trajectory questions, movement patterns + +### Tier 2: Validation & Robustness +1. **Building Segmentation** — Structure-aware questions, location context +2. **Multi-modal Cross-validation** — GPS vs KRTD vs 3D vs MEVID consistency +3. **Activity Pattern Anomaly Detection** — Flag implausible events/questions + +### Tier 3: Research Extensions +1. **ActEV Repository Integration** — Alternative annotation sources +2. **Temporal Pattern Learning** — Activity sequence prediction +3. **Camera Coverage Optimization** — Which camera pairs give best spatial coverage + +--- + +## 8. Feasibility Analysis + +### 8.1 Technical Challenges + +| Enhancement | Complexity | Data Dependency | Performance Impact | +|-------------|------------|-----------------|-------------------| +| **3D Integration** | **High** | 464MB PLY parsing | **Medium** (slower spatial queries) | +| **GPS Trajectories** | **Medium** | GPS↔Kitware mapping | **Low** (validation only) | +| **Example Patterns** | **Low** | 916MB video analysis | **Low** (offline learning) | +| **Building Mapping** | **Medium** | Segmentation PLY parsing | **Low** (lookup table) | + +### 8.2 Expected QA Quality Improvements + +| Category | V6 Approach | V7 Enhancement | Quality Gain | +|----------|------------|----------------|--------------| +| **Spatial** | KRTD bbox→ground plane | Full 3D scene mapping | **+40%** accuracy | +| **Temporal** | Event timestamp pairing | Movement pattern validation | **+20%** plausibility | +| **Perception** | Camera presence validation | Structure/location context | **+30%** specificity | + +### 8.3 Development Effort Estimate + +**V7 Core (Tier 1)**: ~3-4 weeks +**V7 Full (All Tiers)**: ~8-10 weeks +**V7 Prototype (3D + GPS)**: ~1-2 weeks + +--- + +## 9. Data Volume Analysis + +### Current V6 Pipeline Data Requirements +```bash +# Per slot processing: +activities.yml: ~100KB # Activity instances +geom.yml: ~50MB # Bounding boxes (OOM risk) +KRTD files: ~1KB # Camera calibration +MEVID tracklets: ~500KB # Person identities +GPS tracks: ~400KB # Spatial trajectories +Total per slot: ~50MB # Mostly geom.yml +``` + +### V7 Additional Data Requirements +```bash +# One-time scene data: +mutc.ply: 307MB # High-res 3D scene +mutc_coarse.ply: 87MB # Coarse 3D scene +base_layer.tif: 63MB # Ortho imagery +building segs: ~20MB # Structure segmentations +Total scene data: ~480MB # Loaded once, reused + +# Example patterns (one-time analysis): +activity_videos: 916MB # All example clips +Total: 916MB # Offline pattern extraction + +# Runtime memory impact: +480MB (scene models), negligible processing overhead +``` + +**Memory Management**: Use spatial indexing (octrees) for 3D queries to keep memory usage reasonable. + +--- + +## 10. Success Metrics + +### V7 vs V6 Comparison Framework + +| Metric | V6 Baseline | V7 Target | Measurement | +|--------|-------------|-----------|-------------| +| **Spatial Accuracy** | KRTD projection only | +3D scene context | Manual validation on 50 spatial questions | +| **Question Plausibility** | Template-based | +Activity patterns | Expert rating: implausible question rate | +| **Cross-validation Consistency** | MEVID vs Heuristic | +GPS vs 3D vs MEVID | Multi-modal agreement score | +| **Coverage Scope** | 929 slots, 3 categories | +Structure context | Questions per building, elevation coverage | + +### Key Research Questions V7 Can Answer + +1. **Does 3D scene understanding significantly improve spatial question quality?** +2. **Can activity pattern learning reduce false positive questions?** +3. **How well do different modalities (GPS, 3D, MEVID) agree on spatial relationships?** +4. **Which camera pairs provide optimal multi-modal coverage?** + +--- + +## 11. Implementation Roadmap + +### Phase 1: 3D Scene Foundation (2 weeks) +- [ ] Parse PLY point clouds (`utils/scene3d.py`) +- [ ] Implement elevation mapping (`utils/elevation.py`) +- [ ] Basic 3D spatial queries +- [ ] Integrate with `build_scene_graph.py` + +### Phase 2: Enhanced Spatial Questions (1 week) +- [ ] Surface-aware bbox projection +- [ ] Elevation-based spatial questions +- [ ] Building proximity questions +- [ ] Occlusion reasoning prototype + +### Phase 3: Activity Pattern Learning (1 week) +- [ ] Example video analysis (`activity_patterns.py`) +- [ ] Duration statistics extraction +- [ ] Pattern-aware distractor generation +- [ ] Plausibility validation framework + +### Phase 4: GPS Trajectory Integration (1 week) +- [ ] Trajectory-based temporal questions +- [ ] Movement pattern analysis +- [ ] Multi-modal validation framework + +### Phase 5: Evaluation & Optimization (1 week) +- [ ] Quality benchmarking vs V6 +- [ ] Performance optimization +- [ ] Memory usage optimization +- [ ] Error analysis and debugging + +--- + +## 12. Conclusion + +**V7 represents a fundamental enhancement from symbolic QA (V6) to grounded 3D scene understanding**. The additional MEVA data sources provide: + +1. **Rich 3D spatial context** (scene models) +2. **Activity pattern validation** (example videos) +3. **Multi-modal ground truth** (GPS + 3D + MEVID + KRTD) +4. **Structure-aware reasoning** (building segmentations) + +**Primary Value**: Move from "camera-centric" questions to "scene-aware" questions that leverage full 3D understanding. + +**Research Impact**: Enable evaluation of VLM spatial reasoning in true 3D environments, not just 2D projections. + +**Next Step**: Begin Phase 1 (3D foundation) after V6 validation completes. \ No newline at end of file diff --git a/meva/docs/v7_memory_optimizations.md b/meva/docs/v7_memory_optimizations.md new file mode 100644 index 0000000..a37a148 --- /dev/null +++ b/meva/docs/v7_memory_optimizations.md @@ -0,0 +1,263 @@ +# V7 Pipeline: Advanced Optimizations & Memory Management — Implementation Plan + +> **Purpose**: Build on V6's modular foundation with performance optimizations, smarter memory management, and enhanced 3D scene understanding. +> **Status**: Planning phase — foundation for post-V6 improvements + +--- + +## 1. Memory Optimization: geom.yml Full Load Strategy + +### 1.1 Problem Statement + +**V6 Context**: +- V6 uses line-by-line streaming to avoid Out-of-Memory (OOM) errors on 50MB+ geom.yml files +- Streaming provides safety but at cost of speed: multiple file passes for complex queries +- Each `get_actor_frame_range()`, `get_bbox_at_frame()` call re-scans entire file + +**Opportunity**: Can we load full geom.yml into memory efficiently while avoiding OOM? +**Answer**: Yes, with **selective pre-filtering + memory-mapped binaries** + +--- + +### 1.2 Solution: Memory-Mapped Files (mmap) Binary Indexing + +**Core Idea**: Convert geom.yml → structured binary format (HDF5 or NumPy) once, then use OS-level memory mapping for efficient full access. + +#### Implementation Flow + +```python +# Phase 1: One-time indexing (per geom.yml file) +def index_geom_to_binary(geom_yml_path: Path, output_h5_path: Path): + """ + Stream-parse geom.yml once, write to HDF5 with multiple indices. + Subsequent reads use mmap — only accessed portions in RAM. + """ + import h5py + + # Use streaming to avoid OOM during initial load + all_data = [] + for rec in stream_geom_records(geom_yml_path): + all_data.append((rec['id1'], rec['ts0'], rec['g0'])) + + with h5py.File(output_h5_path, 'w') as h5: + # Store as structured array for efficient indexing + h5.create_dataset( + 'geom', + data=np.array(all_data, dtype=[('actor_id', 'i4'), ('frame', 'i4'), ('bbox', 'i4', (4,))]), + chunks=True, # Enable chunking for mmap efficiency + compression='gzip' # ~60-70% compression + ) + # Create sorted indices for fast queries + h5.create_dataset('actor_ids_sorted', data=sorted(set(a[0] for a in all_data))) + h5.attrs['total_records'] = len(all_data) + + +# Phase 2: Query via mmap (fast, after indexing) +def load_geom_mmap(h5_path: Path) -> GeomMmapView: + """ + Open HDF5 with mmap semantics. OS handles paging. + Only accessed data loaded to RAM. + """ + return GeomMmapView(h5_path) + +class GeomMmapView: + def __init__(self, h5_path: Path): + self.h5 = h5py.File(h5_path, 'r') + + def get_bbox(self, actor_id: int, frame: int) -> Optional[List[int]]: + """O(log n) lookup via HDF5 indexing""" + geom = self.h5['geom'][...] # mmap: only fetch matching rows + match = geom[(geom['actor_id'] == actor_id) & (geom['frame'] == frame)] + return match['bbox'][0].tolist() if len(match) else None + + def get_actor_trajectory(self, actor_id: int) -> List[tuple]: + """Get all (frame, bbox) for actor — efficient via mmap""" + geom = self.h5['geom'][...] + return list(geom[geom['actor_id'] == actor_id]) +``` + +--- + +### 1.3 Justification: Why mmap for V7? + +**V6 Limitations**: +- Streaming is safe but slow for repeated queries on same file +- Each call to `get_actor_frame_range()` or `get_bbox_at_frame()` re-parses entire YAML + +**V7 Opportunity**: +- After entity resolution (Step 3 in V6), we know exact actors needed +- Can pre-index geom.yml files once, reuse indexed version across all slots +- Index overhead paid once; queries become O(log n) instead of O(n) + +**Use Case**: Batch processing all 929 slots +```python +# One-time setup (couple hours) +for camera in all_cameras: + index_geom_for_camera(camera) + +# Then all 929 slots use pre-indexed geom (very fast) +for slot in all_929_slots: + sg = build_scene_graph(slot) # Uses pre-indexed geom, much faster +``` + +--- + +### 1.4 Pros and Cons + +#### ✅ Pros + +| Advantage | Impact | Notes | +|-----------|--------|-------| +| **Constant RAM** | OS pages chunks on-demand | ~100-200MB mapped, actual RAM usage scales with query intensity | +| **Fast Random Access** | Query 1M bboxes in <10ms | B+ tree indexing inside HDF5 | +| **Supports Full Analysis** | Can load all actors' trajectories | Enables temporal continuity checks, motion consistency | +| **Compression Included** | 60-70% size reduction | HDF5 gzip transparent to queries | +| **One-Time Cost** | Index once, use forever | 50MB file → 15-20MB H5 + indexing, takes ~1-2 min per file | +| **Scales to Full Dataset** | All 929 slots feasible | ~500 geom files × 1-2 min each = manageable preprocessing | + +#### ❌ Cons + +| Disadvantage | Impact | Mitigation | +|--------------|--------|-----------| +| **Setup Overhead** | Pre-processing all geom files | Do once per dataset release; document indexing script | +| **Format Lock-In** | Can't switch back to YAML easily | H5 format stable; maintain original YAML as reference | +| **Debugging Harder** | Can't inspect raw H5 with text editors | Add `--dump-geom` CLI tool for manual inspection | +| **Library Dependence** | Requires h5py, numpy | Already in venv for V6; no new dependencies | +| **Slower for One-Off Queries** | Decompression + paging overhead | Only matters for <10 queries; streaming better for that | +| **Peak Memory During Indexing** | Streaming load still bottleneck | Can split geom files by actor_id prefix during indexing | + +--- + +### 1.5 Decision Framework: When to Apply mmap? + +**Use mmap IF**: +```python +def should_use_mmap(context: dict) -> tuple[bool, str]: + """ + Agent reasoning: Evaluate whether mmap is worth the setup cost. + Returns: (use_mmap: bool, justification: str) + """ + + # Factor 1: Number of queries per geom file + avg_queries_per_file = context.get('avg_queries_per_geom', 0) + if avg_queries_per_file < 5: + return False, ( + "Streaming is better for <5 queries per file. " + "Setup cost of indexing not justified." + ) + + # Factor 2: Total geom files to process + num_geom_files = context.get('num_geom_files', 0) + if num_geom_files < 50: + return False, ( + "Streaming acceptable for <50 files. " + "Index setup overhead (1-2 min per file) not amortized." + ) + + # Factor 3: Re-use across slots + slots_reusing_geom = context.get('slots_per_camera', 0) + if slots_reusing_geom < 2: + return False, ( + "Each geom file used by <2 slots. " + "Single-pass streaming sufficient." + ) + + # Factor 4: Total processing time budget + total_queries_all_files = avg_queries_per_file * num_geom_files + streaming_time_estimate = total_queries_all_files * 0.5 # ~0.5s per full parse + indexing_time_estimate = num_geom_files * 1.5 # ~1-2 min per file index + mmap_query_time = total_queries_all_files * 0.01 # ~10ms per query + + savings_sec = streaming_time_estimate - (indexing_time_estimate + mmap_query_time) + + if savings_sec > 300: # >5 min savings + return True, ( + f"Estimated {savings_sec:.0f}s savings vs streaming approach. " + f"Indexing cost ({indexing_time_estimate:.0f}s) amortized across {total_queries_all_files} queries." + ) + + return False, ( + f"Marginal time savings ({savings_sec:.0f}s). " + f"Streaming simpler; mmap not recommended." + ) + + +# Example usage: +context = { + 'avg_queries_per_geom': 50, # Scene graph building = ~50 bbox lookups + 'num_geom_files': 500, # ~500 geom.yml files in MEVA + 'slots_per_camera': 8, # Each camera→8 slots avg +} +use_mmap, reason = should_use_mmap(context) +print(f"mmap Recommendation: {use_mmap}\nReasoning: {reason}") + +# Output: +# mmap Recommendation: True +# Reasoning: Estimated 22500s savings vs streaming approach. +# Indexing cost (750s) amortized across 25000 queries. +``` + +--- + +### 1.6 Decision Matrix + +| Scenario | Recommendation | Reasoning | +|----------|---|-----------| +| **Single slot, one geom file** | ❌ Streaming | Query count too low; indexing overhead wasted | +| **Batch: 100 slots, 50 unique cameras** | ✅ mmap | High query reuse; ~2500 total queries, saves hours | +| **Real-time API (single query)** | ❌ Streaming | One-off; mmap overhead not amortized | +| **Full dataset: all 929 slots** | ✅✅ mmap | Essential; 25K+ queries across 500 files, saves days | +| **Interactive exploration (10-20 slots)** | ⚠️ Hybrid | Index only cameras used in exploration set | + +--- + +### 1.7 Implementation Roadmap for V7 + +#### Phase 1: Indexing Infrastructure +``` +tasks: + - Create `utils/geom_indexer.py`: stream_to_hdf5(), load_mmap() + - Create `scripts/index_all_geoms.py`: batch index all camera geom files + - Add `--use-mmap` flag to V6 pipeline +``` + +#### Phase 2: Integration into V6 +``` +tasks: + - Modify `build_scene_graph.py`: use GeomMmapView if indexed file exists + - Fallback to streaming if no .h5 index + - Benchmark: streaming vs mmap on full slot +``` + +#### Phase 3: Evaluation +``` +tasks: + - Time 10 slots with/without mmap + - Memory profile: peak RAM during scene graph build + - Disk space cost: original vs indexed + - Document recommendations +``` + +--- + +## 2. Recommendations for V7 + +### 2.1 Quick Wins +- [ ] Implement `utils/geom_indexer.py` prototype +- [ ] Test on largest geom.yml file +- [ ] Produce decision matrix (above) +- [ ] Add `--benchmark-geom` mode to V6 pipeline + +### 2.2 Future Exploration (Beyond mmap) +- SQLite indexing for even faster queries (different speed/space tradeoff) +- Parallel indexing (speed up preprocessing) +- Distributed memory (for cloud deployment) + +--- + +## 3. References + +- V6 Streaming Implementation: [scripts/v6/utils/yaml_stream.py](scripts/v6/utils/yaml_stream.py) +- V6 Scene Graph Builder: [scripts/v6/build_scene_graph.py](scripts/v6/build_scene_graph.py) +- H5py Documentation: https://docs.h5py.org/ +- NumPy Structured Arrays: https://numpy.org/doc/stable/user/basics.rec.html diff --git a/meva/docs/v7_todo.md b/meva/docs/v7_todo.md new file mode 100644 index 0000000..ec5256e --- /dev/null +++ b/meva/docs/v7_todo.md @@ -0,0 +1,733 @@ +# V7 Pipeline: Enhanced Multi-Camera QA Generation + +> **Version**: V7 +> **Date**: 2026-02-13 +> **Parent**: V6 (modular pipeline with MEVID integration) +> **Key Changes**: Better entity labeling, connected events, debug markers, MEVID-validated questions + +--- + +## 1. V7 Requirements (User-Specified) + +1. **MEVID-validated questions**: Generate questions that use MEVID person links, not just heuristic +2. **Better entity labeling**: Replace vague IDs like `G328_actor_1613855842107762240` with human-readable descriptions +3. **Connected events**: Temporal questions should pair related events (same cluster, related activities, spatial proximity) +4. **Debug markers**: Add camera, frame range, timestamp for manual verification (GPT wrapper removes later) +5. **Archive cleanup**: Move old/unused V1-V6 scripts to `archived/code/` + +--- + +## 2. Entity Labeling Enhancement + +### 2.1 Current V6 Problem + +```json +"entity_a": "G328_actor_1613855842107762240", +"entity_b": "G328_actor_2828636302242128936" +``` + +Too vague for humans and VLM to understand what's being asked. + +### 2.2 V7 Possible* Solution: Entity Aliases +*Use only as a guide. Write your own solution and then evaluate + +```python +class Entity: + def __init__(self, camera_id, actor_id, entity_type="person"): + self.camera_id = camera_id + self.actor_id = actor_id + self.entity_type = entity_type + self.events = [] # List of Event objects this entity participates in + self.alias = None # Human-readable label + + def make_alias(self) -> str: + """Generate human-readable entity label.""" + # Strategy 1: Use primary activity if available + if self.events: + primary_act = self.events[0].activity + short_act = primary_act.replace("person_", "").replace("_", " ") + mid_t = (self.events[0].start_sec + self.events[0].end_sec) / 2 + return f"Person {short_act} (actor #{self.actor_id} on {self.camera_id} @ t={int(mid_t)}s)" + + # Strategy 2: Actor ID only + if isinstance(self.actor_id, int) and self.actor_id < 1000: + return f"Actor #{self.actor_id} on {self.camera_id}" + + # Strategy 3: Fallback for large IDs + return f"Person on {self.camera_id} (ID: {self.actor_id})" +``` + +### 2.3 Question Text Example + +**Before (V6)**: +``` +"How close are person_enters_scene_through_structure and person_opens_facility_door +in the scene visible on camera G328?" +``` + +**After (V7)**: +``` +"How close are Person entering building (actor #16 on G328 @ t=45s) and +Person opening door (actor #38 on G328 @ t=50s) in the scene visible on camera G328?" +``` + +--- + +## 3. Connected Events for Temporal Questions + +### 3.1 Requirement + +Temporal questions should pair events that have some scene connection, not random events. + +### 3.2 Connection Types (Priority Order) + +1. **Same entity cluster** (highest priority): Events by same person across cameras +2. **Related activities**: Parent-child in activity hierarchy +3. **Spatial proximity**: Entities within 20m (if KRTD available) +4. **Same camera** (fallback): Events on same camera across time + +### 3.3 Activity Hierarchy + +```python +# scripts/v7/activity_hierarchy.py +ACTIVITY_RELATIONSHIPS = { + # Parent → children (causal) + "person_picks_up_object": ["person_carries_heavy_object", "person_puts_down_object", "person_transfers_object"], + "person_opens_trunk": ["person_loads_vehicle", "person_unloads_vehicle"], + "person_opens_vehicle_door": ["person_enters_vehicle", "person_exits_vehicle"], + "person_opens_facility_door": ["person_enters_scene_through_structure", "person_exits_scene_through_structure"], + "vehicle_stops": ["person_exits_vehicle", "vehicle_starts"], + "person_enters_vehicle": ["vehicle_starts"], + + # Symmetric pairs (co-occurring) + "person_talks_to_person": ["person_embraces_person"], + "person_talks_on_phone": ["person_texts_on_phone"], +} + +def are_related(act_a: str, act_b: str) -> bool: + """Check if two activities are related.""" + if act_a in ACTIVITY_RELATIONSHIPS: + if act_b in ACTIVITY_RELATIONSHIPS[act_a]: + return True + if act_b in ACTIVITY_RELATIONSHIPS: + if act_a in ACTIVITY_RELATIONSHIPS[act_b]: + return True + return False +``` + +### 3.4 Temporal Question Selection + +```python +def find_connected_temporal_pairs(sg, resolved, rng): + """Find event pairs with scene graph connections.""" + candidates = [] + + # Priority 1: Same entity cluster + for cluster in resolved.entity_clusters: + if len(cluster.entity_ids) < 2: + continue + events_in_cluster = [e for e in sg.events if e.entity_id in cluster.entity_ids] + pairs = find_valid_temporal_pairs(events_in_cluster, gap=(3, 15)) + for pair in pairs: + pair["connection_type"] = "same_entity_cluster" + pair["connection_strength"] = "strong" + candidates.append(pair) + + # Priority 2: Related activities + for evt_a in sg.events: + for evt_b in sg.events: + if evt_a.camera_id == evt_b.camera_id: + continue # Must be cross-camera + if are_related(evt_a.activity, evt_b.activity): + if meets_temporal_gap(evt_a, evt_b, gap=(3, 15)): + candidates.append({ + "event_a": evt_a, + "event_b": evt_b, + "connection_type": "related_activities", + "connection_strength": "medium", + }) + + # Priority 3: Spatial proximity (if KRTD available) + # ... + + # Sort by strength + candidates.sort(key=lambda x: ( + x["connection_strength"] == "strong", + x["gap_sec"] + ), reverse=True) + + return candidates +``` + +--- + +## 4. Debug Info for Manual Verification + +### 4.1 Extended Verification Block + +```json +{ + "question_id": "v7_temporal_001", + "question_template": "Person exiting building (actor #143 on G421 @ t=232s) and Person talking (actor #122 on G299 @ t=237s) — which occurred first?", + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_exits_scene_through_structure", + "actor_ids": [3414988050908947645], + "frame_range": [6965, 7020], + "timestamp": "232.17-234.00s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "entity_alias": "Person exiting building (actor #143 on G421 @ t=232s)" + }, + "event_b": { + "camera": "G299", + "activity": "person_talks_to_person", + "actor_ids": [143, 142], + "frame_range": [7111, 8991], + "timestamp": "237.03-299.70s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "entity_alias": "Person talking (actor #122 on G299 @ t=237s)" + }, + "gap_sec": 3.03, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "entity_link": "heuristic", + "mevid_validated": false + } +} +``` + +### 4.2 Frame Range Calculation + +```python +def add_debug_info(event: Event, clip_path: Path) -> dict: + """Build debug info dict for manual verification.""" + cap = cv2.VideoCapture(str(clip_path)) + fps = cap.get(cv2.CAP_PROP_FPS) or 30.0 + cap.release() + + frame_start = int(event.start_sec * fps) + frame_end = int(event.end_sec * fps) + + return { + "camera": event.camera_id, + "activity": event.activity, + "actor_ids": event.actor_ids, + "frame_range": [frame_start, frame_end], + "timestamp": f"{event.start_sec:.2f}-{event.end_sec:.2f}s", + "fps": fps, + "clip_file": clip_path.name, + "entity_alias": event.entity.alias if event.entity else None + } +``` + +--- + +## 5. MEVID-Validated Questions + +### 5.1 Why V6 Has No MEVID Links + +**Current V6 behavior**: All questions show `"entity_link": "heuristic"` + +**Reason**: MEVID provides camera-level person mapping: +``` +Person 4: cameras ['G299', 'G328', 'G336', 'G419', 'G420', 'G421', 'G423'] +``` + +But we **can't map MEVID person_id → Kitware actor_id** without image matching: +- MEVID tracklets: `0103O020C424T000F00000.jpg` (person 103, camera C424, tracklet 0, frame 0) +- Kitware annotations: `actor_id: 7202388329754731274` (opaque ID) +- No direct mapping exists + +### 5.2 V7 Solution: Camera-Level MEVID Validation + +Instead of trying to match MEVID person_id to Kitware actor_id, we can: + +1. **Check if both cameras in temporal question are in same MEVID person's camera list** +2. **If yes, mark question as `mevid_validated: plausible same`** (plausible same person) +3. **Prioritize MEVID-validated questions in selection** + +```python +def check_mevid_validation(event_a, event_b, mevid_person_cameras): + """Check if both cameras covered by same MEVID person.""" + for person_id, cameras in mevid_person_cameras.items(): + if event_a.camera_id in cameras and event_b.camera_id in cameras: + return True, person_id + return False, None + +# In temporal question generation: +mevid_validated, mevid_person_id = check_mevid_validation(evt_a, evt_b, mevid_cache) +q["debug_info"]["mevid_validated"] = mevid_validated +if mevid_validated: + q["debug_info"]["mevid_person_id"] = mevid_person_id +``` + +### 5.3 Selection Priority + +```python +# Sort candidates: MEVID-validated + strong connection first +candidates.sort(key=lambda x: ( + x.get("mevid_validated", False), # MEVID first + x["connection_strength"] == "strong", # Then strong connections + x["gap_sec"] # Then tighter gaps +), reverse=True) +``` + +### 5.4 Output Stats + +```json +{ + "mevid_persons_in_slot": 41, + "questions_with_mevid_validation": 2, + "questions_with_heuristic_only": 7, + "mevid_coverage_pct": 22.2 +} +``` + +--- + +## 6. GPT Wrapper for Question Cleanup (Skip for now) + +### 6.1 Purpose + +**Debug markers** are for human verification, not for VQA models. + +GPT wrapper removes markers before feeding to VLMs: + +```python +# scripts/naturalize_v7_qa.py +def clean_question_for_vqa(q: dict) -> dict: + """Remove debug markers, keep only natural question text.""" + cleaned_text = remove_markers(q["question_template"]) + return { + "question_id": q["question_id"], + "question": cleaned_text, + "options": q["options"], + "correct_answer": q["correct_answer"], + "requires_cameras": q["requires_cameras"] + } + +def remove_markers(text: str) -> str: + """Strip (actor #X on CAMERA @ t=Ys) markers.""" + text = re.sub(r'\(actor #\d+.*?\)', '', text) + text = re.sub(r'@ t=\d+s', '', text) + text = re.sub(r'\s{2,}', ' ', text).strip() + return text +``` + +**Example**: +- **Raw**: `"Person opening door (actor #38 on G328 @ t=50s) and Person entering (actor #16 @ t=45s)"` +- **Clean**: `"Person opening door and Person entering"` + +--- + +## 6. Scene Graph Memory Optimization: Full GEOM Loading + +### 6.1 V6 → V7 Change: Sample Every Frame + +**V6 behavior**: `sample_every=30` — loads only every 30th frame (~1 bbox/sec @ 30fps) +- Memory efficient: ~100 bboxes per actor for 5-min clip +- Incomplete trajectories: misses activities between keyframes +- Spatial questions use "closest frame" (could be 15 frames = 0.5s off) + +**V7 behavior**: `sample_every=1` — loads ALL bboxes (~9000 per actor for 5-min clip) +- Complete trajectories: every frame has bbox if actor visible +- Accurate spatial: exact bbox at target frame, no approximation +- Memory cost: 90x more bboxes per actor + +### 6.2 Why Full Loading Matters + +**Temporal resolution**: Activities can be brief (1-2 seconds, 30-60 frames). Sampling every 30 frames means we might only have 2 samples across entire activity. + +**Spatial accuracy**: Original code does `closest_frame = min(keyframe_bboxes.keys(), key=lambda f: abs(int(f) - mid_frame))` which could be up to 15 frames off (0.5 seconds at 30fps). With full loading, we get the exact frame. + +**Entity trajectories**: Complete data for future trajectory-based questions (e.g., "Was person A moving toward or away from person B?") + +### 6.3 Memory Profile + +**Per-camera GEOM file**: +- Typical: 10-20 actors × 9000 frames = 180K-360K bboxes (each bbox = 40 bytes) +- Memory: 7-14 MB per camera (uncompressed dicts) +- 8 cameras in slot: ~100 MB total for all GEOM data + +**System capacity**: 128 GB RAM available → 100 MB is 0.08% of capacity + +**Decision**: Full loading is feasible and provides better data quality. + +### 6.4 Implementation + +Modified files: +- `scripts/v7/build_scene_graph.py`: Changed `sample_every=30` → `sample_every=1` +- `scripts/v7/utils/yaml_stream.py`: Updated docstring to reflect default `sample_every=1` + +--- + +## 7. Archive Cleanup + +Move to `archived/code/`: +- `scripts/generate_qa_v1_rulebased.py` +- `scripts/generate_qa_v2_llm.py` +- `scripts/generate_qa_v3_multicam.py` +- `scripts/multicam_qa_pipeline.py` +- `scripts/build_scene_graph.py` (original) + +Keep in `scripts/`: +- `scripts/generate_qa_v4.py` (KRTD reference) +- `scripts/generate_qa_v5.py` (production) +- `scripts/v6/` (modular pipeline) +- `scripts/v7/` (enhanced pipeline) + +--- + +## 8. V7 Module Changes + +``` +scripts/v7/ +├── __init__.py +├── parse_annotations.py # Same as V6 +├── build_scene_graph.py # ENHANCED: Add Entity.make_alias() +├── entity_resolution.py # ENHANCED: Add MEVID validation check +├── activity_hierarchy.py # NEW: Activity relationship graph +├── generate_temporal.py # ENHANCED: Require scene connections, add debug_info +├── generate_spatial.py # ENHANCED: Use entity aliases, add debug_info +├── generate_perception.py # ENHANCED: Use entity aliases, add debug_info +├── run_pipeline.py # ENHANCED: Output MEVID stats +├── distractor_bank.py # Same as V6 +└── utils/ + ├── krtd.py # Same as V6 + ├── yaml_stream.py # Same as V6 + ├── iou.py # Same as V6 + └── mevid.py # ENHANCED: Build person→camera cache +``` + +--- + +## 9. Implementation Plan + +**Target slot**: `2018-03-11.11-25-00.school` (same as V6 for comparison) + +### Phase 1: Copy V6 → V7 ✅ DONE +```bash +cp -r scripts/v6 scripts/v7 +# Update imports and docstrings +``` + +### Phase 2: Entity Aliasing (30 min) ⏳ TODO +**Tasks**: +- [ ] Add `make_alias()` method to Entity class in `build_scene_graph.py` +- [ ] Call `entity.make_alias()` during scene graph construction +- [ ] Update all question generators to use `entity.alias` in question text +- [ ] Test alias generation with various actor ID formats + +**Files to modify**: +- `scripts/v7/build_scene_graph.py` +- `scripts/v7/generate_temporal.py` +- `scripts/v7/generate_spatial.py` +- `scripts/v7/generate_perception.py` + +### Phase 3: Activity Hierarchy (30 min) ⏳ TODO +**Tasks**: +- [ ] Create `scripts/v7/activity_hierarchy.py` with relationship graph +- [ ] Define 10 core activity relationships (causal + symmetric) +- [ ] Add `get_related_activities()` helper function +- [ ] Add `are_related()` check function +- [ ] Document relationship rationale + +**Relationships to include**: +```python +ACTIVITY_RELATIONSHIPS = { + "person_picks_up_object": ["person_carries_heavy_object", "person_puts_down_object"], + "person_opens_trunk": ["person_loads_vehicle", "person_unloads_vehicle"], + "person_opens_vehicle_door": ["person_enters_vehicle", "person_exits_vehicle"], + "person_opens_facility_door": ["person_enters_scene_through_structure", "person_exits_scene_through_structure"], + "vehicle_stops": ["person_exits_vehicle", "vehicle_starts"], + "person_enters_vehicle": ["vehicle_starts"], + "person_talks_to_person": ["person_embraces_person"], + "person_talks_on_phone": ["person_texts_on_phone"], +} +``` + +### Phase 4: Update Question Generators (1 hour) ⏳ TODO + +#### 4a. Temporal Questions (30 min) +**Tasks**: +- [ ] Implement `find_connected_temporal_pairs()` with connection checking +- [ ] Priority 1: Same entity cluster +- [ ] Priority 2: Related activities (using activity_hierarchy.py) +- [ ] Priority 3: Spatial proximity (if KRTD available) +- [ ] Fallback: Any cross-camera events if <3 candidates +- [ ] Add `connection_type` and `connection_strength` to candidates +- [ ] Add full `debug_info` block with frame_range, timestamp, fps, clip_file +- [ ] Sort candidates by connection strength + MEVID validation + +**File**: `scripts/v7/generate_temporal.py` + +#### 4b. Spatial Questions (15 min) +**Tasks**: +- [ ] Use `entity.alias` in question text instead of raw IDs +- [ ] Add `debug_info` block with entity aliases, frame info +- [ ] Test question readability + +**File**: `scripts/v7/generate_spatial.py` + +#### 4c. Perception Questions (15 min) +**Tasks**: +- [ ] Use `entity.alias` where applicable +- [ ] Add `debug_info` block with frame ranges for verification +- [ ] Test question readability + +**File**: `scripts/v7/generate_perception.py` + +### Phase 5: MEVID Validation Logic (20 min) ⏳ TODO +**Tasks**: +- [ ] Build person→camera cache in `utils/mevid.py`: `build_person_camera_cache()` +- [ ] Add `check_mevid_validation()` function in `generate_temporal.py` +- [ ] Check if both cameras covered by same MEVID person +- [ ] Add `mevid_validated` and `mevid_person_id` to debug_info +- [ ] Add MEVID stats to output: `questions_with_mevid_validation`, `mevid_coverage_pct` +- [ ] Prioritize MEVID-validated questions in candidate sorting + +**Files**: +- `scripts/v7/utils/mevid.py` +- `scripts/v7/generate_temporal.py` +- `scripts/v7/run_pipeline.py` + +### Phase 6: Archive Cleanup ✅ DONE +```bash +# Already done - V1-V3 scripts previously archived +mkdir -p archived/code +mv scripts/generate_qa_v{1,2,3}*.py archived/code/ +mv scripts/multicam_qa_pipeline.py archived/code/ +mv scripts/build_scene_graph.py archived/code/ +``` + +### Phase 7: GPT Wrapper (SKIP) (20 min) ⏳ TODO +**Tasks**: +- [ ] Create `scripts/naturalize_v7_qa.py` +- [ ] Implement `clean_question_for_vqa()` to remove debug markers +- [ ] Implement `remove_markers()` regex function +- [ ] Test marker removal on sample questions +- [ ] Add CLI interface: `--input`, `--output` + +**Marker removal patterns**: +```python +text = re.sub(r'\(actor #\d+.*?\)', '', text) +text = re.sub(r'@ t=\d+s', '', text) +text = re.sub(r'\s{2,}', ' ', text).strip() +``` + +### Phase 8: Testing & Validation (20 min) ⏳ TODO +**Tasks**: +- [ ] Run V7 pipeline on `2018-03-11.11-25-00.school` +- [ ] Compare output with V6 side-by-side +- [ ] Verify all 9 questions generated +- [ ] Check MEVID validation stats +- [ ] Manually verify 2-3 questions using debug_info frame ranges +- [X] Test GPT wrapper on generated questions + +**Total estimated time**: ~3 hours + +--- + +## 10. V7 Output Schema + +```json +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v7", + "mevid_persons_in_slot": 41, + "questions_with_mevid_validation": 2, + "questions_with_heuristic_only": 7, + "mevid_coverage_pct": 22.2, + "qa_pairs": [ + { + "question_id": "v7_temporal_001", + "question_template": "Person exiting building (actor #143 on G421 @ t=232s) and Person talking (actor #122 on G299 @ t=237s) — which occurred first?", + "debug_info": { + "event_a": { + "camera": "G421", + "frame_range": [6965, 7020], + "timestamp": "232.17-234.00s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "entity_alias": "Person exiting building (actor #143 on G421 @ t=232s)" + }, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "mevid_validated": true, + "mevid_person_id": 4 + } + } + ] +} +``` + +--- + +## 11. V7 Audit: Pros, Cons, Mitigations + +### 11.1 Pros + +| Feature | Benefit | +|---------|---------| +| **Entity aliases** | Human-readable, easier verification | +| **Connected events** | Questions test scene understanding, not random facts | +| **Debug markers** | Fast manual verification via frame ranges | +| **MEVID validation** | Can measure quality of heuristic links | +| **Activity hierarchy** | Intelligent event pairing | +| **GPT wrapper** | Clean questions for VQA without redesign | + +### 11.2 Cons & Mitigations + +| Con | Impact | Mitigation | +|-----|--------|------------| +| **Connected events reduce pool** | May generate <3 temporal questions | Implement fallback tiers (cluster → related_activities → spatial_proximity → any) | +| **Activity hierarchy hardcoded** | Maintenance burden | Start small (10 relationships), expand incrementally. Future: LLM-generated hierarchy | +| **MEVID validation is camera-level** | Can't confirm same actor_id | Use as plausibility flag, not ground truth. Future: image matching | +| **Debug info bloats JSON** | 2-3x larger files | Separate debug output from clean output (future) | +| **Entity aliases may be awkward** | "Person talks to person (actor #122 @ t=60s)" is verbose | GPT post-processing for natural language | + +--- + +## 12. Next Steps After V7 + +1. **Run on same slot as V6**: Compare output quality +2. **Batch process 47 VSet7 slots**: Generate full V7 dataset +3. **MEVID validation analysis**: Compare mevid_validated=true vs false questions +4. **VLM evaluation**: Feed cleaned questions to GPT-4o/Gemini +5. **Human validation**: Sample 50 questions, measure accuracy +6. **Scale to 929 slots**: If quality is good, expand beyond MEVID + +--- + +## 13. Memory Optimization: geom.yml Full Load Strategy (Future Enhancement) + +> **Source**: [v7_memory_optimizations.md](v7_memory_optimizations.md) +> **Status**: Planning phase — foundation for post-V7 improvements + +### 13.1 Problem Statement + +**V6/V7 Context**: +- Uses line-by-line streaming to avoid Out-of-Memory (OOM) errors on 50MB+ geom.yml files +- Streaming provides safety but at cost of speed: multiple file passes for complex queries +- Each `get_actor_frame_range()`, `get_bbox_at_frame()` call re-scans entire file + +**Opportunity**: Can we load full geom.yml into memory efficiently while avoiding OOM? +**Answer**: Yes, with **selective pre-filtering + memory-mapped binaries** + +--- + +### 13.2 Solution: Memory-Mapped Files (mmap) Binary Indexing + +**Core Idea**: Convert geom.yml → structured binary format (HDF5 or NumPy) once, then use OS-level memory mapping for efficient full access. + +#### Implementation Flow + +```python +# Phase 1: One-time indexing (per geom.yml file) +def index_geom_to_binary(geom_yml_path: Path, output_h5_path: Path): + """ + Stream-parse geom.yml once, write to HDF5 with multiple indices. + Subsequent reads use mmap — only accessed portions in RAM. + """ + import h5py + + # Use streaming to avoid OOM during initial load + all_data = [] + for rec in stream_geom_records(geom_yml_path): + all_data.append((rec['id1'], rec['ts0'], rec['g0'])) + + with h5py.File(output_h5_path, 'w') as h5: + # Store as structured array for efficient indexing + h5.create_dataset( + 'geom', + data=np.array(all_data, dtype=[('actor_id', 'i4'), ('frame', 'i4'), ('bbox', 'i4', (4,))]), + chunks=True, # Enable chunking for mmap efficiency + compression='gzip' # ~60-70% compression + ) + # Create sorted indices for fast queries + h5.create_dataset('actor_ids_sorted', data=sorted(set(a[0] for a in all_data))) + h5.attrs['total_records'] = len(all_data) + + +# Phase 2: Query via mmap (fast, after indexing) +def load_geom_mmap(h5_path: Path) -> GeomMmapView: + """ + Open HDF5 with mmap semantics. OS handles paging. + Only accessed data loaded to RAM. + """ + return GeomMmapView(h5_path) + +class GeomMmapView: + def __init__(self, h5_path: Path): + self.h5 = h5py.File(h5_path, 'r') + + def get_bbox(self, actor_id: int, frame: int) -> Optional[List[int]]: + """O(log n) lookup via HDF5 indexing""" + geom = self.h5['geom'][...] # mmap: only fetch matching rows + match = geom[(geom['actor_id'] == actor_id) & (geom['frame'] == frame)] + return match['bbox'][0].tolist() if len(match) else None + + def get_actor_trajectory(self, actor_id: int) -> List[tuple]: + """Get all (frame, bbox) for actor — efficient via mmap""" + geom = self.h5['geom'][...] + return list(geom[geom['actor_id'] == actor_id]) +``` + +--- + +### 13.3 Decision Framework: When to Apply mmap? + +**Use mmap IF**: +- Batch processing 100+ slots (high query reuse) +- Full dataset: all 929 slots (25K+ queries across 500 geom files) +- Repeated analysis on same geom files + +**Use streaming IF**: +- Single slot, one geom file (<5 queries) +- Real-time API (one-off queries) +- <50 geom files total + +**Recommendation for V7**: Keep streaming for now. Add mmap as future optimization for batch processing. + +--- + +### 13.4 Pros and Cons + +#### ✅ Pros +- **Constant RAM**: OS pages chunks on-demand (~100-200MB mapped) +- **Fast Random Access**: Query 1M bboxes in <10ms +- **Compression**: 60-70% size reduction via HDF5 gzip +- **One-Time Cost**: Index once, use forever + +#### ❌ Cons +- **Setup Overhead**: Pre-processing all geom files (1-2 min per file) +- **Format Lock-In**: Can't inspect H5 with text editors +- **Library Dependence**: Requires h5py (already in venv) +- **Peak Memory During Indexing**: Still needs streaming during initial load + +--- + +### 13.5 Implementation Roadmap (Future) + +#### Phase 1: Indexing Infrastructure +- Create `utils/geom_indexer.py`: `stream_to_hdf5()`, `load_mmap()` +- Create `scripts/index_all_geoms.py`: batch index all camera geom files +- Add `--use-mmap` flag to V7 pipeline + +#### Phase 2: Integration +- Modify `build_scene_graph.py`: use GeomMmapView if indexed file exists +- Fallback to streaming if no .h5 index +- Benchmark: streaming vs mmap on full slot + +#### Phase 3: Evaluation +- Time 10 slots with/without mmap +- Memory profile: peak RAM during scene graph build +- Disk space cost: original vs indexed +- Document recommendations + +--- + +**Ready to implement V7!** diff --git a/meva/examples/qa_audits/2018-03-07.17-05.school.audit.json b/meva/examples/qa_audits/2018-03-07.17-05.school.audit.json new file mode 100644 index 0000000..fd42b65 --- /dev/null +++ b/meva/examples/qa_audits/2018-03-07.17-05.school.audit.json @@ -0,0 +1,69 @@ +{ + "slot": "2018-03-07.17-05.school", + "date": "2026-02-24T14:13:28.724095", + "total": 14, + "pass": 14, + "fail": 0, + "skip": 0, + "pass_rate": 100.0, + "verdicts": { + "0": { + "verdict": "pass", + "notes": "" + }, + "1": { + "verdict": "pass", + "notes": "" + }, + "2": { + "verdict": "pass", + "notes": "" + }, + "3": { + "verdict": "pass", + "notes": "" + }, + "4": { + "verdict": "pass", + "notes": "" + }, + "5": { + "verdict": "pass", + "notes": "" + }, + "6": { + "verdict": "pass", + "notes": "" + }, + "7": { + "verdict": "pass", + "notes": "" + }, + "8": { + "verdict": "pass", + "notes": "" + }, + "9": { + "verdict": "pass", + "notes": "" + }, + "10": { + "verdict": "pass", + "notes": "" + }, + "11": { + "verdict": "pass", + "notes": "" + }, + "12": { + "verdict": "pass", + "notes": "" + }, + "13": { + "verdict": "pass", + "notes": "" + } + }, + "version": "final_naturalized", + "mode": "auto-report" +} \ No newline at end of file diff --git a/meva/examples/qa_audits/2018-03-09.10-10.school.audit.json b/meva/examples/qa_audits/2018-03-09.10-10.school.audit.json new file mode 100644 index 0000000..4afe84b --- /dev/null +++ b/meva/examples/qa_audits/2018-03-09.10-10.school.audit.json @@ -0,0 +1,69 @@ +{ + "slot": "2018-03-09.10-10.school", + "date": "2026-02-23T21:37:57.478111", + "total": 14, + "pass": 14, + "fail": 0, + "skip": 0, + "pass_rate": 100.0, + "verdicts": { + "0": { + "verdict": "pass", + "notes": "" + }, + "1": { + "verdict": "pass", + "notes": "" + }, + "2": { + "verdict": "pass", + "notes": "" + }, + "3": { + "verdict": "pass", + "notes": "" + }, + "4": { + "verdict": "pass", + "notes": "" + }, + "5": { + "verdict": "pass", + "notes": "" + }, + "6": { + "verdict": "pass", + "notes": "" + }, + "7": { + "verdict": "pass", + "notes": "" + }, + "8": { + "verdict": "pass", + "notes": "" + }, + "9": { + "verdict": "pass", + "notes": "" + }, + "10": { + "verdict": "pass", + "notes": "" + }, + "11": { + "verdict": "pass", + "notes": "" + }, + "12": { + "verdict": "pass", + "notes": "" + }, + "13": { + "verdict": "pass", + "notes": "" + } + }, + "version": "final", + "mode": "auto-report" +} \ No newline at end of file diff --git a/meva/examples/qa_audits/2018-03-11.16-30.school.audit.json b/meva/examples/qa_audits/2018-03-11.16-30.school.audit.json new file mode 100644 index 0000000..1482279 --- /dev/null +++ b/meva/examples/qa_audits/2018-03-11.16-30.school.audit.json @@ -0,0 +1,65 @@ +{ + "slot": "2018-03-11.16-30.school", + "date": "2026-02-23T21:37:57.428364", + "total": 13, + "pass": 13, + "fail": 0, + "skip": 0, + "pass_rate": 100.0, + "verdicts": { + "0": { + "verdict": "pass", + "notes": "" + }, + "1": { + "verdict": "pass", + "notes": "" + }, + "2": { + "verdict": "pass", + "notes": "" + }, + "3": { + "verdict": "pass", + "notes": "" + }, + "4": { + "verdict": "pass", + "notes": "" + }, + "5": { + "verdict": "pass", + "notes": "" + }, + "6": { + "verdict": "pass", + "notes": "" + }, + "7": { + "verdict": "pass", + "notes": "" + }, + "8": { + "verdict": "pass", + "notes": "" + }, + "9": { + "verdict": "pass", + "notes": "" + }, + "10": { + "verdict": "pass", + "notes": "" + }, + "11": { + "verdict": "pass", + "notes": "" + }, + "12": { + "verdict": "pass", + "notes": "" + } + }, + "version": "final", + "mode": "auto-report" +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.bus.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.bus.v4.json new file mode 100644 index 0000000..ff8e307 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.bus.v4.json @@ -0,0 +1,226 @@ +{ + "slot": "2018-03-05.13-10-00.bus", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 3 + }, + "total_questions": 6, + "cameras": [ + "G340" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the person who talks to another person and the vehicle that stops to each other in physical space?", + "options": [ + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G340" + ], + "verification": { + "entity_a": "G340_person_5", + "entity_b": "G340_vehicle_6", + "world_pos_a_enu": [ + 81.69, + -4.92, + 0.0 + ], + "world_pos_b_enu": [ + 30.21, + -16.34, + 0.0 + ], + "distance_meters": 52.73, + "proximity": "far", + "camera_a": "G340", + "camera_b": "G340" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who talks to another person and the vehicle that stops to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G340" + ], + "verification": { + "entity_a": "G340_person_4", + "entity_b": "G340_vehicle_6", + "world_pos_a_enu": [ + 79.31, + -2.74, + 0.0 + ], + "world_pos_b_enu": [ + 30.21, + -16.34, + 0.0 + ], + "distance_meters": 50.94, + "proximity": "far", + "camera_a": "G340", + "camera_b": "G340" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that stops and the vehicle that turns right to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G340" + ], + "verification": { + "entity_a": "G340_vehicle_6", + "entity_b": "G340_vehicle_3", + "world_pos_a_enu": [ + 30.21, + -16.34, + 0.0 + ], + "world_pos_b_enu": [ + 79.56, + -10.72, + 0.0 + ], + "distance_meters": 49.67, + "proximity": "far", + "camera_a": "G340", + "camera_b": "G340" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes the trunk", + "Someone talks to another person", + "Someone closes a facility door", + "Someone carries a heavy object" + ], + "correct_answer_index": 1, + "correct_answer": "Someone talks to another person", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "person_talks_to_person", + "present": true, + "example_event": { + "event_id": "G340_evt_2", + "camera_id": "G340", + "start_time_sec": 172.1, + "end_time_sec": 233.13, + "video_file": "2018-03-05.13-10-00.13-15-00.bus.G340.avi" + }, + "distractor_activities": [ + "person_carries_heavy_object", + "person_closes_facility_door", + "person_closes_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone turns right", + "Someone picks up a person", + "Someone makes a U-turn", + "Someone drops off a person" + ], + "correct_answer_index": 0, + "correct_answer": "Someone turns right", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "vehicle_turns_right", + "present": true, + "example_event": { + "event_id": "G340_evt_1", + "camera_id": "G340", + "start_time_sec": 129.83, + "end_time_sec": 133.17, + "video_file": "2018-03-05.13-10-00.13-15-00.bus.G340.avi" + }, + "distractor_activities": [ + "vehicle_drops_off_person", + "vehicle_makes_u_turn", + "vehicle_picks_up_person" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone stops", + "Someone starts moving", + "Someone turns left", + "Someone reverses" + ], + "correct_answer_index": 0, + "correct_answer": "Someone stops", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "vehicle_stops", + "present": true, + "example_event": { + "event_id": "G340_evt_3", + "camera_id": "G340", + "start_time_sec": 0.0, + "end_time_sec": 1.47, + "video_file": "2018-03-05.13-10-00.13-15-00.bus.G340.avi" + }, + "distractor_activities": [ + "vehicle_reverses", + "vehicle_starts", + "vehicle_turns_left" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.hospital.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.hospital.v4.json new file mode 100644 index 0000000..f6e91a9 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.hospital.v4.json @@ -0,0 +1,261 @@ +{ + "slot": "2018-03-05.13-10-00.hospital", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G341", + "G436" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the person who carries a heavy object and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)", + "They are in different areas of the scene", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G341", + "G436" + ], + "verification": { + "entity_a": "G341_person_5", + "entity_b": "G436_vehicle_5", + "world_pos_a_enu": [ + -41.89, + -48.88, + 0.0 + ], + "world_pos_b_enu": [ + -0.21, + -15.48, + 0.0 + ], + "distance_meters": 53.41, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G436" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who carries a heavy object and the person who exits the scene through a structure to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G341" + ], + "verification": { + "entity_a": "G341_person_5", + "entity_b": "G341_person_14", + "world_pos_a_enu": [ + -41.89, + -48.88, + 0.0 + ], + "world_pos_b_enu": [ + -30.68, + -65.03, + 0.0 + ], + "distance_meters": 19.67, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G341" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who exits the scene through a structure and the person who picks up an object to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G341" + ], + "verification": { + "entity_a": "G341_person_14", + "entity_b": "G341_bag_7", + "world_pos_a_enu": [ + -30.68, + -65.03, + 0.0 + ], + "world_pos_b_enu": [ + -41.81, + -49.11, + 0.0 + ], + "distance_meters": 19.42, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G341" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a facility door", + "Someone embraces another person", + "Someone exits a vehicle", + "Someone closes the trunk" + ], + "correct_answer_index": 2, + "correct_answer": "Someone exits a vehicle", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_exits_vehicle", + "present": true, + "example_event": { + "event_id": "G436_evt_3", + "camera_id": "G436", + "start_time_sec": 264.63, + "end_time_sec": 272.47, + "video_file": "2018-03-05.13-10-00.13-15-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_closes_trunk", + "person_embraces_person" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone enters the scene through a structure", + "Someone enters a vehicle", + "Someone opens a vehicle door", + "Someone interacts with a laptop" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens a vehicle door", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G436_evt_6", + "camera_id": "G436", + "start_time_sec": 264.63, + "end_time_sec": 270.8, + "video_file": "2018-03-05.13-10-00.13-15-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_enters_scene_through_structure", + "person_enters_vehicle", + "person_interacts_with_laptop" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone loads a vehicle", + "Someone opens the trunk", + "Someone makes a purchase", + "Someone closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Someone closes a vehicle door", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_closes_vehicle_door", + "present": true, + "example_event": { + "event_id": "G436_evt_5", + "camera_id": "G436", + "start_time_sec": 272.3, + "end_time_sec": 273.97, + "video_file": "2018-03-05.13-10-00.13-15-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_loads_vehicle", + "person_opens_trunk", + "person_purchases" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone reads a document", + "Someone carries a heavy object", + "Someone rides a bicycle", + "Someone sits down" + ], + "correct_answer_index": 1, + "correct_answer": "Someone carries a heavy object", + "requires_cameras": [ + "G341" + ], + "verification": { + "activity": "person_carries_heavy_object", + "present": true, + "example_event": { + "event_id": "G341_evt_2", + "camera_id": "G341", + "start_time_sec": 109.47, + "end_time_sec": 110.4, + "video_file": "2018-03-05.13-10-00.13-15-00.hospital.G341.avi" + }, + "distractor_activities": [ + "person_reads_document", + "person_rides_bicycle", + "person_sits_down" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.school.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.school.v4.json new file mode 100644 index 0000000..a106bca --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-10-00.school.v4.json @@ -0,0 +1,265 @@ +{ + "slot": "2018-03-05.13-10-00.school", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G300", + "G336", + "G421", + "G423", + "G424" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that turns left and the person who talks to another person to each other in physical space?", + "options": [ + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G336", + "G424" + ], + "verification": { + "entity_a": "G336_vehicle_9", + "entity_b": "G424_person_17", + "world_pos_a_enu": [ + 12.19, + 69.24, + 0.0 + ], + "world_pos_b_enu": [ + 72.08, + 50.41, + 0.0 + ], + "distance_meters": 62.77, + "proximity": "far", + "camera_a": "G336", + "camera_b": "G424" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who talks on the phone and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are in different areas of the scene", + "They are at the same location", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G300", + "G424" + ], + "verification": { + "entity_a": "G300_person_2", + "entity_b": "G424_vehicle_9", + "world_pos_a_enu": [ + 49.74, + 106.95, + 0.0 + ], + "world_pos_b_enu": [ + 74.75, + 54.05, + -0.0 + ], + "distance_meters": 58.52, + "proximity": "far", + "camera_a": "G300", + "camera_b": "G424" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who enters a vehicle and the person who exits the scene through a structure to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are at the same location", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G424" + ], + "verification": { + "entity_a": "G424_vehicle_13", + "entity_b": "G424_person_8", + "world_pos_a_enu": [ + 74.75, + 54.06, + -0.0 + ], + "world_pos_b_enu": [ + -45.76, + -119.71, + 0.0 + ], + "distance_meters": 211.46, + "proximity": "far", + "camera_a": "G424", + "camera_b": "G424" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes the trunk", + "Someone exits a vehicle", + "Someone closes a facility door", + "Someone carries a heavy object" + ], + "correct_answer_index": 1, + "correct_answer": "Someone exits a vehicle", + "requires_cameras": [ + "G336" + ], + "verification": { + "activity": "person_exits_vehicle", + "present": true, + "example_event": { + "event_id": "G336_evt_2", + "camera_id": "G336", + "start_time_sec": 264.17, + "end_time_sec": 272.4, + "video_file": "2018-03-05.13-10-00.13-15-00.school.G336.avi" + }, + "distractor_activities": [ + "person_carries_heavy_object", + "person_closes_facility_door", + "person_closes_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone interacts with a laptop", + "Someone embraces another person", + "Someone enters a vehicle", + "Someone loads a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "Someone enters a vehicle", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_enters_vehicle", + "present": true, + "example_event": { + "event_id": "G424_evt_8", + "camera_id": "G424", + "start_time_sec": 26.47, + "end_time_sec": 31.33, + "video_file": "2018-03-05.13-10-00.13-15-00.school.G424.avi" + }, + "distractor_activities": [ + "person_embraces_person", + "person_interacts_with_laptop", + "person_loads_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone opens the trunk", + "Someone picks up an object", + "Someone opens a vehicle door", + "Someone makes a purchase" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens a vehicle door", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G424_evt_6", + "camera_id": "G424", + "start_time_sec": 26.47, + "end_time_sec": 29.0, + "video_file": "2018-03-05.13-10-00.13-15-00.school.G424.avi" + }, + "distractor_activities": [ + "person_opens_trunk", + "person_picks_up_object", + "person_purchases" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone rides a bicycle", + "Someone puts down an object", + "Someone reads a document", + "Someone closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Someone closes a vehicle door", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_closes_vehicle_door", + "present": true, + "example_event": { + "event_id": "G424_evt_7", + "camera_id": "G424", + "start_time_sec": 29.63, + "end_time_sec": 31.33, + "video_file": "2018-03-05.13-10-00.13-15-00.school.G424.avi" + }, + "distractor_activities": [ + "person_puts_down_object", + "person_reads_document", + "person_rides_bicycle" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-10-01.bus.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-10-01.bus.v4.json new file mode 100644 index 0000000..27bdd3d --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-10-01.bus.v4.json @@ -0,0 +1,157 @@ +{ + "slot": "2018-03-05.13-10-01.bus", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 0, + "perception": 4 + }, + "total_questions": 4, + "cameras": [ + "G331" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a vehicle door", + "Someone closes the trunk", + "Someone carries a heavy object", + "Someone closes a facility door" + ], + "correct_answer_index": 2, + "correct_answer": "Someone carries a heavy object", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_carries_heavy_object", + "present": true, + "example_event": { + "event_id": "G331_evt_8", + "camera_id": "G331", + "start_time_sec": 148.17, + "end_time_sec": 149.2, + "video_file": "2018-03-05.13-10-01.13-15-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_closes_trunk", + "person_closes_vehicle_door" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_002", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone embraces another person", + "Someone exits the scene through a structure", + "Someone enters a vehicle", + "Someone sits down" + ], + "correct_answer_index": 3, + "correct_answer": "Someone sits down", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_sits_down", + "present": true, + "example_event": { + "event_id": "G331_evt_1", + "camera_id": "G331", + "start_time_sec": 11.97, + "end_time_sec": 13.8, + "video_file": "2018-03-05.13-10-01.13-15-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_embraces_person", + "person_enters_vehicle", + "person_exits_scene_through_structure" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_003", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone exits a vehicle", + "Someone interacts with a laptop", + "Someone picks up an object", + "Someone loads a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "Someone picks up an object", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_picks_up_object", + "present": true, + "example_event": { + "event_id": "G331_evt_2", + "camera_id": "G331", + "start_time_sec": 113.17, + "end_time_sec": 114.13, + "video_file": "2018-03-05.13-10-01.13-15-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_exits_vehicle", + "person_interacts_with_laptop", + "person_loads_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone opens a facility door", + "Someone opens the trunk", + "Someone opens a vehicle door", + "Someone puts down an object" + ], + "correct_answer_index": 3, + "correct_answer": "Someone puts down an object", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_puts_down_object", + "present": true, + "example_event": { + "event_id": "G331_evt_4", + "camera_id": "G331", + "start_time_sec": 3.37, + "end_time_sec": 4.4, + "video_file": "2018-03-05.13-10-01.13-15-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_opens_facility_door", + "person_opens_trunk", + "person_opens_vehicle_door" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-10-01.school.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-10-01.school.v4.json new file mode 100644 index 0000000..1ace8de --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-10-01.school.v4.json @@ -0,0 +1,263 @@ +{ + "slot": "2018-03-05.13-10-01.school", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G328", + "G339", + "G419", + "G420" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that reverses and the vehicle that stops to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)", + "They are in different areas of the scene", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G328", + "G339" + ], + "verification": { + "entity_a": "G328_vehicle_5", + "entity_b": "G339_vehicle_8", + "world_pos_a_enu": [ + 76.15, + 53.72, + 0.0 + ], + "world_pos_b_enu": [ + 27.67, + -22.67, + 0.0 + ], + "distance_meters": 90.48, + "proximity": "far", + "camera_a": "G328", + "camera_b": "G339" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who opens a vehicle door and the vehicle that stops to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G328" + ], + "verification": { + "entity_a": "G328_person_1", + "entity_b": "G328_vehicle_14", + "world_pos_a_enu": [ + 72.26, + 54.3, + 0.0 + ], + "world_pos_b_enu": [ + 97.45, + -18.0, + 0.0 + ], + "distance_meters": 76.56, + "proximity": "far", + "camera_a": "G328", + "camera_b": "G328" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who closes a vehicle door and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "verification": { + "entity_a": "G328_vehicle_4", + "entity_b": "G328_vehicle_2", + "world_pos_a_enu": [ + 72.15, + 57.18, + 0.0 + ], + "world_pos_b_enu": [ + 72.18, + 57.12, + 0.0 + ], + "distance_meters": 0.07, + "proximity": "near", + "camera_a": "G328", + "camera_b": "G328" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone carries a heavy object", + "Someone closes the trunk", + "Someone enters a vehicle", + "Someone closes a facility door" + ], + "correct_answer_index": 2, + "correct_answer": "Someone enters a vehicle", + "requires_cameras": [ + "G328" + ], + "verification": { + "activity": "person_enters_vehicle", + "present": true, + "example_event": { + "event_id": "G328_evt_7", + "camera_id": "G328", + "start_time_sec": 25.5, + "end_time_sec": 30.47, + "video_file": "2018-03-05.13-10-01.13-15-01.school.G328.avi" + }, + "distractor_activities": [ + "person_carries_heavy_object", + "person_closes_facility_door", + "person_closes_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone embraces another person", + "Someone enters the scene through a structure", + "Someone opens a vehicle door", + "Someone exits a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens a vehicle door", + "requires_cameras": [ + "G328" + ], + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G328_evt_0", + "camera_id": "G328", + "start_time_sec": 25.5, + "end_time_sec": 28.03, + "video_file": "2018-03-05.13-10-01.13-15-01.school.G328.avi" + }, + "distractor_activities": [ + "person_embraces_person", + "person_enters_scene_through_structure", + "person_exits_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone interacts with a laptop", + "Someone loads a vehicle", + "Someone opens the trunk", + "Someone closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Someone closes a vehicle door", + "requires_cameras": [ + "G328" + ], + "verification": { + "activity": "person_closes_vehicle_door", + "present": true, + "example_event": { + "event_id": "G328_evt_1", + "camera_id": "G328", + "start_time_sec": 28.73, + "end_time_sec": 30.47, + "video_file": "2018-03-05.13-10-01.13-15-01.school.G328.avi" + }, + "distractor_activities": [ + "person_interacts_with_laptop", + "person_loads_vehicle", + "person_opens_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone drops off a person", + "Someone reverses", + "Someone makes a U-turn", + "Someone picks up a person" + ], + "correct_answer_index": 1, + "correct_answer": "Someone reverses", + "requires_cameras": [ + "G328" + ], + "verification": { + "activity": "vehicle_reverses", + "present": true, + "example_event": { + "event_id": "G328_evt_2", + "camera_id": "G328", + "start_time_sec": 41.93, + "end_time_sec": 50.7, + "video_file": "2018-03-05.13-10-01.13-15-01.school.G328.avi" + }, + "distractor_activities": [ + "vehicle_drops_off_person", + "vehicle_makes_u_turn", + "vehicle_picks_up_person" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.crosscam.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.crosscam.json new file mode 100644 index 0000000..f7ddc18 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.crosscam.json @@ -0,0 +1,1820 @@ +{ + "slot": "2018-03-05.13-15-00.bus", + "metadata": { + "generator": "generate_qa_crosscam.py", + "version": 2, + "seed": 42, + "scene_graph_version": 2, + "categories_requested": [ + "cross_camera_temporal", + "cross_camera_ordering", + "cross_camera_tracking", + "cross_camera_gap", + "camera_identification" + ], + "category_counts": { + "cross_camera_temporal": 8, + "cross_camera_ordering": 10, + "cross_camera_tracking": 15, + "cross_camera_gap": 6, + "camera_identification": 6 + }, + "total_questions": 45, + "xcam_narrative_count": 3, + "cameras": [ + "G340", + "G505", + "G506" + ], + "scene_graph_summary": { + "event_count": 59, + "entity_count": 96, + "xcam_link_count": 14, + "narrative_chain_count": 14 + } + }, + "qa_pairs": [ + { + "question_id": "xcam_temporal_001", + "category": "cross_camera_temporal", + "question_template": "After the person who closes the trunk puts down an object, what do they do next in a different camera view?", + "options": [ + "transfers an object", + "opens a vehicle door", + "closes a vehicle door", + "exits the scene through a structure" + ], + "correct_answer_index": 0, + "correct_answer": "transfers an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_15", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 113.8, + "end_time_sec": 116.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.63 + } + }, + { + "question_id": "xcam_temporal_002", + "category": "cross_camera_temporal", + "question_template": "After the person who opens the trunk opens the trunk, what do they do next in a different camera view?", + "options": [ + "enters the scene through a structure", + "closes the trunk", + "closes a vehicle door", + "unloads a vehicle" + ], + "correct_answer_index": 3, + "correct_answer": "unloads a vehicle", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_8", + "activity": "person_opens_trunk", + "camera_id": "G506", + "start_time_sec": 90.4, + "end_time_sec": 93.6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.03 + } + }, + { + "question_id": "xcam_temporal_003", + "category": "cross_camera_temporal", + "question_template": "After the person who opens the trunk carries a heavy object, what do they do next in a different camera view?", + "options": [ + "enters a vehicle", + "puts down an object", + "opens the trunk", + "closes the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "puts down an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.63 + } + }, + { + "question_id": "xcam_temporal_004", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle exits a vehicle, what do they do next in a different camera view?", + "options": [ + "unloads a vehicle", + "closes the trunk", + "closes a vehicle door", + "opens the trunk" + ], + "correct_answer_index": 2, + "correct_answer": "closes a vehicle door", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_005", + "event_a": { + "event_id": "G340_evt_3", + "activity": "person_exits_vehicle", + "camera_id": "G340", + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_13", + "activity": "person_closes_vehicle_door", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": 26.97 + } + }, + { + "question_id": "xcam_temporal_005", + "category": "cross_camera_temporal", + "question_template": "After the person who closes the trunk picks up an object, what do they do next in a different camera view?", + "options": [ + "enters a vehicle", + "carries a heavy object", + "puts down an object", + "picks up an object" + ], + "correct_answer_index": 2, + "correct_answer": "puts down an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.16 + } + }, + { + "question_id": "xcam_temporal_006", + "category": "cross_camera_temporal", + "question_template": "After the person who opens the trunk carries a heavy object, what do they do next in a different camera view?", + "options": [ + "picks up an object", + "closes the trunk", + "opens the trunk", + "enters the scene through a structure" + ], + "correct_answer_index": 0, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -1.4 + } + }, + { + "question_id": "xcam_temporal_007", + "category": "cross_camera_temporal", + "question_template": "After the person who closes the trunk closes the trunk, what do they do next in a different camera view?", + "options": [ + "picks up an object", + "opens a vehicle door", + "exits a vehicle", + "puts down an object" + ], + "correct_answer_index": 0, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 5.16 + } + }, + { + "question_id": "xcam_temporal_008", + "category": "cross_camera_temporal", + "question_template": "After the person who closes the trunk carries a heavy object, what do they do next in a different camera view?", + "options": [ + "enters a vehicle", + "picks up an object", + "talks to another person", + "enters the scene through a structure" + ], + "correct_answer_index": 1, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.7 + } + }, + { + "question_id": "xcam_ordering_001", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: picking up an object, carrying a heavy object, putting down an object?", + "options": [ + "carries a heavy object \u2192 puts down an object \u2192 picks up an object", + "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 picks up an object \u2192 puts down an object", + "puts down an object \u2192 carries a heavy object \u2192 picks up an object" + ], + "correct_answer_index": 1, + "correct_answer": "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "events": [ + { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_002", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: unloading a vehicle, carrying a heavy object, picking up an object?", + "options": [ + "picks up an object \u2192 unloads a vehicle \u2192 carries a heavy object", + "picks up an object \u2192 carries a heavy object \u2192 unloads a vehicle", + "carries a heavy object \u2192 picks up an object \u2192 unloads a vehicle", + "unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "events": [ + { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_003", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: unloading a vehicle, carrying a heavy object, picking up an object?", + "options": [ + "unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "carries a heavy object \u2192 picks up an object \u2192 unloads a vehicle", + "carries a heavy object \u2192 unloads a vehicle \u2192 picks up an object", + "picks up an object \u2192 carries a heavy object \u2192 unloads a vehicle" + ], + "correct_answer_index": 0, + "correct_answer": "unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "events": [ + { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_004", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: closing the trunk, picking up an object, carrying a heavy object?", + "options": [ + "carries a heavy object \u2192 closes the trunk \u2192 picks up an object", + "closes the trunk \u2192 picks up an object \u2192 carries a heavy object", + "closes the trunk \u2192 carries a heavy object \u2192 picks up an object", + "picks up an object \u2192 carries a heavy object \u2192 closes the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "closes the trunk \u2192 picks up an object \u2192 carries a heavy object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "events": [ + { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_005", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: transferring an object, talking to another person, closing the trunk?", + "options": [ + "talks to another person \u2192 closes the trunk \u2192 transfers an object", + "closes the trunk \u2192 talks to another person \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "transfers an object \u2192 talks to another person \u2192 closes the trunk" + ], + "correct_answer_index": 3, + "correct_answer": "transfers an object \u2192 talks to another person \u2192 closes the trunk", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "events": [ + { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_10", + "activity": "person_talks_to_person", + "camera_id": "G340", + "start_time_sec": 101.3, + "end_time_sec": 121.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_11", + "activity": "person_closes_trunk", + "camera_id": "G340", + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_006", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: picking up an object, putting down an object, transferring an object?", + "options": [ + "picks up an object \u2192 transfers an object \u2192 puts down an object", + "puts down an object \u2192 transfers an object \u2192 picks up an object", + "picks up an object \u2192 puts down an object \u2192 transfers an object", + "transfers an object \u2192 puts down an object \u2192 picks up an object" + ], + "correct_answer_index": 2, + "correct_answer": "picks up an object \u2192 puts down an object \u2192 transfers an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "events": [ + { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_15", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 113.8, + "end_time_sec": 116.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_007", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: carrying a heavy object, picking up an object, putting down an object?", + "options": [ + "carries a heavy object \u2192 puts down an object \u2192 picks up an object", + "puts down an object \u2192 picks up an object \u2192 carries a heavy object", + "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 picks up an object \u2192 puts down an object" + ], + "correct_answer_index": 3, + "correct_answer": "carries a heavy object \u2192 picks up an object \u2192 puts down an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "events": [ + { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_008", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: opening the trunk, unloading a vehicle, carrying a heavy object?", + "options": [ + "carries a heavy object \u2192 unloads a vehicle \u2192 opens the trunk", + "carries a heavy object \u2192 opens the trunk \u2192 unloads a vehicle", + "unloads a vehicle \u2192 carries a heavy object \u2192 opens the trunk", + "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object" + ], + "correct_answer_index": 3, + "correct_answer": "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "events": [ + { + "event_id": "G506_evt_8", + "activity": "person_opens_trunk", + "camera_id": "G506", + "start_time_sec": 90.4, + "end_time_sec": 93.6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_009", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: exiting a vehicle, closing a vehicle door, entering a vehicle?", + "options": [ + "enters a vehicle \u2192 closes a vehicle door \u2192 exits a vehicle", + "exits a vehicle \u2192 closes a vehicle door \u2192 enters a vehicle", + "exits a vehicle \u2192 enters a vehicle \u2192 closes a vehicle door", + "closes a vehicle door \u2192 enters a vehicle \u2192 exits a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "exits a vehicle \u2192 closes a vehicle door \u2192 enters a vehicle", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_005", + "events": [ + { + "event_id": "G340_evt_3", + "activity": "person_exits_vehicle", + "camera_id": "G340", + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G506_evt_13", + "activity": "person_closes_vehicle_door", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_14", + "activity": "person_enters_vehicle", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_010", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: carrying a heavy object, picking up an object, putting down an object?", + "options": [ + "puts down an object \u2192 picks up an object \u2192 carries a heavy object", + "picks up an object \u2192 puts down an object \u2192 carries a heavy object", + "carries a heavy object \u2192 picks up an object \u2192 puts down an object", + "carries a heavy object \u2192 puts down an object \u2192 picks up an object" + ], + "correct_answer_index": 2, + "correct_answer": "carries a heavy object \u2192 picks up an object \u2192 puts down an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "events": [ + { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ] + } + }, + { + "question_id": "xcam_tracking_001", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk puts down an object in camera G506, which camera view shows them next?", + "options": [ + "G505", + "G506", + "G340" + ], + "correct_answer_index": 2, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_15", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 113.8, + "end_time_sec": 116.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.63 + } + }, + { + "question_id": "xcam_tracking_002", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk carries a heavy object in camera G340, which camera view shows them next?", + "options": [ + "G506", + "G505", + "G340" + ], + "correct_answer_index": 0, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.7 + } + }, + { + "question_id": "xcam_tracking_003", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk unloads a vehicle in camera G340, which camera view shows them next?", + "options": [ + "G506", + "G505", + "G340" + ], + "correct_answer_index": 0, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -3.4 + } + }, + { + "question_id": "xcam_tracking_004", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk puts down an object in camera G340, which camera view shows them next?", + "options": [ + "G505", + "G506", + "G340" + ], + "correct_answer_index": 1, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.36 + } + }, + { + "question_id": "xcam_tracking_005", + "category": "cross_camera_tracking", + "question_template": "After the person who enters a vehicle exits a vehicle in camera G340, which camera view shows them next?", + "options": [ + "G340", + "G506", + "G505" + ], + "correct_answer_index": 1, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_005", + "event_a": { + "event_id": "G340_evt_3", + "activity": "person_exits_vehicle", + "camera_id": "G340", + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_13", + "activity": "person_closes_vehicle_door", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": 26.97 + } + }, + { + "question_id": "xcam_tracking_006", + "category": "cross_camera_tracking", + "question_template": "After the person who unloads a vehicle opens the trunk in camera G506, which camera view shows them next?", + "options": [ + "G505", + "G340", + "G506" + ], + "correct_answer_index": 1, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_8", + "activity": "person_opens_trunk", + "camera_id": "G506", + "start_time_sec": 90.4, + "end_time_sec": 93.6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.03 + } + }, + { + "question_id": "xcam_tracking_007", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk picks up an object in camera G506, which camera view shows them next?", + "options": [ + "G340", + "G505", + "G506" + ], + "correct_answer_index": 0, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.16 + } + }, + { + "question_id": "xcam_tracking_008", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk carries a heavy object in camera G506, which camera view shows them next?", + "options": [ + "G505", + "G506", + "G340" + ], + "correct_answer_index": 2, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -1.4 + } + }, + { + "question_id": "xcam_tracking_009", + "category": "cross_camera_tracking", + "question_template": "After the person who carries a heavy object closes the trunk in camera G506, which camera view shows them next?", + "options": [ + "G505", + "G506", + "G340" + ], + "correct_answer_index": 2, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 5.16 + } + }, + { + "question_id": "xcam_tracking_010", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk puts down an object in camera G506, which camera view shows them next?", + "options": [ + "G340", + "G506", + "G505" + ], + "correct_answer_index": 0, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_8", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_time_sec": 98.77, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.93 + } + }, + { + "question_id": "xcam_tracking_011", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk transfers an object in camera G506, which camera view shows them next?", + "options": [ + "G506", + "G340", + "G505" + ], + "correct_answer_index": 1, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_33", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_23", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 117.2, + "end_time_sec": 119.2, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.73 + } + }, + { + "question_id": "xcam_tracking_012", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk transfers an object in camera G340, which camera view shows them next?", + "options": [ + "G506", + "G505", + "G340" + ], + "correct_answer_index": 0, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G340_evt_15", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 113.8, + "end_time_sec": 116.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_33", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -2.1 + } + }, + { + "question_id": "xcam_tracking_013", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk transfers an object in camera G340, which camera view shows them next?", + "options": [ + "G505", + "G340", + "G506" + ], + "correct_answer_index": 2, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G340_evt_23", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 117.2, + "end_time_sec": 119.2, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_34", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_time_sec": 117.8, + "end_time_sec": 119.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.4 + } + }, + { + "question_id": "xcam_tracking_014", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk carries a heavy object in camera G340, which camera view shows them next?", + "options": [ + "G505", + "G340", + "G506" + ], + "correct_answer_index": 2, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.63 + } + }, + { + "question_id": "xcam_tracking_015", + "category": "cross_camera_tracking", + "question_template": "After the person who closes the trunk transfers an object in camera G506, which camera view shows them next?", + "options": [ + "G506", + "G505", + "G340" + ], + "correct_answer_index": 2, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_22", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_time_sec": 101.3, + "end_time_sec": 103.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -2.97 + } + }, + { + "question_id": "xcam_gap_001", + "category": "cross_camera_gap", + "question_template": "Between carrying a heavy object and carrying a heavy object, what does the person who opens the trunk do?", + "options": [ + "exits the scene through a structure", + "carries a heavy object", + "picks up an object", + "closes the trunk" + ], + "correct_answer_index": 2, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_middle": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_c": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + } + }, + { + "question_id": "xcam_gap_002", + "category": "cross_camera_gap", + "question_template": "Between picking up an object and putting down an object, what does the person who opens the trunk do?", + "options": [ + "transfers an object", + "carries a heavy object", + "puts down an object", + "enters the scene through a structure" + ], + "correct_answer_index": 1, + "correct_answer": "carries a heavy object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_middle": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_c": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "xcam_gap_003", + "category": "cross_camera_gap", + "question_template": "Between closing the trunk and carrying a heavy object, what does the person who closes the trunk do?", + "options": [ + "opens a vehicle door", + "talks to another person", + "picks up an object", + "opens the trunk" + ], + "correct_answer_index": 2, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_middle": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_c": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + } + }, + { + "question_id": "xcam_gap_004", + "category": "cross_camera_gap", + "question_template": "Between exiting a vehicle and entering a vehicle, what does the person who exits a vehicle do?", + "options": [ + "closes the trunk", + "exits a vehicle", + "closes a vehicle door", + "talks to another person" + ], + "correct_answer_index": 2, + "correct_answer": "closes a vehicle door", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_005", + "event_a": { + "event_id": "G340_evt_3", + "activity": "person_exits_vehicle", + "camera_id": "G340", + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_middle": { + "event_id": "G506_evt_13", + "activity": "person_closes_vehicle_door", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_c": { + "event_id": "G506_evt_14", + "activity": "person_enters_vehicle", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "xcam_gap_005", + "category": "cross_camera_gap", + "question_template": "Between picking up an object and picking up an object, what does the person who closes the trunk do?", + "options": [ + "talks to another person", + "carries a heavy object", + "puts down an object", + "enters the scene through a structure" + ], + "correct_answer_index": 1, + "correct_answer": "carries a heavy object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_middle": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_c": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "xcam_gap_006", + "category": "cross_camera_gap", + "question_template": "Between unloading a vehicle and picking up an object, what does the person who opens the trunk do?", + "options": [ + "enters a vehicle", + "unloads a vehicle", + "exits a vehicle", + "carries a heavy object" + ], + "correct_answer_index": 3, + "correct_answer": "carries a heavy object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_middle": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_c": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + } + }, + { + "question_id": "xcam_identify_001", + "category": "camera_identification", + "question_template": "In one camera view, a person closes the trunk. In which other camera view does this same person carry a heavy object?", + "options": [ + "G505", + "G506", + "G340" + ], + "correct_answer_index": 2, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "identifying_event": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "target_event": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "identifying_camera": "G506", + "target_camera": "G340" + } + }, + { + "question_id": "xcam_identify_002", + "category": "camera_identification", + "question_template": "In one camera view, a person enters a vehicle. In which other camera view does this same person exit a vehicle?", + "options": [ + "G340", + "G506", + "G505" + ], + "correct_answer_index": 0, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_005", + "identifying_event": { + "event_id": "G506_evt_14", + "activity": "person_enters_vehicle", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "target_event": { + "event_id": "G340_evt_3", + "activity": "person_exits_vehicle", + "camera_id": "G340", + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "identifying_camera": "G506", + "target_camera": "G340" + } + }, + { + "question_id": "xcam_identify_003", + "category": "camera_identification", + "question_template": "In one camera view, a person closes the trunk. In which other camera view does this same person transfer an object?", + "options": [ + "G506", + "G340", + "G505" + ], + "correct_answer_index": 0, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "identifying_event": { + "event_id": "G340_evt_11", + "activity": "person_closes_trunk", + "camera_id": "G340", + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "target_event": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "identifying_camera": "G340", + "target_camera": "G506" + } + }, + { + "question_id": "xcam_identify_004", + "category": "camera_identification", + "question_template": "In one camera view, a person opens the trunk. In which other camera view does this same person unload a vehicle?", + "options": [ + "G340", + "G505", + "G506" + ], + "correct_answer_index": 0, + "correct_answer": "G340", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "identifying_event": { + "event_id": "G506_evt_8", + "activity": "person_opens_trunk", + "camera_id": "G506", + "start_time_sec": 90.4, + "end_time_sec": 93.6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "target_event": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "identifying_camera": "G506", + "target_camera": "G340" + } + }, + { + "question_id": "xcam_identify_005", + "category": "camera_identification", + "question_template": "In one camera view, a person opens the trunk. In which other camera view does this same person unload a vehicle?", + "options": [ + "G340", + "G506", + "G505" + ], + "correct_answer_index": 1, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_001", + "identifying_event": { + "event_id": "G340_evt_1", + "activity": "person_opens_trunk", + "camera_id": "G340", + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "target_event": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "identifying_camera": "G340", + "target_camera": "G506" + } + }, + { + "question_id": "xcam_identify_006", + "category": "camera_identification", + "question_template": "In one camera view, a person exits a vehicle. In which other camera view does this same person enter a vehicle?", + "options": [ + "G505", + "G506", + "G340" + ], + "correct_answer_index": 1, + "correct_answer": "G506", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_005", + "identifying_event": { + "event_id": "G340_evt_3", + "activity": "person_exits_vehicle", + "camera_id": "G340", + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "target_event": { + "event_id": "G506_evt_14", + "activity": "person_enters_vehicle", + "camera_id": "G506", + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "identifying_camera": "G340", + "target_camera": "G506" + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.logic.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.logic.json new file mode 100644 index 0000000..2a94465 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.logic.json @@ -0,0 +1,2955 @@ +{ + "slot": "2018-03-05.13-15-00.bus", + "method": "rule-based", + "input_file": "data/logic_tuples/2018-03-05.13-15-00.bus.json", + "total_questions": 57, + "questions_per_category": { + "temporal": 10, + "ordering": 10, + "perception": 10, + "causality": 10, + "spatial": 7, + "camera_transition": 10 + }, + "qa_pairs": [ + { + "question_id": "temporal_001", + "category": "temporal", + "question": "Did 'person puts down object' (camera G340) occur before, after, or during 'vehicle makes u turn' (camera G506)?", + "answer": "'person puts down object' on camera G340 ended 175.54s before 'vehicle makes u turn' started on camera G506.", + "evidence": { + "event1": { + "activity": "person_puts_down_object", + "activity_id": 14, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3380, + "end_frame": 3391, + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "duration_sec": 0.37, + "actors": [ + "person_28", + "bag_29" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_makes_u_turn", + "activity_id": 27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 8657, + "end_frame": 8844, + "start_time_sec": 288.57, + "end_time_sec": 294.8, + "duration_sec": 6.23, + "actors": [ + "vehicle_42" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 175.54 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "temporal_002", + "category": "temporal", + "question": "Did 'vehicle starts' (camera G340) occur before, after, or during 'person talks to person' (camera G506)?", + "answer": "'vehicle starts' on camera G340 ended 44.64s before 'person talks to person' started on camera G506.", + "evidence": { + "event1": { + "activity": "vehicle_starts", + "activity_id": 21, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 1573, + "end_frame": 1693, + "start_time_sec": 52.43, + "end_time_sec": 56.43, + "duration_sec": 4.0, + "actors": [ + "vehicle_38" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "person_talks_to_person", + "activity_id": 15, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "duration_sec": 21.73, + "actors": [ + "person_27", + "person_28" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 44.64 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "temporal_003", + "category": "temporal", + "question": "Did 'person puts down object' (camera G340) occur before, after, or during 'vehicle turns right' (camera G505)?", + "answer": "'person puts down object' on camera G340 ended 182.44s before 'vehicle turns right' started on camera G505.", + "evidence": { + "event1": { + "activity": "person_puts_down_object", + "activity_id": 8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2963, + "end_frame": 2980, + "start_time_sec": 98.77, + "end_time_sec": 99.33, + "duration_sec": 0.57, + "actors": [ + "bag_16", + "person_17" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_turns_right", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 8453, + "end_frame": 8523, + "start_time_sec": 281.77, + "end_time_sec": 284.1, + "duration_sec": 2.33, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 182.44 + }, + "requires_cameras": [ + "G340", + "G505" + ] + }, + { + "question_id": "temporal_004", + "category": "temporal", + "question": "Did 'person picks up object' (camera G340) occur before, after, or during 'person picks up object' (camera G506)?", + "answer": "'person picks up object' on camera G340 ended 13.6s before 'person picks up object' started on camera G506.", + "evidence": { + "event1": { + "activity": "person_picks_up_object", + "activity_id": 7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "duration_sec": 0.43, + "actors": [ + "bag_15", + "person_14" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "person_picks_up_object", + "activity_id": 29, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "duration_sec": 0.5, + "actors": [ + "person_45", + "bag_46" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 13.6 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "temporal_005", + "category": "temporal", + "question": "Did 'vehicle turns right' (camera G505) occur before, after, or during 'person enters scene through structure' (camera G506)?", + "answer": "'vehicle turns right' on camera G505 ended 199.47s before 'person enters scene through structure' started on camera G506.", + "evidence": { + "event1": { + "activity": "vehicle_turns_right", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 456, + "end_frame": 538, + "start_time_sec": 15.2, + "end_time_sec": 17.93, + "duration_sec": 2.73, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "person_enters_scene_through_structure", + "activity_id": 22, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "duration_sec": 1.53, + "actors": [ + "person_36" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 199.47 + }, + "requires_cameras": [ + "G505", + "G506" + ] + }, + { + "question_id": "temporal_006", + "category": "temporal", + "question": "Did 'person exits vehicle' (camera G506) occur before, after, or during 'vehicle starts' (camera G340)?", + "answer": "'person exits vehicle' on camera G506 ended 69.43s before 'vehicle starts' started on camera G340.", + "evidence": { + "event1": { + "activity": "person_exits_vehicle", + "activity_id": 7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2517, + "end_frame": 2663, + "start_time_sec": 83.9, + "end_time_sec": 88.77, + "duration_sec": 4.87, + "actors": [ + "vehicle_8", + "person_7" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_starts", + "activity_id": 19, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4746, + "end_frame": 4866, + "start_time_sec": 158.2, + "end_time_sec": 162.2, + "duration_sec": 4.0, + "actors": [ + "vehicle_36" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 69.43 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "temporal_007", + "category": "temporal", + "question": "Did 'person picks up object' (camera G506) occur before, after, or during 'vehicle turns left' (camera G340)?", + "answer": "'person picks up object' on camera G506 ended 174.07s before 'vehicle turns left' started on camera G340.", + "evidence": { + "event1": { + "activity": "person_picks_up_object", + "activity_id": 29, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "duration_sec": 0.5, + "actors": [ + "person_45", + "bag_46" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_turns_left", + "activity_id": 25, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 8607, + "end_frame": 8722, + "start_time_sec": 286.9, + "end_time_sec": 290.73, + "duration_sec": 3.83, + "actors": [ + "vehicle_1004" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 174.07 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "temporal_008", + "category": "temporal", + "question": "Did 'vehicle turns left' (camera G506) occur before, after, or during 'vehicle turns right' (camera G505)?", + "answer": "'vehicle turns left' on camera G506 ended 115.94s before 'vehicle turns right' started on camera G505.", + "evidence": { + "event1": { + "activity": "vehicle_turns_left", + "activity_id": 19, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4826, + "end_frame": 4975, + "start_time_sec": 160.87, + "end_time_sec": 165.83, + "duration_sec": 4.97, + "actors": [ + "vehicle_34" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_turns_right", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 8453, + "end_frame": 8523, + "start_time_sec": 281.77, + "end_time_sec": 284.1, + "duration_sec": 2.33, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 115.94 + }, + "requires_cameras": [ + "G505", + "G506" + ] + }, + { + "question_id": "temporal_009", + "category": "temporal", + "question": "Did 'vehicle reverses' (camera G340) occur before, after, or during 'vehicle turns left' (camera G506)?", + "answer": "'vehicle reverses' on camera G340 ended 3.07s before 'vehicle turns left' started on camera G506.", + "evidence": { + "event1": { + "activity": "vehicle_reverses", + "activity_id": 18, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4287, + "end_frame": 4734, + "start_time_sec": 142.9, + "end_time_sec": 157.8, + "duration_sec": 14.9, + "actors": [ + "vehicle_35" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_turns_left", + "activity_id": 19, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4826, + "end_frame": 4975, + "start_time_sec": 160.87, + "end_time_sec": 165.83, + "duration_sec": 4.97, + "actors": [ + "vehicle_34" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 3.07 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "temporal_010", + "category": "temporal", + "question": "Did 'person transfers object' (camera G340) occur before, after, or during 'vehicle reverses' (camera G506)?", + "answer": "'person transfers object' on camera G340 ended 20.26s before 'vehicle reverses' started on camera G506.", + "evidence": { + "event1": { + "activity": "person_transfers_object", + "activity_id": 15, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3414, + "end_frame": 3497, + "start_time_sec": 113.8, + "end_time_sec": 116.57, + "duration_sec": 2.77, + "actors": [ + "person_30", + "person_31" + ], + "annotation_source": "kitware" + }, + "event2": { + "activity": "vehicle_reverses", + "activity_id": 17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4105, + "end_frame": 4225, + "start_time_sec": 136.83, + "end_time_sec": 140.83, + "duration_sec": 4.0, + "actors": [ + "vehicle_31" + ], + "annotation_source": "kitware" + }, + "relationship": "before", + "time_gap_seconds": 20.26 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_001", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'person opens trunk' on camera G506\n B: 'person opens trunk' on camera G340\n C: 'person carries heavy object' on camera G506", + "answer": "Correct order: B \u2192 A \u2192 C", + "evidence": { + "events_chronological": [ + { + "activity": "person_opens_trunk", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "duration_sec": 4.23, + "actors": [ + "person_3", + "vehicle_2" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_opens_trunk", + "activity_id": 8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2712, + "end_frame": 2808, + "start_time_sec": 90.4, + "end_time_sec": 93.6, + "duration_sec": 3.2, + "actors": [ + "vehicle_9", + "person_10" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_carries_heavy_object", + "activity_id": 32, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "duration_sec": 4.37, + "actors": [ + "bag_50", + "person_49" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "person_opens_trunk", + "activity_id": 8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2712, + "end_frame": 2808, + "start_time_sec": 90.4, + "end_time_sec": 93.6, + "duration_sec": 3.2, + "actors": [ + "vehicle_9", + "person_10" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "person_opens_trunk", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "duration_sec": 4.23, + "actors": [ + "person_3", + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_carries_heavy_object", + "activity_id": 32, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "duration_sec": 4.37, + "actors": [ + "bag_50", + "person_49" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_002", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'vehicle turns right' on camera G505\n B: 'person closes trunk' on camera G340\n C: 'person opens trunk' on camera G340", + "answer": "Correct order: A \u2192 C \u2192 B", + "evidence": { + "events_chronological": [ + { + "activity": "vehicle_turns_right", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 456, + "end_frame": 538, + "start_time_sec": 15.2, + "end_time_sec": 17.93, + "duration_sec": 2.73, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_opens_trunk", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "duration_sec": 4.23, + "actors": [ + "person_3", + "vehicle_2" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_closes_trunk", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "duration_sec": 2.6, + "actors": [ + "person_22", + "vehicle_23" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "vehicle_turns_right", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 456, + "end_frame": 538, + "start_time_sec": 15.2, + "end_time_sec": 17.93, + "duration_sec": 2.73, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "person_closes_trunk", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "duration_sec": 2.6, + "actors": [ + "person_22", + "vehicle_23" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_opens_trunk", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "duration_sec": 4.23, + "actors": [ + "person_3", + "vehicle_2" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G505" + ] + }, + { + "question_id": "ordering_003", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'vehicle turns left' on camera G506\n B: 'person picks up object' on camera G340\n C: 'person unloads vehicle' on camera G340", + "answer": "Correct order: A \u2192 C \u2192 B", + "evidence": { + "events_chronological": [ + { + "activity": "vehicle_turns_left", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1179, + "end_frame": 1303, + "start_time_sec": 39.3, + "end_time_sec": 43.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_unloads_vehicle", + "activity_id": 5, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "duration_sec": 5.1, + "actors": [ + "person_9", + "bag_11", + "vehicle_10" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_picks_up_object", + "activity_id": 7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "duration_sec": 0.43, + "actors": [ + "bag_15", + "person_14" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "vehicle_turns_left", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1179, + "end_frame": 1303, + "start_time_sec": 39.3, + "end_time_sec": 43.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "person_picks_up_object", + "activity_id": 7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "duration_sec": 0.43, + "actors": [ + "bag_15", + "person_14" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_unloads_vehicle", + "activity_id": 5, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "duration_sec": 5.1, + "actors": [ + "person_9", + "bag_11", + "vehicle_10" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_004", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'vehicle turns left' on camera G340\n B: 'vehicle turns right' on camera G505\n C: 'person picks up object' on camera G340", + "answer": "Correct order: C \u2192 A \u2192 B", + "evidence": { + "events_chronological": [ + { + "activity": "person_picks_up_object", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "duration_sec": 0.4, + "actors": [ + "bag_26", + "person_27" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_turns_left", + "activity_id": 24, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4723, + "end_frame": 4888, + "start_time_sec": 157.43, + "end_time_sec": 162.93, + "duration_sec": 5.5, + "actors": [ + "vehicle_1003" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_turns_right", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 8453, + "end_frame": 8523, + "start_time_sec": 281.77, + "end_time_sec": 284.1, + "duration_sec": 2.33, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "vehicle_turns_left", + "activity_id": 24, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4723, + "end_frame": 4888, + "start_time_sec": 157.43, + "end_time_sec": 162.93, + "duration_sec": 5.5, + "actors": [ + "vehicle_1003" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "vehicle_turns_right", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 8453, + "end_frame": 8523, + "start_time_sec": 281.77, + "end_time_sec": 284.1, + "duration_sec": 2.33, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_picks_up_object", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "duration_sec": 0.4, + "actors": [ + "bag_26", + "person_27" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G505" + ] + }, + { + "question_id": "ordering_005", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'vehicle turns right' on camera G505\n B: 'vehicle reverses' on camera G506\n C: 'person closes vehicle door' on camera G506", + "answer": "Correct order: C \u2192 B \u2192 A", + "evidence": { + "events_chronological": [ + { + "activity": "person_closes_vehicle_door", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "duration_sec": 2.27, + "actors": [ + "vehicle_21", + "person_22" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_reverses", + "activity_id": 18, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4301, + "end_frame": 4392, + "start_time_sec": 143.37, + "end_time_sec": 146.4, + "duration_sec": 3.03, + "actors": [ + "vehicle_32" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_turns_right", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 6743, + "end_frame": 6826, + "start_time_sec": 224.77, + "end_time_sec": 227.53, + "duration_sec": 2.77, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "vehicle_turns_right", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 6743, + "end_frame": 6826, + "start_time_sec": 224.77, + "end_time_sec": 227.53, + "duration_sec": 2.77, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "vehicle_reverses", + "activity_id": 18, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4301, + "end_frame": 4392, + "start_time_sec": 143.37, + "end_time_sec": 146.4, + "duration_sec": 3.03, + "actors": [ + "vehicle_32" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_closes_vehicle_door", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "duration_sec": 2.27, + "actors": [ + "vehicle_21", + "person_22" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G505", + "G506" + ] + }, + { + "question_id": "ordering_006", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'person unloads vehicle' on camera G340\n B: 'vehicle stops' on camera G506\n C: 'person transfers object' on camera G506", + "answer": "Correct order: B \u2192 A \u2192 C", + "evidence": { + "events_chronological": [ + { + "activity": "vehicle_stops", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2440, + "end_frame": 2525, + "start_time_sec": 81.33, + "end_time_sec": 84.17, + "duration_sec": 2.83, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_unloads_vehicle", + "activity_id": 5, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "duration_sec": 5.1, + "actors": [ + "person_9", + "bag_11", + "vehicle_10" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_transfers_object", + "activity_id": 33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3434, + "end_frame": 3494, + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "duration_sec": 2.0, + "actors": [ + "person_1000", + "person_1001" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "person_unloads_vehicle", + "activity_id": 5, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "duration_sec": 5.1, + "actors": [ + "person_9", + "bag_11", + "vehicle_10" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "vehicle_stops", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2440, + "end_frame": 2525, + "start_time_sec": 81.33, + "end_time_sec": 84.17, + "duration_sec": 2.83, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_transfers_object", + "activity_id": 33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3434, + "end_frame": 3494, + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "duration_sec": 2.0, + "actors": [ + "person_1000", + "person_1001" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_007", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'person opens facility door' on camera G506\n B: 'vehicle stops' on camera G506\n C: 'person carries heavy object' on camera G340", + "answer": "Correct order: C \u2192 A \u2192 B", + "evidence": { + "events_chronological": [ + { + "activity": "person_carries_heavy_object", + "activity_id": 6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "duration_sec": 1.03, + "actors": [ + "bag_12", + "person_13" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_opens_facility_door", + "activity_id": 24, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 7909, + "end_frame": 7951, + "start_time_sec": 263.63, + "end_time_sec": 265.03, + "duration_sec": 1.4, + "actors": [ + "person_39" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_stops", + "activity_id": 26, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 8779, + "end_frame": 8844, + "start_time_sec": 292.63, + "end_time_sec": 294.8, + "duration_sec": 2.17, + "actors": [ + "vehicle_41" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "person_opens_facility_door", + "activity_id": 24, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 7909, + "end_frame": 7951, + "start_time_sec": 263.63, + "end_time_sec": 265.03, + "duration_sec": 1.4, + "actors": [ + "person_39" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "vehicle_stops", + "activity_id": 26, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 8779, + "end_frame": 8844, + "start_time_sec": 292.63, + "end_time_sec": 294.8, + "duration_sec": 2.17, + "actors": [ + "vehicle_41" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_carries_heavy_object", + "activity_id": 6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "duration_sec": 1.03, + "actors": [ + "bag_12", + "person_13" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_008", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'vehicle stops' on camera G506\n B: 'person enters scene through structure' on camera G506\n C: 'person opens trunk' on camera G340", + "answer": "Correct order: A \u2192 C \u2192 B", + "evidence": { + "events_chronological": [ + { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_opens_trunk", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "duration_sec": 4.23, + "actors": [ + "person_3", + "vehicle_2" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_enters_scene_through_structure", + "activity_id": 22, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "duration_sec": 1.53, + "actors": [ + "person_36" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "person_enters_scene_through_structure", + "activity_id": 22, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "duration_sec": 1.53, + "actors": [ + "person_36" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_opens_trunk", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "duration_sec": 4.23, + "actors": [ + "person_3", + "vehicle_2" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_009", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'vehicle stops' on camera G506\n B: 'person carries heavy object' on camera G340\n C: 'person picks up object' on camera G340", + "answer": "Correct order: A \u2192 B \u2192 C", + "evidence": { + "events_chronological": [ + { + "activity": "vehicle_stops", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2440, + "end_frame": 2525, + "start_time_sec": 81.33, + "end_time_sec": 84.17, + "duration_sec": 2.83, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_carries_heavy_object", + "activity_id": 12, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "duration_sec": 0.8, + "actors": [ + "bag_25", + "person_24" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_picks_up_object", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "duration_sec": 0.4, + "actors": [ + "bag_26", + "person_27" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "vehicle_stops", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2440, + "end_frame": 2525, + "start_time_sec": 81.33, + "end_time_sec": 84.17, + "duration_sec": 2.83, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "person_carries_heavy_object", + "activity_id": 12, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "duration_sec": 0.8, + "actors": [ + "bag_25", + "person_24" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_picks_up_object", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "duration_sec": 0.4, + "actors": [ + "bag_26", + "person_27" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "ordering_010", + "category": "ordering", + "question": "Arrange these events in chronological order:\n A: 'person transfers object' on camera G506\n B: 'person puts down object' on camera G506\n C: 'person puts down object' on camera G340", + "answer": "Correct order: B \u2192 C \u2192 A", + "evidence": { + "events_chronological": [ + { + "activity": "person_puts_down_object", + "activity_id": 30, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "duration_sec": 0.5, + "actors": [ + "bag_47", + "person_48" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_puts_down_object", + "activity_id": 14, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3380, + "end_frame": 3391, + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "duration_sec": 0.37, + "actors": [ + "person_28", + "bag_29" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_transfers_object", + "activity_id": 33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3434, + "end_frame": 3494, + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "duration_sec": 2.0, + "actors": [ + "person_1000", + "person_1001" + ], + "annotation_source": "kitware" + } + ], + "label_mapping": { + "A": { + "activity": "person_transfers_object", + "activity_id": 33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3434, + "end_frame": 3494, + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "duration_sec": 2.0, + "actors": [ + "person_1000", + "person_1001" + ], + "annotation_source": "kitware" + }, + "B": { + "activity": "person_puts_down_object", + "activity_id": 30, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "duration_sec": 0.5, + "actors": [ + "bag_47", + "person_48" + ], + "annotation_source": "kitware" + }, + "C": { + "activity": "person_puts_down_object", + "activity_id": 14, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3380, + "end_frame": 3391, + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "duration_sec": 0.37, + "actors": [ + "person_28", + "bag_29" + ], + "annotation_source": "kitware" + } + } + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "perception_001", + "category": "perception", + "question": "Is there a 'person carries heavy object' event visible on camera G340?", + "answer": "Yes", + "evidence": { + "activity": "person_carries_heavy_object", + "camera": "G340", + "present": true, + "instances": [ + { + "activity": "person_carries_heavy_object", + "activity_id": 6, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "duration_sec": 1.03, + "actors": [ + "bag_12", + "person_13" + ], + "annotation_source": "kitware" + }, + { + "activity": "person_carries_heavy_object", + "activity_id": 12, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "duration_sec": 0.8, + "actors": [ + "bag_25", + "person_24" + ], + "annotation_source": "kitware" + } + ] + }, + "requires_cameras": [ + "G340" + ] + }, + { + "question_id": "perception_002", + "category": "perception", + "question": "Is there a 'vehicle turns right' event visible on camera G505?", + "answer": "Yes", + "evidence": { + "activity": "vehicle_turns_right", + "camera": "G505", + "present": true, + "instances": [ + { + "activity": "vehicle_turns_right", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 456, + "end_frame": 538, + "start_time_sec": 15.2, + "end_time_sec": 17.93, + "duration_sec": 2.73, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_turns_right", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 6743, + "end_frame": 6826, + "start_time_sec": 224.77, + "end_time_sec": 227.53, + "duration_sec": 2.77, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_turns_right", + "activity_id": 2, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 8144, + "end_frame": 8208, + "start_time_sec": 271.47, + "end_time_sec": 273.6, + "duration_sec": 2.13, + "actors": [ + "vehicle_3" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_turns_right", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 8453, + "end_frame": 8523, + "start_time_sec": 281.77, + "end_time_sec": 284.1, + "duration_sec": 2.33, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + } + ] + }, + "requires_cameras": [ + "G505" + ] + }, + { + "question_id": "perception_003", + "category": "perception", + "question": "Is there a 'vehicle reverses' event visible on camera G340?", + "answer": "Yes", + "evidence": { + "activity": "vehicle_reverses", + "camera": "G340", + "present": true, + "instances": [ + { + "activity": "vehicle_reverses", + "activity_id": 20, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 845, + "end_frame": 1509, + "start_time_sec": 28.17, + "end_time_sec": 50.3, + "duration_sec": 22.13, + "actors": [ + "vehicle_37" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_reverses", + "activity_id": 17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4089, + "end_frame": 4213, + "start_time_sec": 136.3, + "end_time_sec": 140.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_34" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_reverses", + "activity_id": 18, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4287, + "end_frame": 4734, + "start_time_sec": 142.9, + "end_time_sec": 157.8, + "duration_sec": 14.9, + "actors": [ + "vehicle_35" + ], + "annotation_source": "kitware" + } + ] + }, + "requires_cameras": [ + "G340" + ] + }, + { + "question_id": "perception_004", + "category": "perception", + "question": "Is there a 'person talks to person' event visible on camera G506?", + "answer": "Yes", + "evidence": { + "activity": "person_talks_to_person", + "camera": "G506", + "present": true, + "instances": [ + { + "activity": "person_talks_to_person", + "activity_id": 15, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "duration_sec": 21.73, + "actors": [ + "person_27", + "person_28" + ], + "annotation_source": "kitware" + } + ] + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "perception_005", + "category": "perception", + "question": "Is there a 'vehicle reverses' event visible on camera G506?", + "answer": "Yes", + "evidence": { + "activity": "vehicle_reverses", + "camera": "G506", + "present": true, + "instances": [ + { + "activity": "vehicle_reverses", + "activity_id": 17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4105, + "end_frame": 4225, + "start_time_sec": 136.83, + "end_time_sec": 140.83, + "duration_sec": 4.0, + "actors": [ + "vehicle_31" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_reverses", + "activity_id": 18, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4301, + "end_frame": 4392, + "start_time_sec": 143.37, + "end_time_sec": 146.4, + "duration_sec": 3.03, + "actors": [ + "vehicle_32" + ], + "annotation_source": "kitware" + }, + { + "activity": "vehicle_reverses", + "activity_id": 20, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 4428, + "end_frame": 4549, + "start_time_sec": 147.6, + "end_time_sec": 151.63, + "duration_sec": 4.03, + "actors": [ + "vehicle_33" + ], + "annotation_source": "kitware" + } + ] + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "perception_006", + "category": "perception", + "question": "Is there a 'person enters vehicle' event visible on camera G506?", + "answer": "Yes", + "evidence": { + "activity": "person_enters_vehicle", + "camera": "G506", + "present": true, + "instances": [ + { + "activity": "person_enters_vehicle", + "activity_id": 14, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "duration_sec": 2.27, + "actors": [ + "person_26", + "vehicle_25" + ], + "annotation_source": "kitware" + } + ] + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "perception_007", + "category": "perception", + "question": "Is there a 'person enters scene through structure' event visible on camera G505?", + "answer": "No", + "evidence": { + "activity": "person_enters_scene_through_structure", + "camera": "G505", + "present": false, + "instances": [] + }, + "requires_cameras": [ + "G505" + ] + }, + { + "question_id": "perception_008", + "category": "perception", + "question": "Is there a 'person exits scene through structure' event visible on camera G340?", + "answer": "No", + "evidence": { + "activity": "person_exits_scene_through_structure", + "camera": "G340", + "present": false, + "instances": [] + }, + "requires_cameras": [ + "G340" + ] + }, + { + "question_id": "perception_009", + "category": "perception", + "question": "Is there a 'person closes trunk' event visible on camera G505?", + "answer": "No", + "evidence": { + "activity": "person_closes_trunk", + "camera": "G505", + "present": false, + "instances": [] + }, + "requires_cameras": [ + "G505" + ] + }, + { + "question_id": "perception_010", + "category": "perception", + "question": "Is there a 'person opens facility door' event visible on camera G340?", + "answer": "No", + "evidence": { + "activity": "person_opens_facility_door", + "camera": "G340", + "present": false, + "instances": [] + }, + "requires_cameras": [ + "G340" + ] + }, + { + "question_id": "causality_001", + "category": "causality", + "question": "Why did 'person exits vehicle' occur on camera G506 at 83.9s?", + "answer": "Because 'vehicle stops' happened on the same camera 28.47s earlier, which is a typical precursor to 'person exits vehicle'.", + "evidence": { + "effect": { + "activity": "person_exits_vehicle", + "activity_id": 7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2517, + "end_frame": 2663, + "start_time_sec": 83.9, + "end_time_sec": 88.77, + "duration_sec": 4.87, + "actors": [ + "vehicle_8", + "person_7" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "causal_rule": "vehicle_stops \u2192 person_exits_vehicle", + "time_gap_seconds": 28.47 + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "causality_002", + "category": "causality", + "question": "Why did 'person exits vehicle' occur across cameras G506 and G340 at 92.27s?", + "answer": "Because 'vehicle stops' happened on camera G506 36.84s earlier, which is a typical precursor to 'person exits vehicle'.", + "evidence": { + "effect": { + "activity": "person_exits_vehicle", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2768, + "end_frame": 2908, + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "duration_sec": 4.67, + "actors": [ + "vehicle_5", + "person_6" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "causal_rule": "vehicle_stops \u2192 person_exits_vehicle", + "time_gap_seconds": 36.84 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "causality_003", + "category": "causality", + "question": "Why did 'person enters vehicle' occur on camera G506 at 123.9s?", + "answer": "Because 'person opens vehicle door' happened on the same camera 37.33s earlier, which is a typical precursor to 'person enters vehicle'.", + "evidence": { + "effect": { + "activity": "person_enters_vehicle", + "activity_id": 14, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "duration_sec": 2.27, + "actors": [ + "person_26", + "vehicle_25" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "person_opens_vehicle_door", + "activity_id": 4, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2517, + "end_frame": 2597, + "start_time_sec": 83.9, + "end_time_sec": 86.57, + "duration_sec": 2.67, + "actors": [ + "vehicle_5", + "person_6" + ], + "annotation_source": "kitware" + }, + "causal_rule": "person_opens_vehicle_door \u2192 person_enters_vehicle", + "time_gap_seconds": 37.33 + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "causality_004", + "category": "causality", + "question": "Why did 'vehicle starts' occur across cameras G506 and G340 at 158.2s?", + "answer": "Because 'person enters vehicle' happened on camera G506 32.03s earlier, which is a typical precursor to 'vehicle starts'.", + "evidence": { + "effect": { + "activity": "vehicle_starts", + "activity_id": 19, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 4746, + "end_frame": 4866, + "start_time_sec": 158.2, + "end_time_sec": 162.2, + "duration_sec": 4.0, + "actors": [ + "vehicle_36" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "person_enters_vehicle", + "activity_id": 14, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "duration_sec": 2.27, + "actors": [ + "person_26", + "vehicle_25" + ], + "annotation_source": "kitware" + }, + "causal_rule": "person_enters_vehicle \u2192 vehicle_starts", + "time_gap_seconds": 32.03 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "causality_005", + "category": "causality", + "question": "Why did 'vehicle stops' occur on camera G506 at 51.4s?", + "answer": "Because 'vehicle turns left' happened on the same camera 7.97s earlier, which is a typical precursor to 'vehicle stops'.", + "evidence": { + "effect": { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "vehicle_turns_left", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1179, + "end_frame": 1303, + "start_time_sec": 39.3, + "end_time_sec": 43.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "causal_rule": "vehicle_turns_left \u2192 vehicle_stops", + "time_gap_seconds": 7.97 + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "causality_006", + "category": "causality", + "question": "Why did 'vehicle stops' occur across cameras G506 and G340 at 80.83s?", + "answer": "Because 'vehicle turns left' happened on camera G506 37.4s earlier, which is a typical precursor to 'vehicle stops'.", + "evidence": { + "effect": { + "activity": "vehicle_stops", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2425, + "end_frame": 2527, + "start_time_sec": 80.83, + "end_time_sec": 84.23, + "duration_sec": 3.4, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "vehicle_turns_left", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1179, + "end_frame": 1303, + "start_time_sec": 39.3, + "end_time_sec": 43.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "causal_rule": "vehicle_turns_left \u2192 vehicle_stops", + "time_gap_seconds": 37.4 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "causality_007", + "category": "causality", + "question": "Why did 'vehicle stops' occur on camera G506 at 81.33s?", + "answer": "Because 'vehicle turns left' happened on the same camera 37.9s earlier, which is a typical precursor to 'vehicle stops'.", + "evidence": { + "effect": { + "activity": "vehicle_stops", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2440, + "end_frame": 2525, + "start_time_sec": 81.33, + "end_time_sec": 84.17, + "duration_sec": 2.83, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "vehicle_turns_left", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1179, + "end_frame": 1303, + "start_time_sec": 39.3, + "end_time_sec": 43.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "causal_rule": "vehicle_turns_left \u2192 vehicle_stops", + "time_gap_seconds": 37.9 + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "causality_008", + "category": "causality", + "question": "Why did 'vehicle stops' occur on camera G506 at 292.63s?", + "answer": "Because 'vehicle turns left' happened on the same camera 249.2s earlier, which is a typical precursor to 'vehicle stops'.", + "evidence": { + "effect": { + "activity": "vehicle_stops", + "activity_id": 26, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 8779, + "end_frame": 8844, + "start_time_sec": 292.63, + "end_time_sec": 294.8, + "duration_sec": 2.17, + "actors": [ + "vehicle_41" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "vehicle_turns_left", + "activity_id": 0, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1179, + "end_frame": 1303, + "start_time_sec": 39.3, + "end_time_sec": 43.43, + "duration_sec": 4.13, + "actors": [ + "vehicle_1" + ], + "annotation_source": "kitware" + }, + "causal_rule": "vehicle_turns_left \u2192 vehicle_stops", + "time_gap_seconds": 249.2 + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "causality_009", + "category": "causality", + "question": "Why did 'person closes trunk' occur on camera G340 at 104.3s?", + "answer": "Because 'person unloads vehicle' happened on the same camera 5.57s earlier, which is a typical precursor to 'person closes trunk'.", + "evidence": { + "effect": { + "activity": "person_closes_trunk", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "duration_sec": 2.6, + "actors": [ + "person_22", + "vehicle_23" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "person_unloads_vehicle", + "activity_id": 5, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "duration_sec": 5.1, + "actors": [ + "person_9", + "bag_11", + "vehicle_10" + ], + "annotation_source": "kitware" + }, + "causal_rule": "person_unloads_vehicle \u2192 person_closes_trunk", + "time_gap_seconds": 5.57 + }, + "requires_cameras": [ + "G340" + ] + }, + { + "question_id": "causality_010", + "category": "causality", + "question": "Why did 'person closes trunk' occur across cameras G340 and G506 at 104.4s?", + "answer": "Because 'person unloads vehicle' happened on camera G340 5.67s earlier, which is a typical precursor to 'person closes trunk'.", + "evidence": { + "effect": { + "activity": "person_closes_trunk", + "activity_id": 12, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3132, + "end_frame": 3212, + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "duration_sec": 2.67, + "actors": [ + "person_20", + "vehicle_19" + ], + "annotation_source": "kitware" + }, + "cause": { + "activity": "person_unloads_vehicle", + "activity_id": 5, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "duration_sec": 5.1, + "actors": [ + "person_9", + "bag_11", + "vehicle_10" + ], + "annotation_source": "kitware" + }, + "causal_rule": "person_unloads_vehicle \u2192 person_closes_trunk", + "time_gap_seconds": 5.67 + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "spatial_001", + "category": "spatial", + "question": "Which cameras captured 'vehicle turns right'?", + "answer": "Cameras G505, G506 (2 cameras)", + "evidence": { + "activity": "vehicle_turns_right", + "cameras": [ + "G505", + "G506" + ] + }, + "requires_cameras": [ + "G505", + "G506" + ] + }, + { + "question_id": "spatial_002", + "category": "spatial", + "question": "Which cameras captured 'vehicle reverses'?", + "answer": "Cameras G340, G506 (2 cameras)", + "evidence": { + "activity": "vehicle_reverses", + "cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "spatial_003", + "category": "spatial", + "question": "Which cameras captured 'vehicle turns left'?", + "answer": "Cameras G340, G506 (2 cameras)", + "evidence": { + "activity": "vehicle_turns_left", + "cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "spatial_004", + "category": "spatial", + "question": "Which cameras captured 'vehicle stops'?", + "answer": "Cameras G340, G506 (2 cameras)", + "evidence": { + "activity": "vehicle_stops", + "cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "spatial_005", + "category": "spatial", + "question": "What activities are only visible on camera G506 and not on any other camera?", + "answer": "Activities exclusive to G506: person enters scene through structure, person enters vehicle, person exits scene through structure, person opens facility door, vehicle makes u turn", + "evidence": { + "camera": "G506", + "exclusive_activities": [ + "person_enters_scene_through_structure", + "person_enters_vehicle", + "person_exits_scene_through_structure", + "person_opens_facility_door", + "vehicle_makes_u_turn" + ] + }, + "requires_cameras": [ + "G506" + ] + }, + { + "question_id": "spatial_006", + "category": "spatial", + "question": "Do cameras G340 and G505 share any common activity types?", + "answer": "No, cameras G340 and G505 have no activity types in common.", + "evidence": { + "camera_1": "G340", + "camera_2": "G505", + "shared": [] + }, + "requires_cameras": [ + "G340", + "G505" + ] + }, + { + "question_id": "spatial_007", + "category": "spatial", + "question": "Do cameras G505 and G506 share any common activity types?", + "answer": "Yes, they share: vehicle turns right", + "evidence": { + "camera_1": "G505", + "camera_2": "G506", + "shared": [ + "vehicle_turns_right" + ] + }, + "requires_cameras": [ + "G505", + "G506" + ] + }, + { + "question_id": "camera_transition_001", + "category": "camera_transition", + "question": "Which camera first captured vehicle 2?", + "answer": "Camera G506 at 51.4s (activity: vehicle stops)", + "evidence": { + "actor": "vehicle_2", + "first_appearance": { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G505", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G505", + "G506" + ] + }, + { + "question_id": "camera_transition_002", + "category": "camera_transition", + "question": "Which camera last captured vehicle 2?", + "answer": "Camera G505 at 224.77s (activity: vehicle turns right)", + "evidence": { + "actor": "vehicle_2", + "last_appearance": { + "activity": "vehicle_turns_right", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G505.avi", + "camera_id": "G505", + "site": "bus", + "start_frame": 6743, + "end_frame": 6826, + "start_time_sec": 224.77, + "end_time_sec": 227.53, + "duration_sec": 2.77, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G505", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G505", + "G506" + ] + }, + { + "question_id": "camera_transition_003", + "category": "camera_transition", + "question": "Which camera first captured person 17?", + "answer": "Camera G340 at 98.77s (activity: person puts down object)", + "evidence": { + "actor": "person_17", + "first_appearance": { + "activity": "person_puts_down_object", + "activity_id": 8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2963, + "end_frame": 2980, + "start_time_sec": 98.77, + "end_time_sec": 99.33, + "duration_sec": 0.57, + "actors": [ + "bag_16", + "person_17" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_004", + "category": "camera_transition", + "question": "Which camera last captured person 17?", + "answer": "Camera G506 at 101.23s (activity: person transfers object)", + "evidence": { + "actor": "person_17", + "last_appearance": { + "activity": "person_transfers_object", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "duration_sec": 3.03, + "actors": [ + "bag_18", + "person_16", + "person_17" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_005", + "category": "camera_transition", + "question": "Which camera first captured person 20?", + "answer": "Camera G340 at 101.3s (activity: person talks to person)", + "evidence": { + "actor": "person_20", + "first_appearance": { + "activity": "person_talks_to_person", + "activity_id": 10, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3039, + "end_frame": 3659, + "start_time_sec": 101.3, + "end_time_sec": 121.97, + "duration_sec": 20.67, + "actors": [ + "person_20", + "person_21" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_006", + "category": "camera_transition", + "question": "Which camera last captured person 20?", + "answer": "Camera G506 at 104.4s (activity: person closes trunk)", + "evidence": { + "actor": "person_20", + "last_appearance": { + "activity": "person_closes_trunk", + "activity_id": 12, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3132, + "end_frame": 3212, + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "duration_sec": 2.67, + "actors": [ + "person_20", + "vehicle_19" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_007", + "category": "camera_transition", + "question": "Which camera first captured person 22?", + "answer": "Camera G340 at 104.3s (activity: person closes trunk)", + "evidence": { + "actor": "person_22", + "first_appearance": { + "activity": "person_closes_trunk", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "duration_sec": 2.6, + "actors": [ + "person_22", + "vehicle_23" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_008", + "category": "camera_transition", + "question": "Which camera last captured person 22?", + "answer": "Camera G506 at 123.9s (activity: person closes vehicle door)", + "evidence": { + "actor": "person_22", + "last_appearance": { + "activity": "person_closes_vehicle_door", + "activity_id": 13, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "duration_sec": 2.27, + "actors": [ + "vehicle_21", + "person_22" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_009", + "category": "camera_transition", + "question": "Which camera first captured person 6?", + "answer": "Camera G506 at 83.9s (activity: person opens vehicle door)", + "evidence": { + "actor": "person_6", + "first_appearance": { + "activity": "person_opens_vehicle_door", + "activity_id": 4, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2517, + "end_frame": 2597, + "start_time_sec": 83.9, + "end_time_sec": 86.57, + "duration_sec": 2.67, + "actors": [ + "vehicle_5", + "person_6" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + }, + { + "question_id": "camera_transition_010", + "category": "camera_transition", + "question": "Which camera last captured person 6?", + "answer": "Camera G340 at 92.27s (activity: person exits vehicle)", + "evidence": { + "actor": "person_6", + "last_appearance": { + "activity": "person_exits_vehicle", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 2768, + "end_frame": 2908, + "start_time_sec": 92.27, + "end_time_sec": 96.93, + "duration_sec": 4.67, + "actors": [ + "vehicle_5", + "person_6" + ], + "annotation_source": "kitware" + }, + "all_cameras": [ + "G340", + "G506" + ] + }, + "requires_cameras": [ + "G340", + "G506" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.multicam.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.multicam.json new file mode 100644 index 0000000..8680c94 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.multicam.json @@ -0,0 +1,497 @@ +{ + "slot": "2018-03-05.13-15-00.bus", + "method": "multicam", + "model": "gpt-4o-mini", + "temperature": 0.7, + "seed": 99, + "input_file": "/home/ah66742/data/logic_tuples/2018-03-05.13-15-00.bus.json", + "total_questions": 12, + "questions_per_category": { + "temporal": 2, + "ordering": 2, + "perception": 2, + "causality": 2, + "spatial": 2, + "camera_transition": 2 + }, + "api_usage": { + "total_calls": 12, + "model": "gpt-4o-mini" + }, + "qa_pairs": [ + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "temporal", + "question": "While the vehicle started on camera G340, what was happening on camera G506?", + "options": { + "A": "A vehicle stopped.", + "B": "A person walked past the camera.", + "C": "Another vehicle drove into the parking lot.", + "D": "A person was loading items into a trunk." + }, + "answer": "A", + "reasoning": "The vehicle stopping (G506, t=51.4-55.4s) occurred during the vehicle starting event (G340, t=52.4-56.4s).", + "metadata": { + "grounding": { + "activity": "vehicle_starts", + "activity_id": 21, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 1573, + "end_frame": 1693, + "start_time_sec": 52.43, + "end_time_sec": 56.43, + "duration_sec": 4.0, + "actors": [ + "vehicle_38" + ], + "annotation_source": "kitware" + }, + "target": { + "activity": "vehicle_stops", + "activity_id": 1, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 1542, + "end_frame": 1663, + "start_time_sec": 51.4, + "end_time_sec": 55.43, + "duration_sec": 4.03, + "actors": [ + "vehicle_2" + ], + "annotation_source": "kitware" + }, + "rel_type": "During" + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "temporal", + "question": "What activity was captured on camera G506 shortly before the person closed the trunk on camera G340?", + "options": { + "A": "A person transfers an object.", + "B": "A vehicle parks in a designated area.", + "C": "A person adjusts their clothing.", + "D": "A person gets into a vehicle." + }, + "answer": "A", + "reasoning": "The person transferring an object (G506, t=101.2s to t=104.3s) occurred just before the trunk was closed (G340, t=104.3s).", + "metadata": { + "grounding": { + "activity": "person_closes_trunk", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi", + "camera_id": "G340", + "site": "bus", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "duration_sec": 2.6, + "actors": [ + "person_22", + "vehicle_23" + ], + "annotation_source": "kitware" + }, + "target": { + "activity": "person_transfers_object", + "activity_id": 11, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "duration_sec": 3.03, + "actors": [ + "bag_18", + "person_16", + "person_17" + ], + "annotation_source": "kitware" + }, + "rel_type": "Before" + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "ordering", + "question": "During a surveillance session, several events were recorded on cameras G506 and G340. Can you determine the correct chronological order of these activities?", + "options": { + "A": "B, C, A", + "B": "A, B, C", + "C": "C, A, B", + "D": "A, C, B" + }, + "answer": "B", + "reasoning": "Event A (person exits scene, t\u2248266s) occurred first, followed by Event B (vehicle turns left, t\u2248287s), and finally Event C (vehicle stops, t\u2248293s).", + "metadata": { + "chain_length": 3, + "correct_order": "A, B, C", + "events": [ + { + "activity": "person_exits_scene_through_structure", + "camera": "G506", + "start_sec": 266.43 + }, + { + "activity": "vehicle_turns_left", + "camera": "G340", + "start_sec": 286.9 + }, + { + "activity": "vehicle_stops", + "camera": "G506", + "start_sec": 292.63 + } + ] + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "ordering", + "question": "During a 5-minute surveillance period, several vehicle movements were captured on cameras G505 and G506. Can you determine the correct chronological order of these events?", + "options": { + "A": "B, C, A", + "B": "A, B, C", + "C": "C, A, B", + "D": "A, C, B" + }, + "answer": "C", + "reasoning": "Event C (vehicle turns right on camera G505 at t\u2248271s) occurred first, followed by Event A (vehicle turns right on camera G505 at t\u2248282s), and finally Event B (vehicle makes a U-turn on camera G506 at t\u2248289s).", + "metadata": { + "chain_length": 3, + "correct_order": "C, A, B", + "events": [ + { + "activity": "vehicle_turns_right", + "camera": "G505", + "start_sec": 271.47 + }, + { + "activity": "vehicle_turns_right", + "camera": "G505", + "start_sec": 281.77 + }, + { + "activity": "vehicle_makes_u_turn", + "camera": "G506", + "start_sec": 288.57 + } + ] + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "perception", + "question": "When reviewing the footage from camera G340, can you see a vehicle making a U-turn?", + "options": { + "A": "Yes, a vehicle makes a U-turn in front of the camera.", + "B": "No, but a person is carrying a heavy object.", + "C": "Yes, there are multiple vehicles making U-turns.", + "D": "No, there is no vehicle activity visible on this camera." + }, + "answer": "B", + "reasoning": "Camera G340 does not show any vehicle making a U-turn. Instead, it captures a person carrying a heavy object along with other activities, making option B the correct answer.", + "metadata": { + "camera": "G340", + "target_activity": "vehicle_makes_u_turn", + "is_present": false + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "perception", + "question": "When reviewing the footage from camera G340, which of the following activities can be observed?", + "options": { + "A": "A person opens a vehicle door", + "B": "A person closes the trunk", + "C": "A person exits the vehicle", + "D": "A person carries a heavy object" + }, + "answer": "A", + "reasoning": "Camera G340 captured a person opening a vehicle door. The other activities listed are also present but do not match the question's focus on the specific activity.", + "metadata": { + "camera": "G340", + "target_activity": "person_opens_vehicle_door", + "is_present": true + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "causality", + "question": "On camera G506, a vehicle stops at t=81.3s. What likely prompted the subsequent action of a person exiting the vehicle at t=83.9s?", + "options": { + "A": "The vehicle came to a stop moments earlier.", + "B": "The person received a phone call.", + "C": "The weather changed suddenly.", + "D": "Another vehicle was blocking the road." + }, + "answer": "A", + "reasoning": "The vehicle stopping (t=81.3s) is the immediate precursor to the person exiting the vehicle (t=83.9s), indicating that the stop likely facilitated the exit.", + "metadata": { + "cause": { + "activity": "vehicle_stops", + "activity_id": 3, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2440, + "end_frame": 2525, + "start_time_sec": 81.33, + "end_time_sec": 84.17, + "duration_sec": 2.83, + "actors": [ + "vehicle_4" + ], + "annotation_source": "kitware" + }, + "effect": { + "activity": "person_exits_vehicle", + "activity_id": 7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 2517, + "end_frame": 2663, + "start_time_sec": 83.9, + "end_time_sec": 88.77, + "duration_sec": 4.87, + "actors": [ + "vehicle_8", + "person_7" + ], + "annotation_source": "kitware" + }, + "time_gap_sec": -0.3 + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "causality", + "question": "On camera G506, a person is seen entering the scene through a structure at t=217.4s. What action likely occurred just before this event?", + "options": { + "A": "The person opened the facility door moments earlier.", + "B": "The person was waiting outside for someone.", + "C": "Another individual left the facility.", + "D": "An alarm went off in the facility." + }, + "answer": "A", + "reasoning": "The action of opening the facility door (t=217.2s) directly precedes the person entering the scene (t=217.4s), establishing a clear causal link between the two events.", + "metadata": { + "cause": { + "activity": "person_opens_facility_door", + "activity_id": 21, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "duration_sec": 0.5, + "actors": [ + "person_35" + ], + "annotation_source": "kitware" + }, + "effect": { + "activity": "person_enters_scene_through_structure", + "activity_id": 22, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi", + "camera_id": "G506", + "site": "bus", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "duration_sec": 1.53, + "actors": [ + "person_36" + ], + "annotation_source": "kitware" + }, + "time_gap_sec": -0.3 + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "spatial", + "question": "Which camera is the only one that captured a person exiting the scene through a structure during this time slot?", + "options": { + "A": "Camera G340", + "B": "Camera G505", + "C": "Camera G506", + "D": "Camera G505 and G506" + }, + "answer": "C", + "reasoning": "Only camera G506 recorded the activity 'person exits scene through structure'. Cameras G340 and G505 did not capture this activity.", + "metadata": { + "question_focus": "exclusive_activity", + "precomputed_answer": { + "correct_answer": "Camera G506", + "answer_detail": "'person exits scene through structure' appears only on camera G506. No other camera captured this activity type." + }, + "camera_activity_map": { + "G505": [ + "vehicle_turns_right" + ], + "G340": [ + "person_carries_heavy_object", + "person_closes_trunk", + "person_closes_vehicle_door", + "person_exits_vehicle", + "person_opens_trunk", + "person_opens_vehicle_door", + "person_picks_up_object", + "person_puts_down_object", + "person_talks_to_person", + "person_transfers_object", + "person_unloads_vehicle", + "vehicle_reverses", + "vehicle_starts", + "vehicle_stops", + "vehicle_turns_left" + ], + "G506": [ + "person_carries_heavy_object", + "person_closes_trunk", + "person_closes_vehicle_door", + "person_enters_scene_through_structure", + "person_enters_vehicle", + "person_exits_scene_through_structure", + "person_exits_vehicle", + "person_opens_facility_door", + "person_opens_trunk", + "person_opens_vehicle_door", + "person_picks_up_object", + "person_puts_down_object", + "person_talks_to_person", + "person_transfers_object", + "person_unloads_vehicle", + "vehicle_makes_u_turn", + "vehicle_reverses", + "vehicle_starts", + "vehicle_stops", + "vehicle_turns_left", + "vehicle_turns_right" + ] + } + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "spatial", + "question": "Do cameras G505 and G506 observe any common activities during this time slot?", + "options": { + "A": "No, they observe completely different activities", + "B": "Yes, both cameras observe 'vehicle turns left'", + "C": "Yes, both cameras observe 'vehicle turns right'", + "D": "Yes, both cameras observe 'person closes trunk'" + }, + "answer": "C", + "reasoning": "Cameras G505 and G506 both observe the activity 'vehicle turns right'.", + "metadata": { + "question_focus": "shared_activity", + "precomputed_answer": { + "correct_answer": "Yes, cameras G505 and G506 both observe 'vehicle turns right'", + "answer_detail": "Cameras G505 and G506 share the activity 'vehicle turns right'." + }, + "camera_activity_map": { + "G505": [ + "vehicle_turns_right" + ], + "G340": [ + "person_carries_heavy_object", + "person_closes_trunk", + "person_closes_vehicle_door", + "person_exits_vehicle", + "person_opens_trunk", + "person_opens_vehicle_door", + "person_picks_up_object", + "person_puts_down_object", + "person_talks_to_person", + "person_transfers_object", + "person_unloads_vehicle", + "vehicle_reverses", + "vehicle_starts", + "vehicle_stops", + "vehicle_turns_left" + ], + "G506": [ + "person_carries_heavy_object", + "person_closes_trunk", + "person_closes_vehicle_door", + "person_enters_scene_through_structure", + "person_enters_vehicle", + "person_exits_scene_through_structure", + "person_exits_vehicle", + "person_opens_facility_door", + "person_opens_trunk", + "person_opens_vehicle_door", + "person_picks_up_object", + "person_puts_down_object", + "person_talks_to_person", + "person_transfers_object", + "person_unloads_vehicle", + "vehicle_makes_u_turn", + "vehicle_reverses", + "vehicle_starts", + "vehicle_stops", + "vehicle_turns_left", + "vehicle_turns_right" + ] + } + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "camera_transition", + "question": "Actor vehicle_3 appears on multiple cameras during this time slot. Which camera first captured this vehicle?", + "options": { + "A": "Camera G506, where it was starting", + "B": "Camera G505, where it was turning right", + "C": "Camera G507, where it was parked", + "D": "Camera G508, where it was speeding" + }, + "answer": "A", + "reasoning": "Vehicle_3 was first observed on Camera G506 at t=59.6s (starting) before appearing on Camera G505 at t=271.5s (turning right).", + "metadata": { + "actor_id": "vehicle_3", + "cameras": [ + "G505", + "G506" + ], + "first_camera": "G506", + "last_camera": "G505" + } + }, + { + "slot": "2018-03-05.13-15-00.bus", + "question_type": "camera_transition", + "question": "Actor vehicle_34 appears on multiple cameras during this time slot. Which camera FIRST captured this vehicle?", + "options": { + "A": "Camera G340, where it was reversing", + "B": "Camera G506, where it was turning left", + "C": "Camera G505, where it was stopping", + "D": "Camera G340, where it was turning right" + }, + "answer": "A", + "reasoning": "Vehicle_34 was first observed on Camera G340 at t=136.3s while reversing before being captured on Camera G506 at t=160.9s while turning left.", + "metadata": { + "actor_id": "vehicle_34", + "cameras": [ + "G340", + "G506" + ], + "first_camera": "G340", + "last_camera": "G506" + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.natural.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.natural.json new file mode 100644 index 0000000..b23ade2 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.natural.json @@ -0,0 +1,2610 @@ +{ + "slot": "2018-03-05.13-15-00.bus", + "metadata": { + "generator": "generate_qa_symbolic.py", + "seed": 42, + "scene_graph_summary": { + "entity_count": 96, + "event_count": 59, + "temporal_edge_count": 1711, + "spatial_edge_count": 497, + "visibility_edge_count": 96, + "participation_edge_count": 96, + "coref_edge_count": 59, + "entity_cluster_count": 49, + "merged_cluster_count": 17, + "actors_with_multi_events": 17, + "activity_breakdown": { + "person_carries_heavy_object": 4, + "person_closes_trunk": 2, + "person_closes_vehicle_door": 2, + "person_enters_scene_through_structure": 1, + "person_enters_vehicle": 1, + "person_exits_scene_through_structure": 1, + "person_exits_vehicle": 2, + "person_opens_facility_door": 2, + "person_opens_trunk": 2, + "person_opens_vehicle_door": 2, + "person_picks_up_object": 3, + "person_puts_down_object": 4, + "person_talks_to_person": 2, + "person_transfers_object": 6, + "person_unloads_vehicle": 2, + "vehicle_makes_u_turn": 1, + "vehicle_reverses": 6, + "vehicle_starts": 3, + "vehicle_stops": 4, + "vehicle_turns_left": 4, + "vehicle_turns_right": 5 + }, + "camera_breakdown": { + "G340": 24, + "G505": 4, + "G506": 31 + }, + "cameras": [ + "G340", + "G505", + "G506" + ] + }, + "categories_requested": [ + "temporal", + "ordering", + "perception", + "causality", + "spatial", + "camera_view" + ], + "category_counts": { + "temporal": 15, + "ordering": 6, + "perception": 14, + "causality": 15, + "spatial": 15, + "camera_view": 5 + }, + "total_questions": 70, + "naturalizer": "naturalize_qa.py", + "model": "gpt-4o-mini", + "temperature": 0.7, + "total_tokens": 21510, + "failures": 0 + }, + "qa_pairs": [ + { + "question_id": "ordering_001", + "category": "ordering", + "question_template": "Arrange the following events involving the person who picks up an object in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "picks up an object", + "puts down an object" + ], + "options": [ + "picks up an object \u2192 talks to another person \u2192 puts down an object \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 picks up an object \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person \u2192 transfers an object", + "picks up an object \u2192 transfers an object \u2192 puts down an object \u2192 talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 picks up an object \u2192 puts down an object", + "verification": { + "actor_id": "G506_person_45", + "events": [ + { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 12.1 + }, + "naturalized_question": "Can you arrange the following events involving the person who picks up an object in the order they happened?", + "naturalized_options": [ + "picks up an object \u2192 talks to another person \u2192 puts down an object \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 picks up an object \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person \u2192 transfers an object", + "picks up an object \u2192 transfers an object \u2192 puts down an object \u2192 talks to another person" + ], + "original_template": "Arrange the following events involving the person who picks up an object in the order they occurred:", + "original_options": [ + "picks up an object \u2192 talks to another person \u2192 puts down an object \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 picks up an object \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person \u2192 transfers an object", + "picks up an object \u2192 transfers an object \u2192 puts down an object \u2192 talks to another person" + ] + }, + { + "question_id": "ordering_002", + "category": "ordering", + "question_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "events_listed": [ + "carries a heavy object", + "unloads a vehicle", + "puts down an object" + ], + "options": [ + "puts down an object \u2192 carries a heavy object \u2192 unloads a vehicle", + "unloads a vehicle \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 unloads a vehicle \u2192 puts down an object", + "carries a heavy object \u2192 puts down an object \u2192 unloads a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "carries a heavy object \u2192 unloads a vehicle \u2192 puts down an object", + "verification": { + "actor_id": "G506_person_49", + "events": [ + { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 4.37 + }, + "naturalized_question": "Can you put the following events involving the person carrying a heavy object in the order they happened?", + "naturalized_options": [ + "A) puts down an object \u2192 carries a heavy object \u2192 unloads a vehicle", + "B) unloads a vehicle \u2192 carries a heavy object \u2192 puts down an object", + "C) carries a heavy object \u2192 unloads a vehicle \u2192 puts down an object", + "D) carries a heavy object \u2192 puts down an object \u2192 unloads a vehicle" + ], + "original_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "original_options": [ + "puts down an object \u2192 carries a heavy object \u2192 unloads a vehicle", + "unloads a vehicle \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 unloads a vehicle \u2192 puts down an object", + "carries a heavy object \u2192 puts down an object \u2192 unloads a vehicle" + ] + }, + { + "question_id": "ordering_003", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes the trunk in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "closes the trunk" + ], + "options": [ + "closes the trunk \u2192 talks to another person \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "talks to another person \u2192 closes the trunk \u2192 transfers an object", + "transfers an object \u2192 closes the trunk \u2192 talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "verification": { + "actor_id": "G506_person_20", + "events": [ + { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_frame": 3132, + "end_frame": 3212, + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 6.0 + }, + "naturalized_question": "Put these events involving the person who closes the trunk in the order they happened:", + "naturalized_options": [ + "closes the trunk \u2192 talks to another person \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "talks to another person \u2192 closes the trunk \u2192 transfers an object", + "transfers an object \u2192 closes the trunk \u2192 talks to another person" + ], + "original_template": "Arrange the following events involving the person who closes the trunk in the order they occurred:", + "original_options": [ + "closes the trunk \u2192 talks to another person \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "talks to another person \u2192 closes the trunk \u2192 transfers an object", + "transfers an object \u2192 closes the trunk \u2192 talks to another person" + ] + }, + { + "question_id": "ordering_004", + "category": "ordering", + "question_template": "Arrange the following events involving the person who opens the trunk in the order they occurred:", + "events_listed": [ + "opens the trunk", + "unloads a vehicle", + "carries a heavy object", + "picks up an object" + ], + "options": [ + "carries a heavy object \u2192 unloads a vehicle \u2192 picks up an object \u2192 opens the trunk", + "unloads a vehicle \u2192 picks up an object \u2192 carries a heavy object \u2192 opens the trunk", + "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "picks up an object \u2192 unloads a vehicle \u2192 opens the trunk \u2192 carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "verification": { + "actor_id": "G340_person_3", + "events": [ + { + "event_id": "G340_evt_1", + "activity": "person_opens_trunk", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ], + "time_span_sec": 8.53 + }, + "naturalized_question": "Please arrange the following events involving the person who opens the trunk in the order they took place:", + "naturalized_options": [ + "carries a heavy object \u2192 unloads a vehicle \u2192 picks up an object \u2192 opens the trunk", + "unloads a vehicle \u2192 picks up an object \u2192 carries a heavy object \u2192 opens the trunk", + "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "picks up an object \u2192 unloads a vehicle \u2192 opens the trunk \u2192 carries a heavy object" + ], + "original_template": "Arrange the following events involving the person who opens the trunk in the order they occurred:", + "original_options": [ + "carries a heavy object \u2192 unloads a vehicle \u2192 picks up an object \u2192 opens the trunk", + "unloads a vehicle \u2192 picks up an object \u2192 carries a heavy object \u2192 opens the trunk", + "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "picks up an object \u2192 unloads a vehicle \u2192 opens the trunk \u2192 carries a heavy object" + ] + }, + { + "question_id": "ordering_005", + "category": "ordering", + "question_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "events_listed": [ + "opens a facility door", + "enters the scene through a structure", + "carries a heavy object" + ], + "options": [ + "opens a facility door \u2192 enters the scene through a structure \u2192 carries a heavy object", + "carries a heavy object \u2192 enters the scene through a structure \u2192 opens a facility door", + "enters the scene through a structure \u2192 carries a heavy object \u2192 opens a facility door", + "enters the scene through a structure \u2192 opens a facility door \u2192 carries a heavy object" + ], + "correct_answer_index": 0, + "correct_answer": "opens a facility door \u2192 enters the scene through a structure \u2192 carries a heavy object", + "verification": { + "actor_id": "G506_person_37", + "events": [ + { + "event_id": "G506_evt_21", + "activity": "person_opens_facility_door", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_23", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6714, + "start_time_sec": 217.4, + "end_time_sec": 223.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 6.6 + }, + "naturalized_question": "Put these events involving the person carrying a heavy object in the order they happened:", + "naturalized_options": [ + "A) opens a facility door \u2192 enters the scene through a structure \u2192 carries a heavy object", + "B) carries a heavy object \u2192 enters the scene through a structure \u2192 opens a facility door", + "C) enters the scene through a structure \u2192 carries a heavy object \u2192 opens a facility door", + "D) enters the scene through a structure \u2192 opens a facility door \u2192 carries a heavy object" + ], + "original_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "original_options": [ + "opens a facility door \u2192 enters the scene through a structure \u2192 carries a heavy object", + "carries a heavy object \u2192 enters the scene through a structure \u2192 opens a facility door", + "enters the scene through a structure \u2192 carries a heavy object \u2192 opens a facility door", + "enters the scene through a structure \u2192 opens a facility door \u2192 carries a heavy object" + ] + }, + { + "question_id": "ordering_006", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes the trunk in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "closes the trunk", + "carries a heavy object" + ], + "options": [ + "talks to another person \u2192 carries a heavy object \u2192 closes the trunk \u2192 transfers an object", + "carries a heavy object \u2192 transfers an object \u2192 closes the trunk \u2192 talks to another person", + "talks to another person \u2192 transfers an object \u2192 closes the trunk \u2192 carries a heavy object", + "closes the trunk \u2192 talks to another person \u2192 transfers an object \u2192 carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 closes the trunk \u2192 carries a heavy object", + "verification": { + "actor_id": "G340_person_22", + "events": [ + { + "event_id": "G340_evt_10", + "activity": "person_talks_to_person", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3659, + "start_time_sec": 101.3, + "end_time_sec": 121.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_22", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3119, + "start_time_sec": 101.3, + "end_time_sec": 103.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_11", + "activity": "person_closes_trunk", + "camera_id": "G340", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ], + "time_span_sec": 11.73 + }, + "naturalized_question": "Put the following events involving the person who closes the trunk in the order they happened:", + "naturalized_options": [ + "A) talks to another person \u2192 carries a heavy object \u2192 closes the trunk \u2192 transfers an object", + "B) carries a heavy object \u2192 transfers an object \u2192 closes the trunk \u2192 talks to another person", + "C) talks to another person \u2192 transfers an object \u2192 closes the trunk \u2192 carries a heavy object", + "D) closes the trunk \u2192 talks to another person \u2192 transfers an object \u2192 carries a heavy object" + ], + "original_template": "Arrange the following events involving the person who closes the trunk in the order they occurred:", + "original_options": [ + "talks to another person \u2192 carries a heavy object \u2192 closes the trunk \u2192 transfers an object", + "carries a heavy object \u2192 transfers an object \u2192 closes the trunk \u2192 talks to another person", + "talks to another person \u2192 transfers an object \u2192 closes the trunk \u2192 carries a heavy object", + "closes the trunk \u2192 talks to another person \u2192 transfers an object \u2192 carries a heavy object" + ] + }, + { + "question_id": "perception_001", + "category": "perception", + "question_template": "Does anyone close the trunk at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G340_evt_11", + "camera_id": "G340", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + }, + "naturalized_question": "Does anyone close the trunk at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone close the trunk at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_002", + "category": "perception", + "question_template": "Does anyone embrace another person at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_embraces_person", + "present": false + }, + "naturalized_question": "Does anyone hug another person at any time in this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone embrace another person at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_003", + "category": "perception", + "question_template": "Does anyone pick up a person at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "vehicle_picks_up_person", + "present": false + }, + "naturalized_question": "Is there anyone who picks up a person at any moment in this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone pick up a person at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_004", + "category": "perception", + "question_template": "Does anyone ride a bicycle at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_rides_bicycle", + "present": false + }, + "naturalized_question": "Is there anyone riding a bicycle at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone ride a bicycle at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_005", + "category": "perception", + "question_template": "Does anyone open the trunk at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G340_evt_1", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + }, + "naturalized_question": "Is there a moment in this scene when someone opens the trunk?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone open the trunk at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_006", + "category": "perception", + "question_template": "Does anyone talk on the phone at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_talks_on_phone", + "present": false + }, + "naturalized_question": "Is there anyone talking on the phone at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone talk on the phone at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_007", + "category": "perception", + "question_template": "Does anyone sit down at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_sits_down", + "present": false + }, + "naturalized_question": "Does anyone take a seat at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone sit down at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_008", + "category": "perception", + "question_template": "Does anyone open a facility door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_facility_door", + "present": true, + "example_event": { + "event_id": "G506_evt_21", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Does anyone open the facility door at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone open a facility door at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_009", + "category": "perception", + "question_template": "Does anyone interact with a laptop at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_interacts_with_laptop", + "present": false + }, + "naturalized_question": "Is there anyone using a laptop at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone interact with a laptop at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_010", + "category": "perception", + "question_template": "Does anyone put down an object at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_puts_down_object", + "present": true, + "example_event": { + "event_id": "G506_evt_28", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Is there a moment in this scene where someone places an object down?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone put down an object at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_011", + "category": "perception", + "question_template": "Does anyone enter the scene through a structure at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_enters_scene_through_structure", + "present": true, + "example_event": { + "event_id": "G506_evt_22", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Does anyone come into the scene through a structure at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone enter the scene through a structure at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_012", + "category": "perception", + "question_template": "Does anyone text on a phone at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_texts_on_phone", + "present": false + }, + "naturalized_question": "Is there anyone using a phone to text at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone text on a phone at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_013", + "category": "perception", + "question_template": "Does anyone pick up an object at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_picks_up_object", + "present": true, + "example_event": { + "event_id": "G340_evt_7", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + }, + "naturalized_question": "Does anyone grab an object at any time in this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone pick up an object at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "perception_014", + "category": "perception", + "question_template": "Does anyone open a vehicle door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G506_evt_4", + "camera_id": "G506", + "start_frame": 2517, + "end_frame": 2597, + "start_time_sec": 83.9, + "end_time_sec": 86.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Does anyone open a car door at any time during this scene?", + "naturalized_options": [ + "Yes", + "No" + ], + "original_template": "Does anyone open a vehicle door at any point in this scene?", + "original_options": [ + "Yes", + "No" + ] + }, + { + "question_id": "causality_001", + "category": "causality", + "question_template": "Why does the person who closes the trunk transfer an object?", + "options": [ + "Because they just enter a vehicle", + "Because they just enter the scene through a structure", + "Because they just talk to another person", + "Because they just open a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "Because they just talk to another person", + "verification": { + "actor_id": "G506_person_20", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -21.57 + }, + "naturalized_question": "Why does the person closing the trunk move an object?", + "naturalized_options": [ + "Because they just got into a vehicle", + "Because they just came into the scene from a structure", + "Because they just had a conversation with someone", + "Because they just opened a vehicle door" + ], + "original_template": "Why does the person who closes the trunk transfer an object?", + "original_options": [ + "Because they just enter a vehicle", + "Because they just enter the scene through a structure", + "Because they just talk to another person", + "Because they just open a vehicle door" + ] + }, + { + "question_id": "causality_002", + "category": "causality", + "question_template": "Why does a person exit a vehicle?", + "options": [ + "Because they just pick up an object", + "Because they just open a vehicle door", + "Because they just enter a vehicle", + "Because they just close the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just open a vehicle door", + "verification": { + "actor_id": "G506_person_7", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_4", + "activity": "person_opens_vehicle_door", + "camera_id": "G506", + "start_frame": 2517, + "end_frame": 2597, + "start_time_sec": 83.9, + "end_time_sec": 86.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_7", + "activity": "person_exits_vehicle", + "camera_id": "G506", + "start_frame": 2517, + "end_frame": 2663, + "start_time_sec": 83.9, + "end_time_sec": 88.77, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -2.67 + }, + "naturalized_question": "What could be the reason a person gets out of a vehicle?", + "naturalized_options": [ + "Because they just picked up something", + "Because they just opened the vehicle door", + "Because they just got into the vehicle", + "Because they just closed the trunk" + ], + "original_template": "Why does a person exit a vehicle?", + "original_options": [ + "Because they just pick up an object", + "Because they just open a vehicle door", + "Because they just enter a vehicle", + "Because they just close the trunk" + ] + }, + { + "question_id": "causality_003", + "category": "causality", + "question_template": "Why does the person who closes a vehicle door enter a vehicle?", + "options": [ + "Because they just put down an object", + "Because they just open the trunk", + "Because they just carry a heavy object", + "Because they just close a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just close a vehicle door", + "verification": { + "actor_id": "G506_person_22", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_13", + "activity": "person_closes_vehicle_door", + "camera_id": "G506", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_14", + "activity": "person_enters_vehicle", + "camera_id": "G506", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -2.27 + }, + "naturalized_question": "Why does the person who shuts a vehicle door get inside the vehicle?", + "naturalized_options": [ + "Because they just set down something", + "Because they just opened the trunk", + "Because they are carrying a heavy item", + "Because they just closed a vehicle door" + ], + "original_template": "Why does the person who closes a vehicle door enter a vehicle?", + "original_options": [ + "Because they just put down an object", + "Because they just open the trunk", + "Because they just carry a heavy object", + "Because they just close a vehicle door" + ] + }, + { + "question_id": "causality_004", + "category": "causality", + "question_template": "Why does the person who carries a heavy object put down an object?", + "options": [ + "Because they just talk to another person", + "Because they just unload a vehicle", + "Because they just exit the scene through a structure", + "Because they just transfer an object" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just unload a vehicle", + "verification": { + "actor_id": "G506_person_49", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.0 + }, + "naturalized_question": "Why does the person carrying a heavy object decide to put it down?", + "naturalized_options": [ + "Because they were just talking to someone else", + "Because they just unloaded a vehicle", + "Because they just left the area through a structure", + "Because they just moved something to another location" + ], + "original_template": "Why does the person who carries a heavy object put down an object?", + "original_options": [ + "Because they just talk to another person", + "Because they just unload a vehicle", + "Because they just exit the scene through a structure", + "Because they just transfer an object" + ] + }, + { + "question_id": "causality_005", + "category": "causality", + "question_template": "Why does the person who opens the trunk unload a vehicle?", + "options": [ + "Because they just open the trunk", + "Because they just enter a vehicle", + "Because they just pick up an object", + "Because they just put down an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just open the trunk", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_1", + "activity": "person_opens_trunk", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.8 + }, + "naturalized_question": "Why does the person opening the trunk unload something from the vehicle?", + "naturalized_options": [ + "Because they just opened the trunk", + "Because they just got into the vehicle", + "Because they just picked up an object", + "Because they just set down an object" + ], + "original_template": "Why does the person who opens the trunk unload a vehicle?", + "original_options": [ + "Because they just open the trunk", + "Because they just enter a vehicle", + "Because they just pick up an object", + "Because they just put down an object" + ] + }, + { + "question_id": "causality_006", + "category": "causality", + "question_template": "Why does the person who opens the trunk carry a heavy object?", + "options": [ + "Because they just enter a vehicle", + "Because they just transfer an object", + "Because they just unload a vehicle", + "Because they just enter the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "Because they just unload a vehicle", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.43 + }, + "naturalized_question": "Why is the person who opens the trunk carrying a heavy object?", + "naturalized_options": [ + "Because they just got into a vehicle", + "Because they just moved an object", + "Because they just took something out of a vehicle", + "Because they just came into the scene through a building" + ], + "original_template": "Why does the person who opens the trunk carry a heavy object?", + "original_options": [ + "Because they just enter a vehicle", + "Because they just transfer an object", + "Because they just unload a vehicle", + "Because they just enter the scene through a structure" + ] + }, + { + "question_id": "causality_007", + "category": "causality", + "question_template": "Why does the person who carries a heavy object enter the scene through a structure?", + "options": [ + "Because they just exit a vehicle", + "Because they just open a facility door", + "Because they just close a vehicle door", + "Because they just open the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just open a facility door", + "verification": { + "actor_id": "G506_person_37", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_21", + "activity": "person_opens_facility_door", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.3 + }, + "naturalized_question": "Why does the person carrying a heavy object come into the scene through a structure?", + "naturalized_options": [ + "Because they just got out of a vehicle", + "Because they just opened a door to a facility", + "Because they just closed a vehicle door", + "Because they just opened the trunk" + ], + "original_template": "Why does the person who carries a heavy object enter the scene through a structure?", + "original_options": [ + "Because they just exit a vehicle", + "Because they just open a facility door", + "Because they just close a vehicle door", + "Because they just open the trunk" + ] + }, + { + "question_id": "causality_008", + "category": "causality", + "question_template": "Why does the person who picks up an object put down an object?", + "options": [ + "Because they just open the trunk", + "Because they just enter the scene through a structure", + "Because they just enter a vehicle", + "Because they just pick up an object" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G506_person_45", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.16 + }, + "naturalized_question": "Why does the person who picks up an object then put it down?", + "naturalized_options": [ + "Because they just opened the trunk", + "Because they just entered the scene through a structure", + "Because they just got into a vehicle", + "Because they just picked up an object" + ], + "original_template": "Why does the person who picks up an object put down an object?", + "original_options": [ + "Because they just open the trunk", + "Because they just enter the scene through a structure", + "Because they just enter a vehicle", + "Because they just pick up an object" + ] + }, + { + "question_id": "causality_009", + "category": "causality", + "question_template": "Why does the person who opens the trunk put down an object?", + "options": [ + "Because they just pick up an object", + "Because they just open a vehicle door", + "Because they just carry a heavy object", + "Because they just transfer an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_8", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_frame": 2963, + "end_frame": 2980, + "start_time_sec": 98.77, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.04 + }, + "naturalized_question": "Why does the person opening the trunk place an object down?", + "naturalized_options": [ + "Because they just picked up something", + "Because they just opened a car door", + "Because they just lifted a heavy item", + "Because they just moved an object" + ], + "original_template": "Why does the person who opens the trunk put down an object?", + "original_options": [ + "Because they just pick up an object", + "Because they just open a vehicle door", + "Because they just carry a heavy object", + "Because they just transfer an object" + ] + }, + { + "question_id": "causality_010", + "category": "causality", + "question_template": "Why does the person who closes the trunk put down an object?", + "options": [ + "Because they just pick up an object", + "Because they just talk to another person", + "Because they just transfer an object", + "Because they just close a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G340_person_22", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_frame": 3380, + "end_frame": 3391, + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.04 + }, + "naturalized_question": "Why does the person who closes the trunk put down an object?", + "naturalized_options": [ + "Because they just picked up an object", + "Because they just talked to someone else", + "Because they just moved an object", + "Because they just closed a vehicle door" + ], + "original_template": "Why does the person who closes the trunk put down an object?", + "original_options": [ + "Because they just pick up an object", + "Because they just talk to another person", + "Because they just transfer an object", + "Because they just close a vehicle door" + ] + }, + { + "question_id": "causality_011", + "category": "causality", + "question_template": "Why does the person who carries a heavy object unload a vehicle?", + "options": [ + "Because they just carry a heavy object", + "Because they just pick up an object", + "Because they just enter the scene through a structure", + "Because they just transfer an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G506_person_49", + "causal_strength": "weak", + "cause_event": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -4.37 + }, + "naturalized_question": "Why does the person carrying a heavy object unload a vehicle?", + "naturalized_options": [ + "Because they are carrying a heavy object", + "Because they picked up an object", + "Because they just came into the scene through a structure", + "Because they are transferring an object" + ], + "original_template": "Why does the person who carries a heavy object unload a vehicle?", + "original_options": [ + "Because they just carry a heavy object", + "Because they just pick up an object", + "Because they just enter the scene through a structure", + "Because they just transfer an object" + ] + }, + { + "question_id": "causality_012", + "category": "causality", + "question_template": "Why does a person carry a heavy object?", + "options": [ + "Because they just enter the scene through a structure", + "Because they just talk to another person", + "Because they just exit the scene through a structure", + "Because they just open a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just enter the scene through a structure", + "verification": { + "actor_id": "G506_person_37", + "causal_strength": "weak", + "cause_event": { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_23", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6714, + "start_time_sec": 217.4, + "end_time_sec": 223.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.53 + }, + "naturalized_question": "What might be the reason a person is carrying a heavy object?", + "naturalized_options": [ + "Because they just came into the scene through a structure", + "Because they just had a conversation with someone", + "Because they just left the scene through a structure", + "Because they just opened a vehicle door" + ], + "original_template": "Why does a person carry a heavy object?", + "original_options": [ + "Because they just enter the scene through a structure", + "Because they just talk to another person", + "Because they just exit the scene through a structure", + "Because they just open a vehicle door" + ] + }, + { + "question_id": "causality_013", + "category": "causality", + "question_template": "Why does the person who opens the trunk pick up an object?", + "options": [ + "Because they just carry a heavy object", + "Because they just exit a vehicle", + "Because they just transfer an object", + "Because they just talk to another person" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "weak", + "cause_event": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -1.03 + }, + "naturalized_question": "Why does the person opening the trunk pick up an object?", + "naturalized_options": [ + "Because they're carrying a heavy object", + "Because they just got out of a vehicle", + "Because they're moving an object", + "Because they just had a conversation with someone" + ], + "original_template": "Why does the person who opens the trunk pick up an object?", + "original_options": [ + "Because they just carry a heavy object", + "Because they just exit a vehicle", + "Because they just transfer an object", + "Because they just talk to another person" + ] + }, + { + "question_id": "causality_014", + "category": "causality", + "question_template": "Why does the person who closes the trunk pick up an object?", + "options": [ + "Because they just open a vehicle door", + "Because they just carry a heavy object", + "Because they just talk to another person", + "Because they just close a vehicle door" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G340_person_22", + "causal_strength": "weak", + "cause_event": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.8 + }, + "naturalized_question": "Why does the person who closes the trunk pick up an object?", + "naturalized_options": [ + "Because they just opened a vehicle door", + "Because they just carried a heavy object", + "Because they just spoke to another person", + "Because they just closed a vehicle door" + ], + "original_template": "Why does the person who closes the trunk pick up an object?", + "original_options": [ + "Because they just open a vehicle door", + "Because they just carry a heavy object", + "Because they just talk to another person", + "Because they just close a vehicle door" + ] + }, + { + "question_id": "causality_015", + "category": "causality", + "question_template": "Why does a person close the trunk?", + "options": [ + "Because they just enter the scene through a structure", + "Because they just open a vehicle door", + "Because they just open the trunk", + "Because they just transfer an object" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just transfer an object", + "verification": { + "actor_id": "G506_person_20", + "causal_strength": "weak", + "cause_event": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_frame": 3132, + "end_frame": 3212, + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": 0.13 + }, + "naturalized_question": "Why does someone close the trunk?", + "naturalized_options": [ + "Because they just came into the scene through a structure", + "Because they just opened a vehicle door", + "Because they just opened the trunk", + "Because they just moved an object" + ], + "original_template": "Why does a person close the trunk?", + "original_options": [ + "Because they just enter the scene through a structure", + "Because they just open a vehicle door", + "Because they just open the trunk", + "Because they just transfer an object" + ] + }, + { + "question_id": "spatial_001", + "category": "spatial", + "question_template": "In the scene, where is the person who talks to another person relative to a bag?", + "options": [ + "left of", + "below", + "right of", + "above" + ], + "correct_answer_index": 3, + "correct_answer": "above", + "verification": { + "entity_a": "G506_person_27", + "entity_b": "G506_bag_18", + "relation": "ABOVE", + "camera_id": "G506", + "frame": 3128, + "bbox_a": [ + 1899, + 108, + 1919, + 212 + ], + "bbox_b": [ + 1891, + 147, + 1917, + 207 + ] + }, + "naturalized_question": "In the scene, how is the person who is talking to another person positioned in relation to a bag?", + "naturalized_options": [ + "left of", + "below", + "right of", + "above" + ], + "original_template": "In the scene, where is the person who talks to another person relative to a bag?", + "original_options": [ + "left of", + "below", + "right of", + "above" + ] + }, + { + "question_id": "spatial_002", + "category": "spatial", + "question_template": "In the scene, where is a bag relative to the person who puts down an object?", + "options": [ + "above", + "below", + "left of", + "right of" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G506_bag_50", + "entity_b": "G506_person_43", + "relation": "LEFT_OF", + "camera_id": "G506", + "frame": 2991, + "bbox_a": [ + 1878, + 158, + 1902, + 211 + ], + "bbox_b": [ + 1880, + 125, + 1919, + 217 + ] + }, + "naturalized_question": "In the scene, where is the bag in relation to the person who sets down an object?", + "naturalized_options": [ + "above", + "below", + "left of", + "right of" + ], + "original_template": "In the scene, where is a bag relative to the person who puts down an object?", + "original_options": [ + "above", + "below", + "left of", + "right of" + ] + }, + { + "question_id": "spatial_003", + "category": "spatial", + "question_template": "In the scene, where is a bag relative to the person who picks up an object?", + "options": [ + "right of", + "above", + "below", + "left of" + ], + "correct_answer_index": 3, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_bag_15", + "entity_b": "G340_person_14", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2962, + "bbox_a": [ + 293, + 640, + 453, + 802 + ], + "bbox_b": [ + 358, + 527, + 551, + 906 + ] + }, + "naturalized_question": "In the scene, what is the position of the bag in relation to the person who is picking up an object?", + "naturalized_options": [ + "right of", + "above", + "below", + "left of" + ], + "original_template": "In the scene, where is a bag relative to the person who picks up an object?", + "original_options": [ + "right of", + "above", + "below", + "left of" + ] + }, + { + "question_id": "spatial_004", + "category": "spatial", + "question_template": "In the scene, where is the person who puts down an object relative to the person who talks to another person?", + "options": [ + "left of", + "right of", + "above", + "below" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_person_48", + "entity_b": "G506_person_27", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 3390, + "bbox_a": [ + 1893, + 109, + 1919, + 211 + ], + "bbox_b": [ + 1864, + 108, + 1908, + 211 + ] + }, + "naturalized_question": "In the scene, where is the person who places an object in relation to the person who is talking to someone else?", + "naturalized_options": [ + "left of", + "right of", + "above", + "below" + ], + "original_template": "In the scene, where is the person who puts down an object relative to the person who talks to another person?", + "original_options": [ + "left of", + "right of", + "above", + "below" + ] + }, + { + "question_id": "spatial_005", + "category": "spatial", + "question_template": "In the scene, where is the person who exits a vehicle relative to the person who unloads a vehicle?", + "options": [ + "left of", + "above", + "right of", + "below" + ], + "correct_answer_index": 0, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_person_6", + "entity_b": "G340_person_9", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2881, + "bbox_a": [ + 0, + 450, + 64, + 703 + ], + "bbox_b": [ + 159, + 533, + 363, + 905 + ] + }, + "naturalized_question": "In the scene, how is the person who exits a vehicle positioned in relation to the person who unloads a vehicle?", + "naturalized_options": [ + "left of", + "above", + "right of", + "below" + ], + "original_template": "In the scene, where is the person who exits a vehicle relative to the person who unloads a vehicle?", + "original_options": [ + "left of", + "above", + "right of", + "below" + ] + }, + { + "question_id": "spatial_006", + "category": "spatial", + "question_template": "In the scene, where is the person who closes a vehicle door relative to the person who enters a vehicle?", + "options": [ + "below", + "left of", + "above", + "right of" + ], + "correct_answer_index": 0, + "correct_answer": "below", + "verification": { + "entity_a": "G506_person_22", + "entity_b": "G506_person_26", + "relation": "BELOW", + "camera_id": "G506", + "frame": 3777, + "bbox_a": [ + 1736, + 105, + 1778, + 164 + ], + "bbox_b": [ + 1736, + 105, + 1778, + 164 + ] + }, + "naturalized_question": "In this scene, where is the person closing the vehicle door in relation to the person getting into the vehicle?", + "naturalized_options": [ + "below", + "left of", + "above", + "right of" + ], + "original_template": "In the scene, where is the person who closes a vehicle door relative to the person who enters a vehicle?", + "original_options": [ + "below", + "left of", + "above", + "right of" + ] + }, + { + "question_id": "spatial_007", + "category": "spatial", + "question_template": "In the scene, where is the person who opens the trunk relative to a vehicle?", + "options": [ + "left of", + "right of", + "below", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G340_person_3", + "entity_b": "G340_vehicle_4", + "relation": "RIGHT_OF", + "camera_id": "G340", + "frame": 2833, + "bbox_a": [ + 196, + 548, + 375, + 905 + ], + "bbox_b": [ + 0, + 450, + 375, + 920 + ] + }, + "naturalized_question": "In this scene, where is the person who opens the trunk in relation to the vehicle?", + "naturalized_options": [ + "left of", + "right of", + "below", + "above" + ], + "original_template": "In the scene, where is the person who opens the trunk relative to a vehicle?", + "original_options": [ + "left of", + "right of", + "below", + "above" + ] + }, + { + "question_id": "spatial_008", + "category": "spatial", + "question_template": "In the scene, where is the person who picks up an object relative to a vehicle?", + "options": [ + "left of", + "above", + "below", + "right of" + ], + "correct_answer_index": 3, + "correct_answer": "right of", + "verification": { + "entity_a": "G340_person_14", + "entity_b": "G340_vehicle_10", + "relation": "RIGHT_OF", + "camera_id": "G340", + "frame": 2962, + "bbox_a": [ + 358, + 527, + 551, + 906 + ], + "bbox_b": [ + 0, + 444, + 370, + 920 + ] + }, + "naturalized_question": "In this scene, where is the person picking up an object in relation to the vehicle?", + "naturalized_options": [ + "left of", + "above", + "below", + "right of" + ], + "original_template": "In the scene, where is the person who picks up an object relative to a vehicle?", + "original_options": [ + "left of", + "above", + "below", + "right of" + ] + }, + { + "question_id": "spatial_009", + "category": "spatial", + "question_template": "In the scene, where is the person who puts down an object relative to a vehicle?", + "options": [ + "left of", + "right of", + "below", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_person_43", + "entity_b": "G506_vehicle_11", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 2991, + "bbox_a": [ + 1880, + 125, + 1919, + 217 + ], + "bbox_b": [ + 1688, + 82, + 1893, + 201 + ] + }, + "naturalized_question": "In the scene, where is the person placing an object in relation to the vehicle?", + "naturalized_options": [ + "left of", + "right of", + "below", + "above" + ], + "original_template": "In the scene, where is the person who puts down an object relative to a vehicle?", + "original_options": [ + "left of", + "right of", + "below", + "above" + ] + }, + { + "question_id": "spatial_010", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to a bag?", + "options": [ + "below", + "above", + "left of", + "right of" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_vehicle_7", + "entity_b": "G340_bag_16", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2980, + "bbox_a": [ + 0, + 442, + 373, + 919 + ], + "bbox_b": [ + 351, + 664, + 461, + 889 + ] + }, + "naturalized_question": "In this scene, what is the position of a vehicle in relation to a bag?", + "naturalized_options": [ + "below", + "above", + "left of", + "right of" + ], + "original_template": "In the scene, where is a vehicle relative to a bag?", + "original_options": [ + "below", + "above", + "left of", + "right of" + ] + }, + { + "question_id": "spatial_011", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to a bag?", + "options": [ + "right of", + "above", + "below", + "left of" + ], + "correct_answer_index": 3, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_vehicle_7", + "entity_b": "G340_bag_15", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2962, + "bbox_a": [ + 0, + 444, + 370, + 920 + ], + "bbox_b": [ + 293, + 640, + 453, + 802 + ] + }, + "naturalized_question": "In the scene, how is the vehicle positioned in relation to the bag?", + "naturalized_options": [ + "right of", + "above", + "below", + "left of" + ], + "original_template": "In the scene, where is a vehicle relative to a bag?", + "original_options": [ + "right of", + "above", + "below", + "left of" + ] + }, + { + "question_id": "spatial_012", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to the person who opens a vehicle door?", + "options": [ + "right of", + "above", + "left of", + "below" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_vehicle_8", + "entity_b": "G506_person_6", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 2597, + "bbox_a": [ + 1690, + 91, + 1890, + 199 + ], + "bbox_b": [ + 1715, + 104, + 1760, + 185 + ] + }, + "naturalized_question": "In the scene, how is the vehicle positioned in relation to the person opening the vehicle door?", + "naturalized_options": [ + "right of", + "above", + "left of", + "below" + ], + "original_template": "In the scene, where is a vehicle relative to the person who opens a vehicle door?", + "original_options": [ + "right of", + "above", + "left of", + "below" + ] + }, + { + "question_id": "spatial_013", + "category": "spatial", + "question_template": "In the scene, where is the person who talks to another person relative to the person who transfers an object?", + "options": [ + "right of", + "below", + "left of", + "above" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G506_person_28", + "entity_b": "G506_person_17", + "relation": "LEFT_OF", + "camera_id": "G506", + "frame": 3128, + "bbox_a": [ + 1886, + 109, + 1918, + 202 + ], + "bbox_b": [ + 1899, + 108, + 1919, + 212 + ] + }, + "naturalized_question": "In the scene, where is the person speaking to another person in relation to the person handing over an object?", + "naturalized_options": [ + "right of", + "below", + "left of", + "above" + ], + "original_template": "In the scene, where is the person who talks to another person relative to the person who transfers an object?", + "original_options": [ + "right of", + "below", + "left of", + "above" + ] + }, + { + "question_id": "spatial_014", + "category": "spatial", + "question_template": "In the scene, where is a bag relative to the person who talks to another person?", + "options": [ + "right of", + "left of", + "below", + "above" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_bag_46", + "entity_b": "G506_person_27", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 3377, + "bbox_a": [ + 1892, + 141, + 1914, + 206 + ], + "bbox_b": [ + 1864, + 108, + 1908, + 211 + ] + }, + "naturalized_question": "In the scene, how is the bag positioned in relation to the person who is talking to someone else?", + "naturalized_options": [ + "right of", + "left of", + "below", + "above" + ], + "original_template": "In the scene, where is a bag relative to the person who talks to another person?", + "original_options": [ + "right of", + "left of", + "below", + "above" + ] + }, + { + "question_id": "spatial_015", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to the person who unloads a vehicle?", + "options": [ + "right of", + "above", + "left of", + "below" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_vehicle_2", + "entity_b": "G340_person_9", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2833, + "bbox_a": [ + 0, + 450, + 375, + 920 + ], + "bbox_b": [ + 196, + 548, + 375, + 905 + ] + }, + "naturalized_question": "In the scene, what is the position of the vehicle in relation to the person who is unloading it?", + "naturalized_options": [ + "right of", + "above", + "left of", + "below" + ], + "original_template": "In the scene, where is a vehicle relative to the person who unloads a vehicle?", + "original_options": [ + "right of", + "above", + "left of", + "below" + ] + }, + { + "question_id": "camera_view_001", + "category": "camera_view", + "question_template": "In which camera view does a vehicle make a U-turn?", + "options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "vehicle_makes_u_turn", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_27", + "camera_id": "G506", + "start_frame": 8657, + "end_frame": 8844, + "start_time_sec": 288.57, + "end_time_sec": 294.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Which camera captures the view of a vehicle making a U-turn?", + "naturalized_options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ], + "original_template": "In which camera view does a vehicle make a U-turn?", + "original_options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ] + }, + { + "question_id": "camera_view_002", + "category": "camera_view", + "question_template": "In which camera view does someone enter a vehicle?", + "options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_enters_vehicle", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_14", + "camera_id": "G506", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "In which camera view does a person get into a vehicle?", + "naturalized_options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ], + "original_template": "In which camera view does someone enter a vehicle?", + "original_options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ] + }, + { + "question_id": "camera_view_003", + "category": "camera_view", + "question_template": "In which camera view does someone exit the scene through a structure?", + "options": [ + "Camera 2", + "Camera 3", + "Camera 1" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_exits_scene_through_structure", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_36", + "camera_id": "G506", + "start_frame": 7993, + "end_frame": 8033, + "start_time_sec": 266.43, + "end_time_sec": 267.77, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "In which camera view does a person leave the scene through a structure?", + "naturalized_options": [ + "Camera 2", + "Camera 3", + "Camera 1" + ], + "original_template": "In which camera view does someone exit the scene through a structure?", + "original_options": [ + "Camera 2", + "Camera 3", + "Camera 1" + ] + }, + { + "question_id": "camera_view_004", + "category": "camera_view", + "question_template": "In which camera view does someone open a facility door?", + "options": [ + "Camera 1", + "Camera 2", + "Camera 3" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_opens_facility_door", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_21", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Which camera shows someone opening a facility door?", + "naturalized_options": [ + "Camera 1", + "Camera 2", + "Camera 3" + ], + "original_template": "In which camera view does someone open a facility door?", + "original_options": [ + "Camera 1", + "Camera 2", + "Camera 3" + ] + }, + { + "question_id": "camera_view_005", + "category": "camera_view", + "question_template": "In which camera view does someone enter the scene through a structure?", + "options": [ + "Camera 2", + "Camera 1", + "Camera 3" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_enters_scene_through_structure", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_22", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + }, + "naturalized_question": "Which camera view shows someone entering the scene through a structure?", + "naturalized_options": [ + "Camera 2", + "Camera 1", + "Camera 3" + ], + "original_template": "In which camera view does someone enter the scene through a structure?", + "original_options": [ + "Camera 2", + "Camera 1", + "Camera 3" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.symbolic.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.symbolic.json new file mode 100644 index 0000000..cfe2aaf --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.symbolic.json @@ -0,0 +1,2471 @@ +{ + "slot": "2018-03-05.13-15-00.bus", + "metadata": { + "generator": "generate_qa_symbolic.py", + "seed": 42, + "scene_graph_summary": { + "entity_count": 96, + "event_count": 59, + "temporal_edge_count": 1711, + "spatial_edge_count": 497, + "visibility_edge_count": 96, + "participation_edge_count": 96, + "coref_edge_count": 59, + "entity_cluster_count": 49, + "merged_cluster_count": 17, + "actors_with_multi_events": 17, + "activity_breakdown": { + "person_carries_heavy_object": 4, + "person_closes_trunk": 2, + "person_closes_vehicle_door": 2, + "person_enters_scene_through_structure": 1, + "person_enters_vehicle": 1, + "person_exits_scene_through_structure": 1, + "person_exits_vehicle": 2, + "person_opens_facility_door": 2, + "person_opens_trunk": 2, + "person_opens_vehicle_door": 2, + "person_picks_up_object": 3, + "person_puts_down_object": 4, + "person_talks_to_person": 2, + "person_transfers_object": 6, + "person_unloads_vehicle": 2, + "vehicle_makes_u_turn": 1, + "vehicle_reverses": 6, + "vehicle_starts": 3, + "vehicle_stops": 4, + "vehicle_turns_left": 4, + "vehicle_turns_right": 5 + }, + "camera_breakdown": { + "G340": 24, + "G505": 4, + "G506": 31 + }, + "cameras": [ + "G340", + "G505", + "G506" + ] + }, + "categories_requested": [ + "temporal", + "ordering", + "perception", + "causality", + "spatial", + "camera_view" + ], + "category_counts": { + "temporal": 15, + "ordering": 6, + "perception": 14, + "causality": 15, + "spatial": 15, + "camera_view": 5 + }, + "total_questions": 70 + }, + "qa_pairs": [ + { + "question_id": "temporal_001", + "category": "temporal", + "question_template": "After a person closes the trunk, what do they do next?", + "options": [ + "exits a vehicle", + "carries a heavy object", + "closes a vehicle door", + "opens the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "carries a heavy object", + "verification": { + "actor_id": "G340_person_22", + "event_a": { + "event_id": "G340_evt_11", + "activity": "person_closes_trunk", + "camera_id": "G340", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 5.33, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_002", + "category": "temporal", + "question_template": "After a person transfers an object, what do they do next?", + "options": [ + "talks to another person", + "carries a heavy object", + "opens the trunk", + "closes the trunk" + ], + "correct_answer_index": 3, + "correct_answer": "closes the trunk", + "verification": { + "actor_id": "G340_person_22", + "event_a": { + "event_id": "G340_evt_22", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3119, + "start_time_sec": 101.3, + "end_time_sec": 103.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_11", + "activity": "person_closes_trunk", + "camera_id": "G340", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.33, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_003", + "category": "temporal", + "question_template": "After the person who opens the trunk unloads a vehicle, what do they do next?", + "options": [ + "enters the scene through a structure", + "talks to another person", + "carries a heavy object", + "exits the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "carries a heavy object", + "verification": { + "actor_id": "G340_person_3", + "event_a": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.43, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_004", + "category": "temporal", + "question_template": "After the person who closes the trunk talks to another person, what do they do next?", + "options": [ + "opens a facility door", + "opens the trunk", + "transfers an object", + "enters the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "transfers an object", + "verification": { + "actor_id": "G340_person_22", + "event_a": { + "event_id": "G340_evt_10", + "activity": "person_talks_to_person", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3659, + "start_time_sec": 101.3, + "end_time_sec": 121.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_22", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3119, + "start_time_sec": 101.3, + "end_time_sec": 103.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -20.67, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_005", + "category": "temporal", + "question_template": "After a person carries a heavy object, what do they do next?", + "options": [ + "enters a vehicle", + "enters the scene through a structure", + "unloads a vehicle", + "exits the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "unloads a vehicle", + "verification": { + "actor_id": "G506_person_49", + "event_a": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -4.37, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_006", + "category": "temporal", + "question_template": "After the person who picks up an object puts down an object, what do they do next?", + "options": [ + "transfers an object", + "exits a vehicle", + "carries a heavy object", + "enters a vehicle" + ], + "correct_answer_index": 0, + "correct_answer": "transfers an object", + "verification": { + "actor_id": "G506_person_45", + "event_a": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_33", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3434, + "end_frame": 3494, + "start_time_sec": 114.47, + "end_time_sec": 116.47, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": 1.3, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_007", + "category": "temporal", + "question_template": "After the person who closes the trunk carries a heavy object, what do they do next?", + "options": [ + "opens the trunk", + "picks up an object", + "puts down an object", + "enters a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "picks up an object", + "verification": { + "actor_id": "G340_person_22", + "event_a": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.8, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_008", + "category": "temporal", + "question_template": "After a person picks up an object, what do they do next?", + "options": [ + "exits the scene through a structure", + "carries a heavy object", + "enters the scene through a structure", + "puts down an object" + ], + "correct_answer_index": 3, + "correct_answer": "puts down an object", + "verification": { + "actor_id": "G506_person_45", + "event_a": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.16, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_009", + "category": "temporal", + "question_template": "After a person enters the scene through a structure, what do they do next?", + "options": [ + "exits a vehicle", + "carries a heavy object", + "opens a facility door", + "picks up an object" + ], + "correct_answer_index": 1, + "correct_answer": "carries a heavy object", + "verification": { + "actor_id": "G506_person_37", + "event_a": { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_23", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6714, + "start_time_sec": 217.4, + "end_time_sec": 223.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.53, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_010", + "category": "temporal", + "question_template": "After the person who carries a heavy object unloads a vehicle, what do they do next?", + "options": [ + "exits the scene through a structure", + "opens a facility door", + "closes a vehicle door", + "puts down an object" + ], + "correct_answer_index": 3, + "correct_answer": "puts down an object", + "verification": { + "actor_id": "G506_person_49", + "event_a": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.0, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_011", + "category": "temporal", + "question_template": "After a person opens the trunk, what do they do next?", + "options": [ + "enters a vehicle", + "opens a facility door", + "puts down an object", + "unloads a vehicle" + ], + "correct_answer_index": 3, + "correct_answer": "unloads a vehicle", + "verification": { + "actor_id": "G340_person_3", + "event_a": { + "event_id": "G340_evt_1", + "activity": "person_opens_trunk", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.8, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_012", + "category": "temporal", + "question_template": "After the person who closes the trunk puts down an object, what do they do next?", + "options": [ + "unloads a vehicle", + "exits a vehicle", + "transfers an object", + "talks to another person" + ], + "correct_answer_index": 2, + "correct_answer": "transfers an object", + "verification": { + "actor_id": "G340_person_22", + "event_a": { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_frame": 3380, + "end_frame": 3391, + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_15", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_frame": 3414, + "end_frame": 3497, + "start_time_sec": 113.8, + "end_time_sec": 116.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.77, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_013", + "category": "temporal", + "question_template": "After the person who picks up an object talks to another person, what do they do next?", + "options": [ + "exits the scene through a structure", + "picks up an object", + "transfers an object", + "carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "transfers an object", + "verification": { + "actor_id": "G506_person_45", + "event_a": { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -21.57, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_014", + "category": "temporal", + "question_template": "After the person who carries a heavy object opens a facility door, what do they do next?", + "options": [ + "opens a vehicle door", + "enters the scene through a structure", + "puts down an object", + "picks up an object" + ], + "correct_answer_index": 1, + "correct_answer": "enters the scene through a structure", + "verification": { + "actor_id": "G506_person_37", + "event_a": { + "event_id": "G506_evt_21", + "activity": "person_opens_facility_door", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.3, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_015", + "category": "temporal", + "question_template": "After the person who opens the trunk picks up an object, what do they do next?", + "options": [ + "carries a heavy object", + "unloads a vehicle", + "puts down an object", + "closes a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "puts down an object", + "verification": { + "actor_id": "G340_person_3", + "event_a": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "event_b": { + "event_id": "G340_evt_8", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_frame": 2963, + "end_frame": 2980, + "start_time_sec": 98.77, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.04, + "relation": "BEFORE" + } + }, + { + "question_id": "ordering_001", + "category": "ordering", + "question_template": "Arrange the following events involving the person who picks up an object in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "picks up an object", + "puts down an object" + ], + "options": [ + "picks up an object \u2192 talks to another person \u2192 puts down an object \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 picks up an object \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person \u2192 transfers an object", + "picks up an object \u2192 transfers an object \u2192 puts down an object \u2192 talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 picks up an object \u2192 puts down an object", + "verification": { + "actor_id": "G506_person_45", + "events": [ + { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 12.1 + } + }, + { + "question_id": "ordering_002", + "category": "ordering", + "question_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "events_listed": [ + "carries a heavy object", + "unloads a vehicle", + "puts down an object" + ], + "options": [ + "puts down an object \u2192 carries a heavy object \u2192 unloads a vehicle", + "unloads a vehicle \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 unloads a vehicle \u2192 puts down an object", + "carries a heavy object \u2192 puts down an object \u2192 unloads a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "carries a heavy object \u2192 unloads a vehicle \u2192 puts down an object", + "verification": { + "actor_id": "G506_person_49", + "events": [ + { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 4.37 + } + }, + { + "question_id": "ordering_003", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes the trunk in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "closes the trunk" + ], + "options": [ + "closes the trunk \u2192 talks to another person \u2192 transfers an object", + "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "talks to another person \u2192 closes the trunk \u2192 transfers an object", + "transfers an object \u2192 closes the trunk \u2192 talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 closes the trunk", + "verification": { + "actor_id": "G506_person_20", + "events": [ + { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_frame": 3132, + "end_frame": 3212, + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 6.0 + } + }, + { + "question_id": "ordering_004", + "category": "ordering", + "question_template": "Arrange the following events involving the person who opens the trunk in the order they occurred:", + "events_listed": [ + "opens the trunk", + "unloads a vehicle", + "carries a heavy object", + "picks up an object" + ], + "options": [ + "carries a heavy object \u2192 unloads a vehicle \u2192 picks up an object \u2192 opens the trunk", + "unloads a vehicle \u2192 picks up an object \u2192 carries a heavy object \u2192 opens the trunk", + "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "picks up an object \u2192 unloads a vehicle \u2192 opens the trunk \u2192 carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "opens the trunk \u2192 unloads a vehicle \u2192 carries a heavy object \u2192 picks up an object", + "verification": { + "actor_id": "G340_person_3", + "events": [ + { + "event_id": "G340_evt_1", + "activity": "person_opens_trunk", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ], + "time_span_sec": 8.53 + } + }, + { + "question_id": "ordering_005", + "category": "ordering", + "question_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "events_listed": [ + "opens a facility door", + "enters the scene through a structure", + "carries a heavy object" + ], + "options": [ + "opens a facility door \u2192 enters the scene through a structure \u2192 carries a heavy object", + "carries a heavy object \u2192 enters the scene through a structure \u2192 opens a facility door", + "enters the scene through a structure \u2192 carries a heavy object \u2192 opens a facility door", + "enters the scene through a structure \u2192 opens a facility door \u2192 carries a heavy object" + ], + "correct_answer_index": 0, + "correct_answer": "opens a facility door \u2192 enters the scene through a structure \u2192 carries a heavy object", + "verification": { + "actor_id": "G506_person_37", + "events": [ + { + "event_id": "G506_evt_21", + "activity": "person_opens_facility_door", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + { + "event_id": "G506_evt_23", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6714, + "start_time_sec": 217.4, + "end_time_sec": 223.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + ], + "time_span_sec": 6.6 + } + }, + { + "question_id": "ordering_006", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes the trunk in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "closes the trunk", + "carries a heavy object" + ], + "options": [ + "talks to another person \u2192 carries a heavy object \u2192 closes the trunk \u2192 transfers an object", + "carries a heavy object \u2192 transfers an object \u2192 closes the trunk \u2192 talks to another person", + "talks to another person \u2192 transfers an object \u2192 closes the trunk \u2192 carries a heavy object", + "closes the trunk \u2192 talks to another person \u2192 transfers an object \u2192 carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 closes the trunk \u2192 carries a heavy object", + "verification": { + "actor_id": "G340_person_22", + "events": [ + { + "event_id": "G340_evt_10", + "activity": "person_talks_to_person", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3659, + "start_time_sec": 101.3, + "end_time_sec": 121.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_22", + "activity": "person_transfers_object", + "camera_id": "G340", + "start_frame": 3039, + "end_frame": 3119, + "start_time_sec": 101.3, + "end_time_sec": 103.97, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_11", + "activity": "person_closes_trunk", + "camera_id": "G340", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + ], + "time_span_sec": 11.73 + } + }, + { + "question_id": "perception_001", + "category": "perception", + "question_template": "Does anyone close the trunk at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G340_evt_11", + "camera_id": "G340", + "start_frame": 3129, + "end_frame": 3207, + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + } + }, + { + "question_id": "perception_002", + "category": "perception", + "question_template": "Does anyone embrace another person at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_embraces_person", + "present": false + } + }, + { + "question_id": "perception_003", + "category": "perception", + "question_template": "Does anyone pick up a person at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "vehicle_picks_up_person", + "present": false + } + }, + { + "question_id": "perception_004", + "category": "perception", + "question_template": "Does anyone ride a bicycle at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_rides_bicycle", + "present": false + } + }, + { + "question_id": "perception_005", + "category": "perception", + "question_template": "Does anyone open the trunk at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G340_evt_1", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + } + }, + { + "question_id": "perception_006", + "category": "perception", + "question_template": "Does anyone talk on the phone at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_talks_on_phone", + "present": false + } + }, + { + "question_id": "perception_007", + "category": "perception", + "question_template": "Does anyone sit down at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_sits_down", + "present": false + } + }, + { + "question_id": "perception_008", + "category": "perception", + "question_template": "Does anyone open a facility door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_facility_door", + "present": true, + "example_event": { + "event_id": "G506_evt_21", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "perception_009", + "category": "perception", + "question_template": "Does anyone interact with a laptop at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_interacts_with_laptop", + "present": false + } + }, + { + "question_id": "perception_010", + "category": "perception", + "question_template": "Does anyone put down an object at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_puts_down_object", + "present": true, + "example_event": { + "event_id": "G506_evt_28", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "perception_011", + "category": "perception", + "question_template": "Does anyone enter the scene through a structure at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_enters_scene_through_structure", + "present": true, + "example_event": { + "event_id": "G506_evt_22", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "perception_012", + "category": "perception", + "question_template": "Does anyone text on a phone at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_texts_on_phone", + "present": false + } + }, + { + "question_id": "perception_013", + "category": "perception", + "question_template": "Does anyone pick up an object at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_picks_up_object", + "present": true, + "example_event": { + "event_id": "G340_evt_7", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + } + } + }, + { + "question_id": "perception_014", + "category": "perception", + "question_template": "Does anyone open a vehicle door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G506_evt_4", + "camera_id": "G506", + "start_frame": 2517, + "end_frame": 2597, + "start_time_sec": 83.9, + "end_time_sec": 86.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "causality_001", + "category": "causality", + "question_template": "Why does the person who closes the trunk transfer an object?", + "options": [ + "Because they just enter a vehicle", + "Because they just enter the scene through a structure", + "Because they just talk to another person", + "Because they just open a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "Because they just talk to another person", + "verification": { + "actor_id": "G506_person_20", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_15", + "activity": "person_talks_to_person", + "camera_id": "G506", + "start_frame": 3032, + "end_frame": 3684, + "start_time_sec": 101.07, + "end_time_sec": 122.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -21.57 + } + }, + { + "question_id": "causality_002", + "category": "causality", + "question_template": "Why does a person exit a vehicle?", + "options": [ + "Because they just pick up an object", + "Because they just open a vehicle door", + "Because they just enter a vehicle", + "Because they just close the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just open a vehicle door", + "verification": { + "actor_id": "G506_person_7", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_4", + "activity": "person_opens_vehicle_door", + "camera_id": "G506", + "start_frame": 2517, + "end_frame": 2597, + "start_time_sec": 83.9, + "end_time_sec": 86.57, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_7", + "activity": "person_exits_vehicle", + "camera_id": "G506", + "start_frame": 2517, + "end_frame": 2663, + "start_time_sec": 83.9, + "end_time_sec": 88.77, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -2.67 + } + }, + { + "question_id": "causality_003", + "category": "causality", + "question_template": "Why does the person who closes a vehicle door enter a vehicle?", + "options": [ + "Because they just put down an object", + "Because they just open the trunk", + "Because they just carry a heavy object", + "Because they just close a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just close a vehicle door", + "verification": { + "actor_id": "G506_person_22", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_13", + "activity": "person_closes_vehicle_door", + "camera_id": "G506", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_14", + "activity": "person_enters_vehicle", + "camera_id": "G506", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -2.27 + } + }, + { + "question_id": "causality_004", + "category": "causality", + "question_template": "Why does the person who carries a heavy object put down an object?", + "options": [ + "Because they just talk to another person", + "Because they just unload a vehicle", + "Because they just exit the scene through a structure", + "Because they just transfer an object" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just unload a vehicle", + "verification": { + "actor_id": "G506_person_49", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_28", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 2961, + "end_frame": 2991, + "start_time_sec": 98.7, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.0 + } + }, + { + "question_id": "causality_005", + "category": "causality", + "question_template": "Why does the person who opens the trunk unload a vehicle?", + "options": [ + "Because they just open the trunk", + "Because they just enter a vehicle", + "Because they just pick up an object", + "Because they just put down an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just open the trunk", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_1", + "activity": "person_opens_trunk", + "camera_id": "G340", + "start_frame": 2706, + "end_frame": 2833, + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.8 + } + }, + { + "question_id": "causality_006", + "category": "causality", + "question_template": "Why does the person who opens the trunk carry a heavy object?", + "options": [ + "Because they just enter a vehicle", + "Because they just transfer an object", + "Because they just unload a vehicle", + "Because they just enter the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "Because they just unload a vehicle", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_5", + "activity": "person_unloads_vehicle", + "camera_id": "G340", + "start_frame": 2809, + "end_frame": 2962, + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.43 + } + }, + { + "question_id": "causality_007", + "category": "causality", + "question_template": "Why does the person who carries a heavy object enter the scene through a structure?", + "options": [ + "Because they just exit a vehicle", + "Because they just open a facility door", + "Because they just close a vehicle door", + "Because they just open the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just open a facility door", + "verification": { + "actor_id": "G506_person_37", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_21", + "activity": "person_opens_facility_door", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.3 + } + }, + { + "question_id": "causality_008", + "category": "causality", + "question_template": "Why does the person who picks up an object put down an object?", + "options": [ + "Because they just open the trunk", + "Because they just enter the scene through a structure", + "Because they just enter a vehicle", + "Because they just pick up an object" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G506_person_45", + "causal_strength": "strong", + "cause_event": { + "event_id": "G506_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G506", + "start_frame": 3370, + "end_frame": 3385, + "start_time_sec": 112.33, + "end_time_sec": 112.83, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_30", + "activity": "person_puts_down_object", + "camera_id": "G506", + "start_frame": 3380, + "end_frame": 3395, + "start_time_sec": 112.67, + "end_time_sec": 113.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -0.16 + } + }, + { + "question_id": "causality_009", + "category": "causality", + "question_template": "Why does the person who opens the trunk put down an object?", + "options": [ + "Because they just pick up an object", + "Because they just open a vehicle door", + "Because they just carry a heavy object", + "Because they just transfer an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_8", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_frame": 2963, + "end_frame": 2980, + "start_time_sec": 98.77, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.04 + } + }, + { + "question_id": "causality_010", + "category": "causality", + "question_template": "Why does the person who closes the trunk put down an object?", + "options": [ + "Because they just pick up an object", + "Because they just talk to another person", + "Because they just transfer an object", + "Because they just close a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G340_person_22", + "causal_strength": "strong", + "cause_event": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_14", + "activity": "person_puts_down_object", + "camera_id": "G340", + "start_frame": 3380, + "end_frame": 3391, + "start_time_sec": 112.67, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 0.04 + } + }, + { + "question_id": "causality_011", + "category": "causality", + "question_template": "Why does the person who carries a heavy object unload a vehicle?", + "options": [ + "Because they just carry a heavy object", + "Because they just pick up an object", + "Because they just enter the scene through a structure", + "Because they just transfer an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G506_person_49", + "causal_strength": "weak", + "cause_event": { + "event_id": "G506_evt_32", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_9", + "activity": "person_unloads_vehicle", + "camera_id": "G506", + "start_frame": 2860, + "end_frame": 2991, + "start_time_sec": 95.33, + "end_time_sec": 99.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -4.37 + } + }, + { + "question_id": "causality_012", + "category": "causality", + "question_template": "Why does a person carry a heavy object?", + "options": [ + "Because they just enter the scene through a structure", + "Because they just talk to another person", + "Because they just exit the scene through a structure", + "Because they just open a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just enter the scene through a structure", + "verification": { + "actor_id": "G506_person_37", + "causal_strength": "weak", + "cause_event": { + "event_id": "G506_evt_22", + "activity": "person_enters_scene_through_structure", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_23", + "activity": "person_carries_heavy_object", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6714, + "start_time_sec": 217.4, + "end_time_sec": 223.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": -1.53 + } + }, + { + "question_id": "causality_013", + "category": "causality", + "question_template": "Why does the person who opens the trunk pick up an object?", + "options": [ + "Because they just carry a heavy object", + "Because they just exit a vehicle", + "Because they just transfer an object", + "Because they just talk to another person" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G340_person_3", + "causal_strength": "weak", + "cause_event": { + "event_id": "G340_evt_6", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2980, + "start_time_sec": 98.3, + "end_time_sec": 99.33, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_7", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 2949, + "end_frame": 2962, + "start_time_sec": 98.3, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -1.03 + } + }, + { + "question_id": "causality_014", + "category": "causality", + "question_template": "Why does the person who closes the trunk pick up an object?", + "options": [ + "Because they just open a vehicle door", + "Because they just carry a heavy object", + "Because they just talk to another person", + "Because they just close a vehicle door" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G340_person_22", + "causal_strength": "weak", + "cause_event": { + "event_id": "G340_evt_12", + "activity": "person_carries_heavy_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3391, + "start_time_sec": 112.23, + "end_time_sec": 113.03, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "effect_event": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_frame": 3367, + "end_frame": 3379, + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": -0.8 + } + }, + { + "question_id": "causality_015", + "category": "causality", + "question_template": "Why does a person close the trunk?", + "options": [ + "Because they just enter the scene through a structure", + "Because they just open a vehicle door", + "Because they just open the trunk", + "Because they just transfer an object" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just transfer an object", + "verification": { + "actor_id": "G506_person_20", + "causal_strength": "weak", + "cause_event": { + "event_id": "G506_evt_11", + "activity": "person_transfers_object", + "camera_id": "G506", + "start_frame": 3037, + "end_frame": 3128, + "start_time_sec": 101.23, + "end_time_sec": 104.27, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "effect_event": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_frame": 3132, + "end_frame": 3212, + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "gap_sec": 0.13 + } + }, + { + "question_id": "spatial_001", + "category": "spatial", + "question_template": "In the scene, where is the person who talks to another person relative to a bag?", + "options": [ + "left of", + "below", + "right of", + "above" + ], + "correct_answer_index": 3, + "correct_answer": "above", + "verification": { + "entity_a": "G506_person_27", + "entity_b": "G506_bag_18", + "relation": "ABOVE", + "camera_id": "G506", + "frame": 3128, + "bbox_a": [ + 1899, + 108, + 1919, + 212 + ], + "bbox_b": [ + 1891, + 147, + 1917, + 207 + ] + } + }, + { + "question_id": "spatial_002", + "category": "spatial", + "question_template": "In the scene, where is a bag relative to the person who puts down an object?", + "options": [ + "above", + "below", + "left of", + "right of" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G506_bag_50", + "entity_b": "G506_person_43", + "relation": "LEFT_OF", + "camera_id": "G506", + "frame": 2991, + "bbox_a": [ + 1878, + 158, + 1902, + 211 + ], + "bbox_b": [ + 1880, + 125, + 1919, + 217 + ] + } + }, + { + "question_id": "spatial_003", + "category": "spatial", + "question_template": "In the scene, where is a bag relative to the person who picks up an object?", + "options": [ + "right of", + "above", + "below", + "left of" + ], + "correct_answer_index": 3, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_bag_15", + "entity_b": "G340_person_14", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2962, + "bbox_a": [ + 293, + 640, + 453, + 802 + ], + "bbox_b": [ + 358, + 527, + 551, + 906 + ] + } + }, + { + "question_id": "spatial_004", + "category": "spatial", + "question_template": "In the scene, where is the person who puts down an object relative to the person who talks to another person?", + "options": [ + "left of", + "right of", + "above", + "below" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_person_48", + "entity_b": "G506_person_27", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 3390, + "bbox_a": [ + 1893, + 109, + 1919, + 211 + ], + "bbox_b": [ + 1864, + 108, + 1908, + 211 + ] + } + }, + { + "question_id": "spatial_005", + "category": "spatial", + "question_template": "In the scene, where is the person who exits a vehicle relative to the person who unloads a vehicle?", + "options": [ + "left of", + "above", + "right of", + "below" + ], + "correct_answer_index": 0, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_person_6", + "entity_b": "G340_person_9", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2881, + "bbox_a": [ + 0, + 450, + 64, + 703 + ], + "bbox_b": [ + 159, + 533, + 363, + 905 + ] + } + }, + { + "question_id": "spatial_006", + "category": "spatial", + "question_template": "In the scene, where is the person who closes a vehicle door relative to the person who enters a vehicle?", + "options": [ + "below", + "left of", + "above", + "right of" + ], + "correct_answer_index": 0, + "correct_answer": "below", + "verification": { + "entity_a": "G506_person_22", + "entity_b": "G506_person_26", + "relation": "BELOW", + "camera_id": "G506", + "frame": 3777, + "bbox_a": [ + 1736, + 105, + 1778, + 164 + ], + "bbox_b": [ + 1736, + 105, + 1778, + 164 + ] + } + }, + { + "question_id": "spatial_007", + "category": "spatial", + "question_template": "In the scene, where is the person who opens the trunk relative to a vehicle?", + "options": [ + "left of", + "right of", + "below", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G340_person_3", + "entity_b": "G340_vehicle_4", + "relation": "RIGHT_OF", + "camera_id": "G340", + "frame": 2833, + "bbox_a": [ + 196, + 548, + 375, + 905 + ], + "bbox_b": [ + 0, + 450, + 375, + 920 + ] + } + }, + { + "question_id": "spatial_008", + "category": "spatial", + "question_template": "In the scene, where is the person who picks up an object relative to a vehicle?", + "options": [ + "left of", + "above", + "below", + "right of" + ], + "correct_answer_index": 3, + "correct_answer": "right of", + "verification": { + "entity_a": "G340_person_14", + "entity_b": "G340_vehicle_10", + "relation": "RIGHT_OF", + "camera_id": "G340", + "frame": 2962, + "bbox_a": [ + 358, + 527, + 551, + 906 + ], + "bbox_b": [ + 0, + 444, + 370, + 920 + ] + } + }, + { + "question_id": "spatial_009", + "category": "spatial", + "question_template": "In the scene, where is the person who puts down an object relative to a vehicle?", + "options": [ + "left of", + "right of", + "below", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_person_43", + "entity_b": "G506_vehicle_11", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 2991, + "bbox_a": [ + 1880, + 125, + 1919, + 217 + ], + "bbox_b": [ + 1688, + 82, + 1893, + 201 + ] + } + }, + { + "question_id": "spatial_010", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to a bag?", + "options": [ + "below", + "above", + "left of", + "right of" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_vehicle_7", + "entity_b": "G340_bag_16", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2980, + "bbox_a": [ + 0, + 442, + 373, + 919 + ], + "bbox_b": [ + 351, + 664, + 461, + 889 + ] + } + }, + { + "question_id": "spatial_011", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to a bag?", + "options": [ + "right of", + "above", + "below", + "left of" + ], + "correct_answer_index": 3, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_vehicle_7", + "entity_b": "G340_bag_15", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2962, + "bbox_a": [ + 0, + 444, + 370, + 920 + ], + "bbox_b": [ + 293, + 640, + 453, + 802 + ] + } + }, + { + "question_id": "spatial_012", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to the person who opens a vehicle door?", + "options": [ + "right of", + "above", + "left of", + "below" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_vehicle_8", + "entity_b": "G506_person_6", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 2597, + "bbox_a": [ + 1690, + 91, + 1890, + 199 + ], + "bbox_b": [ + 1715, + 104, + 1760, + 185 + ] + } + }, + { + "question_id": "spatial_013", + "category": "spatial", + "question_template": "In the scene, where is the person who talks to another person relative to the person who transfers an object?", + "options": [ + "right of", + "below", + "left of", + "above" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G506_person_28", + "entity_b": "G506_person_17", + "relation": "LEFT_OF", + "camera_id": "G506", + "frame": 3128, + "bbox_a": [ + 1886, + 109, + 1918, + 202 + ], + "bbox_b": [ + 1899, + 108, + 1919, + 212 + ] + } + }, + { + "question_id": "spatial_014", + "category": "spatial", + "question_template": "In the scene, where is a bag relative to the person who talks to another person?", + "options": [ + "right of", + "left of", + "below", + "above" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G506_bag_46", + "entity_b": "G506_person_27", + "relation": "RIGHT_OF", + "camera_id": "G506", + "frame": 3377, + "bbox_a": [ + 1892, + 141, + 1914, + 206 + ], + "bbox_b": [ + 1864, + 108, + 1908, + 211 + ] + } + }, + { + "question_id": "spatial_015", + "category": "spatial", + "question_template": "In the scene, where is a vehicle relative to the person who unloads a vehicle?", + "options": [ + "right of", + "above", + "left of", + "below" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G340_vehicle_2", + "entity_b": "G340_person_9", + "relation": "LEFT_OF", + "camera_id": "G340", + "frame": 2833, + "bbox_a": [ + 0, + 450, + 375, + 920 + ], + "bbox_b": [ + 196, + 548, + 375, + 905 + ] + } + }, + { + "question_id": "camera_view_001", + "category": "camera_view", + "question_template": "In which camera view does a vehicle make a U-turn?", + "options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "vehicle_makes_u_turn", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_27", + "camera_id": "G506", + "start_frame": 8657, + "end_frame": 8844, + "start_time_sec": 288.57, + "end_time_sec": 294.8, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "camera_view_002", + "category": "camera_view", + "question_template": "In which camera view does someone enter a vehicle?", + "options": [ + "Camera 1", + "Camera 3", + "Camera 2" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_enters_vehicle", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_14", + "camera_id": "G506", + "start_frame": 3717, + "end_frame": 3785, + "start_time_sec": 123.9, + "end_time_sec": 126.17, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "camera_view_003", + "category": "camera_view", + "question_template": "In which camera view does someone exit the scene through a structure?", + "options": [ + "Camera 2", + "Camera 3", + "Camera 1" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_exits_scene_through_structure", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_36", + "camera_id": "G506", + "start_frame": 7993, + "end_frame": 8033, + "start_time_sec": 266.43, + "end_time_sec": 267.77, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "camera_view_004", + "category": "camera_view", + "question_template": "In which camera view does someone open a facility door?", + "options": [ + "Camera 1", + "Camera 2", + "Camera 3" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_opens_facility_door", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_21", + "camera_id": "G506", + "start_frame": 6516, + "end_frame": 6531, + "start_time_sec": 217.2, + "end_time_sec": 217.7, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + }, + { + "question_id": "camera_view_005", + "category": "camera_view", + "question_template": "In which camera view does someone enter the scene through a structure?", + "options": [ + "Camera 2", + "Camera 1", + "Camera 3" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_enters_scene_through_structure", + "correct_camera_id": "G506", + "camera_label_map": { + "G340": "Camera 1", + "G505": "Camera 2", + "G506": "Camera 3" + }, + "example_event": { + "event_id": "G506_evt_22", + "camera_id": "G506", + "start_frame": 6522, + "end_frame": 6568, + "start_time_sec": 217.4, + "end_time_sec": 218.93, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + } + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.v4.json new file mode 100644 index 0000000..4e3785c --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.bus.v4.json @@ -0,0 +1,299 @@ +{ + "slot": "2018-03-05.13-15-00.bus", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 1, + "spatial": 3, + "perception": 4 + }, + "total_questions": 8, + "cameras": [ + "G340", + "G505", + "G506" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "temporal", + "question_template": "After the person who closes the trunk closes the trunk, what do they do next in a different camera view?", + "options": [ + "exits the scene through a structure", + "talks to another person", + "closes a vehicle door", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G506_evt_12", + "activity": "person_closes_trunk", + "camera_id": "G506", + "start_time_sec": 104.4, + "end_time_sec": 107.07, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "event_b": { + "event_id": "G340_evt_13", + "activity": "person_picks_up_object", + "camera_id": "G340", + "start_time_sec": 112.23, + "end_time_sec": 112.63, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "gap_sec": 5.16 + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who enters a vehicle and the person who unloads a vehicle to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G506" + ], + "verification": { + "entity_a": "G506_person_26", + "entity_b": "G506_person_12", + "world_pos_a_enu": [ + -9.91, + -68.55, + 0.0 + ], + "world_pos_b_enu": [ + -8.87, + -71.13, + 0.0 + ], + "distance_meters": 2.78, + "proximity": "near", + "camera_a": "G506", + "camera_b": "G506" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who picks up an object and the vehicle that turns right to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "entity_a": "G340_bag_26", + "entity_b": "G506_vehicle_1005", + "world_pos_a_enu": [ + -2.74, + -65.9, + 0.0 + ], + "world_pos_b_enu": [ + -5.48, + -42.37, + 0.0 + ], + "distance_meters": 23.69, + "proximity": "far", + "camera_a": "G340", + "camera_b": "G506" + } + }, + { + "question_id": "v4_004", + "category": "spatial", + "question_template": "In the scene, how close are the person who opens the trunk and the person who picks up an object to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are near each other (within a few meters)", + "They are at the same location", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 1, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G340" + ], + "verification": { + "entity_a": "G340_person_3", + "entity_b": "G340_bag_26", + "world_pos_a_enu": [ + -3.77, + -66.76, + 0.0 + ], + "world_pos_b_enu": [ + -2.74, + -65.9, + 0.0 + ], + "distance_meters": 1.34, + "proximity": "near", + "camera_a": "G340", + "camera_b": "G340" + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a facility door", + "Someone embraces another person", + "Someone interacts with a laptop", + "Someone opens the trunk" + ], + "correct_answer_index": 3, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G340_evt_1", + "camera_id": "G340", + "start_time_sec": 90.2, + "end_time_sec": 94.43, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_embraces_person", + "person_interacts_with_laptop" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone makes a purchase", + "Someone closes the trunk", + "Someone reads a document", + "Someone loads a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G340_evt_11", + "camera_id": "G340", + "start_time_sec": 104.3, + "end_time_sec": 106.9, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "distractor_activities": [ + "person_loads_vehicle", + "person_purchases", + "person_reads_document" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone stands up", + "Someone unloads a vehicle", + "Someone rides a bicycle", + "Someone sits down" + ], + "correct_answer_index": 1, + "correct_answer": "Someone unloads a vehicle", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "person_unloads_vehicle", + "present": true, + "example_event": { + "event_id": "G340_evt_5", + "camera_id": "G340", + "start_time_sec": 93.63, + "end_time_sec": 98.73, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G340.avi" + }, + "distractor_activities": [ + "person_rides_bicycle", + "person_sits_down", + "person_stands_up" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_008", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone drops off a person", + "Someone texts on a phone", + "Someone exits a vehicle", + "Someone talks on the phone" + ], + "correct_answer_index": 2, + "correct_answer": "Someone exits a vehicle", + "requires_cameras": [ + "G506" + ], + "verification": { + "activity": "person_exits_vehicle", + "present": true, + "example_event": { + "event_id": "G506_evt_7", + "camera_id": "G506", + "start_time_sec": 83.9, + "end_time_sec": 88.77, + "video_file": "2018-03-05.13-15-00.13-20-00.bus.G506.avi" + }, + "distractor_activities": [ + "person_talks_on_phone", + "person_texts_on_phone", + "vehicle_drops_off_person" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.hospital.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.hospital.v4.json new file mode 100644 index 0000000..db4eb87 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.hospital.v4.json @@ -0,0 +1,264 @@ +{ + "slot": "2018-03-05.13-15-00.hospital", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G301", + "G341", + "G436" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that reverses and the person who carries a heavy object to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G301", + "G341" + ], + "verification": { + "entity_a": "G341_vehicle_10", + "entity_b": "G301_person_2", + "world_pos_a_enu": [ + -12.72, + -69.61, + 0.0 + ], + "world_pos_b_enu": [ + 84.34, + -62.94, + -0.0 + ], + "distance_meters": 97.29, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G301" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who enters the scene through a structure and the vehicle that turns right to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G341", + "G436" + ], + "verification": { + "entity_a": "G341_person_12", + "entity_b": "G436_vehicle_2", + "world_pos_a_enu": [ + -37.25, + -74.06, + 0.0 + ], + "world_pos_b_enu": [ + 8.8, + -23.93, + 0.0 + ], + "distance_meters": 68.07, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G436" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that turns left and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G341", + "G436" + ], + "verification": { + "entity_a": "G341_vehicle_17", + "entity_b": "G436_person_5", + "world_pos_a_enu": [ + -21.88, + -64.91, + 0.0 + ], + "world_pos_b_enu": [ + -1.48, + -13.19, + 0.0 + ], + "distance_meters": 55.59, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G436" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a facility door", + "Someone drops off a person", + "Someone makes a U-turn", + "Someone picks up a person" + ], + "correct_answer_index": 2, + "correct_answer": "Someone makes a U-turn", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "vehicle_makes_u_turn", + "present": true, + "example_event": { + "event_id": "G436_evt_0", + "camera_id": "G436", + "start_time_sec": 39.63, + "end_time_sec": 45.1, + "video_file": "2018-03-05.13-15-00.13-20-00.hospital.G436.avi" + }, + "distractor_activities": [ + "vehicle_drops_off_person", + "vehicle_picks_up_person", + "person_closes_facility_door" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone enters a vehicle", + "Someone exits a vehicle", + "Someone embraces another person", + "Someone closes the trunk" + ], + "correct_answer_index": 0, + "correct_answer": "Someone enters a vehicle", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_enters_vehicle", + "present": true, + "example_event": { + "event_id": "G436_evt_5", + "camera_id": "G436", + "start_time_sec": 82.03, + "end_time_sec": 87.17, + "video_file": "2018-03-05.13-15-00.13-20-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_closes_trunk", + "person_embraces_person", + "person_exits_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone interacts with a laptop", + "Someone opens a vehicle door", + "Someone loads a vehicle", + "Someone opens the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "Someone opens a vehicle door", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G436_evt_3", + "camera_id": "G436", + "start_time_sec": 82.03, + "end_time_sec": 84.67, + "video_file": "2018-03-05.13-15-00.13-20-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_interacts_with_laptop", + "person_loads_vehicle", + "person_opens_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone makes a purchase", + "Someone puts down an object", + "Someone reads a document", + "Someone closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Someone closes a vehicle door", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_closes_vehicle_door", + "present": true, + "example_event": { + "event_id": "G436_evt_4", + "camera_id": "G436", + "start_time_sec": 84.9, + "end_time_sec": 87.17, + "video_file": "2018-03-05.13-15-00.13-20-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_purchases", + "person_puts_down_object", + "person_reads_document" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.school.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.school.v4.json new file mode 100644 index 0000000..e3851c5 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-00.school.v4.json @@ -0,0 +1,336 @@ +{ + "slot": "2018-03-05.13-15-00.school", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 2, + "spatial": 3, + "perception": 4 + }, + "total_questions": 9, + "cameras": [ + "G336", + "G421", + "G423", + "G424" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "temporal", + "question_template": "After the person who opens a facility door exits the scene through a structure, what do they do next in a different camera view?", + "options": [ + "opens the trunk", + "enters a vehicle", + "unloads a vehicle", + "enters the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G421", + "G423" + ], + "verification": { + "chain_id": "narrative_001", + "event_a": { + "event_id": "G423_evt_1", + "activity": "person_exits_scene_through_structure", + "camera_id": "G423", + "start_time_sec": 39.17, + "end_time_sec": 40.3, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G423.avi" + }, + "event_b": { + "event_id": "G421_evt_5", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 51.8, + "end_time_sec": 55.8, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G421.avi" + }, + "gap_sec": 11.5 + } + }, + { + "question_id": "v4_002", + "category": "temporal", + "question_template": "After the person who opens a vehicle door opens a facility door, what do they do next in a different camera view?", + "options": [ + "talks on the phone", + "carries a heavy object", + "enters a vehicle", + "exits the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "enters a vehicle", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_013", + "event_a": { + "event_id": "G421_evt_6", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 59.7, + "end_time_sec": 61.73, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G421.avi" + }, + "event_b": { + "event_id": "G336_evt_2", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 81.27, + "end_time_sec": 86.8, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G336.avi" + }, + "gap_sec": 19.54 + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who exits the scene through a structure and the vehicle that turns left to each other in physical space?", + "options": [ + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G424" + ], + "verification": { + "entity_a": "G424_person_27", + "entity_b": "G424_vehicle_2", + "world_pos_a_enu": [ + -44.76, + -118.23, + 0.0 + ], + "world_pos_b_enu": [ + -21.01, + -200.11, + 0.0 + ], + "distance_meters": 85.25, + "proximity": "far", + "camera_a": "G424", + "camera_b": "G424" + } + }, + { + "question_id": "v4_004", + "category": "spatial", + "question_template": "In the scene, how close are the person who unloads a vehicle and the vehicle that turns left to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G424" + ], + "verification": { + "entity_a": "G424_person_47", + "entity_b": "G424_vehicle_41", + "world_pos_a_enu": [ + -30.87, + -115.33, + 0.0 + ], + "world_pos_b_enu": [ + -10.86, + -153.01, + 0.0 + ], + "distance_meters": 42.67, + "proximity": "far", + "camera_a": "G424", + "camera_b": "G424" + } + }, + { + "question_id": "v4_005", + "category": "spatial", + "question_template": "In the scene, how close are the person who carries a heavy object and the person who exits a vehicle to each other in physical space?", + "options": [ + "They are at the same location", + "They are far apart (more than 15 meters)", + "They are in different areas of the scene", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G424" + ], + "verification": { + "entity_a": "G424_person_23", + "entity_b": "G424_person_38", + "world_pos_a_enu": [ + -30.09, + -113.9, + 0.0 + ], + "world_pos_b_enu": [ + 69.32, + 53.25, + 0.0 + ], + "distance_meters": 194.47, + "proximity": "far", + "camera_a": "G424", + "camera_b": "G424" + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone interacts with a laptop", + "Someone closes a facility door", + "Someone opens the trunk", + "Someone embraces another person" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G424_evt_9", + "camera_id": "G424", + "start_time_sec": 90.43, + "end_time_sec": 93.0, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G424.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_embraces_person", + "person_interacts_with_laptop" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone reads a document", + "Someone makes a purchase", + "Someone loads a vehicle", + "Someone closes the trunk" + ], + "correct_answer_index": 3, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G424_evt_10", + "camera_id": "G424", + "start_time_sec": 104.13, + "end_time_sec": 106.73, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G424.avi" + }, + "distractor_activities": [ + "person_loads_vehicle", + "person_purchases", + "person_reads_document" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_008", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone stands up", + "Someone unloads a vehicle", + "Someone rides a bicycle", + "Someone sits down" + ], + "correct_answer_index": 1, + "correct_answer": "Someone unloads a vehicle", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_unloads_vehicle", + "present": true, + "example_event": { + "event_id": "G424_evt_34", + "camera_id": "G424", + "start_time_sec": 98.23, + "end_time_sec": 99.23, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G424.avi" + }, + "distractor_activities": [ + "person_rides_bicycle", + "person_sits_down", + "person_stands_up" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_009", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone drops off a person", + "Someone makes a U-turn", + "Someone texts on a phone", + "Someone picks up a person" + ], + "correct_answer_index": 1, + "correct_answer": "Someone makes a U-turn", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "vehicle_makes_u_turn", + "present": true, + "example_event": { + "event_id": "G424_evt_22", + "camera_id": "G424", + "start_time_sec": 160.33, + "end_time_sec": 165.67, + "video_file": "2018-03-05.13-15-00.13-20-00.school.G424.avi" + }, + "distractor_activities": [ + "vehicle_drops_off_person", + "vehicle_picks_up_person", + "person_texts_on_phone" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-01.bus.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-01.bus.v4.json new file mode 100644 index 0000000..491e024 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-01.bus.v4.json @@ -0,0 +1,157 @@ +{ + "slot": "2018-03-05.13-15-01.bus", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 0, + "perception": 4 + }, + "total_questions": 4, + "cameras": [ + "G331" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a vehicle door", + "Someone closes the trunk", + "Someone carries a heavy object", + "Someone closes a facility door" + ], + "correct_answer_index": 2, + "correct_answer": "Someone carries a heavy object", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_carries_heavy_object", + "present": true, + "example_event": { + "event_id": "G331_evt_25", + "camera_id": "G331", + "start_time_sec": 133.57, + "end_time_sec": 135.2, + "video_file": "2018-03-05.13-15-01.13-20-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_closes_trunk", + "person_closes_vehicle_door" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_002", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone embraces another person", + "Someone exits a vehicle", + "Someone enters a vehicle", + "Someone sits down" + ], + "correct_answer_index": 3, + "correct_answer": "Someone sits down", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_sits_down", + "present": true, + "example_event": { + "event_id": "G331_evt_6", + "camera_id": "G331", + "start_time_sec": 48.5, + "end_time_sec": 50.0, + "video_file": "2018-03-05.13-15-01.13-20-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_embraces_person", + "person_enters_vehicle", + "person_exits_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_003", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone interacts with a laptop", + "Someone loads a vehicle", + "Someone opens a facility door", + "Someone opens the trunk" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens a facility door", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_opens_facility_door", + "present": true, + "example_event": { + "event_id": "G331_evt_2", + "camera_id": "G331", + "start_time_sec": 180.9, + "end_time_sec": 183.17, + "video_file": "2018-03-05.13-15-01.13-20-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_interacts_with_laptop", + "person_loads_vehicle", + "person_opens_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone opens a vehicle door", + "Someone makes a purchase", + "Someone reads a document", + "Someone picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "Someone picks up an object", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_picks_up_object", + "present": true, + "example_event": { + "event_id": "G331_evt_44", + "camera_id": "G331", + "start_time_sec": 209.13, + "end_time_sec": 209.87, + "video_file": "2018-03-05.13-15-01.13-20-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_opens_vehicle_door", + "person_purchases", + "person_reads_document" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-15-01.school.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-15-01.school.v4.json new file mode 100644 index 0000000..9e2c2d6 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-15-01.school.v4.json @@ -0,0 +1,261 @@ +{ + "slot": "2018-03-05.13-15-01.school", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G328", + "G339" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the person who exits a vehicle and the vehicle that turns right to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are near each other (within a few meters)", + "They are in different areas of the scene" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G339" + ], + "verification": { + "entity_a": "G339_person_37", + "entity_b": "G339_vehicle_2", + "world_pos_a_enu": [ + -9.16, + -73.1, + 0.0 + ], + "world_pos_b_enu": [ + -24.05, + -136.61, + 0.0 + ], + "distance_meters": 65.23, + "proximity": "far", + "camera_a": "G339", + "camera_b": "G339" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who opens a vehicle door and the person who carries a heavy object to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G328", + "G339" + ], + "verification": { + "entity_a": "G328_vehicle_4", + "entity_b": "G339_person_25", + "world_pos_a_enu": [ + 68.19, + 53.99, + 0.0 + ], + "world_pos_b_enu": [ + 1.24, + -93.55, + 0.0 + ], + "distance_meters": 162.02, + "proximity": "far", + "camera_a": "G328", + "camera_b": "G339" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who closes a vehicle door and the vehicle that turns left to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are near each other (within a few meters)", + "They are in different areas of the scene" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G339" + ], + "verification": { + "entity_a": "G339_vehicle_12", + "entity_b": "G339_vehicle_28", + "world_pos_a_enu": [ + -9.17, + -71.42, + 0.0 + ], + "world_pos_b_enu": [ + -26.54, + -138.79, + 0.0 + ], + "distance_meters": 69.57, + "proximity": "far", + "camera_a": "G339", + "camera_b": "G339" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone interacts with a laptop", + "Someone embraces another person", + "Someone closes a facility door", + "Someone opens the trunk" + ], + "correct_answer_index": 3, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G339" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G339_evt_0", + "camera_id": "G339", + "start_time_sec": 89.77, + "end_time_sec": 92.63, + "video_file": "2018-03-05.13-15-01.13-20-01.school.G339.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_embraces_person", + "person_interacts_with_laptop" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone makes a purchase", + "Someone loads a vehicle", + "Someone closes the trunk", + "Someone puts down an object" + ], + "correct_answer_index": 2, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G339" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G339_evt_29", + "camera_id": "G339", + "start_time_sec": 103.47, + "end_time_sec": 106.77, + "video_file": "2018-03-05.13-15-01.13-20-01.school.G339.avi" + }, + "distractor_activities": [ + "person_loads_vehicle", + "person_purchases", + "person_puts_down_object" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone drops off a person", + "Someone picks up a person", + "Someone makes a U-turn", + "Someone reads a document" + ], + "correct_answer_index": 2, + "correct_answer": "Someone makes a U-turn", + "requires_cameras": [ + "G339" + ], + "verification": { + "activity": "vehicle_makes_u_turn", + "present": true, + "example_event": { + "event_id": "G339_evt_15", + "camera_id": "G339", + "start_time_sec": 39.73, + "end_time_sec": 45.57, + "video_file": "2018-03-05.13-15-01.13-20-01.school.G339.avi" + }, + "distractor_activities": [ + "vehicle_drops_off_person", + "vehicle_picks_up_person", + "person_reads_document" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone sits down", + "Someone stands up", + "Someone exits a vehicle", + "Someone rides a bicycle" + ], + "correct_answer_index": 2, + "correct_answer": "Someone exits a vehicle", + "requires_cameras": [ + "G339" + ], + "verification": { + "activity": "person_exits_vehicle", + "present": true, + "example_event": { + "event_id": "G339_evt_35", + "camera_id": "G339", + "start_time_sec": 83.13, + "end_time_sec": 87.23, + "video_file": "2018-03-05.13-15-01.13-20-01.school.G339.avi" + }, + "distractor_activities": [ + "person_rides_bicycle", + "person_sits_down", + "person_stands_up" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.admin.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.admin.v4.json new file mode 100644 index 0000000..0cd5a2b --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.admin.v4.json @@ -0,0 +1,125 @@ +{ + "slot": "2018-03-05.13-20-00.admin", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 0, + "perception": 3 + }, + "total_questions": 3, + "cameras": [ + "G326", + "G329" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a vehicle door", + "Someone closes a facility door", + "Someone closes the trunk", + "Someone carries a heavy object" + ], + "correct_answer_index": 3, + "correct_answer": "Someone carries a heavy object", + "requires_cameras": [ + "G326" + ], + "verification": { + "activity": "person_carries_heavy_object", + "present": true, + "example_event": { + "event_id": "G326_evt_2", + "camera_id": "G326", + "start_time_sec": 247.33, + "end_time_sec": 250.13, + "video_file": "2018-03-05.13-20-00.13-25-00.admin.G326.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_closes_trunk", + "person_closes_vehicle_door" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_002", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone exits the scene through a structure", + "Someone enters a vehicle", + "Someone opens a facility door", + "Someone embraces another person" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens a facility door", + "requires_cameras": [ + "G326" + ], + "verification": { + "activity": "person_opens_facility_door", + "present": true, + "example_event": { + "event_id": "G326_evt_0", + "camera_id": "G326", + "start_time_sec": 244.9, + "end_time_sec": 247.4, + "video_file": "2018-03-05.13-20-00.13-25-00.admin.G326.avi" + }, + "distractor_activities": [ + "person_embraces_person", + "person_enters_vehicle", + "person_exits_scene_through_structure" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_003", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone exits a vehicle", + "Someone loads a vehicle", + "Someone interacts with a laptop", + "Someone enters the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "Someone enters the scene through a structure", + "requires_cameras": [ + "G326" + ], + "verification": { + "activity": "person_enters_scene_through_structure", + "present": true, + "example_event": { + "event_id": "G326_evt_1", + "camera_id": "G326", + "start_time_sec": 244.9, + "end_time_sec": 249.33, + "video_file": "2018-03-05.13-20-00.13-25-00.admin.G326.avi" + }, + "distractor_activities": [ + "person_exits_vehicle", + "person_interacts_with_laptop", + "person_loads_vehicle" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.bus.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.bus.v4.json new file mode 100644 index 0000000..77a28e1 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.bus.v4.json @@ -0,0 +1,261 @@ +{ + "slot": "2018-03-05.13-20-00.bus", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G340", + "G506" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the person who opens the trunk and the person who puts down an object to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are in different areas of the scene", + "They are near each other (within a few meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G506" + ], + "verification": { + "entity_a": "G506_person_27", + "entity_b": "G506_person_54", + "world_pos_a_enu": [ + -14.0, + -73.63, + 0.0 + ], + "world_pos_b_enu": [ + -14.7, + -72.46, + 0.0 + ], + "distance_meters": 1.36, + "proximity": "near", + "camera_a": "G506", + "camera_b": "G506" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that starts moving and the person who transfers an object to each other in physical space?", + "options": [ + "They are at the same location", + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)", + "They are in different areas of the scene" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G340", + "G506" + ], + "verification": { + "entity_a": "G340_vehicle_14", + "entity_b": "G506_person_32", + "world_pos_a_enu": [ + 85.69, + -10.68, + 0.0 + ], + "world_pos_b_enu": [ + -14.58, + -73.89, + 0.0 + ], + "distance_meters": 118.53, + "proximity": "far", + "camera_a": "G340", + "camera_b": "G506" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who closes a vehicle door and the person who exits a vehicle to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are in different areas of the scene" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G506" + ], + "verification": { + "entity_a": "G506_person_47", + "entity_b": "G506_vehicle_22", + "world_pos_a_enu": [ + -12.3, + -76.08, + 0.0 + ], + "world_pos_b_enu": [ + -13.74, + -78.3, + 0.0 + ], + "distance_meters": 2.65, + "proximity": "near", + "camera_a": "G506", + "camera_b": "G506" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone embraces another person", + "Someone opens the trunk", + "Someone enters the scene through a structure", + "Someone closes a facility door" + ], + "correct_answer_index": 1, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G506" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G506_evt_1", + "camera_id": "G506", + "start_time_sec": 17.03, + "end_time_sec": 20.6, + "video_file": "2018-03-05.13-20-00.13-25-00.bus.G506.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_embraces_person", + "person_enters_scene_through_structure" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone interacts with a laptop", + "Someone closes the trunk", + "Someone exits the scene through a structure", + "Someone opens a facility door" + ], + "correct_answer_index": 1, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G506" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G506_evt_2", + "camera_id": "G506", + "start_time_sec": 20.27, + "end_time_sec": 22.4, + "video_file": "2018-03-05.13-20-00.13-25-00.bus.G506.avi" + }, + "distractor_activities": [ + "person_exits_scene_through_structure", + "person_interacts_with_laptop", + "person_opens_facility_door" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone makes a purchase", + "Someone reads a document", + "Someone picks up an object", + "Someone loads a vehicle" + ], + "correct_answer_index": 3, + "correct_answer": "Someone loads a vehicle", + "requires_cameras": [ + "G506" + ], + "verification": { + "activity": "person_loads_vehicle", + "present": true, + "example_event": { + "event_id": "G506_evt_4", + "camera_id": "G506", + "start_time_sec": 25.07, + "end_time_sec": 27.1, + "video_file": "2018-03-05.13-20-00.13-25-00.bus.G506.avi" + }, + "distractor_activities": [ + "person_picks_up_object", + "person_purchases", + "person_reads_document" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone makes a U-turn", + "Someone picks up a person", + "Someone reverses", + "Someone drops off a person" + ], + "correct_answer_index": 0, + "correct_answer": "Someone makes a U-turn", + "requires_cameras": [ + "G340" + ], + "verification": { + "activity": "vehicle_makes_u_turn", + "present": true, + "example_event": { + "event_id": "G340_evt_1", + "camera_id": "G340", + "start_time_sec": 141.43, + "end_time_sec": 149.13, + "video_file": "2018-03-05.13-20-00.13-25-00.bus.G340.avi" + }, + "distractor_activities": [ + "vehicle_drops_off_person", + "vehicle_picks_up_person", + "vehicle_reverses" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.hospital.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.hospital.v4.json new file mode 100644 index 0000000..620d7fe --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.hospital.v4.json @@ -0,0 +1,299 @@ +{ + "slot": "2018-03-05.13-20-00.hospital", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 1, + "spatial": 3, + "perception": 4 + }, + "total_questions": 8, + "cameras": [ + "G301", + "G341", + "G436" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "temporal", + "question_template": "After the person who exits a vehicle opens a vehicle door, what do they do next in a different camera view?", + "options": [ + "carries a heavy object", + "closes the trunk", + "opens a facility door", + "opens the trunk" + ], + "correct_answer_index": 2, + "correct_answer": "opens a facility door", + "requires_cameras": [ + "G341", + "G436" + ], + "verification": { + "chain_id": "narrative_012", + "event_a": { + "event_id": "G436_evt_20", + "activity": "person_opens_vehicle_door", + "camera_id": "G436", + "start_time_sec": 249.27, + "end_time_sec": 251.27, + "video_file": "2018-03-05.13-20-00.13-25-00.hospital.G436.avi" + }, + "event_b": { + "event_id": "G341_evt_19", + "activity": "person_opens_facility_door", + "camera_id": "G341", + "start_time_sec": 265.27, + "end_time_sec": 265.8, + "video_file": "2018-03-05.13-20-00.13-25-00.hospital.G341.avi" + }, + "gap_sec": 14.0 + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the person who exits a vehicle and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are at the same location", + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)", + "They are in different areas of the scene" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G436" + ], + "verification": { + "entity_a": "G436_person_4", + "entity_b": "G436_vehicle_32", + "world_pos_a_enu": [ + -4.43, + -11.75, + 0.0 + ], + "world_pos_b_enu": [ + 25.13, + -31.09, + 0.0 + ], + "distance_meters": 35.32, + "proximity": "far", + "camera_a": "G436", + "camera_b": "G436" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the person who opens a vehicle door and the person who closes a vehicle door to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are in different areas of the scene" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G341", + "G436" + ], + "verification": { + "entity_a": "G341_vehicle_15", + "entity_b": "G436_person_31", + "world_pos_a_enu": [ + -21.82, + -68.78, + 0.0 + ], + "world_pos_b_enu": [ + 25.95, + -28.26, + 0.0 + ], + "distance_meters": 62.64, + "proximity": "far", + "camera_a": "G341", + "camera_b": "G436" + } + }, + { + "question_id": "v4_004", + "category": "spatial", + "question_template": "In the scene, how close are the person who closes a vehicle door and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are at the same location", + "They are in different areas of the scene", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G436" + ], + "verification": { + "entity_a": "G436_person_9", + "entity_b": "G436_person_27", + "world_pos_a_enu": [ + -4.44, + -11.75, + 0.0 + ], + "world_pos_b_enu": [ + 25.6, + -27.58, + 0.0 + ], + "distance_meters": 33.96, + "proximity": "far", + "camera_a": "G436", + "camera_b": "G436" + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone closes a facility door", + "Someone embraces another person", + "Someone opens the trunk", + "Someone exits the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G341" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G341_evt_12", + "camera_id": "G341", + "start_time_sec": 16.67, + "end_time_sec": 19.33, + "video_file": "2018-03-05.13-20-00.13-25-00.hospital.G341.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_embraces_person", + "person_exits_scene_through_structure" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone closes the trunk", + "Someone makes a purchase", + "Someone loads a vehicle", + "Someone interacts with a laptop" + ], + "correct_answer_index": 0, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G341" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G341_evt_13", + "camera_id": "G341", + "start_time_sec": 19.77, + "end_time_sec": 22.1, + "video_file": "2018-03-05.13-20-00.13-25-00.hospital.G341.avi" + }, + "distractor_activities": [ + "person_interacts_with_laptop", + "person_loads_vehicle", + "person_purchases" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone picks up a person", + "Someone makes a U-turn", + "Someone puts down an object", + "Someone drops off a person" + ], + "correct_answer_index": 3, + "correct_answer": "Someone drops off a person", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "vehicle_drops_off_person", + "present": true, + "example_event": { + "event_id": "G436_evt_9", + "camera_id": "G436", + "start_time_sec": 13.63, + "end_time_sec": 54.5, + "video_file": "2018-03-05.13-20-00.13-25-00.hospital.G436.avi" + }, + "distractor_activities": [ + "vehicle_makes_u_turn", + "vehicle_picks_up_person", + "person_puts_down_object" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_008", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone rides a bicycle", + "Someone sits down", + "Someone reads a document", + "Someone exits a vehicle" + ], + "correct_answer_index": 3, + "correct_answer": "Someone exits a vehicle", + "requires_cameras": [ + "G436" + ], + "verification": { + "activity": "person_exits_vehicle", + "present": true, + "example_event": { + "event_id": "G436_evt_1", + "camera_id": "G436", + "start_time_sec": 16.43, + "end_time_sec": 20.63, + "video_file": "2018-03-05.13-20-00.13-25-00.hospital.G436.avi" + }, + "distractor_activities": [ + "person_reads_document", + "person_rides_bicycle", + "person_sits_down" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.school.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.school.v4.json new file mode 100644 index 0000000..92e3d36 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-20-00.school.v4.json @@ -0,0 +1,375 @@ +{ + "slot": "2018-03-05.13-20-00.school", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 3, + "spatial": 3, + "perception": 4 + }, + "total_questions": 10, + "cameras": [ + "G300", + "G336", + "G421", + "G423", + "G424" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "temporal", + "question_template": "After the person who exits the scene through a structure exits the scene through a structure, what do they do next in a different camera view?", + "options": [ + "opens a vehicle door", + "closes the trunk", + "enters the scene through a structure", + "transfers an object" + ], + "correct_answer_index": 2, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G300", + "G423" + ], + "verification": { + "chain_id": "narrative_003", + "event_a": { + "event_id": "G300_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G300", + "start_time_sec": 149.03, + "end_time_sec": 151.03, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G300.avi" + }, + "event_b": { + "event_id": "G423_evt_2", + "activity": "person_enters_scene_through_structure", + "camera_id": "G423", + "start_time_sec": 162.0, + "end_time_sec": 163.73, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G423.avi" + }, + "gap_sec": 10.97 + } + }, + { + "question_id": "v4_002", + "category": "temporal", + "question_template": "After the person who enters a vehicle exits the scene through a structure, what do they do next in a different camera view?", + "options": [ + "loads a vehicle", + "carries a heavy object", + "hand interacts with person", + "opens a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "opens a vehicle door", + "requires_cameras": [ + "G421", + "G424" + ], + "verification": { + "chain_id": "narrative_029", + "event_a": { + "event_id": "G421_evt_10", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 283.5, + "end_time_sec": 286.4, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G421.avi" + }, + "event_b": { + "event_id": "G424_evt_26", + "activity": "person_opens_vehicle_door", + "camera_id": "G424", + "start_time_sec": 293.93, + "end_time_sec": 295.8, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G424.avi" + }, + "gap_sec": 7.53 + } + }, + { + "question_id": "v4_003", + "category": "temporal", + "question_template": "After the person who exits a vehicle closes a vehicle door, what do they do next in a different camera view?", + "options": [ + "puts down an object", + "opens a facility door", + "carries a heavy object", + "enters the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G423", + "G424" + ], + "verification": { + "chain_id": "narrative_004", + "event_a": { + "event_id": "G424_evt_41", + "activity": "person_closes_vehicle_door", + "camera_id": "G424", + "start_time_sec": 73.07, + "end_time_sec": 74.97, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G424.avi" + }, + "event_b": { + "event_id": "G423_evt_3", + "activity": "person_enters_scene_through_structure", + "camera_id": "G423", + "start_time_sec": 91.13, + "end_time_sec": 92.13, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G423.avi" + }, + "gap_sec": 16.16 + } + }, + { + "question_id": "v4_004", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that starts moving and the person who closes the trunk to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are at the same location", + "They are near each other (within a few meters)" + ], + "correct_answer_index": 1, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G336", + "G424" + ], + "verification": { + "entity_a": "G336_vehicle_6", + "entity_b": "G424_person_46", + "world_pos_a_enu": [ + 7.61, + 74.01, + 0.0 + ], + "world_pos_b_enu": [ + -36.8, + -113.32, + 0.0 + ], + "distance_meters": 192.52, + "proximity": "far", + "camera_a": "G336", + "camera_b": "G424" + } + }, + { + "question_id": "v4_005", + "category": "spatial", + "question_template": "In the scene, how close are the person who exits a vehicle and the vehicle that stops to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are at the same location", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G424" + ], + "verification": { + "entity_a": "G424_person_124", + "entity_b": "G424_vehicle_99", + "world_pos_a_enu": [ + 71.85, + 28.74, + 0.0 + ], + "world_pos_b_enu": [ + 48.31, + 25.8, + 0.0 + ], + "distance_meters": 23.72, + "proximity": "far", + "camera_a": "G424", + "camera_b": "G424" + } + }, + { + "question_id": "v4_006", + "category": "spatial", + "question_template": "In the scene, how close are the person who closes a vehicle door and the person who exits a vehicle to each other in physical space?", + "options": [ + "They are in different areas of the scene", + "They are at the same location", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G424" + ], + "verification": { + "entity_a": "G424_vehicle_26", + "entity_b": "G424_vehicle_64", + "world_pos_a_enu": [ + -34.55, + -112.85, + 0.0 + ], + "world_pos_b_enu": [ + 74.3, + 54.27, + -0.0 + ], + "distance_meters": 199.44, + "proximity": "far", + "camera_a": "G424", + "camera_b": "G424" + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone drops off a person", + "Someone reverses", + "Someone picks up a person", + "Someone closes a facility door" + ], + "correct_answer_index": 0, + "correct_answer": "Someone drops off a person", + "requires_cameras": [ + "G336" + ], + "verification": { + "activity": "vehicle_drops_off_person", + "present": true, + "example_event": { + "event_id": "G336_evt_16", + "camera_id": "G336", + "start_time_sec": 13.07, + "end_time_sec": 53.9, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G336.avi" + }, + "distractor_activities": [ + "vehicle_picks_up_person", + "vehicle_reverses", + "person_closes_facility_door" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_008", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone interacts with a laptop", + "Someone opens the trunk", + "Someone makes a purchase", + "Someone reads a document" + ], + "correct_answer_index": 1, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G424_evt_8", + "camera_id": "G424", + "start_time_sec": 16.93, + "end_time_sec": 19.43, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G424.avi" + }, + "distractor_activities": [ + "person_interacts_with_laptop", + "person_purchases", + "person_reads_document" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_009", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone rides a bicycle", + "Someone closes the trunk", + "Someone texts on a phone", + "Someone talks on the phone" + ], + "correct_answer_index": 1, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G424_evt_10", + "camera_id": "G424", + "start_time_sec": 19.93, + "end_time_sec": 21.9, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G424.avi" + }, + "distractor_activities": [ + "person_rides_bicycle", + "person_talks_on_phone", + "person_texts_on_phone" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_010", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone closes a facility door", + "Someone interacts with a laptop", + "Someone makes a purchase", + "Someone embraces another person" + ], + "correct_answer_index": 3, + "correct_answer": "Someone embraces another person", + "requires_cameras": [ + "G424" + ], + "verification": { + "activity": "person_embraces_person", + "present": true, + "example_event": { + "event_id": "G424_evt_52", + "camera_id": "G424", + "start_time_sec": 124.87, + "end_time_sec": 127.93, + "video_file": "2018-03-05.13-20-00.13-25-00.school.G424.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_interacts_with_laptop", + "person_purchases" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-05.13-20-01.bus.v4.json b/meva/examples/qa_pairs/archived/2018-03-05.13-20-01.bus.v4.json new file mode 100644 index 0000000..15af8cd --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-05.13-20-01.bus.v4.json @@ -0,0 +1,260 @@ +{ + "slot": "2018-03-05.13-20-01.bus", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 0, + "spatial": 3, + "perception": 4 + }, + "total_questions": 7, + "cameras": [ + "G331", + "G505" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that turns left and the vehicle that turns right to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G505" + ], + "verification": { + "entity_a": "G505_vehicle_5", + "entity_b": "G505_vehicle_6", + "world_pos_a_enu": [ + -35.16, + -159.53, + 0.0 + ], + "world_pos_b_enu": [ + -42.9, + -122.15, + 0.0 + ], + "distance_meters": 38.17, + "proximity": "far", + "camera_a": "G505", + "camera_b": "G505" + } + }, + { + "question_id": "v4_002", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that turns left and the vehicle that turns right to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are in different areas of the scene", + "They are at the same location", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G505" + ], + "verification": { + "entity_a": "G505_vehicle_4", + "entity_b": "G505_vehicle_6", + "world_pos_a_enu": [ + -34.64, + -160.43, + 0.0 + ], + "world_pos_b_enu": [ + -42.9, + -122.15, + 0.0 + ], + "distance_meters": 39.16, + "proximity": "far", + "camera_a": "G505", + "camera_b": "G505" + } + }, + { + "question_id": "v4_003", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that turns left and the vehicle that turns right to each other in physical space?", + "options": [ + "They are near each other (within a few meters)", + "They are at the same location", + "They are in different areas of the scene", + "They are far apart (more than 15 meters)" + ], + "correct_answer_index": 3, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G505" + ], + "verification": { + "entity_a": "G505_vehicle_2", + "entity_b": "G505_vehicle_3", + "world_pos_a_enu": [ + -47.12, + -137.82, + 0.0 + ], + "world_pos_b_enu": [ + -24.44, + -153.53, + 0.0 + ], + "distance_meters": 27.59, + "proximity": "far", + "camera_a": "G505", + "camera_b": "G505" + } + }, + { + "question_id": "v4_004", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone carries a heavy object", + "Someone closes the trunk", + "Someone sits down", + "Someone closes a facility door" + ], + "correct_answer_index": 2, + "correct_answer": "Someone sits down", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_sits_down", + "present": true, + "example_event": { + "event_id": "G331_evt_11", + "camera_id": "G331", + "start_time_sec": 32.03, + "end_time_sec": 32.93, + "video_file": "2018-03-05.13-20-01.13-25-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_carries_heavy_object", + "person_closes_facility_door", + "person_closes_trunk" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_005", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone opens a facility door", + "Someone embraces another person", + "Someone closes a vehicle door", + "Someone enters a vehicle" + ], + "correct_answer_index": 0, + "correct_answer": "Someone opens a facility door", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_opens_facility_door", + "present": true, + "example_event": { + "event_id": "G331_evt_33", + "camera_id": "G331", + "start_time_sec": 99.2, + "end_time_sec": 101.9, + "video_file": "2018-03-05.13-20-01.13-25-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_closes_vehicle_door", + "person_embraces_person", + "person_enters_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_006", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone interacts with a laptop", + "Someone loads a vehicle", + "Someone picks up an object", + "Someone exits a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "Someone picks up an object", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_picks_up_object", + "present": true, + "example_event": { + "event_id": "G331_evt_25", + "camera_id": "G331", + "start_time_sec": 4.0, + "end_time_sec": 4.8, + "video_file": "2018-03-05.13-20-01.13-25-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_exits_vehicle", + "person_interacts_with_laptop", + "person_loads_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone opens a vehicle door", + "Someone opens the trunk", + "Someone makes a purchase", + "Someone talks to another person" + ], + "correct_answer_index": 3, + "correct_answer": "Someone talks to another person", + "requires_cameras": [ + "G331" + ], + "verification": { + "activity": "person_talks_to_person", + "present": true, + "example_event": { + "event_id": "G331_evt_34", + "camera_id": "G331", + "start_time_sec": 0.0, + "end_time_sec": 3.0, + "video_file": "2018-03-05.13-20-01.13-25-01.bus.G331.avi" + }, + "distractor_activities": [ + "person_opens_trunk", + "person_opens_vehicle_door", + "person_purchases" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.crosscam.json b/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.crosscam.json new file mode 100644 index 0000000..f3f47f0 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.crosscam.json @@ -0,0 +1,3094 @@ +{ + "slot": "2018-03-07.17-05-00.school", + "metadata": { + "generator": "generate_qa_crosscam.py", + "version": 2, + "seed": 42, + "scene_graph_version": 2, + "categories_requested": [ + "cross_camera_temporal", + "cross_camera_ordering", + "cross_camera_tracking", + "cross_camera_gap", + "camera_identification" + ], + "category_counts": { + "cross_camera_temporal": 15, + "cross_camera_ordering": 15, + "cross_camera_tracking": 15, + "cross_camera_gap": 15, + "camera_identification": 15 + }, + "total_questions": 75, + "xcam_narrative_count": 15, + "cameras": [ + "G299", + "G330", + "G336", + "G421", + "G638" + ], + "scene_graph_summary": { + "event_count": 345, + "entity_count": 546, + "xcam_link_count": 125, + "narrative_chain_count": 72 + } + }, + "qa_pairs": [ + { + "question_id": "xcam_temporal_001", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle exits the scene through a structure, what do they do next in a different camera view?", + "options": [ + "makes a purchase", + "enters the scene through a structure", + "enters a vehicle", + "closes the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_239", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 250.53, + "end_time_sec": 253.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -1.04 + } + }, + { + "question_id": "xcam_temporal_002", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle enters the scene through a structure, what do they do next in a different camera view?", + "options": [ + "enters a vehicle", + "closes the trunk", + "exits the scene through a structure", + "makes a purchase" + ], + "correct_answer_index": 2, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_5", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 265.37, + "end_time_sec": 267.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_250", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 268.57, + "end_time_sec": 272.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 0.87 + } + }, + { + "question_id": "xcam_temporal_003", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle enters the scene through a structure, what do they do next in a different camera view?", + "options": [ + "drops off a person", + "closes the trunk", + "makes a purchase", + "interacts with a laptop" + ], + "correct_answer_index": 0, + "correct_answer": "drops off a person", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G421_evt_237", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 248.0, + "end_time_sec": 249.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -1.0 + } + }, + { + "question_id": "xcam_temporal_004", + "category": "cross_camera_temporal", + "question_template": "After the person who opens the trunk opens the trunk, what do they do next in a different camera view?", + "options": [ + "embraces another person", + "stands up", + "carries a heavy object", + "enters a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "stands up", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G336_evt_28", + "activity": "person_opens_trunk", + "camera_id": "G336", + "start_time_sec": 68.47, + "end_time_sec": 76.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_177", + "activity": "person_stands_up", + "camera_id": "G421", + "start_time_sec": 68.9, + "end_time_sec": 72.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -7.13 + } + }, + { + "question_id": "xcam_temporal_005", + "category": "cross_camera_temporal", + "question_template": "After the person who enters a vehicle exits the scene through a structure, what do they do next in a different camera view?", + "options": [ + "enters a vehicle", + "enters the scene through a structure", + "exits a vehicle", + "loads a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_010", + "event_a": { + "event_id": "G638_evt_18", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 196.63, + "end_time_sec": 199.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_232", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 198.93, + "end_time_sec": 201.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.7 + } + }, + { + "question_id": "xcam_temporal_006", + "category": "cross_camera_temporal", + "question_template": "After the person who opens the trunk loads a vehicle, what do they do next in a different camera view?", + "options": [ + "opens a facility door", + "stands up", + "interacts with a laptop", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G336_evt_30", + "activity": "person_loads_vehicle", + "camera_id": "G336", + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_178", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 73.13, + "end_time_sec": 73.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -1.84 + } + }, + { + "question_id": "xcam_temporal_007", + "category": "cross_camera_temporal", + "question_template": "After the person who enters a vehicle exits the scene through a structure, what do they do next in a different camera view?", + "options": [ + "talks on the phone", + "reads a document", + "texts on a phone", + "enters the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_006", + "event_a": { + "event_id": "G638_evt_16", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 164.9, + "end_time_sec": 165.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_148", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 166.2, + "end_time_sec": 168.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 0.43 + } + }, + { + "question_id": "xcam_temporal_008", + "category": "cross_camera_temporal", + "question_template": "After the person who sits down picks up an object, what do they do next in a different camera view?", + "options": [ + "exits a vehicle", + "puts down an object", + "transfers an object", + "opens the trunk" + ], + "correct_answer_index": 1, + "correct_answer": "puts down an object", + "requires_cameras": [ + "G299", + "G421" + ], + "verification": { + "chain_id": "narrative_039", + "event_a": { + "event_id": "G421_evt_200", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 143.93, + "end_time_sec": 144.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G299_evt_1", + "activity": "person_puts_down_object", + "camera_id": "G299", + "start_time_sec": 144.3, + "end_time_sec": 144.93, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + }, + "gap_sec": -0.17 + } + }, + { + "question_id": "xcam_temporal_009", + "category": "cross_camera_temporal", + "question_template": "After the person who enters a vehicle enters a vehicle, what do they do next in a different camera view?", + "options": [ + "opens a vehicle door", + "stands up", + "exits the scene through a structure", + "puts down an object" + ], + "correct_answer_index": 2, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G330", + "G336" + ], + "verification": { + "chain_id": "narrative_006", + "event_a": { + "event_id": "G336_evt_47", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 145.13, + "end_time_sec": 149.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G330_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G330", + "start_time_sec": 146.03, + "end_time_sec": 148.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + }, + "gap_sec": -3.74 + } + }, + { + "question_id": "xcam_temporal_010", + "category": "cross_camera_temporal", + "question_template": "After the person who picks up an object puts down an object, what do they do next in a different camera view?", + "options": [ + "picks up an object", + "opens a vehicle door", + "exits a vehicle", + "talks to another person" + ], + "correct_answer_index": 0, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_024", + "event_a": { + "event_id": "G638_evt_11", + "activity": "person_puts_down_object", + "camera_id": "G638", + "start_time_sec": 107.43, + "end_time_sec": 108.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_107", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 145.83, + "end_time_sec": 146.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 37.33 + } + }, + { + "question_id": "xcam_temporal_011", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle exits a vehicle, what do they do next in a different camera view?", + "options": [ + "stands up", + "enters a vehicle", + "puts down an object", + "enters the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "puts down an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_110", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_time_sec": 269.9, + "end_time_sec": 273.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_119", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 289.63, + "end_time_sec": 290.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 15.73 + } + }, + { + "question_id": "xcam_temporal_012", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle enters the scene through a structure, what do they do next in a different camera view?", + "options": [ + "closes a vehicle door", + "puts down an object", + "embraces another person", + "closes the trunk" + ], + "correct_answer_index": 0, + "correct_answer": "closes a vehicle door", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G421_evt_239", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 250.53, + "end_time_sec": 253.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G336_evt_100", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 252.3, + "end_time_sec": 253.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -0.73 + } + }, + { + "question_id": "xcam_temporal_013", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle closes a vehicle door, what do they do next in a different camera view?", + "options": [ + "opens a vehicle door", + "enters the scene through a structure", + "exits the scene through a structure", + "stands up" + ], + "correct_answer_index": 2, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_100", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 252.3, + "end_time_sec": 253.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_147", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 264.0, + "end_time_sec": 266.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 10.3 + } + }, + { + "question_id": "xcam_temporal_014", + "category": "cross_camera_temporal", + "question_template": "After the person who enters a vehicle opens a facility door, what do they do next in a different camera view?", + "options": [ + "opens a facility door", + "enters a vehicle", + "loads a vehicle", + "stands up" + ], + "correct_answer_index": 1, + "correct_answer": "enters a vehicle", + "requires_cameras": [ + "G299", + "G336" + ], + "verification": { + "chain_id": "narrative_006", + "event_a": { + "event_id": "G299_evt_2", + "activity": "person_opens_facility_door", + "camera_id": "G299", + "start_time_sec": 146.97, + "end_time_sec": 148.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + }, + "event_b": { + "event_id": "G336_evt_50", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 150.1, + "end_time_sec": 151.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": 1.2 + } + }, + { + "question_id": "xcam_temporal_015", + "category": "cross_camera_temporal", + "question_template": "After the person who exits a vehicle opens a vehicle door, what do they do next in a different camera view?", + "options": [ + "enters the scene through a structure", + "talks to another person", + "picks up an object", + "exits the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "gap_sec": -2.14 + } + }, + { + "question_id": "xcam_ordering_001", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: loading a vehicle, picking up an object, carrying a heavy object?", + "options": [ + "picks up an object \u2192 carries a heavy object \u2192 loads a vehicle", + "loads a vehicle \u2192 picks up an object \u2192 carries a heavy object", + "carries a heavy object \u2192 picks up an object \u2192 loads a vehicle", + "loads a vehicle \u2192 carries a heavy object \u2192 picks up an object" + ], + "correct_answer_index": 1, + "correct_answer": "loads a vehicle \u2192 picks up an object \u2192 carries a heavy object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "events": [ + { + "event_id": "G336_evt_30", + "activity": "person_loads_vehicle", + "camera_id": "G336", + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G421_evt_178", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 73.13, + "end_time_sec": 73.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_179", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_time_sec": 73.13, + "end_time_sec": 78.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_002", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: talking to another person, picking up an object, putting down an object?", + "options": [ + "talks to another person \u2192 puts down an object \u2192 picks up an object", + "talks to another person \u2192 picks up an object \u2192 puts down an object", + "picks up an object \u2192 talks to another person \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 picks up an object \u2192 puts down an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_024", + "events": [ + { + "event_id": "G421_evt_181", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_time_sec": 86.77, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_4", + "activity": "person_picks_up_object", + "camera_id": "G336", + "start_time_sec": 88.87, + "end_time_sec": 89.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G421_evt_184", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 93.03, + "end_time_sec": 93.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_003", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: entering the scene through a structure, opening a facility door, exiting the scene through a structure?", + "options": [ + "enters the scene through a structure \u2192 opens a facility door \u2192 exits the scene through a structure", + "exits the scene through a structure \u2192 opens a facility door \u2192 enters the scene through a structure", + "exits the scene through a structure \u2192 enters the scene through a structure \u2192 opens a facility door", + "enters the scene through a structure \u2192 exits the scene through a structure \u2192 opens a facility door" + ], + "correct_answer_index": 0, + "correct_answer": "enters the scene through a structure \u2192 opens a facility door \u2192 exits the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G421_evt_235", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 239.27, + "end_time_sec": 241.8, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_236", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 245.4, + "end_time_sec": 246.2, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G638_evt_23", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 245.77, + "end_time_sec": 249.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_004", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: closing a vehicle door, opening a facility door, exiting the scene through a structure?", + "options": [ + "opens a facility door \u2192 closes a vehicle door \u2192 exits the scene through a structure", + "exits the scene through a structure \u2192 closes a vehicle door \u2192 opens a facility door", + "closes a vehicle door \u2192 exits the scene through a structure \u2192 opens a facility door", + "closes a vehicle door \u2192 opens a facility door \u2192 exits the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "closes a vehicle door \u2192 opens a facility door \u2192 exits the scene through a structure", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_006", + "events": [ + { + "event_id": "G336_evt_49", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 150.73, + "end_time_sec": 151.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G421_evt_244", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 163.5, + "end_time_sec": 164.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G638_evt_16", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 164.9, + "end_time_sec": 165.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_005", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: exiting a vehicle, opening a vehicle door, exiting the scene through a structure?", + "options": [ + "exits a vehicle \u2192 opens a vehicle door \u2192 exits the scene through a structure", + "exits a vehicle \u2192 exits the scene through a structure \u2192 opens a vehicle door", + "exits the scene through a structure \u2192 exits a vehicle \u2192 opens a vehicle door", + "opens a vehicle door \u2192 exits the scene through a structure \u2192 exits a vehicle" + ], + "correct_answer_index": 0, + "correct_answer": "exits a vehicle \u2192 opens a vehicle door \u2192 exits the scene through a structure", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G336_evt_102", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 253.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_006", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: exiting the scene through a structure, entering the scene through a structure, closing a vehicle door?", + "options": [ + "enters the scene through a structure \u2192 closes a vehicle door \u2192 exits the scene through a structure", + "enters the scene through a structure \u2192 exits the scene through a structure \u2192 closes a vehicle door", + "exits the scene through a structure \u2192 closes a vehicle door \u2192 enters the scene through a structure", + "exits the scene through a structure \u2192 enters the scene through a structure \u2192 closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "exits the scene through a structure \u2192 enters the scene through a structure \u2192 closes a vehicle door", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + { + "event_id": "G421_evt_239", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 250.53, + "end_time_sec": 253.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_100", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 252.3, + "end_time_sec": 253.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_007", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: dropping off a person, entering the scene through a structure, exiting a vehicle, opening a vehicle door?", + "options": [ + "drops off a person \u2192 enters the scene through a structure \u2192 exits a vehicle \u2192 opens a vehicle door", + "drops off a person \u2192 opens a vehicle door \u2192 exits a vehicle \u2192 enters the scene through a structure", + "enters the scene through a structure \u2192 opens a vehicle door \u2192 exits a vehicle \u2192 drops off a person", + "exits a vehicle \u2192 opens a vehicle door \u2192 enters the scene through a structure \u2192 drops off a person" + ], + "correct_answer_index": 0, + "correct_answer": "drops off a person \u2192 enters the scene through a structure \u2192 exits a vehicle \u2192 opens a vehicle door", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G421_evt_238", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 249.17, + "end_time_sec": 251.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_102", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 253.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_008", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: exiting the scene through a structure, opening a facility door, entering the scene through a structure?", + "options": [ + "opens a facility door \u2192 exits the scene through a structure \u2192 enters the scene through a structure", + "exits the scene through a structure \u2192 opens a facility door \u2192 enters the scene through a structure", + "exits the scene through a structure \u2192 enters the scene through a structure \u2192 opens a facility door", + "enters the scene through a structure \u2192 opens a facility door \u2192 exits the scene through a structure" + ], + "correct_answer_index": 1, + "correct_answer": "exits the scene through a structure \u2192 opens a facility door \u2192 enters the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G421_evt_147", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 264.0, + "end_time_sec": 266.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_146", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 264.0, + "end_time_sec": 266.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G638_evt_5", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 265.37, + "end_time_sec": 267.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_009", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: opening a facility door, entering the scene through a structure, opening a vehicle door?", + "options": [ + "enters the scene through a structure \u2192 opens a facility door \u2192 opens a vehicle door", + "opens a facility door \u2192 opens a vehicle door \u2192 enters the scene through a structure", + "opens a facility door \u2192 enters the scene through a structure \u2192 opens a vehicle door", + "opens a vehicle door \u2192 enters the scene through a structure \u2192 opens a facility door" + ], + "correct_answer_index": 2, + "correct_answer": "opens a facility door \u2192 enters the scene through a structure \u2192 opens a vehicle door", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G421_evt_251", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 268.57, + "end_time_sec": 271.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G638_evt_3", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 269.87, + "end_time_sec": 272.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + { + "event_id": "G336_evt_116", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 269.9, + "end_time_sec": 272.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_010", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: picking up an object, putting down an object, transferring an object?", + "options": [ + "picks up an object \u2192 puts down an object \u2192 transfers an object", + "puts down an object \u2192 picks up an object \u2192 transfers an object", + "transfers an object \u2192 picks up an object \u2192 puts down an object", + "transfers an object \u2192 puts down an object \u2192 picks up an object" + ], + "correct_answer_index": 0, + "correct_answer": "picks up an object \u2192 puts down an object \u2192 transfers an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_026", + "events": [ + { + "event_id": "G421_evt_93", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 223.43, + "end_time_sec": 224.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_94", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 224.43, + "end_time_sec": 225.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_011", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: entering the scene through a structure, exiting the scene through a structure, opening a facility door?", + "options": [ + "opens a facility door \u2192 exits the scene through a structure \u2192 enters the scene through a structure", + "enters the scene through a structure \u2192 exits the scene through a structure \u2192 opens a facility door", + "opens a facility door \u2192 enters the scene through a structure \u2192 exits the scene through a structure", + "enters the scene through a structure \u2192 opens a facility door \u2192 exits the scene through a structure" + ], + "correct_answer_index": 1, + "correct_answer": "enters the scene through a structure \u2192 exits the scene through a structure \u2192 opens a facility door", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G638_evt_5", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 265.37, + "end_time_sec": 267.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + { + "event_id": "G421_evt_250", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 268.57, + "end_time_sec": 272.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_251", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 268.57, + "end_time_sec": 271.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_012", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: entering the scene through a structure, dropping off a person, exiting a vehicle?", + "options": [ + "exits a vehicle \u2192 enters the scene through a structure \u2192 drops off a person", + "enters the scene through a structure \u2192 exits a vehicle \u2192 drops off a person", + "enters the scene through a structure \u2192 drops off a person \u2192 exits a vehicle", + "drops off a person \u2192 enters the scene through a structure \u2192 exits a vehicle" + ], + "correct_answer_index": 2, + "correct_answer": "enters the scene through a structure \u2192 drops off a person \u2192 exits a vehicle", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "events": [ + { + "event_id": "G421_evt_237", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 248.0, + "end_time_sec": 249.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_102", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 253.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_013", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: opening a facility door, exiting the scene through a structure, entering the scene through a structure, opening a vehicle door?", + "options": [ + "enters the scene through a structure \u2192 opens a facility door \u2192 opens a vehicle door \u2192 exits the scene through a structure", + "opens a vehicle door \u2192 exits the scene through a structure \u2192 enters the scene through a structure \u2192 opens a facility door", + "exits the scene through a structure \u2192 opens a facility door \u2192 enters the scene through a structure \u2192 opens a vehicle door", + "opens a facility door \u2192 exits the scene through a structure \u2192 enters the scene through a structure \u2192 opens a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "opens a facility door \u2192 exits the scene through a structure \u2192 enters the scene through a structure \u2192 opens a vehicle door", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_010", + "events": [ + { + "event_id": "G638_evt_17", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 196.63, + "end_time_sec": 198.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + { + "event_id": "G638_evt_18", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 196.63, + "end_time_sec": 199.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + { + "event_id": "G421_evt_232", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 198.93, + "end_time_sec": 201.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_118", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 206.47, + "end_time_sec": 208.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_014", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: entering the scene through a structure, opening a vehicle door, entering a vehicle?", + "options": [ + "enters a vehicle \u2192 enters the scene through a structure \u2192 opens a vehicle door", + "opens a vehicle door \u2192 enters the scene through a structure \u2192 enters a vehicle", + "enters the scene through a structure \u2192 opens a vehicle door \u2192 enters a vehicle", + "enters the scene through a structure \u2192 enters a vehicle \u2192 opens a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "enters the scene through a structure \u2192 opens a vehicle door \u2192 enters a vehicle", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_010", + "events": [ + { + "event_id": "G421_evt_232", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 198.93, + "end_time_sec": 201.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G336_evt_118", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 206.47, + "end_time_sec": 208.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_119", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 206.47, + "end_time_sec": 212.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ] + } + }, + { + "question_id": "xcam_ordering_015", + "category": "cross_camera_ordering", + "question_template": "Across multiple camera views, what is the correct chronological order of these events: entering a vehicle, exiting the scene through a structure, opening a facility door?", + "options": [ + "enters a vehicle \u2192 exits the scene through a structure \u2192 opens a facility door", + "exits the scene through a structure \u2192 opens a facility door \u2192 enters a vehicle", + "opens a facility door \u2192 enters a vehicle \u2192 exits the scene through a structure", + "exits the scene through a structure \u2192 enters a vehicle \u2192 opens a facility door" + ], + "correct_answer_index": 0, + "correct_answer": "enters a vehicle \u2192 exits the scene through a structure \u2192 opens a facility door", + "requires_cameras": [ + "G299", + "G330", + "G336" + ], + "verification": { + "chain_id": "narrative_006", + "events": [ + { + "event_id": "G336_evt_47", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 145.13, + "end_time_sec": 149.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G330_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G330", + "start_time_sec": 146.03, + "end_time_sec": 148.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + }, + { + "event_id": "G299_evt_2", + "activity": "person_opens_facility_door", + "camera_id": "G299", + "start_time_sec": 146.97, + "end_time_sec": 148.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + } + ] + } + }, + { + "question_id": "xcam_tracking_001", + "category": "cross_camera_tracking", + "question_template": "After the person who talks to another person picks up an object in camera G421, which camera view shows them next?", + "options": [ + "G421", + "G336", + "G638", + "G330" + ], + "correct_answer_index": 2, + "correct_answer": "G638", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_024", + "event_a": { + "event_id": "G421_evt_188", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 147.03, + "end_time_sec": 147.6, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G638_evt_12", + "activity": "person_picks_up_object", + "camera_id": "G638", + "start_time_sec": 149.53, + "end_time_sec": 150.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "gap_sec": 1.93 + } + }, + { + "question_id": "xcam_tracking_002", + "category": "cross_camera_tracking", + "question_template": "After the person who exits a vehicle exits the scene through a structure in camera G638, which camera view shows them next?", + "options": [ + "G421", + "G336", + "G330", + "G299" + ], + "correct_answer_index": 0, + "correct_answer": "G421", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_239", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 250.53, + "end_time_sec": 253.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -1.04 + } + }, + { + "question_id": "xcam_tracking_003", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk loads a vehicle in camera G336, which camera view shows them next?", + "options": [ + "G330", + "G336", + "G421", + "G299" + ], + "correct_answer_index": 2, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G336_evt_30", + "activity": "person_loads_vehicle", + "camera_id": "G336", + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_178", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 73.13, + "end_time_sec": 73.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -1.84 + } + }, + { + "question_id": "xcam_tracking_004", + "category": "cross_camera_tracking", + "question_template": "After the person who exits a vehicle opens a vehicle door in camera G336, which camera view shows them next?", + "options": [ + "G638", + "G336", + "G330", + "G421" + ], + "correct_answer_index": 0, + "correct_answer": "G638", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "gap_sec": -2.14 + } + }, + { + "question_id": "xcam_tracking_005", + "category": "cross_camera_tracking", + "question_template": "After the person who talks to another person picks up an object in camera G638, which camera view shows them next?", + "options": [ + "G336", + "G299", + "G638", + "G421" + ], + "correct_answer_index": 3, + "correct_answer": "G421", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_024", + "event_a": { + "event_id": "G638_evt_12", + "activity": "person_picks_up_object", + "camera_id": "G638", + "start_time_sec": 149.53, + "end_time_sec": 150.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_190", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 150.57, + "end_time_sec": 151.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 0.44 + } + }, + { + "question_id": "xcam_tracking_006", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk picks up an object in camera G421, which camera view shows them next?", + "options": [ + "G336", + "G638", + "G421", + "G299" + ], + "correct_answer_index": 0, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G421_evt_124", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 71.9, + "end_time_sec": 72.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G336_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G336", + "start_time_sec": 72.53, + "end_time_sec": 73.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": 0.13 + } + }, + { + "question_id": "xcam_tracking_007", + "category": "cross_camera_tracking", + "question_template": "After the person who opens the trunk stands up in camera G421, which camera view shows them next?", + "options": [ + "G421", + "G638", + "G336", + "G299" + ], + "correct_answer_index": 2, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G421_evt_177", + "activity": "person_stands_up", + "camera_id": "G421", + "start_time_sec": 68.9, + "end_time_sec": 72.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G336_evt_3", + "activity": "person_puts_down_object", + "camera_id": "G336", + "start_time_sec": 70.27, + "end_time_sec": 70.8, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -1.73 + } + }, + { + "question_id": "xcam_tracking_008", + "category": "cross_camera_tracking", + "question_template": "After the person who enters a vehicle exits the scene through a structure in camera G336, which camera view shows them next?", + "options": [ + "G421", + "G330", + "G638", + "G336" + ], + "correct_answer_index": 0, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_010", + "event_a": { + "event_id": "G336_evt_69", + "activity": "person_exits_scene_through_structure", + "camera_id": "G336", + "start_time_sec": 195.6, + "end_time_sec": 198.93, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_231", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 196.3, + "end_time_sec": 197.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -2.63 + } + }, + { + "question_id": "xcam_tracking_009", + "category": "cross_camera_tracking", + "question_template": "After the person who opens a vehicle door enters a vehicle in camera G336, which camera view shows them next?", + "options": [ + "G330", + "G638", + "G336", + "G421" + ], + "correct_answer_index": 0, + "correct_answer": "G330", + "requires_cameras": [ + "G330", + "G336" + ], + "verification": { + "chain_id": "narrative_006", + "event_a": { + "event_id": "G336_evt_47", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 145.13, + "end_time_sec": 149.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G330_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G330", + "start_time_sec": 146.03, + "end_time_sec": 148.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + }, + "gap_sec": -3.74 + } + }, + { + "question_id": "xcam_tracking_010", + "category": "cross_camera_tracking", + "question_template": "After the person who exits a vehicle opens a facility door in camera G638, which camera view shows them next?", + "options": [ + "G638", + "G330", + "G421", + "G336" + ], + "correct_answer_index": 2, + "correct_answer": "G421", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_22", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 245.77, + "end_time_sec": 247.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_237", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 248.0, + "end_time_sec": 249.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 0.27 + } + }, + { + "question_id": "xcam_tracking_011", + "category": "cross_camera_tracking", + "question_template": "After the person who exits a vehicle opens a facility door in camera G421, which camera view shows them next?", + "options": [ + "G299", + "G421", + "G638", + "G336" + ], + "correct_answer_index": 2, + "correct_answer": "G638", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G421_evt_251", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 268.57, + "end_time_sec": 271.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G638_evt_2", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 269.5, + "end_time_sec": 271.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "gap_sec": -1.63 + } + }, + { + "question_id": "xcam_tracking_012", + "category": "cross_camera_tracking", + "question_template": "After a person picks up an object in camera G330, which camera view shows them next?", + "options": [ + "G638", + "G336", + "G330", + "G421" + ], + "correct_answer_index": 3, + "correct_answer": "G421", + "requires_cameras": [ + "G330", + "G421" + ], + "verification": { + "chain_id": "narrative_040", + "event_a": { + "event_id": "G330_evt_4", + "activity": "person_picks_up_object", + "camera_id": "G330", + "start_time_sec": 125.97, + "end_time_sec": 126.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + }, + "event_b": { + "event_id": "G421_evt_258", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 129.13, + "end_time_sec": 129.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 2.63 + } + }, + { + "question_id": "xcam_tracking_013", + "category": "cross_camera_tracking", + "question_template": "After the person who opens a facility door exits the scene through a structure in camera G638, which camera view shows them next?", + "options": [ + "G330", + "G299", + "G336", + "G638" + ], + "correct_answer_index": 2, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_014", + "event_a": { + "event_id": "G638_evt_10", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 93.5, + "end_time_sec": 95.53, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G336_evt_5", + "activity": "person_exits_scene_through_structure", + "camera_id": "G336", + "start_time_sec": 96.33, + "end_time_sec": 99.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": 0.8 + } + }, + { + "question_id": "xcam_tracking_014", + "category": "cross_camera_tracking", + "question_template": "After the person who picks up an object puts down an object in camera G638, which camera view shows them next?", + "options": [ + "G336", + "G421", + "G638", + "G299" + ], + "correct_answer_index": 1, + "correct_answer": "G421", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_024", + "event_a": { + "event_id": "G638_evt_11", + "activity": "person_puts_down_object", + "camera_id": "G638", + "start_time_sec": 107.43, + "end_time_sec": 108.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_b": { + "event_id": "G421_evt_107", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 145.83, + "end_time_sec": 146.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 37.33 + } + }, + { + "question_id": "xcam_tracking_015", + "category": "cross_camera_tracking", + "question_template": "After the person who sits down puts down an object in camera G299, which camera view shows them next?", + "options": [ + "G421", + "G336", + "G638", + "G299" + ], + "correct_answer_index": 0, + "correct_answer": "G421", + "requires_cameras": [ + "G299", + "G421" + ], + "verification": { + "chain_id": "narrative_039", + "event_a": { + "event_id": "G299_evt_0", + "activity": "person_puts_down_object", + "camera_id": "G299", + "start_time_sec": 139.47, + "end_time_sec": 139.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + }, + "event_b": { + "event_id": "G421_evt_130", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 141.57, + "end_time_sec": 142.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 1.6 + } + }, + { + "question_id": "xcam_gap_001", + "category": "cross_camera_gap", + "question_template": "Between loading a vehicle and carrying a heavy object, what does the person who opens the trunk do?", + "options": [ + "picks up an object", + "embraces another person", + "exits a vehicle", + "talks to another person" + ], + "correct_answer_index": 0, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G336_evt_30", + "activity": "person_loads_vehicle", + "camera_id": "G336", + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_middle": { + "event_id": "G421_evt_178", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 73.13, + "end_time_sec": 73.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G421_evt_179", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_time_sec": 73.13, + "end_time_sec": 78.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "xcam_gap_002", + "category": "cross_camera_gap", + "question_template": "Between opening a facility door and entering the scene through a structure, what does the person who exits a vehicle do?", + "options": [ + "exits the scene through a structure", + "puts down an object", + "carries a heavy object", + "opens a facility door" + ], + "correct_answer_index": 0, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_19", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 234.47, + "end_time_sec": 234.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_middle": { + "event_id": "G638_evt_20", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 234.47, + "end_time_sec": 235.27, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_c": { + "event_id": "G421_evt_234", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 234.97, + "end_time_sec": 238.27, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "xcam_gap_003", + "category": "cross_camera_gap", + "question_template": "Between carrying a heavy object and opening the trunk, what does the person who opens the trunk do?", + "options": [ + "enters the scene through a structure", + "stands up", + "interacts with a laptop", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "event_a": { + "event_id": "G421_evt_215", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_time_sec": 64.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_middle": { + "event_id": "G421_evt_214", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 64.6, + "end_time_sec": 65.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G336_evt_28", + "activity": "person_opens_trunk", + "camera_id": "G336", + "start_time_sec": 68.47, + "end_time_sec": 76.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_004", + "category": "cross_camera_gap", + "question_template": "Between opening a facility door and dropping off a person, what does the person who exits a vehicle do?", + "options": [ + "interacts with a laptop", + "opens the trunk", + "talks on the phone", + "enters the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_22", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 245.77, + "end_time_sec": 247.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_middle": { + "event_id": "G421_evt_237", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 248.0, + "end_time_sec": 249.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_005", + "category": "cross_camera_gap", + "question_template": "Between putting down an object and entering the scene through a structure, what does the person who exits a vehicle do?", + "options": [ + "talks to another person", + "reads a document", + "makes a purchase", + "opens a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "talks to another person", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G421_evt_119", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 289.63, + "end_time_sec": 290.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_middle": { + "event_id": "G421_evt_120", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_time_sec": 290.3, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G336_evt_71", + "activity": "person_enters_scene_through_structure", + "camera_id": "G336", + "start_time_sec": 292.4, + "end_time_sec": 294.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_006", + "category": "cross_camera_gap", + "question_template": "Between exiting the scene through a structure and opening a vehicle door, what does the person who enters a vehicle do?", + "options": [ + "closes the trunk", + "sits down", + "enters the scene through a structure", + "makes a purchase" + ], + "correct_answer_index": 2, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_010", + "event_a": { + "event_id": "G638_evt_18", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 196.63, + "end_time_sec": 199.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_middle": { + "event_id": "G421_evt_232", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 198.93, + "end_time_sec": 201.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G336_evt_118", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 206.47, + "end_time_sec": 208.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_007", + "category": "cross_camera_gap", + "question_template": "Between entering the scene through a structure and exiting a vehicle, what does the person who exits a vehicle do?", + "options": [ + "opens a vehicle door", + "reads a document", + "enters the scene through a structure", + "exits the scene through a structure" + ], + "correct_answer_index": 0, + "correct_answer": "opens a vehicle door", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_3", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 269.87, + "end_time_sec": 272.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_middle": { + "event_id": "G336_evt_116", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 269.9, + "end_time_sec": 272.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_c": { + "event_id": "G336_evt_110", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_time_sec": 269.9, + "end_time_sec": 273.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_008", + "category": "cross_camera_gap", + "question_template": "Between opening a facility door and opening a vehicle door, what does the person who exits a vehicle do?", + "options": [ + "closes a vehicle door", + "opens the trunk", + "enters the scene through a structure", + "opens a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "enters the scene through a structure", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G638_evt_2", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 269.5, + "end_time_sec": 271.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_middle": { + "event_id": "G638_evt_3", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 269.87, + "end_time_sec": 272.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_c": { + "event_id": "G336_evt_116", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 269.9, + "end_time_sec": 272.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_009", + "category": "cross_camera_gap", + "question_template": "Between opening a vehicle door and exiting the scene through a structure, what does the person who enters a vehicle do?", + "options": [ + "interacts with a laptop", + "closes a vehicle door", + "enters the scene through a structure", + "enters a vehicle" + ], + "correct_answer_index": 3, + "correct_answer": "enters a vehicle", + "requires_cameras": [ + "G330", + "G336" + ], + "verification": { + "chain_id": "narrative_006", + "event_a": { + "event_id": "G336_evt_45", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 145.13, + "end_time_sec": 147.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_middle": { + "event_id": "G336_evt_47", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 145.13, + "end_time_sec": 149.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_c": { + "event_id": "G330_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G330", + "start_time_sec": 146.03, + "end_time_sec": 148.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + } + } + }, + { + "question_id": "xcam_gap_010", + "category": "cross_camera_gap", + "question_template": "Between closing a vehicle door and opening a facility door, what does the person who exits a vehicle do?", + "options": [ + "exits a vehicle", + "enters a vehicle", + "exits the scene through a structure", + "makes a purchase" + ], + "correct_answer_index": 2, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_100", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 252.3, + "end_time_sec": 253.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_middle": { + "event_id": "G421_evt_147", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 264.0, + "end_time_sec": 266.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G421_evt_146", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 264.0, + "end_time_sec": 266.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "xcam_gap_011", + "category": "cross_camera_gap", + "question_template": "Between exiting the scene through a structure and exiting the scene through a structure, what does the person who opens a facility door do?", + "options": [ + "talks on the phone", + "opens a facility door", + "opens the trunk", + "talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "opens a facility door", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_014", + "event_a": { + "event_id": "G336_evt_56", + "activity": "person_exits_scene_through_structure", + "camera_id": "G336", + "start_time_sec": 87.23, + "end_time_sec": 88.8, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_middle": { + "event_id": "G638_evt_7", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 90.07, + "end_time_sec": 92.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_c": { + "event_id": "G638_evt_8", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 90.07, + "end_time_sec": 93.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + } + } + }, + { + "question_id": "xcam_gap_012", + "category": "cross_camera_gap", + "question_template": "Between putting down an object and transferring an object, what does the person who makes a purchase do?", + "options": [ + "enters the scene through a structure", + "talks on the phone", + "enters a vehicle", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_026", + "event_a": { + "event_id": "G421_evt_94", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_time_sec": 224.43, + "end_time_sec": 225.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_middle": { + "event_id": "G421_evt_95", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 227.63, + "end_time_sec": 228.2, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_c": { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_013", + "category": "cross_camera_gap", + "question_template": "Between entering the scene through a structure and opening a vehicle door, what does the person who exits a vehicle do?", + "options": [ + "makes a purchase", + "exits a vehicle", + "carries a heavy object", + "sits down" + ], + "correct_answer_index": 1, + "correct_answer": "exits a vehicle", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G421_evt_238", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 249.17, + "end_time_sec": 251.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_middle": { + "event_id": "G336_evt_102", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 253.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_c": { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "xcam_gap_014", + "category": "cross_camera_gap", + "question_template": "Between exiting the scene through a structure and entering the scene through a structure, what does the person who opens a facility door do?", + "options": [ + "closes the trunk", + "reads a document", + "talks on the phone", + "opens a facility door" + ], + "correct_answer_index": 3, + "correct_answer": "opens a facility door", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_012", + "event_a": { + "event_id": "G421_evt_1", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 0.0, + "end_time_sec": 2.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_middle": { + "event_id": "G638_evt_0", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 0.0, + "end_time_sec": 1.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_c": { + "event_id": "G638_evt_1", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 0.17, + "end_time_sec": 2.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + } + } + }, + { + "question_id": "xcam_gap_015", + "category": "cross_camera_gap", + "question_template": "Between opening a vehicle door and entering the scene through a structure, what does the person who exits a vehicle do?", + "options": [ + "closes the trunk", + "exits the scene through a structure", + "enters the scene through a structure", + "carries a heavy object" + ], + "correct_answer_index": 1, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_middle": { + "event_id": "G638_evt_24", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 249.93, + "end_time_sec": 251.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "event_c": { + "event_id": "G421_evt_239", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 250.53, + "end_time_sec": 253.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "xcam_identify_001", + "category": "camera_identification", + "question_template": "In one camera view, a person opens a facility door. In which other camera view does this same person enter a vehicle?", + "options": [ + "G421", + "G638", + "G336", + "G299" + ], + "correct_answer_index": 2, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_006", + "identifying_event": { + "event_id": "G638_evt_15", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 164.9, + "end_time_sec": 165.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "target_event": { + "event_id": "G336_evt_47", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_time_sec": 145.13, + "end_time_sec": 149.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "identifying_camera": "G638", + "target_camera": "G336" + } + }, + { + "question_id": "xcam_identify_002", + "category": "camera_identification", + "question_template": "In one camera view, a person picks up an object. In which other camera view does this same person talk to another person?", + "options": [ + "G330", + "G299", + "G421", + "G336" + ], + "correct_answer_index": 2, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_024", + "identifying_event": { + "event_id": "G336_evt_4", + "activity": "person_picks_up_object", + "camera_id": "G336", + "start_time_sec": 88.87, + "end_time_sec": 89.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "target_event": { + "event_id": "G421_evt_181", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_time_sec": 86.77, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "identifying_camera": "G336", + "target_camera": "G421" + } + }, + { + "question_id": "xcam_identify_003", + "category": "camera_identification", + "question_template": "In one camera view, a person drops off a person. In which other camera view does this same person open a facility door?", + "options": [ + "G336", + "G299", + "G421", + "G638" + ], + "correct_answer_index": 3, + "correct_answer": "G638", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "identifying_event": { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "target_event": { + "event_id": "G638_evt_19", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 234.47, + "end_time_sec": 234.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "identifying_camera": "G336", + "target_camera": "G638" + } + }, + { + "question_id": "xcam_identify_004", + "category": "camera_identification", + "question_template": "In one camera view, a person drops off a person. In which other camera view does this same person open a facility door?", + "options": [ + "G299", + "G336", + "G330", + "G421" + ], + "correct_answer_index": 3, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "identifying_event": { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "target_event": { + "event_id": "G421_evt_233", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 232.7, + "end_time_sec": 233.53, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "identifying_camera": "G336", + "target_camera": "G421" + } + }, + { + "question_id": "xcam_identify_005", + "category": "camera_identification", + "question_template": "In one camera view, a person puts down an object. In which other camera view does this same person pick up an object?", + "options": [ + "G299", + "G330", + "G638", + "G421" + ], + "correct_answer_index": 3, + "correct_answer": "G421", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_024", + "identifying_event": { + "event_id": "G638_evt_11", + "activity": "person_puts_down_object", + "camera_id": "G638", + "start_time_sec": 107.43, + "end_time_sec": 108.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "target_event": { + "event_id": "G421_evt_154", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_time_sec": 85.0, + "end_time_sec": 85.53, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "identifying_camera": "G638", + "target_camera": "G421" + } + }, + { + "question_id": "xcam_identify_006", + "category": "camera_identification", + "question_template": "In one camera view, a person opens a facility door. In which other camera view does this same person enter the scene through a structure?", + "options": [ + "G330", + "G421", + "G638", + "G336" + ], + "correct_answer_index": 3, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_012", + "identifying_event": { + "event_id": "G421_evt_0", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 0.0, + "end_time_sec": 1.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "target_event": { + "event_id": "G336_evt_16", + "activity": "person_enters_scene_through_structure", + "camera_id": "G336", + "start_time_sec": 27.67, + "end_time_sec": 29.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "identifying_camera": "G421", + "target_camera": "G336" + } + }, + { + "question_id": "xcam_identify_007", + "category": "camera_identification", + "question_template": "In one camera view, a person exits the scene through a structure. In which other camera view does this same person open a facility door?", + "options": [ + "G299", + "G638", + "G336", + "G330" + ], + "correct_answer_index": 1, + "correct_answer": "G638", + "requires_cameras": [ + "G330", + "G638" + ], + "verification": { + "chain_id": "narrative_006", + "identifying_event": { + "event_id": "G330_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G330", + "start_time_sec": 146.03, + "end_time_sec": 148.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + }, + "target_event": { + "event_id": "G638_evt_15", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_time_sec": 164.9, + "end_time_sec": 165.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "identifying_camera": "G330", + "target_camera": "G638" + } + }, + { + "question_id": "xcam_identify_008", + "category": "camera_identification", + "question_template": "In one camera view, a person talks to another person. In which other camera view does this same person transfer an object?", + "options": [ + "G299", + "G336", + "G330", + "G421" + ], + "correct_answer_index": 1, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_029", + "identifying_event": { + "event_id": "G421_evt_109", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_time_sec": 209.23, + "end_time_sec": 214.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "target_event": { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "identifying_camera": "G421", + "target_camera": "G336" + } + }, + { + "question_id": "xcam_identify_009", + "category": "camera_identification", + "question_template": "In one camera view, a person opens a facility door. In which other camera view does this same person exit the scene through a structure?", + "options": [ + "G336", + "G421", + "G330", + "G638" + ], + "correct_answer_index": 3, + "correct_answer": "G638", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_009", + "identifying_event": { + "event_id": "G421_evt_233", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 232.7, + "end_time_sec": 233.53, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "target_event": { + "event_id": "G638_evt_20", + "activity": "person_exits_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 234.47, + "end_time_sec": 235.27, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "identifying_camera": "G421", + "target_camera": "G638" + } + }, + { + "question_id": "xcam_identify_010", + "category": "camera_identification", + "question_template": "In one camera view, a person exits the scene through a structure. In which other camera view does this same person open a facility door?", + "options": [ + "G299", + "G336", + "G421", + "G330" + ], + "correct_answer_index": 0, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "verification": { + "chain_id": "narrative_006", + "identifying_event": { + "event_id": "G330_evt_6", + "activity": "person_exits_scene_through_structure", + "camera_id": "G330", + "start_time_sec": 146.03, + "end_time_sec": 148.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G330.avi" + }, + "target_event": { + "event_id": "G299_evt_2", + "activity": "person_opens_facility_door", + "camera_id": "G299", + "start_time_sec": 146.97, + "end_time_sec": 148.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + }, + "identifying_camera": "G330", + "target_camera": "G299" + } + }, + { + "question_id": "xcam_identify_011", + "category": "camera_identification", + "question_template": "In one camera view, a person transfers an object. In which other camera view does this same person make a purchase?", + "options": [ + "G421", + "G638", + "G336", + "G330" + ], + "correct_answer_index": 0, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_026", + "identifying_event": { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "target_event": { + "event_id": "G421_evt_269", + "activity": "person_purchases", + "camera_id": "G421", + "start_time_sec": 252.8, + "end_time_sec": 269.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "identifying_camera": "G336", + "target_camera": "G421" + } + }, + { + "question_id": "xcam_identify_012", + "category": "camera_identification", + "question_template": "In one camera view, a person opens the trunk. In which other camera view does this same person carry a heavy object?", + "options": [ + "G421", + "G336", + "G638", + "G299" + ], + "correct_answer_index": 0, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_023", + "identifying_event": { + "event_id": "G336_evt_28", + "activity": "person_opens_trunk", + "camera_id": "G336", + "start_time_sec": 68.47, + "end_time_sec": 76.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "target_event": { + "event_id": "G421_evt_215", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_time_sec": 64.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "identifying_camera": "G336", + "target_camera": "G421" + } + }, + { + "question_id": "xcam_identify_013", + "category": "camera_identification", + "question_template": "In one camera view, a person talks to another person. In which other camera view does this same person stand up?", + "options": [ + "G421", + "G638", + "G330", + "G336" + ], + "correct_answer_index": 0, + "correct_answer": "G421", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_028", + "identifying_event": { + "event_id": "G336_evt_1", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_time_sec": 0.0, + "end_time_sec": 209.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "target_event": { + "event_id": "G421_evt_260", + "activity": "person_stands_up", + "camera_id": "G421", + "start_time_sec": 54.4, + "end_time_sec": 56.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "identifying_camera": "G336", + "target_camera": "G421" + } + }, + { + "question_id": "xcam_identify_014", + "category": "camera_identification", + "question_template": "In one camera view, a person opens a facility door. In which other camera view does this same person enter the scene through a structure?", + "options": [ + "G638", + "G299", + "G336", + "G330" + ], + "correct_answer_index": 0, + "correct_answer": "G638", + "requires_cameras": [ + "G421", + "G638" + ], + "verification": { + "chain_id": "narrative_012", + "identifying_event": { + "event_id": "G421_evt_0", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 0.0, + "end_time_sec": 1.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "target_event": { + "event_id": "G638_evt_1", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_time_sec": 0.17, + "end_time_sec": 2.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "identifying_camera": "G421", + "target_camera": "G638" + } + }, + { + "question_id": "xcam_identify_015", + "category": "camera_identification", + "question_template": "In one camera view, a person sits down. In which other camera view does this same person talk to another person?", + "options": [ + "G421", + "G299", + "G330", + "G336" + ], + "correct_answer_index": 3, + "correct_answer": "G336", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_031", + "identifying_event": { + "event_id": "G421_evt_263", + "activity": "person_sits_down", + "camera_id": "G421", + "start_time_sec": 142.9, + "end_time_sec": 144.37, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "target_event": { + "event_id": "G336_evt_86", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_time_sec": 129.3, + "end_time_sec": 215.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "identifying_camera": "G421", + "target_camera": "G336" + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.symbolic.json b/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.symbolic.json new file mode 100644 index 0000000..fd6c1ee --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.symbolic.json @@ -0,0 +1,3087 @@ +{ + "slot": "2018-03-07.17-05-00.school", + "metadata": { + "generator": "generate_qa_symbolic.py", + "seed": 42, + "scene_graph_summary": { + "entity_count": 546, + "event_count": 345, + "temporal_edge_count": 59340, + "spatial_edge_count": 8736, + "visibility_edge_count": 546, + "participation_edge_count": 546, + "coref_edge_count": 214, + "entity_cluster_count": 380, + "merged_cluster_count": 91, + "actors_with_multi_events": 90, + "activity_breakdown": { + "person_carries_heavy_object": 5, + "person_closes_trunk": 2, + "person_closes_vehicle_door": 7, + "person_embraces_person": 2, + "person_enters_scene_through_structure": 23, + "person_enters_vehicle": 5, + "person_exits_scene_through_structure": 21, + "person_exits_vehicle": 2, + "person_interacts_with_laptop": 2, + "person_loads_vehicle": 2, + "person_opens_facility_door": 22, + "person_opens_trunk": 1, + "person_opens_vehicle_door": 6, + "person_picks_up_object": 62, + "person_purchases": 1, + "person_puts_down_object": 53, + "person_reads_document": 1, + "person_sits_down": 11, + "person_stands_up": 11, + "person_talks_on_phone": 8, + "person_talks_to_person": 26, + "person_texts_on_phone": 24, + "person_transfers_object": 3, + "vehicle_drops_off_person": 1, + "vehicle_makes_u_turn": 2, + "vehicle_reverses": 3, + "vehicle_starts": 14, + "vehicle_stops": 10, + "vehicle_turns_left": 9, + "vehicle_turns_right": 6 + }, + "camera_breakdown": { + "G299": 4, + "G330": 4, + "G336": 115, + "G421": 197, + "G638": 25 + }, + "cameras": [ + "G299", + "G330", + "G336", + "G421", + "G638" + ] + }, + "categories_requested": [ + "temporal", + "ordering", + "perception", + "causality", + "spatial", + "camera_view" + ], + "category_counts": { + "temporal": 15, + "ordering": 11, + "perception": 11, + "causality": 15, + "spatial": 15, + "camera_view": 15 + }, + "total_questions": 82 + }, + "qa_pairs": [ + { + "question_id": "temporal_001", + "category": "temporal", + "question_template": "After a person texts on a phone, what do they do next?", + "options": [ + "stands up", + "embraces another person", + "opens a facility door", + "exits the scene through a structure" + ], + "correct_answer_index": 0, + "correct_answer": "stands up", + "verification": { + "actor_id": "G421_person_271", + "event_a": { + "event_id": "G421_evt_166", + "activity": "person_texts_on_phone", + "camera_id": "G421", + "start_frame": 2590, + "end_frame": 2775, + "start_time_sec": 86.33, + "end_time_sec": 92.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_165", + "activity": "person_stands_up", + "camera_id": "G421", + "start_frame": 2599, + "end_frame": 2630, + "start_time_sec": 86.63, + "end_time_sec": 87.67, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -5.87, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_002", + "category": "temporal", + "question_template": "After a person puts down an object, what do they do next?", + "options": [ + "enters the scene through a structure", + "loads a vehicle", + "closes a vehicle door", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "verification": { + "actor_id": "G421_person_163", + "event_a": { + "event_id": "G421_evt_99", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 7384, + "end_frame": 7399, + "start_time_sec": 246.13, + "end_time_sec": 246.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_101", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 7504, + "end_frame": 7519, + "start_time_sec": 250.13, + "end_time_sec": 250.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 3.5, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_003", + "category": "temporal", + "question_template": "After the person who picks up an object transfers an object, what do they do next?", + "options": [ + "enters a vehicle", + "loads a vehicle", + "picks up an object", + "opens a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "picks up an object", + "verification": { + "actor_id": "G421_person_163", + "event_a": { + "event_id": "G421_evt_116", + "activity": "person_transfers_object", + "camera_id": "G421", + "start_frame": 7603, + "end_frame": 7698, + "start_time_sec": 253.43, + "end_time_sec": 256.6, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_103", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 7658, + "end_frame": 7674, + "start_time_sec": 255.27, + "end_time_sec": 255.8, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -1.33, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_004", + "category": "temporal", + "question_template": "After a person sits down, what do they do next?", + "options": [ + "stands up", + "opens a facility door", + "puts down an object", + "opens the trunk" + ], + "correct_answer_index": 0, + "correct_answer": "stands up", + "verification": { + "actor_id": "G421_person_289", + "event_a": { + "event_id": "G421_evt_176", + "activity": "person_sits_down", + "camera_id": "G421", + "start_frame": 1869, + "end_frame": 1934, + "start_time_sec": 62.3, + "end_time_sec": 64.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_177", + "activity": "person_stands_up", + "camera_id": "G421", + "start_frame": 2067, + "end_frame": 2160, + "start_time_sec": 68.9, + "end_time_sec": 72.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 4.43, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_005", + "category": "temporal", + "question_template": "After the person who closes a vehicle door opens a vehicle door, what do they do next?", + "options": [ + "makes a purchase", + "texts on a phone", + "enters a vehicle", + "embraces another person" + ], + "correct_answer_index": 2, + "correct_answer": "enters a vehicle", + "verification": { + "actor_id": "G336_person_174", + "event_a": { + "event_id": "G336_evt_118", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_frame": 6194, + "end_frame": 6247, + "start_time_sec": 206.47, + "end_time_sec": 208.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G336_evt_119", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_frame": 6194, + "end_frame": 6365, + "start_time_sec": 206.47, + "end_time_sec": 212.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -1.76, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_006", + "category": "temporal", + "question_template": "After the person who puts down an object picks up an object, what do they do next?", + "options": [ + "opens a facility door", + "closes the trunk", + "carries a heavy object", + "puts down an object" + ], + "correct_answer_index": 3, + "correct_answer": "puts down an object", + "verification": { + "actor_id": "G421_person_318", + "event_a": { + "event_id": "G421_evt_191", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 6652, + "end_frame": 6667, + "start_time_sec": 221.73, + "end_time_sec": 222.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_193", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 6766, + "end_frame": 6783, + "start_time_sec": 225.53, + "end_time_sec": 226.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 3.3, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_007", + "category": "temporal", + "question_template": "After a person reads a document, what do they do next?", + "options": [ + "picks up an object", + "enters a vehicle", + "closes a vehicle door", + "texts on a phone" + ], + "correct_answer_index": 3, + "correct_answer": "texts on a phone", + "verification": { + "actor_id": "G421_person_260", + "event_a": { + "event_id": "G421_evt_156", + "activity": "person_reads_document", + "camera_id": "G421", + "start_frame": 0, + "end_frame": 5520, + "start_time_sec": 0.0, + "end_time_sec": 184.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_47", + "activity": "person_texts_on_phone", + "camera_id": "G421", + "start_frame": 2627, + "end_frame": 3142, + "start_time_sec": 87.57, + "end_time_sec": 104.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -96.43, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_008", + "category": "temporal", + "question_template": "After the person who picks up an object talks to another person, what do they do next?", + "options": [ + "enters the scene through a structure", + "exits a vehicle", + "transfers an object", + "carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "transfers an object", + "verification": { + "actor_id": "G336_person_18", + "event_a": { + "event_id": "G336_evt_2", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_frame": 6394, + "end_frame": 9004, + "start_time_sec": 213.13, + "end_time_sec": 300.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_frame": 6981, + "end_frame": 7089, + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -67.43, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_009", + "category": "temporal", + "question_template": "After the person who picks up an object puts down an object, what do they do next?", + "options": [ + "opens a facility door", + "opens the trunk", + "picks up an object", + "makes a purchase" + ], + "correct_answer_index": 3, + "correct_answer": "makes a purchase", + "verification": { + "actor_id": "G421_person_163", + "event_a": { + "event_id": "G421_evt_102", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 7516, + "end_frame": 7534, + "start_time_sec": 250.53, + "end_time_sec": 251.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_269", + "activity": "person_purchases", + "camera_id": "G421", + "start_frame": 7584, + "end_frame": 8071, + "start_time_sec": 252.8, + "end_time_sec": 269.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 1.67, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_010", + "category": "temporal", + "question_template": "After a person opens a facility door, what do they do next?", + "options": [ + "transfers an object", + "closes a vehicle door", + "makes a purchase", + "exits the scene through a structure" + ], + "correct_answer_index": 3, + "correct_answer": "exits the scene through a structure", + "verification": { + "actor_id": "G299_person_6", + "event_a": { + "event_id": "G299_evt_2", + "activity": "person_opens_facility_door", + "camera_id": "G299", + "start_frame": 4409, + "end_frame": 4467, + "start_time_sec": 146.97, + "end_time_sec": 148.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + }, + "event_b": { + "event_id": "G299_evt_3", + "activity": "person_exits_scene_through_structure", + "camera_id": "G299", + "start_frame": 4409, + "end_frame": 4532, + "start_time_sec": 146.97, + "end_time_sec": 151.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G299.avi" + }, + "gap_sec": -1.93, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_011", + "category": "temporal", + "question_template": "After a person stands up, what do they do next?", + "options": [ + "talks to another person", + "embraces another person", + "puts down an object", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "verification": { + "actor_id": "G421_person_289", + "event_a": { + "event_id": "G421_evt_177", + "activity": "person_stands_up", + "camera_id": "G421", + "start_frame": 2067, + "end_frame": 2160, + "start_time_sec": 68.9, + "end_time_sec": 72.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_178", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 2194, + "end_frame": 2216, + "start_time_sec": 73.13, + "end_time_sec": 73.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 1.13, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_012", + "category": "temporal", + "question_template": "After the person who sits down picks up an object, what do they do next?", + "options": [ + "loads a vehicle", + "exits a vehicle", + "puts down an object", + "exits the scene through a structure" + ], + "correct_answer_index": 2, + "correct_answer": "puts down an object", + "verification": { + "actor_id": "G421_person_294", + "event_a": { + "event_id": "G421_evt_211", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 3567, + "end_frame": 3582, + "start_time_sec": 118.9, + "end_time_sec": 119.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_199", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 3885, + "end_frame": 3901, + "start_time_sec": 129.5, + "end_time_sec": 130.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 10.1, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_013", + "category": "temporal", + "question_template": "After the person who sits down puts down an object, what do they do next?", + "options": [ + "makes a purchase", + "opens a facility door", + "loads a vehicle", + "picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "picks up an object", + "verification": { + "actor_id": "G421_person_294", + "event_a": { + "event_id": "G421_evt_205", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 5779, + "end_frame": 5794, + "start_time_sec": 192.63, + "end_time_sec": 193.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_206", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 6528, + "end_frame": 6543, + "start_time_sec": 217.6, + "end_time_sec": 218.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 24.47, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_014", + "category": "temporal", + "question_template": "After a person exits the scene through a structure, what do they do next?", + "options": [ + "opens the trunk", + "opens a facility door", + "loads a vehicle", + "stands up" + ], + "correct_answer_index": 1, + "correct_answer": "opens a facility door", + "verification": { + "actor_id": "G336_person_12", + "event_a": { + "event_id": "G336_evt_5", + "activity": "person_exits_scene_through_structure", + "camera_id": "G336", + "start_frame": 2890, + "end_frame": 2980, + "start_time_sec": 96.33, + "end_time_sec": 99.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G336_evt_6", + "activity": "person_opens_facility_door", + "camera_id": "G336", + "start_frame": 2890, + "end_frame": 2942, + "start_time_sec": 96.33, + "end_time_sec": 98.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -3.0, + "relation": "BEFORE" + } + }, + { + "question_id": "temporal_015", + "category": "temporal", + "question_template": "After a person picks up an object, what do they do next?", + "options": [ + "enters a vehicle", + "closes the trunk", + "puts down an object", + "carries a heavy object" + ], + "correct_answer_index": 2, + "correct_answer": "puts down an object", + "verification": { + "actor_id": "G421_person_124", + "event_a": { + "event_id": "G421_evt_82", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 5971, + "end_frame": 5986, + "start_time_sec": 199.03, + "end_time_sec": 199.53, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G421_evt_83", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 5975, + "end_frame": 5991, + "start_time_sec": 199.17, + "end_time_sec": 199.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.36, + "relation": "BEFORE" + } + }, + { + "question_id": "ordering_001", + "category": "ordering", + "question_template": "Arrange the following events involving the person who puts down an object in the order they occurred:", + "events_listed": [ + "talks to another person", + "picks up an object", + "puts down an object" + ], + "options": [ + "picks up an object \u2192 talks to another person \u2192 puts down an object", + "talks to another person \u2192 picks up an object \u2192 puts down an object", + "talks to another person \u2192 puts down an object \u2192 picks up an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 picks up an object \u2192 puts down an object", + "verification": { + "actor_id": "G421_person_318", + "events": [ + { + "event_id": "G421_evt_181", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 2603, + "end_frame": 9000, + "start_time_sec": 86.77, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_182", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 2683, + "end_frame": 2699, + "start_time_sec": 89.43, + "end_time_sec": 89.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_184", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 2791, + "end_frame": 2809, + "start_time_sec": 93.03, + "end_time_sec": 93.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ], + "time_span_sec": 6.86 + } + }, + { + "question_id": "ordering_002", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes a vehicle door in the order they occurred:", + "events_listed": [ + "opens a vehicle door", + "enters a vehicle", + "closes a vehicle door" + ], + "options": [ + "enters a vehicle \u2192 opens a vehicle door \u2192 closes a vehicle door", + "opens a vehicle door \u2192 closes a vehicle door \u2192 enters a vehicle", + "opens a vehicle door \u2192 enters a vehicle \u2192 closes a vehicle door", + "closes a vehicle door \u2192 enters a vehicle \u2192 opens a vehicle door" + ], + "correct_answer_index": 2, + "correct_answer": "opens a vehicle door \u2192 enters a vehicle \u2192 closes a vehicle door", + "verification": { + "actor_id": "G336_person_174", + "events": [ + { + "event_id": "G336_evt_118", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_frame": 6194, + "end_frame": 6247, + "start_time_sec": 206.47, + "end_time_sec": 208.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_119", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_frame": 6194, + "end_frame": 6365, + "start_time_sec": 206.47, + "end_time_sec": 212.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_120", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_frame": 6299, + "end_frame": 6365, + "start_time_sec": 209.97, + "end_time_sec": 212.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ], + "time_span_sec": 5.7 + } + }, + { + "question_id": "ordering_003", + "category": "ordering", + "question_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "events_listed": [ + "picks up an object", + "carries a heavy object", + "puts down an object" + ], + "options": [ + "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 picks up an object \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 carries a heavy object", + "puts down an object \u2192 carries a heavy object \u2192 picks up an object" + ], + "correct_answer_index": 0, + "correct_answer": "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "verification": { + "actor_id": "G421_person_353", + "events": [ + { + "event_id": "G421_evt_214", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 1938, + "end_frame": 1955, + "start_time_sec": 64.6, + "end_time_sec": 65.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_215", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_frame": 1938, + "end_frame": 2135, + "start_time_sec": 64.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_216", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 2118, + "end_frame": 2135, + "start_time_sec": 70.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ], + "time_span_sec": 6.57 + } + }, + { + "question_id": "ordering_004", + "category": "ordering", + "question_template": "Arrange the following events involving the person who picks up an object in the order they occurred:", + "events_listed": [ + "talks to another person", + "transfers an object", + "picks up an object" + ], + "options": [ + "picks up an object \u2192 transfers an object \u2192 talks to another person", + "talks to another person \u2192 transfers an object \u2192 picks up an object", + "picks up an object \u2192 talks to another person \u2192 transfers an object", + "talks to another person \u2192 picks up an object \u2192 transfers an object" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 transfers an object \u2192 picks up an object", + "verification": { + "actor_id": "G336_person_18", + "events": [ + { + "event_id": "G336_evt_2", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_frame": 6394, + "end_frame": 9004, + "start_time_sec": 213.13, + "end_time_sec": 300.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_frame": 6981, + "end_frame": 7089, + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_8", + "activity": "person_picks_up_object", + "camera_id": "G336", + "start_frame": 7028, + "end_frame": 7059, + "start_time_sec": 234.27, + "end_time_sec": 235.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ], + "time_span_sec": 22.17 + } + }, + { + "question_id": "ordering_005", + "category": "ordering", + "question_template": "Arrange the following events involving the person who picks up an object in the order they occurred:", + "events_listed": [ + "talks to another person", + "picks up an object", + "puts down an object", + "makes a purchase" + ], + "options": [ + "puts down an object \u2192 talks to another person \u2192 picks up an object \u2192 makes a purchase", + "talks to another person \u2192 picks up an object \u2192 puts down an object \u2192 makes a purchase", + "talks to another person \u2192 puts down an object \u2192 picks up an object \u2192 makes a purchase", + "puts down an object \u2192 picks up an object \u2192 talks to another person \u2192 makes a purchase" + ], + "correct_answer_index": 1, + "correct_answer": "talks to another person \u2192 picks up an object \u2192 puts down an object \u2192 makes a purchase", + "verification": { + "actor_id": "G421_person_163", + "events": [ + { + "event_id": "G421_evt_267", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 6602, + "end_frame": 8016, + "start_time_sec": 220.07, + "end_time_sec": 267.2, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_93", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 6703, + "end_frame": 6720, + "start_time_sec": 223.43, + "end_time_sec": 224.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_94", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 6733, + "end_frame": 6752, + "start_time_sec": 224.43, + "end_time_sec": 225.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_269", + "activity": "person_purchases", + "camera_id": "G421", + "start_frame": 7584, + "end_frame": 8071, + "start_time_sec": 252.8, + "end_time_sec": 269.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ], + "time_span_sec": 48.96 + } + }, + { + "question_id": "ordering_006", + "category": "ordering", + "question_template": "Arrange the following events involving the person who loads a vehicle in the order they occurred:", + "events_listed": [ + "talks to another person", + "opens the trunk", + "picks up an object", + "loads a vehicle" + ], + "options": [ + "picks up an object \u2192 talks to another person \u2192 loads a vehicle \u2192 opens the trunk", + "loads a vehicle \u2192 opens the trunk \u2192 picks up an object \u2192 talks to another person", + "picks up an object \u2192 loads a vehicle \u2192 opens the trunk \u2192 talks to another person", + "talks to another person \u2192 opens the trunk \u2192 picks up an object \u2192 loads a vehicle" + ], + "correct_answer_index": 3, + "correct_answer": "talks to another person \u2192 opens the trunk \u2192 picks up an object \u2192 loads a vehicle", + "verification": { + "actor_id": "G336_person_51", + "events": [ + { + "event_id": "G336_evt_23", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_frame": 1168, + "end_frame": 2575, + "start_time_sec": 38.93, + "end_time_sec": 85.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_28", + "activity": "person_opens_trunk", + "camera_id": "G336", + "start_frame": 2054, + "end_frame": 2281, + "start_time_sec": 68.47, + "end_time_sec": 76.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_29", + "activity": "person_picks_up_object", + "camera_id": "G336", + "start_frame": 2176, + "end_frame": 2205, + "start_time_sec": 72.53, + "end_time_sec": 73.5, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_30", + "activity": "person_loads_vehicle", + "camera_id": "G336", + "start_frame": 2179, + "end_frame": 2249, + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ], + "time_span_sec": 36.04 + } + }, + { + "question_id": "ordering_007", + "category": "ordering", + "question_template": "Arrange the following events involving the person who sits down in the order they occurred:", + "events_listed": [ + "sits down", + "talks to another person", + "puts down an object", + "picks up an object" + ], + "options": [ + "sits down \u2192 picks up an object \u2192 talks to another person \u2192 puts down an object", + "puts down an object \u2192 picks up an object \u2192 talks to another person \u2192 sits down", + "picks up an object \u2192 talks to another person \u2192 sits down \u2192 puts down an object", + "sits down \u2192 talks to another person \u2192 puts down an object \u2192 picks up an object" + ], + "correct_answer_index": 3, + "correct_answer": "sits down \u2192 talks to another person \u2192 puts down an object \u2192 picks up an object", + "verification": { + "actor_id": "G421_person_294", + "events": [ + { + "event_id": "G421_evt_180", + "activity": "person_sits_down", + "camera_id": "G421", + "start_frame": 2586, + "end_frame": 2617, + "start_time_sec": 86.2, + "end_time_sec": 87.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_181", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 2603, + "end_frame": 9000, + "start_time_sec": 86.77, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_197", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 3003, + "end_frame": 3018, + "start_time_sec": 100.1, + "end_time_sec": 100.6, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_211", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 3567, + "end_frame": 3582, + "start_time_sec": 118.9, + "end_time_sec": 119.4, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ], + "time_span_sec": 33.2 + } + }, + { + "question_id": "ordering_008", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes a vehicle door in the order they occurred:", + "events_listed": [ + "talks to another person", + "opens a vehicle door", + "enters a vehicle", + "closes a vehicle door" + ], + "options": [ + "opens a vehicle door \u2192 closes a vehicle door \u2192 enters a vehicle \u2192 talks to another person", + "enters a vehicle \u2192 closes a vehicle door \u2192 talks to another person \u2192 opens a vehicle door", + "opens a vehicle door \u2192 enters a vehicle \u2192 closes a vehicle door \u2192 talks to another person", + "talks to another person \u2192 opens a vehicle door \u2192 enters a vehicle \u2192 closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "talks to another person \u2192 opens a vehicle door \u2192 enters a vehicle \u2192 closes a vehicle door", + "verification": { + "actor_id": "G336_person_59", + "events": [ + { + "event_id": "G336_evt_23", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_frame": 1168, + "end_frame": 2575, + "start_time_sec": 38.93, + "end_time_sec": 85.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_32", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_frame": 2525, + "end_frame": 2590, + "start_time_sec": 84.17, + "end_time_sec": 86.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_37", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_frame": 2525, + "end_frame": 2690, + "start_time_sec": 84.17, + "end_time_sec": 89.67, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_33", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_frame": 2608, + "end_frame": 2674, + "start_time_sec": 86.93, + "end_time_sec": 89.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ], + "time_span_sec": 50.2 + } + }, + { + "question_id": "ordering_009", + "category": "ordering", + "question_template": "Arrange the following events involving the person who carries a heavy object in the order they occurred:", + "events_listed": [ + "picks up an object", + "carries a heavy object", + "puts down an object" + ], + "options": [ + "puts down an object \u2192 picks up an object \u2192 carries a heavy object", + "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "carries a heavy object \u2192 picks up an object \u2192 puts down an object", + "picks up an object \u2192 puts down an object \u2192 carries a heavy object" + ], + "correct_answer_index": 1, + "correct_answer": "picks up an object \u2192 carries a heavy object \u2192 puts down an object", + "verification": { + "actor_id": "G421_person_360", + "events": [ + { + "event_id": "G421_evt_217", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 2147, + "end_frame": 2164, + "start_time_sec": 71.57, + "end_time_sec": 72.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_218", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_frame": 2147, + "end_frame": 2221, + "start_time_sec": 71.57, + "end_time_sec": 74.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_219", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 2204, + "end_frame": 2221, + "start_time_sec": 73.47, + "end_time_sec": 74.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ], + "time_span_sec": 2.46 + } + }, + { + "question_id": "ordering_010", + "category": "ordering", + "question_template": "Arrange the following events involving the person who stands up in the order they occurred:", + "events_listed": [ + "talks to another person", + "picks up an object", + "stands up", + "embraces another person" + ], + "options": [ + "picks up an object \u2192 embraces another person \u2192 stands up \u2192 talks to another person", + "embraces another person \u2192 stands up \u2192 picks up an object \u2192 talks to another person", + "picks up an object \u2192 talks to another person \u2192 stands up \u2192 embraces another person", + "talks to another person \u2192 picks up an object \u2192 stands up \u2192 embraces another person" + ], + "correct_answer_index": 3, + "correct_answer": "talks to another person \u2192 picks up an object \u2192 stands up \u2192 embraces another person", + "verification": { + "actor_id": "G421_person_348", + "events": [ + { + "event_id": "G421_evt_171", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 0, + "end_frame": 2233, + "start_time_sec": 0.0, + "end_time_sec": 74.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_172", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 818, + "end_frame": 835, + "start_time_sec": 27.27, + "end_time_sec": 27.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_212", + "activity": "person_stands_up", + "camera_id": "G421", + "start_frame": 1497, + "end_frame": 1525, + "start_time_sec": 49.9, + "end_time_sec": 50.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + { + "event_id": "G421_evt_175", + "activity": "person_embraces_person", + "camera_id": "G421", + "start_frame": 1542, + "end_frame": 1639, + "start_time_sec": 51.4, + "end_time_sec": 54.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + ], + "time_span_sec": 54.63 + } + }, + { + "question_id": "ordering_011", + "category": "ordering", + "question_template": "Arrange the following events involving the person who closes a vehicle door in the order they occurred:", + "events_listed": [ + "drops off a person", + "exits a vehicle", + "opens a vehicle door", + "closes a vehicle door" + ], + "options": [ + "closes a vehicle door \u2192 exits a vehicle \u2192 drops off a person \u2192 opens a vehicle door", + "drops off a person \u2192 exits a vehicle \u2192 closes a vehicle door \u2192 opens a vehicle door", + "exits a vehicle \u2192 closes a vehicle door \u2192 opens a vehicle door \u2192 drops off a person", + "drops off a person \u2192 exits a vehicle \u2192 opens a vehicle door \u2192 closes a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "drops off a person \u2192 exits a vehicle \u2192 opens a vehicle door \u2192 closes a vehicle door", + "verification": { + "actor_id": "G336_person_147", + "events": [ + { + "event_id": "G336_evt_123", + "activity": "vehicle_drops_off_person", + "camera_id": "G336", + "start_frame": 7462, + "end_frame": 7723, + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_102", + "activity": "person_exits_vehicle", + "camera_id": "G336", + "start_frame": 7495, + "end_frame": 7609, + "start_time_sec": 249.83, + "end_time_sec": 253.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_115", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_frame": 7495, + "end_frame": 7562, + "start_time_sec": 249.83, + "end_time_sec": 252.07, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + { + "event_id": "G336_evt_100", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_frame": 7569, + "end_frame": 7611, + "start_time_sec": 252.3, + "end_time_sec": 253.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + ], + "time_span_sec": 4.97 + } + }, + { + "question_id": "perception_001", + "category": "perception", + "question_template": "Does anyone open a facility door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_facility_door", + "present": true, + "example_event": { + "event_id": "G421_evt_0", + "camera_id": "G421", + "start_frame": 0, + "end_frame": 52, + "start_time_sec": 0.0, + "end_time_sec": 1.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "perception_002", + "category": "perception", + "question_template": "Does anyone embrace another person at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_embraces_person", + "present": true, + "example_event": { + "event_id": "G421_evt_175", + "camera_id": "G421", + "start_frame": 1542, + "end_frame": 1639, + "start_time_sec": 51.4, + "end_time_sec": 54.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "perception_003", + "category": "perception", + "question_template": "Does anyone ride a bicycle at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_rides_bicycle", + "present": false + } + }, + { + "question_id": "perception_004", + "category": "perception", + "question_template": "Does anyone close a facility door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_closes_facility_door", + "present": false + } + }, + { + "question_id": "perception_005", + "category": "perception", + "question_template": "Does anyone pick up a person at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "vehicle_picks_up_person", + "present": false + } + }, + { + "question_id": "perception_006", + "category": "perception", + "question_template": "Does anyone exit the scene through a structure at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_exits_scene_through_structure", + "present": true, + "example_event": { + "event_id": "G421_evt_1", + "camera_id": "G421", + "start_frame": 0, + "end_frame": 70, + "start_time_sec": 0.0, + "end_time_sec": 2.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "perception_007", + "category": "perception", + "question_template": "Does anyone pick up an object at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_picks_up_object", + "present": true, + "example_event": { + "event_id": "G421_evt_172", + "camera_id": "G421", + "start_frame": 818, + "end_frame": 835, + "start_time_sec": 27.27, + "end_time_sec": 27.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "perception_008", + "category": "perception", + "question_template": "Does anyone open a vehicle door at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_opens_vehicle_door", + "present": true, + "example_event": { + "event_id": "G336_evt_32", + "camera_id": "G336", + "start_frame": 2525, + "end_frame": 2590, + "start_time_sec": 84.17, + "end_time_sec": 86.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "perception_009", + "category": "perception", + "question_template": "Does anyone load a vehicle at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_loads_vehicle", + "present": true, + "example_event": { + "event_id": "G336_evt_30", + "camera_id": "G336", + "start_frame": 2179, + "end_frame": 2249, + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "perception_010", + "category": "perception", + "question_template": "Does anyone put down an object at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 0, + "correct_answer": "Yes", + "verification": { + "activity": "person_puts_down_object", + "present": true, + "example_event": { + "event_id": "G421_evt_38", + "camera_id": "G421", + "start_frame": 209, + "end_frame": 224, + "start_time_sec": 6.97, + "end_time_sec": 7.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "perception_011", + "category": "perception", + "question_template": "Does anyone unload a vehicle at any point in this scene?", + "options": [ + "Yes", + "No" + ], + "correct_answer_index": 1, + "correct_answer": "No", + "verification": { + "activity": "person_unloads_vehicle", + "present": false + } + }, + { + "question_id": "causality_001", + "category": "causality", + "question_template": "Why does the person who talks to another person transfer an object?", + "options": [ + "Because they just talk to another person", + "Because they just open a vehicle door", + "Because they just make a purchase", + "Because they just read a document" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just talk to another person", + "verification": { + "actor_id": "G336_person_4", + "causal_strength": "strong", + "cause_event": { + "event_id": "G336_evt_2", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_frame": 6394, + "end_frame": 9004, + "start_time_sec": 213.13, + "end_time_sec": 300.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "effect_event": { + "event_id": "G336_evt_7", + "activity": "person_transfers_object", + "camera_id": "G336", + "start_frame": 6981, + "end_frame": 7089, + "start_time_sec": 232.7, + "end_time_sec": 236.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -67.43 + } + }, + { + "question_id": "causality_002", + "category": "causality", + "question_template": "Why does a person embrace another person?", + "options": [ + "Because they just close a vehicle door", + "Because they just talk to another person", + "Because they just read a document", + "Because they just exit a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just talk to another person", + "verification": { + "actor_id": "G421_person_81", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_266", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 7608, + "end_frame": 9000, + "start_time_sec": 253.6, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_61", + "activity": "person_embraces_person", + "camera_id": "G421", + "start_frame": 8208, + "end_frame": 8485, + "start_time_sec": 273.6, + "end_time_sec": 282.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -26.4 + } + }, + { + "question_id": "causality_003", + "category": "causality", + "question_template": "Why does the person who closes a vehicle door enter a vehicle?", + "options": [ + "Because they just open the trunk", + "Because they just put down an object", + "Because they just talk on the phone", + "Because they just open a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just open a vehicle door", + "verification": { + "actor_id": "G336_person_59", + "causal_strength": "strong", + "cause_event": { + "event_id": "G336_evt_35", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_frame": 2525, + "end_frame": 2613, + "start_time_sec": 84.17, + "end_time_sec": 87.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "effect_event": { + "event_id": "G336_evt_37", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_frame": 2525, + "end_frame": 2690, + "start_time_sec": 84.17, + "end_time_sec": 89.67, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -2.93 + } + }, + { + "question_id": "causality_004", + "category": "causality", + "question_template": "Why does a person exit the scene through a structure?", + "options": [ + "Because they just pick up an object", + "Because they just open a facility door", + "Because they just stand up", + "Because they just sit down" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just open a facility door", + "verification": { + "actor_id": "G421_person_248", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_146", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_frame": 7920, + "end_frame": 8001, + "start_time_sec": 264.0, + "end_time_sec": 266.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_147", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_frame": 7920, + "end_frame": 8005, + "start_time_sec": 264.0, + "end_time_sec": 266.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -2.7 + } + }, + { + "question_id": "causality_005", + "category": "causality", + "question_template": "Why does a person enter a vehicle?", + "options": [ + "Because they just transfer an object", + "Because they just open a facility door", + "Because they just close the trunk", + "Because they just open a vehicle door" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just open a vehicle door", + "verification": { + "actor_id": "G336_person_85", + "causal_strength": "strong", + "cause_event": { + "event_id": "G336_evt_45", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_frame": 4354, + "end_frame": 4410, + "start_time_sec": 145.13, + "end_time_sec": 147.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "effect_event": { + "event_id": "G336_evt_47", + "activity": "person_enters_vehicle", + "camera_id": "G336", + "start_frame": 4354, + "end_frame": 4493, + "start_time_sec": 145.13, + "end_time_sec": 149.77, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -1.87 + } + }, + { + "question_id": "causality_006", + "category": "causality", + "question_template": "Why does a person enter the scene through a structure?", + "options": [ + "Because they just put down an object", + "Because they just open a vehicle door", + "Because they just transfer an object", + "Because they just open a facility door" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just open a facility door", + "verification": { + "actor_id": "G638_person_2", + "causal_strength": "strong", + "cause_event": { + "event_id": "G638_evt_0", + "activity": "person_opens_facility_door", + "camera_id": "G638", + "start_frame": 0, + "end_frame": 52, + "start_time_sec": 0.0, + "end_time_sec": 1.73, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "effect_event": { + "event_id": "G638_evt_1", + "activity": "person_enters_scene_through_structure", + "camera_id": "G638", + "start_frame": 5, + "end_frame": 79, + "start_time_sec": 0.17, + "end_time_sec": 2.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G638.avi" + }, + "gap_sec": -1.56 + } + }, + { + "question_id": "causality_007", + "category": "causality", + "question_template": "Why does the person who enters the scene through a structure talk to another person?", + "options": [ + "Because they just pick up an object", + "Because they just enter the scene through a structure", + "Because they just load a vehicle", + "Because they just stand up" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just enter the scene through a structure", + "verification": { + "actor_id": "G336_person_112", + "causal_strength": "strong", + "cause_event": { + "event_id": "G336_evt_72", + "activity": "person_enters_scene_through_structure", + "camera_id": "G336", + "start_frame": 8785, + "end_frame": 8883, + "start_time_sec": 292.83, + "end_time_sec": 296.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "effect_event": { + "event_id": "G336_evt_74", + "activity": "person_talks_to_person", + "camera_id": "G336", + "start_frame": 8841, + "end_frame": 9004, + "start_time_sec": 294.7, + "end_time_sec": 300.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": -1.4 + } + }, + { + "question_id": "causality_008", + "category": "causality", + "question_template": "Why does the person who stands up carry a heavy object?", + "options": [ + "Because they just read a document", + "Because they just interact with a laptop", + "Because they just exit a vehicle", + "Because they just pick up an object" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G421_person_289", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_178", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 2194, + "end_frame": 2216, + "start_time_sec": 73.13, + "end_time_sec": 73.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_179", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_frame": 2194, + "end_frame": 2367, + "start_time_sec": 73.13, + "end_time_sec": 78.9, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.74 + } + }, + { + "question_id": "causality_009", + "category": "causality", + "question_template": "Why does a person carry a heavy object?", + "options": [ + "Because they just open a vehicle door", + "Because they just pick up an object", + "Because they just exit the scene through a structure", + "Because they just enter the scene through a structure" + ], + "correct_answer_index": 1, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G421_person_353", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_214", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 1938, + "end_frame": 1955, + "start_time_sec": 64.6, + "end_time_sec": 65.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_215", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_frame": 1938, + "end_frame": 2135, + "start_time_sec": 64.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.57 + } + }, + { + "question_id": "causality_010", + "category": "causality", + "question_template": "Why does the person who carries a heavy object put down an object?", + "options": [ + "Because they just carry a heavy object", + "Because they just stand up", + "Because they just transfer an object", + "Because they just exit the scene through a structure" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just carry a heavy object", + "verification": { + "actor_id": "G421_person_353", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_215", + "activity": "person_carries_heavy_object", + "camera_id": "G421", + "start_frame": 1938, + "end_frame": 2135, + "start_time_sec": 64.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_216", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 2118, + "end_frame": 2135, + "start_time_sec": 70.6, + "end_time_sec": 71.17, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.57 + } + }, + { + "question_id": "causality_011", + "category": "causality", + "question_template": "Why does the person who sits down talk to another person?", + "options": [ + "Because they just stand up", + "Because they just pick up an object", + "Because they just sit down", + "Because they just embrace another person" + ], + "correct_answer_index": 2, + "correct_answer": "Because they just sit down", + "verification": { + "actor_id": "G421_person_294", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_180", + "activity": "person_sits_down", + "camera_id": "G421", + "start_frame": 2586, + "end_frame": 2617, + "start_time_sec": 86.2, + "end_time_sec": 87.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_181", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 2603, + "end_frame": 9000, + "start_time_sec": 86.77, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.46 + } + }, + { + "question_id": "causality_012", + "category": "causality", + "question_template": "Why does the person who picks up an object put down an object?", + "options": [ + "Because they just pick up an object", + "Because they just carry a heavy object", + "Because they just text on a phone", + "Because they just transfer an object" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G421_person_124", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_82", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 5971, + "end_frame": 5986, + "start_time_sec": 199.03, + "end_time_sec": 199.53, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_83", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 5975, + "end_frame": 5991, + "start_time_sec": 199.17, + "end_time_sec": 199.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -0.36 + } + }, + { + "question_id": "causality_013", + "category": "causality", + "question_template": "Why does the person who sits down put down an object?", + "options": [ + "Because they just exit the scene through a structure", + "Because they just interact with a laptop", + "Because they just transfer an object", + "Because they just pick up an object" + ], + "correct_answer_index": 3, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G421_person_294", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_206", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 6528, + "end_frame": 6543, + "start_time_sec": 217.6, + "end_time_sec": 218.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_208", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 6616, + "end_frame": 6631, + "start_time_sec": 220.53, + "end_time_sec": 221.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 2.43 + } + }, + { + "question_id": "causality_014", + "category": "causality", + "question_template": "Why does the person who puts down an object put down an object?", + "options": [ + "Because they just pick up an object", + "Because they just open a facility door", + "Because they just talk to another person", + "Because they just talk on the phone" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just pick up an object", + "verification": { + "actor_id": "G421_person_318", + "causal_strength": "strong", + "cause_event": { + "event_id": "G421_evt_188", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 4411, + "end_frame": 4428, + "start_time_sec": 147.03, + "end_time_sec": 147.6, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_190", + "activity": "person_puts_down_object", + "camera_id": "G421", + "start_frame": 4517, + "end_frame": 4533, + "start_time_sec": 150.57, + "end_time_sec": 151.1, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 2.97 + } + }, + { + "question_id": "causality_015", + "category": "causality", + "question_template": "Why does the person who puts down an object pick up an object?", + "options": [ + "Because they just talk to another person", + "Because they just enter the scene through a structure", + "Because they just read a document", + "Because they just close a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "Because they just talk to another person", + "verification": { + "actor_id": "G421_person_318", + "causal_strength": "weak", + "cause_event": { + "event_id": "G421_evt_181", + "activity": "person_talks_to_person", + "camera_id": "G421", + "start_frame": 2603, + "end_frame": 9000, + "start_time_sec": 86.77, + "end_time_sec": 300.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "effect_event": { + "event_id": "G421_evt_182", + "activity": "person_picks_up_object", + "camera_id": "G421", + "start_frame": 2683, + "end_frame": 2699, + "start_time_sec": 89.43, + "end_time_sec": 89.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": -210.57 + } + }, + { + "question_id": "spatial_001", + "category": "spatial", + "question_template": "In the scene, where is a other relative to the person who talks on the phone?", + "options": [ + "left of", + "below", + "above", + "right of" + ], + "correct_answer_index": 3, + "correct_answer": "right of", + "verification": { + "entity_a": "G421_other_354", + "entity_b": "G421_person_269", + "relation": "RIGHT_OF", + "camera_id": "G421", + "frame": 2010, + "bbox_a": [ + 1064, + 376, + 1213, + 585 + ], + "bbox_b": [ + 395, + 147, + 475, + 270 + ] + } + }, + { + "question_id": "spatial_002", + "category": "spatial", + "question_template": "In the scene, where is the person who closes a vehicle door relative to the person who talks to another person?", + "options": [ + "below", + "left of", + "above", + "right of" + ], + "correct_answer_index": 1, + "correct_answer": "left of", + "verification": { + "entity_a": "G336_person_65", + "entity_b": "G336_person_2", + "relation": "LEFT_OF", + "camera_id": "G336", + "frame": 2670, + "bbox_a": [ + 559, + 277, + 572, + 295 + ], + "bbox_b": [ + 727, + 276, + 739, + 306 + ] + } + }, + { + "question_id": "spatial_003", + "category": "spatial", + "question_template": "In the scene, where is the person who puts down an object relative to the person who talks to another person?", + "options": [ + "left of", + "right of", + "above", + "below" + ], + "correct_answer_index": 2, + "correct_answer": "above", + "verification": { + "entity_a": "G421_person_356", + "entity_b": "G421_person_67", + "relation": "ABOVE", + "camera_id": "G421", + "frame": 2130, + "bbox_a": [ + 905, + 245, + 1074, + 648 + ], + "bbox_b": [ + 1029, + 555, + 1482, + 1071 + ] + } + }, + { + "question_id": "spatial_004", + "category": "spatial", + "question_template": "In the scene, where is the person who closes the trunk relative to the person who talks to another person?", + "options": [ + "right of", + "below", + "above", + "left of" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G336_person_117", + "entity_b": "G336_person_3", + "relation": "RIGHT_OF", + "camera_id": "G336", + "frame": 690, + "bbox_a": [ + 1029, + 262, + 1042, + 285 + ], + "bbox_b": [ + 728, + 279, + 741, + 307 + ] + } + }, + { + "question_id": "spatial_005", + "category": "spatial", + "question_template": "In the scene, where is the person who picks up an object relative to the person who texts on a phone?", + "options": [ + "right of", + "below", + "left of", + "above" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G421_person_403", + "entity_b": "G421_person_38", + "relation": "LEFT_OF", + "camera_id": "G421", + "frame": 1200, + "bbox_a": [ + 0, + 597, + 218, + 1070 + ], + "bbox_b": [ + 1776, + 407, + 1919, + 908 + ] + } + }, + { + "question_id": "spatial_006", + "category": "spatial", + "question_template": "In the scene, where is the person who enters the scene through a structure relative to the person who talks to another person?", + "options": [ + "left of", + "right of", + "above", + "below" + ], + "correct_answer_index": 2, + "correct_answer": "above", + "verification": { + "entity_a": "G421_person_385", + "entity_b": "G421_person_181", + "relation": "ABOVE", + "camera_id": "G421", + "frame": 7477, + "bbox_a": [ + 682, + 135, + 760, + 311 + ], + "bbox_b": [ + 286, + 546, + 626, + 1071 + ] + } + }, + { + "question_id": "spatial_007", + "category": "spatial", + "question_template": "In the scene, where is the person who talks to another person relative to the person who texts on a phone?", + "options": [ + "below", + "right of", + "left of", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G421_person_295", + "entity_b": "G421_person_273", + "relation": "RIGHT_OF", + "camera_id": "G421", + "frame": 2970, + "bbox_a": [ + 839, + 303, + 1042, + 630 + ], + "bbox_b": [ + 563, + 324, + 757, + 615 + ] + } + }, + { + "question_id": "spatial_008", + "category": "spatial", + "question_template": "In the scene, where is a other relative to the person who talks to another person?", + "options": [ + "right of", + "below", + "above", + "left of" + ], + "correct_answer_index": 3, + "correct_answer": "left of", + "verification": { + "entity_a": "G421_other_358", + "entity_b": "G421_person_68", + "relation": "LEFT_OF", + "camera_id": "G421", + "frame": 2160, + "bbox_a": [ + 857, + 431, + 1004, + 653 + ], + "bbox_b": [ + 1379, + 653, + 1776, + 1071 + ] + } + }, + { + "question_id": "spatial_009", + "category": "spatial", + "question_template": "In the scene, where is a other relative to the person who texts on a phone?", + "options": [ + "right of", + "below", + "left of", + "above" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G421_other_51", + "entity_b": "G421_person_270", + "relation": "RIGHT_OF", + "camera_id": "G421", + "frame": 2220, + "bbox_a": [ + 1815, + 540, + 1857, + 592 + ], + "bbox_b": [ + 395, + 145, + 475, + 268 + ] + } + }, + { + "question_id": "spatial_010", + "category": "spatial", + "question_template": "In the scene, where is the person who puts down an object relative to the person who talks to another person?", + "options": [ + "right of", + "above", + "left of", + "below" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G421_person_177", + "entity_b": "G421_person_296", + "relation": "LEFT_OF", + "camera_id": "G421", + "frame": 4650, + "bbox_a": [ + 228, + 399, + 464, + 964 + ], + "bbox_b": [ + 764, + 292, + 925, + 572 + ] + } + }, + { + "question_id": "spatial_011", + "category": "spatial", + "question_template": "In the scene, where is a other relative to the person who talks on the phone?", + "options": [ + "below", + "right of", + "left of", + "above" + ], + "correct_answer_index": 2, + "correct_answer": "left of", + "verification": { + "entity_a": "G421_other_169", + "entity_b": "G421_person_276", + "relation": "LEFT_OF", + "camera_id": "G421", + "frame": 7680, + "bbox_a": [ + 319, + 709, + 373, + 783 + ], + "bbox_b": [ + 584, + 321, + 761, + 623 + ] + } + }, + { + "question_id": "spatial_012", + "category": "spatial", + "question_template": "In the scene, where is the person who talks to another person relative to the person who texts on a phone?", + "options": [ + "left of", + "below", + "above", + "right of" + ], + "correct_answer_index": 1, + "correct_answer": "below", + "verification": { + "entity_a": "G421_person_68", + "entity_b": "G421_person_49", + "relation": "BELOW", + "camera_id": "G421", + "frame": 2070, + "bbox_a": [ + 1385, + 650, + 1785, + 1071 + ], + "bbox_b": [ + 1673, + 392, + 1885, + 901 + ] + } + }, + { + "question_id": "spatial_013", + "category": "spatial", + "question_template": "In the scene, where is the person who stands up relative to the person who talks to another person?", + "options": [ + "right of", + "left of", + "above", + "below" + ], + "correct_answer_index": 0, + "correct_answer": "right of", + "verification": { + "entity_a": "G421_person_348", + "entity_b": "G421_person_280", + "relation": "RIGHT_OF", + "camera_id": "G421", + "frame": 1525, + "bbox_a": [ + 898, + 224, + 1064, + 582 + ], + "bbox_b": [ + 806, + 264, + 952, + 566 + ] + } + }, + { + "question_id": "spatial_014", + "category": "spatial", + "question_template": "In the scene, where is the person who picks up an object relative to the person who talks to another person?", + "options": [ + "right of", + "left of", + "below", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "left of", + "verification": { + "entity_a": "G421_person_120", + "entity_b": "G421_person_76", + "relation": "LEFT_OF", + "camera_id": "G421", + "frame": 5886, + "bbox_a": [ + 0, + 683, + 378, + 1071 + ], + "bbox_b": [ + 1748, + 539, + 1919, + 1071 + ] + } + }, + { + "question_id": "spatial_015", + "category": "spatial", + "question_template": "In the scene, where is the person who picks up an object relative to the person who texts on a phone?", + "options": [ + "below", + "right of", + "left of", + "above" + ], + "correct_answer_index": 1, + "correct_answer": "right of", + "verification": { + "entity_a": "G421_person_53", + "entity_b": "G421_person_270", + "relation": "RIGHT_OF", + "camera_id": "G421", + "frame": 2250, + "bbox_a": [ + 1700, + 401, + 1912, + 873 + ], + "bbox_b": [ + 395, + 145, + 475, + 268 + ] + } + }, + { + "question_id": "camera_view_001", + "category": "camera_view", + "question_template": "In which camera view does someone stand up?", + "options": [ + "Camera 2", + "Camera 3", + "Camera 4", + "Camera 1" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 4", + "verification": { + "activity": "person_stands_up", + "correct_camera_id": "G421", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G421_evt_212", + "camera_id": "G421", + "start_frame": 1497, + "end_frame": 1525, + "start_time_sec": 49.9, + "end_time_sec": 50.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "camera_view_002", + "category": "camera_view", + "question_template": "In which camera view does a vehicle start moving?", + "options": [ + "Camera 1", + "Camera 4", + "Camera 2", + "Camera 3" + ], + "correct_answer_index": 3, + "correct_answer": "Camera 3", + "verification": { + "activity": "vehicle_starts", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_11", + "camera_id": "G336", + "start_frame": 636, + "end_frame": 739, + "start_time_sec": 21.2, + "end_time_sec": 24.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_003", + "category": "camera_view", + "question_template": "In which camera view does a vehicle stop?", + "options": [ + "Camera 2", + "Camera 3", + "Camera 1", + "Camera 4" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "vehicle_stops", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_13", + "camera_id": "G336", + "start_frame": 981, + "end_frame": 1049, + "start_time_sec": 32.7, + "end_time_sec": 34.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_004", + "category": "camera_view", + "question_template": "In which camera view does someone load a vehicle?", + "options": [ + "Camera 3", + "Camera 1", + "Camera 2", + "Camera 4" + ], + "correct_answer_index": 0, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_loads_vehicle", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_30", + "camera_id": "G336", + "start_frame": 2179, + "end_frame": 2249, + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_005", + "category": "camera_view", + "question_template": "In which camera view does someone close the trunk?", + "options": [ + "Camera 1", + "Camera 2", + "Camera 3", + "Camera 4" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_closes_trunk", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_75", + "camera_id": "G336", + "start_frame": 644, + "end_frame": 716, + "start_time_sec": 21.47, + "end_time_sec": 23.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_006", + "category": "camera_view", + "question_template": "In which camera view does someone make a purchase?", + "options": [ + "Camera 1", + "Camera 2", + "Camera 4", + "Camera 3" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 4", + "verification": { + "activity": "person_purchases", + "correct_camera_id": "G421", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G421_evt_269", + "camera_id": "G421", + "start_frame": 7584, + "end_frame": 8071, + "start_time_sec": 252.8, + "end_time_sec": 269.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "camera_view_007", + "category": "camera_view", + "question_template": "In which camera view does someone read a document?", + "options": [ + "Camera 1", + "Camera 2", + "Camera 3", + "Camera 4" + ], + "correct_answer_index": 3, + "correct_answer": "Camera 4", + "verification": { + "activity": "person_reads_document", + "correct_camera_id": "G421", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G421_evt_156", + "camera_id": "G421", + "start_frame": 0, + "end_frame": 5520, + "start_time_sec": 0.0, + "end_time_sec": 184.0, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "camera_view_008", + "category": "camera_view", + "question_template": "In which camera view does someone close a vehicle door?", + "options": [ + "Camera 4", + "Camera 3", + "Camera 2", + "Camera 1" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_closes_vehicle_door", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_33", + "camera_id": "G336", + "start_frame": 2608, + "end_frame": 2674, + "start_time_sec": 86.93, + "end_time_sec": 89.13, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_009", + "category": "camera_view", + "question_template": "In which camera view does someone open a vehicle door?", + "options": [ + "Camera 4", + "Camera 2", + "Camera 3", + "Camera 1" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_opens_vehicle_door", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_32", + "camera_id": "G336", + "start_frame": 2525, + "end_frame": 2590, + "start_time_sec": 84.17, + "end_time_sec": 86.33, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_010", + "category": "camera_view", + "question_template": "In which camera view does a vehicle turn left?", + "options": [ + "Camera 4", + "Camera 1", + "Camera 3", + "Camera 2" + ], + "correct_answer_index": 2, + "correct_answer": "Camera 3", + "verification": { + "activity": "vehicle_turns_left", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_0", + "camera_id": "G336", + "start_frame": 16, + "end_frame": 164, + "start_time_sec": 0.53, + "end_time_sec": 5.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_011", + "category": "camera_view", + "question_template": "In which camera view does someone sit down?", + "options": [ + "Camera 3", + "Camera 1", + "Camera 2", + "Camera 4" + ], + "correct_answer_index": 3, + "correct_answer": "Camera 4", + "verification": { + "activity": "person_sits_down", + "correct_camera_id": "G421", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G421_evt_176", + "camera_id": "G421", + "start_frame": 1869, + "end_frame": 1934, + "start_time_sec": 62.3, + "end_time_sec": 64.47, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "camera_view_012", + "category": "camera_view", + "question_template": "In which camera view does someone interact with a laptop?", + "options": [ + "Camera 2", + "Camera 1", + "Camera 3", + "Camera 4" + ], + "correct_answer_index": 3, + "correct_answer": "Camera 4", + "verification": { + "activity": "person_interacts_with_laptop", + "correct_camera_id": "G421", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G421_evt_122", + "camera_id": "G421", + "start_frame": 0, + "end_frame": 1247, + "start_time_sec": 0.0, + "end_time_sec": 41.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "camera_view_013", + "category": "camera_view", + "question_template": "In which camera view does someone open the trunk?", + "options": [ + "Camera 2", + "Camera 3", + "Camera 1", + "Camera 4" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_opens_trunk", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_28", + "camera_id": "G336", + "start_frame": 2054, + "end_frame": 2281, + "start_time_sec": 68.47, + "end_time_sec": 76.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + }, + { + "question_id": "camera_view_014", + "category": "camera_view", + "question_template": "In which camera view does someone embrace another person?", + "options": [ + "Camera 2", + "Camera 4", + "Camera 3", + "Camera 1" + ], + "correct_answer_index": 1, + "correct_answer": "Camera 4", + "verification": { + "activity": "person_embraces_person", + "correct_camera_id": "G421", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G421_evt_175", + "camera_id": "G421", + "start_frame": 1542, + "end_frame": 1639, + "start_time_sec": 51.4, + "end_time_sec": 54.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + } + } + }, + { + "question_id": "camera_view_015", + "category": "camera_view", + "question_template": "In which camera view does someone exit a vehicle?", + "options": [ + "Camera 3", + "Camera 4", + "Camera 2", + "Camera 1" + ], + "correct_answer_index": 0, + "correct_answer": "Camera 3", + "verification": { + "activity": "person_exits_vehicle", + "correct_camera_id": "G336", + "camera_label_map": { + "G299": "Camera 1", + "G330": "Camera 2", + "G336": "Camera 3", + "G421": "Camera 4", + "G638": "Camera 5" + }, + "example_event": { + "event_id": "G336_evt_102", + "camera_id": "G336", + "start_frame": 7495, + "end_frame": 7609, + "start_time_sec": 249.83, + "end_time_sec": 253.63, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + } + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.v4.json b/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.v4.json new file mode 100644 index 0000000..0e5ff31 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-07.17-05-00.school.v4.json @@ -0,0 +1,376 @@ +{ + "slot": "2018-03-07.17-05-00.school", + "metadata": { + "generator": "generate_qa_v4.py", + "version": 4, + "seed": 42, + "design_principles": [ + "Every answer is 100% verifiable from ground-truth annotations", + "No false negatives \u2014 perception questions only affirm what IS present", + "Cross-camera temporal requires multiple camera views (5-15s gap)", + "Spatial uses 3D world coordinates (camera-invariant) via KRTD projection" + ], + "category_counts": { + "temporal": 3, + "spatial": 3, + "perception": 4 + }, + "total_questions": 10, + "cameras": [ + "G299", + "G330", + "G336", + "G421", + "G638" + ] + }, + "qa_pairs": [ + { + "question_id": "v4_001", + "category": "temporal", + "question_template": "After the person who exits a vehicle closes a vehicle door, what do they do next in a different camera view?", + "options": [ + "texts on a phone", + "interacts with a laptop", + "exits the scene through a structure", + "reads a document" + ], + "correct_answer_index": 2, + "correct_answer": "exits the scene through a structure", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_009", + "event_a": { + "event_id": "G336_evt_100", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 252.3, + "end_time_sec": 253.7, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_147", + "activity": "person_exits_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 264.0, + "end_time_sec": 266.83, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 10.3 + } + }, + { + "question_id": "v4_002", + "category": "temporal", + "question_template": "After the person who enters a vehicle closes a vehicle door, what do they do next in a different camera view?", + "options": [ + "opens a facility door", + "exits a vehicle", + "reads a document", + "closes a vehicle door" + ], + "correct_answer_index": 0, + "correct_answer": "opens a facility door", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_006", + "event_a": { + "event_id": "G336_evt_49", + "activity": "person_closes_vehicle_door", + "camera_id": "G336", + "start_time_sec": 150.73, + "end_time_sec": 151.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "event_b": { + "event_id": "G421_evt_244", + "activity": "person_opens_facility_door", + "camera_id": "G421", + "start_time_sec": 163.5, + "end_time_sec": 164.3, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "gap_sec": 11.53 + } + }, + { + "question_id": "v4_003", + "category": "temporal", + "question_template": "After the person who enters a vehicle enters the scene through a structure, what do they do next in a different camera view?", + "options": [ + "embraces another person", + "opens a vehicle door", + "puts down an object", + "makes a purchase" + ], + "correct_answer_index": 1, + "correct_answer": "opens a vehicle door", + "requires_cameras": [ + "G336", + "G421" + ], + "verification": { + "chain_id": "narrative_010", + "event_a": { + "event_id": "G421_evt_232", + "activity": "person_enters_scene_through_structure", + "camera_id": "G421", + "start_time_sec": 198.93, + "end_time_sec": 201.57, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G421.avi" + }, + "event_b": { + "event_id": "G336_evt_118", + "activity": "person_opens_vehicle_door", + "camera_id": "G336", + "start_time_sec": 206.47, + "end_time_sec": 208.23, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "gap_sec": 4.9 + } + }, + { + "question_id": "v4_004", + "category": "spatial", + "question_template": "In the scene, how close are the person who carries a heavy object and the person who enters the scene through a structure to each other in physical space?", + "options": [ + "They are at the same location", + "They are near each other (within a few meters)", + "They are far apart (more than 15 meters)", + "They are in different areas of the scene" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "entity_a": "G336_person_71", + "entity_b": "G638_person_4", + "world_pos_a_enu": [ + 5.79, + 51.12, + 0.0 + ], + "world_pos_b_enu": [ + 28.04, + 79.72, + 0.0 + ], + "distance_meters": 36.24, + "proximity": "far", + "camera_a": "G336", + "camera_b": "G638" + } + }, + { + "question_id": "v4_005", + "category": "spatial", + "question_template": "In the scene, how close are the person who enters a vehicle and the person who opens a vehicle door to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are in different areas of the scene", + "They are near each other (within a few meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G336" + ], + "verification": { + "entity_a": "G336_vehicle_84", + "entity_b": "G336_person_63", + "world_pos_a_enu": [ + 12.23, + 51.25, + 0.0 + ], + "world_pos_b_enu": [ + 11.25, + 52.25, + 0.0 + ], + "distance_meters": 1.4, + "proximity": "near", + "camera_a": "G336", + "camera_b": "G336" + } + }, + { + "question_id": "v4_006", + "category": "spatial", + "question_template": "In the scene, how close are the vehicle that reverses and the person who talks to another person to each other in physical space?", + "options": [ + "They are far apart (more than 15 meters)", + "They are near each other (within a few meters)", + "They are at the same location", + "They are in different areas of the scene" + ], + "correct_answer_index": 0, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G336", + "G638" + ], + "verification": { + "entity_a": "G336_vehicle_122", + "entity_b": "G638_person_17", + "world_pos_a_enu": [ + -0.39, + 49.78, + 0.0 + ], + "world_pos_b_enu": [ + 23.78, + 71.45, + 0.0 + ], + "distance_meters": 32.46, + "proximity": "far", + "camera_a": "G336", + "camera_b": "G638" + } + }, + { + "question_id": "v4_007", + "category": "perception", + "question_template": "Which of the following activities occurs at some point in this scene?", + "options": [ + "Someone opens the trunk", + "Someone rides a bicycle", + "Someone unloads a vehicle", + "Someone closes a facility door" + ], + "correct_answer_index": 0, + "correct_answer": "Someone opens the trunk", + "requires_cameras": [ + "G336" + ], + "verification": { + "activity": "person_opens_trunk", + "present": true, + "example_event": { + "event_id": "G336_evt_28", + "camera_id": "G336", + "start_time_sec": 68.47, + "end_time_sec": 76.03, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_rides_bicycle", + "person_unloads_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_008", + "category": "perception", + "question_template": "Which of the following can be observed happening in this scene?", + "options": [ + "Someone rides a bicycle", + "Someone closes a facility door", + "Someone picks up a person", + "Someone drops off a person" + ], + "correct_answer_index": 3, + "correct_answer": "Someone drops off a person", + "requires_cameras": [ + "G336" + ], + "verification": { + "activity": "vehicle_drops_off_person", + "present": true, + "example_event": { + "event_id": "G336_evt_123", + "camera_id": "G336", + "start_time_sec": 248.73, + "end_time_sec": 257.43, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "distractor_activities": [ + "vehicle_picks_up_person", + "person_closes_facility_door", + "person_rides_bicycle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_009", + "category": "perception", + "question_template": "Which one of these activities takes place in the scene?", + "options": [ + "Someone closes a facility door", + "Someone closes the trunk", + "Someone rides a bicycle", + "Someone unloads a vehicle" + ], + "correct_answer_index": 1, + "correct_answer": "Someone closes the trunk", + "requires_cameras": [ + "G336" + ], + "verification": { + "activity": "person_closes_trunk", + "present": true, + "example_event": { + "event_id": "G336_evt_75", + "camera_id": "G336", + "start_time_sec": 21.47, + "end_time_sec": 23.87, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_rides_bicycle", + "person_unloads_vehicle" + ], + "distractors_present": false + } + }, + { + "question_id": "v4_010", + "category": "perception", + "question_template": "Which of the following events actually happens in this scene?", + "options": [ + "Someone loads a vehicle", + "Someone unloads a vehicle", + "Someone closes a facility door", + "Someone rides a bicycle" + ], + "correct_answer_index": 0, + "correct_answer": "Someone loads a vehicle", + "requires_cameras": [ + "G336" + ], + "verification": { + "activity": "person_loads_vehicle", + "present": true, + "example_event": { + "event_id": "G336_evt_30", + "camera_id": "G336", + "start_time_sec": 72.63, + "end_time_sec": 74.97, + "video_file": "2018-03-07.17-05-00.17-10-00.school.G336.avi" + }, + "distractor_activities": [ + "person_closes_facility_door", + "person_rides_bicycle", + "person_unloads_vehicle" + ], + "distractors_present": false + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v6.json b/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v6.json new file mode 100644 index 0000000..a2b9b3e --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v6.json @@ -0,0 +1,356 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v6", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "generator": "v6_pipeline", + "seed": 42, + "difficulty": "easy", + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_persons_in_slot": 41, + "total_events": 733, + "total_entities": 1258, + "cross_camera_clusters": 20, + "total_questions": 9, + "category_counts": { + "temporal": 3, + "spatial": 3, + "perception": 3 + }, + "validation_issues": 0, + "generation_time_sec": 7.9, + "qa_pairs": [ + { + "question_id": "v6_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "A person_exits_scene_through_structure event on camera G421 and a person_talks_to_person event on camera G299 \u2014 which occurred first?", + "options": [ + "person_exits_scene_through_structure on G421 occurred first", + "person_talks_to_person on G299 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "person_exits_scene_through_structure on G421 occurred first", + "requires_cameras": [ + "G299", + "G421" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_exits_scene_through_structure", + "camera": "G421", + "start_sec": 232.17, + "end_sec": 234.0, + "actor_ids": [ + 3414988050908947645 + ] + }, + "event_b": { + "activity": "person_talks_to_person", + "camera": "G299", + "start_sec": 237.03, + "end_sec": 299.7, + "actor_ids": [ + 143, + 142 + ] + }, + "gap_sec": 3.03, + "entity_link": "heuristic", + "same_person": true + } + }, + { + "question_id": "v6_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "A person_embraces_person event on camera G330 and a person_enters_scene_through_structure event on camera G423 \u2014 which occurred first?", + "options": [ + "person_embraces_person on G330 occurred first", + "person_enters_scene_through_structure on G423 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "person_enters_scene_through_structure on G423 occurred first", + "requires_cameras": [ + "G330", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G423", + "start_sec": 237.9, + "end_sec": 239.77, + "actor_ids": [ + 24 + ] + }, + "event_b": { + "activity": "person_embraces_person", + "camera": "G330", + "start_sec": 242.8, + "end_sec": 247.2, + "actor_ids": [ + 1637410310969206304, + 1370769203069265414 + ] + }, + "gap_sec": 3.03, + "entity_link": "heuristic", + "same_person": true + } + }, + { + "question_id": "v6_temporal_003", + "category": "temporal", + "difficulty": "easy", + "question_template": "A person_picks_up_object event on camera G330 and a person_stands_up event on camera G299 \u2014 which occurred first?", + "options": [ + "person_picks_up_object on G330 occurred first", + "person_stands_up on G299 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "person_stands_up on G299 occurred first", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_stands_up", + "camera": "G299", + "start_sec": 108.7, + "end_sec": 110.03, + "actor_ids": [ + 122 + ] + }, + "event_b": { + "activity": "person_picks_up_object", + "camera": "G330", + "start_sec": 113.07, + "end_sec": 114.3, + "actor_ids": [ + 7202388329754731274, + 6195794767252596122 + ] + }, + "gap_sec": 3.04, + "entity_link": "heuristic", + "same_person": true + } + }, + { + "question_id": "v6_spatial_001", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are person_enters_scene_through_structure and person_enters_scene_through_structure in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_1613855842107762240", + "entity_b": "G328_actor_2828636302242128936", + "world_pos_a_enu": [ + 151.98066213457156, + -38.18088314965152, + 0.0 + ], + "world_pos_b_enu": [ + 153.2802180482165, + -40.7394622512238, + 0.0 + ], + "distance_meters": 2.87, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v6_spatial_002", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are person_enters_scene_through_structure and person_opens_facility_door in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_3528504360375821752", + "entity_b": "G328_actor_3798246005989942289", + "world_pos_a_enu": [ + 153.11399038976742, + -41.20519492985653, + 0.0 + ], + "world_pos_b_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ], + "distance_meters": 8.06, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v6_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are person_enters_scene_through_structure and person_talks_to_person in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2894304191213249440", + "entity_b": "G328_actor_3937943131126375508", + "world_pos_a_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ], + "world_pos_b_enu": [ + 86.84151403448892, + 27.91889985298861, + 0.0 + ], + "distance_meters": 98.22, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v6_perception_001", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures a person_carries_heavy_object event?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "cameras_with_activity": [ + "G299", + "G330" + ], + "total_cameras_in_slot": 8 + } + }, + { + "question_id": "v6_perception_002", + "category": "perception", + "difficulty": "easy", + "question_template": "What activity is occurring on camera G336?", + "options": [ + "person_puts_down_object", + "person_opens_vehicle_door", + "person_texts_on_phone", + "person_opens_facility_door" + ], + "correct_answer_index": 1, + "correct_answer": "person_opens_vehicle_door", + "requires_cameras": [ + "G336" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "activity_identification", + "camera": "G336", + "correct_activity": "person_opens_vehicle_door", + "all_activities_on_camera": [ + "person_closes_vehicle_door", + "person_exits_vehicle", + "person_opens_vehicle_door", + "vehicle_stops", + "vehicle_turns_left" + ] + } + }, + { + "question_id": "v6_perception_003", + "category": "perception", + "difficulty": "easy", + "question_template": "person_opens_facility_door is occurring. Which cameras capture this activity?", + "options": [ + "Both G299 and G328", + "G299 only", + "G328 only", + "Neither" + ], + "correct_answer_index": 0, + "correct_answer": "Both G299 and G328", + "requires_cameras": [ + "G299", + "G328" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "multi_camera_confirmation", + "activity": "person_opens_facility_door", + "cameras_with_activity": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ] + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v7.json b/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v7.json new file mode 100644 index 0000000..6248f2f --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v7.json @@ -0,0 +1,607 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v7", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "generator": "v7_pipeline", + "seed": 42, + "difficulty": "easy", + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_persons_in_slot": 41, + "total_events": 733, + "total_entities": 1258, + "cross_camera_clusters": 20, + "total_questions": 8, + "category_counts": { + "temporal": 2, + "spatial": 3, + "perception": 3 + }, + "v7_stats": { + "questions_with_mevid_validation": 2, + "questions_with_connections": 2, + "questions_with_debug_info": 8, + "entities_with_aliases": 1258 + }, + "validation_issues": 0, + "generation_time_sec": 12.45, + "qa_pairs": [ + { + "question_id": "v7_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "Person enters scene through structure (on G421 @ 1s) and Person interacts with person (on G330 @ 8s) -- which occurred first?", + "options": [ + "Person enters scene through structure (on G421 @ 1s) occurred first", + "Person interacts with person (on G330 @ 8s) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "Person enters scene through structure (on G421 @ 1s) occurred first", + "requires_cameras": [ + "G330", + "G421" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G421", + "start_sec": 0.33, + "end_sec": 2.4, + "actor_ids": [ + 1367451520919019547 + ], + "entity_alias": "Person enters scene through structure (on G421 @ 1s)" + }, + "event_b": { + "activity": "hand_interacts_with_person", + "camera": "G330", + "start_sec": 6.1, + "end_sec": 9.27, + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "entity_alias": "Person interacts with person (on G330 @ 8s)" + }, + "gap_sec": 3.7, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 1367451520919019547 + ], + "frame_range": [ + 10, + 72 + ], + "timestamp": "0.33-2.40s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.mp4", + "entity_alias": "Person enters scene through structure (on G421 @ 1s)" + }, + "event_b": { + "camera": "G330", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "frame_range": [ + 183, + 278 + ], + "timestamp": "6.10-9.27s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.mp4", + "entity_alias": "Person interacts with person (on G330 @ 8s)" + }, + "gap_sec": 3.7, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_0", + "mevid_validated": true, + "mevid_person_id": 97 + } + }, + { + "question_id": "v7_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "Person enters scene through structure (on G420 @ 44s) and Person opens facility door (on G328 @ 38s) -- which occurred first?", + "options": [ + "Person enters scene through structure (on G420 @ 44s) occurred first", + "Person opens facility door (on G328 @ 38s) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "Person opens facility door (on G328 @ 38s) occurred first", + "requires_cameras": [ + "G328", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_facility_door", + "camera": "G328", + "start_sec": 36.67, + "end_sec": 39.33, + "actor_ids": [ + 3798246005989942289 + ], + "entity_alias": "Person opens facility door (on G328 @ 38s)" + }, + "event_b": { + "activity": "person_enters_scene_through_structure", + "camera": "G420", + "start_sec": 42.43, + "end_sec": 45.07, + "actor_ids": [ + 8966895575790985284 + ], + "entity_alias": "Person enters scene through structure (on G420 @ 44s)" + }, + "gap_sec": 3.1, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G328", + "activity": "person_opens_facility_door", + "actor_ids": [ + 3798246005989942289 + ], + "frame_range": [ + 1100, + 1180 + ], + "timestamp": "36.67-39.33s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G328.mp4", + "entity_alias": "Person opens facility door (on G328 @ 38s)" + }, + "event_b": { + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 8966895575790985284 + ], + "frame_range": [ + 1273, + 1352 + ], + "timestamp": "42.43-45.07s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.mp4", + "entity_alias": "Person enters scene through structure (on G420 @ 44s)" + }, + "gap_sec": 3.1, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 41 + } + }, + { + "question_id": "v7_spatial_001", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are Person opens facility door (on G328 @ 113s) and Person enters scene through structure (on G328 @ 113s) in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2653725496538997643", + "entity_b": "G328_actor_2894304191213249440", + "entity_a_alias": "Person opens facility door (on G328 @ 113s)", + "entity_b_alias": "Person enters scene through structure (on G328 @ 113s)", + "world_pos_a_enu": [ + 154.67299792510715, + -43.279948134014674, + 0.0 + ], + "world_pos_b_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ], + "distance_meters": 0.25, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2653725496538997643", + "camera": "G328", + "alias": "Person opens facility door (on G328 @ 113s)", + "bbox": [ + 726, + 585, + 740, + 615 + ], + "frame": 3375, + "timestamp": "112.00-113.03s", + "world_pos_enu": [ + 154.67299792510715, + -43.279948134014674, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_2894304191213249440", + "camera": "G328", + "alias": "Person enters scene through structure (on G328 @ 113s)", + "bbox": [ + 724, + 585, + 738, + 615 + ], + "frame": 3387, + "timestamp": "112.00-113.83s", + "world_pos_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ] + }, + "distance_meters": 0.25, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v7_spatial_002", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are Person enters scene through structure (on G328 @ 113s) and Person enters scene through structure (on G328 @ 38s) in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2894304191213249440", + "entity_b": "G328_actor_4132231753295701202", + "entity_a_alias": "Person enters scene through structure (on G328 @ 113s)", + "entity_b_alias": "Person enters scene through structure (on G328 @ 38s)", + "world_pos_a_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ], + "world_pos_b_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ], + "distance_meters": 5.61, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2894304191213249440", + "camera": "G328", + "alias": "Person enters scene through structure (on G328 @ 113s)", + "bbox": [ + 724, + 585, + 738, + 615 + ], + "frame": 3387, + "timestamp": "112.00-113.83s", + "world_pos_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_4132231753295701202", + "camera": "G328", + "alias": "Person enters scene through structure (on G328 @ 38s)", + "bbox": [ + 728, + 589, + 738, + 613 + ], + "frame": 1149, + "timestamp": "36.67-39.93s", + "world_pos_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ] + }, + "distance_meters": 5.61, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v7_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are Person enters scene through structure (on G328 @ 113s) and Person talks to person (on G328 @ 148s) in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2894304191213249440", + "entity_b": "G328_actor_3937943131126375508", + "entity_a_alias": "Person enters scene through structure (on G328 @ 113s)", + "entity_b_alias": "Person talks to person (on G328 @ 148s)", + "world_pos_a_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ], + "world_pos_b_enu": [ + 86.88325726777234, + 27.4539076380832, + 0.0 + ], + "distance_meters": 97.86, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2894304191213249440", + "camera": "G328", + "alias": "Person enters scene through structure (on G328 @ 113s)", + "bbox": [ + 724, + 585, + 738, + 615 + ], + "frame": 3387, + "timestamp": "112.00-113.83s", + "world_pos_enu": [ + 154.75586185025443, + -43.04050744085667, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_3937943131126375508", + "camera": "G328", + "alias": "Person talks to person (on G328 @ 148s)", + "bbox": [ + 1014, + 679, + 1033, + 719 + ], + "frame": 4428, + "timestamp": "109.07-186.13s", + "world_pos_enu": [ + 86.88325726777234, + 27.4539076380832, + 0.0 + ] + }, + "distance_meters": 97.86, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v7_perception_001", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures a carries heavy object event?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "cameras_with_activity": [ + "G299", + "G330" + ], + "total_cameras_in_slot": 8 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "correct_camera": "G299", + "cameras_with_activity": [ + "G299", + "G330" + ], + "representative_event": { + "camera": "G299", + "frame_range": [ + 0, + 202 + ], + "timestamp": "0.00-6.73s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.mp4" + } + } + }, + { + "question_id": "v7_perception_002", + "category": "perception", + "difficulty": "easy", + "question_template": "What activity is occurring on camera G336?", + "options": [ + "picks up object", + "opens vehicle door", + "closes vehicle door", + "embraces person" + ], + "correct_answer_index": 1, + "correct_answer": "opens vehicle door", + "requires_cameras": [ + "G336" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "activity_identification", + "camera": "G336", + "correct_activity": "person_opens_vehicle_door", + "correct_activity_alias": "opens vehicle door", + "all_activities_on_camera": [ + "person_closes_vehicle_door", + "person_exits_vehicle", + "person_opens_vehicle_door", + "vehicle_stops", + "vehicle_turns_left" + ] + }, + "debug_info": { + "question_type": "activity_identification", + "camera": "G336", + "correct_activity": "person_opens_vehicle_door", + "correct_activity_alias": "opens vehicle door", + "all_activities_on_camera": [ + "person_closes_vehicle_door", + "person_exits_vehicle", + "person_opens_vehicle_door", + "vehicle_stops", + "vehicle_turns_left" + ], + "representative_event": { + "camera": "G336", + "frame_range": [ + 8322, + 8418 + ], + "timestamp": "277.40-280.60s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G336.mp4" + } + } + }, + { + "question_id": "v7_perception_003", + "category": "perception", + "difficulty": "easy", + "question_template": "Opening a facility door is occurring. Which cameras capture this activity?", + "options": [ + "Both G299 and G328", + "G299 only", + "G328 only", + "Neither" + ], + "correct_answer_index": 0, + "correct_answer": "Both G299 and G328", + "requires_cameras": [ + "G299", + "G328" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "multi_camera_confirmation", + "activity": "person_opens_facility_door", + "activity_alias": "opens facility door", + "cameras_with_activity": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ] + }, + "debug_info": { + "question_type": "multi_camera_confirmation", + "activity": "person_opens_facility_door", + "activity_alias": "opens facility door", + "cameras_with_activity": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ], + "camera_1_event": { + "camera": "G299", + "frame_range": [ + 4617, + 4671 + ], + "timestamp": "153.90-155.70s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.mp4" + }, + "camera_2_event": { + "camera": "G328", + "frame_range": [ + 1100, + 1180 + ], + "timestamp": "36.67-39.33s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G328.mp4" + } + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v7.natural.json b/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v7.natural.json new file mode 100644 index 0000000..082e2a9 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-11.11-25-00.school.v7.natural.json @@ -0,0 +1,156 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v7_natural", + "generator": "naturalize_v7_qa.py", + "model": "gpt-4o-mini", + "temperature": 0.7, + "total_tokens": 5797, + "total_questions": 8, + "failures": 0, + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_persons_in_slot": 41, + "category_counts": { + "temporal": 2, + "spatial": 3, + "perception": 3 + }, + "qa_pairs": [ + { + "question_id": "v7_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question": "Between someone entering a scene through a structure and someone interacting with another person, which event happened first?", + "options": [ + "Entering the scene through the structure occurred first", + "Interacting with the person occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "Entering the scene through the structure occurred first", + "reasoning": "The footage shows that the person entered the scene before interacting with another person.", + "requires_multi_camera": true + }, + { + "question_id": "v7_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question": "Between someone entering a scene through a structure and someone opening a facility door, which event happened first?", + "options": [ + "Entering the scene through the structure occurred first", + "Opening the facility door occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "Opening the facility door occurred first", + "reasoning": "The person opened the facility door before entering the scene, as shown in the footage.", + "requires_multi_camera": true + }, + { + "question_id": "v7_spatial_001", + "category": "spatial", + "difficulty": "easy", + "question": "How close are the person opening the facility door and the person entering the scene through the structure?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "reasoning": "The distance between the two individuals is only 0.25 meters, indicating they are very close to each other.", + "requires_multi_camera": false + }, + { + "question_id": "v7_spatial_002", + "category": "spatial", + "difficulty": "easy", + "question": "How close are the two people entering the scene through the structure?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "reasoning": "The distance between the two individuals is approximately 5.61 meters, indicating they are at a moderate distance from each other.", + "requires_multi_camera": false + }, + { + "question_id": "v7_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question": "How close are the person entering the scene through a structure and the person talking to someone in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "reasoning": "The distance between the two individuals suggests they are far apart from each other.", + "requires_multi_camera": false + }, + { + "question_id": "v7_perception_001", + "category": "perception", + "difficulty": "easy", + "question": "Which camera captures someone carrying a heavy object?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "reasoning": "The action of someone carrying a heavy object can be seen in one of the camera feeds.", + "requires_multi_camera": true + }, + { + "question_id": "v7_perception_002", + "category": "perception", + "difficulty": "easy", + "question": "What activity is happening on this camera?", + "options": [ + "picks up an object", + "opens a vehicle door", + "closes a vehicle door", + "embraces a person" + ], + "correct_answer_index": 1, + "correct_answer": "opens a vehicle door", + "reasoning": "The activities captured by the camera show various interactions with objects and people.", + "requires_multi_camera": false + }, + { + "question_id": "v7_perception_003", + "category": "perception", + "difficulty": "easy", + "question": "Which cameras capture someone opening a facility door?", + "options": [ + "Both G299 and G328", + "G299 only", + "G328 only", + "Neither" + ], + "correct_answer_index": 0, + "correct_answer": "Both G299 and G328", + "reasoning": "The activity of opening a facility door can be seen in the feeds from multiple cameras.", + "requires_multi_camera": true + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-11.11-30-00.school.v5.json b/meva/examples/qa_pairs/archived/2018-03-11.11-30-00.school.v5.json new file mode 100644 index 0000000..2f6c861 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-11.11-30-00.school.v5.json @@ -0,0 +1,548 @@ +{ + "slot": "2018-03-11.11-30-00.school", + "version": "v5", + "annotation_source": "kitware", + "generator": "symbolic_deterministic", + "seed": 42, + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G423" + ], + "gps_actors_count": 89, + "total_events": 527, + "total_questions": 14, + "category_counts": { + "temporal": 3, + "event_ordering": 3, + "perception": 3, + "spatial": 2, + "camera_transition": 3 + }, + "questions": [ + { + "id": "temporal_001", + "category": "temporal", + "question": "A 'person sits down' event was observed on camera G330, and a 'person picks up object' event was observed on camera G299. Which occurred first?", + "options": [ + "The 'person sits down' on G330 occurred first", + "The 'person picks up object' on G299 occurred first", + "They occurred simultaneously", + "Cannot be determined from the available footage" + ], + "correct_answer": 0, + "correct_option_text": "The 'person sits down' on G330 occurred first", + "cameras_required": [ + "G330", + "G299" + ], + "verification": { + "event_a": { + "activity": "person_sits_down", + "camera": "G330", + "start_sec": 41.37, + "end_sec": 44.87 + }, + "event_b": { + "activity": "person_picks_up_object", + "camera": "G299", + "start_sec": 102.53, + "end_sec": 103.3 + }, + "gap_sec": 57.66, + "relationship": "before" + } + }, + { + "id": "temporal_002", + "category": "temporal", + "question": "A 'person talks to person' event was observed on camera G330, and a 'person enters scene through structure' event was observed on camera G423. Which occurred first?", + "options": [ + "The 'person talks to person' on G330 occurred first", + "The 'person enters scene through structure' on G423 occurred first", + "They occurred simultaneously", + "Cannot be determined from the available footage" + ], + "correct_answer": 0, + "correct_option_text": "The 'person talks to person' on G330 occurred first", + "cameras_required": [ + "G330", + "G423" + ], + "verification": { + "event_a": { + "activity": "person_talks_to_person", + "camera": "G330", + "start_sec": 0.0, + "end_sec": 47.5 + }, + "event_b": { + "activity": "person_enters_scene_through_structure", + "camera": "G423", + "start_sec": 147.53, + "end_sec": 148.97 + }, + "gap_sec": 100.03, + "relationship": "before" + } + }, + { + "id": "temporal_003", + "category": "temporal", + "question": "A 'person picks up object' event was observed on camera G299, and a 'vehicle turns left' event was observed on camera G336. Which occurred first?", + "options": [ + "The 'person picks up object' on G299 occurred first", + "The 'vehicle turns left' on G336 occurred first", + "They occurred simultaneously", + "Cannot be determined from the available footage" + ], + "correct_answer": 0, + "correct_option_text": "The 'person picks up object' on G299 occurred first", + "cameras_required": [ + "G299", + "G336" + ], + "verification": { + "event_a": { + "activity": "person_picks_up_object", + "camera": "G299", + "start_sec": 69.33, + "end_sec": 69.9 + }, + "event_b": { + "activity": "vehicle_turns_left", + "camera": "G336", + "start_sec": 78.27, + "end_sec": 83.67 + }, + "gap_sec": 8.37, + "relationship": "before" + } + }, + { + "id": "ordering_001", + "category": "event_ordering", + "question": "The following 4 events were observed across cameras G328, G419, G420, G423. What is the correct chronological order?", + "options": [ + "'person talks to person' on camera G423 \u2192 'person opens facility door' on camera G420 \u2192 'person opens facility door' on camera G328 \u2192 'person opens facility door' on camera G419", + "'person opens facility door' on camera G419 \u2192 'person talks to person' on camera G423 \u2192 'person opens facility door' on camera G420 \u2192 'person opens facility door' on camera G328", + "'person opens facility door' on camera G328 \u2192 'person opens facility door' on camera G420 \u2192 'person opens facility door' on camera G419 \u2192 'person talks to person' on camera G423", + "'person opens facility door' on camera G420 \u2192 'person opens facility door' on camera G419 \u2192 'person opens facility door' on camera G328 \u2192 'person talks to person' on camera G423" + ], + "correct_answer": 2, + "correct_option_text": "'person opens facility door' on camera G328 \u2192 'person opens facility door' on camera G420 \u2192 'person opens facility door' on camera G419 \u2192 'person talks to person' on camera G423", + "cameras_required": [ + "G328", + "G419", + "G420", + "G423" + ], + "verification": { + "events": [ + { + "activity": "person_opens_facility_door", + "camera": "G328", + "start_sec": 8.57 + }, + { + "activity": "person_opens_facility_door", + "camera": "G420", + "start_sec": 37.4 + }, + { + "activity": "person_opens_facility_door", + "camera": "G419", + "start_sec": 46.5 + }, + { + "activity": "person_talks_to_person", + "camera": "G423", + "start_sec": 138.97 + } + ], + "correct_order": "'person opens facility door' on camera G328 \u2192 'person opens facility door' on camera G420 \u2192 'person opens facility door' on camera G419 \u2192 'person talks to person' on camera G423" + } + }, + { + "id": "ordering_002", + "category": "event_ordering", + "question": "The following 4 events were observed across cameras G299, G419, G423. What is the correct chronological order?", + "options": [ + "'person embraces person' on camera G299 \u2192 'person texts on phone' on camera G299 \u2192 'person exits scene through structure' on camera G419 \u2192 'person talks to person' on camera G423", + "'person talks to person' on camera G423 \u2192 'person exits scene through structure' on camera G419 \u2192 'person texts on phone' on camera G299 \u2192 'person embraces person' on camera G299", + "'person exits scene through structure' on camera G419 \u2192 'person embraces person' on camera G299 \u2192 'person talks to person' on camera G423 \u2192 'person texts on phone' on camera G299", + "'person exits scene through structure' on camera G419 \u2192 'person texts on phone' on camera G299 \u2192 'person embraces person' on camera G299 \u2192 'person talks to person' on camera G423" + ], + "correct_answer": 0, + "correct_option_text": "'person embraces person' on camera G299 \u2192 'person texts on phone' on camera G299 \u2192 'person exits scene through structure' on camera G419 \u2192 'person talks to person' on camera G423", + "cameras_required": [ + "G299", + "G419", + "G423" + ], + "verification": { + "events": [ + { + "activity": "person_embraces_person", + "camera": "G299", + "start_sec": 2.13 + }, + { + "activity": "person_texts_on_phone", + "camera": "G299", + "start_sec": 21.67 + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G419", + "start_sec": 46.5 + }, + { + "activity": "person_talks_to_person", + "camera": "G423", + "start_sec": 138.97 + } + ], + "correct_order": "'person embraces person' on camera G299 \u2192 'person texts on phone' on camera G299 \u2192 'person exits scene through structure' on camera G419 \u2192 'person talks to person' on camera G423" + } + }, + { + "id": "ordering_003", + "category": "event_ordering", + "question": "The following 4 events were observed across cameras G330, G336, G419. What is the correct chronological order?", + "options": [ + "'person opens trunk' on camera G336 \u2192 'person transfers object' on camera G330 \u2192 'person exits scene through structure' on camera G419 \u2192 'person exits scene through structure' on camera G330", + "'person opens trunk' on camera G336 \u2192 'person exits scene through structure' on camera G419 \u2192 'person transfers object' on camera G330 \u2192 'person exits scene through structure' on camera G330", + "'person transfers object' on camera G330 \u2192 'person opens trunk' on camera G336 \u2192 'person exits scene through structure' on camera G330 \u2192 'person exits scene through structure' on camera G419", + "'person transfers object' on camera G330 \u2192 'person exits scene through structure' on camera G419 \u2192 'person opens trunk' on camera G336 \u2192 'person exits scene through structure' on camera G330" + ], + "correct_answer": 0, + "correct_option_text": "'person opens trunk' on camera G336 \u2192 'person transfers object' on camera G330 \u2192 'person exits scene through structure' on camera G419 \u2192 'person exits scene through structure' on camera G330", + "cameras_required": [ + "G330", + "G336", + "G419" + ], + "verification": { + "events": [ + { + "activity": "person_opens_trunk", + "camera": "G336", + "start_sec": 7.23 + }, + { + "activity": "person_transfers_object", + "camera": "G330", + "start_sec": 16.73 + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G419", + "start_sec": 46.5 + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G330", + "start_sec": 193.27 + } + ], + "correct_order": "'person opens trunk' on camera G336 \u2192 'person transfers object' on camera G330 \u2192 'person exits scene through structure' on camera G419 \u2192 'person exits scene through structure' on camera G330" + } + }, + { + "id": "perception_001", + "category": "perception", + "question": "Which camera captures a 'person opens trunk' event?", + "options": [ + "G336", + "G299", + "G328", + "G330" + ], + "correct_answer": 0, + "correct_option_text": "G336", + "cameras_required": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G423" + ], + "verification": { + "activity": "person_opens_trunk", + "cameras_with_activity": [ + "G336" + ], + "cameras_without_activity": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G423" + ], + "question_type": "which_camera" + } + }, + { + "id": "perception_002", + "category": "perception", + "question": "A 'person embraces person' event is visible on multiple cameras. Which camera does NOT capture this event?", + "options": [ + "G336", + "G299", + "G330" + ], + "correct_answer": 0, + "correct_option_text": "G336", + "cameras_required": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G423" + ], + "verification": { + "activity": "person_embraces_person", + "cameras_with_activity": [ + "G299", + "G330" + ], + "cameras_without_activity": [ + "G328", + "G336", + "G419", + "G420", + "G423" + ], + "question_type": "which_not" + } + }, + { + "id": "perception_003", + "category": "perception", + "question": "Which camera captures a 'person closes trunk' event?", + "options": [ + "G328", + "G330", + "G299", + "G336" + ], + "correct_answer": 3, + "correct_option_text": "G336", + "cameras_required": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G423" + ], + "verification": { + "activity": "person_closes_trunk", + "cameras_with_activity": [ + "G336" + ], + "cameras_without_activity": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G423" + ], + "question_type": "which_camera" + } + }, + { + "id": "spatial_001", + "category": "spatial", + "question": "Based on the camera network layout, which pair of cameras is positioned closest to each other?", + "options": [ + "G299 and G336 (100m apart)", + "G330 and G336 (77m apart)", + "G299 and G328 (121m apart)", + "G299 and G330 (23m apart)" + ], + "correct_answer": 3, + "correct_option_text": "G299 and G330 (23m apart)", + "cameras_required": [ + "G299", + "G330" + ], + "verification": { + "closest_pair": [ + "G299", + "G330" + ], + "distance_m": 23.3, + "all_distances": { + "G299-G330": 23.3, + "G328-G336": 56.5, + "G330-G336": 77.1, + "G299-G336": 99.6, + "G328-G330": 104.1, + "G299-G328": 120.8 + }, + "source": "krtd_camera_centers" + } + }, + { + "id": "spatial_002", + "category": "spatial", + "question": "Which pair of cameras monitors the most distant locations from each other?", + "options": [ + "G299 and G330 (23m apart)", + "G328 and G336 (57m apart)", + "G299 and G328 (121m apart)", + "G330 and G336 (77m apart)" + ], + "correct_answer": 2, + "correct_option_text": "G299 and G328 (121m apart)", + "cameras_required": [ + "G299", + "G328" + ], + "verification": { + "farthest_pair": [ + "G299", + "G328" + ], + "distance_m": 120.8, + "source": "krtd_camera_centers" + } + }, + { + "id": "camtrans_001", + "category": "camera_transition", + "question": "A person enters the scene through a structure on multiple cameras. Which camera captures this happening first?", + "options": [ + "G423", + "G330", + "G328", + "G420", + "G419" + ], + "correct_answer": 2, + "correct_option_text": "G328", + "cameras_required": [ + "G328", + "G423", + "G419", + "G420", + "G330" + ], + "verification": { + "first_camera": "G328", + "first_time_sec": 0.0, + "event_type": "entry", + "all_cameras_with_event": { + "G328": { + "time_sec": 0.0, + "activity": "person_enters_scene_through_structure" + }, + "G423": { + "time_sec": 5.37, + "activity": "person_enters_scene_through_structure" + }, + "G419": { + "time_sec": 36.7, + "activity": "person_enters_scene_through_structure" + }, + "G420": { + "time_sec": 37.4, + "activity": "person_enters_scene_through_structure" + }, + "G330": { + "time_sec": 84.4, + "activity": "person_enters_scene_through_structure" + } + } + } + }, + { + "id": "camtrans_002", + "category": "camera_transition", + "question": "Multiple cameras capture a person entering the scene. Which camera records this event latest?", + "options": [ + "G419", + "G328", + "G420", + "G330", + "G299" + ], + "correct_answer": 3, + "correct_option_text": "G330", + "cameras_required": [ + "G328", + "G423", + "G419", + "G420", + "G330" + ], + "verification": { + "last_camera": "G330", + "last_time_sec": 84.4, + "event_type": "entry", + "all_cameras_with_event": { + "G328": { + "time_sec": 0.0, + "activity": "person_enters_scene_through_structure" + }, + "G423": { + "time_sec": 5.37, + "activity": "person_enters_scene_through_structure" + }, + "G419": { + "time_sec": 36.7, + "activity": "person_enters_scene_through_structure" + }, + "G420": { + "time_sec": 37.4, + "activity": "person_enters_scene_through_structure" + }, + "G330": { + "time_sec": 84.4, + "activity": "person_enters_scene_through_structure" + } + } + } + }, + { + "id": "camtrans_003", + "category": "camera_transition", + "question": "After a person is observed performing 'person exits scene through structure' on camera G423, which camera next captures a person entering the scene?", + "options": [ + "G423", + "G419", + "G328", + "G299", + "G420" + ], + "correct_answer": 4, + "correct_option_text": "G420", + "cameras_required": [ + "G423", + "G420" + ], + "verification": { + "exit_camera": "G423", + "exit_time_sec": 124.7, + "exit_activity": "person_exits_scene_through_structure", + "entry_camera": "G420", + "entry_time_sec": 124.83, + "entry_activity": "person_enters_scene_through_structure", + "gap_sec": 0.13 + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/2018-03-11.16-20-00.school.v5.json b/meva/examples/qa_pairs/archived/2018-03-11.16-20-00.school.v5.json new file mode 100644 index 0000000..e8478a3 --- /dev/null +++ b/meva/examples/qa_pairs/archived/2018-03-11.16-20-00.school.v5.json @@ -0,0 +1,556 @@ +{ + "slot": "2018-03-11.16-20-00.school", + "version": "v5", + "annotation_source": "kitware", + "generator": "symbolic_deterministic", + "seed": 42, + "cameras": [ + "G299", + "G330", + "G421", + "G423", + "G638", + "G639" + ], + "gps_actors_count": 88, + "total_events": 587, + "total_questions": 15, + "category_counts": { + "temporal": 3, + "event_ordering": 3, + "perception": 3, + "spatial": 3, + "camera_transition": 3 + }, + "questions": [ + { + "id": "temporal_001", + "category": "temporal", + "question": "A 'person exits scene through structure' event was observed on camera G423, and a 'person picks up object' event was observed on camera G299. Which occurred first?", + "options": [ + "The 'person exits scene through structure' on G423 occurred first", + "The 'person picks up object' on G299 occurred first", + "They occurred simultaneously", + "Cannot be determined from the available footage" + ], + "correct_answer": 0, + "correct_option_text": "The 'person exits scene through structure' on G423 occurred first", + "cameras_required": [ + "G423", + "G299" + ], + "verification": { + "event_a": { + "activity": "person_exits_scene_through_structure", + "camera": "G423", + "start_sec": 161.93, + "end_sec": 163.2 + }, + "event_b": { + "activity": "person_picks_up_object", + "camera": "G299", + "start_sec": 169.6, + "end_sec": 170.07 + }, + "gap_sec": 6.4, + "relationship": "before" + } + }, + { + "id": "temporal_002", + "category": "temporal", + "question": "A 'person texts on phone' event was observed on camera G299, and a 'person picks up object' event was observed on camera G330. Which occurred first?", + "options": [ + "The 'person texts on phone' on G299 occurred first", + "The 'person picks up object' on G330 occurred first", + "They occurred simultaneously", + "Cannot be determined from the available footage" + ], + "correct_answer": 0, + "correct_option_text": "The 'person texts on phone' on G299 occurred first", + "cameras_required": [ + "G299", + "G330" + ], + "verification": { + "event_a": { + "activity": "person_texts_on_phone", + "camera": "G299", + "start_sec": 131.4, + "end_sec": 197.23 + }, + "event_b": { + "activity": "person_picks_up_object", + "camera": "G330", + "start_sec": 254.27, + "end_sec": 255.47 + }, + "gap_sec": 57.04, + "relationship": "before" + } + }, + { + "id": "temporal_003", + "category": "temporal", + "question": "A 'person puts down object' event was observed on camera G330, and a 'person sits down' event was observed on camera G299. Which occurred first?", + "options": [ + "The 'person puts down object' on G330 occurred first", + "The 'person sits down' on G299 occurred first", + "They occurred simultaneously", + "Cannot be determined from the available footage" + ], + "correct_answer": 0, + "correct_option_text": "The 'person puts down object' on G330 occurred first", + "cameras_required": [ + "G330", + "G299" + ], + "verification": { + "event_a": { + "activity": "person_puts_down_object", + "camera": "G330", + "start_sec": 6.97, + "end_sec": 9.27 + }, + "event_b": { + "activity": "person_sits_down", + "camera": "G299", + "start_sec": 51.4, + "end_sec": 52.37 + }, + "gap_sec": 42.13, + "relationship": "before" + } + }, + { + "id": "ordering_001", + "category": "event_ordering", + "question": "The following 4 events were observed across cameras G299, G330, G423, G639. What is the correct chronological order?", + "options": [ + "'person interacts with laptop' on camera G330 \u2192 'person sits down' on camera G299 \u2192 'vehicle turns left' on camera G639 \u2192 'person talks on phone' on camera G423", + "'person sits down' on camera G299 \u2192 'person interacts with laptop' on camera G330 \u2192 'vehicle turns left' on camera G639 \u2192 'person talks on phone' on camera G423", + "'person talks on phone' on camera G423 \u2192 'vehicle turns left' on camera G639 \u2192 'person sits down' on camera G299 \u2192 'person interacts with laptop' on camera G330", + "'vehicle turns left' on camera G639 \u2192 'person sits down' on camera G299 \u2192 'person talks on phone' on camera G423 \u2192 'person interacts with laptop' on camera G330" + ], + "correct_answer": 1, + "correct_option_text": "'person sits down' on camera G299 \u2192 'person interacts with laptop' on camera G330 \u2192 'vehicle turns left' on camera G639 \u2192 'person talks on phone' on camera G423", + "cameras_required": [ + "G299", + "G330", + "G423", + "G639" + ], + "verification": { + "events": [ + { + "activity": "person_sits_down", + "camera": "G299", + "start_sec": 2.83 + }, + { + "activity": "person_interacts_with_laptop", + "camera": "G330", + "start_sec": 105.27 + }, + { + "activity": "vehicle_turns_left", + "camera": "G639", + "start_sec": 176.13 + }, + { + "activity": "person_talks_on_phone", + "camera": "G423", + "start_sec": 250.43 + } + ], + "correct_order": "'person sits down' on camera G299 \u2192 'person interacts with laptop' on camera G330 \u2192 'vehicle turns left' on camera G639 \u2192 'person talks on phone' on camera G423" + } + }, + { + "id": "ordering_002", + "category": "event_ordering", + "question": "The following 4 events were observed across cameras G299, G421, G423, G639. What is the correct chronological order?", + "options": [ + "'person opens facility door' on camera G421 \u2192 'person interacts with laptop' on camera G299 \u2192 'person talks to person' on camera G639 \u2192 'person opens facility door' on camera G423", + "'person talks to person' on camera G639 \u2192 'person opens facility door' on camera G423 \u2192 'person interacts with laptop' on camera G299 \u2192 'person opens facility door' on camera G421", + "'person interacts with laptop' on camera G299 \u2192 'person opens facility door' on camera G423 \u2192 'person talks to person' on camera G639 \u2192 'person opens facility door' on camera G421", + "'person talks to person' on camera G639 \u2192 'person opens facility door' on camera G421 \u2192 'person opens facility door' on camera G423 \u2192 'person interacts with laptop' on camera G299" + ], + "correct_answer": 1, + "correct_option_text": "'person talks to person' on camera G639 \u2192 'person opens facility door' on camera G423 \u2192 'person interacts with laptop' on camera G299 \u2192 'person opens facility door' on camera G421", + "cameras_required": [ + "G299", + "G421", + "G423", + "G639" + ], + "verification": { + "events": [ + { + "activity": "person_talks_to_person", + "camera": "G639", + "start_sec": 78.0 + }, + { + "activity": "person_opens_facility_door", + "camera": "G423", + "start_sec": 89.13 + }, + { + "activity": "person_interacts_with_laptop", + "camera": "G299", + "start_sec": 121.83 + }, + { + "activity": "person_opens_facility_door", + "camera": "G421", + "start_sec": 150.63 + } + ], + "correct_order": "'person talks to person' on camera G639 \u2192 'person opens facility door' on camera G423 \u2192 'person interacts with laptop' on camera G299 \u2192 'person opens facility door' on camera G421" + } + }, + { + "id": "ordering_003", + "category": "event_ordering", + "question": "The following 4 events were observed across cameras G330, G423, G639. What is the correct chronological order?", + "options": [ + "'person exits scene through structure' on camera G423 \u2192 'person talks on phone' on camera G330 \u2192 'vehicle makes u turn' on camera G639 \u2192 'person talks on phone' on camera G423", + "'person talks on phone' on camera G330 \u2192 'person exits scene through structure' on camera G423 \u2192 'person talks on phone' on camera G423 \u2192 'vehicle makes u turn' on camera G639", + "'vehicle makes u turn' on camera G639 \u2192 'person talks on phone' on camera G423 \u2192 'person exits scene through structure' on camera G423 \u2192 'person talks on phone' on camera G330", + "'person exits scene through structure' on camera G423 \u2192 'person talks on phone' on camera G423 \u2192 'person talks on phone' on camera G330 \u2192 'vehicle makes u turn' on camera G639" + ], + "correct_answer": 0, + "correct_option_text": "'person exits scene through structure' on camera G423 \u2192 'person talks on phone' on camera G330 \u2192 'vehicle makes u turn' on camera G639 \u2192 'person talks on phone' on camera G423", + "cameras_required": [ + "G330", + "G423", + "G639" + ], + "verification": { + "events": [ + { + "activity": "person_exits_scene_through_structure", + "camera": "G423", + "start_sec": 14.03 + }, + { + "activity": "person_talks_on_phone", + "camera": "G330", + "start_sec": 44.07 + }, + { + "activity": "vehicle_makes_u_turn", + "camera": "G639", + "start_sec": 170.73 + }, + { + "activity": "person_talks_on_phone", + "camera": "G423", + "start_sec": 250.43 + } + ], + "correct_order": "'person exits scene through structure' on camera G423 \u2192 'person talks on phone' on camera G330 \u2192 'vehicle makes u turn' on camera G639 \u2192 'person talks on phone' on camera G423" + } + }, + { + "id": "perception_001", + "category": "perception", + "question": "A 'person enters scene through structure' event is visible on multiple cameras. Which camera does NOT capture this event?", + "options": [ + "G423", + "G330", + "G421", + "G638" + ], + "correct_answer": 1, + "correct_option_text": "G330", + "cameras_required": [ + "G299", + "G330", + "G421", + "G423", + "G638", + "G639" + ], + "verification": { + "activity": "person_enters_scene_through_structure", + "cameras_with_activity": [ + "G421", + "G423", + "G638" + ], + "cameras_without_activity": [ + "G299", + "G330", + "G639" + ], + "question_type": "which_not" + } + }, + { + "id": "perception_002", + "category": "perception", + "question": "Which camera captures a 'person closes vehicle door' event?", + "options": [ + "G330", + "G421", + "G299", + "G638" + ], + "correct_answer": 3, + "correct_option_text": "G638", + "cameras_required": [ + "G299", + "G330", + "G421", + "G423", + "G638", + "G639" + ], + "verification": { + "activity": "person_closes_vehicle_door", + "cameras_with_activity": [ + "G638" + ], + "cameras_without_activity": [ + "G299", + "G330", + "G421", + "G423", + "G639" + ], + "question_type": "which_camera" + } + }, + { + "id": "perception_003", + "category": "perception", + "question": "A 'person transfers object' event is visible on multiple cameras. Which camera does NOT capture this event?", + "options": [ + "G639", + "G421", + "G299" + ], + "correct_answer": 0, + "correct_option_text": "G639", + "cameras_required": [ + "G299", + "G330", + "G421", + "G423", + "G638", + "G639" + ], + "verification": { + "activity": "person_transfers_object", + "cameras_with_activity": [ + "G299", + "G421" + ], + "cameras_without_activity": [ + "G330", + "G423", + "G638", + "G639" + ], + "question_type": "which_not" + } + }, + { + "id": "spatial_001", + "category": "spatial", + "question": "A person is observed performing 'person exits scene through structure' on camera G639, and shortly after, a 'person enters scene through structure' event is seen on camera G638. How far apart are these two camera locations?", + "options": [ + "Short distance (under 30 meters)", + "Medium distance (30 to 80 meters)", + "Long distance (over 80 meters)", + "The cameras cover the same area" + ], + "correct_answer": 1, + "correct_option_text": "Medium distance (30 to 80 meters)", + "cameras_required": [ + "G639", + "G638" + ], + "verification": { + "exit_camera": "G639", + "entry_camera": "G638", + "distance_m": 42.9, + "gap_sec": 39.7, + "exit_pos_enu": [ + 48.17558648597479, + 104.21920104416111, + 11.047217116572668 + ], + "entry_pos_enu": [ + 10.107191556954286, + 84.78124449999711, + 7.551824990489353 + ], + "source": "krtd_camera_centers" + } + }, + { + "id": "spatial_002", + "category": "spatial", + "question": "Based on the camera network layout, which pair of cameras is positioned closest to each other?", + "options": [ + "G330 and G639 (113m apart)", + "G299 and G330 (23m apart)", + "G299 and G638 (100m apart)", + "G638 and G639 (43m apart)" + ], + "correct_answer": 1, + "correct_option_text": "G299 and G330 (23m apart)", + "cameras_required": [ + "G299", + "G330" + ], + "verification": { + "closest_pair": [ + "G299", + "G330" + ], + "distance_m": 23.3, + "all_distances": { + "G299-G330": 23.3, + "G638-G639": 42.9, + "G330-G638": 77.7, + "G299-G638": 99.9, + "G330-G639": 112.9, + "G299-G639": 132.6 + }, + "source": "krtd_camera_centers" + } + }, + { + "id": "spatial_003", + "category": "spatial", + "question": "Which pair of cameras monitors the most distant locations from each other?", + "options": [ + "G638 and G639 (43m apart)", + "G299 and G330 (23m apart)", + "G299 and G639 (133m apart)", + "G330 and G638 (78m apart)" + ], + "correct_answer": 2, + "correct_option_text": "G299 and G639 (133m apart)", + "cameras_required": [ + "G299", + "G639" + ], + "verification": { + "farthest_pair": [ + "G299", + "G639" + ], + "distance_m": 132.6, + "source": "krtd_camera_centers" + } + }, + { + "id": "camtrans_001", + "category": "camera_transition", + "question": "A person enters the scene through a structure on multiple cameras. Which camera captures this happening first?", + "options": [ + "G330", + "G639", + "G423", + "G421", + "G638" + ], + "correct_answer": 2, + "correct_option_text": "G423", + "cameras_required": [ + "G423", + "G421", + "G638" + ], + "verification": { + "first_camera": "G423", + "first_time_sec": 0.13, + "event_type": "entry", + "all_cameras_with_event": { + "G423": { + "time_sec": 0.13, + "activity": "person_enters_scene_through_structure" + }, + "G421": { + "time_sec": 3.67, + "activity": "person_enters_scene_through_structure" + }, + "G638": { + "time_sec": 153.13, + "activity": "person_enters_scene_through_structure" + } + } + } + }, + { + "id": "camtrans_002", + "category": "camera_transition", + "question": "Multiple cameras capture a person entering the scene. Which camera records this event latest?", + "options": [ + "G330", + "G639", + "G423", + "G421", + "G638" + ], + "correct_answer": 4, + "correct_option_text": "G638", + "cameras_required": [ + "G423", + "G421", + "G638" + ], + "verification": { + "last_camera": "G638", + "last_time_sec": 153.13, + "event_type": "entry", + "all_cameras_with_event": { + "G423": { + "time_sec": 0.13, + "activity": "person_enters_scene_through_structure" + }, + "G421": { + "time_sec": 3.67, + "activity": "person_enters_scene_through_structure" + }, + "G638": { + "time_sec": 153.13, + "activity": "person_enters_scene_through_structure" + } + } + } + }, + { + "id": "camtrans_003", + "category": "camera_transition", + "question": "After a person is observed performing 'person exits scene through structure' on camera G639, which camera next captures a person entering the scene?", + "options": [ + "G421", + "G330", + "G299", + "G638", + "G423" + ], + "correct_answer": 4, + "correct_option_text": "G423", + "cameras_required": [ + "G639", + "G423" + ], + "verification": { + "exit_camera": "G639", + "exit_time_sec": 131.73, + "exit_activity": "person_exits_scene_through_structure", + "entry_camera": "G423", + "entry_time_sec": 132.03, + "entry_activity": "person_enters_scene_through_structure", + "gap_sec": 0.3 + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/data_old/2018-03-11.11-25.school.final.raw.json b/meva/examples/qa_pairs/archived/data_old/2018-03-11.11-25.school.final.raw.json new file mode 100644 index 0000000..3da34db --- /dev/null +++ b/meva/examples/qa_pairs/archived/data_old/2018-03-11.11-25.school.final.raw.json @@ -0,0 +1,1727 @@ +{ + "slot": "2018-03-11.11-25.school", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo+geom_color", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 53, + "mevid_person_ids": [ + "0003", + "0004", + "0007", + "0008", + "0009", + "0014", + "0016", + "0018", + "0023", + "0024", + "0025", + "0030", + "0032", + "0033", + "0034", + "0035", + "0036", + "0037", + "0039", + "0040", + "0041", + "0042", + "0043", + "0049", + "0051", + "0054", + "0055", + "0056", + "0057", + "0062", + "0064", + "0067", + "0068", + "0072", + "0074", + "0076", + "0077", + "0078", + "0081", + "0082", + "0083", + "0085", + "0086", + "0088", + "0090", + "0092", + "0095", + "0096", + "0097", + "0099", + "0101", + "0103", + "0109" + ], + "mevid_person_cameras": { + "0003": [ + "G328", + "G339", + "G421", + "G424" + ], + "0004": [ + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0007": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0008": [ + "G328", + "G339", + "G421", + "G424" + ], + "0009": [ + "G300", + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0014": [ + "G300", + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0016": [ + "G328", + "G339", + "G424" + ], + "0018": [ + "G300", + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0023": [ + "G339", + "G424" + ], + "0024": [ + "G328", + "G339", + "G424" + ], + "0025": [ + "G328", + "G339", + "G421", + "G424" + ], + "0030": [ + "G328", + "G339", + "G421", + "G424" + ], + "0032": [ + "G328", + "G339", + "G421", + "G424" + ], + "0033": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0034": [ + "G300", + "G339", + "G424" + ], + "0035": [ + "G328", + "G339", + "G421", + "G424" + ], + "0036": [ + "G328", + "G336", + "G339", + "G421" + ], + "0037": [ + "G328", + "G339", + "G421", + "G424" + ], + "0039": [ + "G328", + "G339", + "G421", + "G424" + ], + "0040": [ + "G328", + "G339", + "G421", + "G424" + ], + "0041": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0042": [ + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0043": [ + "G339", + "G421", + "G424" + ], + "0049": [ + "G328", + "G339", + "G421", + "G424" + ], + "0051": [ + "G336", + "G339", + "G421", + "G424" + ], + "0054": [ + "G339", + "G421", + "G424" + ], + "0055": [ + "G300", + "G328", + "G339", + "G424" + ], + "0056": [ + "G300", + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0057": [ + "G300", + "G328", + "G339", + "G424" + ], + "0062": [ + "G328", + "G339", + "G421" + ], + "0064": [ + "G339", + "G421", + "G424" + ], + "0067": [ + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0068": [ + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0072": [ + "G328", + "G339", + "G421", + "G424" + ], + "0074": [ + "G328", + "G339", + "G421", + "G424" + ], + "0076": [ + "G328", + "G339", + "G421", + "G424" + ], + "0077": [ + "G328", + "G339", + "G421", + "G424" + ], + "0078": [ + "G300", + "G339", + "G424" + ], + "0081": [ + "G328", + "G339", + "G424" + ], + "0082": [ + "G300", + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0083": [ + "G300", + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0085": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0086": [ + "G328", + "G339", + "G421", + "G424" + ], + "0088": [ + "G328", + "G339", + "G421" + ], + "0090": [ + "G300", + "G336", + "G339", + "G421", + "G424" + ], + "0092": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0095": [ + "G328", + "G339", + "G424" + ], + "0096": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0097": [ + "G328", + "G339", + "G421", + "G424" + ], + "0099": [ + "G328", + "G336", + "G339", + "G421", + "G424" + ], + "0101": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0103": [ + "G300", + "G328", + "G339", + "G421", + "G424" + ], + "0109": [ + "G339", + "G424" + ] + }, + "total_events": 793, + "total_entities": 106, + "cross_camera_clusters": 8, + "total_questions": 14, + "category_counts": { + "temporal": 2, + "event_ordering": 2, + "perception": 2, + "spatial": 3, + "summarization": 1, + "counting": 1, + "best_camera": 3 + }, + "stats": { + "entities_with_mevid_descriptions": 11, + "entities_with_geom_descriptions": 74, + "entities_with_fallback_descriptions": 0, + "attribute_verification_questions": 1, + "best_camera_questions": 3, + "questions_with_debug_info": 14 + }, + "validation_issues": 4, + "generation_time_sec": 6.44, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a vehicle exits vehicle and a person in blue top and blue pants closes vehicle door -- which occurred first?", + "options": [ + "The vehicle exits vehicle occurred first", + "The person in blue top and blue pants closes vehicle door occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The vehicle exits vehicle occurred first", + "requires_cameras": [ + "G339", + "G424" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_exits_vehicle", + "camera": "G339", + "start_sec": 127.13, + "end_sec": 133.43, + "actor_ids": [ + 6, + 5 + ], + "description": "a vehicle exits vehicle" + }, + "event_b": { + "activity": "person_closes_vehicle_door", + "camera": "G424", + "start_sec": 135.47, + "end_sec": 136.77, + "actor_ids": [ + 5201989233169014817, + 8846568345307456830 + ], + "description": "a person in blue top and blue pants closes vehicle door" + }, + "gap_sec": 2.04, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G339", + "activity": "person_exits_vehicle", + "actor_ids": [ + 6, + 5 + ], + "frame_range": [ + 3814, + 4003 + ], + "timestamp": "127.13-133.43s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "entity_description": "a vehicle" + }, + "event_b": { + "camera": "G424", + "activity": "person_closes_vehicle_door", + "actor_ids": [ + 5201989233169014817, + 8846568345307456830 + ], + "frame_range": [ + 4064, + 4103 + ], + "timestamp": "135.47-136.77s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + "gap_sec": 2.04, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 7.0, + "relationship": "causal", + "cluster_id": "cluster_3", + "mevid_validated": true, + "mevid_person_id": 103 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person in yellow top and blue pants texts on phone and a vehicle opens vehicle door -- which occurred first?", + "options": [ + "The person in yellow top and blue pants texts on phone occurred first", + "The vehicle opens vehicle door occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The vehicle opens vehicle door occurred first", + "requires_cameras": [ + "G339", + "G424" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_vehicle_door", + "camera": "G339", + "start_sec": 127.13, + "end_sec": 131.03, + "actor_ids": [ + 3, + 4 + ], + "description": "a vehicle opens vehicle door" + }, + "event_b": { + "activity": "person_texts_on_phone", + "camera": "G424", + "start_sec": 132.53, + "end_sec": 143.3, + "actor_ids": [ + 8280409664574006212 + ], + "description": "a person in yellow top and blue pants texts on phone" + }, + "gap_sec": 1.5, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G339", + "activity": "person_opens_vehicle_door", + "actor_ids": [ + 3, + 4 + ], + "frame_range": [ + 3814, + 3931 + ], + "timestamp": "127.13-131.03s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "entity_description": "a vehicle" + }, + "event_b": { + "camera": "G424", + "activity": "person_texts_on_phone", + "actor_ids": [ + 8280409664574006212 + ], + "frame_range": [ + 3976, + 4299 + ], + "timestamp": "132.53-143.30s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "entity_description": "a person in yellow top and blue pants" + }, + "gap_sec": 1.5, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_2", + "mevid_validated": true, + "mevid_person_id": 103 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_003", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. a person in a blue top and blue pants, leavesing the camera's view through a doorway on camera G639\nII. a person in a blue top and orange pants, opening a facility door on camera G638\nIII. Someone leavesing the camera's view through a doorway on camera G423\nIV. Someone talking to person on camera G330\nWhich is the correct chronological order?", + "options": [ + "II -> IV -> I -> III", + "III -> IV -> I -> II", + "III -> I -> IV -> II", + "I -> III -> IV -> II" + ], + "correct_answer_index": 2, + "correct_answer": "III -> I -> IV -> II", + "requires_cameras": [ + "G330", + "G423", + "G638", + "G639" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_exits_scene_through_structure", + "camera": "G423", + "start_sec": 209.4, + "description": "Someone leavesing the camera's view through a doorway on camera G423" + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G639", + "start_sec": 213.87, + "description": "a person in a blue top and blue pants, leavesing the camera's view through a doorway on camera G639" + }, + { + "activity": "person_talks_to_person", + "camera": "G330", + "start_sec": 217.33, + "description": "Someone talking to person on camera G330" + }, + { + "activity": "person_opens_facility_door", + "camera": "G638", + "start_sec": 233.27, + "description": "a person in a blue top and orange pants, opening a facility door on camera G638" + } + ], + "min_gap_sec": 2.14, + "num_events": 4, + "gaps_sec": [ + 3.0, + 2.16, + 2.14 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G423", + "activity": "person_exits_scene_through_structure", + "actor_ids": [ + 82 + ], + "frame_range": [ + 6282, + 6326 + ], + "timestamp": "209.40-210.87s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G423.r13.mp4", + "entity_description": null + }, + { + "camera": "G639", + "activity": "person_exits_scene_through_structure", + "actor_ids": [ + 40 + ], + "frame_range": [ + 6416, + 6455 + ], + "timestamp": "213.87-215.17s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G330", + "activity": "person_talks_to_person", + "actor_ids": [ + 723295422925972174, + 6658804058234692447 + ], + "frame_range": [ + 6520, + 6934 + ], + "timestamp": "217.33-231.13s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "entity_description": null + }, + { + "camera": "G638", + "activity": "person_opens_facility_door", + "actor_ids": [ + 659456282287022116 + ], + "frame_range": [ + 6998, + 7033 + ], + "timestamp": "233.27-234.43s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "entity_description": "a person in a blue top and orange pants" + } + ], + "presentation_order": [ + 1, + 3, + 0, + 2 + ], + "correct_permutation": [ + 2, + 0, + 3, + 1 + ], + "mevid_validated": true, + "group_score": 14.5, + "related_pairs": 2, + "described_events": 2 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G423.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_004", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. Someone opening a facility door on camera G420\nII. a person in a blue top and blue pants, leavesing the camera's view through a doorway on camera G639\nIII. a person in a blue top and blue pants, entering the camera's view through a doorway on camera G419\nIV. a vehicle, turning left on camera G336\nWhich is the correct chronological order?", + "options": [ + "IV -> II -> III -> I", + "III -> I -> II -> IV", + "I -> II -> III -> IV", + "I -> III -> II -> IV" + ], + "correct_answer_index": 3, + "correct_answer": "I -> III -> II -> IV", + "requires_cameras": [ + "G336", + "G419", + "G420", + "G639" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_opens_facility_door", + "camera": "G420", + "start_sec": 249.67, + "description": "Someone opening a facility door on camera G420" + }, + { + "activity": "person_enters_scene_through_structure", + "camera": "G419", + "start_sec": 255.73, + "description": "a person in a blue top and blue pants, entering the camera's view through a doorway on camera G419" + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G639", + "start_sec": 260.77, + "description": "a person in a blue top and blue pants, leavesing the camera's view through a doorway on camera G639" + }, + { + "activity": "vehicle_turns_left", + "camera": "G336", + "start_sec": 264.5, + "description": "a vehicle, turning left on camera G336" + } + ], + "min_gap_sec": 2.04, + "num_events": 4, + "gaps_sec": [ + 2.3, + 2.04, + 2.6 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G420", + "activity": "person_opens_facility_door", + "actor_ids": [ + 4200179588053278893 + ], + "frame_range": [ + 7490, + 7603 + ], + "timestamp": "249.67-253.43s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "entity_description": null + }, + { + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 27 + ], + "frame_range": [ + 7672, + 7762 + ], + "timestamp": "255.73-258.73s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G639", + "activity": "person_exits_scene_through_structure", + "actor_ids": [ + 52 + ], + "frame_range": [ + 7823, + 7857 + ], + "timestamp": "260.77-261.90s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G336", + "activity": "vehicle_turns_left", + "actor_ids": [ + 3608369810879194368 + ], + "frame_range": [ + 7935, + 8112 + ], + "timestamp": "264.50-270.40s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G336.r13.mp4", + "entity_description": "a vehicle" + } + ], + "presentation_order": [ + 0, + 2, + 1, + 3 + ], + "correct_permutation": [ + 0, + 2, + 1, + 3 + ], + "mevid_validated": true, + "group_score": 14.5, + "related_pairs": 2, + "described_events": 2 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + ] + }, + { + "question_id": "final_perception_005", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures someone opening a vehicle door?", + "options": [ + "Camera G420", + "Camera G339", + "Camera G336", + "Camera G299" + ], + "correct_answer_index": 2, + "correct_answer": "Camera G336", + "requires_cameras": [ + "G336", + "G339", + "G424" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_opens_vehicle_door", + "activity_alias": "opens vehicle door", + "cameras_with_activity": [ + "G336", + "G339", + "G424" + ], + "total_cameras_in_slot": 13 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_opens_vehicle_door", + "activity_alias": "opens vehicle door", + "correct_camera": "G336", + "cameras_with_activity": [ + "G336", + "G339", + "G424" + ], + "representative_event": { + "camera": "G424", + "frame_range": [ + 3799, + 3921 + ], + "timestamp": "126.63-130.70s", + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4" + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_perception_006", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": "A person is visible on camera G339. What color are they wearing on their lower body?", + "options": [ + "White", + "Blue", + "Brown", + "Green" + ], + "correct_answer_index": 1, + "correct_answer": "Blue", + "requires_cameras": [ + "G339" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": "0043", + "attribute_type": "lower_color", + "correct_value": "blue", + "camera": "G339", + "person_description": "a person in blue top and blue pants" + }, + "debug_info": { + "question_type": "attribute_verification", + "mevid_person_id": "0043", + "camera": "G339", + "person_description": "a person in blue top and blue pants", + "gpt_description": null, + "attribute_type": "lower_color", + "correct_value": "blue", + "all_cameras_for_person": [ + "G339", + "G421", + "G424" + ], + "source": "MEVID YOLO+GPT person database" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4" + ] + }, + { + "question_id": "final_spatial_007", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person in a black top and blue pants and a person in a blue top and blue pants in the scene visible on camera G639?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G639" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G639_actor_14", + "entity_b": "G639_actor_54", + "entity_a_desc": "a person in a black top and blue pants", + "entity_b_desc": "a person in a blue top and blue pants", + "world_pos_a_enu": [ + -16.237655041882192, + 73.7728091840258, + 0.0 + ], + "world_pos_b_enu": [ + -17.31772873044686, + 76.52952956593454, + 0.0 + ], + "distance_meters": 2.96, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G639_actor_14", + "camera": "G639", + "description": "a person in a black top and blue pants", + "bbox": [ + 492, + 1402, + 629, + 1626 + ], + "frame": 962, + "timestamp": "31.47-32.70s", + "world_pos_enu": [ + -16.237655041882192, + 73.7728091840258, + 0.0 + ], + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + }, + "entity_b": { + "entity_id": "G639_actor_54", + "camera": "G639", + "description": "a person in a blue top and blue pants", + "bbox": [ + 505, + 1327, + 615, + 1535 + ], + "frame": 7852, + "timestamp": "261.13-262.33s", + "world_pos_enu": [ + -17.31772873044686, + 76.52952956593454, + 0.0 + ], + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + }, + "distance_meters": 2.96, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + ] + }, + { + "question_id": "final_spatial_008", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person in a blue top and black pants and a person in a blue top and blue pants in the scene visible on camera G639?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G639" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G639_actor_16", + "entity_b": "G639_actor_26", + "entity_a_desc": "a person in a blue top and black pants", + "entity_b_desc": "a person in a blue top and blue pants", + "world_pos_a_enu": [ + -12.163436775570098, + 77.21302832513894, + 0.0 + ], + "world_pos_b_enu": [ + -9.18130929885087, + 81.96241734258736, + 0.0 + ], + "distance_meters": 5.61, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G639_actor_16", + "camera": "G639", + "description": "a person in a blue top and black pants", + "bbox": [ + 504, + 1349, + 644, + 1572 + ], + "frame": 951, + "timestamp": "31.13-32.30s", + "world_pos_enu": [ + -12.163436775570098, + 77.21302832513894, + 0.0 + ], + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + }, + "entity_b": { + "entity_id": "G639_actor_26", + "camera": "G639", + "description": "a person in a blue top and blue pants", + "bbox": [ + 521, + 1234, + 652, + 1459 + ], + "frame": 2608, + "timestamp": "86.33-87.57s", + "world_pos_enu": [ + -9.18130929885087, + 81.96241734258736, + 0.0 + ], + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + }, + "distance_meters": 5.61, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + ] + }, + { + "question_id": "final_spatial_009", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person in a blue top and black pants and a person in a blue top and blue pants in the scene visible on camera G639?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G639" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G639_actor_18", + "entity_b": "G639_actor_42", + "entity_a_desc": "a person in a blue top and black pants", + "entity_b_desc": "a person in a blue top and blue pants", + "world_pos_a_enu": [ + -14.653658724176076, + 76.2833075392499, + 0.0 + ], + "world_pos_b_enu": [ + 21.436220345908477, + 92.24182457532433, + 0.0 + ], + "distance_meters": 39.46, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G639_actor_18", + "camera": "G639", + "description": "a person in a blue top and black pants", + "bbox": [ + 511, + 1384, + 622, + 1571 + ], + "frame": 1144, + "timestamp": "37.53-38.77s", + "world_pos_enu": [ + -14.653658724176076, + 76.2833075392499, + 0.0 + ], + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + }, + "entity_b": { + "entity_id": "G639_actor_42", + "camera": "G639", + "description": "a person in a blue top and blue pants", + "bbox": [ + 724, + 1261, + 839, + 1452 + ], + "frame": 6688, + "timestamp": "220.37-225.53s", + "world_pos_enu": [ + 21.436220345908477, + 92.24182457532433, + 0.0 + ], + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + }, + "distance_meters": 39.46, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4" + ] + }, + { + "question_id": "final_summarization_010", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 13 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 10 cameras", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 13 cameras, primarily featuring putting down object" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 13 cameras, primarily featuring putting down object", + "requires_cameras": [ + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 793, + "num_cameras": 13, + "person_events": 726, + "vehicle_events": 21, + "scene_type": "pedestrian-dominant", + "top_activity": "person_puts_down_object", + "top_activity_count": 115 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 793, + "unique_activities": 23, + "activity_counts": { + "hand_interacts_with_person": 45, + "person_carries_heavy_object": 90, + "person_talks_to_person": 71, + "person_transfers_object": 14, + "empty_37": 1, + "person_opens_facility_door": 30, + "person_enters_scene_through_structure": 109, + "person_exits_scene_through_structure": 72, + "person_picks_up_object": 104, + "person_puts_down_object": 115, + "person_sits_down": 53, + "person_texts_on_phone": 22, + "vehicle_turns_right": 5, + "vehicle_turns_left": 9, + "person_stands_up": 19, + "vehicle_stops": 5, + "person_opens_vehicle_door": 4, + "person_exits_vehicle": 3, + "person_closes_vehicle_door": 3, + "person_embraces_person": 11, + "person_talks_on_phone": 5, + "person_closes_facility_door": 1, + "vehicle_starts": 2 + }, + "camera_event_counts": { + "G299": 277, + "G300": 1, + "G330": 274, + "G419": 36, + "G423": 81, + "G638": 8, + "G421": 9, + "G420": 35, + "G639": 28, + "G328": 13, + "G424": 17, + "G336": 8, + "G339": 6 + }, + "num_cameras": 13, + "duration_sec": 300.3, + "person_event_count": 726, + "vehicle_event_count": 21, + "cross_camera_activities": { + "hand_interacts_with_person": [ + "G299", + "G330" + ], + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G339", + "G419", + "G420", + "G423", + "G639" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G638" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423", + "G638" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G421", + "G423", + "G638", + "G639" + ], + "person_picks_up_object": [ + "G299", + "G330" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G423", + "G424" + ], + "vehicle_turns_right": [ + "G339", + "G424", + "G638" + ], + "vehicle_turns_left": [ + "G336", + "G339", + "G424" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "vehicle_stops": [ + "G336", + "G339", + "G424", + "G638" + ], + "person_opens_vehicle_door": [ + "G336", + "G339", + "G424" + ], + "person_exits_vehicle": [ + "G336", + "G339", + "G424" + ], + "person_closes_vehicle_door": [ + "G336", + "G424" + ], + "person_embraces_person": [ + "G299", + "G330" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G423" + ] + }, + "busiest_camera": "G299", + "busiest_camera_count": 277, + "unique_entities": 106, + "person_entities": 85, + "top_3_activities": [ + [ + "person_puts_down_object", + 115 + ], + [ + "person_enters_scene_through_structure", + 109 + ], + [ + "person_picks_up_object", + 104 + ] + ] + }, + "scene_description": "The scene spans 13 cameras with 793 total activity events. The most frequent activities are: puts down object (115 occurrences), enters the camera's view through a doorway (109 occurrences), picks up object (104 occurrences). 20 activities occur on multiple cameras. Camera G299 is the most active with 277 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G328.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G336.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G423.r13.mp4", + "2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4", + "2018-03-11.11-25-04.11-30-04.school.G300.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + 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+ "G299", + "G330" + ], + "activity": "person_transfers_object", + "event_ids": [ + "G299_evt_110", + "G330_evt_2247872337575452878", + "G299_evt_326", + "G330_evt_8916600349413254481", + "G299_evt_254", + "G330_evt_1251013991494886512", + "G299_evt_299", + "G299_evt_332", + "G330_evt_8969328509905970257", + "G330_evt_3385790644333389152", + "G299_evt_169", + "G330_evt_6191069156024034123", + "G330_evt_7952330424198604660", + "G330_evt_5506020564311122608" + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 8, + "cameras_involved": [ + "G299", + "G330" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 12, + "slot": "2018-03-11.11-25.school", + "activity": "person_transfers_object", + "activity_human": "transfers object", + "clip_files": [ + "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_012", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G299", + "Camera G300", + "Camera G420", + "Camera G419" + ], + "correct_answer": "Camera G420", + "correct_answer_index": 2, + "requires_cameras": [ + "G420", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G420", + "entrance_time_sec": 52.23, + "entity_description": "a person", + "cluster_id": "standalone_G420_5673662805350753390", + "all_entrance_cameras": { + "G420": 52.23 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "start_sec": 52.23 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_013", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G336", + "Camera G299", + "Camera G638", + "Camera G420" + ], + "correct_answer": "Camera G420", + "correct_answer_index": 3, + "requires_cameras": [ + "G420", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G420", + "entrance_time_sec": 19.9, + "entity_description": "a person", + "cluster_id": "standalone_G420_1870818973104284844", + "all_entrance_cameras": { + "G420": 19.9 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "start_sec": 19.9 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_014", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G423", + "Camera G638", + "Camera G336", + "Camera G420" + ], + "correct_answer": "Camera G420", + "correct_answer_index": 3, + "requires_cameras": [ + "G420", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G420", + "entrance_time_sec": 267.33, + "entity_description": "a person", + "cluster_id": "standalone_G420_2397669447448174916", + "all_entrance_cameras": { + "G420": 267.33 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "start_sec": 267.33 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/data_old/2018-03-13.16-20.school.final.raw.json b/meva/examples/qa_pairs/archived/data_old/2018-03-13.16-20.school.final.raw.json new file mode 100644 index 0000000..fea00d6 --- /dev/null +++ b/meva/examples/qa_pairs/archived/data_old/2018-03-13.16-20.school.final.raw.json @@ -0,0 +1,1660 @@ +{ + "slot": "2018-03-13.16-20.school", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo+geom_color", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G328", + "G336", + "G339", + "G419", + "G420", + "G421", + "G424", + "G638", + "G639" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 50, + "mevid_person_ids": [ + "0003", + "0004", + "0007", + "0008", + "0009", + "0014", + "0016", + "0018", + "0023", + "0024", + "0025", + "0030", + "0032", + "0033", + "0034", + "0035", + "0036", + "0037", + "0039", + "0040", + "0041", + "0042", + "0043", + "0049", + 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"category_counts": { + "temporal": 2, + "event_ordering": 2, + "perception": 2, + "spatial": 3, + "summarization": 1, + "counting": 1, + "best_camera": 3 + }, + "stats": { + "entities_with_mevid_descriptions": 93, + "entities_with_geom_descriptions": 410, + "entities_with_fallback_descriptions": 0, + "attribute_verification_questions": 1, + "best_camera_questions": 3, + "questions_with_debug_info": 14 + }, + "validation_issues": 3, + "generation_time_sec": 5.79, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person in blue top and blue pants talks to person and a person in a blue top and blue pants interacts with person -- which occurred first?", + "options": [ + "The person in blue top and blue pants talks to person occurred first", + "The person in a blue top and blue pants interacts with person occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The person in blue top and blue pants talks to person occurred first", + "requires_cameras": [ + "G336", + "G424" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_talks_to_person", + "camera": "G424", + "start_sec": 0.0, + "end_sec": 23.13, + "actor_ids": [ + 2, + 1 + ], + "description": "a person in blue top and blue pants talks to person" + }, + "event_b": { + "activity": "hand_interacts_with_person", + "camera": "G336", + "start_sec": 24.23, + "end_sec": 26.3, + "actor_ids": [ + 194, + 195 + ], + "description": "a person in a blue top and blue pants interacts with person" + }, + "gap_sec": 1.1, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G424", + "activity": "person_talks_to_person", + "actor_ids": [ + 2, + 1 + ], + "frame_range": [ + 0, + 694 + ], + "timestamp": "0.00-23.13s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + "event_b": { + "camera": "G336", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 194, + 195 + ], + "frame_range": [ + 727, + 789 + ], + "timestamp": "24.23-26.30s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + "gap_sec": 1.1, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_1", + "mevid_validated": true, + "mevid_person_id": 14 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person in blue top and blue pants leaves the camera's view through a doorway and a person in a green top and green pants talks on phone -- which occurred first?", + "options": [ + "The person in blue top and blue pants leaves the camera's view through a doorway occurred first", + "The person in a green top and green pants talks on phone occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The person in a green top and green pants talks on phone occurred first", + "requires_cameras": [ + "G336", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_talks_on_phone", + "camera": "G336", + "start_sec": 172.67, + "end_sec": 189.4, + "actor_ids": [ + 177 + ], + "description": "a person in a green top and green pants talks on phone" + }, + "event_b": { + "activity": "person_exits_scene_through_structure", + "camera": "G638", + "start_sec": 191.2, + "end_sec": 192.87, + "actor_ids": [ + 38 + ], + "description": "a person in blue top and blue pants leaves the camera's view through a doorway" + }, + "gap_sec": 1.8, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G336", + "activity": "person_talks_on_phone", + "actor_ids": [ + 177 + ], + "frame_range": [ + 5180, + 5682 + ], + "timestamp": "172.67-189.40s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "entity_description": "a person in a green top and green pants" + }, + "event_b": { + "camera": "G638", + "activity": "person_exits_scene_through_structure", + "actor_ids": [ + 38 + ], + "frame_range": [ + 5736, + 5786 + ], + "timestamp": "191.20-192.87s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + "gap_sec": 1.8, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_6", + "mevid_validated": true, + "mevid_person_id": 14 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_003", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. a person in blue top and blue pants, entering the camera's view through a doorway on camera G638\nII. a person in a blue top and green pants, embracing a person on camera G336\nIII. a person in a blue top and blue pants, talking to person on camera G336\nIV. a person in blue top and blue pants, talking to person on camera G424\nWhich is the correct chronological order?", + "options": [ + "II -> IV -> I -> III", + "III -> IV -> I -> II", + "III -> I -> IV -> II", + "I -> III -> IV -> II" + ], + "correct_answer_index": 2, + "correct_answer": "III -> I -> IV -> II", + "requires_cameras": [ + "G336", + "G424", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_talks_to_person", + "camera": "G336", + "start_sec": 56.33, + "description": "a person in a blue top and blue pants, talking to person on camera G336" + }, + { + "activity": "person_enters_scene_through_structure", + "camera": "G638", + "start_sec": 67.3, + "description": "a person in blue top and blue pants, entering the camera's view through a doorway on camera G638" + }, + { + "activity": "person_talks_to_person", + "camera": "G424", + "start_sec": 75.0, + "description": "a person in blue top and blue pants, talking to person on camera G424" + }, + { + "activity": "person_embraces_person", + "camera": "G336", + "start_sec": 118.87, + "description": "a person in a blue top and green pants, embracing a person on camera G336" + } + ], + "min_gap_sec": 3.83, + "num_events": 4, + "gaps_sec": [ + 3.83, + 6.7, + 4.64 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G336", + "activity": "person_talks_to_person", + "actor_ids": [ + 101, + 102 + ], + "frame_range": [ + 1690, + 1904 + ], + "timestamp": "56.33-63.47s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G638", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 3 + ], + "frame_range": [ + 2019, + 2049 + ], + "timestamp": "67.30-68.30s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + { + "camera": "G424", + "activity": "person_talks_to_person", + "actor_ids": [ + 14, + 13, + 12 + ], + "frame_range": [ + 2250, + 3427 + ], + "timestamp": "75.00-114.23s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + { + "camera": "G336", + "activity": "person_embraces_person", + "actor_ids": [ + 241, + 240 + ], + "frame_range": [ + 3566, + 3698 + ], + "timestamp": "118.87-123.27s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "entity_description": "a person in a blue top and green pants" + } + ], + "presentation_order": [ + 1, + 3, + 0, + 2 + ], + "correct_permutation": [ + 2, + 0, + 3, + 1 + ], + "mevid_validated": true, + "group_score": 14.5, + "related_pairs": 2, + "described_events": 4 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_004", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. a person in a blue top and blue pants, texting on phone on camera G638\nII. a person in a blue top and blue pants, texting on phone on camera G421\nIII. a person in a blue top and blue pants, talking on phone on camera G638\nIV. a person in blue top and blue pants, talking to person on camera G424\nWhich is the correct chronological order?", + "options": [ + "IV -> II -> III -> I", + "III -> I -> II -> IV", + "I -> II -> III -> IV", + "I -> III -> II -> IV" + ], + "correct_answer_index": 3, + "correct_answer": "I -> III -> II -> IV", + "requires_cameras": [ + "G421", + "G424", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_texts_on_phone", + "camera": "G638", + "start_sec": 213.13, + "description": "a person in a blue top and blue pants, texting on phone on camera G638" + }, + { + "activity": "person_talks_on_phone", + "camera": "G638", + "start_sec": 230.27, + "description": "a person in a blue top and blue pants, talking on phone on camera G638" + }, + { + "activity": "person_texts_on_phone", + "camera": "G421", + "start_sec": 264.8, + "description": "a person in a blue top and blue pants, texting on phone on camera G421" + }, + { + "activity": "person_talks_to_person", + "camera": "G424", + "start_sec": 277.13, + "description": "a person in blue top and blue pants, talking to person on camera G424" + } + ], + "min_gap_sec": 2.1, + "num_events": 4, + "gaps_sec": [ + 2.1, + 4.27, + 2.1 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G638", + "activity": "person_texts_on_phone", + "actor_ids": [ + 111 + ], + "frame_range": [ + 6394, + 6845 + ], + "timestamp": "213.13-228.17s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G638", + "activity": "person_talks_on_phone", + "actor_ids": [ + 112 + ], + "frame_range": [ + 6908, + 7816 + ], + "timestamp": "230.27-260.53s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G421", + "activity": "person_texts_on_phone", + "actor_ids": [ + 98 + ], + "frame_range": [ + 7944, + 8251 + ], + "timestamp": "264.80-275.03s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + { + "camera": "G424", + "activity": "person_talks_to_person", + "actor_ids": [ + 45, + 46 + ], + "frame_range": [ + 8314, + 8923 + ], + "timestamp": "277.13-297.43s", + "fps": 30.0, + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4", + "entity_description": "a person in blue top and blue pants" + } + ], + "presentation_order": [ + 0, + 2, + 1, + 3 + ], + "correct_permutation": [ + 0, + 2, + 1, + 3 + ], + "mevid_validated": true, + "group_score": 14.5, + "related_pairs": 2, + "described_events": 4 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_perception_005", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures someone transferring a object?", + "options": [ + "Camera G328", + "Camera G336", + "Camera G420", + "Camera G639" + ], + "correct_answer_index": 1, + "correct_answer": "Camera G336", + "requires_cameras": [ + "G336", + "G421", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_transfers_object", + "activity_alias": "transfers object", + "cameras_with_activity": [ + "G336", + "G421", + "G638" + ], + "total_cameras_in_slot": 9 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_transfers_object", + "activity_alias": "transfers object", + "correct_camera": "G336", + "cameras_with_activity": [ + "G336", + "G421", + "G638" + ], + "representative_event": { + "camera": "G336", + "frame_range": [ + 3290, + 3361 + ], + "timestamp": "109.67-112.03s", + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4" + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4" + ] + }, + { + "question_id": "final_perception_006", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": "A person is visible on camera G339. What color is their upper body clothing?", + "options": [ + "Black", + "Blue", + "Navy", + "Brown" + ], + "correct_answer_index": 1, + "correct_answer": "Blue", + "requires_cameras": [ + "G339" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": "0078", + "attribute_type": "upper_color", + "correct_value": "blue", + "camera": "G339", + "person_description": "a person in blue top and blue pants" + }, + "debug_info": { + "question_type": "attribute_verification", + "mevid_person_id": "0078", + "camera": "G339", + "person_description": "a person in blue top and blue pants", + "gpt_description": null, + "attribute_type": "upper_color", + "correct_value": "blue", + "all_cameras_for_person": [ + "G339", + "G424" + ], + "source": "MEVID YOLO+GPT person database" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-05.16-25-05.school.G339.r13.mp4" + ] + }, + { + "question_id": "final_spatial_007", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person in blue top and blue pants and a person in a blue top and green pants in the scene visible on camera G638?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G638" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G638_actor_33", + "entity_b": "G638_actor_49", + "entity_a_desc": "a person in blue top and blue pants", + "entity_b_desc": "a person in a blue top and green pants", + "world_pos_a_enu": [ + 28.39536508044327, + 72.31722510110839, + 0.0 + ], + "world_pos_b_enu": [ + 28.273459336760418, + 72.7330302062804, + -8.881784197001252e-16 + ], + "distance_meters": 0.43, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G638_actor_33", + "camera": "G638", + "description": "a person in blue top and blue pants", + "bbox": [ + 907, + 411, + 950, + 492 + ], + "frame": 5374, + "timestamp": "176.30-181.97s", + "world_pos_enu": [ + 28.39536508044327, + 72.31722510110839, + 0.0 + ], + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + }, + "entity_b": { + "entity_id": "G638_actor_49", + "camera": "G638", + "description": "a person in a blue top and green pants", + "bbox": [ + 892, + 420, + 929, + 498 + ], + "frame": 6155, + "timestamp": "204.40-205.93s", + "world_pos_enu": [ + 28.273459336760418, + 72.7330302062804, + -8.881784197001252e-16 + ], + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + }, + "distance_meters": 0.43, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_spatial_008", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person in a green top and blue pants and a person in a black top and blue pants in the scene visible on camera G336?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G336" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G336_actor_13", + "entity_b": "G336_actor_214", + "entity_a_desc": "a person in a green top and blue pants", + "entity_b_desc": "a person in a black top and blue pants", + "world_pos_a_enu": [ + 11.007337881815683, + 68.7509899510339, + 0.0 + ], + "world_pos_b_enu": [ + 4.825056267140974, + 67.75607475981411, + 0.0 + ], + "distance_meters": 6.26, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G336_actor_13", + "camera": "G336", + "description": "a person in a green top and blue pants", + "bbox": [ + 300, + 628, + 360, + 736 + ], + "frame": 634, + "timestamp": "19.67-22.60s", + "world_pos_enu": [ + 11.007337881815683, + 68.7509899510339, + 0.0 + ], + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4" + }, + "entity_b": { + "entity_id": "G336_actor_214", + "camera": "G336", + "description": "a person in a black top and blue pants", + "bbox": [ + 938, + 611, + 985, + 706 + ], + "frame": 2293, + "timestamp": "37.63-115.23s", + "world_pos_enu": [ + 4.825056267140974, + 67.75607475981411, + 0.0 + ], + "clip_file": "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4" + }, + "distance_meters": 6.26, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4" + ] + }, + { + "question_id": "final_spatial_009", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person in a blue top and blue pants and a person in a black top and blue pants in the scene visible on camera G638?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 2, + "correct_answer": "They are far apart (more than 15 meters)", + "requires_cameras": [ + "G638" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G638_actor_168", + "entity_b": "G638_actor_55", + "entity_a_desc": "a person in a blue top and blue pants", + "entity_b_desc": "a person in a black top and blue pants", + "world_pos_a_enu": [ + 25.129211087283636, + 47.49929469155531, + 0.0 + ], + "world_pos_b_enu": [ + 28.55446651381429, + 72.31742866565779, + 0.0 + ], + "distance_meters": 25.05, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G638_actor_168", + "camera": "G638", + "description": "a person in a blue top and blue pants", + "bbox": [ + 1878, + 326, + 1913, + 388 + ], + "frame": 7951, + "timestamp": "263.10-267.00s", + "world_pos_enu": [ + 25.129211087283636, + 47.49929469155531, + 0.0 + ], + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + }, + "entity_b": { + "entity_id": "G638_actor_55", + "camera": "G638", + "description": "a person in a black top and blue pants", + "bbox": [ + 901, + 396, + 945, + 489 + ], + "frame": 6525, + "timestamp": "216.93-218.10s", + "world_pos_enu": [ + 28.55446651381429, + 72.31742866565779, + 0.0 + ], + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + }, + "distance_meters": 25.05, + "proximity": "far", + "projection_method": "krtd_bbox_foot" + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_summarization_010", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 9 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 6 cameras", + "A single-camera scene showing only indoor activities", + "A vehicle-focused scene with mostly parking and driving activity", + "A pedestrian-dominant scene across 9 cameras, primarily featuring leavesing the camera's view through a doorway" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 9 cameras, primarily featuring leavesing the camera's view through a doorway", + "requires_cameras": [ + "G328", + "G336", + "G339", + "G419", + "G420", + "G421", + "G424", + "G638", + "G639" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 560, + "num_cameras": 9, + "person_events": 454, + "vehicle_events": 49, + "scene_type": "pedestrian-dominant", + "top_activity": "person_exits_scene_through_structure", + "top_activity_count": 162 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 560, + "unique_activities": 25, + "activity_counts": { + "person_talks_to_person": 99, + "person_texts_on_phone": 26, + "vehicle_reverses": 7, + "person_talks_on_phone": 4, + "hand_interacts_with_person": 57, + "person_exits_scene_through_structure": 162, + "person_opens_facility_door": 15, + "person_enters_scene_through_structure": 64, + "vehicle_starts": 12, + "vehicle_turns_right": 8, + "vehicle_turns_left": 10, + "vehicle_stops": 9, + "person_embraces_person": 7, + "person_stands_up": 5, + "person_sits_down": 2, + "person_carries_heavy_object": 12, + "person_puts_down_object": 6, + "person_picks_up_object": 12, + "person_transfers_object": 4, + "person_enters_vehicle": 10, + "person_opens_vehicle_door": 13, + "person_closes_vehicle_door": 12, + "vehicle_makes_u_turn": 2, + "person_closes_facility_door": 1, + "vehicle_picks_up_person": 1 + }, + "camera_event_counts": { + "G328": 13, + "G336": 138, + "G421": 116, + "G424": 33, + "G638": 120, + "G339": 24, + "G419": 35, + "G420": 44, + "G639": 37 + }, + "num_cameras": 9, + "duration_sec": 300.2, + "person_event_count": 454, + "vehicle_event_count": 49, + "cross_camera_activities": { + "person_talks_to_person": [ + "G328", + "G336", + "G339", + "G419", + "G420", + "G421", + "G424", + "G638", + "G639" + ], + "person_texts_on_phone": [ + "G336", + "G339", + "G420", + "G421", + "G638" + ], + "vehicle_reverses": [ + "G328", + "G336", + "G339", + "G424", + "G638" + ], + "person_talks_on_phone": [ + "G336", + "G421", + "G638" + ], + "hand_interacts_with_person": [ + "G336", + "G419", + "G420", + "G421", + "G638" + ], + "person_exits_scene_through_structure": [ + "G419", + "G420", + "G421", + "G638", + "G639" + ], + "person_opens_facility_door": [ + "G419", + "G420", + "G421", + "G638" + ], + "person_enters_scene_through_structure": [ + "G421", + "G638" + ], + "vehicle_starts": [ + "G336", + "G339", + "G424", + "G638" + ], + "vehicle_turns_right": [ + "G328", + "G336", + "G339", + "G424" + ], + "vehicle_turns_left": [ + "G328", + "G336", + "G424" + ], + "vehicle_stops": [ + "G336", + "G424", + "G638" + ], + "person_embraces_person": [ + "G336", + "G638" + ], + "person_stands_up": [ + "G336", + "G421" + ], + "person_carries_heavy_object": [ + "G336", + "G421", + "G638" + ], + "person_puts_down_object": [ + "G336", + "G421" + ], + "person_picks_up_object": [ + "G336", + "G421", + "G639" + ], + "person_transfers_object": [ + "G336", + "G421", + "G638" + ], + "person_enters_vehicle": [ + "G339", + "G424", + "G638" + ], + "person_opens_vehicle_door": [ + "G339", + "G424", + "G638" + ], + "person_closes_vehicle_door": [ + "G339", + "G424", + "G638" + ], + "vehicle_makes_u_turn": [ + "G336", + "G424" + ] + }, + "busiest_camera": "G336", + "busiest_camera_count": 138, + "unique_entities": 577, + "person_entities": 503, + "top_3_activities": [ + [ + "person_exits_scene_through_structure", + 162 + ], + [ + "person_talks_to_person", + 99 + ], + [ + "person_enters_scene_through_structure", + 64 + ] + ] + }, + "scene_description": "The scene spans 9 cameras with 560 total activity events. The most frequent activities are: leaves the camera's view through a doorway (162 occurrences), talks to person (99 occurrences), enters the camera's view through a doorway (64 occurrences). 22 activities occur on multiple cameras. Camera G336 is the most active with 138 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-13.16-20-00.16-25-00.school.G328.r13.mp4", + "2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4", + "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "2018-03-13.16-20-00.16-25-00.school.G639.r13.mp4", + "2018-03-13.16-20-01.16-25-01.school.G419.r13.mp4", + "2018-03-13.16-20-01.16-25-01.school.G420.r13.mp4", + "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4", + "2018-03-13.16-20-05.16-25-05.school.G339.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G328.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + 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"correct_answer_index": 1, + "correct_answer": "5", + "requires_cameras": [ + "G328", + "G336", + "G339", + "G424", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 5, + "cameras_involved": [ + "G328", + "G336", + "G339", + "G424", + "G638" + ], + "activity": "vehicle_reverses", + "event_ids": [ + "G336_evt_65", + "G424_evt_1", + "G328_evt_5", + "G339_evt_18", + "G424_evt_13", + "G638_evt_109", + "G424_evt_33" + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 5, + "cameras_involved": [ + "G328", + "G336", + "G339", + "G424", + "G638" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 18, + "slot": "2018-03-13.16-20.school", + "activity": "vehicle_reverses", + "activity_human": "reverses", + "clip_files": [ + "2018-03-13.16-20-00.16-25-00.school.G328.r13.mp4", + "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4", + "2018-03-13.16-20-05.16-25-05.school.G339.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G328.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-03.16-25-03.school.G424.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-05.16-25-05.school.G339.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_012", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G336", + "Camera G638", + "Camera G420", + "Camera G639" + ], + "correct_answer": "Camera G638", + "correct_answer_index": 1, + "requires_cameras": [ + "G638", + "G328", + "G336", + "G339", + "G419", + "G420", + "G421", + "G424", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G638", + "entrance_time_sec": 239.43, + "entity_description": "a person", + "cluster_id": "standalone_G638_166", + "all_entrance_cameras": { + "G638": 239.43 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4", + "camera": "G638", + "activity": "person_enters_scene_through_structure", + "start_sec": 239.43 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_013", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G424", + "Camera G421", + "Camera G328", + "Camera G339" + ], + "correct_answer": "Camera G421", + "correct_answer_index": 1, + "requires_cameras": [ + "G421", + "G328", + "G336", + "G339", + "G419", + "G420", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G421", + "entrance_time_sec": 201.33, + "entity_description": "a person", + "cluster_id": "standalone_G421_49", + "all_entrance_cameras": { + "G421": 201.33 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4", + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "start_sec": 201.33 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_014", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G419", + "Camera G638", + "Camera G336", + "Camera G421" + ], + "correct_answer": "Camera G421", + "correct_answer_index": 3, + "requires_cameras": [ + "G421", + "G328", + "G336", + "G339", + "G419", + "G420", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G421", + "entrance_time_sec": 195.57, + "entity_description": "a person", + "cluster_id": "standalone_G421_45", + "all_entrance_cameras": { + "G421": 195.57 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4", + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "start_sec": 195.57 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-20.school/2018-03-13.16-20-00.16-25-00.school.G421.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.json b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.json new file mode 100644 index 0000000..980ffbf --- /dev/null +++ b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.json @@ -0,0 +1,982 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v8", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo_gpt", + "generator": "v8_pipeline", + "seed": 42, + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 21, + "mevid_person_ids": [ + "0008", + "0010", + "0032", + "0040", + "0048", + "0053", + "0054", + "0074", + "0076", + "0080", + "0097", + "0110", + "0202", + "0211", + "0229", + "0238", + "0260", + "0268", + "0271", + "0280", + "0292" + ], + "mevid_person_cameras": { + "0008": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0010": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0032": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0040": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0048": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0053": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0054": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0074": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0076": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0080": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0097": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0110": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0202": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0211": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0229": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0238": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0260": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0268": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0271": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0280": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0292": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ] + }, + "total_events": 733, + "total_entities": 1258, + "entities_with_mevid_descriptions": 132, + "cross_camera_clusters": 20, + "total_questions": 9, + "category_counts": { + "temporal": 2, + "spatial": 2, + "perception": 2, + "re_identification": 2, + "scene_summary": 1 + }, + "v8_stats": { + "entities_with_mevid_descriptions": 132, + "entities_with_geom_descriptions": 352, + "entities_with_fallback_descriptions": 442, + "attribute_verification_questions": 1, + "reid_questions": 2, + "scene_summary_questions": 1, + "questions_with_debug_info": 9 + }, + "validation_issues": 0, + "generation_time_sec": 8.17, + "qa_pairs": [ + { + "question_id": "v8_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person wearing a gray top and green pants, and they are carrying a black backpack enters scene through structure on camera G421 and a person in a blue top and green pants interacts with person on camera G330 -- which occurred first?", + "options": [ + "The event on camera G421 occurred first", + "The event on camera G330 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The event on camera G421 occurred first", + "requires_cameras": [ + "G330", + "G421" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G421", + "start_sec": 0.33, + "end_sec": 2.4, + "actor_ids": [ + 1367451520919019547 + ], + "description": "a person wearing a gray top and green pants, and they are carrying a black backpack enters scene through structure on camera G421" + }, + "event_b": { + "activity": "hand_interacts_with_person", + "camera": "G330", + "start_sec": 6.1, + "end_sec": 9.27, + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "description": "a person in a blue top and green pants interacts with person on camera G330" + }, + "gap_sec": 3.7, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 1367451520919019547 + ], + "frame_range": [ + 10, + 72 + ], + "timestamp": "0.33-2.40s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.mp4", + "entity_description": "a person wearing a gray top and green pants, and they are carrying a black backpack" + }, + "event_b": { + "camera": "G330", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "frame_range": [ + 183, + 278 + ], + "timestamp": "6.10-9.27s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.mp4", + "entity_description": "a person in a blue top and green pants" + }, + "gap_sec": 3.7, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_0", + "mevid_validated": true, + "mevid_person_id": 97 + } + }, + { + "question_id": "v8_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person in a blue top and blue pants enters scene through structure on camera G420 and a person wearing a gray top and green pants, and they are carrying a black backpack opens facility door on camera G328 -- which occurred first?", + "options": [ + "The event on camera G420 occurred first", + "The event on camera G328 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The event on camera G328 occurred first", + "requires_cameras": [ + "G328", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_facility_door", + "camera": "G328", + "start_sec": 36.67, + "end_sec": 39.33, + "actor_ids": [ + 3798246005989942289 + ], + "description": "a person wearing a gray top and green pants, and they are carrying a black backpack opens facility door on camera G328" + }, + "event_b": { + "activity": "person_enters_scene_through_structure", + "camera": "G420", + "start_sec": 42.43, + "end_sec": 45.07, + "actor_ids": [ + 8966895575790985284 + ], + "description": "a person in a blue top and blue pants enters scene through structure on camera G420" + }, + "gap_sec": 3.1, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G328", + "activity": "person_opens_facility_door", + "actor_ids": [ + 3798246005989942289 + ], + "frame_range": [ + 1100, + 1180 + ], + "timestamp": "36.67-39.33s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G328.mp4", + "entity_description": "a person wearing a gray top and green pants, and they are carrying a black backpack" + }, + "event_b": { + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 8966895575790985284 + ], + "frame_range": [ + 1273, + 1352 + ], + "timestamp": "42.43-45.07s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + "gap_sec": 3.1, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 41 + } + }, + { + "question_id": "v8_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and a person wearing a blue top and blue pants in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_1613855842107762240", + "entity_b": "G328_actor_2828636302242128936", + "entity_a_desc": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "entity_b_desc": "a person wearing a blue top and blue pants", + "world_pos_a_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ], + "world_pos_b_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ], + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_1613855842107762240", + "camera": "G328", + "description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "bbox": [ + 716, + 588, + 730, + 618 + ], + "frame": 3003, + "timestamp": "98.97-101.23s", + "world_pos_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_2828636302242128936", + "camera": "G328", + "description": "a person wearing a blue top and blue pants", + "bbox": [ + 721, + 587, + 735, + 617 + ], + "frame": 3065, + "timestamp": "101.30-103.03s", + "world_pos_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ] + }, + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v8_spatial_004", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person wearing a blue top and blue pants, and is carrying a bottle and a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene visible on camera G328?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2837885804119270893", + "entity_b": "G328_actor_4132231753295701202", + "entity_a_desc": "a person wearing a blue top and blue pants, and is carrying a bottle", + "entity_b_desc": "a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap", + "world_pos_a_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ], + "world_pos_b_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ], + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2837885804119270893", + "camera": "G328", + "description": "a person wearing a blue top and blue pants, and is carrying a bottle", + "bbox": [ + 722, + 585, + 736, + 615 + ], + "frame": 2985, + "timestamp": "98.97-100.07s", + "world_pos_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_4132231753295701202", + "camera": "G328", + "description": "a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap", + "bbox": [ + 728, + 589, + 738, + 613 + ], + "frame": 1149, + "timestamp": "36.67-39.93s", + "world_pos_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ] + }, + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + } + }, + { + "question_id": "v8_perception_005", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures a carries heavy object event?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "cameras_with_activity": [ + "G299", + "G330" + ], + "total_cameras_in_slot": 8 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "correct_camera": "G299", + "cameras_with_activity": [ + "G299", + "G330" + ], + "representative_event": { + "camera": "G299", + "frame_range": [ + 0, + 202 + ], + "timestamp": "0.00-6.73s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.mp4" + } + } + }, + { + "question_id": "v8_perception_006", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": "A person is visible on camera G423. What color are they wearing on their lower body?", + "options": [ + "Gray", + "Navy", + "Blue", + "Brown" + ], + "correct_answer_index": 2, + "correct_answer": "Blue", + "requires_cameras": [ + "G423" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "attribute_type": "lower_color", + "correct_value": "blue", + "camera": "G423", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back" + }, + "debug_info": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "camera": "G423", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "gpt_description": "The person is wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "attribute_type": "lower_color", + "correct_value": "blue", + "all_cameras_for_person": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "source": "MEVID YOLO+GPT person database" + } + }, + { + "question_id": "v8_re_identification_007", + "category": "re_identification", + "difficulty": "medium", + "question_template": "On camera G419, a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back is visible. Which other camera also shows this same person?", + "options": [ + "G423", + "G299", + "G328", + "None of these cameras" + ], + "correct_answer_index": 0, + "correct_answer": "G423", + "requires_cameras": [ + "G419", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "source_camera": "G419", + "correct_target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ] + }, + "debug_info": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "source_camera": "G419", + "target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back" + } + }, + { + "question_id": "v8_re_identification_008", + "category": "re_identification", + "difficulty": "medium", + "question_template": "A person wearing a blue top and blue pants, with short dark hair and glasses is observed on camera G419. Is this the same person visible on camera G420?", + "options": [ + "Yes, it is the same person", + "No, they are different people", + "Cannot be determined from the footage", + "The person is not visible on the second camera" + ], + "correct_answer_index": 0, + "correct_answer": "Yes, it is the same person", + "requires_cameras": [ + "G419", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "person_description": "a person wearing a blue top and blue pants, with short dark hair and glasses", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "ground_truth": "same_person" + }, + "debug_info": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "a person wearing a blue top and blue pants, with short dark hair and glasses" + } + }, + { + "question_id": "v8_scene_summary_009", + "category": "scene_summary", + "difficulty": "hard", + "question_template": "Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 5 cameras", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity", + "requires_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 733, + "num_cameras": 8, + "person_events": 683, + "vehicle_events": 5, + "scene_type": "pedestrian-dominant", + "top_activity": "person_puts_down_object", + "top_activity_count": 115 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 733, + "unique_activities": 20, + "activity_counts": { + "hand_interacts_with_person": 45, + "person_carries_heavy_object": 90, + "person_talks_to_person": 66, + "person_transfers_object": 14, + "person_enters_scene_through_structure": 108, + "person_picks_up_object": 104, + "person_puts_down_object": 115, + "person_opens_facility_door": 28, + "person_sits_down": 53, + "person_texts_on_phone": 21, + "person_exits_scene_through_structure": 45, + "vehicle_turns_left": 4, + "person_stands_up": 19, + "person_embraces_person": 11, + "person_talks_on_phone": 5, + "person_closes_facility_door": 1, + "vehicle_stops": 1, + "person_opens_vehicle_door": 1, + "person_exits_vehicle": 1, + "person_closes_vehicle_door": 1 + }, + "camera_event_counts": { + "G299": 277, + "G330": 274, + "G419": 36, + "G423": 81, + "G421": 9, + "G420": 35, + "G328": 13, + "G336": 8 + }, + "num_cameras": 8, + "duration_sec": 300.3, + "person_event_count": 683, + "vehicle_event_count": 5, + "cross_camera_activities": { + "hand_interacts_with_person": [ + "G299", + "G330" + ], + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G423" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423" + ], + "person_picks_up_object": [ + "G299", + "G330" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G423" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G421", + "G423" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "person_embraces_person": [ + "G299", + "G330" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G423" + ] + }, + "busiest_camera": "G299", + "busiest_camera_count": 277, + "unique_entities": 1258, + "person_entities": 926, + "top_3_activities": [ + [ + "person_puts_down_object", + 115 + ], + [ + "person_enters_scene_through_structure", + 108 + ], + [ + "person_picks_up_object", + 104 + ] + ] + }, + "scene_description": "The scene spans 8 cameras with 733 total activity events. The most frequent activities are: puts down object (115 occurrences), enters scene through structure (108 occurrences), picks up object (104 occurrences). 14 activities occur on multiple cameras. Camera G299 is the most active with 277 events.", + "scene_type": "pedestrian-dominant" + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.natural.v2.json b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.natural.v2.json new file mode 100644 index 0000000..b856095 --- /dev/null +++ b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.natural.v2.json @@ -0,0 +1,840 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v8_natural_v2", + "generator": "naturalize_v8_qa_v2.py", + "preprocessor": "v2_preprocess", + "model": "gpt-4o-mini", + "temperature": 0.7, + "total_tokens": 10104, + "total_questions": 9, + "failures": 0, + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 21, + "category_counts": { + "temporal": 2, + "spatial": 2, + "perception": 2, + "re_identification": 2, + "scene_summary": 1 + }, + "v8_stats": { + "entities_with_mevid_descriptions": 132, + "entities_with_geom_descriptions": 352, + "entities_with_fallback_descriptions": 442, + "attribute_verification_questions": 1, + "reid_questions": 2, + "scene_summary_questions": 1, + "questions_with_debug_info": 9 + }, + "qa_pairs": [ + { + "question_id": "v8_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "Consider two events in this multi-camera scene: (1) A person wearing a gray top and green pants, carrying a black backpack, entering a scene through a structure on camera G421. (2) A person in a blue top and green pants, interacting with a person on camera G330. Which event occurred first?", + "options": [ + "Entering a scene through a structure (camera G421) occurred first", + "Interacting with a person (camera G330) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "Entering a scene through a structure (camera G421) occurred first", + "requires_cameras": [ + "G330", + "G421" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G421", + "start_sec": 0.33, + "end_sec": 2.4, + "actor_ids": [ + 1367451520919019547 + ], + "description": "a person wearing a gray top and green pants, and they are carrying a black backpack enters scene through structure on camera G421" + }, + "event_b": { + "activity": "hand_interacts_with_person", + "camera": "G330", + "start_sec": 6.1, + "end_sec": 9.27, + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "description": "a person in a blue top and green pants interacts with person on camera G330" + }, + "gap_sec": 3.7, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 1367451520919019547 + ], + "frame_range": [ + 10, + 72 + ], + "timestamp": "0.33-2.40s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.mp4", + "entity_description": "a person wearing a gray top and green pants, and they are carrying a black backpack" + }, + "event_b": { + "camera": "G330", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "frame_range": [ + 183, + 278 + ], + "timestamp": "6.10-9.27s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.mp4", + "entity_description": "a person in a blue top and green pants" + }, + "gap_sec": 3.7, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_0", + "mevid_validated": true, + "mevid_person_id": 97 + }, + "original_template": "a person wearing a gray top and green pants, and they are carrying a black backpack enters scene through structure on camera G421 and a person in a blue top and green pants interacts with person on camera G330 -- which occurred first?", + "original_options": [ + "The event on camera G421 occurred first", + "The event on camera G330 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "naturalized_question": "In this multi-camera scene, we have two events: (1) A person dressed in a gray top and green pants, carrying a black backpack, enters through a structure on camera G421. (2) A person in a blue top and green pants is seen interacting with someone on camera G330. Which of these events took place first?", + "naturalized_options": [ + "Entering a scene through a structure (camera G421) occurred first", + "Interacting with a person (camera G330) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "reasoning": "The video shows that the entry through the structure on camera G421 happened before the interaction on camera G330, with a gap of 3.7 seconds between them.", + "grammar_checked": true + }, + { + "question_id": "v8_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "Consider two events in this multi-camera scene: (1) A person in a blue top and blue pants, entering a scene through a structure on camera G420. (2) A person wearing a gray top and green pants, carrying a black backpack, opening a facility door on camera G328. Which event occurred first?", + "options": [ + "Entering a scene through a structure (camera G420) occurred first", + "Opening a facility door (camera G328) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "Opening a facility door (camera G328) occurred first", + "requires_cameras": [ + "G328", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_facility_door", + "camera": "G328", + "start_sec": 36.67, + "end_sec": 39.33, + "actor_ids": [ + 3798246005989942289 + ], + "description": "a person wearing a gray top and green pants, and they are carrying a black backpack opens facility door on camera G328" + }, + "event_b": { + "activity": "person_enters_scene_through_structure", + "camera": "G420", + "start_sec": 42.43, + "end_sec": 45.07, + "actor_ids": [ + 8966895575790985284 + ], + "description": "a person in a blue top and blue pants enters scene through structure on camera G420" + }, + "gap_sec": 3.1, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G328", + "activity": "person_opens_facility_door", + "actor_ids": [ + 3798246005989942289 + ], + "frame_range": [ + 1100, + 1180 + ], + "timestamp": "36.67-39.33s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G328.mp4", + "entity_description": "a person wearing a gray top and green pants, and they are carrying a black backpack" + }, + "event_b": { + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 8966895575790985284 + ], + "frame_range": [ + 1273, + 1352 + ], + "timestamp": "42.43-45.07s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + "gap_sec": 3.1, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 41 + }, + "original_template": "a person in a blue top and blue pants enters scene through structure on camera G420 and a person wearing a gray top and green pants, and they are carrying a black backpack opens facility door on camera G328 -- which occurred first?", + "original_options": [ + "The event on camera G420 occurred first", + "The event on camera G328 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "naturalized_question": "In this multi-camera scene, we have two events: (1) A person wearing a blue top and blue pants enters through a structure on camera G420. (2) A person in a gray top and green pants, carrying a black backpack, opens a facility door on camera G328. Which of these events took place first?", + "naturalized_options": [ + "Entering a scene through a structure (camera G420) occurred first", + "Opening a facility door (camera G328) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "reasoning": "Given that there is a 3.1-second gap between the two events, the entry through the structure on camera G420 must have occurred before the door opening on camera G328.", + "grammar_checked": true + }, + { + "question_id": "v8_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and a person wearing a blue top and blue pants in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_1613855842107762240", + "entity_b": "G328_actor_2828636302242128936", + "entity_a_desc": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "entity_b_desc": "a person wearing a blue top and blue pants", + "world_pos_a_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ], + "world_pos_b_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ], + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_1613855842107762240", + "camera": "G328", + "description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "bbox": [ + 716, + 588, + 730, + 618 + ], + "frame": 3003, + "timestamp": "98.97-101.23s", + "world_pos_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_2828636302242128936", + "camera": "G328", + "description": "a person wearing a blue top and blue pants", + "bbox": [ + 721, + 587, + 735, + 617 + ], + "frame": 3065, + "timestamp": "101.30-103.03s", + "world_pos_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ] + }, + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "original_template": "How close are a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and a person wearing a blue top and blue pants in the scene visible on camera G328?", + "naturalized_question": "In the scene, how far apart are the person in a blue top and blue pants with a black graphic hoodie and the person also in a blue top and blue pants?", + "naturalized_options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "reasoning": "Given that the distance between them is 2.77 meters, they are considered to be near each other.", + "grammar_checked": true + }, + { + "question_id": "v8_spatial_004", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person wearing a blue top and blue pants, carrying a bottle and a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2837885804119270893", + "entity_b": "G328_actor_4132231753295701202", + "entity_a_desc": "a person wearing a blue top and blue pants, and is carrying a bottle", + "entity_b_desc": "a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap", + "world_pos_a_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ], + "world_pos_b_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ], + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2837885804119270893", + "camera": "G328", + "description": "a person wearing a blue top and blue pants, and is carrying a bottle", + "bbox": [ + 722, + 585, + 736, + 615 + ], + "frame": 2985, + "timestamp": "98.97-100.07s", + "world_pos_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_4132231753295701202", + "camera": "G328", + "description": "a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap", + "bbox": [ + 728, + 589, + 738, + 613 + ], + "frame": 1149, + "timestamp": "36.67-39.93s", + "world_pos_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ] + }, + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "original_template": "How close are a person wearing a blue top and blue pants, and is carrying a bottle and a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene visible on camera G328?", + "naturalized_question": "In the scene, how far apart are the person in a blue top and blue pants carrying a bottle and the person in a white Puma hoodie with camouflage pants and a camouflage cap?", + "naturalized_options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "reasoning": "Given that the distance between them is approximately 5.76 meters, they are at a moderate distance from each other.", + "grammar_checked": true + }, + { + "question_id": "v8_perception_005", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures carrying a heavy object?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "cameras_with_activity": [ + "G299", + "G330" + ], + "total_cameras_in_slot": 8 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "correct_camera": "G299", + "cameras_with_activity": [ + "G299", + "G330" + ], + "representative_event": { + "camera": "G299", + "frame_range": [ + 0, + 202 + ], + "timestamp": "0.00-6.73s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.mp4" + } + }, + "original_template": "Which camera captures a carries heavy object event?", + "naturalized_question": "Which camera shows someone carrying a heavy object?", + "naturalized_options": [ + "G336", + "G299", + "G421", + "G419" + ], + "reasoning": "The correct camera is the one that clearly captures an individual with a heavy object.", + "grammar_checked": true + }, + { + "question_id": "v8_perception_006", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": "A person is visible on camera G423. What color are they wearing on their lower body?", + "options": [ + "Gray", + "Navy", + "Blue", + "Brown" + ], + "correct_answer_index": 2, + "correct_answer": "Blue", + "requires_cameras": [ + "G423" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "attribute_type": "lower_color", + "correct_value": "blue", + "camera": "G423", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back" + }, + "debug_info": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "camera": "G423", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "gpt_description": "The person is wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "attribute_type": "lower_color", + "correct_value": "blue", + "all_cameras_for_person": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "source": "MEVID YOLO+GPT person database" + }, + "naturalized_question": "Looking at camera G423, what color is the person visible wearing on their lower body?", + "naturalized_options": [ + "Gray", + "Navy", + "Blue", + "Brown" + ], + "reasoning": "The person on camera G423 is wearing blue pants, making blue the correct answer.", + "grammar_checked": true + }, + { + "question_id": "v8_re_identification_007", + "category": "re_identification", + "difficulty": "medium", + "question_template": "On camera G419, a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back is visible. Which other camera also shows this same person?", + "options": [ + "G423", + "G299", + "G328", + "None of these cameras" + ], + "correct_answer_index": 0, + "correct_answer": "G423", + "requires_cameras": [ + "G419", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "source_camera": "G419", + "correct_target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ] + }, + "debug_info": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "source_camera": "G419", + "target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back" + }, + "naturalized_question": "A person wearing a blue top and blue pants, along with a black hoodie that has a graphic design on the back, is visible on camera G419. Which other camera also captures this same person?", + "naturalized_options": [ + "G423", + "G299", + "G328", + "None of these cameras" + ], + "reasoning": "The individual in the blue top and pants with the distinctive black graphic hoodie can also be seen on camera G423.", + "grammar_checked": true + }, + { + "question_id": "v8_re_identification_008", + "category": "re_identification", + "difficulty": "medium", + "question_template": "A person wearing a blue top and blue pants, with short dark hair and glasses is observed on camera G419. Is this the same person visible on camera G420?", + "options": [ + "Yes, it is the same person", + "No, they are different people", + "Cannot be determined from the footage", + "The person is not visible on the second camera" + ], + "correct_answer_index": 0, + "correct_answer": "Yes, it is the same person", + "requires_cameras": [ + "G419", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "person_description": "a person wearing a blue top and blue pants, with short dark hair and glasses", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "ground_truth": "same_person" + }, + "debug_info": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "a person wearing a blue top and blue pants, with short dark hair and glasses" + }, + "naturalized_question": "A person in a blue top and blue pants with short dark hair and glasses is seen on camera G419. Is this the same person shown on camera G420?", + "naturalized_options": [ + "Yes, it is the same person", + "No, they are different people", + "Cannot be determined from the footage", + "The person is not visible on the second camera" + ], + "reasoning": "The question asks if the person observed on camera G419, described specifically, matches the appearance of anyone on camera G420.", + "grammar_checked": true + }, + { + "question_id": "v8_scene_summary_009", + "category": "scene_summary", + "difficulty": "hard", + "question_template": "Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 5 cameras", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity", + "requires_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 733, + "num_cameras": 8, + "person_events": 683, + "vehicle_events": 5, + "scene_type": "pedestrian-dominant", + "top_activity": "person_puts_down_object", + "top_activity_count": 115 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 733, + "unique_activities": 20, + "activity_counts": { + "hand_interacts_with_person": 45, + "person_carries_heavy_object": 90, + "person_talks_to_person": 66, + "person_transfers_object": 14, + "person_enters_scene_through_structure": 108, + "person_picks_up_object": 104, + "person_puts_down_object": 115, + "person_opens_facility_door": 28, + "person_sits_down": 53, + "person_texts_on_phone": 21, + "person_exits_scene_through_structure": 45, + "vehicle_turns_left": 4, + "person_stands_up": 19, + "person_embraces_person": 11, + "person_talks_on_phone": 5, + "person_closes_facility_door": 1, + "vehicle_stops": 1, + "person_opens_vehicle_door": 1, + "person_exits_vehicle": 1, + "person_closes_vehicle_door": 1 + }, + "camera_event_counts": { + "G299": 277, + "G330": 274, + "G419": 36, + "G423": 81, + "G421": 9, + "G420": 35, + "G328": 13, + "G336": 8 + }, + "num_cameras": 8, + "duration_sec": 300.3, + "person_event_count": 683, + "vehicle_event_count": 5, + "cross_camera_activities": { + "hand_interacts_with_person": [ + "G299", + "G330" + ], + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G423" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423" + ], + "person_picks_up_object": [ + "G299", + "G330" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G423" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G421", + "G423" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "person_embraces_person": [ + "G299", + "G330" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G423" + ] + }, + "busiest_camera": "G299", + "busiest_camera_count": 277, + "unique_entities": 1258, + "person_entities": 926, + "top_3_activities": [ + [ + "person_puts_down_object", + 115 + ], + [ + "person_enters_scene_through_structure", + 108 + ], + [ + "person_picks_up_object", + 104 + ] + ] + }, + "scene_description": "The scene spans 8 cameras with 733 total activity events. The most frequent activities are: puts down object (115 occurrences), enters scene through structure (108 occurrences), picks up object (104 occurrences). 14 activities occur on multiple cameras. Camera G299 is the most active with 277 events.", + "scene_type": "pedestrian-dominant" + }, + "naturalized_question": "Taking into account all 8 camera feeds during this time, which description best represents the overall scene?", + "naturalized_options": [ + "An empty scene with very little activity, shown on 5 cameras", + "A scene centered around vehicles, with mainly parking and driving activities", + "A scene observed from just one camera, displaying only indoor activities", + "A scene dominated by pedestrians across 8 cameras, mainly involving putting down objects" + ], + "reasoning": "The predominant activities observed across all 8 cameras are pedestrian-related, particularly the action of putting down objects.", + "grammar_checked": true + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.natural.v3.json b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.natural.v3.json new file mode 100644 index 0000000..38f9cf2 --- /dev/null +++ b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.natural.v3.json @@ -0,0 +1,840 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v8_natural_v3", + "generator": "naturalize_v8_qa_v2.py", + "preprocessor": "v3_preprocess", + "model": "gpt-4o-mini", + "temperature": 0.95, + "total_tokens": 10558, + "total_questions": 9, + "failures": 0, + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 21, + "category_counts": { + "temporal": 2, + "spatial": 2, + "perception": 2, + "re_identification": 2, + "scene_summary": 1 + }, + "v8_stats": { + "entities_with_mevid_descriptions": 132, + "entities_with_geom_descriptions": 352, + "entities_with_fallback_descriptions": 442, + "attribute_verification_questions": 1, + "reid_questions": 2, + "scene_summary_questions": 1, + "questions_with_debug_info": 9 + }, + "qa_pairs": [ + { + "question_id": "v8_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "Consider two events in this multi-camera scene: (1) A person wearing a gray top and green pants, carrying a black backpack, entering a scene through a structure. (2) A person in a blue top and green pants, interacting with a person. Which event occurred first?", + "options": [ + "Entering a scene through a structure (camera G421) occurred first", + "Interacting with a person (camera G330) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "Entering a scene through a structure (camera G421) occurred first", + "requires_cameras": [ + "G330", + "G421" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G421", + "start_sec": 0.33, + "end_sec": 2.4, + "actor_ids": [ + 1367451520919019547 + ], + "description": "a person wearing a gray top and green pants, and they are carrying a black backpack enters scene through structure on camera G421" + }, + "event_b": { + "activity": "hand_interacts_with_person", + "camera": "G330", + "start_sec": 6.1, + "end_sec": 9.27, + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "description": "a person in a blue top and green pants interacts with person on camera G330" + }, + "gap_sec": 3.7, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 1367451520919019547 + ], + "frame_range": [ + 10, + 72 + ], + "timestamp": "0.33-2.40s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.mp4", + "entity_description": "a person wearing a gray top and green pants, and they are carrying a black backpack" + }, + "event_b": { + "camera": "G330", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "frame_range": [ + 183, + 278 + ], + "timestamp": "6.10-9.27s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.mp4", + "entity_description": "a person in a blue top and green pants" + }, + "gap_sec": 3.7, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_0", + "mevid_validated": true, + "mevid_person_id": 97 + }, + "original_template": "a person wearing a gray top and green pants, and they are carrying a black backpack enters scene through structure on camera G421 and a person in a blue top and green pants interacts with person on camera G330 -- which occurred first?", + "original_options": [ + "The event on camera G421 occurred first", + "The event on camera G330 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "naturalized_question": "Can you identify which event took place first: the individual in gray who had a black backpack enter the scene through a structure, or the interaction involving the person dressed in blue with green pants?", + "naturalized_options": [ + "Entering a scene through a structure (camera G421) occurred first", + "Interacting with a person (camera G330) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "reasoning": "The timeline shows that the individual in gray entered first, followed by the interaction with the person in blue.", + "grammar_checked": true + }, + { + "question_id": "v8_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "Consider two events in this multi-camera scene: (1) A person in a blue top and blue pants, entering a scene through a structure. (2) A person wearing a gray top and green pants, carrying a black backpack, opening a facility door. Which event occurred first?", + "options": [ + "Entering a scene through a structure (camera G420) occurred first", + "Opening a facility door (camera G328) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "Opening a facility door (camera G328) occurred first", + "requires_cameras": [ + "G328", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_facility_door", + "camera": "G328", + "start_sec": 36.67, + "end_sec": 39.33, + "actor_ids": [ + 3798246005989942289 + ], + "description": "a person wearing a gray top and green pants, and they are carrying a black backpack opens facility door on camera G328" + }, + "event_b": { + "activity": "person_enters_scene_through_structure", + "camera": "G420", + "start_sec": 42.43, + "end_sec": 45.07, + "actor_ids": [ + 8966895575790985284 + ], + "description": "a person in a blue top and blue pants enters scene through structure on camera G420" + }, + "gap_sec": 3.1, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G328", + "activity": "person_opens_facility_door", + "actor_ids": [ + 3798246005989942289 + ], + "frame_range": [ + 1100, + 1180 + ], + "timestamp": "36.67-39.33s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G328.mp4", + "entity_description": "a person wearing a gray top and green pants, and they are carrying a black backpack" + }, + "event_b": { + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 8966895575790985284 + ], + "frame_range": [ + 1273, + 1352 + ], + "timestamp": "42.43-45.07s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.mp4", + "entity_description": "a person in a blue top and blue pants" + }, + "gap_sec": 3.1, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 41 + }, + "original_template": "a person in a blue top and blue pants enters scene through structure on camera G420 and a person wearing a gray top and green pants, and they are carrying a black backpack opens facility door on camera G328 -- which occurred first?", + "original_options": [ + "The event on camera G420 occurred first", + "The event on camera G328 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "naturalized_question": "Can you identify which event took place first: a person in a blue top and blue pants walking through a structure, or an individual with a gray top and green pants who is opening a door while carrying a black backpack?", + "naturalized_options": [ + "Entering a scene through a structure (camera G420) occurred first", + "Opening a facility door (camera G328) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "reasoning": "The person in blue entered the scene before the gray-clad individual opened the door, as there is a 3.1-second gap between the events.", + "grammar_checked": true + }, + { + "question_id": "v8_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and a person wearing a blue top and blue pants in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_1613855842107762240", + "entity_b": "G328_actor_2828636302242128936", + "entity_a_desc": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "entity_b_desc": "a person wearing a blue top and blue pants", + "world_pos_a_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ], + "world_pos_b_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ], + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_1613855842107762240", + "camera": "G328", + "description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "bbox": [ + 716, + 588, + 730, + 618 + ], + "frame": 3003, + "timestamp": "98.97-101.23s", + "world_pos_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_2828636302242128936", + "camera": "G328", + "description": "a person wearing a blue top and blue pants", + "bbox": [ + 721, + 587, + 735, + 617 + ], + "frame": 3065, + "timestamp": "101.30-103.03s", + "world_pos_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ] + }, + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "original_template": "How close are a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and a person wearing a blue top and blue pants in the scene visible on camera G328?", + "naturalized_question": "Can you estimate the distance between the individual dressed in a blue top and blue pants with a black graphic hoodie and another person also sporting a blue top and blue pants in the same scene?", + "naturalized_options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "reasoning": "The footage indicates they are approximately 2.77 meters apart, placing them close together.", + "grammar_checked": true + }, + { + "question_id": "v8_spatial_004", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are a person wearing a blue top and blue pants, carrying a bottle and a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2837885804119270893", + "entity_b": "G328_actor_4132231753295701202", + "entity_a_desc": "a person wearing a blue top and blue pants, and is carrying a bottle", + "entity_b_desc": "a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap", + "world_pos_a_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ], + "world_pos_b_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ], + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2837885804119270893", + "camera": "G328", + "description": "a person wearing a blue top and blue pants, and is carrying a bottle", + "bbox": [ + 722, + 585, + 736, + 615 + ], + "frame": 2985, + "timestamp": "98.97-100.07s", + "world_pos_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_4132231753295701202", + "camera": "G328", + "description": "a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap", + "bbox": [ + 728, + 589, + 738, + 613 + ], + "frame": 1149, + "timestamp": "36.67-39.93s", + "world_pos_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ] + }, + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "original_template": "How close are a person wearing a blue top and blue pants, and is carrying a bottle and a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene visible on camera G328?", + "naturalized_question": "What would you say is the distance between the individual dressed in blue with a bottle and the one in a white Puma hoodie and camo pants?", + "naturalized_options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "reasoning": "The two individuals are approximately 5.76 meters apart in the scene.", + "grammar_checked": true + }, + { + "question_id": "v8_perception_005", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures carrying a heavy object?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "cameras_with_activity": [ + "G299", + "G330" + ], + "total_cameras_in_slot": 8 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "correct_camera": "G299", + "cameras_with_activity": [ + "G299", + "G330" + ], + "representative_event": { + "camera": "G299", + "frame_range": [ + 0, + 202 + ], + "timestamp": "0.00-6.73s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.mp4" + } + }, + "original_template": "Which camera captures a carries heavy object event?", + "naturalized_question": "Can you tell me which camera shows someone carrying a heavy object?", + "naturalized_options": [ + "G336", + "G299", + "G421", + "G419" + ], + "reasoning": "The footage from camera G336 depicts an individual struggling with a heavy item.", + "grammar_checked": true + }, + { + "question_id": "v8_perception_006", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": "A person is visible on camera G423. What color are they wearing on their lower body?", + "options": [ + "Gray", + "Navy", + "Blue", + "Brown" + ], + "correct_answer_index": 2, + "correct_answer": "Blue", + "requires_cameras": [ + "G423" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "attribute_type": "lower_color", + "correct_value": "blue", + "camera": "G423", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back" + }, + "debug_info": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "camera": "G423", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "gpt_description": "The person is wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "attribute_type": "lower_color", + "correct_value": "blue", + "all_cameras_for_person": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "source": "MEVID YOLO+GPT person database" + }, + "naturalized_question": "On camera G423, can you identify the color of the person's pants?", + "naturalized_options": [ + "Gray", + "Navy", + "Blue", + "Brown" + ], + "reasoning": "The individual seen on G423 is wearing blue on their lower half.", + "grammar_checked": true + }, + { + "question_id": "v8_re_identification_007", + "category": "re_identification", + "difficulty": "medium", + "question_template": "On camera G419, a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back is visible. Which other camera also shows this same person?", + "options": [ + "G423", + "G299", + "G328", + "None of these cameras" + ], + "correct_answer_index": 0, + "correct_answer": "G423", + "requires_cameras": [ + "G419", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back", + "source_camera": "G419", + "correct_target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ] + }, + "debug_info": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "source_camera": "G419", + "target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back" + }, + "naturalized_question": "A person is spotted on camera G419 dressed in a blue top and blue pants, complemented by a black hoodie that has a graphic design on the back. Can you identify the same individual on another camera?", + "naturalized_options": [ + "G423", + "G299", + "G328", + "None of these cameras" + ], + "reasoning": "The unique graphic on the hoodie makes it identifiable, and this person is seen on both G419 and G423.", + "grammar_checked": true + }, + { + "question_id": "v8_re_identification_008", + "category": "re_identification", + "difficulty": "medium", + "question_template": "A person wearing a blue top and blue pants, with short dark hair and glasses is observed on camera G419. Is this the same person visible on camera G420?", + "options": [ + "Yes, it is the same person", + "No, they are different people", + "Cannot be determined from the footage", + "The person is not visible on the second camera" + ], + "correct_answer_index": 0, + "correct_answer": "Yes, it is the same person", + "requires_cameras": [ + "G419", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "person_description": "a person wearing a blue top and blue pants, with short dark hair and glasses", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "ground_truth": "same_person" + }, + "debug_info": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "a person wearing a blue top and blue pants, with short dark hair and glasses" + }, + "naturalized_question": "On camera G419, there's an individual in a blue top and blue pants, sporting short dark hair and wearing glasses. Do we see this same person on camera G420 as well?", + "naturalized_options": [ + "A) Yes, it is the same person", + "B) No, they are different people", + "C) Cannot be determined from the footage", + "D) The person is not visible on the second camera" + ], + "reasoning": "The person's distinct features help verify their presence on both cameras.", + "grammar_checked": true + }, + { + "question_id": "v8_scene_summary_009", + "category": "scene_summary", + "difficulty": "hard", + "question_template": "Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 5 cameras", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity", + "requires_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 733, + "num_cameras": 8, + "person_events": 683, + "vehicle_events": 5, + "scene_type": "pedestrian-dominant", + "top_activity": "person_puts_down_object", + "top_activity_count": 115 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 733, + "unique_activities": 20, + "activity_counts": { + "hand_interacts_with_person": 45, + "person_carries_heavy_object": 90, + "person_talks_to_person": 66, + "person_transfers_object": 14, + "person_enters_scene_through_structure": 108, + "person_picks_up_object": 104, + "person_puts_down_object": 115, + "person_opens_facility_door": 28, + "person_sits_down": 53, + "person_texts_on_phone": 21, + "person_exits_scene_through_structure": 45, + "vehicle_turns_left": 4, + "person_stands_up": 19, + "person_embraces_person": 11, + "person_talks_on_phone": 5, + "person_closes_facility_door": 1, + "vehicle_stops": 1, + "person_opens_vehicle_door": 1, + "person_exits_vehicle": 1, + "person_closes_vehicle_door": 1 + }, + "camera_event_counts": { + "G299": 277, + "G330": 274, + "G419": 36, + "G423": 81, + "G421": 9, + "G420": 35, + "G328": 13, + "G336": 8 + }, + "num_cameras": 8, + "duration_sec": 300.3, + "person_event_count": 683, + "vehicle_event_count": 5, + "cross_camera_activities": { + "hand_interacts_with_person": [ + "G299", + "G330" + ], + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G423" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423" + ], + "person_picks_up_object": [ + "G299", + "G330" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G423" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G421", + "G423" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "person_embraces_person": [ + "G299", + "G330" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G423" + ] + }, + "busiest_camera": "G299", + "busiest_camera_count": 277, + "unique_entities": 1258, + "person_entities": 926, + "top_3_activities": [ + [ + "person_puts_down_object", + 115 + ], + [ + "person_enters_scene_through_structure", + 108 + ], + [ + "person_picks_up_object", + 104 + ] + ] + }, + "scene_description": "The scene spans 8 cameras with 733 total activity events. The most frequent activities are: puts down object (115 occurrences), enters scene through structure (108 occurrences), picks up object (104 occurrences). 14 activities occur on multiple cameras. Camera G299 is the most active with 277 events.", + "scene_type": "pedestrian-dominant" + }, + "naturalized_question": "How would you summarize the events captured by all 8 cameras during this time frame?", + "naturalized_options": [ + "An empty scene with minimal activity, captured on 5 cameras", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 8 cameras, primarily featuring the act of putting down objects" + ], + "reasoning": "The footage reveals that pedestrians are the main focus across all 8 feeds, with the act of putting down objects being frequently observed.", + "grammar_checked": true + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.preprocessed.json b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.preprocessed.json new file mode 100644 index 0000000..3b0774e --- /dev/null +++ b/meva/examples/qa_pairs/archived/v8_outputs/2018-03-11.11-25-00.school.v8.preprocessed.json @@ -0,0 +1,1001 @@ +{ + "slot": "2018-03-11.11-25-00.school", + "version": "v8_preprocessed", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo_gpt", + "generator": "v8_pipeline", + "seed": 42, + "cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 21, + "mevid_person_ids": [ + "0008", + "0010", + "0032", + "0040", + "0048", + "0053", + "0054", + "0074", + "0076", + "0080", + "0097", + "0110", + "0202", + "0211", + "0229", + "0238", + "0260", + "0268", + "0271", + "0280", + "0292" + ], + "mevid_person_cameras": { + "0008": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0010": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0032": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0040": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0048": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0053": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0054": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0074": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0076": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0080": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0097": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0110": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0202": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0211": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0229": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0238": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0260": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0268": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0271": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0280": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "0292": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ] + }, + "total_events": 733, + "total_entities": 1258, + "entities_with_mevid_descriptions": 132, + "cross_camera_clusters": 20, + "total_questions": 9, + "category_counts": { + "temporal": 2, + "spatial": 2, + "perception": 2, + "re_identification": 2, + "scene_summary": 1 + }, + "v8_stats": { + "entities_with_mevid_descriptions": 132, + "entities_with_fallback_descriptions": 1126, + "attribute_verification_questions": 1, + "reid_questions": 2, + "scene_summary_questions": 1, + "questions_with_debug_info": 9 + }, + "validation_issues": 0, + "generation_time_sec": 8.15, + "preprocessor": "naturalize_v8_qa_v2.py", + "qa_pairs": [ + { + "question_id": "v8_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "Consider two events in this multi-camera scene: (1) A person wearing a gray top and green pants, carrying a black backpack, entering a scene through a structure on camera G421. (2) A person interacting with a person on camera G330. Which event occurred first?", + "options": [ + "Entering a scene through a structure (camera G421) occurred first", + "Interacting with a person (camera G330) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "Entering a scene through a structure (camera G421) occurred first", + "requires_cameras": [ + "G330", + "G421" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G421", + "start_sec": 0.33, + "end_sec": 2.4, + "actor_ids": [ + 1367451520919019547 + ], + "description": "the person wearing a gray upper body garment and green lower body clothing, and they are carrying a black backpack. enters scene through structure on camera G421" + }, + "event_b": { + "activity": "hand_interacts_with_person", + "camera": "G330", + "start_sec": 6.1, + "end_sec": 9.27, + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "description": "a person interacts with person interacts with person on camera G330" + }, + "gap_sec": 3.7, + "entity_link": "mevid_validated", + "same_person": true + }, + "debug_info": { + "event_a": { + "camera": "G421", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 1367451520919019547 + ], + "frame_range": [ + 10, + 72 + ], + "timestamp": "0.33-2.40s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-01.school.G421.mp4", + "entity_description": "the person wearing a gray upper body garment and green lower body clothing, and they are carrying a black backpack." + }, + "event_b": { + "camera": "G330", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 7001363839426372650, + 4592107427582189901 + ], + "frame_range": [ + 183, + 278 + ], + "timestamp": "6.10-9.27s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.mp4", + "entity_description": "a person interacts with person" + }, + "gap_sec": 3.7, + "connection_type": "same_entity_cluster", + "connection_strength": "strong", + "connection_score": 5.0, + "relationship": null, + "cluster_id": "cluster_0", + "mevid_validated": true, + "mevid_person_id": 97 + }, + "original_template": "the person wearing a gray upper body garment and green lower body clothing, and they are carrying a black backpack. enters scene through structure on camera G421 and a person interacts with person interacts with person on camera G330 -- which occurred first?", + "original_options": [ + "The event on camera G421 occurred first", + "The event on camera G330 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ] + }, + { + "question_id": "v8_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "Consider two events in this multi-camera scene: (1) A person entering a scene through a structure on camera G420. (2) A person wearing a gray top and green pants, carrying a black backpack, opening a facility door on camera G328. Which event occurred first?", + "options": [ + "Entering a scene through a structure (camera G420) occurred first", + "Opening a facility door (camera G328) occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "Opening a facility door (camera G328) occurred first", + "requires_cameras": [ + "G328", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_facility_door", + "camera": "G328", + "start_sec": 36.67, + "end_sec": 39.33, + "actor_ids": [ + 3798246005989942289 + ], + "description": "the person wearing a gray upper body garment and green lower body clothing, and they are carrying a black backpack. opens facility door on camera G328" + }, + "event_b": { + "activity": "person_enters_scene_through_structure", + "camera": "G420", + "start_sec": 42.43, + "end_sec": 45.07, + "actor_ids": [ + 8966895575790985284 + ], + "description": "a person enters scene through structure enters scene through structure on camera G420" + }, + "gap_sec": 3.1, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G328", + "activity": "person_opens_facility_door", + "actor_ids": [ + 3798246005989942289 + ], + "frame_range": [ + 1100, + 1180 + ], + "timestamp": "36.67-39.33s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G328.mp4", + "entity_description": "the person wearing a gray upper body garment and green lower body clothing, and they are carrying a black backpack." + }, + "event_b": { + "camera": "G420", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 8966895575790985284 + ], + "frame_range": [ + 1273, + 1352 + ], + "timestamp": "42.43-45.07s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G420.mp4", + "entity_description": "a person enters scene through structure" + }, + "gap_sec": 3.1, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 41 + }, + "original_template": "a person enters scene through structure enters scene through structure on camera G420 and the person wearing a gray upper body garment and green lower body clothing, and they are carrying a black backpack. opens facility door on camera G328 -- which occurred first?", + "original_options": [ + "The event on camera G420 occurred first", + "The event on camera G328 occurred first", + "They occurred simultaneously", + "Cannot be determined" + ] + }, + { + "question_id": "v8_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are the person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and the person wearing a blue top and blue pants in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 0, + "correct_answer": "They are near each other (within a few meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_1613855842107762240", + "entity_b": "G328_actor_2828636302242128936", + "entity_a_desc": "the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "entity_b_desc": "the person wearing a blue upper body garment and blue lower body garment.", + "world_pos_a_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ], + "world_pos_b_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ], + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_1613855842107762240", + "camera": "G328", + "description": "the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "bbox": [ + 716, + 588, + 730, + 618 + ], + "frame": 3003, + "timestamp": "98.97-101.23s", + "world_pos_enu": [ + 150.56448736717743, + -36.15586021380905, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_2828636302242128936", + "camera": "G328", + "description": "the person wearing a blue upper body garment and blue lower body garment.", + "bbox": [ + 721, + 587, + 735, + 617 + ], + "frame": 3065, + "timestamp": "101.30-103.03s", + "world_pos_enu": [ + 151.81478939723144, + -38.632104952473, + -8.881784197001252e-16 + ] + }, + "distance_meters": 2.77, + "proximity": "near", + "projection_method": "krtd_bbox_foot" + }, + "original_template": "How close are the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back. and the person wearing a blue upper body garment and blue lower body garment. in the scene visible on camera G328?" + }, + { + "question_id": "v8_spatial_004", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close are the person wearing a blue top and blue pants, carrying a bottle and the person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?", + "options": [ + "They are near each other (within a few meters)", + "They are at a moderate distance (5-15 meters)", + "They are far apart (more than 15 meters)", + "They are at the same location" + ], + "correct_answer_index": 1, + "correct_answer": "They are at a moderate distance (5-15 meters)", + "requires_cameras": [ + "G328" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G328_actor_2837885804119270893", + "entity_b": "G328_actor_4132231753295701202", + "entity_a_desc": "the person wearing a blue upper body garment and blue lower body clothing, and is carrying a bottle.", + "entity_b_desc": "the person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap.", + "world_pos_a_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ], + "world_pos_b_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ], + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "debug_info": { + "entity_a": { + "entity_id": "G328_actor_2837885804119270893", + "camera": "G328", + "description": "the person wearing a blue upper body garment and blue lower body clothing, and is carrying a bottle.", + "bbox": [ + 722, + 585, + 736, + 615 + ], + "frame": 2985, + "timestamp": "98.97-100.07s", + "world_pos_enu": [ + 154.83853886241715, + -42.80160684490623, + 0.0 + ] + }, + "entity_b": { + "entity_id": "G328_actor_4132231753295701202", + "camera": "G328", + "description": "the person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap.", + "bbox": [ + 728, + 589, + 738, + 613 + ], + "frame": 1149, + "timestamp": "36.67-39.93s", + "world_pos_enu": [ + 157.95625593288253, + -47.64936259629016, + 0.0 + ] + }, + "distance_meters": 5.76, + "proximity": "moderate", + "projection_method": "krtd_bbox_foot" + }, + "original_template": "How close are the person wearing a blue upper body garment and blue lower body clothing, and is carrying a bottle. and the person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap. in the scene visible on camera G328?" + }, + { + "question_id": "v8_perception_005", + "category": "perception", + "difficulty": "easy", + "question_template": "Which camera captures carrying a heavy object?", + "options": [ + "G336", + "G299", + "G421", + "G419" + ], + "correct_answer_index": 1, + "correct_answer": "G299", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "cameras_with_activity": [ + "G299", + "G330" + ], + "total_cameras_in_slot": 8 + }, + "debug_info": { + "question_type": "which_camera", + "activity": "person_carries_heavy_object", + "activity_alias": "carries heavy object", + "correct_camera": "G299", + "cameras_with_activity": [ + "G299", + "G330" + ], + "representative_event": { + "camera": "G299", + "frame_range": [ + 0, + 202 + ], + "timestamp": "0.00-6.73s", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.mp4" + } + }, + "original_template": "Which camera captures a carries heavy object event?" + }, + { + "question_id": "v8_perception_006", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": "A person is visible on camera G423. What color are they wearing on their lower body?", + "options": [ + "Gray", + "Navy", + "Blue", + "Brown" + ], + "correct_answer_index": 2, + "correct_answer": "Blue", + "requires_cameras": [ + "G423" + ], + "requires_multi_camera": false, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "attribute_type": "lower_color", + "correct_value": "blue", + "camera": "G423", + "person_description": "the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back." + }, + "debug_info": { + "question_type": "attribute_verification", + "mevid_person_id": "0008", + "camera": "G423", + "person_description": "the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "gpt_description": "The person is wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "attribute_type": "lower_color", + "correct_value": "blue", + "all_cameras_for_person": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "source": "MEVID YOLO+GPT person database" + } + }, + { + "question_id": "v8_re_identification_007", + "category": "re_identification", + "difficulty": "medium", + "question_template": "On camera G419, a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back is visible. Which other camera also shows this same person?", + "options": [ + "G423", + "G299", + "G328", + "None of these cameras" + ], + "correct_answer_index": 0, + "correct_answer": "G423", + "requires_cameras": [ + "G419", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "person_description": "the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back.", + "source_camera": "G419", + "correct_target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ] + }, + "debug_info": { + "question_type": "which_camera_reid", + "mevid_person_id": "0008", + "source_camera": "G419", + "target_camera": "G423", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back." + }, + "original_template": "On camera G419, the person wearing a blue upper body garment and blue lower body clothing, with a black hoodie featuring a graphic design on the back. is visible. Which other camera also shows this same person?" + }, + { + "question_id": "v8_re_identification_008", + "category": "re_identification", + "difficulty": "medium", + "question_template": "A person wearing a blue top and blue pants, with short dark hair and glasses is observed on camera G419. Is this the same person visible on camera G420?", + "options": [ + "Yes, it is the same person", + "No, they are different people", + "Cannot be determined from the footage", + "The person is not visible on the second camera" + ], + "correct_answer_index": 0, + "correct_answer": "Yes, it is the same person", + "requires_cameras": [ + "G419", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "person_description": "the person wearing a blue upper body garment and blue lower body clothing, with short dark hair and glasses.", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "ground_truth": "same_person" + }, + "debug_info": { + "question_type": "same_person_confirmation", + "mevid_person_id": "0010", + "camera_a": "G419", + "camera_b": "G420", + "all_person_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "person_description": "the person wearing a blue upper body garment and blue lower body clothing, with short dark hair and glasses." + }, + "original_template": "The person wearing a blue upper body garment and blue lower body clothing, with short dark hair and glasses. is observed on camera G419. Is this the same person visible on camera G420?" + }, + { + "question_id": "v8_scene_summary_009", + "category": "scene_summary", + "difficulty": "hard", + "question_template": "Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 5 cameras", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 8 cameras, primarily featuring puts down object activity", + "requires_cameras": [ + "G299", + "G328", + "G330", + "G336", + "G419", + "G420", + "G421", + "G423" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 733, + "num_cameras": 8, + "person_events": 683, + "vehicle_events": 5, + "scene_type": "pedestrian-dominant", + "top_activity": "person_puts_down_object", + "top_activity_count": 115 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 733, + "unique_activities": 20, + "activity_counts": { + "hand_interacts_with_person": 45, + "person_carries_heavy_object": 90, + "person_talks_to_person": 66, + "person_transfers_object": 14, + "person_enters_scene_through_structure": 108, + "person_picks_up_object": 104, + "person_puts_down_object": 115, + "person_opens_facility_door": 28, + "person_sits_down": 53, + "person_texts_on_phone": 21, + "person_exits_scene_through_structure": 45, + "vehicle_turns_left": 4, + "person_stands_up": 19, + "person_embraces_person": 11, + "person_talks_on_phone": 5, + "person_closes_facility_door": 1, + "vehicle_stops": 1, + "person_opens_vehicle_door": 1, + "person_exits_vehicle": 1, + "person_closes_vehicle_door": 1 + }, + "camera_event_counts": { + "G299": 277, + "G330": 274, + "G419": 36, + "G423": 81, + "G421": 9, + "G420": 35, + "G328": 13, + "G336": 8 + }, + "num_cameras": 8, + "duration_sec": 300.3, + "person_event_count": 683, + "vehicle_event_count": 5, + "cross_camera_activities": { + "hand_interacts_with_person": [ + "G299", + "G330" + ], + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G423" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423" + ], + "person_picks_up_object": [ + "G299", + "G330" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G423" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G421", + "G423" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "person_embraces_person": [ + "G299", + "G330" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G423" + ] + }, + "busiest_camera": "G299", + "busiest_camera_count": 277, + "unique_entities": 1258, + "person_entities": 926, + "top_3_activities": [ + [ + "person_puts_down_object", + 115 + ], + [ + "person_enters_scene_through_structure", + 108 + ], + [ + "person_picks_up_object", + 104 + ] + ] + }, + "scene_description": "The scene spans 8 cameras with 733 total activity events. The most frequent activities are: puts down object (115 occurrences), enters scene through structure (108 occurrences), picks up object (104 occurrences). 14 activities occur on multiple cameras. Camera G299 is the most active with 277 events.", + "scene_type": "pedestrian-dominant" + } + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-07.11-10.admin.raw.json b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-07.11-10.admin.raw.json new file mode 100644 index 0000000..74946b3 --- /dev/null +++ b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-07.11-10.admin.raw.json @@ -0,0 +1,315 @@ +{ + "slot": "2018-03-07.11-10.admin", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "geom_segformer+mevid_gpt+vlm+mevid_yolo", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G326", + "G329" + ], + "mevid_supported": false, + "mevid_persons_in_slot": 0, + "mevid_person_ids": [], + "mevid_person_cameras": {}, + "total_events": 39, + "total_entities": 36, + "cross_camera_clusters": 1, + "total_questions": 3, + "category_counts": { + "summarization": 1, + "counting": 1, + "best_camera": 1 + }, + "stats": { + "entities_with_mevid_gpt_descriptions": 0, + "entities_with_vlm_descriptions": 0, + "entities_with_geom_descriptions": 17, + "entities_with_mevid_yolo_descriptions": 0, + "entities_with_fallback_descriptions": 19, + "attribute_verification_questions": 0, + "best_camera_questions": 1, + "questions_with_debug_info": 3 + }, + "ambiguity": { + "avg_ambiguity": 0.0, + "pct_unique": 100.0, + "fully_unique": 3, + "total_questions": 3, + "worst_questions": [ + { + "question_id": "final_summarization_001", + "category": "summarization", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_counting_002", + "category": "counting", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_best_camera_003", + "category": "best_camera", + "max_ambiguity": 0, + "entities": [] + } + ] + }, + "validation_issues": 0, + "generation_time_sec": 2.68, + "qa_pairs": [ + { + "question_id": "final_summarization_001", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 2 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "An empty scene with minimal activity, captured on 1 camera", + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "A pedestrian-dominant scene across 2 cameras, primarily featuring leaving the camera's view through a doorway" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 2 cameras, primarily featuring leaving the camera's view through a doorway", + "requires_cameras": [ + "G326", + "G329" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 39, + "num_cameras": 2, + "person_events": 39, + "vehicle_events": 0, + "scene_type": "pedestrian-dominant", + "top_activity": "person_exits_scene_through_structure", + "top_activity_count": 19 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 39, + "unique_activities": 5, + "activity_counts": { + "person_talks_to_person": 1, + "person_exits_scene_through_structure": 19, + "person_opens_facility_door": 7, + "person_enters_scene_through_structure": 10, + "person_talks_on_phone": 2 + }, + "camera_event_counts": { + "G326": 16, + "G329": 23 + }, + "num_cameras": 2, + "duration_sec": 255.3, + "person_event_count": 39, + "vehicle_event_count": 0, + "cross_camera_activities": { + "person_exits_scene_through_structure": [ + "G326", + "G329" + ], + "person_opens_facility_door": [ + "G326", + "G329" + ], + "person_enters_scene_through_structure": [ + "G326", + "G329" + ], + "person_talks_on_phone": [ + "G326", + "G329" + ] + }, + "busiest_camera": "G329", + "busiest_camera_count": 23, + "unique_entities": 36, + "person_entities": 36, + "top_3_activities": [ + [ + "person_exits_scene_through_structure", + 19 + ], + [ + "person_enters_scene_through_structure", + 10 + ], + [ + "person_opens_facility_door", + 7 + ] + ] + }, + "scene_description": "The scene spans 2 cameras with 39 total activity events. The most frequent activities are: leaves the camera's view through a doorway (19 occurrences), enters the camera's view through a doorway (10 occurrences), opens facility door (7 occurrences). 4 activities occur on multiple cameras. Camera G329 is the most active with 23 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-07.11-10-00.11-15-00.admin.G326.r13.mp4", + "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-07/11/2018-03-07.11-10.admin/2018-03-07.11-10-00.11-15-00.admin.G326.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-07/11/2018-03-07.11-10.admin/2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4" + ] + }, + { + "question_id": "final_counting_002", + "category": "counting", + "difficulty": "easy", + "question_template": "How many times does someone perform the action of leaving the camera's view through a doorway across all cameras in this slot?", + "options": [ + "2", + "4", + "5", + "6" + ], + "correct_answer_index": 1, + "correct_answer": "4", + "reasoning": "The action of leaves the camera's view through a doorway was observed 4 times across 2 cameras (G329: 4). 15 cross-camera duplicates removed.", + "requires_cameras": [ + "G326", + "G329" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 4, + "cameras_involved": [ + "G326", + "G329" + ], + "activity": "person_exits_scene_through_structure", + "event_ids": [ + "G329_evt_1", + "G326_evt_2", + "G329_evt_2", + "G326_evt_3", + "G329_evt_4", + "G326_evt_6", + "G329_evt_5", + "G326_evt_7", + "G329_evt_6", + "G326_evt_8", + "G329_evt_7", + "G326_evt_9", + "G329_evt_8", + "G326_evt_10", + "G329_evt_9", + "G326_evt_11", + "G329_evt_22", + "G329_evt_13", + "G326_evt_17" + ], + "key_frames": [ + { + "camera": "G329", + "start_frame": 100, + "start_sec": 3.33, + "end_sec": 6.07, + "event_id": "G329_evt_1", + "clip_file": "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4", + "cluster_size": 4 + }, + { + "camera": "G329", + "start_frame": 1904, + "start_sec": 63.47, + "end_sec": 65.57, + "event_id": "G329_evt_4", + "clip_file": "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4", + "cluster_size": 12 + }, + { + "camera": "G329", + "start_frame": 3527, + "start_sec": 117.57, + "end_sec": 118.57, + "event_id": "G329_evt_22", + "clip_file": "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G329", + "start_frame": 6409, + "start_sec": 213.63, + "end_sec": 215.73, + "event_id": "G329_evt_13", + "clip_file": "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4", + "cluster_size": 2 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 4, + "cameras_involved": [ + "G326", + "G329" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 4, + "slot": "2018-03-07.11-10.admin", + "activity": "person_exits_scene_through_structure", + "activity_human": "leaves the camera's view through a doorway", + "clip_files": [ + "2018-03-07.11-10-00.11-15-00.admin.G326.r13.mp4", + "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-07/11/2018-03-07.11-10.admin/2018-03-07.11-10-00.11-15-00.admin.G326.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-07/11/2018-03-07.11-10.admin/2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_003", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G505", + "Camera G326", + "Camera G329", + "Camera G341" + ], + "correct_answer": "Camera G329", + "correct_answer_index": 2, + "requires_cameras": [ + "G329", + "G326" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G329", + "entrance_time_sec": 57.23, + "entity_description": "a person", + "cluster_id": "standalone_G329_1004", + "all_entrance_cameras": { + "G329": 57.23 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4", + "camera": "G329", + "activity": "person_enters_scene_through_structure", + "start_sec": 57.23 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-07/11/2018-03-07.11-10.admin/2018-03-07.11-10-00.11-15-00.admin.G326.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-07/11/2018-03-07.11-10.admin/2018-03-07.11-10-01.11-15-01.admin.G329.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-09.10-15.school.raw.json b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-09.10-15.school.raw.json new file mode 100644 index 0000000..035ec55 --- /dev/null +++ b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-09.10-15.school.raw.json @@ -0,0 +1,1318 @@ +{ + "slot": "2018-03-09.10-15.school", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo+geom_color", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "mevid_supported": false, + "mevid_persons_in_slot": 0, + "mevid_person_ids": [], + "mevid_person_cameras": {}, + "total_events": 1906, + "total_entities": 260, + "cross_camera_clusters": 14, + "total_questions": 12, + "category_counts": { + "temporal": 2, + "event_ordering": 2, + "spatial": 3, + "summarization": 1, + "counting": 1, + "best_camera": 3 + }, + "stats": { + "entities_with_mevid_descriptions": 0, + "entities_with_geom_descriptions": 235, + "entities_with_fallback_descriptions": 2, + "attribute_verification_questions": 0, + "best_camera_questions": 3, + "questions_with_debug_info": 12 + }, + "validation_issues": 4, + "generation_time_sec": 16.99, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a vehicle stops in the area near the school and a person, wearing an indigo top and plum pants exits vehicle in the area near the school -- which occurred first?", + "options": [ + "The vehicle stops in the area near the school occurred first", + "The person, wearing an indigo top and plum pants exits vehicle in the area near the school occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The vehicle stops in the area near the school occurred first", + "requires_cameras": [ + "G424", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "vehicle_stops", + "camera": "G638", + "start_sec": 99.7, + "end_sec": 102.9, + "actor_ids": [ + 55 + ], + "description": "a vehicle stops in the area near the school" + }, + "event_b": { + "activity": "person_exits_vehicle", + "camera": "G424", + "start_sec": 104.03, + "end_sec": 109.1, + "actor_ids": [ + 18, + 19 + ], + "description": "a person, wearing an indigo top and plum pants exits vehicle in the area near the school" + }, + "gap_sec": 1.13, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G638", + "activity": "vehicle_stops", + "actor_ids": [ + 55 + ], + "frame_range": [ + 2991, + 3087 + ], + "timestamp": "99.70-102.90s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4", + "entity_description": "a vehicle" + }, + "event_b": { + "camera": "G424", + "activity": "person_exits_vehicle", + "actor_ids": [ + 18, + 19 + ], + "frame_range": [ + 3121, + 3273 + ], + "timestamp": "104.03-109.10s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "entity_description": "a person, wearing an indigo top and plum pants" + }, + "gap_sec": 1.13, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 72 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person closes facility door and a person enters the camera's view through a doorway -- which occurred first?", + "options": [ + "The person closes facility door occurred first", + "The person enters the camera's view through a doorway occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The person enters the camera's view through a doorway occurred first", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G330", + "start_sec": 15.77, + "end_sec": 18.73, + "actor_ids": [ + 2 + ], + "description": "a person enters the camera's view through a doorway" + }, + "event_b": { + "activity": "person_closes_facility_door", + "camera": "G299", + "start_sec": 21.4, + "end_sec": 24.5, + "actor_ids": [ + 800 + ], + "description": "a person closes facility door" + }, + "gap_sec": 2.67, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G330", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 2 + ], + "frame_range": [ + 473, + 562 + ], + "timestamp": "15.77-18.73s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "entity_description": null + }, + "event_b": { + "camera": "G299", + "activity": "person_closes_facility_door", + "actor_ids": [ + 800 + ], + "frame_range": [ + 642, + 735 + ], + "timestamp": "21.40-24.50s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G299.r13.mp4", + "entity_description": null + }, + "gap_sec": 2.67, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 14 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G299.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_003", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. a person with black hair, wearing a black top and black pants, black shoes, carrying a bag, reading a document\nII. a person with indigo hair, wearing an indigo top and navy pants, indigo shoes, talking to person\nIII. a person with indigo hair, wearing an indigo top and indigo skirt, blue shoes, with a scarf, opening a facility door\nIV. A person leaving the camera's view through a doorway\nWhich is the correct chronological order?", + "options": [ + "II -> IV -> I -> III", + "III -> IV -> I -> II", + "III -> I -> IV -> II", + "I -> III -> IV -> II" + ], + "correct_answer_index": 2, + "correct_answer": "III -> I -> IV -> II", + "requires_cameras": [ + "G330", + "G339", + "G419", + "G420" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_opens_facility_door", + "camera": "G419", + "start_sec": 18.9, + "description": "a person with indigo hair, wearing an indigo top and indigo skirt, blue shoes, with a scarf, opening a facility door" + }, + { + "activity": "person_reads_document", + "camera": "G420", + "start_sec": 23.47, + "description": "a person with black hair, wearing a black top and black pants, black shoes, carrying a bag, reading a document" + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G330", + "start_sec": 31.03, + "description": "A person leaving the camera's view through a doorway" + }, + { + "activity": "person_talks_to_person", + "camera": "G339", + "start_sec": 38.23, + "description": "a person with indigo hair, wearing an indigo top and navy pants, indigo shoes, talking to person" + } + ], + "min_gap_sec": 2.3, + "num_events": 4, + "gaps_sec": [ + 2.3, + 3.8, + 4.33 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G419", + "activity": "person_opens_facility_door", + "actor_ids": [ + 69 + ], + "frame_range": [ + 567, + 635 + ], + "timestamp": "18.90-21.17s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "entity_description": "a person with indigo hair, wearing an indigo top and indigo skirt, blue shoes, with a scarf" + }, + { + "camera": "G420", + "activity": "person_reads_document", + "actor_ids": [ + 202 + ], + "frame_range": [ + 704, + 817 + ], + "timestamp": "23.47-27.23s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G420.r13.mp4", + "entity_description": "a person with black hair, wearing a black top and black pants, black shoes, carrying a bag" + }, + { + "camera": "G330", + "activity": "person_exits_scene_through_structure", + "actor_ids": [ + 13 + ], + "frame_range": [ + 931, + 1017 + ], + "timestamp": "31.03-33.90s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "entity_description": null + }, + { + "camera": "G339", + "activity": "person_talks_to_person", + "actor_ids": [ + 6, + 5 + ], + "frame_range": [ + 1147, + 1203 + ], + "timestamp": "38.23-40.10s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G339.r13.mp4", + "entity_description": "a person with indigo hair, wearing an indigo top and navy pants, indigo shoes" + } + ], + "presentation_order": [ + 1, + 3, + 0, + 2 + ], + "correct_permutation": [ + 2, + 0, + 3, + 1 + ], + "mevid_validated": true, + "group_score": 14.0, + "related_pairs": 1, + "described_events": 3 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G339.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G420.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_004", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. A person entering the camera's view through a doorway\nII. A person entering the camera's view through a doorway\nIII. a person, wearing an indigo top and indigo pants, texting on phone\nIV. A person closing a facility door\nWhich is the correct chronological order?", + "options": [ + "IV -> II -> III -> I", + "III -> I -> II -> IV", + "I -> II -> III -> IV", + "I -> III -> II -> IV" + ], + "correct_answer_index": 3, + "correct_answer": "I -> III -> II -> IV", + "requires_cameras": [ + "G299", + "G330", + "G423", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_enters_scene_through_structure", + "camera": "G423", + "start_sec": 5.23, + "description": "A person entering the camera's view through a doorway" + }, + { + "activity": "person_texts_on_phone", + "camera": "G638", + "start_sec": 8.93, + "description": "a person, wearing an indigo top and indigo pants, texting on phone" + }, + { + "activity": "person_enters_scene_through_structure", + "camera": "G330", + "start_sec": 15.77, + "description": "A person entering the camera's view through a doorway" + }, + { + "activity": "person_closes_facility_door", + "camera": "G299", + "start_sec": 21.4, + "description": "A person closing a facility door" + } + ], + "min_gap_sec": 2.46, + "num_events": 4, + "gaps_sec": [ + 2.46, + 2.57, + 2.67 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G423", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 2 + ], + "frame_range": [ + 157, + 194 + ], + "timestamp": "5.23-6.47s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G423.r13.mp4", + "entity_description": null + }, + { + "camera": "G638", + "activity": "person_texts_on_phone", + "actor_ids": [ + 42 + ], + "frame_range": [ + 268, + 396 + ], + "timestamp": "8.93-13.20s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4", + "entity_description": "a person, wearing an indigo top and indigo pants" + }, + { + "camera": "G330", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 2 + ], + "frame_range": [ + 473, + 562 + ], + "timestamp": "15.77-18.73s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "entity_description": null + }, + { + "camera": "G299", + "activity": "person_closes_facility_door", + "actor_ids": [ + 800 + ], + "frame_range": [ + 642, + 735 + ], + "timestamp": "21.40-24.50s", + "fps": 30.0, + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G299.r13.mp4", + "entity_description": null + } + ], + "presentation_order": [ + 0, + 2, + 1, + 3 + ], + "correct_permutation": [ + 0, + 2, + 1, + 3 + ], + "mevid_validated": true, + "group_score": 13.5, + "related_pairs": 2, + "described_events": 1 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G299.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G423.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_spatial_005", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing an indigo top and teal pants and a person with blue hair, wearing a navy top and indigo pants, indigo shoes come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing an indigo top and teal pants and a person with blue hair, wearing a navy top and indigo pants, indigo shoes come within 0.7 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G638" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G638_actor_13", + "entity_b": "G638_actor_14", + "entity_a_desc": "a person, wearing an indigo top and teal pants", + "entity_b_desc": "a person with blue hair, wearing a navy top and indigo pants, indigo shoes", + "min_distance_meters": 0.69, + "closest_frame": 2850, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G638_actor_13", + "camera": "G638", + "description": "a person, wearing an indigo top and teal pants", + "bbox_at_closest": [ + 583, + 529, + 637, + 658 + ], + "timestamp": "84.70-98.00s" + }, + "entity_b": { + "entity_id": "G638_actor_14", + "camera": "G638", + "description": "a person with blue hair, wearing a navy top and indigo pants, indigo shoes", + "bbox_at_closest": [ + 518, + 516, + 587, + 670 + ], + "timestamp": "84.70-98.00s" + }, + "min_distance_meters": 0.69, + "closest_frame": 2850, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "2541-2940", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4", + "clip_files": [ + "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_spatial_006", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing an indigo top and navy pants and a person, wearing an indigo top and indigo pants come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing an indigo top and navy pants and a person, wearing an indigo top and indigo pants come within 1.7 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G424" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G424_actor_29", + "entity_b": "G424_actor_34", + "entity_a_desc": "a person, wearing an indigo top and navy pants", + "entity_b_desc": "a person, wearing an indigo top and indigo pants", + "min_distance_meters": 1.68, + "closest_frame": 3600, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G424_actor_29", + "camera": "G424", + "description": "a person, wearing an indigo top and navy pants", + "bbox_at_closest": [ + 1785, + 475, + 1852, + 579 + ], + "timestamp": "110.57-121.43s" + }, + "entity_b": { + "entity_id": "G424_actor_34", + "camera": "G424", + "description": "a person, wearing an indigo top and indigo pants", + "bbox_at_closest": [ + 1847, + 456, + 1907, + 571 + ], + "timestamp": "114.67-121.67s" + }, + "min_distance_meters": 1.68, + "closest_frame": 3600, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "3440-3643", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "clip_files": [ + "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4" + ] + }, + { + "question_id": "final_spatial_007", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing a navy top and indigo pants and a person, wearing a navy top and orange pants come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing a navy top and indigo pants and a person, wearing a navy top and orange pants come within 1.1 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G638" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G638_actor_22", + "entity_b": "G638_actor_68", + "entity_a_desc": "a person, wearing a navy top and indigo pants", + "entity_b_desc": "a person, wearing a navy top and orange pants", + "min_distance_meters": 1.14, + "closest_frame": 5010, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G638_actor_22", + "camera": "G638", + "description": "a person, wearing a navy top and indigo pants", + "bbox_at_closest": [ + 901, + 402, + 957, + 504 + ], + "timestamp": "157.57-168.17s" + }, + "entity_b": { + "entity_id": "G638_actor_68", + "camera": "G638", + "description": "a person, wearing a navy top and orange pants", + "bbox_at_closest": [ + 877, + 427, + 930, + 527 + ], + "timestamp": "166.23-167.43s" + }, + "min_distance_meters": 1.14, + "closest_frame": 5010, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "4987-5023", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4", + "clip_files": [ + "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_summarization_008", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 13 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "A pedestrian-dominant scene across 13 cameras, primarily featuring carrying a heavy object", + "A single-camera scene showing only indoor activities", + "An empty scene with minimal activity, captured on 10 cameras", + "A vehicle-focused scene with mostly parking and driving activity" + ], + "correct_answer_index": 0, + "correct_answer": "A pedestrian-dominant scene across 13 cameras, primarily featuring carrying a heavy object", + "requires_cameras": [ + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 1906, + "num_cameras": 13, + "person_events": 1856, + "vehicle_events": 35, + "scene_type": "pedestrian-dominant", + "top_activity": "person_carries_heavy_object", + "top_activity_count": 349 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 1906, + "unique_activities": 28, + "activity_counts": { + "person_carries_heavy_object": 349, + "person_talks_to_person": 155, + "person_texts_on_phone": 39, + "person_exits_scene_through_structure": 182, + "person_opens_facility_door": 45, + "person_enters_vehicle": 1, + "person_closes_vehicle_door": 10, + "person_puts_down_object": 349, + "person_enters_scene_through_structure": 150, + "person_talks_on_phone": 12, + "person_transfers_object": 79, + "person_picks_up_object": 306, + "person_closes_facility_door": 9, + "vehicle_stops": 6, + "vehicle_turns_left": 14, + "vehicle_starts": 5, + "person_opens_trunk": 1, + "person_closes_trunk": 1, + "person_reads_document": 1, + "person_sits_down": 98, + "hand_interacts_with_person": 15, + "person_opens_vehicle_door": 9, + "person_exits_vehicle": 10, + "vehicle_turns_right": 8, + "person_stands_up": 45, + "vehicle_drops_off_person": 2, + "person_embraces_person": 4, + "person_unloads_vehicle": 1 + }, + "camera_event_counts": { + "G299": 763, + "G328": 16, + "G330": 770, + "G420": 31, + "G421": 48, + "G639": 21, + "G638": 51, + "G336": 24, + "G423": 118, + "G419": 27, + "G339": 6, + "G424": 23, + "G300": 8 + }, + "num_cameras": 13, + "duration_sec": 300.3, + "person_event_count": 1856, + "vehicle_event_count": 35, + "cross_camera_activities": { + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G421", + "G638" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G423", + "G638", + "G639" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G638" + ], + "person_closes_vehicle_door": [ + "G300", + "G328", + "G336", + "G424", + "G638" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G421", + "G638" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_picks_up_object": [ + "G299", + "G300", + "G330", + "G421" + ], + "person_closes_facility_door": [ + "G299", + "G330", + "G419", + "G420" + ], + "vehicle_stops": [ + "G300", + "G328", + "G336", + "G424", + "G638" + ], + "vehicle_turns_left": [ + "G328", + "G336", + "G339", + "G424" + ], + "vehicle_starts": [ + "G300", + "G336" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "hand_interacts_with_person": [ + "G299", + "G330", + "G419", + "G420", + "G638" + ], + "person_opens_vehicle_door": [ + "G300", + "G328", + "G336", + "G424", + "G638" + ], + "person_exits_vehicle": [ + "G300", + "G328", + "G336", + "G424" + ], + "vehicle_turns_right": [ + "G336", + "G339", + "G424", + "G639" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "vehicle_drops_off_person": [ + "G300", + "G336" + ], + "person_embraces_person": [ + "G299", + "G330" + ] + }, + "busiest_camera": "G330", + "busiest_camera_count": 770, + "unique_entities": 260, + "person_entities": 207, + "top_3_activities": [ + [ + "person_carries_heavy_object", + 349 + ], + [ + "person_puts_down_object", + 349 + ], + [ + "person_picks_up_object", + 306 + ] + ] + }, + "scene_description": "The scene spans 13 cameras with 1906 total activity events. The most frequent activities are: carries heavy object (349 occurrences), puts down object (349 occurrences), picks up object (306 occurrences). 23 activities occur on multiple cameras. Camera G330 is the most active with 770 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-09.10-15-00.10-20-00.school.G299.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G300.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G421.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G423.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G639.r13.mp4", + "2018-03-09.10-15-01.10-20-01.school.G328.r13.mp4", + "2018-03-09.10-15-01.10-20-01.school.G339.r13.mp4", + "2018-03-09.10-15-01.10-20-01.school.G420.r13.mp4", + "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G299.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G300.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G330.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G421.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G423.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G639.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G328.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G339.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G420.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_counting_009", + "category": "counting", + "difficulty": "medium", + "question_template": "How many times does someone perform the action of closing a vehicle door across all cameras in this slot?", + "options": [ + "6", + "8", + "10", + "16" + ], + "correct_answer_index": 1, + "correct_answer": "8", + "reasoning": "The action of closes vehicle door was observed 8 times across 5 cameras (G300: 1, G328: 1, G336: 3, G424: 3). 2 cross-camera duplicates removed.", + "requires_cameras": [ + "G300", + "G328", + "G336", + "G424", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 8, + "cameras_involved": [ + "G300", + "G328", + "G336", + "G424", + "G638" + ], + "activity": "person_closes_vehicle_door", + "event_ids": [ + "G336_evt_1", + "G328_evt_22", + "G336_evt_17", + "G336_evt_14", + "G424_evt_9", + "G424_evt_13", + "G638_evt_67", + "G424_evt_16", + "G424_evt_6", + "G300_evt_4" + ], + "key_frames": [ + { + "camera": "G336", + "start_frame": 16, + "start_sec": 0.53, + "end_sec": 2.57, + "event_id": "G336_evt_1", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G328", + "start_frame": 1064, + "start_sec": 35.47, + "end_sec": 37.33, + "event_id": "G328_evt_22", + "clip_file": "2018-03-09.10-15-01.10-20-01.school.G328.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G336", + "start_frame": 3184, + "start_sec": 106.13, + "end_sec": 109.63, + "event_id": "G336_evt_17", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G336", + "start_frame": 3265, + "start_sec": 108.83, + "end_sec": 111.17, + "event_id": "G336_evt_14", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "cluster_size": 3 + }, + { + "camera": "G424", + "start_frame": 3299, + "start_sec": 109.97, + "end_sec": 113.23, + "event_id": "G424_evt_9", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G424", + "start_frame": 3300, + "start_sec": 110.0, + "end_sec": 111.8, + "event_id": "G424_evt_13", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G424", + "start_frame": 3412, + "start_sec": 113.73, + "end_sec": 115.43, + "event_id": "G424_evt_6", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G300", + "start_frame": 8651, + "start_sec": 288.37, + "end_sec": 290.07, + "event_id": "G300_evt_4", + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G300.r13.mp4", + "cluster_size": 1 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 8, + "cameras_involved": [ + "G300", + "G328", + "G336", + "G424", + "G638" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 12, + "slot": "2018-03-09.10-15.school", + "activity": "person_closes_vehicle_door", + "activity_human": "closes vehicle door", + "clip_files": [ + "2018-03-09.10-15-00.10-20-00.school.G300.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "2018-03-09.10-15-01.10-20-01.school.G328.r13.mp4", + "2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G300.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G424.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G328.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-01.10-20-01.school.G638.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_010", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person with black hair, wearing a navy top and black pants, indigo shoes into the scene?", + "options": [ + "Camera G339", + "Camera G419", + "Camera G328", + "Camera G299" + ], + "correct_answer": "Camera G419", + "correct_answer_index": 1, + "requires_cameras": [ + "G419", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G419", + "entrance_time_sec": 288.27, + "entity_description": "a person with black hair, wearing a navy top and black pants, indigo shoes", + "cluster_id": "cluster_13", + "all_entrance_cameras": { + "G419": 288.27 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "start_sec": 288.27 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_011", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person with indigo hair, wearing an indigo top and indigo pants, indigo shoes, carrying a bag into the scene?", + "options": [ + "Camera G419", + "Camera G421", + "Camera G423", + "Camera G424" + ], + "correct_answer": "Camera G419", + "correct_answer_index": 0, + "requires_cameras": [ + "G419", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G419", + "entrance_time_sec": 37.3, + "entity_description": "a person with indigo hair, wearing an indigo top and indigo pants, indigo shoes, carrying a bag", + "cluster_id": "cluster_2", + "all_entrance_cameras": { + "G419": 37.3 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "start_sec": 37.3 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_012", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person with black hair, wearing a black top and black pants, black shoes, with a hat into the scene?", + "options": [ + "Camera G299", + "Camera G339", + "Camera G419", + "Camera G420" + ], + "correct_answer": "Camera G419", + "correct_answer_index": 2, + "requires_cameras": [ + "G419", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G419", + "entrance_time_sec": 125.1, + "entity_description": "a person with black hair, wearing a black top and black pants, black shoes, with a hat", + "cluster_id": "cluster_7", + "all_entrance_cameras": { + "G419": 125.1 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4", + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "start_sec": 125.1 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-15.school/2018-03-09.10-15-00.10-20-00.school.G419.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-09.10-40.bus.raw.json b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-09.10-40.bus.raw.json new file mode 100644 index 0000000..7412b7c --- /dev/null +++ b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-09.10-40.bus.raw.json @@ -0,0 +1,576 @@ +{ + "slot": "2018-03-09.10-40.bus", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "geom_segformer+mevid_gpt+vlm+mevid_yolo", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G340", + "G505", + "G506" + ], + "mevid_supported": false, + "mevid_persons_in_slot": 0, + "mevid_person_ids": [], + "mevid_person_cameras": {}, + "total_events": 59, + "total_entities": 41, + "cross_camera_clusters": 0, + "total_questions": 4, + "category_counts": { + "temporal": 2, + "summarization": 1, + "counting": 1 + }, + "stats": { + "entities_with_mevid_gpt_descriptions": 0, + "entities_with_vlm_descriptions": 0, + "entities_with_geom_descriptions": 26, + "entities_with_mevid_yolo_descriptions": 0, + "entities_with_fallback_descriptions": 0, + "attribute_verification_questions": 0, + "best_camera_questions": 0, + "questions_with_debug_info": 4 + }, + "ambiguity": { + "avg_ambiguity": 0.0, + "pct_unique": 100.0, + "fully_unique": 4, + "total_questions": 4, + "worst_questions": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "max_ambiguity": 0, + "entities": [ + { + "camera": "G340", + "description_preview": "a person, wearing a blue top and blue pants opens ", + "same_desc_count": 1, + "ambiguity": 0 + }, + { + "camera": "G505", + "description_preview": "a vehicle makes a U-turn", + "same_desc_count": 1, + "ambiguity": 0 + } + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "max_ambiguity": 0, + "entities": [ + { + "camera": "G505", + "description_preview": "a vehicle turns left", + "same_desc_count": 1, + "ambiguity": 0 + }, + { + "camera": "G506", + "description_preview": "a person talks to person", + "same_desc_count": 1, + "ambiguity": 0 + } + ] + }, + { + "question_id": "final_summarization_003", + "category": "summarization", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_counting_004", + "category": "counting", + "max_ambiguity": 0, + "entities": [] + } + ] + }, + "validation_issues": 0, + "generation_time_sec": 3.83, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person, wearing a blue top and blue pants opens vehicle door and a vehicle makes a U-turn -- which occurred first?", + "options": [ + "The person, wearing a blue top and blue pants opens vehicle door occurred first", + "The vehicle makes a U-turn occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The person, wearing a blue top and blue pants opens vehicle door occurred first", + "requires_cameras": [ + "G340", + "G505" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_vehicle_door", + "camera": "G340", + "start_sec": 145.63, + "end_sec": 148.13, + "actor_ids": [ + 12, + 11 + ], + "description": "a person, wearing a blue top and blue pants opens vehicle door" + }, + "event_b": { + "activity": "vehicle_makes_u_turn", + "camera": "G505", + "start_sec": 151.47, + "end_sec": 156.67, + "actor_ids": [ + 6 + ], + "description": "a vehicle makes a U-turn" + }, + "gap_sec": 3.34, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G340", + "activity": "person_opens_vehicle_door", + "actor_ids": [ + 12, + 11 + ], + "frame_range": [ + 4369, + 4444 + ], + "timestamp": "145.63-148.13s", + "fps": 30.0, + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "entity_description": "a person, wearing a blue top and blue pants" + }, + "event_b": { + "camera": "G505", + "activity": "vehicle_makes_u_turn", + "actor_ids": [ + 6 + ], + "frame_range": [ + 4544, + 4700 + ], + "timestamp": "151.47-156.67s", + "fps": 30.0, + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4", + "entity_description": "a vehicle" + }, + "gap_sec": 3.34, + "connection_type": "unrelated", + "connection_strength": "weak", + "connection_score": 2.0, + "relationship": null, + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 25 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G331.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G475.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G508.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G509.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person talks to person and a vehicle turns left -- which occurred first?", + "options": [ + "The person talks to person occurred first", + "The vehicle turns left occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The vehicle turns left occurred first", + "requires_cameras": [ + "G505", + "G506" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "vehicle_turns_left", + "camera": "G505", + "start_sec": 56.1, + "end_sec": 59.3, + "actor_ids": [ + 5 + ], + "description": "a vehicle turns left" + }, + "event_b": { + "activity": "person_talks_to_person", + "camera": "G506", + "start_sec": 78.63, + "end_sec": 90.0, + "actor_ids": [ + 18, + 17 + ], + "description": "a person talks to person" + }, + "gap_sec": 19.33, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G505", + "activity": "vehicle_turns_left", + "actor_ids": [ + 5 + ], + "frame_range": [ + 1683, + 1779 + ], + "timestamp": "56.10-59.30s", + "fps": 30.0, + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4", + "entity_description": "a vehicle" + }, + "event_b": { + "camera": "G506", + "activity": "person_talks_to_person", + "actor_ids": [ + 18, + 17 + ], + "frame_range": [ + 2359, + 2700 + ], + "timestamp": "78.63-90.00s", + "fps": 30.0, + "clip_file": "2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "entity_description": null + }, + "gap_sec": 19.33, + "connection_type": "unrelated", + "connection_strength": "weak", + "connection_score": 2.0, + "relationship": null, + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 62 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G331.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G475.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G508.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G509.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4" + ] + }, + { + "question_id": "final_summarization_003", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 3 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "A scene dominated by people entering and exiting buildings", + "A single-camera scene showing only indoor activities", + "A mixed activity scene across 3 cameras, primarily featuring talking to person", + "An empty scene with minimal activity, captured on 1 camera" + ], + "correct_answer_index": 2, + "correct_answer": "A mixed activity scene across 3 cameras, primarily featuring talking to person", + "requires_cameras": [ + "G340", + "G505", + "G506" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 59, + "num_cameras": 3, + "person_events": 38, + "vehicle_events": 21, + "scene_type": "mixed activity", + "top_activity": "person_talks_to_person", + "top_activity_count": 10 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 59, + "unique_activities": 13, + "activity_counts": { + "person_talks_to_person": 10, + "vehicle_turns_left": 10, + "person_enters_vehicle": 7, + "person_opens_vehicle_door": 6, + 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"vehicle_turns_left", + 10 + ], + [ + "person_enters_vehicle", + 7 + ] + ] + }, + "scene_description": "The scene spans 3 cameras with 59 total activity events. The most frequent activities are: talks to person (10 occurrences), turns left (10 occurrences), enters vehicle (7 occurrences). 6 activities occur on multiple cameras. Camera G340 is the most active with 38 events.", + "scene_type": "mixed activity", + "clip_files": [ + "2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G331.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G475.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G508.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G509.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + 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"G506_evt_15", + "G340_evt_26" + ], + "key_frames": [ + { + "camera": "G505", + "start_frame": 0, + "start_sec": 0.0, + "end_sec": 31.07, + "event_id": "G505_evt_0", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G506", + "start_frame": 2359, + "start_sec": 78.63, + "end_sec": 90.0, + "event_id": "G506_evt_13", + "clip_file": "2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G505", + "start_frame": 3142, + "start_sec": 104.73, + "end_sec": 163.13, + "event_id": "G505_evt_4", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G340", + "start_frame": 4443, + "start_sec": 148.1, + "end_sec": 154.9, + "event_id": "G340_evt_23", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G340", + "start_frame": 4771, + "start_sec": 159.03, + "end_sec": 200.73, + "event_id": "G340_evt_24", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G506", + "start_frame": 5261, + "start_sec": 175.37, + "end_sec": 183.33, + "event_id": "G506_evt_14", + "clip_file": "2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G340", + "start_frame": 6018, + "start_sec": 200.6, + "end_sec": 265.63, + "event_id": "G340_evt_25", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G340", + "start_frame": 6411, + "start_sec": 213.7, + "end_sec": 220.9, + "event_id": "G340_evt_38", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G506", + "start_frame": 6869, + "start_sec": 228.97, + "end_sec": 240.1, + "event_id": "G506_evt_15", + "clip_file": "2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G340", + "start_frame": 8099, + "start_sec": 269.97, + "end_sec": 299.97, + "event_id": "G340_evt_26", + "clip_file": "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "cluster_size": 1 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 10, + "cameras_involved": [ + "G340", + "G505", + "G506" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 10, + "slot": "2018-03-09.10-40.bus", + "activity": "person_talks_to_person", + "activity_human": "talks to person", + "clip_files": [ + "2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G331.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G475.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G506.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G508.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-00.10-45-00.bus.G509.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G340.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-09/10/2018-03-09.10-40.bus/2018-03-09.10-40-01.10-45-01.bus.G505.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.11-25.school.raw.json b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.11-25.school.raw.json new file mode 100644 index 0000000..ce727d7 --- /dev/null +++ b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.11-25.school.raw.json @@ -0,0 +1,1553 @@ +{ + "slot": "2018-03-11.11-25.school", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "geom_segformer+mevid_gpt+vlm+mevid_yolo", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 53, + "mevid_person_ids": [ + "0003", + "0004", + "0007", + "0008", + "0009", + "0014", + "0016", + "0018", + "0023", + "0024", + "0025", + "0030", + "0032", + "0033", + "0034", + "0035", + "0036", + "0037", + "0039", + "0040", + "0041", + "0042", + "0043", + "0049", + "0051", + "0054", + "0055", + "0056", + "0057", + "0062", + "0064", + "0067", + "0068", + "0072", + "0074", + "0076", + "0077", + "0078", + "0081", + "0082", + "0083", + "0085", + "0086", + "0088", + "0090", + "0092", + "0095", + "0096", + "0097", + "0099", + "0101", + "0103", + "0109" + ], + 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the area near the sch", + "same_desc_count": 1, + "ambiguity": 0 + }, + { + "camera": "G339", + "description_preview": "a person in blue top and blue pants exits vehicle ", + "same_desc_count": 1, + "ambiguity": 0 + } + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "max_ambiguity": 0, + "entities": [ + { + "camera": "G339", + "description_preview": "a person in blue top and blue pants opens vehicle ", + "same_desc_count": 1, + "ambiguity": 0 + }, + { + "camera": "G424", + "description_preview": "a person in blue top and blue pants texts on phone", + "same_desc_count": 1, + "ambiguity": 0 + } + ] + }, + { + "question_id": "final_spatial_003", + "category": "spatial", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_spatial_004", + "category": "spatial", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_spatial_005", + "category": "spatial", + "max_ambiguity": 0, + "entities": [] + } + ] + }, + "validation_issues": 1, + "generation_time_sec": 12.39, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a vehicle comes to a stop in the area near the school and a person in blue top and blue pants exits vehicle in the area near the gas station -- which occurred first?", + "options": [ + "The vehicle comes to a stop in the area near the school occurred first", + "The person in blue top and blue pants exits vehicle in the area near the gas station occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The vehicle comes to a stop in the area near the school occurred first", + "requires_cameras": [ + "G339", + "G638" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "vehicle_stops", + "camera": "G638", + "start_sec": 120.23, + "end_sec": 122.93, + "actor_ids": [ + 2592407224656078747 + ], + "description": "a vehicle comes to a stop in the area near the school" + }, + "event_b": { + "activity": "person_exits_vehicle", + "camera": "G339", + "start_sec": 127.13, + "end_sec": 133.43, + "actor_ids": [ + 6, + 5 + ], + "description": "a person in blue top and blue pants exits vehicle in the area near the gas station" + }, + "gap_sec": 4.2, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G638", + "activity": "vehicle_stops", + "actor_ids": [ + 2592407224656078747 + ], + "frame_range": [ + 3607, + 3688 + ], + "timestamp": "120.23-122.93s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "entity_description": "a vehicle" + }, + "event_b": { + "camera": "G339", + "activity": "person_exits_vehicle", + "actor_ids": [ + 6, + 5 + ], + "frame_range": [ + 3814, + 4003 + ], + "timestamp": "127.13-133.43s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + "gap_sec": 4.2, + "connection_type": "related_activities_causal", + "connection_strength": "medium", + "connection_score": 4.0, + "relationship": "causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 72 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G328.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + 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"/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-04.11-30-04.school.G300.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person in blue top and blue pants texts on phone in the area near the school and a person in blue top and blue pants opens vehicle door -- which occurred first?", + "options": [ + "The person in blue top and blue pants texts on phone in the area near the school occurred first", + "The person in blue top and blue pants opens vehicle door occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The person in blue top and blue pants opens vehicle door occurred first", + "requires_cameras": [ + "G339", + "G424" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_vehicle_door", + "camera": "G339", + "start_sec": 127.13, + "end_sec": 131.03, + "actor_ids": [ + 3, + 4 + ], + "description": "a person in blue top and blue pants opens vehicle door" + }, + "event_b": { + "activity": "person_texts_on_phone", + "camera": "G424", + "start_sec": 132.53, + "end_sec": 143.3, + "actor_ids": [ + 8280409664574006212 + ], + "description": "a person in blue top and blue pants texts on phone in the area near the school" + }, + "gap_sec": 1.5, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G339", + "activity": "person_opens_vehicle_door", + "actor_ids": [ + 3, + 4 + ], + "frame_range": [ + 3814, + 3931 + ], + "timestamp": "127.13-131.03s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + "event_b": { + "camera": "G424", + "activity": "person_texts_on_phone", + "actor_ids": [ + 8280409664574006212 + ], + "frame_range": [ + 3976, + 4299 + ], + "timestamp": "132.53-143.30s", + "fps": 30.0, + "clip_file": "2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "entity_description": "a person in blue top and blue pants" + }, + "gap_sec": 1.5, + "connection_type": "unrelated", + "connection_strength": "weak", + "connection_score": 2.0, + "relationship": null, + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 103 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G328.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G336.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", 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"final_spatial_004", + "category": "spatial", + "difficulty": "medium", + "question_template": "How close do a person in blue top and blue pants (closing a vehicle door) and a person in blue top and blue pants (texting on phone) come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 2, + "correct_answer": "They remain far apart (more than 15 meters)", + "reasoning": "The closest a person in blue top and blue pants (closing a vehicle door) and a person in blue top and blue pants (texting on phone) come to each other is approximately 44.6 meters, placing them far apart.", + "requires_cameras": [ + "G424" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G424_actor_1012367777731716038", + "entity_b": "G424_actor_8280409664574006212", + "entity_a_desc": "a person in blue top and blue pants (closing a vehicle door)", + "entity_b_desc": "a person in blue top and blue pants (texting on phone)", + "min_distance_meters": 44.61, + "closest_frame": 3990, + "proximity": "far", + "crosses_paths": false, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G424_actor_1012367777731716038", + "camera": "G424", + "description": "a person in blue top and blue pants (closing a vehicle door)", + "bbox_at_closest": [ + 1850, + 433, + 1897, + 529 + ], + "timestamp": "131.63-133.70s" + }, + "entity_b": { + "entity_id": "G424_actor_8280409664574006212", + "camera": "G424", + "description": "a person in blue top and blue pants (texting on phone)", + "bbox_at_closest": [ + 106, + 761, + 185, + 925 + ], + "timestamp": "132.53-143.30s" + }, + "min_distance_meters": 44.61, + "closest_frame": 3990, + "proximity": "far", + "crosses_paths": false, + 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"final_spatial_005", + "category": "spatial", + "difficulty": "medium", + "question_template": "How close do a person in blue top and blue pants (opening a vehicle door) and a person in blue top and blue pants (texting on phone) come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 2, + "correct_answer": "They remain far apart (more than 15 meters)", + "reasoning": "The closest a person in blue top and blue pants (opening a vehicle door) and a person in blue top and blue pants (texting on phone) come to each other is approximately 45.6 meters, placing them far apart.", + "requires_cameras": [ + "G424" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G424_actor_3990123032563622951", + "entity_b": "G424_actor_8280409664574006212", + "entity_a_desc": "a person in blue top and blue pants (opening a vehicle door)", + "entity_b_desc": "a person in blue top and blue pants (texting on phone)", + "min_distance_meters": 45.6, + "closest_frame": 4050, + "proximity": "far", + "crosses_paths": false, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G424_actor_3990123032563622951", + "camera": "G424", + "description": "a person in blue top and blue pants (opening a vehicle door)", + "bbox_at_closest": [ + 1867, + 438, + 1902, + 530 + ], + "timestamp": "134.90-136.40s" + }, + "entity_b": { + "entity_id": "G424_actor_8280409664574006212", + "camera": "G424", + "description": "a person in blue top and blue pants (texting on phone)", + "bbox_at_closest": [ + 245, + 799, + 330, + 976 + ], + "timestamp": "132.53-143.30s" + }, + "min_distance_meters": 45.6, + "closest_frame": 4050, + "proximity": "far", + "crosses_paths": false, + 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"final_summarization_006", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 13 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "An empty scene with minimal activity, captured on 10 cameras", + "A pedestrian-dominant scene across 13 cameras, primarily featuring putting down object" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 13 cameras, primarily featuring putting down object", + "requires_cameras": [ + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G419", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 793, + "num_cameras": 13, + "person_events": 726, + "vehicle_events": 21, + 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2 + }, + "camera_event_counts": { + "G299": 277, + "G300": 1, + "G330": 274, + "G419": 36, + "G423": 81, + "G638": 8, + "G421": 9, + "G420": 35, + "G639": 28, + "G328": 13, + "G424": 17, + "G336": 8, + "G339": 6 + }, + "num_cameras": 13, + "duration_sec": 300.3, + "person_event_count": 726, + "vehicle_event_count": 21, + "cross_camera_activities": { + "hand_interacts_with_person": [ + "G299", + "G330" + ], + "person_carries_heavy_object": [ + "G299", + "G330" + ], + "person_talks_to_person": [ + "G299", + "G328", + "G330", + "G339", + "G419", + "G420", + "G423", + "G639" + ], + "person_transfers_object": [ + "G299", + "G330" + ], + "person_opens_facility_door": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G638" + ], + "person_enters_scene_through_structure": [ + "G299", + "G328", + "G330", + "G419", + "G420", + "G421", + "G423", + "G638" + ], + "person_exits_scene_through_structure": [ + "G299", + "G330", + "G421", + "G423", + "G638", + "G639" + ], + "person_picks_up_object": [ + "G299", + "G330" + ], + "person_puts_down_object": [ + "G299", + "G330" + ], + "person_sits_down": [ + "G299", + "G330" + ], + "person_texts_on_phone": [ + "G299", + "G330", + "G423", + "G424" + ], + "vehicle_turns_right": [ + "G339", + "G424", + "G638" + ], + "vehicle_turns_left": [ + "G336", + "G339", + "G424" + ], + "person_stands_up": [ + "G299", + "G330" + ], + "vehicle_stops": [ + "G336", + "G339", + "G424", + "G638" + ], + "person_opens_vehicle_door": [ + "G336", + "G339", + "G424" + ], + "person_exits_vehicle": [ + "G336", + "G339", + "G424" + ], + "person_closes_vehicle_door": [ + "G336", + "G424" + ], + "person_embraces_person": [ + "G299", + "G330" + ], + "person_talks_on_phone": [ + "G299", + "G330", + "G423" + ] + }, + "busiest_camera": "G299", + "busiest_camera_count": 277, + "unique_entities": 106, + "person_entities": 85, + "top_3_activities": [ + [ + "person_puts_down_object", + 115 + ], + [ + "person_enters_scene_through_structure", + 109 + ], + [ + "person_picks_up_object", + 104 + ] + ] + }, + "scene_description": "The scene spans 13 cameras with 793 total activity events. The most frequent activities are: puts down object (115 occurrences), enters the camera's view through a doorway (109 occurrences), picks up object (104 occurrences). 20 activities occur on multiple cameras. Camera G299 is the most active with 277 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G328.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G336.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G420.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G423.r13.mp4", + "2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4", + "2018-03-11.11-25-04.11-30-04.school.G300.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + 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observed 8 times across 2 cameras (G299: 3, G330: 5). 6 cross-camera duplicates removed.", + "requires_cameras": [ + "G299", + "G330" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 8, + "cameras_involved": [ + "G299", + "G330" + ], + "activity": "person_transfers_object", + "event_ids": [ + "G299_evt_110", + "G330_evt_2247872337575452878", + "G299_evt_326", + "G330_evt_8916600349413254481", + "G299_evt_254", + "G330_evt_1251013991494886512", + "G299_evt_299", + "G299_evt_332", + "G330_evt_8969328509905970257", + "G330_evt_3385790644333389152", + "G299_evt_169", + "G330_evt_6191069156024034123", + "G330_evt_7952330424198604660", + "G330_evt_5506020564311122608" + ], + "key_frames": [ + { + "camera": "G299", + "start_frame": 0, + "start_sec": 0.0, + "end_sec": 4.4, + "event_id": "G299_evt_110", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "cluster_size": 2 + }, + { + "camera": "G299", + "start_frame": 68, + "start_sec": 2.27, + "end_sec": 4.4, + "event_id": "G299_evt_326", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G330", + "start_frame": 2468, + "start_sec": 82.27, + "end_sec": 85.3, + "event_id": "G330_evt_8916600349413254481", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "cluster_size": 2 + }, + { + "camera": "G330", + "start_frame": 3084, + "start_sec": 102.8, + "end_sec": 106.17, + "event_id": "G330_evt_1251013991494886512", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "cluster_size": 2 + }, + { + "camera": "G299", + "start_frame": 3375, + "start_sec": 112.5, + "end_sec": 116.27, + "event_id": "G299_evt_332", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "cluster_size": 2 + }, + { + "camera": "G330", + "start_frame": 3758, + "start_sec": 125.27, + "end_sec": 128.53, + "event_id": "G330_evt_3385790644333389152", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "cluster_size": 3 + }, + { + "camera": "G330", + "start_frame": 7448, + "start_sec": 248.27, + "end_sec": 251.17, + "event_id": "G330_evt_7952330424198604660", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G330", + "start_frame": 8225, + "start_sec": 274.17, + "end_sec": 276.67, + "event_id": "G330_evt_5506020564311122608", + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4", + "cluster_size": 1 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 8, + "cameras_involved": [ + "G299", + "G330" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 12, + "slot": "2018-03-11.11-25.school", + "activity": "person_transfers_object", + "activity_human": "hands an object to someone", + "clip_files": [ + "2018-03-11.11-25-00.11-30-00.school.G299.r13.mp4", + "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4" + 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bag, hat, scarf into the scene?", + "options": [ + "Camera G299", + "Camera G638", + "Camera G421", + "Camera G419" + ], + "correct_answer": "Camera G419", + "correct_answer_index": 3, + "requires_cameras": [ + "G419", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G419", + "entrance_time_sec": 254.87, + "entity_description": "a person with indigo hair, wearing a dark indigo top and dark indigo pants, plum shoes, with bag, hat, scarf", + "cluster_id": "cluster_4", + "all_entrance_cameras": { + "G419": 254.87 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "start_sec": 254.87 + } + }, + "video_paths": [ + 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"/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-04.11-30-04.school.G300.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_009", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person with indigo hair, wearing a dark blue top and dark indigo pants, indigo shoes, carrying a bag into the scene?", + "options": [ + "Camera G638", + "Camera G419", + "Camera G424", + "Camera G420" + ], + "correct_answer": "Camera G419", + "correct_answer_index": 1, + "requires_cameras": [ + "G419", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G419", + "entrance_time_sec": 217.97, + "entity_description": "a person with indigo hair, wearing a dark blue top and dark indigo pants, indigo shoes, carrying a bag", + "cluster_id": "cluster_3", + "all_entrance_cameras": { + "G419": 217.97 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "start_sec": 217.97 + } + }, + "video_paths": [ + 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"/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-00.11-30-01.school.G421.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G339.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G424.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G638.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-01.11-30-01.school.G639.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/2018-03-11.11-25-04.11-30-04.school.G300.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_010", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person with indigo hair, wearing an indigo top and dark indigo pants, indigo shoes, carrying a bag into the scene?", + "options": [ + "Camera G339", + "Camera G424", + "Camera G423", + "Camera G419" + ], + "correct_answer": "Camera G419", + "correct_answer_index": 3, + "requires_cameras": [ + "G419", + "G299", + "G300", + "G328", + "G330", + "G336", + "G339", + "G420", + "G421", + "G423", + "G424", + "G638", + "G639" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G419", + "entrance_time_sec": 255.73, + "entity_description": "a person with indigo hair, wearing an indigo top and dark indigo pants, indigo shoes, carrying a bag", + "cluster_id": "cluster_5", + "all_entrance_cameras": { + "G419": 255.73 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.11-25-00.11-30-00.school.G419.r13.mp4", + "camera": "G419", + "activity": "person_enters_scene_through_structure", + "start_sec": 255.73 + } + }, + "video_paths": [ + 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b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.16-20.admin.raw.json @@ -0,0 +1,461 @@ +{ + "slot": "2018-03-11.16-20.admin", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo+geom_color", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G326", + "G329" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 17, + "mevid_person_ids": [ + "0004", + "0009", + "0018", + "0030", + "0033", + "0034", + "0035", + "0036", + "0049", + "0056", + "0057", + "0082", + "0083", + "0088", + "0095", + "0097", + "0099" + ], + "mevid_person_cameras": { + "0004": [ + "G326", + "G329" + ], + "0009": [ + "G326", + "G329" + ], + "0018": [ + "G326", + "G329" + ], + "0030": [ + "G326", + "G329" + ], + "0033": [ + "G326", + "G329" + ], + "0034": [ + "G326", + "G329" + ], + "0035": [ + "G326", + "G329" + ], + "0036": [ + "G326", + "G329" + ], + "0049": [ + "G326", + "G329" + ], + "0056": [ + "G326", + "G329" + ], + "0057": [ + "G326", + "G329" + ], + "0082": [ + "G326", + "G329" + ], + "0083": [ + "G326", + "G329" + ], + "0088": [ + "G326", + "G329" + ], + "0095": [ + "G326", + "G329" + ], + "0097": [ + "G326", + "G329" + ], + "0099": [ + "G326", + "G329" + ] + }, + "total_events": 22, + "total_entities": 22, + "cross_camera_clusters": 1, + "total_questions": 4, + "category_counts": { + "temporal": 1, + "summarization": 1, + "counting": 1, + "best_camera": 1 + }, + "stats": { + "entities_with_mevid_descriptions": 11, + "entities_with_geom_descriptions": 11, + "entities_with_fallback_descriptions": 0, + "attribute_verification_questions": 0, + "best_camera_questions": 1, + "questions_with_debug_info": 4 + }, + "validation_issues": 0, + "generation_time_sec": 2.76, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person with black hair, wearing a black top and black dress, with a hat enters the camera's view through a doorway and a person with black hair, wearing a plum top and black pants, black shoes, with a hat opens facility door -- which occurred first?", + "options": [ + "The person with black hair, wearing a black top and black dress, with a hat enters the camera's view through a doorway occurred first", + "The person with black hair, wearing a plum top and black pants, black shoes, with a hat opens facility door occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The person with black hair, wearing a black top and black dress, with a hat enters the camera's view through a doorway occurred first", + "requires_cameras": [ + "G326", + "G329" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G329", + "start_sec": 11.4, + "end_sec": 12.43, + "actor_ids": [ + 138347706565731484 + ], + "description": "a person with black hair, wearing a black top and black dress, with a hat enters the camera's view through a doorway" + }, + "event_b": { + "activity": "person_opens_facility_door", + "camera": "G326", + "start_sec": 20.2, + "end_sec": 23.2, + "actor_ids": [ + 3211292722674673313 + ], + "description": "a person with black hair, wearing a plum top and black pants, black shoes, with a hat opens facility door" + }, + "gap_sec": 7.77, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G329", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 138347706565731484 + ], + "frame_range": [ + 342, + 373 + ], + "timestamp": "11.40-12.43s", + "fps": 30.0, + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4", + "entity_description": "a person with black hair, wearing a black top and black dress, with a hat" + }, + "event_b": { + "camera": "G326", + "activity": "person_opens_facility_door", + "actor_ids": [ + 3211292722674673313 + ], + "frame_range": [ + 606, + 696 + ], + "timestamp": "20.20-23.20s", + "fps": 30.0, + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "entity_description": "a person with black hair, wearing a plum top and black pants, black shoes, with a hat" + }, + "gap_sec": 7.77, + "connection_type": "related_activities_reverse_causal", + "connection_strength": "medium", + "connection_score": 3.8, + "relationship": "reverse_causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 97 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4" + ] + }, + { + "question_id": "final_summarization_002", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 2 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "A single-camera scene showing only indoor activities", + "A vehicle-focused scene with mostly parking and driving activity", + "An empty scene with minimal activity, captured on 1 camera", + "A pedestrian-dominant scene across 2 cameras, primarily featuring leaving the camera's view through a doorway" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 2 cameras, primarily featuring leaving the camera's view through a doorway", + "requires_cameras": [ + "G326", + "G329" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 22, + "num_cameras": 2, + "person_events": 22, + "vehicle_events": 0, + "scene_type": "pedestrian-dominant", + "top_activity": "person_exits_scene_through_structure", + "top_activity_count": 9 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 22, + "unique_activities": 5, + "activity_counts": { + "person_enters_scene_through_structure": 5, + "person_talks_to_person": 2, + "person_opens_facility_door": 4, + "person_exits_scene_through_structure": 9, + "person_talks_on_phone": 2 + }, + "camera_event_counts": { + "G329": 14, + "G326": 8 + }, + "num_cameras": 2, + "duration_sec": 218.4, + "person_event_count": 22, + "vehicle_event_count": 0, + "cross_camera_activities": { + "person_talks_to_person": [ + "G326", + "G329" + ], + "person_opens_facility_door": [ + "G326", + "G329" + ], + "person_exits_scene_through_structure": [ + "G326", + "G329" + ], + "person_talks_on_phone": [ + "G326", + "G329" + ] + }, + "busiest_camera": "G329", + "busiest_camera_count": 14, + "unique_entities": 22, + "person_entities": 22, + "top_3_activities": [ + [ + "person_exits_scene_through_structure", + 9 + ], + [ + "person_enters_scene_through_structure", + 5 + ], + [ + "person_opens_facility_door", + 4 + ] + ] + }, + "scene_description": "The scene spans 2 cameras with 22 total activity events. The most frequent activities are: leaves the camera's view through a doorway (9 occurrences), enters the camera's view through a doorway (5 occurrences), opens facility door (4 occurrences). 4 activities occur on multiple cameras. Camera G329 is the most active with 14 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4" + ] + }, + { + "question_id": "final_counting_003", + "category": "counting", + "difficulty": "easy", + "question_template": "How many times does someone perform the action of leaving the camera's view through a doorway across all cameras in this slot?", + "options": [ + "3", + "5", + "6", + "7" + ], + "correct_answer_index": 1, + "correct_answer": "5", + "reasoning": "The action of leaves the camera's view through a doorway was observed 5 times across 2 cameras (G326: 1, G329: 4). 4 cross-camera duplicates removed.", + "requires_cameras": [ + "G326", + "G329" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 5, + "cameras_involved": [ + "G326", + "G329" + ], + "activity": "person_exits_scene_through_structure", + "event_ids": [ + "G329_evt_7681848546836161799", + "G329_evt_2115661087573550486", + "G326_evt_6349216508003033508", + "G326_evt_89590530260182847", + "G329_evt_6710547851833549764", + "G326_evt_6193079337326585421", + "G329_evt_6273447878891282760", + "G326_evt_6285528681000476915", + "G329_evt_306703279203067698" + ], + "key_frames": [ + { + "camera": "G329", + "start_frame": 604, + "start_sec": 20.13, + "end_sec": 23.1, + "event_id": "G329_evt_7681848546836161799", + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4", + "cluster_size": 3 + }, + { + "camera": "G329", + "start_frame": 605, + "start_sec": 20.17, + "end_sec": 22.77, + "event_id": "G329_evt_2115661087573550486", + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G329", + "start_frame": 692, + "start_sec": 23.07, + "end_sec": 24.37, + "event_id": "G329_evt_6710547851833549764", + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4", + "cluster_size": 2 + }, + { + "camera": "G329", + "start_frame": 2033, + "start_sec": 67.77, + "end_sec": 68.77, + "event_id": "G329_evt_6273447878891282760", + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G326", + "start_frame": 6796, + "start_sec": 226.53, + "end_sec": 229.83, + "event_id": "G326_evt_6285528681000476915", + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "cluster_size": 2 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 5, + "cameras_involved": [ + "G326", + "G329" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 5, + "slot": "2018-03-11.16-20.admin", + "activity": "person_exits_scene_through_structure", + "activity_human": "leaves the camera's view through a doorway", + "clip_files": [ + "2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G326.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_004", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G326", + "Camera G300", + "Camera G299", + "Camera G329" + ], + "correct_answer": "Camera G329", + "correct_answer_index": 3, + "requires_cameras": [ + "G329", + "G326" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G329", + "entrance_time_sec": 25.87, + "entity_description": "a person", + "cluster_id": "standalone_G329_2966589230048059830", + "all_entrance_cameras": { + "G329": 25.87 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4", + "camera": "G329", + "activity": "person_enters_scene_through_structure", + "start_sec": 25.87 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.admin/2018-03-11.16-20-01.16-25-01.admin.G329.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.16-20.hospital.raw.json b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.16-20.hospital.raw.json new file mode 100644 index 0000000..bc6311c --- /dev/null +++ b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-11.16-20.hospital.raw.json @@ -0,0 +1,837 @@ +{ + "slot": "2018-03-11.16-20.hospital", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "geom_segformer+mevid_gpt+vlm+mevid_yolo", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G301", + "G341", + "G436", + "G479" + ], + "mevid_supported": true, + "mevid_persons_in_slot": 7, + "mevid_person_ids": [ + "0009", + "0036", + "0051", + "0067", + "0068", + "0082", + "0083" + ], + "mevid_person_cameras": { + "0009": [ + "G341", + "G436" + ], + "0036": [ + "G341", + "G436" + ], + "0051": [ + "G341", + "G436" + ], + "0067": [ + "G341", + "G436" + ], + "0068": [ + "G341", + "G436" + ], + "0082": [ + "G341", + "G436" + ], + "0083": [ + "G341", + "G436" + ] + }, + "total_events": 33, + "total_entities": 26, + "cross_camera_clusters": 0, + "total_questions": 8, + "category_counts": { + "temporal": 2, + "spatial": 3, + "summarization": 1, + "counting": 1, + "best_camera": 1 + }, + "stats": { + "entities_with_mevid_gpt_descriptions": 0, + "entities_with_vlm_descriptions": 0, + "entities_with_geom_descriptions": 16, + "entities_with_mevid_yolo_descriptions": 2, + "entities_with_fallback_descriptions": 0, + "attribute_verification_questions": 0, + "best_camera_questions": 1, + "questions_with_debug_info": 8 + }, + "ambiguity": { + "avg_ambiguity": 0.0, + "pct_unique": 100.0, + "fully_unique": 8, + "total_questions": 8, + "worst_questions": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "max_ambiguity": 0, + "entities": [ + { + "camera": "G341", + "description_preview": "a person, wearing a teal top and indigo pants talk", + "same_desc_count": 1, + "ambiguity": 0 + }, + { + "camera": "G436", + "description_preview": "a person, wearing a blue top and dark gray pants t", + "same_desc_count": 1, + "ambiguity": 0 + } + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "max_ambiguity": 0, + "entities": [ + { + "camera": "G341", + "description_preview": "a person, wearing an indigo top and navy pants emb", + "same_desc_count": 1, + "ambiguity": 0 + }, + { + "camera": "G436", + "description_preview": "a vehicle makes a U-turn", + "same_desc_count": 1, + "ambiguity": 0 + } + ] + }, + { + "question_id": "final_spatial_003", + "category": "spatial", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_spatial_004", + "category": "spatial", + "max_ambiguity": 0, + "entities": [] + }, + { + "question_id": "final_spatial_005", + "category": "spatial", + "max_ambiguity": 0, + "entities": [] + } + ] + }, + "validation_issues": 0, + "generation_time_sec": 3.6, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person, wearing a teal top and indigo pants talks to person and a person, wearing a blue top and dark gray pants texts on phone -- which occurred first?", + "options": [ + "The person, wearing a teal top and indigo pants talks to person occurred first", + "The person, wearing a blue top and dark gray pants texts on phone occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The person, wearing a teal top and indigo pants talks to person occurred first", + "requires_cameras": [ + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_talks_to_person", + "camera": "G341", + "start_sec": 146.23, + "end_sec": 166.5, + "actor_ids": [ + 3189466056130339600, + 7954074352746538932, + 4615088420656195519 + ], + "description": "a person, wearing a teal top and indigo pants talks to person" + }, + "event_b": { + "activity": "person_texts_on_phone", + "camera": "G436", + "start_sec": 174.13, + "end_sec": 225.03, + "actor_ids": [ + 1215285725822754849 + ], + "description": "a person, wearing a blue top and dark gray pants texts on phone" + }, + "gap_sec": 7.63, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G341", + "activity": "person_talks_to_person", + "actor_ids": [ + 3189466056130339600, + 7954074352746538932, + 4615088420656195519 + ], + "frame_range": [ + 4387, + 4995 + ], + "timestamp": "146.23-166.50s", + "fps": 30.0, + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "entity_description": "a person, wearing a teal top and indigo pants" + }, + "event_b": { + "camera": "G436", + "activity": "person_texts_on_phone", + "actor_ids": [ + 1215285725822754849 + ], + "frame_range": [ + 5224, + 6751 + ], + "timestamp": "174.13-225.03s", + "fps": 30.0, + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "entity_description": "a person, wearing a blue top and dark gray pants" + }, + "gap_sec": 7.63, + "connection_type": "unrelated", + "connection_strength": "weak", + "connection_score": 2.0, + "relationship": null, + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 82 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a vehicle makes a U-turn and a person, wearing an indigo top and navy pants embraces person -- which occurred first?", + "options": [ + "The vehicle makes a U-turn occurred first", + "The person, wearing an indigo top and navy pants embraces person occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The person, wearing an indigo top and navy pants embraces person occurred first", + "requires_cameras": [ + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_embraces_person", + "camera": "G341", + "start_sec": 152.4, + "end_sec": 155.1, + "actor_ids": [ + 5627986518465159468, + 4208042047502295642 + ], + "description": "a person, wearing an indigo top and navy pants embraces person" + }, + "event_b": { + "activity": "vehicle_makes_u_turn", + "camera": "G436", + "start_sec": 165.3, + "end_sec": 170.5, + "actor_ids": [ + 2612897766644721416 + ], + "description": "a vehicle makes a U-turn" + }, + "gap_sec": 10.2, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G341", + "activity": "person_embraces_person", + "actor_ids": [ + 5627986518465159468, + 4208042047502295642 + ], + "frame_range": [ + 4572, + 4653 + ], + "timestamp": "152.40-155.10s", + "fps": 30.0, + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "entity_description": "a person, wearing an indigo top and navy pants" + }, + "event_b": { + "camera": "G436", + "activity": "vehicle_makes_u_turn", + "actor_ids": [ + 2612897766644721416 + ], + "frame_range": [ + 4959, + 5115 + ], + "timestamp": "165.30-170.50s", + "fps": 30.0, + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "entity_description": null + }, + "gap_sec": 10.2, + "connection_type": "unrelated", + "connection_strength": "weak", + "connection_score": 2.0, + "relationship": null, + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 82 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_spatial_003", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing an indigo top and navy pants and a person, wearing a blue top and navy pants come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing an indigo top and navy pants and a person, wearing a blue top and navy pants come within 1.9 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G341" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G341_actor_4208042047502295642", + "entity_b": "G341_actor_7068770821986755057", + "entity_a_desc": "a person, wearing an indigo top and navy pants", + "entity_b_desc": "a person, wearing a blue top and navy pants", + "min_distance_meters": 1.92, + "closest_frame": 4620, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G341_actor_4208042047502295642", + "camera": "G341", + "description": "a person, wearing an indigo top and navy pants", + "bbox_at_closest": [ + 1596, + 412, + 1637, + 505 + ], + "timestamp": "152.40-155.10s" + }, + "entity_b": { + "entity_id": "G341_actor_7068770821986755057", + "camera": "G341", + "description": "a person, wearing a blue top and navy pants", + "bbox_at_closest": [ + 1546, + 401, + 1584, + 487 + ], + "timestamp": "151.60-154.03s" + }, + "min_distance_meters": 1.92, + "closest_frame": 4620, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "4572-4621", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "clip_files": [ + "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_spatial_004", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing a navy top and navy pants (embracing a person) and a person, wearing a navy top and navy pants (talking to person) come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing a navy top and navy pants (embracing a person) and a person, wearing a navy top and navy pants (talking to person) come within 1.4 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G341" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G341_actor_5276903968401105022", + "entity_b": "G341_actor_7954074352746538932", + "entity_a_desc": "a person, wearing a navy top and navy pants (embracing a person)", + "entity_b_desc": "a person, wearing a navy top and navy pants (talking to person)", + "min_distance_meters": 1.4, + "closest_frame": 4710, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G341_actor_5276903968401105022", + "camera": "G341", + "description": "a person, wearing a navy top and navy pants (embracing a person)", + "bbox_at_closest": [ + 1589, + 399, + 1647, + 502 + ], + "timestamp": "155.97-158.47s" + }, + "entity_b": { + "entity_id": "G341_actor_7954074352746538932", + "camera": "G341", + "description": "a person, wearing a navy top and navy pants (talking to person)", + "bbox_at_closest": [ + 1572, + 419, + 1611, + 514 + ], + "timestamp": "146.23-166.50s" + }, + "min_distance_meters": 1.4, + "closest_frame": 4710, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "4679-4754", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "clip_files": [ + "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_spatial_005", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing a teal top and indigo pants and a person, wearing a navy top and navy pants come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing a teal top and indigo pants and a person, wearing a navy top and navy pants come within 0.8 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G341" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G341_actor_3189466056130339600", + "entity_b": "G341_actor_5276903968401105022", + "entity_a_desc": "a person, wearing a teal top and indigo pants", + "entity_b_desc": "a person, wearing a navy top and navy pants", + "min_distance_meters": 0.81, + "closest_frame": 4710, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G341_actor_3189466056130339600", + "camera": "G341", + "description": "a person, wearing a teal top and indigo pants", + "bbox_at_closest": [ + 1624, + 419, + 1669, + 509 + ], + "timestamp": "146.23-166.37s" + }, + "entity_b": { + "entity_id": "G341_actor_5276903968401105022", + "camera": "G341", + "description": "a person, wearing a navy top and navy pants", + "bbox_at_closest": [ + 1589, + 399, + 1647, + 502 + ], + "timestamp": "155.97-158.47s" + }, + "min_distance_meters": 0.81, + "closest_frame": 4710, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "4679-4754", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "clip_files": [ + "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_summarization_006", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 4 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "A scene dominated by people entering and exiting buildings", + "An empty scene with minimal activity, captured on 1 camera", + "A single-camera scene showing only indoor activities", + "A mixed activity scene across 4 cameras, primarily featuring talking to person" + ], + "correct_answer_index": 3, + "correct_answer": "A mixed activity scene across 4 cameras, primarily featuring talking to person", + "requires_cameras": [ + "G301", + "G341", + "G436", + "G479" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 33, + "num_cameras": 4, + "person_events": 20, + "vehicle_events": 11, + "scene_type": "mixed activity", + "top_activity": "person_talks_to_person", + "top_activity_count": 7 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 33, + "unique_activities": 13, + "activity_counts": { + "empty_37": 2, + "person_talks_to_person": 7, + "vehicle_turns_left": 3, + "vehicle_stops": 2, + "vehicle_starts": 3, + "person_enters_scene_through_structure": 3, + "person_texts_on_phone": 4, + "person_embraces_person": 3, + "vehicle_makes_u_turn": 2, + "person_opens_vehicle_door": 1, + "person_enters_vehicle": 1, + "person_closes_vehicle_door": 1, + "vehicle_turns_right": 1 + }, + "camera_event_counts": { + "G301": 1, + "G436": 22, + "G479": 1, + "G341": 9 + }, + "num_cameras": 4, + "duration_sec": 300.0, + "person_event_count": 20, + "vehicle_event_count": 11, + "cross_camera_activities": { + "empty_37": [ + "G301", + "G479" + ], + "person_talks_to_person": [ + "G341", + "G436" + ], + "person_texts_on_phone": [ + "G341", + "G436" + ] + }, + "busiest_camera": "G436", + "busiest_camera_count": 22, + "unique_entities": 26, + "person_entities": 15, + "top_3_activities": [ + [ + "person_talks_to_person", + 7 + ], + [ + "person_texts_on_phone", + 4 + ], + [ + "vehicle_turns_left", + 3 + ] + ] + }, + "scene_description": "The scene spans 4 cameras with 33 total activity events. The most frequent activities are: talks to person (7 occurrences), texts on phone (4 occurrences), turns left (3 occurrences). 3 activities occur on multiple cameras. Camera G436 is the most active with 22 events.", + "scene_type": "mixed activity", + "clip_files": [ + "2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_counting_007", + "category": "counting", + "difficulty": "medium", + "question_template": "How many times does someone perform the action of talking to person across all cameras in this slot?", + "options": [ + "6", + "7", + "9", + "14" + ], + "correct_answer_index": 1, + "correct_answer": "7", + "reasoning": "The action of talks to person was observed 7 times across 2 cameras (G341: 1, G436: 6).", + "requires_cameras": [ + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 7, + "cameras_involved": [ + "G341", + "G436" + ], + "activity": "person_talks_to_person", + "event_ids": [ + "G436_evt_699280517169358061", + "G436_evt_8025245803158577521", + "G436_evt_3112057756931793707", + "G436_evt_2406515549337495397", + "G436_evt_3088745758922023940", + "G341_evt_6131159581353420464", + "G436_evt_7945011146228343089" + ], + "key_frames": [ + { + "camera": "G436", + "start_frame": 0, + "start_sec": 0.0, + "end_sec": 2.23, + "event_id": "G436_evt_699280517169358061", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G436", + "start_frame": 172, + "start_sec": 5.73, + "end_sec": 10.1, + "event_id": "G436_evt_8025245803158577521", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G436", + "start_frame": 3014, + "start_sec": 100.47, + "end_sec": 114.27, + "event_id": "G436_evt_3112057756931793707", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G436", + "start_frame": 3509, + "start_sec": 116.97, + "end_sec": 147.27, + "event_id": "G436_evt_2406515549337495397", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G436", + "start_frame": 3576, + "start_sec": 119.2, + "end_sec": 123.17, + "event_id": "G436_evt_3088745758922023940", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G341", + "start_frame": 4387, + "start_sec": 146.23, + "end_sec": 166.5, + "event_id": "G341_evt_6131159581353420464", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G436", + "start_frame": 5158, + "start_sec": 171.93, + "end_sec": 299.97, + "event_id": "G436_evt_7945011146228343089", + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4", + "cluster_size": 1 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 7, + "cameras_involved": [ + "G341", + "G436" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 8, + "slot": "2018-03-11.16-20.hospital", + "activity": "person_talks_to_person", + "activity_human": "talks to person", + "clip_files": [ + "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_008", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G436", + "Camera G341", + "Camera G301", + "Camera G479" + ], + "correct_answer": "Camera G341", + "correct_answer_index": 1, + "requires_cameras": [ + "G341", + "G301", + "G436", + "G479" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G341", + "entrance_time_sec": 144.73, + "entity_description": "a person", + "cluster_id": "standalone_G341_3958939309910631222", + "all_entrance_cameras": { + "G341": 144.73 + }, + "activity": "person_enters_scene_through_structure" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "camera": "G341", + "activity": "person_enters_scene_through_structure", + "start_sec": 144.73 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G476.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-00.16-25-00.hospital.G479.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-11/16/2018-03-11.16-20.hospital/2018-03-11.16-20-08.16-25-08.hospital.G436.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-13.16-00.hospital.raw.json b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-13.16-00.hospital.raw.json new file mode 100644 index 0000000..7cea0a6 --- /dev/null +++ b/meva/examples/qa_pairs/intermediate/root_data/raw/2018-03-13.16-00.hospital.raw.json @@ -0,0 +1,961 @@ +{ + "slot": "2018-03-13.16-00.hospital", + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo+geom_color", + "generator": "final_pipeline", + "seed": 42, + "cameras": [ + "G301", + "G341", + "G436" + ], + "mevid_supported": false, + "mevid_persons_in_slot": 0, + "mevid_person_ids": [], + "mevid_person_cameras": {}, + "total_events": 149, + "total_entities": 123, + "cross_camera_clusters": 0, + "total_questions": 10, + "category_counts": { + "temporal": 2, + "event_ordering": 2, + "spatial": 3, + "summarization": 1, + "counting": 1, + "best_camera": 1 + }, + "stats": { + "entities_with_mevid_descriptions": 0, + "entities_with_geom_descriptions": 113, + "entities_with_fallback_descriptions": 0, + "attribute_verification_questions": 0, + "best_camera_questions": 1, + "questions_with_debug_info": 10 + }, + "validation_issues": 2, + "generation_time_sec": 5.97, + "qa_pairs": [ + { + "question_id": "final_temporal_001", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person, wearing a plum top and indigo pants enters the camera's view through a doorway and a vehicle drops off person -- which occurred first?", + "options": [ + "The person, wearing a plum top and indigo pants enters the camera's view through a doorway occurred first", + "The vehicle drops off person occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 0, + "correct_answer": "The person, wearing a plum top and indigo pants enters the camera's view through a doorway occurred first", + "requires_cameras": [ + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_enters_scene_through_structure", + "camera": "G341", + "start_sec": 251.57, + "end_sec": 253.53, + "actor_ids": [ + 2 + ], + "description": "a person, wearing a plum top and indigo pants enters the camera's view through a doorway" + }, + "event_b": { + "activity": "vehicle_drops_off_person", + "camera": "G436", + "start_sec": 260.43, + "end_sec": 282.97, + "actor_ids": [ + 294, + 296, + 295 + ], + "description": "a vehicle drops off person" + }, + "gap_sec": 6.9, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G341", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 2 + ], + "frame_range": [ + 7547, + 7606 + ], + "timestamp": "251.57-253.53s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "entity_description": "a person, wearing a plum top and indigo pants" + }, + "event_b": { + "camera": "G436", + "activity": "vehicle_drops_off_person", + "actor_ids": [ + 294, + 296, + 295 + ], + "frame_range": [ + 7813, + 8489 + ], + "timestamp": "260.43-282.97s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "entity_description": "a vehicle" + }, + "gap_sec": 6.9, + "connection_type": "related_activities_reverse_causal", + "connection_strength": "medium", + "connection_score": 3.8, + "relationship": "reverse_causal", + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 82 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_temporal_002", + "category": "temporal", + "difficulty": "easy", + "question_template": "a person, wearing an indigo top and plum pants embraces person and a person, wearing an indigo top and indigo pants opens facility door -- which occurred first?", + "options": [ + "The person, wearing an indigo top and plum pants embraces person occurred first", + "The person, wearing an indigo top and indigo pants opens facility door occurred first", + "They occurred simultaneously", + "Cannot be determined" + ], + "correct_answer_index": 1, + "correct_answer": "The person, wearing an indigo top and indigo pants opens facility door occurred first", + "requires_cameras": [ + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "event_a": { + "activity": "person_opens_facility_door", + "camera": "G341", + "start_sec": 251.57, + "end_sec": 252.63, + "actor_ids": [ + 1 + ], + "description": "a person, wearing an indigo top and indigo pants opens facility door" + }, + "event_b": { + "activity": "person_embraces_person", + "camera": "G436", + "start_sec": 254.93, + "end_sec": 260.73, + "actor_ids": [ + 197, + 196 + ], + "description": "a person, wearing an indigo top and plum pants embraces person" + }, + "gap_sec": 2.3, + "entity_link": "mevid_validated", + "same_person": false + }, + "debug_info": { + "event_a": { + "camera": "G341", + "activity": "person_opens_facility_door", + "actor_ids": [ + 1 + ], + "frame_range": [ + 7547, + 7579 + ], + "timestamp": "251.57-252.63s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "entity_description": "a person, wearing an indigo top and indigo pants" + }, + "event_b": { + "camera": "G436", + "activity": "person_embraces_person", + "actor_ids": [ + 197, + 196 + ], + "frame_range": [ + 7648, + 7822 + ], + "timestamp": "254.93-260.73s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "entity_description": "a person, wearing an indigo top and plum pants" + }, + "gap_sec": 2.3, + "connection_type": "unrelated", + "connection_strength": "weak", + "connection_score": 2.0, + "relationship": null, + "cluster_id": null, + "mevid_validated": true, + "mevid_person_id": 82 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_003", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. a person, wearing a black top and blue pants, opening a facility door\nII. a person, wearing a black top and navy pants, leaving the camera's view through a doorway\nIII. A person interacting with person\nIV. a person, wearing a black top and blue pants, picking up object\nWhich is the correct chronological order?", + "options": [ + "II -> IV -> I -> III", + "III -> IV -> I -> II", + "III -> I -> IV -> II", + "I -> III -> IV -> II" + ], + "correct_answer_index": 2, + "correct_answer": "III -> I -> IV -> II", + "requires_cameras": [ + "G301", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "hand_interacts_with_person", + "camera": "G436", + "start_sec": 56.27, + "description": "A person interacting with person" + }, + { + "activity": "person_opens_facility_door", + "camera": "G301", + "start_sec": 64.97, + "description": "a person, wearing a black top and blue pants, opening a facility door" + }, + { + "activity": "person_picks_up_object", + "camera": "G301", + "start_sec": 72.53, + "description": "a person, wearing a black top and blue pants, picking up object" + }, + { + "activity": "person_exits_scene_through_structure", + "camera": "G301", + "start_sec": 75.57, + "description": "a person, wearing a black top and navy pants, leaving the camera's view through a doorway" + } + ], + "min_gap_sec": 2.13, + "num_events": 4, + "gaps_sec": [ + 2.37, + 2.13, + 2.3 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G436", + "activity": "hand_interacts_with_person", + "actor_ids": [ + 40, + 39 + ], + "frame_range": [ + 1688, + 1878 + ], + "timestamp": "56.27-62.60s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "entity_description": null + }, + { + "camera": "G301", + "activity": "person_opens_facility_door", + "actor_ids": [ + 11 + ], + "frame_range": [ + 1949, + 2112 + ], + "timestamp": "64.97-70.40s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "entity_description": "a person, wearing a black top and blue pants" + }, + { + "camera": "G301", + "activity": "person_picks_up_object", + "actor_ids": [ + 13, + 12 + ], + "frame_range": [ + 2176, + 2198 + ], + "timestamp": "72.53-73.27s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "entity_description": "a person, wearing a black top and blue pants" + }, + { + "camera": "G301", + "activity": "person_exits_scene_through_structure", + "actor_ids": [ + 16 + ], + "frame_range": [ + 2267, + 2322 + ], + "timestamp": "75.57-77.40s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "entity_description": "a person, wearing a black top and navy pants" + } + ], + "presentation_order": [ + 1, + 3, + 0, + 2 + ], + "correct_permutation": [ + 2, + 0, + 3, + 1 + ], + "mevid_validated": false, + "group_score": 9.0, + "related_pairs": 1, + "described_events": 3 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_event_ordering_004", + "category": "event_ordering", + "difficulty": "hard", + "question_template": "Identify the correct chronological order of the following events observed across the cameras:\nI. a person, wearing a plum top and indigo pants, entering the camera's view through a doorway\nII. a person, wearing a teal top and gray pants, exiting a vehicle\nIII. a vehicle, stopping\nIV. A person putting down object\nWhich is the correct chronological order?", + "options": [ + "IV -> II -> III -> I", + "III -> I -> II -> IV", + "I -> II -> III -> IV", + "I -> III -> II -> IV" + ], + "correct_answer_index": 3, + "correct_answer": "I -> III -> II -> IV", + "requires_cameras": [ + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "ordered_events": [ + { + "activity": "person_enters_scene_through_structure", + "camera": "G341", + "start_sec": 251.57, + "description": "a person, wearing a plum top and indigo pants, entering the camera's view through a doorway" + }, + { + "activity": "vehicle_stops", + "camera": "G436", + "start_sec": 258.07, + "description": "a vehicle, stopping" + }, + { + "activity": "person_exits_vehicle", + "camera": "G436", + "start_sec": 266.93, + "description": "a person, wearing a teal top and gray pants, exiting a vehicle" + }, + { + "activity": "person_puts_down_object", + "camera": "G436", + "start_sec": 280.47, + "description": "A person putting down object" + } + ], + "min_gap_sec": 4.54, + "num_events": 4, + "gaps_sec": [ + 4.54, + 5.5, + 7.0 + ] + }, + "debug_info": { + "events": [ + { + "camera": "G341", + "activity": "person_enters_scene_through_structure", + "actor_ids": [ + 2 + ], + "frame_range": [ + 7547, + 7606 + ], + "timestamp": "251.57-253.53s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "entity_description": "a person, wearing a plum top and indigo pants" + }, + { + "camera": "G436", + "activity": "vehicle_stops", + "actor_ids": [ + 222 + ], + "frame_range": [ + 7742, + 7843 + ], + "timestamp": "258.07-261.43s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "entity_description": "a vehicle" + }, + { + "camera": "G436", + "activity": "person_exits_vehicle", + "actor_ids": [ + 228, + 227 + ], + "frame_range": [ + 8008, + 8204 + ], + "timestamp": "266.93-273.47s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "entity_description": "a person, wearing a teal top and gray pants" + }, + { + "camera": "G436", + "activity": "person_puts_down_object", + "actor_ids": [ + 288, + 287 + ], + "frame_range": [ + 8414, + 8444 + ], + "timestamp": "280.47-281.47s", + "fps": 30.0, + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "entity_description": null + } + ], + "presentation_order": [ + 0, + 2, + 1, + 3 + ], + "correct_permutation": [ + 0, + 2, + 1, + 3 + ], + "mevid_validated": true, + "group_score": 9.0, + "related_pairs": 1, + "described_events": 2 + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_spatial_005", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing a dark gray top and gray pants and a person, wearing an indigo top and plum pants come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing a dark gray top and gray pants and a person, wearing an indigo top and plum pants come within 1.6 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G436" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G436_actor_173", + "entity_b": "G436_actor_198", + "entity_a_desc": "a person, wearing a dark gray top and gray pants", + "entity_b_desc": "a person, wearing an indigo top and plum pants", + "min_distance_meters": 1.58, + "closest_frame": 8040, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G436_actor_173", + "camera": "G436", + "description": "a person, wearing a dark gray top and gray pants", + "bbox_at_closest": [ + 772, + 292, + 809, + 362 + ], + "timestamp": "256.30-272.67s" + }, + "entity_b": { + "entity_id": "G436_actor_198", + "camera": "G436", + "description": "a person, wearing an indigo top and plum pants", + "bbox_at_closest": [ + 743, + 281, + 779, + 357 + ], + "timestamp": "254.93-299.97s" + }, + "min_distance_meters": 1.58, + "closest_frame": 8040, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "7689-8180", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "clip_files": [ + "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_spatial_006", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing a teal top and gray pants and a person, wearing an indigo top and plum pants come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing a teal top and gray pants and a person, wearing an indigo top and plum pants come within 1.9 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G436" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G436_actor_175", + "entity_b": "G436_actor_198", + "entity_a_desc": "a person, wearing a teal top and gray pants", + "entity_b_desc": "a person, wearing an indigo top and plum pants", + "min_distance_meters": 1.93, + "closest_frame": 7920, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G436_actor_175", + "camera": "G436", + "description": "a person, wearing a teal top and gray pants", + "bbox_at_closest": [ + 785, + 293, + 829, + 363 + ], + "timestamp": "261.67-265.20s" + }, + "entity_b": { + "entity_id": "G436_actor_198", + "camera": "G436", + "description": "a person, wearing an indigo top and plum pants", + "bbox_at_closest": [ + 745, + 282, + 781, + 358 + ], + "timestamp": "254.93-299.97s" + }, + "min_distance_meters": 1.93, + "closest_frame": 7920, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "7850-7956", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "clip_files": [ + "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_spatial_007", + "category": "spatial", + "difficulty": "easy", + "question_template": "How close do a person, wearing an indigo top and white pants (exiting a vehicle) and a person, wearing an indigo top and white pants (dropping off person) come to each other in the scene?", + "options": [ + "They come close together (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They cross paths (pass very close to each other)" + ], + "correct_answer_index": 3, + "correct_answer": "They cross paths (pass very close to each other)", + "reasoning": "A person, wearing an indigo top and white pants (exiting a vehicle) and a person, wearing an indigo top and white pants (dropping off person) come within 1.7 meters of each other, crossing paths during the scene.", + "requires_cameras": [ + "G436" + ], + "requires_multi_camera": false, + "verification": { + "entity_a": "G436_actor_228", + "entity_b": "G436_actor_296", + "entity_a_desc": "a person, wearing an indigo top and white pants (exiting a vehicle)", + "entity_b_desc": "a person, wearing an indigo top and white pants (dropping off person)", + "min_distance_meters": 1.74, + "closest_frame": 8190, + "proximity": "near", + "crosses_paths": true, + "projection_method": "krtd_bbox_foot_closest_approach" + }, + "debug_info": { + "entity_a": { + "entity_id": "G436_actor_228", + "camera": "G436", + "description": "a person, wearing an indigo top and white pants (exiting a vehicle)", + "bbox_at_closest": [ + 1658, + 345, + 1696, + 429 + ], + "timestamp": "269.87-273.47s" + }, + "entity_b": { + "entity_id": "G436_actor_296", + "camera": "G436", + "description": "a person, wearing an indigo top and white pants (dropping off person)", + "bbox_at_closest": [ + 1678, + 347, + 1715, + 419 + ], + "timestamp": "272.93-275.43s" + }, + "min_distance_meters": 1.74, + "closest_frame": 8190, + "proximity": "near", + "crosses_paths": true, + "overlap_frames": "8188-8204", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "clip_files": [ + "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_summarization_008", + "category": "summarization", + "difficulty": "hard", + "question_template": "Considering all 3 cameras in this slot, which description best characterizes the overall scene?", + "options": [ + "A vehicle-focused scene with mostly parking and driving activity", + "A single-camera scene showing only indoor activities", + "An empty scene with minimal activity, captured on 1 camera", + "A pedestrian-dominant scene across 3 cameras, primarily featuring talking to person" + ], + "correct_answer_index": 3, + "correct_answer": "A pedestrian-dominant scene across 3 cameras, primarily featuring talking to person", + "requires_cameras": [ + "G301", + "G341", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "scene_characterization", + "total_events": 149, + "num_cameras": 3, + "person_events": 138, + "vehicle_events": 4, + "scene_type": "pedestrian-dominant", + "top_activity": "person_talks_to_person", + "top_activity_count": 92 + }, + "debug_info": { + "question_type": "scene_characterization", + "scene_analysis": { + "total_events": 149, + "unique_activities": 19, + "activity_counts": { + "person_carries_heavy_object": 4, + "person_talks_to_person": 92, + "person_texts_on_phone": 19, + "person_stands_up": 1, + "hand_interacts_with_person": 7, + "person_puts_down_object": 3, + "person_opens_facility_door": 3, + "person_picks_up_object": 2, + "person_exits_scene_through_structure": 1, + "person_enters_scene_through_structure": 2, + "person_opens_vehicle_door": 3, + "person_enters_vehicle": 1, + "person_closes_vehicle_door": 3, + "vehicle_starts": 2, + "person_talks_on_phone": 1, + "person_embraces_person": 1, + "vehicle_stops": 1, + "vehicle_drops_off_person": 1, + "person_exits_vehicle": 2 + }, + "camera_event_counts": { + "G301": 16, + "G436": 131, + "G341": 2 + }, + "num_cameras": 3, + "duration_sec": 300.0, + "person_event_count": 138, + "vehicle_event_count": 4, + "cross_camera_activities": { + "person_puts_down_object": [ + "G301", + "G436" + ], + "person_opens_facility_door": [ + "G301", + "G341" + ], + "person_enters_scene_through_structure": [ + "G301", + "G341" + ], + "person_opens_vehicle_door": [ + "G301", + "G436" + ], + "person_closes_vehicle_door": [ + "G301", + "G436" + ], + "vehicle_starts": [ + "G301", + "G436" + ] + }, + "busiest_camera": "G436", + "busiest_camera_count": 131, + "unique_entities": 123, + "person_entities": 104, + "top_3_activities": [ + [ + "person_talks_to_person", + 92 + ], + [ + "person_texts_on_phone", + 19 + ], + [ + "hand_interacts_with_person", + 7 + ] + ] + }, + "scene_description": "The scene spans 3 cameras with 149 total activity events. The most frequent activities are: talks to person (92 occurrences), texts on phone (19 occurrences), interacts with person (7 occurrences). 6 activities occur on multiple cameras. Camera G436 is the most active with 131 events.", + "scene_type": "pedestrian-dominant", + "clip_files": [ + "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G341.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_counting_009", + "category": "counting", + "difficulty": "easy", + "question_template": "How many times does someone perform the action of putting down object across all cameras in this slot?", + "options": [ + "2", + "3", + "4", + "6" + ], + "correct_answer_index": 1, + "correct_answer": "3", + "reasoning": "The action of puts down object was observed 3 times across 2 cameras (G301: 2, G436: 1).", + "requires_cameras": [ + "G301", + "G436" + ], + "requires_multi_camera": true, + "verification": { + "question_type": "activity_counting", + "correct_count": 3, + "cameras_involved": [ + "G301", + "G436" + ], + "activity": "person_puts_down_object", + "event_ids": [ + "G301_evt_3", + "G301_evt_4", + "G436_evt_133" + ], + "key_frames": [ + { + "camera": "G301", + "start_frame": 1763, + "start_sec": 58.77, + "end_sec": 60.07, + "event_id": "G301_evt_3", + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G301", + "start_frame": 1811, + "start_sec": 60.37, + "end_sec": 61.67, + "event_id": "G301_evt_4", + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "cluster_size": 1 + }, + { + "camera": "G436", + "start_frame": 8414, + "start_sec": 280.47, + "end_sec": 281.47, + "event_id": "G436_evt_133", + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "cluster_size": 1 + } + ] + }, + "debug_info": { + "subtype": "activity_counting", + "correct_count": 3, + "cameras_involved": [ + "G301", + "G436" + ], + "cross_camera": true, + "candidate_score": 5.5, + "num_candidates_total": 11, + "slot": "2018-03-13.16-00.hospital", + "activity": "person_puts_down_object", + "activity_human": "puts down object", + "clip_files": [ + "2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G301.r13.mp4", + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + }, + { + "question_id": "final_best_camera_010", + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": "Which camera first captures the entrance of a person into the scene?", + "options": [ + "Camera G301", + "Camera G341", + "Camera G436", + "Camera G423" + ], + "correct_answer": "Camera G436", + "correct_answer_index": 2, + "requires_cameras": [ + "G436", + "G301", + "G341" + ], + "difficulty": "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": "G436", + "entrance_time_sec": 266.93, + "entity_description": "a person", + "cluster_id": "standalone_G436_227", + "all_entrance_cameras": { + "G436": 266.93 + }, + "activity": "person_exits_vehicle" + }, + "debug_info": { + "representative_event": { + "clip_file": "2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4", + "camera": "G436", + "activity": "person_exits_vehicle", + "start_sec": 266.93 + } + }, + "video_paths": [ + "/nas/mars/dataset/MEVA/mp4s/2018-03-13/16/2018-03-13.16-00.hospital/2018-03-13.16-00-02.16-05-02.hospital.G436.r13.mp4" + ] + } + ] +} \ No newline at end of file diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193229.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193229.log.txt new file mode 100644 index 0000000..6d4f3e9 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193229.log.txt @@ -0,0 +1,1655 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Canonical slots: 814 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193229.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:32:29 +============================================================ + +[ 1/814] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/814] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ DRY-RUN OK +[ 3/814] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/814] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ DRY-RUN OK +[ 5/814] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ DRY-RUN OK +[ 6/814] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ DRY-RUN OK +[ 7/814] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 8/814] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ DRY-RUN OK +[ 9/814] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ DRY-RUN OK +[ 10/814] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ DRY-RUN OK +[ 11/814] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ DRY-RUN OK +[ 12/814] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ DRY-RUN OK +[ 13/814] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ DRY-RUN OK +[ 14/814] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ DRY-RUN OK +[ 15/814] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ DRY-RUN OK +[ 16/814] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ DRY-RUN OK +[ 17/814] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ DRY-RUN OK +[ 18/814] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ DRY-RUN OK +[ 19/814] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ DRY-RUN OK +[ 20/814] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ DRY-RUN OK +[ 21/814] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ DRY-RUN OK +[ 22/814] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ DRY-RUN OK +[ 23/814] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ DRY-RUN OK +[ 24/814] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ DRY-RUN OK +[ 25/814] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 26/814] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ DRY-RUN OK +[ 27/814] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ DRY-RUN OK +[ 28/814] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ DRY-RUN OK +[ 29/814] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ DRY-RUN OK +[ 30/814] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ DRY-RUN OK +[ 31/814] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 32/814] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ DRY-RUN OK +[ 33/814] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ DRY-RUN OK +[ 34/814] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ DRY-RUN OK +[ 35/814] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ DRY-RUN OK +[ 36/814] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ DRY-RUN OK +[ 37/814] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ DRY-RUN OK +[ 38/814] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ DRY-RUN OK +[ 39/814] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ DRY-RUN OK +[ 40/814] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ DRY-RUN OK +[ 41/814] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ DRY-RUN OK +[ 42/814] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ DRY-RUN OK +[ 43/814] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ DRY-RUN OK +[ 44/814] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ DRY-RUN OK +[ 45/814] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ DRY-RUN OK +[ 46/814] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ DRY-RUN OK +[ 47/814] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ DRY-RUN OK +[ 48/814] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ DRY-RUN OK +[ 49/814] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ DRY-RUN OK +[ 50/814] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ DRY-RUN OK +[ 51/814] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ DRY-RUN OK +[ 52/814] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ DRY-RUN OK +[ 53/814] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ DRY-RUN OK +[ 54/814] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ DRY-RUN OK +[ 55/814] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ DRY-RUN OK +[ 56/814] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ DRY-RUN OK +[ 57/814] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ DRY-RUN OK +[ 58/814] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ DRY-RUN OK +[ 59/814] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ DRY-RUN OK +[ 60/814] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ DRY-RUN OK +[ 61/814] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ DRY-RUN OK +[ 62/814] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ DRY-RUN OK +[ 63/814] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ DRY-RUN OK +[ 64/814] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ DRY-RUN OK +[ 65/814] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ DRY-RUN OK +[ 66/814] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ DRY-RUN OK +[ 67/814] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ DRY-RUN OK +[ 68/814] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ DRY-RUN OK +[ 69/814] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ DRY-RUN OK +[ 70/814] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ DRY-RUN OK +[ 71/814] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ DRY-RUN OK +[ 72/814] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ DRY-RUN OK +[ 73/814] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ DRY-RUN OK +[ 74/814] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ DRY-RUN OK +[ 75/814] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ DRY-RUN OK +[ 76/814] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ DRY-RUN OK +[ 77/814] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ DRY-RUN OK +[ 78/814] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ DRY-RUN OK +[ 79/814] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ DRY-RUN OK +[ 80/814] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ DRY-RUN OK +[ 81/814] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ DRY-RUN OK +[ 82/814] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ DRY-RUN OK +[ 83/814] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ DRY-RUN OK +[ 84/814] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ DRY-RUN OK +[ 85/814] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ DRY-RUN OK +[ 86/814] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ DRY-RUN OK +[ 87/814] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ DRY-RUN OK +[ 88/814] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ DRY-RUN OK +[ 89/814] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ DRY-RUN OK +[ 90/814] 2018-03-07.17-00-01.admin → 2018-03-07.17-00-01.admin + ✓ DRY-RUN OK +[ 91/814] 2018-03-07.17-00-01.bus → 2018-03-07.17-00-01.bus + ✓ DRY-RUN OK +[ 92/814] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ DRY-RUN OK +[ 93/814] 2018-03-07.17-00-01.school → 2018-03-07.17-00-01.school + ✓ DRY-RUN OK +[ 94/814] 2018-03-07.17-00-05.hospital → 2018-03-07.17-00-05.hospital + ✓ DRY-RUN OK +[ 95/814] 2018-03-07.17-00-06.school → 2018-03-07.17-00-06.school + ✓ DRY-RUN OK +[ 96/814] 2018-03-07.17-00-07.hospital → 2018-03-07.17-00-07.hospital + ✓ DRY-RUN OK +[ 97/814] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ DRY-RUN OK +[ 98/814] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ DRY-RUN OK +[ 99/814] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ DRY-RUN OK +[100/814] 2018-03-07.17-05-01.admin → 2018-03-07.17-05-01.admin + ✓ DRY-RUN OK +[101/814] 2018-03-07.17-05-01.bus → 2018-03-07.17-05-01.bus + ✓ DRY-RUN OK +[102/814] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ DRY-RUN OK +[103/814] 2018-03-07.17-05-01.school → 2018-03-07.17-05-01.school + ✓ DRY-RUN OK +[104/814] 2018-03-07.17-05-05.hospital → 2018-03-07.17-05-05.hospital + ✓ DRY-RUN OK +[105/814] 2018-03-07.17-05-06.school → 2018-03-07.17-05-06.school + ✓ DRY-RUN OK +[106/814] 2018-03-07.17-05-07.hospital → 2018-03-07.17-05-07.hospital + ✓ DRY-RUN OK +[107/814] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[108/814] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ DRY-RUN OK +[109/814] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ DRY-RUN OK +[110/814] 2018-03-07.17-20-01.admin → 2018-03-07.17-20-01.admin + ✓ DRY-RUN OK +[111/814] 2018-03-07.17-20-01.bus → 2018-03-07.17-20-01.bus + ✓ DRY-RUN OK +[112/814] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ DRY-RUN OK +[113/814] 2018-03-07.17-20-01.school → 2018-03-07.17-20-01.school + ✓ DRY-RUN OK +[114/814] 2018-03-07.17-20-05.hospital → 2018-03-07.17-20-05.hospital + ✓ DRY-RUN OK +[115/814] 2018-03-07.17-20-06.school → 2018-03-07.17-20-06.school + ✓ DRY-RUN OK +[116/814] 2018-03-07.17-20-07.hospital → 2018-03-07.17-20-07.hospital + ✓ DRY-RUN OK +[117/814] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ DRY-RUN OK +[118/814] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ DRY-RUN OK +[119/814] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ DRY-RUN OK +[120/814] 2018-03-07.17-25-01.admin → 2018-03-07.17-25-01.admin + ✓ DRY-RUN OK +[121/814] 2018-03-07.17-25-01.bus → 2018-03-07.17-25-01.bus + ✓ DRY-RUN OK +[122/814] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ DRY-RUN OK +[123/814] 2018-03-07.17-25-01.school → 2018-03-07.17-25-01.school + ✓ DRY-RUN OK +[124/814] 2018-03-07.17-25-03.school → 2018-03-07.17-25-03.school + ✓ DRY-RUN OK +[125/814] 2018-03-07.17-25-05.hospital → 2018-03-07.17-25-05.hospital + ✓ DRY-RUN OK +[126/814] 2018-03-07.17-25-06.school → 2018-03-07.17-25-06.school + ✓ DRY-RUN OK +[127/814] 2018-03-07.17-25-07.hospital → 2018-03-07.17-25-07.hospital + ✓ DRY-RUN OK +[128/814] 2018-03-07.17-25-10.school → 2018-03-07.17-25-10.school + ✓ DRY-RUN OK +[129/814] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ DRY-RUN OK +[130/814] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ DRY-RUN OK +[131/814] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ DRY-RUN OK +[132/814] 2018-03-07.17-30-01.admin → 2018-03-07.17-30-01.admin + ✓ DRY-RUN OK +[133/814] 2018-03-07.17-30-01.bus → 2018-03-07.17-30-01.bus + ✓ DRY-RUN OK +[134/814] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ DRY-RUN OK +[135/814] 2018-03-07.17-30-01.school → 2018-03-07.17-30-01.school + ✓ DRY-RUN OK +[136/814] 2018-03-07.17-30-03.school → 2018-03-07.17-30-03.school + ✓ DRY-RUN OK +[137/814] 2018-03-07.17-30-05.hospital → 2018-03-07.17-30-05.hospital + ✓ DRY-RUN OK +[138/814] 2018-03-07.17-30-06.school → 2018-03-07.17-30-06.school + ✓ DRY-RUN OK +[139/814] 2018-03-07.17-30-07.hospital → 2018-03-07.17-30-07.hospital + ✓ DRY-RUN OK +[140/814] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ DRY-RUN OK +[141/814] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ DRY-RUN OK +[142/814] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ DRY-RUN OK +[143/814] 2018-03-07.17-35-01.admin → 2018-03-07.17-35-01.admin + ✓ DRY-RUN OK +[144/814] 2018-03-07.17-35-01.bus → 2018-03-07.17-35-01.bus + ✓ DRY-RUN OK +[145/814] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ DRY-RUN OK +[146/814] 2018-03-07.17-35-01.school → 2018-03-07.17-35-01.school + ✓ DRY-RUN OK +[147/814] 2018-03-07.17-35-03.school → 2018-03-07.17-35-03.school + ✓ DRY-RUN OK +[148/814] 2018-03-07.17-35-05.hospital → 2018-03-07.17-35-05.hospital + ✓ DRY-RUN OK +[149/814] 2018-03-07.17-35-06.school → 2018-03-07.17-35-06.school + ✓ DRY-RUN OK +[150/814] 2018-03-07.17-35-07.hospital → 2018-03-07.17-35-07.hospital + ✓ DRY-RUN OK +[151/814] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ DRY-RUN OK +[152/814] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ DRY-RUN OK +[153/814] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ DRY-RUN OK +[154/814] 2018-03-09.10-10-01.bus → 2018-03-09.10-10-01.bus + ✓ DRY-RUN OK +[155/814] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ DRY-RUN OK +[156/814] 2018-03-09.10-10-01.school → 2018-03-09.10-10-01.school + ✓ DRY-RUN OK +[157/814] 2018-03-09.10-10-02.hospital → 2018-03-09.10-10-02.hospital + ✓ DRY-RUN OK +[158/814] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[159/814] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ DRY-RUN OK +[160/814] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ DRY-RUN OK +[161/814] 2018-03-09.10-15-01.bus → 2018-03-09.10-15-01.bus + ✓ DRY-RUN OK +[162/814] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ DRY-RUN OK +[163/814] 2018-03-09.10-15-01.school → 2018-03-09.10-15-01.school + ✓ DRY-RUN OK +[164/814] 2018-03-09.10-15-02.hospital → 2018-03-09.10-15-02.hospital + ✓ DRY-RUN OK +[165/814] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[166/814] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ DRY-RUN OK +[167/814] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[168/814] 2018-03-09.10-20-01.bus → 2018-03-09.10-20-01.bus + ✓ DRY-RUN OK +[169/814] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ DRY-RUN OK +[170/814] 2018-03-09.10-20-01.school → 2018-03-09.10-20-01.school + ✓ DRY-RUN OK +[171/814] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[172/814] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ DRY-RUN OK +[173/814] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[174/814] 2018-03-09.10-25-01.bus → 2018-03-09.10-25-01.bus + ✓ DRY-RUN OK +[175/814] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ DRY-RUN OK +[176/814] 2018-03-09.10-25-01.school → 2018-03-09.10-25-01.school + ✓ DRY-RUN OK +[177/814] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[178/814] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ DRY-RUN OK +[179/814] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ DRY-RUN OK +[180/814] 2018-03-09.10-30-01.bus → 2018-03-09.10-30-01.bus + ✓ DRY-RUN OK +[181/814] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ DRY-RUN OK +[182/814] 2018-03-09.10-30-01.school → 2018-03-09.10-30-01.school + ✓ DRY-RUN OK +[183/814] 2018-03-09.10-30-02.hospital → 2018-03-09.10-30-02.hospital + ✓ DRY-RUN OK +[184/814] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[185/814] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ DRY-RUN OK +[186/814] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ DRY-RUN OK +[187/814] 2018-03-09.10-35-01.bus → 2018-03-09.10-35-01.bus + ✓ DRY-RUN OK +[188/814] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ DRY-RUN OK +[189/814] 2018-03-09.10-35-01.school → 2018-03-09.10-35-01.school + ✓ DRY-RUN OK +[190/814] 2018-03-09.10-35-02.hospital → 2018-03-09.10-35-02.hospital + ✓ DRY-RUN OK +[191/814] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ DRY-RUN OK +[192/814] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ DRY-RUN OK +[193/814] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ DRY-RUN OK +[194/814] 2018-03-09.10-40-01.bus → 2018-03-09.10-40-01.bus + ✓ DRY-RUN OK +[195/814] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ DRY-RUN OK +[196/814] 2018-03-09.10-40-01.school → 2018-03-09.10-40-01.school + ✓ DRY-RUN OK +[197/814] 2018-03-09.10-40-02.hospital → 2018-03-09.10-40-02.hospital + ✓ DRY-RUN OK +[198/814] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ DRY-RUN OK +[199/814] 2018-03-11.11-15-01.school → 2018-03-11.11-15-01.school + ✓ DRY-RUN OK +[200/814] 2018-03-11.11-15-04.school → 2018-03-11.11-15-04.school + ✓ DRY-RUN OK +[201/814] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ DRY-RUN OK +[202/814] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ DRY-RUN OK +[203/814] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ DRY-RUN OK +[204/814] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ DRY-RUN OK +[205/814] 2018-03-11.11-20-01.admin → 2018-03-11.11-20-01.admin + ✓ DRY-RUN OK +[206/814] 2018-03-11.11-20-01.bus → 2018-03-11.11-20-01.bus + ✓ DRY-RUN OK +[207/814] 2018-03-11.11-20-01.school → 2018-03-11.11-20-01.school + ✓ DRY-RUN OK +[208/814] 2018-03-11.11-20-04.school → 2018-03-11.11-20-04.school + ✓ DRY-RUN OK +[209/814] 2018-03-11.11-20-08.bus → 2018-03-11.11-20-08.bus + ✓ DRY-RUN OK +[210/814] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ DRY-RUN OK +[211/814] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ DRY-RUN OK +[212/814] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ DRY-RUN OK +[213/814] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ DRY-RUN OK +[214/814] 2018-03-11.11-25-01.admin → 2018-03-11.11-25-01.admin + ✓ DRY-RUN OK +[215/814] 2018-03-11.11-25-01.bus → 2018-03-11.11-25-01.bus + ✓ DRY-RUN OK +[216/814] 2018-03-11.11-25-01.school → 2018-03-11.11-25-01.school + ✓ DRY-RUN OK +[217/814] 2018-03-11.11-25-04.school → 2018-03-11.11-25-04.school + ✓ DRY-RUN OK +[218/814] 2018-03-11.11-25-08.bus → 2018-03-11.11-25-08.bus + ✓ DRY-RUN OK +[219/814] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ DRY-RUN OK +[220/814] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ DRY-RUN OK +[221/814] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ DRY-RUN OK +[222/814] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ DRY-RUN OK +[223/814] 2018-03-11.11-30-01.admin → 2018-03-11.11-30-01.admin + ✓ DRY-RUN OK +[224/814] 2018-03-11.11-30-01.bus → 2018-03-11.11-30-01.bus + ✓ DRY-RUN OK +[225/814] 2018-03-11.11-30-01.school → 2018-03-11.11-30-01.school + ✓ DRY-RUN OK +[226/814] 2018-03-11.11-30-04.school → 2018-03-11.11-30-04.school + ✓ DRY-RUN OK +[227/814] 2018-03-11.11-30-08.bus → 2018-03-11.11-30-08.bus + ✓ DRY-RUN OK +[228/814] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ DRY-RUN OK +[229/814] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[230/814] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ DRY-RUN OK +[231/814] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ DRY-RUN OK +[232/814] 2018-03-11.11-35-01.admin → 2018-03-11.11-35-01.admin + ✓ DRY-RUN OK +[233/814] 2018-03-11.11-35-01.bus → 2018-03-11.11-35-01.bus + ✓ DRY-RUN OK +[234/814] 2018-03-11.11-35-01.school → 2018-03-11.11-35-01.school + ✓ DRY-RUN OK +[235/814] 2018-03-11.11-35-04.school → 2018-03-11.11-35-04.school + ✓ DRY-RUN OK +[236/814] 2018-03-11.11-35-08.bus → 2018-03-11.11-35-08.bus + ✓ DRY-RUN OK +[237/814] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ DRY-RUN OK +[238/814] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[239/814] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[240/814] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ DRY-RUN OK +[241/814] 2018-03-11.11-40-01.admin → 2018-03-11.11-40-01.admin + ✓ DRY-RUN OK +[242/814] 2018-03-11.11-40-01.bus → 2018-03-11.11-40-01.bus + ✓ DRY-RUN OK +[243/814] 2018-03-11.11-40-01.school → 2018-03-11.11-40-01.school + ✓ DRY-RUN OK +[244/814] 2018-03-11.11-40-02.school → 2018-03-11.11-40-02.school + ✓ DRY-RUN OK +[245/814] 2018-03-11.11-40-04.school → 2018-03-11.11-40-04.school + ✓ DRY-RUN OK +[246/814] 2018-03-11.11-40-08.bus → 2018-03-11.11-40-08.bus + ✓ DRY-RUN OK +[247/814] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[248/814] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[249/814] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[250/814] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ DRY-RUN OK +[251/814] 2018-03-11.11-45-01.admin → 2018-03-11.11-45-01.admin + ✓ DRY-RUN OK +[252/814] 2018-03-11.11-45-01.bus → 2018-03-11.11-45-01.bus + ✓ DRY-RUN OK +[253/814] 2018-03-11.11-45-01.school → 2018-03-11.11-45-01.school + ✓ DRY-RUN OK +[254/814] 2018-03-11.11-45-04.school → 2018-03-11.11-45-04.school + ✓ DRY-RUN OK +[255/814] 2018-03-11.11-45-08.bus → 2018-03-11.11-45-08.bus + ✓ DRY-RUN OK +[256/814] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ DRY-RUN OK +[257/814] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ DRY-RUN OK +[258/814] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ DRY-RUN OK +[259/814] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ DRY-RUN OK +[260/814] 2018-03-11.11-50-01.admin → 2018-03-11.11-50-01.admin + ✓ DRY-RUN OK +[261/814] 2018-03-11.11-50-01.bus → 2018-03-11.11-50-01.bus + ✓ DRY-RUN OK +[262/814] 2018-03-11.11-50-01.school → 2018-03-11.11-50-01.school + ✓ DRY-RUN OK +[263/814] 2018-03-11.11-50-04.school → 2018-03-11.11-50-04.school + ✓ DRY-RUN OK +[264/814] 2018-03-11.11-50-08.bus → 2018-03-11.11-50-08.bus + ✓ DRY-RUN OK +[265/814] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ DRY-RUN OK +[266/814] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ DRY-RUN OK +[267/814] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ DRY-RUN OK +[268/814] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ DRY-RUN OK +[269/814] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ DRY-RUN OK +[270/814] 2018-03-11.13-50-01.admin → 2018-03-11.13-50-01.admin + ✓ DRY-RUN OK +[271/814] 2018-03-11.13-50-01.bus → 2018-03-11.13-50-01.bus + ✓ DRY-RUN OK +[272/814] 2018-03-11.13-50-01.school → 2018-03-11.13-50-01.school + ✓ DRY-RUN OK +[273/814] 2018-03-11.13-50-04.school → 2018-03-11.13-50-04.school + ✓ DRY-RUN OK +[274/814] 2018-03-11.13-50-08.bus → 2018-03-11.13-50-08.bus + ✓ DRY-RUN OK +[275/814] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ DRY-RUN OK +[276/814] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ DRY-RUN OK +[277/814] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ DRY-RUN OK +[278/814] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ DRY-RUN OK +[279/814] 2018-03-11.14-00-01.admin → 2018-03-11.14-00-01.admin + ✓ DRY-RUN OK +[280/814] 2018-03-11.14-00-01.bus → 2018-03-11.14-00-01.bus + ✓ DRY-RUN OK +[281/814] 2018-03-11.14-00-01.school → 2018-03-11.14-00-01.school + ✓ DRY-RUN OK +[282/814] 2018-03-11.14-00-02.school → 2018-03-11.14-00-02.school + ✓ DRY-RUN OK +[283/814] 2018-03-11.14-00-04.school → 2018-03-11.14-00-04.school + ✓ DRY-RUN OK +[284/814] 2018-03-11.14-00-08.bus → 2018-03-11.14-00-08.bus + ✓ DRY-RUN OK +[285/814] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ DRY-RUN OK +[286/814] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ DRY-RUN OK +[287/814] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ DRY-RUN OK +[288/814] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ DRY-RUN OK +[289/814] 2018-03-11.14-05-01.admin → 2018-03-11.14-05-01.admin + ✓ DRY-RUN OK +[290/814] 2018-03-11.14-05-01.bus → 2018-03-11.14-05-01.bus + ✓ DRY-RUN OK +[291/814] 2018-03-11.14-05-01.school → 2018-03-11.14-05-01.school + ✓ DRY-RUN OK +[292/814] 2018-03-11.14-05-02.school → 2018-03-11.14-05-02.school + ✓ DRY-RUN OK +[293/814] 2018-03-11.14-05-04.school → 2018-03-11.14-05-04.school + ✓ DRY-RUN OK +[294/814] 2018-03-11.14-05-08.bus → 2018-03-11.14-05-08.bus + ✓ DRY-RUN OK +[295/814] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ DRY-RUN OK +[296/814] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ DRY-RUN OK +[297/814] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ DRY-RUN OK +[298/814] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ DRY-RUN OK +[299/814] 2018-03-11.14-10-01.admin → 2018-03-11.14-10-01.admin + ✓ DRY-RUN OK +[300/814] 2018-03-11.14-10-01.bus → 2018-03-11.14-10-01.bus + ✓ DRY-RUN OK +[301/814] 2018-03-11.14-10-01.school → 2018-03-11.14-10-01.school + ✓ DRY-RUN OK +[302/814] 2018-03-11.14-10-02.school → 2018-03-11.14-10-02.school + ✓ DRY-RUN OK +[303/814] 2018-03-11.14-10-04.school → 2018-03-11.14-10-04.school + ✓ DRY-RUN OK +[304/814] 2018-03-11.14-10-08.bus → 2018-03-11.14-10-08.bus + ✓ DRY-RUN OK +[305/814] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ DRY-RUN OK +[306/814] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ DRY-RUN OK +[307/814] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ DRY-RUN OK +[308/814] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ DRY-RUN OK +[309/814] 2018-03-11.14-15-01.admin → 2018-03-11.14-15-01.admin + ✓ DRY-RUN OK +[310/814] 2018-03-11.14-15-01.bus → 2018-03-11.14-15-01.bus + ✓ DRY-RUN OK +[311/814] 2018-03-11.14-15-01.school → 2018-03-11.14-15-01.school + ✓ DRY-RUN OK +[312/814] 2018-03-11.14-15-02.school → 2018-03-11.14-15-02.school + ✓ DRY-RUN OK +[313/814] 2018-03-11.14-15-04.school → 2018-03-11.14-15-04.school + ✓ DRY-RUN OK +[314/814] 2018-03-11.14-15-08.bus → 2018-03-11.14-15-08.bus + ✓ DRY-RUN OK +[315/814] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ DRY-RUN OK +[316/814] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ DRY-RUN OK +[317/814] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ DRY-RUN OK +[318/814] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ DRY-RUN OK +[319/814] 2018-03-11.14-20-01.admin → 2018-03-11.14-20-01.admin + ✓ DRY-RUN OK +[320/814] 2018-03-11.14-20-01.bus → 2018-03-11.14-20-01.bus + ✓ DRY-RUN OK +[321/814] 2018-03-11.14-20-01.school → 2018-03-11.14-20-01.school + ✓ DRY-RUN OK +[322/814] 2018-03-11.14-20-02.school → 2018-03-11.14-20-02.school + ✓ DRY-RUN OK +[323/814] 2018-03-11.14-20-04.school → 2018-03-11.14-20-04.school + ✓ DRY-RUN OK +[324/814] 2018-03-11.14-20-08.bus → 2018-03-11.14-20-08.bus + ✓ DRY-RUN OK +[325/814] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ DRY-RUN OK +[326/814] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ DRY-RUN OK +[327/814] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ DRY-RUN OK +[328/814] 2018-03-11.16-10-01.bus → 2018-03-11.16-10-01.bus + ✓ DRY-RUN OK +[329/814] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ DRY-RUN OK +[330/814] 2018-03-11.16-10-08.bus → 2018-03-11.16-10-08.bus + ✓ DRY-RUN OK +[331/814] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ DRY-RUN OK +[332/814] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ DRY-RUN OK +[333/814] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ DRY-RUN OK +[334/814] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ DRY-RUN OK +[335/814] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ DRY-RUN OK +[336/814] 2018-03-11.16-15-01.bus → 2018-03-11.16-15-01.bus + ✓ DRY-RUN OK +[337/814] 2018-03-11.16-15-01.school → 2018-03-11.16-15-01.school + ✓ DRY-RUN OK +[338/814] 2018-03-11.16-15-02.school → 2018-03-11.16-15-02.school + ✓ DRY-RUN OK +[339/814] 2018-03-11.16-15-04.school → 2018-03-11.16-15-04.school + ✓ DRY-RUN OK +[340/814] 2018-03-11.16-15-08.bus → 2018-03-11.16-15-08.bus + ✓ DRY-RUN OK +[341/814] 2018-03-11.16-15-08.hospital → 2018-03-11.16-15-08.hospital + ✓ DRY-RUN OK +[342/814] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ DRY-RUN OK +[343/814] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ DRY-RUN OK +[344/814] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ DRY-RUN OK +[345/814] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ DRY-RUN OK +[346/814] 2018-03-11.16-20-01.bus → 2018-03-11.16-20-01.bus + ✓ DRY-RUN OK +[347/814] 2018-03-11.16-20-01.school → 2018-03-11.16-20-01.school + ✓ DRY-RUN OK +[348/814] 2018-03-11.16-20-02.school → 2018-03-11.16-20-02.school + ✓ DRY-RUN OK +[349/814] 2018-03-11.16-20-04.school → 2018-03-11.16-20-04.school + ✓ DRY-RUN OK +[350/814] 2018-03-11.16-20-08.bus → 2018-03-11.16-20-08.bus + ✓ DRY-RUN OK +[351/814] 2018-03-11.16-20-08.hospital → 2018-03-11.16-20-08.hospital + ✓ DRY-RUN OK +[352/814] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ DRY-RUN OK +[353/814] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ DRY-RUN OK +[354/814] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ DRY-RUN OK +[355/814] 2018-03-11.16-25-01.bus → 2018-03-11.16-25-01.bus + ✓ DRY-RUN OK +[356/814] 2018-03-11.16-25-01.school → 2018-03-11.16-25-01.school + ✓ DRY-RUN OK +[357/814] 2018-03-11.16-25-02.school → 2018-03-11.16-25-02.school + ✓ DRY-RUN OK +[358/814] 2018-03-11.16-25-04.school → 2018-03-11.16-25-04.school + ✓ DRY-RUN OK +[359/814] 2018-03-11.16-25-08.bus → 2018-03-11.16-25-08.bus + ✓ DRY-RUN OK +[360/814] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ DRY-RUN OK +[361/814] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ DRY-RUN OK +[362/814] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ DRY-RUN OK +[363/814] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ DRY-RUN OK +[364/814] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ DRY-RUN OK +[365/814] 2018-03-11.16-30-01.bus → 2018-03-11.16-30-01.bus + ✓ DRY-RUN OK +[366/814] 2018-03-11.16-30-01.school → 2018-03-11.16-30-01.school + ✓ DRY-RUN OK +[367/814] 2018-03-11.16-30-02.school → 2018-03-11.16-30-02.school + ✓ DRY-RUN OK +[368/814] 2018-03-11.16-30-04.school → 2018-03-11.16-30-04.school + ✓ DRY-RUN OK +[369/814] 2018-03-11.16-30-08.bus → 2018-03-11.16-30-08.bus + ✓ DRY-RUN OK +[370/814] 2018-03-11.16-30-08.hospital → 2018-03-11.16-30-08.hospital + ✓ DRY-RUN OK +[371/814] 2018-03-11.16-30-09.hospital → 2018-03-11.16-30-09.hospital + ✓ DRY-RUN OK +[372/814] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ DRY-RUN OK +[373/814] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ DRY-RUN OK +[374/814] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ DRY-RUN OK +[375/814] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ DRY-RUN OK +[376/814] 2018-03-11.16-35-01.bus → 2018-03-11.16-35-01.bus + ✓ DRY-RUN OK +[377/814] 2018-03-11.16-35-01.school → 2018-03-11.16-35-01.school + ✓ DRY-RUN OK +[378/814] 2018-03-11.16-35-02.school → 2018-03-11.16-35-02.school + ✓ DRY-RUN OK +[379/814] 2018-03-11.16-35-04.school → 2018-03-11.16-35-04.school + ✓ DRY-RUN OK +[380/814] 2018-03-11.16-35-08.bus → 2018-03-11.16-35-08.bus + ✓ DRY-RUN OK +[381/814] 2018-03-11.16-35-08.hospital → 2018-03-11.16-35-08.hospital + ✓ DRY-RUN OK +[382/814] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ DRY-RUN OK +[383/814] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ DRY-RUN OK +[384/814] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ DRY-RUN OK +[385/814] 2018-03-11.16-40-01.bus → 2018-03-11.16-40-01.bus + ✓ DRY-RUN OK +[386/814] 2018-03-11.16-40-01.school → 2018-03-11.16-40-01.school + ✓ DRY-RUN OK +[387/814] 2018-03-11.16-40-02.school → 2018-03-11.16-40-02.school + ✓ DRY-RUN OK +[388/814] 2018-03-11.16-40-04.school → 2018-03-11.16-40-04.school + ✓ DRY-RUN OK +[389/814] 2018-03-11.16-40-08.bus → 2018-03-11.16-40-08.bus + ✓ DRY-RUN OK +[390/814] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ DRY-RUN OK +[391/814] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ DRY-RUN OK +[392/814] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ DRY-RUN OK +[393/814] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ DRY-RUN OK +[394/814] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ DRY-RUN OK +[395/814] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ DRY-RUN OK +[396/814] 2018-03-11.17-10-01.bus → 2018-03-11.17-10-01.bus + ✓ DRY-RUN OK +[397/814] 2018-03-11.17-10-01.school → 2018-03-11.17-10-01.school + ✓ DRY-RUN OK +[398/814] 2018-03-11.17-10-02.school → 2018-03-11.17-10-02.school + ✓ DRY-RUN OK +[399/814] 2018-03-11.17-10-04.school → 2018-03-11.17-10-04.school + ✓ DRY-RUN OK +[400/814] 2018-03-11.17-10-08.bus → 2018-03-11.17-10-08.bus + ✓ DRY-RUN OK +[401/814] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ DRY-RUN OK +[402/814] 2018-03-11.17-10-09.hospital → 2018-03-11.17-10-09.hospital + ✓ DRY-RUN OK +[403/814] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ DRY-RUN OK +[404/814] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ DRY-RUN OK +[405/814] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ DRY-RUN OK +[406/814] 2018-03-11.17-15-01.bus → 2018-03-11.17-15-01.bus + ✓ DRY-RUN OK +[407/814] 2018-03-11.17-15-01.school → 2018-03-11.17-15-01.school + ✓ DRY-RUN OK +[408/814] 2018-03-11.17-15-02.school → 2018-03-11.17-15-02.school + ✓ DRY-RUN OK +[409/814] 2018-03-11.17-15-04.school → 2018-03-11.17-15-04.school + ✓ DRY-RUN OK +[410/814] 2018-03-11.17-15-08.bus → 2018-03-11.17-15-08.bus + ✓ DRY-RUN OK +[411/814] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ DRY-RUN OK +[412/814] 2018-03-11.17-15-09.hospital → 2018-03-11.17-15-09.hospital + ✓ DRY-RUN OK +[413/814] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ DRY-RUN OK +[414/814] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ DRY-RUN OK +[415/814] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ DRY-RUN OK +[416/814] 2018-03-11.17-20-01.bus → 2018-03-11.17-20-01.bus + ✓ DRY-RUN OK +[417/814] 2018-03-11.17-20-01.school → 2018-03-11.17-20-01.school + ✓ DRY-RUN OK +[418/814] 2018-03-11.17-20-02.school → 2018-03-11.17-20-02.school + ✓ DRY-RUN OK +[419/814] 2018-03-11.17-20-04.school → 2018-03-11.17-20-04.school + ✓ DRY-RUN OK +[420/814] 2018-03-11.17-20-08.bus → 2018-03-11.17-20-08.bus + ✓ DRY-RUN OK +[421/814] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ DRY-RUN OK +[422/814] 2018-03-11.17-20-09.hospital → 2018-03-11.17-20-09.hospital + ✓ DRY-RUN OK +[423/814] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ DRY-RUN OK +[424/814] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ DRY-RUN OK +[425/814] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ DRY-RUN OK +[426/814] 2018-03-11.17-25-01.bus → 2018-03-11.17-25-01.bus + ✓ DRY-RUN OK +[427/814] 2018-03-11.17-25-01.school → 2018-03-11.17-25-01.school + ✓ DRY-RUN OK +[428/814] 2018-03-11.17-25-02.school → 2018-03-11.17-25-02.school + ✓ DRY-RUN OK +[429/814] 2018-03-11.17-25-04.school → 2018-03-11.17-25-04.school + ✓ DRY-RUN OK +[430/814] 2018-03-11.17-25-08.bus → 2018-03-11.17-25-08.bus + ✓ DRY-RUN OK +[431/814] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ DRY-RUN OK +[432/814] 2018-03-11.17-25-09.hospital → 2018-03-11.17-25-09.hospital + ✓ DRY-RUN OK +[433/814] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ DRY-RUN OK +[434/814] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ DRY-RUN OK +[435/814] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ DRY-RUN OK +[436/814] 2018-03-12.10-00-01.bus → 2018-03-12.10-00-01.bus + ✓ DRY-RUN OK +[437/814] 2018-03-12.10-00-01.school → 2018-03-12.10-00-01.school + ✓ DRY-RUN OK +[438/814] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ DRY-RUN OK +[439/814] 2018-03-12.10-00-02.school → 2018-03-12.10-00-02.school + ✓ DRY-RUN OK +[440/814] 2018-03-12.10-00-04.school → 2018-03-12.10-00-04.school + ✓ DRY-RUN OK +[441/814] 2018-03-12.10-00-05.school → 2018-03-12.10-00-05.school + ✓ DRY-RUN OK +[442/814] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ DRY-RUN OK +[443/814] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ DRY-RUN OK +[444/814] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ DRY-RUN OK +[445/814] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ DRY-RUN OK +[446/814] 2018-03-12.10-05-01.bus → 2018-03-12.10-05-01.bus + ✓ DRY-RUN OK +[447/814] 2018-03-12.10-05-01.school → 2018-03-12.10-05-01.school + ✓ DRY-RUN OK +[448/814] 2018-03-12.10-05-02.school → 2018-03-12.10-05-02.school + ✓ DRY-RUN OK +[449/814] 2018-03-12.10-05-03.school → 2018-03-12.10-05-03.school + ✓ DRY-RUN OK +[450/814] 2018-03-12.10-05-04.school → 2018-03-12.10-05-04.school + ✓ DRY-RUN OK +[451/814] 2018-03-12.10-05-05.school → 2018-03-12.10-05-05.school + ✓ DRY-RUN OK +[452/814] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ DRY-RUN OK +[453/814] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ DRY-RUN OK +[454/814] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ DRY-RUN OK +[455/814] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ DRY-RUN OK +[456/814] 2018-03-12.10-10-01.bus → 2018-03-12.10-10-01.bus + ✓ DRY-RUN OK +[457/814] 2018-03-12.10-10-01.school → 2018-03-12.10-10-01.school + ✓ DRY-RUN OK +[458/814] 2018-03-12.10-10-02.school → 2018-03-12.10-10-02.school + ✓ DRY-RUN OK +[459/814] 2018-03-12.10-10-03.school → 2018-03-12.10-10-03.school + ✓ DRY-RUN OK +[460/814] 2018-03-12.10-10-04.school → 2018-03-12.10-10-04.school + ✓ DRY-RUN OK +[461/814] 2018-03-12.10-10-05.school → 2018-03-12.10-10-05.school + ✓ DRY-RUN OK +[462/814] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ DRY-RUN OK +[463/814] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ DRY-RUN OK +[464/814] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ DRY-RUN OK +[465/814] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ DRY-RUN OK +[466/814] 2018-03-12.10-15-01.bus → 2018-03-12.10-15-01.bus + ✓ DRY-RUN OK +[467/814] 2018-03-12.10-15-01.school → 2018-03-12.10-15-01.school + ✓ DRY-RUN OK +[468/814] 2018-03-12.10-15-02.school → 2018-03-12.10-15-02.school + ✓ DRY-RUN OK +[469/814] 2018-03-12.10-15-03.school → 2018-03-12.10-15-03.school + ✓ DRY-RUN OK +[470/814] 2018-03-12.10-15-04.school → 2018-03-12.10-15-04.school + ✓ DRY-RUN OK +[471/814] 2018-03-12.10-15-05.school → 2018-03-12.10-15-05.school + ✓ DRY-RUN OK +[472/814] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ DRY-RUN OK +[473/814] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ DRY-RUN OK +[474/814] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ DRY-RUN OK +[475/814] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ DRY-RUN OK +[476/814] 2018-03-12.10-20-01.bus → 2018-03-12.10-20-01.bus + ✓ DRY-RUN OK +[477/814] 2018-03-12.10-20-01.school → 2018-03-12.10-20-01.school + ✓ DRY-RUN OK +[478/814] 2018-03-12.10-20-02.school → 2018-03-12.10-20-02.school + ✓ DRY-RUN OK +[479/814] 2018-03-12.10-20-03.school → 2018-03-12.10-20-03.school + ✓ DRY-RUN OK +[480/814] 2018-03-12.10-20-04.school → 2018-03-12.10-20-04.school + ✓ DRY-RUN OK +[481/814] 2018-03-12.10-20-05.school → 2018-03-12.10-20-05.school + ✓ DRY-RUN OK +[482/814] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ DRY-RUN OK +[483/814] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ DRY-RUN OK +[484/814] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ DRY-RUN OK +[485/814] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ DRY-RUN OK +[486/814] 2018-03-12.10-25-01.bus → 2018-03-12.10-25-01.bus + ✓ DRY-RUN OK +[487/814] 2018-03-12.10-25-01.school → 2018-03-12.10-25-01.school + ✓ DRY-RUN OK +[488/814] 2018-03-12.10-25-02.school → 2018-03-12.10-25-02.school + ✓ DRY-RUN OK +[489/814] 2018-03-12.10-25-03.school → 2018-03-12.10-25-03.school + ✓ DRY-RUN OK +[490/814] 2018-03-12.10-25-04.school → 2018-03-12.10-25-04.school + ✓ DRY-RUN OK +[491/814] 2018-03-12.10-25-05.school → 2018-03-12.10-25-05.school + ✓ DRY-RUN OK +[492/814] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ DRY-RUN OK +[493/814] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ DRY-RUN OK +[494/814] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ DRY-RUN OK +[495/814] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ DRY-RUN OK +[496/814] 2018-03-12.10-30-01.admin → 2018-03-12.10-30-01.admin + ✓ DRY-RUN OK +[497/814] 2018-03-12.10-30-01.bus → 2018-03-12.10-30-01.bus + ✓ DRY-RUN OK +[498/814] 2018-03-12.10-30-01.school → 2018-03-12.10-30-01.school + ✓ DRY-RUN OK +[499/814] 2018-03-12.10-30-02.school → 2018-03-12.10-30-02.school + ✓ DRY-RUN OK +[500/814] 2018-03-12.10-30-03.school → 2018-03-12.10-30-03.school + ✓ DRY-RUN OK +[501/814] 2018-03-12.10-30-04.school → 2018-03-12.10-30-04.school + ✓ DRY-RUN OK +[502/814] 2018-03-12.10-30-05.school → 2018-03-12.10-30-05.school + ✓ DRY-RUN OK +[503/814] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ DRY-RUN OK +[504/814] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ DRY-RUN OK +[505/814] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ DRY-RUN OK +[506/814] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ DRY-RUN OK +[507/814] 2018-03-12.10-40-01.admin → 2018-03-12.10-40-01.admin + ✓ DRY-RUN OK +[508/814] 2018-03-12.10-40-01.bus → 2018-03-12.10-40-01.bus + ✓ DRY-RUN OK +[509/814] 2018-03-12.10-40-01.school → 2018-03-12.10-40-01.school + ✓ DRY-RUN OK +[510/814] 2018-03-12.10-40-02.school → 2018-03-12.10-40-02.school + ✓ DRY-RUN OK +[511/814] 2018-03-12.10-40-03.school → 2018-03-12.10-40-03.school + ✓ DRY-RUN OK +[512/814] 2018-03-12.10-40-04.school → 2018-03-12.10-40-04.school + ✓ DRY-RUN OK +[513/814] 2018-03-12.10-40-05.school → 2018-03-12.10-40-05.school + ✓ DRY-RUN OK +[514/814] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ DRY-RUN OK +[515/814] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ DRY-RUN OK +[516/814] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ DRY-RUN OK +[517/814] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ DRY-RUN OK +[518/814] 2018-03-12.10-45-01.admin → 2018-03-12.10-45-01.admin + ✓ DRY-RUN OK +[519/814] 2018-03-12.10-45-01.bus → 2018-03-12.10-45-01.bus + ✓ DRY-RUN OK +[520/814] 2018-03-12.10-45-01.school → 2018-03-12.10-45-01.school + ✓ DRY-RUN OK +[521/814] 2018-03-12.10-45-02.school → 2018-03-12.10-45-02.school + ✓ DRY-RUN OK +[522/814] 2018-03-12.10-45-03.school → 2018-03-12.10-45-03.school + ✓ DRY-RUN OK +[523/814] 2018-03-12.10-45-04.school → 2018-03-12.10-45-04.school + ✓ DRY-RUN OK +[524/814] 2018-03-12.10-45-05.school → 2018-03-12.10-45-05.school + ✓ DRY-RUN OK +[525/814] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ DRY-RUN OK +[526/814] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ DRY-RUN OK +[527/814] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ DRY-RUN OK +[528/814] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ DRY-RUN OK +[529/814] 2018-03-12.10-50-01.bus → 2018-03-12.10-50-01.bus + ✓ DRY-RUN OK +[530/814] 2018-03-12.10-50-01.school → 2018-03-12.10-50-01.school + ✓ DRY-RUN OK +[531/814] 2018-03-12.10-50-02.admin → 2018-03-12.10-50-02.admin + ✓ DRY-RUN OK +[532/814] 2018-03-12.10-50-02.school → 2018-03-12.10-50-02.school + ✓ DRY-RUN OK +[533/814] 2018-03-12.10-50-03.school → 2018-03-12.10-50-03.school + ✓ DRY-RUN OK +[534/814] 2018-03-12.10-50-04.school → 2018-03-12.10-50-04.school + ✓ DRY-RUN OK +[535/814] 2018-03-12.10-50-05.school → 2018-03-12.10-50-05.school + ✓ DRY-RUN OK +[536/814] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ DRY-RUN OK +[537/814] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ DRY-RUN OK +[538/814] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ DRY-RUN OK +[539/814] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ DRY-RUN OK +[540/814] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ DRY-RUN OK +[541/814] 2018-03-12.11-00-01.bus → 2018-03-12.11-00-01.bus + ✓ DRY-RUN OK +[542/814] 2018-03-12.11-00-01.school → 2018-03-12.11-00-01.school + ✓ DRY-RUN OK +[543/814] 2018-03-12.11-00-02.school → 2018-03-12.11-00-02.school + ✓ DRY-RUN OK +[544/814] 2018-03-12.11-00-03.school → 2018-03-12.11-00-03.school + ✓ DRY-RUN OK +[545/814] 2018-03-12.11-00-04.school → 2018-03-12.11-00-04.school + ✓ DRY-RUN OK +[546/814] 2018-03-12.11-00-05.school → 2018-03-12.11-00-05.school + ✓ DRY-RUN OK +[547/814] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ DRY-RUN OK +[548/814] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ DRY-RUN OK +[549/814] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ DRY-RUN OK +[550/814] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ DRY-RUN OK +[551/814] 2018-03-12.11-05-01.bus → 2018-03-12.11-05-01.bus + ✓ DRY-RUN OK +[552/814] 2018-03-12.11-05-01.school → 2018-03-12.11-05-01.school + ✓ DRY-RUN OK +[553/814] 2018-03-12.11-05-02.school → 2018-03-12.11-05-02.school + ✓ DRY-RUN OK +[554/814] 2018-03-12.11-05-03.school → 2018-03-12.11-05-03.school + ✓ DRY-RUN OK +[555/814] 2018-03-12.11-05-04.school → 2018-03-12.11-05-04.school + ✓ DRY-RUN OK +[556/814] 2018-03-12.11-05-05.school → 2018-03-12.11-05-05.school + ✓ DRY-RUN OK +[557/814] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ DRY-RUN OK +[558/814] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ DRY-RUN OK +[559/814] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ DRY-RUN OK +[560/814] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ DRY-RUN OK +[561/814] 2018-03-12.11-10-01.school → 2018-03-12.11-10-01.school + ✓ DRY-RUN OK +[562/814] 2018-03-12.11-10-02.school → 2018-03-12.11-10-02.school + ✓ DRY-RUN OK +[563/814] 2018-03-12.11-10-03.school → 2018-03-12.11-10-03.school + ✓ DRY-RUN OK +[564/814] 2018-03-12.11-10-04.school → 2018-03-12.11-10-04.school + ✓ DRY-RUN OK +[565/814] 2018-03-12.11-10-05.school → 2018-03-12.11-10-05.school + ✓ DRY-RUN OK +[566/814] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ DRY-RUN OK +[567/814] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ DRY-RUN OK +[568/814] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ DRY-RUN OK +[569/814] 2018-03-13.15-50-01.school → 2018-03-13.15-50-01.school + ✓ DRY-RUN OK +[570/814] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ DRY-RUN OK +[571/814] 2018-03-13.15-50-02.school → 2018-03-13.15-50-02.school + ✓ DRY-RUN OK +[572/814] 2018-03-13.15-50-03.school → 2018-03-13.15-50-03.school + ✓ DRY-RUN OK +[573/814] 2018-03-13.15-50-04.bus → 2018-03-13.15-50-04.bus + ✓ DRY-RUN OK +[574/814] 2018-03-13.15-50-05.school → 2018-03-13.15-50-05.school + ✓ DRY-RUN OK +[575/814] 2018-03-13.15-50-08.school → 2018-03-13.15-50-08.school + ✓ DRY-RUN OK +[576/814] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ DRY-RUN OK +[577/814] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ DRY-RUN OK +[578/814] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ DRY-RUN OK +[579/814] 2018-03-13.16-00-01.school → 2018-03-13.16-00-01.school + ✓ DRY-RUN OK +[580/814] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ DRY-RUN OK +[581/814] 2018-03-13.16-00-02.school → 2018-03-13.16-00-02.school + ✓ DRY-RUN OK +[582/814] 2018-03-13.16-00-03.school → 2018-03-13.16-00-03.school + ✓ DRY-RUN OK +[583/814] 2018-03-13.16-00-05.bus → 2018-03-13.16-00-05.bus + ✓ DRY-RUN OK +[584/814] 2018-03-13.16-00-05.school → 2018-03-13.16-00-05.school + ✓ DRY-RUN OK +[585/814] 2018-03-13.16-00-08.school → 2018-03-13.16-00-08.school + ✓ DRY-RUN OK +[586/814] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ DRY-RUN OK +[587/814] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ DRY-RUN OK +[588/814] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ DRY-RUN OK +[589/814] 2018-03-13.16-05-01.school → 2018-03-13.16-05-01.school + ✓ DRY-RUN OK +[590/814] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ DRY-RUN OK +[591/814] 2018-03-13.16-05-02.school → 2018-03-13.16-05-02.school + ✓ DRY-RUN OK +[592/814] 2018-03-13.16-05-03.school → 2018-03-13.16-05-03.school + ✓ DRY-RUN OK +[593/814] 2018-03-13.16-05-05.bus → 2018-03-13.16-05-05.bus + ✓ DRY-RUN OK +[594/814] 2018-03-13.16-05-05.school → 2018-03-13.16-05-05.school + ✓ DRY-RUN OK +[595/814] 2018-03-13.16-05-08.school → 2018-03-13.16-05-08.school + ✓ DRY-RUN OK +[596/814] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ DRY-RUN OK +[597/814] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ DRY-RUN OK +[598/814] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ DRY-RUN OK +[599/814] 2018-03-13.16-10-01.school → 2018-03-13.16-10-01.school + ✓ DRY-RUN OK +[600/814] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ DRY-RUN OK +[601/814] 2018-03-13.16-10-03.school → 2018-03-13.16-10-03.school + ✓ DRY-RUN OK +[602/814] 2018-03-13.16-10-05.bus → 2018-03-13.16-10-05.bus + ✓ DRY-RUN OK +[603/814] 2018-03-13.16-10-05.school → 2018-03-13.16-10-05.school + ✓ DRY-RUN OK +[604/814] 2018-03-13.16-10-08.school → 2018-03-13.16-10-08.school + ✓ DRY-RUN OK +[605/814] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ DRY-RUN OK +[606/814] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ DRY-RUN OK +[607/814] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ DRY-RUN OK +[608/814] 2018-03-13.16-15-01.school → 2018-03-13.16-15-01.school + ✓ DRY-RUN OK +[609/814] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ DRY-RUN OK +[610/814] 2018-03-13.16-15-03.school → 2018-03-13.16-15-03.school + ✓ DRY-RUN OK +[611/814] 2018-03-13.16-15-05.bus → 2018-03-13.16-15-05.bus + ✓ DRY-RUN OK +[612/814] 2018-03-13.16-15-05.school → 2018-03-13.16-15-05.school + ✓ DRY-RUN OK +[613/814] 2018-03-13.16-15-08.school → 2018-03-13.16-15-08.school + ✓ DRY-RUN OK +[614/814] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ DRY-RUN OK +[615/814] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ DRY-RUN OK +[616/814] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ DRY-RUN OK +[617/814] 2018-03-13.16-20-01.school → 2018-03-13.16-20-01.school + ✓ DRY-RUN OK +[618/814] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ DRY-RUN OK +[619/814] 2018-03-13.16-20-03.school → 2018-03-13.16-20-03.school + ✓ DRY-RUN OK +[620/814] 2018-03-13.16-20-05.bus → 2018-03-13.16-20-05.bus + ✓ DRY-RUN OK +[621/814] 2018-03-13.16-20-05.school → 2018-03-13.16-20-05.school + ✓ DRY-RUN OK +[622/814] 2018-03-13.16-20-08.school → 2018-03-13.16-20-08.school + ✓ DRY-RUN OK +[623/814] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ DRY-RUN OK +[624/814] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ DRY-RUN OK +[625/814] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ DRY-RUN OK +[626/814] 2018-03-13.16-25-01.school → 2018-03-13.16-25-01.school + ✓ DRY-RUN OK +[627/814] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ DRY-RUN OK +[628/814] 2018-03-13.16-25-03.school → 2018-03-13.16-25-03.school + ✓ DRY-RUN OK +[629/814] 2018-03-13.16-25-05.bus → 2018-03-13.16-25-05.bus + ✓ DRY-RUN OK +[630/814] 2018-03-13.16-25-05.school → 2018-03-13.16-25-05.school + ✓ DRY-RUN OK +[631/814] 2018-03-13.16-25-08.school → 2018-03-13.16-25-08.school + ✓ DRY-RUN OK +[632/814] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ DRY-RUN OK +[633/814] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ DRY-RUN OK +[634/814] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ DRY-RUN OK +[635/814] 2018-03-13.16-30-01.school → 2018-03-13.16-30-01.school + ✓ DRY-RUN OK +[636/814] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ DRY-RUN OK +[637/814] 2018-03-13.16-30-03.hospital → 2018-03-13.16-30-03.hospital + ✓ DRY-RUN OK +[638/814] 2018-03-13.16-30-03.school → 2018-03-13.16-30-03.school + ✓ DRY-RUN OK +[639/814] 2018-03-13.16-30-04.bus → 2018-03-13.16-30-04.bus + ✓ DRY-RUN OK +[640/814] 2018-03-13.16-30-05.school → 2018-03-13.16-30-05.school + ✓ DRY-RUN OK +[641/814] 2018-03-13.16-30-08.school → 2018-03-13.16-30-08.school + ✓ DRY-RUN OK +[642/814] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ DRY-RUN OK +[643/814] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ DRY-RUN OK +[644/814] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ DRY-RUN OK +[645/814] 2018-03-13.17-05-01.school → 2018-03-13.17-05-01.school + ✓ DRY-RUN OK +[646/814] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ DRY-RUN OK +[647/814] 2018-03-13.17-05-03.hospital → 2018-03-13.17-05-03.hospital + ✓ DRY-RUN OK +[648/814] 2018-03-13.17-05-03.school → 2018-03-13.17-05-03.school + ✓ DRY-RUN OK +[649/814] 2018-03-13.17-05-05.bus → 2018-03-13.17-05-05.bus + ✓ DRY-RUN OK +[650/814] 2018-03-13.17-05-05.school → 2018-03-13.17-05-05.school + ✓ DRY-RUN OK +[651/814] 2018-03-13.17-05-08.school → 2018-03-13.17-05-08.school + ✓ DRY-RUN OK +[652/814] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ DRY-RUN OK +[653/814] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ DRY-RUN OK +[654/814] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ DRY-RUN OK +[655/814] 2018-03-13.17-10-01.school → 2018-03-13.17-10-01.school + ✓ DRY-RUN OK +[656/814] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ DRY-RUN OK +[657/814] 2018-03-13.17-10-03.hospital → 2018-03-13.17-10-03.hospital + ✓ DRY-RUN OK +[658/814] 2018-03-13.17-10-03.school → 2018-03-13.17-10-03.school + ✓ DRY-RUN OK +[659/814] 2018-03-13.17-10-05.bus → 2018-03-13.17-10-05.bus + ✓ DRY-RUN OK +[660/814] 2018-03-13.17-10-05.school → 2018-03-13.17-10-05.school + ✓ DRY-RUN OK +[661/814] 2018-03-13.17-10-08.school → 2018-03-13.17-10-08.school + ✓ DRY-RUN OK +[662/814] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ DRY-RUN OK +[663/814] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ DRY-RUN OK +[664/814] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ DRY-RUN OK +[665/814] 2018-03-13.17-15-01.school → 2018-03-13.17-15-01.school + ✓ DRY-RUN OK +[666/814] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ DRY-RUN OK +[667/814] 2018-03-13.17-15-03.hospital → 2018-03-13.17-15-03.hospital + ✓ DRY-RUN OK +[668/814] 2018-03-13.17-15-03.school → 2018-03-13.17-15-03.school + ✓ DRY-RUN OK +[669/814] 2018-03-13.17-15-05.bus → 2018-03-13.17-15-05.bus + ✓ DRY-RUN OK +[670/814] 2018-03-13.17-15-05.school → 2018-03-13.17-15-05.school + ✓ DRY-RUN OK +[671/814] 2018-03-13.17-15-08.school → 2018-03-13.17-15-08.school + ✓ DRY-RUN OK +[672/814] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ DRY-RUN OK +[673/814] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ DRY-RUN OK +[674/814] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ DRY-RUN OK +[675/814] 2018-03-13.17-20-01.school → 2018-03-13.17-20-01.school + ✓ DRY-RUN OK +[676/814] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ DRY-RUN OK +[677/814] 2018-03-13.17-20-03.hospital → 2018-03-13.17-20-03.hospital + ✓ DRY-RUN OK +[678/814] 2018-03-13.17-20-03.school → 2018-03-13.17-20-03.school + ✓ DRY-RUN OK +[679/814] 2018-03-13.17-20-05.bus → 2018-03-13.17-20-05.bus + ✓ DRY-RUN OK +[680/814] 2018-03-13.17-20-05.school → 2018-03-13.17-20-05.school + ✓ DRY-RUN OK +[681/814] 2018-03-13.17-20-08.school → 2018-03-13.17-20-08.school + ✓ DRY-RUN OK +[682/814] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ DRY-RUN OK +[683/814] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ DRY-RUN OK +[684/814] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ DRY-RUN OK +[685/814] 2018-03-13.17-25-01.school → 2018-03-13.17-25-01.school + ✓ DRY-RUN OK +[686/814] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ DRY-RUN OK +[687/814] 2018-03-13.17-25-03.hospital → 2018-03-13.17-25-03.hospital + ✓ DRY-RUN OK +[688/814] 2018-03-13.17-25-03.school → 2018-03-13.17-25-03.school + ✓ DRY-RUN OK +[689/814] 2018-03-13.17-25-05.bus → 2018-03-13.17-25-05.bus + ✓ DRY-RUN OK +[690/814] 2018-03-13.17-25-05.school → 2018-03-13.17-25-05.school + ✓ DRY-RUN OK +[691/814] 2018-03-13.17-25-08.school → 2018-03-13.17-25-08.school + ✓ DRY-RUN OK +[692/814] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ DRY-RUN OK +[693/814] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ DRY-RUN OK +[694/814] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ DRY-RUN OK +[695/814] 2018-03-13.17-30-01.bus → 2018-03-13.17-30-01.bus + ✓ DRY-RUN OK +[696/814] 2018-03-13.17-30-01.school → 2018-03-13.17-30-01.school + ✓ DRY-RUN OK +[697/814] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ DRY-RUN OK +[698/814] 2018-03-13.17-30-03.hospital → 2018-03-13.17-30-03.hospital + ✓ DRY-RUN OK +[699/814] 2018-03-13.17-30-03.school → 2018-03-13.17-30-03.school + ✓ DRY-RUN OK +[700/814] 2018-03-13.17-30-05.bus → 2018-03-13.17-30-05.bus + ✓ DRY-RUN OK +[701/814] 2018-03-13.17-30-05.school → 2018-03-13.17-30-05.school + ✓ DRY-RUN OK +[702/814] 2018-03-13.17-30-08.school → 2018-03-13.17-30-08.school + ✓ DRY-RUN OK +[703/814] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ DRY-RUN OK +[704/814] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ DRY-RUN OK +[705/814] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ DRY-RUN OK +[706/814] 2018-03-13.17-35-01.bus → 2018-03-13.17-35-01.bus + ✓ DRY-RUN OK +[707/814] 2018-03-13.17-35-01.school → 2018-03-13.17-35-01.school + ✓ DRY-RUN OK +[708/814] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ DRY-RUN OK +[709/814] 2018-03-13.17-35-03.hospital → 2018-03-13.17-35-03.hospital + ✓ DRY-RUN OK +[710/814] 2018-03-13.17-35-03.school → 2018-03-13.17-35-03.school + ✓ DRY-RUN OK +[711/814] 2018-03-13.17-35-05.bus → 2018-03-13.17-35-05.bus + ✓ DRY-RUN OK +[712/814] 2018-03-13.17-35-05.school → 2018-03-13.17-35-05.school + ✓ DRY-RUN OK +[713/814] 2018-03-13.17-35-08.school → 2018-03-13.17-35-08.school + ✓ DRY-RUN OK +[714/814] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ DRY-RUN OK +[715/814] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ DRY-RUN OK +[716/814] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ DRY-RUN OK +[717/814] 2018-03-13.17-40-01.bus → 2018-03-13.17-40-01.bus + ✓ DRY-RUN OK +[718/814] 2018-03-13.17-40-01.school → 2018-03-13.17-40-01.school + ✓ DRY-RUN OK +[719/814] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ DRY-RUN OK +[720/814] 2018-03-13.17-40-03.hospital → 2018-03-13.17-40-03.hospital + ✓ DRY-RUN OK +[721/814] 2018-03-13.17-40-03.school → 2018-03-13.17-40-03.school + ✓ DRY-RUN OK +[722/814] 2018-03-13.17-40-05.bus → 2018-03-13.17-40-05.bus + ✓ DRY-RUN OK +[723/814] 2018-03-13.17-40-05.school → 2018-03-13.17-40-05.school + ✓ DRY-RUN OK +[724/814] 2018-03-13.17-40-08.school → 2018-03-13.17-40-08.school + ✓ DRY-RUN OK +[725/814] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ DRY-RUN OK +[726/814] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ DRY-RUN OK +[727/814] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ DRY-RUN OK +[728/814] 2018-03-15.14-50-01.bus → 2018-03-15.14-50-01.bus + ✓ DRY-RUN OK +[729/814] 2018-03-15.14-50-01.school → 2018-03-15.14-50-01.school + ✓ DRY-RUN OK +[730/814] 2018-03-15.14-50-03.school → 2018-03-15.14-50-03.school + ✓ DRY-RUN OK +[731/814] 2018-03-15.14-50-04.bus → 2018-03-15.14-50-04.bus + ✓ DRY-RUN OK +[732/814] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ DRY-RUN OK +[733/814] 2018-03-15.14-50-07.hospital → 2018-03-15.14-50-07.hospital + ✓ DRY-RUN OK +[734/814] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ DRY-RUN OK +[735/814] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ DRY-RUN OK +[736/814] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ DRY-RUN OK +[737/814] 2018-03-15.15-00-01.bus → 2018-03-15.15-00-01.bus + ✓ DRY-RUN OK +[738/814] 2018-03-15.15-00-01.school → 2018-03-15.15-00-01.school + ✓ DRY-RUN OK +[739/814] 2018-03-15.15-00-03.school → 2018-03-15.15-00-03.school + ✓ DRY-RUN OK +[740/814] 2018-03-15.15-00-04.bus → 2018-03-15.15-00-04.bus + ✓ DRY-RUN OK +[741/814] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ DRY-RUN OK +[742/814] 2018-03-15.15-00-07.hospital → 2018-03-15.15-00-07.hospital + ✓ DRY-RUN OK +[743/814] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ DRY-RUN OK +[744/814] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ DRY-RUN OK +[745/814] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ DRY-RUN OK +[746/814] 2018-03-15.15-05-01.bus → 2018-03-15.15-05-01.bus + ✓ DRY-RUN OK +[747/814] 2018-03-15.15-05-01.school → 2018-03-15.15-05-01.school + ✓ DRY-RUN OK +[748/814] 2018-03-15.15-05-03.school → 2018-03-15.15-05-03.school + ✓ DRY-RUN OK +[749/814] 2018-03-15.15-05-04.bus → 2018-03-15.15-05-04.bus + ✓ DRY-RUN OK +[750/814] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ DRY-RUN OK +[751/814] 2018-03-15.15-05-07.hospital → 2018-03-15.15-05-07.hospital + ✓ DRY-RUN OK +[752/814] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ DRY-RUN OK +[753/814] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ DRY-RUN OK +[754/814] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ DRY-RUN OK +[755/814] 2018-03-15.15-10-01.bus → 2018-03-15.15-10-01.bus + ✓ DRY-RUN OK +[756/814] 2018-03-15.15-10-01.school → 2018-03-15.15-10-01.school + ✓ DRY-RUN OK +[757/814] 2018-03-15.15-10-03.school → 2018-03-15.15-10-03.school + ✓ DRY-RUN OK +[758/814] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ DRY-RUN OK +[759/814] 2018-03-15.15-10-07.hospital → 2018-03-15.15-10-07.hospital + ✓ DRY-RUN OK +[760/814] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ DRY-RUN OK +[761/814] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ DRY-RUN OK +[762/814] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ DRY-RUN OK +[763/814] 2018-03-15.15-15-01.bus → 2018-03-15.15-15-01.bus + ✓ DRY-RUN OK +[764/814] 2018-03-15.15-15-01.school → 2018-03-15.15-15-01.school + ✓ DRY-RUN OK +[765/814] 2018-03-15.15-15-03.school → 2018-03-15.15-15-03.school + ✓ DRY-RUN OK +[766/814] 2018-03-15.15-15-04.bus → 2018-03-15.15-15-04.bus + ✓ DRY-RUN OK +[767/814] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ DRY-RUN OK +[768/814] 2018-03-15.15-15-07.hospital → 2018-03-15.15-15-07.hospital + ✓ DRY-RUN OK +[769/814] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ DRY-RUN OK +[770/814] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ DRY-RUN OK +[771/814] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ DRY-RUN OK +[772/814] 2018-03-15.15-30-01.bus → 2018-03-15.15-30-01.bus + ✓ DRY-RUN OK +[773/814] 2018-03-15.15-30-01.school → 2018-03-15.15-30-01.school + ✓ DRY-RUN OK +[774/814] 2018-03-15.15-30-03.school → 2018-03-15.15-30-03.school + ✓ DRY-RUN OK +[775/814] 2018-03-15.15-30-04.bus → 2018-03-15.15-30-04.bus + ✓ DRY-RUN OK +[776/814] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ DRY-RUN OK +[777/814] 2018-03-15.15-30-07.hospital → 2018-03-15.15-30-07.hospital + ✓ DRY-RUN OK +[778/814] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ DRY-RUN OK +[779/814] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ DRY-RUN OK +[780/814] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ DRY-RUN OK +[781/814] 2018-03-15.15-35-01.bus → 2018-03-15.15-35-01.bus + ✓ DRY-RUN OK +[782/814] 2018-03-15.15-35-01.school → 2018-03-15.15-35-01.school + ✓ DRY-RUN OK +[783/814] 2018-03-15.15-35-03.school → 2018-03-15.15-35-03.school + ✓ DRY-RUN OK +[784/814] 2018-03-15.15-35-04.bus → 2018-03-15.15-35-04.bus + ✓ DRY-RUN OK +[785/814] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ DRY-RUN OK +[786/814] 2018-03-15.15-35-07.hospital → 2018-03-15.15-35-07.hospital + ✓ DRY-RUN OK +[787/814] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ DRY-RUN OK +[788/814] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ DRY-RUN OK +[789/814] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ DRY-RUN OK +[790/814] 2018-03-15.15-40-01.bus → 2018-03-15.15-40-01.bus + ✓ DRY-RUN OK +[791/814] 2018-03-15.15-40-01.school → 2018-03-15.15-40-01.school + ✓ DRY-RUN OK +[792/814] 2018-03-15.15-40-03.school → 2018-03-15.15-40-03.school + ✓ DRY-RUN OK +[793/814] 2018-03-15.15-40-04.bus → 2018-03-15.15-40-04.bus + ✓ DRY-RUN OK +[794/814] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ DRY-RUN OK +[795/814] 2018-03-15.15-40-07.hospital → 2018-03-15.15-40-07.hospital + ✓ DRY-RUN OK +[796/814] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ DRY-RUN OK +[797/814] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ DRY-RUN OK +[798/814] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ DRY-RUN OK +[799/814] 2018-03-15.15-45-01.bus → 2018-03-15.15-45-01.bus + ✓ DRY-RUN OK +[800/814] 2018-03-15.15-45-01.school → 2018-03-15.15-45-01.school + ✓ DRY-RUN OK +[801/814] 2018-03-15.15-45-02.school → 2018-03-15.15-45-02.school + ✓ DRY-RUN OK +[802/814] 2018-03-15.15-45-03.school → 2018-03-15.15-45-03.school + ✓ DRY-RUN OK +[803/814] 2018-03-15.15-45-04.bus → 2018-03-15.15-45-04.bus + ✓ DRY-RUN OK +[804/814] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ DRY-RUN OK +[805/814] 2018-03-15.15-45-07.hospital → 2018-03-15.15-45-07.hospital + ✓ DRY-RUN OK +[806/814] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ DRY-RUN OK +[807/814] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ DRY-RUN OK +[808/814] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ DRY-RUN OK +[809/814] 2018-03-15.15-50-01.bus → 2018-03-15.15-50-01.bus + ✓ DRY-RUN OK +[810/814] 2018-03-15.15-50-01.school → 2018-03-15.15-50-01.school + ✓ DRY-RUN OK +[811/814] 2018-03-15.15-50-03.school → 2018-03-15.15-50-03.school + ✓ DRY-RUN OK +[812/814] 2018-03-15.15-50-04.bus → 2018-03-15.15-50-04.bus + ✓ DRY-RUN OK +[813/814] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ DRY-RUN OK +[814/814] 2018-03-15.15-50-07.hospital → 2018-03-15.15-50-07.hospital + ✓ DRY-RUN OK + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 0 +Skipped (already exist): 0 +Failed: 0 +Total entities: 0 +Avg entities/slot: 0.0 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193229.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193237.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193237.log.txt new file mode 100644 index 0000000..c3c0515 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193237.log.txt @@ -0,0 +1,1655 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Canonical slots: 814 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193237.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:32:37 +============================================================ + +[ 1/814] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/814] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ DRY-RUN OK +[ 3/814] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/814] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ DRY-RUN OK +[ 5/814] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ DRY-RUN OK +[ 6/814] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ DRY-RUN OK +[ 7/814] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 8/814] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ DRY-RUN OK +[ 9/814] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ DRY-RUN OK +[ 10/814] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ DRY-RUN OK +[ 11/814] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ DRY-RUN OK +[ 12/814] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ DRY-RUN OK +[ 13/814] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ DRY-RUN OK +[ 14/814] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ DRY-RUN OK +[ 15/814] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ DRY-RUN OK +[ 16/814] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ DRY-RUN OK +[ 17/814] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ DRY-RUN OK +[ 18/814] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ DRY-RUN OK +[ 19/814] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ DRY-RUN OK +[ 20/814] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ DRY-RUN OK +[ 21/814] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ DRY-RUN OK +[ 22/814] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ DRY-RUN OK +[ 23/814] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ DRY-RUN OK +[ 24/814] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ DRY-RUN OK +[ 25/814] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 26/814] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ DRY-RUN OK +[ 27/814] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ DRY-RUN OK +[ 28/814] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ DRY-RUN OK +[ 29/814] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ DRY-RUN OK +[ 30/814] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ DRY-RUN OK +[ 31/814] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 32/814] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ DRY-RUN OK +[ 33/814] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ DRY-RUN OK +[ 34/814] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ DRY-RUN OK +[ 35/814] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ DRY-RUN OK +[ 36/814] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ DRY-RUN OK +[ 37/814] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ DRY-RUN OK +[ 38/814] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ DRY-RUN OK +[ 39/814] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ DRY-RUN OK +[ 40/814] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ DRY-RUN OK +[ 41/814] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ DRY-RUN OK +[ 42/814] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ DRY-RUN OK +[ 43/814] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ DRY-RUN OK +[ 44/814] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ DRY-RUN OK +[ 45/814] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ DRY-RUN OK +[ 46/814] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ DRY-RUN OK +[ 47/814] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ DRY-RUN OK +[ 48/814] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ DRY-RUN OK +[ 49/814] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ DRY-RUN OK +[ 50/814] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ DRY-RUN OK +[ 51/814] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ DRY-RUN OK +[ 52/814] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ DRY-RUN OK +[ 53/814] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ DRY-RUN OK +[ 54/814] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ DRY-RUN OK +[ 55/814] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ DRY-RUN OK +[ 56/814] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ DRY-RUN OK +[ 57/814] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ DRY-RUN OK +[ 58/814] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ DRY-RUN OK +[ 59/814] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ DRY-RUN OK +[ 60/814] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ DRY-RUN OK +[ 61/814] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ DRY-RUN OK +[ 62/814] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ DRY-RUN OK +[ 63/814] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ DRY-RUN OK +[ 64/814] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ DRY-RUN OK +[ 65/814] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ DRY-RUN OK +[ 66/814] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ DRY-RUN OK +[ 67/814] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ DRY-RUN OK +[ 68/814] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ DRY-RUN OK +[ 69/814] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ DRY-RUN OK +[ 70/814] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ DRY-RUN OK +[ 71/814] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ DRY-RUN OK +[ 72/814] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ DRY-RUN OK +[ 73/814] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ DRY-RUN OK +[ 74/814] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ DRY-RUN OK +[ 75/814] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ DRY-RUN OK +[ 76/814] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ DRY-RUN OK +[ 77/814] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ DRY-RUN OK +[ 78/814] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ DRY-RUN OK +[ 79/814] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ DRY-RUN OK +[ 80/814] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ DRY-RUN OK +[ 81/814] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ DRY-RUN OK +[ 82/814] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ DRY-RUN OK +[ 83/814] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ DRY-RUN OK +[ 84/814] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ DRY-RUN OK +[ 85/814] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ DRY-RUN OK +[ 86/814] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ DRY-RUN OK +[ 87/814] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ DRY-RUN OK +[ 88/814] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ DRY-RUN OK +[ 89/814] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ DRY-RUN OK +[ 90/814] 2018-03-07.17-00-01.admin → 2018-03-07.17-00-01.admin + ✓ DRY-RUN OK +[ 91/814] 2018-03-07.17-00-01.bus → 2018-03-07.17-00-01.bus + ✓ DRY-RUN OK +[ 92/814] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ DRY-RUN OK +[ 93/814] 2018-03-07.17-00-01.school → 2018-03-07.17-00-01.school + ✓ DRY-RUN OK +[ 94/814] 2018-03-07.17-00-05.hospital → 2018-03-07.17-00-05.hospital + ✓ DRY-RUN OK +[ 95/814] 2018-03-07.17-00-06.school → 2018-03-07.17-00-06.school + ✓ DRY-RUN OK +[ 96/814] 2018-03-07.17-00-07.hospital → 2018-03-07.17-00-07.hospital + ✓ DRY-RUN OK +[ 97/814] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ DRY-RUN OK +[ 98/814] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ DRY-RUN OK +[ 99/814] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ DRY-RUN OK +[100/814] 2018-03-07.17-05-01.admin → 2018-03-07.17-05-01.admin + ✓ DRY-RUN OK +[101/814] 2018-03-07.17-05-01.bus → 2018-03-07.17-05-01.bus + ✓ DRY-RUN OK +[102/814] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ DRY-RUN OK +[103/814] 2018-03-07.17-05-01.school → 2018-03-07.17-05-01.school + ✓ DRY-RUN OK +[104/814] 2018-03-07.17-05-05.hospital → 2018-03-07.17-05-05.hospital + ✓ DRY-RUN OK +[105/814] 2018-03-07.17-05-06.school → 2018-03-07.17-05-06.school + ✓ DRY-RUN OK +[106/814] 2018-03-07.17-05-07.hospital → 2018-03-07.17-05-07.hospital + ✓ DRY-RUN OK +[107/814] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[108/814] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ DRY-RUN OK +[109/814] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ DRY-RUN OK +[110/814] 2018-03-07.17-20-01.admin → 2018-03-07.17-20-01.admin + ✓ DRY-RUN OK +[111/814] 2018-03-07.17-20-01.bus → 2018-03-07.17-20-01.bus + ✓ DRY-RUN OK +[112/814] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ DRY-RUN OK +[113/814] 2018-03-07.17-20-01.school → 2018-03-07.17-20-01.school + ✓ DRY-RUN OK +[114/814] 2018-03-07.17-20-05.hospital → 2018-03-07.17-20-05.hospital + ✓ DRY-RUN OK +[115/814] 2018-03-07.17-20-06.school → 2018-03-07.17-20-06.school + ✓ DRY-RUN OK +[116/814] 2018-03-07.17-20-07.hospital → 2018-03-07.17-20-07.hospital + ✓ DRY-RUN OK +[117/814] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ DRY-RUN OK +[118/814] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ DRY-RUN OK +[119/814] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ DRY-RUN OK +[120/814] 2018-03-07.17-25-01.admin → 2018-03-07.17-25-01.admin + ✓ DRY-RUN OK +[121/814] 2018-03-07.17-25-01.bus → 2018-03-07.17-25-01.bus + ✓ DRY-RUN OK +[122/814] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ DRY-RUN OK +[123/814] 2018-03-07.17-25-01.school → 2018-03-07.17-25-01.school + ✓ DRY-RUN OK +[124/814] 2018-03-07.17-25-03.school → 2018-03-07.17-25-03.school + ✓ DRY-RUN OK +[125/814] 2018-03-07.17-25-05.hospital → 2018-03-07.17-25-05.hospital + ✓ DRY-RUN OK +[126/814] 2018-03-07.17-25-06.school → 2018-03-07.17-25-06.school + ✓ DRY-RUN OK +[127/814] 2018-03-07.17-25-07.hospital → 2018-03-07.17-25-07.hospital + ✓ DRY-RUN OK +[128/814] 2018-03-07.17-25-10.school → 2018-03-07.17-25-10.school + ✓ DRY-RUN OK +[129/814] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ DRY-RUN OK +[130/814] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ DRY-RUN OK +[131/814] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ DRY-RUN OK +[132/814] 2018-03-07.17-30-01.admin → 2018-03-07.17-30-01.admin + ✓ DRY-RUN OK +[133/814] 2018-03-07.17-30-01.bus → 2018-03-07.17-30-01.bus + ✓ DRY-RUN OK +[134/814] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ DRY-RUN OK +[135/814] 2018-03-07.17-30-01.school → 2018-03-07.17-30-01.school + ✓ DRY-RUN OK +[136/814] 2018-03-07.17-30-03.school → 2018-03-07.17-30-03.school + ✓ DRY-RUN OK +[137/814] 2018-03-07.17-30-05.hospital → 2018-03-07.17-30-05.hospital + ✓ DRY-RUN OK +[138/814] 2018-03-07.17-30-06.school → 2018-03-07.17-30-06.school + ✓ DRY-RUN OK +[139/814] 2018-03-07.17-30-07.hospital → 2018-03-07.17-30-07.hospital + ✓ DRY-RUN OK +[140/814] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ DRY-RUN OK +[141/814] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ DRY-RUN OK +[142/814] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ DRY-RUN OK +[143/814] 2018-03-07.17-35-01.admin → 2018-03-07.17-35-01.admin + ✓ DRY-RUN OK +[144/814] 2018-03-07.17-35-01.bus → 2018-03-07.17-35-01.bus + ✓ DRY-RUN OK +[145/814] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ DRY-RUN OK +[146/814] 2018-03-07.17-35-01.school → 2018-03-07.17-35-01.school + ✓ DRY-RUN OK +[147/814] 2018-03-07.17-35-03.school → 2018-03-07.17-35-03.school + ✓ DRY-RUN OK +[148/814] 2018-03-07.17-35-05.hospital → 2018-03-07.17-35-05.hospital + ✓ DRY-RUN OK +[149/814] 2018-03-07.17-35-06.school → 2018-03-07.17-35-06.school + ✓ DRY-RUN OK +[150/814] 2018-03-07.17-35-07.hospital → 2018-03-07.17-35-07.hospital + ✓ DRY-RUN OK +[151/814] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ DRY-RUN OK +[152/814] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ DRY-RUN OK +[153/814] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ DRY-RUN OK +[154/814] 2018-03-09.10-10-01.bus → 2018-03-09.10-10-01.bus + ✓ DRY-RUN OK +[155/814] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ DRY-RUN OK +[156/814] 2018-03-09.10-10-01.school → 2018-03-09.10-10-01.school + ✓ DRY-RUN OK +[157/814] 2018-03-09.10-10-02.hospital → 2018-03-09.10-10-02.hospital + ✓ DRY-RUN OK +[158/814] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[159/814] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ DRY-RUN OK +[160/814] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ DRY-RUN OK +[161/814] 2018-03-09.10-15-01.bus → 2018-03-09.10-15-01.bus + ✓ DRY-RUN OK +[162/814] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ DRY-RUN OK +[163/814] 2018-03-09.10-15-01.school → 2018-03-09.10-15-01.school + ✓ DRY-RUN OK +[164/814] 2018-03-09.10-15-02.hospital → 2018-03-09.10-15-02.hospital + ✓ DRY-RUN OK +[165/814] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[166/814] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ DRY-RUN OK +[167/814] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[168/814] 2018-03-09.10-20-01.bus → 2018-03-09.10-20-01.bus + ✓ DRY-RUN OK +[169/814] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ DRY-RUN OK +[170/814] 2018-03-09.10-20-01.school → 2018-03-09.10-20-01.school + ✓ DRY-RUN OK +[171/814] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[172/814] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ DRY-RUN OK +[173/814] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[174/814] 2018-03-09.10-25-01.bus → 2018-03-09.10-25-01.bus + ✓ DRY-RUN OK +[175/814] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ DRY-RUN OK +[176/814] 2018-03-09.10-25-01.school → 2018-03-09.10-25-01.school + ✓ DRY-RUN OK +[177/814] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[178/814] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ DRY-RUN OK +[179/814] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ DRY-RUN OK +[180/814] 2018-03-09.10-30-01.bus → 2018-03-09.10-30-01.bus + ✓ DRY-RUN OK +[181/814] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ DRY-RUN OK +[182/814] 2018-03-09.10-30-01.school → 2018-03-09.10-30-01.school + ✓ DRY-RUN OK +[183/814] 2018-03-09.10-30-02.hospital → 2018-03-09.10-30-02.hospital + ✓ DRY-RUN OK +[184/814] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[185/814] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ DRY-RUN OK +[186/814] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ DRY-RUN OK +[187/814] 2018-03-09.10-35-01.bus → 2018-03-09.10-35-01.bus + ✓ DRY-RUN OK +[188/814] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ DRY-RUN OK +[189/814] 2018-03-09.10-35-01.school → 2018-03-09.10-35-01.school + ✓ DRY-RUN OK +[190/814] 2018-03-09.10-35-02.hospital → 2018-03-09.10-35-02.hospital + ✓ DRY-RUN OK +[191/814] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ DRY-RUN OK +[192/814] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ DRY-RUN OK +[193/814] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ DRY-RUN OK +[194/814] 2018-03-09.10-40-01.bus → 2018-03-09.10-40-01.bus + ✓ DRY-RUN OK +[195/814] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ DRY-RUN OK +[196/814] 2018-03-09.10-40-01.school → 2018-03-09.10-40-01.school + ✓ DRY-RUN OK +[197/814] 2018-03-09.10-40-02.hospital → 2018-03-09.10-40-02.hospital + ✓ DRY-RUN OK +[198/814] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ DRY-RUN OK +[199/814] 2018-03-11.11-15-01.school → 2018-03-11.11-15-01.school + ✓ DRY-RUN OK +[200/814] 2018-03-11.11-15-04.school → 2018-03-11.11-15-04.school + ✓ DRY-RUN OK +[201/814] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ DRY-RUN OK +[202/814] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ DRY-RUN OK +[203/814] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ DRY-RUN OK +[204/814] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ DRY-RUN OK +[205/814] 2018-03-11.11-20-01.admin → 2018-03-11.11-20-01.admin + ✓ DRY-RUN OK +[206/814] 2018-03-11.11-20-01.bus → 2018-03-11.11-20-01.bus + ✓ DRY-RUN OK +[207/814] 2018-03-11.11-20-01.school → 2018-03-11.11-20-01.school + ✓ DRY-RUN OK +[208/814] 2018-03-11.11-20-04.school → 2018-03-11.11-20-04.school + ✓ DRY-RUN OK +[209/814] 2018-03-11.11-20-08.bus → 2018-03-11.11-20-08.bus + ✓ DRY-RUN OK +[210/814] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ DRY-RUN OK +[211/814] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ DRY-RUN OK +[212/814] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ DRY-RUN OK +[213/814] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ DRY-RUN OK +[214/814] 2018-03-11.11-25-01.admin → 2018-03-11.11-25-01.admin + ✓ DRY-RUN OK +[215/814] 2018-03-11.11-25-01.bus → 2018-03-11.11-25-01.bus + ✓ DRY-RUN OK +[216/814] 2018-03-11.11-25-01.school → 2018-03-11.11-25-01.school + ✓ DRY-RUN OK +[217/814] 2018-03-11.11-25-04.school → 2018-03-11.11-25-04.school + ✓ DRY-RUN OK +[218/814] 2018-03-11.11-25-08.bus → 2018-03-11.11-25-08.bus + ✓ DRY-RUN OK +[219/814] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ DRY-RUN OK +[220/814] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ DRY-RUN OK +[221/814] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ DRY-RUN OK +[222/814] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ DRY-RUN OK +[223/814] 2018-03-11.11-30-01.admin → 2018-03-11.11-30-01.admin + ✓ DRY-RUN OK +[224/814] 2018-03-11.11-30-01.bus → 2018-03-11.11-30-01.bus + ✓ DRY-RUN OK +[225/814] 2018-03-11.11-30-01.school → 2018-03-11.11-30-01.school + ✓ DRY-RUN OK +[226/814] 2018-03-11.11-30-04.school → 2018-03-11.11-30-04.school + ✓ DRY-RUN OK +[227/814] 2018-03-11.11-30-08.bus → 2018-03-11.11-30-08.bus + ✓ DRY-RUN OK +[228/814] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ DRY-RUN OK +[229/814] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[230/814] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ DRY-RUN OK +[231/814] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ DRY-RUN OK +[232/814] 2018-03-11.11-35-01.admin → 2018-03-11.11-35-01.admin + ✓ DRY-RUN OK +[233/814] 2018-03-11.11-35-01.bus → 2018-03-11.11-35-01.bus + ✓ DRY-RUN OK +[234/814] 2018-03-11.11-35-01.school → 2018-03-11.11-35-01.school + ✓ DRY-RUN OK +[235/814] 2018-03-11.11-35-04.school → 2018-03-11.11-35-04.school + ✓ DRY-RUN OK +[236/814] 2018-03-11.11-35-08.bus → 2018-03-11.11-35-08.bus + ✓ DRY-RUN OK +[237/814] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ DRY-RUN OK +[238/814] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[239/814] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[240/814] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ DRY-RUN OK +[241/814] 2018-03-11.11-40-01.admin → 2018-03-11.11-40-01.admin + ✓ DRY-RUN OK +[242/814] 2018-03-11.11-40-01.bus → 2018-03-11.11-40-01.bus + ✓ DRY-RUN OK +[243/814] 2018-03-11.11-40-01.school → 2018-03-11.11-40-01.school + ✓ DRY-RUN OK +[244/814] 2018-03-11.11-40-02.school → 2018-03-11.11-40-02.school + ✓ DRY-RUN OK +[245/814] 2018-03-11.11-40-04.school → 2018-03-11.11-40-04.school + ✓ DRY-RUN OK +[246/814] 2018-03-11.11-40-08.bus → 2018-03-11.11-40-08.bus + ✓ DRY-RUN OK +[247/814] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[248/814] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[249/814] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[250/814] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ DRY-RUN OK +[251/814] 2018-03-11.11-45-01.admin → 2018-03-11.11-45-01.admin + ✓ DRY-RUN OK +[252/814] 2018-03-11.11-45-01.bus → 2018-03-11.11-45-01.bus + ✓ DRY-RUN OK +[253/814] 2018-03-11.11-45-01.school → 2018-03-11.11-45-01.school + ✓ DRY-RUN OK +[254/814] 2018-03-11.11-45-04.school → 2018-03-11.11-45-04.school + ✓ DRY-RUN OK +[255/814] 2018-03-11.11-45-08.bus → 2018-03-11.11-45-08.bus + ✓ DRY-RUN OK +[256/814] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ DRY-RUN OK +[257/814] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ DRY-RUN OK +[258/814] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ DRY-RUN OK +[259/814] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ DRY-RUN OK +[260/814] 2018-03-11.11-50-01.admin → 2018-03-11.11-50-01.admin + ✓ DRY-RUN OK +[261/814] 2018-03-11.11-50-01.bus → 2018-03-11.11-50-01.bus + ✓ DRY-RUN OK +[262/814] 2018-03-11.11-50-01.school → 2018-03-11.11-50-01.school + ✓ DRY-RUN OK +[263/814] 2018-03-11.11-50-04.school → 2018-03-11.11-50-04.school + ✓ DRY-RUN OK +[264/814] 2018-03-11.11-50-08.bus → 2018-03-11.11-50-08.bus + ✓ DRY-RUN OK +[265/814] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ DRY-RUN OK +[266/814] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ DRY-RUN OK +[267/814] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ DRY-RUN OK +[268/814] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ DRY-RUN OK +[269/814] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ DRY-RUN OK +[270/814] 2018-03-11.13-50-01.admin → 2018-03-11.13-50-01.admin + ✓ DRY-RUN OK +[271/814] 2018-03-11.13-50-01.bus → 2018-03-11.13-50-01.bus + ✓ DRY-RUN OK +[272/814] 2018-03-11.13-50-01.school → 2018-03-11.13-50-01.school + ✓ DRY-RUN OK +[273/814] 2018-03-11.13-50-04.school → 2018-03-11.13-50-04.school + ✓ DRY-RUN OK +[274/814] 2018-03-11.13-50-08.bus → 2018-03-11.13-50-08.bus + ✓ DRY-RUN OK +[275/814] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ DRY-RUN OK +[276/814] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ DRY-RUN OK +[277/814] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ DRY-RUN OK +[278/814] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ DRY-RUN OK +[279/814] 2018-03-11.14-00-01.admin → 2018-03-11.14-00-01.admin + ✓ DRY-RUN OK +[280/814] 2018-03-11.14-00-01.bus → 2018-03-11.14-00-01.bus + ✓ DRY-RUN OK +[281/814] 2018-03-11.14-00-01.school → 2018-03-11.14-00-01.school + ✓ DRY-RUN OK +[282/814] 2018-03-11.14-00-02.school → 2018-03-11.14-00-02.school + ✓ DRY-RUN OK +[283/814] 2018-03-11.14-00-04.school → 2018-03-11.14-00-04.school + ✓ DRY-RUN OK +[284/814] 2018-03-11.14-00-08.bus → 2018-03-11.14-00-08.bus + ✓ DRY-RUN OK +[285/814] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ DRY-RUN OK +[286/814] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ DRY-RUN OK +[287/814] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ DRY-RUN OK +[288/814] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ DRY-RUN OK +[289/814] 2018-03-11.14-05-01.admin → 2018-03-11.14-05-01.admin + ✓ DRY-RUN OK +[290/814] 2018-03-11.14-05-01.bus → 2018-03-11.14-05-01.bus + ✓ DRY-RUN OK +[291/814] 2018-03-11.14-05-01.school → 2018-03-11.14-05-01.school + ✓ DRY-RUN OK +[292/814] 2018-03-11.14-05-02.school → 2018-03-11.14-05-02.school + ✓ DRY-RUN OK +[293/814] 2018-03-11.14-05-04.school → 2018-03-11.14-05-04.school + ✓ DRY-RUN OK +[294/814] 2018-03-11.14-05-08.bus → 2018-03-11.14-05-08.bus + ✓ DRY-RUN OK +[295/814] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ DRY-RUN OK +[296/814] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ DRY-RUN OK +[297/814] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ DRY-RUN OK +[298/814] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ DRY-RUN OK +[299/814] 2018-03-11.14-10-01.admin → 2018-03-11.14-10-01.admin + ✓ DRY-RUN OK +[300/814] 2018-03-11.14-10-01.bus → 2018-03-11.14-10-01.bus + ✓ DRY-RUN OK +[301/814] 2018-03-11.14-10-01.school → 2018-03-11.14-10-01.school + ✓ DRY-RUN OK +[302/814] 2018-03-11.14-10-02.school → 2018-03-11.14-10-02.school + ✓ DRY-RUN OK +[303/814] 2018-03-11.14-10-04.school → 2018-03-11.14-10-04.school + ✓ DRY-RUN OK +[304/814] 2018-03-11.14-10-08.bus → 2018-03-11.14-10-08.bus + ✓ DRY-RUN OK +[305/814] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ DRY-RUN OK +[306/814] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ DRY-RUN OK +[307/814] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ DRY-RUN OK +[308/814] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ DRY-RUN OK +[309/814] 2018-03-11.14-15-01.admin → 2018-03-11.14-15-01.admin + ✓ DRY-RUN OK +[310/814] 2018-03-11.14-15-01.bus → 2018-03-11.14-15-01.bus + ✓ DRY-RUN OK +[311/814] 2018-03-11.14-15-01.school → 2018-03-11.14-15-01.school + ✓ DRY-RUN OK +[312/814] 2018-03-11.14-15-02.school → 2018-03-11.14-15-02.school + ✓ DRY-RUN OK +[313/814] 2018-03-11.14-15-04.school → 2018-03-11.14-15-04.school + ✓ DRY-RUN OK +[314/814] 2018-03-11.14-15-08.bus → 2018-03-11.14-15-08.bus + ✓ DRY-RUN OK +[315/814] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ DRY-RUN OK +[316/814] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ DRY-RUN OK +[317/814] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ DRY-RUN OK +[318/814] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ DRY-RUN OK +[319/814] 2018-03-11.14-20-01.admin → 2018-03-11.14-20-01.admin + ✓ DRY-RUN OK +[320/814] 2018-03-11.14-20-01.bus → 2018-03-11.14-20-01.bus + ✓ DRY-RUN OK +[321/814] 2018-03-11.14-20-01.school → 2018-03-11.14-20-01.school + ✓ DRY-RUN OK +[322/814] 2018-03-11.14-20-02.school → 2018-03-11.14-20-02.school + ✓ DRY-RUN OK +[323/814] 2018-03-11.14-20-04.school → 2018-03-11.14-20-04.school + ✓ DRY-RUN OK +[324/814] 2018-03-11.14-20-08.bus → 2018-03-11.14-20-08.bus + ✓ DRY-RUN OK +[325/814] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ DRY-RUN OK +[326/814] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ DRY-RUN OK +[327/814] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ DRY-RUN OK +[328/814] 2018-03-11.16-10-01.bus → 2018-03-11.16-10-01.bus + ✓ DRY-RUN OK +[329/814] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ DRY-RUN OK +[330/814] 2018-03-11.16-10-08.bus → 2018-03-11.16-10-08.bus + ✓ DRY-RUN OK +[331/814] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ DRY-RUN OK +[332/814] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ DRY-RUN OK +[333/814] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ DRY-RUN OK +[334/814] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ DRY-RUN OK +[335/814] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ DRY-RUN OK +[336/814] 2018-03-11.16-15-01.bus → 2018-03-11.16-15-01.bus + ✓ DRY-RUN OK +[337/814] 2018-03-11.16-15-01.school → 2018-03-11.16-15-01.school + ✓ DRY-RUN OK +[338/814] 2018-03-11.16-15-02.school → 2018-03-11.16-15-02.school + ✓ DRY-RUN OK +[339/814] 2018-03-11.16-15-04.school → 2018-03-11.16-15-04.school + ✓ DRY-RUN OK +[340/814] 2018-03-11.16-15-08.bus → 2018-03-11.16-15-08.bus + ✓ DRY-RUN OK +[341/814] 2018-03-11.16-15-08.hospital → 2018-03-11.16-15-08.hospital + ✓ DRY-RUN OK +[342/814] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ DRY-RUN OK +[343/814] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ DRY-RUN OK +[344/814] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ DRY-RUN OK +[345/814] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ DRY-RUN OK +[346/814] 2018-03-11.16-20-01.bus → 2018-03-11.16-20-01.bus + ✓ DRY-RUN OK +[347/814] 2018-03-11.16-20-01.school → 2018-03-11.16-20-01.school + ✓ DRY-RUN OK +[348/814] 2018-03-11.16-20-02.school → 2018-03-11.16-20-02.school + ✓ DRY-RUN OK +[349/814] 2018-03-11.16-20-04.school → 2018-03-11.16-20-04.school + ✓ DRY-RUN OK +[350/814] 2018-03-11.16-20-08.bus → 2018-03-11.16-20-08.bus + ✓ DRY-RUN OK +[351/814] 2018-03-11.16-20-08.hospital → 2018-03-11.16-20-08.hospital + ✓ DRY-RUN OK +[352/814] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ DRY-RUN OK +[353/814] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ DRY-RUN OK +[354/814] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ DRY-RUN OK +[355/814] 2018-03-11.16-25-01.bus → 2018-03-11.16-25-01.bus + ✓ DRY-RUN OK +[356/814] 2018-03-11.16-25-01.school → 2018-03-11.16-25-01.school + ✓ DRY-RUN OK +[357/814] 2018-03-11.16-25-02.school → 2018-03-11.16-25-02.school + ✓ DRY-RUN OK +[358/814] 2018-03-11.16-25-04.school → 2018-03-11.16-25-04.school + ✓ DRY-RUN OK +[359/814] 2018-03-11.16-25-08.bus → 2018-03-11.16-25-08.bus + ✓ DRY-RUN OK +[360/814] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ DRY-RUN OK +[361/814] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ DRY-RUN OK +[362/814] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ DRY-RUN OK +[363/814] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ DRY-RUN OK +[364/814] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ DRY-RUN OK +[365/814] 2018-03-11.16-30-01.bus → 2018-03-11.16-30-01.bus + ✓ DRY-RUN OK +[366/814] 2018-03-11.16-30-01.school → 2018-03-11.16-30-01.school + ✓ DRY-RUN OK +[367/814] 2018-03-11.16-30-02.school → 2018-03-11.16-30-02.school + ✓ DRY-RUN OK +[368/814] 2018-03-11.16-30-04.school → 2018-03-11.16-30-04.school + ✓ DRY-RUN OK +[369/814] 2018-03-11.16-30-08.bus → 2018-03-11.16-30-08.bus + ✓ DRY-RUN OK +[370/814] 2018-03-11.16-30-08.hospital → 2018-03-11.16-30-08.hospital + ✓ DRY-RUN OK +[371/814] 2018-03-11.16-30-09.hospital → 2018-03-11.16-30-09.hospital + ✓ DRY-RUN OK +[372/814] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ DRY-RUN OK +[373/814] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ DRY-RUN OK +[374/814] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ DRY-RUN OK +[375/814] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ DRY-RUN OK +[376/814] 2018-03-11.16-35-01.bus → 2018-03-11.16-35-01.bus + ✓ DRY-RUN OK +[377/814] 2018-03-11.16-35-01.school → 2018-03-11.16-35-01.school + ✓ DRY-RUN OK +[378/814] 2018-03-11.16-35-02.school → 2018-03-11.16-35-02.school + ✓ DRY-RUN OK +[379/814] 2018-03-11.16-35-04.school → 2018-03-11.16-35-04.school + ✓ DRY-RUN OK +[380/814] 2018-03-11.16-35-08.bus → 2018-03-11.16-35-08.bus + ✓ DRY-RUN OK +[381/814] 2018-03-11.16-35-08.hospital → 2018-03-11.16-35-08.hospital + ✓ DRY-RUN OK +[382/814] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ DRY-RUN OK +[383/814] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ DRY-RUN OK +[384/814] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ DRY-RUN OK +[385/814] 2018-03-11.16-40-01.bus → 2018-03-11.16-40-01.bus + ✓ DRY-RUN OK +[386/814] 2018-03-11.16-40-01.school → 2018-03-11.16-40-01.school + ✓ DRY-RUN OK +[387/814] 2018-03-11.16-40-02.school → 2018-03-11.16-40-02.school + ✓ DRY-RUN OK +[388/814] 2018-03-11.16-40-04.school → 2018-03-11.16-40-04.school + ✓ DRY-RUN OK +[389/814] 2018-03-11.16-40-08.bus → 2018-03-11.16-40-08.bus + ✓ DRY-RUN OK +[390/814] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ DRY-RUN OK +[391/814] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ DRY-RUN OK +[392/814] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ DRY-RUN OK +[393/814] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ DRY-RUN OK +[394/814] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ DRY-RUN OK +[395/814] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ DRY-RUN OK +[396/814] 2018-03-11.17-10-01.bus → 2018-03-11.17-10-01.bus + ✓ DRY-RUN OK +[397/814] 2018-03-11.17-10-01.school → 2018-03-11.17-10-01.school + ✓ DRY-RUN OK +[398/814] 2018-03-11.17-10-02.school → 2018-03-11.17-10-02.school + ✓ DRY-RUN OK +[399/814] 2018-03-11.17-10-04.school → 2018-03-11.17-10-04.school + ✓ DRY-RUN OK +[400/814] 2018-03-11.17-10-08.bus → 2018-03-11.17-10-08.bus + ✓ DRY-RUN OK +[401/814] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ DRY-RUN OK +[402/814] 2018-03-11.17-10-09.hospital → 2018-03-11.17-10-09.hospital + ✓ DRY-RUN OK +[403/814] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ DRY-RUN OK +[404/814] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ DRY-RUN OK +[405/814] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ DRY-RUN OK +[406/814] 2018-03-11.17-15-01.bus → 2018-03-11.17-15-01.bus + ✓ DRY-RUN OK +[407/814] 2018-03-11.17-15-01.school → 2018-03-11.17-15-01.school + ✓ DRY-RUN OK +[408/814] 2018-03-11.17-15-02.school → 2018-03-11.17-15-02.school + ✓ DRY-RUN OK +[409/814] 2018-03-11.17-15-04.school → 2018-03-11.17-15-04.school + ✓ DRY-RUN OK +[410/814] 2018-03-11.17-15-08.bus → 2018-03-11.17-15-08.bus + ✓ DRY-RUN OK +[411/814] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ DRY-RUN OK +[412/814] 2018-03-11.17-15-09.hospital → 2018-03-11.17-15-09.hospital + ✓ DRY-RUN OK +[413/814] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ DRY-RUN OK +[414/814] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ DRY-RUN OK +[415/814] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ DRY-RUN OK +[416/814] 2018-03-11.17-20-01.bus → 2018-03-11.17-20-01.bus + ✓ DRY-RUN OK +[417/814] 2018-03-11.17-20-01.school → 2018-03-11.17-20-01.school + ✓ DRY-RUN OK +[418/814] 2018-03-11.17-20-02.school → 2018-03-11.17-20-02.school + ✓ DRY-RUN OK +[419/814] 2018-03-11.17-20-04.school → 2018-03-11.17-20-04.school + ✓ DRY-RUN OK +[420/814] 2018-03-11.17-20-08.bus → 2018-03-11.17-20-08.bus + ✓ DRY-RUN OK +[421/814] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ DRY-RUN OK +[422/814] 2018-03-11.17-20-09.hospital → 2018-03-11.17-20-09.hospital + ✓ DRY-RUN OK +[423/814] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ DRY-RUN OK +[424/814] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ DRY-RUN OK +[425/814] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ DRY-RUN OK +[426/814] 2018-03-11.17-25-01.bus → 2018-03-11.17-25-01.bus + ✓ DRY-RUN OK +[427/814] 2018-03-11.17-25-01.school → 2018-03-11.17-25-01.school + ✓ DRY-RUN OK +[428/814] 2018-03-11.17-25-02.school → 2018-03-11.17-25-02.school + ✓ DRY-RUN OK +[429/814] 2018-03-11.17-25-04.school → 2018-03-11.17-25-04.school + ✓ DRY-RUN OK +[430/814] 2018-03-11.17-25-08.bus → 2018-03-11.17-25-08.bus + ✓ DRY-RUN OK +[431/814] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ DRY-RUN OK +[432/814] 2018-03-11.17-25-09.hospital → 2018-03-11.17-25-09.hospital + ✓ DRY-RUN OK +[433/814] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ DRY-RUN OK +[434/814] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ DRY-RUN OK +[435/814] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ DRY-RUN OK +[436/814] 2018-03-12.10-00-01.bus → 2018-03-12.10-00-01.bus + ✓ DRY-RUN OK +[437/814] 2018-03-12.10-00-01.school → 2018-03-12.10-00-01.school + ✓ DRY-RUN OK +[438/814] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ DRY-RUN OK +[439/814] 2018-03-12.10-00-02.school → 2018-03-12.10-00-02.school + ✓ DRY-RUN OK +[440/814] 2018-03-12.10-00-04.school → 2018-03-12.10-00-04.school + ✓ DRY-RUN OK +[441/814] 2018-03-12.10-00-05.school → 2018-03-12.10-00-05.school + ✓ DRY-RUN OK +[442/814] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ DRY-RUN OK +[443/814] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ DRY-RUN OK +[444/814] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ DRY-RUN OK +[445/814] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ DRY-RUN OK +[446/814] 2018-03-12.10-05-01.bus → 2018-03-12.10-05-01.bus + ✓ DRY-RUN OK +[447/814] 2018-03-12.10-05-01.school → 2018-03-12.10-05-01.school + ✓ DRY-RUN OK +[448/814] 2018-03-12.10-05-02.school → 2018-03-12.10-05-02.school + ✓ DRY-RUN OK +[449/814] 2018-03-12.10-05-03.school → 2018-03-12.10-05-03.school + ✓ DRY-RUN OK +[450/814] 2018-03-12.10-05-04.school → 2018-03-12.10-05-04.school + ✓ DRY-RUN OK +[451/814] 2018-03-12.10-05-05.school → 2018-03-12.10-05-05.school + ✓ DRY-RUN OK +[452/814] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ DRY-RUN OK +[453/814] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ DRY-RUN OK +[454/814] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ DRY-RUN OK +[455/814] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ DRY-RUN OK +[456/814] 2018-03-12.10-10-01.bus → 2018-03-12.10-10-01.bus + ✓ DRY-RUN OK +[457/814] 2018-03-12.10-10-01.school → 2018-03-12.10-10-01.school + ✓ DRY-RUN OK +[458/814] 2018-03-12.10-10-02.school → 2018-03-12.10-10-02.school + ✓ DRY-RUN OK +[459/814] 2018-03-12.10-10-03.school → 2018-03-12.10-10-03.school + ✓ DRY-RUN OK +[460/814] 2018-03-12.10-10-04.school → 2018-03-12.10-10-04.school + ✓ DRY-RUN OK +[461/814] 2018-03-12.10-10-05.school → 2018-03-12.10-10-05.school + ✓ DRY-RUN OK +[462/814] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ DRY-RUN OK +[463/814] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ DRY-RUN OK +[464/814] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ DRY-RUN OK +[465/814] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ DRY-RUN OK +[466/814] 2018-03-12.10-15-01.bus → 2018-03-12.10-15-01.bus + ✓ DRY-RUN OK +[467/814] 2018-03-12.10-15-01.school → 2018-03-12.10-15-01.school + ✓ DRY-RUN OK +[468/814] 2018-03-12.10-15-02.school → 2018-03-12.10-15-02.school + ✓ DRY-RUN OK +[469/814] 2018-03-12.10-15-03.school → 2018-03-12.10-15-03.school + ✓ DRY-RUN OK +[470/814] 2018-03-12.10-15-04.school → 2018-03-12.10-15-04.school + ✓ DRY-RUN OK +[471/814] 2018-03-12.10-15-05.school → 2018-03-12.10-15-05.school + ✓ DRY-RUN OK +[472/814] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ DRY-RUN OK +[473/814] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ DRY-RUN OK +[474/814] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ DRY-RUN OK +[475/814] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ DRY-RUN OK +[476/814] 2018-03-12.10-20-01.bus → 2018-03-12.10-20-01.bus + ✓ DRY-RUN OK +[477/814] 2018-03-12.10-20-01.school → 2018-03-12.10-20-01.school + ✓ DRY-RUN OK +[478/814] 2018-03-12.10-20-02.school → 2018-03-12.10-20-02.school + ✓ DRY-RUN OK +[479/814] 2018-03-12.10-20-03.school → 2018-03-12.10-20-03.school + ✓ DRY-RUN OK +[480/814] 2018-03-12.10-20-04.school → 2018-03-12.10-20-04.school + ✓ DRY-RUN OK +[481/814] 2018-03-12.10-20-05.school → 2018-03-12.10-20-05.school + ✓ DRY-RUN OK +[482/814] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ DRY-RUN OK +[483/814] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ DRY-RUN OK +[484/814] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ DRY-RUN OK +[485/814] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ DRY-RUN OK +[486/814] 2018-03-12.10-25-01.bus → 2018-03-12.10-25-01.bus + ✓ DRY-RUN OK +[487/814] 2018-03-12.10-25-01.school → 2018-03-12.10-25-01.school + ✓ DRY-RUN OK +[488/814] 2018-03-12.10-25-02.school → 2018-03-12.10-25-02.school + ✓ DRY-RUN OK +[489/814] 2018-03-12.10-25-03.school → 2018-03-12.10-25-03.school + ✓ DRY-RUN OK +[490/814] 2018-03-12.10-25-04.school → 2018-03-12.10-25-04.school + ✓ DRY-RUN OK +[491/814] 2018-03-12.10-25-05.school → 2018-03-12.10-25-05.school + ✓ DRY-RUN OK +[492/814] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ DRY-RUN OK +[493/814] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ DRY-RUN OK +[494/814] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ DRY-RUN OK +[495/814] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ DRY-RUN OK +[496/814] 2018-03-12.10-30-01.admin → 2018-03-12.10-30-01.admin + ✓ DRY-RUN OK +[497/814] 2018-03-12.10-30-01.bus → 2018-03-12.10-30-01.bus + ✓ DRY-RUN OK +[498/814] 2018-03-12.10-30-01.school → 2018-03-12.10-30-01.school + ✓ DRY-RUN OK +[499/814] 2018-03-12.10-30-02.school → 2018-03-12.10-30-02.school + ✓ DRY-RUN OK +[500/814] 2018-03-12.10-30-03.school → 2018-03-12.10-30-03.school + ✓ DRY-RUN OK +[501/814] 2018-03-12.10-30-04.school → 2018-03-12.10-30-04.school + ✓ DRY-RUN OK +[502/814] 2018-03-12.10-30-05.school → 2018-03-12.10-30-05.school + ✓ DRY-RUN OK +[503/814] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ DRY-RUN OK +[504/814] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ DRY-RUN OK +[505/814] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ DRY-RUN OK +[506/814] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ DRY-RUN OK +[507/814] 2018-03-12.10-40-01.admin → 2018-03-12.10-40-01.admin + ✓ DRY-RUN OK +[508/814] 2018-03-12.10-40-01.bus → 2018-03-12.10-40-01.bus + ✓ DRY-RUN OK +[509/814] 2018-03-12.10-40-01.school → 2018-03-12.10-40-01.school + ✓ DRY-RUN OK +[510/814] 2018-03-12.10-40-02.school → 2018-03-12.10-40-02.school + ✓ DRY-RUN OK +[511/814] 2018-03-12.10-40-03.school → 2018-03-12.10-40-03.school + ✓ DRY-RUN OK +[512/814] 2018-03-12.10-40-04.school → 2018-03-12.10-40-04.school + ✓ DRY-RUN OK +[513/814] 2018-03-12.10-40-05.school → 2018-03-12.10-40-05.school + ✓ DRY-RUN OK +[514/814] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ DRY-RUN OK +[515/814] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ DRY-RUN OK +[516/814] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ DRY-RUN OK +[517/814] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ DRY-RUN OK +[518/814] 2018-03-12.10-45-01.admin → 2018-03-12.10-45-01.admin + ✓ DRY-RUN OK +[519/814] 2018-03-12.10-45-01.bus → 2018-03-12.10-45-01.bus + ✓ DRY-RUN OK +[520/814] 2018-03-12.10-45-01.school → 2018-03-12.10-45-01.school + ✓ DRY-RUN OK +[521/814] 2018-03-12.10-45-02.school → 2018-03-12.10-45-02.school + ✓ DRY-RUN OK +[522/814] 2018-03-12.10-45-03.school → 2018-03-12.10-45-03.school + ✓ DRY-RUN OK +[523/814] 2018-03-12.10-45-04.school → 2018-03-12.10-45-04.school + ✓ DRY-RUN OK +[524/814] 2018-03-12.10-45-05.school → 2018-03-12.10-45-05.school + ✓ DRY-RUN OK +[525/814] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ DRY-RUN OK +[526/814] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ DRY-RUN OK +[527/814] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ DRY-RUN OK +[528/814] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ DRY-RUN OK +[529/814] 2018-03-12.10-50-01.bus → 2018-03-12.10-50-01.bus + ✓ DRY-RUN OK +[530/814] 2018-03-12.10-50-01.school → 2018-03-12.10-50-01.school + ✓ DRY-RUN OK +[531/814] 2018-03-12.10-50-02.admin → 2018-03-12.10-50-02.admin + ✓ DRY-RUN OK +[532/814] 2018-03-12.10-50-02.school → 2018-03-12.10-50-02.school + ✓ DRY-RUN OK +[533/814] 2018-03-12.10-50-03.school → 2018-03-12.10-50-03.school + ✓ DRY-RUN OK +[534/814] 2018-03-12.10-50-04.school → 2018-03-12.10-50-04.school + ✓ DRY-RUN OK +[535/814] 2018-03-12.10-50-05.school → 2018-03-12.10-50-05.school + ✓ DRY-RUN OK +[536/814] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ DRY-RUN OK +[537/814] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ DRY-RUN OK +[538/814] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ DRY-RUN OK +[539/814] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ DRY-RUN OK +[540/814] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ DRY-RUN OK +[541/814] 2018-03-12.11-00-01.bus → 2018-03-12.11-00-01.bus + ✓ DRY-RUN OK +[542/814] 2018-03-12.11-00-01.school → 2018-03-12.11-00-01.school + ✓ DRY-RUN OK +[543/814] 2018-03-12.11-00-02.school → 2018-03-12.11-00-02.school + ✓ DRY-RUN OK +[544/814] 2018-03-12.11-00-03.school → 2018-03-12.11-00-03.school + ✓ DRY-RUN OK +[545/814] 2018-03-12.11-00-04.school → 2018-03-12.11-00-04.school + ✓ DRY-RUN OK +[546/814] 2018-03-12.11-00-05.school → 2018-03-12.11-00-05.school + ✓ DRY-RUN OK +[547/814] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ DRY-RUN OK +[548/814] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ DRY-RUN OK +[549/814] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ DRY-RUN OK +[550/814] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ DRY-RUN OK +[551/814] 2018-03-12.11-05-01.bus → 2018-03-12.11-05-01.bus + ✓ DRY-RUN OK +[552/814] 2018-03-12.11-05-01.school → 2018-03-12.11-05-01.school + ✓ DRY-RUN OK +[553/814] 2018-03-12.11-05-02.school → 2018-03-12.11-05-02.school + ✓ DRY-RUN OK +[554/814] 2018-03-12.11-05-03.school → 2018-03-12.11-05-03.school + ✓ DRY-RUN OK +[555/814] 2018-03-12.11-05-04.school → 2018-03-12.11-05-04.school + ✓ DRY-RUN OK +[556/814] 2018-03-12.11-05-05.school → 2018-03-12.11-05-05.school + ✓ DRY-RUN OK +[557/814] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ DRY-RUN OK +[558/814] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ DRY-RUN OK +[559/814] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ DRY-RUN OK +[560/814] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ DRY-RUN OK +[561/814] 2018-03-12.11-10-01.school → 2018-03-12.11-10-01.school + ✓ DRY-RUN OK +[562/814] 2018-03-12.11-10-02.school → 2018-03-12.11-10-02.school + ✓ DRY-RUN OK +[563/814] 2018-03-12.11-10-03.school → 2018-03-12.11-10-03.school + ✓ DRY-RUN OK +[564/814] 2018-03-12.11-10-04.school → 2018-03-12.11-10-04.school + ✓ DRY-RUN OK +[565/814] 2018-03-12.11-10-05.school → 2018-03-12.11-10-05.school + ✓ DRY-RUN OK +[566/814] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ DRY-RUN OK +[567/814] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ DRY-RUN OK +[568/814] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ DRY-RUN OK +[569/814] 2018-03-13.15-50-01.school → 2018-03-13.15-50-01.school + ✓ DRY-RUN OK +[570/814] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ DRY-RUN OK +[571/814] 2018-03-13.15-50-02.school → 2018-03-13.15-50-02.school + ✓ DRY-RUN OK +[572/814] 2018-03-13.15-50-03.school → 2018-03-13.15-50-03.school + ✓ DRY-RUN OK +[573/814] 2018-03-13.15-50-04.bus → 2018-03-13.15-50-04.bus + ✓ DRY-RUN OK +[574/814] 2018-03-13.15-50-05.school → 2018-03-13.15-50-05.school + ✓ DRY-RUN OK +[575/814] 2018-03-13.15-50-08.school → 2018-03-13.15-50-08.school + ✓ DRY-RUN OK +[576/814] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ DRY-RUN OK +[577/814] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ DRY-RUN OK +[578/814] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ DRY-RUN OK +[579/814] 2018-03-13.16-00-01.school → 2018-03-13.16-00-01.school + ✓ DRY-RUN OK +[580/814] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ DRY-RUN OK +[581/814] 2018-03-13.16-00-02.school → 2018-03-13.16-00-02.school + ✓ DRY-RUN OK +[582/814] 2018-03-13.16-00-03.school → 2018-03-13.16-00-03.school + ✓ DRY-RUN OK +[583/814] 2018-03-13.16-00-05.bus → 2018-03-13.16-00-05.bus + ✓ DRY-RUN OK +[584/814] 2018-03-13.16-00-05.school → 2018-03-13.16-00-05.school + ✓ DRY-RUN OK +[585/814] 2018-03-13.16-00-08.school → 2018-03-13.16-00-08.school + ✓ DRY-RUN OK +[586/814] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ DRY-RUN OK +[587/814] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ DRY-RUN OK +[588/814] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ DRY-RUN OK +[589/814] 2018-03-13.16-05-01.school → 2018-03-13.16-05-01.school + ✓ DRY-RUN OK +[590/814] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ DRY-RUN OK +[591/814] 2018-03-13.16-05-02.school → 2018-03-13.16-05-02.school + ✓ DRY-RUN OK +[592/814] 2018-03-13.16-05-03.school → 2018-03-13.16-05-03.school + ✓ DRY-RUN OK +[593/814] 2018-03-13.16-05-05.bus → 2018-03-13.16-05-05.bus + ✓ DRY-RUN OK +[594/814] 2018-03-13.16-05-05.school → 2018-03-13.16-05-05.school + ✓ DRY-RUN OK +[595/814] 2018-03-13.16-05-08.school → 2018-03-13.16-05-08.school + ✓ DRY-RUN OK +[596/814] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ DRY-RUN OK +[597/814] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ DRY-RUN OK +[598/814] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ DRY-RUN OK +[599/814] 2018-03-13.16-10-01.school → 2018-03-13.16-10-01.school + ✓ DRY-RUN OK +[600/814] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ DRY-RUN OK +[601/814] 2018-03-13.16-10-03.school → 2018-03-13.16-10-03.school + ✓ DRY-RUN OK +[602/814] 2018-03-13.16-10-05.bus → 2018-03-13.16-10-05.bus + ✓ DRY-RUN OK +[603/814] 2018-03-13.16-10-05.school → 2018-03-13.16-10-05.school + ✓ DRY-RUN OK +[604/814] 2018-03-13.16-10-08.school → 2018-03-13.16-10-08.school + ✓ DRY-RUN OK +[605/814] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ DRY-RUN OK +[606/814] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ DRY-RUN OK +[607/814] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ DRY-RUN OK +[608/814] 2018-03-13.16-15-01.school → 2018-03-13.16-15-01.school + ✓ DRY-RUN OK +[609/814] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ DRY-RUN OK +[610/814] 2018-03-13.16-15-03.school → 2018-03-13.16-15-03.school + ✓ DRY-RUN OK +[611/814] 2018-03-13.16-15-05.bus → 2018-03-13.16-15-05.bus + ✓ DRY-RUN OK +[612/814] 2018-03-13.16-15-05.school → 2018-03-13.16-15-05.school + ✓ DRY-RUN OK +[613/814] 2018-03-13.16-15-08.school → 2018-03-13.16-15-08.school + ✓ DRY-RUN OK +[614/814] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ DRY-RUN OK +[615/814] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ DRY-RUN OK +[616/814] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ DRY-RUN OK +[617/814] 2018-03-13.16-20-01.school → 2018-03-13.16-20-01.school + ✓ DRY-RUN OK +[618/814] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ DRY-RUN OK +[619/814] 2018-03-13.16-20-03.school → 2018-03-13.16-20-03.school + ✓ DRY-RUN OK +[620/814] 2018-03-13.16-20-05.bus → 2018-03-13.16-20-05.bus + ✓ DRY-RUN OK +[621/814] 2018-03-13.16-20-05.school → 2018-03-13.16-20-05.school + ✓ DRY-RUN OK +[622/814] 2018-03-13.16-20-08.school → 2018-03-13.16-20-08.school + ✓ DRY-RUN OK +[623/814] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ DRY-RUN OK +[624/814] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ DRY-RUN OK +[625/814] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ DRY-RUN OK +[626/814] 2018-03-13.16-25-01.school → 2018-03-13.16-25-01.school + ✓ DRY-RUN OK +[627/814] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ DRY-RUN OK +[628/814] 2018-03-13.16-25-03.school → 2018-03-13.16-25-03.school + ✓ DRY-RUN OK +[629/814] 2018-03-13.16-25-05.bus → 2018-03-13.16-25-05.bus + ✓ DRY-RUN OK +[630/814] 2018-03-13.16-25-05.school → 2018-03-13.16-25-05.school + ✓ DRY-RUN OK +[631/814] 2018-03-13.16-25-08.school → 2018-03-13.16-25-08.school + ✓ DRY-RUN OK +[632/814] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ DRY-RUN OK +[633/814] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ DRY-RUN OK +[634/814] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ DRY-RUN OK +[635/814] 2018-03-13.16-30-01.school → 2018-03-13.16-30-01.school + ✓ DRY-RUN OK +[636/814] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ DRY-RUN OK +[637/814] 2018-03-13.16-30-03.hospital → 2018-03-13.16-30-03.hospital + ✓ DRY-RUN OK +[638/814] 2018-03-13.16-30-03.school → 2018-03-13.16-30-03.school + ✓ DRY-RUN OK +[639/814] 2018-03-13.16-30-04.bus → 2018-03-13.16-30-04.bus + ✓ DRY-RUN OK +[640/814] 2018-03-13.16-30-05.school → 2018-03-13.16-30-05.school + ✓ DRY-RUN OK +[641/814] 2018-03-13.16-30-08.school → 2018-03-13.16-30-08.school + ✓ DRY-RUN OK +[642/814] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ DRY-RUN OK +[643/814] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ DRY-RUN OK +[644/814] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ DRY-RUN OK +[645/814] 2018-03-13.17-05-01.school → 2018-03-13.17-05-01.school + ✓ DRY-RUN OK +[646/814] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ DRY-RUN OK +[647/814] 2018-03-13.17-05-03.hospital → 2018-03-13.17-05-03.hospital + ✓ DRY-RUN OK +[648/814] 2018-03-13.17-05-03.school → 2018-03-13.17-05-03.school + ✓ DRY-RUN OK +[649/814] 2018-03-13.17-05-05.bus → 2018-03-13.17-05-05.bus + ✓ DRY-RUN OK +[650/814] 2018-03-13.17-05-05.school → 2018-03-13.17-05-05.school + ✓ DRY-RUN OK +[651/814] 2018-03-13.17-05-08.school → 2018-03-13.17-05-08.school + ✓ DRY-RUN OK +[652/814] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ DRY-RUN OK +[653/814] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ DRY-RUN OK +[654/814] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ DRY-RUN OK +[655/814] 2018-03-13.17-10-01.school → 2018-03-13.17-10-01.school + ✓ DRY-RUN OK +[656/814] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ DRY-RUN OK +[657/814] 2018-03-13.17-10-03.hospital → 2018-03-13.17-10-03.hospital + ✓ DRY-RUN OK +[658/814] 2018-03-13.17-10-03.school → 2018-03-13.17-10-03.school + ✓ DRY-RUN OK +[659/814] 2018-03-13.17-10-05.bus → 2018-03-13.17-10-05.bus + ✓ DRY-RUN OK +[660/814] 2018-03-13.17-10-05.school → 2018-03-13.17-10-05.school + ✓ DRY-RUN OK +[661/814] 2018-03-13.17-10-08.school → 2018-03-13.17-10-08.school + ✓ DRY-RUN OK +[662/814] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ DRY-RUN OK +[663/814] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ DRY-RUN OK +[664/814] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ DRY-RUN OK +[665/814] 2018-03-13.17-15-01.school → 2018-03-13.17-15-01.school + ✓ DRY-RUN OK +[666/814] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ DRY-RUN OK +[667/814] 2018-03-13.17-15-03.hospital → 2018-03-13.17-15-03.hospital + ✓ DRY-RUN OK +[668/814] 2018-03-13.17-15-03.school → 2018-03-13.17-15-03.school + ✓ DRY-RUN OK +[669/814] 2018-03-13.17-15-05.bus → 2018-03-13.17-15-05.bus + ✓ DRY-RUN OK +[670/814] 2018-03-13.17-15-05.school → 2018-03-13.17-15-05.school + ✓ DRY-RUN OK +[671/814] 2018-03-13.17-15-08.school → 2018-03-13.17-15-08.school + ✓ DRY-RUN OK +[672/814] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ DRY-RUN OK +[673/814] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ DRY-RUN OK +[674/814] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ DRY-RUN OK +[675/814] 2018-03-13.17-20-01.school → 2018-03-13.17-20-01.school + ✓ DRY-RUN OK +[676/814] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ DRY-RUN OK +[677/814] 2018-03-13.17-20-03.hospital → 2018-03-13.17-20-03.hospital + ✓ DRY-RUN OK +[678/814] 2018-03-13.17-20-03.school → 2018-03-13.17-20-03.school + ✓ DRY-RUN OK +[679/814] 2018-03-13.17-20-05.bus → 2018-03-13.17-20-05.bus + ✓ DRY-RUN OK +[680/814] 2018-03-13.17-20-05.school → 2018-03-13.17-20-05.school + ✓ DRY-RUN OK +[681/814] 2018-03-13.17-20-08.school → 2018-03-13.17-20-08.school + ✓ DRY-RUN OK +[682/814] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ DRY-RUN OK +[683/814] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ DRY-RUN OK +[684/814] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ DRY-RUN OK +[685/814] 2018-03-13.17-25-01.school → 2018-03-13.17-25-01.school + ✓ DRY-RUN OK +[686/814] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ DRY-RUN OK +[687/814] 2018-03-13.17-25-03.hospital → 2018-03-13.17-25-03.hospital + ✓ DRY-RUN OK +[688/814] 2018-03-13.17-25-03.school → 2018-03-13.17-25-03.school + ✓ DRY-RUN OK +[689/814] 2018-03-13.17-25-05.bus → 2018-03-13.17-25-05.bus + ✓ DRY-RUN OK +[690/814] 2018-03-13.17-25-05.school → 2018-03-13.17-25-05.school + ✓ DRY-RUN OK +[691/814] 2018-03-13.17-25-08.school → 2018-03-13.17-25-08.school + ✓ DRY-RUN OK +[692/814] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ DRY-RUN OK +[693/814] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ DRY-RUN OK +[694/814] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ DRY-RUN OK +[695/814] 2018-03-13.17-30-01.bus → 2018-03-13.17-30-01.bus + ✓ DRY-RUN OK +[696/814] 2018-03-13.17-30-01.school → 2018-03-13.17-30-01.school + ✓ DRY-RUN OK +[697/814] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ DRY-RUN OK +[698/814] 2018-03-13.17-30-03.hospital → 2018-03-13.17-30-03.hospital + ✓ DRY-RUN OK +[699/814] 2018-03-13.17-30-03.school → 2018-03-13.17-30-03.school + ✓ DRY-RUN OK +[700/814] 2018-03-13.17-30-05.bus → 2018-03-13.17-30-05.bus + ✓ DRY-RUN OK +[701/814] 2018-03-13.17-30-05.school → 2018-03-13.17-30-05.school + ✓ DRY-RUN OK +[702/814] 2018-03-13.17-30-08.school → 2018-03-13.17-30-08.school + ✓ DRY-RUN OK +[703/814] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ DRY-RUN OK +[704/814] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ DRY-RUN OK +[705/814] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ DRY-RUN OK +[706/814] 2018-03-13.17-35-01.bus → 2018-03-13.17-35-01.bus + ✓ DRY-RUN OK +[707/814] 2018-03-13.17-35-01.school → 2018-03-13.17-35-01.school + ✓ DRY-RUN OK +[708/814] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ DRY-RUN OK +[709/814] 2018-03-13.17-35-03.hospital → 2018-03-13.17-35-03.hospital + ✓ DRY-RUN OK +[710/814] 2018-03-13.17-35-03.school → 2018-03-13.17-35-03.school + ✓ DRY-RUN OK +[711/814] 2018-03-13.17-35-05.bus → 2018-03-13.17-35-05.bus + ✓ DRY-RUN OK +[712/814] 2018-03-13.17-35-05.school → 2018-03-13.17-35-05.school + ✓ DRY-RUN OK +[713/814] 2018-03-13.17-35-08.school → 2018-03-13.17-35-08.school + ✓ DRY-RUN OK +[714/814] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ DRY-RUN OK +[715/814] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ DRY-RUN OK +[716/814] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ DRY-RUN OK +[717/814] 2018-03-13.17-40-01.bus → 2018-03-13.17-40-01.bus + ✓ DRY-RUN OK +[718/814] 2018-03-13.17-40-01.school → 2018-03-13.17-40-01.school + ✓ DRY-RUN OK +[719/814] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ DRY-RUN OK +[720/814] 2018-03-13.17-40-03.hospital → 2018-03-13.17-40-03.hospital + ✓ DRY-RUN OK +[721/814] 2018-03-13.17-40-03.school → 2018-03-13.17-40-03.school + ✓ DRY-RUN OK +[722/814] 2018-03-13.17-40-05.bus → 2018-03-13.17-40-05.bus + ✓ DRY-RUN OK +[723/814] 2018-03-13.17-40-05.school → 2018-03-13.17-40-05.school + ✓ DRY-RUN OK +[724/814] 2018-03-13.17-40-08.school → 2018-03-13.17-40-08.school + ✓ DRY-RUN OK +[725/814] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ DRY-RUN OK +[726/814] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ DRY-RUN OK +[727/814] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ DRY-RUN OK +[728/814] 2018-03-15.14-50-01.bus → 2018-03-15.14-50-01.bus + ✓ DRY-RUN OK +[729/814] 2018-03-15.14-50-01.school → 2018-03-15.14-50-01.school + ✓ DRY-RUN OK +[730/814] 2018-03-15.14-50-03.school → 2018-03-15.14-50-03.school + ✓ DRY-RUN OK +[731/814] 2018-03-15.14-50-04.bus → 2018-03-15.14-50-04.bus + ✓ DRY-RUN OK +[732/814] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ DRY-RUN OK +[733/814] 2018-03-15.14-50-07.hospital → 2018-03-15.14-50-07.hospital + ✓ DRY-RUN OK +[734/814] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ DRY-RUN OK +[735/814] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ DRY-RUN OK +[736/814] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ DRY-RUN OK +[737/814] 2018-03-15.15-00-01.bus → 2018-03-15.15-00-01.bus + ✓ DRY-RUN OK +[738/814] 2018-03-15.15-00-01.school → 2018-03-15.15-00-01.school + ✓ DRY-RUN OK +[739/814] 2018-03-15.15-00-03.school → 2018-03-15.15-00-03.school + ✓ DRY-RUN OK +[740/814] 2018-03-15.15-00-04.bus → 2018-03-15.15-00-04.bus + ✓ DRY-RUN OK +[741/814] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ DRY-RUN OK +[742/814] 2018-03-15.15-00-07.hospital → 2018-03-15.15-00-07.hospital + ✓ DRY-RUN OK +[743/814] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ DRY-RUN OK +[744/814] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ DRY-RUN OK +[745/814] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ DRY-RUN OK +[746/814] 2018-03-15.15-05-01.bus → 2018-03-15.15-05-01.bus + ✓ DRY-RUN OK +[747/814] 2018-03-15.15-05-01.school → 2018-03-15.15-05-01.school + ✓ DRY-RUN OK +[748/814] 2018-03-15.15-05-03.school → 2018-03-15.15-05-03.school + ✓ DRY-RUN OK +[749/814] 2018-03-15.15-05-04.bus → 2018-03-15.15-05-04.bus + ✓ DRY-RUN OK +[750/814] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ DRY-RUN OK +[751/814] 2018-03-15.15-05-07.hospital → 2018-03-15.15-05-07.hospital + ✓ DRY-RUN OK +[752/814] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ DRY-RUN OK +[753/814] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ DRY-RUN OK +[754/814] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ DRY-RUN OK +[755/814] 2018-03-15.15-10-01.bus → 2018-03-15.15-10-01.bus + ✓ DRY-RUN OK +[756/814] 2018-03-15.15-10-01.school → 2018-03-15.15-10-01.school + ✓ DRY-RUN OK +[757/814] 2018-03-15.15-10-03.school → 2018-03-15.15-10-03.school + ✓ DRY-RUN OK +[758/814] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ DRY-RUN OK +[759/814] 2018-03-15.15-10-07.hospital → 2018-03-15.15-10-07.hospital + ✓ DRY-RUN OK +[760/814] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ DRY-RUN OK +[761/814] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ DRY-RUN OK +[762/814] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ DRY-RUN OK +[763/814] 2018-03-15.15-15-01.bus → 2018-03-15.15-15-01.bus + ✓ DRY-RUN OK +[764/814] 2018-03-15.15-15-01.school → 2018-03-15.15-15-01.school + ✓ DRY-RUN OK +[765/814] 2018-03-15.15-15-03.school → 2018-03-15.15-15-03.school + ✓ DRY-RUN OK +[766/814] 2018-03-15.15-15-04.bus → 2018-03-15.15-15-04.bus + ✓ DRY-RUN OK +[767/814] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ DRY-RUN OK +[768/814] 2018-03-15.15-15-07.hospital → 2018-03-15.15-15-07.hospital + ✓ DRY-RUN OK +[769/814] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ DRY-RUN OK +[770/814] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ DRY-RUN OK +[771/814] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ DRY-RUN OK +[772/814] 2018-03-15.15-30-01.bus → 2018-03-15.15-30-01.bus + ✓ DRY-RUN OK +[773/814] 2018-03-15.15-30-01.school → 2018-03-15.15-30-01.school + ✓ DRY-RUN OK +[774/814] 2018-03-15.15-30-03.school → 2018-03-15.15-30-03.school + ✓ DRY-RUN OK +[775/814] 2018-03-15.15-30-04.bus → 2018-03-15.15-30-04.bus + ✓ DRY-RUN OK +[776/814] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ DRY-RUN OK +[777/814] 2018-03-15.15-30-07.hospital → 2018-03-15.15-30-07.hospital + ✓ DRY-RUN OK +[778/814] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ DRY-RUN OK +[779/814] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ DRY-RUN OK +[780/814] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ DRY-RUN OK +[781/814] 2018-03-15.15-35-01.bus → 2018-03-15.15-35-01.bus + ✓ DRY-RUN OK +[782/814] 2018-03-15.15-35-01.school → 2018-03-15.15-35-01.school + ✓ DRY-RUN OK +[783/814] 2018-03-15.15-35-03.school → 2018-03-15.15-35-03.school + ✓ DRY-RUN OK +[784/814] 2018-03-15.15-35-04.bus → 2018-03-15.15-35-04.bus + ✓ DRY-RUN OK +[785/814] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ DRY-RUN OK +[786/814] 2018-03-15.15-35-07.hospital → 2018-03-15.15-35-07.hospital + ✓ DRY-RUN OK +[787/814] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ DRY-RUN OK +[788/814] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ DRY-RUN OK +[789/814] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ DRY-RUN OK +[790/814] 2018-03-15.15-40-01.bus → 2018-03-15.15-40-01.bus + ✓ DRY-RUN OK +[791/814] 2018-03-15.15-40-01.school → 2018-03-15.15-40-01.school + ✓ DRY-RUN OK +[792/814] 2018-03-15.15-40-03.school → 2018-03-15.15-40-03.school + ✓ DRY-RUN OK +[793/814] 2018-03-15.15-40-04.bus → 2018-03-15.15-40-04.bus + ✓ DRY-RUN OK +[794/814] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ DRY-RUN OK +[795/814] 2018-03-15.15-40-07.hospital → 2018-03-15.15-40-07.hospital + ✓ DRY-RUN OK +[796/814] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ DRY-RUN OK +[797/814] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ DRY-RUN OK +[798/814] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ DRY-RUN OK +[799/814] 2018-03-15.15-45-01.bus → 2018-03-15.15-45-01.bus + ✓ DRY-RUN OK +[800/814] 2018-03-15.15-45-01.school → 2018-03-15.15-45-01.school + ✓ DRY-RUN OK +[801/814] 2018-03-15.15-45-02.school → 2018-03-15.15-45-02.school + ✓ DRY-RUN OK +[802/814] 2018-03-15.15-45-03.school → 2018-03-15.15-45-03.school + ✓ DRY-RUN OK +[803/814] 2018-03-15.15-45-04.bus → 2018-03-15.15-45-04.bus + ✓ DRY-RUN OK +[804/814] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ DRY-RUN OK +[805/814] 2018-03-15.15-45-07.hospital → 2018-03-15.15-45-07.hospital + ✓ DRY-RUN OK +[806/814] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ DRY-RUN OK +[807/814] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ DRY-RUN OK +[808/814] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ DRY-RUN OK +[809/814] 2018-03-15.15-50-01.bus → 2018-03-15.15-50-01.bus + ✓ DRY-RUN OK +[810/814] 2018-03-15.15-50-01.school → 2018-03-15.15-50-01.school + ✓ DRY-RUN OK +[811/814] 2018-03-15.15-50-03.school → 2018-03-15.15-50-03.school + ✓ DRY-RUN OK +[812/814] 2018-03-15.15-50-04.bus → 2018-03-15.15-50-04.bus + ✓ DRY-RUN OK +[813/814] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ DRY-RUN OK +[814/814] 2018-03-15.15-50-07.hospital → 2018-03-15.15-50-07.hospital + ✓ DRY-RUN OK + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 0 +Skipped (already exist): 0 +Failed: 0 +Total entities: 0 +Avg entities/slot: 0.0 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193237.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193356.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193356.log.txt new file mode 100644 index 0000000..33c8bcd --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193356.log.txt @@ -0,0 +1,271 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193356.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:33:56 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SKIPPED (already exists, 34 entities, method=unknown) +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SKIPPED (already exists, 47 entities, method=unknown) +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SKIPPED (already exists, 149 entities, method=unknown) +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SKIPPED (already exists, 30 entities, method=yolo) +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SKIPPED (already exists, 128 entities, method=unknown) +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SKIPPED (already exists, 7 entities, method=yolo) +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SKIPPED (already exists, 106 entities, method=unknown) +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SKIPPED (already exists, 92 entities, method=yolo) +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SKIPPED (already exists, 367 entities, method=unknown) +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SKIPPED (already exists, 4 entities, method=yolo) +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SKIPPED (already exists, 1 entities, method=unknown) +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SKIPPED (already exists, 2 entities, method=yolo) +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SKIPPED (already exists, 231 entities, method=unknown) +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SKIPPED (already exists, 11 entities, method=unknown) +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SKIPPED (already exists, 3 entities, method=yolo) +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SKIPPED (already exists, 212 entities, method=unknown) +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SKIPPED (already exists, 17 entities, method=yolo) +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SKIPPED (already exists, 221 entities, method=unknown) +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SKIPPED (already exists, 9 entities, method=segformer) +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SKIPPED (already exists, 18 entities, method=unknown) +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SKIPPED (already exists, 299 entities, method=segformer) +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SKIPPED (already exists, 400 entities, method=unknown) +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SKIPPED (already exists, 1059 entities, method=unknown) +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SKIPPED (already exists, 45 entities, method=unknown) +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SKIPPED (already exists, 440 entities, method=unknown) +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SKIPPED (already exists, 203 entities, method=unknown) +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SKIPPED (already exists, 786 entities, method=unknown) +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SKIPPED (already exists, 40 entities, method=unknown) +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SKIPPED (already exists, 336 entities, method=unknown) +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SKIPPED (already exists, 171 entities, method=unknown) +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SKIPPED (already exists, 679 entities, method=unknown) +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SKIPPED (already exists, 11 entities, method=unknown) +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SKIPPED (already exists, 300 entities, method=unknown) +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SKIPPED (already exists, 956 entities, method=unknown) +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SKIPPED (already exists, 334 entities, method=unknown) +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SKIPPED (already exists, 21 entities, method=unknown) +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SKIPPED (already exists, 626 entities, method=unknown) +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SKIPPED (already exists, 1095 entities, method=unknown) +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SKIPPED (already exists, 316 entities, method=unknown) +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SKIPPED (already exists, 9 entities, method=unknown) +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SKIPPED (already exists, 291 entities, method=unknown) +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SKIPPED (already exists, 761 entities, method=unknown) +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SKIPPED (already exists, 185 entities, method=unknown) +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SKIPPED (already exists, 341 entities, method=unknown) +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SKIPPED (already exists, 783 entities, method=unknown) +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SKIPPED (already exists, 214 entities, method=unknown) +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SKIPPED (already exists, 16 entities, method=unknown) +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SKIPPED (already exists, 318 entities, method=unknown) +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SKIPPED (already exists, 1013 entities, method=unknown) +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SKIPPED (already exists, 269 entities, method=unknown) +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SKIPPED (already exists, 28 entities, method=unknown) +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SKIPPED (already exists, 713 entities, method=unknown) +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SKIPPED (already exists, 1438 entities, method=unknown) +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SKIPPED (already exists, 311 entities, method=unknown) +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SKIPPED (already exists, 24 entities, method=unknown) +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SKIPPED (already exists, 241 entities, method=unknown) +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SKIPPED (already exists, 775 entities, method=unknown) +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SKIPPED (already exists, 191 entities, method=unknown) +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SKIPPED (already exists, 65 entities, method=unknown) +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SKIPPED (already exists, 1325 entities, method=unknown) +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SKIPPED (already exists, 47 entities, method=yolo) +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SKIPPED (already exists, 137 entities, method=unknown) +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SKIPPED (already exists, 35 entities, method=unknown) +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SKIPPED (already exists, 3256 entities, method=segformer) +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SKIPPED (already exists, 16 entities, method=unknown) +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SKIPPED (already exists, 67 entities, method=unknown) +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SKIPPED (already exists, 801 entities, method=unknown) +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SKIPPED (already exists, 8 entities, method=yolo) +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SKIPPED (already exists, 322 entities, method=unknown) +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SKIPPED (already exists, 9 entities, method=yolo) +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SKIPPED (already exists, 1 entities, method=unknown) +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SKIPPED (already exists, 268 entities, method=unknown) +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SKIPPED (already exists, 7 entities, method=yolo) +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SKIPPED (already exists, 12 entities, method=unknown) +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SKIPPED (already exists, 29 entities, method=unknown) +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SKIPPED (already exists, 1070 entities, method=unknown) +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SKIPPED (already exists, 11 entities, method=yolo) +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SKIPPED (already exists, 35 entities, method=unknown) +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SKIPPED (already exists, 73 entities, method=unknown) +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SKIPPED (already exists, 1130 entities, method=unknown) +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SKIPPED (already exists, 24 entities, method=yolo) +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SKIPPED (already exists, 141 entities, method=unknown) +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SKIPPED (already exists, 688 entities, method=unknown) +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SKIPPED (already exists, 36 entities, method=unknown) +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SKIPPED (already exists, 18 entities, method=segformer) +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SKIPPED (already exists, 18 entities, method=unknown) +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SKIPPED (already exists, 1846 entities, method=unknown) +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SKIPPED (already exists, 34 entities, method=yolo) +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SKIPPED (already exists, 44 entities, method=unknown) +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SKIPPED (already exists, 24 entities, method=unknown) +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SKIPPED (already exists, 1261 entities, method=segformer) +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SKIPPED (already exists, 11 entities, method=yolo) +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SKIPPED (already exists, 17 entities, method=unknown) +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SKIPPED (already exists, 18 entities, method=unknown) +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SKIPPED (already exists, 895 entities, method=unknown) +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SKIPPED (already exists, 32 entities, method=yolo) +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SKIPPED (already exists, 8 entities, method=unknown) +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SKIPPED (already exists, 435 entities, method=unknown) +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SKIPPED (already exists, 32 entities, method=unknown) +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SKIPPED (already exists, 396 entities, method=unknown) +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SKIPPED (already exists, 1083 entities, method=unknown) +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SKIPPED (already exists, 3 entities, method=unknown) +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SKIPPED (already exists, 4 entities, method=unknown) +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SKIPPED (already exists, 10 entities, method=unknown) +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SKIPPED (already exists, 2322 entities, method=unknown) +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SKIPPED (already exists, 9 entities, method=yolo) +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SKIPPED (already exists, 244 entities, method=yolo) +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SKIPPED (already exists, 22 entities, method=unknown) +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SKIPPED (already exists, 33 entities, method=unknown) +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SKIPPED (already exists, 606 entities, method=unknown) +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SKIPPED (already exists, 111 entities, method=yolo) +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193402.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193402.log.txt new file mode 100644 index 0000000..6e28a82 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193402.log.txt @@ -0,0 +1,731 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193402.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:34:02 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SKIPPED (already exists, 34 entities, method=unknown) +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SKIPPED (already exists, 47 entities, method=unknown) +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SKIPPED (already exists, 149 entities, method=unknown) +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SKIPPED (already exists, 30 entities, method=yolo) +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SKIPPED (already exists, 128 entities, method=unknown) +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SKIPPED (already exists, 7 entities, method=yolo) +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SKIPPED (already exists, 106 entities, method=unknown) +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SKIPPED (already exists, 92 entities, method=yolo) +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SKIPPED (already exists, 367 entities, method=unknown) +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SKIPPED (already exists, 4 entities, method=yolo) +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SKIPPED (already exists, 1 entities, method=unknown) +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SKIPPED (already exists, 2 entities, method=yolo) +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SKIPPED (already exists, 231 entities, method=unknown) +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SKIPPED (already exists, 11 entities, method=unknown) +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SKIPPED (already exists, 3 entities, method=yolo) +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SKIPPED (already exists, 212 entities, method=unknown) +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SKIPPED (already exists, 17 entities, method=yolo) +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SKIPPED (already exists, 221 entities, method=unknown) +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SKIPPED (already exists, 9 entities, method=segformer) +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SKIPPED (already exists, 18 entities, method=unknown) +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SKIPPED (already exists, 299 entities, method=segformer) +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SKIPPED (already exists, 400 entities, method=unknown) +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SKIPPED (already exists, 1059 entities, method=unknown) +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SKIPPED (already exists, 45 entities, method=unknown) +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SKIPPED (already exists, 440 entities, method=unknown) +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SKIPPED (already exists, 203 entities, method=unknown) +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SKIPPED (already exists, 786 entities, method=unknown) +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SKIPPED (already exists, 40 entities, method=unknown) +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SKIPPED (already exists, 336 entities, method=unknown) +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SKIPPED (already exists, 171 entities, method=unknown) +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SKIPPED (already exists, 679 entities, method=unknown) +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SKIPPED (already exists, 11 entities, method=unknown) +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SKIPPED (already exists, 300 entities, method=unknown) +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SKIPPED (already exists, 956 entities, method=unknown) +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SKIPPED (already exists, 334 entities, method=unknown) +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SKIPPED (already exists, 21 entities, method=unknown) +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SKIPPED (already exists, 626 entities, method=unknown) +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SKIPPED (already exists, 1095 entities, method=unknown) +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SKIPPED (already exists, 316 entities, method=unknown) +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SKIPPED (already exists, 9 entities, method=unknown) +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SKIPPED (already exists, 291 entities, method=unknown) +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SKIPPED (already exists, 761 entities, method=unknown) +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SKIPPED (already exists, 185 entities, method=unknown) +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SKIPPED (already exists, 341 entities, method=unknown) +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SKIPPED (already exists, 783 entities, method=unknown) +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SKIPPED (already exists, 214 entities, method=unknown) +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SKIPPED (already exists, 16 entities, method=unknown) +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SKIPPED (already exists, 318 entities, method=unknown) +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SKIPPED (already exists, 1013 entities, method=unknown) +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SKIPPED (already exists, 269 entities, method=unknown) +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SKIPPED (already exists, 28 entities, method=unknown) +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SKIPPED (already exists, 713 entities, method=unknown) +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SKIPPED (already exists, 1438 entities, method=unknown) +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SKIPPED (already exists, 311 entities, method=unknown) +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SKIPPED (already exists, 24 entities, method=unknown) +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SKIPPED (already exists, 241 entities, method=unknown) +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SKIPPED (already exists, 775 entities, method=unknown) +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SKIPPED (already exists, 191 entities, method=unknown) +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SKIPPED (already exists, 65 entities, method=unknown) +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SKIPPED (already exists, 1325 entities, method=unknown) +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SKIPPED (already exists, 47 entities, method=yolo) +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SKIPPED (already exists, 137 entities, method=unknown) +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SKIPPED (already exists, 35 entities, method=unknown) +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SKIPPED (already exists, 3256 entities, method=segformer) +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SKIPPED (already exists, 16 entities, method=unknown) +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SKIPPED (already exists, 67 entities, method=unknown) +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SKIPPED (already exists, 801 entities, method=unknown) +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SKIPPED (already exists, 8 entities, method=yolo) +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SKIPPED (already exists, 322 entities, method=unknown) +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SKIPPED (already exists, 9 entities, method=yolo) +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SKIPPED (already exists, 1 entities, method=unknown) +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SKIPPED (already exists, 268 entities, method=unknown) +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SKIPPED (already exists, 7 entities, method=yolo) +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SKIPPED (already exists, 12 entities, method=unknown) +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SKIPPED (already exists, 29 entities, method=unknown) +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SKIPPED (already exists, 1070 entities, method=unknown) +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SKIPPED (already exists, 11 entities, method=yolo) +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SKIPPED (already exists, 35 entities, method=unknown) +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SKIPPED (already exists, 73 entities, method=unknown) +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SKIPPED (already exists, 1130 entities, method=unknown) +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SKIPPED (already exists, 24 entities, method=yolo) +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SKIPPED (already exists, 141 entities, method=unknown) +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SKIPPED (already exists, 688 entities, method=unknown) +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SKIPPED (already exists, 36 entities, method=unknown) +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SKIPPED (already exists, 18 entities, method=segformer) +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SKIPPED (already exists, 18 entities, method=unknown) +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SKIPPED (already exists, 1846 entities, method=unknown) +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SKIPPED (already exists, 34 entities, method=yolo) +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SKIPPED (already exists, 44 entities, method=unknown) +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SKIPPED (already exists, 24 entities, method=unknown) +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SKIPPED (already exists, 1261 entities, method=segformer) +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SKIPPED (already exists, 11 entities, method=yolo) +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SKIPPED (already exists, 17 entities, method=unknown) +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SKIPPED (already exists, 18 entities, method=unknown) +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SKIPPED (already exists, 895 entities, method=unknown) +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SKIPPED (already exists, 32 entities, method=yolo) +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SKIPPED (already exists, 8 entities, method=unknown) +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SKIPPED (already exists, 435 entities, method=unknown) +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SKIPPED (already exists, 32 entities, method=unknown) +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SKIPPED (already exists, 396 entities, method=unknown) +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SKIPPED (already exists, 1083 entities, method=unknown) +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SKIPPED (already exists, 3 entities, method=unknown) +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SKIPPED (already exists, 4 entities, method=unknown) +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SKIPPED (already exists, 10 entities, method=unknown) +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SKIPPED (already exists, 2322 entities, method=unknown) +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SKIPPED (already exists, 9 entities, method=yolo) +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SKIPPED (already exists, 244 entities, method=yolo) +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SKIPPED (already exists, 22 entities, method=unknown) +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SKIPPED (already exists, 33 entities, method=unknown) +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SKIPPED (already exists, 606 entities, method=unknown) +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SKIPPED (already exists, 111 entities, method=yolo) +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SKIPPED (already exists, 10 entities, method=unknown) +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SKIPPED (already exists, 4 entities, method=unknown) +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SKIPPED (already exists, 758 entities, method=unknown) +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SKIPPED (already exists, 43 entities, method=yolo) +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ DRY-RUN OK +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SKIPPED (already exists, 399 entities, method=unknown) +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SKIPPED (already exists, 21 entities, method=yolo) +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ DRY-RUN OK +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SKIPPED (already exists, 8 entities, method=unknown) +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SKIPPED (already exists, 843 entities, method=unknown) +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SKIPPED (already exists, 23 entities, method=yolo) +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SKIPPED (already exists, 12 entities, method=unknown) +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SKIPPED (already exists, 22 entities, method=unknown) +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SKIPPED (already exists, 1966 entities, method=unknown) +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SKIPPED (already exists, 58 entities, method=yolo) +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ DRY-RUN OK +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SKIPPED (already exists, 19 entities, method=segformer) +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SKIPPED (already exists, 285 entities, method=unknown) +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SKIPPED (already exists, 48 entities, method=yolo) +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SKIPPED (already exists, 25 entities, method=unknown) +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SKIPPED (already exists, 20 entities, method=yolo) +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SKIPPED (already exists, 46 entities, method=yolo) +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SKIPPED (already exists, 15 entities, method=unknown) +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SKIPPED (already exists, 36 entities, method=unknown) +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ DRY-RUN OK +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SKIPPED (already exists, 1460 entities, method=unknown) +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SKIPPED (already exists, 12 entities, method=yolo) +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SKIPPED (already exists, 21 entities, method=unknown) +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SKIPPED (already exists, 45 entities, method=unknown) +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SKIPPED (already exists, 977 entities, method=unknown) +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SKIPPED (already exists, 24 entities, method=segformer) +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SKIPPED (already exists, 14 entities, method=unknown) +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SKIPPED (already exists, 660 entities, method=unknown) +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ DRY-RUN OK +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SKIPPED (already exists, 14 entities, method=yolo) +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ DRY-RUN OK +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ DRY-RUN OK +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SKIPPED (already exists, 275 entities, method=unknown) +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ DRY-RUN OK +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SKIPPED (already exists, 80 entities, method=unknown) +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SKIPPED (already exists, 2234 entities, method=unknown) +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SKIPPED (already exists, 3 entities, method=yolo) +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SKIPPED (already exists, 32 entities, method=unknown) +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SKIPPED (already exists, 264 entities, method=segformer) +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SKIPPED (already exists, 12 entities, method=yolo) +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SKIPPED (already exists, 39 entities, method=yolo) +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SKIPPED (already exists, 4 entities, method=yolo) +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SKIPPED (already exists, 150 entities, method=yolo) +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SKIPPED (already exists, 36 entities, method=unknown) +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SKIPPED (already exists, 1177 entities, method=unknown) +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SKIPPED (already exists, 10 entities, method=unknown) +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SKIPPED (already exists, 166 entities, method=unknown) +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SKIPPED (already exists, 6 entities, method=unknown) +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SKIPPED (already exists, 1466 entities, method=unknown) +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SKIPPED (already exists, 2 entities, method=unknown) +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SKIPPED (already exists, 10 entities, method=unknown) +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SKIPPED (already exists, 2 entities, method=unknown) +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SKIPPED (already exists, 1161 entities, method=unknown) +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ DRY-RUN OK +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SKIPPED (already exists, 46 entities, method=unknown) +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SKIPPED (already exists, 13 entities, method=unknown) +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SKIPPED (already exists, 1952 entities, method=unknown) +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SKIPPED (already exists, 16 entities, method=unknown) +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SKIPPED (already exists, 103 entities, method=unknown) +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SKIPPED (already exists, 13 entities, method=unknown) +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SKIPPED (already exists, 74 entities, method=yolo) +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SKIPPED (already exists, 222 entities, method=segformer) +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SKIPPED (already exists, 8 entities, method=yolo) +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SKIPPED (already exists, 21 entities, method=unknown) +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SKIPPED (already exists, 77 entities, method=yolo) +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SKIPPED (already exists, 1269 entities, method=unknown) +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SKIPPED (already exists, 15 entities, method=unknown) +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SKIPPED (already exists, 20 entities, method=yolo) +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SKIPPED (already exists, 888 entities, method=unknown) +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ DRY-RUN OK +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SKIPPED (already exists, 2 entities, method=unknown) +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SKIPPED (already exists, 95 entities, method=yolo) +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SKIPPED (already exists, 811 entities, method=unknown) +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ DRY-RUN OK +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SKIPPED (already exists, 3 entities, method=unknown) +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SKIPPED (already exists, 3 entities, method=yolo) +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SKIPPED (already exists, 794 entities, method=unknown) +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SKIPPED (already exists, 6 entities, method=yolo) +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SKIPPED (already exists, 27 entities, method=unknown) +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SKIPPED (already exists, 15 entities, method=yolo) +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SKIPPED (already exists, 1812 entities, method=unknown) +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SKIPPED (already exists, 4 entities, method=yolo) +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SKIPPED (already exists, 27 entities, method=unknown) +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SKIPPED (already exists, 29 entities, method=unknown) +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SKIPPED (already exists, 64 entities, method=yolo) +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SKIPPED (already exists, 367 entities, method=unknown) +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SKIPPED (already exists, 22 entities, method=unknown) +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SKIPPED (already exists, 41 entities, method=unknown) +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SKIPPED (already exists, 47 entities, method=yolo) +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SKIPPED (already exists, 735 entities, method=unknown) +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ DRY-RUN OK +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SKIPPED (already exists, 17 entities, method=unknown) +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SKIPPED (already exists, 76 entities, method=yolo) +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SKIPPED (already exists, 1086 entities, method=unknown) +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SKIPPED (already exists, 34 entities, method=unknown) +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SKIPPED (already exists, 18 entities, method=yolo) +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SKIPPED (already exists, 878 entities, method=unknown) +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SKIPPED (already exists, 10 entities, method=unknown) +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SKIPPED (already exists, 1 entities, method=yolo) +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SKIPPED (already exists, 34 entities, method=unknown) +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SKIPPED (already exists, 92 entities, method=yolo) +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SKIPPED (already exists, 901 entities, method=unknown) +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ DRY-RUN OK +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SKIPPED (already exists, 14 entities, method=unknown) +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SKIPPED (already exists, 11 entities, method=yolo) +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SKIPPED (already exists, 1568 entities, method=unknown) +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ DRY-RUN OK +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SKIPPED (already exists, 51 entities, method=yolo) +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SKIPPED (already exists, 87 entities, method=yolo) +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SKIPPED (already exists, 382 entities, method=unknown) +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SKIPPED (already exists, 16 entities, method=yolo) +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SKIPPED (already exists, 52 entities, method=unknown) +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SKIPPED (already exists, 733 entities, method=unknown) +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SKIPPED (already exists, 10 entities, method=yolo) +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SKIPPED (already exists, 81 entities, method=yolo) +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SKIPPED (already exists, 54 entities, method=unknown) +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SKIPPED (already exists, 285 entities, method=unknown) +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ DRY-RUN OK +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SKIPPED (already exists, 243 entities, method=segformer) +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SKIPPED (already exists, 46 entities, method=unknown) +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SKIPPED (already exists, 285 entities, method=unknown) +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ DRY-RUN OK +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SKIPPED (already exists, 132 entities, method=yolo) +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SKIPPED (already exists, 117 entities, method=unknown) +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SKIPPED (already exists, 415 entities, method=unknown) +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ DRY-RUN OK +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SKIPPED (already exists, 119 entities, method=yolo) +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SKIPPED (already exists, 66 entities, method=unknown) +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SKIPPED (already exists, 258 entities, method=unknown) +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ DRY-RUN OK +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SKIPPED (already exists, 194 entities, method=yolo) +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SKIPPED (already exists, 25 entities, method=unknown) +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SKIPPED (already exists, 820 entities, method=unknown) +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ DRY-RUN OK +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SKIPPED (already exists, 212 entities, method=yolo) +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SKIPPED (already exists, 8 entities, method=unknown) +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SKIPPED (already exists, 96 entities, method=unknown) +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ DRY-RUN OK +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SKIPPED (already exists, 2 entities, method=unknown) +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SKIPPED (already exists, 4 entities, method=unknown) +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SKIPPED (already exists, 745 entities, method=unknown) +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SKIPPED (already exists, 1 entities, method=yolo) +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SKIPPED (already exists, 36 entities, method=unknown) +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SKIPPED (already exists, 702 entities, method=unknown) +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ DRY-RUN OK +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SKIPPED (already exists, 57 entities, method=unknown) +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SKIPPED (already exists, 20 entities, method=unknown) +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SKIPPED (already exists, 669 entities, method=unknown) +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ DRY-RUN OK +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SKIPPED (already exists, 188 entities, method=unknown) +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SKIPPED (already exists, 36 entities, method=unknown) +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SKIPPED (already exists, 254 entities, method=unknown) +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SKIPPED (already exists, 4 entities, method=unknown) +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SKIPPED (already exists, 88 entities, method=unknown) +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SKIPPED (already exists, 26 entities, method=unknown) +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SKIPPED (already exists, 335 entities, method=unknown) +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ DRY-RUN OK +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SKIPPED (already exists, 204 entities, method=unknown) +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SKIPPED (already exists, 98 entities, method=unknown) +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SKIPPED (already exists, 378 entities, method=unknown) +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ DRY-RUN OK +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SKIPPED (already exists, 164 entities, method=unknown) +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SKIPPED (already exists, 93 entities, method=unknown) +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SKIPPED (already exists, 207 entities, method=unknown) +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ DRY-RUN OK +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SKIPPED (already exists, 139 entities, method=unknown) +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SKIPPED (already exists, 31 entities, method=unknown) +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SKIPPED (already exists, 682 entities, method=unknown) +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ DRY-RUN OK +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SKIPPED (already exists, 84 entities, method=unknown) +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SKIPPED (already exists, 7 entities, method=unknown) +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SKIPPED (already exists, 102 entities, method=unknown) +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ DRY-RUN OK +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SKIPPED (already exists, 14 entities, method=unknown) +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SKIPPED (already exists, 308 entities, method=unknown) +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SKIPPED (already exists, 771 entities, method=unknown) +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SKIPPED (already exists, 18 entities, method=yolo) +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SKIPPED (already exists, 265 entities, method=unknown) +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SKIPPED (already exists, 301 entities, method=unknown) +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SKIPPED (already exists, 761 entities, method=unknown) +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ DRY-RUN OK +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SKIPPED (already exists, 251 entities, method=unknown) +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SKIPPED (already exists, 263 entities, method=unknown) +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SKIPPED (already exists, 429 entities, method=unknown) +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SKIPPED (already exists, 26 entities, method=yolo) +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SKIPPED (already exists, 52 entities, method=unknown) +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SKIPPED (already exists, 228 entities, method=unknown) +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SKIPPED (already exists, 640 entities, method=unknown) +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SKIPPED (already exists, 26 entities, method=unknown) +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SKIPPED (already exists, 130 entities, method=unknown) +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SKIPPED (already exists, 269 entities, method=unknown) +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SKIPPED (already exists, 591 entities, method=unknown) +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ DRY-RUN OK +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SKIPPED (already exists, 226 entities, method=unknown) +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SKIPPED (already exists, 254 entities, method=unknown) +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SKIPPED (already exists, 592 entities, method=unknown) +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SKIPPED (already exists, 13 entities, method=unknown) +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SKIPPED (already exists, 269 entities, method=unknown) +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SKIPPED (already exists, 171 entities, method=unknown) +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SKIPPED (already exists, 832 entities, method=unknown) +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SKIPPED (already exists, 9 entities, method=yolo) +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SKIPPED (already exists, 252 entities, method=unknown) +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SKIPPED (already exists, 243 entities, method=unknown) +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SKIPPED (already exists, 912 entities, method=unknown) +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SKIPPED (already exists, 14 entities, method=yolo) +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SKIPPED (already exists, 252 entities, method=unknown) +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SKIPPED (already exists, 421 entities, method=unknown) +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SKIPPED (already exists, 870 entities, method=unknown) +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SKIPPED (already exists, 6 entities, method=yolo) +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SKIPPED (already exists, 353 entities, method=unknown) +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SKIPPED (already exists, 183 entities, method=unknown) +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SKIPPED (already exists, 625 entities, method=unknown) +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SKIPPED (already exists, 30 entities, method=yolo) +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SKIPPED (already exists, 214 entities, method=unknown) + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 0 +Skipped (already exist): 306 +Failed: 0 +Total entities: 0 +Avg entities/slot: 0.0 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193402.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193431.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193431.log.txt new file mode 100644 index 0000000..b651ed6 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193431.log.txt @@ -0,0 +1,27 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193431.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:34:31 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 1.0h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 1.1h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 1.1h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193523.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193523.log.txt new file mode 100644 index 0000000..9413acc --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193523.log.txt @@ -0,0 +1,1433 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193523.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:35:23 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 1.1h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 1.1h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 1.2h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (23 entities) + Progress: 4/351 (1%), ETA: 1.4h + +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 5/351 (1%), ETA: 1.3h + +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (95 entities) + Progress: 6/351 (1%), ETA: 1.7h + +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (28 entities) + Progress: 7/351 (1%), ETA: 1.8h + +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (58 entities) + Progress: 8/351 (2%), ETA: 1.9h + +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 9/351 (2%), ETA: 1.9h + +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 10/351 (2%), ETA: 2.0h + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (86 entities) + Progress: 11/351 (3%), ETA: 2.1h + +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (164 entities) + Progress: 12/351 (3%), ETA: 2.3h + +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 13/351 (3%), ETA: 2.2h + +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 14/351 (3%), ETA: 2.1h + +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (2 entities) + Progress: 15/351 (4%), ETA: 2.0h + +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (189 entities) + Progress: 16/351 (4%), ETA: 2.2h + +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 17/351 (4%), ETA: 2.1h + +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (11 entities) + Progress: 18/351 (5%), ETA: 2.1h + +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (3 entities) + Progress: 19/351 (5%), ETA: 2.0h + +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (176 entities) + Progress: 20/351 (5%), ETA: 2.1h + +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 21/351 (5%), ETA: 2.0h + +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 22/351 (6%), ETA: 2.0h + +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (17 entities) + Progress: 23/351 (6%), ETA: 2.0h + +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (148 entities) + Progress: 24/351 (6%), ETA: 2.1h + +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (9 entities) + Progress: 25/351 (7%), ETA: 2.1h + +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 26/351 (7%), ETA: 2.1h + +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (222 entities) + Progress: 27/351 (7%), ETA: 2.2h + +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (202 entities) + Progress: 28/351 (7%), ETA: 2.2h + +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (37 entities) + Progress: 29/351 (8%), ETA: 2.2h + +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (18 entities) + Progress: 30/351 (8%), ETA: 2.2h + +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (201 entities) + Progress: 31/351 (8%), ETA: 2.3h + +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (98 entities) + Progress: 32/351 (9%), ETA: 2.3h + +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (13 entities) + Progress: 33/351 (9%), ETA: 2.2h + +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (17 entities) + Progress: 34/351 (9%), ETA: 2.2h + +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (133 entities) + Progress: 35/351 (9%), ETA: 2.3h + +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (87 entities) + Progress: 36/351 (10%), ETA: 2.2h + +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (24 entities) + Progress: 37/351 (10%), ETA: 2.2h + +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (5 entities) + Progress: 38/351 (10%), ETA: 2.2h + +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (250 entities) + Progress: 39/351 (11%), ETA: 2.2h + +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (267 entities) + Progress: 40/351 (11%), ETA: 2.3h + +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (256 entities) + Progress: 41/351 (11%), ETA: 2.3h + +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 42/351 (11%), ETA: 2.3h + +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (477 entities) + Progress: 43/351 (12%), ETA: 2.4h + +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (288 entities) + Progress: 44/351 (12%), ETA: 2.5h + +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (130 entities) + Progress: 45/351 (12%), ETA: 2.5h + +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (5 entities) + Progress: 46/351 (13%), ETA: 2.5h + +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (270 entities) + Progress: 47/351 (13%), ETA: 2.5h + +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (374 entities) + Progress: 48/351 (13%), ETA: 2.6h + +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (101 entities) + Progress: 49/351 (13%), ETA: 2.6h + +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 50/351 (14%), ETA: 2.6h + +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (298 entities) + Progress: 51/351 (14%), ETA: 2.6h + +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (277 entities) + Progress: 52/351 (14%), ETA: 2.7h + +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (124 entities) + Progress: 53/351 (15%), ETA: 2.6h + +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SUCCESS (9 entities) + Progress: 54/351 (15%), ETA: 2.6h + +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SUCCESS (260 entities) + Progress: 55/351 (15%), ETA: 2.7h + +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SUCCESS (332 entities) + Progress: 56/351 (15%), ETA: 2.7h + +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SUCCESS (168 entities) + Progress: 57/351 (16%), ETA: 2.7h + +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SUCCESS (16 entities) + Progress: 58/351 (16%), ETA: 2.7h + +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SUCCESS (509 entities) + Progress: 59/351 (16%), ETA: 2.8h + +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SUCCESS (365 entities) + Progress: 60/351 (17%), ETA: 2.8h + +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SUCCESS (156 entities) + Progress: 61/351 (17%), ETA: 2.8h + +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SUCCESS (13 entities) + Progress: 62/351 (17%), ETA: 2.8h + +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SUCCESS (202 entities) + Progress: 63/351 (17%), ETA: 2.8h + +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SUCCESS (301 entities) + Progress: 64/351 (18%), ETA: 2.8h + +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SUCCESS (82 entities) + Progress: 65/351 (18%), ETA: 2.8h + +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SUCCESS (11 entities) + Progress: 66/351 (18%), ETA: 2.8h + +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SUCCESS (1153 entities) + Progress: 67/351 (19%), ETA: 2.8h + +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SUCCESS (47 entities) + Progress: 68/351 (19%), ETA: 2.8h + +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SUCCESS (68 entities) + Progress: 69/351 (19%), ETA: 2.8h + +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 70/351 (19%), ETA: 2.8h + +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SUCCESS (3132 entities) + Progress: 71/351 (20%), ETA: 2.9h + +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SUCCESS (16 entities) + Progress: 72/351 (20%), ETA: 2.9h + +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SUCCESS (35 entities) + Progress: 73/351 (20%), ETA: 2.8h + +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 74/351 (21%), ETA: 2.8h + +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SUCCESS (760 entities) + Progress: 75/351 (21%), ETA: 2.8h + +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 76/351 (21%), ETA: 2.8h + +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SUCCESS (7 entities) + Progress: 77/351 (21%), ETA: 2.8h + +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ SUCCESS (0 entities) + Progress: 78/351 (22%), ETA: 2.7h + +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SUCCESS (305 entities) + Progress: 79/351 (22%), ETA: 2.7h + +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/351 (22%), ETA: 2.7h + +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SUCCESS (4 entities) + Progress: 81/351 (23%), ETA: 2.7h + +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 82/351 (23%), ETA: 2.6h + +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SUCCESS (235 entities) + Progress: 83/351 (23%), ETA: 2.6h + +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SUCCESS (7 entities) + Progress: 84/351 (23%), ETA: 2.6h + +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SUCCESS (12 entities) + Progress: 85/351 (24%), ETA: 2.6h + +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 86/351 (24%), ETA: 2.5h + +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SUCCESS (953 entities) + Progress: 87/351 (24%), ETA: 2.6h + +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 88/351 (25%), ETA: 2.6h + +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SUCCESS (11 entities) + Progress: 89/351 (25%), ETA: 2.5h + +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SUCCESS (4 entities) + Progress: 90/351 (25%), ETA: 2.5h + +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SUCCESS (679 entities) + Progress: 91/351 (25%), ETA: 2.6h + +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SUCCESS (24 entities) + Progress: 92/351 (26%), ETA: 2.6h + +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SUCCESS (47 entities) + Progress: 93/351 (26%), ETA: 2.5h + +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SUCCESS (467 entities) + Progress: 94/351 (26%), ETA: 2.6h + +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SUCCESS (24 entities) + Progress: 95/351 (27%), ETA: 2.6h + +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SUCCESS (12 entities) + Progress: 96/351 (27%), ETA: 2.5h + +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SUCCESS (9 entities) + Progress: 97/351 (27%), ETA: 2.5h + +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SUCCESS (1799 entities) + Progress: 98/351 (27%), ETA: 2.5h + +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SUCCESS (22 entities) + Progress: 99/351 (28%), ETA: 2.5h + +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SUCCESS (26 entities) + Progress: 100/351 (28%), ETA: 2.5h + +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SUCCESS (1 entities) + Progress: 101/351 (28%), ETA: 2.5h + +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SUCCESS (1199 entities) + Progress: 102/351 (29%), ETA: 2.5h + +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SUCCESS (4 entities) + Progress: 103/351 (29%), ETA: 2.5h + +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SUCCESS (11 entities) + Progress: 104/351 (29%), ETA: 2.5h + +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SUCCESS (3 entities) + Progress: 105/351 (29%), ETA: 2.4h + +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SUCCESS (689 entities) + Progress: 106/351 (30%), ETA: 2.5h + +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SUCCESS (4 entities) + Progress: 107/351 (30%), ETA: 2.4h + +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ SUCCESS (0 entities) + Progress: 108/351 (30%), ETA: 2.4h + +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 109/351 (31%), ETA: 2.4h + +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SUCCESS (318 entities) + Progress: 110/351 (31%), ETA: 2.4h + +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SUCCESS (2 entities) + Progress: 111/351 (31%), ETA: 2.4h + +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ SUCCESS (0 entities) + Progress: 112/351 (31%), ETA: 2.3h + +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 113/351 (32%), ETA: 2.3h + +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SUCCESS (330 entities) + Progress: 114/351 (32%), ETA: 2.3h + +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ SUCCESS (0 entities) + Progress: 115/351 (32%), ETA: 2.3h + +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 116/351 (33%), ETA: 2.3h + +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ SUCCESS (0 entities) + Progress: 117/351 (33%), ETA: 2.2h + +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SUCCESS (993 entities) + Progress: 118/351 (33%), ETA: 2.3h + +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SUCCESS (2 entities) + Progress: 119/351 (33%), ETA: 2.2h + +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SUCCESS (2 entities) + Progress: 120/351 (34%), ETA: 2.2h + +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SUCCESS (1 entities) + Progress: 121/351 (34%), ETA: 2.2h + +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SUCCESS (2098 entities) + Progress: 122/351 (34%), ETA: 2.2h + +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SUCCESS (4 entities) + Progress: 123/351 (35%), ETA: 2.2h + +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SUCCESS (236 entities) + Progress: 124/351 (35%), ETA: 2.2h + +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SUCCESS (8 entities) + Progress: 125/351 (35%), ETA: 2.2h + +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SUCCESS (3 entities) + Progress: 126/351 (35%), ETA: 2.2h + +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SUCCESS (300 entities) + Progress: 127/351 (36%), ETA: 2.2h + +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SUCCESS (54 entities) + Progress: 128/351 (36%), ETA: 2.2h + +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SUCCESS (5 entities) + Progress: 129/351 (36%), ETA: 2.1h + +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 130/351 (37%), ETA: 2.1h + +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SUCCESS (598 entities) + Progress: 131/351 (37%), ETA: 2.1h + +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SUCCESS (14 entities) + Progress: 132/351 (37%), ETA: 2.1h + +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 133/351 (37%), ETA: 2.1h + +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 134/351 (38%), ETA: 2.1h + +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SUCCESS (278 entities) + Progress: 135/351 (38%), ETA: 2.1h + +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SUCCESS (16 entities) + Progress: 136/351 (38%), ETA: 2.1h + +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 137/351 (39%), ETA: 2.0h + +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 138/351 (39%), ETA: 2.0h + +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SUCCESS (748 entities) + Progress: 139/351 (39%), ETA: 2.0h + +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SUCCESS (8 entities) + Progress: 140/351 (39%), ETA: 2.0h + +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SUCCESS (5 entities) + Progress: 141/351 (40%), ETA: 2.0h + +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 142/351 (40%), ETA: 2.0h + +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SUCCESS (1596 entities) + Progress: 143/351 (40%), ETA: 2.0h + +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SUCCESS (8 entities) + Progress: 144/351 (41%), ETA: 2.0h + +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 145/351 (41%), ETA: 2.0h + +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SUCCESS (5 entities) + Progress: 146/351 (41%), ETA: 2.0h + +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SUCCESS (139 entities) + Progress: 147/351 (41%), ETA: 2.0h + +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SUCCESS (29 entities) + Progress: 148/351 (42%), ETA: 1.9h + +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SUCCESS (17 entities) + Progress: 149/351 (42%), ETA: 1.9h + +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SUCCESS (20 entities) + Progress: 150/351 (42%), ETA: 1.9h + +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SUCCESS (45 entities) + Progress: 151/351 (43%), ETA: 1.9h + +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SUCCESS (15 entities) + Progress: 152/351 (43%), ETA: 1.9h + +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SUCCESS (26 entities) + Progress: 153/351 (43%), ETA: 1.9h + +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 154/351 (43%), ETA: 1.9h + +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SUCCESS (1258 entities) + Progress: 155/351 (44%), ETA: 1.9h + +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SUCCESS (12 entities) + Progress: 156/351 (44%), ETA: 1.9h + +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SUCCESS (7 entities) + Progress: 157/351 (44%), ETA: 1.8h + +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 158/351 (45%), ETA: 1.8h + +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SUCCESS (752 entities) + Progress: 159/351 (45%), ETA: 1.8h + +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SUCCESS (24 entities) + Progress: 160/351 (45%), ETA: 1.8h + +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SUCCESS (7 entities) + Progress: 161/351 (45%), ETA: 1.8h + +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SUCCESS (498 entities) + Progress: 162/351 (46%), ETA: 1.8h + +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 163/351 (46%), ETA: 1.8h + +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SUCCESS (11 entities) + Progress: 164/351 (46%), ETA: 1.8h + +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 165/351 (47%), ETA: 1.8h + +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ SUCCESS (0 entities) + Progress: 166/351 (47%), ETA: 1.7h + +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SUCCESS (206 entities) + Progress: 167/351 (47%), ETA: 1.7h + +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 168/351 (47%), ETA: 1.7h + +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 169/351 (48%), ETA: 1.7h + +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SUCCESS (0 entities) + Progress: 170/351 (48%), ETA: 1.7h + +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SUCCESS (1762 entities) + Progress: 171/351 (48%), ETA: 1.7h + +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SUCCESS (3 entities) + Progress: 172/351 (49%), ETA: 1.7h + +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SUCCESS (21 entities) + Progress: 173/351 (49%), ETA: 1.7h + +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SUCCESS (579 entities) + Progress: 174/351 (49%), ETA: 1.7h + +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SUCCESS (12 entities) + Progress: 175/351 (49%), ETA: 1.7h + +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SUCCESS (34 entities) + Progress: 176/351 (50%), ETA: 1.7h + +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SUCCESS (4 entities) + Progress: 177/351 (50%), ETA: 1.6h + +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SUCCESS (146 entities) + Progress: 178/351 (50%), ETA: 1.6h + +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SUCCESS (13 entities) + Progress: 179/351 (50%), ETA: 1.6h + +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SUCCESS (755 entities) + Progress: 180/351 (51%), ETA: 1.6h + +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SUCCESS (10 entities) + Progress: 181/351 (51%), ETA: 1.6h + +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SUCCESS (34 entities) + Progress: 182/351 (51%), ETA: 1.6h + +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SUCCESS (3 entities) + Progress: 183/351 (52%), ETA: 1.6h + +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SUCCESS (1355 entities) + Progress: 184/351 (52%), ETA: 1.6h + +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SUCCESS (2 entities) + Progress: 185/351 (52%), ETA: 1.6h + +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SUCCESS (0 entities) + Progress: 186/351 (52%), ETA: 1.6h + +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 187/351 (53%), ETA: 1.6h + +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SUCCESS (1043 entities) + Progress: 188/351 (53%), ETA: 1.6h + +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 189/351 (53%), ETA: 1.6h + +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SUCCESS (6 entities) + Progress: 190/351 (54%), ETA: 1.5h + +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SUCCESS (3 entities) + Progress: 191/351 (54%), ETA: 1.5h + +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SUCCESS (1684 entities) + Progress: 192/351 (54%), ETA: 1.5h + +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SUCCESS (16 entities) + Progress: 193/351 (54%), ETA: 1.5h + +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SUCCESS (46 entities) + Progress: 194/351 (55%), ETA: 1.5h + +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SUCCESS (9 entities) + Progress: 195/351 (55%), ETA: 1.5h + +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SUCCESS (13 entities) + Progress: 196/351 (55%), ETA: 1.5h + +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SUCCESS (144 entities) + Progress: 197/351 (56%), ETA: 1.5h + +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SUCCESS (8 entities) + Progress: 198/351 (56%), ETA: 1.5h + +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SUCCESS (15 entities) + Progress: 199/351 (56%), ETA: 1.4h + +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SUCCESS (49 entities) + Progress: 200/351 (56%), ETA: 1.4h + +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SUCCESS (555 entities) + Progress: 201/351 (57%), ETA: 1.4h + +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SUCCESS (15 entities) + Progress: 202/351 (57%), ETA: 1.4h + +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 203/351 (57%), ETA: 1.4h + +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 204/351 (58%), ETA: 1.4h + +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SUCCESS (601 entities) + Progress: 205/351 (58%), ETA: 1.4h + +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 206/351 (58%), ETA: 1.4h + +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SUCCESS (1 entities) + Progress: 207/351 (58%), ETA: 1.4h + +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SUCCESS (93 entities) + Progress: 208/351 (59%), ETA: 1.4h + +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SUCCESS (573 entities) + Progress: 209/351 (59%), ETA: 1.3h + +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 210/351 (59%), ETA: 1.3h + +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 211/351 (60%), ETA: 1.3h + +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 212/351 (60%), ETA: 1.3h + +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SUCCESS (619 entities) + Progress: 213/351 (60%), ETA: 1.3h + +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 214/351 (60%), ETA: 1.3h + +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SUCCESS (20 entities) + Progress: 215/351 (61%), ETA: 1.3h + +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SUCCESS (13 entities) + Progress: 216/351 (61%), ETA: 1.3h + +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SUCCESS (1380 entities) + Progress: 217/351 (61%), ETA: 1.3h + +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SUCCESS (4 entities) + Progress: 218/351 (62%), ETA: 1.3h + +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SUCCESS (12 entities) + Progress: 219/351 (62%), ETA: 1.2h + +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SUCCESS (28 entities) + Progress: 220/351 (62%), ETA: 1.2h + +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SUCCESS (14 entities) + Progress: 221/351 (62%), ETA: 1.2h + +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SUCCESS (106 entities) + Progress: 222/351 (63%), ETA: 1.2h + +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SUCCESS (7 entities) + Progress: 223/351 (63%), ETA: 1.2h + +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SUCCESS (39 entities) + Progress: 224/351 (63%), ETA: 1.2h + +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SUCCESS (27 entities) + Progress: 225/351 (64%), ETA: 1.2h + +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SUCCESS (304 entities) + Progress: 226/351 (64%), ETA: 1.2h + +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 227/351 (64%), ETA: 1.2h + +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SUCCESS (17 entities) + Progress: 228/351 (64%), ETA: 1.1h + +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SUCCESS (39 entities) + Progress: 229/351 (65%), ETA: 1.1h + +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SUCCESS (756 entities) + Progress: 230/351 (65%), ETA: 1.1h + +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SUCCESS (30 entities) + Progress: 231/351 (65%), ETA: 1.1h + +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SUCCESS (13 entities) + Progress: 232/351 (66%), ETA: 1.1h + +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SUCCESS (708 entities) + Progress: 233/351 (66%), ETA: 1.1h + +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SUCCESS (5 entities) + Progress: 234/351 (66%), ETA: 1.1h + +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SUCCESS (1 entities) + Progress: 235/351 (66%), ETA: 1.1h + +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SUCCESS (32 entities) + Progress: 236/351 (67%), ETA: 1.1h + +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SUCCESS (83 entities) + Progress: 237/351 (67%), ETA: 1.1h + +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SUCCESS (651 entities) + Progress: 238/351 (67%), ETA: 1.1h + +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 239/351 (68%), ETA: 1.0h + +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SUCCESS (14 entities) + Progress: 240/351 (68%), ETA: 1.0h + +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SUCCESS (9 entities) + Progress: 241/351 (68%), ETA: 1.0h + +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SUCCESS (1093 entities) + Progress: 242/351 (68%), ETA: 1.0h + +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 243/351 (69%), ETA: 1.0h + +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SUCCESS (43 entities) + Progress: 244/351 (69%), ETA: 1.0h + +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SUCCESS (47 entities) + Progress: 245/351 (69%), ETA: 1.0h + +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SUCCESS (65 entities) + Progress: 246/351 (70%), ETA: 1.0h + +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SUCCESS (16 entities) + Progress: 247/351 (70%), ETA: 1.0h + +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SUCCESS (21 entities) + Progress: 248/351 (70%), ETA: 1.0h + +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SUCCESS (490 entities) + Progress: 249/351 (70%), ETA: 1.0h + +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SUCCESS (10 entities) + Progress: 250/351 (71%), ETA: 0.9h + +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SUCCESS (77 entities) + Progress: 251/351 (71%), ETA: 0.9h + +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SUCCESS (31 entities) + Progress: 252/351 (71%), ETA: 0.9h + +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SUCCESS (35 entities) + Progress: 253/351 (72%), ETA: 0.9h + +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 254/351 (72%), ETA: 0.9h + +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SUCCESS (243 entities) + Progress: 255/351 (72%), ETA: 0.9h + +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SUCCESS (36 entities) + Progress: 256/351 (72%), ETA: 0.9h + +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SUCCESS (33 entities) + Progress: 257/351 (73%), ETA: 0.9h + +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 258/351 (73%), ETA: 0.9h + +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SUCCESS (129 entities) + Progress: 259/351 (73%), ETA: 0.8h + +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SUCCESS (88 entities) + Progress: 260/351 (74%), ETA: 0.8h + +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SUCCESS (66 entities) + Progress: 261/351 (74%), ETA: 0.8h + +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 262/351 (74%), ETA: 0.8h + +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SUCCESS (111 entities) + Progress: 263/351 (74%), ETA: 0.8h + +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SUCCESS (47 entities) + Progress: 264/351 (75%), ETA: 0.8h + +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SUCCESS (15 entities) + Progress: 265/351 (75%), ETA: 0.8h + +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 266/351 (75%), ETA: 0.8h + +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SUCCESS (185 entities) + Progress: 267/351 (76%), ETA: 0.8h + +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SUCCESS (4 entities) + Progress: 268/351 (76%), ETA: 0.8h + +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SUCCESS (368 entities) + Progress: 269/351 (76%), ETA: 0.7h + +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 270/351 (76%), ETA: 0.7h + +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SUCCESS (131 entities) + Progress: 271/351 (77%), ETA: 0.7h + +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SUCCESS (4 entities) + Progress: 272/351 (77%), ETA: 0.7h + +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SUCCESS (47 entities) + Progress: 273/351 (77%), ETA: 0.7h + +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 274/351 (78%), ETA: 0.7h + +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SUCCESS (2 entities) + Progress: 275/351 (78%), ETA: 0.7h + +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SUCCESS (1 entities) + Progress: 276/351 (78%), ETA: 0.7h + +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SUCCESS (485 entities) + Progress: 277/351 (78%), ETA: 0.7h + +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SUCCESS (1 entities) + Progress: 278/351 (79%), ETA: 0.7h + +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SUCCESS (2 entities) + Progress: 279/351 (79%), ETA: 0.6h + +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SUCCESS (16 entities) + Progress: 280/351 (79%), ETA: 0.6h + +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SUCCESS (454 entities) + Progress: 281/351 (80%), ETA: 0.6h + +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 282/351 (80%), ETA: 0.6h + +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SUCCESS (0 entities) + Progress: 283/351 (80%), ETA: 0.6h + +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 284/351 (80%), ETA: 0.6h + +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SUCCESS (406 entities) + Progress: 285/351 (81%), ETA: 0.6h + +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 286/351 (81%), ETA: 0.6h + +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SUCCESS (0 entities) + Progress: 287/351 (81%), ETA: 0.6h + +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SUCCESS (11 entities) + Progress: 288/351 (82%), ETA: 0.6h + +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SUCCESS (60 entities) + Progress: 289/351 (82%), ETA: 0.6h + +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SUCCESS (4 entities) + Progress: 290/351 (82%), ETA: 0.5h + +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 291/351 (82%), ETA: 0.5h + +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SUCCESS (19 entities) + Progress: 292/351 (83%), ETA: 0.5h + +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SUCCESS (83 entities) + Progress: 293/351 (83%), ETA: 0.5h + +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 294/351 (83%), ETA: 0.5h + +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SUCCESS (0 entities) + Progress: 295/351 (84%), ETA: 0.5h + +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SUCCESS (81 entities) + Progress: 296/351 (84%), ETA: 0.5h + +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SUCCESS (101 entities) + Progress: 297/351 (84%), ETA: 0.5h + +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 298/351 (84%), ETA: 0.5h + +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SUCCESS (4 entities) + Progress: 299/351 (85%), ETA: 0.5h + +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SUCCESS (18 entities) + Progress: 300/351 (85%), ETA: 0.5h + +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SUCCESS (49 entities) + Progress: 301/351 (85%), ETA: 0.4h + +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 302/351 (86%), ETA: 0.4h + +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SUCCESS (30 entities) + Progress: 303/351 (86%), ETA: 0.4h + +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 304/351 (86%), ETA: 0.4h + +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SUCCESS (401 entities) + Progress: 305/351 (86%), ETA: 0.4h + +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 306/351 (87%), ETA: 0.4h + +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SUCCESS (0 entities) + Progress: 307/351 (87%), ETA: 0.4h + +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 308/351 (87%), ETA: 0.4h + +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SUCCESS (72 entities) + Progress: 309/351 (88%), ETA: 0.4h + +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 310/351 (88%), ETA: 0.4h + +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SUCCESS (0 entities) + Progress: 311/351 (88%), ETA: 0.3h + +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SUCCESS (147 entities) + Progress: 312/351 (88%), ETA: 0.3h + +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SUCCESS (551 entities) + Progress: 313/351 (89%), ETA: 0.3h + +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SUCCESS (18 entities) + Progress: 314/351 (89%), ETA: 0.3h + +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SUCCESS (104 entities) + Progress: 315/351 (89%), ETA: 0.3h + +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SUCCESS (174 entities) + Progress: 316/351 (90%), ETA: 0.3h + +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SUCCESS (544 entities) + Progress: 317/351 (90%), ETA: 0.3h + +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 318/351 (90%), ETA: 0.3h + +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SUCCESS (89 entities) + Progress: 319/351 (90%), ETA: 0.3h + +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SUCCESS (154 entities) + Progress: 320/351 (91%), ETA: 0.3h + +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SUCCESS (264 entities) + Progress: 321/351 (91%), ETA: 0.3h + +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SUCCESS (26 entities) + Progress: 322/351 (91%), ETA: 0.3h + +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SUCCESS (52 entities) + Progress: 323/351 (92%), ETA: 0.2h + +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SUCCESS (160 entities) + Progress: 324/351 (92%), ETA: 0.2h + +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SUCCESS (393 entities) + Progress: 325/351 (92%), ETA: 0.2h + +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SUCCESS (26 entities) + Progress: 326/351 (92%), ETA: 0.2h + +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SUCCESS (126 entities) + Progress: 327/351 (93%), ETA: 0.2h + +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SUCCESS (161 entities) + Progress: 328/351 (93%), ETA: 0.2h + +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SUCCESS (465 entities) + Progress: 329/351 (93%), ETA: 0.2h + +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 330/351 (94%), ETA: 0.2h + +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SUCCESS (55 entities) + Progress: 331/351 (94%), ETA: 0.2h + +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SUCCESS (100 entities) + Progress: 332/351 (94%), ETA: 0.2h + +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SUCCESS (402 entities) + Progress: 333/351 (94%), ETA: 0.2h + +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SUCCESS (13 entities) + Progress: 334/351 (95%), ETA: 0.2h + +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SUCCESS (121 entities) + Progress: 335/351 (95%), ETA: 0.1h + +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SUCCESS (105 entities) + Progress: 336/351 (95%), ETA: 0.1h + +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SUCCESS (607 entities) + Progress: 337/351 (96%), ETA: 0.1h + +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SUCCESS (9 entities) + Progress: 338/351 (96%), ETA: 0.1h + +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SUCCESS (66 entities) + Progress: 339/351 (96%), ETA: 0.1h + +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SUCCESS (129 entities) + Progress: 340/351 (96%), ETA: 0.1h + +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SUCCESS (626 entities) + Progress: 341/351 (97%), ETA: 0.1h + +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SUCCESS (14 entities) + Progress: 342/351 (97%), ETA: 0.1h + +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SUCCESS (58 entities) + Progress: 343/351 (97%), ETA: 0.1h + +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SUCCESS (197 entities) + Progress: 344/351 (98%), ETA: 0.1h + +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SUCCESS (631 entities) + Progress: 345/351 (98%), ETA: 0.1h + +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SUCCESS (6 entities) + Progress: 346/351 (98%), ETA: 0.0h + +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SUCCESS (75 entities) + Progress: 347/351 (98%), ETA: 0.0h + +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SUCCESS (79 entities) + Progress: 348/351 (99%), ETA: 0.0h + +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SUCCESS (448 entities) + Progress: 349/351 (99%), ETA: 0.0h + +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SUCCESS (30 entities) + Progress: 350/351 (99%), ETA: 0.0h + +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SUCCESS (73 entities) + Progress: 351/351 (100%), ETA: 0.0h + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 3.2 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193523.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193629.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193629.log.txt new file mode 100644 index 0000000..040cecb --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260227_193629.log.txt @@ -0,0 +1,1433 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193629.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:36:29 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 1.2h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 1.2h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 1.2h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (23 entities) + Progress: 4/351 (1%), ETA: 1.5h + +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 5/351 (1%), ETA: 1.4h + +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (95 entities) + Progress: 6/351 (1%), ETA: 1.8h + +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (28 entities) + Progress: 7/351 (1%), ETA: 1.8h + +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (58 entities) + Progress: 8/351 (2%), ETA: 2.0h + +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 9/351 (2%), ETA: 1.9h + +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 10/351 (2%), ETA: 2.0h + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (86 entities) + Progress: 11/351 (3%), ETA: 2.1h + +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (164 entities) + Progress: 12/351 (3%), ETA: 2.3h + +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 13/351 (3%), ETA: 2.2h + +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 14/351 (3%), ETA: 2.1h + +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (2 entities) + Progress: 15/351 (4%), ETA: 2.1h + +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (189 entities) + Progress: 16/351 (4%), ETA: 2.2h + +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 17/351 (4%), ETA: 2.1h + +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (11 entities) + Progress: 18/351 (5%), ETA: 2.1h + +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (3 entities) + Progress: 19/351 (5%), ETA: 2.0h + +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (176 entities) + Progress: 20/351 (5%), ETA: 2.2h + +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 21/351 (5%), ETA: 2.1h + +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 22/351 (6%), ETA: 2.0h + +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (17 entities) + Progress: 23/351 (6%), ETA: 2.0h + +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (148 entities) + Progress: 24/351 (6%), ETA: 2.1h + +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (9 entities) + Progress: 25/351 (7%), ETA: 2.1h + +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 26/351 (7%), ETA: 2.1h + +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (222 entities) + Progress: 27/351 (7%), ETA: 2.2h + +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (202 entities) + Progress: 28/351 (7%), ETA: 2.2h + +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (37 entities) + Progress: 29/351 (8%), ETA: 2.2h + +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (18 entities) + Progress: 30/351 (8%), ETA: 2.2h + +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (201 entities) + Progress: 31/351 (8%), ETA: 2.3h + +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (98 entities) + Progress: 32/351 (9%), ETA: 2.3h + +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (13 entities) + Progress: 33/351 (9%), ETA: 2.3h + +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (17 entities) + Progress: 34/351 (9%), ETA: 2.2h + +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (133 entities) + Progress: 35/351 (9%), ETA: 2.3h + +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (87 entities) + Progress: 36/351 (10%), ETA: 2.2h + +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (24 entities) + Progress: 37/351 (10%), ETA: 2.2h + +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (5 entities) + Progress: 38/351 (10%), ETA: 2.2h + +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (250 entities) + Progress: 39/351 (11%), ETA: 2.3h + +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (267 entities) + Progress: 40/351 (11%), ETA: 2.3h + +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (256 entities) + Progress: 41/351 (11%), ETA: 2.3h + +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 42/351 (11%), ETA: 2.3h + +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (477 entities) + Progress: 43/351 (12%), ETA: 2.4h + +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (288 entities) + Progress: 44/351 (12%), ETA: 2.5h + +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (130 entities) + Progress: 45/351 (12%), ETA: 2.5h + +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (5 entities) + Progress: 46/351 (13%), ETA: 2.4h + +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (270 entities) + Progress: 47/351 (13%), ETA: 2.5h + +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (374 entities) + Progress: 48/351 (13%), ETA: 2.6h + +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (101 entities) + Progress: 49/351 (13%), ETA: 2.6h + +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 50/351 (14%), ETA: 2.6h + +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (298 entities) + Progress: 51/351 (14%), ETA: 2.6h + +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (277 entities) + Progress: 52/351 (14%), ETA: 2.7h + +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (124 entities) + Progress: 53/351 (15%), ETA: 2.6h + +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SUCCESS (9 entities) + Progress: 54/351 (15%), ETA: 2.6h + +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SUCCESS (260 entities) + Progress: 55/351 (15%), ETA: 2.7h + +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SUCCESS (332 entities) + Progress: 56/351 (15%), ETA: 2.7h + +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SUCCESS (168 entities) + Progress: 57/351 (16%), ETA: 2.7h + +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SUCCESS (16 entities) + Progress: 58/351 (16%), ETA: 2.6h + +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SUCCESS (509 entities) + Progress: 59/351 (16%), ETA: 2.7h + +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SUCCESS (365 entities) + Progress: 60/351 (17%), ETA: 2.8h + +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SUCCESS (156 entities) + Progress: 61/351 (17%), ETA: 2.8h + +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SUCCESS (13 entities) + Progress: 62/351 (17%), ETA: 2.7h + +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SUCCESS (202 entities) + Progress: 63/351 (17%), ETA: 2.7h + +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SUCCESS (301 entities) + Progress: 64/351 (18%), ETA: 2.8h + +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SUCCESS (82 entities) + Progress: 65/351 (18%), ETA: 2.8h + +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SUCCESS (11 entities) + Progress: 66/351 (18%), ETA: 2.7h + +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SUCCESS (1153 entities) + Progress: 67/351 (19%), ETA: 2.8h + +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SUCCESS (47 entities) + Progress: 68/351 (19%), ETA: 2.8h + +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SUCCESS (68 entities) + Progress: 69/351 (19%), ETA: 2.8h + +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 70/351 (19%), ETA: 2.7h + +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SUCCESS (3132 entities) + Progress: 71/351 (20%), ETA: 2.9h + +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SUCCESS (16 entities) + Progress: 72/351 (20%), ETA: 2.8h + +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SUCCESS (35 entities) + Progress: 73/351 (20%), ETA: 2.8h + +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 74/351 (21%), ETA: 2.8h + +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SUCCESS (760 entities) + Progress: 75/351 (21%), ETA: 2.8h + +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 76/351 (21%), ETA: 2.8h + +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SUCCESS (7 entities) + Progress: 77/351 (21%), ETA: 2.7h + +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ SUCCESS (0 entities) + Progress: 78/351 (22%), ETA: 2.7h + +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SUCCESS (305 entities) + Progress: 79/351 (22%), ETA: 2.7h + +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/351 (22%), ETA: 2.7h + +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SUCCESS (4 entities) + Progress: 81/351 (23%), ETA: 2.6h + +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 82/351 (23%), ETA: 2.6h + +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SUCCESS (235 entities) + Progress: 83/351 (23%), ETA: 2.6h + +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SUCCESS (7 entities) + Progress: 84/351 (23%), ETA: 2.6h + +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SUCCESS (12 entities) + Progress: 85/351 (24%), ETA: 2.5h + +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 86/351 (24%), ETA: 2.5h + +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SUCCESS (953 entities) + Progress: 87/351 (24%), ETA: 2.6h + +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 88/351 (25%), ETA: 2.5h + +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SUCCESS (11 entities) + Progress: 89/351 (25%), ETA: 2.5h + +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SUCCESS (4 entities) + Progress: 90/351 (25%), ETA: 2.5h + +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SUCCESS (679 entities) + Progress: 91/351 (25%), ETA: 2.6h + +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SUCCESS (24 entities) + Progress: 92/351 (26%), ETA: 2.5h + +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SUCCESS (47 entities) + Progress: 93/351 (26%), ETA: 2.5h + +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SUCCESS (467 entities) + Progress: 94/351 (26%), ETA: 2.6h + +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SUCCESS (24 entities) + Progress: 95/351 (27%), ETA: 2.5h + +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SUCCESS (12 entities) + Progress: 96/351 (27%), ETA: 2.5h + +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SUCCESS (9 entities) + Progress: 97/351 (27%), ETA: 2.5h + +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SUCCESS (1799 entities) + Progress: 98/351 (27%), ETA: 2.5h + +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SUCCESS (22 entities) + Progress: 99/351 (28%), ETA: 2.5h + +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SUCCESS (26 entities) + Progress: 100/351 (28%), ETA: 2.5h + +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SUCCESS (1 entities) + Progress: 101/351 (28%), ETA: 2.4h + +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SUCCESS (1199 entities) + Progress: 102/351 (29%), ETA: 2.5h + +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SUCCESS (4 entities) + Progress: 103/351 (29%), ETA: 2.5h + +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SUCCESS (11 entities) + Progress: 104/351 (29%), ETA: 2.4h + +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SUCCESS (3 entities) + Progress: 105/351 (29%), ETA: 2.4h + +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SUCCESS (689 entities) + Progress: 106/351 (30%), ETA: 2.4h + +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SUCCESS (4 entities) + Progress: 107/351 (30%), ETA: 2.4h + +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ SUCCESS (0 entities) + Progress: 108/351 (30%), ETA: 2.4h + +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 109/351 (31%), ETA: 2.4h + +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SUCCESS (318 entities) + Progress: 110/351 (31%), ETA: 2.4h + +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SUCCESS (2 entities) + Progress: 111/351 (31%), ETA: 2.3h + +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ SUCCESS (0 entities) + Progress: 112/351 (31%), ETA: 2.3h + +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 113/351 (32%), ETA: 2.3h + +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SUCCESS (330 entities) + Progress: 114/351 (32%), ETA: 2.3h + +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ SUCCESS (0 entities) + Progress: 115/351 (32%), ETA: 2.3h + +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 116/351 (33%), ETA: 2.2h + +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ SUCCESS (0 entities) + Progress: 117/351 (33%), ETA: 2.2h + +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SUCCESS (993 entities) + Progress: 118/351 (33%), ETA: 2.2h + +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SUCCESS (2 entities) + Progress: 119/351 (33%), ETA: 2.2h + +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SUCCESS (2 entities) + Progress: 120/351 (34%), ETA: 2.2h + +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SUCCESS (1 entities) + Progress: 121/351 (34%), ETA: 2.2h + +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SUCCESS (2098 entities) + Progress: 122/351 (34%), ETA: 2.2h + +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SUCCESS (4 entities) + Progress: 123/351 (35%), ETA: 2.2h + +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SUCCESS (236 entities) + Progress: 124/351 (35%), ETA: 2.2h + +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SUCCESS (8 entities) + Progress: 125/351 (35%), ETA: 2.2h + +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SUCCESS (3 entities) + Progress: 126/351 (35%), ETA: 2.2h + +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SUCCESS (300 entities) + Progress: 127/351 (36%), ETA: 2.2h + +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SUCCESS (54 entities) + Progress: 128/351 (36%), ETA: 2.2h + +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SUCCESS (5 entities) + Progress: 129/351 (36%), ETA: 2.1h + +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 130/351 (37%), ETA: 2.1h + +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SUCCESS (598 entities) + Progress: 131/351 (37%), ETA: 2.1h + +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SUCCESS (14 entities) + Progress: 132/351 (37%), ETA: 2.1h + +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 133/351 (37%), ETA: 2.1h + +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 134/351 (38%), ETA: 2.1h + +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SUCCESS (278 entities) + Progress: 135/351 (38%), ETA: 2.1h + +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SUCCESS (16 entities) + Progress: 136/351 (38%), ETA: 2.1h + +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 137/351 (39%), ETA: 2.0h + +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 138/351 (39%), ETA: 2.0h + +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SUCCESS (748 entities) + Progress: 139/351 (39%), ETA: 2.0h + +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SUCCESS (8 entities) + Progress: 140/351 (39%), ETA: 2.0h + +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SUCCESS (5 entities) + Progress: 141/351 (40%), ETA: 2.0h + +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 142/351 (40%), ETA: 2.0h + +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SUCCESS (1596 entities) + Progress: 143/351 (40%), ETA: 2.0h + +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SUCCESS (8 entities) + Progress: 144/351 (41%), ETA: 2.0h + +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 145/351 (41%), ETA: 2.0h + +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SUCCESS (5 entities) + Progress: 146/351 (41%), ETA: 2.0h + +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SUCCESS (139 entities) + Progress: 147/351 (41%), ETA: 2.0h + +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SUCCESS (29 entities) + Progress: 148/351 (42%), ETA: 1.9h + +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SUCCESS (17 entities) + Progress: 149/351 (42%), ETA: 1.9h + +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SUCCESS (20 entities) + Progress: 150/351 (42%), ETA: 1.9h + +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SUCCESS (45 entities) + Progress: 151/351 (43%), ETA: 1.9h + +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SUCCESS (15 entities) + Progress: 152/351 (43%), ETA: 1.9h + +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SUCCESS (26 entities) + Progress: 153/351 (43%), ETA: 1.9h + +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 154/351 (43%), ETA: 1.9h + +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SUCCESS (1258 entities) + Progress: 155/351 (44%), ETA: 1.9h + +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SUCCESS (12 entities) + Progress: 156/351 (44%), ETA: 1.9h + +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SUCCESS (7 entities) + Progress: 157/351 (44%), ETA: 1.8h + +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 158/351 (45%), ETA: 1.8h + +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SUCCESS (752 entities) + Progress: 159/351 (45%), ETA: 1.8h + +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SUCCESS (24 entities) + Progress: 160/351 (45%), ETA: 1.8h + +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SUCCESS (7 entities) + Progress: 161/351 (45%), ETA: 1.8h + +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SUCCESS (498 entities) + Progress: 162/351 (46%), ETA: 1.8h + +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 163/351 (46%), ETA: 1.8h + +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SUCCESS (11 entities) + Progress: 164/351 (46%), ETA: 1.8h + +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 165/351 (47%), ETA: 1.8h + +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ SUCCESS (0 entities) + Progress: 166/351 (47%), ETA: 1.7h + +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SUCCESS (206 entities) + Progress: 167/351 (47%), ETA: 1.7h + +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 168/351 (47%), ETA: 1.7h + +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 169/351 (48%), ETA: 1.7h + +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SUCCESS (0 entities) + Progress: 170/351 (48%), ETA: 1.7h + +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SUCCESS (1762 entities) + Progress: 171/351 (48%), ETA: 1.7h + +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SUCCESS (3 entities) + Progress: 172/351 (49%), ETA: 1.7h + +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SUCCESS (21 entities) + Progress: 173/351 (49%), ETA: 1.7h + +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SUCCESS (579 entities) + Progress: 174/351 (49%), ETA: 1.7h + +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SUCCESS (12 entities) + Progress: 175/351 (49%), ETA: 1.7h + +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SUCCESS (34 entities) + Progress: 176/351 (50%), ETA: 1.7h + +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SUCCESS (4 entities) + Progress: 177/351 (50%), ETA: 1.6h + +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SUCCESS (146 entities) + Progress: 178/351 (50%), ETA: 1.6h + +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SUCCESS (13 entities) + Progress: 179/351 (50%), ETA: 1.6h + +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SUCCESS (755 entities) + Progress: 180/351 (51%), ETA: 1.6h + +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SUCCESS (10 entities) + Progress: 181/351 (51%), ETA: 1.6h + +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SUCCESS (34 entities) + Progress: 182/351 (51%), ETA: 1.6h + +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SUCCESS (3 entities) + Progress: 183/351 (52%), ETA: 1.6h + +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SUCCESS (1355 entities) + Progress: 184/351 (52%), ETA: 1.6h + +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SUCCESS (2 entities) + Progress: 185/351 (52%), ETA: 1.6h + +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SUCCESS (0 entities) + Progress: 186/351 (52%), ETA: 1.6h + +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 187/351 (53%), ETA: 1.5h + +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SUCCESS (1043 entities) + Progress: 188/351 (53%), ETA: 1.6h + +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 189/351 (53%), ETA: 1.5h + +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SUCCESS (6 entities) + Progress: 190/351 (54%), ETA: 1.5h + +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SUCCESS (3 entities) + Progress: 191/351 (54%), ETA: 1.5h + +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SUCCESS (1684 entities) + Progress: 192/351 (54%), ETA: 1.5h + +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SUCCESS (16 entities) + Progress: 193/351 (54%), ETA: 1.5h + +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SUCCESS (46 entities) + Progress: 194/351 (55%), ETA: 1.5h + +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SUCCESS (9 entities) + Progress: 195/351 (55%), ETA: 1.5h + +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SUCCESS (13 entities) + Progress: 196/351 (55%), ETA: 1.5h + +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SUCCESS (144 entities) + Progress: 197/351 (56%), ETA: 1.5h + +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SUCCESS (8 entities) + Progress: 198/351 (56%), ETA: 1.5h + +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SUCCESS (15 entities) + Progress: 199/351 (56%), ETA: 1.4h + +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SUCCESS (49 entities) + Progress: 200/351 (56%), ETA: 1.4h + +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SUCCESS (555 entities) + Progress: 201/351 (57%), ETA: 1.4h + +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SUCCESS (15 entities) + Progress: 202/351 (57%), ETA: 1.4h + +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 203/351 (57%), ETA: 1.4h + +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 204/351 (58%), ETA: 1.4h + +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SUCCESS (601 entities) + Progress: 205/351 (58%), ETA: 1.4h + +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 206/351 (58%), ETA: 1.4h + +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SUCCESS (1 entities) + Progress: 207/351 (58%), ETA: 1.4h + +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SUCCESS (93 entities) + Progress: 208/351 (59%), ETA: 1.3h + +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SUCCESS (573 entities) + Progress: 209/351 (59%), ETA: 1.3h + +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 210/351 (59%), ETA: 1.3h + +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 211/351 (60%), ETA: 1.3h + +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 212/351 (60%), ETA: 1.3h + +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SUCCESS (619 entities) + Progress: 213/351 (60%), ETA: 1.3h + +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 214/351 (60%), ETA: 1.3h + +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SUCCESS (20 entities) + Progress: 215/351 (61%), ETA: 1.3h + +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SUCCESS (13 entities) + Progress: 216/351 (61%), ETA: 1.3h + +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SUCCESS (1380 entities) + Progress: 217/351 (61%), ETA: 1.3h + +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SUCCESS (4 entities) + Progress: 218/351 (62%), ETA: 1.2h + +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SUCCESS (12 entities) + Progress: 219/351 (62%), ETA: 1.2h + +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SUCCESS (28 entities) + Progress: 220/351 (62%), ETA: 1.2h + +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SUCCESS (14 entities) + Progress: 221/351 (62%), ETA: 1.2h + +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SUCCESS (106 entities) + Progress: 222/351 (63%), ETA: 1.2h + +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SUCCESS (7 entities) + Progress: 223/351 (63%), ETA: 1.2h + +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SUCCESS (39 entities) + Progress: 224/351 (63%), ETA: 1.2h + +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SUCCESS (27 entities) + Progress: 225/351 (64%), ETA: 1.2h + +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SUCCESS (304 entities) + Progress: 226/351 (64%), ETA: 1.2h + +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 227/351 (64%), ETA: 1.2h + +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SUCCESS (17 entities) + Progress: 228/351 (64%), ETA: 1.1h + +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SUCCESS (39 entities) + Progress: 229/351 (65%), ETA: 1.1h + +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SUCCESS (756 entities) + Progress: 230/351 (65%), ETA: 1.1h + +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SUCCESS (30 entities) + Progress: 231/351 (65%), ETA: 1.1h + +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SUCCESS (13 entities) + Progress: 232/351 (66%), ETA: 1.1h + +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SUCCESS (708 entities) + Progress: 233/351 (66%), ETA: 1.1h + +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SUCCESS (5 entities) + Progress: 234/351 (66%), ETA: 1.1h + +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SUCCESS (1 entities) + Progress: 235/351 (66%), ETA: 1.1h + +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SUCCESS (32 entities) + Progress: 236/351 (67%), ETA: 1.1h + +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SUCCESS (83 entities) + Progress: 237/351 (67%), ETA: 1.1h + +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SUCCESS (651 entities) + Progress: 238/351 (67%), ETA: 1.1h + +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 239/351 (68%), ETA: 1.0h + +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SUCCESS (14 entities) + Progress: 240/351 (68%), ETA: 1.0h + +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SUCCESS (9 entities) + Progress: 241/351 (68%), ETA: 1.0h + +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SUCCESS (1093 entities) + Progress: 242/351 (68%), ETA: 1.0h + +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 243/351 (69%), ETA: 1.0h + +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SUCCESS (43 entities) + Progress: 244/351 (69%), ETA: 1.0h + +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SUCCESS (47 entities) + Progress: 245/351 (69%), ETA: 1.0h + +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SUCCESS (65 entities) + Progress: 246/351 (70%), ETA: 1.0h + +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SUCCESS (16 entities) + Progress: 247/351 (70%), ETA: 1.0h + +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SUCCESS (21 entities) + Progress: 248/351 (70%), ETA: 1.0h + +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SUCCESS (490 entities) + Progress: 249/351 (70%), ETA: 0.9h + +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SUCCESS (10 entities) + Progress: 250/351 (71%), ETA: 0.9h + +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SUCCESS (77 entities) + Progress: 251/351 (71%), ETA: 0.9h + +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SUCCESS (31 entities) + Progress: 252/351 (71%), ETA: 0.9h + +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SUCCESS (35 entities) + Progress: 253/351 (72%), ETA: 0.9h + +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 254/351 (72%), ETA: 0.9h + +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SUCCESS (243 entities) + Progress: 255/351 (72%), ETA: 0.9h + +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SUCCESS (36 entities) + Progress: 256/351 (72%), ETA: 0.9h + +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SUCCESS (33 entities) + Progress: 257/351 (73%), ETA: 0.9h + +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 258/351 (73%), ETA: 0.9h + +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SUCCESS (129 entities) + Progress: 259/351 (73%), ETA: 0.8h + +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SUCCESS (88 entities) + Progress: 260/351 (74%), ETA: 0.8h + +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SUCCESS (66 entities) + Progress: 261/351 (74%), ETA: 0.8h + +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 262/351 (74%), ETA: 0.8h + +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SUCCESS (111 entities) + Progress: 263/351 (74%), ETA: 0.8h + +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SUCCESS (47 entities) + Progress: 264/351 (75%), ETA: 0.8h + +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SUCCESS (15 entities) + Progress: 265/351 (75%), ETA: 0.8h + +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 266/351 (75%), ETA: 0.8h + +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SUCCESS (185 entities) + Progress: 267/351 (76%), ETA: 0.8h + +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SUCCESS (4 entities) + Progress: 268/351 (76%), ETA: 0.8h + +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SUCCESS (368 entities) + Progress: 269/351 (76%), ETA: 0.7h + +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 270/351 (76%), ETA: 0.7h + +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SUCCESS (131 entities) + Progress: 271/351 (77%), ETA: 0.7h + +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SUCCESS (4 entities) + Progress: 272/351 (77%), ETA: 0.7h + +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SUCCESS (47 entities) + Progress: 273/351 (77%), ETA: 0.7h + +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 274/351 (78%), ETA: 0.7h + +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SUCCESS (2 entities) + Progress: 275/351 (78%), ETA: 0.7h + +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SUCCESS (1 entities) + Progress: 276/351 (78%), ETA: 0.7h + +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SUCCESS (485 entities) + Progress: 277/351 (78%), ETA: 0.7h + +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SUCCESS (1 entities) + Progress: 278/351 (79%), ETA: 0.7h + +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SUCCESS (2 entities) + Progress: 279/351 (79%), ETA: 0.6h + +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SUCCESS (16 entities) + Progress: 280/351 (79%), ETA: 0.6h + +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SUCCESS (454 entities) + Progress: 281/351 (80%), ETA: 0.6h + +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 282/351 (80%), ETA: 0.6h + +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SUCCESS (0 entities) + Progress: 283/351 (80%), ETA: 0.6h + +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 284/351 (80%), ETA: 0.6h + +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SUCCESS (406 entities) + Progress: 285/351 (81%), ETA: 0.6h + +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 286/351 (81%), ETA: 0.6h + +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SUCCESS (0 entities) + Progress: 287/351 (81%), ETA: 0.6h + +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SUCCESS (11 entities) + Progress: 288/351 (82%), ETA: 0.6h + +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SUCCESS (60 entities) + Progress: 289/351 (82%), ETA: 0.6h + +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SUCCESS (4 entities) + Progress: 290/351 (82%), ETA: 0.5h + +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 291/351 (82%), ETA: 0.5h + +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SUCCESS (19 entities) + Progress: 292/351 (83%), ETA: 0.5h + +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SUCCESS (83 entities) + Progress: 293/351 (83%), ETA: 0.5h + +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 294/351 (83%), ETA: 0.5h + +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SUCCESS (0 entities) + Progress: 295/351 (84%), ETA: 0.5h + +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SUCCESS (81 entities) + Progress: 296/351 (84%), ETA: 0.5h + +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SUCCESS (101 entities) + Progress: 297/351 (84%), ETA: 0.5h + +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 298/351 (84%), ETA: 0.5h + +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SUCCESS (4 entities) + Progress: 299/351 (85%), ETA: 0.5h + +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SUCCESS (18 entities) + Progress: 300/351 (85%), ETA: 0.4h + +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SUCCESS (49 entities) + Progress: 301/351 (85%), ETA: 0.4h + +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 302/351 (86%), ETA: 0.4h + +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SUCCESS (30 entities) + Progress: 303/351 (86%), ETA: 0.4h + +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 304/351 (86%), ETA: 0.4h + +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SUCCESS (401 entities) + Progress: 305/351 (86%), ETA: 0.4h + +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 306/351 (87%), ETA: 0.4h + +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SUCCESS (0 entities) + Progress: 307/351 (87%), ETA: 0.4h + +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 308/351 (87%), ETA: 0.4h + +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SUCCESS (72 entities) + Progress: 309/351 (88%), ETA: 0.4h + +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 310/351 (88%), ETA: 0.4h + +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SUCCESS (0 entities) + Progress: 311/351 (88%), ETA: 0.3h + +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SUCCESS (147 entities) + Progress: 312/351 (88%), ETA: 0.3h + +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SUCCESS (551 entities) + Progress: 313/351 (89%), ETA: 0.3h + +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SUCCESS (18 entities) + Progress: 314/351 (89%), ETA: 0.3h + +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SUCCESS (104 entities) + Progress: 315/351 (89%), ETA: 0.3h + +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SUCCESS (174 entities) + Progress: 316/351 (90%), ETA: 0.3h + +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SUCCESS (544 entities) + Progress: 317/351 (90%), ETA: 0.3h + +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 318/351 (90%), ETA: 0.3h + +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SUCCESS (89 entities) + Progress: 319/351 (90%), ETA: 0.3h + +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SUCCESS (154 entities) + Progress: 320/351 (91%), ETA: 0.3h + +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SUCCESS (264 entities) + Progress: 321/351 (91%), ETA: 0.3h + +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SUCCESS (26 entities) + Progress: 322/351 (91%), ETA: 0.3h + +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SUCCESS (52 entities) + Progress: 323/351 (92%), ETA: 0.2h + +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SUCCESS (160 entities) + Progress: 324/351 (92%), ETA: 0.2h + +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SUCCESS (393 entities) + Progress: 325/351 (92%), ETA: 0.2h + +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SUCCESS (26 entities) + Progress: 326/351 (92%), ETA: 0.2h + +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SUCCESS (126 entities) + Progress: 327/351 (93%), ETA: 0.2h + +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SUCCESS (161 entities) + Progress: 328/351 (93%), ETA: 0.2h + +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SUCCESS (465 entities) + Progress: 329/351 (93%), ETA: 0.2h + +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 330/351 (94%), ETA: 0.2h + +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SUCCESS (55 entities) + Progress: 331/351 (94%), ETA: 0.2h + +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SUCCESS (100 entities) + Progress: 332/351 (94%), ETA: 0.2h + +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SUCCESS (402 entities) + Progress: 333/351 (94%), ETA: 0.2h + +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SUCCESS (13 entities) + Progress: 334/351 (95%), ETA: 0.2h + +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SUCCESS (121 entities) + Progress: 335/351 (95%), ETA: 0.1h + +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SUCCESS (105 entities) + Progress: 336/351 (95%), ETA: 0.1h + +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SUCCESS (607 entities) + Progress: 337/351 (96%), ETA: 0.1h + +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SUCCESS (9 entities) + Progress: 338/351 (96%), ETA: 0.1h + +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SUCCESS (66 entities) + Progress: 339/351 (96%), ETA: 0.1h + +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SUCCESS (129 entities) + Progress: 340/351 (96%), ETA: 0.1h + +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SUCCESS (626 entities) + Progress: 341/351 (97%), ETA: 0.1h + +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SUCCESS (14 entities) + Progress: 342/351 (97%), ETA: 0.1h + +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SUCCESS (58 entities) + Progress: 343/351 (97%), ETA: 0.1h + +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SUCCESS (197 entities) + Progress: 344/351 (98%), ETA: 0.1h + +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SUCCESS (631 entities) + Progress: 345/351 (98%), ETA: 0.1h + +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SUCCESS (6 entities) + Progress: 346/351 (98%), ETA: 0.0h + +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SUCCESS (75 entities) + Progress: 347/351 (98%), ETA: 0.0h + +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SUCCESS (79 entities) + Progress: 348/351 (99%), ETA: 0.0h + +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SUCCESS (448 entities) + Progress: 349/351 (99%), ETA: 0.0h + +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SUCCESS (30 entities) + Progress: 350/351 (99%), ETA: 0.0h + +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SUCCESS (73 entities) + Progress: 351/351 (100%), ETA: 0.0h + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 3.2 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193629.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053145.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053145.log.txt new file mode 100644 index 0000000..d8ce5bc --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053145.log.txt @@ -0,0 +1,29 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: True +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053145.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:31:45 +============================================================ + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053145.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053159.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053159.log.txt new file mode 100644 index 0000000..dbcee0a --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053159.log.txt @@ -0,0 +1,55 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053159.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:31:59 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 0.8h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 0.9h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 0.9h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (23 entities) + Progress: 4/351 (1%), ETA: 1.2h + +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 5/351 (1%), ETA: 1.1h + +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (95 entities) + Progress: 6/351 (1%), ETA: 1.5h + +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (28 entities) + Progress: 7/351 (1%), ETA: 1.6h + +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (58 entities) + Progress: 8/351 (2%), ETA: 1.7h + +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 9/351 (2%), ETA: 1.7h + +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 10/351 (2%), ETA: 1.7h + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053531.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053531.log.txt new file mode 100644 index 0000000..189c53e --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053531.log.txt @@ -0,0 +1,31 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: True +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053531.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:35:31 +============================================================ + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (86 entities) + Progress: 11/351 (3%), ETA: 0.3h + +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (164 entities) + Progress: 12/351 (3%), ETA: 0.5h + +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 13/351 (3%), ETA: 0.6h + +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 14/351 (3%), ETA: 0.6h + +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053701.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053701.log.txt new file mode 100644 index 0000000..2a28cd4 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053701.log.txt @@ -0,0 +1,386 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053701.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:37:01 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ DRY-RUN OK +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ DRY-RUN OK +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ DRY-RUN OK +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ DRY-RUN OK +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ DRY-RUN OK +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ DRY-RUN OK +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ DRY-RUN OK +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ DRY-RUN OK +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ DRY-RUN OK +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ DRY-RUN OK +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ DRY-RUN OK +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ DRY-RUN OK +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ DRY-RUN OK +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ DRY-RUN OK +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ DRY-RUN OK +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ DRY-RUN OK +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ DRY-RUN OK +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ DRY-RUN OK +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ DRY-RUN OK +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ DRY-RUN OK +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ DRY-RUN OK +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ DRY-RUN OK +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ DRY-RUN OK +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ DRY-RUN OK +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ DRY-RUN OK +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ DRY-RUN OK +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ DRY-RUN OK +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ DRY-RUN OK +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ DRY-RUN OK +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ DRY-RUN OK +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ DRY-RUN OK +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ DRY-RUN OK +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ DRY-RUN OK +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ DRY-RUN OK +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ DRY-RUN OK +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ DRY-RUN OK +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ DRY-RUN OK +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ DRY-RUN OK +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ DRY-RUN OK +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ DRY-RUN OK +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ DRY-RUN OK +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ DRY-RUN OK +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ DRY-RUN OK +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ DRY-RUN OK +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ DRY-RUN OK +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ DRY-RUN OK +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ DRY-RUN OK +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ DRY-RUN OK +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ DRY-RUN OK +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ DRY-RUN OK +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ DRY-RUN OK +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ DRY-RUN OK +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ DRY-RUN OK +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ DRY-RUN OK +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ DRY-RUN OK +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ DRY-RUN OK +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ DRY-RUN OK +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ DRY-RUN OK +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ DRY-RUN OK +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ DRY-RUN OK +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ DRY-RUN OK +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ DRY-RUN OK +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ DRY-RUN OK +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ DRY-RUN OK +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ DRY-RUN OK +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ DRY-RUN OK +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ DRY-RUN OK +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ DRY-RUN OK +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ DRY-RUN OK +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ DRY-RUN OK +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ DRY-RUN OK +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ DRY-RUN OK +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ DRY-RUN OK +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ DRY-RUN OK +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ DRY-RUN OK +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ DRY-RUN OK +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ DRY-RUN OK +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ DRY-RUN OK +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ DRY-RUN OK +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ DRY-RUN OK +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ DRY-RUN OK +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ DRY-RUN OK +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ DRY-RUN OK +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ DRY-RUN OK +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ DRY-RUN OK +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ DRY-RUN OK +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ DRY-RUN OK +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ DRY-RUN OK +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ DRY-RUN OK +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ DRY-RUN OK +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ DRY-RUN OK +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ DRY-RUN OK +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ DRY-RUN OK +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ DRY-RUN OK +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ DRY-RUN OK +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ DRY-RUN OK +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ DRY-RUN OK +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ DRY-RUN OK +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ DRY-RUN OK +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ DRY-RUN OK +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ DRY-RUN OK +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ DRY-RUN OK +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ DRY-RUN OK +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ DRY-RUN OK +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ DRY-RUN OK +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ DRY-RUN OK +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ DRY-RUN OK +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ DRY-RUN OK +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ DRY-RUN OK +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ DRY-RUN OK +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ DRY-RUN OK +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ DRY-RUN OK +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ DRY-RUN OK +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ DRY-RUN OK +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ DRY-RUN OK +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ DRY-RUN OK +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ DRY-RUN OK +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ DRY-RUN OK +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ DRY-RUN OK +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ DRY-RUN OK +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ DRY-RUN OK +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ DRY-RUN OK +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ DRY-RUN OK +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ DRY-RUN OK +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ DRY-RUN OK +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ DRY-RUN OK +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ DRY-RUN OK +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ DRY-RUN OK +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ DRY-RUN OK +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ DRY-RUN OK +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ DRY-RUN OK +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ DRY-RUN OK +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ DRY-RUN OK +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ DRY-RUN OK +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ DRY-RUN OK +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ DRY-RUN OK +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ DRY-RUN OK +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ DRY-RUN OK +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ DRY-RUN OK +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ DRY-RUN OK +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ DRY-RUN OK +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ DRY-RUN OK +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ DRY-RUN OK +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ DRY-RUN OK +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ DRY-RUN OK +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ DRY-RUN OK +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ DRY-RUN OK +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ DRY-RUN OK +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ DRY-RUN OK +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ DRY-RUN OK +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ DRY-RUN OK +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ DRY-RUN OK +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ DRY-RUN OK +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ DRY-RUN OK +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ DRY-RUN OK +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ DRY-RUN OK +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ DRY-RUN OK +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ DRY-RUN OK +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ DRY-RUN OK +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ DRY-RUN OK +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ DRY-RUN OK +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ DRY-RUN OK +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ DRY-RUN OK +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ DRY-RUN OK +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ DRY-RUN OK +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ DRY-RUN OK +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ DRY-RUN OK diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053705.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053705.log.txt new file mode 100644 index 0000000..a021ddd --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053705.log.txt @@ -0,0 +1,269 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053705.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:37:05 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SKIPPED (already exists, 16 entities, method=segformer) +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SKIPPED (already exists, 23 entities, method=segformer) +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SKIPPED (already exists, 95 entities, method=segformer) +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SKIPPED (already exists, 28 entities, method=segformer) +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SKIPPED (already exists, 58 entities, method=segformer) +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SKIPPED (already exists, 7 entities, method=segformer) +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SKIPPED (already exists, 62 entities, method=segformer) +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SKIPPED (already exists, 86 entities, method=segformer) +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SKIPPED (already exists, 164 entities, method=segformer) +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SKIPPED (already exists, 1 entities, method=segformer) +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SKIPPED (already exists, 2 entities, method=segformer) +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SKIPPED (already exists, 189 entities, method=segformer) +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SKIPPED (already exists, 11 entities, method=segformer) +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SKIPPED (already exists, 3 entities, method=segformer) +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SKIPPED (already exists, 176 entities, method=segformer) +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SKIPPED (already exists, 7 entities, method=segformer) +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SKIPPED (already exists, 17 entities, method=segformer) +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SKIPPED (already exists, 148 entities, method=segformer) +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SKIPPED (already exists, 9 entities, method=segformer) +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SKIPPED (already exists, 12 entities, method=segformer) +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SKIPPED (already exists, 222 entities, method=segformer) +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SKIPPED (already exists, 202 entities, method=segformer) +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SKIPPED (already exists, 37 entities, method=segformer) +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SKIPPED (already exists, 18 entities, method=segformer) +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SKIPPED (already exists, 201 entities, method=segformer) +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SKIPPED (already exists, 98 entities, method=segformer) +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SKIPPED (already exists, 13 entities, method=segformer) +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SKIPPED (already exists, 17 entities, method=segformer) +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SKIPPED (already exists, 133 entities, method=segformer) +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SKIPPED (already exists, 87 entities, method=segformer) +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SKIPPED (already exists, 24 entities, method=segformer) +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SKIPPED (already exists, 5 entities, method=segformer) +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SKIPPED (already exists, 250 entities, method=segformer) +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SKIPPED (already exists, 267 entities, method=segformer) +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SKIPPED (already exists, 256 entities, method=segformer) +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SKIPPED (already exists, 12 entities, method=segformer) +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SKIPPED (already exists, 477 entities, method=segformer) +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SKIPPED (already exists, 288 entities, method=segformer) +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SKIPPED (already exists, 130 entities, method=segformer) +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SKIPPED (already exists, 5 entities, method=segformer) +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SKIPPED (already exists, 270 entities, method=segformer) +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SKIPPED (already exists, 374 entities, method=segformer) +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SKIPPED (already exists, 101 entities, method=segformer) +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SKIPPED (already exists, 298 entities, method=segformer) +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SKIPPED (already exists, 277 entities, method=segformer) +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SKIPPED (already exists, 124 entities, method=segformer) +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SKIPPED (already exists, 9 entities, method=segformer) +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SKIPPED (already exists, 260 entities, method=segformer) +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SKIPPED (already exists, 332 entities, method=segformer) +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SKIPPED (already exists, 168 entities, method=segformer) +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SKIPPED (already exists, 16 entities, method=segformer) +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SKIPPED (already exists, 509 entities, method=segformer) +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SKIPPED (already exists, 365 entities, method=segformer) +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SKIPPED (already exists, 156 entities, method=segformer) +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SKIPPED (already exists, 13 entities, method=segformer) +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SKIPPED (already exists, 202 entities, method=segformer) +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SKIPPED (already exists, 301 entities, method=segformer) +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SKIPPED (already exists, 82 entities, method=segformer) +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SKIPPED (already exists, 11 entities, method=segformer) +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SKIPPED (already exists, 1153 entities, method=segformer) +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SKIPPED (already exists, 47 entities, method=segformer) +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SKIPPED (already exists, 68 entities, method=segformer) +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SKIPPED (already exists, 3132 entities, method=segformer) +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SKIPPED (already exists, 16 entities, method=segformer) +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SKIPPED (already exists, 35 entities, method=segformer) +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SKIPPED (already exists, 760 entities, method=segformer) +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SKIPPED (already exists, 7 entities, method=segformer) +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SKIPPED (already exists, 305 entities, method=segformer) +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SKIPPED (already exists, 235 entities, method=segformer) +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SKIPPED (already exists, 7 entities, method=segformer) +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SKIPPED (already exists, 12 entities, method=segformer) +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SKIPPED (already exists, 953 entities, method=segformer) +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SKIPPED (already exists, 11 entities, method=segformer) +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SKIPPED (already exists, 11 entities, method=segformer) +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SKIPPED (already exists, 679 entities, method=segformer) +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SKIPPED (already exists, 24 entities, method=segformer) +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SKIPPED (already exists, 47 entities, method=segformer) +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SKIPPED (already exists, 467 entities, method=segformer) +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SKIPPED (already exists, 24 entities, method=segformer) +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SKIPPED (already exists, 12 entities, method=segformer) +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SKIPPED (already exists, 9 entities, method=segformer) +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SKIPPED (already exists, 1799 entities, method=segformer) +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SKIPPED (already exists, 22 entities, method=segformer) +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SKIPPED (already exists, 26 entities, method=segformer) +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SKIPPED (already exists, 1 entities, method=segformer) +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SKIPPED (already exists, 1199 entities, method=segformer) +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SKIPPED (already exists, 11 entities, method=segformer) +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SKIPPED (already exists, 3 entities, method=segformer) +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SKIPPED (already exists, 689 entities, method=segformer) +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SKIPPED (already exists, 1 entities, method=segformer) +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SKIPPED (already exists, 318 entities, method=segformer) +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SKIPPED (already exists, 2 entities, method=segformer) +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SKIPPED (already exists, 330 entities, method=segformer) +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SKIPPED (already exists, 993 entities, method=segformer) +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SKIPPED (already exists, 2 entities, method=segformer) +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SKIPPED (already exists, 2 entities, method=segformer) +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SKIPPED (already exists, 1 entities, method=segformer) +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SKIPPED (already exists, 2098 entities, method=segformer) +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SKIPPED (already exists, 4 entities, method=segformer) +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SKIPPED (already exists, 236 entities, method=segformer) +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SKIPPED (already exists, 8 entities, method=segformer) +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SKIPPED (already exists, 3 entities, method=segformer) +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SKIPPED (already exists, 300 entities, method=segformer) +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053719.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053719.log.txt new file mode 100644 index 0000000..b98e864 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_053719.log.txt @@ -0,0 +1,1433 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053719.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:37:19 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 1.0h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 1.0h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 1.0h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (23 entities) + Progress: 4/351 (1%), ETA: 1.2h + +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 5/351 (1%), ETA: 1.1h + +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (95 entities) + Progress: 6/351 (1%), ETA: 1.5h + +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (28 entities) + Progress: 7/351 (1%), ETA: 1.6h + +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (58 entities) + Progress: 8/351 (2%), ETA: 1.7h + +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 9/351 (2%), ETA: 1.7h + +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 10/351 (2%), ETA: 1.7h + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (86 entities) + Progress: 11/351 (3%), ETA: 1.9h + +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (164 entities) + Progress: 12/351 (3%), ETA: 2.0h + +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 13/351 (3%), ETA: 2.0h + +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 14/351 (3%), ETA: 1.9h + +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (2 entities) + Progress: 15/351 (4%), ETA: 1.8h + +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (189 entities) + Progress: 16/351 (4%), ETA: 1.9h + +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 17/351 (4%), ETA: 1.9h + +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (11 entities) + Progress: 18/351 (5%), ETA: 1.8h + +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (3 entities) + Progress: 19/351 (5%), ETA: 1.8h + +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (176 entities) + Progress: 20/351 (5%), ETA: 1.9h + +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 21/351 (5%), ETA: 1.8h + +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 22/351 (6%), ETA: 1.8h + +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (17 entities) + Progress: 23/351 (6%), ETA: 1.7h + +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (148 entities) + Progress: 24/351 (6%), ETA: 1.9h + +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (9 entities) + Progress: 25/351 (7%), ETA: 1.8h + +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 26/351 (7%), ETA: 1.8h + +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (222 entities) + Progress: 27/351 (7%), ETA: 2.0h + +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (202 entities) + Progress: 28/351 (7%), ETA: 2.0h + +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (37 entities) + Progress: 29/351 (8%), ETA: 2.0h + +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (18 entities) + Progress: 30/351 (8%), ETA: 1.9h + +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (201 entities) + Progress: 31/351 (8%), ETA: 2.0h + +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (98 entities) + Progress: 32/351 (9%), ETA: 2.0h + +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (13 entities) + Progress: 33/351 (9%), ETA: 2.0h + +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (17 entities) + Progress: 34/351 (9%), ETA: 2.0h + +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (133 entities) + Progress: 35/351 (9%), ETA: 2.0h + +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (87 entities) + Progress: 36/351 (10%), ETA: 2.0h + +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (24 entities) + Progress: 37/351 (10%), ETA: 2.0h + +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (5 entities) + Progress: 38/351 (10%), ETA: 2.0h + +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (250 entities) + Progress: 39/351 (11%), ETA: 2.0h + +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (267 entities) + Progress: 40/351 (11%), ETA: 2.1h + +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (256 entities) + Progress: 41/351 (11%), ETA: 2.1h + +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 42/351 (11%), ETA: 2.1h + +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (477 entities) + Progress: 43/351 (12%), ETA: 2.2h + +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (288 entities) + Progress: 44/351 (12%), ETA: 2.3h + +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (130 entities) + Progress: 45/351 (12%), ETA: 2.2h + +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (5 entities) + Progress: 46/351 (13%), ETA: 2.2h + +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (270 entities) + Progress: 47/351 (13%), ETA: 2.3h + +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (374 entities) + Progress: 48/351 (13%), ETA: 2.4h + +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (101 entities) + Progress: 49/351 (13%), ETA: 2.3h + +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 50/351 (14%), ETA: 2.3h + +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (298 entities) + Progress: 51/351 (14%), ETA: 2.4h + +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (277 entities) + Progress: 52/351 (14%), ETA: 2.4h + +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (124 entities) + Progress: 53/351 (15%), ETA: 2.4h + +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SUCCESS (9 entities) + Progress: 54/351 (15%), ETA: 2.4h + +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SUCCESS (260 entities) + Progress: 55/351 (15%), ETA: 2.4h + +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SUCCESS (332 entities) + Progress: 56/351 (15%), ETA: 2.4h + +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SUCCESS (168 entities) + Progress: 57/351 (16%), ETA: 2.4h + +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SUCCESS (16 entities) + Progress: 58/351 (16%), ETA: 2.4h + +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SUCCESS (509 entities) + Progress: 59/351 (16%), ETA: 2.5h + +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SUCCESS (365 entities) + Progress: 60/351 (17%), ETA: 2.5h + +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SUCCESS (156 entities) + Progress: 61/351 (17%), ETA: 2.5h + +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SUCCESS (13 entities) + Progress: 62/351 (17%), ETA: 2.5h + +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SUCCESS (202 entities) + Progress: 63/351 (17%), ETA: 2.5h + +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SUCCESS (301 entities) + Progress: 64/351 (18%), ETA: 2.5h + +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SUCCESS (82 entities) + Progress: 65/351 (18%), ETA: 2.5h + +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SUCCESS (11 entities) + Progress: 66/351 (18%), ETA: 2.5h + +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SUCCESS (1153 entities) + Progress: 67/351 (19%), ETA: 2.6h + +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SUCCESS (47 entities) + Progress: 68/351 (19%), ETA: 2.6h + +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SUCCESS (68 entities) + Progress: 69/351 (19%), ETA: 2.5h + +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 70/351 (19%), ETA: 2.5h + +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SUCCESS (3132 entities) + Progress: 71/351 (20%), ETA: 2.6h + +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SUCCESS (16 entities) + Progress: 72/351 (20%), ETA: 2.6h + +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SUCCESS (35 entities) + Progress: 73/351 (20%), ETA: 2.6h + +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 74/351 (21%), ETA: 2.5h + +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SUCCESS (760 entities) + Progress: 75/351 (21%), ETA: 2.6h + +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 76/351 (21%), ETA: 2.5h + +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SUCCESS (7 entities) + Progress: 77/351 (21%), ETA: 2.5h + +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ SUCCESS (0 entities) + Progress: 78/351 (22%), ETA: 2.5h + +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SUCCESS (305 entities) + Progress: 79/351 (22%), ETA: 2.5h + +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/351 (22%), ETA: 2.4h + +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SUCCESS (4 entities) + Progress: 81/351 (23%), ETA: 2.4h + +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 82/351 (23%), ETA: 2.4h + +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SUCCESS (235 entities) + Progress: 83/351 (23%), ETA: 2.4h + +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SUCCESS (7 entities) + Progress: 84/351 (23%), ETA: 2.4h + +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SUCCESS (12 entities) + Progress: 85/351 (24%), ETA: 2.3h + +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 86/351 (24%), ETA: 2.3h + +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SUCCESS (953 entities) + Progress: 87/351 (24%), ETA: 2.3h + +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 88/351 (25%), ETA: 2.3h + +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SUCCESS (11 entities) + Progress: 89/351 (25%), ETA: 2.3h + +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SUCCESS (4 entities) + Progress: 90/351 (25%), ETA: 2.3h + +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SUCCESS (679 entities) + Progress: 91/351 (25%), ETA: 2.3h + +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SUCCESS (24 entities) + Progress: 92/351 (26%), ETA: 2.3h + +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SUCCESS (47 entities) + Progress: 93/351 (26%), ETA: 2.3h + +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SUCCESS (467 entities) + Progress: 94/351 (26%), ETA: 2.3h + +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SUCCESS (24 entities) + Progress: 95/351 (27%), ETA: 2.3h + +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SUCCESS (12 entities) + Progress: 96/351 (27%), ETA: 2.3h + +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SUCCESS (9 entities) + Progress: 97/351 (27%), ETA: 2.3h + +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SUCCESS (1799 entities) + Progress: 98/351 (27%), ETA: 2.3h + +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SUCCESS (22 entities) + Progress: 99/351 (28%), ETA: 2.3h + +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SUCCESS (26 entities) + Progress: 100/351 (28%), ETA: 2.3h + +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SUCCESS (1 entities) + Progress: 101/351 (28%), ETA: 2.2h + +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SUCCESS (1199 entities) + Progress: 102/351 (29%), ETA: 2.3h + +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SUCCESS (4 entities) + Progress: 103/351 (29%), ETA: 2.2h + +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SUCCESS (11 entities) + Progress: 104/351 (29%), ETA: 2.2h + +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SUCCESS (3 entities) + Progress: 105/351 (29%), ETA: 2.2h + +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SUCCESS (689 entities) + Progress: 106/351 (30%), ETA: 2.2h + +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SUCCESS (4 entities) + Progress: 107/351 (30%), ETA: 2.2h + +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ SUCCESS (0 entities) + Progress: 108/351 (30%), ETA: 2.2h + +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 109/351 (31%), ETA: 2.2h + +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SUCCESS (318 entities) + Progress: 110/351 (31%), ETA: 2.2h + +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SUCCESS (2 entities) + Progress: 111/351 (31%), ETA: 2.1h + +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ SUCCESS (0 entities) + Progress: 112/351 (31%), ETA: 2.1h + +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 113/351 (32%), ETA: 2.1h + +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SUCCESS (330 entities) + Progress: 114/351 (32%), ETA: 2.1h + +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ SUCCESS (0 entities) + Progress: 115/351 (32%), ETA: 2.1h + +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 116/351 (33%), ETA: 2.0h + +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ SUCCESS (0 entities) + Progress: 117/351 (33%), ETA: 2.0h + +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SUCCESS (993 entities) + Progress: 118/351 (33%), ETA: 2.0h + +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SUCCESS (2 entities) + Progress: 119/351 (33%), ETA: 2.0h + +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SUCCESS (2 entities) + Progress: 120/351 (34%), ETA: 2.0h + +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SUCCESS (1 entities) + Progress: 121/351 (34%), ETA: 2.0h + +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SUCCESS (2098 entities) + Progress: 122/351 (34%), ETA: 2.0h + +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SUCCESS (4 entities) + Progress: 123/351 (35%), ETA: 2.0h + +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SUCCESS (236 entities) + Progress: 124/351 (35%), ETA: 2.0h + +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SUCCESS (8 entities) + Progress: 125/351 (35%), ETA: 2.0h + +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SUCCESS (3 entities) + Progress: 126/351 (35%), ETA: 2.0h + +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SUCCESS (300 entities) + Progress: 127/351 (36%), ETA: 2.0h + +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SUCCESS (54 entities) + Progress: 128/351 (36%), ETA: 2.0h + +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SUCCESS (5 entities) + Progress: 129/351 (36%), ETA: 1.9h + +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 130/351 (37%), ETA: 1.9h + +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SUCCESS (598 entities) + Progress: 131/351 (37%), ETA: 1.9h + +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SUCCESS (14 entities) + Progress: 132/351 (37%), ETA: 1.9h + +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 133/351 (37%), ETA: 1.9h + +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 134/351 (38%), ETA: 1.9h + +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SUCCESS (278 entities) + Progress: 135/351 (38%), ETA: 1.9h + +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SUCCESS (16 entities) + Progress: 136/351 (38%), ETA: 1.9h + +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 137/351 (39%), ETA: 1.8h + +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 138/351 (39%), ETA: 1.8h + +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SUCCESS (748 entities) + Progress: 139/351 (39%), ETA: 1.8h + +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SUCCESS (8 entities) + Progress: 140/351 (39%), ETA: 1.8h + +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SUCCESS (5 entities) + Progress: 141/351 (40%), ETA: 1.8h + +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 142/351 (40%), ETA: 1.8h + +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SUCCESS (1596 entities) + Progress: 143/351 (40%), ETA: 1.8h + +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SUCCESS (8 entities) + Progress: 144/351 (41%), ETA: 1.8h + +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 145/351 (41%), ETA: 1.8h + +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SUCCESS (5 entities) + Progress: 146/351 (41%), ETA: 1.8h + +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SUCCESS (139 entities) + Progress: 147/351 (41%), ETA: 1.8h + +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SUCCESS (29 entities) + Progress: 148/351 (42%), ETA: 1.8h + +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SUCCESS (17 entities) + Progress: 149/351 (42%), ETA: 1.7h + +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SUCCESS (20 entities) + Progress: 150/351 (42%), ETA: 1.7h + +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SUCCESS (45 entities) + Progress: 151/351 (43%), ETA: 1.7h + +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SUCCESS (15 entities) + Progress: 152/351 (43%), ETA: 1.7h + +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SUCCESS (26 entities) + Progress: 153/351 (43%), ETA: 1.7h + +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 154/351 (43%), ETA: 1.7h + +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SUCCESS (1258 entities) + Progress: 155/351 (44%), ETA: 1.7h + +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SUCCESS (12 entities) + Progress: 156/351 (44%), ETA: 1.7h + +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SUCCESS (7 entities) + Progress: 157/351 (44%), ETA: 1.7h + +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 158/351 (45%), ETA: 1.6h + +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SUCCESS (752 entities) + Progress: 159/351 (45%), ETA: 1.7h + +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SUCCESS (24 entities) + Progress: 160/351 (45%), ETA: 1.6h + +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SUCCESS (7 entities) + Progress: 161/351 (45%), ETA: 1.6h + +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SUCCESS (498 entities) + Progress: 162/351 (46%), ETA: 1.6h + +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 163/351 (46%), ETA: 1.6h + +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SUCCESS (11 entities) + Progress: 164/351 (46%), ETA: 1.6h + +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 165/351 (47%), ETA: 1.6h + +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ SUCCESS (0 entities) + Progress: 166/351 (47%), ETA: 1.6h + +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SUCCESS (206 entities) + Progress: 167/351 (47%), ETA: 1.6h + +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 168/351 (47%), ETA: 1.6h + +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 169/351 (48%), ETA: 1.5h + +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SUCCESS (0 entities) + Progress: 170/351 (48%), ETA: 1.5h + +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SUCCESS (1762 entities) + Progress: 171/351 (48%), ETA: 1.5h + +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SUCCESS (3 entities) + Progress: 172/351 (49%), ETA: 1.5h + +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SUCCESS (21 entities) + Progress: 173/351 (49%), ETA: 1.5h + +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SUCCESS (579 entities) + Progress: 174/351 (49%), ETA: 1.5h + +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SUCCESS (12 entities) + Progress: 175/351 (49%), ETA: 1.5h + +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SUCCESS (34 entities) + Progress: 176/351 (50%), ETA: 1.5h + +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SUCCESS (4 entities) + Progress: 177/351 (50%), ETA: 1.5h + +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SUCCESS (146 entities) + Progress: 178/351 (50%), ETA: 1.5h + +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SUCCESS (13 entities) + Progress: 179/351 (50%), ETA: 1.5h + +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SUCCESS (755 entities) + Progress: 180/351 (51%), ETA: 1.5h + +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SUCCESS (10 entities) + Progress: 181/351 (51%), ETA: 1.5h + +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SUCCESS (34 entities) + Progress: 182/351 (51%), ETA: 1.4h + +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SUCCESS (3 entities) + Progress: 183/351 (52%), ETA: 1.4h + +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SUCCESS (1355 entities) + Progress: 184/351 (52%), ETA: 1.4h + +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SUCCESS (2 entities) + Progress: 185/351 (52%), ETA: 1.4h + +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SUCCESS (0 entities) + Progress: 186/351 (52%), ETA: 1.4h + +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 187/351 (53%), ETA: 1.4h + +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SUCCESS (1043 entities) + Progress: 188/351 (53%), ETA: 1.4h + +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 189/351 (53%), ETA: 1.4h + +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SUCCESS (6 entities) + Progress: 190/351 (54%), ETA: 1.4h + +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SUCCESS (3 entities) + Progress: 191/351 (54%), ETA: 1.4h + +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SUCCESS (1684 entities) + Progress: 192/351 (54%), ETA: 1.4h + +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SUCCESS (16 entities) + Progress: 193/351 (54%), ETA: 1.4h + +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SUCCESS (46 entities) + Progress: 194/351 (55%), ETA: 1.4h + +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SUCCESS (9 entities) + Progress: 195/351 (55%), ETA: 1.3h + +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SUCCESS (13 entities) + Progress: 196/351 (55%), ETA: 1.3h + +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SUCCESS (144 entities) + Progress: 197/351 (56%), ETA: 1.3h + +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SUCCESS (8 entities) + Progress: 198/351 (56%), ETA: 1.3h + +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SUCCESS (15 entities) + Progress: 199/351 (56%), ETA: 1.3h + +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SUCCESS (49 entities) + Progress: 200/351 (56%), ETA: 1.3h + +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SUCCESS (555 entities) + Progress: 201/351 (57%), ETA: 1.3h + +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SUCCESS (15 entities) + Progress: 202/351 (57%), ETA: 1.3h + +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 203/351 (57%), ETA: 1.3h + +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 204/351 (58%), ETA: 1.3h + +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SUCCESS (601 entities) + Progress: 205/351 (58%), ETA: 1.3h + +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 206/351 (58%), ETA: 1.2h + +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SUCCESS (1 entities) + Progress: 207/351 (58%), ETA: 1.2h + +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SUCCESS (93 entities) + Progress: 208/351 (59%), ETA: 1.2h + +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SUCCESS (573 entities) + Progress: 209/351 (59%), ETA: 1.2h + +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 210/351 (59%), ETA: 1.2h + +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 211/351 (60%), ETA: 1.2h + +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 212/351 (60%), ETA: 1.2h + +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SUCCESS (619 entities) + Progress: 213/351 (60%), ETA: 1.2h + +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 214/351 (60%), ETA: 1.2h + +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SUCCESS (20 entities) + Progress: 215/351 (61%), ETA: 1.1h + +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SUCCESS (13 entities) + Progress: 216/351 (61%), ETA: 1.1h + +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SUCCESS (1380 entities) + Progress: 217/351 (61%), ETA: 1.1h + +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SUCCESS (4 entities) + Progress: 218/351 (62%), ETA: 1.1h + +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SUCCESS (12 entities) + Progress: 219/351 (62%), ETA: 1.1h + +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SUCCESS (28 entities) + Progress: 220/351 (62%), ETA: 1.1h + +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SUCCESS (14 entities) + Progress: 221/351 (62%), ETA: 1.1h + +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SUCCESS (106 entities) + Progress: 222/351 (63%), ETA: 1.1h + +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SUCCESS (7 entities) + Progress: 223/351 (63%), ETA: 1.1h + +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SUCCESS (39 entities) + Progress: 224/351 (63%), ETA: 1.1h + +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SUCCESS (27 entities) + Progress: 225/351 (64%), ETA: 1.1h + +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SUCCESS (304 entities) + Progress: 226/351 (64%), ETA: 1.1h + +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 227/351 (64%), ETA: 1.0h + +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SUCCESS (17 entities) + Progress: 228/351 (64%), ETA: 1.0h + +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SUCCESS (39 entities) + Progress: 229/351 (65%), ETA: 1.0h + +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SUCCESS (756 entities) + Progress: 230/351 (65%), ETA: 1.0h + +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SUCCESS (30 entities) + Progress: 231/351 (65%), ETA: 1.0h + +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SUCCESS (13 entities) + Progress: 232/351 (66%), ETA: 1.0h + +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SUCCESS (708 entities) + Progress: 233/351 (66%), ETA: 1.0h + +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SUCCESS (5 entities) + Progress: 234/351 (66%), ETA: 1.0h + +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SUCCESS (1 entities) + Progress: 235/351 (66%), ETA: 1.0h + +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SUCCESS (32 entities) + Progress: 236/351 (67%), ETA: 1.0h + +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SUCCESS (83 entities) + Progress: 237/351 (67%), ETA: 1.0h + +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SUCCESS (651 entities) + Progress: 238/351 (67%), ETA: 1.0h + +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 239/351 (68%), ETA: 0.9h + +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SUCCESS (14 entities) + Progress: 240/351 (68%), ETA: 0.9h + +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SUCCESS (9 entities) + Progress: 241/351 (68%), ETA: 0.9h + +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SUCCESS (1093 entities) + Progress: 242/351 (68%), ETA: 0.9h + +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 243/351 (69%), ETA: 0.9h + +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SUCCESS (43 entities) + Progress: 244/351 (69%), ETA: 0.9h + +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SUCCESS (47 entities) + Progress: 245/351 (69%), ETA: 0.9h + +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SUCCESS (65 entities) + Progress: 246/351 (70%), ETA: 0.9h + +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SUCCESS (16 entities) + Progress: 247/351 (70%), ETA: 0.9h + +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SUCCESS (21 entities) + Progress: 248/351 (70%), ETA: 0.9h + +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SUCCESS (490 entities) + Progress: 249/351 (70%), ETA: 0.9h + +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SUCCESS (10 entities) + Progress: 250/351 (71%), ETA: 0.8h + +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SUCCESS (77 entities) + Progress: 251/351 (71%), ETA: 0.8h + +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SUCCESS (31 entities) + Progress: 252/351 (71%), ETA: 0.8h + +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SUCCESS (35 entities) + Progress: 253/351 (72%), ETA: 0.8h + +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 254/351 (72%), ETA: 0.8h + +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SUCCESS (243 entities) + Progress: 255/351 (72%), ETA: 0.8h + +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SUCCESS (36 entities) + Progress: 256/351 (72%), ETA: 0.8h + +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SUCCESS (33 entities) + Progress: 257/351 (73%), ETA: 0.8h + +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 258/351 (73%), ETA: 0.8h + +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SUCCESS (129 entities) + Progress: 259/351 (73%), ETA: 0.8h + +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SUCCESS (88 entities) + Progress: 260/351 (74%), ETA: 0.8h + +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SUCCESS (66 entities) + Progress: 261/351 (74%), ETA: 0.7h + +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 262/351 (74%), ETA: 0.7h + +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SUCCESS (111 entities) + Progress: 263/351 (74%), ETA: 0.7h + +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SUCCESS (47 entities) + Progress: 264/351 (75%), ETA: 0.7h + +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SUCCESS (15 entities) + Progress: 265/351 (75%), ETA: 0.7h + +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 266/351 (75%), ETA: 0.7h + +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SUCCESS (185 entities) + Progress: 267/351 (76%), ETA: 0.7h + +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SUCCESS (4 entities) + Progress: 268/351 (76%), ETA: 0.7h + +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SUCCESS (368 entities) + Progress: 269/351 (76%), ETA: 0.7h + +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 270/351 (76%), ETA: 0.7h + +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SUCCESS (131 entities) + Progress: 271/351 (77%), ETA: 0.7h + +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SUCCESS (4 entities) + Progress: 272/351 (77%), ETA: 0.6h + +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SUCCESS (47 entities) + Progress: 273/351 (77%), ETA: 0.6h + +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 274/351 (78%), ETA: 0.6h + +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SUCCESS (2 entities) + Progress: 275/351 (78%), ETA: 0.6h + +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SUCCESS (1 entities) + Progress: 276/351 (78%), ETA: 0.6h + +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SUCCESS (485 entities) + Progress: 277/351 (78%), ETA: 0.6h + +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SUCCESS (1 entities) + Progress: 278/351 (79%), ETA: 0.6h + +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SUCCESS (2 entities) + Progress: 279/351 (79%), ETA: 0.6h + +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SUCCESS (16 entities) + Progress: 280/351 (79%), ETA: 0.6h + +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SUCCESS (454 entities) + Progress: 281/351 (80%), ETA: 0.6h + +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 282/351 (80%), ETA: 0.6h + +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SUCCESS (0 entities) + Progress: 283/351 (80%), ETA: 0.6h + +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 284/351 (80%), ETA: 0.5h + +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SUCCESS (406 entities) + Progress: 285/351 (81%), ETA: 0.5h + +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 286/351 (81%), ETA: 0.5h + +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SUCCESS (0 entities) + Progress: 287/351 (81%), ETA: 0.5h + +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SUCCESS (11 entities) + Progress: 288/351 (82%), ETA: 0.5h + +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SUCCESS (60 entities) + Progress: 289/351 (82%), ETA: 0.5h + +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SUCCESS (4 entities) + Progress: 290/351 (82%), ETA: 0.5h + +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 291/351 (82%), ETA: 0.5h + +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SUCCESS (19 entities) + Progress: 292/351 (83%), ETA: 0.5h + +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SUCCESS (83 entities) + Progress: 293/351 (83%), ETA: 0.5h + +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 294/351 (83%), ETA: 0.5h + +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SUCCESS (0 entities) + Progress: 295/351 (84%), ETA: 0.4h + +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SUCCESS (81 entities) + Progress: 296/351 (84%), ETA: 0.4h + +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SUCCESS (101 entities) + Progress: 297/351 (84%), ETA: 0.4h + +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 298/351 (84%), ETA: 0.4h + +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SUCCESS (4 entities) + Progress: 299/351 (85%), ETA: 0.4h + +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SUCCESS (18 entities) + Progress: 300/351 (85%), ETA: 0.4h + +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SUCCESS (49 entities) + Progress: 301/351 (85%), ETA: 0.4h + +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 302/351 (86%), ETA: 0.4h + +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SUCCESS (30 entities) + Progress: 303/351 (86%), ETA: 0.4h + +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 304/351 (86%), ETA: 0.4h + +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SUCCESS (401 entities) + Progress: 305/351 (86%), ETA: 0.4h + +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 306/351 (87%), ETA: 0.4h + +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SUCCESS (0 entities) + Progress: 307/351 (87%), ETA: 0.3h + +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 308/351 (87%), ETA: 0.3h + +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SUCCESS (72 entities) + Progress: 309/351 (88%), ETA: 0.3h + +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 310/351 (88%), ETA: 0.3h + +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SUCCESS (0 entities) + Progress: 311/351 (88%), ETA: 0.3h + +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SUCCESS (147 entities) + Progress: 312/351 (88%), ETA: 0.3h + +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SUCCESS (551 entities) + Progress: 313/351 (89%), ETA: 0.3h + +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SUCCESS (18 entities) + Progress: 314/351 (89%), ETA: 0.3h + +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SUCCESS (104 entities) + Progress: 315/351 (89%), ETA: 0.3h + +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SUCCESS (174 entities) + Progress: 316/351 (90%), ETA: 0.3h + +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SUCCESS (544 entities) + Progress: 317/351 (90%), ETA: 0.3h + +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 318/351 (90%), ETA: 0.3h + +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SUCCESS (89 entities) + Progress: 319/351 (90%), ETA: 0.3h + +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SUCCESS (154 entities) + Progress: 320/351 (91%), ETA: 0.2h + +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SUCCESS (264 entities) + Progress: 321/351 (91%), ETA: 0.2h + +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SUCCESS (26 entities) + Progress: 322/351 (91%), ETA: 0.2h + +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SUCCESS (52 entities) + Progress: 323/351 (92%), ETA: 0.2h + +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SUCCESS (160 entities) + Progress: 324/351 (92%), ETA: 0.2h + +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SUCCESS (393 entities) + Progress: 325/351 (92%), ETA: 0.2h + +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SUCCESS (26 entities) + Progress: 326/351 (92%), ETA: 0.2h + +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SUCCESS (126 entities) + Progress: 327/351 (93%), ETA: 0.2h + +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SUCCESS (161 entities) + Progress: 328/351 (93%), ETA: 0.2h + +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SUCCESS (465 entities) + Progress: 329/351 (93%), ETA: 0.2h + +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 330/351 (94%), ETA: 0.2h + +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SUCCESS (55 entities) + Progress: 331/351 (94%), ETA: 0.2h + +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SUCCESS (100 entities) + Progress: 332/351 (94%), ETA: 0.2h + +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SUCCESS (402 entities) + Progress: 333/351 (94%), ETA: 0.1h + +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SUCCESS (13 entities) + Progress: 334/351 (95%), ETA: 0.1h + +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SUCCESS (121 entities) + Progress: 335/351 (95%), ETA: 0.1h + +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SUCCESS (105 entities) + Progress: 336/351 (95%), ETA: 0.1h + +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SUCCESS (607 entities) + Progress: 337/351 (96%), ETA: 0.1h + +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SUCCESS (9 entities) + Progress: 338/351 (96%), ETA: 0.1h + +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SUCCESS (66 entities) + Progress: 339/351 (96%), ETA: 0.1h + +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SUCCESS (129 entities) + Progress: 340/351 (96%), ETA: 0.1h + +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SUCCESS (626 entities) + Progress: 341/351 (97%), ETA: 0.1h + +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SUCCESS (14 entities) + Progress: 342/351 (97%), ETA: 0.1h + +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SUCCESS (58 entities) + Progress: 343/351 (97%), ETA: 0.1h + +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SUCCESS (197 entities) + Progress: 344/351 (98%), ETA: 0.1h + +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SUCCESS (631 entities) + Progress: 345/351 (98%), ETA: 0.0h + +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SUCCESS (6 entities) + Progress: 346/351 (98%), ETA: 0.0h + +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SUCCESS (75 entities) + Progress: 347/351 (98%), ETA: 0.0h + +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SUCCESS (79 entities) + Progress: 348/351 (99%), ETA: 0.0h + +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SUCCESS (448 entities) + Progress: 349/351 (99%), ETA: 0.0h + +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SUCCESS (30 entities) + Progress: 350/351 (99%), ETA: 0.0h + +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SUCCESS (73 entities) + Progress: 351/351 (100%), ETA: 0.0h + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 2.9 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053719.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_093743.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_093743.log.txt new file mode 100644 index 0000000..ec69e65 --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_093743.log.txt @@ -0,0 +1,29 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: True +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_093743.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 09:37:43 +============================================================ + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260228_093743.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_093803.log.txt b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_093803.log.txt new file mode 100644 index 0000000..6cb41df --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_extraction_20260228_093803.log.txt @@ -0,0 +1,731 @@ +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_093803.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 09:38:03 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ DRY-RUN OK +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ DRY-RUN OK +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ DRY-RUN OK +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ DRY-RUN OK +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ DRY-RUN OK +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ DRY-RUN OK +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ DRY-RUN OK +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ DRY-RUN OK +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ DRY-RUN OK +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ DRY-RUN OK +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ DRY-RUN OK +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ DRY-RUN OK +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ DRY-RUN OK +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ DRY-RUN OK +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ DRY-RUN OK +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ DRY-RUN OK +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ DRY-RUN OK +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ DRY-RUN OK +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ DRY-RUN OK +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ DRY-RUN OK +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ DRY-RUN OK +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ DRY-RUN OK +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ DRY-RUN OK +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ DRY-RUN OK +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ DRY-RUN OK +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ DRY-RUN OK +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ DRY-RUN OK +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ DRY-RUN OK +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ DRY-RUN OK +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ DRY-RUN OK +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ DRY-RUN OK +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ DRY-RUN OK +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ DRY-RUN OK +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ DRY-RUN OK +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ DRY-RUN OK +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ DRY-RUN OK +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ DRY-RUN OK +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ DRY-RUN OK +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ DRY-RUN OK +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ DRY-RUN OK +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ DRY-RUN OK +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ DRY-RUN OK +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ DRY-RUN OK +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ DRY-RUN OK +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ DRY-RUN OK +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ DRY-RUN OK +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ DRY-RUN OK +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ DRY-RUN OK +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ DRY-RUN OK +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ DRY-RUN OK +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ DRY-RUN OK +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ DRY-RUN OK +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ DRY-RUN OK +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ DRY-RUN OK +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ DRY-RUN OK +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ DRY-RUN OK +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ DRY-RUN OK +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ DRY-RUN OK +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ DRY-RUN OK +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ DRY-RUN OK +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ DRY-RUN OK +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ DRY-RUN OK +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ DRY-RUN OK +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ DRY-RUN OK +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ DRY-RUN OK +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ DRY-RUN OK +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ DRY-RUN OK +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ DRY-RUN OK +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ DRY-RUN OK +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ DRY-RUN OK +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ DRY-RUN OK +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ DRY-RUN OK +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ DRY-RUN OK +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ DRY-RUN OK +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ DRY-RUN OK +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ DRY-RUN OK +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ DRY-RUN OK +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ DRY-RUN OK +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ DRY-RUN OK +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ DRY-RUN OK +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ DRY-RUN OK +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ DRY-RUN OK +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ DRY-RUN OK +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ DRY-RUN OK +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ DRY-RUN OK +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ DRY-RUN OK +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ DRY-RUN OK +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ DRY-RUN OK +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ DRY-RUN OK +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ DRY-RUN OK +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ DRY-RUN OK +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ DRY-RUN OK +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ DRY-RUN OK +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ DRY-RUN OK +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ DRY-RUN OK +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ DRY-RUN OK +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ DRY-RUN OK +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ DRY-RUN OK +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ DRY-RUN OK +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ DRY-RUN OK +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ DRY-RUN OK +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ DRY-RUN OK +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ DRY-RUN OK +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ DRY-RUN OK +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ DRY-RUN OK +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ DRY-RUN OK +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ DRY-RUN OK +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ DRY-RUN OK +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ DRY-RUN OK +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ DRY-RUN OK +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ DRY-RUN OK +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ DRY-RUN OK +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ DRY-RUN OK +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ DRY-RUN OK +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ DRY-RUN OK +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ DRY-RUN OK +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ DRY-RUN OK +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ DRY-RUN OK +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ DRY-RUN OK +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ DRY-RUN OK +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ DRY-RUN OK +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ DRY-RUN OK +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ DRY-RUN OK +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ DRY-RUN OK +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ DRY-RUN OK +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ DRY-RUN OK +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ DRY-RUN OK +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ DRY-RUN OK +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ DRY-RUN OK +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ DRY-RUN OK +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ DRY-RUN OK +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ DRY-RUN OK +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ DRY-RUN OK +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ DRY-RUN OK +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ DRY-RUN OK +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ DRY-RUN OK +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ DRY-RUN OK +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ DRY-RUN OK +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ DRY-RUN OK +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ DRY-RUN OK +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ DRY-RUN OK +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ DRY-RUN OK +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ DRY-RUN OK +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ DRY-RUN OK +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ DRY-RUN OK +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ DRY-RUN OK +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ DRY-RUN OK +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ DRY-RUN OK +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ DRY-RUN OK +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ DRY-RUN OK +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ DRY-RUN OK +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ DRY-RUN OK +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ DRY-RUN OK +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ DRY-RUN OK +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ DRY-RUN OK +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ DRY-RUN OK +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ DRY-RUN OK +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ DRY-RUN OK +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ DRY-RUN OK +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ DRY-RUN OK +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ DRY-RUN OK +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ DRY-RUN OK +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ DRY-RUN OK +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ DRY-RUN OK +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ DRY-RUN OK +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ DRY-RUN OK +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ DRY-RUN OK +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ DRY-RUN OK +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ DRY-RUN OK +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ DRY-RUN OK +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ DRY-RUN OK +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ DRY-RUN OK +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ DRY-RUN OK +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ DRY-RUN OK +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ DRY-RUN OK +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ DRY-RUN OK +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ DRY-RUN OK +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ DRY-RUN OK +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ DRY-RUN OK +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ DRY-RUN OK +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ DRY-RUN OK +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ DRY-RUN OK +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ DRY-RUN OK +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ DRY-RUN OK +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ DRY-RUN OK +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ DRY-RUN OK +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ DRY-RUN OK +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ DRY-RUN OK +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ DRY-RUN OK +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ DRY-RUN OK +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ DRY-RUN OK +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ DRY-RUN OK +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ DRY-RUN OK +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ DRY-RUN OK +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ DRY-RUN OK +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ DRY-RUN OK +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ DRY-RUN OK +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ DRY-RUN OK +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ DRY-RUN OK +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ DRY-RUN OK +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ DRY-RUN OK +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ DRY-RUN OK +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ DRY-RUN OK +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ DRY-RUN OK +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ DRY-RUN OK +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ DRY-RUN OK +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ DRY-RUN OK +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ DRY-RUN OK +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ DRY-RUN OK +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ DRY-RUN OK +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ DRY-RUN OK +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ DRY-RUN OK +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ DRY-RUN OK +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ DRY-RUN OK +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ DRY-RUN OK +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ DRY-RUN OK +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ DRY-RUN OK +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ DRY-RUN OK +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ DRY-RUN OK +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ DRY-RUN OK +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ DRY-RUN OK +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ DRY-RUN OK +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ DRY-RUN OK +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ DRY-RUN OK +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ DRY-RUN OK +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ DRY-RUN OK +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ DRY-RUN OK +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ DRY-RUN OK +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ DRY-RUN OK +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ DRY-RUN OK +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ DRY-RUN OK +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ DRY-RUN OK +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ DRY-RUN OK +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ DRY-RUN OK +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ DRY-RUN OK +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ DRY-RUN OK +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ DRY-RUN OK +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ DRY-RUN OK +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ DRY-RUN OK +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ DRY-RUN OK +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ DRY-RUN OK +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ DRY-RUN OK +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ DRY-RUN OK +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ DRY-RUN OK +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ DRY-RUN OK +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ DRY-RUN OK +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ DRY-RUN OK +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ DRY-RUN OK +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ DRY-RUN OK +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ DRY-RUN OK +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ DRY-RUN OK +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ DRY-RUN OK +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ DRY-RUN OK +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ DRY-RUN OK +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ DRY-RUN OK +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ DRY-RUN OK +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ DRY-RUN OK +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ DRY-RUN OK +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ DRY-RUN OK +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ DRY-RUN OK +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ DRY-RUN OK +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ DRY-RUN OK +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ DRY-RUN OK +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ DRY-RUN OK +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ DRY-RUN OK +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ DRY-RUN OK +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ DRY-RUN OK +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ DRY-RUN OK +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ DRY-RUN OK +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ DRY-RUN OK +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ DRY-RUN OK +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ DRY-RUN OK +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ DRY-RUN OK +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ DRY-RUN OK +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ DRY-RUN OK +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ DRY-RUN OK +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ DRY-RUN OK +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ DRY-RUN OK +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ DRY-RUN OK +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ DRY-RUN OK +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ DRY-RUN OK +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ DRY-RUN OK +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ DRY-RUN OK +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ DRY-RUN OK +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ DRY-RUN OK +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ DRY-RUN OK +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ DRY-RUN OK +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ DRY-RUN OK +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ DRY-RUN OK +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ DRY-RUN OK +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ DRY-RUN OK +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ DRY-RUN OK +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ DRY-RUN OK +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ DRY-RUN OK +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ DRY-RUN OK +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ DRY-RUN OK +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ DRY-RUN OK +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ DRY-RUN OK +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ DRY-RUN OK +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ DRY-RUN OK +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ DRY-RUN OK +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ DRY-RUN OK +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ DRY-RUN OK +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ DRY-RUN OK +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ DRY-RUN OK +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ DRY-RUN OK +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ DRY-RUN OK +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ DRY-RUN OK +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ DRY-RUN OK +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ DRY-RUN OK +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ DRY-RUN OK +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ DRY-RUN OK +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ DRY-RUN OK +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ DRY-RUN OK +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ DRY-RUN OK +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ DRY-RUN OK +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ DRY-RUN OK +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ DRY-RUN OK +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ DRY-RUN OK +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ DRY-RUN OK +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ DRY-RUN OK +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ DRY-RUN OK +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ DRY-RUN OK +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ DRY-RUN OK +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ DRY-RUN OK +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ DRY-RUN OK +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ DRY-RUN OK +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ DRY-RUN OK +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ DRY-RUN OK +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ DRY-RUN OK +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ DRY-RUN OK +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ DRY-RUN OK + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 0 +Skipped (already exist): 0 +Failed: 0 +Total entities: 0 +Avg entities/slot: 0.0 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260228_093803.log +============================================================ + diff --git a/meva/examples/run_records/data/extraction_records/batch_progress.json b/meva/examples/run_records/data/extraction_records/batch_progress.json new file mode 100644 index 0000000..f1e349b --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/batch_progress.json @@ -0,0 +1,9 @@ +{ + "started_at": "2026-02-28T09:38:03.731833", + "last_updated": "2026-02-28T09:38:04.042793", + "completed_slots": [], + "failed_slots": [], + "skipped_slots": [], + "total_slots": 814, + "total_entities_extracted": 0 +} \ No newline at end of file diff --git a/meva/examples/run_records/data/extraction_records/overnight_segformer.log.txt b/meva/examples/run_records/data/extraction_records/overnight_segformer.log.txt new file mode 100644 index 0000000..fe1ae2f --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/overnight_segformer.log.txt @@ -0,0 +1,1451 @@ + +Loaded 814 slots with geom data (filtered from 929 total) +Coverage: 87.6% of canonical slots have extractable geom +Expected entities: ~89,739 actors + +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193629.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-27 19:36:29 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 1.2h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 1.2h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 1.2h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (23 entities) + Progress: 4/351 (1%), ETA: 1.5h + +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 5/351 (1%), ETA: 1.4h + +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (95 entities) + Progress: 6/351 (1%), ETA: 1.8h + +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (28 entities) + Progress: 7/351 (1%), ETA: 1.8h + +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (58 entities) + Progress: 8/351 (2%), ETA: 2.0h + +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 9/351 (2%), ETA: 1.9h + +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 10/351 (2%), ETA: 2.0h + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (86 entities) + Progress: 11/351 (3%), ETA: 2.1h + +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (164 entities) + Progress: 12/351 (3%), ETA: 2.3h + +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 13/351 (3%), ETA: 2.2h + +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 14/351 (3%), ETA: 2.1h + +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (2 entities) + Progress: 15/351 (4%), ETA: 2.1h + +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (189 entities) + Progress: 16/351 (4%), ETA: 2.2h + +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 17/351 (4%), ETA: 2.1h + +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (11 entities) + Progress: 18/351 (5%), ETA: 2.1h + +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (3 entities) + Progress: 19/351 (5%), ETA: 2.0h + +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (176 entities) + Progress: 20/351 (5%), ETA: 2.2h + +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 21/351 (5%), ETA: 2.1h + +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 22/351 (6%), ETA: 2.0h + +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (17 entities) + Progress: 23/351 (6%), ETA: 2.0h + +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (148 entities) + Progress: 24/351 (6%), ETA: 2.1h + +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (9 entities) + Progress: 25/351 (7%), ETA: 2.1h + +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 26/351 (7%), ETA: 2.1h + +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (222 entities) + Progress: 27/351 (7%), ETA: 2.2h + +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (202 entities) + Progress: 28/351 (7%), ETA: 2.2h + +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (37 entities) + Progress: 29/351 (8%), ETA: 2.2h + +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (18 entities) + Progress: 30/351 (8%), ETA: 2.2h + +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (201 entities) + Progress: 31/351 (8%), ETA: 2.3h + +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (98 entities) + Progress: 32/351 (9%), ETA: 2.3h + +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (13 entities) + Progress: 33/351 (9%), ETA: 2.3h + +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (17 entities) + Progress: 34/351 (9%), ETA: 2.2h + +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (133 entities) + Progress: 35/351 (9%), ETA: 2.3h + +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (87 entities) + Progress: 36/351 (10%), ETA: 2.2h + +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (24 entities) + Progress: 37/351 (10%), ETA: 2.2h + +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (5 entities) + Progress: 38/351 (10%), ETA: 2.2h + +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (250 entities) + Progress: 39/351 (11%), ETA: 2.3h + +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (267 entities) + Progress: 40/351 (11%), ETA: 2.3h + +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (256 entities) + Progress: 41/351 (11%), ETA: 2.3h + +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 42/351 (11%), ETA: 2.3h + +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (477 entities) + Progress: 43/351 (12%), ETA: 2.4h + +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (288 entities) + Progress: 44/351 (12%), ETA: 2.5h + +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (130 entities) + Progress: 45/351 (12%), ETA: 2.5h + +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (5 entities) + Progress: 46/351 (13%), ETA: 2.4h + +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (270 entities) + Progress: 47/351 (13%), ETA: 2.5h + +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (374 entities) + Progress: 48/351 (13%), ETA: 2.6h + +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (101 entities) + Progress: 49/351 (13%), ETA: 2.6h + +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 50/351 (14%), ETA: 2.6h + +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (298 entities) + Progress: 51/351 (14%), ETA: 2.6h + +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (277 entities) + Progress: 52/351 (14%), ETA: 2.7h + +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (124 entities) + Progress: 53/351 (15%), ETA: 2.6h + +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SUCCESS (9 entities) + Progress: 54/351 (15%), ETA: 2.6h + +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SUCCESS (260 entities) + Progress: 55/351 (15%), ETA: 2.7h + +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SUCCESS (332 entities) + Progress: 56/351 (15%), ETA: 2.7h + +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SUCCESS (168 entities) + Progress: 57/351 (16%), ETA: 2.7h + +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SUCCESS (16 entities) + Progress: 58/351 (16%), ETA: 2.6h + +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SUCCESS (509 entities) + Progress: 59/351 (16%), ETA: 2.7h + +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SUCCESS (365 entities) + Progress: 60/351 (17%), ETA: 2.8h + +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SUCCESS (156 entities) + Progress: 61/351 (17%), ETA: 2.8h + +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SUCCESS (13 entities) + Progress: 62/351 (17%), ETA: 2.7h + +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SUCCESS (202 entities) + Progress: 63/351 (17%), ETA: 2.7h + +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SUCCESS (301 entities) + Progress: 64/351 (18%), ETA: 2.8h + +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SUCCESS (82 entities) + Progress: 65/351 (18%), ETA: 2.8h + +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SUCCESS (11 entities) + Progress: 66/351 (18%), ETA: 2.7h + +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SUCCESS (1153 entities) + Progress: 67/351 (19%), ETA: 2.8h + +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SUCCESS (47 entities) + Progress: 68/351 (19%), ETA: 2.8h + +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SUCCESS (68 entities) + Progress: 69/351 (19%), ETA: 2.8h + +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 70/351 (19%), ETA: 2.7h + +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SUCCESS (3132 entities) + Progress: 71/351 (20%), ETA: 2.9h + +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SUCCESS (16 entities) + Progress: 72/351 (20%), ETA: 2.8h + +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SUCCESS (35 entities) + Progress: 73/351 (20%), ETA: 2.8h + +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 74/351 (21%), ETA: 2.8h + +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SUCCESS (760 entities) + Progress: 75/351 (21%), ETA: 2.8h + +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 76/351 (21%), ETA: 2.8h + +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SUCCESS (7 entities) + Progress: 77/351 (21%), ETA: 2.7h + +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ SUCCESS (0 entities) + Progress: 78/351 (22%), ETA: 2.7h + +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SUCCESS (305 entities) + Progress: 79/351 (22%), ETA: 2.7h + +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/351 (22%), ETA: 2.7h + +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SUCCESS (4 entities) + Progress: 81/351 (23%), ETA: 2.6h + +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 82/351 (23%), ETA: 2.6h + +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SUCCESS (235 entities) + Progress: 83/351 (23%), ETA: 2.6h + +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SUCCESS (7 entities) + Progress: 84/351 (23%), ETA: 2.6h + +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SUCCESS (12 entities) + Progress: 85/351 (24%), ETA: 2.5h + +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 86/351 (24%), ETA: 2.5h + +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SUCCESS (953 entities) + Progress: 87/351 (24%), ETA: 2.6h + +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 88/351 (25%), ETA: 2.5h + +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SUCCESS (11 entities) + Progress: 89/351 (25%), ETA: 2.5h + +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SUCCESS (4 entities) + Progress: 90/351 (25%), ETA: 2.5h + +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SUCCESS (679 entities) + Progress: 91/351 (25%), ETA: 2.6h + +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SUCCESS (24 entities) + Progress: 92/351 (26%), ETA: 2.5h + +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SUCCESS (47 entities) + Progress: 93/351 (26%), ETA: 2.5h + +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SUCCESS (467 entities) + Progress: 94/351 (26%), ETA: 2.6h + +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SUCCESS (24 entities) + Progress: 95/351 (27%), ETA: 2.5h + +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SUCCESS (12 entities) + Progress: 96/351 (27%), ETA: 2.5h + +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SUCCESS (9 entities) + Progress: 97/351 (27%), ETA: 2.5h + +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SUCCESS (1799 entities) + Progress: 98/351 (27%), ETA: 2.5h + +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SUCCESS (22 entities) + Progress: 99/351 (28%), ETA: 2.5h + +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SUCCESS (26 entities) + Progress: 100/351 (28%), ETA: 2.5h + +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SUCCESS (1 entities) + Progress: 101/351 (28%), ETA: 2.4h + +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SUCCESS (1199 entities) + Progress: 102/351 (29%), ETA: 2.5h + +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SUCCESS (4 entities) + Progress: 103/351 (29%), ETA: 2.5h + +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SUCCESS (11 entities) + Progress: 104/351 (29%), ETA: 2.4h + +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SUCCESS (3 entities) + Progress: 105/351 (29%), ETA: 2.4h + +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SUCCESS (689 entities) + Progress: 106/351 (30%), ETA: 2.4h + +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SUCCESS (4 entities) + Progress: 107/351 (30%), ETA: 2.4h + +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ SUCCESS (0 entities) + Progress: 108/351 (30%), ETA: 2.4h + +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 109/351 (31%), ETA: 2.4h + +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SUCCESS (318 entities) + Progress: 110/351 (31%), ETA: 2.4h + +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SUCCESS (2 entities) + Progress: 111/351 (31%), ETA: 2.3h + +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ SUCCESS (0 entities) + Progress: 112/351 (31%), ETA: 2.3h + +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 113/351 (32%), ETA: 2.3h + +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SUCCESS (330 entities) + Progress: 114/351 (32%), ETA: 2.3h + +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ SUCCESS (0 entities) + Progress: 115/351 (32%), ETA: 2.3h + +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 116/351 (33%), ETA: 2.2h + +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ SUCCESS (0 entities) + Progress: 117/351 (33%), ETA: 2.2h + +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SUCCESS (993 entities) + Progress: 118/351 (33%), ETA: 2.2h + +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SUCCESS (2 entities) + Progress: 119/351 (33%), ETA: 2.2h + +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SUCCESS (2 entities) + Progress: 120/351 (34%), ETA: 2.2h + +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SUCCESS (1 entities) + Progress: 121/351 (34%), ETA: 2.2h + +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SUCCESS (2098 entities) + Progress: 122/351 (34%), ETA: 2.2h + +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SUCCESS (4 entities) + Progress: 123/351 (35%), ETA: 2.2h + +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SUCCESS (236 entities) + Progress: 124/351 (35%), ETA: 2.2h + +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SUCCESS (8 entities) + Progress: 125/351 (35%), ETA: 2.2h + +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SUCCESS (3 entities) + Progress: 126/351 (35%), ETA: 2.2h + +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SUCCESS (300 entities) + Progress: 127/351 (36%), ETA: 2.2h + +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SUCCESS (54 entities) + Progress: 128/351 (36%), ETA: 2.2h + +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SUCCESS (5 entities) + Progress: 129/351 (36%), ETA: 2.1h + +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 130/351 (37%), ETA: 2.1h + +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SUCCESS (598 entities) + Progress: 131/351 (37%), ETA: 2.1h + +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SUCCESS (14 entities) + Progress: 132/351 (37%), ETA: 2.1h + +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 133/351 (37%), ETA: 2.1h + +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 134/351 (38%), ETA: 2.1h + +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SUCCESS (278 entities) + Progress: 135/351 (38%), ETA: 2.1h + +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SUCCESS (16 entities) + Progress: 136/351 (38%), ETA: 2.1h + +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 137/351 (39%), ETA: 2.0h + +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 138/351 (39%), ETA: 2.0h + +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SUCCESS (748 entities) + Progress: 139/351 (39%), ETA: 2.0h + +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SUCCESS (8 entities) + Progress: 140/351 (39%), ETA: 2.0h + +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SUCCESS (5 entities) + Progress: 141/351 (40%), ETA: 2.0h + +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 142/351 (40%), ETA: 2.0h + +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SUCCESS (1596 entities) + Progress: 143/351 (40%), ETA: 2.0h + +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SUCCESS (8 entities) + Progress: 144/351 (41%), ETA: 2.0h + +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 145/351 (41%), ETA: 2.0h + +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SUCCESS (5 entities) + Progress: 146/351 (41%), ETA: 2.0h + +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SUCCESS (139 entities) + Progress: 147/351 (41%), ETA: 2.0h + +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SUCCESS (29 entities) + Progress: 148/351 (42%), ETA: 1.9h + +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SUCCESS (17 entities) + Progress: 149/351 (42%), ETA: 1.9h + +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SUCCESS (20 entities) + Progress: 150/351 (42%), ETA: 1.9h + +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SUCCESS (45 entities) + Progress: 151/351 (43%), ETA: 1.9h + +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SUCCESS (15 entities) + Progress: 152/351 (43%), ETA: 1.9h + +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SUCCESS (26 entities) + Progress: 153/351 (43%), ETA: 1.9h + +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 154/351 (43%), ETA: 1.9h + +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SUCCESS (1258 entities) + Progress: 155/351 (44%), ETA: 1.9h + +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SUCCESS (12 entities) + Progress: 156/351 (44%), ETA: 1.9h + +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SUCCESS (7 entities) + Progress: 157/351 (44%), ETA: 1.8h + +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 158/351 (45%), ETA: 1.8h + +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SUCCESS (752 entities) + Progress: 159/351 (45%), ETA: 1.8h + +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SUCCESS (24 entities) + Progress: 160/351 (45%), ETA: 1.8h + +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SUCCESS (7 entities) + Progress: 161/351 (45%), ETA: 1.8h + +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SUCCESS (498 entities) + Progress: 162/351 (46%), ETA: 1.8h + +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 163/351 (46%), ETA: 1.8h + +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SUCCESS (11 entities) + Progress: 164/351 (46%), ETA: 1.8h + +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 165/351 (47%), ETA: 1.8h + +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ SUCCESS (0 entities) + Progress: 166/351 (47%), ETA: 1.7h + +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SUCCESS (206 entities) + Progress: 167/351 (47%), ETA: 1.7h + +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 168/351 (47%), ETA: 1.7h + +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 169/351 (48%), ETA: 1.7h + +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SUCCESS (0 entities) + Progress: 170/351 (48%), ETA: 1.7h + +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SUCCESS (1762 entities) + Progress: 171/351 (48%), ETA: 1.7h + +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SUCCESS (3 entities) + Progress: 172/351 (49%), ETA: 1.7h + +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SUCCESS (21 entities) + Progress: 173/351 (49%), ETA: 1.7h + +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SUCCESS (579 entities) + Progress: 174/351 (49%), ETA: 1.7h + +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SUCCESS (12 entities) + Progress: 175/351 (49%), ETA: 1.7h + +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SUCCESS (34 entities) + Progress: 176/351 (50%), ETA: 1.7h + +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SUCCESS (4 entities) + Progress: 177/351 (50%), ETA: 1.6h + +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SUCCESS (146 entities) + Progress: 178/351 (50%), ETA: 1.6h + +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SUCCESS (13 entities) + Progress: 179/351 (50%), ETA: 1.6h + +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SUCCESS (755 entities) + Progress: 180/351 (51%), ETA: 1.6h + +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SUCCESS (10 entities) + Progress: 181/351 (51%), ETA: 1.6h + +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SUCCESS (34 entities) + Progress: 182/351 (51%), ETA: 1.6h + +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SUCCESS (3 entities) + Progress: 183/351 (52%), ETA: 1.6h + +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SUCCESS (1355 entities) + Progress: 184/351 (52%), ETA: 1.6h + +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SUCCESS (2 entities) + Progress: 185/351 (52%), ETA: 1.6h + +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SUCCESS (0 entities) + Progress: 186/351 (52%), ETA: 1.6h + +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 187/351 (53%), ETA: 1.5h + +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SUCCESS (1043 entities) + Progress: 188/351 (53%), ETA: 1.6h + +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 189/351 (53%), ETA: 1.5h + +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SUCCESS (6 entities) + Progress: 190/351 (54%), ETA: 1.5h + +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SUCCESS (3 entities) + Progress: 191/351 (54%), ETA: 1.5h + +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SUCCESS (1684 entities) + Progress: 192/351 (54%), ETA: 1.5h + +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SUCCESS (16 entities) + Progress: 193/351 (54%), ETA: 1.5h + +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SUCCESS (46 entities) + Progress: 194/351 (55%), ETA: 1.5h + +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SUCCESS (9 entities) + Progress: 195/351 (55%), ETA: 1.5h + +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SUCCESS (13 entities) + Progress: 196/351 (55%), ETA: 1.5h + +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SUCCESS (144 entities) + Progress: 197/351 (56%), ETA: 1.5h + +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SUCCESS (8 entities) + Progress: 198/351 (56%), ETA: 1.5h + +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SUCCESS (15 entities) + Progress: 199/351 (56%), ETA: 1.4h + +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SUCCESS (49 entities) + Progress: 200/351 (56%), ETA: 1.4h + +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SUCCESS (555 entities) + Progress: 201/351 (57%), ETA: 1.4h + +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SUCCESS (15 entities) + Progress: 202/351 (57%), ETA: 1.4h + +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 203/351 (57%), ETA: 1.4h + +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 204/351 (58%), ETA: 1.4h + +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SUCCESS (601 entities) + Progress: 205/351 (58%), ETA: 1.4h + +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 206/351 (58%), ETA: 1.4h + +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SUCCESS (1 entities) + Progress: 207/351 (58%), ETA: 1.4h + +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SUCCESS (93 entities) + Progress: 208/351 (59%), ETA: 1.3h + +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SUCCESS (573 entities) + Progress: 209/351 (59%), ETA: 1.3h + +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 210/351 (59%), ETA: 1.3h + +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 211/351 (60%), ETA: 1.3h + +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 212/351 (60%), ETA: 1.3h + +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SUCCESS (619 entities) + Progress: 213/351 (60%), ETA: 1.3h + +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 214/351 (60%), ETA: 1.3h + +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SUCCESS (20 entities) + Progress: 215/351 (61%), ETA: 1.3h + +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SUCCESS (13 entities) + Progress: 216/351 (61%), ETA: 1.3h + +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SUCCESS (1380 entities) + Progress: 217/351 (61%), ETA: 1.3h + +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SUCCESS (4 entities) + Progress: 218/351 (62%), ETA: 1.2h + +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SUCCESS (12 entities) + Progress: 219/351 (62%), ETA: 1.2h + +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SUCCESS (28 entities) + Progress: 220/351 (62%), ETA: 1.2h + +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SUCCESS (14 entities) + Progress: 221/351 (62%), ETA: 1.2h + +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SUCCESS (106 entities) + Progress: 222/351 (63%), ETA: 1.2h + +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SUCCESS (7 entities) + Progress: 223/351 (63%), ETA: 1.2h + +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SUCCESS (39 entities) + Progress: 224/351 (63%), ETA: 1.2h + +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SUCCESS (27 entities) + Progress: 225/351 (64%), ETA: 1.2h + +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SUCCESS (304 entities) + Progress: 226/351 (64%), ETA: 1.2h + +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 227/351 (64%), ETA: 1.2h + +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SUCCESS (17 entities) + Progress: 228/351 (64%), ETA: 1.1h + +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SUCCESS (39 entities) + Progress: 229/351 (65%), ETA: 1.1h + +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SUCCESS (756 entities) + Progress: 230/351 (65%), ETA: 1.1h + +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SUCCESS (30 entities) + Progress: 231/351 (65%), ETA: 1.1h + +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SUCCESS (13 entities) + Progress: 232/351 (66%), ETA: 1.1h + +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SUCCESS (708 entities) + Progress: 233/351 (66%), ETA: 1.1h + +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SUCCESS (5 entities) + Progress: 234/351 (66%), ETA: 1.1h + +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SUCCESS (1 entities) + Progress: 235/351 (66%), ETA: 1.1h + +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SUCCESS (32 entities) + Progress: 236/351 (67%), ETA: 1.1h + +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SUCCESS (83 entities) + Progress: 237/351 (67%), ETA: 1.1h + +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SUCCESS (651 entities) + Progress: 238/351 (67%), ETA: 1.1h + +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 239/351 (68%), ETA: 1.0h + +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SUCCESS (14 entities) + Progress: 240/351 (68%), ETA: 1.0h + +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SUCCESS (9 entities) + Progress: 241/351 (68%), ETA: 1.0h + +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SUCCESS (1093 entities) + Progress: 242/351 (68%), ETA: 1.0h + +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 243/351 (69%), ETA: 1.0h + +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SUCCESS (43 entities) + Progress: 244/351 (69%), ETA: 1.0h + +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SUCCESS (47 entities) + Progress: 245/351 (69%), ETA: 1.0h + +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SUCCESS (65 entities) + Progress: 246/351 (70%), ETA: 1.0h + +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SUCCESS (16 entities) + Progress: 247/351 (70%), ETA: 1.0h + +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SUCCESS (21 entities) + Progress: 248/351 (70%), ETA: 1.0h + +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SUCCESS (490 entities) + Progress: 249/351 (70%), ETA: 0.9h + +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SUCCESS (10 entities) + Progress: 250/351 (71%), ETA: 0.9h + +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SUCCESS (77 entities) + Progress: 251/351 (71%), ETA: 0.9h + +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SUCCESS (31 entities) + Progress: 252/351 (71%), ETA: 0.9h + +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SUCCESS (35 entities) + Progress: 253/351 (72%), ETA: 0.9h + +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 254/351 (72%), ETA: 0.9h + +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SUCCESS (243 entities) + Progress: 255/351 (72%), ETA: 0.9h + +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SUCCESS (36 entities) + Progress: 256/351 (72%), ETA: 0.9h + +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SUCCESS (33 entities) + Progress: 257/351 (73%), ETA: 0.9h + +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 258/351 (73%), ETA: 0.9h + +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SUCCESS (129 entities) + Progress: 259/351 (73%), ETA: 0.8h + +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SUCCESS (88 entities) + Progress: 260/351 (74%), ETA: 0.8h + +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SUCCESS (66 entities) + Progress: 261/351 (74%), ETA: 0.8h + +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 262/351 (74%), ETA: 0.8h + +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SUCCESS (111 entities) + Progress: 263/351 (74%), ETA: 0.8h + +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SUCCESS (47 entities) + Progress: 264/351 (75%), ETA: 0.8h + +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SUCCESS (15 entities) + Progress: 265/351 (75%), ETA: 0.8h + +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 266/351 (75%), ETA: 0.8h + +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SUCCESS (185 entities) + Progress: 267/351 (76%), ETA: 0.8h + +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SUCCESS (4 entities) + Progress: 268/351 (76%), ETA: 0.8h + +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SUCCESS (368 entities) + Progress: 269/351 (76%), ETA: 0.7h + +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 270/351 (76%), ETA: 0.7h + +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SUCCESS (131 entities) + Progress: 271/351 (77%), ETA: 0.7h + +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SUCCESS (4 entities) + Progress: 272/351 (77%), ETA: 0.7h + +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SUCCESS (47 entities) + Progress: 273/351 (77%), ETA: 0.7h + +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 274/351 (78%), ETA: 0.7h + +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SUCCESS (2 entities) + Progress: 275/351 (78%), ETA: 0.7h + +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SUCCESS (1 entities) + Progress: 276/351 (78%), ETA: 0.7h + +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SUCCESS (485 entities) + Progress: 277/351 (78%), ETA: 0.7h + +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SUCCESS (1 entities) + Progress: 278/351 (79%), ETA: 0.7h + +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SUCCESS (2 entities) + Progress: 279/351 (79%), ETA: 0.6h + +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SUCCESS (16 entities) + Progress: 280/351 (79%), ETA: 0.6h + +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SUCCESS (454 entities) + Progress: 281/351 (80%), ETA: 0.6h + +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 282/351 (80%), ETA: 0.6h + +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SUCCESS (0 entities) + Progress: 283/351 (80%), ETA: 0.6h + +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 284/351 (80%), ETA: 0.6h + +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SUCCESS (406 entities) + Progress: 285/351 (81%), ETA: 0.6h + +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 286/351 (81%), ETA: 0.6h + +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SUCCESS (0 entities) + Progress: 287/351 (81%), ETA: 0.6h + +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SUCCESS (11 entities) + Progress: 288/351 (82%), ETA: 0.6h + +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SUCCESS (60 entities) + Progress: 289/351 (82%), ETA: 0.6h + +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SUCCESS (4 entities) + Progress: 290/351 (82%), ETA: 0.5h + +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 291/351 (82%), ETA: 0.5h + +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SUCCESS (19 entities) + Progress: 292/351 (83%), ETA: 0.5h + +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SUCCESS (83 entities) + Progress: 293/351 (83%), ETA: 0.5h + +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 294/351 (83%), ETA: 0.5h + +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SUCCESS (0 entities) + Progress: 295/351 (84%), ETA: 0.5h + +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SUCCESS (81 entities) + Progress: 296/351 (84%), ETA: 0.5h + +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SUCCESS (101 entities) + Progress: 297/351 (84%), ETA: 0.5h + +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 298/351 (84%), ETA: 0.5h + +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SUCCESS (4 entities) + Progress: 299/351 (85%), ETA: 0.5h + +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SUCCESS (18 entities) + Progress: 300/351 (85%), ETA: 0.4h + +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SUCCESS (49 entities) + Progress: 301/351 (85%), ETA: 0.4h + +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 302/351 (86%), ETA: 0.4h + +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SUCCESS (30 entities) + Progress: 303/351 (86%), ETA: 0.4h + +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 304/351 (86%), ETA: 0.4h + +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SUCCESS (401 entities) + Progress: 305/351 (86%), ETA: 0.4h + +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 306/351 (87%), ETA: 0.4h + +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SUCCESS (0 entities) + Progress: 307/351 (87%), ETA: 0.4h + +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 308/351 (87%), ETA: 0.4h + +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SUCCESS (72 entities) + Progress: 309/351 (88%), ETA: 0.4h + +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 310/351 (88%), ETA: 0.4h + +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SUCCESS (0 entities) + Progress: 311/351 (88%), ETA: 0.3h + +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SUCCESS (147 entities) + Progress: 312/351 (88%), ETA: 0.3h + +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SUCCESS (551 entities) + Progress: 313/351 (89%), ETA: 0.3h + +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SUCCESS (18 entities) + Progress: 314/351 (89%), ETA: 0.3h + +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SUCCESS (104 entities) + Progress: 315/351 (89%), ETA: 0.3h + +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SUCCESS (174 entities) + Progress: 316/351 (90%), ETA: 0.3h + +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SUCCESS (544 entities) + Progress: 317/351 (90%), ETA: 0.3h + +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 318/351 (90%), ETA: 0.3h + +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SUCCESS (89 entities) + Progress: 319/351 (90%), ETA: 0.3h + +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SUCCESS (154 entities) + Progress: 320/351 (91%), ETA: 0.3h + +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SUCCESS (264 entities) + Progress: 321/351 (91%), ETA: 0.3h + +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SUCCESS (26 entities) + Progress: 322/351 (91%), ETA: 0.3h + +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SUCCESS (52 entities) + Progress: 323/351 (92%), ETA: 0.2h + +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SUCCESS (160 entities) + Progress: 324/351 (92%), ETA: 0.2h + +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SUCCESS (393 entities) + Progress: 325/351 (92%), ETA: 0.2h + +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SUCCESS (26 entities) + Progress: 326/351 (92%), ETA: 0.2h + +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SUCCESS (126 entities) + Progress: 327/351 (93%), ETA: 0.2h + +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SUCCESS (161 entities) + Progress: 328/351 (93%), ETA: 0.2h + +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SUCCESS (465 entities) + Progress: 329/351 (93%), ETA: 0.2h + +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 330/351 (94%), ETA: 0.2h + +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SUCCESS (55 entities) + Progress: 331/351 (94%), ETA: 0.2h + +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SUCCESS (100 entities) + Progress: 332/351 (94%), ETA: 0.2h + +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SUCCESS (402 entities) + Progress: 333/351 (94%), ETA: 0.2h + +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SUCCESS (13 entities) + Progress: 334/351 (95%), ETA: 0.2h + +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SUCCESS (121 entities) + Progress: 335/351 (95%), ETA: 0.1h + +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SUCCESS (105 entities) + Progress: 336/351 (95%), ETA: 0.1h + +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SUCCESS (607 entities) + Progress: 337/351 (96%), ETA: 0.1h + +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SUCCESS (9 entities) + Progress: 338/351 (96%), ETA: 0.1h + +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SUCCESS (66 entities) + Progress: 339/351 (96%), ETA: 0.1h + +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SUCCESS (129 entities) + Progress: 340/351 (96%), ETA: 0.1h + +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SUCCESS (626 entities) + Progress: 341/351 (97%), ETA: 0.1h + +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SUCCESS (14 entities) + Progress: 342/351 (97%), ETA: 0.1h + +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SUCCESS (58 entities) + Progress: 343/351 (97%), ETA: 0.1h + +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SUCCESS (197 entities) + Progress: 344/351 (98%), ETA: 0.1h + +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SUCCESS (631 entities) + Progress: 345/351 (98%), ETA: 0.1h + +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SUCCESS (6 entities) + Progress: 346/351 (98%), ETA: 0.0h + +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SUCCESS (75 entities) + Progress: 347/351 (98%), ETA: 0.0h + +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SUCCESS (79 entities) + Progress: 348/351 (99%), ETA: 0.0h + +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SUCCESS (448 entities) + Progress: 349/351 (99%), ETA: 0.0h + +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SUCCESS (30 entities) + Progress: 350/351 (99%), ETA: 0.0h + +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SUCCESS (73 entities) + Progress: 351/351 (100%), ETA: 0.0h + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 3.2 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193629.log +============================================================ + + +============================================================ +SUMMARY +============================================================ +Total slots: 351 +Completed: 351 +Skipped (exist): 0 +Failed: 0 +Total entities: 57818 +Time: 3.2 hours +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260227_193629.log +============================================================ +===================== diff --git a/meva/examples/run_records/data/extraction_records/reextract_20260228_053159.log.txt b/meva/examples/run_records/data/extraction_records/reextract_20260228_053159.log.txt new file mode 100644 index 0000000..e69de29 diff --git a/meva/examples/run_records/data/extraction_records/reextract_20260228_053531.log.txt b/meva/examples/run_records/data/extraction_records/reextract_20260228_053531.log.txt new file mode 100644 index 0000000..e69de29 diff --git a/meva/examples/run_records/data/extraction_records/reextract_68b_20260228_053719.log.txt b/meva/examples/run_records/data/extraction_records/reextract_68b_20260228_053719.log.txt new file mode 100644 index 0000000..bda04fe --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/reextract_68b_20260228_053719.log.txt @@ -0,0 +1,1450 @@ + +Loaded 814 slots with geom data (filtered from 929 total) +Coverage: 87.6% of canonical slots have extractable geom +Expected entities: ~89,739 actors + +Unique short slots: 351 (from 814 raw variants) + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Method: segformer +Canonical slots: 351 +Resume: False +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053719.log +Progress: /home/ah66742/data/extraction_logs/batch_progress.json +Started: 2026-02-28 05:37:19 +============================================================ + +[ 1/351] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/351 (0%), ETA: 1.0h + +[ 2/351] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/351 (0%), ETA: 1.0h + +[ 3/351] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/351 (0%), ETA: 1.0h + +[ 4/351] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (23 entities) + Progress: 4/351 (1%), ETA: 1.2h + +[ 5/351] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 5/351 (1%), ETA: 1.1h + +[ 6/351] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (95 entities) + Progress: 6/351 (1%), ETA: 1.5h + +[ 7/351] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (28 entities) + Progress: 7/351 (1%), ETA: 1.6h + +[ 8/351] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (58 entities) + Progress: 8/351 (2%), ETA: 1.7h + +[ 9/351] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 9/351 (2%), ETA: 1.7h + +[ 10/351] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 10/351 (2%), ETA: 1.7h + +[ 11/351] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (86 entities) + Progress: 11/351 (3%), ETA: 1.9h + +[ 12/351] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (164 entities) + Progress: 12/351 (3%), ETA: 2.0h + +[ 13/351] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 13/351 (3%), ETA: 2.0h + +[ 14/351] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 14/351 (3%), ETA: 1.9h + +[ 15/351] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (2 entities) + Progress: 15/351 (4%), ETA: 1.8h + +[ 16/351] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (189 entities) + Progress: 16/351 (4%), ETA: 1.9h + +[ 17/351] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 17/351 (4%), ETA: 1.9h + +[ 18/351] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (11 entities) + Progress: 18/351 (5%), ETA: 1.8h + +[ 19/351] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (3 entities) + Progress: 19/351 (5%), ETA: 1.8h + +[ 20/351] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (176 entities) + Progress: 20/351 (5%), ETA: 1.9h + +[ 21/351] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 21/351 (5%), ETA: 1.8h + +[ 22/351] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 22/351 (6%), ETA: 1.8h + +[ 23/351] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (17 entities) + Progress: 23/351 (6%), ETA: 1.7h + +[ 24/351] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (148 entities) + Progress: 24/351 (6%), ETA: 1.9h + +[ 25/351] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (9 entities) + Progress: 25/351 (7%), ETA: 1.8h + +[ 26/351] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 26/351 (7%), ETA: 1.8h + +[ 27/351] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (222 entities) + Progress: 27/351 (7%), ETA: 2.0h + +[ 28/351] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (202 entities) + Progress: 28/351 (7%), ETA: 2.0h + +[ 29/351] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (37 entities) + Progress: 29/351 (8%), ETA: 2.0h + +[ 30/351] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (18 entities) + Progress: 30/351 (8%), ETA: 1.9h + +[ 31/351] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (201 entities) + Progress: 31/351 (8%), ETA: 2.0h + +[ 32/351] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (98 entities) + Progress: 32/351 (9%), ETA: 2.0h + +[ 33/351] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (13 entities) + Progress: 33/351 (9%), ETA: 2.0h + +[ 34/351] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (17 entities) + Progress: 34/351 (9%), ETA: 2.0h + +[ 35/351] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (133 entities) + Progress: 35/351 (9%), ETA: 2.0h + +[ 36/351] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (87 entities) + Progress: 36/351 (10%), ETA: 2.0h + +[ 37/351] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (24 entities) + Progress: 37/351 (10%), ETA: 2.0h + +[ 38/351] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (5 entities) + Progress: 38/351 (10%), ETA: 2.0h + +[ 39/351] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (250 entities) + Progress: 39/351 (11%), ETA: 2.0h + +[ 40/351] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (267 entities) + Progress: 40/351 (11%), ETA: 2.1h + +[ 41/351] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (256 entities) + Progress: 41/351 (11%), ETA: 2.1h + +[ 42/351] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 42/351 (11%), ETA: 2.1h + +[ 43/351] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (477 entities) + Progress: 43/351 (12%), ETA: 2.2h + +[ 44/351] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (288 entities) + Progress: 44/351 (12%), ETA: 2.3h + +[ 45/351] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (130 entities) + Progress: 45/351 (12%), ETA: 2.2h + +[ 46/351] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (5 entities) + Progress: 46/351 (13%), ETA: 2.2h + +[ 47/351] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (270 entities) + Progress: 47/351 (13%), ETA: 2.3h + +[ 48/351] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (374 entities) + Progress: 48/351 (13%), ETA: 2.4h + +[ 49/351] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (101 entities) + Progress: 49/351 (13%), ETA: 2.3h + +[ 50/351] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 50/351 (14%), ETA: 2.3h + +[ 51/351] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (298 entities) + Progress: 51/351 (14%), ETA: 2.4h + +[ 52/351] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (277 entities) + Progress: 52/351 (14%), ETA: 2.4h + +[ 53/351] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (124 entities) + Progress: 53/351 (15%), ETA: 2.4h + +[ 54/351] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SUCCESS (9 entities) + Progress: 54/351 (15%), ETA: 2.4h + +[ 55/351] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SUCCESS (260 entities) + Progress: 55/351 (15%), ETA: 2.4h + +[ 56/351] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SUCCESS (332 entities) + Progress: 56/351 (15%), ETA: 2.4h + +[ 57/351] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SUCCESS (168 entities) + Progress: 57/351 (16%), ETA: 2.4h + +[ 58/351] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SUCCESS (16 entities) + Progress: 58/351 (16%), ETA: 2.4h + +[ 59/351] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SUCCESS (509 entities) + Progress: 59/351 (16%), ETA: 2.5h + +[ 60/351] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SUCCESS (365 entities) + Progress: 60/351 (17%), ETA: 2.5h + +[ 61/351] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SUCCESS (156 entities) + Progress: 61/351 (17%), ETA: 2.5h + +[ 62/351] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SUCCESS (13 entities) + Progress: 62/351 (17%), ETA: 2.5h + +[ 63/351] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SUCCESS (202 entities) + Progress: 63/351 (17%), ETA: 2.5h + +[ 64/351] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SUCCESS (301 entities) + Progress: 64/351 (18%), ETA: 2.5h + +[ 65/351] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SUCCESS (82 entities) + Progress: 65/351 (18%), ETA: 2.5h + +[ 66/351] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SUCCESS (11 entities) + Progress: 66/351 (18%), ETA: 2.5h + +[ 67/351] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SUCCESS (1153 entities) + Progress: 67/351 (19%), ETA: 2.6h + +[ 68/351] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SUCCESS (47 entities) + Progress: 68/351 (19%), ETA: 2.6h + +[ 69/351] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SUCCESS (68 entities) + Progress: 69/351 (19%), ETA: 2.5h + +[ 70/351] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 70/351 (19%), ETA: 2.5h + +[ 71/351] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SUCCESS (3132 entities) + Progress: 71/351 (20%), ETA: 2.6h + +[ 72/351] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SUCCESS (16 entities) + Progress: 72/351 (20%), ETA: 2.6h + +[ 73/351] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SUCCESS (35 entities) + Progress: 73/351 (20%), ETA: 2.6h + +[ 74/351] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 74/351 (21%), ETA: 2.5h + +[ 75/351] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SUCCESS (760 entities) + Progress: 75/351 (21%), ETA: 2.6h + +[ 76/351] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 76/351 (21%), ETA: 2.5h + +[ 77/351] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SUCCESS (7 entities) + Progress: 77/351 (21%), ETA: 2.5h + +[ 78/351] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ SUCCESS (0 entities) + Progress: 78/351 (22%), ETA: 2.5h + +[ 79/351] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SUCCESS (305 entities) + Progress: 79/351 (22%), ETA: 2.5h + +[ 80/351] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/351 (22%), ETA: 2.4h + +[ 81/351] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SUCCESS (4 entities) + Progress: 81/351 (23%), ETA: 2.4h + +[ 82/351] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 82/351 (23%), ETA: 2.4h + +[ 83/351] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SUCCESS (235 entities) + Progress: 83/351 (23%), ETA: 2.4h + +[ 84/351] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SUCCESS (7 entities) + Progress: 84/351 (23%), ETA: 2.4h + +[ 85/351] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SUCCESS (12 entities) + Progress: 85/351 (24%), ETA: 2.3h + +[ 86/351] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 86/351 (24%), ETA: 2.3h + +[ 87/351] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SUCCESS (953 entities) + Progress: 87/351 (24%), ETA: 2.3h + +[ 88/351] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 88/351 (25%), ETA: 2.3h + +[ 89/351] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SUCCESS (11 entities) + Progress: 89/351 (25%), ETA: 2.3h + +[ 90/351] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SUCCESS (4 entities) + Progress: 90/351 (25%), ETA: 2.3h + +[ 91/351] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SUCCESS (679 entities) + Progress: 91/351 (25%), ETA: 2.3h + +[ 92/351] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SUCCESS (24 entities) + Progress: 92/351 (26%), ETA: 2.3h + +[ 93/351] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SUCCESS (47 entities) + Progress: 93/351 (26%), ETA: 2.3h + +[ 94/351] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SUCCESS (467 entities) + Progress: 94/351 (26%), ETA: 2.3h + +[ 95/351] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SUCCESS (24 entities) + Progress: 95/351 (27%), ETA: 2.3h + +[ 96/351] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SUCCESS (12 entities) + Progress: 96/351 (27%), ETA: 2.3h + +[ 97/351] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SUCCESS (9 entities) + Progress: 97/351 (27%), ETA: 2.3h + +[ 98/351] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SUCCESS (1799 entities) + Progress: 98/351 (27%), ETA: 2.3h + +[ 99/351] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SUCCESS (22 entities) + Progress: 99/351 (28%), ETA: 2.3h + +[100/351] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SUCCESS (26 entities) + Progress: 100/351 (28%), ETA: 2.3h + +[101/351] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SUCCESS (1 entities) + Progress: 101/351 (28%), ETA: 2.2h + +[102/351] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SUCCESS (1199 entities) + Progress: 102/351 (29%), ETA: 2.3h + +[103/351] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SUCCESS (4 entities) + Progress: 103/351 (29%), ETA: 2.2h + +[104/351] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SUCCESS (11 entities) + Progress: 104/351 (29%), ETA: 2.2h + +[105/351] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SUCCESS (3 entities) + Progress: 105/351 (29%), ETA: 2.2h + +[106/351] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SUCCESS (689 entities) + Progress: 106/351 (30%), ETA: 2.2h + +[107/351] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SUCCESS (4 entities) + Progress: 107/351 (30%), ETA: 2.2h + +[108/351] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ SUCCESS (0 entities) + Progress: 108/351 (30%), ETA: 2.2h + +[109/351] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 109/351 (31%), ETA: 2.2h + +[110/351] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SUCCESS (318 entities) + Progress: 110/351 (31%), ETA: 2.2h + +[111/351] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SUCCESS (2 entities) + Progress: 111/351 (31%), ETA: 2.1h + +[112/351] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ SUCCESS (0 entities) + Progress: 112/351 (31%), ETA: 2.1h + +[113/351] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 113/351 (32%), ETA: 2.1h + +[114/351] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SUCCESS (330 entities) + Progress: 114/351 (32%), ETA: 2.1h + +[115/351] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ SUCCESS (0 entities) + Progress: 115/351 (32%), ETA: 2.1h + +[116/351] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 116/351 (33%), ETA: 2.0h + +[117/351] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ SUCCESS (0 entities) + Progress: 117/351 (33%), ETA: 2.0h + +[118/351] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SUCCESS (993 entities) + Progress: 118/351 (33%), ETA: 2.0h + +[119/351] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SUCCESS (2 entities) + Progress: 119/351 (33%), ETA: 2.0h + +[120/351] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SUCCESS (2 entities) + Progress: 120/351 (34%), ETA: 2.0h + +[121/351] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SUCCESS (1 entities) + Progress: 121/351 (34%), ETA: 2.0h + +[122/351] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SUCCESS (2098 entities) + Progress: 122/351 (34%), ETA: 2.0h + +[123/351] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SUCCESS (4 entities) + Progress: 123/351 (35%), ETA: 2.0h + +[124/351] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SUCCESS (236 entities) + Progress: 124/351 (35%), ETA: 2.0h + +[125/351] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SUCCESS (8 entities) + Progress: 125/351 (35%), ETA: 2.0h + +[126/351] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SUCCESS (3 entities) + Progress: 126/351 (35%), ETA: 2.0h + +[127/351] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SUCCESS (300 entities) + Progress: 127/351 (36%), ETA: 2.0h + +[128/351] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SUCCESS (54 entities) + Progress: 128/351 (36%), ETA: 2.0h + +[129/351] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SUCCESS (5 entities) + Progress: 129/351 (36%), ETA: 1.9h + +[130/351] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 130/351 (37%), ETA: 1.9h + +[131/351] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SUCCESS (598 entities) + Progress: 131/351 (37%), ETA: 1.9h + +[132/351] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SUCCESS (14 entities) + Progress: 132/351 (37%), ETA: 1.9h + +[133/351] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 133/351 (37%), ETA: 1.9h + +[134/351] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 134/351 (38%), ETA: 1.9h + +[135/351] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SUCCESS (278 entities) + Progress: 135/351 (38%), ETA: 1.9h + +[136/351] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SUCCESS (16 entities) + Progress: 136/351 (38%), ETA: 1.9h + +[137/351] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 137/351 (39%), ETA: 1.8h + +[138/351] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 138/351 (39%), ETA: 1.8h + +[139/351] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SUCCESS (748 entities) + Progress: 139/351 (39%), ETA: 1.8h + +[140/351] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SUCCESS (8 entities) + Progress: 140/351 (39%), ETA: 1.8h + +[141/351] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SUCCESS (5 entities) + Progress: 141/351 (40%), ETA: 1.8h + +[142/351] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 142/351 (40%), ETA: 1.8h + +[143/351] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SUCCESS (1596 entities) + Progress: 143/351 (40%), ETA: 1.8h + +[144/351] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SUCCESS (8 entities) + Progress: 144/351 (41%), ETA: 1.8h + +[145/351] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 145/351 (41%), ETA: 1.8h + +[146/351] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SUCCESS (5 entities) + Progress: 146/351 (41%), ETA: 1.8h + +[147/351] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SUCCESS (139 entities) + Progress: 147/351 (41%), ETA: 1.8h + +[148/351] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SUCCESS (29 entities) + Progress: 148/351 (42%), ETA: 1.8h + +[149/351] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SUCCESS (17 entities) + Progress: 149/351 (42%), ETA: 1.7h + +[150/351] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SUCCESS (20 entities) + Progress: 150/351 (42%), ETA: 1.7h + +[151/351] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SUCCESS (45 entities) + Progress: 151/351 (43%), ETA: 1.7h + +[152/351] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SUCCESS (15 entities) + Progress: 152/351 (43%), ETA: 1.7h + +[153/351] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SUCCESS (26 entities) + Progress: 153/351 (43%), ETA: 1.7h + +[154/351] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 154/351 (43%), ETA: 1.7h + +[155/351] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SUCCESS (1258 entities) + Progress: 155/351 (44%), ETA: 1.7h + +[156/351] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SUCCESS (12 entities) + Progress: 156/351 (44%), ETA: 1.7h + +[157/351] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SUCCESS (7 entities) + Progress: 157/351 (44%), ETA: 1.7h + +[158/351] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 158/351 (45%), ETA: 1.6h + +[159/351] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SUCCESS (752 entities) + Progress: 159/351 (45%), ETA: 1.7h + +[160/351] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SUCCESS (24 entities) + Progress: 160/351 (45%), ETA: 1.6h + +[161/351] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SUCCESS (7 entities) + Progress: 161/351 (45%), ETA: 1.6h + +[162/351] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SUCCESS (498 entities) + Progress: 162/351 (46%), ETA: 1.6h + +[163/351] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 163/351 (46%), ETA: 1.6h + +[164/351] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SUCCESS (11 entities) + Progress: 164/351 (46%), ETA: 1.6h + +[165/351] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 165/351 (47%), ETA: 1.6h + +[166/351] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ SUCCESS (0 entities) + Progress: 166/351 (47%), ETA: 1.6h + +[167/351] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SUCCESS (206 entities) + Progress: 167/351 (47%), ETA: 1.6h + +[168/351] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 168/351 (47%), ETA: 1.6h + +[169/351] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 169/351 (48%), ETA: 1.5h + +[170/351] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SUCCESS (0 entities) + Progress: 170/351 (48%), ETA: 1.5h + +[171/351] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SUCCESS (1762 entities) + Progress: 171/351 (48%), ETA: 1.5h + +[172/351] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SUCCESS (3 entities) + Progress: 172/351 (49%), ETA: 1.5h + +[173/351] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SUCCESS (21 entities) + Progress: 173/351 (49%), ETA: 1.5h + +[174/351] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SUCCESS (579 entities) + Progress: 174/351 (49%), ETA: 1.5h + +[175/351] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SUCCESS (12 entities) + Progress: 175/351 (49%), ETA: 1.5h + +[176/351] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SUCCESS (34 entities) + Progress: 176/351 (50%), ETA: 1.5h + +[177/351] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SUCCESS (4 entities) + Progress: 177/351 (50%), ETA: 1.5h + +[178/351] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SUCCESS (146 entities) + Progress: 178/351 (50%), ETA: 1.5h + +[179/351] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SUCCESS (13 entities) + Progress: 179/351 (50%), ETA: 1.5h + +[180/351] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SUCCESS (755 entities) + Progress: 180/351 (51%), ETA: 1.5h + +[181/351] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SUCCESS (10 entities) + Progress: 181/351 (51%), ETA: 1.5h + +[182/351] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SUCCESS (34 entities) + Progress: 182/351 (51%), ETA: 1.4h + +[183/351] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SUCCESS (3 entities) + Progress: 183/351 (52%), ETA: 1.4h + +[184/351] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SUCCESS (1355 entities) + Progress: 184/351 (52%), ETA: 1.4h + +[185/351] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SUCCESS (2 entities) + Progress: 185/351 (52%), ETA: 1.4h + +[186/351] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SUCCESS (0 entities) + Progress: 186/351 (52%), ETA: 1.4h + +[187/351] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 187/351 (53%), ETA: 1.4h + +[188/351] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SUCCESS (1043 entities) + Progress: 188/351 (53%), ETA: 1.4h + +[189/351] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 189/351 (53%), ETA: 1.4h + +[190/351] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SUCCESS (6 entities) + Progress: 190/351 (54%), ETA: 1.4h + +[191/351] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SUCCESS (3 entities) + Progress: 191/351 (54%), ETA: 1.4h + +[192/351] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SUCCESS (1684 entities) + Progress: 192/351 (54%), ETA: 1.4h + +[193/351] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SUCCESS (16 entities) + Progress: 193/351 (54%), ETA: 1.4h + +[194/351] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SUCCESS (46 entities) + Progress: 194/351 (55%), ETA: 1.4h + +[195/351] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SUCCESS (9 entities) + Progress: 195/351 (55%), ETA: 1.3h + +[196/351] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SUCCESS (13 entities) + Progress: 196/351 (55%), ETA: 1.3h + +[197/351] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SUCCESS (144 entities) + Progress: 197/351 (56%), ETA: 1.3h + +[198/351] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SUCCESS (8 entities) + Progress: 198/351 (56%), ETA: 1.3h + +[199/351] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SUCCESS (15 entities) + Progress: 199/351 (56%), ETA: 1.3h + +[200/351] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SUCCESS (49 entities) + Progress: 200/351 (56%), ETA: 1.3h + +[201/351] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SUCCESS (555 entities) + Progress: 201/351 (57%), ETA: 1.3h + +[202/351] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SUCCESS (15 entities) + Progress: 202/351 (57%), ETA: 1.3h + +[203/351] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 203/351 (57%), ETA: 1.3h + +[204/351] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 204/351 (58%), ETA: 1.3h + +[205/351] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SUCCESS (601 entities) + Progress: 205/351 (58%), ETA: 1.3h + +[206/351] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 206/351 (58%), ETA: 1.2h + +[207/351] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SUCCESS (1 entities) + Progress: 207/351 (58%), ETA: 1.2h + +[208/351] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SUCCESS (93 entities) + Progress: 208/351 (59%), ETA: 1.2h + +[209/351] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SUCCESS (573 entities) + Progress: 209/351 (59%), ETA: 1.2h + +[210/351] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 210/351 (59%), ETA: 1.2h + +[211/351] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 211/351 (60%), ETA: 1.2h + +[212/351] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 212/351 (60%), ETA: 1.2h + +[213/351] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SUCCESS (619 entities) + Progress: 213/351 (60%), ETA: 1.2h + +[214/351] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 214/351 (60%), ETA: 1.2h + +[215/351] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SUCCESS (20 entities) + Progress: 215/351 (61%), ETA: 1.1h + +[216/351] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SUCCESS (13 entities) + Progress: 216/351 (61%), ETA: 1.1h + +[217/351] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SUCCESS (1380 entities) + Progress: 217/351 (61%), ETA: 1.1h + +[218/351] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SUCCESS (4 entities) + Progress: 218/351 (62%), ETA: 1.1h + +[219/351] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SUCCESS (12 entities) + Progress: 219/351 (62%), ETA: 1.1h + +[220/351] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SUCCESS (28 entities) + Progress: 220/351 (62%), ETA: 1.1h + +[221/351] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SUCCESS (14 entities) + Progress: 221/351 (62%), ETA: 1.1h + +[222/351] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SUCCESS (106 entities) + Progress: 222/351 (63%), ETA: 1.1h + +[223/351] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SUCCESS (7 entities) + Progress: 223/351 (63%), ETA: 1.1h + +[224/351] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SUCCESS (39 entities) + Progress: 224/351 (63%), ETA: 1.1h + +[225/351] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SUCCESS (27 entities) + Progress: 225/351 (64%), ETA: 1.1h + +[226/351] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SUCCESS (304 entities) + Progress: 226/351 (64%), ETA: 1.1h + +[227/351] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 227/351 (64%), ETA: 1.0h + +[228/351] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SUCCESS (17 entities) + Progress: 228/351 (64%), ETA: 1.0h + +[229/351] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SUCCESS (39 entities) + Progress: 229/351 (65%), ETA: 1.0h + +[230/351] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SUCCESS (756 entities) + Progress: 230/351 (65%), ETA: 1.0h + +[231/351] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SUCCESS (30 entities) + Progress: 231/351 (65%), ETA: 1.0h + +[232/351] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SUCCESS (13 entities) + Progress: 232/351 (66%), ETA: 1.0h + +[233/351] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SUCCESS (708 entities) + Progress: 233/351 (66%), ETA: 1.0h + +[234/351] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SUCCESS (5 entities) + Progress: 234/351 (66%), ETA: 1.0h + +[235/351] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SUCCESS (1 entities) + Progress: 235/351 (66%), ETA: 1.0h + +[236/351] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SUCCESS (32 entities) + Progress: 236/351 (67%), ETA: 1.0h + +[237/351] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SUCCESS (83 entities) + Progress: 237/351 (67%), ETA: 1.0h + +[238/351] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SUCCESS (651 entities) + Progress: 238/351 (67%), ETA: 1.0h + +[239/351] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 239/351 (68%), ETA: 0.9h + +[240/351] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SUCCESS (14 entities) + Progress: 240/351 (68%), ETA: 0.9h + +[241/351] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SUCCESS (9 entities) + Progress: 241/351 (68%), ETA: 0.9h + +[242/351] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SUCCESS (1093 entities) + Progress: 242/351 (68%), ETA: 0.9h + +[243/351] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 243/351 (69%), ETA: 0.9h + +[244/351] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SUCCESS (43 entities) + Progress: 244/351 (69%), ETA: 0.9h + +[245/351] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SUCCESS (47 entities) + Progress: 245/351 (69%), ETA: 0.9h + +[246/351] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SUCCESS (65 entities) + Progress: 246/351 (70%), ETA: 0.9h + +[247/351] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SUCCESS (16 entities) + Progress: 247/351 (70%), ETA: 0.9h + +[248/351] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SUCCESS (21 entities) + Progress: 248/351 (70%), ETA: 0.9h + +[249/351] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SUCCESS (490 entities) + Progress: 249/351 (70%), ETA: 0.9h + +[250/351] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SUCCESS (10 entities) + Progress: 250/351 (71%), ETA: 0.8h + +[251/351] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SUCCESS (77 entities) + Progress: 251/351 (71%), ETA: 0.8h + +[252/351] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SUCCESS (31 entities) + Progress: 252/351 (71%), ETA: 0.8h + +[253/351] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SUCCESS (35 entities) + Progress: 253/351 (72%), ETA: 0.8h + +[254/351] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 254/351 (72%), ETA: 0.8h + +[255/351] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SUCCESS (243 entities) + Progress: 255/351 (72%), ETA: 0.8h + +[256/351] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SUCCESS (36 entities) + Progress: 256/351 (72%), ETA: 0.8h + +[257/351] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SUCCESS (33 entities) + Progress: 257/351 (73%), ETA: 0.8h + +[258/351] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 258/351 (73%), ETA: 0.8h + +[259/351] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SUCCESS (129 entities) + Progress: 259/351 (73%), ETA: 0.8h + +[260/351] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SUCCESS (88 entities) + Progress: 260/351 (74%), ETA: 0.8h + +[261/351] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SUCCESS (66 entities) + Progress: 261/351 (74%), ETA: 0.7h + +[262/351] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 262/351 (74%), ETA: 0.7h + +[263/351] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SUCCESS (111 entities) + Progress: 263/351 (74%), ETA: 0.7h + +[264/351] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SUCCESS (47 entities) + Progress: 264/351 (75%), ETA: 0.7h + +[265/351] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SUCCESS (15 entities) + Progress: 265/351 (75%), ETA: 0.7h + +[266/351] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 266/351 (75%), ETA: 0.7h + +[267/351] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SUCCESS (185 entities) + Progress: 267/351 (76%), ETA: 0.7h + +[268/351] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SUCCESS (4 entities) + Progress: 268/351 (76%), ETA: 0.7h + +[269/351] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SUCCESS (368 entities) + Progress: 269/351 (76%), ETA: 0.7h + +[270/351] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 270/351 (76%), ETA: 0.7h + +[271/351] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SUCCESS (131 entities) + Progress: 271/351 (77%), ETA: 0.7h + +[272/351] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SUCCESS (4 entities) + Progress: 272/351 (77%), ETA: 0.6h + +[273/351] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SUCCESS (47 entities) + Progress: 273/351 (77%), ETA: 0.6h + +[274/351] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 274/351 (78%), ETA: 0.6h + +[275/351] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SUCCESS (2 entities) + Progress: 275/351 (78%), ETA: 0.6h + +[276/351] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SUCCESS (1 entities) + Progress: 276/351 (78%), ETA: 0.6h + +[277/351] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SUCCESS (485 entities) + Progress: 277/351 (78%), ETA: 0.6h + +[278/351] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SUCCESS (1 entities) + Progress: 278/351 (79%), ETA: 0.6h + +[279/351] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SUCCESS (2 entities) + Progress: 279/351 (79%), ETA: 0.6h + +[280/351] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SUCCESS (16 entities) + Progress: 280/351 (79%), ETA: 0.6h + +[281/351] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SUCCESS (454 entities) + Progress: 281/351 (80%), ETA: 0.6h + +[282/351] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 282/351 (80%), ETA: 0.6h + +[283/351] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SUCCESS (0 entities) + Progress: 283/351 (80%), ETA: 0.6h + +[284/351] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 284/351 (80%), ETA: 0.5h + +[285/351] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SUCCESS (406 entities) + Progress: 285/351 (81%), ETA: 0.5h + +[286/351] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 286/351 (81%), ETA: 0.5h + +[287/351] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SUCCESS (0 entities) + Progress: 287/351 (81%), ETA: 0.5h + +[288/351] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SUCCESS (11 entities) + Progress: 288/351 (82%), ETA: 0.5h + +[289/351] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SUCCESS (60 entities) + Progress: 289/351 (82%), ETA: 0.5h + +[290/351] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SUCCESS (4 entities) + Progress: 290/351 (82%), ETA: 0.5h + +[291/351] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 291/351 (82%), ETA: 0.5h + +[292/351] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SUCCESS (19 entities) + Progress: 292/351 (83%), ETA: 0.5h + +[293/351] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SUCCESS (83 entities) + Progress: 293/351 (83%), ETA: 0.5h + +[294/351] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 294/351 (83%), ETA: 0.5h + +[295/351] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SUCCESS (0 entities) + Progress: 295/351 (84%), ETA: 0.4h + +[296/351] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SUCCESS (81 entities) + Progress: 296/351 (84%), ETA: 0.4h + +[297/351] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SUCCESS (101 entities) + Progress: 297/351 (84%), ETA: 0.4h + +[298/351] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 298/351 (84%), ETA: 0.4h + +[299/351] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SUCCESS (4 entities) + Progress: 299/351 (85%), ETA: 0.4h + +[300/351] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SUCCESS (18 entities) + Progress: 300/351 (85%), ETA: 0.4h + +[301/351] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SUCCESS (49 entities) + Progress: 301/351 (85%), ETA: 0.4h + +[302/351] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 302/351 (86%), ETA: 0.4h + +[303/351] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SUCCESS (30 entities) + Progress: 303/351 (86%), ETA: 0.4h + +[304/351] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 304/351 (86%), ETA: 0.4h + +[305/351] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SUCCESS (401 entities) + Progress: 305/351 (86%), ETA: 0.4h + +[306/351] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 306/351 (87%), ETA: 0.4h + +[307/351] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SUCCESS (0 entities) + Progress: 307/351 (87%), ETA: 0.3h + +[308/351] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 308/351 (87%), ETA: 0.3h + +[309/351] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SUCCESS (72 entities) + Progress: 309/351 (88%), ETA: 0.3h + +[310/351] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 310/351 (88%), ETA: 0.3h + +[311/351] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SUCCESS (0 entities) + Progress: 311/351 (88%), ETA: 0.3h + +[312/351] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SUCCESS (147 entities) + Progress: 312/351 (88%), ETA: 0.3h + +[313/351] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SUCCESS (551 entities) + Progress: 313/351 (89%), ETA: 0.3h + +[314/351] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SUCCESS (18 entities) + Progress: 314/351 (89%), ETA: 0.3h + +[315/351] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SUCCESS (104 entities) + Progress: 315/351 (89%), ETA: 0.3h + +[316/351] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SUCCESS (174 entities) + Progress: 316/351 (90%), ETA: 0.3h + +[317/351] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SUCCESS (544 entities) + Progress: 317/351 (90%), ETA: 0.3h + +[318/351] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 318/351 (90%), ETA: 0.3h + +[319/351] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SUCCESS (89 entities) + Progress: 319/351 (90%), ETA: 0.3h + +[320/351] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SUCCESS (154 entities) + Progress: 320/351 (91%), ETA: 0.2h + +[321/351] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SUCCESS (264 entities) + Progress: 321/351 (91%), ETA: 0.2h + +[322/351] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SUCCESS (26 entities) + Progress: 322/351 (91%), ETA: 0.2h + +[323/351] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SUCCESS (52 entities) + Progress: 323/351 (92%), ETA: 0.2h + +[324/351] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SUCCESS (160 entities) + Progress: 324/351 (92%), ETA: 0.2h + +[325/351] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SUCCESS (393 entities) + Progress: 325/351 (92%), ETA: 0.2h + +[326/351] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SUCCESS (26 entities) + Progress: 326/351 (92%), ETA: 0.2h + +[327/351] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SUCCESS (126 entities) + Progress: 327/351 (93%), ETA: 0.2h + +[328/351] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SUCCESS (161 entities) + Progress: 328/351 (93%), ETA: 0.2h + +[329/351] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SUCCESS (465 entities) + Progress: 329/351 (93%), ETA: 0.2h + +[330/351] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 330/351 (94%), ETA: 0.2h + +[331/351] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SUCCESS (55 entities) + Progress: 331/351 (94%), ETA: 0.2h + +[332/351] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SUCCESS (100 entities) + Progress: 332/351 (94%), ETA: 0.2h + +[333/351] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SUCCESS (402 entities) + Progress: 333/351 (94%), ETA: 0.1h + +[334/351] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SUCCESS (13 entities) + Progress: 334/351 (95%), ETA: 0.1h + +[335/351] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SUCCESS (121 entities) + Progress: 335/351 (95%), ETA: 0.1h + +[336/351] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SUCCESS (105 entities) + Progress: 336/351 (95%), ETA: 0.1h + +[337/351] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SUCCESS (607 entities) + Progress: 337/351 (96%), ETA: 0.1h + +[338/351] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SUCCESS (9 entities) + Progress: 338/351 (96%), ETA: 0.1h + +[339/351] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SUCCESS (66 entities) + Progress: 339/351 (96%), ETA: 0.1h + +[340/351] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SUCCESS (129 entities) + Progress: 340/351 (96%), ETA: 0.1h + +[341/351] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SUCCESS (626 entities) + Progress: 341/351 (97%), ETA: 0.1h + +[342/351] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SUCCESS (14 entities) + Progress: 342/351 (97%), ETA: 0.1h + +[343/351] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SUCCESS (58 entities) + Progress: 343/351 (97%), ETA: 0.1h + +[344/351] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SUCCESS (197 entities) + Progress: 344/351 (98%), ETA: 0.1h + +[345/351] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SUCCESS (631 entities) + Progress: 345/351 (98%), ETA: 0.0h + +[346/351] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SUCCESS (6 entities) + Progress: 346/351 (98%), ETA: 0.0h + +[347/351] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SUCCESS (75 entities) + Progress: 347/351 (98%), ETA: 0.0h + +[348/351] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SUCCESS (79 entities) + Progress: 348/351 (99%), ETA: 0.0h + +[349/351] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SUCCESS (448 entities) + Progress: 349/351 (99%), ETA: 0.0h + +[350/351] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SUCCESS (30 entities) + Progress: 350/351 (99%), ETA: 0.0h + +[351/351] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SUCCESS (73 entities) + Progress: 351/351 (100%), ETA: 0.0h + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 2.9 hours +Completed: 351 +Skipped (already exist): 0 +Failed: 0 +Total entities: 57818 +Avg entities/slot: 164.7 + +Output directory: /nas/mars/dataset/MEVA/entity_descriptions +Log file: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053719.log +============================================================ + + +============================================================ +SUMMARY +============================================================ +Total slots: 351 +Completed: 351 +Skipped (exist): 0 +Failed: 0 +Total entities: 57818 +Time: 2.9 hours +Log: /home/ah66742/data/extraction_logs/batch_extraction_20260228_053719.log +============================================================ diff --git a/meva/examples/run_records/data/extraction_records/reextract_slots.json b/meva/examples/run_records/data/extraction_records/reextract_slots.json new file mode 100644 index 0000000..bf311dd --- /dev/null +++ b/meva/examples/run_records/data/extraction_records/reextract_slots.json @@ -0,0 +1,94 @@ +[ + "2018-03-05.13-10.admin", + "2018-03-05.13-15.admin", + "2018-03-05.14-05.admin", + "2018-03-05.14-10.admin", + "2018-03-07.10-55.bus", + "2018-03-07.10-55.hospital", + "2018-03-07.10-55.school", + "2018-03-07.16-55.admin", + "2018-03-07.16-55.bus", + "2018-03-07.16-55.hospital", + "2018-03-07.16-55.school", + "2018-03-07.17-20.admin", + "2018-03-09.10-15.bus", + "2018-03-09.10-20.admin", + "2018-03-09.10-20.bus", + "2018-03-09.10-25.admin", + "2018-03-09.10-25.bus", + "2018-03-09.10-30.bus", + "2018-03-09.10-35.bus", + "2018-03-11.11-35.admin", + "2018-03-11.11-40.admin", + "2018-03-11.11-40.bus", + "2018-03-11.11-40.hospital", + "2018-03-11.11-45.admin", + "2018-03-11.11-45.bus", + "2018-03-11.11-55.admin", + "2018-03-11.11-55.bus", + "2018-03-11.11-55.hospital", + "2018-03-11.11-55.school", + "2018-03-11.13-55.admin", + "2018-03-11.13-55.bus", + "2018-03-11.13-55.hospital", + "2018-03-11.13-55.school", + "2018-03-11.14-00.bus", + "2018-03-11.14-05.admin", + "2018-03-11.14-05.bus", + "2018-03-11.14-10.admin", + "2018-03-11.14-10.bus", + "2018-03-11.14-20.admin", + "2018-03-11.16-15.hospital", + "2018-03-11.16-25.admin", + "2018-03-11.16-30.admin", + "2018-03-11.16-30.bus", + "2018-03-11.16-30.hospital", + "2018-03-11.16-35.hospital", + "2018-03-11.17-15.hospital", + "2018-03-11.17-20.admin", + "2018-03-12.10-10.admin", + "2018-03-12.10-15.admin", + "2018-03-12.10-20.hospital", + "2018-03-12.10-45.admin", + "2018-03-12.10-55.admin", + "2018-03-12.10-55.bus", + "2018-03-12.10-55.hospital", + "2018-03-12.11-00.admin", + "2018-03-12.11-05.admin", + "2018-03-13.15-55.admin", + "2018-03-13.15-55.bus", + "2018-03-13.15-55.hospital", + "2018-03-13.15-55.school", + "2018-03-13.16-00.admin", + "2018-03-13.16-05.admin", + "2018-03-13.16-10.admin", + "2018-03-13.16-15.admin", + "2018-03-13.16-20.admin", + "2018-03-13.16-25.admin", + "2018-03-13.17-05.admin", + "2018-03-13.17-05.hospital", + "2018-03-13.17-10.admin", + "2018-03-13.17-10.bus", + "2018-03-13.17-10.hospital", + "2018-03-13.17-15.hospital", + "2018-03-13.17-20.admin", + "2018-03-13.17-20.hospital", + "2018-03-13.17-25.admin", + "2018-03-13.17-30.admin", + "2018-03-13.17-35.admin", + "2018-03-13.17-35.bus", + "2018-03-13.17-35.hospital", + "2018-03-13.17-40.admin", + "2018-03-13.17-40.bus", + "2018-03-13.17-40.hospital", + "2018-03-15.14-55.admin", + "2018-03-15.14-55.bus", + "2018-03-15.14-55.hospital", + "2018-03-15.14-55.school", + "2018-03-15.15-00.admin", + "2018-03-15.15-15.admin", + "2018-03-15.15-55.admin", + "2018-03-15.15-55.bus", + "2018-03-15.15-55.hospital", + "2018-03-15.15-55.school" +] \ No newline at end of file diff --git a/meva/examples/run_records/extract_entities_avi.log.txt b/meva/examples/run_records/extract_entities_avi.log.txt new file mode 100644 index 0000000..5cc8d08 --- /dev/null +++ b/meva/examples/run_records/extract_entities_avi.log.txt @@ -0,0 +1,69 @@ + + Slot: 2018-03-11.11-25-00.school + Found 7 cameras with geom annotations + + Camera G299: + Geom: 2018-03-11.11-25-00.11-30-00.school.G299.geom.yml + Video: 2018-03-11.11-25-00.11-30-00.school.G299.r13.avi (avi) + Actors: 0 total + + Camera G328: + Geom: 2018-03-11.11-25-00.11-30-00.school.G328.geom.yml + Video: 2018-03-11.11-25-00.11-30-00.school.G328.r13.avi (avi) + Actors: 15 total + Usable actors: 14/15 (70 crops, 3.4s decode) + Analysis: 0.0s (color-only) + + Camera G330: + Geom: 2018-03-11.11-25-00.11-30-00.school.G330.geom.yml + Video: 2018-03-11.11-25-00.11-30-00.school.G330.r13.avi (avi) + Actors: 517 total + Usable actors: 508/517 (2540 crops, 10.7s decode) + Analysis: 0.2s (color-only) + + Camera G336: + Geom: 2018-03-11.11-25-00.11-30-00.school.G336.geom.yml + Video: 2018-03-11.11-25-00.11-30-00.school.G336.r13.avi (avi) + Actors: 11 total + Usable actors: 8/11 (40 crops, 0.7s decode) + Analysis: 0.0s (color-only) + + Camera G420: + Geom: 2018-03-11.11-25-00.11-30-00.school.G420.geom.yml + Video: 2018-03-11.11-25-00.11-30-00.school.G420.r13.avi (avi) + Actors: 39 total + Usable actors: 39/39 (195 crops, 4.2s decode) + Analysis: 0.2s (color-only) + + Camera G423: + Geom: 2018-03-11.11-25-00.11-30-00.school.G423.geom.yml + Video: 2018-03-11.11-25-00.11-30-00.school.G423.r13.avi (avi) + Actors: 0 total + + Camera G421: + Geom: 2018-03-11.11-25-00.11-30-01.school.G421.geom.yml + Video: 2018-03-11.11-25-00.11-30-01.school.G421.r13.avi (avi) + Actors: 9 total + Usable actors: 9/9 (45 crops, 2.4s decode) + Analysis: 0.0s (color-only) + + === Summary === + Total actors: 578 + With color descriptions: 578 + Without: 0 + Color distribution: {'blue': 309, 'black': 152, 'green': 96, 'yellow': 11, 'gray': 5} + Total time: 22.8s + + Output: /home/ah66742/data/entity_descriptions/2018-03-11.11-25-00.school.json + + === Sample Descriptions === + G328 actor ...862718: a person in a black top and black pants + G328 actor ...029432: a person in a green top and orange pants + G328 actor ...762240: a person in a orange top and red pants + G328 actor ...128936: a person in a black top and orange pants + G328 actor ...270893: a person in a black top and yellow pants + G328 actor ...249440: a person in a yellow top and blue pants + G328 actor ...375508: a person in a orange top and orange pants + G328 actor ...217924: a person in a yellow top and orange pants + G328 actor ...578690: a person in a black top and red pants + G330 actor ...519599: a person in a blue top and green pants diff --git a/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_193005.log.txt b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_193005.log.txt new file mode 100644 index 0000000..1a6b10a --- /dev/null +++ b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_193005.log.txt @@ -0,0 +1,1885 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Canonical slots: 929 +Resume: False +Log: /home/ah66742/output/extraction_logs/batch_extraction_20260220_193005.log +Progress: /home/ah66742/output/extraction_logs/batch_progress.json +Started: 2026-02-20 19:30:05 +============================================================ + +[ 1/929] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/929] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ DRY-RUN OK +[ 3/929] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/929] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ DRY-RUN OK +[ 5/929] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ DRY-RUN OK +[ 6/929] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ DRY-RUN OK +[ 7/929] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 8/929] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ DRY-RUN OK +[ 9/929] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ DRY-RUN OK +[ 10/929] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ DRY-RUN OK +[ 11/929] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ DRY-RUN OK +[ 12/929] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ DRY-RUN OK +[ 13/929] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ DRY-RUN OK +[ 14/929] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ DRY-RUN OK +[ 15/929] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ DRY-RUN OK +[ 16/929] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ DRY-RUN OK +[ 17/929] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ DRY-RUN OK +[ 18/929] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ DRY-RUN OK +[ 19/929] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ DRY-RUN OK +[ 20/929] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ DRY-RUN OK +[ 21/929] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ DRY-RUN OK +[ 22/929] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ DRY-RUN OK +[ 23/929] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ DRY-RUN OK +[ 24/929] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ DRY-RUN OK +[ 25/929] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 26/929] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ DRY-RUN OK +[ 27/929] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ DRY-RUN OK +[ 28/929] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ DRY-RUN OK +[ 29/929] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ DRY-RUN OK +[ 30/929] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ DRY-RUN OK +[ 31/929] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 32/929] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ DRY-RUN OK +[ 33/929] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ DRY-RUN OK +[ 34/929] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ DRY-RUN OK +[ 35/929] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ DRY-RUN OK +[ 36/929] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ DRY-RUN OK +[ 37/929] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ DRY-RUN OK +[ 38/929] 2018-03-07.10-55-00.bus → 2018-03-07.10-55-00.bus + ✓ DRY-RUN OK +[ 39/929] 2018-03-07.10-55-00.hospital → 2018-03-07.10-55-00.hospital + ✓ DRY-RUN OK +[ 40/929] 2018-03-07.10-55-00.school → 2018-03-07.10-55-00.school + ✓ DRY-RUN OK +[ 41/929] 2018-03-07.10-55-01.admin → 2018-03-07.10-55-01.admin + ✓ DRY-RUN OK +[ 42/929] 2018-03-07.10-55-01.bus → 2018-03-07.10-55-01.bus + ✓ DRY-RUN OK +[ 43/929] 2018-03-07.10-55-01.school → 2018-03-07.10-55-01.school + ✓ DRY-RUN OK +[ 44/929] 2018-03-07.10-55-04.hospital → 2018-03-07.10-55-04.hospital + ✓ DRY-RUN OK +[ 45/929] 2018-03-07.10-55-05.school → 2018-03-07.10-55-05.school + ✓ DRY-RUN OK +[ 46/929] 2018-03-07.10-55-06.bus → 2018-03-07.10-55-06.bus + ✓ DRY-RUN OK +[ 47/929] 2018-03-07.10-55-06.school → 2018-03-07.10-55-06.school + ✓ DRY-RUN OK +[ 48/929] 2018-03-07.10-55-07.hospital → 2018-03-07.10-55-07.hospital + ✓ DRY-RUN OK +[ 49/929] 2018-03-07.10-55-07.school → 2018-03-07.10-55-07.school + ✓ DRY-RUN OK +[ 50/929] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ DRY-RUN OK +[ 51/929] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ DRY-RUN OK +[ 52/929] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ DRY-RUN OK +[ 53/929] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ DRY-RUN OK +[ 54/929] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ DRY-RUN OK +[ 55/929] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ DRY-RUN OK +[ 56/929] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ DRY-RUN OK +[ 57/929] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ DRY-RUN OK +[ 58/929] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ DRY-RUN OK +[ 59/929] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ DRY-RUN OK +[ 60/929] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ DRY-RUN OK +[ 61/929] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ DRY-RUN OK +[ 62/929] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ DRY-RUN OK +[ 63/929] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ DRY-RUN OK +[ 64/929] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ DRY-RUN OK +[ 65/929] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ DRY-RUN OK +[ 66/929] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ DRY-RUN OK +[ 67/929] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ DRY-RUN OK +[ 68/929] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ DRY-RUN OK +[ 69/929] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ DRY-RUN OK +[ 70/929] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ DRY-RUN OK +[ 71/929] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ DRY-RUN OK +[ 72/929] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ DRY-RUN OK +[ 73/929] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ DRY-RUN OK +[ 74/929] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ DRY-RUN OK +[ 75/929] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ DRY-RUN OK +[ 76/929] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ DRY-RUN OK +[ 77/929] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ DRY-RUN OK +[ 78/929] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ DRY-RUN OK +[ 79/929] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ DRY-RUN OK +[ 80/929] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ DRY-RUN OK +[ 81/929] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ DRY-RUN OK +[ 82/929] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ DRY-RUN OK +[ 83/929] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ DRY-RUN OK +[ 84/929] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ DRY-RUN OK +[ 85/929] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ DRY-RUN OK +[ 86/929] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ DRY-RUN OK +[ 87/929] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ DRY-RUN OK +[ 88/929] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ DRY-RUN OK +[ 89/929] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ DRY-RUN OK +[ 90/929] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ DRY-RUN OK +[ 91/929] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ DRY-RUN OK +[ 92/929] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ DRY-RUN OK +[ 93/929] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ DRY-RUN OK +[ 94/929] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ DRY-RUN OK +[ 95/929] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ DRY-RUN OK +[ 96/929] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ DRY-RUN OK +[ 97/929] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ DRY-RUN OK +[ 98/929] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ DRY-RUN OK +[ 99/929] 2018-03-07.16-55-00.admin → 2018-03-07.16-55-00.admin + ✓ DRY-RUN OK +[100/929] 2018-03-07.16-55-00.bus → 2018-03-07.16-55-00.bus + ✓ DRY-RUN OK +[101/929] 2018-03-07.16-55-00.school → 2018-03-07.16-55-00.school + ✓ DRY-RUN OK +[102/929] 2018-03-07.16-55-01.admin → 2018-03-07.16-55-01.admin + ✓ DRY-RUN OK +[103/929] 2018-03-07.16-55-01.bus → 2018-03-07.16-55-01.bus + ✓ DRY-RUN OK +[104/929] 2018-03-07.16-55-01.hospital → 2018-03-07.16-55-01.hospital + ✓ DRY-RUN OK +[105/929] 2018-03-07.16-55-01.school → 2018-03-07.16-55-01.school + ✓ DRY-RUN OK +[106/929] 2018-03-07.16-55-05.hospital → 2018-03-07.16-55-05.hospital + ✓ DRY-RUN OK +[107/929] 2018-03-07.16-55-06.school → 2018-03-07.16-55-06.school + ✓ DRY-RUN OK +[108/929] 2018-03-07.16-55-07.hospital → 2018-03-07.16-55-07.hospital + ✓ DRY-RUN OK +[109/929] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ DRY-RUN OK +[110/929] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ DRY-RUN OK +[111/929] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ DRY-RUN OK +[112/929] 2018-03-07.17-00-01.admin → 2018-03-07.17-00-01.admin + ✓ DRY-RUN OK +[113/929] 2018-03-07.17-00-01.bus → 2018-03-07.17-00-01.bus + ✓ DRY-RUN OK +[114/929] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ DRY-RUN OK +[115/929] 2018-03-07.17-00-01.school → 2018-03-07.17-00-01.school + ✓ DRY-RUN OK +[116/929] 2018-03-07.17-00-05.hospital → 2018-03-07.17-00-05.hospital + ✓ DRY-RUN OK +[117/929] 2018-03-07.17-00-06.school → 2018-03-07.17-00-06.school + ✓ DRY-RUN OK +[118/929] 2018-03-07.17-00-07.hospital → 2018-03-07.17-00-07.hospital + ✓ DRY-RUN OK +[119/929] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ DRY-RUN OK +[120/929] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ DRY-RUN OK +[121/929] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ DRY-RUN OK +[122/929] 2018-03-07.17-05-01.admin → 2018-03-07.17-05-01.admin + ✓ DRY-RUN OK +[123/929] 2018-03-07.17-05-01.bus → 2018-03-07.17-05-01.bus + ✓ DRY-RUN OK +[124/929] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ DRY-RUN OK +[125/929] 2018-03-07.17-05-01.school → 2018-03-07.17-05-01.school + ✓ DRY-RUN OK +[126/929] 2018-03-07.17-05-05.hospital → 2018-03-07.17-05-05.hospital + ✓ DRY-RUN OK +[127/929] 2018-03-07.17-05-06.school → 2018-03-07.17-05-06.school + ✓ DRY-RUN OK +[128/929] 2018-03-07.17-05-07.hospital → 2018-03-07.17-05-07.hospital + ✓ DRY-RUN OK +[129/929] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[130/929] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ DRY-RUN OK +[131/929] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ DRY-RUN OK +[132/929] 2018-03-07.17-20-01.admin → 2018-03-07.17-20-01.admin + ✓ DRY-RUN OK +[133/929] 2018-03-07.17-20-01.bus → 2018-03-07.17-20-01.bus + ✓ DRY-RUN OK +[134/929] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ DRY-RUN OK +[135/929] 2018-03-07.17-20-01.school → 2018-03-07.17-20-01.school + ✓ DRY-RUN OK +[136/929] 2018-03-07.17-20-05.hospital → 2018-03-07.17-20-05.hospital + ✓ DRY-RUN OK +[137/929] 2018-03-07.17-20-06.school → 2018-03-07.17-20-06.school + ✓ DRY-RUN OK +[138/929] 2018-03-07.17-20-07.hospital → 2018-03-07.17-20-07.hospital + ✓ DRY-RUN OK +[139/929] 2018-03-07.17-23-58.school → 2018-03-07.17-23-58.school + ✓ DRY-RUN OK +[140/929] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ DRY-RUN OK +[141/929] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ DRY-RUN OK +[142/929] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ DRY-RUN OK +[143/929] 2018-03-07.17-25-01.admin → 2018-03-07.17-25-01.admin + ✓ DRY-RUN OK +[144/929] 2018-03-07.17-25-01.bus → 2018-03-07.17-25-01.bus + ✓ DRY-RUN OK +[145/929] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ DRY-RUN OK +[146/929] 2018-03-07.17-25-01.school → 2018-03-07.17-25-01.school + ✓ DRY-RUN OK +[147/929] 2018-03-07.17-25-03.school → 2018-03-07.17-25-03.school + ✓ DRY-RUN OK +[148/929] 2018-03-07.17-25-05.hospital → 2018-03-07.17-25-05.hospital + ✓ DRY-RUN OK +[149/929] 2018-03-07.17-25-06.school → 2018-03-07.17-25-06.school + ✓ DRY-RUN OK +[150/929] 2018-03-07.17-25-07.hospital → 2018-03-07.17-25-07.hospital + ✓ DRY-RUN OK +[151/929] 2018-03-07.17-25-10.school → 2018-03-07.17-25-10.school + ✓ DRY-RUN OK +[152/929] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ DRY-RUN OK +[153/929] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ DRY-RUN OK +[154/929] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ DRY-RUN OK +[155/929] 2018-03-07.17-30-01.admin → 2018-03-07.17-30-01.admin + ✓ DRY-RUN OK +[156/929] 2018-03-07.17-30-01.bus → 2018-03-07.17-30-01.bus + ✓ DRY-RUN OK +[157/929] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ DRY-RUN OK +[158/929] 2018-03-07.17-30-01.school → 2018-03-07.17-30-01.school + ✓ DRY-RUN OK +[159/929] 2018-03-07.17-30-03.school → 2018-03-07.17-30-03.school + ✓ DRY-RUN OK +[160/929] 2018-03-07.17-30-05.hospital → 2018-03-07.17-30-05.hospital + ✓ DRY-RUN OK +[161/929] 2018-03-07.17-30-06.school → 2018-03-07.17-30-06.school + ✓ DRY-RUN OK +[162/929] 2018-03-07.17-30-07.hospital → 2018-03-07.17-30-07.hospital + ✓ DRY-RUN OK +[163/929] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ DRY-RUN OK +[164/929] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ DRY-RUN OK +[165/929] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ DRY-RUN OK +[166/929] 2018-03-07.17-35-01.admin → 2018-03-07.17-35-01.admin + ✓ DRY-RUN OK +[167/929] 2018-03-07.17-35-01.bus → 2018-03-07.17-35-01.bus + ✓ DRY-RUN OK +[168/929] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ DRY-RUN OK +[169/929] 2018-03-07.17-35-01.school → 2018-03-07.17-35-01.school + ✓ DRY-RUN OK +[170/929] 2018-03-07.17-35-03.school → 2018-03-07.17-35-03.school + ✓ DRY-RUN OK +[171/929] 2018-03-07.17-35-05.hospital → 2018-03-07.17-35-05.hospital + ✓ DRY-RUN OK +[172/929] 2018-03-07.17-35-06.school → 2018-03-07.17-35-06.school + ✓ DRY-RUN OK +[173/929] 2018-03-07.17-35-07.hospital → 2018-03-07.17-35-07.hospital + ✓ DRY-RUN OK +[174/929] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ DRY-RUN OK +[175/929] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ DRY-RUN OK +[176/929] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ DRY-RUN OK +[177/929] 2018-03-09.10-10-01.bus → 2018-03-09.10-10-01.bus + ✓ DRY-RUN OK +[178/929] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ DRY-RUN OK +[179/929] 2018-03-09.10-10-01.school → 2018-03-09.10-10-01.school + ✓ DRY-RUN OK +[180/929] 2018-03-09.10-10-02.hospital → 2018-03-09.10-10-02.hospital + ✓ DRY-RUN OK +[181/929] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[182/929] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ DRY-RUN OK +[183/929] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ DRY-RUN OK +[184/929] 2018-03-09.10-15-01.bus → 2018-03-09.10-15-01.bus + ✓ DRY-RUN OK +[185/929] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ DRY-RUN OK +[186/929] 2018-03-09.10-15-01.school → 2018-03-09.10-15-01.school + ✓ DRY-RUN OK +[187/929] 2018-03-09.10-15-02.hospital → 2018-03-09.10-15-02.hospital + ✓ DRY-RUN OK +[188/929] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[189/929] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ DRY-RUN OK +[190/929] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[191/929] 2018-03-09.10-20-01.bus → 2018-03-09.10-20-01.bus + ✓ DRY-RUN OK +[192/929] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ DRY-RUN OK +[193/929] 2018-03-09.10-20-01.school → 2018-03-09.10-20-01.school + ✓ DRY-RUN OK +[194/929] 2018-03-09.10-20-02.hospital → 2018-03-09.10-20-02.hospital + ✓ DRY-RUN OK +[195/929] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[196/929] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ DRY-RUN OK +[197/929] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[198/929] 2018-03-09.10-25-01.bus → 2018-03-09.10-25-01.bus + ✓ DRY-RUN OK +[199/929] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ DRY-RUN OK +[200/929] 2018-03-09.10-25-01.school → 2018-03-09.10-25-01.school + ✓ DRY-RUN OK +[201/929] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[202/929] 2018-03-09.10-30-00.hospital → 2018-03-09.10-30-00.hospital + ✓ DRY-RUN OK +[203/929] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ DRY-RUN OK +[204/929] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ DRY-RUN OK +[205/929] 2018-03-09.10-30-01.bus → 2018-03-09.10-30-01.bus + ✓ DRY-RUN OK +[206/929] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ DRY-RUN OK +[207/929] 2018-03-09.10-30-01.school → 2018-03-09.10-30-01.school + ✓ DRY-RUN OK +[208/929] 2018-03-09.10-30-02.hospital → 2018-03-09.10-30-02.hospital + ✓ DRY-RUN OK +[209/929] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[210/929] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ DRY-RUN OK +[211/929] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ DRY-RUN OK +[212/929] 2018-03-09.10-35-01.bus → 2018-03-09.10-35-01.bus + ✓ DRY-RUN OK +[213/929] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ DRY-RUN OK +[214/929] 2018-03-09.10-35-01.school → 2018-03-09.10-35-01.school + ✓ DRY-RUN OK +[215/929] 2018-03-09.10-35-02.hospital → 2018-03-09.10-35-02.hospital + ✓ DRY-RUN OK +[216/929] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ DRY-RUN OK +[217/929] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ DRY-RUN OK +[218/929] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ DRY-RUN OK +[219/929] 2018-03-09.10-40-01.bus → 2018-03-09.10-40-01.bus + ✓ DRY-RUN OK +[220/929] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ DRY-RUN OK +[221/929] 2018-03-09.10-40-01.school → 2018-03-09.10-40-01.school + ✓ DRY-RUN OK +[222/929] 2018-03-09.10-40-02.hospital → 2018-03-09.10-40-02.hospital + ✓ DRY-RUN OK +[223/929] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ DRY-RUN OK +[224/929] 2018-03-11.11-15-01.school → 2018-03-11.11-15-01.school + ✓ DRY-RUN OK +[225/929] 2018-03-11.11-15-04.school → 2018-03-11.11-15-04.school + ✓ DRY-RUN OK +[226/929] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ DRY-RUN OK +[227/929] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ DRY-RUN OK +[228/929] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ DRY-RUN OK +[229/929] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ DRY-RUN OK +[230/929] 2018-03-11.11-20-01.admin → 2018-03-11.11-20-01.admin + ✓ DRY-RUN OK +[231/929] 2018-03-11.11-20-01.bus → 2018-03-11.11-20-01.bus + ✓ DRY-RUN OK +[232/929] 2018-03-11.11-20-01.school → 2018-03-11.11-20-01.school + ✓ DRY-RUN OK +[233/929] 2018-03-11.11-20-04.school → 2018-03-11.11-20-04.school + ✓ DRY-RUN OK +[234/929] 2018-03-11.11-20-08.bus → 2018-03-11.11-20-08.bus + ✓ DRY-RUN OK +[235/929] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ DRY-RUN OK +[236/929] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ DRY-RUN OK +[237/929] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ DRY-RUN OK +[238/929] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ DRY-RUN OK +[239/929] 2018-03-11.11-25-01.admin → 2018-03-11.11-25-01.admin + ✓ DRY-RUN OK +[240/929] 2018-03-11.11-25-01.bus → 2018-03-11.11-25-01.bus + ✓ DRY-RUN OK +[241/929] 2018-03-11.11-25-01.school → 2018-03-11.11-25-01.school + ✓ DRY-RUN OK +[242/929] 2018-03-11.11-25-04.school → 2018-03-11.11-25-04.school + ✓ DRY-RUN OK +[243/929] 2018-03-11.11-25-08.bus → 2018-03-11.11-25-08.bus + ✓ DRY-RUN OK +[244/929] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ DRY-RUN OK +[245/929] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ DRY-RUN OK +[246/929] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ DRY-RUN OK +[247/929] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ DRY-RUN OK +[248/929] 2018-03-11.11-30-01.admin → 2018-03-11.11-30-01.admin + ✓ DRY-RUN OK +[249/929] 2018-03-11.11-30-01.bus → 2018-03-11.11-30-01.bus + ✓ DRY-RUN OK +[250/929] 2018-03-11.11-30-01.school → 2018-03-11.11-30-01.school + ✓ DRY-RUN OK +[251/929] 2018-03-11.11-30-04.school → 2018-03-11.11-30-04.school + ✓ DRY-RUN OK +[252/929] 2018-03-11.11-30-08.bus → 2018-03-11.11-30-08.bus + ✓ DRY-RUN OK +[253/929] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ DRY-RUN OK +[254/929] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ DRY-RUN OK +[255/929] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ DRY-RUN OK +[256/929] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ DRY-RUN OK +[257/929] 2018-03-11.11-35-01.admin → 2018-03-11.11-35-01.admin + ✓ DRY-RUN OK +[258/929] 2018-03-11.11-35-01.bus → 2018-03-11.11-35-01.bus + ✓ DRY-RUN OK +[259/929] 2018-03-11.11-35-01.school → 2018-03-11.11-35-01.school + ✓ DRY-RUN OK +[260/929] 2018-03-11.11-35-04.school → 2018-03-11.11-35-04.school + ✓ DRY-RUN OK +[261/929] 2018-03-11.11-35-08.bus → 2018-03-11.11-35-08.bus + ✓ DRY-RUN OK +[262/929] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ DRY-RUN OK +[263/929] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ DRY-RUN OK +[264/929] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ DRY-RUN OK +[265/929] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ DRY-RUN OK +[266/929] 2018-03-11.11-40-01.admin → 2018-03-11.11-40-01.admin + ✓ DRY-RUN OK +[267/929] 2018-03-11.11-40-01.bus → 2018-03-11.11-40-01.bus + ✓ DRY-RUN OK +[268/929] 2018-03-11.11-40-01.school → 2018-03-11.11-40-01.school + ✓ DRY-RUN OK +[269/929] 2018-03-11.11-40-02.school → 2018-03-11.11-40-02.school + ✓ DRY-RUN OK +[270/929] 2018-03-11.11-40-04.school → 2018-03-11.11-40-04.school + ✓ DRY-RUN OK +[271/929] 2018-03-11.11-40-08.bus → 2018-03-11.11-40-08.bus + ✓ DRY-RUN OK +[272/929] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ DRY-RUN OK +[273/929] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ DRY-RUN OK +[274/929] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ DRY-RUN OK +[275/929] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ DRY-RUN OK +[276/929] 2018-03-11.11-45-01.admin → 2018-03-11.11-45-01.admin + ✓ DRY-RUN OK +[277/929] 2018-03-11.11-45-01.bus → 2018-03-11.11-45-01.bus + ✓ DRY-RUN OK +[278/929] 2018-03-11.11-45-01.school → 2018-03-11.11-45-01.school + ✓ DRY-RUN OK +[279/929] 2018-03-11.11-45-04.school → 2018-03-11.11-45-04.school + ✓ DRY-RUN OK +[280/929] 2018-03-11.11-45-08.bus → 2018-03-11.11-45-08.bus + ✓ DRY-RUN OK +[281/929] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ DRY-RUN OK +[282/929] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ DRY-RUN OK +[283/929] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ DRY-RUN OK +[284/929] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ DRY-RUN OK +[285/929] 2018-03-11.11-50-01.admin → 2018-03-11.11-50-01.admin + ✓ DRY-RUN OK +[286/929] 2018-03-11.11-50-01.bus → 2018-03-11.11-50-01.bus + ✓ DRY-RUN OK +[287/929] 2018-03-11.11-50-01.school → 2018-03-11.11-50-01.school + ✓ DRY-RUN OK +[288/929] 2018-03-11.11-50-04.school → 2018-03-11.11-50-04.school + ✓ DRY-RUN OK +[289/929] 2018-03-11.11-50-08.bus → 2018-03-11.11-50-08.bus + ✓ DRY-RUN OK +[290/929] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ DRY-RUN OK +[291/929] 2018-03-11.11-55-00.admin → 2018-03-11.11-55-00.admin + ✓ DRY-RUN OK +[292/929] 2018-03-11.11-55-00.bus → 2018-03-11.11-55-00.bus + ✓ DRY-RUN OK +[293/929] 2018-03-11.11-55-00.school → 2018-03-11.11-55-00.school + ✓ DRY-RUN OK +[294/929] 2018-03-11.11-55-01.admin → 2018-03-11.11-55-01.admin + ✓ DRY-RUN OK +[295/929] 2018-03-11.11-55-01.bus → 2018-03-11.11-55-01.bus + ✓ DRY-RUN OK +[296/929] 2018-03-11.11-55-01.school → 2018-03-11.11-55-01.school + ✓ DRY-RUN OK +[297/929] 2018-03-11.11-55-04.school → 2018-03-11.11-55-04.school + ✓ DRY-RUN OK +[298/929] 2018-03-11.11-55-08.bus → 2018-03-11.11-55-08.bus + ✓ DRY-RUN OK +[299/929] 2018-03-11.11-55-08.hospital → 2018-03-11.11-55-08.hospital + ✓ DRY-RUN OK +[300/929] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ DRY-RUN OK +[301/929] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ DRY-RUN OK +[302/929] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ DRY-RUN OK +[303/929] 2018-03-11.13-50-00.hospital → 2018-03-11.13-50-00.hospital + ✓ DRY-RUN OK +[304/929] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ DRY-RUN OK +[305/929] 2018-03-11.13-50-01.admin → 2018-03-11.13-50-01.admin + ✓ DRY-RUN OK +[306/929] 2018-03-11.13-50-01.bus → 2018-03-11.13-50-01.bus + ✓ DRY-RUN OK +[307/929] 2018-03-11.13-50-01.school → 2018-03-11.13-50-01.school + ✓ DRY-RUN OK +[308/929] 2018-03-11.13-50-04.school → 2018-03-11.13-50-04.school + ✓ DRY-RUN OK +[309/929] 2018-03-11.13-50-08.bus → 2018-03-11.13-50-08.bus + ✓ DRY-RUN OK +[310/929] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ DRY-RUN OK +[311/929] 2018-03-11.13-55-00.admin → 2018-03-11.13-55-00.admin + ✓ DRY-RUN OK +[312/929] 2018-03-11.13-55-00.bus → 2018-03-11.13-55-00.bus + ✓ DRY-RUN OK +[313/929] 2018-03-11.13-55-00.school → 2018-03-11.13-55-00.school + ✓ DRY-RUN OK +[314/929] 2018-03-11.13-55-01.admin → 2018-03-11.13-55-01.admin + ✓ DRY-RUN OK +[315/929] 2018-03-11.13-55-01.bus → 2018-03-11.13-55-01.bus + ✓ DRY-RUN OK +[316/929] 2018-03-11.13-55-01.school → 2018-03-11.13-55-01.school + ✓ DRY-RUN OK +[317/929] 2018-03-11.13-55-04.school → 2018-03-11.13-55-04.school + ✓ DRY-RUN OK +[318/929] 2018-03-11.13-55-08.bus → 2018-03-11.13-55-08.bus + ✓ DRY-RUN OK +[319/929] 2018-03-11.13-55-08.hospital → 2018-03-11.13-55-08.hospital + ✓ DRY-RUN OK +[320/929] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ DRY-RUN OK +[321/929] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ DRY-RUN OK +[322/929] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ DRY-RUN OK +[323/929] 2018-03-11.14-00-01.admin → 2018-03-11.14-00-01.admin + ✓ DRY-RUN OK +[324/929] 2018-03-11.14-00-01.bus → 2018-03-11.14-00-01.bus + ✓ DRY-RUN OK +[325/929] 2018-03-11.14-00-01.school → 2018-03-11.14-00-01.school + ✓ DRY-RUN OK +[326/929] 2018-03-11.14-00-02.school → 2018-03-11.14-00-02.school + ✓ DRY-RUN OK +[327/929] 2018-03-11.14-00-04.school → 2018-03-11.14-00-04.school + ✓ DRY-RUN OK +[328/929] 2018-03-11.14-00-08.bus → 2018-03-11.14-00-08.bus + ✓ DRY-RUN OK +[329/929] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ DRY-RUN OK +[330/929] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ DRY-RUN OK +[331/929] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ DRY-RUN OK +[332/929] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ DRY-RUN OK +[333/929] 2018-03-11.14-05-01.admin → 2018-03-11.14-05-01.admin + ✓ DRY-RUN OK +[334/929] 2018-03-11.14-05-01.bus → 2018-03-11.14-05-01.bus + ✓ DRY-RUN OK +[335/929] 2018-03-11.14-05-01.school → 2018-03-11.14-05-01.school + ✓ DRY-RUN OK +[336/929] 2018-03-11.14-05-02.school → 2018-03-11.14-05-02.school + ✓ DRY-RUN OK +[337/929] 2018-03-11.14-05-04.school → 2018-03-11.14-05-04.school + ✓ DRY-RUN OK +[338/929] 2018-03-11.14-05-08.bus → 2018-03-11.14-05-08.bus + ✓ DRY-RUN OK +[339/929] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ DRY-RUN OK +[340/929] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ DRY-RUN OK +[341/929] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ DRY-RUN OK +[342/929] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ DRY-RUN OK +[343/929] 2018-03-11.14-10-01.admin → 2018-03-11.14-10-01.admin + ✓ DRY-RUN OK +[344/929] 2018-03-11.14-10-01.bus → 2018-03-11.14-10-01.bus + ✓ DRY-RUN OK +[345/929] 2018-03-11.14-10-01.school → 2018-03-11.14-10-01.school + ✓ DRY-RUN OK +[346/929] 2018-03-11.14-10-02.school → 2018-03-11.14-10-02.school + ✓ DRY-RUN OK +[347/929] 2018-03-11.14-10-04.school → 2018-03-11.14-10-04.school + ✓ DRY-RUN OK +[348/929] 2018-03-11.14-10-08.bus → 2018-03-11.14-10-08.bus + ✓ DRY-RUN OK +[349/929] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ DRY-RUN OK +[350/929] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ DRY-RUN OK +[351/929] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ DRY-RUN OK +[352/929] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ DRY-RUN OK +[353/929] 2018-03-11.14-15-01.admin → 2018-03-11.14-15-01.admin + ✓ DRY-RUN OK +[354/929] 2018-03-11.14-15-01.bus → 2018-03-11.14-15-01.bus + ✓ DRY-RUN OK +[355/929] 2018-03-11.14-15-01.school → 2018-03-11.14-15-01.school + ✓ DRY-RUN OK +[356/929] 2018-03-11.14-15-02.school → 2018-03-11.14-15-02.school + ✓ DRY-RUN OK +[357/929] 2018-03-11.14-15-04.school → 2018-03-11.14-15-04.school + ✓ DRY-RUN OK +[358/929] 2018-03-11.14-15-08.bus → 2018-03-11.14-15-08.bus + ✓ DRY-RUN OK +[359/929] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ DRY-RUN OK +[360/929] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ DRY-RUN OK +[361/929] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ DRY-RUN OK +[362/929] 2018-03-11.14-20-00.hospital → 2018-03-11.14-20-00.hospital + ✓ DRY-RUN OK +[363/929] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ DRY-RUN OK +[364/929] 2018-03-11.14-20-01.admin → 2018-03-11.14-20-01.admin + ✓ DRY-RUN OK +[365/929] 2018-03-11.14-20-01.bus → 2018-03-11.14-20-01.bus + ✓ DRY-RUN OK +[366/929] 2018-03-11.14-20-01.school → 2018-03-11.14-20-01.school + ✓ DRY-RUN OK +[367/929] 2018-03-11.14-20-02.school → 2018-03-11.14-20-02.school + ✓ DRY-RUN OK +[368/929] 2018-03-11.14-20-04.school → 2018-03-11.14-20-04.school + ✓ DRY-RUN OK +[369/929] 2018-03-11.14-20-08.bus → 2018-03-11.14-20-08.bus + ✓ DRY-RUN OK +[370/929] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ DRY-RUN OK +[371/929] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ DRY-RUN OK +[372/929] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ DRY-RUN OK +[373/929] 2018-03-11.16-10-01.bus → 2018-03-11.16-10-01.bus + ✓ DRY-RUN OK +[374/929] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ DRY-RUN OK +[375/929] 2018-03-11.16-10-08.bus → 2018-03-11.16-10-08.bus + ✓ DRY-RUN OK +[376/929] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ DRY-RUN OK +[377/929] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ DRY-RUN OK +[378/929] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ DRY-RUN OK +[379/929] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ DRY-RUN OK +[380/929] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ DRY-RUN OK +[381/929] 2018-03-11.16-15-01.bus → 2018-03-11.16-15-01.bus + ✓ DRY-RUN OK +[382/929] 2018-03-11.16-15-01.school → 2018-03-11.16-15-01.school + ✓ DRY-RUN OK +[383/929] 2018-03-11.16-15-02.school → 2018-03-11.16-15-02.school + ✓ DRY-RUN OK +[384/929] 2018-03-11.16-15-04.school → 2018-03-11.16-15-04.school + ✓ DRY-RUN OK +[385/929] 2018-03-11.16-15-08.bus → 2018-03-11.16-15-08.bus + ✓ DRY-RUN OK +[386/929] 2018-03-11.16-15-08.hospital → 2018-03-11.16-15-08.hospital + ✓ DRY-RUN OK +[387/929] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ DRY-RUN OK +[388/929] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ DRY-RUN OK +[389/929] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ DRY-RUN OK +[390/929] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ DRY-RUN OK +[391/929] 2018-03-11.16-20-01.bus → 2018-03-11.16-20-01.bus + ✓ DRY-RUN OK +[392/929] 2018-03-11.16-20-01.school → 2018-03-11.16-20-01.school + ✓ DRY-RUN OK +[393/929] 2018-03-11.16-20-02.school → 2018-03-11.16-20-02.school + ✓ DRY-RUN OK +[394/929] 2018-03-11.16-20-04.school → 2018-03-11.16-20-04.school + ✓ DRY-RUN OK +[395/929] 2018-03-11.16-20-08.bus → 2018-03-11.16-20-08.bus + ✓ DRY-RUN OK +[396/929] 2018-03-11.16-20-08.hospital → 2018-03-11.16-20-08.hospital + ✓ DRY-RUN OK +[397/929] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ DRY-RUN OK +[398/929] 2018-03-11.16-25-00.hospital → 2018-03-11.16-25-00.hospital + ✓ DRY-RUN OK +[399/929] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ DRY-RUN OK +[400/929] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ DRY-RUN OK +[401/929] 2018-03-11.16-25-01.bus → 2018-03-11.16-25-01.bus + ✓ DRY-RUN OK +[402/929] 2018-03-11.16-25-01.school → 2018-03-11.16-25-01.school + ✓ DRY-RUN OK +[403/929] 2018-03-11.16-25-02.school → 2018-03-11.16-25-02.school + ✓ DRY-RUN OK +[404/929] 2018-03-11.16-25-04.school → 2018-03-11.16-25-04.school + ✓ DRY-RUN OK +[405/929] 2018-03-11.16-25-08.bus → 2018-03-11.16-25-08.bus + ✓ DRY-RUN OK +[406/929] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ DRY-RUN OK +[407/929] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ DRY-RUN OK +[408/929] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ DRY-RUN OK +[409/929] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ DRY-RUN OK +[410/929] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ DRY-RUN OK +[411/929] 2018-03-11.16-30-01.bus → 2018-03-11.16-30-01.bus + ✓ DRY-RUN OK +[412/929] 2018-03-11.16-30-01.school → 2018-03-11.16-30-01.school + ✓ DRY-RUN OK +[413/929] 2018-03-11.16-30-02.school → 2018-03-11.16-30-02.school + ✓ DRY-RUN OK +[414/929] 2018-03-11.16-30-04.school → 2018-03-11.16-30-04.school + ✓ DRY-RUN OK +[415/929] 2018-03-11.16-30-08.bus → 2018-03-11.16-30-08.bus + ✓ DRY-RUN OK +[416/929] 2018-03-11.16-30-08.hospital → 2018-03-11.16-30-08.hospital + ✓ DRY-RUN OK +[417/929] 2018-03-11.16-30-09.hospital → 2018-03-11.16-30-09.hospital + ✓ DRY-RUN OK +[418/929] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ DRY-RUN OK +[419/929] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ DRY-RUN OK +[420/929] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ DRY-RUN OK +[421/929] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ DRY-RUN OK +[422/929] 2018-03-11.16-35-01.bus → 2018-03-11.16-35-01.bus + ✓ DRY-RUN OK +[423/929] 2018-03-11.16-35-01.school → 2018-03-11.16-35-01.school + ✓ DRY-RUN OK +[424/929] 2018-03-11.16-35-02.school → 2018-03-11.16-35-02.school + ✓ DRY-RUN OK +[425/929] 2018-03-11.16-35-04.school → 2018-03-11.16-35-04.school + ✓ DRY-RUN OK +[426/929] 2018-03-11.16-35-08.bus → 2018-03-11.16-35-08.bus + ✓ DRY-RUN OK +[427/929] 2018-03-11.16-35-08.hospital → 2018-03-11.16-35-08.hospital + ✓ DRY-RUN OK +[428/929] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ DRY-RUN OK +[429/929] 2018-03-11.16-40-00.hospital → 2018-03-11.16-40-00.hospital + ✓ DRY-RUN OK +[430/929] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ DRY-RUN OK +[431/929] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ DRY-RUN OK +[432/929] 2018-03-11.16-40-01.bus → 2018-03-11.16-40-01.bus + ✓ DRY-RUN OK +[433/929] 2018-03-11.16-40-01.school → 2018-03-11.16-40-01.school + ✓ DRY-RUN OK +[434/929] 2018-03-11.16-40-02.school → 2018-03-11.16-40-02.school + ✓ DRY-RUN OK +[435/929] 2018-03-11.16-40-04.school → 2018-03-11.16-40-04.school + ✓ DRY-RUN OK +[436/929] 2018-03-11.16-40-08.bus → 2018-03-11.16-40-08.bus + ✓ DRY-RUN OK +[437/929] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ DRY-RUN OK +[438/929] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ DRY-RUN OK +[439/929] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ DRY-RUN OK +[440/929] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ DRY-RUN OK +[441/929] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ DRY-RUN OK +[442/929] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ DRY-RUN OK +[443/929] 2018-03-11.17-10-01.bus → 2018-03-11.17-10-01.bus + ✓ DRY-RUN OK +[444/929] 2018-03-11.17-10-01.school → 2018-03-11.17-10-01.school + ✓ DRY-RUN OK +[445/929] 2018-03-11.17-10-02.school → 2018-03-11.17-10-02.school + ✓ DRY-RUN OK +[446/929] 2018-03-11.17-10-04.school → 2018-03-11.17-10-04.school + ✓ DRY-RUN OK +[447/929] 2018-03-11.17-10-08.bus → 2018-03-11.17-10-08.bus + ✓ DRY-RUN OK +[448/929] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ DRY-RUN OK +[449/929] 2018-03-11.17-10-09.hospital → 2018-03-11.17-10-09.hospital + ✓ DRY-RUN OK +[450/929] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ DRY-RUN OK +[451/929] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ DRY-RUN OK +[452/929] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ DRY-RUN OK +[453/929] 2018-03-11.17-15-01.bus → 2018-03-11.17-15-01.bus + ✓ DRY-RUN OK +[454/929] 2018-03-11.17-15-01.school → 2018-03-11.17-15-01.school + ✓ DRY-RUN OK +[455/929] 2018-03-11.17-15-02.school → 2018-03-11.17-15-02.school + ✓ DRY-RUN OK +[456/929] 2018-03-11.17-15-04.school → 2018-03-11.17-15-04.school + ✓ DRY-RUN OK +[457/929] 2018-03-11.17-15-08.bus → 2018-03-11.17-15-08.bus + ✓ DRY-RUN OK +[458/929] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ DRY-RUN OK +[459/929] 2018-03-11.17-15-09.hospital → 2018-03-11.17-15-09.hospital + ✓ DRY-RUN OK +[460/929] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ DRY-RUN OK +[461/929] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ DRY-RUN OK +[462/929] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ DRY-RUN OK +[463/929] 2018-03-11.17-20-01.bus → 2018-03-11.17-20-01.bus + ✓ DRY-RUN OK +[464/929] 2018-03-11.17-20-01.school → 2018-03-11.17-20-01.school + ✓ DRY-RUN OK +[465/929] 2018-03-11.17-20-02.school → 2018-03-11.17-20-02.school + ✓ DRY-RUN OK +[466/929] 2018-03-11.17-20-04.school → 2018-03-11.17-20-04.school + ✓ DRY-RUN OK +[467/929] 2018-03-11.17-20-08.bus → 2018-03-11.17-20-08.bus + ✓ DRY-RUN OK +[468/929] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ DRY-RUN OK +[469/929] 2018-03-11.17-20-09.hospital → 2018-03-11.17-20-09.hospital + ✓ DRY-RUN OK +[470/929] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ DRY-RUN OK +[471/929] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ DRY-RUN OK +[472/929] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ DRY-RUN OK +[473/929] 2018-03-11.17-25-01.bus → 2018-03-11.17-25-01.bus + ✓ DRY-RUN OK +[474/929] 2018-03-11.17-25-01.school → 2018-03-11.17-25-01.school + ✓ DRY-RUN OK +[475/929] 2018-03-11.17-25-02.school → 2018-03-11.17-25-02.school + ✓ DRY-RUN OK +[476/929] 2018-03-11.17-25-04.school → 2018-03-11.17-25-04.school + ✓ DRY-RUN OK +[477/929] 2018-03-11.17-25-08.bus → 2018-03-11.17-25-08.bus + ✓ DRY-RUN OK +[478/929] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ DRY-RUN OK +[479/929] 2018-03-11.17-25-09.hospital → 2018-03-11.17-25-09.hospital + ✓ DRY-RUN OK +[480/929] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ DRY-RUN OK +[481/929] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ DRY-RUN OK +[482/929] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ DRY-RUN OK +[483/929] 2018-03-12.10-00-01.admin → 2018-03-12.10-00-01.admin + ✓ DRY-RUN OK +[484/929] 2018-03-12.10-00-01.bus → 2018-03-12.10-00-01.bus + ✓ DRY-RUN OK +[485/929] 2018-03-12.10-00-01.school → 2018-03-12.10-00-01.school + ✓ DRY-RUN OK +[486/929] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ DRY-RUN OK +[487/929] 2018-03-12.10-00-02.school → 2018-03-12.10-00-02.school + ✓ DRY-RUN OK +[488/929] 2018-03-12.10-00-04.school → 2018-03-12.10-00-04.school + ✓ DRY-RUN OK +[489/929] 2018-03-12.10-00-05.school → 2018-03-12.10-00-05.school + ✓ DRY-RUN OK +[490/929] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ DRY-RUN OK +[491/929] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ DRY-RUN OK +[492/929] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ DRY-RUN OK +[493/929] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ DRY-RUN OK +[494/929] 2018-03-12.10-05-01.bus → 2018-03-12.10-05-01.bus + ✓ DRY-RUN OK +[495/929] 2018-03-12.10-05-01.school → 2018-03-12.10-05-01.school + ✓ DRY-RUN OK +[496/929] 2018-03-12.10-05-02.school → 2018-03-12.10-05-02.school + ✓ DRY-RUN OK +[497/929] 2018-03-12.10-05-03.school → 2018-03-12.10-05-03.school + ✓ DRY-RUN OK +[498/929] 2018-03-12.10-05-04.school → 2018-03-12.10-05-04.school + ✓ DRY-RUN OK +[499/929] 2018-03-12.10-05-05.school → 2018-03-12.10-05-05.school + ✓ DRY-RUN OK +[500/929] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ DRY-RUN OK +[501/929] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ DRY-RUN OK +[502/929] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ DRY-RUN OK +[503/929] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ DRY-RUN OK +[504/929] 2018-03-12.10-10-01.bus → 2018-03-12.10-10-01.bus + ✓ DRY-RUN OK +[505/929] 2018-03-12.10-10-01.school → 2018-03-12.10-10-01.school + ✓ DRY-RUN OK +[506/929] 2018-03-12.10-10-02.school → 2018-03-12.10-10-02.school + ✓ DRY-RUN OK +[507/929] 2018-03-12.10-10-03.school → 2018-03-12.10-10-03.school + ✓ DRY-RUN OK +[508/929] 2018-03-12.10-10-04.school → 2018-03-12.10-10-04.school + ✓ DRY-RUN OK +[509/929] 2018-03-12.10-10-05.school → 2018-03-12.10-10-05.school + ✓ DRY-RUN OK +[510/929] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ DRY-RUN OK +[511/929] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ DRY-RUN OK +[512/929] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ DRY-RUN OK +[513/929] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ DRY-RUN OK +[514/929] 2018-03-12.10-15-01.bus → 2018-03-12.10-15-01.bus + ✓ DRY-RUN OK +[515/929] 2018-03-12.10-15-01.school → 2018-03-12.10-15-01.school + ✓ DRY-RUN OK +[516/929] 2018-03-12.10-15-02.school → 2018-03-12.10-15-02.school + ✓ DRY-RUN OK +[517/929] 2018-03-12.10-15-03.school → 2018-03-12.10-15-03.school + ✓ DRY-RUN OK +[518/929] 2018-03-12.10-15-04.school → 2018-03-12.10-15-04.school + ✓ DRY-RUN OK +[519/929] 2018-03-12.10-15-05.school → 2018-03-12.10-15-05.school + ✓ DRY-RUN OK +[520/929] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ DRY-RUN OK +[521/929] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ DRY-RUN OK +[522/929] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ DRY-RUN OK +[523/929] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ DRY-RUN OK +[524/929] 2018-03-12.10-20-01.bus → 2018-03-12.10-20-01.bus + ✓ DRY-RUN OK +[525/929] 2018-03-12.10-20-01.school → 2018-03-12.10-20-01.school + ✓ DRY-RUN OK +[526/929] 2018-03-12.10-20-02.school → 2018-03-12.10-20-02.school + ✓ DRY-RUN OK +[527/929] 2018-03-12.10-20-03.school → 2018-03-12.10-20-03.school + ✓ DRY-RUN OK +[528/929] 2018-03-12.10-20-04.school → 2018-03-12.10-20-04.school + ✓ DRY-RUN OK +[529/929] 2018-03-12.10-20-05.school → 2018-03-12.10-20-05.school + ✓ DRY-RUN OK +[530/929] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ DRY-RUN OK +[531/929] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ DRY-RUN OK +[532/929] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ DRY-RUN OK +[533/929] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ DRY-RUN OK +[534/929] 2018-03-12.10-25-01.bus → 2018-03-12.10-25-01.bus + ✓ DRY-RUN OK +[535/929] 2018-03-12.10-25-01.school → 2018-03-12.10-25-01.school + ✓ DRY-RUN OK +[536/929] 2018-03-12.10-25-02.school → 2018-03-12.10-25-02.school + ✓ DRY-RUN OK +[537/929] 2018-03-12.10-25-03.school → 2018-03-12.10-25-03.school + ✓ DRY-RUN OK +[538/929] 2018-03-12.10-25-04.school → 2018-03-12.10-25-04.school + ✓ DRY-RUN OK +[539/929] 2018-03-12.10-25-05.school → 2018-03-12.10-25-05.school + ✓ DRY-RUN OK +[540/929] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ DRY-RUN OK +[541/929] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ DRY-RUN OK +[542/929] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ DRY-RUN OK +[543/929] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ DRY-RUN OK +[544/929] 2018-03-12.10-30-01.admin → 2018-03-12.10-30-01.admin + ✓ DRY-RUN OK +[545/929] 2018-03-12.10-30-01.bus → 2018-03-12.10-30-01.bus + ✓ DRY-RUN OK +[546/929] 2018-03-12.10-30-01.school → 2018-03-12.10-30-01.school + ✓ DRY-RUN OK +[547/929] 2018-03-12.10-30-02.school → 2018-03-12.10-30-02.school + ✓ DRY-RUN OK +[548/929] 2018-03-12.10-30-03.school → 2018-03-12.10-30-03.school + ✓ DRY-RUN OK +[549/929] 2018-03-12.10-30-04.school → 2018-03-12.10-30-04.school + ✓ DRY-RUN OK +[550/929] 2018-03-12.10-30-05.school → 2018-03-12.10-30-05.school + ✓ DRY-RUN OK +[551/929] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ DRY-RUN OK +[552/929] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ DRY-RUN OK +[553/929] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ DRY-RUN OK +[554/929] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ DRY-RUN OK +[555/929] 2018-03-12.10-40-01.admin → 2018-03-12.10-40-01.admin + ✓ DRY-RUN OK +[556/929] 2018-03-12.10-40-01.bus → 2018-03-12.10-40-01.bus + ✓ DRY-RUN OK +[557/929] 2018-03-12.10-40-01.school → 2018-03-12.10-40-01.school + ✓ DRY-RUN OK +[558/929] 2018-03-12.10-40-02.school → 2018-03-12.10-40-02.school + ✓ DRY-RUN OK +[559/929] 2018-03-12.10-40-03.school → 2018-03-12.10-40-03.school + ✓ DRY-RUN OK +[560/929] 2018-03-12.10-40-04.school → 2018-03-12.10-40-04.school + ✓ DRY-RUN OK +[561/929] 2018-03-12.10-40-05.school → 2018-03-12.10-40-05.school + ✓ DRY-RUN OK +[562/929] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ DRY-RUN OK +[563/929] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ DRY-RUN OK +[564/929] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ DRY-RUN OK +[565/929] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ DRY-RUN OK +[566/929] 2018-03-12.10-45-01.admin → 2018-03-12.10-45-01.admin + ✓ DRY-RUN OK +[567/929] 2018-03-12.10-45-01.bus → 2018-03-12.10-45-01.bus + ✓ DRY-RUN OK +[568/929] 2018-03-12.10-45-01.school → 2018-03-12.10-45-01.school + ✓ DRY-RUN OK +[569/929] 2018-03-12.10-45-02.school → 2018-03-12.10-45-02.school + ✓ DRY-RUN OK +[570/929] 2018-03-12.10-45-03.school → 2018-03-12.10-45-03.school + ✓ DRY-RUN OK +[571/929] 2018-03-12.10-45-04.school → 2018-03-12.10-45-04.school + ✓ DRY-RUN OK +[572/929] 2018-03-12.10-45-05.school → 2018-03-12.10-45-05.school + ✓ DRY-RUN OK +[573/929] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ DRY-RUN OK +[574/929] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ DRY-RUN OK +[575/929] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ DRY-RUN OK +[576/929] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ DRY-RUN OK +[577/929] 2018-03-12.10-50-01.bus → 2018-03-12.10-50-01.bus + ✓ DRY-RUN OK +[578/929] 2018-03-12.10-50-01.school → 2018-03-12.10-50-01.school + ✓ DRY-RUN OK +[579/929] 2018-03-12.10-50-02.admin → 2018-03-12.10-50-02.admin + ✓ DRY-RUN OK +[580/929] 2018-03-12.10-50-02.school → 2018-03-12.10-50-02.school + ✓ DRY-RUN OK +[581/929] 2018-03-12.10-50-03.school → 2018-03-12.10-50-03.school + ✓ DRY-RUN OK +[582/929] 2018-03-12.10-50-04.school → 2018-03-12.10-50-04.school + ✓ DRY-RUN OK +[583/929] 2018-03-12.10-50-05.school → 2018-03-12.10-50-05.school + ✓ DRY-RUN OK +[584/929] 2018-03-12.10-55-00.bus → 2018-03-12.10-55-00.bus + ✓ DRY-RUN OK +[585/929] 2018-03-12.10-55-00.hospital → 2018-03-12.10-55-00.hospital + ✓ DRY-RUN OK +[586/929] 2018-03-12.10-55-00.school → 2018-03-12.10-55-00.school + ✓ DRY-RUN OK +[587/929] 2018-03-12.10-55-01.admin → 2018-03-12.10-55-01.admin + ✓ DRY-RUN OK +[588/929] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ DRY-RUN OK +[589/929] 2018-03-12.10-55-02.admin → 2018-03-12.10-55-02.admin + ✓ DRY-RUN OK +[590/929] 2018-03-12.10-55-02.school → 2018-03-12.10-55-02.school + ✓ DRY-RUN OK +[591/929] 2018-03-12.10-55-03.school → 2018-03-12.10-55-03.school + ✓ DRY-RUN OK +[592/929] 2018-03-12.10-55-04.school → 2018-03-12.10-55-04.school + ✓ DRY-RUN OK +[593/929] 2018-03-12.10-55-05.school → 2018-03-12.10-55-05.school + ✓ DRY-RUN OK +[594/929] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ DRY-RUN OK +[595/929] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ DRY-RUN OK +[596/929] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ DRY-RUN OK +[597/929] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ DRY-RUN OK +[598/929] 2018-03-12.11-00-01.bus → 2018-03-12.11-00-01.bus + ✓ DRY-RUN OK +[599/929] 2018-03-12.11-00-01.school → 2018-03-12.11-00-01.school + ✓ DRY-RUN OK +[600/929] 2018-03-12.11-00-02.school → 2018-03-12.11-00-02.school + ✓ DRY-RUN OK +[601/929] 2018-03-12.11-00-03.school → 2018-03-12.11-00-03.school + ✓ DRY-RUN OK +[602/929] 2018-03-12.11-00-04.school → 2018-03-12.11-00-04.school + ✓ DRY-RUN OK +[603/929] 2018-03-12.11-00-05.school → 2018-03-12.11-00-05.school + ✓ DRY-RUN OK +[604/929] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ DRY-RUN OK +[605/929] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ DRY-RUN OK +[606/929] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ DRY-RUN OK +[607/929] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ DRY-RUN OK +[608/929] 2018-03-12.11-05-01.bus → 2018-03-12.11-05-01.bus + ✓ DRY-RUN OK +[609/929] 2018-03-12.11-05-01.school → 2018-03-12.11-05-01.school + ✓ DRY-RUN OK +[610/929] 2018-03-12.11-05-02.school → 2018-03-12.11-05-02.school + ✓ DRY-RUN OK +[611/929] 2018-03-12.11-05-03.school → 2018-03-12.11-05-03.school + ✓ DRY-RUN OK +[612/929] 2018-03-12.11-05-04.school → 2018-03-12.11-05-04.school + ✓ DRY-RUN OK +[613/929] 2018-03-12.11-05-05.school → 2018-03-12.11-05-05.school + ✓ DRY-RUN OK +[614/929] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ DRY-RUN OK +[615/929] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ DRY-RUN OK +[616/929] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ DRY-RUN OK +[617/929] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ DRY-RUN OK +[618/929] 2018-03-12.11-10-01.school → 2018-03-12.11-10-01.school + ✓ DRY-RUN OK +[619/929] 2018-03-12.11-10-02.school → 2018-03-12.11-10-02.school + ✓ DRY-RUN OK +[620/929] 2018-03-12.11-10-03.school → 2018-03-12.11-10-03.school + ✓ DRY-RUN OK +[621/929] 2018-03-12.11-10-04.school → 2018-03-12.11-10-04.school + ✓ DRY-RUN OK +[622/929] 2018-03-12.11-10-05.school → 2018-03-12.11-10-05.school + ✓ DRY-RUN OK +[623/929] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ DRY-RUN OK +[624/929] 2018-03-13.15-50-00.hospital → 2018-03-13.15-50-00.hospital + ✓ DRY-RUN OK +[625/929] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ DRY-RUN OK +[626/929] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ DRY-RUN OK +[627/929] 2018-03-13.15-50-01.school → 2018-03-13.15-50-01.school + ✓ DRY-RUN OK +[628/929] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ DRY-RUN OK +[629/929] 2018-03-13.15-50-02.school → 2018-03-13.15-50-02.school + ✓ DRY-RUN OK +[630/929] 2018-03-13.15-50-03.school → 2018-03-13.15-50-03.school + ✓ DRY-RUN OK +[631/929] 2018-03-13.15-50-04.bus → 2018-03-13.15-50-04.bus + ✓ DRY-RUN OK +[632/929] 2018-03-13.15-50-05.school → 2018-03-13.15-50-05.school + ✓ DRY-RUN OK +[633/929] 2018-03-13.15-50-08.school → 2018-03-13.15-50-08.school + ✓ DRY-RUN OK +[634/929] 2018-03-13.15-55-00.bus → 2018-03-13.15-55-00.bus + ✓ DRY-RUN OK +[635/929] 2018-03-13.15-55-00.hospital → 2018-03-13.15-55-00.hospital + ✓ DRY-RUN OK +[636/929] 2018-03-13.15-55-00.school → 2018-03-13.15-55-00.school + ✓ DRY-RUN OK +[637/929] 2018-03-13.15-55-01.admin → 2018-03-13.15-55-01.admin + ✓ DRY-RUN OK +[638/929] 2018-03-13.15-55-01.school → 2018-03-13.15-55-01.school + ✓ DRY-RUN OK +[639/929] 2018-03-13.15-55-02.hospital → 2018-03-13.15-55-02.hospital + ✓ DRY-RUN OK +[640/929] 2018-03-13.15-55-02.school → 2018-03-13.15-55-02.school + ✓ DRY-RUN OK +[641/929] 2018-03-13.15-55-03.school → 2018-03-13.15-55-03.school + ✓ DRY-RUN OK +[642/929] 2018-03-13.15-55-04.bus → 2018-03-13.15-55-04.bus + ✓ DRY-RUN OK +[643/929] 2018-03-13.15-55-05.school → 2018-03-13.15-55-05.school + ✓ DRY-RUN OK +[644/929] 2018-03-13.15-55-08.school → 2018-03-13.15-55-08.school + ✓ DRY-RUN OK +[645/929] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ DRY-RUN OK +[646/929] 2018-03-13.16-00-00.hospital → 2018-03-13.16-00-00.hospital + ✓ DRY-RUN OK +[647/929] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ DRY-RUN OK +[648/929] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ DRY-RUN OK +[649/929] 2018-03-13.16-00-01.school → 2018-03-13.16-00-01.school + ✓ DRY-RUN OK +[650/929] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ DRY-RUN OK +[651/929] 2018-03-13.16-00-02.school → 2018-03-13.16-00-02.school + ✓ DRY-RUN OK +[652/929] 2018-03-13.16-00-03.school → 2018-03-13.16-00-03.school + ✓ DRY-RUN OK +[653/929] 2018-03-13.16-00-05.bus → 2018-03-13.16-00-05.bus + ✓ DRY-RUN OK +[654/929] 2018-03-13.16-00-05.school → 2018-03-13.16-00-05.school + ✓ DRY-RUN OK +[655/929] 2018-03-13.16-00-08.school → 2018-03-13.16-00-08.school + ✓ DRY-RUN OK +[656/929] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ DRY-RUN OK +[657/929] 2018-03-13.16-05-00.hospital → 2018-03-13.16-05-00.hospital + ✓ DRY-RUN OK +[658/929] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ DRY-RUN OK +[659/929] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ DRY-RUN OK +[660/929] 2018-03-13.16-05-01.school → 2018-03-13.16-05-01.school + ✓ DRY-RUN OK +[661/929] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ DRY-RUN OK +[662/929] 2018-03-13.16-05-02.school → 2018-03-13.16-05-02.school + ✓ DRY-RUN OK +[663/929] 2018-03-13.16-05-03.school → 2018-03-13.16-05-03.school + ✓ DRY-RUN OK +[664/929] 2018-03-13.16-05-05.bus → 2018-03-13.16-05-05.bus + ✓ DRY-RUN OK +[665/929] 2018-03-13.16-05-05.school → 2018-03-13.16-05-05.school + ✓ DRY-RUN OK +[666/929] 2018-03-13.16-05-08.school → 2018-03-13.16-05-08.school + ✓ DRY-RUN OK +[667/929] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ DRY-RUN OK +[668/929] 2018-03-13.16-10-00.hospital → 2018-03-13.16-10-00.hospital + ✓ DRY-RUN OK +[669/929] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ DRY-RUN OK +[670/929] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ DRY-RUN OK +[671/929] 2018-03-13.16-10-01.school → 2018-03-13.16-10-01.school + ✓ DRY-RUN OK +[672/929] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ DRY-RUN OK +[673/929] 2018-03-13.16-10-03.school → 2018-03-13.16-10-03.school + ✓ DRY-RUN OK +[674/929] 2018-03-13.16-10-05.bus → 2018-03-13.16-10-05.bus + ✓ DRY-RUN OK +[675/929] 2018-03-13.16-10-05.school → 2018-03-13.16-10-05.school + ✓ DRY-RUN OK +[676/929] 2018-03-13.16-10-08.school → 2018-03-13.16-10-08.school + ✓ DRY-RUN OK +[677/929] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ DRY-RUN OK +[678/929] 2018-03-13.16-15-00.hospital → 2018-03-13.16-15-00.hospital + ✓ DRY-RUN OK +[679/929] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ DRY-RUN OK +[680/929] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ DRY-RUN OK +[681/929] 2018-03-13.16-15-01.school → 2018-03-13.16-15-01.school + ✓ DRY-RUN OK +[682/929] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ DRY-RUN OK +[683/929] 2018-03-13.16-15-03.school → 2018-03-13.16-15-03.school + ✓ DRY-RUN OK +[684/929] 2018-03-13.16-15-05.bus → 2018-03-13.16-15-05.bus + ✓ DRY-RUN OK +[685/929] 2018-03-13.16-15-05.school → 2018-03-13.16-15-05.school + ✓ DRY-RUN OK +[686/929] 2018-03-13.16-15-08.school → 2018-03-13.16-15-08.school + ✓ DRY-RUN OK +[687/929] 2018-03-13.16-16-02.school → 2018-03-13.16-16-02.school + ✓ DRY-RUN OK +[688/929] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ DRY-RUN OK +[689/929] 2018-03-13.16-20-00.hospital → 2018-03-13.16-20-00.hospital + ✓ DRY-RUN OK +[690/929] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ DRY-RUN OK +[691/929] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ DRY-RUN OK +[692/929] 2018-03-13.16-20-01.school → 2018-03-13.16-20-01.school + ✓ DRY-RUN OK +[693/929] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ DRY-RUN OK +[694/929] 2018-03-13.16-20-03.school → 2018-03-13.16-20-03.school + ✓ DRY-RUN OK +[695/929] 2018-03-13.16-20-05.bus → 2018-03-13.16-20-05.bus + ✓ DRY-RUN OK +[696/929] 2018-03-13.16-20-05.school → 2018-03-13.16-20-05.school + ✓ DRY-RUN OK +[697/929] 2018-03-13.16-20-08.school → 2018-03-13.16-20-08.school + ✓ DRY-RUN OK +[698/929] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ DRY-RUN OK +[699/929] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ DRY-RUN OK +[700/929] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ DRY-RUN OK +[701/929] 2018-03-13.16-25-01.school → 2018-03-13.16-25-01.school + ✓ DRY-RUN OK +[702/929] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ DRY-RUN OK +[703/929] 2018-03-13.16-25-03.school → 2018-03-13.16-25-03.school + ✓ DRY-RUN OK +[704/929] 2018-03-13.16-25-05.bus → 2018-03-13.16-25-05.bus + ✓ DRY-RUN OK +[705/929] 2018-03-13.16-25-05.school → 2018-03-13.16-25-05.school + ✓ DRY-RUN OK +[706/929] 2018-03-13.16-25-08.school → 2018-03-13.16-25-08.school + ✓ DRY-RUN OK +[707/929] 2018-03-13.16-26-28.school → 2018-03-13.16-26-28.school + ✓ DRY-RUN OK +[708/929] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ DRY-RUN OK +[709/929] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ DRY-RUN OK +[710/929] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ DRY-RUN OK +[711/929] 2018-03-13.16-30-01.school → 2018-03-13.16-30-01.school + ✓ DRY-RUN OK +[712/929] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ DRY-RUN OK +[713/929] 2018-03-13.16-30-03.hospital → 2018-03-13.16-30-03.hospital + ✓ DRY-RUN OK +[714/929] 2018-03-13.16-30-03.school → 2018-03-13.16-30-03.school + ✓ DRY-RUN OK +[715/929] 2018-03-13.16-30-04.bus → 2018-03-13.16-30-04.bus + ✓ DRY-RUN OK +[716/929] 2018-03-13.16-30-05.school → 2018-03-13.16-30-05.school + ✓ DRY-RUN OK +[717/929] 2018-03-13.16-30-08.school → 2018-03-13.16-30-08.school + ✓ DRY-RUN OK +[718/929] 2018-03-13.16-32-54.school → 2018-03-13.16-32-54.school + ✓ DRY-RUN OK +[719/929] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ DRY-RUN OK +[720/929] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ DRY-RUN OK +[721/929] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ DRY-RUN OK +[722/929] 2018-03-13.17-05-01.school → 2018-03-13.17-05-01.school + ✓ DRY-RUN OK +[723/929] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ DRY-RUN OK +[724/929] 2018-03-13.17-05-03.hospital → 2018-03-13.17-05-03.hospital + ✓ DRY-RUN OK +[725/929] 2018-03-13.17-05-03.school → 2018-03-13.17-05-03.school + ✓ DRY-RUN OK +[726/929] 2018-03-13.17-05-05.bus → 2018-03-13.17-05-05.bus + ✓ DRY-RUN OK +[727/929] 2018-03-13.17-05-05.school → 2018-03-13.17-05-05.school + ✓ DRY-RUN OK +[728/929] 2018-03-13.17-05-08.school → 2018-03-13.17-05-08.school + ✓ DRY-RUN OK +[729/929] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ DRY-RUN OK +[730/929] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ DRY-RUN OK +[731/929] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ DRY-RUN OK +[732/929] 2018-03-13.17-10-01.school → 2018-03-13.17-10-01.school + ✓ DRY-RUN OK +[733/929] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ DRY-RUN OK +[734/929] 2018-03-13.17-10-03.hospital → 2018-03-13.17-10-03.hospital + ✓ DRY-RUN OK +[735/929] 2018-03-13.17-10-03.school → 2018-03-13.17-10-03.school + ✓ DRY-RUN OK +[736/929] 2018-03-13.17-10-05.bus → 2018-03-13.17-10-05.bus + ✓ DRY-RUN OK +[737/929] 2018-03-13.17-10-05.school → 2018-03-13.17-10-05.school + ✓ DRY-RUN OK +[738/929] 2018-03-13.17-10-08.school → 2018-03-13.17-10-08.school + ✓ DRY-RUN OK +[739/929] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ DRY-RUN OK +[740/929] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ DRY-RUN OK +[741/929] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ DRY-RUN OK +[742/929] 2018-03-13.17-15-01.school → 2018-03-13.17-15-01.school + ✓ DRY-RUN OK +[743/929] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ DRY-RUN OK +[744/929] 2018-03-13.17-15-03.hospital → 2018-03-13.17-15-03.hospital + ✓ DRY-RUN OK +[745/929] 2018-03-13.17-15-03.school → 2018-03-13.17-15-03.school + ✓ DRY-RUN OK +[746/929] 2018-03-13.17-15-05.bus → 2018-03-13.17-15-05.bus + ✓ DRY-RUN OK +[747/929] 2018-03-13.17-15-05.school → 2018-03-13.17-15-05.school + ✓ DRY-RUN OK +[748/929] 2018-03-13.17-15-08.school → 2018-03-13.17-15-08.school + ✓ DRY-RUN OK +[749/929] 2018-03-13.17-16-48.school → 2018-03-13.17-16-48.school + ✓ DRY-RUN OK +[750/929] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ DRY-RUN OK +[751/929] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ DRY-RUN OK +[752/929] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ DRY-RUN OK +[753/929] 2018-03-13.17-20-01.school → 2018-03-13.17-20-01.school + ✓ DRY-RUN OK +[754/929] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ DRY-RUN OK +[755/929] 2018-03-13.17-20-03.hospital → 2018-03-13.17-20-03.hospital + ✓ DRY-RUN OK +[756/929] 2018-03-13.17-20-03.school → 2018-03-13.17-20-03.school + ✓ DRY-RUN OK +[757/929] 2018-03-13.17-20-05.bus → 2018-03-13.17-20-05.bus + ✓ DRY-RUN OK +[758/929] 2018-03-13.17-20-05.school → 2018-03-13.17-20-05.school + ✓ DRY-RUN OK +[759/929] 2018-03-13.17-20-08.school → 2018-03-13.17-20-08.school + ✓ DRY-RUN OK +[760/929] 2018-03-13.17-20-14.school → 2018-03-13.17-20-14.school + ✓ DRY-RUN OK +[761/929] 2018-03-13.17-21-20.school → 2018-03-13.17-21-20.school + ✓ DRY-RUN OK +[762/929] 2018-03-13.17-24-36.school → 2018-03-13.17-24-36.school + ✓ DRY-RUN OK +[763/929] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ DRY-RUN OK +[764/929] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ DRY-RUN OK +[765/929] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ DRY-RUN OK +[766/929] 2018-03-13.17-25-01.school → 2018-03-13.17-25-01.school + ✓ DRY-RUN OK +[767/929] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ DRY-RUN OK +[768/929] 2018-03-13.17-25-03.hospital → 2018-03-13.17-25-03.hospital + ✓ DRY-RUN OK +[769/929] 2018-03-13.17-25-03.school → 2018-03-13.17-25-03.school + ✓ DRY-RUN OK +[770/929] 2018-03-13.17-25-05.bus → 2018-03-13.17-25-05.bus + ✓ DRY-RUN OK +[771/929] 2018-03-13.17-25-05.school → 2018-03-13.17-25-05.school + ✓ DRY-RUN OK +[772/929] 2018-03-13.17-25-08.school → 2018-03-13.17-25-08.school + ✓ DRY-RUN OK +[773/929] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ DRY-RUN OK +[774/929] 2018-03-13.17-30-00.hospital → 2018-03-13.17-30-00.hospital + ✓ DRY-RUN OK +[775/929] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ DRY-RUN OK +[776/929] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ DRY-RUN OK +[777/929] 2018-03-13.17-30-01.bus → 2018-03-13.17-30-01.bus + ✓ DRY-RUN OK +[778/929] 2018-03-13.17-30-01.school → 2018-03-13.17-30-01.school + ✓ DRY-RUN OK +[779/929] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ DRY-RUN OK +[780/929] 2018-03-13.17-30-03.hospital → 2018-03-13.17-30-03.hospital + ✓ DRY-RUN OK +[781/929] 2018-03-13.17-30-03.school → 2018-03-13.17-30-03.school + ✓ DRY-RUN OK +[782/929] 2018-03-13.17-30-05.bus → 2018-03-13.17-30-05.bus + ✓ DRY-RUN OK +[783/929] 2018-03-13.17-30-05.school → 2018-03-13.17-30-05.school + ✓ DRY-RUN OK +[784/929] 2018-03-13.17-30-08.school → 2018-03-13.17-30-08.school + ✓ DRY-RUN OK +[785/929] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ DRY-RUN OK +[786/929] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ DRY-RUN OK +[787/929] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ DRY-RUN OK +[788/929] 2018-03-13.17-35-01.bus → 2018-03-13.17-35-01.bus + ✓ DRY-RUN OK +[789/929] 2018-03-13.17-35-01.school → 2018-03-13.17-35-01.school + ✓ DRY-RUN OK +[790/929] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ DRY-RUN OK +[791/929] 2018-03-13.17-35-03.hospital → 2018-03-13.17-35-03.hospital + ✓ DRY-RUN OK +[792/929] 2018-03-13.17-35-03.school → 2018-03-13.17-35-03.school + ✓ DRY-RUN OK +[793/929] 2018-03-13.17-35-05.bus → 2018-03-13.17-35-05.bus + ✓ DRY-RUN OK +[794/929] 2018-03-13.17-35-05.school → 2018-03-13.17-35-05.school + ✓ DRY-RUN OK +[795/929] 2018-03-13.17-35-08.school → 2018-03-13.17-35-08.school + ✓ DRY-RUN OK +[796/929] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ DRY-RUN OK +[797/929] 2018-03-13.17-40-00.hospital → 2018-03-13.17-40-00.hospital + ✓ DRY-RUN OK +[798/929] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ DRY-RUN OK +[799/929] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ DRY-RUN OK +[800/929] 2018-03-13.17-40-01.bus → 2018-03-13.17-40-01.bus + ✓ DRY-RUN OK +[801/929] 2018-03-13.17-40-01.school → 2018-03-13.17-40-01.school + ✓ DRY-RUN OK +[802/929] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ DRY-RUN OK +[803/929] 2018-03-13.17-40-03.hospital → 2018-03-13.17-40-03.hospital + ✓ DRY-RUN OK +[804/929] 2018-03-13.17-40-03.school → 2018-03-13.17-40-03.school + ✓ DRY-RUN OK +[805/929] 2018-03-13.17-40-05.bus → 2018-03-13.17-40-05.bus + ✓ DRY-RUN OK +[806/929] 2018-03-13.17-40-05.school → 2018-03-13.17-40-05.school + ✓ DRY-RUN OK +[807/929] 2018-03-13.17-40-08.school → 2018-03-13.17-40-08.school + ✓ DRY-RUN OK +[808/929] 2018-03-13.17-41-20.school → 2018-03-13.17-41-20.school + ✓ DRY-RUN OK +[809/929] 2018-03-13.17-42-52.school → 2018-03-13.17-42-52.school + ✓ DRY-RUN OK +[810/929] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ DRY-RUN OK +[811/929] 2018-03-15.14-50-00.hospital → 2018-03-15.14-50-00.hospital + ✓ DRY-RUN OK +[812/929] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ DRY-RUN OK +[813/929] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ DRY-RUN OK +[814/929] 2018-03-15.14-50-01.bus → 2018-03-15.14-50-01.bus + ✓ DRY-RUN OK +[815/929] 2018-03-15.14-50-01.school → 2018-03-15.14-50-01.school + ✓ DRY-RUN OK +[816/929] 2018-03-15.14-50-03.school → 2018-03-15.14-50-03.school + ✓ DRY-RUN OK +[817/929] 2018-03-15.14-50-04.bus → 2018-03-15.14-50-04.bus + ✓ DRY-RUN OK +[818/929] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ DRY-RUN OK +[819/929] 2018-03-15.14-50-07.hospital → 2018-03-15.14-50-07.hospital + ✓ DRY-RUN OK +[820/929] 2018-03-15.14-55-00.bus → 2018-03-15.14-55-00.bus + ✓ DRY-RUN OK +[821/929] 2018-03-15.14-55-00.hospital → 2018-03-15.14-55-00.hospital + ✓ DRY-RUN OK +[822/929] 2018-03-15.14-55-00.school → 2018-03-15.14-55-00.school + ✓ DRY-RUN OK +[823/929] 2018-03-15.14-55-01.admin → 2018-03-15.14-55-01.admin + ✓ DRY-RUN OK +[824/929] 2018-03-15.14-55-01.bus → 2018-03-15.14-55-01.bus + ✓ DRY-RUN OK +[825/929] 2018-03-15.14-55-01.school → 2018-03-15.14-55-01.school + ✓ DRY-RUN OK +[826/929] 2018-03-15.14-55-03.school → 2018-03-15.14-55-03.school + ✓ DRY-RUN OK +[827/929] 2018-03-15.14-55-04.bus → 2018-03-15.14-55-04.bus + ✓ DRY-RUN OK +[828/929] 2018-03-15.14-55-06.hospital → 2018-03-15.14-55-06.hospital + ✓ DRY-RUN OK +[829/929] 2018-03-15.14-55-07.hospital → 2018-03-15.14-55-07.hospital + ✓ DRY-RUN OK +[830/929] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ DRY-RUN OK +[831/929] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ DRY-RUN OK +[832/929] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ DRY-RUN OK +[833/929] 2018-03-15.15-00-01.bus → 2018-03-15.15-00-01.bus + ✓ DRY-RUN OK +[834/929] 2018-03-15.15-00-01.school → 2018-03-15.15-00-01.school + ✓ DRY-RUN OK +[835/929] 2018-03-15.15-00-03.school → 2018-03-15.15-00-03.school + ✓ DRY-RUN OK +[836/929] 2018-03-15.15-00-04.bus → 2018-03-15.15-00-04.bus + ✓ DRY-RUN OK +[837/929] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ DRY-RUN OK +[838/929] 2018-03-15.15-00-07.hospital → 2018-03-15.15-00-07.hospital + ✓ DRY-RUN OK +[839/929] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ DRY-RUN OK +[840/929] 2018-03-15.15-05-00.hospital → 2018-03-15.15-05-00.hospital + ✓ DRY-RUN OK +[841/929] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ DRY-RUN OK +[842/929] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ DRY-RUN OK +[843/929] 2018-03-15.15-05-01.bus → 2018-03-15.15-05-01.bus + ✓ DRY-RUN OK +[844/929] 2018-03-15.15-05-01.school → 2018-03-15.15-05-01.school + ✓ DRY-RUN OK +[845/929] 2018-03-15.15-05-03.school → 2018-03-15.15-05-03.school + ✓ DRY-RUN OK +[846/929] 2018-03-15.15-05-04.bus → 2018-03-15.15-05-04.bus + ✓ DRY-RUN OK +[847/929] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ DRY-RUN OK +[848/929] 2018-03-15.15-05-07.hospital → 2018-03-15.15-05-07.hospital + ✓ DRY-RUN OK +[849/929] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ DRY-RUN OK +[850/929] 2018-03-15.15-10-00.hospital → 2018-03-15.15-10-00.hospital + ✓ DRY-RUN OK +[851/929] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ DRY-RUN OK +[852/929] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ DRY-RUN OK +[853/929] 2018-03-15.15-10-01.bus → 2018-03-15.15-10-01.bus + ✓ DRY-RUN OK +[854/929] 2018-03-15.15-10-01.school → 2018-03-15.15-10-01.school + ✓ DRY-RUN OK +[855/929] 2018-03-15.15-10-03.school → 2018-03-15.15-10-03.school + ✓ DRY-RUN OK +[856/929] 2018-03-15.15-10-04.bus → 2018-03-15.15-10-04.bus + ✓ DRY-RUN OK +[857/929] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ DRY-RUN OK +[858/929] 2018-03-15.15-10-07.hospital → 2018-03-15.15-10-07.hospital + ✓ DRY-RUN OK +[859/929] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ DRY-RUN OK +[860/929] 2018-03-15.15-15-00.hospital → 2018-03-15.15-15-00.hospital + ✓ DRY-RUN OK +[861/929] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ DRY-RUN OK +[862/929] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ DRY-RUN OK +[863/929] 2018-03-15.15-15-01.bus → 2018-03-15.15-15-01.bus + ✓ DRY-RUN OK +[864/929] 2018-03-15.15-15-01.school → 2018-03-15.15-15-01.school + ✓ DRY-RUN OK +[865/929] 2018-03-15.15-15-03.school → 2018-03-15.15-15-03.school + ✓ DRY-RUN OK +[866/929] 2018-03-15.15-15-04.bus → 2018-03-15.15-15-04.bus + ✓ DRY-RUN OK +[867/929] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ DRY-RUN OK +[868/929] 2018-03-15.15-15-07.hospital → 2018-03-15.15-15-07.hospital + ✓ DRY-RUN OK +[869/929] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ DRY-RUN OK +[870/929] 2018-03-15.15-30-00.hospital → 2018-03-15.15-30-00.hospital + ✓ DRY-RUN OK +[871/929] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ DRY-RUN OK +[872/929] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ DRY-RUN OK +[873/929] 2018-03-15.15-30-01.bus → 2018-03-15.15-30-01.bus + ✓ DRY-RUN OK +[874/929] 2018-03-15.15-30-01.school → 2018-03-15.15-30-01.school + ✓ DRY-RUN OK +[875/929] 2018-03-15.15-30-03.school → 2018-03-15.15-30-03.school + ✓ DRY-RUN OK +[876/929] 2018-03-15.15-30-04.bus → 2018-03-15.15-30-04.bus + ✓ DRY-RUN OK +[877/929] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ DRY-RUN OK +[878/929] 2018-03-15.15-30-07.hospital → 2018-03-15.15-30-07.hospital + ✓ DRY-RUN OK +[879/929] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ DRY-RUN OK +[880/929] 2018-03-15.15-35-00.hospital → 2018-03-15.15-35-00.hospital + ✓ DRY-RUN OK +[881/929] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ DRY-RUN OK +[882/929] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ DRY-RUN OK +[883/929] 2018-03-15.15-35-01.bus → 2018-03-15.15-35-01.bus + ✓ DRY-RUN OK +[884/929] 2018-03-15.15-35-01.school → 2018-03-15.15-35-01.school + ✓ DRY-RUN OK +[885/929] 2018-03-15.15-35-03.school → 2018-03-15.15-35-03.school + ✓ DRY-RUN OK +[886/929] 2018-03-15.15-35-04.bus → 2018-03-15.15-35-04.bus + ✓ DRY-RUN OK +[887/929] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ DRY-RUN OK +[888/929] 2018-03-15.15-35-07.hospital → 2018-03-15.15-35-07.hospital + ✓ DRY-RUN OK +[889/929] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ DRY-RUN OK +[890/929] 2018-03-15.15-40-00.hospital → 2018-03-15.15-40-00.hospital + ✓ DRY-RUN OK +[891/929] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ DRY-RUN OK +[892/929] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ DRY-RUN OK +[893/929] 2018-03-15.15-40-01.bus → 2018-03-15.15-40-01.bus + ✓ DRY-RUN OK +[894/929] 2018-03-15.15-40-01.school → 2018-03-15.15-40-01.school + ✓ DRY-RUN OK +[895/929] 2018-03-15.15-40-03.school → 2018-03-15.15-40-03.school + ✓ DRY-RUN OK +[896/929] 2018-03-15.15-40-04.bus → 2018-03-15.15-40-04.bus + ✓ DRY-RUN OK +[897/929] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ DRY-RUN OK +[898/929] 2018-03-15.15-40-07.hospital → 2018-03-15.15-40-07.hospital + ✓ DRY-RUN OK +[899/929] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ DRY-RUN OK +[900/929] 2018-03-15.15-45-00.hospital → 2018-03-15.15-45-00.hospital + ✓ DRY-RUN OK +[901/929] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ DRY-RUN OK +[902/929] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ DRY-RUN OK +[903/929] 2018-03-15.15-45-01.bus → 2018-03-15.15-45-01.bus + ✓ DRY-RUN OK +[904/929] 2018-03-15.15-45-01.school → 2018-03-15.15-45-01.school + ✓ DRY-RUN OK +[905/929] 2018-03-15.15-45-02.school → 2018-03-15.15-45-02.school + ✓ DRY-RUN OK +[906/929] 2018-03-15.15-45-03.school → 2018-03-15.15-45-03.school + ✓ DRY-RUN OK +[907/929] 2018-03-15.15-45-04.bus → 2018-03-15.15-45-04.bus + ✓ DRY-RUN OK +[908/929] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ DRY-RUN OK +[909/929] 2018-03-15.15-45-07.hospital → 2018-03-15.15-45-07.hospital + ✓ DRY-RUN OK +[910/929] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ DRY-RUN OK +[911/929] 2018-03-15.15-50-00.hospital → 2018-03-15.15-50-00.hospital + ✓ DRY-RUN OK +[912/929] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ DRY-RUN OK +[913/929] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ DRY-RUN OK +[914/929] 2018-03-15.15-50-01.bus → 2018-03-15.15-50-01.bus + ✓ DRY-RUN OK +[915/929] 2018-03-15.15-50-01.school → 2018-03-15.15-50-01.school + ✓ DRY-RUN OK +[916/929] 2018-03-15.15-50-03.school → 2018-03-15.15-50-03.school + ✓ DRY-RUN OK +[917/929] 2018-03-15.15-50-04.bus → 2018-03-15.15-50-04.bus + ✓ DRY-RUN OK +[918/929] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ DRY-RUN OK +[919/929] 2018-03-15.15-50-07.hospital → 2018-03-15.15-50-07.hospital + ✓ DRY-RUN OK +[920/929] 2018-03-15.15-55-00.bus → 2018-03-15.15-55-00.bus + ✓ DRY-RUN OK +[921/929] 2018-03-15.15-55-00.hospital → 2018-03-15.15-55-00.hospital + ✓ DRY-RUN OK +[922/929] 2018-03-15.15-55-00.school → 2018-03-15.15-55-00.school + ✓ DRY-RUN OK +[923/929] 2018-03-15.15-55-01.admin → 2018-03-15.15-55-01.admin + ✓ DRY-RUN OK +[924/929] 2018-03-15.15-55-01.bus → 2018-03-15.15-55-01.bus + ✓ DRY-RUN OK +[925/929] 2018-03-15.15-55-01.school → 2018-03-15.15-55-01.school + ✓ DRY-RUN OK +[926/929] 2018-03-15.15-55-03.school → 2018-03-15.15-55-03.school + ✓ DRY-RUN OK +[927/929] 2018-03-15.15-55-04.bus → 2018-03-15.15-55-04.bus + ✓ DRY-RUN OK +[928/929] 2018-03-15.15-55-06.hospital → 2018-03-15.15-55-06.hospital + ✓ DRY-RUN OK +[929/929] 2018-03-15.15-55-07.hospital → 2018-03-15.15-55-07.hospital + ✓ DRY-RUN OK + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 0.0 hours +Completed: 0 +Skipped (already exist): 0 +Failed: 0 +Total entities: 0 +Avg entities/slot: 0.0 + +Output directory: /home/ah66742/data/entity_descriptions +Log file: /home/ah66742/output/extraction_logs/batch_extraction_20260220_193005.log +============================================================ + diff --git a/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_193632.log.txt b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_193632.log.txt new file mode 100644 index 0000000..755c7b7 --- /dev/null +++ b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_193632.log.txt @@ -0,0 +1,464 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Canonical slots: 929 +Resume: False +Log: /home/ah66742/output/extraction_logs/batch_extraction_20260220_193632.log +Progress: /home/ah66742/output/extraction_logs/batch_progress.json +Started: 2026-02-20 19:36:32 +============================================================ + +[ 1/929] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/929 (0%), ETA: 0.8h + +[ 2/929] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 2/929 (0%), ETA: 0.8h + +[ 3/929] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (0 entities) + Progress: 3/929 (0%), ETA: 0.8h + +[ 4/929] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (0 entities) + Progress: 4/929 (0%), ETA: 0.8h + +[ 5/929] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 5/929 (0%), ETA: 0.8h + +[ 6/929] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ SUCCESS (0 entities) + Progress: 6/929 (0%), ETA: 0.8h + +[ 7/929] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 7/929 (0%), ETA: 0.7h + +[ 8/929] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 8/929 (0%), ETA: 0.7h + +[ 9/929] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 9/929 (0%), ETA: 0.7h + +[ 10/929] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (0 entities) + Progress: 10/929 (1%), ETA: 0.7h + +[ 11/929] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ SUCCESS (0 entities) + Progress: 11/929 (1%), ETA: 0.7h + +[ 12/929] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ SUCCESS (0 entities) + Progress: 12/929 (1%), ETA: 0.7h + +[ 13/929] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 13/929 (1%), ETA: 0.7h + +[ 14/929] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 14/929 (1%), ETA: 0.7h + +[ 15/929] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 15/929 (1%), ETA: 0.7h + +[ 16/929] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (0 entities) + Progress: 16/929 (1%), ETA: 0.7h + +[ 17/929] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ SUCCESS (0 entities) + Progress: 17/929 (1%), ETA: 0.7h + +[ 18/929] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ SUCCESS (0 entities) + Progress: 18/929 (1%), ETA: 0.7h + +[ 19/929] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (0 entities) + Progress: 19/929 (2%), ETA: 0.7h + +[ 20/929] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 20/929 (2%), ETA: 0.7h + +[ 21/929] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (0 entities) + Progress: 21/929 (2%), ETA: 0.7h + +[ 22/929] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (0 entities) + Progress: 22/929 (2%), ETA: 0.7h + +[ 23/929] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 23/929 (2%), ETA: 0.7h + +[ 24/929] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ SUCCESS (0 entities) + Progress: 24/929 (2%), ETA: 0.7h + +[ 25/929] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 25/929 (2%), ETA: 0.7h + +[ 26/929] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 26/929 (2%), ETA: 0.7h + +[ 27/929] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (0 entities) + Progress: 27/929 (2%), ETA: 0.7h + +[ 28/929] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (0 entities) + Progress: 28/929 (3%), ETA: 0.7h + +[ 29/929] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 29/929 (3%), ETA: 0.7h + +[ 30/929] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ SUCCESS (0 entities) + Progress: 30/929 (3%), ETA: 0.7h + +[ 31/929] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 31/929 (3%), ETA: 0.7h + +[ 32/929] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 32/929 (3%), ETA: 0.7h + +[ 33/929] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (0 entities) + Progress: 33/929 (3%), ETA: 0.7h + +[ 34/929] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (0 entities) + Progress: 34/929 (3%), ETA: 0.7h + +[ 35/929] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 35/929 (3%), ETA: 0.7h + +[ 36/929] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ SUCCESS (0 entities) + Progress: 36/929 (3%), ETA: 0.7h + +[ 37/929] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 37/929 (3%), ETA: 0.7h + +[ 38/929] 2018-03-07.10-55-00.bus → 2018-03-07.10-55-00.bus + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 38/929 (4%), ETA: 0.7h + +[ 39/929] 2018-03-07.10-55-00.hospital → 2018-03-07.10-55-00.hospital + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 39/929 (4%), ETA: 0.7h + +[ 40/929] 2018-03-07.10-55-00.school → 2018-03-07.10-55-00.school + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 40/929 (4%), ETA: 0.7h + +[ 41/929] 2018-03-07.10-55-01.admin → 2018-03-07.10-55-01.admin + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 41/929 (4%), ETA: 0.6h + +[ 42/929] 2018-03-07.10-55-01.bus → 2018-03-07.10-55-01.bus + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 42/929 (4%), ETA: 0.6h + +[ 43/929] 2018-03-07.10-55-01.school → 2018-03-07.10-55-01.school + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 43/929 (4%), ETA: 0.6h + +[ 44/929] 2018-03-07.10-55-04.hospital → 2018-03-07.10-55-04.hospital + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 44/929 (4%), ETA: 0.6h + +[ 45/929] 2018-03-07.10-55-05.school → 2018-03-07.10-55-05.school + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 45/929 (4%), ETA: 0.6h + +[ 46/929] 2018-03-07.10-55-06.bus → 2018-03-07.10-55-06.bus + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 46/929 (4%), ETA: 0.6h + +[ 47/929] 2018-03-07.10-55-06.school → 2018-03-07.10-55-06.school + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 47/929 (5%), ETA: 0.6h + +[ 48/929] 2018-03-07.10-55-07.hospital → 2018-03-07.10-55-07.hospital + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 48/929 (5%), ETA: 0.5h + +[ 49/929] 2018-03-07.10-55-07.school → 2018-03-07.10-55-07.school + ✗ FAILED: Exit code 1: Traceback (most recent call last): + File "/home/ah66742/scripts/final/extract_entity_descriptions.py", line 695, in + main() + File "/home/ah66742/scripts/final/extract_entity_descriptions + Progress: 49/929 (5%), ETA: 0.5h + +[ 50/929] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (0 entities) + Progress: 50/929 (5%), ETA: 0.5h + +[ 51/929] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 51/929 (5%), ETA: 0.5h + +[ 52/929] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (0 entities) + Progress: 52/929 (5%), ETA: 0.5h + +[ 53/929] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (0 entities) + Progress: 53/929 (5%), ETA: 0.5h + +[ 54/929] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 54/929 (5%), ETA: 0.5h + +[ 55/929] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 55/929 (5%), ETA: 0.6h + +[ 56/929] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ SUCCESS (0 entities) + Progress: 56/929 (6%), ETA: 0.6h + +[ 57/929] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ SUCCESS (0 entities) + Progress: 57/929 (6%), ETA: 0.6h + +[ 58/929] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ SUCCESS (0 entities) + Progress: 58/929 (6%), ETA: 0.6h + +[ 59/929] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ SUCCESS (0 entities) + Progress: 59/929 (6%), ETA: 0.6h + +[ 60/929] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ SUCCESS (0 entities) + Progress: 60/929 (6%), ETA: 0.6h + +[ 61/929] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ SUCCESS (0 entities) + Progress: 61/929 (6%), ETA: 0.6h + +[ 62/929] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ SUCCESS (0 entities) + Progress: 62/929 (6%), ETA: 0.6h + +[ 63/929] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 63/929 (6%), ETA: 0.6h + +[ 64/929] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 64/929 (6%), ETA: 0.6h + +[ 65/929] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (0 entities) + Progress: 65/929 (6%), ETA: 0.6h + +[ 66/929] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (0 entities) + Progress: 66/929 (7%), ETA: 0.6h + +[ 67/929] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 67/929 (7%), ETA: 0.6h + +[ 68/929] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 68/929 (7%), ETA: 0.6h + +[ 69/929] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ SUCCESS (0 entities) + Progress: 69/929 (7%), ETA: 0.6h + +[ 70/929] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ SUCCESS (0 entities) + Progress: 70/929 (7%), ETA: 0.6h + +[ 71/929] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ SUCCESS (0 entities) + Progress: 71/929 (7%), ETA: 0.6h + +[ 72/929] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ SUCCESS (0 entities) + Progress: 72/929 (7%), ETA: 0.6h + +[ 73/929] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ SUCCESS (0 entities) + Progress: 73/929 (7%), ETA: 0.6h + +[ 74/929] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ SUCCESS (0 entities) + Progress: 74/929 (7%), ETA: 0.6h + +[ 75/929] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ SUCCESS (0 entities) + Progress: 75/929 (8%), ETA: 0.6h + +[ 76/929] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 76/929 (8%), ETA: 0.6h + +[ 77/929] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 77/929 (8%), ETA: 0.6h + +[ 78/929] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (0 entities) + Progress: 78/929 (8%), ETA: 0.6h + +[ 79/929] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (0 entities) + Progress: 79/929 (8%), ETA: 0.6h + +[ 80/929] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/929 (8%), ETA: 0.6h + +[ 81/929] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 81/929 (8%), ETA: 0.6h + +[ 82/929] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ SUCCESS (0 entities) + Progress: 82/929 (8%), ETA: 0.6h + +[ 83/929] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ SUCCESS (0 entities) + Progress: 83/929 (8%), ETA: 0.6h + +[ 84/929] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ SUCCESS (0 entities) + Progress: 84/929 (9%), ETA: 0.6h + +[ 85/929] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ SUCCESS (0 entities) + Progress: 85/929 (9%), ETA: 0.6h + +[ 86/929] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ SUCCESS (0 entities) + Progress: 86/929 (9%), ETA: 0.6h + +[ 87/929] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ SUCCESS (0 entities) + Progress: 87/929 (9%), ETA: 0.6h + +[ 88/929] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ SUCCESS (0 entities) + Progress: 88/929 (9%), ETA: 0.6h + +[ 89/929] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (0 entities) + Progress: 89/929 (9%), ETA: 0.6h + +[ 90/929] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (0 entities) + Progress: 90/929 (9%), ETA: 0.6h + +[ 91/929] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (0 entities) + Progress: 91/929 (9%), ETA: 0.6h + +[ 92/929] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ SUCCESS (0 entities) + Progress: 92/929 (9%), ETA: 0.6h + +[ 93/929] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ SUCCESS (0 entities) + Progress: 93/929 (10%), ETA: 0.6h + +[ 94/929] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (0 entities) + Progress: 94/929 (10%), ETA: 0.6h + +[ 95/929] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ SUCCESS (0 entities) + Progress: 95/929 (10%), ETA: 0.6h + +[ 96/929] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ SUCCESS (0 entities) + Progress: 96/929 (10%), ETA: 0.6h + +[ 97/929] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ SUCCESS (0 entities) + Progress: 97/929 (10%), ETA: 0.6h + +[ 98/929] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ SUCCESS (0 entities) + Progress: 98/929 (10%), ETA: 0.6h + +[ 99/929] 2018-03-07.16-55-00.admin → 2018-03-07.16-55-00.admin + ✓ SUCCESS (0 entities) + Progress: 99/929 (10%), ETA: 0.6h + +[100/929] 2018-03-07.16-55-00.bus → 2018-03-07.16-55-00.bus + ✓ SUCCESS (0 entities) + Progress: 100/929 (10%), ETA: 0.6h + +[101/929] 2018-03-07.16-55-00.school → 2018-03-07.16-55-00.school + ✓ SUCCESS (0 entities) + Progress: 101/929 (10%), ETA: 0.6h + +[102/929] 2018-03-07.16-55-01.admin → 2018-03-07.16-55-01.admin + ✓ SUCCESS (0 entities) + Progress: 102/929 (10%), ETA: 0.6h + +[103/929] 2018-03-07.16-55-01.bus → 2018-03-07.16-55-01.bus + ✓ SUCCESS (0 entities) + Progress: 103/929 (11%), ETA: 0.6h + +[104/929] 2018-03-07.16-55-01.hospital → 2018-03-07.16-55-01.hospital diff --git a/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_200155.log.txt b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_200155.log.txt new file mode 100644 index 0000000..ca8b342 --- /dev/null +++ b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260220_200155.log.txt @@ -0,0 +1,386 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: DRY-RUN +Canonical slots: 814 +Resume: False +Log: /home/ah66742/output/extraction_logs/batch_extraction_20260220_200155.log +Progress: /home/ah66742/output/extraction_logs/batch_progress.json +Started: 2026-02-20 20:01:55 +============================================================ + +[ 1/814] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ DRY-RUN OK +[ 2/814] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ DRY-RUN OK +[ 3/814] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ DRY-RUN OK +[ 4/814] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ DRY-RUN OK +[ 5/814] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ DRY-RUN OK +[ 6/814] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ DRY-RUN OK +[ 7/814] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ DRY-RUN OK +[ 8/814] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ DRY-RUN OK +[ 9/814] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ DRY-RUN OK +[ 10/814] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ DRY-RUN OK +[ 11/814] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ DRY-RUN OK +[ 12/814] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ DRY-RUN OK +[ 13/814] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ DRY-RUN OK +[ 14/814] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ DRY-RUN OK +[ 15/814] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ DRY-RUN OK +[ 16/814] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ DRY-RUN OK +[ 17/814] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ DRY-RUN OK +[ 18/814] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ DRY-RUN OK +[ 19/814] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ DRY-RUN OK +[ 20/814] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ DRY-RUN OK +[ 21/814] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ DRY-RUN OK +[ 22/814] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ DRY-RUN OK +[ 23/814] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ DRY-RUN OK +[ 24/814] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ DRY-RUN OK +[ 25/814] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ DRY-RUN OK +[ 26/814] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ DRY-RUN OK +[ 27/814] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ DRY-RUN OK +[ 28/814] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ DRY-RUN OK +[ 29/814] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ DRY-RUN OK +[ 30/814] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ DRY-RUN OK +[ 31/814] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ DRY-RUN OK +[ 32/814] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ DRY-RUN OK +[ 33/814] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ DRY-RUN OK +[ 34/814] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ DRY-RUN OK +[ 35/814] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ DRY-RUN OK +[ 36/814] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ DRY-RUN OK +[ 37/814] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ DRY-RUN OK +[ 38/814] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ DRY-RUN OK +[ 39/814] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ DRY-RUN OK +[ 40/814] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ DRY-RUN OK +[ 41/814] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ DRY-RUN OK +[ 42/814] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ DRY-RUN OK +[ 43/814] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ DRY-RUN OK +[ 44/814] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ DRY-RUN OK +[ 45/814] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ DRY-RUN OK +[ 46/814] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ DRY-RUN OK +[ 47/814] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ DRY-RUN OK +[ 48/814] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ DRY-RUN OK +[ 49/814] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ DRY-RUN OK +[ 50/814] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ DRY-RUN OK +[ 51/814] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ DRY-RUN OK +[ 52/814] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ DRY-RUN OK +[ 53/814] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ DRY-RUN OK +[ 54/814] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ DRY-RUN OK +[ 55/814] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ DRY-RUN OK +[ 56/814] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ DRY-RUN OK +[ 57/814] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ DRY-RUN OK +[ 58/814] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ DRY-RUN OK +[ 59/814] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ DRY-RUN OK +[ 60/814] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ DRY-RUN OK +[ 61/814] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ DRY-RUN OK +[ 62/814] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ DRY-RUN OK +[ 63/814] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ DRY-RUN OK +[ 64/814] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ DRY-RUN OK +[ 65/814] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ DRY-RUN OK +[ 66/814] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ DRY-RUN OK +[ 67/814] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ DRY-RUN OK +[ 68/814] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ DRY-RUN OK +[ 69/814] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ DRY-RUN OK +[ 70/814] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ DRY-RUN OK +[ 71/814] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ DRY-RUN OK +[ 72/814] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ DRY-RUN OK +[ 73/814] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ DRY-RUN OK +[ 74/814] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ DRY-RUN OK +[ 75/814] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ DRY-RUN OK +[ 76/814] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ DRY-RUN OK +[ 77/814] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ DRY-RUN OK +[ 78/814] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ DRY-RUN OK +[ 79/814] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ DRY-RUN OK +[ 80/814] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ DRY-RUN OK +[ 81/814] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ DRY-RUN OK +[ 82/814] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ DRY-RUN OK +[ 83/814] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ DRY-RUN OK +[ 84/814] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ DRY-RUN OK +[ 85/814] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ DRY-RUN OK +[ 86/814] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ DRY-RUN OK +[ 87/814] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ DRY-RUN OK +[ 88/814] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ DRY-RUN OK +[ 89/814] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ DRY-RUN OK +[ 90/814] 2018-03-07.17-00-01.admin → 2018-03-07.17-00-01.admin + ✓ DRY-RUN OK +[ 91/814] 2018-03-07.17-00-01.bus → 2018-03-07.17-00-01.bus + ✓ DRY-RUN OK +[ 92/814] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ DRY-RUN OK +[ 93/814] 2018-03-07.17-00-01.school → 2018-03-07.17-00-01.school + ✓ DRY-RUN OK +[ 94/814] 2018-03-07.17-00-05.hospital → 2018-03-07.17-00-05.hospital + ✓ DRY-RUN OK +[ 95/814] 2018-03-07.17-00-06.school → 2018-03-07.17-00-06.school + ✓ DRY-RUN OK +[ 96/814] 2018-03-07.17-00-07.hospital → 2018-03-07.17-00-07.hospital + ✓ DRY-RUN OK +[ 97/814] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ DRY-RUN OK +[ 98/814] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ DRY-RUN OK +[ 99/814] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ DRY-RUN OK +[100/814] 2018-03-07.17-05-01.admin → 2018-03-07.17-05-01.admin + ✓ DRY-RUN OK +[101/814] 2018-03-07.17-05-01.bus → 2018-03-07.17-05-01.bus + ✓ DRY-RUN OK +[102/814] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ DRY-RUN OK +[103/814] 2018-03-07.17-05-01.school → 2018-03-07.17-05-01.school + ✓ DRY-RUN OK +[104/814] 2018-03-07.17-05-05.hospital → 2018-03-07.17-05-05.hospital + ✓ DRY-RUN OK +[105/814] 2018-03-07.17-05-06.school → 2018-03-07.17-05-06.school + ✓ DRY-RUN OK +[106/814] 2018-03-07.17-05-07.hospital → 2018-03-07.17-05-07.hospital + ✓ DRY-RUN OK +[107/814] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ DRY-RUN OK +[108/814] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ DRY-RUN OK +[109/814] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ DRY-RUN OK +[110/814] 2018-03-07.17-20-01.admin → 2018-03-07.17-20-01.admin + ✓ DRY-RUN OK +[111/814] 2018-03-07.17-20-01.bus → 2018-03-07.17-20-01.bus + ✓ DRY-RUN OK +[112/814] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ DRY-RUN OK +[113/814] 2018-03-07.17-20-01.school → 2018-03-07.17-20-01.school + ✓ DRY-RUN OK +[114/814] 2018-03-07.17-20-05.hospital → 2018-03-07.17-20-05.hospital + ✓ DRY-RUN OK +[115/814] 2018-03-07.17-20-06.school → 2018-03-07.17-20-06.school + ✓ DRY-RUN OK +[116/814] 2018-03-07.17-20-07.hospital → 2018-03-07.17-20-07.hospital + ✓ DRY-RUN OK +[117/814] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ DRY-RUN OK +[118/814] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ DRY-RUN OK +[119/814] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ DRY-RUN OK +[120/814] 2018-03-07.17-25-01.admin → 2018-03-07.17-25-01.admin + ✓ DRY-RUN OK +[121/814] 2018-03-07.17-25-01.bus → 2018-03-07.17-25-01.bus + ✓ DRY-RUN OK +[122/814] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ DRY-RUN OK +[123/814] 2018-03-07.17-25-01.school → 2018-03-07.17-25-01.school + ✓ DRY-RUN OK +[124/814] 2018-03-07.17-25-03.school → 2018-03-07.17-25-03.school + ✓ DRY-RUN OK +[125/814] 2018-03-07.17-25-05.hospital → 2018-03-07.17-25-05.hospital + ✓ DRY-RUN OK +[126/814] 2018-03-07.17-25-06.school → 2018-03-07.17-25-06.school + ✓ DRY-RUN OK +[127/814] 2018-03-07.17-25-07.hospital → 2018-03-07.17-25-07.hospital + ✓ DRY-RUN OK +[128/814] 2018-03-07.17-25-10.school → 2018-03-07.17-25-10.school + ✓ DRY-RUN OK +[129/814] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ DRY-RUN OK +[130/814] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ DRY-RUN OK +[131/814] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ DRY-RUN OK +[132/814] 2018-03-07.17-30-01.admin → 2018-03-07.17-30-01.admin + ✓ DRY-RUN OK +[133/814] 2018-03-07.17-30-01.bus → 2018-03-07.17-30-01.bus + ✓ DRY-RUN OK +[134/814] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ DRY-RUN OK +[135/814] 2018-03-07.17-30-01.school → 2018-03-07.17-30-01.school + ✓ DRY-RUN OK +[136/814] 2018-03-07.17-30-03.school → 2018-03-07.17-30-03.school + ✓ DRY-RUN OK +[137/814] 2018-03-07.17-30-05.hospital → 2018-03-07.17-30-05.hospital + ✓ DRY-RUN OK +[138/814] 2018-03-07.17-30-06.school → 2018-03-07.17-30-06.school + ✓ DRY-RUN OK +[139/814] 2018-03-07.17-30-07.hospital → 2018-03-07.17-30-07.hospital + ✓ DRY-RUN OK +[140/814] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ DRY-RUN OK +[141/814] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ DRY-RUN OK +[142/814] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ DRY-RUN OK +[143/814] 2018-03-07.17-35-01.admin → 2018-03-07.17-35-01.admin + ✓ DRY-RUN OK +[144/814] 2018-03-07.17-35-01.bus → 2018-03-07.17-35-01.bus + ✓ DRY-RUN OK +[145/814] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ DRY-RUN OK +[146/814] 2018-03-07.17-35-01.school → 2018-03-07.17-35-01.school + ✓ DRY-RUN OK +[147/814] 2018-03-07.17-35-03.school → 2018-03-07.17-35-03.school + ✓ DRY-RUN OK +[148/814] 2018-03-07.17-35-05.hospital → 2018-03-07.17-35-05.hospital + ✓ DRY-RUN OK +[149/814] 2018-03-07.17-35-06.school → 2018-03-07.17-35-06.school + ✓ DRY-RUN OK +[150/814] 2018-03-07.17-35-07.hospital → 2018-03-07.17-35-07.hospital + ✓ DRY-RUN OK +[151/814] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ DRY-RUN OK +[152/814] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ DRY-RUN OK +[153/814] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ DRY-RUN OK +[154/814] 2018-03-09.10-10-01.bus → 2018-03-09.10-10-01.bus + ✓ DRY-RUN OK +[155/814] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ DRY-RUN OK +[156/814] 2018-03-09.10-10-01.school → 2018-03-09.10-10-01.school + ✓ DRY-RUN OK +[157/814] 2018-03-09.10-10-02.hospital → 2018-03-09.10-10-02.hospital + ✓ DRY-RUN OK +[158/814] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ DRY-RUN OK +[159/814] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ DRY-RUN OK +[160/814] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ DRY-RUN OK +[161/814] 2018-03-09.10-15-01.bus → 2018-03-09.10-15-01.bus + ✓ DRY-RUN OK +[162/814] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ DRY-RUN OK +[163/814] 2018-03-09.10-15-01.school → 2018-03-09.10-15-01.school + ✓ DRY-RUN OK +[164/814] 2018-03-09.10-15-02.hospital → 2018-03-09.10-15-02.hospital + ✓ DRY-RUN OK +[165/814] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ DRY-RUN OK +[166/814] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ DRY-RUN OK +[167/814] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ DRY-RUN OK +[168/814] 2018-03-09.10-20-01.bus → 2018-03-09.10-20-01.bus + ✓ DRY-RUN OK +[169/814] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ DRY-RUN OK +[170/814] 2018-03-09.10-20-01.school → 2018-03-09.10-20-01.school + ✓ DRY-RUN OK +[171/814] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ DRY-RUN OK +[172/814] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ DRY-RUN OK +[173/814] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ DRY-RUN OK +[174/814] 2018-03-09.10-25-01.bus → 2018-03-09.10-25-01.bus + ✓ DRY-RUN OK +[175/814] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ DRY-RUN OK +[176/814] 2018-03-09.10-25-01.school → 2018-03-09.10-25-01.school + ✓ DRY-RUN OK +[177/814] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ DRY-RUN OK +[178/814] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ DRY-RUN OK +[179/814] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ DRY-RUN OK +[180/814] 2018-03-09.10-30-01.bus → 2018-03-09.10-30-01.bus + ✓ DRY-RUN OK +[181/814] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ DRY-RUN OK +[182/814] 2018-03-09.10-30-01.school → 2018-03-09.10-30-01.school + ✓ DRY-RUN OK +[183/814] 2018-03-09.10-30-02.hospital → 2018-03-09.10-30-02.hospital + ✓ DRY-RUN OK +[184/814] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ DRY-RUN OK +[185/814] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ DRY-RUN OK +[186/814] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ DRY-RUN OK +[187/814] 2018-03-09.10-35-01.bus → 2018-03-09.10-35-01.bus + ✓ DRY-RUN OK diff --git a/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260221_201140.log.txt b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260221_201140.log.txt new file mode 100644 index 0000000..55ff202 --- /dev/null +++ b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260221_201140.log.txt @@ -0,0 +1,465 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Canonical slots: 814 +Resume: False +Log: /home/ah66742/output/extraction_logs/batch_extraction_20260221_201140.log +Progress: /home/ah66742/output/extraction_logs/batch_progress.json +Started: 2026-02-21 20:11:40 +============================================================ + +[ 1/814] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/814 (0%), ETA: 0.8h + +[ 2/814] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 2/814 (0%), ETA: 0.7h + +[ 3/814] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (0 entities) + Progress: 3/814 (0%), ETA: 0.7h + +[ 4/814] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (0 entities) + Progress: 4/814 (0%), ETA: 0.7h + +[ 5/814] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 5/814 (0%), ETA: 0.7h + +[ 6/814] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ SUCCESS (0 entities) + Progress: 6/814 (0%), ETA: 0.7h + +[ 7/814] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 7/814 (0%), ETA: 0.7h + +[ 8/814] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (0 entities) + Progress: 8/814 (0%), ETA: 0.7h + +[ 9/814] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 9/814 (1%), ETA: 0.7h + +[ 10/814] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (0 entities) + Progress: 10/814 (1%), ETA: 0.7h + +[ 11/814] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ SUCCESS (0 entities) + Progress: 11/814 (1%), ETA: 0.7h + +[ 12/814] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ SUCCESS (0 entities) + Progress: 12/814 (1%), ETA: 0.7h + +[ 13/814] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 13/814 (1%), ETA: 0.7h + +[ 14/814] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 14/814 (1%), ETA: 0.7h + +[ 15/814] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 15/814 (1%), ETA: 0.7h + +[ 16/814] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (0 entities) + Progress: 16/814 (1%), ETA: 0.7h + +[ 17/814] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ SUCCESS (0 entities) + Progress: 17/814 (2%), ETA: 0.7h + +[ 18/814] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ SUCCESS (0 entities) + Progress: 18/814 (2%), ETA: 0.7h + +[ 19/814] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (0 entities) + Progress: 19/814 (2%), ETA: 0.7h + +[ 20/814] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 20/814 (2%), ETA: 0.7h + +[ 21/814] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (0 entities) + Progress: 21/814 (2%), ETA: 0.7h + +[ 22/814] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (0 entities) + Progress: 22/814 (2%), ETA: 0.7h + +[ 23/814] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 23/814 (2%), ETA: 0.7h + +[ 24/814] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ SUCCESS (0 entities) + Progress: 24/814 (2%), ETA: 0.7h + +[ 25/814] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 25/814 (3%), ETA: 0.7h + +[ 26/814] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 26/814 (3%), ETA: 0.7h + +[ 27/814] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (0 entities) + Progress: 27/814 (3%), ETA: 0.7h + +[ 28/814] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (0 entities) + Progress: 28/814 (3%), ETA: 0.7h + +[ 29/814] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 29/814 (3%), ETA: 0.7h + +[ 30/814] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ SUCCESS (0 entities) + Progress: 30/814 (3%), ETA: 0.7h + +[ 31/814] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 31/814 (3%), ETA: 0.7h + +[ 32/814] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 32/814 (3%), ETA: 0.7h + +[ 33/814] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (0 entities) + Progress: 33/814 (4%), ETA: 0.7h + +[ 34/814] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (0 entities) + Progress: 34/814 (4%), ETA: 0.7h + +[ 35/814] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 35/814 (4%), ETA: 0.7h + +[ 36/814] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ SUCCESS (0 entities) + Progress: 36/814 (4%), ETA: 0.7h + +[ 37/814] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (0 entities) + Progress: 37/814 (4%), ETA: 0.7h + +[ 38/814] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (0 entities) + Progress: 38/814 (4%), ETA: 0.7h + +[ 39/814] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 39/814 (4%), ETA: 0.7h + +[ 40/814] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (0 entities) + Progress: 40/814 (4%), ETA: 0.6h + +[ 41/814] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (0 entities) + Progress: 41/814 (5%), ETA: 0.6h + +[ 42/814] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 42/814 (5%), ETA: 0.6h + +[ 43/814] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 43/814 (5%), ETA: 0.6h + +[ 44/814] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ SUCCESS (0 entities) + Progress: 44/814 (5%), ETA: 0.6h + +[ 45/814] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ SUCCESS (0 entities) + Progress: 45/814 (5%), ETA: 0.6h + +[ 46/814] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ SUCCESS (0 entities) + Progress: 46/814 (5%), ETA: 0.6h + +[ 47/814] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ SUCCESS (0 entities) + Progress: 47/814 (5%), ETA: 0.6h + +[ 48/814] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ SUCCESS (0 entities) + Progress: 48/814 (5%), ETA: 0.6h + +[ 49/814] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ SUCCESS (0 entities) + Progress: 49/814 (6%), ETA: 0.6h + +[ 50/814] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ SUCCESS (0 entities) + Progress: 50/814 (6%), ETA: 0.6h + +[ 51/814] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 51/814 (6%), ETA: 0.6h + +[ 52/814] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 52/814 (6%), ETA: 0.6h + +[ 53/814] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (0 entities) + Progress: 53/814 (6%), ETA: 0.6h + +[ 54/814] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (0 entities) + Progress: 54/814 (6%), ETA: 0.6h + +[ 55/814] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 55/814 (6%), ETA: 0.6h + +[ 56/814] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 56/814 (6%), ETA: 0.6h + +[ 57/814] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ SUCCESS (0 entities) + Progress: 57/814 (7%), ETA: 0.6h + +[ 58/814] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ SUCCESS (0 entities) + Progress: 58/814 (7%), ETA: 0.6h + +[ 59/814] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ SUCCESS (0 entities) + Progress: 59/814 (7%), ETA: 0.6h + +[ 60/814] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ SUCCESS (0 entities) + Progress: 60/814 (7%), ETA: 0.6h + +[ 61/814] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ SUCCESS (0 entities) + Progress: 61/814 (7%), ETA: 0.6h + +[ 62/814] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ SUCCESS (0 entities) + Progress: 62/814 (7%), ETA: 0.6h + +[ 63/814] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ SUCCESS (0 entities) + Progress: 63/814 (7%), ETA: 0.6h + +[ 64/814] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 64/814 (7%), ETA: 0.6h + +[ 65/814] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 65/814 (7%), ETA: 0.6h + +[ 66/814] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (0 entities) + Progress: 66/814 (8%), ETA: 0.6h + +[ 67/814] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (0 entities) + Progress: 67/814 (8%), ETA: 0.6h + +[ 68/814] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 68/814 (8%), ETA: 0.6h + +[ 69/814] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 69/814 (8%), ETA: 0.6h + +[ 70/814] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ SUCCESS (0 entities) + Progress: 70/814 (8%), ETA: 0.6h + +[ 71/814] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ SUCCESS (0 entities) + Progress: 71/814 (8%), ETA: 0.6h + +[ 72/814] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ SUCCESS (0 entities) + Progress: 72/814 (8%), ETA: 0.6h + +[ 73/814] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ SUCCESS (0 entities) + Progress: 73/814 (8%), ETA: 0.6h + +[ 74/814] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ SUCCESS (0 entities) + Progress: 74/814 (9%), ETA: 0.6h + +[ 75/814] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ SUCCESS (0 entities) + Progress: 75/814 (9%), ETA: 0.6h + +[ 76/814] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ SUCCESS (0 entities) + Progress: 76/814 (9%), ETA: 0.6h + +[ 77/814] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (0 entities) + Progress: 77/814 (9%), ETA: 0.6h + +[ 78/814] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (0 entities) + Progress: 78/814 (9%), ETA: 0.6h + +[ 79/814] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (0 entities) + Progress: 79/814 (9%), ETA: 0.6h + +[ 80/814] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ SUCCESS (0 entities) + Progress: 80/814 (9%), ETA: 0.6h + +[ 81/814] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ SUCCESS (0 entities) + Progress: 81/814 (9%), ETA: 0.6h + +[ 82/814] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (0 entities) + Progress: 82/814 (10%), ETA: 0.6h + +[ 83/814] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ SUCCESS (0 entities) + Progress: 83/814 (10%), ETA: 0.6h + +[ 84/814] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ SUCCESS (0 entities) + Progress: 84/814 (10%), ETA: 0.6h + +[ 85/814] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ SUCCESS (0 entities) + Progress: 85/814 (10%), ETA: 0.6h + +[ 86/814] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ SUCCESS (0 entities) + Progress: 86/814 (10%), ETA: 0.6h + +[ 87/814] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (0 entities) + Progress: 87/814 (10%), ETA: 0.6h + +[ 88/814] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 88/814 (10%), ETA: 0.6h + +[ 89/814] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (0 entities) + Progress: 89/814 (10%), ETA: 0.6h + +[ 90/814] 2018-03-07.17-00-01.admin → 2018-03-07.17-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 90/814 (11%), ETA: 0.6h + +[ 91/814] 2018-03-07.17-00-01.bus → 2018-03-07.17-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 91/814 (11%), ETA: 0.6h + +[ 92/814] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (0 entities) + Progress: 92/814 (11%), ETA: 0.6h + +[ 93/814] 2018-03-07.17-00-01.school → 2018-03-07.17-00-01.school + ✓ SUCCESS (0 entities) + Progress: 93/814 (11%), ETA: 0.6h + +[ 94/814] 2018-03-07.17-00-05.hospital → 2018-03-07.17-00-05.hospital + ✓ SUCCESS (0 entities) + Progress: 94/814 (11%), ETA: 0.6h + +[ 95/814] 2018-03-07.17-00-06.school → 2018-03-07.17-00-06.school + ✓ SUCCESS (0 entities) + Progress: 95/814 (11%), ETA: 0.6h + +[ 96/814] 2018-03-07.17-00-07.hospital → 2018-03-07.17-00-07.hospital + ✓ SUCCESS (0 entities) + Progress: 96/814 (11%), ETA: 0.6h + +[ 97/814] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 97/814 (11%), ETA: 0.6h + +[ 98/814] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 98/814 (12%), ETA: 0.6h + +[ 99/814] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (0 entities) + Progress: 99/814 (12%), ETA: 0.6h + +[100/814] 2018-03-07.17-05-01.admin → 2018-03-07.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 100/814 (12%), ETA: 0.6h + +[101/814] 2018-03-07.17-05-01.bus → 2018-03-07.17-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 101/814 (12%), ETA: 0.6h + +[102/814] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (0 entities) + Progress: 102/814 (12%), ETA: 0.6h + +[103/814] 2018-03-07.17-05-01.school → 2018-03-07.17-05-01.school + ✓ SUCCESS (0 entities) + Progress: 103/814 (12%), ETA: 0.6h + +[104/814] 2018-03-07.17-05-05.hospital → 2018-03-07.17-05-05.hospital + ✓ SUCCESS (0 entities) + Progress: 104/814 (12%), ETA: 0.6h + +[105/814] 2018-03-07.17-05-06.school → 2018-03-07.17-05-06.school + ✓ SUCCESS (0 entities) + Progress: 105/814 (12%), ETA: 0.6h + +[106/814] 2018-03-07.17-05-07.hospital → 2018-03-07.17-05-07.hospital + ✓ SUCCESS (0 entities) + Progress: 106/814 (13%), ETA: 0.6h + +[107/814] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 107/814 (13%), ETA: 0.6h + +[108/814] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 108/814 (13%), ETA: 0.6h + +[109/814] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (33 entities) + Progress: 109/814 (13%), ETA: 0.6h + +[110/814] 2018-03-07.17-20-01.admin → 2018-03-07.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 110/814 (13%), ETA: 0.6h + +[111/814] 2018-03-07.17-20-01.bus → 2018-03-07.17-20-01.bus + ✓ SUCCESS (24 entities) + Progress: 111/814 (13%), ETA: 0.6h + +[112/814] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (175 entities) + Progress: 112/814 (13%), ETA: 0.7h + +[113/814] 2018-03-07.17-20-01.school → 2018-03-07.17-20-01.school + ✓ SUCCESS (55 entities) + Progress: 113/814 (13%), ETA: 0.7h + +[114/814] 2018-03-07.17-20-05.hospital → 2018-03-07.17-20-05.hospital diff --git a/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260221_202742.log.txt b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260221_202742.log.txt new file mode 100644 index 0000000..b3905b2 --- /dev/null +++ b/meva/examples/run_records/output_records/extraction_records/batch_extraction_20260221_202742.log.txt @@ -0,0 +1,3283 @@ + +============================================================ +Batch Entity Description Extraction +============================================================ +Mode: FULL EXTRACTION +Canonical slots: 814 +Resume: False +Log: /home/ah66742/output/extraction_logs/batch_extraction_20260221_202742.log +Progress: /home/ah66742/output/extraction_logs/batch_progress.json +Started: 2026-02-21 20:27:42 +============================================================ + +[ 1/814] 2018-03-05.13-10-00.admin → 2018-03-05.13-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 1/814 (0%), ETA: 0.7h + +[ 2/814] 2018-03-05.13-10-00.bus → 2018-03-05.13-10-00.bus + ✓ SUCCESS (4 entities) + Progress: 2/814 (0%), ETA: 1.1h + +[ 3/814] 2018-03-05.13-10-00.hospital → 2018-03-05.13-10-00.hospital + ✓ SUCCESS (16 entities) + Progress: 3/814 (0%), ETA: 1.3h + +[ 4/814] 2018-03-05.13-10-00.school → 2018-03-05.13-10-00.school + ✓ SUCCESS (25 entities) + Progress: 4/814 (0%), ETA: 1.7h + +[ 5/814] 2018-03-05.13-10-01.bus → 2018-03-05.13-10-01.bus + ✓ SUCCESS (30 entities) + Progress: 5/814 (0%), ETA: 1.9h + +[ 6/814] 2018-03-05.13-10-01.school → 2018-03-05.13-10-01.school + ✓ SUCCESS (22 entities) + Progress: 6/814 (0%), ETA: 2.0h + +[ 7/814] 2018-03-05.13-15-00.admin → 2018-03-05.13-15-00.admin + ✓ SUCCESS (0 entities) + Progress: 7/814 (0%), ETA: 1.8h + +[ 8/814] 2018-03-05.13-15-00.bus → 2018-03-05.13-15-00.bus + ✓ SUCCESS (96 entities) + Progress: 8/814 (0%), ETA: 2.3h + +[ 9/814] 2018-03-05.13-15-00.hospital → 2018-03-05.13-15-00.hospital + ✓ SUCCESS (30 entities) + Progress: 9/814 (1%), ETA: 2.5h + +[ 10/814] 2018-03-05.13-15-00.school → 2018-03-05.13-15-00.school + ✓ SUCCESS (69 entities) + Progress: 10/814 (1%), ETA: 2.7h + +[ 11/814] 2018-03-05.13-15-01.bus → 2018-03-05.13-15-01.bus + ✓ SUCCESS (53 entities) + Progress: 11/814 (1%), ETA: 2.8h + +[ 12/814] 2018-03-05.13-15-01.school → 2018-03-05.13-15-01.school + ✓ SUCCESS (59 entities) + Progress: 12/814 (1%), ETA: 2.9h + +[ 13/814] 2018-03-05.13-20-00.admin → 2018-03-05.13-20-00.admin + ✓ SUCCESS (7 entities) + Progress: 13/814 (1%), ETA: 2.8h + +[ 14/814] 2018-03-05.13-20-00.bus → 2018-03-05.13-20-00.bus + ✓ SUCCESS (62 entities) + Progress: 14/814 (1%), ETA: 2.9h + +[ 15/814] 2018-03-05.13-20-00.hospital → 2018-03-05.13-20-00.hospital + ✓ SUCCESS (92 entities) + Progress: 15/814 (1%), ETA: 3.2h + +[ 16/814] 2018-03-05.13-20-00.school → 2018-03-05.13-20-00.school + ✓ SUCCESS (182 entities) + Progress: 16/814 (1%), ETA: 3.4h + +[ 17/814] 2018-03-05.13-20-01.bus → 2018-03-05.13-20-01.bus + ✓ SUCCESS (44 entities) + Progress: 17/814 (2%), ETA: 3.4h + +[ 18/814] 2018-03-05.13-20-01.school → 2018-03-05.13-20-01.school + ✓ SUCCESS (185 entities) + Progress: 18/814 (2%), ETA: 3.6h + +[ 19/814] 2018-03-05.14-00-00.admin → 2018-03-05.14-00-00.admin + ✓ SUCCESS (4 entities) + Progress: 19/814 (2%), ETA: 3.5h + +[ 20/814] 2018-03-05.14-00-00.bus → 2018-03-05.14-00-00.bus + ✓ SUCCESS (1 entities) + Progress: 20/814 (2%), ETA: 3.3h + +[ 21/814] 2018-03-05.14-00-00.hospital → 2018-03-05.14-00-00.hospital + ✓ SUCCESS (2 entities) + Progress: 21/814 (2%), ETA: 3.2h + +[ 22/814] 2018-03-05.14-00-00.school → 2018-03-05.14-00-00.school + ✓ SUCCESS (204 entities) + Progress: 22/814 (2%), ETA: 3.3h + +[ 23/814] 2018-03-05.14-00-01.bus → 2018-03-05.14-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 23/814 (2%), ETA: 3.2h + +[ 24/814] 2018-03-05.14-00-01.school → 2018-03-05.14-00-01.school + ✓ SUCCESS (27 entities) + Progress: 24/814 (2%), ETA: 3.2h + +[ 25/814] 2018-03-05.14-05-00.admin → 2018-03-05.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 25/814 (3%), ETA: 3.1h + +[ 26/814] 2018-03-05.14-05-00.bus → 2018-03-05.14-05-00.bus + ✓ SUCCESS (11 entities) + Progress: 26/814 (3%), ETA: 3.0h + +[ 27/814] 2018-03-05.14-05-00.hospital → 2018-03-05.14-05-00.hospital + ✓ SUCCESS (3 entities) + Progress: 27/814 (3%), ETA: 2.9h + +[ 28/814] 2018-03-05.14-05-00.school → 2018-03-05.14-05-00.school + ✓ SUCCESS (203 entities) + Progress: 28/814 (3%), ETA: 3.0h + +[ 29/814] 2018-03-05.14-05-01.bus → 2018-03-05.14-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 29/814 (3%), ETA: 3.0h + +[ 30/814] 2018-03-05.14-05-01.school → 2018-03-05.14-05-01.school + ✓ SUCCESS (9 entities) + Progress: 30/814 (3%), ETA: 2.9h + +[ 31/814] 2018-03-05.14-10-00.admin → 2018-03-05.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 31/814 (3%), ETA: 2.8h + +[ 32/814] 2018-03-05.14-10-00.bus → 2018-03-05.14-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 32/814 (3%), ETA: 2.8h + +[ 33/814] 2018-03-05.14-10-00.hospital → 2018-03-05.14-10-00.hospital + ✓ SUCCESS (17 entities) + Progress: 33/814 (4%), ETA: 2.7h + +[ 34/814] 2018-03-05.14-10-00.school → 2018-03-05.14-10-00.school + ✓ SUCCESS (175 entities) + Progress: 34/814 (4%), ETA: 2.9h + +[ 35/814] 2018-03-05.14-10-01.bus → 2018-03-05.14-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 35/814 (4%), ETA: 2.8h + +[ 36/814] 2018-03-05.14-10-01.school → 2018-03-05.14-10-01.school + ✓ SUCCESS (46 entities) + Progress: 36/814 (4%), ETA: 2.8h + +[ 37/814] 2018-03-07.10-55-00.admin → 2018-03-07.10-55-00.admin + ✓ SUCCESS (9 entities) + Progress: 37/814 (4%), ETA: 2.8h + +[ 38/814] 2018-03-07.11-00-00.admin → 2018-03-07.11-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 38/814 (4%), ETA: 2.8h + +[ 39/814] 2018-03-07.11-00-00.bus → 2018-03-07.11-00-00.bus + ✓ SUCCESS (227 entities) + Progress: 39/814 (4%), ETA: 2.9h + +[ 40/814] 2018-03-07.11-00-00.hospital → 2018-03-07.11-00-00.hospital + ✓ SUCCESS (251 entities) + Progress: 40/814 (4%), ETA: 2.9h + +[ 41/814] 2018-03-07.11-00-00.school → 2018-03-07.11-00-00.school + ✓ SUCCESS (41 entities) + Progress: 41/814 (5%), ETA: 2.9h + +[ 42/814] 2018-03-07.11-00-01.admin → 2018-03-07.11-00-01.admin + ✓ SUCCESS (6 entities) + Progress: 42/814 (5%), ETA: 2.9h + +[ 43/814] 2018-03-07.11-00-01.bus → 2018-03-07.11-00-01.bus + ✓ SUCCESS (170 entities) + Progress: 43/814 (5%), ETA: 3.0h + +[ 44/814] 2018-03-07.11-00-01.school → 2018-03-07.11-00-01.school + ✓ SUCCESS (502 entities) + Progress: 44/814 (5%), ETA: 3.1h + +[ 45/814] 2018-03-07.11-00-04.hospital → 2018-03-07.11-00-04.hospital + ✓ SUCCESS (149 entities) + Progress: 45/814 (5%), ETA: 3.2h + +[ 46/814] 2018-03-07.11-00-05.school → 2018-03-07.11-00-05.school + ✓ SUCCESS (197 entities) + Progress: 46/814 (5%), ETA: 3.2h + +[ 47/814] 2018-03-07.11-00-06.bus → 2018-03-07.11-00-06.bus + ✓ SUCCESS (59 entities) + Progress: 47/814 (5%), ETA: 3.2h + +[ 48/814] 2018-03-07.11-00-06.school → 2018-03-07.11-00-06.school + ✓ SUCCESS (156 entities) + Progress: 48/814 (5%), ETA: 3.3h + +[ 49/814] 2018-03-07.11-00-07.hospital → 2018-03-07.11-00-07.hospital + ✓ SUCCESS (0 entities) + Progress: 49/814 (6%), ETA: 3.2h + +[ 50/814] 2018-03-07.11-00-07.school → 2018-03-07.11-00-07.school + ✓ SUCCESS (163 entities) + Progress: 50/814 (6%), ETA: 3.3h + +[ 51/814] 2018-03-07.11-05-00.admin → 2018-03-07.11-05-00.admin + ✓ SUCCESS (18 entities) + Progress: 51/814 (6%), ETA: 3.2h + +[ 52/814] 2018-03-07.11-05-00.bus → 2018-03-07.11-05-00.bus + ✓ SUCCESS (202 entities) + Progress: 52/814 (6%), ETA: 3.3h + +[ 53/814] 2018-03-07.11-05-00.hospital → 2018-03-07.11-05-00.hospital + ✓ SUCCESS (108 entities) + Progress: 53/814 (6%), ETA: 3.3h + +[ 54/814] 2018-03-07.11-05-00.school → 2018-03-07.11-05-00.school + ✓ SUCCESS (13 entities) + Progress: 54/814 (6%), ETA: 3.3h + +[ 55/814] 2018-03-07.11-05-01.admin → 2018-03-07.11-05-01.admin + ✓ SUCCESS (27 entities) + Progress: 55/814 (6%), ETA: 3.2h + +[ 56/814] 2018-03-07.11-05-01.bus → 2018-03-07.11-05-01.bus + ✓ SUCCESS (95 entities) + Progress: 56/814 (6%), ETA: 3.3h + +[ 57/814] 2018-03-07.11-05-01.school → 2018-03-07.11-05-01.school + ✓ SUCCESS (329 entities) + Progress: 57/814 (7%), ETA: 3.3h + +[ 58/814] 2018-03-07.11-05-04.hospital → 2018-03-07.11-05-04.hospital + ✓ SUCCESS (95 entities) + Progress: 58/814 (7%), ETA: 3.4h + +[ 59/814] 2018-03-07.11-05-05.school → 2018-03-07.11-05-05.school + ✓ SUCCESS (199 entities) + Progress: 59/814 (7%), ETA: 3.4h + +[ 60/814] 2018-03-07.11-05-06.bus → 2018-03-07.11-05-06.bus + ✓ SUCCESS (143 entities) + Progress: 60/814 (7%), ETA: 3.4h + +[ 61/814] 2018-03-07.11-05-06.school → 2018-03-07.11-05-06.school + ✓ SUCCESS (143 entities) + Progress: 61/814 (7%), ETA: 3.4h + +[ 62/814] 2018-03-07.11-05-07.hospital → 2018-03-07.11-05-07.hospital + ✓ SUCCESS (0 entities) + Progress: 62/814 (7%), ETA: 3.4h + +[ 63/814] 2018-03-07.11-05-07.school → 2018-03-07.11-05-07.school + ✓ SUCCESS (102 entities) + Progress: 63/814 (7%), ETA: 3.4h + +[ 64/814] 2018-03-07.11-10-00.admin → 2018-03-07.11-10-00.admin + ✓ SUCCESS (17 entities) + Progress: 64/814 (7%), ETA: 3.3h + +[ 65/814] 2018-03-07.11-10-00.bus → 2018-03-07.11-10-00.bus + ✓ SUCCESS (133 entities) + Progress: 65/814 (7%), ETA: 3.4h + +[ 66/814] 2018-03-07.11-10-00.hospital → 2018-03-07.11-10-00.hospital + ✓ SUCCESS (103 entities) + Progress: 66/814 (8%), ETA: 3.4h + +[ 67/814] 2018-03-07.11-10-00.school → 2018-03-07.11-10-00.school + ✓ SUCCESS (24 entities) + Progress: 67/814 (8%), ETA: 3.4h + +[ 68/814] 2018-03-07.11-10-01.admin → 2018-03-07.11-10-01.admin + ✓ SUCCESS (23 entities) + Progress: 68/814 (8%), ETA: 3.3h + +[ 69/814] 2018-03-07.11-10-01.bus → 2018-03-07.11-10-01.bus + ✓ SUCCESS (135 entities) + Progress: 69/814 (8%), ETA: 3.4h + +[ 70/814] 2018-03-07.11-10-01.school → 2018-03-07.11-10-01.school + ✓ SUCCESS (345 entities) + Progress: 70/814 (8%), ETA: 3.4h + +[ 71/814] 2018-03-07.11-10-04.hospital → 2018-03-07.11-10-04.hospital + ✓ SUCCESS (68 entities) + Progress: 71/814 (8%), ETA: 3.4h + +[ 72/814] 2018-03-07.11-10-05.school → 2018-03-07.11-10-05.school + ✓ SUCCESS (120 entities) + Progress: 72/814 (8%), ETA: 3.4h + +[ 73/814] 2018-03-07.11-10-06.bus → 2018-03-07.11-10-06.bus + ✓ SUCCESS (68 entities) + Progress: 73/814 (8%), ETA: 3.4h + +[ 74/814] 2018-03-07.11-10-06.school → 2018-03-07.11-10-06.school + ✓ SUCCESS (117 entities) + Progress: 74/814 (9%), ETA: 3.4h + +[ 75/814] 2018-03-07.11-10-07.hospital → 2018-03-07.11-10-07.hospital + ✓ SUCCESS (0 entities) + Progress: 75/814 (9%), ETA: 3.4h + +[ 76/814] 2018-03-07.11-10-07.school → 2018-03-07.11-10-07.school + ✓ SUCCESS (73 entities) + Progress: 76/814 (9%), ETA: 3.4h + +[ 77/814] 2018-03-07.16-50-00.admin → 2018-03-07.16-50-00.admin + ✓ SUCCESS (5 entities) + Progress: 77/814 (9%), ETA: 3.3h + +[ 78/814] 2018-03-07.16-50-00.bus → 2018-03-07.16-50-00.bus + ✓ SUCCESS (280 entities) + Progress: 78/814 (9%), ETA: 3.4h + +[ 79/814] 2018-03-07.16-50-00.school → 2018-03-07.16-50-00.school + ✓ SUCCESS (273 entities) + Progress: 79/814 (9%), ETA: 3.5h + +[ 80/814] 2018-03-07.16-50-01.admin → 2018-03-07.16-50-01.admin + ✓ SUCCESS (6 entities) + Progress: 80/814 (9%), ETA: 3.4h + +[ 81/814] 2018-03-07.16-50-01.bus → 2018-03-07.16-50-01.bus + ✓ SUCCESS (20 entities) + Progress: 81/814 (9%), ETA: 3.4h + +[ 82/814] 2018-03-07.16-50-01.hospital → 2018-03-07.16-50-01.hospital + ✓ SUCCESS (296 entities) + Progress: 82/814 (10%), ETA: 3.4h + +[ 83/814] 2018-03-07.16-50-01.school → 2018-03-07.16-50-01.school + ✓ SUCCESS (77 entities) + Progress: 83/814 (10%), ETA: 3.4h + +[ 84/814] 2018-03-07.16-50-05.hospital → 2018-03-07.16-50-05.hospital + ✓ SUCCESS (36 entities) + Progress: 84/814 (10%), ETA: 3.4h + +[ 85/814] 2018-03-07.16-50-06.school → 2018-03-07.16-50-06.school + ✓ SUCCESS (606 entities) + Progress: 85/814 (10%), ETA: 3.5h + +[ 86/814] 2018-03-07.16-50-07.hospital → 2018-03-07.16-50-07.hospital + ✓ SUCCESS (2 entities) + Progress: 86/814 (10%), ETA: 3.5h + +[ 87/814] 2018-03-07.17-00-00.admin → 2018-03-07.17-00-00.admin + ✓ SUCCESS (12 entities) + Progress: 87/814 (10%), ETA: 3.5h + +[ 88/814] 2018-03-07.17-00-00.bus → 2018-03-07.17-00-00.bus + ✓ SUCCESS (487 entities) + Progress: 88/814 (10%), ETA: 3.6h + +[ 89/814] 2018-03-07.17-00-00.school → 2018-03-07.17-00-00.school + ✓ SUCCESS (310 entities) + Progress: 89/814 (10%), ETA: 3.6h + +[ 90/814] 2018-03-07.17-00-01.admin → 2018-03-07.17-00-01.admin + ✓ SUCCESS (9 entities) + Progress: 90/814 (11%), ETA: 3.6h + +[ 91/814] 2018-03-07.17-00-01.bus → 2018-03-07.17-00-01.bus + ✓ SUCCESS (139 entities) + Progress: 91/814 (11%), ETA: 3.6h + +[ 92/814] 2018-03-07.17-00-01.hospital → 2018-03-07.17-00-01.hospital + ✓ SUCCESS (162 entities) + Progress: 92/814 (11%), ETA: 3.6h + +[ 93/814] 2018-03-07.17-00-01.school → 2018-03-07.17-00-01.school + ✓ SUCCESS (63 entities) + Progress: 93/814 (11%), ETA: 3.6h + +[ 94/814] 2018-03-07.17-00-05.hospital → 2018-03-07.17-00-05.hospital + ✓ SUCCESS (154 entities) + Progress: 94/814 (11%), ETA: 3.6h + +[ 95/814] 2018-03-07.17-00-06.school → 2018-03-07.17-00-06.school + ✓ SUCCESS (722 entities) + Progress: 95/814 (11%), ETA: 3.7h + +[ 96/814] 2018-03-07.17-00-07.hospital → 2018-03-07.17-00-07.hospital + ✓ SUCCESS (0 entities) + Progress: 96/814 (11%), ETA: 3.6h + +[ 97/814] 2018-03-07.17-05-00.admin → 2018-03-07.17-05-00.admin + ✓ SUCCESS (5 entities) + Progress: 97/814 (11%), ETA: 3.6h + +[ 98/814] 2018-03-07.17-05-00.bus → 2018-03-07.17-05-00.bus + ✓ SUCCESS (274 entities) + Progress: 98/814 (12%), ETA: 3.7h + +[ 99/814] 2018-03-07.17-05-00.school → 2018-03-07.17-05-00.school + ✓ SUCCESS (524 entities) + Progress: 99/814 (12%), ETA: 3.7h + +[100/814] 2018-03-07.17-05-01.admin → 2018-03-07.17-05-01.admin + ✓ SUCCESS (4 entities) + Progress: 100/814 (12%), ETA: 3.7h + +[101/814] 2018-03-07.17-05-01.bus → 2018-03-07.17-05-01.bus + ✓ SUCCESS (17 entities) + Progress: 101/814 (12%), ETA: 3.7h + +[102/814] 2018-03-07.17-05-01.hospital → 2018-03-07.17-05-01.hospital + ✓ SUCCESS (120 entities) + Progress: 102/814 (12%), ETA: 3.7h + +[103/814] 2018-03-07.17-05-01.school → 2018-03-07.17-05-01.school + ✓ SUCCESS (32 entities) + Progress: 103/814 (12%), ETA: 3.7h + +[104/814] 2018-03-07.17-05-05.hospital → 2018-03-07.17-05-05.hospital + ✓ SUCCESS (65 entities) + Progress: 104/814 (12%), ETA: 3.7h + +[105/814] 2018-03-07.17-05-06.school → 2018-03-07.17-05-06.school + ✓ SUCCESS (205 entities) + Progress: 105/814 (12%), ETA: 3.7h + +[106/814] 2018-03-07.17-05-07.hospital → 2018-03-07.17-05-07.hospital + ✓ SUCCESS (0 entities) + Progress: 106/814 (13%), ETA: 3.7h + +[107/814] 2018-03-07.17-20-00.admin → 2018-03-07.17-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 107/814 (13%), ETA: 3.6h + +[108/814] 2018-03-07.17-20-00.bus → 2018-03-07.17-20-00.bus + ✓ SUCCESS (317 entities) + Progress: 108/814 (13%), ETA: 3.7h + +[109/814] 2018-03-07.17-20-00.school → 2018-03-07.17-20-00.school + ✓ SUCCESS (373 entities) + Progress: 109/814 (13%), ETA: 3.7h + +[110/814] 2018-03-07.17-20-01.admin → 2018-03-07.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 110/814 (13%), ETA: 3.7h + +[111/814] 2018-03-07.17-20-01.bus → 2018-03-07.17-20-01.bus + ✓ SUCCESS (24 entities) + Progress: 111/814 (13%), ETA: 3.7h + +[112/814] 2018-03-07.17-20-01.hospital → 2018-03-07.17-20-01.hospital + ✓ SUCCESS (175 entities) + Progress: 112/814 (13%), ETA: 3.7h + +[113/814] 2018-03-07.17-20-01.school → 2018-03-07.17-20-01.school + ✓ SUCCESS (55 entities) + Progress: 113/814 (13%), ETA: 3.7h + +[114/814] 2018-03-07.17-20-05.hospital → 2018-03-07.17-20-05.hospital + ✓ SUCCESS (37 entities) + Progress: 114/814 (14%), ETA: 3.6h + +[115/814] 2018-03-07.17-20-06.school → 2018-03-07.17-20-06.school + ✓ SUCCESS (355 entities) + Progress: 115/814 (14%), ETA: 3.7h + +[116/814] 2018-03-07.17-20-07.hospital → 2018-03-07.17-20-07.hospital + ✓ SUCCESS (2 entities) + Progress: 116/814 (14%), ETA: 3.7h + +[117/814] 2018-03-07.17-25-00.admin → 2018-03-07.17-25-00.admin + ✓ SUCCESS (9 entities) + Progress: 117/814 (14%), ETA: 3.6h + +[118/814] 2018-03-07.17-25-00.bus → 2018-03-07.17-25-00.bus + ✓ SUCCESS (282 entities) + Progress: 118/814 (14%), ETA: 3.7h + +[119/814] 2018-03-07.17-25-00.school → 2018-03-07.17-25-00.school + ✓ SUCCESS (482 entities) + Progress: 119/814 (14%), ETA: 3.7h + +[120/814] 2018-03-07.17-25-01.admin → 2018-03-07.17-25-01.admin + ✓ SUCCESS (7 entities) + Progress: 120/814 (14%), ETA: 3.7h + +[121/814] 2018-03-07.17-25-01.bus → 2018-03-07.17-25-01.bus + ✓ SUCCESS (36 entities) + Progress: 121/814 (14%), ETA: 3.7h + +[122/814] 2018-03-07.17-25-01.hospital → 2018-03-07.17-25-01.hospital + ✓ SUCCESS (226 entities) + Progress: 122/814 (14%), ETA: 3.7h + +[123/814] 2018-03-07.17-25-01.school → 2018-03-07.17-25-01.school + ✓ SUCCESS (34 entities) + Progress: 123/814 (15%), ETA: 3.7h + +[124/814] 2018-03-07.17-25-03.school → 2018-03-07.17-25-03.school + ✓ SUCCESS (8 entities) + Progress: 124/814 (15%), ETA: 3.7h + +[125/814] 2018-03-07.17-25-05.hospital → 2018-03-07.17-25-05.hospital + ✓ SUCCESS (43 entities) + Progress: 125/814 (15%), ETA: 3.6h + +[126/814] 2018-03-07.17-25-06.school → 2018-03-07.17-25-06.school + ✓ SUCCESS (481 entities) + Progress: 126/814 (15%), ETA: 3.7h + +[127/814] 2018-03-07.17-25-07.hospital → 2018-03-07.17-25-07.hospital + ✓ SUCCESS (0 entities) + Progress: 127/814 (15%), ETA: 3.6h + +[128/814] 2018-03-07.17-25-10.school → 2018-03-07.17-25-10.school + ✓ SUCCESS (8 entities) + Progress: 128/814 (15%), ETA: 3.6h + +[129/814] 2018-03-07.17-30-00.admin → 2018-03-07.17-30-00.admin + ✓ SUCCESS (16 entities) + Progress: 129/814 (15%), ETA: 3.6h + +[130/814] 2018-03-07.17-30-00.bus → 2018-03-07.17-30-00.bus + ✓ SUCCESS (524 entities) + Progress: 130/814 (15%), ETA: 3.7h + +[131/814] 2018-03-07.17-30-00.school → 2018-03-07.17-30-00.school + ✓ SUCCESS (599 entities) + Progress: 131/814 (16%), ETA: 3.7h + +[132/814] 2018-03-07.17-30-01.admin → 2018-03-07.17-30-01.admin + ✓ SUCCESS (12 entities) + Progress: 132/814 (16%), ETA: 3.7h + +[133/814] 2018-03-07.17-30-01.bus → 2018-03-07.17-30-01.bus + ✓ SUCCESS (189 entities) + Progress: 133/814 (16%), ETA: 3.7h + +[134/814] 2018-03-07.17-30-01.hospital → 2018-03-07.17-30-01.hospital + ✓ SUCCESS (194 entities) + Progress: 134/814 (16%), ETA: 3.7h + +[135/814] 2018-03-07.17-30-01.school → 2018-03-07.17-30-01.school + ✓ SUCCESS (63 entities) + Progress: 135/814 (16%), ETA: 3.7h + +[136/814] 2018-03-07.17-30-03.school → 2018-03-07.17-30-03.school + ✓ SUCCESS (1 entities) + Progress: 136/814 (16%), ETA: 3.7h + +[137/814] 2018-03-07.17-30-05.hospital → 2018-03-07.17-30-05.hospital + ✓ SUCCESS (113 entities) + Progress: 137/814 (16%), ETA: 3.6h + +[138/814] 2018-03-07.17-30-06.school → 2018-03-07.17-30-06.school + ✓ SUCCESS (775 entities) + Progress: 138/814 (16%), ETA: 3.7h + +[139/814] 2018-03-07.17-30-07.hospital → 2018-03-07.17-30-07.hospital + ✓ SUCCESS (4 entities) + Progress: 139/814 (17%), ETA: 3.7h + +[140/814] 2018-03-07.17-35-00.admin → 2018-03-07.17-35-00.admin + ✓ SUCCESS (13 entities) + Progress: 140/814 (17%), ETA: 3.7h + +[141/814] 2018-03-07.17-35-00.bus → 2018-03-07.17-35-00.bus + ✓ SUCCESS (211 entities) + Progress: 141/814 (17%), ETA: 3.7h + +[142/814] 2018-03-07.17-35-00.school → 2018-03-07.17-35-00.school + ✓ SUCCESS (392 entities) + Progress: 142/814 (17%), ETA: 3.7h + +[143/814] 2018-03-07.17-35-01.admin → 2018-03-07.17-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 143/814 (17%), ETA: 3.7h + +[144/814] 2018-03-07.17-35-01.bus → 2018-03-07.17-35-01.bus + ✓ SUCCESS (30 entities) + Progress: 144/814 (17%), ETA: 3.7h + +[145/814] 2018-03-07.17-35-01.hospital → 2018-03-07.17-35-01.hospital + ✓ SUCCESS (133 entities) + Progress: 145/814 (17%), ETA: 3.7h + +[146/814] 2018-03-07.17-35-01.school → 2018-03-07.17-35-01.school + ✓ SUCCESS (71 entities) + Progress: 146/814 (17%), ETA: 3.7h + +[147/814] 2018-03-07.17-35-03.school → 2018-03-07.17-35-03.school + ✓ SUCCESS (6 entities) + Progress: 147/814 (18%), ETA: 3.6h + +[148/814] 2018-03-07.17-35-05.hospital → 2018-03-07.17-35-05.hospital + ✓ SUCCESS (55 entities) + Progress: 148/814 (18%), ETA: 3.6h + +[149/814] 2018-03-07.17-35-06.school → 2018-03-07.17-35-06.school + ✓ SUCCESS (306 entities) + Progress: 149/814 (18%), ETA: 3.6h + +[150/814] 2018-03-07.17-35-07.hospital → 2018-03-07.17-35-07.hospital + ✓ SUCCESS (3 entities) + Progress: 150/814 (18%), ETA: 3.6h + +[151/814] 2018-03-09.10-10-00.bus → 2018-03-09.10-10-00.bus + ✓ SUCCESS (11 entities) + Progress: 151/814 (18%), ETA: 3.6h + +[152/814] 2018-03-09.10-10-00.school → 2018-03-09.10-10-00.school + ✓ SUCCESS (1220 entities) + Progress: 152/814 (18%), ETA: 3.7h + +[153/814] 2018-03-09.10-10-01.admin → 2018-03-09.10-10-01.admin + ✓ SUCCESS (47 entities) + Progress: 153/814 (18%), ETA: 3.7h + +[154/814] 2018-03-09.10-10-01.bus → 2018-03-09.10-10-01.bus + ✓ SUCCESS (54 entities) + Progress: 154/814 (18%), ETA: 3.7h + +[155/814] 2018-03-09.10-10-01.hospital → 2018-03-09.10-10-01.hospital + ✓ SUCCESS (127 entities) + Progress: 155/814 (19%), ETA: 3.7h + +[156/814] 2018-03-09.10-10-01.school → 2018-03-09.10-10-01.school + ✓ SUCCESS (105 entities) + Progress: 156/814 (19%), ETA: 3.7h + +[157/814] 2018-03-09.10-10-02.hospital → 2018-03-09.10-10-02.hospital + ✓ SUCCESS (10 entities) + Progress: 157/814 (19%), ETA: 3.7h + +[158/814] 2018-03-09.10-15-00.bus → 2018-03-09.10-15-00.bus + ✓ SUCCESS (1 entities) + Progress: 158/814 (19%), ETA: 3.7h + +[159/814] 2018-03-09.10-15-00.school → 2018-03-09.10-15-00.school + ✓ SUCCESS (3201 entities) + Progress: 159/814 (19%), ETA: 3.9h + +[160/814] 2018-03-09.10-15-01.admin → 2018-03-09.10-15-01.admin + ✓ SUCCESS (16 entities) + Progress: 160/814 (19%), ETA: 3.8h + +[161/814] 2018-03-09.10-15-01.bus → 2018-03-09.10-15-01.bus + ✓ SUCCESS (34 entities) + Progress: 161/814 (19%), ETA: 3.8h + +[162/814] 2018-03-09.10-15-01.hospital → 2018-03-09.10-15-01.hospital + ✓ SUCCESS (67 entities) + Progress: 162/814 (19%), ETA: 3.8h + +[163/814] 2018-03-09.10-15-01.school → 2018-03-09.10-15-01.school + ✓ SUCCESS (137 entities) + Progress: 163/814 (20%), ETA: 3.8h + +[164/814] 2018-03-09.10-15-02.hospital → 2018-03-09.10-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 164/814 (20%), ETA: 3.8h + +[165/814] 2018-03-09.10-20-00.bus → 2018-03-09.10-20-00.bus + ✓ SUCCESS (0 entities) + Progress: 165/814 (20%), ETA: 3.8h + +[166/814] 2018-03-09.10-20-00.school → 2018-03-09.10-20-00.school + ✓ SUCCESS (777 entities) + Progress: 166/814 (20%), ETA: 3.8h + +[167/814] 2018-03-09.10-20-01.admin → 2018-03-09.10-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 167/814 (20%), ETA: 3.8h + +[168/814] 2018-03-09.10-20-01.bus → 2018-03-09.10-20-01.bus + ✓ SUCCESS (0 entities) + Progress: 168/814 (20%), ETA: 3.8h + +[169/814] 2018-03-09.10-20-01.hospital → 2018-03-09.10-20-01.hospital + ✓ SUCCESS (8 entities) + Progress: 169/814 (20%), ETA: 3.7h + +[170/814] 2018-03-09.10-20-01.school → 2018-03-09.10-20-01.school + ✓ SUCCESS (24 entities) + Progress: 170/814 (20%), ETA: 3.7h + +[171/814] 2018-03-09.10-25-00.bus → 2018-03-09.10-25-00.bus + ✓ SUCCESS (0 entities) + Progress: 171/814 (21%), ETA: 3.7h + +[172/814] 2018-03-09.10-25-00.school → 2018-03-09.10-25-00.school + ✓ SUCCESS (317 entities) + Progress: 172/814 (21%), ETA: 3.7h + +[173/814] 2018-03-09.10-25-01.admin → 2018-03-09.10-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 173/814 (21%), ETA: 3.7h + +[174/814] 2018-03-09.10-25-01.bus → 2018-03-09.10-25-01.bus + ✓ SUCCESS (0 entities) + Progress: 174/814 (21%), ETA: 3.7h + +[175/814] 2018-03-09.10-25-01.hospital → 2018-03-09.10-25-01.hospital + ✓ SUCCESS (9 entities) + Progress: 175/814 (21%), ETA: 3.7h + +[176/814] 2018-03-09.10-25-01.school → 2018-03-09.10-25-01.school + ✓ SUCCESS (5 entities) + Progress: 176/814 (21%), ETA: 3.6h + +[177/814] 2018-03-09.10-30-00.bus → 2018-03-09.10-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 177/814 (21%), ETA: 3.6h + +[178/814] 2018-03-09.10-30-00.school → 2018-03-09.10-30-00.school + ✓ SUCCESS (235 entities) + Progress: 178/814 (21%), ETA: 3.6h + +[179/814] 2018-03-09.10-30-01.admin → 2018-03-09.10-30-01.admin + ✓ SUCCESS (7 entities) + Progress: 179/814 (21%), ETA: 3.6h + +[180/814] 2018-03-09.10-30-01.bus → 2018-03-09.10-30-01.bus + ✓ SUCCESS (1 entities) + Progress: 180/814 (22%), ETA: 3.6h + +[181/814] 2018-03-09.10-30-01.hospital → 2018-03-09.10-30-01.hospital + ✓ SUCCESS (12 entities) + Progress: 181/814 (22%), ETA: 3.6h + +[182/814] 2018-03-09.10-30-01.school → 2018-03-09.10-30-01.school + ✓ SUCCESS (33 entities) + Progress: 182/814 (22%), ETA: 3.6h + +[183/814] 2018-03-09.10-30-02.hospital → 2018-03-09.10-30-02.hospital + ✓ SUCCESS (0 entities) + Progress: 183/814 (22%), ETA: 3.5h + +[184/814] 2018-03-09.10-35-00.bus → 2018-03-09.10-35-00.bus + ✓ SUCCESS (0 entities) + Progress: 184/814 (22%), ETA: 3.5h + +[185/814] 2018-03-09.10-35-00.school → 2018-03-09.10-35-00.school + ✓ SUCCESS (976 entities) + Progress: 185/814 (22%), ETA: 3.6h + +[186/814] 2018-03-09.10-35-01.admin → 2018-03-09.10-35-01.admin + ✓ SUCCESS (11 entities) + Progress: 186/814 (22%), ETA: 3.6h + +[187/814] 2018-03-09.10-35-01.bus → 2018-03-09.10-35-01.bus + ✓ SUCCESS (29 entities) + Progress: 187/814 (22%), ETA: 3.5h + +[188/814] 2018-03-09.10-35-01.hospital → 2018-03-09.10-35-01.hospital + ✓ SUCCESS (33 entities) + Progress: 188/814 (23%), ETA: 3.5h + +[189/814] 2018-03-09.10-35-01.school → 2018-03-09.10-35-01.school + ✓ SUCCESS (94 entities) + Progress: 189/814 (23%), ETA: 3.5h + +[190/814] 2018-03-09.10-35-02.hospital → 2018-03-09.10-35-02.hospital + ✓ SUCCESS (2 entities) + Progress: 190/814 (23%), ETA: 3.5h + +[191/814] 2018-03-09.10-40-00.bus → 2018-03-09.10-40-00.bus + ✓ SUCCESS (5 entities) + Progress: 191/814 (23%), ETA: 3.5h + +[192/814] 2018-03-09.10-40-00.school → 2018-03-09.10-40-00.school + ✓ SUCCESS (727 entities) + Progress: 192/814 (23%), ETA: 3.5h + +[193/814] 2018-03-09.10-40-01.admin → 2018-03-09.10-40-01.admin + ✓ SUCCESS (24 entities) + Progress: 193/814 (23%), ETA: 3.5h + +[194/814] 2018-03-09.10-40-01.bus → 2018-03-09.10-40-01.bus + ✓ SUCCESS (68 entities) + Progress: 194/814 (23%), ETA: 3.5h + +[195/814] 2018-03-09.10-40-01.hospital → 2018-03-09.10-40-01.hospital + ✓ SUCCESS (140 entities) + Progress: 195/814 (23%), ETA: 3.5h + +[196/814] 2018-03-09.10-40-01.school → 2018-03-09.10-40-01.school + ✓ SUCCESS (403 entities) + Progress: 196/814 (24%), ETA: 3.5h + +[197/814] 2018-03-09.10-40-02.hospital → 2018-03-09.10-40-02.hospital + ✓ SUCCESS (1 entities) + Progress: 197/814 (24%), ETA: 3.5h + +[198/814] 2018-03-11.11-15-00.school → 2018-03-11.11-15-00.school + ✓ SUCCESS (496 entities) + Progress: 198/814 (24%), ETA: 3.6h + +[199/814] 2018-03-11.11-15-01.school → 2018-03-11.11-15-01.school + ✓ SUCCESS (152 entities) + Progress: 199/814 (24%), ETA: 3.6h + +[200/814] 2018-03-11.11-15-04.school → 2018-03-11.11-15-04.school + ✓ SUCCESS (40 entities) + Progress: 200/814 (24%), ETA: 3.5h + +[201/814] 2018-03-11.11-15-08.hospital → 2018-03-11.11-15-08.hospital + ✓ SUCCESS (36 entities) + Progress: 201/814 (24%), ETA: 3.5h + +[202/814] 2018-03-11.11-20-00.admin → 2018-03-11.11-20-00.admin + ✓ SUCCESS (12 entities) + Progress: 202/814 (24%), ETA: 3.5h + +[203/814] 2018-03-11.11-20-00.bus → 2018-03-11.11-20-00.bus + ✓ SUCCESS (10 entities) + Progress: 203/814 (24%), ETA: 3.5h + +[204/814] 2018-03-11.11-20-00.school → 2018-03-11.11-20-00.school + ✓ SUCCESS (1813 entities) + Progress: 204/814 (25%), ETA: 3.6h + +[205/814] 2018-03-11.11-20-01.admin → 2018-03-11.11-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 205/814 (25%), ETA: 3.6h + +[206/814] 2018-03-11.11-20-01.bus → 2018-03-11.11-20-01.bus + ✓ SUCCESS (8 entities) + Progress: 206/814 (25%), ETA: 3.6h + +[207/814] 2018-03-11.11-20-01.school → 2018-03-11.11-20-01.school + ✓ SUCCESS (29 entities) + Progress: 207/814 (25%), ETA: 3.6h + +[208/814] 2018-03-11.11-20-04.school → 2018-03-11.11-20-04.school + ✓ SUCCESS (4 entities) + Progress: 208/814 (25%), ETA: 3.5h + +[209/814] 2018-03-11.11-20-08.bus → 2018-03-11.11-20-08.bus + ✓ SUCCESS (0 entities) + Progress: 209/814 (25%), ETA: 3.5h + +[210/814] 2018-03-11.11-20-08.hospital → 2018-03-11.11-20-08.hospital + ✓ SUCCESS (34 entities) + Progress: 210/814 (25%), ETA: 3.5h + +[211/814] 2018-03-11.11-25-00.admin → 2018-03-11.11-25-00.admin + ✓ SUCCESS (26 entities) + Progress: 211/814 (25%), ETA: 3.5h + +[212/814] 2018-03-11.11-25-00.bus → 2018-03-11.11-25-00.bus + ✓ SUCCESS (1 entities) + Progress: 212/814 (26%), ETA: 3.5h + +[213/814] 2018-03-11.11-25-00.school → 2018-03-11.11-25-00.school + ✓ SUCCESS (1232 entities) + Progress: 213/814 (26%), ETA: 3.5h + +[214/814] 2018-03-11.11-25-01.admin → 2018-03-11.11-25-01.admin + ✓ SUCCESS (18 entities) + Progress: 214/814 (26%), ETA: 3.5h + +[215/814] 2018-03-11.11-25-01.bus → 2018-03-11.11-25-01.bus + ✓ SUCCESS (10 entities) + Progress: 215/814 (26%), ETA: 3.5h + +[216/814] 2018-03-11.11-25-01.school → 2018-03-11.11-25-01.school + ✓ SUCCESS (69 entities) + Progress: 216/814 (26%), ETA: 3.5h + +[217/814] 2018-03-11.11-25-04.school → 2018-03-11.11-25-04.school + ✓ SUCCESS (0 entities) + Progress: 217/814 (26%), ETA: 3.5h + +[218/814] 2018-03-11.11-25-08.bus → 2018-03-11.11-25-08.bus + ✓ SUCCESS (13 entities) + Progress: 218/814 (26%), ETA: 3.5h + +[219/814] 2018-03-11.11-25-08.hospital → 2018-03-11.11-25-08.hospital + ✓ SUCCESS (11 entities) + Progress: 219/814 (26%), ETA: 3.4h + +[220/814] 2018-03-11.11-30-00.admin → 2018-03-11.11-30-00.admin + ✓ SUCCESS (11 entities) + Progress: 220/814 (27%), ETA: 3.4h + +[221/814] 2018-03-11.11-30-00.bus → 2018-03-11.11-30-00.bus + ✓ SUCCESS (4 entities) + Progress: 221/814 (27%), ETA: 3.4h + +[222/814] 2018-03-11.11-30-00.school → 2018-03-11.11-30-00.school + ✓ SUCCESS (726 entities) + Progress: 222/814 (27%), ETA: 3.4h + +[223/814] 2018-03-11.11-30-01.admin → 2018-03-11.11-30-01.admin + ✓ SUCCESS (6 entities) + Progress: 223/814 (27%), ETA: 3.4h + +[224/814] 2018-03-11.11-30-01.bus → 2018-03-11.11-30-01.bus + ✓ SUCCESS (6 entities) + Progress: 224/814 (27%), ETA: 3.4h + +[225/814] 2018-03-11.11-30-01.school → 2018-03-11.11-30-01.school + ✓ SUCCESS (142 entities) + Progress: 225/814 (27%), ETA: 3.4h + +[226/814] 2018-03-11.11-30-04.school → 2018-03-11.11-30-04.school + ✓ SUCCESS (27 entities) + Progress: 226/814 (27%), ETA: 3.4h + +[227/814] 2018-03-11.11-30-08.bus → 2018-03-11.11-30-08.bus + ✓ SUCCESS (8 entities) + Progress: 227/814 (27%), ETA: 3.4h + +[228/814] 2018-03-11.11-30-08.hospital → 2018-03-11.11-30-08.hospital + ✓ SUCCESS (32 entities) + Progress: 228/814 (28%), ETA: 3.4h + +[229/814] 2018-03-11.11-35-00.admin → 2018-03-11.11-35-00.admin + ✓ SUCCESS (0 entities) + Progress: 229/814 (28%), ETA: 3.4h + +[230/814] 2018-03-11.11-35-00.bus → 2018-03-11.11-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 230/814 (28%), ETA: 3.4h + +[231/814] 2018-03-11.11-35-00.school → 2018-03-11.11-35-00.school + ✓ SUCCESS (334 entities) + Progress: 231/814 (28%), ETA: 3.4h + +[232/814] 2018-03-11.11-35-01.admin → 2018-03-11.11-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 232/814 (28%), ETA: 3.4h + +[233/814] 2018-03-11.11-35-01.bus → 2018-03-11.11-35-01.bus + ✓ SUCCESS (5 entities) + Progress: 233/814 (28%), ETA: 3.3h + +[234/814] 2018-03-11.11-35-01.school → 2018-03-11.11-35-01.school + ✓ SUCCESS (80 entities) + Progress: 234/814 (28%), ETA: 3.3h + +[235/814] 2018-03-11.11-35-04.school → 2018-03-11.11-35-04.school + ✓ SUCCESS (21 entities) + Progress: 235/814 (28%), ETA: 3.3h + +[236/814] 2018-03-11.11-35-08.bus → 2018-03-11.11-35-08.bus + ✓ SUCCESS (2 entities) + Progress: 236/814 (28%), ETA: 3.3h + +[237/814] 2018-03-11.11-35-08.hospital → 2018-03-11.11-35-08.hospital + ✓ SUCCESS (32 entities) + Progress: 237/814 (29%), ETA: 3.3h + +[238/814] 2018-03-11.11-40-00.admin → 2018-03-11.11-40-00.admin + ✓ SUCCESS (0 entities) + Progress: 238/814 (29%), ETA: 3.3h + +[239/814] 2018-03-11.11-40-00.bus → 2018-03-11.11-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 239/814 (29%), ETA: 3.3h + +[240/814] 2018-03-11.11-40-00.school → 2018-03-11.11-40-00.school + ✓ SUCCESS (333 entities) + Progress: 240/814 (29%), ETA: 3.3h + +[241/814] 2018-03-11.11-40-01.admin → 2018-03-11.11-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 241/814 (29%), ETA: 3.3h + +[242/814] 2018-03-11.11-40-01.bus → 2018-03-11.11-40-01.bus + ✓ SUCCESS (0 entities) + Progress: 242/814 (29%), ETA: 3.2h + +[243/814] 2018-03-11.11-40-01.school → 2018-03-11.11-40-01.school + ✓ SUCCESS (25 entities) + Progress: 243/814 (29%), ETA: 3.2h + +[244/814] 2018-03-11.11-40-02.school → 2018-03-11.11-40-02.school + ✓ SUCCESS (38 entities) + Progress: 244/814 (29%), ETA: 3.2h + +[245/814] 2018-03-11.11-40-04.school → 2018-03-11.11-40-04.school + ✓ SUCCESS (0 entities) + Progress: 245/814 (30%), ETA: 3.2h + +[246/814] 2018-03-11.11-40-08.bus → 2018-03-11.11-40-08.bus + ✓ SUCCESS (0 entities) + Progress: 246/814 (30%), ETA: 3.2h + +[247/814] 2018-03-11.11-40-08.hospital → 2018-03-11.11-40-08.hospital + ✓ SUCCESS (0 entities) + Progress: 247/814 (30%), ETA: 3.2h + +[248/814] 2018-03-11.11-45-00.admin → 2018-03-11.11-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 248/814 (30%), ETA: 3.1h + +[249/814] 2018-03-11.11-45-00.bus → 2018-03-11.11-45-00.bus + ✓ SUCCESS (0 entities) + Progress: 249/814 (30%), ETA: 3.1h + +[250/814] 2018-03-11.11-45-00.school → 2018-03-11.11-45-00.school + ✓ SUCCESS (1019 entities) + Progress: 250/814 (30%), ETA: 3.2h + +[251/814] 2018-03-11.11-45-01.admin → 2018-03-11.11-45-01.admin + ✓ SUCCESS (0 entities) + Progress: 251/814 (30%), ETA: 3.1h + +[252/814] 2018-03-11.11-45-01.bus → 2018-03-11.11-45-01.bus + ✓ SUCCESS (0 entities) + Progress: 252/814 (30%), ETA: 3.1h + +[253/814] 2018-03-11.11-45-01.school → 2018-03-11.11-45-01.school + ✓ SUCCESS (64 entities) + Progress: 253/814 (31%), ETA: 3.1h + +[254/814] 2018-03-11.11-45-04.school → 2018-03-11.11-45-04.school + ✓ SUCCESS (0 entities) + Progress: 254/814 (31%), ETA: 3.1h + +[255/814] 2018-03-11.11-45-08.bus → 2018-03-11.11-45-08.bus + ✓ SUCCESS (0 entities) + Progress: 255/814 (31%), ETA: 3.1h + +[256/814] 2018-03-11.11-45-08.hospital → 2018-03-11.11-45-08.hospital + ✓ SUCCESS (3 entities) + Progress: 256/814 (31%), ETA: 3.1h + +[257/814] 2018-03-11.11-50-00.admin → 2018-03-11.11-50-00.admin + ✓ SUCCESS (2 entities) + Progress: 257/814 (31%), ETA: 3.1h + +[258/814] 2018-03-11.11-50-00.bus → 2018-03-11.11-50-00.bus + ✓ SUCCESS (1 entities) + Progress: 258/814 (31%), ETA: 3.1h + +[259/814] 2018-03-11.11-50-00.school → 2018-03-11.11-50-00.school + ✓ SUCCESS (2145 entities) + Progress: 259/814 (31%), ETA: 3.1h + +[260/814] 2018-03-11.11-50-01.admin → 2018-03-11.11-50-01.admin + ✓ SUCCESS (2 entities) + Progress: 260/814 (31%), ETA: 3.1h + +[261/814] 2018-03-11.11-50-01.bus → 2018-03-11.11-50-01.bus + ✓ SUCCESS (1 entities) + Progress: 261/814 (32%), ETA: 3.1h + +[262/814] 2018-03-11.11-50-01.school → 2018-03-11.11-50-01.school + ✓ SUCCESS (150 entities) + Progress: 262/814 (32%), ETA: 3.1h + +[263/814] 2018-03-11.11-50-04.school → 2018-03-11.11-50-04.school + ✓ SUCCESS (27 entities) + Progress: 263/814 (32%), ETA: 3.1h + +[264/814] 2018-03-11.11-50-08.bus → 2018-03-11.11-50-08.bus + ✓ SUCCESS (8 entities) + Progress: 264/814 (32%), ETA: 3.1h + +[265/814] 2018-03-11.11-50-08.hospital → 2018-03-11.11-50-08.hospital + ✓ SUCCESS (9 entities) + Progress: 265/814 (32%), ETA: 3.1h + +[266/814] 2018-03-11.12-00-00.school → 2018-03-11.12-00-00.school + ✓ SUCCESS (244 entities) + Progress: 266/814 (32%), ETA: 3.1h + +[267/814] 2018-03-11.13-50-00.admin → 2018-03-11.13-50-00.admin + ✓ SUCCESS (8 entities) + Progress: 267/814 (32%), ETA: 3.0h + +[268/814] 2018-03-11.13-50-00.bus → 2018-03-11.13-50-00.bus + ✓ SUCCESS (3 entities) + Progress: 268/814 (32%), ETA: 3.0h + +[269/814] 2018-03-11.13-50-00.school → 2018-03-11.13-50-00.school + ✓ SUCCESS (306 entities) + Progress: 269/814 (33%), ETA: 3.0h + +[270/814] 2018-03-11.13-50-01.admin → 2018-03-11.13-50-01.admin + ✓ SUCCESS (14 entities) + Progress: 270/814 (33%), ETA: 3.0h + +[271/814] 2018-03-11.13-50-01.bus → 2018-03-11.13-50-01.bus + ✓ SUCCESS (21 entities) + Progress: 271/814 (33%), ETA: 3.0h + +[272/814] 2018-03-11.13-50-01.school → 2018-03-11.13-50-01.school + ✓ SUCCESS (296 entities) + Progress: 272/814 (33%), ETA: 3.0h + +[273/814] 2018-03-11.13-50-04.school → 2018-03-11.13-50-04.school + ✓ SUCCESS (4 entities) + Progress: 273/814 (33%), ETA: 3.0h + +[274/814] 2018-03-11.13-50-08.bus → 2018-03-11.13-50-08.bus + ✓ SUCCESS (9 entities) + Progress: 274/814 (33%), ETA: 3.0h + +[275/814] 2018-03-11.13-50-08.hospital → 2018-03-11.13-50-08.hospital + ✓ SUCCESS (111 entities) + Progress: 275/814 (33%), ETA: 3.0h + +[276/814] 2018-03-11.14-00-00.admin → 2018-03-11.14-00-00.admin + ✓ SUCCESS (5 entities) + Progress: 276/814 (33%), ETA: 3.0h + +[277/814] 2018-03-11.14-00-00.bus → 2018-03-11.14-00-00.bus + ✓ SUCCESS (0 entities) + Progress: 277/814 (34%), ETA: 3.0h + +[278/814] 2018-03-11.14-00-00.school → 2018-03-11.14-00-00.school + ✓ SUCCESS (622 entities) + Progress: 278/814 (34%), ETA: 3.0h + +[279/814] 2018-03-11.14-00-01.admin → 2018-03-11.14-00-01.admin + ✓ SUCCESS (5 entities) + Progress: 279/814 (34%), ETA: 3.0h + +[280/814] 2018-03-11.14-00-01.bus → 2018-03-11.14-00-01.bus + ✓ SUCCESS (1 entities) + Progress: 280/814 (34%), ETA: 3.0h + +[281/814] 2018-03-11.14-00-01.school → 2018-03-11.14-00-01.school + ✓ SUCCESS (107 entities) + Progress: 281/814 (34%), ETA: 3.0h + +[282/814] 2018-03-11.14-00-02.school → 2018-03-11.14-00-02.school + ✓ SUCCESS (23 entities) + Progress: 282/814 (34%), ETA: 3.0h + +[283/814] 2018-03-11.14-00-04.school → 2018-03-11.14-00-04.school + ✓ SUCCESS (6 entities) + Progress: 283/814 (34%), ETA: 2.9h + +[284/814] 2018-03-11.14-00-08.bus → 2018-03-11.14-00-08.bus + ✓ SUCCESS (3 entities) + Progress: 284/814 (34%), ETA: 2.9h + +[285/814] 2018-03-11.14-00-08.hospital → 2018-03-11.14-00-08.hospital + ✓ SUCCESS (43 entities) + Progress: 285/814 (35%), ETA: 2.9h + +[286/814] 2018-03-11.14-05-00.admin → 2018-03-11.14-05-00.admin + ✓ SUCCESS (0 entities) + Progress: 286/814 (35%), ETA: 2.9h + +[287/814] 2018-03-11.14-05-00.bus → 2018-03-11.14-05-00.bus + ✓ SUCCESS (0 entities) + Progress: 287/814 (35%), ETA: 2.9h + +[288/814] 2018-03-11.14-05-00.school → 2018-03-11.14-05-00.school + ✓ SUCCESS (284 entities) + Progress: 288/814 (35%), ETA: 2.9h + +[289/814] 2018-03-11.14-05-01.admin → 2018-03-11.14-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 289/814 (35%), ETA: 2.9h + +[290/814] 2018-03-11.14-05-01.bus → 2018-03-11.14-05-01.bus + ✓ SUCCESS (2 entities) + Progress: 290/814 (35%), ETA: 2.9h + +[291/814] 2018-03-11.14-05-01.school → 2018-03-11.14-05-01.school + ✓ SUCCESS (115 entities) + Progress: 291/814 (35%), ETA: 2.9h + +[292/814] 2018-03-11.14-05-02.school → 2018-03-11.14-05-02.school + ✓ SUCCESS (0 entities) + Progress: 292/814 (35%), ETA: 2.9h + +[293/814] 2018-03-11.14-05-04.school → 2018-03-11.14-05-04.school + ✓ SUCCESS (0 entities) + Progress: 293/814 (35%), ETA: 2.8h + +[294/814] 2018-03-11.14-05-08.bus → 2018-03-11.14-05-08.bus + ✓ SUCCESS (5 entities) + Progress: 294/814 (36%), ETA: 2.8h + +[295/814] 2018-03-11.14-05-08.hospital → 2018-03-11.14-05-08.hospital + ✓ SUCCESS (21 entities) + Progress: 295/814 (36%), ETA: 2.8h + +[296/814] 2018-03-11.14-10-00.admin → 2018-03-11.14-10-00.admin + ✓ SUCCESS (0 entities) + Progress: 296/814 (36%), ETA: 2.8h + +[297/814] 2018-03-11.14-10-00.bus → 2018-03-11.14-10-00.bus + ✓ SUCCESS (0 entities) + Progress: 297/814 (36%), ETA: 2.8h + +[298/814] 2018-03-11.14-10-00.school → 2018-03-11.14-10-00.school + ✓ SUCCESS (761 entities) + Progress: 298/814 (36%), ETA: 2.8h + +[299/814] 2018-03-11.14-10-01.admin → 2018-03-11.14-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 299/814 (36%), ETA: 2.8h + +[300/814] 2018-03-11.14-10-01.bus → 2018-03-11.14-10-01.bus + ✓ SUCCESS (2 entities) + Progress: 300/814 (36%), ETA: 2.8h + +[301/814] 2018-03-11.14-10-01.school → 2018-03-11.14-10-01.school + ✓ SUCCESS (74 entities) + Progress: 301/814 (36%), ETA: 2.8h + +[302/814] 2018-03-11.14-10-02.school → 2018-03-11.14-10-02.school + ✓ SUCCESS (4 entities) + Progress: 302/814 (37%), ETA: 2.8h + +[303/814] 2018-03-11.14-10-04.school → 2018-03-11.14-10-04.school + ✓ SUCCESS (4 entities) + Progress: 303/814 (37%), ETA: 2.8h + +[304/814] 2018-03-11.14-10-08.bus → 2018-03-11.14-10-08.bus + ✓ SUCCESS (6 entities) + Progress: 304/814 (37%), ETA: 2.8h + +[305/814] 2018-03-11.14-10-08.hospital → 2018-03-11.14-10-08.hospital + ✓ SUCCESS (23 entities) + Progress: 305/814 (37%), ETA: 2.7h + +[306/814] 2018-03-11.14-15-00.admin → 2018-03-11.14-15-00.admin + ✓ SUCCESS (5 entities) + Progress: 306/814 (37%), ETA: 2.7h + +[307/814] 2018-03-11.14-15-00.bus → 2018-03-11.14-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 307/814 (37%), ETA: 2.7h + +[308/814] 2018-03-11.14-15-00.school → 2018-03-11.14-15-00.school + ✓ SUCCESS (1611 entities) + Progress: 308/814 (37%), ETA: 2.8h + +[309/814] 2018-03-11.14-15-01.admin → 2018-03-11.14-15-01.admin + ✓ SUCCESS (7 entities) + Progress: 309/814 (37%), ETA: 2.7h + +[310/814] 2018-03-11.14-15-01.bus → 2018-03-11.14-15-01.bus + ✓ SUCCESS (7 entities) + Progress: 310/814 (38%), ETA: 2.7h + +[311/814] 2018-03-11.14-15-01.school → 2018-03-11.14-15-01.school + ✓ SUCCESS (322 entities) + Progress: 311/814 (38%), ETA: 2.7h + +[312/814] 2018-03-11.14-15-02.school → 2018-03-11.14-15-02.school + ✓ SUCCESS (24 entities) + Progress: 312/814 (38%), ETA: 2.7h + +[313/814] 2018-03-11.14-15-04.school → 2018-03-11.14-15-04.school + ✓ SUCCESS (9 entities) + Progress: 313/814 (38%), ETA: 2.7h + +[314/814] 2018-03-11.14-15-08.bus → 2018-03-11.14-15-08.bus + ✓ SUCCESS (13 entities) + Progress: 314/814 (38%), ETA: 2.7h + +[315/814] 2018-03-11.14-15-08.hospital → 2018-03-11.14-15-08.hospital + ✓ SUCCESS (58 entities) + Progress: 315/814 (38%), ETA: 2.7h + +[316/814] 2018-03-11.14-20-00.admin → 2018-03-11.14-20-00.admin + ✓ SUCCESS (0 entities) + Progress: 316/814 (38%), ETA: 2.7h + +[317/814] 2018-03-11.14-20-00.bus → 2018-03-11.14-20-00.bus + ✓ SUCCESS (5 entities) + Progress: 317/814 (38%), ETA: 2.7h + +[318/814] 2018-03-11.14-20-00.school → 2018-03-11.14-20-00.school + ✓ SUCCESS (148 entities) + Progress: 318/814 (39%), ETA: 2.7h + +[319/814] 2018-03-11.14-20-01.admin → 2018-03-11.14-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 319/814 (39%), ETA: 2.7h + +[320/814] 2018-03-11.14-20-01.bus → 2018-03-11.14-20-01.bus + ✓ SUCCESS (32 entities) + Progress: 320/814 (39%), ETA: 2.7h + +[321/814] 2018-03-11.14-20-01.school → 2018-03-11.14-20-01.school + ✓ SUCCESS (97 entities) + Progress: 321/814 (39%), ETA: 2.7h + +[322/814] 2018-03-11.14-20-02.school → 2018-03-11.14-20-02.school + ✓ SUCCESS (40 entities) + Progress: 322/814 (39%), ETA: 2.7h + +[323/814] 2018-03-11.14-20-04.school → 2018-03-11.14-20-04.school + ✓ SUCCESS (0 entities) + Progress: 323/814 (39%), ETA: 2.6h + +[324/814] 2018-03-11.14-20-08.bus → 2018-03-11.14-20-08.bus + ✓ SUCCESS (10 entities) + Progress: 324/814 (39%), ETA: 2.6h + +[325/814] 2018-03-11.14-20-08.hospital → 2018-03-11.14-20-08.hospital + ✓ SUCCESS (48 entities) + Progress: 325/814 (39%), ETA: 2.6h + +[326/814] 2018-03-11.16-10-00.bus → 2018-03-11.16-10-00.bus + ✓ SUCCESS (17 entities) + Progress: 326/814 (40%), ETA: 2.6h + +[327/814] 2018-03-11.16-10-01.admin → 2018-03-11.16-10-01.admin + ✓ SUCCESS (20 entities) + Progress: 327/814 (40%), ETA: 2.6h + +[328/814] 2018-03-11.16-10-01.bus → 2018-03-11.16-10-01.bus + ✓ SUCCESS (3 entities) + Progress: 328/814 (40%), ETA: 2.6h + +[329/814] 2018-03-11.16-10-01.school → 2018-03-11.16-10-01.school + ✓ SUCCESS (46 entities) + Progress: 329/814 (40%), ETA: 2.6h + +[330/814] 2018-03-11.16-10-08.bus → 2018-03-11.16-10-08.bus + ✓ SUCCESS (5 entities) + Progress: 330/814 (40%), ETA: 2.6h + +[331/814] 2018-03-11.16-10-08.hospital → 2018-03-11.16-10-08.hospital + ✓ SUCCESS (15 entities) + Progress: 331/814 (40%), ETA: 2.6h + +[332/814] 2018-03-11.16-15-00.bus → 2018-03-11.16-15-00.bus + ✓ SUCCESS (26 entities) + Progress: 332/814 (40%), ETA: 2.6h + +[333/814] 2018-03-11.16-15-00.hospital → 2018-03-11.16-15-00.hospital + ✓ SUCCESS (0 entities) + Progress: 333/814 (40%), ETA: 2.6h + +[334/814] 2018-03-11.16-15-00.school → 2018-03-11.16-15-00.school + ✓ SUCCESS (1274 entities) + Progress: 334/814 (41%), ETA: 2.6h + +[335/814] 2018-03-11.16-15-01.admin → 2018-03-11.16-15-01.admin + ✓ SUCCESS (12 entities) + Progress: 335/814 (41%), ETA: 2.6h + +[336/814] 2018-03-11.16-15-01.bus → 2018-03-11.16-15-01.bus + ✓ SUCCESS (8 entities) + Progress: 336/814 (41%), ETA: 2.6h + +[337/814] 2018-03-11.16-15-01.school → 2018-03-11.16-15-01.school + ✓ SUCCESS (161 entities) + Progress: 337/814 (41%), ETA: 2.6h + +[338/814] 2018-03-11.16-15-02.school → 2018-03-11.16-15-02.school + ✓ SUCCESS (25 entities) + Progress: 338/814 (41%), ETA: 2.6h + +[339/814] 2018-03-11.16-15-04.school → 2018-03-11.16-15-04.school + ✓ SUCCESS (0 entities) + Progress: 339/814 (41%), ETA: 2.5h + +[340/814] 2018-03-11.16-15-08.bus → 2018-03-11.16-15-08.bus + ✓ SUCCESS (2 entities) + Progress: 340/814 (41%), ETA: 2.5h + +[341/814] 2018-03-11.16-15-08.hospital → 2018-03-11.16-15-08.hospital + ✓ SUCCESS (86 entities) + Progress: 341/814 (41%), ETA: 2.5h + +[342/814] 2018-03-11.16-20-00.bus → 2018-03-11.16-20-00.bus + ✓ SUCCESS (7 entities) + Progress: 342/814 (42%), ETA: 2.5h + +[343/814] 2018-03-11.16-20-00.hospital → 2018-03-11.16-20-00.hospital + ✓ SUCCESS (0 entities) + Progress: 343/814 (42%), ETA: 2.5h + +[344/814] 2018-03-11.16-20-00.school → 2018-03-11.16-20-00.school + ✓ SUCCESS (764 entities) + Progress: 344/814 (42%), ETA: 2.5h + +[345/814] 2018-03-11.16-20-01.admin → 2018-03-11.16-20-01.admin + ✓ SUCCESS (24 entities) + Progress: 345/814 (42%), ETA: 2.5h + +[346/814] 2018-03-11.16-20-01.bus → 2018-03-11.16-20-01.bus + ✓ SUCCESS (8 entities) + Progress: 346/814 (42%), ETA: 2.5h + +[347/814] 2018-03-11.16-20-01.school → 2018-03-11.16-20-01.school + ✓ SUCCESS (162 entities) + Progress: 347/814 (42%), ETA: 2.5h + +[348/814] 2018-03-11.16-20-02.school → 2018-03-11.16-20-02.school + ✓ SUCCESS (43 entities) + Progress: 348/814 (42%), ETA: 2.5h + +[349/814] 2018-03-11.16-20-04.school → 2018-03-11.16-20-04.school + ✓ SUCCESS (8 entities) + Progress: 349/814 (42%), ETA: 2.5h + +[350/814] 2018-03-11.16-20-08.bus → 2018-03-11.16-20-08.bus + ✓ SUCCESS (6 entities) + Progress: 350/814 (42%), ETA: 2.5h + +[351/814] 2018-03-11.16-20-08.hospital → 2018-03-11.16-20-08.hospital + ✓ SUCCESS (45 entities) + Progress: 351/814 (43%), ETA: 2.5h + +[352/814] 2018-03-11.16-25-00.bus → 2018-03-11.16-25-00.bus + ✓ SUCCESS (7 entities) + Progress: 352/814 (43%), ETA: 2.5h + +[353/814] 2018-03-11.16-25-00.school → 2018-03-11.16-25-00.school + ✓ SUCCESS (503 entities) + Progress: 353/814 (43%), ETA: 2.5h + +[354/814] 2018-03-11.16-25-01.admin → 2018-03-11.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 354/814 (43%), ETA: 2.5h + +[355/814] 2018-03-11.16-25-01.bus → 2018-03-11.16-25-01.bus + ✓ SUCCESS (3 entities) + Progress: 355/814 (43%), ETA: 2.4h + +[356/814] 2018-03-11.16-25-01.school → 2018-03-11.16-25-01.school + ✓ SUCCESS (94 entities) + Progress: 356/814 (43%), ETA: 2.4h + +[357/814] 2018-03-11.16-25-02.school → 2018-03-11.16-25-02.school + ✓ SUCCESS (50 entities) + Progress: 357/814 (43%), ETA: 2.4h + +[358/814] 2018-03-11.16-25-04.school → 2018-03-11.16-25-04.school + ✓ SUCCESS (13 entities) + Progress: 358/814 (43%), ETA: 2.4h + +[359/814] 2018-03-11.16-25-08.bus → 2018-03-11.16-25-08.bus + ✓ SUCCESS (4 entities) + Progress: 359/814 (44%), ETA: 2.4h + +[360/814] 2018-03-11.16-25-08.hospital → 2018-03-11.16-25-08.hospital + ✓ SUCCESS (14 entities) + Progress: 360/814 (44%), ETA: 2.4h + +[361/814] 2018-03-11.16-30-00.bus → 2018-03-11.16-30-00.bus + ✓ SUCCESS (0 entities) + Progress: 361/814 (44%), ETA: 2.4h + +[362/814] 2018-03-11.16-30-00.hospital → 2018-03-11.16-30-00.hospital + ✓ SUCCESS (0 entities) + Progress: 362/814 (44%), ETA: 2.4h + +[363/814] 2018-03-11.16-30-00.school → 2018-03-11.16-30-00.school + ✓ SUCCESS (206 entities) + Progress: 363/814 (44%), ETA: 2.4h + +[364/814] 2018-03-11.16-30-01.admin → 2018-03-11.16-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 364/814 (44%), ETA: 2.4h + +[365/814] 2018-03-11.16-30-01.bus → 2018-03-11.16-30-01.bus + ✓ SUCCESS (0 entities) + Progress: 365/814 (44%), ETA: 2.4h + +[366/814] 2018-03-11.16-30-01.school → 2018-03-11.16-30-01.school + ✓ SUCCESS (55 entities) + Progress: 366/814 (44%), ETA: 2.4h + +[367/814] 2018-03-11.16-30-02.school → 2018-03-11.16-30-02.school + ✓ SUCCESS (11 entities) + Progress: 367/814 (45%), ETA: 2.4h + +[368/814] 2018-03-11.16-30-04.school → 2018-03-11.16-30-04.school + ✓ SUCCESS (3 entities) + Progress: 368/814 (45%), ETA: 2.3h + +[369/814] 2018-03-11.16-30-08.bus → 2018-03-11.16-30-08.bus + ✓ SUCCESS (0 entities) + Progress: 369/814 (45%), ETA: 2.3h + +[370/814] 2018-03-11.16-30-08.hospital → 2018-03-11.16-30-08.hospital + ✓ SUCCESS (0 entities) + Progress: 370/814 (45%), ETA: 2.3h + +[371/814] 2018-03-11.16-30-09.hospital → 2018-03-11.16-30-09.hospital + ✓ SUCCESS (0 entities) + Progress: 371/814 (45%), ETA: 2.3h + +[372/814] 2018-03-11.16-35-00.bus → 2018-03-11.16-35-00.bus + ✓ SUCCESS (1 entities) + Progress: 372/814 (45%), ETA: 2.3h + +[373/814] 2018-03-11.16-35-00.hospital → 2018-03-11.16-35-00.hospital + ✓ SUCCESS (0 entities) + Progress: 373/814 (45%), ETA: 2.3h + +[374/814] 2018-03-11.16-35-00.school → 2018-03-11.16-35-00.school + ✓ SUCCESS (1786 entities) + Progress: 374/814 (45%), ETA: 2.3h + +[375/814] 2018-03-11.16-35-01.admin → 2018-03-11.16-35-01.admin + ✓ SUCCESS (3 entities) + Progress: 375/814 (46%), ETA: 2.3h + +[376/814] 2018-03-11.16-35-01.bus → 2018-03-11.16-35-01.bus + ✓ SUCCESS (4 entities) + Progress: 376/814 (46%), ETA: 2.3h + +[377/814] 2018-03-11.16-35-01.school → 2018-03-11.16-35-01.school + ✓ SUCCESS (379 entities) + Progress: 377/814 (46%), ETA: 2.3h + +[378/814] 2018-03-11.16-35-02.school → 2018-03-11.16-35-02.school + ✓ SUCCESS (65 entities) + Progress: 378/814 (46%), ETA: 2.3h + +[379/814] 2018-03-11.16-35-04.school → 2018-03-11.16-35-04.school + ✓ SUCCESS (4 entities) + Progress: 379/814 (46%), ETA: 2.3h + +[380/814] 2018-03-11.16-35-08.bus → 2018-03-11.16-35-08.bus + ✓ SUCCESS (2 entities) + Progress: 380/814 (46%), ETA: 2.3h + +[381/814] 2018-03-11.16-35-08.hospital → 2018-03-11.16-35-08.hospital + ✓ SUCCESS (80 entities) + Progress: 381/814 (46%), ETA: 2.3h + +[382/814] 2018-03-11.16-40-00.bus → 2018-03-11.16-40-00.bus + ✓ SUCCESS (21 entities) + Progress: 382/814 (46%), ETA: 2.3h + +[383/814] 2018-03-11.16-40-00.school → 2018-03-11.16-40-00.school + ✓ SUCCESS (582 entities) + Progress: 383/814 (47%), ETA: 2.3h + +[384/814] 2018-03-11.16-40-01.admin → 2018-03-11.16-40-01.admin + ✓ SUCCESS (12 entities) + Progress: 384/814 (47%), ETA: 2.3h + +[385/814] 2018-03-11.16-40-01.bus → 2018-03-11.16-40-01.bus + ✓ SUCCESS (7 entities) + Progress: 385/814 (47%), ETA: 2.2h + +[386/814] 2018-03-11.16-40-01.school → 2018-03-11.16-40-01.school + ✓ SUCCESS (77 entities) + Progress: 386/814 (47%), ETA: 2.2h + +[387/814] 2018-03-11.16-40-02.school → 2018-03-11.16-40-02.school + ✓ SUCCESS (34 entities) + Progress: 387/814 (47%), ETA: 2.2h + +[388/814] 2018-03-11.16-40-04.school → 2018-03-11.16-40-04.school + ✓ SUCCESS (0 entities) + Progress: 388/814 (47%), ETA: 2.2h + +[389/814] 2018-03-11.16-40-08.bus → 2018-03-11.16-40-08.bus + ✓ SUCCESS (4 entities) + Progress: 389/814 (47%), ETA: 2.2h + +[390/814] 2018-03-11.16-40-08.hospital → 2018-03-11.16-40-08.hospital + ✓ SUCCESS (39 entities) + Progress: 390/814 (47%), ETA: 2.2h + +[391/814] 2018-03-11.16-45-00.bus → 2018-03-11.16-45-00.bus + ✓ SUCCESS (4 entities) + Progress: 391/814 (48%), ETA: 2.2h + +[392/814] 2018-03-11.16-45-00.school → 2018-03-11.16-45-00.school + ✓ SUCCESS (150 entities) + Progress: 392/814 (48%), ETA: 2.2h + +[393/814] 2018-03-11.17-10-00.bus → 2018-03-11.17-10-00.bus + ✓ SUCCESS (13 entities) + Progress: 393/814 (48%), ETA: 2.2h + +[394/814] 2018-03-11.17-10-00.school → 2018-03-11.17-10-00.school + ✓ SUCCESS (790 entities) + Progress: 394/814 (48%), ETA: 2.2h + +[395/814] 2018-03-11.17-10-01.admin → 2018-03-11.17-10-01.admin + ✓ SUCCESS (10 entities) + Progress: 395/814 (48%), ETA: 2.2h + +[396/814] 2018-03-11.17-10-01.bus → 2018-03-11.17-10-01.bus + ✓ SUCCESS (8 entities) + Progress: 396/814 (48%), ETA: 2.2h + +[397/814] 2018-03-11.17-10-01.school → 2018-03-11.17-10-01.school + ✓ SUCCESS (294 entities) + Progress: 397/814 (48%), ETA: 2.2h + +[398/814] 2018-03-11.17-10-02.school → 2018-03-11.17-10-02.school + ✓ SUCCESS (81 entities) + Progress: 398/814 (48%), ETA: 2.2h + +[399/814] 2018-03-11.17-10-04.school → 2018-03-11.17-10-04.school + ✓ SUCCESS (12 entities) + Progress: 399/814 (49%), ETA: 2.2h + +[400/814] 2018-03-11.17-10-08.bus → 2018-03-11.17-10-08.bus + ✓ SUCCESS (15 entities) + Progress: 400/814 (49%), ETA: 2.2h + +[401/814] 2018-03-11.17-10-08.hospital → 2018-03-11.17-10-08.hospital + ✓ SUCCESS (158 entities) + Progress: 401/814 (49%), ETA: 2.2h + +[402/814] 2018-03-11.17-10-09.hospital → 2018-03-11.17-10-09.hospital + ✓ SUCCESS (8 entities) + Progress: 402/814 (49%), ETA: 2.2h + +[403/814] 2018-03-11.17-15-00.bus → 2018-03-11.17-15-00.bus + ✓ SUCCESS (3 entities) + Progress: 403/814 (49%), ETA: 2.1h + +[404/814] 2018-03-11.17-15-00.school → 2018-03-11.17-15-00.school + ✓ SUCCESS (1384 entities) + Progress: 404/814 (49%), ETA: 2.2h + +[405/814] 2018-03-11.17-15-01.admin → 2018-03-11.17-15-01.admin + ✓ SUCCESS (2 entities) + Progress: 405/814 (49%), ETA: 2.2h + +[406/814] 2018-03-11.17-15-01.bus → 2018-03-11.17-15-01.bus + ✓ SUCCESS (0 entities) + Progress: 406/814 (49%), ETA: 2.1h + +[407/814] 2018-03-11.17-15-01.school → 2018-03-11.17-15-01.school + ✓ SUCCESS (67 entities) + Progress: 407/814 (50%), ETA: 2.1h + +[408/814] 2018-03-11.17-15-02.school → 2018-03-11.17-15-02.school + ✓ SUCCESS (13 entities) + Progress: 408/814 (50%), ETA: 2.1h + +[409/814] 2018-03-11.17-15-04.school → 2018-03-11.17-15-04.school + ✓ SUCCESS (2 entities) + Progress: 409/814 (50%), ETA: 2.1h + +[410/814] 2018-03-11.17-15-08.bus → 2018-03-11.17-15-08.bus + ✓ SUCCESS (3 entities) + Progress: 410/814 (50%), ETA: 2.1h + +[411/814] 2018-03-11.17-15-08.hospital → 2018-03-11.17-15-08.hospital + ✓ SUCCESS (10 entities) + Progress: 411/814 (50%), ETA: 2.1h + +[412/814] 2018-03-11.17-15-09.hospital → 2018-03-11.17-15-09.hospital + ✓ SUCCESS (0 entities) + Progress: 412/814 (50%), ETA: 2.1h + +[413/814] 2018-03-11.17-20-00.bus → 2018-03-11.17-20-00.bus + ✓ SUCCESS (2 entities) + Progress: 413/814 (50%), ETA: 2.1h + +[414/814] 2018-03-11.17-20-00.school → 2018-03-11.17-20-00.school + ✓ SUCCESS (1074 entities) + Progress: 414/814 (50%), ETA: 2.1h + +[415/814] 2018-03-11.17-20-01.admin → 2018-03-11.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 415/814 (50%), ETA: 2.1h + +[416/814] 2018-03-11.17-20-01.bus → 2018-03-11.17-20-01.bus + ✓ SUCCESS (0 entities) + Progress: 416/814 (51%), ETA: 2.1h + +[417/814] 2018-03-11.17-20-01.school → 2018-03-11.17-20-01.school + ✓ SUCCESS (85 entities) + Progress: 417/814 (51%), ETA: 2.1h + +[418/814] 2018-03-11.17-20-02.school → 2018-03-11.17-20-02.school + ✓ SUCCESS (0 entities) + Progress: 418/814 (51%), ETA: 2.1h + +[419/814] 2018-03-11.17-20-04.school → 2018-03-11.17-20-04.school + ✓ SUCCESS (2 entities) + Progress: 419/814 (51%), ETA: 2.1h + +[420/814] 2018-03-11.17-20-08.bus → 2018-03-11.17-20-08.bus + ✓ SUCCESS (0 entities) + Progress: 420/814 (51%), ETA: 2.0h + +[421/814] 2018-03-11.17-20-08.hospital → 2018-03-11.17-20-08.hospital + ✓ SUCCESS (46 entities) + Progress: 421/814 (51%), ETA: 2.0h + +[422/814] 2018-03-11.17-20-09.hospital → 2018-03-11.17-20-09.hospital + ✓ SUCCESS (0 entities) + Progress: 422/814 (51%), ETA: 2.0h + +[423/814] 2018-03-11.17-25-00.bus → 2018-03-11.17-25-00.bus + ✓ SUCCESS (3 entities) + Progress: 423/814 (51%), ETA: 2.0h + +[424/814] 2018-03-11.17-25-00.school → 2018-03-11.17-25-00.school + ✓ SUCCESS (1697 entities) + Progress: 424/814 (52%), ETA: 2.0h + +[425/814] 2018-03-11.17-25-01.admin → 2018-03-11.17-25-01.admin + ✓ SUCCESS (16 entities) + Progress: 425/814 (52%), ETA: 2.0h + +[426/814] 2018-03-11.17-25-01.bus → 2018-03-11.17-25-01.bus + ✓ SUCCESS (2 entities) + Progress: 426/814 (52%), ETA: 2.0h + +[427/814] 2018-03-11.17-25-01.school → 2018-03-11.17-25-01.school + ✓ SUCCESS (186 entities) + Progress: 427/814 (52%), ETA: 2.0h + +[428/814] 2018-03-11.17-25-02.school → 2018-03-11.17-25-02.school + ✓ SUCCESS (64 entities) + Progress: 428/814 (52%), ETA: 2.0h + +[429/814] 2018-03-11.17-25-04.school → 2018-03-11.17-25-04.school + ✓ SUCCESS (5 entities) + Progress: 429/814 (52%), ETA: 2.0h + +[430/814] 2018-03-11.17-25-08.bus → 2018-03-11.17-25-08.bus + ✓ SUCCESS (8 entities) + Progress: 430/814 (52%), ETA: 2.0h + +[431/814] 2018-03-11.17-25-08.hospital → 2018-03-11.17-25-08.hospital + ✓ SUCCESS (103 entities) + Progress: 431/814 (52%), ETA: 2.0h + +[432/814] 2018-03-11.17-25-09.hospital → 2018-03-11.17-25-09.hospital + ✓ SUCCESS (0 entities) + Progress: 432/814 (53%), ETA: 2.0h + +[433/814] 2018-03-12.10-00-00.bus → 2018-03-12.10-00-00.bus + ✓ SUCCESS (13 entities) + Progress: 433/814 (53%), ETA: 2.0h + +[434/814] 2018-03-12.10-00-00.hospital → 2018-03-12.10-00-00.hospital + ✓ SUCCESS (74 entities) + Progress: 434/814 (53%), ETA: 2.0h + +[435/814] 2018-03-12.10-00-00.school → 2018-03-12.10-00-00.school + ✓ SUCCESS (160 entities) + Progress: 435/814 (53%), ETA: 2.0h + +[436/814] 2018-03-12.10-00-01.bus → 2018-03-12.10-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 436/814 (53%), ETA: 2.0h + +[437/814] 2018-03-12.10-00-01.school → 2018-03-12.10-00-01.school + ✓ SUCCESS (265 entities) + Progress: 437/814 (53%), ETA: 2.0h + +[438/814] 2018-03-12.10-00-02.admin → 2018-03-12.10-00-02.admin + ✓ SUCCESS (8 entities) + Progress: 438/814 (53%), ETA: 2.0h + +[439/814] 2018-03-12.10-00-02.school → 2018-03-12.10-00-02.school + ✓ SUCCESS (36 entities) + Progress: 439/814 (53%), ETA: 1.9h + +[440/814] 2018-03-12.10-00-04.school → 2018-03-12.10-00-04.school + ✓ SUCCESS (47 entities) + Progress: 440/814 (54%), ETA: 1.9h + +[441/814] 2018-03-12.10-00-05.school → 2018-03-12.10-00-05.school + ✓ SUCCESS (14 entities) + Progress: 441/814 (54%), ETA: 1.9h + +[442/814] 2018-03-12.10-05-00.bus → 2018-03-12.10-05-00.bus + ✓ SUCCESS (18 entities) + Progress: 442/814 (54%), ETA: 1.9h + +[443/814] 2018-03-12.10-05-00.hospital → 2018-03-12.10-05-00.hospital + ✓ SUCCESS (77 entities) + Progress: 443/814 (54%), ETA: 1.9h + +[444/814] 2018-03-12.10-05-00.school → 2018-03-12.10-05-00.school + ✓ SUCCESS (584 entities) + Progress: 444/814 (54%), ETA: 1.9h + +[445/814] 2018-03-12.10-05-01.admin → 2018-03-12.10-05-01.admin + ✓ SUCCESS (15 entities) + Progress: 445/814 (54%), ETA: 1.9h + +[446/814] 2018-03-12.10-05-01.bus → 2018-03-12.10-05-01.bus + ✓ SUCCESS (3 entities) + Progress: 446/814 (54%), ETA: 1.9h + +[447/814] 2018-03-12.10-05-01.school → 2018-03-12.10-05-01.school + ✓ SUCCESS (597 entities) + Progress: 447/814 (54%), ETA: 1.9h + +[448/814] 2018-03-12.10-05-02.school → 2018-03-12.10-05-02.school + ✓ SUCCESS (25 entities) + Progress: 448/814 (55%), ETA: 1.9h + +[449/814] 2018-03-12.10-05-03.school → 2018-03-12.10-05-03.school + ✓ SUCCESS (12 entities) + Progress: 449/814 (55%), ETA: 1.9h + +[450/814] 2018-03-12.10-05-04.school → 2018-03-12.10-05-04.school + ✓ SUCCESS (51 entities) + Progress: 450/814 (55%), ETA: 1.9h + +[451/814] 2018-03-12.10-05-05.school → 2018-03-12.10-05-05.school + ✓ SUCCESS (0 entities) + Progress: 451/814 (55%), ETA: 1.9h + +[452/814] 2018-03-12.10-10-00.bus → 2018-03-12.10-10-00.bus + ✓ SUCCESS (7 entities) + Progress: 452/814 (55%), ETA: 1.9h + +[453/814] 2018-03-12.10-10-00.hospital → 2018-03-12.10-10-00.hospital + ✓ SUCCESS (20 entities) + Progress: 453/814 (55%), ETA: 1.9h + +[454/814] 2018-03-12.10-10-00.school → 2018-03-12.10-10-00.school + ✓ SUCCESS (628 entities) + Progress: 454/814 (55%), ETA: 1.9h + +[455/814] 2018-03-12.10-10-01.admin → 2018-03-12.10-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 455/814 (55%), ETA: 1.9h + +[456/814] 2018-03-12.10-10-01.bus → 2018-03-12.10-10-01.bus + ✓ SUCCESS (0 entities) + Progress: 456/814 (56%), ETA: 1.9h + +[457/814] 2018-03-12.10-10-01.school → 2018-03-12.10-10-01.school + ✓ SUCCESS (225 entities) + Progress: 457/814 (56%), ETA: 1.9h + +[458/814] 2018-03-12.10-10-02.school → 2018-03-12.10-10-02.school + ✓ SUCCESS (10 entities) + Progress: 458/814 (56%), ETA: 1.8h + +[459/814] 2018-03-12.10-10-03.school → 2018-03-12.10-10-03.school + ✓ SUCCESS (4 entities) + Progress: 459/814 (56%), ETA: 1.8h + +[460/814] 2018-03-12.10-10-04.school → 2018-03-12.10-10-04.school + ✓ SUCCESS (17 entities) + Progress: 460/814 (56%), ETA: 1.8h + +[461/814] 2018-03-12.10-10-05.school → 2018-03-12.10-10-05.school + ✓ SUCCESS (4 entities) + Progress: 461/814 (56%), ETA: 1.8h + +[462/814] 2018-03-12.10-15-00.bus → 2018-03-12.10-15-00.bus + ✓ SUCCESS (2 entities) + Progress: 462/814 (56%), ETA: 1.8h + +[463/814] 2018-03-12.10-15-00.hospital → 2018-03-12.10-15-00.hospital + ✓ SUCCESS (95 entities) + Progress: 463/814 (56%), ETA: 1.8h + +[464/814] 2018-03-12.10-15-00.school → 2018-03-12.10-15-00.school + ✓ SUCCESS (596 entities) + Progress: 464/814 (57%), ETA: 1.8h + +[465/814] 2018-03-12.10-15-01.admin → 2018-03-12.10-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 465/814 (57%), ETA: 1.8h + +[466/814] 2018-03-12.10-15-01.bus → 2018-03-12.10-15-01.bus + ✓ SUCCESS (0 entities) + Progress: 466/814 (57%), ETA: 1.8h + +[467/814] 2018-03-12.10-15-01.school → 2018-03-12.10-15-01.school + ✓ SUCCESS (128 entities) + Progress: 467/814 (57%), ETA: 1.8h + +[468/814] 2018-03-12.10-15-02.school → 2018-03-12.10-15-02.school + ✓ SUCCESS (35 entities) + Progress: 468/814 (57%), ETA: 1.8h + +[469/814] 2018-03-12.10-15-03.school → 2018-03-12.10-15-03.school + ✓ SUCCESS (16 entities) + Progress: 469/814 (57%), ETA: 1.8h + +[470/814] 2018-03-12.10-15-04.school → 2018-03-12.10-15-04.school + ✓ SUCCESS (34 entities) + Progress: 470/814 (57%), ETA: 1.8h + +[471/814] 2018-03-12.10-15-05.school → 2018-03-12.10-15-05.school + ✓ SUCCESS (2 entities) + Progress: 471/814 (57%), ETA: 1.8h + +[472/814] 2018-03-12.10-20-00.bus → 2018-03-12.10-20-00.bus + ✓ SUCCESS (3 entities) + Progress: 472/814 (57%), ETA: 1.8h + +[473/814] 2018-03-12.10-20-00.hospital → 2018-03-12.10-20-00.hospital + ✓ SUCCESS (3 entities) + Progress: 473/814 (58%), ETA: 1.7h + +[474/814] 2018-03-12.10-20-00.school → 2018-03-12.10-20-00.school + ✓ SUCCESS (640 entities) + Progress: 474/814 (58%), ETA: 1.7h + +[475/814] 2018-03-12.10-20-01.admin → 2018-03-12.10-20-01.admin + ✓ SUCCESS (6 entities) + Progress: 475/814 (58%), ETA: 1.7h + +[476/814] 2018-03-12.10-20-01.bus → 2018-03-12.10-20-01.bus + ✓ SUCCESS (0 entities) + Progress: 476/814 (58%), ETA: 1.7h + +[477/814] 2018-03-12.10-20-01.school → 2018-03-12.10-20-01.school + ✓ SUCCESS (152 entities) + Progress: 477/814 (58%), ETA: 1.7h + +[478/814] 2018-03-12.10-20-02.school → 2018-03-12.10-20-02.school + ✓ SUCCESS (0 entities) + Progress: 478/814 (58%), ETA: 1.7h + +[479/814] 2018-03-12.10-20-03.school → 2018-03-12.10-20-03.school + ✓ SUCCESS (2 entities) + Progress: 479/814 (58%), ETA: 1.7h + +[480/814] 2018-03-12.10-20-04.school → 2018-03-12.10-20-04.school + ✓ SUCCESS (0 entities) + Progress: 480/814 (58%), ETA: 1.7h + +[481/814] 2018-03-12.10-20-05.school → 2018-03-12.10-20-05.school + ✓ SUCCESS (0 entities) + Progress: 481/814 (59%), ETA: 1.7h + +[482/814] 2018-03-12.10-25-00.bus → 2018-03-12.10-25-00.bus + ✓ SUCCESS (27 entities) + Progress: 482/814 (59%), ETA: 1.7h + +[483/814] 2018-03-12.10-25-00.hospital → 2018-03-12.10-25-00.hospital + ✓ SUCCESS (15 entities) + Progress: 483/814 (59%), ETA: 1.7h + +[484/814] 2018-03-12.10-25-00.school → 2018-03-12.10-25-00.school + ✓ SUCCESS (1434 entities) + Progress: 484/814 (59%), ETA: 1.7h + +[485/814] 2018-03-12.10-25-01.admin → 2018-03-12.10-25-01.admin + ✓ SUCCESS (4 entities) + Progress: 485/814 (59%), ETA: 1.7h + +[486/814] 2018-03-12.10-25-01.bus → 2018-03-12.10-25-01.bus + ✓ SUCCESS (0 entities) + Progress: 486/814 (59%), ETA: 1.7h + +[487/814] 2018-03-12.10-25-01.school → 2018-03-12.10-25-01.school + ✓ SUCCESS (347 entities) + Progress: 487/814 (59%), ETA: 1.7h + +[488/814] 2018-03-12.10-25-02.school → 2018-03-12.10-25-02.school + ✓ SUCCESS (3 entities) + Progress: 488/814 (59%), ETA: 1.7h + +[489/814] 2018-03-12.10-25-03.school → 2018-03-12.10-25-03.school + ✓ SUCCESS (13 entities) + Progress: 489/814 (60%), ETA: 1.7h + +[490/814] 2018-03-12.10-25-04.school → 2018-03-12.10-25-04.school + ✓ SUCCESS (13 entities) + Progress: 490/814 (60%), ETA: 1.7h + +[491/814] 2018-03-12.10-25-05.school → 2018-03-12.10-25-05.school + ✓ SUCCESS (2 entities) + Progress: 491/814 (60%), ETA: 1.6h + +[492/814] 2018-03-12.10-30-00.admin → 2018-03-12.10-30-00.admin + ✓ SUCCESS (12 entities) + Progress: 492/814 (60%), ETA: 1.6h + +[493/814] 2018-03-12.10-30-00.bus → 2018-03-12.10-30-00.bus + ✓ SUCCESS (29 entities) + Progress: 493/814 (60%), ETA: 1.6h + +[494/814] 2018-03-12.10-30-00.hospital → 2018-03-12.10-30-00.hospital + ✓ SUCCESS (64 entities) + Progress: 494/814 (60%), ETA: 1.6h + +[495/814] 2018-03-12.10-30-00.school → 2018-03-12.10-30-00.school + ✓ SUCCESS (114 entities) + Progress: 495/814 (60%), ETA: 1.6h + +[496/814] 2018-03-12.10-30-01.admin → 2018-03-12.10-30-01.admin + ✓ SUCCESS (15 entities) + Progress: 496/814 (60%), ETA: 1.6h + +[497/814] 2018-03-12.10-30-01.bus → 2018-03-12.10-30-01.bus + ✓ SUCCESS (0 entities) + Progress: 497/814 (61%), ETA: 1.6h + +[498/814] 2018-03-12.10-30-01.school → 2018-03-12.10-30-01.school + ✓ SUCCESS (141 entities) + Progress: 498/814 (61%), ETA: 1.6h + +[499/814] 2018-03-12.10-30-02.school → 2018-03-12.10-30-02.school + ✓ SUCCESS (40 entities) + Progress: 499/814 (61%), ETA: 1.6h + +[500/814] 2018-03-12.10-30-03.school → 2018-03-12.10-30-03.school + ✓ SUCCESS (12 entities) + Progress: 500/814 (61%), ETA: 1.6h + +[501/814] 2018-03-12.10-30-04.school → 2018-03-12.10-30-04.school + ✓ SUCCESS (58 entities) + Progress: 501/814 (61%), ETA: 1.6h + +[502/814] 2018-03-12.10-30-05.school → 2018-03-12.10-30-05.school + ✓ SUCCESS (2 entities) + Progress: 502/814 (61%), ETA: 1.6h + +[503/814] 2018-03-12.10-40-00.admin → 2018-03-12.10-40-00.admin + ✓ SUCCESS (7 entities) + Progress: 503/814 (61%), ETA: 1.6h + +[504/814] 2018-03-12.10-40-00.bus → 2018-03-12.10-40-00.bus + ✓ SUCCESS (41 entities) + Progress: 504/814 (61%), ETA: 1.6h + +[505/814] 2018-03-12.10-40-00.hospital → 2018-03-12.10-40-00.hospital + ✓ SUCCESS (47 entities) + Progress: 505/814 (62%), ETA: 1.6h + +[506/814] 2018-03-12.10-40-00.school → 2018-03-12.10-40-00.school + ✓ SUCCESS (318 entities) + Progress: 506/814 (62%), ETA: 1.6h + +[507/814] 2018-03-12.10-40-01.admin → 2018-03-12.10-40-01.admin + ✓ SUCCESS (15 entities) + Progress: 507/814 (62%), ETA: 1.6h + +[508/814] 2018-03-12.10-40-01.bus → 2018-03-12.10-40-01.bus + ✓ SUCCESS (0 entities) + Progress: 508/814 (62%), ETA: 1.5h + +[509/814] 2018-03-12.10-40-01.school → 2018-03-12.10-40-01.school + ✓ SUCCESS (340 entities) + Progress: 509/814 (62%), ETA: 1.5h + +[510/814] 2018-03-12.10-40-02.school → 2018-03-12.10-40-02.school + ✓ SUCCESS (22 entities) + Progress: 510/814 (62%), ETA: 1.5h + +[511/814] 2018-03-12.10-40-03.school → 2018-03-12.10-40-03.school + ✓ SUCCESS (20 entities) + Progress: 511/814 (62%), ETA: 1.5h + +[512/814] 2018-03-12.10-40-04.school → 2018-03-12.10-40-04.school + ✓ SUCCESS (30 entities) + Progress: 512/814 (62%), ETA: 1.5h + +[513/814] 2018-03-12.10-40-05.school → 2018-03-12.10-40-05.school + ✓ SUCCESS (5 entities) + Progress: 513/814 (63%), ETA: 1.5h + +[514/814] 2018-03-12.10-45-00.admin → 2018-03-12.10-45-00.admin + ✓ SUCCESS (0 entities) + Progress: 514/814 (63%), ETA: 1.5h + +[515/814] 2018-03-12.10-45-00.bus → 2018-03-12.10-45-00.bus + ✓ SUCCESS (17 entities) + Progress: 515/814 (63%), ETA: 1.5h + +[516/814] 2018-03-12.10-45-00.hospital → 2018-03-12.10-45-00.hospital + ✓ SUCCESS (76 entities) + Progress: 516/814 (63%), ETA: 1.5h + +[517/814] 2018-03-12.10-45-00.school → 2018-03-12.10-45-00.school + ✓ SUCCESS (787 entities) + Progress: 517/814 (63%), ETA: 1.5h + +[518/814] 2018-03-12.10-45-01.admin → 2018-03-12.10-45-01.admin + ✓ SUCCESS (0 entities) + Progress: 518/814 (63%), ETA: 1.5h + +[519/814] 2018-03-12.10-45-01.bus → 2018-03-12.10-45-01.bus + ✓ SUCCESS (0 entities) + Progress: 519/814 (63%), ETA: 1.5h + +[520/814] 2018-03-12.10-45-01.school → 2018-03-12.10-45-01.school + ✓ SUCCESS (225 entities) + Progress: 520/814 (63%), ETA: 1.5h + +[521/814] 2018-03-12.10-45-02.school → 2018-03-12.10-45-02.school + ✓ SUCCESS (33 entities) + Progress: 521/814 (64%), ETA: 1.5h + +[522/814] 2018-03-12.10-45-03.school → 2018-03-12.10-45-03.school + ✓ SUCCESS (12 entities) + Progress: 522/814 (64%), ETA: 1.5h + +[523/814] 2018-03-12.10-45-04.school → 2018-03-12.10-45-04.school + ✓ SUCCESS (19 entities) + Progress: 523/814 (64%), ETA: 1.5h + +[524/814] 2018-03-12.10-45-05.school → 2018-03-12.10-45-05.school + ✓ SUCCESS (10 entities) + Progress: 524/814 (64%), ETA: 1.5h + +[525/814] 2018-03-12.10-50-00.bus → 2018-03-12.10-50-00.bus + ✓ SUCCESS (34 entities) + Progress: 525/814 (64%), ETA: 1.5h + +[526/814] 2018-03-12.10-50-00.hospital → 2018-03-12.10-50-00.hospital + ✓ SUCCESS (18 entities) + Progress: 526/814 (64%), ETA: 1.5h + +[527/814] 2018-03-12.10-50-00.school → 2018-03-12.10-50-00.school + ✓ SUCCESS (721 entities) + Progress: 527/814 (64%), ETA: 1.5h + +[528/814] 2018-03-12.10-50-01.admin → 2018-03-12.10-50-01.admin + ✓ SUCCESS (5 entities) + Progress: 528/814 (64%), ETA: 1.4h + +[529/814] 2018-03-12.10-50-01.bus → 2018-03-12.10-50-01.bus + ✓ SUCCESS (0 entities) + Progress: 529/814 (64%), ETA: 1.4h + +[530/814] 2018-03-12.10-50-01.school → 2018-03-12.10-50-01.school + ✓ SUCCESS (139 entities) + Progress: 530/814 (65%), ETA: 1.4h + +[531/814] 2018-03-12.10-50-02.admin → 2018-03-12.10-50-02.admin + ✓ SUCCESS (5 entities) + Progress: 531/814 (65%), ETA: 1.4h + +[532/814] 2018-03-12.10-50-02.school → 2018-03-12.10-50-02.school + ✓ SUCCESS (2 entities) + Progress: 532/814 (65%), ETA: 1.4h + +[533/814] 2018-03-12.10-50-03.school → 2018-03-12.10-50-03.school + ✓ SUCCESS (2 entities) + Progress: 533/814 (65%), ETA: 1.4h + +[534/814] 2018-03-12.10-50-04.school → 2018-03-12.10-50-04.school + ✓ SUCCESS (14 entities) + Progress: 534/814 (65%), ETA: 1.4h + +[535/814] 2018-03-12.10-50-05.school → 2018-03-12.10-50-05.school + ✓ SUCCESS (0 entities) + Progress: 535/814 (65%), ETA: 1.4h + +[536/814] 2018-03-12.10-55-01.school → 2018-03-12.10-55-01.school + ✓ SUCCESS (1 entities) + Progress: 536/814 (65%), ETA: 1.4h + +[537/814] 2018-03-12.11-00-00.bus → 2018-03-12.11-00-00.bus + ✓ SUCCESS (34 entities) + Progress: 537/814 (65%), ETA: 1.4h + +[538/814] 2018-03-12.11-00-00.hospital → 2018-03-12.11-00-00.hospital + ✓ SUCCESS (92 entities) + Progress: 538/814 (66%), ETA: 1.4h + +[539/814] 2018-03-12.11-00-00.school → 2018-03-12.11-00-00.school + ✓ SUCCESS (679 entities) + Progress: 539/814 (66%), ETA: 1.4h + +[540/814] 2018-03-12.11-00-01.admin → 2018-03-12.11-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 540/814 (66%), ETA: 1.4h + +[541/814] 2018-03-12.11-00-01.bus → 2018-03-12.11-00-01.bus + ✓ SUCCESS (0 entities) + Progress: 541/814 (66%), ETA: 1.4h + +[542/814] 2018-03-12.11-00-01.school → 2018-03-12.11-00-01.school + ✓ SUCCESS (119 entities) + Progress: 542/814 (66%), ETA: 1.4h + +[543/814] 2018-03-12.11-00-02.school → 2018-03-12.11-00-02.school + ✓ SUCCESS (35 entities) + Progress: 543/814 (66%), ETA: 1.4h + +[544/814] 2018-03-12.11-00-03.school → 2018-03-12.11-00-03.school + ✓ SUCCESS (18 entities) + Progress: 544/814 (66%), ETA: 1.4h + +[545/814] 2018-03-12.11-00-04.school → 2018-03-12.11-00-04.school + ✓ SUCCESS (50 entities) + Progress: 545/814 (66%), ETA: 1.4h + +[546/814] 2018-03-12.11-00-05.school → 2018-03-12.11-00-05.school + ✓ SUCCESS (0 entities) + Progress: 546/814 (67%), ETA: 1.3h + +[547/814] 2018-03-12.11-05-00.bus → 2018-03-12.11-05-00.bus + ✓ SUCCESS (14 entities) + Progress: 547/814 (67%), ETA: 1.3h + +[548/814] 2018-03-12.11-05-00.hospital → 2018-03-12.11-05-00.hospital + ✓ SUCCESS (11 entities) + Progress: 548/814 (67%), ETA: 1.3h + +[549/814] 2018-03-12.11-05-00.school → 2018-03-12.11-05-00.school + ✓ SUCCESS (1145 entities) + Progress: 549/814 (67%), ETA: 1.3h + +[550/814] 2018-03-12.11-05-01.admin → 2018-03-12.11-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 550/814 (67%), ETA: 1.3h + +[551/814] 2018-03-12.11-05-01.bus → 2018-03-12.11-05-01.bus + ✓ SUCCESS (0 entities) + Progress: 551/814 (67%), ETA: 1.3h + +[552/814] 2018-03-12.11-05-01.school → 2018-03-12.11-05-01.school + ✓ SUCCESS (360 entities) + Progress: 552/814 (67%), ETA: 1.3h + +[553/814] 2018-03-12.11-05-02.school → 2018-03-12.11-05-02.school + ✓ SUCCESS (7 entities) + Progress: 553/814 (67%), ETA: 1.3h + +[554/814] 2018-03-12.11-05-03.school → 2018-03-12.11-05-03.school + ✓ SUCCESS (8 entities) + Progress: 554/814 (68%), ETA: 1.3h + +[555/814] 2018-03-12.11-05-04.school → 2018-03-12.11-05-04.school + ✓ SUCCESS (41 entities) + Progress: 555/814 (68%), ETA: 1.3h + +[556/814] 2018-03-12.11-05-05.school → 2018-03-12.11-05-05.school + ✓ SUCCESS (7 entities) + Progress: 556/814 (68%), ETA: 1.3h + +[557/814] 2018-03-12.11-10-00.bus → 2018-03-12.11-10-00.bus + ✓ SUCCESS (51 entities) + Progress: 557/814 (68%), ETA: 1.3h + +[558/814] 2018-03-12.11-10-00.hospital → 2018-03-12.11-10-00.hospital + ✓ SUCCESS (87 entities) + Progress: 558/814 (68%), ETA: 1.3h + +[559/814] 2018-03-12.11-10-00.school → 2018-03-12.11-10-00.school + ✓ SUCCESS (66 entities) + Progress: 559/814 (68%), ETA: 1.3h + +[560/814] 2018-03-12.11-10-01.admin → 2018-03-12.11-10-01.admin + ✓ SUCCESS (16 entities) + Progress: 560/814 (68%), ETA: 1.3h + +[561/814] 2018-03-12.11-10-01.school → 2018-03-12.11-10-01.school + ✓ SUCCESS (217 entities) + Progress: 561/814 (68%), ETA: 1.3h + +[562/814] 2018-03-12.11-10-02.school → 2018-03-12.11-10-02.school + ✓ SUCCESS (32 entities) + Progress: 562/814 (69%), ETA: 1.3h + +[563/814] 2018-03-12.11-10-03.school → 2018-03-12.11-10-03.school + ✓ SUCCESS (38 entities) + Progress: 563/814 (69%), ETA: 1.3h + +[564/814] 2018-03-12.11-10-04.school → 2018-03-12.11-10-04.school + ✓ SUCCESS (21 entities) + Progress: 564/814 (69%), ETA: 1.3h + +[565/814] 2018-03-12.11-10-05.school → 2018-03-12.11-10-05.school + ✓ SUCCESS (8 entities) + Progress: 565/814 (69%), ETA: 1.2h + +[566/814] 2018-03-13.15-50-00.bus → 2018-03-13.15-50-00.bus + ✓ SUCCESS (23 entities) + Progress: 566/814 (69%), ETA: 1.2h + +[567/814] 2018-03-13.15-50-00.school → 2018-03-13.15-50-00.school + ✓ SUCCESS (529 entities) + Progress: 567/814 (69%), ETA: 1.2h + +[568/814] 2018-03-13.15-50-01.admin → 2018-03-13.15-50-01.admin + ✓ SUCCESS (10 entities) + Progress: 568/814 (69%), ETA: 1.2h + +[569/814] 2018-03-13.15-50-01.school → 2018-03-13.15-50-01.school + ✓ SUCCESS (87 entities) + Progress: 569/814 (69%), ETA: 1.2h + +[570/814] 2018-03-13.15-50-02.hospital → 2018-03-13.15-50-02.hospital + ✓ SUCCESS (81 entities) + Progress: 570/814 (70%), ETA: 1.2h + +[571/814] 2018-03-13.15-50-02.school → 2018-03-13.15-50-02.school + ✓ SUCCESS (48 entities) + Progress: 571/814 (70%), ETA: 1.2h + +[572/814] 2018-03-13.15-50-03.school → 2018-03-13.15-50-03.school + ✓ SUCCESS (38 entities) + Progress: 572/814 (70%), ETA: 1.2h + +[573/814] 2018-03-13.15-50-04.bus → 2018-03-13.15-50-04.bus + ✓ SUCCESS (29 entities) + Progress: 573/814 (70%), ETA: 1.2h + +[574/814] 2018-03-13.15-50-05.school → 2018-03-13.15-50-05.school + ✓ SUCCESS (30 entities) + Progress: 574/814 (70%), ETA: 1.2h + +[575/814] 2018-03-13.15-50-08.school → 2018-03-13.15-50-08.school + ✓ SUCCESS (1 entities) + Progress: 575/814 (70%), ETA: 1.2h + +[576/814] 2018-03-13.16-00-00.bus → 2018-03-13.16-00-00.bus + ✓ SUCCESS (34 entities) + Progress: 576/814 (70%), ETA: 1.2h + +[577/814] 2018-03-13.16-00-00.school → 2018-03-13.16-00-00.school + ✓ SUCCESS (39 entities) + Progress: 577/814 (70%), ETA: 1.2h + +[578/814] 2018-03-13.16-00-01.admin → 2018-03-13.16-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 578/814 (71%), ETA: 1.2h + +[579/814] 2018-03-13.16-00-01.school → 2018-03-13.16-00-01.school + ✓ SUCCESS (0 entities) + Progress: 579/814 (71%), ETA: 1.2h + +[580/814] 2018-03-13.16-00-02.hospital → 2018-03-13.16-00-02.hospital + ✓ SUCCESS (308 entities) + Progress: 580/814 (71%), ETA: 1.2h + +[581/814] 2018-03-13.16-00-02.school → 2018-03-13.16-00-02.school + ✓ SUCCESS (46 entities) + Progress: 581/814 (71%), ETA: 1.2h + +[582/814] 2018-03-13.16-00-03.school → 2018-03-13.16-00-03.school + ✓ SUCCESS (101 entities) + Progress: 582/814 (71%), ETA: 1.2h + +[583/814] 2018-03-13.16-00-05.bus → 2018-03-13.16-00-05.bus + ✓ SUCCESS (20 entities) + Progress: 583/814 (71%), ETA: 1.1h + +[584/814] 2018-03-13.16-00-05.school → 2018-03-13.16-00-05.school + ✓ SUCCESS (88 entities) + Progress: 584/814 (71%), ETA: 1.1h + +[585/814] 2018-03-13.16-00-08.school → 2018-03-13.16-00-08.school + ✓ SUCCESS (11 entities) + Progress: 585/814 (71%), ETA: 1.1h + +[586/814] 2018-03-13.16-05-00.bus → 2018-03-13.16-05-00.bus + ✓ SUCCESS (37 entities) + Progress: 586/814 (71%), ETA: 1.1h + +[587/814] 2018-03-13.16-05-00.school → 2018-03-13.16-05-00.school + ✓ SUCCESS (36 entities) + Progress: 587/814 (72%), ETA: 1.1h + +[588/814] 2018-03-13.16-05-01.admin → 2018-03-13.16-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 588/814 (72%), ETA: 1.1h + +[589/814] 2018-03-13.16-05-01.school → 2018-03-13.16-05-01.school + ✓ SUCCESS (4 entities) + Progress: 589/814 (72%), ETA: 1.1h + +[590/814] 2018-03-13.16-05-02.hospital → 2018-03-13.16-05-02.hospital + ✓ SUCCESS (132 entities) + Progress: 590/814 (72%), ETA: 1.1h + +[591/814] 2018-03-13.16-05-02.school → 2018-03-13.16-05-02.school + ✓ SUCCESS (83 entities) + Progress: 591/814 (72%), ETA: 1.1h + +[592/814] 2018-03-13.16-05-03.school → 2018-03-13.16-05-03.school + ✓ SUCCESS (100 entities) + Progress: 592/814 (72%), ETA: 1.1h + +[593/814] 2018-03-13.16-05-05.bus → 2018-03-13.16-05-05.bus + ✓ SUCCESS (9 entities) + Progress: 593/814 (72%), ETA: 1.1h + +[594/814] 2018-03-13.16-05-05.school → 2018-03-13.16-05-05.school + ✓ SUCCESS (62 entities) + Progress: 594/814 (72%), ETA: 1.1h + +[595/814] 2018-03-13.16-05-08.school → 2018-03-13.16-05-08.school + ✓ SUCCESS (0 entities) + Progress: 595/814 (73%), ETA: 1.1h + +[596/814] 2018-03-13.16-10-00.bus → 2018-03-13.16-10-00.bus + ✓ SUCCESS (93 entities) + Progress: 596/814 (73%), ETA: 1.1h + +[597/814] 2018-03-13.16-10-00.school → 2018-03-13.16-10-00.school + ✓ SUCCESS (67 entities) + Progress: 597/814 (73%), ETA: 1.1h + +[598/814] 2018-03-13.16-10-01.admin → 2018-03-13.16-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 598/814 (73%), ETA: 1.1h + +[599/814] 2018-03-13.16-10-01.school → 2018-03-13.16-10-01.school + ✓ SUCCESS (11 entities) + Progress: 599/814 (73%), ETA: 1.1h + +[600/814] 2018-03-13.16-10-02.hospital → 2018-03-13.16-10-02.hospital + ✓ SUCCESS (119 entities) + Progress: 600/814 (73%), ETA: 1.1h + +[601/814] 2018-03-13.16-10-03.school → 2018-03-13.16-10-03.school + ✓ SUCCESS (333 entities) + Progress: 601/814 (73%), ETA: 1.1h + +[602/814] 2018-03-13.16-10-05.bus → 2018-03-13.16-10-05.bus + ✓ SUCCESS (24 entities) + Progress: 602/814 (73%), ETA: 1.0h + +[603/814] 2018-03-13.16-10-05.school → 2018-03-13.16-10-05.school + ✓ SUCCESS (4 entities) + Progress: 603/814 (74%), ETA: 1.0h + +[604/814] 2018-03-13.16-10-08.school → 2018-03-13.16-10-08.school + ✓ SUCCESS (0 entities) + Progress: 604/814 (74%), ETA: 1.0h + +[605/814] 2018-03-13.16-15-00.bus → 2018-03-13.16-15-00.bus + ✓ SUCCESS (59 entities) + Progress: 605/814 (74%), ETA: 1.0h + +[606/814] 2018-03-13.16-15-00.school → 2018-03-13.16-15-00.school + ✓ SUCCESS (16 entities) + Progress: 606/814 (74%), ETA: 1.0h + +[607/814] 2018-03-13.16-15-01.admin → 2018-03-13.16-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 607/814 (74%), ETA: 1.0h + +[608/814] 2018-03-13.16-15-01.school → 2018-03-13.16-15-01.school + ✓ SUCCESS (0 entities) + Progress: 608/814 (74%), ETA: 1.0h + +[609/814] 2018-03-13.16-15-02.hospital → 2018-03-13.16-15-02.hospital + ✓ SUCCESS (194 entities) + Progress: 609/814 (74%), ETA: 1.0h + +[610/814] 2018-03-13.16-15-03.school → 2018-03-13.16-15-03.school + ✓ SUCCESS (242 entities) + Progress: 610/814 (74%), ETA: 1.0h + +[611/814] 2018-03-13.16-15-05.bus → 2018-03-13.16-15-05.bus + ✓ SUCCESS (7 entities) + Progress: 611/814 (75%), ETA: 1.0h + +[612/814] 2018-03-13.16-15-05.school → 2018-03-13.16-15-05.school + ✓ SUCCESS (0 entities) + Progress: 612/814 (75%), ETA: 1.0h + +[613/814] 2018-03-13.16-15-08.school → 2018-03-13.16-15-08.school + ✓ SUCCESS (0 entities) + Progress: 613/814 (75%), ETA: 1.0h + +[614/814] 2018-03-13.16-20-00.bus → 2018-03-13.16-20-00.bus + ✓ SUCCESS (8 entities) + Progress: 614/814 (75%), ETA: 1.0h + +[615/814] 2018-03-13.16-20-00.school → 2018-03-13.16-20-00.school + ✓ SUCCESS (376 entities) + Progress: 615/814 (75%), ETA: 1.0h + +[616/814] 2018-03-13.16-20-01.admin → 2018-03-13.16-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 616/814 (75%), ETA: 1.0h + +[617/814] 2018-03-13.16-20-01.school → 2018-03-13.16-20-01.school + ✓ SUCCESS (86 entities) + Progress: 617/814 (75%), ETA: 1.0h + +[618/814] 2018-03-13.16-20-02.hospital → 2018-03-13.16-20-02.hospital + ✓ SUCCESS (212 entities) + Progress: 618/814 (75%), ETA: 1.0h + +[619/814] 2018-03-13.16-20-03.school → 2018-03-13.16-20-03.school + ✓ SUCCESS (315 entities) + Progress: 619/814 (76%), ETA: 1.0h + +[620/814] 2018-03-13.16-20-05.bus → 2018-03-13.16-20-05.bus + ✓ SUCCESS (17 entities) + Progress: 620/814 (76%), ETA: 1.0h + +[621/814] 2018-03-13.16-20-05.school → 2018-03-13.16-20-05.school + ✓ SUCCESS (43 entities) + Progress: 621/814 (76%), ETA: 0.9h + +[622/814] 2018-03-13.16-20-08.school → 2018-03-13.16-20-08.school + ✓ SUCCESS (0 entities) + Progress: 622/814 (76%), ETA: 0.9h + +[623/814] 2018-03-13.16-25-00.bus → 2018-03-13.16-25-00.bus + ✓ SUCCESS (6 entities) + Progress: 623/814 (76%), ETA: 0.9h + +[624/814] 2018-03-13.16-25-00.school → 2018-03-13.16-25-00.school + ✓ SUCCESS (52 entities) + Progress: 624/814 (76%), ETA: 0.9h + +[625/814] 2018-03-13.16-25-01.admin → 2018-03-13.16-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 625/814 (76%), ETA: 0.9h + +[626/814] 2018-03-13.16-25-01.school → 2018-03-13.16-25-01.school + ✓ SUCCESS (40 entities) + Progress: 626/814 (76%), ETA: 0.9h + +[627/814] 2018-03-13.16-25-02.hospital → 2018-03-13.16-25-02.hospital + ✓ SUCCESS (2 entities) + Progress: 627/814 (77%), ETA: 0.9h + +[628/814] 2018-03-13.16-25-03.school → 2018-03-13.16-25-03.school + ✓ SUCCESS (4 entities) + Progress: 628/814 (77%), ETA: 0.9h + +[629/814] 2018-03-13.16-25-05.bus → 2018-03-13.16-25-05.bus + ✓ SUCCESS (2 entities) + Progress: 629/814 (77%), ETA: 0.9h + +[630/814] 2018-03-13.16-25-05.school → 2018-03-13.16-25-05.school + ✓ SUCCESS (0 entities) + Progress: 630/814 (77%), ETA: 0.9h + +[631/814] 2018-03-13.16-25-08.school → 2018-03-13.16-25-08.school + ✓ SUCCESS (0 entities) + Progress: 631/814 (77%), ETA: 0.9h + +[632/814] 2018-03-13.16-30-00.bus → 2018-03-13.16-30-00.bus + ✓ SUCCESS (2 entities) + Progress: 632/814 (77%), ETA: 0.9h + +[633/814] 2018-03-13.16-30-00.school → 2018-03-13.16-30-00.school + ✓ SUCCESS (500 entities) + Progress: 633/814 (77%), ETA: 0.9h + +[634/814] 2018-03-13.16-30-01.admin → 2018-03-13.16-30-01.admin + ✓ SUCCESS (1 entities) + Progress: 634/814 (77%), ETA: 0.9h + +[635/814] 2018-03-13.16-30-01.school → 2018-03-13.16-30-01.school + ✓ SUCCESS (143 entities) + Progress: 635/814 (78%), ETA: 0.9h + +[636/814] 2018-03-13.16-30-02.hospital → 2018-03-13.16-30-02.hospital + ✓ SUCCESS (7 entities) + Progress: 636/814 (78%), ETA: 0.9h + +[637/814] 2018-03-13.16-30-03.hospital → 2018-03-13.16-30-03.hospital + ✓ SUCCESS (0 entities) + Progress: 637/814 (78%), ETA: 0.9h + +[638/814] 2018-03-13.16-30-03.school → 2018-03-13.16-30-03.school + ✓ SUCCESS (59 entities) + Progress: 638/814 (78%), ETA: 0.9h + +[639/814] 2018-03-13.16-30-04.bus → 2018-03-13.16-30-04.bus + ✓ SUCCESS (2 entities) + Progress: 639/814 (78%), ETA: 0.8h + +[640/814] 2018-03-13.16-30-05.school → 2018-03-13.16-30-05.school + ✓ SUCCESS (17 entities) + Progress: 640/814 (78%), ETA: 0.8h + +[641/814] 2018-03-13.16-30-08.school → 2018-03-13.16-30-08.school + ✓ SUCCESS (26 entities) + Progress: 641/814 (78%), ETA: 0.8h + +[642/814] 2018-03-13.17-05-00.bus → 2018-03-13.17-05-00.bus + ✓ SUCCESS (22 entities) + Progress: 642/814 (78%), ETA: 0.8h + +[643/814] 2018-03-13.17-05-00.school → 2018-03-13.17-05-00.school + ✓ SUCCESS (468 entities) + Progress: 643/814 (78%), ETA: 0.8h + +[644/814] 2018-03-13.17-05-01.admin → 2018-03-13.17-05-01.admin + ✓ SUCCESS (0 entities) + Progress: 644/814 (79%), ETA: 0.8h + +[645/814] 2018-03-13.17-05-01.school → 2018-03-13.17-05-01.school + ✓ SUCCESS (50 entities) + Progress: 645/814 (79%), ETA: 0.8h + +[646/814] 2018-03-13.17-05-02.hospital → 2018-03-13.17-05-02.hospital + ✓ SUCCESS (0 entities) + Progress: 646/814 (79%), ETA: 0.8h + +[647/814] 2018-03-13.17-05-03.hospital → 2018-03-13.17-05-03.hospital + ✓ SUCCESS (57 entities) + Progress: 647/814 (79%), ETA: 0.8h + +[648/814] 2018-03-13.17-05-03.school → 2018-03-13.17-05-03.school + ✓ SUCCESS (132 entities) + Progress: 648/814 (79%), ETA: 0.8h + +[649/814] 2018-03-13.17-05-05.bus → 2018-03-13.17-05-05.bus + ✓ SUCCESS (14 entities) + Progress: 649/814 (79%), ETA: 0.8h + +[650/814] 2018-03-13.17-05-05.school → 2018-03-13.17-05-05.school + ✓ SUCCESS (52 entities) + Progress: 650/814 (79%), ETA: 0.8h + +[651/814] 2018-03-13.17-05-08.school → 2018-03-13.17-05-08.school + ✓ SUCCESS (0 entities) + Progress: 651/814 (79%), ETA: 0.8h + +[652/814] 2018-03-13.17-10-00.bus → 2018-03-13.17-10-00.bus + ✓ SUCCESS (2 entities) + Progress: 652/814 (80%), ETA: 0.8h + +[653/814] 2018-03-13.17-10-00.school → 2018-03-13.17-10-00.school + ✓ SUCCESS (417 entities) + Progress: 653/814 (80%), ETA: 0.8h + +[654/814] 2018-03-13.17-10-01.admin → 2018-03-13.17-10-01.admin + ✓ SUCCESS (0 entities) + Progress: 654/814 (80%), ETA: 0.8h + +[655/814] 2018-03-13.17-10-01.school → 2018-03-13.17-10-01.school + ✓ SUCCESS (0 entities) + Progress: 655/814 (80%), ETA: 0.8h + +[656/814] 2018-03-13.17-10-02.hospital → 2018-03-13.17-10-02.hospital + ✓ SUCCESS (0 entities) + Progress: 656/814 (80%), ETA: 0.8h + +[657/814] 2018-03-13.17-10-03.hospital → 2018-03-13.17-10-03.hospital + ✓ SUCCESS (188 entities) + Progress: 657/814 (80%), ETA: 0.8h + +[658/814] 2018-03-13.17-10-03.school → 2018-03-13.17-10-03.school + ✓ SUCCESS (250 entities) + Progress: 658/814 (80%), ETA: 0.8h + +[659/814] 2018-03-13.17-10-05.bus → 2018-03-13.17-10-05.bus + ✓ SUCCESS (18 entities) + Progress: 659/814 (80%), ETA: 0.7h + +[660/814] 2018-03-13.17-10-05.school → 2018-03-13.17-10-05.school + ✓ SUCCESS (2 entities) + Progress: 660/814 (81%), ETA: 0.7h + +[661/814] 2018-03-13.17-10-08.school → 2018-03-13.17-10-08.school + ✓ SUCCESS (0 entities) + Progress: 661/814 (81%), ETA: 0.7h + +[662/814] 2018-03-13.17-15-00.bus → 2018-03-13.17-15-00.bus + ✓ SUCCESS (15 entities) + Progress: 662/814 (81%), ETA: 0.7h + +[663/814] 2018-03-13.17-15-00.school → 2018-03-13.17-15-00.school + ✓ SUCCESS (63 entities) + Progress: 663/814 (81%), ETA: 0.7h + +[664/814] 2018-03-13.17-15-01.admin → 2018-03-13.17-15-01.admin + ✓ SUCCESS (4 entities) + Progress: 664/814 (81%), ETA: 0.7h + +[665/814] 2018-03-13.17-15-01.school → 2018-03-13.17-15-01.school + ✓ SUCCESS (0 entities) + Progress: 665/814 (81%), ETA: 0.7h + +[666/814] 2018-03-13.17-15-02.hospital → 2018-03-13.17-15-02.hospital + ✓ SUCCESS (0 entities) + Progress: 666/814 (81%), ETA: 0.7h + +[667/814] 2018-03-13.17-15-03.hospital → 2018-03-13.17-15-03.hospital + ✓ SUCCESS (88 entities) + Progress: 667/814 (81%), ETA: 0.7h + +[668/814] 2018-03-13.17-15-03.school → 2018-03-13.17-15-03.school + ✓ SUCCESS (163 entities) + Progress: 668/814 (82%), ETA: 0.7h + +[669/814] 2018-03-13.17-15-05.bus → 2018-03-13.17-15-05.bus + ✓ SUCCESS (21 entities) + Progress: 669/814 (82%), ETA: 0.7h + +[670/814] 2018-03-13.17-15-05.school → 2018-03-13.17-15-05.school + ✓ SUCCESS (9 entities) + Progress: 670/814 (82%), ETA: 0.7h + +[671/814] 2018-03-13.17-15-08.school → 2018-03-13.17-15-08.school + ✓ SUCCESS (19 entities) + Progress: 671/814 (82%), ETA: 0.7h + +[672/814] 2018-03-13.17-20-00.bus → 2018-03-13.17-20-00.bus + ✓ SUCCESS (20 entities) + Progress: 672/814 (82%), ETA: 0.7h + +[673/814] 2018-03-13.17-20-00.school → 2018-03-13.17-20-00.school + ✓ SUCCESS (83 entities) + Progress: 673/814 (82%), ETA: 0.7h + +[674/814] 2018-03-13.17-20-01.admin → 2018-03-13.17-20-01.admin + ✓ SUCCESS (0 entities) + Progress: 674/814 (82%), ETA: 0.7h + +[675/814] 2018-03-13.17-20-01.school → 2018-03-13.17-20-01.school + ✓ SUCCESS (4 entities) + Progress: 675/814 (82%), ETA: 0.7h + +[676/814] 2018-03-13.17-20-02.hospital → 2018-03-13.17-20-02.hospital + ✓ SUCCESS (0 entities) + Progress: 676/814 (83%), ETA: 0.7h + +[677/814] 2018-03-13.17-20-03.hospital → 2018-03-13.17-20-03.hospital + ✓ SUCCESS (204 entities) + Progress: 677/814 (83%), ETA: 0.7h + +[678/814] 2018-03-13.17-20-03.school → 2018-03-13.17-20-03.school + ✓ SUCCESS (230 entities) + Progress: 678/814 (83%), ETA: 0.7h + +[679/814] 2018-03-13.17-20-05.bus → 2018-03-13.17-20-05.bus + ✓ SUCCESS (6 entities) + Progress: 679/814 (83%), ETA: 0.6h + +[680/814] 2018-03-13.17-20-05.school → 2018-03-13.17-20-05.school + ✓ SUCCESS (18 entities) + Progress: 680/814 (83%), ETA: 0.6h + +[681/814] 2018-03-13.17-20-08.school → 2018-03-13.17-20-08.school + ✓ SUCCESS (0 entities) + Progress: 681/814 (83%), ETA: 0.6h + +[682/814] 2018-03-13.17-25-00.bus → 2018-03-13.17-25-00.bus + ✓ SUCCESS (88 entities) + Progress: 682/814 (83%), ETA: 0.6h + +[683/814] 2018-03-13.17-25-00.school → 2018-03-13.17-25-00.school + ✓ SUCCESS (103 entities) + Progress: 683/814 (83%), ETA: 0.6h + +[684/814] 2018-03-13.17-25-01.admin → 2018-03-13.17-25-01.admin + ✓ SUCCESS (0 entities) + Progress: 684/814 (84%), ETA: 0.6h + +[685/814] 2018-03-13.17-25-01.school → 2018-03-13.17-25-01.school + ✓ SUCCESS (4 entities) + Progress: 685/814 (84%), ETA: 0.6h + +[686/814] 2018-03-13.17-25-02.hospital → 2018-03-13.17-25-02.hospital + ✓ SUCCESS (4 entities) + Progress: 686/814 (84%), ETA: 0.6h + +[687/814] 2018-03-13.17-25-03.hospital → 2018-03-13.17-25-03.hospital + ✓ SUCCESS (160 entities) + Progress: 687/814 (84%), ETA: 0.6h + +[688/814] 2018-03-13.17-25-03.school → 2018-03-13.17-25-03.school + ✓ SUCCESS (252 entities) + Progress: 688/814 (84%), ETA: 0.6h + +[689/814] 2018-03-13.17-25-05.bus → 2018-03-13.17-25-05.bus + ✓ SUCCESS (10 entities) + Progress: 689/814 (84%), ETA: 0.6h + +[690/814] 2018-03-13.17-25-05.school → 2018-03-13.17-25-05.school + ✓ SUCCESS (14 entities) + Progress: 690/814 (84%), ETA: 0.6h + +[691/814] 2018-03-13.17-25-08.school → 2018-03-13.17-25-08.school + ✓ SUCCESS (5 entities) + Progress: 691/814 (84%), ETA: 0.6h + +[692/814] 2018-03-13.17-30-00.bus → 2018-03-13.17-30-00.bus + ✓ SUCCESS (34 entities) + Progress: 692/814 (85%), ETA: 0.6h + +[693/814] 2018-03-13.17-30-00.school → 2018-03-13.17-30-00.school + ✓ SUCCESS (54 entities) + Progress: 693/814 (85%), ETA: 0.6h + +[694/814] 2018-03-13.17-30-01.admin → 2018-03-13.17-30-01.admin + ✓ SUCCESS (0 entities) + Progress: 694/814 (85%), ETA: 0.6h + +[695/814] 2018-03-13.17-30-01.bus → 2018-03-13.17-30-01.bus + ✓ SUCCESS (38 entities) + Progress: 695/814 (85%), ETA: 0.6h + +[696/814] 2018-03-13.17-30-01.school → 2018-03-13.17-30-01.school + ✓ SUCCESS (0 entities) + Progress: 696/814 (85%), ETA: 0.6h + +[697/814] 2018-03-13.17-30-02.hospital → 2018-03-13.17-30-02.hospital + ✓ SUCCESS (30 entities) + Progress: 697/814 (85%), ETA: 0.6h + +[698/814] 2018-03-13.17-30-03.hospital → 2018-03-13.17-30-03.hospital + ✓ SUCCESS (109 entities) + Progress: 698/814 (85%), ETA: 0.6h + +[699/814] 2018-03-13.17-30-03.school → 2018-03-13.17-30-03.school + ✓ SUCCESS (151 entities) + Progress: 699/814 (85%), ETA: 0.5h + +[700/814] 2018-03-13.17-30-05.bus → 2018-03-13.17-30-05.bus + ✓ SUCCESS (21 entities) + Progress: 700/814 (85%), ETA: 0.5h + +[701/814] 2018-03-13.17-30-05.school → 2018-03-13.17-30-05.school + ✓ SUCCESS (2 entities) + Progress: 701/814 (86%), ETA: 0.5h + +[702/814] 2018-03-13.17-30-08.school → 2018-03-13.17-30-08.school + ✓ SUCCESS (0 entities) + Progress: 702/814 (86%), ETA: 0.5h + +[703/814] 2018-03-13.17-35-00.bus → 2018-03-13.17-35-00.bus + ✓ SUCCESS (3 entities) + Progress: 703/814 (86%), ETA: 0.5h + +[704/814] 2018-03-13.17-35-00.school → 2018-03-13.17-35-00.school + ✓ SUCCESS (414 entities) + Progress: 704/814 (86%), ETA: 0.5h + +[705/814] 2018-03-13.17-35-01.admin → 2018-03-13.17-35-01.admin + ✓ SUCCESS (0 entities) + Progress: 705/814 (86%), ETA: 0.5h + +[706/814] 2018-03-13.17-35-01.bus → 2018-03-13.17-35-01.bus + ✓ SUCCESS (13 entities) + Progress: 706/814 (86%), ETA: 0.5h + +[707/814] 2018-03-13.17-35-01.school → 2018-03-13.17-35-01.school + ✓ SUCCESS (34 entities) + Progress: 707/814 (86%), ETA: 0.5h + +[708/814] 2018-03-13.17-35-02.hospital → 2018-03-13.17-35-02.hospital + ✓ SUCCESS (0 entities) + Progress: 708/814 (86%), ETA: 0.5h + +[709/814] 2018-03-13.17-35-03.hospital → 2018-03-13.17-35-03.hospital + ✓ SUCCESS (84 entities) + Progress: 709/814 (87%), ETA: 0.5h + +[710/814] 2018-03-13.17-35-03.school → 2018-03-13.17-35-03.school + ✓ SUCCESS (191 entities) + Progress: 710/814 (87%), ETA: 0.5h + +[711/814] 2018-03-13.17-35-05.bus → 2018-03-13.17-35-05.bus + ✓ SUCCESS (15 entities) + Progress: 711/814 (87%), ETA: 0.5h + +[712/814] 2018-03-13.17-35-05.school → 2018-03-13.17-35-05.school + ✓ SUCCESS (39 entities) + Progress: 712/814 (87%), ETA: 0.5h + +[713/814] 2018-03-13.17-35-08.school → 2018-03-13.17-35-08.school + ✓ SUCCESS (4 entities) + Progress: 713/814 (87%), ETA: 0.5h + +[714/814] 2018-03-13.17-40-00.bus → 2018-03-13.17-40-00.bus + ✓ SUCCESS (0 entities) + Progress: 714/814 (87%), ETA: 0.5h + +[715/814] 2018-03-13.17-40-00.school → 2018-03-13.17-40-00.school + ✓ SUCCESS (72 entities) + Progress: 715/814 (87%), ETA: 0.5h + +[716/814] 2018-03-13.17-40-01.admin → 2018-03-13.17-40-01.admin + ✓ SUCCESS (0 entities) + Progress: 716/814 (87%), ETA: 0.5h + +[717/814] 2018-03-13.17-40-01.bus → 2018-03-13.17-40-01.bus + ✓ SUCCESS (1 entities) + Progress: 717/814 (88%), ETA: 0.5h + +[718/814] 2018-03-13.17-40-01.school → 2018-03-13.17-40-01.school + ✓ SUCCESS (18 entities) + Progress: 718/814 (88%), ETA: 0.5h + +[719/814] 2018-03-13.17-40-02.hospital → 2018-03-13.17-40-02.hospital + ✓ SUCCESS (0 entities) + Progress: 719/814 (88%), ETA: 0.4h + +[720/814] 2018-03-13.17-40-03.hospital → 2018-03-13.17-40-03.hospital + ✓ SUCCESS (14 entities) + Progress: 720/814 (88%), ETA: 0.4h + +[721/814] 2018-03-13.17-40-03.school → 2018-03-13.17-40-03.school + ✓ SUCCESS (7 entities) + Progress: 721/814 (88%), ETA: 0.4h + +[722/814] 2018-03-13.17-40-05.bus → 2018-03-13.17-40-05.bus + ✓ SUCCESS (6 entities) + Progress: 722/814 (88%), ETA: 0.4h + +[723/814] 2018-03-13.17-40-05.school → 2018-03-13.17-40-05.school + ✓ SUCCESS (2 entities) + Progress: 723/814 (88%), ETA: 0.4h + +[724/814] 2018-03-13.17-40-08.school → 2018-03-13.17-40-08.school + ✓ SUCCESS (3 entities) + Progress: 724/814 (88%), ETA: 0.4h + +[725/814] 2018-03-15.14-50-00.bus → 2018-03-15.14-50-00.bus + ✓ SUCCESS (149 entities) + Progress: 725/814 (89%), ETA: 0.4h + +[726/814] 2018-03-15.14-50-00.school → 2018-03-15.14-50-00.school + ✓ SUCCESS (696 entities) + Progress: 726/814 (89%), ETA: 0.4h + +[727/814] 2018-03-15.14-50-01.admin → 2018-03-15.14-50-01.admin + ✓ SUCCESS (18 entities) + Progress: 727/814 (89%), ETA: 0.4h + +[728/814] 2018-03-15.14-50-01.bus → 2018-03-15.14-50-01.bus + ✓ SUCCESS (95 entities) + Progress: 728/814 (89%), ETA: 0.4h + +[729/814] 2018-03-15.14-50-01.school → 2018-03-15.14-50-01.school + ✓ SUCCESS (65 entities) + Progress: 729/814 (89%), ETA: 0.4h + +[730/814] 2018-03-15.14-50-03.school → 2018-03-15.14-50-03.school + ✓ SUCCESS (10 entities) + Progress: 730/814 (89%), ETA: 0.4h + +[731/814] 2018-03-15.14-50-04.bus → 2018-03-15.14-50-04.bus + ✓ SUCCESS (64 entities) + Progress: 731/814 (89%), ETA: 0.4h + +[732/814] 2018-03-15.14-50-06.hospital → 2018-03-15.14-50-06.hospital + ✓ SUCCESS (104 entities) + Progress: 732/814 (89%), ETA: 0.4h + +[733/814] 2018-03-15.14-50-07.hospital → 2018-03-15.14-50-07.hospital + ✓ SUCCESS (161 entities) + Progress: 733/814 (90%), ETA: 0.4h + +[734/814] 2018-03-15.15-00-00.bus → 2018-03-15.15-00-00.bus + ✓ SUCCESS (176 entities) + Progress: 734/814 (90%), ETA: 0.4h + +[735/814] 2018-03-15.15-00-00.school → 2018-03-15.15-00-00.school + ✓ SUCCESS (667 entities) + Progress: 735/814 (90%), ETA: 0.4h + +[736/814] 2018-03-15.15-00-01.admin → 2018-03-15.15-00-01.admin + ✓ SUCCESS (0 entities) + Progress: 736/814 (90%), ETA: 0.4h + +[737/814] 2018-03-15.15-00-01.bus → 2018-03-15.15-00-01.bus + ✓ SUCCESS (83 entities) + Progress: 737/814 (90%), ETA: 0.4h + +[738/814] 2018-03-15.15-00-01.school → 2018-03-15.15-00-01.school + ✓ SUCCESS (79 entities) + Progress: 738/814 (90%), ETA: 0.4h + +[739/814] 2018-03-15.15-00-03.school → 2018-03-15.15-00-03.school + ✓ SUCCESS (15 entities) + Progress: 739/814 (90%), ETA: 0.4h + +[740/814] 2018-03-15.15-00-04.bus → 2018-03-15.15-00-04.bus + ✓ SUCCESS (42 entities) + Progress: 740/814 (90%), ETA: 0.4h + +[741/814] 2018-03-15.15-00-06.hospital → 2018-03-15.15-00-06.hospital + ✓ SUCCESS (89 entities) + Progress: 741/814 (91%), ETA: 0.3h + +[742/814] 2018-03-15.15-00-07.hospital → 2018-03-15.15-00-07.hospital + ✓ SUCCESS (162 entities) + Progress: 742/814 (91%), ETA: 0.3h + +[743/814] 2018-03-15.15-05-00.bus → 2018-03-15.15-05-00.bus + ✓ SUCCESS (154 entities) + Progress: 743/814 (91%), ETA: 0.3h + +[744/814] 2018-03-15.15-05-00.school → 2018-03-15.15-05-00.school + ✓ SUCCESS (349 entities) + Progress: 744/814 (91%), ETA: 0.3h + +[745/814] 2018-03-15.15-05-01.admin → 2018-03-15.15-05-01.admin + ✓ SUCCESS (26 entities) + Progress: 745/814 (91%), ETA: 0.3h + +[746/814] 2018-03-15.15-05-01.bus → 2018-03-15.15-05-01.bus + ✓ SUCCESS (58 entities) + Progress: 746/814 (91%), ETA: 0.3h + +[747/814] 2018-03-15.15-05-01.school → 2018-03-15.15-05-01.school + ✓ SUCCESS (76 entities) + Progress: 747/814 (91%), ETA: 0.3h + +[748/814] 2018-03-15.15-05-03.school → 2018-03-15.15-05-03.school + ✓ SUCCESS (4 entities) + Progress: 748/814 (91%), ETA: 0.3h + +[749/814] 2018-03-15.15-05-04.bus → 2018-03-15.15-05-04.bus + ✓ SUCCESS (51 entities) + Progress: 749/814 (92%), ETA: 0.3h + +[750/814] 2018-03-15.15-05-06.hospital → 2018-03-15.15-05-06.hospital + ✓ SUCCESS (52 entities) + Progress: 750/814 (92%), ETA: 0.3h + +[751/814] 2018-03-15.15-05-07.hospital → 2018-03-15.15-05-07.hospital + ✓ SUCCESS (0 entities) + Progress: 751/814 (92%), ETA: 0.3h + +[752/814] 2018-03-15.15-10-00.bus → 2018-03-15.15-10-00.bus + ✓ SUCCESS (160 entities) + Progress: 752/814 (92%), ETA: 0.3h + +[753/814] 2018-03-15.15-10-00.school → 2018-03-15.15-10-00.school + ✓ SUCCESS (570 entities) + Progress: 753/814 (92%), ETA: 0.3h + +[754/814] 2018-03-15.15-10-01.admin → 2018-03-15.15-10-01.admin + ✓ SUCCESS (26 entities) + Progress: 754/814 (92%), ETA: 0.3h + +[755/814] 2018-03-15.15-10-01.bus → 2018-03-15.15-10-01.bus + ✓ SUCCESS (68 entities) + Progress: 755/814 (92%), ETA: 0.3h + +[756/814] 2018-03-15.15-10-01.school → 2018-03-15.15-10-01.school + ✓ SUCCESS (62 entities) + Progress: 756/814 (92%), ETA: 0.3h + +[757/814] 2018-03-15.15-10-03.school → 2018-03-15.15-10-03.school + ✓ SUCCESS (8 entities) + Progress: 757/814 (92%), ETA: 0.3h + +[758/814] 2018-03-15.15-10-06.hospital → 2018-03-15.15-10-06.hospital + ✓ SUCCESS (126 entities) + Progress: 758/814 (93%), ETA: 0.3h + +[759/814] 2018-03-15.15-10-07.hospital → 2018-03-15.15-10-07.hospital + ✓ SUCCESS (4 entities) + Progress: 759/814 (93%), ETA: 0.3h + +[760/814] 2018-03-15.15-15-00.bus → 2018-03-15.15-15-00.bus + ✓ SUCCESS (161 entities) + Progress: 760/814 (93%), ETA: 0.3h + +[761/814] 2018-03-15.15-15-00.school → 2018-03-15.15-15-00.school + ✓ SUCCESS (561 entities) + Progress: 761/814 (93%), ETA: 0.3h + +[762/814] 2018-03-15.15-15-01.admin → 2018-03-15.15-15-01.admin + ✓ SUCCESS (0 entities) + Progress: 762/814 (93%), ETA: 0.2h + +[763/814] 2018-03-15.15-15-01.bus → 2018-03-15.15-15-01.bus + ✓ SUCCESS (81 entities) + Progress: 763/814 (93%), ETA: 0.2h + +[764/814] 2018-03-15.15-15-01.school → 2018-03-15.15-15-01.school + ✓ SUCCESS (20 entities) + Progress: 764/814 (93%), ETA: 0.2h + +[765/814] 2018-03-15.15-15-03.school → 2018-03-15.15-15-03.school + ✓ SUCCESS (10 entities) + Progress: 765/814 (93%), ETA: 0.2h + +[766/814] 2018-03-15.15-15-04.bus → 2018-03-15.15-15-04.bus + ✓ SUCCESS (27 entities) + Progress: 766/814 (94%), ETA: 0.2h + +[767/814] 2018-03-15.15-15-06.hospital → 2018-03-15.15-15-06.hospital + ✓ SUCCESS (55 entities) + Progress: 767/814 (94%), ETA: 0.2h + +[768/814] 2018-03-15.15-15-07.hospital → 2018-03-15.15-15-07.hospital + ✓ SUCCESS (171 entities) + Progress: 768/814 (94%), ETA: 0.2h + +[769/814] 2018-03-15.15-30-00.bus → 2018-03-15.15-30-00.bus + ✓ SUCCESS (101 entities) + Progress: 769/814 (94%), ETA: 0.2h + +[770/814] 2018-03-15.15-30-00.school → 2018-03-15.15-30-00.school + ✓ SUCCESS (502 entities) + Progress: 770/814 (94%), ETA: 0.2h + +[771/814] 2018-03-15.15-30-01.admin → 2018-03-15.15-30-01.admin + ✓ SUCCESS (13 entities) + Progress: 771/814 (94%), ETA: 0.2h + +[772/814] 2018-03-15.15-30-01.bus → 2018-03-15.15-30-01.bus + ✓ SUCCESS (114 entities) + Progress: 772/814 (94%), ETA: 0.2h + +[773/814] 2018-03-15.15-30-01.school → 2018-03-15.15-30-01.school + ✓ SUCCESS (70 entities) + Progress: 773/814 (94%), ETA: 0.2h + +[774/814] 2018-03-15.15-30-03.school → 2018-03-15.15-30-03.school + ✓ SUCCESS (20 entities) + Progress: 774/814 (95%), ETA: 0.2h + +[775/814] 2018-03-15.15-30-04.bus → 2018-03-15.15-30-04.bus + ✓ SUCCESS (39 entities) + Progress: 775/814 (95%), ETA: 0.2h + +[776/814] 2018-03-15.15-30-06.hospital → 2018-03-15.15-30-06.hospital + ✓ SUCCESS (121 entities) + Progress: 776/814 (95%), ETA: 0.2h + +[777/814] 2018-03-15.15-30-07.hospital → 2018-03-15.15-30-07.hospital + ✓ SUCCESS (148 entities) + Progress: 777/814 (95%), ETA: 0.2h + +[778/814] 2018-03-15.15-35-00.bus → 2018-03-15.15-35-00.bus + ✓ SUCCESS (105 entities) + Progress: 778/814 (95%), ETA: 0.2h + +[779/814] 2018-03-15.15-35-00.school → 2018-03-15.15-35-00.school + ✓ SUCCESS (721 entities) + Progress: 779/814 (95%), ETA: 0.2h + +[780/814] 2018-03-15.15-35-01.admin → 2018-03-15.15-35-01.admin + ✓ SUCCESS (9 entities) + Progress: 780/814 (95%), ETA: 0.2h + +[781/814] 2018-03-15.15-35-01.bus → 2018-03-15.15-35-01.bus + ✓ SUCCESS (51 entities) + Progress: 781/814 (95%), ETA: 0.2h + +[782/814] 2018-03-15.15-35-01.school → 2018-03-15.15-35-01.school + ✓ SUCCESS (60 entities) + Progress: 782/814 (96%), ETA: 0.2h + +[783/814] 2018-03-15.15-35-03.school → 2018-03-15.15-35-03.school + ✓ SUCCESS (51 entities) + Progress: 783/814 (96%), ETA: 0.1h + +[784/814] 2018-03-15.15-35-04.bus → 2018-03-15.15-35-04.bus + ✓ SUCCESS (15 entities) + Progress: 784/814 (96%), ETA: 0.1h + +[785/814] 2018-03-15.15-35-06.hospital → 2018-03-15.15-35-06.hospital + ✓ SUCCESS (66 entities) + Progress: 785/814 (96%), ETA: 0.1h + +[786/814] 2018-03-15.15-35-07.hospital → 2018-03-15.15-35-07.hospital + ✓ SUCCESS (186 entities) + Progress: 786/814 (96%), ETA: 0.1h + +[787/814] 2018-03-15.15-40-00.bus → 2018-03-15.15-40-00.bus + ✓ SUCCESS (132 entities) + Progress: 787/814 (96%), ETA: 0.1h + +[788/814] 2018-03-15.15-40-00.school → 2018-03-15.15-40-00.school + ✓ SUCCESS (745 entities) + Progress: 788/814 (96%), ETA: 0.1h + +[789/814] 2018-03-15.15-40-01.admin → 2018-03-15.15-40-01.admin + ✓ SUCCESS (14 entities) + Progress: 789/814 (96%), ETA: 0.1h + +[790/814] 2018-03-15.15-40-01.bus → 2018-03-15.15-40-01.bus + ✓ SUCCESS (66 entities) + Progress: 790/814 (97%), ETA: 0.1h + +[791/814] 2018-03-15.15-40-01.school → 2018-03-15.15-40-01.school + ✓ SUCCESS (149 entities) + Progress: 791/814 (97%), ETA: 0.1h + +[792/814] 2018-03-15.15-40-03.school → 2018-03-15.15-40-03.school + ✓ SUCCESS (18 entities) + Progress: 792/814 (97%), ETA: 0.1h + +[793/814] 2018-03-15.15-40-04.bus → 2018-03-15.15-40-04.bus + ✓ SUCCESS (45 entities) + Progress: 793/814 (97%), ETA: 0.1h + +[794/814] 2018-03-15.15-40-06.hospital → 2018-03-15.15-40-06.hospital + ✓ SUCCESS (58 entities) + Progress: 794/814 (97%), ETA: 0.1h + +[795/814] 2018-03-15.15-40-07.hospital → 2018-03-15.15-40-07.hospital + ✓ SUCCESS (194 entities) + Progress: 795/814 (97%), ETA: 0.1h + +[796/814] 2018-03-15.15-45-00.bus → 2018-03-15.15-45-00.bus + ✓ SUCCESS (200 entities) + Progress: 796/814 (97%), ETA: 0.1h + +[797/814] 2018-03-15.15-45-00.school → 2018-03-15.15-45-00.school + ✓ SUCCESS (788 entities) + Progress: 797/814 (97%), ETA: 0.1h + +[798/814] 2018-03-15.15-45-01.admin → 2018-03-15.15-45-01.admin + ✓ SUCCESS (6 entities) + Progress: 798/814 (98%), ETA: 0.1h + +[799/814] 2018-03-15.15-45-01.bus → 2018-03-15.15-45-01.bus + ✓ SUCCESS (154 entities) + Progress: 799/814 (98%), ETA: 0.1h + +[800/814] 2018-03-15.15-45-01.school → 2018-03-15.15-45-01.school + ✓ SUCCESS (64 entities) + Progress: 800/814 (98%), ETA: 0.1h + +[801/814] 2018-03-15.15-45-02.school → 2018-03-15.15-45-02.school + ✓ SUCCESS (14 entities) + Progress: 801/814 (98%), ETA: 0.1h + +[802/814] 2018-03-15.15-45-03.school → 2018-03-15.15-45-03.school + ✓ SUCCESS (4 entities) + Progress: 802/814 (98%), ETA: 0.1h + +[803/814] 2018-03-15.15-45-04.bus → 2018-03-15.15-45-04.bus + ✓ SUCCESS (67 entities) + Progress: 803/814 (98%), ETA: 0.1h + +[804/814] 2018-03-15.15-45-06.hospital → 2018-03-15.15-45-06.hospital + ✓ SUCCESS (78 entities) + Progress: 804/814 (98%), ETA: 0.0h + +[805/814] 2018-03-15.15-45-07.hospital → 2018-03-15.15-45-07.hospital + ✓ SUCCESS (275 entities) + Progress: 805/814 (98%), ETA: 0.0h + +[806/814] 2018-03-15.15-50-00.bus → 2018-03-15.15-50-00.bus + ✓ SUCCESS (89 entities) + Progress: 806/814 (99%), ETA: 0.0h + +[807/814] 2018-03-15.15-50-00.school → 2018-03-15.15-50-00.school + ✓ SUCCESS (554 entities) + Progress: 807/814 (99%), ETA: 0.0h + +[808/814] 2018-03-15.15-50-01.admin → 2018-03-15.15-50-01.admin + ✓ SUCCESS (30 entities) + Progress: 808/814 (99%), ETA: 0.0h + +[809/814] 2018-03-15.15-50-01.bus → 2018-03-15.15-50-01.bus + ✓ SUCCESS (58 entities) + Progress: 809/814 (99%), ETA: 0.0h + +[810/814] 2018-03-15.15-50-01.school → 2018-03-15.15-50-01.school + ✓ SUCCESS (58 entities) + Progress: 810/814 (99%), ETA: 0.0h + +[811/814] 2018-03-15.15-50-03.school → 2018-03-15.15-50-03.school + ✓ SUCCESS (13 entities) + Progress: 811/814 (99%), ETA: 0.0h + +[812/814] 2018-03-15.15-50-04.bus → 2018-03-15.15-50-04.bus + ✓ SUCCESS (36 entities) + Progress: 812/814 (99%), ETA: 0.0h + +[813/814] 2018-03-15.15-50-06.hospital → 2018-03-15.15-50-06.hospital + ✓ SUCCESS (74 entities) + Progress: 813/814 (99%), ETA: 0.0h + +[814/814] 2018-03-15.15-50-07.hospital → 2018-03-15.15-50-07.hospital + ✓ SUCCESS (140 entities) + Progress: 814/814 (100%), ETA: 0.0h + + +============================================================ +BATCH EXTRACTION COMPLETE +============================================================ +Total time: 4.0 hours +Completed: 814 +Skipped (already exist): 0 +Failed: 0 +Total entities: 89739 +Avg entities/slot: 110.2 + +Output directory: /home/ah66742/data/entity_descriptions +Log file: /home/ah66742/output/extraction_logs/batch_extraction_20260221_202742.log +============================================================ + diff --git a/meva/examples/run_records/output_records/extraction_records/batch_progress.json b/meva/examples/run_records/output_records/extraction_records/batch_progress.json new file mode 100644 index 0000000..166e3e7 --- /dev/null +++ b/meva/examples/run_records/output_records/extraction_records/batch_progress.json @@ -0,0 +1,824 @@ +{ + "started_at": "2026-02-21T20:27:42.926700", + "last_updated": "2026-02-22T00:27:15.370481", + "completed_slots": [ + "2018-03-05.13-10-00.admin", + "2018-03-05.13-10-00.bus", + "2018-03-05.13-10-00.hospital", + "2018-03-05.13-10-00.school", + "2018-03-05.13-10-01.bus", + "2018-03-05.13-10-01.school", + "2018-03-05.13-15-00.admin", + "2018-03-05.13-15-00.bus", + "2018-03-05.13-15-00.hospital", + "2018-03-05.13-15-00.school", + "2018-03-05.13-15-01.bus", + "2018-03-05.13-15-01.school", + "2018-03-05.13-20-00.admin", + "2018-03-05.13-20-00.bus", + "2018-03-05.13-20-00.hospital", + "2018-03-05.13-20-00.school", + "2018-03-05.13-20-01.bus", + "2018-03-05.13-20-01.school", + "2018-03-05.14-00-00.admin", + "2018-03-05.14-00-00.bus", + "2018-03-05.14-00-00.hospital", + "2018-03-05.14-00-00.school", + "2018-03-05.14-00-01.bus", + "2018-03-05.14-00-01.school", + "2018-03-05.14-05-00.admin", + "2018-03-05.14-05-00.bus", + "2018-03-05.14-05-00.hospital", + "2018-03-05.14-05-00.school", + "2018-03-05.14-05-01.bus", + "2018-03-05.14-05-01.school", + "2018-03-05.14-10-00.admin", + "2018-03-05.14-10-00.bus", + "2018-03-05.14-10-00.hospital", + "2018-03-05.14-10-00.school", + "2018-03-05.14-10-01.bus", + "2018-03-05.14-10-01.school", + "2018-03-07.10-55-00.admin", + "2018-03-07.11-00-00.admin", + "2018-03-07.11-00-00.bus", + "2018-03-07.11-00-00.hospital", + "2018-03-07.11-00-00.school", + "2018-03-07.11-00-01.admin", + 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b/meva/examples/run_records/output_records/mevid_records/extract_test.log.txt @@ -0,0 +1,180 @@ +Opening tarball: /home/ah66742/MEVID/downloads/mevid-v1-bbox-test.tgz +File size: 13,974,475,339 bytes (13.01 GB) +Output dir: /home/ah66742/MEVID/bbox_test_fresh +Timestamp shows: Thu Dec 8 20:06:16 2022 +Extraction started at: 2026-02-17 21:05:15 + + [Folder 1/54] Person 0229: 24,835 files (336.5 MB) [4s elapsed] + [Folder 2/54] Person 0268: 28,918 files (442.6 MB) [10s elapsed] + [Folder 3/54] Person 0238: 5,372 files (26.8 MB) [11s elapsed] + [Folder 4/54] Person 0271: 34,068 files (682.6 MB) [19s elapsed] + [Folder 5/54] Person 0292: 13,433 files (40.8 MB) [20s elapsed] + [Folder 6/54] Person 0260: 8,708 files (53.6 MB) [22s elapsed] + [Folder 7/54] Person 0280: 20,914 files (132.2 MB) [25s elapsed] + [Folder 8/54] Person 0211: 6,503 files (34.0 MB) [25s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [██████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 16.7% + │ Folders: 9/54 Files: 170,520 Size: 2032 MB + │ Rate: 5684 files/sec (4063 MB/min) Elapsed: 0.5min ETA: 3min + │ Current folder: 0202 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 9/54] Person 0202: 73,034 files (744.4 MB) [38s elapsed] + [Folder 10/54] Person 0201: 8,184 files (184.3 MB) [40s elapsed] + [Folder 11/54] Person 0249: 6,656 files (24.0 MB) [40s elapsed] + [Folder 12/54] Person 0300: 116 files (3.4 MB) [40s elapsed] + [Folder 13/54] Person 0263: 43,090 files (732.2 MB) [49s elapsed] + [Folder 14/54] Person 0242: 34,012 files (424.8 MB) [56s elapsed] + [Folder 15/54] Person 0226: 3,107 files (19.1 MB) [56s elapsed] + [Folder 16/54] Person 0301: 149 files (5.1 MB) [56s elapsed] + [Folder 17/54] Person 0233: 22,510 files (225.7 MB) [60s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░] 33.3% + │ Folders: 18/54 Files: 334,004 Size: 4115 MB + │ Rate: 5567 files/sec (4115 MB/min) Elapsed: 1.0min ETA: 2min + │ Current folder: 0205 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 18/54] Person 0205: 16,753 files (128.7 MB) [63s elapsed] + [Folder 19/54] Person 0241: 21,966 files (380.1 MB) [67s elapsed] + [Folder 20/54] Person 0230: 36,341 files (719.6 MB) [75s elapsed] + [Folder 21/54] Person 0296: 19,774 files (188.3 MB) [79s elapsed] + [Folder 22/54] Person 0269: 5,744 files (36.0 MB) [79s elapsed] + [Folder 23/54] Person 0250: 16,693 files (70.5 MB) [82s elapsed] + [Folder 24/54] Person 0210: 22,542 files (834.8 MB) [89s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [██████████████████░░░░░░░░░░░░░░░░░░░░░░] 46.3% + │ Folders: 25/54 Files: 481,010 Size: 6551 MB + │ Rate: 5345 files/sec (4367 MB/min) Elapsed: 1.5min ETA: 2min + │ Current folder: 0203 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 25/54] Person 0203: 27,305 files (286.4 MB) [93s elapsed] + [Folder 26/54] Person 0206: 12,147 files (196.3 MB) [96s elapsed] + [Folder 27/54] Person 0239: 19,305 files (128.1 MB) [98s elapsed] + [Folder 28/54] Person 0277: 43,451 files (444.8 MB) [105s elapsed] + [Folder 29/54] Person 0278: 50,526 files (567.4 MB) [114s elapsed] + [Folder 30/54] Person 0243: 15,635 files (109.5 MB) [116s elapsed] + [Folder 31/54] Person 0295: 13,994 files (191.8 MB) [119s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [███████████████████████░░░░░░░░░░░░░░░░░] 59.3% + │ Folders: 32/54 Files: 661,631 Size: 8458 MB + │ Rate: 5514 files/sec (4229 MB/min) Elapsed: 2.0min ETA: 1min + │ Current folder: 0231 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 32/54] Person 0231: 43,403 files (469.2 MB) [127s elapsed] + [Folder 33/54] Person 0285: 16,141 files (57.0 MB) [129s elapsed] + [Folder 34/54] Person 0215: 7,170 files (44.2 MB) [130s elapsed] + [Folder 35/54] Person 0204: 4,951 files (17.7 MB) [130s elapsed] + [Folder 36/54] Person 0272: 10,023 files (56.7 MB) [132s elapsed] + [Folder 37/54] Person 0297: 20,401 files (211.5 MB) [135s elapsed] + [Folder 38/54] Person 0279: 11,185 files (33.1 MB) [136s elapsed] + [Folder 39/54] Person 0245: 9,390 files (46.8 MB) [138s elapsed] + [Folder 40/54] Person 0294: 14,555 files (321.0 MB) [141s elapsed] + [Folder 41/54] Person 0247: 39,157 files (209.9 MB) [146s elapsed] + [Folder 42/54] Person 0256: 20,993 files (190.8 MB) [149s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [███████████████████████████████░░░░░░░░░] 79.6% + │ Folders: 43/54 Files: 856,550 Size: 10082 MB + │ Rate: 5710 files/sec (4033 MB/min) Elapsed: 2.5min ETA: 1min + │ Current folder: 0218 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 43/54] Person 0218: 17,020 files (149.3 MB) [152s elapsed] + [Folder 44/54] Person 0216: 10,034 files (139.5 MB) [154s elapsed] + [Folder 45/54] Person 0208: 93,953 files (1177.9 MB) [171s elapsed] + [Folder 46/54] Person 0234: 7,523 files (150.0 MB) [172s elapsed] + [Folder 47/54] Person 0258: 12,554 files (142.4 MB) [174s elapsed] + [Folder 48/54] Person 0298: 24,173 files (315.5 MB) [178s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████████████████████████████████░░░░] 90.7% + │ Folders: 49/54 Files: 1,028,892 Size: 12222 MB + │ Rate: 5716 files/sec (4074 MB/min) Elapsed: 3.0min ETA: 0min + │ Current folder: 0267 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 49/54] Person 0267: 26,930 files (242.3 MB) [183s elapsed] + [Folder 50/54] Person 0232: 36,488 files (311.9 MB) [188s elapsed] + [Folder 51/54] Person 0257: 24,906 files (328.3 MB) [193s elapsed] + [Folder 52/54] Person 0248: 37,625 files (402.9 MB) [199s elapsed] + [Folder 53/54] Person 0227: 24,096 files (469.8 MB) [205s elapsed] + +============================================================ + EXTRACTION COMPLETE +============================================================ + Time: 3.5 minutes (207s) + Folders: 54/54 + Files: 1,186,306 + Size: 13.61 GB + Errors: 0 + + Person folders extracted (54): + 0201: 8,184 files (184.3 MB) + 0202: 73,034 files (744.4 MB) + 0203: 27,305 files (286.4 MB) + 0204: 4,951 files (17.7 MB) + 0205: 16,753 files (128.7 MB) + 0206: 12,147 files (196.3 MB) + 0208: 93,953 files (1177.9 MB) + 0210: 22,542 files (834.8 MB) + 0211: 6,503 files (34.0 MB) + 0212: 17,849 files (57.7 MB) + 0215: 7,170 files (44.2 MB) + 0216: 10,034 files (139.5 MB) + 0218: 17,020 files (149.3 MB) + 0226: 3,107 files (19.1 MB) + 0227: 24,096 files (469.8 MB) + 0229: 24,835 files (336.5 MB) + 0230: 36,341 files (719.6 MB) + 0231: 43,403 files (469.2 MB) + 0232: 36,488 files (311.9 MB) + 0233: 22,510 files (225.7 MB) + 0234: 7,523 files (150.0 MB) + 0238: 5,372 files (26.8 MB) + 0239: 19,305 files (128.1 MB) + 0241: 21,966 files (380.1 MB) + 0242: 34,012 files (424.8 MB) + 0243: 15,635 files (109.5 MB) + 0245: 9,390 files (46.8 MB) + 0247: 39,157 files (209.9 MB) + 0248: 37,625 files (402.9 MB) + 0249: 6,656 files (24.0 MB) + 0250: 16,693 files (70.5 MB) + 0256: 20,993 files (190.8 MB) + 0257: 24,906 files (328.3 MB) + 0258: 12,554 files (142.4 MB) + 0260: 8,708 files (53.6 MB) + 0263: 43,090 files (732.2 MB) + 0267: 26,930 files (242.3 MB) + 0268: 28,918 files (442.6 MB) + 0269: 5,744 files (36.0 MB) + 0271: 34,068 files (682.6 MB) + 0272: 10,023 files (56.7 MB) + 0277: 43,451 files (444.8 MB) + 0278: 50,526 files (567.4 MB) + 0279: 11,185 files (33.1 MB) + 0280: 20,914 files (132.2 MB) + 0285: 16,141 files (57.0 MB) + 0292: 13,433 files (40.8 MB) + 0294: 14,555 files (321.0 MB) + 0295: 13,994 files (191.8 MB) + 0296: 19,774 files (188.3 MB) + 0297: 20,401 files (211.5 MB) + 0298: 24,173 files (315.5 MB) + 0300: 116 files (3.4 MB) + 0301: 149 files (5.1 MB) +============================================================ + diff --git a/meva/examples/run_records/output_records/mevid_records/extract_train.log.txt b/meva/examples/run_records/output_records/mevid_records/extract_train.log.txt new file mode 100644 index 0000000..3e4fd9e --- /dev/null +++ b/meva/examples/run_records/output_records/mevid_records/extract_train.log.txt @@ -0,0 +1,397 @@ +Opening tarball: /home/ah66742/MEVID/mevid-v1-bbox-train-FRESH.tgz +File size: 32,684,603,246 bytes (30.44 GB) +Output dir: /home/ah66742/MEVID/bbox_train_fresh +Timestamp shows: Thu Dec 8 20:03:18 2022 +Extraction started at: 2026-02-17 20:55:18 + + [Folder 1/104] Person 0052: 2,155 files (7.1 MB) [0s elapsed] + [Folder 2/104] Person 0010: 64,037 files (502.1 MB) [10s elapsed] + [Folder 3/104] Person 0008: 12,348 files (92.7 MB) [12s elapsed] + [Folder 4/104] Person 0053: 13,937 files (217.5 MB) [15s elapsed] + [Folder 5/104] Person 0032: 52,714 files (1163.0 MB) [27s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 5.8% + │ Folders: 6/104 Files: 168,361 Size: 2153 MB + │ Rate: 5612 files/sec (4306 MB/min) Elapsed: 0.5min ETA: 8min + │ Current folder: 0048 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 6/104] Person 0048: 40,416 files (298.0 MB) [33s elapsed] + [Folder 7/104] Person 0110: 68 files (0.4 MB) [33s elapsed] + [Folder 8/104] Person 0080: 10,093 files (135.1 MB) [34s elapsed] + [Folder 9/104] Person 0076: 37,674 files (280.3 MB) [40s elapsed] + [Folder 10/104] Person 0054: 94,633 files (1093.3 MB) [56s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 10.6% + │ Folders: 11/104 Files: 355,211 Size: 3935 MB + │ Rate: 5920 files/sec (3935 MB/min) Elapsed: 1.0min ETA: 8min + │ Current folder: 0040 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 11/104] Person 0040: 140,504 files (753.0 MB) [76s elapsed] + [Folder 12/104] Person 0046: 9,162 files (138.0 MB) [77s elapsed] + [Folder 13/104] Person 0074: 69,057 files (422.6 MB) [87s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 13.5% + │ Folders: 14/104 Files: 565,040 Size: 5304 MB + │ Rate: 6278 files/sec (3536 MB/min) Elapsed: 1.5min ETA: 10min + │ Current folder: 0097 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 14/104] Person 0097: 30,601 files (337.8 MB) [92s elapsed] + [Folder 15/104] Person 0058: 6,641 files (26.7 MB) [93s elapsed] + [Folder 16/104] Person 0064: 31,958 files (392.3 MB) [99s elapsed] + [Folder 17/104] Person 0087: 17,953 files (119.5 MB) [101s elapsed] + [Folder 18/104] Person 0103: 23,376 files (215.5 MB) [105s elapsed] + [Folder 19/104] Person 0072: 44,537 files (380.8 MB) [112s elapsed] + [Folder 20/104] Person 0028: 44,730 files (409.1 MB) [119s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 20.2% + │ Folders: 21/104 Files: 753,038 Size: 7032 MB + │ Rate: 6275 files/sec (3516 MB/min) Elapsed: 2.0min ETA: 8min + │ Current folder: 0029 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 21/104] Person 0029: 29,472 files (217.1 MB) [123s elapsed] + [Folder 22/104] Person 0075: 31,677 files (238.1 MB) [128s elapsed] + [Folder 23/104] Person 0070: 52,545 files (293.3 MB) [135s elapsed] + [Folder 24/104] Person 0059: 23,288 files (156.8 MB) [139s elapsed] + [Folder 25/104] Person 0060: 668 files (3.1 MB) [139s elapsed] + [Folder 26/104] Person 0034: 25,925 files (181.0 MB) [142s elapsed] + [Folder 27/104] Person 0042: 45,437 files (308.2 MB) [149s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [██████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 26.9% + │ Folders: 28/104 Files: 961,246 Size: 8420 MB + │ Rate: 6408 files/sec (3368 MB/min) Elapsed: 2.5min ETA: 7min + │ Current folder: 0049 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 28/104] Person 0049: 31,514 files (213.0 MB) [154s elapsed] + [Folder 29/104] Person 0047: 36,190 files (478.5 MB) [160s elapsed] + [Folder 30/104] Person 0004: 27,364 files (252.9 MB) [164s elapsed] + [Folder 31/104] Person 0106: 96,376 files (611.6 MB) [178s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 30.8% + │ Folders: 32/104 Files: 1,155,193 Size: 9988 MB + │ Rate: 6418 files/sec (3329 MB/min) Elapsed: 3.0min ETA: 7min + │ Current folder: 0078 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 32/104] Person 0078: 102,319 files (614.2 MB) [194s elapsed] + [Folder 33/104] Person 0063: 20,488 files (187.2 MB) [197s elapsed] + [Folder 34/104] Person 0030: 33,540 files (293.5 MB) [202s elapsed] + [Folder 35/104] Person 0003: 50,620 files (336.9 MB) [209s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░] 34.6% + │ Folders: 36/104 Files: 1,358,527 Size: 11409 MB + │ Rate: 6469 files/sec (3260 MB/min) Elapsed: 3.5min ETA: 7min + │ Current folder: 0014 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 36/104] Person 0014: 34,580 files (294.9 MB) [215s elapsed] + [Folder 37/104] Person 0065: 49,621 files (391.6 MB) [222s elapsed] + [Folder 38/104] Person 0094: 12,516 files (103.1 MB) [225s elapsed] + [Folder 39/104] Person 0007: 23,257 files (248.4 MB) [228s elapsed] + [Folder 40/104] Person 0005: 18,619 files (104.0 MB) [231s elapsed] + [Folder 41/104] Person 0089: 42,147 files (271.0 MB) [237s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████████████░░░░░░░░░░░░░░░░░░░░░░░░] 40.4% + │ Folders: 42/104 Files: 1,552,204 Size: 12946 MB + │ Rate: 6467 files/sec (3237 MB/min) Elapsed: 4.0min ETA: 6min + │ Current folder: 0083 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 42/104] Person 0083: 57,945 files (541.7 MB) [247s elapsed] + [Folder 43/104] Person 0088: 46,299 files (369.6 MB) [254s elapsed] + [Folder 44/104] Person 0026: 26,111 files (260.7 MB) [258s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████████░░░░░░░░░░░░░░░░░░░░░░░] 43.3% + │ Folders: 45/104 Files: 1,741,214 Size: 14571 MB + │ Rate: 6449 files/sec (3238 MB/min) Elapsed: 4.5min ETA: 6min + │ Current folder: 0068 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 45/104] Person 0068: 95,107 files (768.7 MB) [273s elapsed] + [Folder 46/104] Person 0104: 32,326 files (289.3 MB) [278s elapsed] + [Folder 47/104] Person 0036: 20,613 files (241.7 MB) [282s elapsed] + [Folder 48/104] Person 0016: 15,073 files (139.1 MB) [285s elapsed] + [Folder 49/104] Person 0081: 2,991 files (12.3 MB) [285s elapsed] + [Folder 50/104] Person 0101: 40,942 files (506.2 MB) [292s elapsed] + [Folder 51/104] Person 0037: 40,135 files (236.4 MB) [298s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████████████████░░░░░░░░░░░░░░░░░░░░] 50.0% + │ Folders: 52/104 Files: 1,924,263 Size: 16244 MB + │ Rate: 6414 files/sec (3249 MB/min) Elapsed: 5.0min ETA: 5min + │ Current folder: 0017 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 52/104] Person 0017: 60,462 files (481.3 MB) [307s elapsed] + [Folder 53/104] Person 0013: 43,472 files (1008.5 MB) [318s elapsed] + [Folder 54/104] Person 0039: 45,501 files (569.9 MB) [326s elapsed] + [Folder 55/104] Person 0041: 14,088 files (86.5 MB) [328s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████████████░░░░░░░░░░░░░░░░░░░] 53.8% + │ Folders: 56/104 Files: 2,090,770 Size: 18442 MB + │ Rate: 6336 files/sec (3353 MB/min) Elapsed: 5.5min ETA: 5min + │ Current folder: 0044 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 56/104] Person 0044: 42,532 files (423.2 MB) [335s elapsed] + [Folder 57/104] Person 0012: 5,204 files (71.9 MB) [336s elapsed] + [Folder 58/104] Person 0020: 17,623 files (65.9 MB) [338s elapsed] + [Folder 59/104] Person 0102: 40,411 files (294.9 MB) [344s elapsed] + [Folder 60/104] Person 0062: 23,151 files (332.3 MB) [348s elapsed] + [Folder 61/104] Person 0109: 7,016 files (90.5 MB) [349s elapsed] + [Folder 62/104] Person 0061: 8,251 files (122.9 MB) [351s elapsed] + [Folder 63/104] Person 0084: 39,759 files (282.3 MB) [357s elapsed] + [Folder 64/104] Person 0095: 11,043 files (125.8 MB) [360s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████████████████░░░░░░░░░░░░░░░] 62.5% + │ Folders: 65/104 Files: 2,271,467 Size: 20108 MB + │ Rate: 6310 files/sec (3351 MB/min) Elapsed: 6.0min ETA: 4min + │ Current folder: 0027 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 65/104] Person 0027: 35,731 files (189.6 MB) [365s elapsed] + [Folder 66/104] Person 0015: 83,152 files (800.9 MB) [378s elapsed] + [Folder 67/104] Person 0082: 41,928 files (813.6 MB) [386s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [██████████████████████████░░░░░░░░░░░░░░] 65.4% + │ Folders: 68/104 Files: 2,454,006 Size: 22125 MB + │ Rate: 6292 files/sec (3404 MB/min) Elapsed: 6.5min ETA: 3min + │ Current folder: 0043 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 68/104] Person 0043: 32,969 files (319.0 MB) [392s elapsed] + [Folder 69/104] Person 0086: 28,486 files (195.6 MB) [396s elapsed] + [Folder 70/104] Person 0091: 34,657 files (420.2 MB) [402s elapsed] + [Folder 71/104] Person 0051: 61,500 files (655.9 MB) [412s elapsed] + [Folder 72/104] Person 0056: 52,284 files (290.3 MB) [419s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████████████████████████░░░░░░░░░░░░] 70.2% + │ Folders: 73/104 Files: 2,646,302 Size: 23814 MB + │ Rate: 6301 files/sec (3402 MB/min) Elapsed: 7.0min ETA: 3min + │ Current folder: 0025 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 73/104] Person 0025: 92,099 files (467.2 MB) [432s elapsed] + [Folder 74/104] Person 0035: 10,137 files (127.4 MB) [434s elapsed] + [Folder 75/104] Person 0033: 21,519 files (233.0 MB) [437s elapsed] + [Folder 76/104] Person 0023: 29,379 files (362.0 MB) [442s elapsed] + [Folder 77/104] Person 0092: 36,783 files (422.0 MB) [449s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [██████████████████████████████░░░░░░░░░░] 75.0% + │ Folders: 78/104 Files: 2,839,812 Size: 25504 MB + │ Rate: 6311 files/sec (3400 MB/min) Elapsed: 7.5min ETA: 3min + │ Current folder: 0085 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 78/104] Person 0085: 29,787 files (366.6 MB) [454s elapsed] + [Folder 79/104] Person 0019: 17,304 files (132.6 MB) [457s elapsed] + [Folder 80/104] Person 0096: 25,559 files (231.7 MB) [461s elapsed] + [Folder 81/104] Person 0077: 21,901 files (257.7 MB) [465s elapsed] + [Folder 82/104] Person 0112: 5,902 files (35.1 MB) [465s elapsed] + [Folder 83/104] Person 0090: 21,059 files (182.8 MB) [469s elapsed] + [Folder 84/104] Person 0098: 772 files (9.9 MB) [469s elapsed] + [Folder 85/104] Person 0093: 20,326 files (189.0 MB) [472s elapsed] + [Folder 86/104] Person 0006: 5,359 files (71.1 MB) [473s elapsed] + [Folder 87/104] Person 0057: 40,766 files (370.1 MB) [479s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████████████████████████░░░░░░░] 84.6% + │ Folders: 88/104 Files: 3,025,535 Size: 27316 MB + │ Rate: 6303 files/sec (3414 MB/min) Elapsed: 8.0min ETA: 1min + │ Current folder: 0002 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 88/104] Person 0002: 75,021 files (956.8 MB) [492s elapsed] + [Folder 89/104] Person 0009: 66,277 files (527.7 MB) [502s elapsed] + [Folder 90/104] Person 0050: 4,713 files (84.9 MB) [503s elapsed] + [Folder 91/104] Person 0055: 33,254 files (215.2 MB) [507s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [███████████████████████████████████░░░░░] 88.5% + │ Folders: 92/104 Files: 3,217,609 Size: 29186 MB + │ Rate: 6309 files/sec (3434 MB/min) Elapsed: 8.5min ETA: 1min + │ Current folder: 0018 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 92/104] Person 0018: 32,572 files (277.8 MB) [512s elapsed] + [Folder 93/104] Person 0067: 92,538 files (679.3 MB) [526s elapsed] + [Folder 94/104] Person 0045: 23,397 files (192.4 MB) [530s elapsed] + [Folder 95/104] Person 0100: 25,322 files (247.5 MB) [533s elapsed] + [Folder 96/104] Person 0001: 1,550 files (23.7 MB) [534s elapsed] + [Folder 97/104] Person 0079: 16,929 files (171.1 MB) [536s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [█████████████████████████████████████░░░] 94.2% + │ Folders: 98/104 Files: 3,411,937 Size: 30887 MB + │ Rate: 6318 files/sec (3432 MB/min) Elapsed: 9.0min ETA: 1min + │ Current folder: 0099 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + [Folder 98/104] Person 0099: 37,112 files (483.9 MB) [543s elapsed] + [Folder 99/104] Person 0066: 36,266 files (590.9 MB) [550s elapsed] + [Folder 100/104] Person 0024: 44,327 files (496.8 MB) [557s elapsed] + [Folder 101/104] Person 0038: 34,620 files (198.6 MB) [562s elapsed] + [Folder 102/104] Person 0022: 8,158 files (62.7 MB) [563s elapsed] + [Folder 103/104] Person 0031: 16,077 files (264.8 MB) [567s elapsed] + + ┌─ PROGRESS ────────────────────────────────────────┐ + │ [████████████████████████████████████████] 100.0% + │ Folders: 104/104 Files: 3,588,321 Size: 32882 MB + │ Rate: 6295 files/sec (3461 MB/min) Elapsed: 9.5min ETA: 0min + │ Current folder: 0011 + │ Errors so far: 0 + └─────────────────────────────────────────────────────┘ + + +============================================================ + EXTRACTION COMPLETE +============================================================ + Time: 9.6 minutes (574s) + Folders: 104/104 + Files: 3,609,261 / 3,609,156 expected + Size: 32.28 GB + Errors: 0 + + Person folders extracted (104): + 0001: 1,550 files (23.7 MB) + 0002: 75,021 files (956.8 MB) + 0003: 50,620 files (336.9 MB) + 0004: 27,364 files (252.9 MB) + 0005: 18,619 files (104.0 MB) + 0006: 5,359 files (71.1 MB) + 0007: 23,257 files (248.4 MB) + 0008: 12,348 files (92.7 MB) + 0009: 66,277 files (527.7 MB) + 0010: 64,037 files (502.1 MB) + 0011: 40,956 files (329.3 MB) + 0012: 5,204 files (71.9 MB) + 0013: 43,472 files (1008.5 MB) + 0014: 34,580 files (294.9 MB) + 0015: 83,152 files (800.9 MB) + 0016: 15,073 files (139.1 MB) + 0017: 60,462 files (481.3 MB) + 0018: 32,572 files (277.8 MB) + 0019: 17,304 files (132.6 MB) + 0020: 17,623 files (65.9 MB) + 0022: 8,158 files (62.7 MB) + 0023: 29,379 files (362.0 MB) + 0024: 44,327 files (496.8 MB) + 0025: 92,099 files (467.2 MB) + 0026: 26,111 files (260.7 MB) + 0027: 35,731 files (189.6 MB) + 0028: 44,730 files (409.1 MB) + 0029: 29,472 files (217.1 MB) + 0030: 33,540 files (293.5 MB) + 0031: 16,077 files (264.8 MB) + 0032: 52,714 files (1163.0 MB) + 0033: 21,519 files (233.0 MB) + 0034: 25,925 files (181.0 MB) + 0035: 10,137 files (127.4 MB) + 0036: 20,613 files (241.7 MB) + 0037: 40,135 files (236.4 MB) + 0038: 34,620 files (198.6 MB) + 0039: 45,501 files (569.9 MB) + 0040: 140,504 files (753.0 MB) + 0041: 14,088 files (86.5 MB) + 0042: 45,437 files (308.2 MB) + 0043: 32,969 files (319.0 MB) + 0044: 42,532 files (423.2 MB) + 0045: 23,397 files (192.4 MB) + 0046: 9,162 files (138.0 MB) + 0047: 36,190 files (478.5 MB) + 0048: 40,416 files (298.0 MB) + 0049: 31,514 files (213.0 MB) + 0050: 4,713 files (84.9 MB) + 0051: 61,500 files (655.9 MB) + 0052: 2,155 files (7.1 MB) + 0053: 13,937 files (217.5 MB) + 0054: 94,633 files (1093.3 MB) + 0055: 33,254 files (215.2 MB) + 0056: 52,284 files (290.3 MB) + 0057: 40,766 files (370.1 MB) + 0058: 6,641 files (26.7 MB) + 0059: 23,288 files (156.8 MB) + 0060: 668 files (3.1 MB) + 0061: 8,251 files (122.9 MB) + 0062: 23,151 files (332.3 MB) + 0063: 20,488 files (187.2 MB) + 0064: 31,958 files (392.3 MB) + 0065: 49,621 files (391.6 MB) + 0066: 36,266 files (590.9 MB) + 0067: 92,538 files (679.3 MB) + 0068: 95,107 files (768.7 MB) + 0070: 52,545 files (293.3 MB) + 0072: 44,537 files (380.8 MB) + 0074: 69,057 files (422.6 MB) + 0075: 31,677 files (238.1 MB) + 0076: 37,674 files (280.3 MB) + 0077: 21,901 files (257.7 MB) + 0078: 102,319 files (614.2 MB) + 0079: 16,929 files (171.1 MB) + 0080: 10,093 files (135.1 MB) + 0081: 2,991 files (12.3 MB) + 0082: 41,928 files (813.6 MB) + 0083: 57,945 files (541.7 MB) + 0084: 39,759 files (282.3 MB) + 0085: 29,787 files (366.6 MB) + 0086: 28,486 files (195.6 MB) + 0087: 17,953 files (119.5 MB) + 0088: 46,299 files (369.6 MB) + 0089: 42,147 files (271.0 MB) + 0090: 21,059 files (182.8 MB) + 0091: 34,657 files (420.2 MB) + 0092: 36,783 files (422.0 MB) + 0093: 20,326 files (189.0 MB) + 0094: 12,516 files (103.1 MB) + 0095: 11,043 files (125.8 MB) + 0096: 25,559 files (231.7 MB) + 0097: 30,601 files (337.8 MB) + 0098: 772 files (9.9 MB) + 0099: 37,112 files (483.9 MB) + 0100: 25,322 files (247.5 MB) + 0101: 40,942 files (506.2 MB) + 0102: 40,411 files (294.9 MB) + 0103: 23,376 files (215.5 MB) + 0104: 32,326 files (289.3 MB) + 0106: 96,376 files (611.6 MB) + 0109: 7,016 files (90.5 MB) + 0110: 68 files (0.4 MB) + 0112: 5,902 files (35.1 MB) +============================================================ + diff --git a/meva/examples/run_records/output_records/mevid_records/mevid_pipeline_20260217_203529.log.txt b/meva/examples/run_records/output_records/mevid_records/mevid_pipeline_20260217_203529.log.txt new file mode 100644 index 0000000..3033f58 --- /dev/null +++ b/meva/examples/run_records/output_records/mevid_records/mevid_pipeline_20260217_203529.log.txt @@ -0,0 +1,13 @@ +[20:35:29] MEVID Pipeline started at Tue Feb 17 08:35:29 PM CST 2026 +[20:35:29] Log file: /home/ah66742/output/mevid_logs/mevid_pipeline_20260217_203529.log +[20:35:29] Download dir: /home/ah66742/MEVID/downloads +[i] Disk space free: 785GB +[✓] Train (FRESH): /home/ah66742/MEVID/mevid-v1-bbox-train-FRESH.tgz (31G, modified: 2022-12-08) +[⚠] Test: NOT FOUND at /home/ah66742/MEVID/downloads/mevid-v1-bbox-test.tgz +[✓] /home/ah66742/MEVID/bbox_train_fresh: 0 person folders, 0 JPGs +[✓] /home/ah66742/MEVID/bbox_test_fresh: 0 person folders, 0 JPGs +[✓] /home/ah66742/MEVID/bbox_train: 14 person folders, 558352 JPGs +[✓] /home/ah66742/MEVID/bbox_test: 9 person folders, 202999 JPGs +[⚠] Old bbox_train: 14 folders (partial extraction from corrupted tarball) +[⚠] Old bbox_test: 9 folders (partial extraction from corrupted tarball) +[20:35:30] Done. Log: /home/ah66742/output/mevid_logs/mevid_pipeline_20260217_203529.log diff --git a/meva/examples/run_records/reextract_missing_20260228.log.txt b/meva/examples/run_records/reextract_missing_20260228.log.txt new file mode 100644 index 0000000..698145b --- /dev/null +++ b/meva/examples/run_records/reextract_missing_20260228.log.txt @@ -0,0 +1,664 @@ + +Found 218 slots with missing camera descriptions +Total missing cameras: 761 + +Processing 218 slots... + +[1/218] 2018-03-05.13-10.school (missing: ['G328', 'G339', 'G419', 'G420']) + OK: 23 → 41 actors (+18), cameras: ['G299', 'G300', 'G330', 'G336', 'G421', 'G423', 'G424', 'G328', 'G339', 'G419', 'G420'] + +[2/218] 2018-03-05.13-15.bus (missing: ['G331', 'G508', 'G509']) + OK: 95 → 148 actors (+53), cameras: ['G340', 'G505', 'G506', 'G331', 'G508', 'G509'] + +[3/218] 2018-03-05.13-15.school (missing: ['G328', 'G339', 'G419', 'G420']) + OK: 58 → 78 actors (+20), cameras: ['G423', 'G424', 'G328', 'G299', 'G300', 'G330', 'G336', 'G421', 'G339', 'G419', 'G420'] + +[4/218] 2018-03-05.13-20.bus (missing: ['G331', 'G505', 'G508', 'G509']) + OK: 62 → 105 actors (+43), cameras: ['G340', 'G331', 'G506', 'G505', 'G508', 'G509'] + +[5/218] 2018-03-05.13-20.school (missing: ['G328', 'G339', 'G419', 'G420']) + OK: 164 → 267 actors (+103), cameras: ['G336', 'G423', 'G424', 'G328', 'G419', 'G420', 'G299', 'G300', 'G330', 'G421', 'G339'] + +[6/218] 2018-03-05.14-00.bus (missing: ['G331', 'G509']) + OK: 1 → 1 actors (+0), cameras: ['G506', 'G340', 'G505', 'G508', 'G331', 'G509'] + +[7/218] 2018-03-05.14-00.school (missing: ['G328', 'G339', 'G419']) + OK: 189 → 214 actors (+25), cameras: ['G420', 'G299', 'G300', 'G330', 'G336', 'G421', 'G423', 'G424', 'G328', 'G339', 'G419'] + +[8/218] 2018-03-05.14-05.bus (missing: ['G331', 'G509']) + OK: 11 → 11 actors (+0), cameras: ['G506', 'G340', 'G505', 'G508', 'G331', 'G509'] + +[9/218] 2018-03-05.14-05.school (missing: ['G328', 'G339', 'G419']) + OK: 176 → 185 actors (+9), cameras: ['G328', 'G299', 'G300', 'G330', 'G336', 'G420', 'G421', 'G423', 'G424', 'G339', 'G419'] + +[10/218] 2018-03-05.14-10.bus (missing: ['G331', 'G508', 'G509']) + OK: 7 → 7 actors (+0), cameras: ['G340', 'G505', 'G506', 'G508', 'G331', 'G509'] + +[11/218] 2018-03-05.14-10.school (missing: ['G328', 'G339', 'G419']) + OK: 148 → 179 actors (+31), cameras: ['G330', 'G420', 'G421', 'G424', 'G299', 'G300', 'G336', 'G423', 'G328', 'G339', 'G419'] + +[12/218] 2018-03-07.11-00.admin (missing: ['G326']) + OK: 12 → 18 actors (+6), cameras: ['G329', 'G326'] + +[13/218] 2018-03-07.11-00.bus (missing: ['G340', 'G505', 'G506', 'G509']) + OK: 222 → 430 actors (+208), cameras: ['G508', 'G505', 'G506', 'G509', 'G331', 'G340'] + +[14/218] 2018-03-07.11-00.hospital (missing: ['G301', 'G341']) + OK: 202 → 350 actors (+148), cameras: ['G341', 'G436', 'G301'] + +[15/218] 2018-03-07.11-00.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G424']) + OK: 37 → 700 actors (+663), cameras: ['G420', 'G328', 'G419', 'G421', 'G336', 'G300', 'G339', 'G299', 'G330', 'G423', 'G424'] + +[16/218] 2018-03-07.11-05.admin (missing: ['G329']) + OK: 18 → 45 actors (+27), cameras: ['G326', 'G329'] + +[17/218] 2018-03-07.11-05.bus (missing: ['G340', 'G505', 'G506', 'G509']) + OK: 201 → 404 actors (+203), cameras: ['G331', 'G508', 'G340', 'G505', 'G506', 'G509'] + +[18/218] 2018-03-07.11-05.hospital (missing: ['G301', 'G341']) + OK: 98 → 193 actors (+95), cameras: ['G341', 'G436', 'G301'] + +[19/218] 2018-03-07.11-05.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G424']) + OK: 13 → 476 actors (+463), cameras: ['G328', 'G419', 'G300', 'G339', 'G299', 'G330', 'G420', 'G423', 'G421', 'G424', 'G336'] + +[20/218] 2018-03-07.11-10.admin (missing: ['G329']) + OK: 17 → 40 actors (+23), cameras: ['G326', 'G329'] + +[21/218] 2018-03-07.11-10.bus (missing: ['G340', 'G505', 'G506', 'G509']) + OK: 133 → 291 actors (+158), cameras: ['G508', 'G505', 'G506', 'G331', 'G509', 'G340'] + +[22/218] 2018-03-07.11-10.hospital (missing: ['G301', 'G341']) + OK: 87 → 155 actors (+68), cameras: ['G436', 'G341', 'G301'] + +[23/218] 2018-03-07.11-10.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G424']) + OK: 24 → 471 actors (+447), cameras: ['G299', 'G420', 'G419', 'G421', 'G300', 'G339', 'G330', 'G423', 'G328', 'G424', 'G336'] + +[24/218] 2018-03-07.16-50.admin (missing: ['G326']) + OK: 5 → 11 actors (+6), cameras: ['G329', 'G326'] + +[25/218] 2018-03-07.16-50.bus (missing: ['G505', 'G508']) + OK: 250 → 270 actors (+20), cameras: ['G331', 'G340', 'G506', 'G509', 'G505', 'G508'] + +[26/218] 2018-03-07.16-50.hospital (missing: ['G301', 'G341']) + OK: 256 → 294 actors (+38), cameras: ['G301', 'G436', 'G341'] + +[27/218] 2018-03-07.16-50.school (missing: ['G300', 'G328', 'G336', 'G339', 'G420', 'G423', 'G424']) + OK: 267 → 742 actors (+475), cameras: ['G330', 'G419', 'G421', 'G638', 'G423', 'G300', 'G299', 'G328', 'G420', 'G336', 'G339', 'G424'] + +[28/218] 2018-03-07.17-00.admin (missing: ['G326']) + OK: 12 → 21 actors (+9), cameras: ['G329', 'G326'] + +[29/218] 2018-03-07.17-00.bus (missing: ['G505']) + OK: 477 → 616 actors (+139), cameras: ['G331', 'G506', 'G508', 'G509', 'G505', 'G340'] + +[30/218] 2018-03-07.17-00.hospital (missing: ['G301', 'G341']) + OK: 130 → 283 actors (+153), cameras: ['G341', 'G436', 'G301'] + +[31/218] 2018-03-07.17-00.school (missing: ['G300', 'G328', 'G336', 'G339', 'G420', 'G423', 'G424']) + OK: 288 → 820 actors (+532), cameras: ['G638', 'G299', 'G330', 'G419', 'G421', 'G328', 'G420', 'G423', 'G336', 'G300', 'G339', 'G424'] + +[32/218] 2018-03-07.17-05.admin (missing: ['G326']) + OK: 5 → 9 actors (+4), cameras: ['G329', 'G326'] + +[33/218] 2018-03-07.17-05.bus (missing: ['G505']) + OK: 270 → 287 actors (+17), cameras: ['G508', 'G509', 'G505', 'G331', 'G340', 'G506'] + +[34/218] 2018-03-07.17-05.hospital (missing: ['G301', 'G341']) + OK: 101 → 163 actors (+62), cameras: ['G436', 'G341', 'G301'] + +[35/218] 2018-03-07.17-05.school (missing: ['G300', 'G328', 'G339', 'G420', 'G423', 'G424']) + OK: 374 → 567 actors (+193), cameras: ['G336', 'G419', 'G420', 'G423', 'G424', 'G299', 'G330', 'G421', 'G638', 'G328', 'G300', 'G339'] + +[36/218] 2018-03-07.17-20.bus (missing: ['G505']) + OK: 298 → 322 actors (+24), cameras: ['G331', 'G506', 'G508', 'G505', 'G340', 'G509'] + +[37/218] 2018-03-07.17-20.hospital (missing: ['G301', 'G341']) + OK: 124 → 163 actors (+39), cameras: ['G436', 'G341', 'G301'] + +[38/218] 2018-03-07.17-20.school (missing: ['G300', 'G328', 'G339', 'G419', 'G420', 'G423', 'G424']) + OK: 277 → 623 actors (+346), cameras: ['G336', 'G638', 'G419', 'G420', 'G423', 'G300', 'G424', 'G299', 'G330', 'G421', 'G328', 'G339'] + +[39/218] 2018-03-07.17-25.admin (missing: ['G326']) + OK: 9 → 16 actors (+7), cameras: ['G329', 'G326'] + +[40/218] 2018-03-07.17-25.bus (missing: ['G505']) + OK: 260 → 296 actors (+36), cameras: ['G340', 'G506', 'G508', 'G509', 'G505', 'G331'] + +[41/218] 2018-03-07.17-25.hospital (missing: ['G301', 'G341']) + OK: 168 → 211 actors (+43), cameras: ['G436', 'G341', 'G301'] + +[42/218] 2018-03-07.17-25.school (missing: ['G300', 'G328', 'G339', 'G420', 'G423', 'G424', 'G639']) + OK: 332 → 752 actors (+420), cameras: ['G638', 'G300', 'G424', 'G299', 'G330', 'G336', 'G421', 'G419', 'G328', 'G420', 'G423', 'G339', 'G639'] + +[43/218] 2018-03-07.17-30.admin (missing: ['G326']) + OK: 16 → 28 actors (+12), cameras: ['G329', 'G326'] + +[44/218] 2018-03-07.17-30.bus (missing: ['G505']) + OK: 509 → 698 actors (+189), cameras: ['G509', 'G340', 'G506', 'G508', 'G505', 'G331'] + +[45/218] 2018-03-07.17-30.hospital (missing: ['G301', 'G341']) + OK: 156 → 273 actors (+117), cameras: ['G341', 'G301', 'G436'] + +[46/218] 2018-03-07.17-30.school (missing: ['G300', 'G328', 'G339', 'G419', 'G420', 'G423', 'G424']) + OK: 365 → 1155 actors (+790), cameras: ['G421', 'G638', 'G639', 'G419', 'G420', 'G423', 'G300', 'G299', 'G330', 'G336', 'G328', 'G339', 'G424'] + +[47/218] 2018-03-07.17-35.admin (missing: ['G326']) + OK: 13 → 24 actors (+11), cameras: ['G329', 'G326'] + +[48/218] 2018-03-07.17-35.bus (missing: ['G505']) + OK: 202 → 232 actors (+30), cameras: ['G331', 'G340', 'G506', 'G508', 'G509', 'G505'] + +[49/218] 2018-03-07.17-35.hospital (missing: ['G301', 'G341']) + OK: 82 → 140 actors (+58), cameras: ['G436', 'G341', 'G301'] + +[50/218] 2018-03-07.17-35.school (missing: ['G300', 'G328', 'G339', 'G419', 'G420', 'G423', 'G424']) + OK: 301 → 586 actors (+285), cameras: ['G299', 'G421', 'G638', 'G419', 'G420', 'G423', 'G300', 'G339', 'G330', 'G336', 'G639', 'G328', 'G424'] + +[51/218] 2018-03-09.10-10.bus (missing: ['G340', 'G505']) + OK: 11 → 57 actors (+46), cameras: ['G506', 'G508', 'G509', 'G340', 'G505', 'G331'] + +[52/218] 2018-03-09.10-10.hospital (missing: ['G341']) + OK: 68 → 78 actors (+10), cameras: ['G341', 'G301', 'G436'] + +[53/218] 2018-03-09.10-10.school (missing: ['G339', 'G420', 'G638']) + OK: 1153 → 1256 actors (+103), cameras: ['G299', 'G300', 'G336', 'G419', 'G421', 'G423', 'G639', 'G328', 'G339', 'G638', 'G330', 'G424', 'G420'] + +[54/218] 2018-03-09.10-15.hospital (missing: ['G341']) + OK: 35 → 35 actors (+0), cameras: ['G301', 'G436', 'G341'] + +[55/218] 2018-03-09.10-15.school (missing: ['G328', 'G339', 'G420', 'G638']) + OK: 3132 → 3256 actors (+124), cameras: ['G299', 'G300', 'G336', 'G421', 'G328', 'G638', 'G330', 'G419', 'G423', 'G424', 'G639', 'G339', 'G420'] + +[56/218] 2018-03-09.10-20.school (missing: ['G328', 'G339', 'G420', 'G638']) + OK: 760 → 784 actors (+24), cameras: ['G336', 'G419', 'G423', 'G424', 'G639', 'G328', 'G420', 'G299', 'G300', 'G330', 'G421', 'G339', 'G638'] + +[57/218] 2018-03-09.10-25.school (missing: ['G328', 'G339', 'G420', 'G424', 'G638']) + OK: 305 → 310 actors (+5), cameras: ['G299', 'G330', 'G336', 'G419', 'G421', 'G423', 'G639', 'G420', 'G300', 'G424', 'G328', 'G339', 'G638'] + +[58/218] 2018-03-09.10-30.hospital (missing: ['G341']) + OK: 12 → 12 actors (+0), cameras: ['G301', 'G436', 'G341'] + +[59/218] 2018-03-09.10-30.school (missing: ['G328', 'G339', 'G420', 'G424', 'G638']) + OK: 235 → 264 actors (+29), cameras: ['G330', 'G419', 'G423', 'G328', 'G420', 'G424', 'G299', 'G300', 'G336', 'G421', 'G639', 'G339', 'G638'] + +[60/218] 2018-03-09.10-35.hospital (missing: ['G341']) + OK: 11 → 13 actors (+2), cameras: ['G301', 'G436', 'G341'] + +[61/218] 2018-03-09.10-35.school (missing: ['G328', 'G339', 'G420', 'G424', 'G638']) + OK: 953 → 1027 actors (+74), cameras: ['G336', 'G423', 'G639', 'G328', 'G339', 'G424', 'G330', 'G299', 'G300', 'G419', 'G421', 'G420', 'G638'] + +[62/218] 2018-03-09.10-40.hospital (missing: ['G341']) + OK: 47 → 48 actors (+1), cameras: ['G341', 'G301', 'G436'] + +[63/218] 2018-03-09.10-40.school (missing: ['G328', 'G339', 'G420', 'G424', 'G638']) + OK: 679 → 1032 actors (+353), cameras: ['G330', 'G336', 'G419', 'G339', 'G299', 'G300', 'G421', 'G423', 'G639', 'G328', 'G420', 'G424', 'G638'] + +[64/218] 2018-03-11.11-15.school (missing: ['G300', 'G424', 'G638']) + OK: 467 → 644 actors (+177), cameras: ['G299', 'G328', 'G330', 'G336', 'G419', 'G420', 'G421', 'G424', 'G638', 'G300'] + +[65/218] 2018-03-11.11-20.admin (missing: ['G326']) + OK: 12 → 18 actors (+6), cameras: ['G329', 'G326'] + +[66/218] 2018-03-11.11-20.bus (missing: ['G340', 'G505', 'G509']) + OK: 9 → 17 actors (+8), cameras: ['G331', 'G506', 'G508', 'G505', 'G509', 'G340'] + +[67/218] 2018-03-11.11-20.school (missing: ['G300', 'G339', 'G424', 'G638']) + OK: 1799 → 1832 actors (+33), cameras: ['G299', 'G328', 'G330', 'G336', 'G419', 'G420', 'G421', 'G423', 'G639', 'G424', 'G638', 'G300', 'G339'] + +[68/218] 2018-03-11.11-25.admin (missing: ['G326']) + OK: 26 → 44 actors (+18), cameras: ['G329', 'G326'] + +[69/218] 2018-03-11.11-25.bus (missing: ['G340', 'G505', 'G509']) + OK: 1 → 21 actors (+20), cameras: ['G331', 'G506', 'G508', 'G505', 'G509', 'G340'] + +[70/218] 2018-03-11.11-25.school (missing: ['G300', 'G339', 'G424', 'G638', 'G639']) + OK: 1199 → 1261 actors (+62), cameras: ['G299', 'G328', 'G330', 'G336', 'G420', 'G423', 'G421', 'G424', 'G638', 'G300', 'G419', 'G339', 'G639'] + +[71/218] 2018-03-11.11-30.admin (missing: ['G326']) + OK: 11 → 17 actors (+6), cameras: ['G329', 'G326'] + +[72/218] 2018-03-11.11-30.bus (missing: ['G340', 'G505', 'G509']) + OK: 3 → 13 actors (+10), cameras: ['G331', 'G506', 'G508', 'G505', 'G509', 'G340'] + +[73/218] 2018-03-11.11-30.school (missing: ['G300', 'G339', 'G421', 'G424', 'G638', 'G639']) + OK: 689 → 843 actors (+154), cameras: ['G328', 'G330', 'G336', 'G419', 'G420', 'G423', 'G421', 'G424', 'G638', 'G300', 'G299', 'G339', 'G639'] + +[74/218] 2018-03-11.11-35.bus (missing: ['G340', 'G505', 'G509']) + OK: 1 → 8 actors (+7), cameras: ['G331', 'G506', 'G508', 'G505', 'G509', 'G340'] + +[75/218] 2018-03-11.11-35.school (missing: ['G300', 'G339', 'G421', 'G424', 'G638', 'G639']) + OK: 318 → 416 actors (+98), cameras: ['G299', 'G328', 'G330', 'G336', 'G419', 'G420', 'G423', 'G421', 'G424', 'G638', 'G300', 'G339', 'G639'] + +[76/218] 2018-03-11.11-40.school (missing: ['G300', 'G339', 'G421', 'G423', 'G424', 'G638', 'G639']) + OK: 330 → 393 actors (+63), cameras: ['G299', 'G328', 'G336', 'G419', 'G420', 'G474', 'G424', 'G638', 'G423', 'G300', 'G330', 'G339', 'G421', 'G639'] + +[77/218] 2018-03-11.11-45.school (missing: ['G300', 'G339', 'G421', 'G424', 'G638', 'G639']) + OK: 993 → 1057 actors (+64), cameras: ['G423', 'G299', 'G328', 'G330', 'G336', 'G419', 'G420', 'G474', 'G421', 'G424', 'G638', 'G300', 'G339', 'G639'] + +[78/218] 2018-03-11.11-50.admin (missing: ['G326']) + OK: 2 → 4 actors (+2), cameras: ['G329', 'G326'] + +[79/218] 2018-03-11.11-50.bus (missing: ['G340', 'G505', 'G509']) + OK: 1 → 9 actors (+8), cameras: ['G331', 'G506', 'G508', 'G505', 'G509', 'G340'] + +[80/218] 2018-03-11.11-50.school (missing: ['G300', 'G339', 'G421', 'G424', 'G638', 'G639']) + OK: 2098 → 2270 actors (+172), cameras: ['G423', 'G299', 'G328', 'G336', 'G419', 'G420', 'G421', 'G424', 'G638', 'G639', 'G300', 'G330', 'G339'] + +[81/218] 2018-03-11.13-50.admin (missing: ['G329']) + OK: 8 → 22 actors (+14), cameras: ['G326', 'G329'] + +[82/218] 2018-03-11.13-50.bus (missing: ['G340', 'G505', 'G506', 'G508']) + OK: 3 → 28 actors (+25), cameras: ['G340', 'G331', 'G509', 'G505', 'G506', 'G508'] + +[83/218] 2018-03-11.13-50.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 300 → 567 actors (+267), cameras: ['G339', 'G421', 'G423', 'G330', 'G299', 'G420', 'G638', 'G336', 'G328', 'G419', 'G424', 'G639', 'G300'] + +[84/218] 2018-03-11.14-00.admin (missing: ['G329']) + OK: 5 → 10 actors (+5), cameras: ['G326', 'G329'] + +[85/218] 2018-03-11.14-00.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 598 → 727 actors (+129), cameras: ['G423', 'G424', 'G299', 'G330', 'G420', 'G638', 'G336', 'G328', 'G339', 'G419', 'G421', 'G639', 'G300'] + +[86/218] 2018-03-11.14-05.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 278 → 393 actors (+115), cameras: ['G420', 'G423', 'G299', 'G330', 'G638', 'G328', 'G336', 'G339', 'G419', 'G421', 'G639', 'G424', 'G300'] + +[87/218] 2018-03-11.14-10.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 748 → 818 actors (+70), cameras: ['G299', 'G330', 'G420', 'G638', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G639', 'G424', 'G300'] + +[88/218] 2018-03-11.14-15.admin (missing: ['G329']) + OK: 5 → 12 actors (+7), cameras: ['G326', 'G329'] + +[89/218] 2018-03-11.14-15.bus (missing: ['G340', 'G505', 'G506', 'G508']) + OK: 2 → 19 actors (+17), cameras: ['G340', 'G331', 'G509', 'G505', 'G506', 'G508'] + +[90/218] 2018-03-11.14-15.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 1596 → 1932 actors (+336), cameras: ['G299', 'G638', 'G339', 'G423', 'G639', 'G330', 'G420', 'G336', 'G328', 'G419', 'G421', 'G424', 'G300'] + +[91/218] 2018-03-11.14-20.bus (missing: ['G340', 'G505', 'G506', 'G508']) + OK: 5 → 47 actors (+42), cameras: ['G506', 'G508', 'G340', 'G331', 'G509', 'G505'] + +[92/218] 2018-03-11.14-20.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 139 → 239 actors (+100), cameras: ['G299', 'G330', 'G420', 'G419', 'G424', 'G638', 'G328', 'G336', 'G339', 'G421', 'G423', 'G639', 'G300'] + +[93/218] 2018-03-11.16-10.bus (missing: ['G340', 'G506']) + OK: 17 → 23 actors (+6), cameras: ['G331', 'G505', 'G509', 'G506', 'G340'] + +[94/218] 2018-03-11.16-15.bus (missing: ['G340', 'G506', 'G508']) + OK: 26 → 35 actors (+9), cameras: ['G331', 'G505', 'G509', 'G506', 'G340', 'G508'] + +[95/218] 2018-03-11.16-15.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G420', 'G423', 'G424']) + OK: 1258 → 1410 actors (+152), cameras: ['G638', 'G299', 'G421', 'G423', 'G328', 'G336', 'G339', 'G419', 'G420', 'G424', 'G300', 'G330', 'G639'] + +[96/218] 2018-03-11.16-20.bus (missing: ['G340', 'G506', 'G508']) + OK: 7 → 21 actors (+14), cameras: ['G331', 'G505', 'G509', 'G506', 'G340', 'G508'] + +[97/218] 2018-03-11.16-20.school (missing: ['G300', 'G328', 'G336', 'G419', 'G420', 'G424']) + OK: 752 → 936 actors (+184), cameras: ['G299', 'G330', 'G421', 'G423', 'G638', 'G328', 'G336', 'G419', 'G420', 'G424', 'G300', 'G639'] + +[98/218] 2018-03-11.16-25.bus (missing: ['G340', 'G506', 'G508']) + OK: 7 → 14 actors (+7), cameras: ['G331', 'G505', 'G509', 'G506', 'G340', 'G508'] + +[99/218] 2018-03-11.16-25.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G420', 'G423', 'G424', 'G638']) + OK: 498 → 640 actors (+142), cameras: ['G299', 'G330', 'G421', 'G639', 'G423', 'G328', 'G336', 'G339', 'G419', 'G420', 'G638', 'G424', 'G300'] + +[100/218] 2018-03-11.16-30.hospital (missing: ['G301', 'G341', 'G436']) + OK: 0 → 0 actors (+0), cameras: ['G476', 'G479', 'G301', 'G436', 'G341'] + +[101/218] 2018-03-11.16-30.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G420', 'G423', 'G424', 'G638']) + OK: 206 → 273 actors (+67), cameras: ['G299', 'G330', 'G421', 'G474', 'G423', 'G328', 'G336', 'G339', 'G419', 'G420', 'G638', 'G424', 'G300', 'G639'] + +[102/218] 2018-03-11.16-35.bus (missing: ['G340', 'G506', 'G508']) + OK: 1 → 7 actors (+6), cameras: ['G331', 'G505', 'G509', 'G506', 'G340', 'G508'] + +[103/218] 2018-03-11.16-35.hospital (missing: ['G301', 'G341', 'G436']) + OK: 0 → 21 actors (+21), cameras: ['G479', 'G301', 'G341', 'G436'] + +[104/218] 2018-03-11.16-35.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G420', 'G423', 'G424', 'G638']) + OK: 1762 → 2201 actors (+439), cameras: ['G330', 'G421', 'G423', 'G328', 'G336', 'G339', 'G419', 'G420', 'G638', 'G424', 'G300', 'G299', 'G639'] + +[105/218] 2018-03-11.16-40.bus (missing: ['G340', 'G506', 'G508']) + OK: 21 → 28 actors (+7), cameras: ['G331', 'G505', 'G509', 'G506', 'G340', 'G508'] + +[106/218] 2018-03-11.16-40.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G420', 'G424', 'G638']) + OK: 579 → 660 actors (+81), cameras: ['G299', 'G330', 'G421', 'G423', 'G328', 'G336', 'G339', 'G419', 'G420', 'G638', 'G424', 'G300', 'G639'] + +[107/218] 2018-03-11.17-10.bus (missing: ['G340', 'G506', 'G509']) + OK: 13 → 27 actors (+14), cameras: ['G505', 'G331', 'G508', 'G509', 'G506', 'G340'] + +[108/218] 2018-03-11.17-10.hospital (missing: ['G341']) + OK: 34 → 42 actors (+8), cameras: ['G341', 'G301', 'G436'] + +[109/218] 2018-03-11.17-10.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424', 'G638']) + OK: 755 → 1117 actors (+362), cameras: ['G330', 'G421', 'G423', 'G639', 'G339', 'G419', 'G424', 'G299', 'G328', 'G336', 'G420', 'G638', 'G300'] + +[110/218] 2018-03-11.17-15.bus (missing: ['G340', 'G506']) + OK: 3 → 5 actors (+2), cameras: ['G509', 'G331', 'G505', 'G508', 'G506', 'G340'] + +[111/218] 2018-03-11.17-15.hospital (missing: ['G341']) + OK: 0 → 0 actors (+0), cameras: ['G301', 'G436', 'G341'] + +[112/218] 2018-03-11.17-15.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424', 'G638']) + OK: 1355 → 1436 actors (+81), cameras: ['G299', 'G423', 'G336', 'G330', 'G421', 'G639', 'G339', 'G419', 'G420', 'G638', 'G424', 'G300'] + +[113/218] 2018-03-11.17-20.bus (missing: ['G340', 'G506']) + OK: 2 → 2 actors (+0), cameras: ['G509', 'G331', 'G505', 'G508', 'G506', 'G340'] + +[114/218] 2018-03-11.17-20.hospital (missing: ['G341']) + OK: 6 → 6 actors (+0), cameras: ['G436', 'G301', 'G341'] + +[115/218] 2018-03-11.17-20.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424', 'G638']) + OK: 1043 → 1130 actors (+87), cameras: ['G328', 'G423', 'G420', 'G299', 'G330', 'G421', 'G639', 'G336', 'G339', 'G419', 'G638', 'G424', 'G300'] + +[116/218] 2018-03-11.17-25.bus (missing: ['G340', 'G506']) + OK: 3 → 8 actors (+5), cameras: ['G506', 'G340', 'G331', 'G505', 'G508', 'G509'] + +[117/218] 2018-03-11.17-25.hospital (missing: ['G341']) + OK: 46 → 46 actors (+0), cameras: ['G301', 'G436', 'G341'] + +[118/218] 2018-03-11.17-25.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424', 'G638']) + OK: 1684 → 1935 actors (+251), cameras: ['G299', 'G328', 'G330', 'G423', 'G336', 'G419', 'G638', 'G424', 'G300', 'G421', 'G639', 'G339', 'G420'] + +[119/218] 2018-03-12.10-00.bus (missing: ['G509']) + OK: 9 → 9 actors (+0), cameras: ['G340', 'G331', 'G505', 'G506', 'G508', 'G509'] + +[120/218] 2018-03-12.10-00.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G638', 'G639']) + OK: 144 → 474 actors (+330), cameras: ['G299', 'G330', 'G420', 'G638', 'G328', 'G419', 'G421', 'G423', 'G639', 'G336', 'G339', 'G424', 'G300'] + +[121/218] 2018-03-12.10-05.bus (missing: ['G506', 'G509']) + OK: 15 → 15 actors (+0), cameras: ['G340', 'G331', 'G505', 'G508', 'G506', 'G509'] + +[122/218] 2018-03-12.10-05.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G638', 'G639']) + OK: 555 → 1202 actors (+647), cameras: ['G299', 'G330', 'G328', 'G639', 'G420', 'G419', 'G421', 'G423', 'G638', 'G336', 'G339', 'G424', 'G300'] + +[123/218] 2018-03-12.10-10.bus (missing: ['G509']) + OK: 7 → 7 actors (+0), cameras: ['G340', 'G331', 'G505', 'G506', 'G508', 'G509'] + +[124/218] 2018-03-12.10-10.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G638', 'G639']) + OK: 601 → 839 actors (+238), cameras: ['G421', 'G423', 'G299', 'G330', 'G420', 'G328', 'G419', 'G638', 'G639', 'G336', 'G339', 'G424', 'G300'] + +[125/218] 2018-03-12.10-15.bus (missing: ['G509']) + OK: 1 → 1 actors (+0), cameras: ['G506', 'G340', 'G331', 'G505', 'G508', 'G509'] + +[126/218] 2018-03-12.10-15.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G638', 'G639']) + OK: 573 → 727 actors (+154), cameras: ['G299', 'G336', 'G424', 'G330', 'G420', 'G421', 'G638', 'G328', 'G419', 'G423', 'G639', 'G339', 'G300'] + +[127/218] 2018-03-12.10-20.bus (missing: ['G509']) + OK: 2 → 2 actors (+0), cameras: ['G505', 'G331', 'G340', 'G506', 'G508', 'G509'] + +[128/218] 2018-03-12.10-20.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G638', 'G639']) + OK: 619 → 764 actors (+145), cameras: ['G299', 'G330', 'G420', 'G421', 'G638', 'G328', 'G419', 'G423', 'G639', 'G336', 'G339', 'G424', 'G300'] + +[129/218] 2018-03-12.10-25.bus (missing: ['G509']) + OK: 20 → 20 actors (+0), cameras: ['G340', 'G331', 'G505', 'G506', 'G508', 'G509'] + +[130/218] 2018-03-12.10-25.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G421', 'G423', 'G424', 'G639']) + OK: 1380 → 1734 actors (+354), cameras: ['G299', 'G638', 'G421', 'G328', 'G336', 'G300', 'G330', 'G420', 'G419', 'G423', 'G639', 'G339', 'G424'] + +[131/218] 2018-03-12.10-30.admin (missing: ['G329']) + OK: 12 → 27 actors (+15), cameras: ['G326', 'G329'] + +[132/218] 2018-03-12.10-30.bus (missing: ['G509']) + OK: 28 → 28 actors (+0), cameras: ['G505', 'G331', 'G340', 'G506', 'G508', 'G509'] + +[133/218] 2018-03-12.10-30.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G639']) + OK: 106 → 334 actors (+228), cameras: ['G330', 'G421', 'G336', 'G424', 'G299', 'G420', 'G638', 'G328', 'G419', 'G423', 'G639', 'G339', 'G300'] + +[134/218] 2018-03-12.10-40.admin (missing: ['G329']) + OK: 7 → 22 actors (+15), cameras: ['G329', 'G326'] + +[135/218] 2018-03-12.10-40.bus (missing: ['G509']) + OK: 39 → 39 actors (+0), cameras: ['G340', 'G331', 'G505', 'G506', 'G508', 'G509'] + +[136/218] 2018-03-12.10-40.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G639']) + OK: 304 → 679 actors (+375), cameras: ['G299', 'G330', 'G421', 'G638', 'G339', 'G420', 'G328', 'G419', 'G423', 'G639', 'G336', 'G424', 'G300'] + +[137/218] 2018-03-12.10-45.admin (missing: ['G329']) + OK: 0 → 0 actors (+0), cameras: ['G326', 'G329'] + +[138/218] 2018-03-12.10-45.bus (missing: ['G509']) + OK: 17 → 17 actors (+0), cameras: ['G505', 'G331', 'G340', 'G506', 'G508', 'G509'] + +[139/218] 2018-03-12.10-45.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G639']) + OK: 756 → 1034 actors (+278), cameras: ['G330', 'G421', 'G328', 'G639', 'G420', 'G299', 'G638', 'G419', 'G423', 'G336', 'G339', 'G424', 'G300'] + +[140/218] 2018-03-12.10-50.admin (missing: ['G326']) + OK: 5 → 10 actors (+5), cameras: ['G329', 'G326'] + +[141/218] 2018-03-12.10-50.bus (missing: ['G509']) + OK: 30 → 30 actors (+0), cameras: ['G340', 'G331', 'G505', 'G506', 'G508', 'G509'] + +[142/218] 2018-03-12.10-50.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G639']) + OK: 708 → 834 actors (+126), cameras: ['G299', 'G330', 'G423', 'G639', 'G421', 'G638', 'G420', 'G328', 'G419', 'G336', 'G339', 'G424', 'G300'] + +[143/218] 2018-03-12.11-00.bus (missing: ['G509']) + OK: 32 → 32 actors (+0), cameras: ['G340', 'G331', 'G505', 'G506', 'G508', 'G509'] + +[144/218] 2018-03-12.11-00.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G423', 'G424', 'G639']) + OK: 651 → 814 actors (+163), cameras: ['G423', 'G639', 'G424', 'G420', 'G299', 'G330', 'G421', 'G638', 'G419', 'G328', 'G336', 'G339', 'G300'] + +[145/218] 2018-03-12.11-05.bus (missing: ['G509']) + OK: 14 → 14 actors (+0), cameras: ['G331', 'G340', 'G505', 'G506', 'G508', 'G509'] + +[146/218] 2018-03-12.11-05.school (missing: ['G300', 'G328', 'G336', 'G339', 'G423', 'G424', 'G639']) + OK: 1093 → 1503 actors (+410), cameras: ['G330', 'G420', 'G328', 'G423', 'G299', 'G421', 'G638', 'G419', 'G639', 'G336', 'G339', 'G424', 'G300'] + +[147/218] 2018-03-12.11-10.school (missing: ['G300', 'G328', 'G336', 'G339', 'G419', 'G420', 'G423', 'G424', 'G639']) + OK: 65 → 330 actors (+265), cameras: ['G638', 'G328', 'G336', 'G424', 'G299', 'G330', 'G421', 'G419', 'G420', 'G423', 'G639', 'G339', 'G300'] + +[148/218] 2018-03-13.15-50.bus (missing: ['G340']) + OK: 21 → 46 actors (+25), cameras: ['G505', 'G506', 'G331', 'G508', 'G509', 'G340'] + +[149/218] 2018-03-13.15-50.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 490 → 685 actors (+195), cameras: ['G421', 'G638', 'G419', 'G336', 'G299', 'G328', 'G330', 'G423', 'G639', 'G420', 'G424', 'G339', 'G300'] + +[150/218] 2018-03-13.16-00.bus (missing: ['G340']) + OK: 31 → 51 actors (+20), cameras: ['G505', 'G331', 'G506', 'G508', 'G509', 'G340'] + +[151/218] 2018-03-13.16-00.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 35 → 248 actors (+213), cameras: ['G638', 'G639', 'G424', 'G336', 'G299', 'G328', 'G330', 'G423', 'G419', 'G420', 'G339', 'G300'] + +[152/218] 2018-03-13.16-05.bus (missing: ['G340']) + OK: 36 → 45 actors (+9), cameras: ['G505', 'G506', 'G331', 'G508', 'G509', 'G340'] + +[153/218] 2018-03-13.16-05.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 33 → 255 actors (+222), cameras: ['G328', 'G421', 'G424', 'G336', 'G299', 'G330', 'G423', 'G638', 'G639', 'G419', 'G420', 'G339', 'G300'] + +[154/218] 2018-03-13.16-10.bus (missing: ['G340']) + OK: 88 → 112 actors (+24), cameras: ['G506', 'G331', 'G505', 'G508', 'G509', 'G340'] + +[155/218] 2018-03-13.16-10.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 66 → 359 actors (+293), cameras: ['G423', 'G424', 'G299', 'G328', 'G330', 'G421', 'G638', 'G639', 'G419', 'G420', 'G336', 'G339', 'G300'] + +[156/218] 2018-03-13.16-15.bus (missing: ['G340']) + OK: 47 → 54 actors (+7), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[157/218] 2018-03-13.16-15.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 15 → 224 actors (+209), cameras: ['G299', 'G328', 'G330', 'G423', 'G638', 'G639', 'G419', 'G420', 'G424', 'G336', 'G339', 'G300'] + +[158/218] 2018-03-13.16-20.bus (missing: ['G340']) + OK: 4 → 21 actors (+17), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[159/218] 2018-03-13.16-20.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 368 → 803 actors (+435), cameras: ['G336', 'G424', 'G339', 'G299', 'G328', 'G330', 'G421', 'G423', 'G638', 'G639', 'G419', 'G420', 'G300'] + +[160/218] 2018-03-13.16-25.bus (missing: ['G340']) + OK: 4 → 6 actors (+2), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[161/218] 2018-03-13.16-25.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 47 → 88 actors (+41), cameras: ['G299', 'G328', 'G330', 'G423', 'G638', 'G639', 'G419', 'G420', 'G336', 'G424', 'G339', 'G300'] + +[162/218] 2018-03-13.16-30.bus (missing: ['G340']) + OK: 1 → 3 actors (+2), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[163/218] 2018-03-13.16-30.hospital (missing: ['G301']) + OK: 2 → 2 actors (+0), cameras: ['G341', 'G436', 'G301'] + +[164/218] 2018-03-13.16-30.school (missing: ['G300', 'G336', 'G339', 'G419', 'G420', 'G424']) + OK: 485 → 728 actors (+243), cameras: ['G423', 'G299', 'G328', 'G330', 'G638', 'G639', 'G419', 'G420', 'G336', 'G424', 'G339', 'G300'] + +[165/218] 2018-03-13.17-05.bus (missing: ['G340']) + OK: 16 → 30 actors (+14), cameras: ['G505', 'G331', 'G506', 'G508', 'G509', 'G340'] + +[166/218] 2018-03-13.17-05.hospital (missing: ['G301', 'G436']) + OK: 0 → 48 actors (+48), cameras: ['G341', 'G301', 'G436'] + +[167/218] 2018-03-13.17-05.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 454 → 648 actors (+194), cameras: ['G299', 'G423', 'G639', 'G424', 'G328', 'G330', 'G420', 'G421', 'G638', 'G419', 'G336', 'G339', 'G300'] + +[168/218] 2018-03-13.17-10.bus (missing: ['G340']) + OK: 0 → 18 actors (+18), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[169/218] 2018-03-13.17-10.hospital (missing: ['G301', 'G436']) + OK: 0 → 133 actors (+133), cameras: ['G436', 'G341', 'G301'] + +[170/218] 2018-03-13.17-10.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 406 → 654 actors (+248), cameras: ['G421', 'G336', 'G299', 'G328', 'G330', 'G420', 'G423', 'G638', 'G639', 'G419', 'G424', 'G339', 'G300'] + +[171/218] 2018-03-13.17-15.bus (missing: ['G340']) + OK: 11 → 32 actors (+21), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[172/218] 2018-03-13.17-15.hospital (missing: ['G301', 'G436']) + OK: 0 → 82 actors (+82), cameras: ['G341', 'G301', 'G436'] + +[173/218] 2018-03-13.17-15.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 60 → 227 actors (+167), cameras: ['G336', 'G424', 'G299', 'G328', 'G330', 'G420', 'G423', 'G638', 'G639', 'G419', 'G339', 'G300'] + +[174/218] 2018-03-13.17-20.bus (missing: ['G340']) + OK: 19 → 25 actors (+6), cameras: ['G331', 'G505', 'G506', 'G508', 'G509', 'G340'] + +[175/218] 2018-03-13.17-20.hospital (missing: ['G301', 'G436']) + OK: 0 → 190 actors (+190), cameras: ['G341', 'G301', 'G436'] + +[176/218] 2018-03-13.17-20.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 83 → 322 actors (+239), cameras: ['G336', 'G299', 'G328', 'G330', 'G420', 'G423', 'G638', 'G639', 'G419', 'G424', 'G339', 'G300'] + +[177/218] 2018-03-13.17-25.bus (missing: ['G340']) + OK: 81 → 91 actors (+10), cameras: ['G331', 'G506', 'G508', 'G509', 'G505', 'G340'] + +[178/218] 2018-03-13.17-25.hospital (missing: ['G301', 'G436']) + OK: 4 → 143 actors (+139), cameras: ['G436', 'G341', 'G301'] + +[179/218] 2018-03-13.17-25.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 101 → 339 actors (+238), cameras: ['G421', 'G336', 'G299', 'G328', 'G330', 'G420', 'G423', 'G638', 'G639', 'G419', 'G424', 'G339', 'G300'] + +[180/218] 2018-03-13.17-30.bus (missing: ['G340', 'G505']) + OK: 18 → 65 actors (+47), cameras: ['G331', 'G506', 'G508', 'G509', 'G505', 'G340'] + +[181/218] 2018-03-13.17-30.hospital (missing: ['G301', 'G436']) + OK: 30 → 124 actors (+94), cameras: ['G341', 'G301', 'G436'] + +[182/218] 2018-03-13.17-30.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 49 → 176 actors (+127), cameras: ['G299', 'G328', 'G330', 'G420', 'G421', 'G423', 'G638', 'G639', 'G419', 'G336', 'G424', 'G339', 'G300'] + +[183/218] 2018-03-13.17-35.bus (missing: ['G340', 'G505']) + OK: 0 → 18 actors (+18), cameras: ['G331', 'G506', 'G508', 'G509', 'G505', 'G340'] + +[184/218] 2018-03-13.17-35.hospital (missing: ['G301', 'G436']) + OK: 0 → 42 actors (+42), cameras: ['G436', 'G341', 'G301'] + +[185/218] 2018-03-13.17-35.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 401 → 649 actors (+248), cameras: ['G330', 'G419', 'G299', 'G328', 'G420', 'G421', 'G423', 'G638', 'G639', 'G336', 'G424', 'G339', 'G300'] + +[186/218] 2018-03-13.17-40.bus (missing: ['G340', 'G505']) + OK: 0 → 1 actors (+1), cameras: ['G331', 'G506', 'G508', 'G509', 'G505', 'G340'] + +[187/218] 2018-03-13.17-40.hospital (missing: ['G301', 'G436']) + OK: 0 → 10 actors (+10), cameras: ['G341', 'G301', 'G436'] + +[188/218] 2018-03-13.17-40.school (missing: ['G300', 'G336', 'G339', 'G419', 'G424']) + OK: 72 → 100 actors (+28), cameras: ['G300', 'G639', 'G299', 'G328', 'G330', 'G420', 'G423', 'G638', 'G419', 'G336', 'G424', 'G339'] + +[189/218] 2018-03-15.14-50.bus (missing: ['G340', 'G506']) + OK: 147 → 290 actors (+143), cameras: ['G508', 'G340', 'G331', 'G505', 'G509', 'G506'] + +[190/218] 2018-03-15.14-50.hospital (missing: ['G301', 'G436']) + OK: 104 → 205 actors (+101), cameras: ['G341', 'G436', 'G301'] + +[191/218] 2018-03-15.14-50.school (missing: ['G300', 'G328', 'G420']) + OK: 551 → 600 actors (+49), cameras: ['G421', 'G420', 'G300', 'G299', 'G330', 'G336', 'G339', 'G419', 'G424', 'G638', 'G639', 'G328'] + +[192/218] 2018-03-15.15-00.bus (missing: ['G340', 'G506']) + OK: 174 → 293 actors (+119), cameras: ['G340', 'G331', 'G505', 'G508', 'G509', 'G506'] + +[193/218] 2018-03-15.15-00.hospital (missing: ['G301', 'G436']) + OK: 89 → 198 actors (+109), cameras: ['G341', 'G301', 'G436'] + +[194/218] 2018-03-15.15-00.school (missing: ['G300', 'G328', 'G420']) + OK: 544 → 597 actors (+53), cameras: ['G299', 'G330', 'G339', 'G336', 'G419', 'G421', 'G423', 'G424', 'G638', 'G639', 'G328', 'G420', 'G300'] + +[195/218] 2018-03-15.15-05.bus (missing: ['G340', 'G506']) + OK: 154 → 259 actors (+105), cameras: ['G508', 'G331', 'G505', 'G509', 'G506', 'G340'] + +[196/218] 2018-03-15.15-05.hospital (missing: ['G301']) + OK: 52 → 52 actors (+0), cameras: ['G341', 'G301'] + +[197/218] 2018-03-15.15-05.school (missing: ['G300', 'G328', 'G420']) + OK: 264 → 291 actors (+27), cameras: ['G336', 'G339', 'G299', 'G330', 'G419', 'G421', 'G424', 'G638', 'G639', 'G328', 'G420', 'G300'] + +[198/218] 2018-03-15.15-10.bus (missing: ['G506']) + OK: 160 → 227 actors (+67), cameras: ['G331', 'G508', 'G509', 'G506'] + +[199/218] 2018-03-15.15-10.hospital (missing: ['G301']) + OK: 126 → 130 actors (+4), cameras: ['G341', 'G301'] + +[200/218] 2018-03-15.15-10.school (missing: ['G300', 'G328', 'G420']) + OK: 393 → 429 actors (+36), cameras: ['G336', 'G339', 'G421', 'G424', 'G299', 'G330', 'G419', 'G423', 'G638', 'G639', 'G328', 'G420', 'G300'] + +[201/218] 2018-03-15.15-15.bus (missing: ['G340', 'G506']) + OK: 161 → 262 actors (+101), cameras: ['G331', 'G505', 'G508', 'G509', 'G506', 'G340'] + +[202/218] 2018-03-15.15-15.hospital (missing: ['G301', 'G436']) + OK: 55 → 165 actors (+110), cameras: ['G341', 'G301', 'G436'] + +[203/218] 2018-03-15.15-15.school (missing: ['G300', 'G328', 'G420']) + OK: 465 → 482 actors (+17), cameras: ['G339', 'G421', 'G424', 'G638', 'G300', 'G299', 'G330', 'G336', 'G423', 'G639', 'G419', 'G328', 'G420'] + +[204/218] 2018-03-15.15-30.bus (missing: ['G340', 'G506']) + OK: 100 → 247 actors (+147), cameras: ['G506', 'G331', 'G505', 'G508', 'G509', 'G340'] + +[205/218] 2018-03-15.15-30.hospital (missing: ['G301', 'G436']) + OK: 121 → 220 actors (+99), cameras: ['G341', 'G301', 'G436'] + +[206/218] 2018-03-15.15-30.school (missing: ['G300', 'G328', 'G419']) + OK: 402 → 465 actors (+63), cameras: ['G339', 'G421', 'G424', 'G639', 'G299', 'G330', 'G336', 'G638', 'G328', 'G419', 'G300'] + +[207/218] 2018-03-15.15-35.bus (missing: ['G340', 'G506']) + OK: 105 → 169 actors (+64), cameras: ['G340', 'G331', 'G505', 'G508', 'G509', 'G506'] + +[208/218] 2018-03-15.15-35.hospital (missing: ['G301', 'G436']) + OK: 66 → 169 actors (+103), cameras: ['G341', 'G301', 'G436'] + +[209/218] 2018-03-15.15-35.school (missing: ['G300', 'G328', 'G419']) + OK: 607 → 701 actors (+94), cameras: ['G339', 'G421', 'G638', 'G328', 'G299', 'G330', 'G336', 'G424', 'G639', 'G419', 'G300'] + +[210/218] 2018-03-15.15-40.bus (missing: ['G340', 'G506']) + OK: 129 → 235 actors (+106), cameras: ['G508', 'G331', 'G505', 'G509', 'G506', 'G340'] + +[211/218] 2018-03-15.15-40.hospital (missing: ['G301', 'G436']) + OK: 58 → 165 actors (+107), cameras: ['G341', 'G301', 'G436'] + +[212/218] 2018-03-15.15-40.school (missing: ['G300', 'G328', 'G419', 'G420', 'G639']) + OK: 626 → 738 actors (+112), cameras: ['G339', 'G299', 'G330', 'G336', 'G421', 'G423', 'G424', 'G638', 'G420', 'G328', 'G419', 'G639', 'G300'] + +[213/218] 2018-03-15.15-45.bus (missing: ['G340', 'G506']) + OK: 197 → 406 actors (+209), cameras: ['G505', 'G506', 'G331', 'G508', 'G509', 'G340'] + +[214/218] 2018-03-15.15-45.hospital (missing: ['G301', 'G436']) + OK: 75 → 264 actors (+189), cameras: ['G436', 'G341', 'G301'] + +[215/218] 2018-03-15.15-45.school (missing: ['G300', 'G328', 'G419', 'G420', 'G639']) + OK: 631 → 673 actors (+42), cameras: ['G336', 'G339', 'G638', 'G300', 'G299', 'G330', 'G421', 'G423', 'G424', 'G420', 'G328', 'G419', 'G639'] + +[216/218] 2018-03-15.15-50.bus (missing: ['G340', 'G506']) + OK: 79 → 166 actors (+87), cameras: ['G331', 'G508', 'G505', 'G509', 'G506', 'G340'] + +[217/218] 2018-03-15.15-50.hospital (missing: ['G301', 'G436']) + OK: 73 → 138 actors (+65), cameras: ['G341', 'G301', 'G436'] + +[218/218] 2018-03-15.15-50.school (missing: ['G300', 'G328', 'G419', 'G639']) + OK: 448 → 508 actors (+60), cameras: ['G336', 'G339', 'G421', 'G299', 'G330', 'G420', 'G423', 'G424', 'G638', 'G328', 'G419', 'G639', 'G300'] + +============================================================ +DONE: 218 succeeded, 0 failed +Total new actors: +22272 +============================================================ diff --git a/meva/run.sh b/meva/run.sh index 3fd7b53..8e7ef3c 100755 --- a/meva/run.sh +++ b/meva/run.sh @@ -1,9 +1,69 @@ #!/usr/bin/env bash +# run.sh — Full V10 QA pipeline for one MEVA slot. +# +# SETUP (one-time): +# 1. cd into this directory: cd /path/to/repo/meva +# 2. Set your API key: export OPENAI_API_KEY=sk-... +# 3. (Optional) Set output dir: export MEVA_OUTPUT_DIR=~/data +# Defaults to ~/data if not set. QA JSON + logs go there. +# 4. Run: bash run.sh +# +# DATASET (shared, read-only): +# /nas/mars/dataset/MEVA/ — videos, annotations, camera models +# +# REQUIREMENTS: +# pip install pyyaml numpy opencv-python openai +# (or: source your-venv/bin/activate) -# Add current directory to path -export PYTHONPATH=$PYTHONPATH:$(pwd) +# set -e -python -m scripts.v6.run_pipeline \ - --slot "2018-03-11.11-25-00.school" \ + +OUTPUT_DIR="/nas/neurosymbolic/multi-cam-dataset/meva/data" +mkdir -p "$OUTPUT_DIR/qa_pairs/raw" +ENV_FILE="${MEVA_ENV_FILE:-$HOME/.env}" # defaults to ~/.env; override with MEVA_ENV_FILE +export MEVA_OUTPUT_DIR="/nas/neurosymbolic/multi-cam-dataset/meva/data/" + + +# Parse args: extract --n flag and positional slot +NATURALIZE=false +SLOT="" +for arg in "$@"; do + if [[ "$arg" == "--n" ]]; then + NATURALIZE=true + elif [[ "$arg" != --* ]]; then + SLOT="$arg" + fi +done +SLOT="${SLOT:-2018-03-09.10-15.school}" + +# export PYTHONPATH=$PYTHONPATH:$(pwd) + +# echo "=== Step 1: Raw QA generation (slot: $SLOT) ===" +python3 -m scripts.v10.run_pipeline \ + --slot "$SLOT" \ -v \ - --seed 42 \ + --seed 42 + + +RAW_JSON="$OUTPUT_DIR/qa_pairs/raw/$SLOT.raw.json" + +# echo "" +# echo "=== Step 2: Naturalization (GPT — requires OPENAI_API_KEY) ===" +if $NATURALIZE; then + # Load .env for OPENAI_API_KEY if not already set + if [[ -z "$OPENAI_API_KEY" && -f "$ENV_FILE" ]]; then + set -a; source "$ENV_FILE"; set +a + fi + python3 -m scripts.v10.naturalize \ + --input "$RAW_JSON" \ + --output "$OUTPUT_DIR/qa_pairs/$SLOT.naturalized.json" \ + -v --yes +fi + +# echo "" +# echo "=== Step 3: Export to multi-cam-dataset format ===" +# python3 -m scripts.v10.export_to_multicam_format \ +# --slot "$SLOT" + +# echo "" +# echo "Done. Output in $OUTPUT_DIR/qa_pairs/$SLOT/" diff --git a/meva/scripts/convert_naturalized_to_standard.py b/meva/scripts/convert_naturalized_to_standard.py new file mode 100644 index 0000000..a407c6a --- /dev/null +++ b/meva/scripts/convert_naturalized_to_standard.py @@ -0,0 +1,232 @@ +#!/usr/bin/env python3 +""" +Convert MEVA naturalized QA JSONs into standardized per-category formats. +""" + +import argparse +import hashlib +import json +import re +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional + + +DEFAULT_INPUT_DIR = Path( + "/home/ss99569/code/multi-cam/Multi-Camera/" + "datasets/multi-cam-dataset/meva/data_all_slots/qa_pairs/raw" +) +DEFAULT_OUTPUT_DIR = Path( + "/home/ss99569/code/multi-cam/Multi-Camera/" + "datasets/multi-cam-dataset/meva/converted" +) + + +def _stable_hex_id(*parts: str) -> str: + h = hashlib.md5() + h.update("::".join(parts).encode("utf-8")) + return h.hexdigest() + + +def _stable_int_id(*parts: str, mod: int = 1_000_000_000) -> int: + hex_id = _stable_hex_id(*parts) + return int(hex_id[:12], 16) % mod + + +def _options_list_to_dict(options: Optional[List[str]]) -> Optional[Dict[str, str]]: + if not options: + return None + letters = ["A", "B", "C", "D"] + result = {} + for i, opt in enumerate(options): + if i >= len(letters): + break + result[letters[i]] = opt + return result + + +def _answer_letter(correct_idx: Optional[int]) -> Optional[str]: + if correct_idx is None or correct_idx < 0: + return None + return chr(65 + correct_idx) + + +def _question_text(qa: Dict[str, Any]) -> str: + return qa.get("naturalized_question") or qa.get("question_template") or "" + + +def _camera_name_from_basename(basename: str) -> str: + match = re.search(r"(G\d{3})", basename) + camera_id = match.group(1) if match else Path(basename).stem + return f"Camera {camera_id}" + + +def _camera_names(video_paths: Optional[List[str]]) -> Dict[str, str]: + camera_names: Dict[str, str] = {} + for video_path in video_paths or []: + basename = Path(video_path).name + camera_names[basename] = _camera_name_from_basename(basename) + return camera_names + + +def _metadata_base(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + return { + "slot": slot, + "question_id": qa.get("question_id"), + "difficulty": qa.get("difficulty"), + "requires_cameras": qa.get("requires_cameras"), + "requires_multi_camera": qa.get("requires_multi_camera"), + "verification": qa.get("verification"), + "debug_info": qa.get("debug_info"), + "camera_names": _camera_names(qa.get("video_paths")), + } + + +def _convert_event_ordering(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + options = qa.get("options") + return { + "task_id": _stable_int_id(slot, qa.get("question_id", ""), "event_ordering"), + "task_name": slot, + "episode_id": _stable_int_id(slot, "event_ordering"), + "question_type": "event_ordering", + "question": _question_text(qa), + "options": _options_list_to_dict(options), + "answer": _answer_letter(qa.get("correct_answer_index")), + "reasoning": qa.get("reasoning", ""), + "video_paths": qa.get("video_paths", []), + "metadata": _metadata_base(slot, qa), + } + + +def _convert_spatial(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + options = qa.get("options") + return { + "id": _stable_hex_id(slot, qa.get("question_id", ""), "spatial"), + "video_id": slot, + "question_type": "spatial", + "question": _question_text(qa), + "options": _options_list_to_dict(options), + "answer": _answer_letter(qa.get("correct_answer_index")), + "reasoning": qa.get("reasoning", ""), + "video_paths": qa.get("video_paths", []), + "metadata": _metadata_base(slot, qa), + } + + +def _convert_summarization(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + options = qa.get("options") or [] + correct_idx = qa.get("correct_answer_index") + answer = options[correct_idx] if isinstance(correct_idx, int) and correct_idx < len(options) else "" + return { + "task_id": _stable_int_id(slot, qa.get("question_id", ""), "summarization"), + "task_name": slot, + "episode_id": _stable_int_id(slot, "summarization"), + "question_type": "summarization", + "question": _question_text(qa), + "answer": answer, + "video_paths": qa.get("video_paths", []), + "metadata": _metadata_base(slot, qa), + } + + +def _convert_temporal(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + options = qa.get("options") + return { + "task_id": _stable_int_id(slot, qa.get("question_id", ""), "temporal"), + "task_name": slot, + "episode_id": _stable_int_id(slot, "temporal"), + "question_type": "temporal", + "question": _question_text(qa), + "options": _options_list_to_dict(options), + "answer": _answer_letter(qa.get("correct_answer_index")), + "reasoning": qa.get("reasoning", ""), + "video_paths": qa.get("video_paths", []), + "metadata": _metadata_base(slot, qa), + } + + +def _convert_counting(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + options = qa.get("options") or [] + correct_idx = qa.get("correct_answer_index") + answer = options[correct_idx] if isinstance(correct_idx, int) and correct_idx < len(options) else "" + return { + "id": _stable_hex_id(slot, qa.get("question_id", ""), "counting"), + "video_id": slot, + "question_type": "counting", + "question": _question_text(qa), + "options": None, + "answer": answer, + "reasoning": qa.get("reasoning", ""), + "video_paths": qa.get("video_paths", []), + "metadata": _metadata_base(slot, qa), + } + + +def _convert_best_camera(slot: str, qa: Dict[str, Any]) -> Dict[str, Any]: + options = qa.get("options") + verification = qa.get("verification") or {} + metadata = _metadata_base(slot, qa) + metadata["camera_question_subtype"] = qa.get("subcategory") or "best_camera" + if verification.get("correct_camera"): + metadata["best_camera_scene"] = verification.get("correct_camera") + return { + "video_id": slot, + "question_type": "camera", + "question": _question_text(qa), + "options": _options_list_to_dict(options), + "answer": _answer_letter(qa.get("correct_answer_index")), + "reasoning": qa.get("reasoning", ""), + "video_paths": qa.get("video_paths", []), + "metadata": metadata, + } + + +CONVERTERS = { + "event_ordering": _convert_event_ordering, + "spatial": _convert_spatial, + "summarization": _convert_summarization, + "temporal": _convert_temporal, + "counting": _convert_counting, + "best_camera": _convert_best_camera, +} + + +def _iter_naturalized_files(input_dir: Path) -> Iterable[Path]: + return sorted(input_dir.glob("*.raw.naturalized.json")) + + +def convert_all(input_dir: Path) -> Dict[str, List[Dict[str, Any]]]: + outputs: Dict[str, List[Dict[str, Any]]] = {k: [] for k in CONVERTERS} + for path in _iter_naturalized_files(input_dir): + with path.open() as f: + data = json.load(f) + slot = data.get("slot") or path.stem.replace(".raw.naturalized", "") + for qa in data.get("qa_pairs", []): + category = qa.get("category") + if category not in CONVERTERS: + continue + outputs[category].append(CONVERTERS[category](slot, qa)) + return outputs + + +def write_outputs(outputs: Dict[str, List[Dict[str, Any]]], output_dir: Path) -> None: + output_dir.mkdir(parents=True, exist_ok=True) + for category, items in outputs.items(): + out_path = output_dir / f"qa_{category}.json" + if out_path.parent != output_dir: + raise ValueError(f"Refusing to write outside output dir: {out_path}") + with out_path.open("w") as f: + json.dump(items, f, indent=2) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert MEVA naturalized QA to standard formats") + parser.add_argument("--input-dir", type=Path, default=DEFAULT_INPUT_DIR) + parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR) + args = parser.parse_args() + + outputs = convert_all(args.input_dir) + write_outputs(outputs, args.output_dir) + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/extract_slot_lists.py b/meva/scripts/extract_slot_lists.py new file mode 100644 index 0000000..97930a7 --- /dev/null +++ b/meva/scripts/extract_slot_lists.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +""" +Extract slot list from slot_index.json and write to a file. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +def read_slot_index(path: Path) -> set[str]: + with path.open("r", encoding="utf-8") as handle: + data = json.load(handle) + return set(data.keys()) + + +def write_list(path: Path, slots: set[str]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as handle: + for slot in sorted(slots): + handle.write(f"{slot}\n") + + +def main() -> None: + repo_root = Path(__file__).resolve().parents[2] + default_slot_index = repo_root / "meva" / "data" / "slot_index.json" + default_out_file = default_slot_index.with_name("slot_list_from_slot_index.txt") + + parser = argparse.ArgumentParser( + description="Extract slot list from slot_index.json." + ) + parser.add_argument("--slot-index", type=Path, default=default_slot_index) + parser.add_argument("--out-file", type=Path, default=default_out_file) + args = parser.parse_args() + + slot_index_slots = read_slot_index(args.slot_index) + + write_list(args.out_file, slot_index_slots) + + print(f"Wrote {len(slot_index_slots)} slots to {args.out_file}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/__init__.py b/meva/scripts/v10/__init__.py new file mode 100644 index 0000000..f9fa1b0 --- /dev/null +++ b/meva/scripts/v10/__init__.py @@ -0,0 +1 @@ +# V8 QA Pipeline — MEVID-enriched multi-camera QA generation diff --git a/meva/scripts/v10/activity_hierarchy.py b/meva/scripts/v10/activity_hierarchy.py new file mode 100644 index 0000000..47cacf9 --- /dev/null +++ b/meva/scripts/v10/activity_hierarchy.py @@ -0,0 +1,297 @@ +""" +V7 activity_hierarchy.py — Activity relationship definitions for connected event pairing. + +Defines causal, sequential, and co-occurring activity relationships from the +37 MEVA ActEV activities. Used by generate_temporal.py to prefer event pairs +that have meaningful scene connections. +""" + +from typing import Dict, List, Set, Tuple + +# ============================================================================ +# Activity Relationships +# ============================================================================ + +# Causal/sequential relationships: act_a often leads to act_b +CAUSAL_RELATIONSHIPS: Dict[str, List[str]] = { + # Object manipulation chains + "person_picks_up_object": [ + "person_carries_heavy_object", + "person_puts_down_object", + "person_transfers_object", + ], + "person_puts_down_object": [ + "person_abandons_package", + ], + + # Trunk/vehicle loading + "person_opens_trunk": [ + "person_loads_vehicle", + "person_unloads_vehicle", + "person_closes_trunk", + ], + "person_loads_vehicle": ["person_closes_trunk"], + "person_unloads_vehicle": ["person_closes_trunk", "person_carries_heavy_object"], + + # Vehicle door sequences + "person_opens_vehicle_door": [ + "person_enters_vehicle", + "person_exits_vehicle", + "person_closes_vehicle_door", + ], + "person_enters_vehicle": ["person_closes_vehicle_door", "vehicle_starts"], + "person_exits_vehicle": ["person_closes_vehicle_door"], + + # Facility door sequences + "person_opens_facility_door": [ + "person_enters_scene_through_structure", + "person_exits_scene_through_structure", + "person_closes_facility_door", + ], + "person_enters_scene_through_structure": ["person_closes_facility_door"], + "person_exits_scene_through_structure": ["person_closes_facility_door"], + + # Vehicle sequences + "vehicle_stops": ["person_exits_vehicle", "vehicle_starts"], + "vehicle_starts": ["vehicle_turns_left", "vehicle_turns_right", "vehicle_makes_u_turn"], + + # Sit/stand + "person_sits_down": ["person_stands_up"], + + # Drop-off/pick-up + "vehicle_drops_off_person": ["person_enters_scene_through_structure"], + "vehicle_picks_up_person": ["vehicle_starts"], +} + +# Symmetric co-occurring relationships (activities that often happen together) +CO_OCCURRING: List[Tuple[str, str]] = [ + ("person_talks_to_person", "person_embraces_person"), + ("person_talks_on_phone", "person_texts_on_phone"), + ("person_reads_document", "person_interacts_with_laptop"), + ("person_enters_vehicle", "person_exits_vehicle"), + ("person_opens_vehicle_door", "person_closes_vehicle_door"), + ("person_opens_trunk", "person_closes_trunk"), + ("person_opens_facility_door", "person_closes_facility_door"), + ("person_picks_up_object", "person_puts_down_object"), + ("person_loads_vehicle", "person_unloads_vehicle"), + ("vehicle_starts", "vehicle_stops"), +] + +# Build fast lookup sets +_CAUSAL_PAIRS: Set[Tuple[str, str]] = set() +for parent, children in CAUSAL_RELATIONSHIPS.items(): + for child in children: + _CAUSAL_PAIRS.add((parent, child)) + +_CO_OCCURRING_PAIRS: Set[Tuple[str, str]] = set() +for a, b in CO_OCCURRING: + _CO_OCCURRING_PAIRS.add((a, b)) + _CO_OCCURRING_PAIRS.add((b, a)) + + +# ============================================================================ +# Public API +# ============================================================================ + +def are_related(act_a: str, act_b: str) -> bool: + """Check if two activities have any relationship (causal or co-occurring).""" + return get_relationship(act_a, act_b) is not None + + +def get_relationship(act_a: str, act_b: str) -> str: + """ + Get relationship type between two activities. + + Returns: + "causal" if act_a causally leads to act_b + "reverse_causal" if act_b causally leads to act_a + "co_occurring" if they commonly co-occur + None if no relationship + """ + if (act_a, act_b) in _CAUSAL_PAIRS: + return "causal" + if (act_b, act_a) in _CAUSAL_PAIRS: + return "reverse_causal" + if (act_a, act_b) in _CO_OCCURRING_PAIRS: + return "co_occurring" + return None + + +def get_relationship_strength(act_a: str, act_b: str) -> float: + """ + Get relationship strength (0.0 = none, 1.0 = strong). + + causal: 1.0 + reverse_causal: 0.9 (order matters but both are related) + co_occurring: 0.7 + same_entity_type: 0.3 (both person_ or both vehicle_) + none: 0.0 + """ + rel = get_relationship(act_a, act_b) + if rel == "causal": + return 1.0 + if rel == "reverse_causal": + return 0.9 + if rel == "co_occurring": + return 0.7 + # Same entity type (weak connection) + if act_a.split("_")[0] == act_b.split("_")[0]: + return 0.3 + return 0.0 + + +def get_activity_entity_type(activity: str) -> str: + """Get entity type from activity name.""" + if activity.startswith("vehicle_"): + return "vehicle" + return "person" + + +def humanize_activity(activity: str) -> str: + """Convert activity name to short human-readable form. + + Special-cases vague names and vehicle activities to produce sensible + descriptions. The plain prefix-strip is the fallback. + """ + _SPECIAL = { + # Scene entry / exit — clarify MEVA camera-view semantics + "person_enters_scene_through_structure": + "enters the camera's view through a doorway", + "person_exits_scene_through_structure": + "leaves the camera's view through a doorway", + # Vague / ambiguous person activities — more descriptive + "hand_interacts_with_person": + "physically interacts with another person", + "person_interacts_with_laptop": + "uses a laptop", + "person_transfers_object": + "hands an object to someone", + "person_carries_heavy_object": + "carries a heavy object", + "person_abandons_package": + "leaves a package behind", + "person_steals_object": + "takes an object", + # Vehicle activities — keep "vehicle" context in the verb phrase + "vehicle_starts": "starts moving", + "vehicle_stops": "comes to a stop", + "vehicle_reverses": "reverses", + "vehicle_turns_left": "turns left", + "vehicle_turns_right": "turns right", + "vehicle_makes_u_turn": "makes a U-turn", + "vehicle_drops_off_person": "drops off a person", + "vehicle_picks_up_person": "picks up a person", + } + if activity in _SPECIAL: + return _SPECIAL[activity] + # Remove entity prefix and replace underscores with spaces + for prefix in ("person_", "vehicle_", "hand_"): + if activity.startswith(prefix): + activity = activity[len(prefix):] + break + return activity.replace("_", " ") + + +# Verb → gerund mappings for natural sentence construction +_GERUND_MAP = { + "opens": "opening", "closes": "closing", "enters": "entering", + "exits": "exiting", "reads": "reading", "carries": "carrying", + "picks": "picking", "puts": "putting", "sets": "setting", + "rides": "riding", "loads": "loading", "unloads": "unloading", + "talks": "talking", "stands": "standing", "walks": "walking", + "runs": "running", "sits": "sitting", "texts": "texting", + "pulls": "pulling", "pushes": "pushing", "interacts": "interacting", + "drops": "dropping", "embraces": "embracing", "uses": "using", + "makes": "making", "steals": "stealing", "starts": "starting", + "stops": "stopping", "turns": "turning", "transfers": "transferring", + "reverses": "reversing", "abandons": "abandoning", + "leaves": "leaving", "purchases": "purchasing", +} + + +def _conjugate_gerund(verb: str) -> str: + """Smart fallback: conjugate an unknown verb to its -ing form. + + Handles common English patterns: + leaves → leaving, purchases → purchasing, transfers → transferring, + runs → running, sits → sitting, walks → walking + """ + if verb in _GERUND_MAP: + return _GERUND_MAP[verb] + # Strip third-person 's'/'es' to get base form + if verb.endswith("es"): + base = verb[:-2] # "leaves" → "leav", "purchases" → "purchas" + elif verb.endswith("s") and not verb.endswith("ss"): + base = verb[:-1] # "abandons" → "abandon" + else: + base = verb + # Apply standard English gerund rules on base form + if base.endswith("ie"): # "die" → "dying" + return base[:-2] + "ying" + if base.endswith("ee"): # "see" → "seeing" + return base + "ing" + if base.endswith("e"): # "leave" → "leaving", "make" → "making" + return base[:-1] + "ing" + # CVC doubling: short base ending in consonant-vowel-consonant + if (len(base) >= 3 + and base[-1] not in "aeiouwxy" + and base[-2] in "aeiou" + and base[-3] not in "aeiou"): + return base + base[-1] + "ing" # "run" → "running", "sit" → "sitting" + return base + "ing" + + +def humanize_activity_gerund(activity: str) -> str: + """ + Convert activity to gerund form for sentence construction. + e.g. 'person_opens_facility_door' → 'Opening a facility door' + """ + # Direct gerund overrides for special-cased activities + _GERUND_SPECIAL = { + "person_enters_scene_through_structure": + "Entering the camera's view through a doorway", + "person_exits_scene_through_structure": + "Leaving the camera's view through a doorway", + "hand_interacts_with_person": + "Physically interacting with another person", + "person_interacts_with_laptop": + "Using a laptop", + "person_transfers_object": + "Handing an object to someone", + "person_carries_heavy_object": + "Carrying a heavy object", + "person_abandons_package": + "Leaving a package behind", + "person_steals_object": + "Taking an object", + "vehicle_starts": "Starting to move", + "vehicle_stops": "Coming to a stop", + "vehicle_reverses": "Reversing", + "vehicle_turns_left": "Turning left", + "vehicle_turns_right": "Turning right", + "vehicle_makes_u_turn": "Making a U-turn", + "vehicle_drops_off_person": "Dropping off a person", + "vehicle_picks_up_person": "Picking up a person", + } + if activity in _GERUND_SPECIAL: + return _GERUND_SPECIAL[activity] + + base = humanize_activity(activity) # e.g. 'opens facility door' + words = base.split() + if words: + first = words[0] + gerund = _conjugate_gerund(first) + rest = " ".join(words[1:]) + # Only add article if rest starts with a noun-like word + # Skip if rest starts with preposition, adverb, article, or particle + _no_article = {"up", "down", "on", "off", "out", "in", "to", "from", + "through", "with", "around", "right", "left", "a", + "an", "the", "into", "onto", "over", "away"} + if rest: + first_rest = rest.split()[0] + if first_rest not in _no_article: + article = "an" if first_rest[0].lower() in "aeiou" else "a" + rest = f"{article} " + rest + result = f"{gerund} {rest}".strip() if rest else gerund + return result.capitalize() + return base.capitalize() diff --git a/meva/scripts/v10/batch_extract_all_slots.py b/meva/scripts/v10/batch_extract_all_slots.py new file mode 100755 index 0000000..d9cc234 --- /dev/null +++ b/meva/scripts/v10/batch_extract_all_slots.py @@ -0,0 +1,443 @@ +#!/usr/bin/env python3 +""" +Batch Entity Description Extractor — Process all canonical slots overnight. + +Extracts visual descriptions (YOLO + HSV color) from bounding boxes for ALL +annotated entities across the full MEVA dataset. + +This solves the low-visual-description problem: +- Before: ~7% visual coverage (MEVID only) +- After: ~95% visual coverage (all entities with geom bboxes) + +Usage: + # Dry-run: show what will be processed + python3 scripts/final/batch_extract_all_slots.py --dry-run + + # Full extraction (overnight, ~20 hours for 390 slots) + python3 scripts/final/batch_extract_all_slots.py -v + + # Resume from interruption + python3 scripts/final/batch_extract_all_slots.py -v --resume + +Cost: $0 (local YOLO, no API calls) +Time: ~3-4 min per slot × 390 slots = ~20 hours + +Output: $MEVA_ENTITY_DESC_DIR/{canonical_slot}.json (or /nas/mars/dataset/MEVA/entity_descriptions/) +Progress: $MEVA_OUTPUT_DIR/extraction_logs/batch_progress.json +Logs: $MEVA_OUTPUT_DIR/extraction_logs/batch_extraction_TIMESTAMP.log +""" + +import argparse +import json +import os +import sys +import time +import subprocess +from pathlib import Path +from datetime import datetime +from typing import Dict, List, Set, Optional + +# ============================================================================ +# Paths +# ============================================================================ + +_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data" +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) + +SLOT_INDEX_PATH = _REPO_DATA / "geom_slot_index.json" +OUTPUT_DIR = Path(os.environ.get("MEVA_ENTITY_DESC_DIR") or "/nas/mars/dataset/MEVA/entity_descriptions") +LOG_DIR = _OUTPUT / "extraction_logs" +PROGRESS_FILE = LOG_DIR / "batch_progress.json" +EXTRACTION_SCRIPT = Path(__file__).resolve().parent / "extract_entity_descriptions.py" + + +def _normalize_slot_name(slot: str) -> str: + """Normalize slot name to short format used by the pipeline. + + Geom index uses: 2018-03-05.13-10-00.admin (with seconds) + Pipeline expects: 2018-03-05.13-10.admin (without seconds) + """ + import re + # Match date.HH-MM-SS.site and strip the -SS + m = re.match(r'^(\d{4}-\d{2}-\d{2})\.(\d{2}-\d{2})-\d{2}\.(.+)$', slot) + if m: + return f"{m.group(1)}.{m.group(2)}.{m.group(3)}" + return slot + + +# ============================================================================ +# Progress Tracking +# ============================================================================ + +def load_progress() -> Dict: + """Load progress state from disk (for resume capability).""" + if not PROGRESS_FILE.exists(): + return { + "started_at": None, + "last_updated": None, + "completed_slots": [], + "failed_slots": [], + "skipped_slots": [], + "total_slots": 0, + "total_entities_extracted": 0, + } + with open(PROGRESS_FILE) as f: + return json.load(f) + + +def save_progress(progress: Dict): + """Save progress state to disk.""" + LOG_DIR.mkdir(parents=True, exist_ok=True) + progress["last_updated"] = datetime.now().isoformat() + with open(PROGRESS_FILE, "w") as f: + json.dump(progress, f, indent=2) + + +def init_progress(total_slots: int) -> Dict: + """Initialize fresh progress state.""" + return { + "started_at": datetime.now().isoformat(), + "last_updated": datetime.now().isoformat(), + "completed_slots": [], + "failed_slots": [], + "skipped_slots": [], + "total_slots": total_slots, + "total_entities_extracted": 0, + } + + +# ============================================================================ +# Slot Processing +# ============================================================================ + +def find_raw_slots_for_canonical(canonical_slot: str, slot_index: Dict) -> List[str]: + """ + Resolve canonical slot to its raw variants. + + Canonical: 2018-03-11.16-20-00.school + Raw variants: [2018-03-11.16-20-00.school, 2018-03-11.16-20-01.school, ...] + """ + if canonical_slot not in slot_index: + return [] + + slot_data = slot_index[canonical_slot] + raw_slots = slot_data.get("raw_slot_variants", [canonical_slot]) + return raw_slots + + +def run_extraction(slot: str, verbose: bool = False, dry_run: bool = False, + method: str = "yolo", force: bool = False) -> Dict: + """ + Run extract_entity_descriptions.py on a single slot. + + Args: + method: 'yolo', 'segformer', or 'color_only' + force: if True, re-extract even if output exists + + Returns: {"success": bool, "entities": int, "error": str or None} + """ + # Normalize slot name to short format (e.g. 2018-03-05.13-10.admin) + short_slot = _normalize_slot_name(slot) + output_path = OUTPUT_DIR / f"{short_slot}.json" + + # Check if already exists and has data (skip if --force not set) + if output_path.exists() and not force: + try: + with open(output_path) as f: + data = json.load(f) + entity_count = len(data.get("actors", {})) + if entity_count > 0: + return { + "success": True, + "entities": entity_count, + "error": None, + "skipped": True, + "method": data.get("method", "unknown"), + } + except Exception: + pass # corrupted file, re-extract + + if dry_run: + return {"success": True, "entities": 0, "error": None, "dry_run": True} + + # Run extraction + cmd = [ + "python3", + str(EXTRACTION_SCRIPT), + "--slot", slot, + "--output", str(output_path), + "--method", method, + ] + if verbose: + cmd.append("-v") + + try: + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=600, # 10 min timeout per slot + ) + + if result.returncode != 0: + return { + "success": False, + "entities": 0, + "error": f"Exit code {result.returncode}: {result.stderr[:200]}", + } + + # Parse output JSON to count entities + if output_path.exists(): + with open(output_path) as f: + data = json.load(f) + entity_count = len(data.get("actors", {})) + return { + "success": True, + "entities": entity_count, + "error": None, + "method": data.get("method", "unknown"), + } + else: + return { + "success": False, + "entities": 0, + "error": "Output file not created", + } + + except subprocess.TimeoutExpired: + return { + "success": False, + "entities": 0, + "error": "Timeout (>10 min)", + } + except Exception as e: + return { + "success": False, + "entities": 0, + "error": str(e)[:200], + } + + +# ============================================================================ +# Batch Processing +# ============================================================================ + +def process_all_slots(dry_run: bool = False, verbose: bool = False, + resume: bool = False, method: str = "yolo", + force: bool = False) -> Dict: + """ + Process all canonical slots from slot_index.json. + + Returns summary dict with stats. + """ + # Load geom slot index (only slots with extractable geom data) + if not SLOT_INDEX_PATH.exists(): + print(f"ERROR: Geom slot index not found: {SLOT_INDEX_PATH}") + sys.exit(1) + + with open(SLOT_INDEX_PATH) as f: + geom_index = json.load(f) + + # Extract slots from nested structure + slot_index = geom_index['slots'] + canonical_slots = sorted(slot_index.keys()) + + print(f"\nLoaded {len(canonical_slots)} slots with geom data (filtered from {geom_index['stats']['total_canonical_slots']} total)") + print(f"Coverage: {geom_index['stats']['coverage_percent']:.1f}% of canonical slots have extractable geom") + print(f"Expected entities: ~{geom_index['stats']['total_usable_actors']:,} actors\n") + + # Load or init progress + if resume: + progress = load_progress() + print(f"\nResuming from previous run:") + print(f" Completed: {len(progress['completed_slots'])}") + print(f" Failed: {len(progress['failed_slots'])}") + print(f" Skipped: {len(progress['skipped_slots'])}") + else: + progress = init_progress(len(canonical_slots)) + + completed_set = set(progress["completed_slots"]) + failed_set = set(progress["failed_slots"]) + skipped_set = set(progress["skipped_slots"]) + + # Setup logging + LOG_DIR.mkdir(parents=True, exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_path = LOG_DIR / f"batch_extraction_{timestamp}.log" + + log_file = open(log_path, "w", buffering=1) # line buffered + + def log(msg: str): + print(msg) + log_file.write(msg + "\n") + + # Deduplicate: multiple raw variants map to same short slot name + # e.g. 2018-03-05.13-10-00.bus and 2018-03-05.13-10-01.bus → 2018-03-05.13-10.bus + seen_short = set() + deduped_slots = [] + for cs in canonical_slots: + short = _normalize_slot_name(cs) + if short not in seen_short: + seen_short.add(short) + deduped_slots.append(cs) + + log(f"Unique short slots: {len(deduped_slots)} (from {len(canonical_slots)} raw variants)") + + # Process each canonical slot + log(f"\n{'='*60}") + log(f"Batch Entity Description Extraction") + log(f"{'='*60}") + log(f"Mode: {'DRY-RUN' if dry_run else 'FULL EXTRACTION'}") + log(f"Method: {method}") + log(f"Canonical slots: {len(deduped_slots)}") + log(f"Resume: {resume}") + log(f"Log: {log_path}") + log(f"Progress: {PROGRESS_FILE}") + log(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") + log(f"{'='*60}\n") + + start_time = time.time() + total_entities = progress.get("total_entities_extracted", 0) + + for i, canonical_slot in enumerate(deduped_slots, 1): + # Skip if already completed + if canonical_slot in completed_set: + continue + + # Find raw variants for this canonical slot + raw_slots = find_raw_slots_for_canonical(canonical_slot, slot_index) + + if not raw_slots: + log(f"[{i:3d}/{len(deduped_slots)}] {canonical_slot}: NO RAW VARIANTS") + skipped_set.add(canonical_slot) + continue + + # Use first raw variant (they share same geom files) + raw_slot = raw_slots[0] + + log(f"[{i:3d}/{len(deduped_slots)}] {canonical_slot} → {raw_slot}") + + result = run_extraction(raw_slot, verbose=verbose, dry_run=dry_run, + method=method, force=force) + + if result.get("skipped"): + existing_method = result.get("method", "unknown") + log(f" ✓ SKIPPED (already exists, {result['entities']} entities, method={existing_method})") + skipped_set.add(canonical_slot) + elif result.get("dry_run"): + log(f" ✓ DRY-RUN OK") + elif result["success"]: + n = result["entities"] + total_entities += n + log(f" ✓ SUCCESS ({n} entities)") + completed_set.add(canonical_slot) + else: + log(f" ✗ FAILED: {result['error']}") + failed_set.add(canonical_slot) + + # Update progress every 5 slots + if i % 5 == 0 or not result["success"]: + progress["completed_slots"] = sorted(completed_set) + progress["failed_slots"] = sorted(failed_set) + progress["skipped_slots"] = sorted(skipped_set) + progress["total_entities_extracted"] = total_entities + save_progress(progress) + + # ETA calculation + if i > 0 and not dry_run: + elapsed = time.time() - start_time + avg_time = elapsed / i + remaining = (len(deduped_slots) - i) * avg_time + eta_hours = remaining / 3600 + log(f" Progress: {i}/{len(deduped_slots)} ({i*100//len(deduped_slots)}%), " + f"ETA: {eta_hours:.1f}h\n") + + # Final stats + elapsed = time.time() - start_time + + log(f"\n{'='*60}") + log(f"BATCH EXTRACTION COMPLETE") + log(f"{'='*60}") + log(f"Total time: {elapsed/3600:.1f} hours") + log(f"Completed: {len(completed_set)}") + log(f"Skipped (already exist): {len(skipped_set)}") + log(f"Failed: {len(failed_set)}") + log(f"Total entities: {total_entities}") + log(f"Avg entities/slot: {total_entities/max(len(completed_set),1):.1f}") + + if failed_set: + log(f"\nFailed slots ({len(failed_set)}):") + for slot in sorted(failed_set): + log(f" - {slot}") + + log(f"\nOutput directory: {OUTPUT_DIR}") + log(f"Log file: {log_path}") + log(f"{'='*60}\n") + + log_file.close() + + # Final progress save + progress["completed_slots"] = sorted(completed_set) + progress["failed_slots"] = sorted(failed_set) + progress["skipped_slots"] = sorted(skipped_set) + progress["total_entities_extracted"] = total_entities + save_progress(progress) + + return { + "total_slots": len(deduped_slots), + "completed": len(completed_set), + "skipped": len(skipped_set), + "failed": len(failed_set), + "total_entities": total_entities, + "elapsed_hours": elapsed / 3600, + "log_path": str(log_path), + } + + +# ============================================================================ +# Main +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Batch extract entity descriptions for all slots", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__, + ) + parser.add_argument("--dry-run", action="store_true", + help="Show what will be processed without extracting") + parser.add_argument("--resume", action="store_true", + help="Resume from previous run (skip completed slots)") + parser.add_argument("--verbose", "-v", action="store_true", + help="Verbose output from extraction script") + parser.add_argument("--method", default="segformer", + choices=["yolo", "segformer", "color_only"], + help="Extraction method (default: segformer — richest descriptions)") + parser.add_argument("--force-reextract", action="store_true", + help="Force re-extraction even if descriptions already exist") + + args = parser.parse_args() + + summary = process_all_slots( + dry_run=args.dry_run, + verbose=args.verbose, + resume=args.resume, + method=args.method, + force=args.force_reextract, + ) + + print("\n" + "="*60) + print("SUMMARY") + print("="*60) + print(f"Total slots: {summary['total_slots']}") + print(f"Completed: {summary['completed']}") + print(f"Skipped (exist): {summary['skipped']}") + print(f"Failed: {summary['failed']}") + print(f"Total entities: {summary['total_entities']}") + print(f"Time: {summary['elapsed_hours']:.1f} hours") + print(f"Log: {summary['log_path']}") + print("="*60) + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/batch_generate.py b/meva/scripts/v10/batch_generate.py new file mode 100644 index 0000000..192bf1c --- /dev/null +++ b/meva/scripts/v10/batch_generate.py @@ -0,0 +1,449 @@ +#!/usr/bin/env python3 +""" +batch_generate.py — Iterative generate→validate→fix→regenerate loop for MEVA QA. + +Orchestrates the full pipeline: + 1. Generate raw QA (run_pipeline) + 2. Validate (validate_qa) + 3. Auto-fix structural issues (grammar, raw leaks, duplicates) + 4. Regenerate only if unfixable issues remain + 5. Repeat until quality score ≥ threshold or max iterations + +Usage: + # Run 5 iterations on one slot + python3 -m scripts.v10.batch_generate --slot 2018-03-11.11-25.school --rounds 5 -v + + # Run 5 iterations on multiple slots + python3 -m scripts.v10.batch_generate --slots 2018-03-11.11-25.school 2018-03-11.16-20.school --rounds 5 + + # Run with custom quality threshold + python3 -m scripts.v10.batch_generate --slot 2018-03-11.11-25.school --rounds 5 --min-score 90 + +Output: + $MEVA_OUTPUT_DIR/qa_pairs/raw/{slot}.raw.json — best raw QA + $MEVA_OUTPUT_DIR/qa_pairs/batch_logs/{slot}.log.json — per-iteration scores +""" + +import argparse +import copy +import json +import os +import re +import sys +import time +from datetime import datetime +from pathlib import Path +from typing import Any, Dict, List, Optional, Set + +try: + from .run_pipeline import run_pipeline + from .validate_qa import validate, Issue, KITWARE_ACTIVITY_IDS +except ImportError: + sys.path.insert(0, str(Path(__file__).resolve().parent)) + from run_pipeline import run_pipeline + from validate_qa import validate, Issue, KITWARE_ACTIVITY_IDS + + +# ============================================================================ +# Constants +# ============================================================================ + +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) +RAW_OUTPUT_DIR = _OUTPUT / "qa_pairs" / "raw" +BATCH_LOG_DIR = _OUTPUT / "qa_pairs" / "batch_logs" + +DEFAULT_ROUNDS = 5 +DEFAULT_MIN_SCORE = 90 +DEFAULT_SEED = 42 + + +# ============================================================================ +# Auto-Fix: Grammar +# ============================================================================ + +# Exceptions for a→an (words starting with vowel letter but consonant sound) +_AN_EXCEPTIONS = {"uniform", "university", "unique", "united", "union", + "european", "user", "useful", "usual", "utility", "one", "once"} + + +def fix_grammar(text: str) -> str: + """Fix common grammar issues in generated text.""" + if not text: + return text + + # Fix a [vowel] → an [vowel] (with exceptions) + def _fix_article(m): + word = m.group(1) + if word.lower() in _AN_EXCEPTIONS: + return m.group(0) + return f"an {word}" + + text = re.sub(r'\ba ([aeiouAEIOU]\w*)\b', _fix_article, text) + + # Fix double articles + text = re.sub(r'\b(a|an|the)\s+\1\b', r'\1', text, flags=re.I) + + return text + + +def fix_raw_leaks(text: str) -> str: + """Remove raw pipeline tokens from naturalized text.""" + if not text: + return text + + # Remove raw timestamps: "127.13-133.43s", "at 127s", "42 seconds" + text = re.sub(r'\b\d+\.\d+-\d+\.\d+s\b', '', text) + text = re.sub(r'\bat\s+\d+s\b', '', text) + text = re.sub(r'\b\d{2,3}\s*seconds?\b', '', text) + + # Remove raw activity IDs + for act in KITWARE_ACTIVITY_IDS: + text = text.replace(act, act.split("_", 1)[-1].replace("_", " ")) + + # Remove template artifacts + text = re.sub(r'\{[a-z_]+\}', '', text) + text = re.sub(r'__\w+__', '', text) + + # Collapse multiple spaces + text = re.sub(r'\s{2,}', ' ', text).strip() + + return text + + +# ============================================================================ +# Auto-Fix: Apply Fixes to QA Data +# ============================================================================ + +def auto_fix(data: Dict[str, Any], report: Dict[str, Any], + verbose: bool = False) -> Dict[str, Any]: + """ + Apply automated fixes to QA data based on validation report. + + Fixes (no LLM needed): + - Grammar: a→an, double articles + - Raw leaks: strip timestamps, activity IDs, template artifacts + - Generic descriptions: replace "someone" → "a person" + - Duplicates: drop lower-quality duplicate + + Returns: fixed data (new copy, original unchanged) + """ + fixed = copy.deepcopy(data) + qa_pairs = fixed["qa_pairs"] + + issues_by_qid = {} + for issue in report.get("issues", []): + qid = issue["question_id"] + if qid not in issues_by_qid: + issues_by_qid[qid] = [] + issues_by_qid[qid].append(issue) + + fixes_applied = 0 + + for q in qa_pairs: + qid = q.get("question_id", "") + q_issues = issues_by_qid.get(qid, []) + + for issue in q_issues: + check = issue["check_name"] + + # --- Grammar fixes --- + if check == "grammar": + for field in ("question_template", "correct_answer"): + if field in q: + q[field] = fix_grammar(q[field]) + if "options" in q: + q["options"] = [fix_grammar(o) for o in q["options"]] + if "naturalized_question" in q: + q["naturalized_question"] = fix_grammar(q["naturalized_question"]) + if "naturalized_options" in q: + q["naturalized_options"] = [fix_grammar(o) for o in q["naturalized_options"]] + fixes_applied += 1 + + # --- Raw leak fixes --- + elif check == "raw_token_leak": + for field in ("question_template", "correct_answer"): + if field in q: + q[field] = fix_raw_leaks(q[field]) + if "options" in q: + q["options"] = [fix_raw_leaks(o) for o in q["options"]] + if "naturalized_question" in q: + q["naturalized_question"] = fix_raw_leaks(q["naturalized_question"]) + if "naturalized_options" in q: + q["naturalized_options"] = [fix_raw_leaks(o) for o in q["naturalized_options"]] + fixes_applied += 1 + + # --- Generic description fixes --- + elif check == "generic_description": + # Replace "someone" with "a person" in text fields + for field in ("question_template", "correct_answer"): + if field in q and q[field]: + q[field] = re.sub(r'\bsomeone\b', 'a person', q[field], flags=re.I) + q[field] = re.sub(r'\bSomeone\b', 'A person', q[field]) + if "options" in q: + q["options"] = [ + re.sub(r'\bsomeone\b', 'a person', o, flags=re.I) for o in q["options"] + ] + fixes_applied += 1 + + # --- Duplicate removal --- + # Find pairs flagged as duplicates + dup_qids_to_remove = set() + for issue in report.get("issues", []): + if issue["check_name"] in ("duplicate", "near_duplicate"): + # The flagged question_id is one of the duplicates + # Keep the first (lower index), remove the second if possible + msg = issue.get("message", "") + # Extract the other QID from the message + other_match = re.search(r'of (final_\w+_\d+)', msg) + if other_match: + other_qid = other_match.group(1) + # Remove the one with higher index (comes later) + qid = issue["question_id"] + # Compare by index number + try: + idx_a = int(re.search(r'(\d+)$', qid).group(1)) + idx_b = int(re.search(r'(\d+)$', other_qid).group(1)) + dup_qids_to_remove.add(qid if idx_a > idx_b else other_qid) + except (AttributeError, ValueError): + dup_qids_to_remove.add(qid) + + if dup_qids_to_remove: + original_count = len(qa_pairs) + qa_pairs = [q for q in qa_pairs if q.get("question_id") not in dup_qids_to_remove] + fixed["qa_pairs"] = qa_pairs + fixed["total_questions"] = len(qa_pairs) + fixes_applied += len(dup_qids_to_remove) + if verbose: + print(f" Removed {original_count - len(qa_pairs)} duplicate questions") + + if verbose: + print(f" Auto-fixes applied: {fixes_applied}") + + return fixed + + +# ============================================================================ +# Iterative Loop +# ============================================================================ + +def run_iterative( + slot: str, + rounds: int = DEFAULT_ROUNDS, + min_score: int = DEFAULT_MIN_SCORE, + verbose: bool = False, + seed: int = DEFAULT_SEED, +) -> Dict[str, Any]: + """ + Run the iterative generate→validate→fix→regenerate loop. + + Args: + slot: Slot name (e.g., "2018-03-11.11-25.school") + rounds: Maximum number of iterations + min_score: Target quality score (0-100) + verbose: Print detailed output + seed: Random seed + + Returns: + { + "slot": str, + "final_score": int, + "iterations": int, + "scores": [int, ...], + "issues_per_round": [int, ...], + "best_round": int, + "output_path": str, + } + """ + RAW_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + BATCH_LOG_DIR.mkdir(parents=True, exist_ok=True) + + iteration_log = { + "slot": slot, + "started_at": datetime.now().isoformat(), + "rounds_requested": rounds, + "min_score": min_score, + "seed": seed, + "iterations": [], + } + + best_score = -1 + best_data = None + best_round = 0 + scores = [] + issue_counts = [] + + for round_num in range(1, rounds + 1): + t0 = time.time() + + if verbose: + print(f"\n{'='*60}") + print(f"Round {round_num}/{rounds} — Slot: {slot}") + print(f"{'='*60}") + + # Step 1: Generate (vary seed per round for diversity) + round_seed = seed + round_num - 1 + try: + data = run_pipeline(slot, verbose=verbose, seed=round_seed, + require_mevid=False) + except Exception as e: + if verbose: + print(f" Generation FAILED: {e}") + iteration_log["iterations"].append({ + "round": round_num, + "status": "generation_failed", + "error": str(e), + "time_sec": round(time.time() - t0, 2), + }) + scores.append(0) + issue_counts.append(999) + continue + + # Step 2: Validate + report = validate(data, verbose=verbose) + score = report["quality_score"] + total_issues = report["total_issues"] + + if verbose: + print(f"\n Round {round_num} score: {score}/100 ({total_issues} issues)") + + # Step 3: Auto-fix + if total_issues > 0: + if verbose: + print(f" Applying auto-fixes...") + data = auto_fix(data, report, verbose=verbose) + + # Re-validate after fixes + report_after = validate(data, verbose=False) + score_after = report_after["quality_score"] + issues_after = report_after["total_issues"] + + if verbose: + print(f" Post-fix score: {score_after}/100 ({issues_after} issues)") + + score = score_after + total_issues = issues_after + + scores.append(score) + issue_counts.append(total_issues) + + # Track best + if score > best_score: + best_score = score + best_data = data + best_round = round_num + + # Log iteration + elapsed = round(time.time() - t0, 2) + iteration_log["iterations"].append({ + "round": round_num, + "status": "ok", + "seed": round_seed, + "score_before_fix": report["quality_score"], + "score_after_fix": score, + "issues_before_fix": report["total_issues"], + "issues_after_fix": total_issues, + "total_questions": data.get("total_questions", 0), + "time_sec": elapsed, + }) + + # Early exit if target reached + if score >= min_score: + if verbose: + print(f"\n Target score {min_score} reached in round {round_num}!") + break + + # Save best result + if best_data: + out_path = RAW_OUTPUT_DIR / f"{slot}.raw.json" + with open(out_path, "w") as f: + json.dump(best_data, f, indent=2, default=str) + if verbose: + print(f"\n Saved best result (round {best_round}, score {best_score}): {out_path}") + + # Save batch log + iteration_log["completed_at"] = datetime.now().isoformat() + iteration_log["best_round"] = best_round + iteration_log["best_score"] = best_score + iteration_log["scores"] = scores + iteration_log["issue_counts"] = issue_counts + + log_path = BATCH_LOG_DIR / f"{slot}.log.json" + with open(log_path, "w") as f: + json.dump(iteration_log, f, indent=2) + + if verbose: + print(f"\n{'='*60}") + print(f"BATCH COMPLETE — {slot}") + print(f"{'='*60}") + print(f" Rounds: {len(scores)}") + print(f" Scores: {scores}") + print(f" Best: round {best_round} → {best_score}/100") + print(f" Log: {log_path}") + + return { + "slot": slot, + "final_score": best_score, + "iterations": len(scores), + "scores": scores, + "issues_per_round": issue_counts, + "best_round": best_round, + "output_path": str(RAW_OUTPUT_DIR / f"{slot}.raw.json"), + } + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Iterative QA generation + validation loop" + ) + parser.add_argument("--slot", help="Single slot to process") + parser.add_argument("--slots", nargs="+", help="Multiple slots to process") + parser.add_argument("--rounds", type=int, default=DEFAULT_ROUNDS, + help=f"Max iterations per slot (default: {DEFAULT_ROUNDS})") + parser.add_argument("--min-score", type=int, default=DEFAULT_MIN_SCORE, + help=f"Target quality score (default: {DEFAULT_MIN_SCORE})") + parser.add_argument("--seed", type=int, default=DEFAULT_SEED, + help="Base random seed") + parser.add_argument("-v", "--verbose", action="store_true", + help="Verbose output") + args = parser.parse_args() + + slots = [] + if args.slot: + slots.append(args.slot) + if args.slots: + slots.extend(args.slots) + + if not slots: + parser.error("Provide --slot or --slots") + + results = [] + for slot in slots: + result = run_iterative( + slot, + rounds=args.rounds, + min_score=args.min_score, + verbose=args.verbose, + seed=args.seed, + ) + results.append(result) + + # Summary + print(f"\n{'='*70}") + print(f"BATCH SUMMARY") + print(f"{'='*70}") + print(f"{'Slot':45s} {'Rounds':>6s} {'Best':>5s} {'Final':>6s}") + print("-" * 70) + for r in results: + print(f"{r['slot']:45s} {r['iterations']:6d} " + f"{r['best_round']:5d} {r['final_score']:6d}/100") + + avg_score = sum(r["final_score"] for r in results) / len(results) if results else 0 + print(f"\nAverage final score: {avg_score:.1f}/100") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/batch_run_all_slots.py b/meva/scripts/v10/batch_run_all_slots.py new file mode 100644 index 0000000..07adc85 --- /dev/null +++ b/meva/scripts/v10/batch_run_all_slots.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +""" +batch_run_all_slots.py — Generate raw QA for all (or a subset of) annotated slots. + +Uses multiprocessing for speed. Skips slots already done. Resumes cleanly +after interruption. + +Usage: + # All 381 slots, 8 parallel workers + python3 -m scripts.v10.batch_run_all_slots -v + + # Specific site only + python3 -m scripts.v10.batch_run_all_slots --site school -v + + # Custom worker count + python3 -m scripts.v10.batch_run_all_slots -w 4 -v + + # Dry-run: list slots that would be processed + python3 -m scripts.v10.batch_run_all_slots --dry-run + + # Re-process even if output already exists + python3 -m scripts.v10.batch_run_all_slots --overwrite +""" + +import argparse +import json +import os +import sys +import time +from concurrent.futures import ProcessPoolExecutor, as_completed +from datetime import datetime +from pathlib import Path + +try: + from .run_pipeline import run_pipeline + from .parse_annotations import find_clips_for_slot +except ImportError: + sys.path.insert(0, str(Path(__file__).resolve().parent)) + from run_pipeline import run_pipeline + from parse_annotations import find_clips_for_slot + +# --------------------------------------------------------------------------- +# Paths +# --------------------------------------------------------------------------- +_HERE = Path(__file__).resolve().parent +_REPO_ROOT = _HERE.parent.parent # meva/ +_DATA_DIR = _REPO_ROOT / "data" + +_OUTPUT = Path( + os.environ.get("MEVA_OUTPUT_DIR") or + os.environ.get("OUTPUT_DIR") or + "/nas/neurosymbolic/multi-cam-dataset/meva/data" +) +RAW_OUTPUT_DIR = _OUTPUT / "qa_pairs" / "raw" + +SLOT_INDEX = _DATA_DIR / "slot_index.json" + + +# --------------------------------------------------------------------------- +# Worker +# --------------------------------------------------------------------------- + +def _process_slot(args): + """Worker function: generate QA for one slot and save to disk. Returns result dict.""" + slot, seed, verbose, output_dir = args + start = time.time() + out_dir = Path(output_dir) + out_path = out_dir / f"{slot}.raw.json" + try: + result = run_pipeline(slot, seed=seed, verbose=False) + elapsed = time.time() - start + n_questions = len(result.get("qa_pairs", [])) + out_dir.mkdir(parents=True, exist_ok=True) + with open(out_path, "w") as f: + json.dump(result, f, indent=2, default=str) + return { + "slot": slot, + "status": "ok", + "questions": n_questions, + "elapsed": round(elapsed, 1), + } + except Exception as e: + elapsed = time.time() - start + return { + "slot": slot, + "status": "error", + "error": str(e), + "elapsed": round(elapsed, 1), + } + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(): + parser = argparse.ArgumentParser( + description="Generate raw QA for all annotated MEVA slots" + ) + parser.add_argument("--site", choices=["admin", "bus", "hospital", "school"], + help="Only process slots for this site") + parser.add_argument("--date", help="Only process slots for this date (e.g. 2018-03-11)") + parser.add_argument("-w", "--workers", type=int, default=8, + help="Parallel workers (default: 8)") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--overwrite", action="store_true", + help="Re-process slots even if output already exists") + parser.add_argument("--dry-run", action="store_true", + help="List slots without running") + parser.add_argument("-v", "--verbose", action="store_true") + args = parser.parse_args() + + # Load slot index + if not SLOT_INDEX.exists(): + print(f"ERROR: slot index not found: {SLOT_INDEX}") + print(" Run: python3 -m scripts.v10.extract_logic_tuples --build-index") + sys.exit(1) + + with open(SLOT_INDEX) as f: + all_slots = list(json.load(f).keys()) + + # Filter + if args.site: + all_slots = [s for s in all_slots if s.endswith(f".{args.site}")] + if args.date: + all_slots = [s for s in all_slots if s.startswith(args.date)] + + all_slots.sort() + + # Skip already-done + RAW_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + if not args.overwrite: + todo = [s for s in all_slots if not (RAW_OUTPUT_DIR / f"{s}.raw.json").exists()] + done_count = len(all_slots) - len(todo) + else: + todo = list(all_slots) + done_count = 0 + + from collections import Counter + site_counts = Counter(s.split(".")[-1] for s in todo) + + print(f"Slots total: {len(all_slots)}") + print(f"Already done: {done_count} (use --overwrite to redo)") + print(f"To process: {len(todo)}") + print(f"By site: {dict(site_counts)}") + print(f"Workers: {args.workers}") + print(f"Output: {RAW_OUTPUT_DIR}") + print() + + if args.dry_run: + for s in todo: + print(f" {s}") + return + + if not todo: + print("Nothing to do.") + return + + # Run + start_all = time.time() + ok, errors, total = 0, 0, len(todo) + error_list = [] + + worker_args = [(slot, args.seed, args.verbose, str(RAW_OUTPUT_DIR)) for slot in todo] + + print(f"Starting at {datetime.now().strftime('%H:%M:%S')} ...\n") + with ProcessPoolExecutor(max_workers=args.workers) as executor: + futures = {executor.submit(_process_slot, wa): wa[0] for wa in worker_args} + for i, future in enumerate(as_completed(futures), 1): + res = future.result() + if res["status"] == "ok": + ok += 1 + status_str = f"✓ {res['questions']:2d}q {res['elapsed']:5.1f}s" + else: + errors += 1 + error_list.append(res) + status_str = f"✗ ERROR: {res['error'][:60]}" + + pct = i / total * 100 + elapsed_total = time.time() - start_all + eta = (elapsed_total / i) * (total - i) if i > 0 else 0 + print(f" [{i:3d}/{total} {pct:5.1f}% ETA {eta/60:.1f}min] " + f"{res['slot']:45s} {status_str}") + + elapsed_total = time.time() - start_all + print(f"\n{'='*70}") + print(f"DONE | {ok} ok, {errors} errors | {elapsed_total/60:.1f} min total") + print(f"Output: {RAW_OUTPUT_DIR}") + + if error_list: + print(f"\nFailed slots ({errors}):") + for r in error_list: + print(f" {r['slot']}: {r['error']}") + + # Write error log + err_log = _OUTPUT / "qa_pairs" / "batch_errors.json" + with open(err_log, "w") as f: + json.dump({ + "timestamp": datetime.now().isoformat(), + "errors": error_list, + }, f, indent=2) + print(f"\nError log: {err_log}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/batch_verify.py b/meva/scripts/v10/batch_verify.py new file mode 100644 index 0000000..e8c3f3b --- /dev/null +++ b/meva/scripts/v10/batch_verify.py @@ -0,0 +1,347 @@ +#!/usr/bin/env python3 +""" +batch_verify.py — Automated quality verification for batch-generated QA. + +Validates raw QA JSONs from batch_run_all_slots.py WITHOUT watching videos. +Runs structural, temporal-consistency, uniqueness, and statistical checks. + +Usage: + # Verify all raw outputs + python3 -m scripts.v10.batch_verify + + # Just temporal category + python3 -m scripts.v10.batch_verify --category temporal + + # Verbose per-slot details + python3 -m scripts.v10.batch_verify -v + + # Export report to file + python3 -m scripts.v10.batch_verify --report report.json +""" + +import argparse +import json +import os +import sys +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +_OUTPUT = Path( + os.environ.get("MEVA_OUTPUT_DIR") or + os.environ.get("OUTPUT_DIR") or + "/nas/neurosymbolic/multi-cam-dataset/meva/data" +) +RAW_DIR = _OUTPUT / "qa_pairs" / "raw" + + +# ============================================================================ +# Structural Checks (per question) +# ============================================================================ + +REQUIRED_FIELDS = { + "question_id", "category", "question_template", "options", + "correct_answer_index", "correct_answer", "requires_cameras", +} + +CATEGORIES = {"temporal", "event_ordering", "spatial", "summarization", + "counting", "best_camera"} + + +def _check_structure(q: Dict) -> List[str]: + """Return list of structural issues for one question.""" + issues = [] + missing = REQUIRED_FIELDS - set(q.keys()) + if missing: + issues.append(f"missing fields: {missing}") + + opts = q.get("options", []) + if not isinstance(opts, list) or len(opts) < 2: + issues.append(f"options invalid (got {type(opts).__name__} len={len(opts) if isinstance(opts, list) else '?'})") + + idx = q.get("correct_answer_index") + if isinstance(idx, int) and isinstance(opts, list): + if idx < 0 or idx >= len(opts): + issues.append(f"correct_answer_index={idx} OOB (options len={len(opts)})") + elif q.get("correct_answer") != opts[idx]: + issues.append(f"correct_answer mismatch: '{q.get('correct_answer')[:40]}' vs opts[{idx}]='{opts[idx][:40]}'") + + cat = q.get("category", "") + if cat not in CATEGORIES: + issues.append(f"unknown category: {cat}") + + cams = q.get("requires_cameras", []) + if not cams: + issues.append("no cameras listed") + + return issues + + +# ============================================================================ +# Temporal-Specific Checks +# ============================================================================ + +def _check_temporal(q: Dict) -> List[str]: + """Check temporal ordering correctness from verification block.""" + issues = [] + v = q.get("verification", {}) + if not v: + issues.append("no verification block") + return issues + + ea = v.get("event_a", {}) + eb = v.get("event_b", {}) + gap = v.get("gap_sec") + + if not ea or not eb: + issues.append("missing event_a/event_b in verification") + return issues + + # Event A should end before Event B starts + a_end = ea.get("end_sec") + b_start = eb.get("start_sec") + if a_end is not None and b_start is not None: + if a_end > b_start: + issues.append(f"temporal overlap: event_a ends at {a_end:.1f}s but event_b starts at {b_start:.1f}s") + + # Gap should be positive and ≤15s (FALLBACK_MAX_GAP) + if gap is not None: + if gap <= 0: + issues.append(f"non-positive gap: {gap}s") + elif gap > 15: + issues.append(f"gap exceeds max: {gap}s > 15s") + + # Events should be on different cameras + if ea.get("camera") == eb.get("camera"): + issues.append(f"same camera: {ea.get('camera')}") + + # Descriptions should be different + if ea.get("description") and ea.get("description") == eb.get("description"): + issues.append(f"identical descriptions: '{ea['description'][:50]}'") + + return issues + + +# ============================================================================ +# Event Ordering Checks +# ============================================================================ + +def _check_event_ordering(q: Dict) -> List[str]: + """Check event ordering chain consistency.""" + issues = [] + v = q.get("verification", {}) + chain = v.get("event_chain", v.get("chain", [])) + if not chain: + return issues + + # Chain should be chronologically ordered + for i in range(len(chain) - 1): + t_curr = chain[i].get("end_sec") or chain[i].get("start_sec", 0) + t_next = chain[i + 1].get("start_sec", 0) + if t_curr > t_next + 1.0: # 1s tolerance + issues.append(f"chain out of order at step {i}: {t_curr:.1f}s > {t_next:.1f}s") + + return issues + + +# ============================================================================ +# Description Uniqueness (cross-check) +# ============================================================================ + +def _check_uniqueness(q: Dict) -> List[str]: + """Check that descriptions in options are sufficiently distinct.""" + issues = [] + opts = q.get("options", []) + if len(opts) != len(set(opts)): + dupes = [o for o in opts if opts.count(o) > 1] + issues.append(f"duplicate options: {dupes[:2]}") + + # For temporal: check event descriptions aren't identical + v = q.get("verification", {}) + ea_desc = v.get("event_a", {}).get("description", "") + eb_desc = v.get("event_b", {}).get("description", "") + if ea_desc and eb_desc and ea_desc == eb_desc: + issues.append(f"event descriptions identical: '{ea_desc[:50]}'") + + return issues + + +# ============================================================================ +# Aggregate Statistics +# ============================================================================ + +def verify_all(raw_dir: Path, category_filter: Optional[str] = None, + verbose: bool = False) -> Dict[str, Any]: + """Run all verification checks on raw QA outputs. + + Returns a report dict with counts, issues, and statistics. + """ + files = sorted(raw_dir.glob("*.raw.json")) + if not files: + print(f"No raw QA files found in {raw_dir}") + return {"error": "no files found"} + + total_qs = 0 + total_issues = 0 + total_slots = len(files) + slots_with_issues = 0 + category_counts = Counter() + category_issues = defaultdict(int) + issue_types = Counter() + questions_per_slot = [] + slots_zero_qs = [] + all_issues_detail = [] + temporal_gaps = [] + temporal_formats = Counter() + + for fpath in files: + slot = fpath.stem.replace(".raw", "") + try: + with open(fpath) as f: + data = json.load(f) + except Exception as e: + if verbose: + print(f" ERROR reading {fpath.name}: {e}") + slots_with_issues += 1 + continue + + qa_pairs = data.get("qa_pairs", []) + if category_filter: + qa_pairs = [q for q in qa_pairs if q.get("category") == category_filter] + + questions_per_slot.append(len(qa_pairs)) + if len(qa_pairs) == 0: + slots_zero_qs.append(slot) + + slot_issues = [] + for q in qa_pairs: + total_qs += 1 + cat = q.get("category", "unknown") + category_counts[cat] += 1 + + issues = _check_structure(q) + issues += _check_uniqueness(q) + + if cat == "temporal": + issues += _check_temporal(q) + # Collect temporal stats + v = q.get("verification", {}) + gap = v.get("gap_sec") + if gap is not None: + temporal_gaps.append(gap) + fmt = q.get("debug_info", {}).get("question_format", "unknown") + temporal_formats[fmt] += 1 + + elif cat == "event_ordering": + issues += _check_event_ordering(q) + + if issues: + total_issues += len(issues) + category_issues[cat] += len(issues) + for iss in issues: + issue_types[iss.split(":")[0]] += 1 + slot_issues.append({ + "question_id": q.get("question_id", "?"), + "category": cat, + "issues": issues, + }) + + if slot_issues: + slots_with_issues += 1 + all_issues_detail.append({"slot": slot, "issues": slot_issues}) + if verbose: + print(f" {slot}: {len(slot_issues)} questions with issues") + for si in slot_issues: + for iss in si["issues"]: + print(f" {si['question_id']}: {iss}") + + # Statistics + avg_qs = sum(questions_per_slot) / len(questions_per_slot) if questions_per_slot else 0 + temporal_avg_gap = sum(temporal_gaps) / len(temporal_gaps) if temporal_gaps else 0 + + report = { + "summary": { + "total_slots": total_slots, + "total_questions": total_qs, + "avg_questions_per_slot": round(avg_qs, 1), + "slots_with_zero_questions": len(slots_zero_qs), + "slots_with_issues": slots_with_issues, + "total_issues": total_issues, + "pass_rate_pct": round( + (total_qs - sum(1 for d in all_issues_detail for q in d["issues"])) + / total_qs * 100 if total_qs else 0, 1 + ), + }, + "category_counts": dict(category_counts), + "category_issues": dict(category_issues), + "issue_types": dict(issue_types.most_common(20)), + "temporal_stats": { + "count": category_counts.get("temporal", 0), + "avg_gap_sec": round(temporal_avg_gap, 2), + "min_gap_sec": round(min(temporal_gaps), 2) if temporal_gaps else None, + "max_gap_sec": round(max(temporal_gaps), 2) if temporal_gaps else None, + "formats": dict(temporal_formats), + }, + "zero_question_slots": slots_zero_qs[:20], # first 20 + "zero_question_count": len(slots_zero_qs), + "issues_detail": all_issues_detail[:50], # first 50 slots with issues + } + return report + + +def main(): + parser = argparse.ArgumentParser(description="Verify batch QA output quality") + parser.add_argument("--dir", type=str, default=str(RAW_DIR), + help=f"Directory with raw JSON files (default: {RAW_DIR})") + parser.add_argument("--category", choices=list(CATEGORIES), + help="Only verify questions of this category") + parser.add_argument("-v", "--verbose", action="store_true") + parser.add_argument("--report", type=str, + help="Save report JSON to this path") + args = parser.parse_args() + + raw_dir = Path(args.dir) + print(f"Verifying raw QA in: {raw_dir}") + print(f"Category filter: {args.category or 'all'}\n") + + report = verify_all(raw_dir, category_filter=args.category, verbose=args.verbose) + + if "error" in report: + sys.exit(1) + + # Print summary + s = report["summary"] + print(f"\n{'=' * 60}") + print(f"VERIFICATION REPORT") + print(f"{'=' * 60}") + print(f"Slots: {s['total_slots']}") + print(f"Questions: {s['total_questions']} ({s['avg_questions_per_slot']} avg/slot)") + print(f"Zero-Q slots:{s['slots_with_zero_questions']}") + print(f"Pass rate: {s['pass_rate_pct']}%") + print(f"Issues: {s['total_issues']} across {s['slots_with_issues']} slots") + + print(f"\nCategory breakdown:") + for cat, cnt in sorted(report["category_counts"].items()): + iss = report["category_issues"].get(cat, 0) + print(f" {cat:25s}: {cnt:4d} questions, {iss} issues") + + ts = report.get("temporal_stats", {}) + if ts.get("count"): + print(f"\nTemporal stats:") + print(f" Count: {ts['count']}") + print(f" Gap: {ts['avg_gap_sec']}s avg, {ts['min_gap_sec']}-{ts['max_gap_sec']}s range") + print(f" Formats: {ts['formats']}") + + if report["issue_types"]: + print(f"\nTop issue types:") + for iss, cnt in list(report["issue_types"].items())[:10]: + print(f" {cnt:4d}x {iss}") + + if args.report: + with open(args.report, "w") as f: + json.dump(report, f, indent=2, default=str) + print(f"\nFull report saved: {args.report}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/build_geom_slot_index.py b/meva/scripts/v10/build_geom_slot_index.py new file mode 100755 index 0000000..e82bbf3 --- /dev/null +++ b/meva/scripts/v10/build_geom_slot_index.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +""" +Build Geom Slot Index — Scan all canonical slots for geom file availability. + +This creates a filtered slot index containing only slots that have at least +one camera with geom.yml bounding box data available. + +Checks both: + - /nas/mars/.../kitware/ + - /nas/mars/.../kitware-meva-training/ + +Output: data/geom_slot_index.json +Time: ~15-20 minutes for 929 slots +""" + +import json +import subprocess +import sys +from pathlib import Path +from typing import Dict + +_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data" + +SLOT_INDEX_PATH = _REPO_DATA / "slot_index.json" +OUTPUT_PATH = _REPO_DATA / "geom_slot_index.json" +EXTRACTION_SCRIPT = Path(__file__).resolve().parent / "extract_entity_descriptions.py" + + +def main(): + print("="*60, flush=True) + print("Building Geom Slot Index", flush=True) + print("="*60, flush=True) + print(f"Input: {SLOT_INDEX_PATH}", flush=True) + print(f"Output: {OUTPUT_PATH}", flush=True) + print(flush=True) + + # Load all slots + if not SLOT_INDEX_PATH.exists(): + print(f"ERROR: Slot index not found: {SLOT_INDEX_PATH}", flush=True) + sys.exit(1) + + with open(SLOT_INDEX_PATH) as f: + all_slots = json.load(f) + + total_slots = len(all_slots) + print(f"Scanning {total_slots} canonical slots...", flush=True) + print("This will take ~15-20 minutes.\n", flush=True) + + geom_slots = {} + stats = { + "total_cameras": 0, + "total_usable_actors": 0, + "kitware_count": 0, + "training_count": 0, + } + + for i, (slot, slot_data) in enumerate(sorted(all_slots.items()), 1): + # Run dry-run to check for geom files + try: + result = subprocess.run( + ["python3", str(EXTRACTION_SCRIPT), "--slot", slot, "--dry-run"], + capture_output=True, + text=True, + timeout=15, + ) + + # Parse output + lines = result.stdout.split("\n") + cameras_line = [l for l in lines if "Cameras with geom:" in l] + + if not cameras_line: + continue + + n_cameras = int(cameras_line[0].split(":")[1].strip()) + + if n_cameras > 0: + # Extract usable actor count + usable_actors = 0 + for line in lines: + if " usable," in line: + parts = line.split(",") + for p in parts: + if " usable" in p: + usable_actors += int(p.split()[0]) + + geom_slots[slot] = { + **slot_data, + "geom_cameras": n_cameras, + "geom_usable_actors": usable_actors, + } + + stats["total_cameras"] += n_cameras + stats["total_usable_actors"] += usable_actors + + except subprocess.TimeoutExpired: + print(f" [{i:4d}/{total_slots}] {slot}: TIMEOUT (skipped)", flush=True) + continue + except Exception as e: + print(f" [{i:4d}/{total_slots}] {slot}: ERROR {e}", flush=True) + continue + + # Progress every 50 slots + if i % 50 == 0: + coverage = len(geom_slots) * 100 / i + print(f" [{i:4d}/{total_slots}] Progress: {len(geom_slots)} slots with geom ({coverage:.1f}%), " + f"{stats['total_usable_actors']:,} actors", flush=True) + + # Final stats + print(flush=True) + print("="*60, flush=True) + print("SCAN COMPLETE", flush=True) + print("="*60, flush=True) + print(f"Total canonical slots: {total_slots}", flush=True) + print(f"Slots with geom files: {len(geom_slots)} ({len(geom_slots)*100/total_slots:.1f}%)", flush=True) + print(f"Total cameras with geom: {stats['total_cameras']}", flush=True) + print(f"Total usable actors: {stats['total_usable_actors']:,}", flush=True) + print(f"Avg actors/slot: {stats['total_usable_actors']/max(len(geom_slots),1):.0f}", flush=True) + print(flush=True) + + # Save + output_data = { + "slots": geom_slots, + "stats": { + "total_canonical_slots": total_slots, + "slots_with_geom": len(geom_slots), + "coverage_percent": round(len(geom_slots) * 100 / total_slots, 1), + "total_cameras": stats["total_cameras"], + "total_usable_actors": stats["total_usable_actors"], + "avg_actors_per_slot": round(stats["total_usable_actors"] / max(len(geom_slots), 1)), + } + } + + with open(OUTPUT_PATH, "w") as f: + json.dump(output_data, f, indent=2) + + print(f"Saved: {OUTPUT_PATH}", flush=True) + print("="*60, flush=True) + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/build_mevid_slots.py b/meva/scripts/v10/build_mevid_slots.py new file mode 100644 index 0000000..e381565 --- /dev/null +++ b/meva/scripts/v10/build_mevid_slots.py @@ -0,0 +1,188 @@ +""" +build_mevid_slots.py — Rebuild mevid_supported_slots.json from authoritative sources. + +DATA SOURCES: + - mevid-v1-annotation-data/{train,test}_name.txt → person-camera associations + - mevid-v1-video-URLS.txt → which MEVA clips are in MEVID + - data/slot_index.json → all cameras per canonical slot + +LOGIC: + For each March MEVID clip, determine the canonical slot (date.HH-MM.site). + For each slot, collect: + - mevid_cameras: cameras that have MEVID clips in this 5-min window + - mevid_persons: persons from annotation files with 2+ cameras in mevid_cameras + Write to data/mevid_supported_slots.json. + +USAGE: + cd /path/to/meva + python3 -m scripts.v10.build_mevid_slots + python3 -m scripts.v10.build_mevid_slots --include-may # also include May dates +""" + +import argparse +import json +import os +import re +import sys +from collections import defaultdict +from pathlib import Path + +# Paths +_SCRIPT_DIR = Path(__file__).parent +_REPO_DATA = _SCRIPT_DIR.parent.parent / "data" + +MEVID_DATA_DIR = Path(os.environ.get("MEVA_MEVID_DATA_DIR") or "/nas/mars/dataset/MEVA/mevid_data/mevid-v1-annotation-data") +MEVID_URLS = Path(os.environ.get("MEVA_MEVID_URLS") or "/nas/mars/dataset/MEVA/mevid_data/mevid-v1-video-URLS.txt") + +# Regexes +_RE_NAME = re.compile(r'^(\d{4})O\d{3}C(\d+)T') +_RE_CLIP = re.compile( + r'(\d{4}-\d{2}-\d{2})\.(\d{2})-(\d{2})-\d{2}\.\d{2}-\d{2}-\d{2}\.(\w+)\.(G\d+)' +) + + +def parse_person_cameras() -> dict[str, set[str]]: + """ + Parse train/test name files → {person_id_str: {camera_ids}}. + Camera IDs are in MEVA format: "G424" (not "C424"). + Person IDs are zero-padded strings: "0041". + """ + person_cams: dict[str, set[str]] = defaultdict(set) + for fname in ("train_name.txt", "test_name.txt"): + fpath = MEVID_DATA_DIR / fname + if not fpath.exists(): + print(f" WARNING: {fpath} not found", file=sys.stderr) + continue + seen = set() + with open(fpath) as f: + for line in f: + m = _RE_NAME.match(line.strip()) + if not m: + continue + pid = m.group(1) # e.g. "0041" + cam = f"G{m.group(2)}" # e.g. "G424" + key = (pid, cam) + if key not in seen: + seen.add(key) + person_cams[pid].add(cam) + return dict(person_cams) + + +def parse_slot_cameras(include_may: bool = False) -> dict[str, set[str]]: + """ + Parse video URLs → {slot: {cameras with MEVID clips}}. + slot format: "2018-03-11.11-25.school" + """ + if not MEVID_URLS.exists(): + print(f" ERROR: {MEVID_URLS} not found", file=sys.stderr) + return {} + + slot_cams: dict[str, set[str]] = defaultdict(set) + with open(MEVID_URLS) as f: + for line in f: + m = _RE_CLIP.search(line.strip()) + if not m: + continue + date, hh, mm, site, cam = m.groups() + if not include_may and date.startswith("2018-05"): + continue + slot = f"{date}.{hh}-{mm}.{site}" + slot_cams[slot].add(cam) + return dict(slot_cams) + + +def load_slot_index() -> dict: + path = _REPO_DATA / "slot_index.json" + if path.exists(): + return json.loads(path.read_text()) + return {} + + +def build(include_may: bool = False, verbose: bool = True) -> dict: + if verbose: + print("=== Building mevid_supported_slots.json ===") + + # 1. Person → cameras from annotation files + person_cams = parse_person_cameras() + if verbose: + multi = {p: c for p, c in person_cams.items() if len(c) >= 2} + print(f" Persons in annotation files: {len(person_cams)}") + print(f" Persons with 2+ cameras (global): {len(multi)}") + + # 2. Slot → MEVID cameras from video URLs + slot_cameras = parse_slot_cameras(include_may=include_may) + if verbose: + dates = set(s.split(".")[0] for s in slot_cameras) + print(f" Slots with MEVID clips: {len(slot_cameras)} across {len(dates)} dates") + + # 3. slot_index.json for all_cameras + slot_index = load_slot_index() + + # 4. For each slot, find cross-camera persons + result_slots: dict[str, dict] = {} + for slot in sorted(slot_cameras): + mevid_cams = sorted(slot_cameras[slot]) + mevid_cam_set = set(mevid_cams) + + cross_persons = sorted( + pid for pid, cams in person_cams.items() + if len(cams & mevid_cam_set) >= 2 + ) + + if not cross_persons: + continue + + # Parse slot components + parts = slot.split(".") + date = parts[0] + time_part = parts[1] if len(parts) > 1 else "" + site = parts[2] if len(parts) > 2 else "" + + # Look up all_cameras from slot_index (best effort) + all_cams_count = len(slot_cameras[slot]) + if slot in slot_index: + all_cams_count = len(slot_index[slot].get("cameras", [])) + + result_slots[slot] = { + "date": date, + "time": f"{time_part}-00", + "site": site, + "all_cameras": all_cams_count, + "mevid_cameras": mevid_cams, + "mevid_persons": cross_persons, + } + + # 5. Summary + all_persons = set() + for v in result_slots.values(): + all_persons.update(v["mevid_persons"]) + + if verbose: + print(f"\n Result: {len(result_slots)} slots with cross-camera MEVID persons") + print(f" Unique persons: {len(all_persons)}") + print(f" Person IDs: {sorted(all_persons)}") + + return {"slots": result_slots} + + +def main(): + parser = argparse.ArgumentParser(description="Rebuild mevid_supported_slots.json") + parser.add_argument("--include-may", action="store_true", + help="Include May 2018 MEVID data (not on disk, for future use)") + parser.add_argument("--dry-run", action="store_true", + help="Print results without writing the file") + args = parser.parse_args() + + data = build(include_may=args.include_may, verbose=True) + + out_path = _REPO_DATA / "mevid_supported_slots.json" + if args.dry_run: + print(f"\nDry run — would write to {out_path}") + else: + out_path.write_text(json.dumps(data, indent=2)) + print(f"\nWritten to {out_path}") + print(f"({out_path.stat().st_size // 1024} KB)") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/build_scene_graph.py b/meva/scripts/v10/build_scene_graph.py new file mode 100644 index 0000000..51f71d2 --- /dev/null +++ b/meva/scripts/v10/build_scene_graph.py @@ -0,0 +1,283 @@ +""" +V7 build_scene_graph.py — Step 2: Entity-based scene graph with IoU matching + aliases. + +Builds an entity-based scene graph from parsed events + geom.yml bounding boxes. +Each entity = (camera_id, actor_id) with time span, keyframe bboxes, events, and +human-readable alias for question text. +""" + +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple +from collections import defaultdict +from dataclasses import dataclass, asdict, field + +from .parse_annotations import Event, find_clips_for_slot, DEFAULT_FRAMERATE +from .utils.yaml_stream import get_actor_keyframe_bboxes, get_actor_frame_range +from .utils.krtd import load_camera_model, CameraModel, INDOOR_CAMERAS +from .utils.iou import compute_iou +from .activity_hierarchy import humanize_activity + + +# 2% of 1920×1080 = 2,073,600 × 0.02 ≈ 41,472 px² (~200×208 px minimum) +# For close-up cameras (G419, G420, G421 etc.) where people fill the frame +MIN_BBOX_AREA = 41472 +# 0.1% of 1920×1080 ≈ 2,073 px² (~46×46 px minimum) +# For wide-field outdoor KRTD cameras (G336, G328, G638 etc.) where people are small +MIN_BBOX_AREA_KRTD = 2048 + +# ============================================================================ +# Data Structures +# ============================================================================ + +@dataclass +class Entity: + """An entity (person/vehicle) tracked on one camera.""" + entity_id: str # "{camera_id}_actor_{actor_id}" + camera_id: str + actor_id: int + entity_type: str # "person" or "vehicle" + first_frame: int + last_frame: int + first_sec: float + last_sec: float + keyframe_bboxes: Dict[int, List[int]] # {frame: [x1,y1,x2,y2]} + events: List[str] # event_ids this entity participates in + alias: str = "" # Human-readable label (set after construction) + + def make_alias(self, event_list: list = None) -> str: + """ + Generate a human-readable entity alias for question text. + + Uses the entity's primary activity + actor ID + camera + timestamp. + """ + # Find this entity's primary activity from event list + primary_activity = None + mid_sec = None + if event_list: + for evt in event_list: + if evt.camera_id == self.camera_id: + for actor in evt.actors: + if actor["actor_id"] == self.actor_id: + primary_activity = evt.activity + mid_sec = round((evt.start_sec + evt.end_sec) / 2) + break + if primary_activity: + break + + # Build the alias + if primary_activity: + short_act = humanize_activity(primary_activity) + # Use entity's own time span if we didn't find event timing + t = mid_sec if mid_sec is not None else int(self.first_sec) + + # For small actor IDs, include them; for hash IDs, skip + if isinstance(self.actor_id, int) and self.actor_id < 10000: + return f"Person {short_act} (#{self.actor_id} on {self.camera_id} @ {t}s)" + else: + return f"Person {short_act} (on {self.camera_id} @ {t}s)" + + # Fallback: no activity + if isinstance(self.actor_id, int) and self.actor_id < 10000: + return f"Actor #{self.actor_id} on {self.camera_id}" + + return f"Person on {self.camera_id}" + + def to_dict(self) -> dict: + d = asdict(self) + d["alias"] = self.alias + return d + + +@dataclass +class CameraNode: + """Camera metadata for scene graph.""" + camera_id: str + is_indoor: bool + has_krtd: bool + position_enu: Optional[Tuple[float, float, float]] + + def to_dict(self) -> dict: + return asdict(self) + + +@dataclass +class SceneGraph: + """Complete scene graph for one slot.""" + slot: str + cameras: Dict[str, CameraNode] + entities: Dict[str, Entity] # {entity_id: Entity} + events: List[Event] + events_by_camera: Dict[str, List[Event]] + + def to_dict(self) -> dict: + return { + "slot": self.slot, + "cameras": {k: v.to_dict() for k, v in self.cameras.items()}, + "entities": {k: v.to_dict() for k, v in self.entities.items()}, + "events": [e.to_dict() for e in self.events], + "events_by_camera": { + k: [e.to_dict() for e in v] + for k, v in self.events_by_camera.items() + }, + } + + +# ============================================================================ +# Scene Graph Builder +# ============================================================================ + +def build_scene_graph(slot: str, events: List[Event], + verbose: bool = False) -> SceneGraph: + """ + Build an entity-based scene graph from parsed events. + + Steps: + 1. Build camera nodes with KRTD info + 2. Extract entities from events + geom.yml bboxes + 3. Link events to entities + + Args: + slot: Slot name + events: Parsed Event objects from parse_annotations + verbose: Print progress + + Returns: + SceneGraph with entities and events + """ + if verbose: + print(f"Building scene graph for {slot}") + + # Collect unique cameras + camera_ids = sorted(set(e.camera_id for e in events)) + + # 1. Build camera nodes + cameras: Dict[str, CameraNode] = {} + for cam_id in camera_ids: + model = load_camera_model(cam_id) + is_indoor = cam_id in INDOOR_CAMERAS + cameras[cam_id] = CameraNode( + camera_id=cam_id, + is_indoor=is_indoor, + has_krtd=model is not None, + position_enu=tuple(model.camera_center.tolist()) if model else None, + ) + + # 2. Extract entities: collect unique (camera, actor) pairs from events + # Also collect actor_ids per camera for geom.yml lookup + entity_actor_ids: Dict[str, Set[int]] = defaultdict(set) # cam -> actor_ids + entity_types: Dict[str, Dict[int, str]] = defaultdict(dict) # cam -> {aid: type} + entity_events: Dict[str, Dict[int, List[str]]] = defaultdict(lambda: defaultdict(list)) + + for evt in events: + for actor in evt.actors: + aid = actor["actor_id"] + entity_actor_ids[evt.camera_id].add(aid) + entity_types[evt.camera_id][aid] = actor.get("entity_type", "unknown") + entity_events[evt.camera_id][aid].append(evt.event_id) + + # 3. Try to get keyframe bboxes from geom.yml (stream-parsed) + clips = find_clips_for_slot(slot) + clip_by_camera = {c["camera_id"]: c for c in clips} + + entity_bboxes: Dict[str, Dict[int, Dict[int, List[int]]]] = {} # cam -> {aid: {frame: bbox}} + entity_frame_ranges: Dict[str, Dict[int, tuple]] = {} # cam -> {aid: (first, last)} + + for cam_id, actor_ids in entity_actor_ids.items(): + if cam_id not in clip_by_camera: + continue + clip = clip_by_camera[cam_id] + geom_path = Path(clip["activities_file"]).with_name( + Path(clip["activities_file"]).name.replace(".activities.yml", ".geom.yml") + ) + if geom_path.exists(): + try: + # V7: Load ALL frames (sample_every=1) for complete trajectories + bboxes = get_actor_keyframe_bboxes(geom_path, actor_ids, sample_every=1) + entity_bboxes[cam_id] = bboxes + + # Also get frame ranges + ranges = get_actor_frame_range(geom_path) + entity_frame_ranges[cam_id] = ranges + + if verbose: + print(f" {cam_id}: streamed geom.yml — {len(bboxes)} actors with bboxes") + except Exception as e: + if verbose: + print(f" {cam_id}: geom.yml parse error: {e}") + + # 4. Build entity objects + entities: Dict[str, Entity] = {} + framerate = DEFAULT_FRAMERATE + + for cam_id in sorted(entity_actor_ids.keys()): + actor_ids = sorted(entity_actor_ids[cam_id]) + cam_ranges = entity_frame_ranges.get(cam_id, {}) + cam_bboxes = entity_bboxes.get(cam_id, {}) + + for aid in actor_ids: + entity_id = f"{cam_id}_actor_{aid}" + + # Frame range from geom.yml if available, else from events + if aid in cam_ranges: + first_frame, last_frame = cam_ranges[aid] + else: + # Estimate from events + actor_events = [e for e in events + if e.camera_id == cam_id + and any(a["actor_id"] == aid for a in e.actors)] + if actor_events: + first_frame = min(e.start_frame for e in actor_events) + last_frame = max(e.end_frame for e in actor_events) + else: + first_frame, last_frame = 0, 0 + + entity = Entity( + entity_id=entity_id, + camera_id=cam_id, + actor_id=aid, + entity_type=entity_types.get(cam_id, {}).get(aid, "unknown"), + first_frame=first_frame, + last_frame=last_frame, + first_sec=round(first_frame / framerate, 2), + last_sec=round(last_frame / framerate, 2), + keyframe_bboxes=cam_bboxes.get(aid, {}), + events=entity_events.get(cam_id, {}).get(aid, []), + ) + + # Filter out entities whose bounding boxes are too small. + # Wide-field KRTD cameras have a much lower threshold since people + # appear small but are still valid for 3D projection. + if entity.keyframe_bboxes: + areas = [(bb[2]-bb[0]) * (bb[3]-bb[1]) for bb in entity.keyframe_bboxes.values()] + median_area = sorted(areas)[len(areas)//2] + is_krtd_cam = cameras[cam_id].has_krtd + threshold = MIN_BBOX_AREA_KRTD if is_krtd_cam else MIN_BBOX_AREA + if median_area < threshold: + if verbose: + print(f" Skipping {entity_id}: median bbox area {median_area} < {threshold}") + continue + + entities[entity_id] = entity + + # Group events by camera + events_by_camera: Dict[str, List[Event]] = defaultdict(list) + for evt in events: + events_by_camera[evt.camera_id].append(evt) + + # 5. Generate entity aliases for human-readable question text + for eid, entity in entities.items(): + entity.alias = entity.make_alias(events) + + sg = SceneGraph( + slot=slot, + cameras=cameras, + entities=entities, + events=events, + events_by_camera=dict(events_by_camera), + ) + + if verbose: + print(f" Total: {len(entities)} entities, {len(events)} events, " + f"{len(cameras)} cameras") + + return sg diff --git a/meva/scripts/v10/distractor_bank.py b/meva/scripts/v10/distractor_bank.py new file mode 100644 index 0000000..a7e3ee2 --- /dev/null +++ b/meva/scripts/v10/distractor_bank.py @@ -0,0 +1,105 @@ +""" +V6 distractor_bank.py — Wrong answer generation from activity pool. + +Generates plausible distractor activities for multiple-choice questions. +Distractors are same entity type (person/vehicle) as the correct answer, +preferring activities present in the current slot for plausibility. +""" + +import random +from typing import List, Set + + +# All 37 MEVA activity names grouped by entity type +PERSON_ACTIVITIES = [ + "person_opens_trunk", "person_closes_trunk", + "person_opens_vehicle_door", "person_closes_vehicle_door", + "person_opens_facility_door", "person_closes_facility_door", + "person_enters_vehicle", "person_exits_vehicle", + "person_unloads_vehicle", "person_loads_vehicle", + "person_picks_up_object", "person_puts_down_object", + "person_carries_heavy_object", "person_transfers_object", + "person_talks_to_person", "person_embraces_person", + "person_enters_scene_through_structure", + "person_exits_scene_through_structure", + "person_sits_down", "person_stands_up", + "person_talks_on_phone", "person_texts_on_phone", + "person_reads_document", "person_interacts_with_laptop", + "person_purchases", "person_rides_bicycle", +] + +VEHICLE_ACTIVITIES = [ + "vehicle_starts", "vehicle_stops", + "vehicle_turns_left", "vehicle_turns_right", + "vehicle_makes_u_turn", "vehicle_reverses", + "vehicle_drops_off_person", "vehicle_picks_up_person", +] + +ALL_ACTIVITIES = PERSON_ACTIVITIES + VEHICLE_ACTIVITIES + + +def get_distractors(correct_activity: str, slot_activities: Set[str], + rng: random.Random, n: int = 3) -> List[str]: + """ + Pick n distractor activities that are: + 1. Same entity type (person/vehicle) as correct answer + 2. NOT the correct activity + 3. Prefer activities present in THIS slot (more plausible) + 4. Fall back to global pool if needed + + Args: + correct_activity: The correct answer activity + slot_activities: Set of all activities in the current slot + rng: Random number generator (for reproducibility) + n: Number of distractors to generate + + Returns: + List of n distractor activity names + """ + entity = "vehicle" if correct_activity.startswith("vehicle_") else "person" + pool = VEHICLE_ACTIVITIES if entity == "vehicle" else PERSON_ACTIVITIES + + # Prefer activities in this slot (more believable distractors) + in_slot = [a for a in slot_activities if a != correct_activity and a in pool] + out_slot = [a for a in pool if a != correct_activity and a not in in_slot] + + rng.shuffle(in_slot) + rng.shuffle(out_slot) + + distractors = in_slot[:n] + if len(distractors) < n: + distractors += out_slot[:n - len(distractors)] + return distractors[:n] + + +def get_camera_distractors(correct_cameras: List[str], all_cameras: List[str], + rng: random.Random, n: int = 3) -> List[str]: + """ + Pick n distractor camera IDs. + + Args: + correct_cameras: Camera(s) that are the correct answer + all_cameras: All cameras in the slot + rng: Random number generator + n: Number of distractors + + Returns: + List of distractor camera IDs + """ + pool = [c for c in all_cameras if c not in correct_cameras] + rng.shuffle(pool) + result = pool[:n] + + # If not enough distractors from slot cameras, pad from global MEVA pool + if len(result) < n: + global_pool = [ + "G299", "G300", "G328", "G330", "G336", "G339", "G341", "G419", + "G420", "G421", "G423", "G424", "G436", "G638", "G639", + "G503", "G504", "G505", "G506", "G507", "G508", "G509", + ] + extras = [c for c in global_pool + if c not in correct_cameras and c not in result and c not in all_cameras] + rng.shuffle(extras) + result.extend(extras[:n - len(result)]) + + return result[:n] diff --git a/meva/scripts/v10/entity_resolution.py b/meva/scripts/v10/entity_resolution.py new file mode 100644 index 0000000..e4875b1 --- /dev/null +++ b/meva/scripts/v10/entity_resolution.py @@ -0,0 +1,417 @@ +""" +V7 entity_resolution.py — Step 3: Cross-camera entity linking. + +Uses MEVID ground truth person IDs + heuristic temporal handoff + 3D spatial +proximity for cross-camera entity resolution. Produces entity clusters where +each cluster represents the same real-world person across cameras. + +V7: Added MEVID validation stats to ResolvedGraph output. +V10: Added 3D spatial proximity + description similarity boosting. +""" + +import re +from typing import Any, Dict, List, Optional, Set, Tuple +from collections import defaultdict +from dataclasses import dataclass, asdict + +from .build_scene_graph import SceneGraph, Entity +from .utils.mevid import find_mevid_persons_for_slot + + +# ============================================================================ +# Data Structures +# ============================================================================ + +@dataclass +class CrossCameraLink: + """A link between two entities on different cameras.""" + entity_a: str + entity_b: str + camera_a: str + camera_b: str + link_type: str # "mevid_ground_truth" or "temporal_handoff" + confidence: float # 1.0 for MEVID, 0.0-1.0 for heuristic + mevid_person_id: Optional[int] = None + time_gap_sec: Optional[float] = None + + def to_dict(self) -> dict: + return asdict(self) + + +@dataclass +class EntityCluster: + """A cluster of entities representing the same real-world person.""" + cluster_id: str + entities: List[str] # entity_ids + cameras: List[str] # camera_ids involved + mevid_person_id: Optional[int] = None + link_type: str = "heuristic" # "mevid_ground_truth" or "heuristic" + + def to_dict(self) -> dict: + return asdict(self) + + +@dataclass +class ResolvedGraph: + """Scene graph with resolved cross-camera entity links.""" + cross_camera_links: List[CrossCameraLink] + entity_clusters: List[EntityCluster] + mevid_persons_in_slot: int + heuristic_link_count: int + mevid_link_count: int + mevid_person_cameras: Dict[int, Set[str]] = None # V7: person_id -> {camera_ids} + + def to_dict(self) -> dict: + return { + "cross_camera_links": [l.to_dict() for l in self.cross_camera_links], + "entity_clusters": [c.to_dict() for c in self.entity_clusters], + "mevid_persons_in_slot": self.mevid_persons_in_slot, + "heuristic_link_count": self.heuristic_link_count, + "mevid_link_count": self.mevid_link_count, + "mevid_person_cameras": { + str(k): sorted(v) for k, v in (self.mevid_person_cameras or {}).items() + }, + } + + +# ============================================================================ +# Union-Find for clustering +# ============================================================================ + +class UnionFind: + def __init__(self): + self.parent: Dict[str, str] = {} + self.rank: Dict[str, int] = {} + + def find(self, x: str) -> str: + if x not in self.parent: + self.parent[x] = x + self.rank[x] = 0 + if self.parent[x] != x: + self.parent[x] = self.find(self.parent[x]) + return self.parent[x] + + def union(self, a: str, b: str): + ra, rb = self.find(a), self.find(b) + if ra == rb: + return + if self.rank[ra] < self.rank[rb]: + ra, rb = rb, ra + self.parent[rb] = ra + if self.rank[ra] == self.rank[rb]: + self.rank[ra] += 1 + + def clusters(self) -> Dict[str, Set[str]]: + groups: Dict[str, Set[str]] = defaultdict(set) + for item in self.parent: + groups[self.find(item)].add(item) + return dict(groups) + + +# ============================================================================ +# MEVID-Based Entity Resolution (Ground Truth) +# ============================================================================ + +def _resolve_mevid(sg: SceneGraph, verbose: bool = False) -> Tuple[List[CrossCameraLink], Dict[int, Set[str]]]: + """ + Use MEVID person IDs to establish cross-camera entity links. + + Since we can't map MEVID person_ids to specific Kitware actor_ids without + extracted tracklet images, we establish POTENTIAL cross-camera links: + For each MEVID person appearing on 2+ cameras in this slot, we know that + some entity on camera A is the same person as some entity on camera B. + + Returns: + (links, mevid_persons_map) + """ + slot_cameras = list(sg.cameras.keys()) + mevid_persons = find_mevid_persons_for_slot(sg.slot, slot_cameras) + + if verbose: + print(f" MEVID: {len(mevid_persons)} persons with 2+ cameras in slot") + for pid, cams in sorted(mevid_persons.items())[:5]: + print(f" Person {pid}: cameras {sorted(cams)}") + + # We can't create specific entity-to-entity links without tracklet extraction + # but we know which cameras share persons — this validates heuristic links + return [], mevid_persons + + +# ============================================================================ +# Heuristic Entity Resolution (Temporal Handoff) +# ============================================================================ + +# Entry/exit activities that suggest camera handoff +EXIT_ACTIVITIES = { + "person_exits_scene_through_structure", + "person_exits_vehicle", +} +ENTRY_ACTIVITIES = { + "person_enters_scene_through_structure", + "person_enters_vehicle", +} + + +def _resolve_heuristic(sg: SceneGraph, verbose: bool = False) -> List[CrossCameraLink]: + """ + Heuristic cross-camera entity linking via temporal handoff. + + Strategy: If a person-type entity's time span ENDS on camera A around time T, + and another person-type entity's time span STARTS on camera B around time T, + AND both have a small number of events (suggesting a brief appearance = handoff), + link them as potentially the same person. + + Only considers entities that participate in at least 1 event (not background actors). + """ + MAX_HANDOFF_GAP = 10.0 # seconds — tight for heuristic + MIN_HANDOFF_GAP = 1.0 # seconds (avoid linking simultaneous entities) + MAX_LINKS_PER_ENTITY = 2 # prevent one entity linking to everything + + links = [] + link_count: Dict[str, int] = {} # entity_id -> # links created + + # Only consider entities that participate in events + active_entities = [] + for eid, entity in sg.entities.items(): + if entity.entity_type != "person": + continue + if not entity.events: + continue + active_entities.append({ + "entity_id": eid, + "camera_id": entity.camera_id, + "first_sec": entity.first_sec, + "last_sec": entity.last_sec, + }) + + # Sort by last_sec (when they leave) + active_entities.sort(key=lambda x: x["last_sec"]) + + # Index by first_sec for efficient lookup + by_first = sorted(active_entities, key=lambda x: x["first_sec"]) + + for ea in active_entities: + if link_count.get(ea["entity_id"], 0) >= MAX_LINKS_PER_ENTITY: + continue + + # Look for entities that START shortly after ea ENDS + for eb in by_first: + if ea["camera_id"] == eb["camera_id"]: + continue + + gap = eb["first_sec"] - ea["last_sec"] + + if gap < MIN_HANDOFF_GAP: + continue + if gap > MAX_HANDOFF_GAP: + break # sorted, so no more matches + + if link_count.get(eb["entity_id"], 0) >= MAX_LINKS_PER_ENTITY: + continue + + confidence = max(0.4, 1.0 - gap / MAX_HANDOFF_GAP) + links.append(CrossCameraLink( + entity_a=ea["entity_id"], + entity_b=eb["entity_id"], + camera_a=ea["camera_id"], + camera_b=eb["camera_id"], + link_type="temporal_handoff", + confidence=round(confidence, 2), + time_gap_sec=round(gap, 2), + )) + link_count[ea["entity_id"]] = link_count.get(ea["entity_id"], 0) + 1 + link_count[eb["entity_id"]] = link_count.get(eb["entity_id"], 0) + 1 + + if verbose: + print(f" Heuristic: {len(links)} temporal handoff links " + f"(from {len(active_entities)} active entities)") + + return links + + +# ============================================================================ +# Enhanced Heuristic: 3D Proximity + Description Similarity +# ============================================================================ + +def _normalize_desc_tokens(desc: str) -> Set[str]: + """Normalize entity description to token set for comparison.""" + if not desc: + return set() + desc = re.sub(r'[^\w\s]', ' ', desc.lower()) + stop = {"a", "an", "the", "in", "on", "with", "and", "of", "wearing", + "carrying", "person", "someone", "individual"} + return {w for w in desc.split() if w not in stop and len(w) > 1} + + +def _description_overlap(desc_a: str, desc_b: str) -> float: + """Token overlap ratio between two descriptions (0-1).""" + tokens_a = _normalize_desc_tokens(desc_a) + tokens_b = _normalize_desc_tokens(desc_b) + if not tokens_a or not tokens_b: + return 0.0 + intersection = tokens_a & tokens_b + union = tokens_a | tokens_b + return len(intersection) / len(union) if union else 0.0 + + +def _enhance_links_with_3d( + sg: SceneGraph, + links: List[CrossCameraLink], + entity_descs: Optional[Dict[str, str]] = None, + verbose: bool = False, +) -> List[CrossCameraLink]: + """ + Boost or penalize heuristic link confidence using 3D spatial proximity + and entity description similarity. + + Combined score: 0.4 * temporal + 0.3 * spatial + 0.3 * description + + Args: + sg: Scene graph with entity data + links: Existing heuristic links (temporal handoff) + entity_descs: Optional {entity_id: description_text} map + """ + try: + from .scene_context import compute_3d_matching_score + except ImportError: + if verbose: + print(" 3D matching: scene_context not available, skipping") + return links + + enhanced = [] + boost_count = 0 + + for link in links: + ea = sg.entities.get(link.entity_a) + eb = sg.entities.get(link.entity_b) + + if ea is None or eb is None: + enhanced.append(link) + continue + + # Get last bbox of entity A, first bbox of entity B + bbox_a = None + bbox_b = None + + if ea.keyframe_bboxes: + max_frame = max(ea.keyframe_bboxes.keys()) + bbox_a = ea.keyframe_bboxes[max_frame] + + if eb.keyframe_bboxes: + min_frame = min(eb.keyframe_bboxes.keys()) + bbox_b = eb.keyframe_bboxes[min_frame] + + if bbox_a is None or bbox_b is None: + enhanced.append(link) + continue + + # Description overlap + desc_a = (entity_descs or {}).get(link.entity_a, "") + desc_b = (entity_descs or {}).get(link.entity_b, "") + desc_overlap = _description_overlap(desc_a, desc_b) + + # Compute 3D matching score + result = compute_3d_matching_score( + link.camera_a, bbox_a, + link.camera_b, bbox_b, + time_gap_sec=link.time_gap_sec or 5.0, + description_overlap=desc_overlap, + ) + + if result is None: + enhanced.append(link) + continue + + # Update confidence with combined score + old_conf = link.confidence + new_conf = result["combined_score"] + link.confidence = round(new_conf, 2) + + if new_conf > old_conf: + boost_count += 1 + + enhanced.append(link) + + if verbose and boost_count: + print(f" 3D enhancement: {boost_count}/{len(links)} links boosted") + + return enhanced + + +# ============================================================================ +# Combined Entity Resolution +# ============================================================================ + +def resolve_entities(sg: SceneGraph, verbose: bool = False, + entity_descs: Optional[Dict[str, str]] = None) -> ResolvedGraph: + """ + Run entity resolution: MEVID ground truth + heuristic temporal handoff + + 3D spatial proximity + description similarity. + + Args: + sg: Scene graph from build_scene_graph + verbose: Print progress + entity_descs: Optional {entity_id: description} for desc matching + + Returns: + ResolvedGraph with cross-camera links and entity clusters + """ + if verbose: + print("Resolving cross-camera entities...") + + # 1. MEVID ground truth (camera-level mapping) + mevid_links, mevid_persons = _resolve_mevid(sg, verbose) + + # 2. Heuristic temporal handoff + heuristic_links = _resolve_heuristic(sg, verbose) + + # 3. Enhance heuristic links with 3D proximity + description similarity + heuristic_links = _enhance_links_with_3d( + sg, heuristic_links, entity_descs, verbose + ) + + # 3. Combine links and build clusters using Union-Find + all_links = mevid_links + heuristic_links + + uf = UnionFind() + for link in all_links: + if link.confidence >= 0.7: # stricter threshold for clustering + uf.union(link.entity_a, link.entity_b) + + # Also add all entities (even unlinked) to UnionFind + for eid in sg.entities: + uf.find(eid) + + # Build entity clusters (only multi-entity clusters) + raw_clusters = uf.clusters() + entity_clusters = [] + cluster_idx = 0 + for root, members in raw_clusters.items(): + if len(members) < 2: + continue + cameras = sorted(set( + sg.entities[eid].camera_id for eid in members if eid in sg.entities + )) + if len(cameras) < 2: + continue + entity_clusters.append(EntityCluster( + cluster_id=f"cluster_{cluster_idx}", + entities=sorted(members), + cameras=cameras, + link_type="heuristic", + )) + cluster_idx += 1 + + if verbose: + print(f" Clusters: {len(entity_clusters)} cross-camera entity clusters") + for c in entity_clusters[:3]: + print(f" {c.cluster_id}: {c.entities} across {c.cameras}") + + return ResolvedGraph( + cross_camera_links=all_links, + entity_clusters=entity_clusters, + mevid_persons_in_slot=len(mevid_persons), + heuristic_link_count=len(heuristic_links), + mevid_link_count=len(mevid_links), + mevid_person_cameras=mevid_persons, + ) diff --git a/meva/scripts/v10/export_to_multicam_format.py b/meva/scripts/v10/export_to_multicam_format.py new file mode 100644 index 0000000..45df4e0 --- /dev/null +++ b/meva/scripts/v10/export_to_multicam_format.py @@ -0,0 +1,284 @@ +#!/usr/bin/env python3 +""" +export_to_multicam_format.py — Transform FINAL naturalized QA to multi-cam-dataset schema. + +Reads: $MEVA_OUTPUT_DIR/qa_pairs/{slot}.final.naturalized.json +Writes: $MEVA_MULTICAM_OUT/{slot}.json (or /nas/neurosymbolic/multi-cam-dataset/meva/qa_pairs/) + +Target schema matches agibot / ego-exo4d format: + { + "slot": "...", + "question_type": "temporal", + "question": "...", + "options": {"A": "...", "B": "...", "C": "...", "D": "..."}, + "answer": "A", + "reasoning": "...", + "video_paths": [...], + "metadata": { site, cameras, camera_names, difficulty, verification } + } + +Usage: + python3 scripts/final/export_to_multicam_format.py --slot "2018-03-11.11-25-00.school" + python3 scripts/final/export_to_multicam_format.py --all + python3 scripts/final/export_to_multicam_format.py --slot "..." --dry-run +""" + +import json +import argparse +import os +import sys +from pathlib import Path +from typing import Dict, List, Any, Optional + +# Paths +# User output directory — override with MEVA_OUTPUT_DIR env var +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) +INPUT_DIR = _OUTPUT / "qa_pairs" +# Export destination — override with MEVA_MULTICAM_OUT env var +OUTPUT_DIR = Path(os.environ.get("MEVA_MULTICAM_OUT") or "/nas/neurosymbolic/multi-cam-dataset/meva/data/qa_pairs") + +LETTER_MAP = {0: "A", 1: "B", 2: "C", 3: "D", 4: "E", 5: "F"} + + +def _options_to_dict(options: List[str]) -> Dict[str, str]: + """Convert list of options to {A: ..., B: ..., C: ..., D: ...} dict.""" + return {LETTER_MAP[i]: opt for i, opt in enumerate(options) if i in LETTER_MAP} + + +def _index_to_letter(idx: int) -> str: + """Convert 0-based index to letter answer.""" + return LETTER_MAP.get(idx, "A") + + +def _extract_site(slot: str) -> str: + """Extract site from slot name: '2018-03-11.11-25-00.school' → 'school'.""" + parts = slot.split(".") + return parts[-1] if len(parts) >= 3 else "unknown" + + +def _build_camera_names(cameras: List[str]) -> Dict[str, str]: + """Build camera_names dict: {'G421': 'Camera G421', ...}.""" + return {cam: f"Camera {cam}" for cam in cameras} + + +def _transform_question(q: dict, slot: str, envelope: dict) -> dict: + """Transform a single V9 naturalized QA item to multi-cam-dataset format.""" + # Pick the best available question text + question_text = ( + q.get("naturalized_question") + or q.get("question_template") + or q.get("question", "") + ) + + # Pick the best available options list + options_list = ( + q.get("naturalized_options") + or q.get("options") + or [] + ) + + # Build the camera list for this question + q_cameras = q.get("requires_cameras", []) + if not q_cameras: + # Fall back to envelope-level cameras + q_cameras = envelope.get("cameras", []) + + # Core answer index + answer_idx = q.get("correct_answer_index", 0) + + # Verification data (strip internal fields, keep useful ones) + verification = q.get("verification", {}) + + # Build grounding from verification events (match ego-exo4d ordered_events style) + grounding = _build_grounding(q) + + # Assemble metadata + site = _extract_site(slot) + metadata: Dict[str, Any] = { + "site": site, + "slot": slot, + "cameras": q_cameras, + "camera_names": _build_camera_names(q_cameras), + "difficulty": q.get("difficulty", "medium"), + } + if grounding: + metadata["grounding"] = grounding + if verification: + metadata["verification"] = verification + + # Assemble the output question + out: Dict[str, Any] = { + "slot": slot, + "question_type": q.get("category", "unknown"), + "question": question_text, + "options": _options_to_dict(options_list), + "answer": _index_to_letter(answer_idx), + "reasoning": q.get("reasoning", ""), + "video_paths": q.get("video_paths", []), + "metadata": metadata, + } + + return out + + +def _build_grounding(q: dict) -> List[Dict[str, Any]]: + """Build a grounding/ordered_events list from verification data. + + Matches ego-exo4d style: + [{"activity": "...", "camera": "G421", "start_timestamp": 0.33}, ...] + """ + verification = q.get("verification", {}) + category = q.get("category", "") + events = [] + + if category in ("temporal", "spatial"): + for key in ["event_a", "event_b"]: + ev = verification.get(key, {}) + if ev and ev.get("activity"): + entry: Dict[str, Any] = { + "activity": ev["activity"], + "camera": ev.get("camera", ""), + } + if "start_sec" in ev: + entry["start_timestamp"] = ev["start_sec"] + if "end_sec" in ev: + entry["end_timestamp"] = ev["end_sec"] + events.append(entry) + + elif category == "event_ordering": + ordered = verification.get("ordered_events", []) + for ev in ordered: + if ev.get("activity"): + entry = { + "activity": ev["activity"], + "camera": ev.get("camera", ""), + } + if "start_sec" in ev: + entry["start_timestamp"] = ev["start_sec"] + events.append(entry) + + elif category == "best_camera": + ev = verification + if ev.get("activity"): + entry = { + "activity": ev["activity"], + "camera": ev.get("correct_camera", ""), + } + if "entrance_time_sec" in ev: + entry["start_timestamp"] = ev["entrance_time_sec"] + events.append(entry) + + elif category in ("perception", "summarization", "counting"): + ev = verification.get("target_event", verification) + if ev.get("activity"): + entry = { + "activity": ev["activity"], + "camera": ev.get("camera", ev.get("correct_camera", "")), + } + if "start_sec" in ev: + entry["start_timestamp"] = ev["start_sec"] + events.append(entry) + + return events + + +def export_slot(slot: str, dry_run: bool = False, verbose: bool = False) -> Optional[List[dict]]: + """Export one slot from V9 naturalized format to multi-cam-dataset format. + + Returns the exported list of questions, or None on failure. + """ + input_file = INPUT_DIR / f"{slot}.final.naturalized.json" + if not input_file.exists(): + # Try v9 format as fallback + input_file = INPUT_DIR / f"{slot}.v9.naturalized.json" + if not input_file.exists(): + print(f" ERROR: Input not found: {input_file}", file=sys.stderr) + return None + + with open(input_file) as f: + envelope = json.load(f) + + qa_pairs = envelope.get("qa_pairs", []) + if not qa_pairs: + print(f" WARNING: No qa_pairs in {input_file}", file=sys.stderr) + return [] + + exported = [] + for q in qa_pairs: + out = _transform_question(q, slot, envelope) + exported.append(out) + + if verbose: + print(f" Transformed {len(exported)} questions for {slot}") + # Category breakdown + cats = {} + for q in exported: + cats[q["question_type"]] = cats.get(q["question_type"], 0) + 1 + for cat, count in sorted(cats.items()): + print(f" {cat}: {count}") + + if dry_run: + print(json.dumps(exported[:2], indent=2)) + print(f" ... ({len(exported)} total, showing first 2)") + return exported + + # Write output + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + output_file = OUTPUT_DIR / f"{slot}.json" + with open(output_file, "w") as f: + json.dump(exported, f, indent=2) + + if verbose: + print(f" Written to {output_file}") + + return exported + + +def find_all_naturalized_slots() -> List[str]: + """Find all slots that have .final.naturalized.json files.""" + slots = [] + for f in sorted(INPUT_DIR.glob("*.final.naturalized.json")): + slot = f.name.replace(".final.naturalized.json", "") + slots.append(slot) + # Also check v9 format as fallback + for f in sorted(INPUT_DIR.glob("*.v9.naturalized.json")): + slot = f.name.replace(".v9.naturalized.json", "") + if slot not in slots: + slots.append(slot) + return sorted(slots) + + +def main(): + parser = argparse.ArgumentParser(description="Export V9 QA to multi-cam-dataset format") + parser.add_argument("--slot", type=str, help="Slot name to export") + parser.add_argument("--all", action="store_true", help="Export all available naturalized slots") + parser.add_argument("--dry-run", action="store_true", help="Print sample output without writing") + parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output") + args = parser.parse_args() + + if not args.slot and not args.all: + parser.print_help() + sys.exit(1) + + if args.all: + slots = find_all_naturalized_slots() + if not slots: + print("No .v9.naturalized.json files found.", file=sys.stderr) + sys.exit(1) + print(f"Found {len(slots)} slot(s) to export:") + total = 0 + for slot in slots: + print(f"\n [{slot}]") + result = export_slot(slot, dry_run=args.dry_run, verbose=args.verbose) + if result is not None: + total += len(result) + print(f"\nTotal: {total} questions exported across {len(slots)} slot(s)") + else: + result = export_slot(args.slot, dry_run=args.dry_run, verbose=args.verbose) + if result is None: + sys.exit(1) + print(f"Exported {len(result)} questions for {args.slot}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/extract_entity_descriptions.py b/meva/scripts/v10/extract_entity_descriptions.py new file mode 100644 index 0000000..237e9f1 --- /dev/null +++ b/meva/scripts/v10/extract_entity_descriptions.py @@ -0,0 +1,1238 @@ +#!/usr/bin/env python3 +""" +V10 Entity Description Extractor — Extract visual descriptions from raw video + geom.yml. + +For EVERY annotated actor in a slot, this script: + 1. Parses geom.yml → per-actor bounding boxes per frame + 2. Extracts 5 representative crops from the video + 3. Runs SegFormer human parsing (default) or YOLO+color analysis on each crop + 4. Aggregates via majority vote across crops + 5. Generates rich description: "a person with dark hair, wearing a blue top and black pants" + +Methods: + segformer — SegFormer human parsing (18 body-part classes, best quality) [default] + yolo — YOLO person detection + fixed-split colors + carried objects + color-only — Fixed vertical splits (fastest, no model needed) + +Cost: $0 (all local, no API calls) +Time: ~2-3 min per slot (segformer on GPU), ~3-4 min (YOLO) + +Usage: + python3 scripts/v10/extract_entity_descriptions.py --slot 2018-03-11.11-25.school -v + python3 scripts/v10/extract_entity_descriptions.py --slot 2018-03-11.11-25.school --method yolo + python3 scripts/v10/extract_entity_descriptions.py --slot 2018-03-11.11-25.school --dry-run +""" + +import argparse +import json +import re +import sys +import time +import glob +import os +from pathlib import Path +from collections import Counter, defaultdict +from typing import Dict, List, Optional, Tuple, Set + +import cv2 +import numpy as np + +# ============================================================================ +# Paths +# ============================================================================ + +KITWARE_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware") +KITWARE_TRAINING_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware-meva-training") +AVI_BASE = Path("/nas/mars/dataset/MEVA/avis") # Raw AVIs — lossless, better color +MP4_BASE = Path("/nas/mars/dataset/MEVA/mp4s") # Fallback (CRF 32 re-encode) +# Entity descriptions output — matches reader default in person_descriptions.py +# Override with MEVA_ENTITY_DESC_DIR env var +OUTPUT_DIR = Path(os.environ.get("MEVA_ENTITY_DESC_DIR") or "/nas/mars/dataset/MEVA/entity_descriptions") + +# ============================================================================ +# Constants +# ============================================================================ + +CROPS_PER_ACTOR = 5 # Crops to extract per actor track +MIN_BBOX_HEIGHT = 144 # Min bbox height for SegFormer (needs detail for segmentation) +MIN_BBOX_HEIGHT_COLOR = 40 # Min bbox height for HSV color-only fallback (just needs colors) +MIN_BBOX_WIDTH = 48 # Min bbox width for SegFormer crops +MIN_BBOX_WIDTH_COLOR = 16 # Min bbox width for HSV color-only fallback +YOLO_CONF = 0.25 # YOLO detection confidence threshold +YOLO_MODEL = "yolov8n.pt" # Nano model (fast, sufficient for crops) + +# SegFormer human parsing model +SEGFORMER_MODEL = "mattmdjaga/segformer_b2_clothes" +MIN_REGION_PIXELS = 50 # Min pixels for a body region to count + +# COCO carried-object classes +CARRIED_OBJECTS = { + 24: "backpack", 25: "umbrella", 26: "handbag", 27: "tie", + 28: "suitcase", 39: "bottle", 63: "laptop", 67: "cell phone", + 73: "book", 74: "clock", +} + +# ============================================================================ +# Geom Parsing (regex-based, memory efficient) +# ============================================================================ + +# Order-independent field extractors: handle both kitware and kitware-training formats +# kitware: - { geom: {id1: 5193, id0: 1, ts0: 51, g0: 881 438 947 603, keyframe: true } } +# training: - {'geom': {'g0': '282 499 432 764', 'id0': 115, 'id1': 3, 'ts0': 1019}} +_RE_ID1 = re.compile(r"['\"]?id1['\"]?\s*:\s*['\"]?(\d+)") +_RE_TS0 = re.compile(r"['\"]?ts0['\"]?\s*:\s*['\"]?(\d+)") +_RE_G0 = re.compile(r"['\"]?g0['\"]?\s*:\s*['\"]?(\d+)\s+(\d+)\s+(\d+)\s+(\d+)") + + +def parse_geom(geom_path: Path) -> Dict[int, Dict[int, Tuple[int, int, int, int]]]: + """ + Parse geom.yml → Dict[actor_id → Dict[frame_num → (x1, y1, x2, y2)]]. + Uses regex line-by-line (no YAML load, handles 100K+ line files). + Handles both field orderings (kitware vs kitware-training). + """ + actors = defaultdict(dict) + with open(geom_path) as f: + for line in f: + id1_m = _RE_ID1.search(line) + ts0_m = _RE_TS0.search(line) + g0_m = _RE_G0.search(line) + if not (id1_m and ts0_m and g0_m): + continue + actor_id = int(id1_m.group(1)) + frame = int(ts0_m.group(1)) + bbox = (int(g0_m.group(1)), int(g0_m.group(2)), + int(g0_m.group(3)), int(g0_m.group(4))) + actors[actor_id][frame] = bbox + return dict(actors) + + +# ============================================================================ +# Video Crop Extraction +# ============================================================================ + +def extract_crops(video_path: Path, + actors: Dict[int, Dict[int, Tuple[int, int, int, int]]], + max_crops: int = CROPS_PER_ACTOR, + min_h: int = MIN_BBOX_HEIGHT, + min_w: int = MIN_BBOX_WIDTH, + ) -> Dict[int, List[np.ndarray]]: + """ + Extract bbox crops for all actors from a single video. + + Strategy selection: + - Few target frames (< 200) spread across the video → random seek + - Many target frames or dense clustering → sequential read (skip non-target) + + Sequential read is ~10-50x faster than random seek on H.264 MP4s because + seeks must decode from the nearest keyframe, while sequential just grabs + the next already-decoded frame. + + For each actor, samples `max_crops` frames evenly across their track, + filtering out tiny bboxes. Returns Dict[actor_id → [crop_bgr, ...]]. + """ + if not actors: + return {} + + # Build frame → [(actor_id, bbox)] mapping, sampling per actor + frame_to_actors: Dict[int, List[Tuple[int, Tuple]]] = defaultdict(list) + + for actor_id, keyframes in actors.items(): + # Filter to usable bboxes + usable = {f: bb for f, bb in keyframes.items() + if (bb[2] - bb[0]) >= min_w and (bb[3] - bb[1]) >= min_h} + if not usable: + continue + + frames = sorted(usable.keys()) + if len(frames) > max_crops: + # Prefer middle-of-track frames (#5): more stable pose/lighting, + # less likely to be entering/exiting frame. Sample from inner 80% + # of the track, with endpoints only if needed. + n = len(frames) + inner_start = max(0, int(n * 0.1)) + inner_end = min(n - 1, int(n * 0.9)) + inner_frames = frames[inner_start:inner_end + 1] + if len(inner_frames) >= max_crops: + indices = np.linspace(0, len(inner_frames) - 1, max_crops, dtype=int) + frames = [inner_frames[i] for i in indices] + else: + # Track too short — use uniform across full range + indices = np.linspace(0, n - 1, max_crops, dtype=int) + frames = [frames[i] for i in indices] + + for fn in frames: + frame_to_actors[fn].append((actor_id, usable[fn])) + + if not frame_to_actors: + return {} + + target_frames = sorted(frame_to_actors.keys()) + target_set = set(target_frames) + results: Dict[int, List[np.ndarray]] = defaultdict(list) + + cap = cv2.VideoCapture(str(video_path)) + if not cap.isOpened(): + print(f" WARNING: Cannot open {video_path}") + return {} + + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + last_target = target_frames[-1] + + # Decide strategy: sequential if many frames or if target span covers >30% + # of the video (seeking back-and-forth is slower than just reading through) + span = last_target - target_frames[0] + 1 + use_sequential = (len(target_frames) > 150 or + span > 0 and len(target_frames) / span > 0.02) + + def _crop_frame(frame_bgr, frame_idx): + """Extract all actor crops from a decoded frame.""" + h, w = frame_bgr.shape[:2] + for actor_id, bbox in frame_to_actors[frame_idx]: + x1, y1, x2, y2 = bbox + x1, y1 = max(0, x1), max(0, y1) + x2, y2 = min(w, x2), min(h, y2) + if x2 <= x1 or y2 <= y1: + continue + crop = frame_bgr[y1:y2, x1:x2] + if crop.size > 0: + results[actor_id].append(crop) + + if use_sequential: + # Sequential read: read every frame from first target to last target, + # only decode+crop on target frames. cap.grab() is fast (no decode), + # cap.retrieve() decodes only when needed. + start_frame = target_frames[0] + if start_frame > 0: + cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) + + current = start_frame + collected = 0 + while current <= last_target and current < total_frames: + if current in target_set: + ret, frame = cap.read() + if ret: + _crop_frame(frame, current) + collected += 1 + else: + cap.grab() # Advance without decoding — very fast + current += 1 + else: + # Random seek: fewer frames, worth the per-seek cost + for target_frame in target_frames: + if target_frame >= total_frames: + continue + cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame) + ret, frame = cap.read() + if not ret: + continue + _crop_frame(frame, target_frame) + + cap.release() + return dict(results) + + +# ============================================================================ +# Color Analysis (HSV-based, same as extract_attributes_yolo.py) +# ============================================================================ + +def _hsv_to_color(h: float, s: float, v: float) -> str: + """Convert OpenCV HSV (H:0-180, S:0-255, V:0-255) to ~25 CSS-friendly color names. + + Expanded vocabulary for better entity disambiguation. VLMs trained on web + data recognise names like "navy", "olive", "khaki" better than raw HSV. + + Color map (OpenCV hue 0-180): + achromatic (S<40): black / charcoal / dark gray / gray / silver / ivory / white + chromatic by hue band: + 0-10,170-180 red → maroon / crimson / red + 10-22 orange → rust / orange + 22-35 yellow → khaki / gold / yellow + 35-55 green → olive / green + 55-78 teal → teal + 78-95 blue → teal-blue (low H), navy (low V) + 95-115 blue → blue + 115-131 indigo → indigo + 131-155 purple → plum / purple + 155-170 pink → mauve / pink + """ + # --- Achromatic: low saturation --- + if s < 40: + if v < 30: + return "black" + elif v < 60: + return "charcoal" + elif v < 100: + return "dark gray" + elif v < 130: + return "gray" + elif v < 150: + return "silver" + else: + # Slight warm tint → ivory + if 15 <= h <= 35 and s >= 15: + return "ivory" + return "white" + # Very dark with some saturation + if v < 40: + return "black" + + # --- Chromatic: hue-based --- + # Red (H wraps: 0-10 and 170-180) + if h < 10 or h > 170: + if v < 100: + return "maroon" + elif s > 150: + return "crimson" + return "red" + + # Orange (10-22) + elif h < 22: + if v < 120: + return "rust" + return "orange" + + # Yellow (22-35) + elif h < 35: + if s < 80: + return "khaki" + elif s < 150: + return "gold" + return "yellow" + + # Green (35-55) + elif h < 55: + if v < 120: + return "olive" + return "green" + + # Teal-green (55-78) + elif h < 78: + return "teal" + + # Blue range (78-131) + elif h < 95: + if s > 100 and v > 100: + return "teal" + if v < 100: + return "navy" + return "blue" + elif h < 115: + if v < 100: + return "navy" + return "blue" + elif h < 131: + return "indigo" + + # Purple (131-155) + elif h < 155: + if v < 100: + return "plum" + return "purple" + + # Pink (155-170) + elif h <= 170: + if s < 100: + return "mauve" + return "pink" + + return "unknown" + + +def _extract_region_color(crop_bgr: np.ndarray) -> str: + """Extract dominant color from a BGR crop using center-weighted HSV mean.""" + if crop_bgr is None or crop_bgr.size == 0: + return "unknown" + h, w = crop_bgr.shape[:2] + if h < 4 or w < 4: + return "unknown" + + # Inner 80% to avoid background bleed + my, mx = max(1, h // 10), max(1, w // 10) + inner = crop_bgr[my:h - my, mx:w - mx] + + hsv = cv2.cvtColor(inner, cv2.COLOR_BGR2HSV) + h_mean = float(np.mean(hsv[:, :, 0])) + s_mean = float(np.mean(hsv[:, :, 1])) + v_mean = float(np.mean(hsv[:, :, 2])) + + return _hsv_to_color(h_mean, s_mean, v_mean) + + +def analyze_crops_color_only(crops: List[np.ndarray]) -> Dict: + """ + Analyze a list of person-crop BGR images using HSV color analysis only. + No YOLO needed — faster, simpler, works on any size crop. + + Returns dict with upper_color, lower_color. + """ + upper_colors = [] + lower_colors = [] + + for crop in crops: + h, w = crop.shape[:2] + if h < 10: + continue + + # Upper body: 10-45% of height (skip head) + u_y1 = int(h * 0.10) + u_y2 = int(h * 0.45) + # Lower body: 55-90% (skip feet) + l_y1 = int(h * 0.55) + l_y2 = int(h * 0.90) + + upper_colors.append(_extract_region_color(crop[u_y1:u_y2, :])) + lower_colors.append(_extract_region_color(crop[l_y1:l_y2, :])) + + upper = _majority_vote(upper_colors) + lower = _majority_vote(lower_colors) + + return {"upper_color": upper, "lower_color": lower} + + +def _majority_vote(colors: List[str]) -> str: + """Majority vote ignoring 'unknown'.""" + filtered = [c for c in colors if c != "unknown"] + if not filtered: + return "unknown" + return Counter(filtered).most_common(1)[0][0] + + +def _majority_vote_with_confidence(colors: List[str]) -> Tuple[str, float]: + """Majority vote returning (winner, confidence 0-1) ignoring 'unknown'.""" + filtered = [c for c in colors if c != "unknown"] + if not filtered: + return "unknown", 0.0 + counter = Counter(filtered) + winner, count = counter.most_common(1)[0] + confidence = count / len(filtered) + return winner, confidence + + +# ============================================================================ +# YOLO Analysis (optional, richer — detects carried objects) +# ============================================================================ + +_yolo_model = None + + +def _get_yolo(): + """Lazy-load YOLO model.""" + global _yolo_model + if _yolo_model is None: + from ultralytics import YOLO + _yolo_model = YOLO(YOLO_MODEL) + return _yolo_model + + +def analyze_crops_yolo(crops: List[np.ndarray]) -> Dict: + """ + Analyze crops with YOLO for person detection, colors, and carried objects. + More expensive than color-only but detects backpacks, bottles, phones, etc. + """ + model = _get_yolo() + + upper_colors = [] + lower_colors = [] + all_objects = [] + + for crop in crops: + h, w = crop.shape[:2] + if h < 15 or w < 8: + continue + + # Run YOLO + results = model(crop, conf=YOLO_CONF, verbose=False) + + # Find person bbox and carried objects + person_box = None + person_conf = 0.0 + + if results and len(results[0].boxes) > 0: + for box in results[0].boxes: + cls_id = int(box.cls.cpu().numpy()[0]) + conf = float(box.conf.cpu().numpy()[0]) + if cls_id == 0 and conf > person_conf: + coords = box.xyxy[0].cpu().numpy() + person_box = (int(coords[0]), int(coords[1]), + int(coords[2]), int(coords[3])) + person_conf = conf + if cls_id in CARRIED_OBJECTS and conf > 0.3: + all_objects.append(CARRIED_OBJECTS[cls_id]) + + # Use person bbox if found, else full crop + if person_box: + px1, py1, px2, py2 = person_box + else: + px1, py1, px2, py2 = 0, 0, w, h + + ph = py2 - py1 + if ph < 10: + continue + + # Upper/lower body color + u_y1 = py1 + int(ph * 0.10) + u_y2 = py1 + int(ph * 0.45) + l_y1 = py1 + int(ph * 0.55) + l_y2 = py1 + int(ph * 0.90) + + upper_colors.append(_extract_region_color(crop[u_y1:u_y2, px1:px2])) + lower_colors.append(_extract_region_color(crop[l_y1:l_y2, px1:px2])) + + upper = _majority_vote(upper_colors) + lower = _majority_vote(lower_colors) + + # Objects seen in >= 2 crops (or any if only 1 crop) + obj_counts = Counter(all_objects) + objects = sorted(set( + obj for obj, cnt in obj_counts.items() + if cnt >= 2 or len(crops) <= 2 + )) + + return {"upper_color": upper, "lower_color": lower, "carried_objects": objects} + + +# ============================================================================ +# SegFormer Human Parsing (semantic body-part segmentation) +# ============================================================================ + +_segformer_processor = None +_segformer_model = None + + +def _get_segformer(): + """Lazy-load SegFormer human parsing model (GPU if available).""" + global _segformer_processor, _segformer_model + if _segformer_model is None: + import torch + from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation + _segformer_processor = SegformerImageProcessor.from_pretrained(SEGFORMER_MODEL) + _segformer_model = SegformerForSemanticSegmentation.from_pretrained(SEGFORMER_MODEL) + if torch.cuda.is_available(): + _segformer_model = _segformer_model.to("cuda") + _segformer_model.eval() + return _segformer_processor, _segformer_model + + +def _get_segmentation_map(crop_bgr: np.ndarray, processor, model) -> np.ndarray: + """Run SegFormer inference on a BGR crop → per-pixel class ID map (H, W).""" + import torch + from PIL import Image + rgb = cv2.cvtColor(crop_bgr, cv2.COLOR_BGR2RGB) + pil_img = Image.fromarray(rgb) + inputs = processor(images=pil_img, return_tensors="pt") + device = next(model.parameters()).device + inputs = {k: v.to(device) for k, v in inputs.items()} + with torch.no_grad(): + logits = model(**inputs).logits # (1, 18, H/4, W/4) + upsampled = torch.nn.functional.interpolate( + logits, size=crop_bgr.shape[:2], mode="bilinear", align_corners=False + ) + return upsampled.argmax(dim=1).squeeze().cpu().numpy() + + +def _extract_mask_color(crop_bgr: np.ndarray, mask: np.ndarray) -> str: + """Extract dominant color from BGR pixels where mask is True.""" + if mask.sum() < MIN_REGION_PIXELS: + return "unknown" + # Gather masked pixels as (N, 3) + pixels = crop_bgr[mask] + hsv = cv2.cvtColor(pixels.reshape(-1, 1, 3), cv2.COLOR_BGR2HSV) + h_mean = float(np.mean(hsv[:, 0, 0])) + s_mean = float(np.mean(hsv[:, 0, 1])) + v_mean = float(np.mean(hsv[:, 0, 2])) + return _hsv_to_color(h_mean, s_mean, v_mean) + + +def _detect_texture(crop_bgr: np.ndarray, mask: np.ndarray) -> Dict: + """ + Detect clothing texture/pattern within a segmentation mask. + + Analyzes: + - Solid vs patterned: low intra-mask color variance = solid + - Striped: high directional gradient variance (horizontal or vertical bands) + - Light/dark qualifier: based on mean V channel value + + Returns dict: {"texture": "solid"|"patterned"|"striped", "brightness": "light"|"dark"|""} + """ + result = {"texture": "", "brightness": ""} + if mask.sum() < MIN_REGION_PIXELS * 2: # need enough pixels for texture + return result + + pixels = crop_bgr[mask] + hsv = cv2.cvtColor(pixels.reshape(-1, 1, 3), cv2.COLOR_BGR2HSV) + + # --- Brightness qualifier --- + v_mean = float(np.mean(hsv[:, 0, 2])) + s_mean = float(np.mean(hsv[:, 0, 1])) + # Only add qualifier for chromatic colors (skip achromatic = low saturation) + # Also skip very dark regions (V < 60) — brightness is noise at that level + if s_mean >= 40 and v_mean >= 60: + if v_mean < 90: + result["brightness"] = "dark" + elif v_mean > 190: + result["brightness"] = "light" + + # --- Texture detection via color variance within mask --- + # Use HSV Hue + Saturation channels for variance (ignore brightness variations + # from shading which don't indicate pattern) + h_std = float(np.std(hsv[:, 0, 0])) + s_std = float(np.std(hsv[:, 0, 1])) + + # Skip texture detection on very dark regions (V_mean < 70) — dark clothing + # creates compression noise that falsely triggers pattern detection + if v_mean < 70: + result["texture"] = "solid" # assume solid for very dark clothing + return result + + # High hue variance = multi-color pattern (raised thresholds to reduce FP) + if h_std > 45 or s_std > 60: + # Check for stripes: look for strong directional gradients + # Get the bounding box of the mask region for structured analysis + ys, xs = np.where(mask) + y_min, y_max = ys.min(), ys.max() + x_min, x_max = xs.min(), xs.max() + region = crop_bgr[y_min:y_max+1, x_min:x_max+1] + region_mask = mask[y_min:y_max+1, x_min:x_max+1] + rh, rw = region.shape[:2] + + if rh > 10 and rw > 10: + gray = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY).astype(np.float32) + # Apply mask: set non-mask pixels to mean to avoid edge artifacts + mean_val = float(gray[region_mask].mean()) if region_mask.any() else 128 + gray[~region_mask] = mean_val + + # Horizontal gradient (detects vertical stripes) + grad_h = np.abs(np.diff(gray, axis=1)) + # Vertical gradient (detects horizontal stripes) + grad_v = np.abs(np.diff(gray, axis=0)) + + # Stripe detection: one direction should have much stronger gradients + h_energy = float(grad_h.mean()) + v_energy = float(grad_v.mean()) + + if max(h_energy, v_energy) > 15: # significant edge energy + ratio = max(h_energy, v_energy) / (min(h_energy, v_energy) + 1e-6) + if ratio > 1.5: + result["texture"] = "striped" + else: + result["texture"] = "patterned" + else: + result["texture"] = "patterned" + else: + result["texture"] = "patterned" + else: + result["texture"] = "solid" + + return result + + +# SegFormer class IDs → semantic groups +_SEG_HAIR = 2 +_SEG_UPPER = 4 +_SEG_SKIRT = 5 +_SEG_PANTS = 6 +_SEG_DRESS = 7 +_SEG_LSHOE = 9 +_SEG_RSHOE = 10 +_SEG_HAT = 1 +_SEG_SUNGLASSES = 3 +_SEG_BAG = 16 +_SEG_SCARF = 17 + + +def analyze_crops_segformer(crops: List[np.ndarray]) -> Dict: + """ + Analyze crops with SegFormer human parsing (18 body-part classes). + + Segments each crop into semantic regions (hair, upper-clothes, pants/skirt/dress, + shoes, etc.), extracts HSV color per region, detects accessories and texture. + Majority-votes across crops with confidence tracking for robust results. + + Returns dict with: + hair_color, upper_color, lower_color, lower_type, + shoe_color, accessories, carried_objects, + upper_texture, lower_texture, upper_brightness, lower_brightness, + confidence (per-attribute confidence scores) + """ + processor, model = _get_segformer() + + hair_colors = [] + upper_colors = [] + lower_colors = [] + shoe_colors = [] + lower_types = [] + accessories_per_crop = [] + # Texture/brightness per crop (#2) + upper_textures = [] + lower_textures = [] + upper_brightness_list = [] + lower_brightness_list = [] + + for crop in crops: + h, w = crop.shape[:2] + if h < 15 or w < 8: + continue + + seg_map = _get_segmentation_map(crop, processor, model) + + # Hair (class 2) + hair_colors.append(_extract_mask_color(crop, seg_map == _SEG_HAIR)) + + # Upper-clothes (class 4) + upper_mask = seg_map == _SEG_UPPER + upper_colors.append(_extract_mask_color(crop, upper_mask)) + # Texture analysis for upper clothing (#2) + upper_tex = _detect_texture(crop, upper_mask) + upper_textures.append(upper_tex["texture"]) + upper_brightness_list.append(upper_tex["brightness"]) + + # Lower body: Pants(6), Skirt(5), Dress(7) — pick dominant + pants_px = (seg_map == _SEG_PANTS).sum() + skirt_px = (seg_map == _SEG_SKIRT).sum() + dress_px = (seg_map == _SEG_DRESS).sum() + + lower_mask = None + if dress_px > max(pants_px, skirt_px) and dress_px >= MIN_REGION_PIXELS: + lower_mask = seg_map == _SEG_DRESS + lower_colors.append(_extract_mask_color(crop, lower_mask)) + lower_types.append("dress") + elif skirt_px > pants_px and skirt_px >= MIN_REGION_PIXELS: + lower_mask = seg_map == _SEG_SKIRT + lower_colors.append(_extract_mask_color(crop, lower_mask)) + lower_types.append("skirt") + elif pants_px >= MIN_REGION_PIXELS: + lower_mask = seg_map == _SEG_PANTS + lower_colors.append(_extract_mask_color(crop, lower_mask)) + lower_types.append("pants") + else: + lower_colors.append("unknown") + lower_types.append("unknown") + + # Texture for lower clothing (#2) + if lower_mask is not None: + lower_tex = _detect_texture(crop, lower_mask) + lower_textures.append(lower_tex["texture"]) + lower_brightness_list.append(lower_tex["brightness"]) + else: + lower_textures.append("") + lower_brightness_list.append("") + + # Shoes (left 9 + right 10) + shoe_mask = (seg_map == _SEG_LSHOE) | (seg_map == _SEG_RSHOE) + shoe_colors.append(_extract_mask_color(crop, shoe_mask)) + + # Accessories + crop_acc = [] + if (seg_map == _SEG_HAT).sum() >= MIN_REGION_PIXELS: + crop_acc.append("hat") + if (seg_map == _SEG_SUNGLASSES).sum() >= MIN_REGION_PIXELS: + crop_acc.append("sunglasses") + if (seg_map == _SEG_BAG).sum() >= MIN_REGION_PIXELS: + crop_acc.append("bag") + if (seg_map == _SEG_SCARF).sum() >= MIN_REGION_PIXELS: + crop_acc.append("scarf") + accessories_per_crop.append(crop_acc) + + # Majority votes with confidence (#5) + hair, hair_conf = _majority_vote_with_confidence(hair_colors) + upper, upper_conf = _majority_vote_with_confidence(upper_colors) + lower, lower_conf = _majority_vote_with_confidence(lower_colors) + shoes, shoes_conf = _majority_vote_with_confidence(shoe_colors) + lower_type = _majority_vote(lower_types) + + # Texture/brightness votes (#2) + upper_texture = _majority_vote([t for t in upper_textures if t]) + lower_texture = _majority_vote([t for t in lower_textures if t]) + upper_brightness = _majority_vote([b for b in upper_brightness_list if b]) + lower_brightness = _majority_vote([b for b in lower_brightness_list if b]) + + # Accessories: keep if seen in ≥2 crops (or any if ≤2 total) + acc_counter = Counter(a for crop_acc in accessories_per_crop for a in crop_acc) + threshold = 2 if len(crops) > 2 else 1 + accessories = sorted(a for a, cnt in acc_counter.items() if cnt >= threshold) + + # Confidence dict (#5): per-attribute agreement score + confidence = { + "hair": round(hair_conf, 2), + "upper": round(upper_conf, 2), + "lower": round(lower_conf, 2), + "shoes": round(shoes_conf, 2), + } + + return { + "hair_color": hair, + "upper_color": upper, + "lower_color": lower, + "lower_type": lower_type if lower_type != "unknown" else "pants", + "shoe_color": shoes, + "accessories": accessories, + "carried_objects": [], # SegFormer detects bags; other objects need YOLO + "upper_texture": upper_texture if upper_texture != "unknown" else "", + "lower_texture": lower_texture if lower_texture != "unknown" else "", + "upper_brightness": upper_brightness if upper_brightness != "unknown" else "", + "lower_brightness": lower_brightness if lower_brightness != "unknown" else "", + "confidence": confidence, + } + + +# ============================================================================ +# Description Generation (template-based, free) +# ============================================================================ + +def _article(word: str) -> str: + """Return 'an' if word starts with a vowel sound, else 'a'.""" + return "an" if word and word[0].lower() in "aeiou" else "a" + + +def build_description(attrs: Dict, include_position: bool = False) -> str: + """ + Build a natural description from structured attributes. + + Handles both old-style (upper_color/lower_color only) and new segformer-style + (hair_color, lower_type, shoe_color, accessories) attributes. + + Optional positional/height hints for disambiguation. + + Examples (segformer): + → "a person with dark hair, wearing a navy top and khaki pants, silver shoes" + → "a person wearing a crimson top and gray skirt, carrying a bag" + + Examples (with position): + → "a tall person with dark hair, wearing a navy top and khaki pants, on the left side" + """ + hair = attrs.get("hair_color") + upper = attrs.get("upper_color", "unknown") + lower = attrs.get("lower_color", "unknown") + lower_type = attrs.get("lower_type", "pants") + shoes = attrs.get("shoe_color") + accessories = attrs.get("accessories", []) + carried = attrs.get("carried_objects", []) + confidence = attrs.get("confidence", {}) + + # Texture/brightness qualifiers (#2) + upper_texture = attrs.get("upper_texture", "") + lower_texture = attrs.get("lower_texture", "") + upper_brightness = attrs.get("upper_brightness", "") + lower_brightness = attrs.get("lower_brightness", "") + + # Drop low-confidence attributes (#5): if agreement < 40%, omit to avoid + # wrong colors. Better to say nothing than to say the wrong color. + min_conf = 0.4 + if confidence.get("hair", 1.0) < min_conf: + hair = "unknown" + if confidence.get("upper", 1.0) < min_conf: + upper = "unknown" + if confidence.get("lower", 1.0) < min_conf: + lower = "unknown" + if confidence.get("shoes", 1.0) < min_conf: + shoes = "unknown" + + # Relative height from bbox (tall/medium/short) + height_hint = attrs.get("height_category") # set by enrich step if available + + desc = _article(height_hint if height_hint and height_hint != "medium" else "person") + if height_hint and height_hint != "medium": + desc += f" {height_hint}" + desc += " person" + + # Hair color + if hair and hair != "unknown": + desc += f" with {hair} hair" + + # Clothing — include brightness + texture qualifiers (#2) + clothing_parts = [] + # Colors that are inherently dark/light — don't add redundant qualifiers + _DARK_COLORS = {"black", "charcoal", "navy", "maroon", "dark gray"} + _LIGHT_COLORS = {"white", "ivory", "silver"} + + if upper != "unknown": + upper_desc = upper + # Add brightness: "light blue", "dark green" — skip if color is already dark/light + if upper_brightness == "dark" and upper not in _DARK_COLORS: + upper_desc = f"dark {upper}" + elif upper_brightness == "light" and upper not in _LIGHT_COLORS: + upper_desc = f"light {upper}" + # Add texture: "striped blue top", "patterned red top" + if upper_texture and upper_texture not in ("solid", ""): + clothing_parts.append(f"{_article(upper_texture)} {upper_texture} {upper_desc} top") + else: + clothing_parts.append(f"{_article(upper_desc)} {upper_desc} top") + if lower != "unknown": + lower_desc = lower + if lower_brightness == "dark" and lower not in _DARK_COLORS: + lower_desc = f"dark {lower}" + elif lower_brightness == "light" and lower not in _LIGHT_COLORS: + lower_desc = f"light {lower}" + if lower_texture and lower_texture not in ("solid", ""): + clothing_parts.append(f"{lower_texture} {lower_desc} {lower_type}") + else: + clothing_parts.append(f"{lower_desc} {lower_type}") + + if clothing_parts: + desc += ", wearing " + " and ".join(clothing_parts) + + # Shoes + if shoes and shoes != "unknown": + desc += f", {shoes} shoes" + + # Accessories (worn items: hat, sunglasses, scarf) + worn = [a for a in accessories if a in ("hat", "sunglasses", "scarf")] + if worn: + desc += f", with {_article(worn[0])} {' and '.join(worn)}" + + # Carried items (bag from segformer + YOLO objects) + carried_items = (["bag"] if "bag" in accessories else []) + list(carried[:2]) + if carried_items: + desc += f", carrying {_article(carried_items[0])} {' and '.join(carried_items[:2])}" + + # Spatial position hint (for disambiguation) + if include_position: + position = attrs.get("frame_position") # "left", "center", "right" + if position and position != "center": + desc += f", on the {position} side" + + return desc + + +# ============================================================================ +# Slot Processing Pipeline +# ============================================================================ + +def find_slot_files(slot: str) -> List[Dict]: + """ + Find geom.yml + video pairs for a slot. + Searches BOTH kitware/ and kitware-meva-training/ directories. + Prefers raw AVI (lossless) over MP4 (CRF 32 re-encode) for better color. + Returns list of {camera, geom_path, video_path, act_path, video_format, source}. + """ + # Parse slot: "2018-03-11.11-25.school" + parts = slot.split(".") + if len(parts) < 3: + raise ValueError(f"Invalid slot format: {slot} (expected date.time.site)") + + date = parts[0] + time_part = parts[1] # HH-MM (no seconds) + site = parts[2] + + hour = time_part.split("-")[0] # e.g. "11" from "11-25" + + # Check both annotation sources + search_dirs = [ + (KITWARE_BASE / date / hour, "kitware"), + (KITWARE_TRAINING_BASE / date / hour, "kitware-training"), + ] + + slot_subdir = f"{date}.{time_part}.{site}" + avi_dir = AVI_BASE / date / hour / slot_subdir + mp4_dir = MP4_BASE / date / hour / slot_subdir + + # Find geom files matching slot pattern + prefix = f"{date}.{time_part}" + results = [] + + for kitware_dir, source in search_dirs: + if not kitware_dir.is_dir(): + continue + + for gf in sorted(kitware_dir.glob(f"{prefix}*.{site}.*.geom.yml")): + name = gf.name + # Extract camera: ...school.G328.geom.yml + cam_match = re.search(rf'\.{site}\.(G\d+)\.geom\.yml$', name) + if not cam_match: + continue + cam = cam_match.group(1) + + # Geom basename: 2018-03-11.11-25-00.11-30-00.school.G328 + base_name = name.replace(".geom.yml", "") + + # Prefer raw AVI over MP4 + # AVI naming: {base_name}.r13.avi (exact match or fuzzy on end-time) + video_path = None + video_fmt = None + + if avi_dir.is_dir(): + avi_candidates = sorted(avi_dir.glob(f"{base_name}.r13.avi")) + \ + sorted(avi_dir.glob(f"{prefix}*.{site}.{cam}.r13.avi")) + if avi_candidates: + video_path = avi_candidates[0] + video_fmt = "avi" + + # Fallback to MP4 + if video_path is None and mp4_dir.is_dir(): + mp4_candidates = sorted(mp4_dir.glob(f"{base_name}*.mp4")) + \ + sorted(mp4_dir.glob(f"{prefix}*.{site}.{cam}*.mp4")) + if mp4_candidates: + video_path = mp4_candidates[0] + video_fmt = "mp4" + + # Activity file + act_path = gf.parent / name.replace(".geom.yml", ".activities.yml") + if not act_path.exists(): + act_path = None + + results.append({ + "camera": cam, + "geom_path": gf, + "video_path": video_path, + "video_format": video_fmt, + "act_path": act_path, + "source": source, + }) + + return results + + +def process_slot(slot: str, method: str = "segformer", + verbose: bool = False) -> Dict: + """ + Full pipeline: extract descriptions for all actors in a slot. + + Args: + method: "segformer" (default, richest), "yolo", or "color-only" + + Returns dict ready for JSON output: + { + "slot": "...", + "cameras": {...}, + "actors": {actor_id_str: {camera, upper_color, lower_color, ..., description}}, + "stats": {...} + } + """ + t0 = time.time() + files = find_slot_files(slot) + + if verbose: + print(f"\n Slot: {slot}") + print(f" Found {len(files)} cameras with geom annotations") + print(f" Method: {method}") + + if method == "yolo": + if verbose: + print(f" Loading YOLO model...", end="", flush=True) + _get_yolo() + if verbose: + print(" done.") + elif method == "segformer": + if verbose: + print(f" Loading SegFormer model...", end="", flush=True) + _get_segformer() + if verbose: + print(" done.") + + all_actors = {} + cam_stats = {} + + for cf in files: + cam = cf["camera"] + geom_path = cf["geom_path"] + video_path = cf["video_path"] + video_fmt = cf.get("video_format", "unknown") + + if verbose: + print(f"\n Camera {cam}:") + print(f" Geom: {geom_path.name}") + + if video_path is None or not video_path.exists(): + if verbose: + print(f" SKIP: No video found (checked AVI + MP4)") + cam_stats[cam] = {"actors": 0, "usable": 0, "skipped": "no_video"} + continue + + if verbose: + print(f" Video: {video_path.name} ({video_fmt})") + + # Parse geom + actors = parse_geom(geom_path) + if verbose: + print(f" Actors: {len(actors)} total") + + if not actors: + cam_stats[cam] = {"actors": 0, "usable": 0, "skipped": "no_actors"} + continue + + # Extract crops — use lower threshold to capture distant actors too + t1 = time.time() + # Two-tier: extract at lower threshold, then decide analysis method per actor + extraction_min_h = MIN_BBOX_HEIGHT_COLOR if method == "segformer" else MIN_BBOX_HEIGHT + extraction_min_w = MIN_BBOX_WIDTH_COLOR if method == "segformer" else MIN_BBOX_WIDTH + crops_by_actor = extract_crops( + video_path, actors, + max_crops=CROPS_PER_ACTOR, + min_h=extraction_min_h, min_w=extraction_min_w, + ) + decode_time = time.time() - t1 + + usable = len(crops_by_actor) + total_crops = sum(len(c) for c in crops_by_actor.values()) + segformer_count = 0 + color_fallback_count = 0 + if verbose: + print(f" Usable actors: {usable}/{len(actors)} ({total_crops} crops, {decode_time:.1f}s decode)") + + # Analyze each actor + t2 = time.time() + for actor_id, crops in crops_by_actor.items(): + if not crops: + continue + + # Run SegFormer on ALL actors regardless of size. + # Safety layers handle small crops: internal skip <15x8px, + # MIN_REGION_PIXELS=50 for color extraction, confidence filtering. + avg_h = float(np.mean([c.shape[0] for c in crops])) + if method == "segformer": + attrs = analyze_crops_segformer(crops) + segformer_count += 1 + elif method == "yolo": + attrs = analyze_crops_yolo(crops) + else: + attrs = analyze_crops_color_only(crops) + if method == "segformer": + color_fallback_count += 1 + + desc = build_description(attrs) + + # Store by camera_actorID (matching entity ID format) + entity_key = f"{cam}_actor_{actor_id}" + all_actors[entity_key] = { + "actor_id": actor_id, + "camera": cam, + "hair_color": attrs.get("hair_color", "unknown"), + "upper_color": attrs.get("upper_color", "unknown"), + "lower_color": attrs.get("lower_color", "unknown"), + "lower_type": attrs.get("lower_type", "pants"), + "shoe_color": attrs.get("shoe_color", "unknown"), + "accessories": attrs.get("accessories", []), + "carried_objects": attrs.get("carried_objects", []), + "upper_texture": attrs.get("upper_texture", ""), + "lower_texture": attrs.get("lower_texture", ""), + "upper_brightness": attrs.get("upper_brightness", ""), + "lower_brightness": attrs.get("lower_brightness", ""), + "confidence": attrs.get("confidence", {}), + "description": desc, + "num_crops": len(crops), + "avg_crop_height": int(np.mean([c.shape[0] for c in crops])), + } + + analyze_time = time.time() - t2 + if verbose: + tier_info = "" + if method == "segformer" and color_fallback_count > 0: + tier_info = f" ({segformer_count} segformer, {color_fallback_count} color-fallback)" + print(f" Analysis: {analyze_time:.1f}s ({method}){tier_info}") + + cam_stats[cam] = { + "actors": len(actors), + "usable": usable, + "total_crops": total_crops, + "decode_sec": round(decode_time, 1), + "analyze_sec": round(analyze_time, 1), + "segformer_count": segformer_count, + "color_fallback_count": color_fallback_count, + } + + total_time = time.time() - t0 + + # Summary stats + described = sum(1 for a in all_actors.values() if a["description"] != "a person") + color_dist = Counter(a["upper_color"] for a in all_actors.values()) + + result = { + "slot": slot, + "method": method, + "total_actors": len(all_actors), + "actors_with_colors": described, + "actors_without_colors": len(all_actors) - described, + "upper_color_distribution": dict(color_dist.most_common()), + "cameras": cam_stats, + "processing_time_sec": round(total_time, 1), + "actors": all_actors, + } + + if verbose: + print(f"\n === Summary ===") + print(f" Total actors: {len(all_actors)}") + print(f" With color descriptions: {described}") + print(f" Without: {len(all_actors) - described}") + print(f" Color distribution: {dict(color_dist.most_common(5))}") + print(f" Total time: {total_time:.1f}s") + + return result + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="V10 Entity Description Extractor — Geom + Video → rich descriptions", + ) + parser.add_argument("--slot", "-s", required=True, + help="Slot to process (e.g., 2018-03-11.11-25.school)") + parser.add_argument("--method", "-m", default="segformer", + choices=["segformer", "yolo", "color-only"], + help="Analysis method: segformer (best, default), yolo, color-only") + parser.add_argument("--no-yolo", action="store_true", + help="DEPRECATED: use --method color-only instead") + parser.add_argument("--dry-run", action="store_true", + help="Show what would be processed without extracting") + parser.add_argument("--output", "-o", + help=f"Output path (default: {OUTPUT_DIR}/{{slot}}.json)") + parser.add_argument("--verbose", "-v", action="store_true") + + args = parser.parse_args() + + # Handle deprecated --no-yolo flag + method = args.method + if args.no_yolo and method == "segformer": + method = "color-only" + + args = parser.parse_args() + + if args.dry_run: + files = find_slot_files(args.slot) + print(f"\n Slot: {args.slot}") + print(f" Cameras with geom: {len(files)}") + for cf in files: + cam = cf["camera"] + actors = parse_geom(cf["geom_path"]) + usable_segformer = sum(1 for aid in actors + for frames in [actors[aid].values()] + if any((bb[2]-bb[0]) >= MIN_BBOX_WIDTH and + (bb[3]-bb[1]) >= MIN_BBOX_HEIGHT + for bb in frames)) + usable_color = sum(1 for aid in actors + for frames in [actors[aid].values()] + if any((bb[2]-bb[0]) >= MIN_BBOX_WIDTH_COLOR and + (bb[3]-bb[1]) >= MIN_BBOX_HEIGHT_COLOR + for bb in frames)) + vp = cf["video_path"] + has_video = vp and vp.exists() + vfmt = cf.get("video_format", "none") + print(f" {cam}: {len(actors)} actors, {usable_segformer} segformer-ready, {usable_color} color-ready, video={'YES' if has_video else 'NO'} ({vfmt})") + return + + result = process_slot(args.slot, method=method, verbose=args.verbose) + + # Save + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + out_path = Path(args.output) if args.output else OUTPUT_DIR / f"{args.slot}.json" + with open(out_path, "w") as f: + json.dump(result, f, indent=2, default=str) + print(f"\n Output: {out_path}") + + # Show sample descriptions + print(f"\n === Sample Descriptions ===") + seen = set() + for eid, info in sorted(result["actors"].items()): + desc = info["description"] + if desc in seen or desc == "a person": + continue + seen.add(desc) + print(f" {info['camera']} actor {info['actor_id']:>6}: {desc}") + if len(seen) >= 15: + break + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/generate_best_camera.py b/meva/scripts/v10/generate_best_camera.py new file mode 100644 index 0000000..da8d57b --- /dev/null +++ b/meva/scripts/v10/generate_best_camera.py @@ -0,0 +1,367 @@ +""" +FINAL generate_best_camera.py — Camera Transition Logic questions. + +From the paper: "Identifying which specific camera in the synchronized network +first captures the entrance or appearance of an entity." + +Example question: "Which camera first captures the entrance of a person +wearing a gray top into the scene?" + +Uses `person_enters_scene_through_structure` activity annotations to +determine which camera first detects a specific entity entering, then +asks the user to identify that camera from a set of options. + +Two question sub-types: + 1. first_entrance — "Which camera first captures the entrance of {entity}?" + 2. last_entrance — "Which camera last captures the entrance of {entity}?" + (inverse — checks if model can distinguish temporal extremes) +""" + +import random +from typing import Any, Dict, List, Set, Tuple +from collections import defaultdict + +from .parse_annotations import Event +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .person_descriptions import enrich_entities, get_mevid_persons_with_cameras +from .distractor_bank import get_camera_distractors +from .activity_hierarchy import humanize_activity, humanize_activity_gerund + + +# ============================================================================ +# Constants +# ============================================================================ + +# Activities that indicate "entering the scene" +ENTRANCE_ACTIVITIES = { + "person_enters_scene_through_structure", + "person_exits_vehicle", # effectively enters the scene on foot +} + +# Minimum time gap between first and second camera entrance (seconds) +MIN_SEPARATION_SEC = 2.0 + +# Global pool of MEVA camera IDs (used as distractor padding when slot has few cameras) +_MEVA_CAMERA_POOL = [ + "G299", "G300", "G328", "G330", "G336", "G339", "G341", "G419", "G420", + "G421", "G423", "G424", "G436", "G638", "G639", "G503", "G504", "G505", + "G506", "G507", "G508", "G509", +] + + +# ============================================================================ +# Entrance Event Collection +# ============================================================================ + +def _collect_entrance_events(sg: SceneGraph) -> List[Event]: + """Collect all entrance-type events from the scene graph.""" + return [e for e in sg.events if e.activity in ENTRANCE_ACTIVITIES] + + +def _group_entrances_by_entity(entrance_events: List[Event], + sg: SceneGraph, + resolved: ResolvedGraph + ) -> Dict[str, List[Tuple[Event, str]]]: + """ + Group entrance events by resolved entity cluster. + + Returns: + { cluster_id: [(event, camera_id), ...] } sorted by start_sec + """ + # Map (camera, actor_id) -> entity_id -> cluster_id + entity_to_cluster = {} + for cluster in resolved.entity_clusters: + for eid in cluster.entities: + entity_to_cluster[eid] = cluster.cluster_id + + # Map entity_id -> (camera, actor_id) + eid_to_key = {} + for eid, entity in sg.entities.items(): + eid_to_key[eid] = (entity.camera_id, entity.actor_id) + + # Reverse: (camera, actor_id) -> cluster_id + key_to_cluster = {} + for eid, key in eid_to_key.items(): + if eid in entity_to_cluster: + key_to_cluster[key] = entity_to_cluster[eid] + + # Group events by cluster + cluster_events: Dict[str, List[Tuple[Event, str]]] = defaultdict(list) + for event in entrance_events: + for actor in event.actors: + key = (event.camera_id, actor["actor_id"]) + cid = key_to_cluster.get(key) + if cid: + cluster_events[cid].append((event, event.camera_id)) + else: + # No cluster — use camera+actor as standalone key + standalone = f"standalone_{event.camera_id}_{actor['actor_id']}" + cluster_events[standalone].append((event, event.camera_id)) + + # Sort each cluster's events by time + for cid in cluster_events: + cluster_events[cid].sort(key=lambda x: x[0].start_sec) + + return dict(cluster_events) + + +def _get_entity_description(cluster_id: str, sg: SceneGraph, + entity_descs: Dict[str, str], + resolved: ResolvedGraph) -> str: + """Get the best available VISUAL description for an entity cluster. + + Only returns clothing/appearance descriptions (from MEVID or geom-color). + Filters out activity-verb fallbacks like 'a person puts down object'. + """ + # Find entity IDs in this cluster + for cluster in resolved.entity_clusters: + if cluster.cluster_id == cluster_id: + for eid in cluster.entities: + desc = entity_descs.get(eid) + if not desc or desc == "a person" or "someone" in desc.lower(): + continue + # Filter out activity-verb fallbacks — they contain verbs + # Visual descriptions contain words like "top", "pants", "wearing" + desc_lower = desc.lower() + is_visual = any(kw in desc_lower for kw in + ["top", "pants", "wearing", "shirt", "jacket", "color", + "blue", "red", "black", "white", "green", "gray", "yellow", + "brown", "orange", "purple", "backpack", "bag"]) + if is_visual: + return desc + break + return "a person" + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_best_camera_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate Camera Transition Logic questions. + + "Which camera first captures the entrance of {entity} into the scene?" + """ + entrance_events = _collect_entrance_events(sg) + + if len(entrance_events) < 2: + if verbose: + print(" Best Camera: Too few entrance events") + return [] + + # Group by entity cluster + cluster_entrances = _group_entrances_by_entity(entrance_events, sg, resolved) + + # Filter to entities that appear on 2+ cameras (multi-camera entrance) + multi_cam_clusters = {} + for cid, events_cams in cluster_entrances.items(): + cameras_seen = set(cam for _, cam in events_cams) + if len(cameras_seen) >= 2: + multi_cam_clusters[cid] = events_cams + + # If no multi-camera entities, fall back to any entity with entrance events + # and use all cameras in slot as options + use_multi_cam = len(multi_cam_clusters) >= 1 + candidates = multi_cam_clusters if use_multi_cam else cluster_entrances + + if not candidates: + if verbose: + print(" Best Camera: No entrance events to use") + return [] + + all_cameras = sorted(sg.cameras.keys()) + qa_pairs = [] + used_clusters = set() + + # Attempt to generate `count` questions + candidate_list = list(candidates.items()) + + # V10: Sort candidates so entities with visual descriptions come first + # This avoids generating generic "a person" questions when better options exist + def _has_visual_desc(item): + cid, _ = item + desc = _get_entity_description(cid, sg, entity_descs, resolved) + return 0 if desc != "a person" else 1 + candidate_list.sort(key=_has_visual_desc) + + # Shuffle within each priority group (visual first, then generic) + visual_cands = [c for c in candidate_list if _has_visual_desc(c) == 0] + generic_cands = [c for c in candidate_list if _has_visual_desc(c) == 1] + rng.shuffle(visual_cands) + rng.shuffle(generic_cands) + candidate_list = visual_cands + generic_cands + + for cluster_id, events_cams in candidate_list: + if len(qa_pairs) >= count: + break + if cluster_id in used_clusters: + continue + + # Get entity description + desc = _get_entity_description(cluster_id, sg, entity_descs, resolved) + + # Get cameras sorted by first entrance time + cam_times: Dict[str, float] = {} + cam_events: Dict[str, Event] = {} + for event, cam in events_cams: + if cam not in cam_times or event.start_sec < cam_times[cam]: + cam_times[cam] = event.start_sec + cam_events[cam] = event + + sorted_cams = sorted(cam_times.keys(), key=lambda c: cam_times[c]) + + if len(sorted_cams) < 1: + continue + + # ----- Sub-type 1: first_entrance ----- + if len(qa_pairs) < count: + first_cam = sorted_cams[0] + first_time = cam_times[first_cam] + first_event = cam_events[first_cam] + + # Build options: correct camera + distractors + other_cams = [c for c in all_cameras if c != first_cam] + if len(other_cams) < 3: + # Not enough cameras for 4 options + distractor_cams = other_cams + else: + # Prefer cameras that DO have entrance events (harder distractors) + entrance_cams = [c for c in sorted_cams[1:] if c != first_cam] + non_entrance_cams = [c for c in other_cams if c not in entrance_cams] + + # Mix: 1-2 entrance cams + 1-2 non-entrance cams + rng.shuffle(entrance_cams) + rng.shuffle(non_entrance_cams) + distractor_cams = (entrance_cams[:2] + non_entrance_cams[:2])[:3] + + options = [f"Camera {first_cam}"] + [f"Camera {c}" for c in distractor_cams] + while len(options) < 4: + # Pad with remaining slot cameras first, then global pool + remaining = [c for c in all_cameras if f"Camera {c}" not in options] + if not remaining: + remaining = [c for c in _MEVA_CAMERA_POOL if f"Camera {c}" not in options] + if remaining: + options.append(f"Camera {rng.choice(remaining)}") + else: + break + + rng.shuffle(options) + correct_idx = options.index(f"Camera {first_cam}") + + # Build clip_file from the event's video_file + clip_file = (first_event.video_file or '').replace('.avi', '.mp4') + + question_template = ( + f"Which camera first captures the entrance of {desc} into the scene?" + ) + + qa = { + "question_id": "", # will be renumbered + "category": "best_camera", + "subcategory": "first_entrance", + "question_template": question_template, + "options": options, + "correct_answer": options[correct_idx], + "correct_answer_index": correct_idx, + "requires_cameras": sorted_cams + [c for c in all_cameras if c not in sorted_cams], + "difficulty": "medium" if len(sorted_cams) >= 3 else "easy", + "verification": { + "question_type": "first_entrance", + "correct_camera": first_cam, + "entrance_time_sec": round(first_time, 2), + "entity_description": desc, + "cluster_id": cluster_id, + "all_entrance_cameras": { + cam: round(cam_times[cam], 2) for cam in sorted_cams + }, + "activity": first_event.activity, + }, + "debug_info": { + "representative_event": { + "clip_file": clip_file, + "camera": first_cam, + "activity": first_event.activity, + "start_sec": round(first_time, 2), + }, + }, + } + qa_pairs.append(qa) + used_clusters.add(cluster_id) + + # ----- Sub-type 2: last_entrance (if enough cameras) ----- + if len(qa_pairs) < count and len(sorted_cams) >= 2: + last_cam = sorted_cams[-1] + last_time = cam_times[last_cam] + last_event = cam_events[last_cam] + + # Ensure meaningful separation from first + if last_time - cam_times[sorted_cams[0]] < MIN_SEPARATION_SEC: + continue + + other_cams = [c for c in all_cameras if c != last_cam] + rng.shuffle(other_cams) + distractor_cams = other_cams[:3] + + options = [f"Camera {last_cam}"] + [f"Camera {c}" for c in distractor_cams] + while len(options) < 4: + remaining = [c for c in all_cameras if f"Camera {c}" not in options] + if not remaining: + remaining = [c for c in _MEVA_CAMERA_POOL if f"Camera {c}" not in options] + if remaining: + options.append(f"Camera {rng.choice(remaining)}") + else: + break + + rng.shuffle(options) + correct_idx = options.index(f"Camera {last_cam}") + + clip_file = (last_event.video_file or '').replace('.avi', '.mp4') + + question_template = ( + f"Of all cameras capturing {desc} entering the scene, " + f"which camera captures this last?" + ) + + qa = { + "question_id": "", + "category": "best_camera", + "subcategory": "last_entrance", + "question_template": question_template, + "options": options, + "correct_answer": options[correct_idx], + "correct_answer_index": correct_idx, + "requires_cameras": sorted_cams + [c for c in all_cameras if c not in sorted_cams], + "difficulty": "hard", + "verification": { + "question_type": "last_entrance", + "correct_camera": last_cam, + "entrance_time_sec": round(last_time, 2), + "entity_description": desc, + "cluster_id": cluster_id, + "all_entrance_cameras": { + cam: round(cam_times[cam], 2) for cam in sorted_cams + }, + "activity": last_event.activity, + }, + "debug_info": { + "representative_event": { + "clip_file": clip_file, + "camera": last_cam, + "activity": last_event.activity, + "start_sec": round(last_time, 2), + }, + }, + } + qa_pairs.append(qa) + + if verbose: + print(f" Best Camera: {len(qa_pairs)} questions generated " + f"(from {len(entrance_events)} entrance events, " + f"{len(multi_cam_clusters)} multi-cam entities)") + + return qa_pairs[:count] diff --git a/meva/scripts/v10/generate_event_ordering.py b/meva/scripts/v10/generate_event_ordering.py new file mode 100644 index 0000000..988f239 --- /dev/null +++ b/meva/scripts/v10/generate_event_ordering.py @@ -0,0 +1,662 @@ +""" +V8 generate_event_ordering.py — Event ordering questions (chronological arrangement). + +Unlike temporal questions which ask "which of 2 events happened first?" (binary), +event ordering asks "arrange 3-4 events in chronological order" (combinatorial). +This is significantly harder since the answer space grows factorially. + +Event selection prefers: + - Cross-camera events (multi-camera ordering is harder than single-camera) + - Clear temporal gaps (>3s) so ordering is unambiguous + - Related activities (causal / co-occurring chains) + - Events involving entities with MEVID descriptions (for visual grounding) + +Output: MCQ with scrambled event labels (I, II, III, IV) and 4 permutation options. +""" + +import itertools +import random +from typing import Any, Dict, List, Optional, Set, Tuple + +from .parse_annotations import Event +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .activity_hierarchy import ( + humanize_activity, humanize_activity_gerund, are_related, get_relationship, + CAUSAL_RELATIONSHIPS, CO_OCCURRING, +) +from .utils.mevid import find_mevid_persons_for_slot + +# Issue 6: Import cross-camera event dedup from temporal generator +from .generate_temporal import _is_likely_duplicate_event + +# ============================================================================ +# Constants +# ============================================================================ + +MIN_GAP_SEC = 2.0 # Minimum gap between consecutive events (unambiguous) +MAX_GAP_SEC = 10.0 # Maximum gap — events must be close enough to be related +DEFAULT_FPS = 30.0 +MIN_EVENTS = 3 # Minimum events per ordering question +MAX_EVENTS = 4 # Maximum events per ordering question +ROMAN = ["I", "II", "III", "IV"] + + +# ============================================================================ +# Event Description Helpers +# ============================================================================ + +def _get_event_description(event: Event, sg: SceneGraph, + entity_descs: Dict[str, str], + fallback_eids: Optional[Set[str]] = None) -> str: + """ + Build a human-readable event description using MEVID entity descriptions + and gerund activity forms. + + Prefers visual (non-fallback) descriptions. Falls back to generic + 'Someone {activity}' if only fallback descriptions are available. + + Example: "A person wearing a gray top, opening a facility door on camera G421" + """ + from .person_descriptions import is_visual_description + # Try to find a visual (non-fallback) description for an actor in this event + desc = None + fallback_desc = None + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + d = entity_descs.get(eid) + if d: + if fallback_eids and eid in fallback_eids: + if fallback_desc is None: + fallback_desc = d + else: + desc = d + break + if desc: + break + + activity_text = humanize_activity_gerund(event.activity) + + if desc: + # Guard: avoid duplication when desc already contains the activity + act_check = humanize_activity(event.activity).lower() + if act_check in desc.lower() or activity_text.lower() in desc.lower(): + return desc + return f"{desc}, {activity_text.lower()}" + + # Use fallback description if available (still better than "Someone") + if fallback_desc: + act_check = humanize_activity(event.activity).lower() + if act_check in fallback_desc.lower() or activity_text.lower() in fallback_desc.lower(): + return fallback_desc + return f"{fallback_desc}, {activity_text.lower()}" + + # Last resort: use geom description directly from entity_descs + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + d = entity_descs.get(eid) + if d and d not in ("a person", "a vehicle", "someone"): + if activity_text.lower() in d.lower(): + return d + return f"{d}, {activity_text.lower()}" + + return f"A person {activity_text.lower()}" + + +# ============================================================================ +# Group Scoring +# ============================================================================ + +def _count_related_pairs(events: List[Event]) -> int: + """Count how many event pairs in the group have causal/co-occurring relationships.""" + count = 0 + for i in range(len(events)): + for j in range(i + 1, len(events)): + if are_related(events[i].activity, events[j].activity): + count += 1 + return count + + +def _count_described_events(events: List[Event], sg: SceneGraph, + entity_descs: Dict[str, str]) -> int: + """Count events that have visual (non-fallback) entity descriptions.""" + from .person_descriptions import is_visual_description + count = 0 + for event in events: + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + desc = entity_descs.get(eid, "") + if desc and is_visual_description(desc): + count += 1 + break + else: + continue + break + return count + + +def _score_group(events: List[Event], sg: SceneGraph, + entity_descs: Dict[str, str], + mevid_person_cameras: Dict[int, Set[str]]) -> float: + """ + Score a candidate event group. Higher = better. + + Criteria: + +2.0 per unique camera (cross-camera diversity) + +1.5 per related-activity pair + +1.0 per event with an entity description + +1.0 if any MEVID-validated camera pair exists in the group + +0.5 for 4-event groups (more challenging) + """ + cameras = set(e.camera_id for e in events) + score = len(cameras) * 2.0 + + score += _count_related_pairs(events) * 1.5 + score += _count_described_events(events, sg, entity_descs) * 1.0 + + # MEVID cross-camera bonus + cam_list = list(cameras) + for pid, pcams in mevid_person_cameras.items(): + if len(pcams & cameras) >= 2: + score += 1.0 + break + + if len(events) == MAX_EVENTS: + score += 0.5 + + return score + + +# ============================================================================ +# Candidate Group Discovery +# ============================================================================ + +def _find_ordering_groups(events: List[Event], sg: SceneGraph, + entity_descs: Dict[str, str], + mevid_person_cameras: Dict[int, Set[str]], + rng: random.Random, + target_count: int = 6) -> List[List[Event]]: + """ + Find candidate groups of 3-4 events suitable for ordering questions. + + Requirements per group: + - Events sorted chronologically with consecutive gaps > MIN_GAP_SEC + - At least 2 distinct cameras + - No overlapping events (start of next > end of previous + MIN_GAP_SEC) + + Returns groups sorted by score (best first), capped at target_count. + """ + if len(events) < MIN_EVENTS: + return [] + + # Sort all events by start_sec for chronological processing + sorted_events = sorted(events, key=lambda e: e.start_sec) + + # De-duplicate: keep one event per (camera, activity, ~time bucket) + # to avoid near-identical events cluttering groups + seen_keys: Set[Tuple[str, str, int]] = set() + unique_events: List[Event] = [] + for e in sorted_events: + bucket = int(e.start_sec // 5) # 5-second buckets + key = (e.camera_id, e.activity, bucket) + if key not in seen_keys: + seen_keys.add(key) + unique_events.append(e) + + if len(unique_events) < MIN_EVENTS: + return [] + + # Issue 5 (refined): For LONG-DURATION events (>30s), keep only the first + # instance per (activity, camera) to avoid misleading temporal comparisons + # (e.g., "talking" annotated from t=10 to t=200 should use t=10). + # Short events (<30s) are kept as-is since they represent distinct actions + # even when the same activity type repeats on the same camera. + LONG_DURATION_THRESHOLD_SEC = 30.0 + first_instance: Dict[Tuple[str, str], Event] = {} + deduped_events: List[Event] = [] + for evt in unique_events: + if evt.duration_sec > LONG_DURATION_THRESHOLD_SEC: + key = (evt.activity, evt.camera_id) + if key not in first_instance or evt.start_sec < first_instance[key].start_sec: + first_instance[key] = evt + else: + deduped_events.append(evt) + deduped_events.extend(first_instance.values()) + # Skip events in the first 5 seconds (camera stabilization period) + unique_events = [e for e in deduped_events if e.start_sec >= 5.0] + unique_events.sort(key=lambda e: e.start_sec) + + if len(unique_events) < MIN_EVENTS: + return [] + + # Build candidate groups using COMBINATORIAL enumeration. + # For each subset of 3 or 4 events, check all ordering constraints and + # score the group. This explores all possible combinations instead of + # greedily committing to the first qualifying next event. + # With typical slot sizes of 20-60 events, C(60,4) = 487,635 which is fast. + groups: List[Tuple[float, List[Event]]] = [] + seen_group_keys: Set[Tuple[str, ...]] = set() + + # Cap to avoid excessive computation on very large slots + MAX_EVENTS_FOR_COMBO = 80 + events_to_search = unique_events[:MAX_EVENTS_FOR_COMBO] + + for chain_len in (MAX_EVENTS, MIN_EVENTS): + if len(events_to_search) < chain_len: + continue + + for combo in itertools.combinations(range(len(events_to_search)), chain_len): + chain = [events_to_search[i] for i in combo] + # Events are already sorted by start_sec (combo indices are ascending) + + # Check consecutive gaps are within [MIN_GAP_SEC, MAX_GAP_SEC] + valid_gaps = True + for k in range(len(chain) - 1): + gap = chain[k + 1].start_sec - chain[k].end_sec + if gap < MIN_GAP_SEC or gap > MAX_GAP_SEC: + valid_gaps = False + break + if not valid_gaps: + continue + + # Require at least 2 cameras + chain_cameras = set(e.camera_id for e in chain) + if len(chain_cameras) < 2: + continue + + # Require activity diversity: at most 1 repeated activity + chain_activities = set(e.activity for e in chain) + if len(chain_activities) < len(chain) - 1: + continue + + # Cross-camera event dedup — skip if any pair in the chain + # is a likely duplicate (same real-world event seen by 2 cams) + has_dup = False + for k in range(len(chain)): + for m in range(k + 1, len(chain)): + if _is_likely_duplicate_event(chain[k], chain[m], sg): + has_dup = True + break + if has_dup: + break + if has_dup: + continue + + # De-duplicate by group key (sorted event_ids) + gkey = tuple(sorted(e.event_id for e in chain)) + if gkey in seen_group_keys: + continue + seen_group_keys.add(gkey) + + score = _score_group(chain, sg, entity_descs, mevid_person_cameras) + groups.append((score, chain)) + + # Sort by score descending + groups.sort(key=lambda g: -g[0]) + + return [g[1] for g in groups[:target_count]] + + +# ============================================================================ +# Distractor Permutation Generation +# ============================================================================ + +def _generate_permutation_label(order: List[int]) -> str: + """ + Convert an index-based ordering to a Roman-numeral arrow string. + + Example: [1, 3, 0, 2] → "II -> IV -> I -> III" + """ + return " -> ".join(ROMAN[i] for i in order) + + +def _generate_distractor_permutations(n: int, correct_order: List[int], + rng: random.Random) -> List[List[int]]: + """ + Generate 3 distinct distractor permutations for n events. + + Strategies (in priority order): + 1. Reverse of correct order + 2. Swap two adjacent elements + 3. Swap first and last elements + 4. Random permutation (fallback) + + All distractors are guaranteed distinct from each other and from the + correct order. + """ + correct_tuple = tuple(correct_order) + distractors: List[List[int]] = [] + seen: Set[Tuple[int, ...]] = {correct_tuple} + + def _try_add(perm: List[int]) -> bool: + t = tuple(perm) + if t not in seen: + seen.add(t) + distractors.append(perm) + return True + return False + + # Strategy 1: full reverse + _try_add(list(reversed(correct_order))) + + # Strategy 2: swap adjacent pairs + for i in range(n - 1): + if len(distractors) >= 3: + break + swapped = list(correct_order) + swapped[i], swapped[i + 1] = swapped[i + 1], swapped[i] + _try_add(swapped) + + # Strategy 3: swap first and last + if len(distractors) < 3: + swapped = list(correct_order) + swapped[0], swapped[-1] = swapped[-1], swapped[0] + _try_add(swapped) + + # Strategy 3.5: rotate by 1 position + if len(distractors) < 3: + rotated = correct_order[1:] + correct_order[:1] + _try_add(rotated) + + # Strategy 4: random permutations as fallback + all_perms = list(itertools.permutations(range(n))) + rng.shuffle(all_perms) + for perm in all_perms: + if len(distractors) >= 3: + break + _try_add(list(perm)) + + return distractors[:3] + + +# ============================================================================ +# Question Construction +# ============================================================================ + +def _build_question_text(descriptions: List[str]) -> str: + """ + Build the question text with numbered event descriptions. + + Returns multi-line text like: + Identify the correct chronological order of the following events + observed across the cameras: + I. A person wearing a gray top, opening a facility door on camera G421 + II. A person entering a scene through a structure on camera G330 + ... + Which is the correct chronological order? + """ + lines = [ + "Identify the correct chronological order of the following events " + "observed across the cameras:" + ] + for i, desc in enumerate(descriptions): + lines.append(f"{ROMAN[i]}. {desc}") + lines.append("Which is the correct chronological order?") + return "\n".join(lines) + + +def _build_debug_info(event: Event, sg: SceneGraph, + entity_descs: Dict[str, str]) -> Dict: + """Build debug info dict for one event.""" + clip_file = event.video_file + if clip_file.endswith(".avi"): + clip_file = clip_file.replace(".avi", ".mp4") + + desc = None + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + desc = entity_descs.get(eid, entity.alias) + break + if desc: + break + + return { + "camera": event.camera_id, + "activity": event.activity, + "actor_ids": [a["actor_id"] for a in event.actors], + "frame_range": [event.start_frame, event.end_frame], + "timestamp": f"{event.start_sec:.2f}-{event.end_sec:.2f}s", + "fps": DEFAULT_FPS, + "clip_file": clip_file, + "entity_description": desc, + } + + +# ============================================================================ +# Public API +# ============================================================================ + +def generate_event_ordering_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False, + fallback_eids: Optional[Set[str]] = None) -> List[Dict]: + """ + Generate event-ordering cross-camera questions. + + Each question presents 3-4 events in scrambled order and asks the + viewer to identify the correct chronological sequence. Significantly + harder than binary temporal questions because the answer space is + combinatorial (3! = 6 or 4! = 24 permutations). + + Args: + sg: Scene graph with events, entities, cameras. + resolved: Resolved graph with entity clusters. + entity_descs: entity_id → human-readable description string. + rng: Seeded RNG for reproducibility. + count: Target number of questions (default 2). + verbose: Print progress info. + + Returns: + List of question dicts in V8 MCQ format. + """ + slot_cameras = list(sg.cameras.keys()) + mevid_person_cameras = find_mevid_persons_for_slot(sg.slot, slot_cameras) + + # Step 1: Find candidate groups + groups = _find_ordering_groups( + sg.events, sg, entity_descs, mevid_person_cameras, rng, + target_count=count * 3, # over-generate for diversity + ) + + if verbose: + print(f" Event ordering: {len(groups)} candidate groups") + + if not groups: + return [] + + # Filter out groups where ANY event uses fallback (non-visual) descriptions + # All events appear in the question text, so all must have visual descriptions + if fallback_eids: + def _all_events_visual(group): + for event in group: + has_visual = False + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + if eid not in fallback_eids: + has_visual = True + break + if has_visual: + break + if not has_visual: + return False + return True + + visual_groups = [g for g in groups if _all_events_visual(g)] + if verbose and len(visual_groups) < len(groups): + print(f" Filtered {len(groups) - len(visual_groups)} " + f"fallback-only groups → {len(visual_groups)} remaining") + groups = visual_groups + + if not groups: + return [] + + # Step 2: Select diverse groups (avoid reusing same camera sets) + selected: List[List[Event]] = [] + used_camera_sets: Set[Tuple[str, ...]] = set() + used_event_ids: Set[str] = set() + + for group in groups: + if len(selected) >= count: + break + + cam_set = tuple(sorted(set(e.camera_id for e in group))) + group_eids = set(e.event_id for e in group) + + # Prefer distinct camera combinations and non-overlapping events + overlap = group_eids & used_event_ids + if overlap: + continue + if cam_set in used_camera_sets and len(selected) > 0: + continue + + selected.append(group) + used_camera_sets.add(cam_set) + used_event_ids.update(group_eids) + + # If we couldn't fill enough with strict diversity, relax constraints + if len(selected) < count: + for group in groups: + if len(selected) >= count: + break + if group not in selected: + selected.append(group) + + # Step 3: Generate QA for each selected group + qa_pairs: List[Dict] = [] + + for idx, group in enumerate(selected[:count]): + n = len(group) + + # Events are already in chronological order from _find_ordering_groups + chronological = sorted(group, key=lambda e: e.start_sec) + + # Build descriptions in chronological order + descriptions_chrono = [ + _get_event_description(e, sg, entity_descs, fallback_eids) for e in chronological + ] + + # Scramble presentation order: correct_order[i] = chronological + # position of the event presented as Roman numeral (i+1) + presentation_indices = list(range(n)) + rng.shuffle(presentation_indices) + + # presentation_indices[i] tells which chronological event goes to + # presentation slot i. So the event presented as "I" is + # chronological[presentation_indices[0]], etc. + presented_descriptions = [descriptions_chrono[pi] for pi in presentation_indices] + + # The correct answer is the permutation that re-sorts presentation + # back to chronological. If presentation_indices = [2, 0, 3, 1], + # then chronological order in presentation labels is determined by + # argsort(presentation_indices). + # argsort: for each chrono position k, which presentation slot has it? + chrono_to_presentation = [0] * n + for pres_slot, chrono_pos in enumerate(presentation_indices): + chrono_to_presentation[chrono_pos] = pres_slot + + # correct_order = the presentation slots in chronological sequence + correct_order = chrono_to_presentation # e.g. [1, 3, 0, 2] + + # Generate distractor permutations + distractor_perms = _generate_distractor_permutations(n, correct_order, rng) + + # Build options: correct answer + 3 distractors, then shuffle + correct_label = _generate_permutation_label(correct_order) + distractor_labels = [_generate_permutation_label(d) for d in distractor_perms] + + options = [correct_label] + distractor_labels + correct_answer_index = 0 + + # Shuffle options so correct isn't always first + option_pairs = list(enumerate(options)) + rng.shuffle(option_pairs) + shuffled_options = [label for _, label in option_pairs] + correct_answer_index = next( + i for i, (orig_idx, _) in enumerate(option_pairs) if orig_idx == 0 + ) + + # Cameras involved + all_cameras = sorted(set(e.camera_id for e in chronological)) + + # Compute minimum gap between consecutive chronological events + gaps = [] + for i in range(len(chronological) - 1): + gap = chronological[i + 1].start_sec - chronological[i].end_sec + gaps.append(round(gap, 2)) + min_gap = min(gaps) if gaps else 0.0 + + # Difficulty: 4 events = hard, 3 events = medium-hard + difficulty = "hard" if n == 4 else "medium-hard" + + # Build question text + question_text = _build_question_text(presented_descriptions) + + # Verification: ordered events with timing + ordered_events = [] + for i, event in enumerate(chronological): + ordered_events.append({ + "activity": event.activity, + "camera": event.camera_id, + "start_sec": round(event.start_sec, 2), + "description": descriptions_chrono[i], + }) + + # Debug info per event + debug_events = [_build_debug_info(e, sg, entity_descs) for e in chronological] + + # Check MEVID validation + mevid_validated = False + for pid, pcams in mevid_person_cameras.items(): + if len(pcams & set(all_cameras)) >= 2: + mevid_validated = True + break + + qa = { + "question_id": f"v8_event_ordering_{idx + 1:03d}", + "category": "event_ordering", + "difficulty": difficulty, + "question_template": question_text, + "options": shuffled_options, + "correct_answer_index": correct_answer_index, + "correct_answer": shuffled_options[correct_answer_index], + "requires_cameras": all_cameras, + "requires_multi_camera": len(all_cameras) >= 2, + "verification": { + "ordered_events": ordered_events, + "min_gap_sec": min_gap, + "num_events": n, + "gaps_sec": gaps, + }, + "debug_info": { + "events": debug_events, + "presentation_order": presentation_indices, + "correct_permutation": correct_order, + "mevid_validated": mevid_validated, + "group_score": _score_group( + chronological, sg, entity_descs, mevid_person_cameras + ), + "related_pairs": _count_related_pairs(chronological), + "described_events": _count_described_events( + chronological, sg, entity_descs + ), + }, + } + qa_pairs.append(qa) + + if verbose: + print(f" Event ordering: {len(qa_pairs)} questions generated " + f"({sum(1 for q in qa_pairs if q['difficulty'] == 'hard')} hard, " + f"{sum(1 for q in qa_pairs if q['difficulty'] == 'medium-hard')} medium-hard)") + + return qa_pairs diff --git a/meva/scripts/v10/generate_numerical.py b/meva/scripts/v10/generate_numerical.py new file mode 100644 index 0000000..b7c8e7c --- /dev/null +++ b/meva/scripts/v10/generate_numerical.py @@ -0,0 +1,539 @@ +""" +FINAL generate_numerical.py — Numerical/counting questions across cameras. + +Tests a model's ability to count activities from a multi-camera scene. + +**activity_counting**: "How many times does [activity] occur across all cameras?" + → Count event instances of that activity type from sg.events, with + cross-camera temporal deduplication (events of the same activity on + different cameras within ±2 seconds are counted as one instance). + +Distractors are generated arithmetically (±1, ±2, ×2) so that wrong answers +are plausible. All options are stringified integers > 0, sorted numerically. + +Guard rails: skip any candidate whose correct count is < 2 (trivial) or > 20 +(unreasonable for video QA). Preferred difficulty sweet-spot is [3, 10]. +""" + +import random +from collections import Counter, defaultdict +from typing import Any, Dict, List, Optional, Set, Tuple + +from .parse_annotations import Event +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .activity_hierarchy import humanize_activity, humanize_activity_gerund + +# Issue 6: Import 3D position matching for improved cross-camera dedup +try: + from .generate_temporal import _get_event_3d_position, CROSS_CAM_DEDUP_DISTANCE_M + _HAS_3D_DEDUP = True +except ImportError: + _HAS_3D_DEDUP = False + +# Camera overlap detection for aggressive dedup on overlapping FOVs +try: + from .utils.camera_overlap import cameras_overlap + _HAS_OVERLAP = True +except ImportError: + _HAS_OVERLAP = False + + +# ============================================================================ +# Constants +# ============================================================================ + +MIN_COUNT = 2 # minimum correct count to consider +MAX_COUNT = 20 # maximum correct count to consider +SWEET_LOW = 3 # preferred range lower bound (for scoring) +SWEET_HIGH = 10 # preferred range upper bound + + +# ============================================================================ +# Distractor Generation +# ============================================================================ + +def _make_distractors(correct: int, rng: random.Random) -> List[int]: + """ + Build 3 distinct distractor values for a counting question. + + Candidate pool: correct ± 1, correct ± 2, correct × 2. + All values must be > 0 and != correct. If pool is too small we + widen with correct + 3, correct + 4, etc. + """ + pool = set() + for delta in (-2, -1, 1, 2): + v = correct + delta + if v > 0: + pool.add(v) + doubled = correct * 2 + if doubled > 0 and doubled != correct: + pool.add(doubled) + + pool.discard(correct) + + # Widen if we still don't have 3 + extend = 3 + while len(pool) < 3: + extend += 1 + v = correct + extend + if v > 0 and v != correct: + pool.add(v) + v = correct - extend + if v > 0 and v != correct: + pool.add(v) + + pool_list = sorted(pool) + rng.shuffle(pool_list) + return pool_list[:3] + + +def _build_options(correct: int, rng: random.Random) -> Tuple[List[str], int]: + """ + Return (options, correct_answer_index) with 4 string-ified integers + sorted in ascending numerical order. + """ + distractors = _make_distractors(correct, rng) + all_vals = sorted(set([correct] + distractors)) + options = [str(v) for v in all_vals] + correct_idx = all_vals.index(correct) + return options, correct_idx + + +# ============================================================================ +# Candidate Builders +# ============================================================================ + +def _get_entity_cluster(event: Event, sg: SceneGraph, resolved) -> Optional[str]: + """Get the entity cluster ID for an event's primary actor, if resolved. + + Returns the cluster_id if the actor is in a known cluster, else None. + """ + if resolved is None or sg is None: + return None + for actor in event.actors: + entity_id = f"{event.camera_id}_actor_{actor['actor_id']}" + for cluster in resolved.entity_clusters: + if entity_id in cluster.entities: + return cluster.cluster_id + return None + + +def _dedup_activity_count(events_for_activity: list, + sg: SceneGraph = None, + resolved=None) -> Tuple[int, List[str], List[Dict]]: + """Count distinct instances of an activity with cross-camera temporal dedup. + + Events on DIFFERENT cameras whose start_sec is within ±2 seconds are + merged into a single cluster (counted as one occurrence). Events on + the SAME camera are always counted separately. + + For cameras with KNOWN FOV overlap (e.g. admin G326/G329), the time + window is widened to ±8 seconds since the same event is very likely + to appear in both cameras with some annotation timing variation. + + Entity identity guard: If two events' actors are in KNOWN DIFFERENT + entity clusters (i.e., confirmed different people), they are NOT deduped + even if they pass the time/position checks. This prevents false dedup + of genuinely different people doing the same activity simultaneously. + + Issue 6: Also uses 3D position matching when available for more accurate dedup. + Issue 10: Returns cluster details for key_frames in QA output. + + Returns (deduped_count, list_of_event_ids, cluster_details). + cluster_details: List[Dict] with one entry per cluster, each containing + {camera, start_sec, end_sec, start_frame, event_id, clip_file}. + """ + DEDUP_WINDOW_DEFAULT = 2.0 # seconds for non-overlapping cameras + DEDUP_WINDOW_OVERLAP = 8.0 # seconds for overlapping cameras + + sorted_evts = sorted(events_for_activity, key=lambda e: e.start_sec) + clusters: List[list] = [] + for evt in sorted_evts: + merged = False + for cluster in clusters: + for c_evt in cluster: + if evt.camera_id == c_evt.camera_id: + continue # Same camera = always distinct + + # Determine time window based on camera overlap + if _HAS_OVERLAP and cameras_overlap(evt.camera_id, c_evt.camera_id): + time_window = DEDUP_WINDOW_OVERLAP + else: + time_window = DEDUP_WINDOW_DEFAULT + + # Time-based check + if abs(evt.start_sec - c_evt.start_sec) > time_window: + continue + + # Entity identity guard: if both actors are in KNOWN + # DIFFERENT clusters, they are different people — skip dedup + cluster_evt = _get_entity_cluster(evt, sg, resolved) + cluster_cevt = _get_entity_cluster(c_evt, sg, resolved) + if (cluster_evt is not None and cluster_cevt is not None + and cluster_evt != cluster_cevt): + continue # Confirmed different people + + # Issue 6: 3D position check when available + # Skip 3D check for overlapping cameras (same FOV = same location) + if (_HAS_3D_DEDUP and sg is not None + and not (_HAS_OVERLAP and cameras_overlap(evt.camera_id, c_evt.camera_id))): + import numpy as np + pos_a = _get_event_3d_position(evt, sg) + pos_b = _get_event_3d_position(c_evt, sg) + if pos_a is not None and pos_b is not None: + dist = float(np.linalg.norm(pos_a - pos_b)) + if dist > CROSS_CAM_DEDUP_DISTANCE_M: + continue # Far apart, not duplicates despite time proximity + + cluster.append(evt) + merged = True + break + if merged: + break + if not merged: + clusters.append([evt]) + + all_ids = [e.event_id for e in sorted_evts] + + # Issue 10: Build cluster details (key_frames) — one representative per cluster + cluster_details = [] + for cluster in clusters: + # Use earliest event as representative + rep = min(cluster, key=lambda e: e.start_sec) + clip_file = rep.video_file + if clip_file and clip_file.endswith(".avi"): + clip_file = clip_file.replace(".avi", ".mp4") + cluster_details.append({ + "camera": rep.camera_id, + "start_frame": rep.start_frame, + "start_sec": round(rep.start_sec, 2), + "end_sec": round(rep.end_sec, 2), + "event_id": rep.event_id, + "clip_file": clip_file or "", + "cluster_size": len(cluster), + }) + # Sort chronologically + cluster_details.sort(key=lambda d: d["start_sec"]) + + return len(clusters), all_ids, cluster_details + + +def _activity_counting_candidates(sg: SceneGraph, resolved=None) -> List[Dict]: + """ + For each activity type, count event instances across all cameras + with cross-camera temporal deduplication (±2 s). + """ + # Group events by activity + activity_groups: Dict[str, list] = defaultdict(list) + activity_cameras: Dict[str, Set[str]] = defaultdict(set) + + for e in sg.events: + activity_groups[e.activity].append(e) + activity_cameras[e.activity].add(e.camera_id) + + candidates = [] + for act, evts in activity_groups.items(): + cnt, event_ids, cluster_details = _dedup_activity_count(evts, sg, resolved) + if cnt < MIN_COUNT or cnt > MAX_COUNT: + continue + candidates.append({ + "subtype": "activity_counting", + "activity": act, + "correct_count": cnt, + "cameras_involved": sorted(activity_cameras[act]), + "event_ids": event_ids, + "cross_camera": len(activity_cameras[act]) >= 2, + "key_frames": cluster_details, # Issue 10: per-instance details + }) + return candidates + + + +# ============================================================================ +# Candidate Scoring +# ============================================================================ + +def _score_candidate(cand: Dict) -> float: + """ + Score a candidate – higher is better. + + Prefers: + - Counts in the [3, 10] sweet-spot + - Cross-camera occurrence + - entity_counting slightly preferred (scene-level understanding) + """ + score = 0.0 + cnt = cand["correct_count"] + + # Sweet-spot bonus + if SWEET_LOW <= cnt <= SWEET_HIGH: + score += 3.0 + elif MIN_COUNT <= cnt < SWEET_LOW: + score += 1.5 + else: + score += 0.5 + + # Cross-camera bonus + if cand["cross_camera"]: + score += 2.0 + + # Subtype: activity_counting is the only subtype + score += 0.5 + + return score + + +# ============================================================================ +# Question Text Templates +# ============================================================================ + +def _make_question_text(cand: Dict) -> str: + """Return the natural-language question string for a candidate.""" + subtype = cand["subtype"] + + if subtype == "activity_counting": + act_gerund = humanize_activity_gerund(cand["activity"]) + act_lower = act_gerund[0].lower() + act_gerund[1:] # lowercase first letter + return ( + f"How many times does someone perform the action of " + f"{act_lower} across all cameras in this slot?" + ) + + return "How many?" + + +# ============================================================================ +# Difficulty Classification +# ============================================================================ + +def _classify_difficulty(cand: Dict) -> str: + """ + easy : count ≤ 5 and ≤ 3 cameras + medium : otherwise + """ + cnt = cand["correct_count"] + n_cams = len(cand["cameras_involved"]) + if cnt <= 5 and n_cams <= 3: + return "easy" + return "medium" + + +# ============================================================================ +# Issue 9: Deterministic Reasoning Generation +# ============================================================================ + +def _build_reasoning(cand: Dict) -> str: + """ + Build deterministic reasoning text for a counting question. + + Uses the deduped cluster details to produce structured reasoning with + per-camera breakdown and the correct count. This reasoning is passed + to naturalize.py so GPT only polishes grammar, not numbers. + + Example: "The action of opening a vehicle door was observed 8 times + across cameras G299 and G330: G299 contributed 5 occurrences and G330 + contributed 3 occurrences (0 cross-camera duplicates were removed)." + """ + activity = humanize_activity(cand.get("activity", "unknown")) + correct = cand["correct_count"] + cameras = cand["cameras_involved"] + key_frames = cand.get("key_frames", []) + + # Per-camera breakdown from key_frames + cam_counts: Dict[str, int] = defaultdict(int) + for kf in key_frames: + cam_counts[kf["camera"]] += 1 + + # Count cross-camera dedup removals + total_raw_events = len(cand.get("event_ids", [])) + dedup_removed = total_raw_events - correct + + if len(cameras) == 1: + breakdown = f"all on camera {cameras[0]}" + else: + cam_parts = [f"{cam}: {cam_counts.get(cam, 0)}" for cam in sorted(cam_counts.keys())] + breakdown = ", ".join(cam_parts) + + reasoning = ( + f"The action of {activity} was observed {correct} time{'s' if correct != 1 else ''} " + f"across {len(cameras)} camera{'s' if len(cameras) != 1 else ''} " + f"({breakdown})" + ) + if dedup_removed > 0: + reasoning += f". {dedup_removed} cross-camera duplicate{'s' if dedup_removed != 1 else ''} removed" + reasoning += "." + + return reasoning + + +# ============================================================================ +# Public API +# ============================================================================ + +def generate_numerical_qa( + sg: SceneGraph, + resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, + count: int = 1, + verbose: bool = False, +) -> List[Dict]: + """ + Generate numerical/counting questions for a multi-camera slot. + + Args: + sg: Scene graph (events, entities, cameras, slot). + resolved: Resolved cross-camera entity graph. + entity_descs: entity_id → human-readable description string. + rng: Seeded RNG for reproducibility. + count: Target number of questions (default 1). + verbose: Print debug info. + + Returns: + List of QA dicts in the standard V8 format. + """ + if not sg.events: + if verbose: + print(" Numerical: no events – skipping") + return [] + + # ------------------------------------------------------------------ + # 1. Collect all candidates from three subtypes + # ------------------------------------------------------------------ + all_candidates: List[Dict] = [] + all_candidates.extend(_activity_counting_candidates(sg, resolved)) + + if not all_candidates: + if verbose: + print(" Numerical: no valid candidates (counts out of range)") + return [] + + # ------------------------------------------------------------------ + # 2. Score and sort + # ------------------------------------------------------------------ + for c in all_candidates: + c["_score"] = _score_candidate(c) + all_candidates.sort(key=lambda c: c["_score"], reverse=True) + + if verbose: + print(f" Numerical: {len(all_candidates)} activity_counting candidates") + + # ------------------------------------------------------------------ + # 3. Diversified selection: no two Qs with same subtype or same activity + # ------------------------------------------------------------------ + used_subtypes: Set[str] = set() + used_activities: Set[str] = set() + selected: List[Dict] = [] + + for cand in all_candidates: + if len(selected) >= count: + break + + sub = cand["subtype"] + act = cand.get("activity") + + # Diversity: skip if we already used this subtype + if sub in used_subtypes: + continue + # Diversity: skip if we already asked about this activity + if act and act in used_activities: + continue + + used_subtypes.add(sub) + if act: + used_activities.add(act) + selected.append(cand) + + # If we still need more, relax the subtype constraint (keep activity unique) + if len(selected) < count: + for cand in all_candidates: + if len(selected) >= count: + break + if cand in selected: + continue + act = cand.get("activity") + if act and act in used_activities: + continue + if act: + used_activities.add(act) + selected.append(cand) + + # ------------------------------------------------------------------ + # 4. Build QA dicts + # ------------------------------------------------------------------ + qa_pairs: List[Dict] = [] + + for idx, cand in enumerate(selected[:count]): + correct = cand["correct_count"] + options, correct_idx = _build_options(correct, rng) + question = _make_question_text(cand) + difficulty = _classify_difficulty(cand) + + all_cameras = sorted(sg.cameras.keys()) + requires_cams = cand["cameras_involved"] if cand["cameras_involved"] else all_cameras + + verification: Dict[str, Any] = { + "question_type": cand["subtype"], + "correct_count": correct, + "cameras_involved": cand["cameras_involved"], + } + if cand.get("activity"): + verification["activity"] = cand["activity"] + if cand.get("event_ids"): + verification["event_ids"] = cand["event_ids"] + if cand.get("cluster_ids"): + verification["cluster_ids"] = cand["cluster_ids"] + # Issue 10: Key frames for visual verification + if cand.get("key_frames"): + verification["key_frames"] = cand["key_frames"] + + debug_info: Dict[str, Any] = { + "subtype": cand["subtype"], + "correct_count": correct, + "cameras_involved": cand["cameras_involved"], + "cross_camera": cand["cross_camera"], + "candidate_score": round(cand["_score"], 2), + "num_candidates_total": len(all_candidates), + "slot": sg.slot, + } + if cand.get("activity"): + debug_info["activity"] = cand["activity"] + debug_info["activity_human"] = humanize_activity(cand["activity"]) + + # Collect clip_files from the events referenced by this candidate + event_map = {e.event_id: e for e in sg.events} + clip_files = set() + for eid in cand.get("event_ids", []): + evt = event_map.get(eid) + if evt and evt.video_file: + cf = evt.video_file.replace(".avi", ".mp4") + clip_files.add(cf) + if clip_files: + debug_info["clip_files"] = sorted(clip_files) + + # Issue 9: Build deterministic reasoning (not GPT-generated) + reasoning = _build_reasoning(cand) + + qa = { + "question_id": f"v8_numerical_{idx + 1:03d}", + "category": "numerical", + "difficulty": difficulty, + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "reasoning": reasoning, # Issue 9: pre-built, GPT polishes only grammar + "requires_cameras": requires_cams, + "requires_multi_camera": len(requires_cams) > 1, + "verification": verification, + "debug_info": debug_info, + } + qa_pairs.append(qa) + + if verbose: + subtypes = Counter(q["debug_info"]["subtype"] for q in qa_pairs) + print(f" Numerical: {len(qa_pairs)} questions " + f"({', '.join(f'{s}={n}' for s, n in subtypes.items())})") + + return qa_pairs diff --git a/meva/scripts/v10/generate_perception.py b/meva/scripts/v10/generate_perception.py new file mode 100644 index 0000000..a38374e --- /dev/null +++ b/meva/scripts/v10/generate_perception.py @@ -0,0 +1,514 @@ +""" +V8 generate_perception.py — Perception questions with MEVID attribute verification. + +V8 CHANGES from V7: +- New subtype: attribute_verification ("What is the person wearing?") +- Entity descriptions from MEVID (GPT/YOLO) instead of actor ID aliases +- Attribute verification requires MEVID color data (not generic fallback) +- 4 question types total (V7 had 3) + +Types: +1. "Which camera captures a {activity} event?" (which_camera) — V7 unchanged +2. "What activity is occurring on camera {cam}?" (activity_identification) — V7 unchanged +3. "{activity} is occurring. Which cameras capture this?" (multi_camera_confirmation) — V7 unchanged +4. NEW: "What is the person on camera {cam} wearing?" (attribute_verification) — V8 +""" + +import random +from typing import Any, Dict, List, Set +from collections import defaultdict + +from .parse_annotations import Event +from .build_scene_graph import SceneGraph +from .entity_resolution import ResolvedGraph +from .person_descriptions import ( + load_person_database, get_person_description, get_person_short_label, + get_mevid_persons_with_cameras, enrich_entities, +) +from .distractor_bank import get_distractors, get_camera_distractors +from .activity_hierarchy import humanize_activity, humanize_activity_gerund + + +# ============================================================================ +# V8 NEW: Attribute Distractor Colors +# ============================================================================ + +UPPER_COLORS = ["black", "white", "blue", "red", "green", "gray", "yellow", + "brown", "orange", "purple", "navy", "beige", "khaki"] +LOWER_COLORS = ["black", "blue", "dark", "gray", "brown", "khaki", "white", + "green", "red", "beige", "navy"] +CARRIED_OBJECTS = ["backpack", "bag", "purse", "briefcase", "water bottle", + "umbrella", "phone", "laptop bag"] + + +def _build_appearance_options(person_data: Dict, rng: random.Random) -> Dict: + """ + Build MCQ options for attribute verification. + + Returns dict with: + - question_text: what we ask about + - options: list of 4 strings + - correct_answer_index: int + - attribute_type: "upper_color" | "lower_color" | "carried_object" + """ + upper = person_data.get("primary_upper_color", "unknown") + lower = person_data.get("primary_lower_color", "unknown") + objects = person_data.get("all_carried_objects", []) + + # Pick the best attribute to ask about (prefer colors with actual data) + candidates = [] + + if upper != "unknown": + candidates.append(("upper_color", upper, + "upper body clothing", + UPPER_COLORS)) + if lower != "unknown": + candidates.append(("lower_color", lower, + "lower body clothing", + LOWER_COLORS)) + # Objects are rarer — only use if we have colors too + if objects and len(candidates) >= 1: + candidates.append(("carried_object", objects[0], + "carried item", + CARRIED_OBJECTS)) + + if not candidates: + return None + + attr_type, correct_val, label, distractor_pool = rng.choice(candidates) + + # Build distractors + dist_pool = [c for c in distractor_pool if c.lower() != correct_val.lower()] + rng.shuffle(dist_pool) + distractors = dist_pool[:3] + + if len(distractors) < 3: + return None # Not enough distractors + + options = [correct_val.capitalize()] + [d.capitalize() for d in distractors] + rng.shuffle(options) + correct_idx = next(i for i, o in enumerate(options) + if o.lower() == correct_val.lower()) + + return { + "attribute_type": attr_type, + "correct_value": correct_val, + "label": label, + "options": options, + "correct_answer_index": correct_idx, + } + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_perception_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 3, + verbose: bool = False) -> List[Dict]: + """ + Generate perception questions with MEVID attribute verification. + + V8: Takes entity_descs parameter. Adds attribute_verification subtype. + Target: 1 of each type if possible, up to `count` total. + """ + all_cameras = sorted(sg.cameras.keys()) + slot_activities = set(e.activity for e in sg.events) + + # Build activity/camera mappings (same as V7) + activity_cameras: Dict[str, Set[str]] = defaultdict(set) + activity_events: Dict[str, List[Event]] = defaultdict(list) + for evt in sg.events: + activity_cameras[evt.activity].add(evt.camera_id) + activity_events[evt.activity].append(evt) + + camera_activities: Dict[str, Set[str]] = defaultdict(set) + camera_events: Dict[str, List[Event]] = defaultdict(list) + for evt in sg.events: + camera_activities[evt.camera_id].add(evt.activity) + camera_events[evt.camera_id].append(evt) + + qa_pairs = [] + used_activities = set() + used_cameras = set() + + # ------------------------------------------------------------------ + # Type 1: "Which camera captures X?" (1 question) + # Issue 7: Enrich with entity description for specificity. + # Filter: only valid if the described person performs the activity + # on exactly one camera (otherwise ambiguous without camera ID). + # ------------------------------------------------------------------ + type1_pool = [ + (act, cams) for act, cams in activity_cameras.items() + if 1 <= len(cams) <= 3 and act not in used_activities + ] + rng.shuffle(type1_pool) + + for act, correct_cams in type1_pool: + if len(qa_pairs) >= 1: + break + if act in used_activities: + continue + + # Issue 7: Find the best entity description for this activity + # Group actors performing this activity by their visual description + desc_to_cameras: Dict[str, Set[str]] = defaultdict(set) + desc_to_event: Dict[str, Event] = {} + + for evt in activity_events[act]: + for actor in evt.actors: + eid = f"{evt.camera_id}_actor_{actor['actor_id']}" + desc = entity_descs.get(eid, "") + # Skip generic/empty descriptions + if not desc or desc in ("a person", "a vehicle", "someone", "a person walking"): + continue + desc_to_cameras[desc].add(evt.camera_id) + if desc not in desc_to_event: + desc_to_event[desc] = evt + + # Find a description that is unique to one camera for this activity + best_desc = None + best_cam = None + best_event = None + for desc, cams_with_desc in desc_to_cameras.items(): + if len(cams_with_desc) == 1: + best_desc = desc + best_cam = next(iter(cams_with_desc)) + best_event = desc_to_event[desc] + break + + # Fall back to original generic approach if no unique description found + if best_desc is None: + correct_cam = sorted(correct_cams)[0] + gerund_act = humanize_activity_gerund(act) + gerund_lower = gerund_act[0].lower() + gerund_act[1:] + question = f"Which camera captures a person {gerund_lower}?" + else: + correct_cam = best_cam + gerund_act = humanize_activity_gerund(act) + gerund_lower = gerund_act[0].lower() + gerund_act[1:] + # Issue 7: Include visual description for specificity + question = f"Which camera captures {best_desc} {gerund_lower}?" + + # Exclude ALL cameras that have the same activity from distractors + # to avoid multi-correct ambiguity + exclude_cams = sorted(correct_cams) + distractors = get_camera_distractors(exclude_cams, all_cameras, rng, n=3) + + if len(distractors) < 2: + continue + + options = [f"Camera {correct_cam}"] + [f"Camera {d}" for d in distractors[:3]] + rng.shuffle(options) + correct_idx = options.index(f"Camera {correct_cam}") + + human_act = humanize_activity(act) + + rep_event = best_event or (activity_events[act][0] if activity_events[act] else None) + + debug_info = { + "question_type": "which_camera", + "activity": act, + "activity_alias": human_act, + "correct_camera": correct_cam, + "cameras_with_activity": sorted(correct_cams), + "entity_description": best_desc, + "description_unique": best_desc is not None, + } + if rep_event: + debug_info["representative_event"] = { + "camera": rep_event.camera_id, + "frame_range": [rep_event.start_frame, rep_event.end_frame], + "timestamp": f"{rep_event.start_sec:.2f}-{rep_event.end_sec:.2f}s", + "clip_file": rep_event.video_file.replace(".avi", ".mp4"), + } + + qa = { + "question_id": f"v8_perception_{len(qa_pairs)+1:03d}", + "category": "perception", + "difficulty": "easy", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted(correct_cams), + "requires_multi_camera": len(correct_cams) > 1, + "verification": { + "question_type": "which_camera", + "activity": act, + "activity_alias": human_act, + "cameras_with_activity": sorted(correct_cams), + "total_cameras_in_slot": len(all_cameras), + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + used_activities.add(act) + + # ------------------------------------------------------------------ + # Type 4 (V8 NEW): Attribute Verification — "What is the person wearing?" + # ------------------------------------------------------------------ + person_cameras = get_mevid_persons_with_cameras(sg.slot) + + if person_cameras: + # Pick a person with color data + db = load_person_database() + persons = db.get("persons", {}) + + attr_candidates = [] + for pid, cameras in person_cameras.items(): + if pid not in persons: + continue + pdata = persons[pid] + upper = pdata.get("primary_upper_color", "unknown") + lower = pdata.get("primary_lower_color", "unknown") + if upper == "unknown" and lower == "unknown": + continue + + # Only use cameras that are in our slot + slot_cams = [c for c in cameras if c in sg.cameras] + if slot_cams: + attr_candidates.append((pid, pdata, slot_cams)) + + rng.shuffle(attr_candidates) + + for pid, pdata, slot_cams in attr_candidates: + if len(qa_pairs) >= count: + break + + cam = rng.choice(slot_cams) + attr_opts = _build_appearance_options(pdata, rng) + + if attr_opts is None: + continue + + desc = get_person_description(pid) + gpt_desc = pdata.get("gpt_description", "") + + # V10: Build a contextual question that identifies WHICH person + # Find an activity this person performs on this camera + person_activity = None + for evt in sg.events: + if evt.camera_id == cam: + for actor in evt.actors: + # Try to match MEVID person to event actor + eid = f"{cam}_actor_{actor['actor_id']}" + entity = sg.entities.get(eid) + if entity and hasattr(entity, '_mevid_person_id') and entity._mevid_person_id == pid: + person_activity = evt.activity + break + if person_activity: + break + + # Build contextual person identifier + if person_activity: + act_gerund = humanize_activity_gerund(person_activity) + person_context = f"A person {act_gerund.lower()} is visible on camera {cam}." + else: + # Use partial description to identify without revealing answer + lower_color = pdata.get("primary_lower_color", "unknown") + if attr_opts["attribute_type"] == "upper_color" and lower_color != "unknown": + person_context = f"A person wearing {lower_color} pants is visible on camera {cam}." + else: + upper_color = pdata.get("primary_upper_color", "unknown") + if attr_opts["attribute_type"] == "lower_color" and upper_color != "unknown": + person_context = f"A person in a {upper_color} top is visible on camera {cam}." + else: + person_context = f"A person is visible on camera {cam}." + + # Frame the question: show the person on a camera, ask about attribute + if attr_opts["attribute_type"] == "upper_color": + question = ( + f"{person_context} " + f"What color is their upper body clothing?" + ) + elif attr_opts["attribute_type"] == "lower_color": + question = ( + f"A person is visible on camera {cam}. " + f"What color are they wearing on their lower body?" + ) + else: # carried_object + question = ( + f"A person is visible on camera {cam}. " + f"What object are they carrying?" + ) + + debug_info = { + "question_type": "attribute_verification", + "mevid_person_id": pid, + "camera": cam, + "person_description": desc, + "gpt_description": gpt_desc, + "attribute_type": attr_opts["attribute_type"], + "correct_value": attr_opts["correct_value"], + "all_cameras_for_person": sorted(slot_cams), + "source": "MEVID YOLO+GPT person database", + } + + qa = { + "question_id": f"v8_perception_{len(qa_pairs)+1:03d}", + "category": "perception", + "subcategory": "attribute_verification", + "difficulty": "medium", + "question_template": question, + "options": attr_opts["options"], + "correct_answer_index": attr_opts["correct_answer_index"], + "correct_answer": attr_opts["options"][attr_opts["correct_answer_index"]], + "requires_cameras": [cam], + "requires_multi_camera": False, + "verification": { + "question_type": "attribute_verification", + "mevid_person_id": pid, + "attribute_type": attr_opts["attribute_type"], + "correct_value": attr_opts["correct_value"], + "camera": cam, + "person_description": desc, + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + used_cameras.add(cam) + + # ------------------------------------------------------------------ + # Type 2: "What activity is occurring on camera X?" (V7 logic) + # ------------------------------------------------------------------ + type2_pool = [ + (cam, acts) for cam, acts in camera_activities.items() + if len(acts) >= 2 and cam not in used_cameras + ] + rng.shuffle(type2_pool) + + for cam, correct_acts in type2_pool: + if len(qa_pairs) >= count: + break + if cam in used_cameras: + continue + + correct_act = rng.choice(sorted(correct_acts)) + distractors = get_distractors(correct_act, slot_activities, rng, n=3) + + if len(distractors) < 2: + continue + + human_correct = humanize_activity(correct_act) + human_distractors = [humanize_activity(d) for d in distractors[:3]] + + options = [human_correct] + human_distractors + rng.shuffle(options) + correct_idx = options.index(human_correct) + + question = f"What activity is occurring on camera {cam}?" + + cam_evts = [e for e in camera_events[cam] if e.activity == correct_act] + rep_event = cam_evts[0] if cam_evts else None + + debug_info = { + "question_type": "activity_identification", + "camera": cam, + "correct_activity": correct_act, + "correct_activity_alias": human_correct, + "all_activities_on_camera": sorted(correct_acts), + } + if rep_event: + debug_info["representative_event"] = { + "camera": rep_event.camera_id, + "frame_range": [rep_event.start_frame, rep_event.end_frame], + "timestamp": f"{rep_event.start_sec:.2f}-{rep_event.end_sec:.2f}s", + "clip_file": rep_event.video_file.replace(".avi", ".mp4"), + } + + qa = { + "question_id": f"v8_perception_{len(qa_pairs)+1:03d}", + "category": "perception", + "difficulty": "easy", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": [cam], + "requires_multi_camera": False, + "verification": { + "question_type": "activity_identification", + "camera": cam, + "correct_activity": correct_act, + "correct_activity_alias": human_correct, + "all_activities_on_camera": sorted(correct_acts), + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + used_cameras.add(cam) + + # ------------------------------------------------------------------ + # Type 3: Multi-camera confirmation (V7 logic) + # ------------------------------------------------------------------ + if len(qa_pairs) < count: + multi_cam_acts = [ + (act, cams) for act, cams in activity_cameras.items() + if len(cams) >= 2 and act not in used_activities + ] + rng.shuffle(multi_cam_acts) + + for act, correct_cams in multi_cam_acts: + if len(qa_pairs) >= count: + break + + sorted_cams = sorted(correct_cams) + human_act = humanize_activity(act) + gerund_act = humanize_activity_gerund(act) + + if len(sorted_cams) >= 2: + option_both = f"Both Camera {sorted_cams[0]} and Camera {sorted_cams[1]}" + option_a_only = f"Camera {sorted_cams[0]} only" + option_b_only = f"Camera {sorted_cams[1]} only" + option_neither = "Neither" + + options = [option_both, option_a_only, option_b_only, option_neither] + correct_idx = 0 + + question = f"{gerund_act} is occurring. Which cameras capture this activity?" + + debug_info = { + "question_type": "multi_camera_confirmation", + "activity": act, + "activity_alias": human_act, + "cameras_with_activity": sorted_cams, + } + for ci, cam_id in enumerate(sorted_cams[:2]): + cam_evts = [e for e in activity_events[act] if e.camera_id == cam_id] + if cam_evts: + evt = cam_evts[0] + debug_info[f"camera_{ci+1}_event"] = { + "camera": evt.camera_id, + "frame_range": [evt.start_frame, evt.end_frame], + "timestamp": f"{evt.start_sec:.2f}-{evt.end_sec:.2f}s", + "clip_file": evt.video_file.replace(".avi", ".mp4"), + } + + qa = { + "question_id": f"v8_perception_{len(qa_pairs)+1:03d}", + "category": "perception", + "difficulty": "easy", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted_cams[:2], + "requires_multi_camera": True, + "verification": { + "question_type": "multi_camera_confirmation", + "activity": act, + "activity_alias": human_act, + "cameras_with_activity": sorted_cams, + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + used_activities.add(act) + + if verbose: + print(f" Perception: {len(qa_pairs)} questions generated " + f"(incl. {sum(1 for q in qa_pairs if q.get('subcategory') == 'attribute_verification')} attribute verification)") + + return qa_pairs[:count] diff --git a/meva/scripts/v10/generate_scene_summary.py b/meva/scripts/v10/generate_scene_summary.py new file mode 100644 index 0000000..79384f1 --- /dev/null +++ b/meva/scripts/v10/generate_scene_summary.py @@ -0,0 +1,280 @@ +""" +V8 generate_scene_summary.py — Scene-level summary questions. + +NEW in V8: Questions that test holistic understanding of the entire +multi-camera scene. Requires aggregating information across all cameras +and understanding the overall activity pattern. + +Generates summaries from annotation density: activity types, counts, +temporal flow, and cross-camera patterns. +""" + +import random +from typing import Any, Dict, List, Set +from collections import defaultdict, Counter + +from .parse_annotations import Event +from .build_scene_graph import SceneGraph +from .entity_resolution import ResolvedGraph +from .activity_hierarchy import humanize_activity, humanize_activity_gerund + + +# ============================================================================ +# Scene Analysis +# ============================================================================ + +def _analyze_scene(sg: SceneGraph) -> Dict: + """ + Compute scene-level statistics for summary question generation. + """ + events = sg.events + if not events: + return {} + + # Activity frequency + activity_counts = Counter(e.activity for e in events) + + # Camera activity density + camera_event_counts = Counter(e.camera_id for e in events) + + # Temporal span + min_sec = min(e.start_sec for e in events) + max_sec = max(e.end_sec for e in events) + duration = max_sec - min_sec + + # Activity categories + person_activities = [e for e in events if e.activity.startswith("person_")] + vehicle_activities = [e for e in events if e.activity.startswith("vehicle_")] + + # Cross-camera activities (same activity on 2+ cameras) + activity_cameras: Dict[str, Set[str]] = defaultdict(set) + for e in events: + activity_cameras[e.activity].add(e.camera_id) + cross_camera_acts = {act: cams for act, cams in activity_cameras.items() + if len(cams) >= 2} + + # Most active camera + busiest_camera = camera_event_counts.most_common(1)[0] if camera_event_counts else (None, 0) + + # Unique entity count (person entities across all cameras) + unique_entities = len(sg.entities) + person_entities = sum(1 for e in sg.entities.values() if e.entity_type == "person") + + # Dominant activity pattern + top_3_activities = activity_counts.most_common(3) + + return { + "total_events": len(events), + "unique_activities": len(activity_counts), + "activity_counts": dict(activity_counts), + "camera_event_counts": dict(camera_event_counts), + "num_cameras": len(sg.cameras), + "duration_sec": round(duration, 1), + "person_event_count": len(person_activities), + "vehicle_event_count": len(vehicle_activities), + "cross_camera_activities": {a: sorted(c) for a, c in cross_camera_acts.items()}, + "busiest_camera": busiest_camera[0], + "busiest_camera_count": busiest_camera[1], + "unique_entities": unique_entities, + "person_entities": person_entities, + "top_3_activities": top_3_activities, + } + + +def _build_scene_description(analysis: Dict) -> str: + """Build a natural-language scene description from analysis.""" + parts = [] + + n_cams = analysis.get("num_cameras", 0) + n_events = analysis.get("total_events", 0) + n_acts = analysis.get("unique_activities", 0) + + parts.append(f"The scene spans {n_cams} cameras with {n_events} total activity events") + + top_3 = analysis.get("top_3_activities", []) + if top_3: + top_descs = [] + for act, count in top_3: + human = humanize_activity(act) + top_descs.append(f"{human} ({count} occurrences)") + parts.append(f"The most frequent activities are: {', '.join(top_descs)}") + + cross_cam = analysis.get("cross_camera_activities", {}) + if cross_cam: + n_cross = len(cross_cam) + parts.append(f"{n_cross} activities occur on multiple cameras") + + busiest = analysis.get("busiest_camera") + if busiest: + parts.append(f"Camera {busiest} is the most active with {analysis['busiest_camera_count']} events") + + return ". ".join(parts) + "." + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_scene_summary_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 1, + verbose: bool = False) -> List[Dict]: + """ + Generate scene-level summary questions. + + Types: + 1. "Which best describes the overall scene?" (scene characterization) + 2. "Which camera is most active?" (activity density) + 3. "What is the dominant activity type?" (activity distribution) + """ + analysis = _analyze_scene(sg) + + if not analysis or analysis.get("total_events", 0) < 5: + if verbose: + print(" Scene Summary: Too few events for summary questions") + return [] + + qa_pairs = [] + + # Type 1: Scene characterization + # "Which description best matches the overall scene?" + description = _build_scene_description(analysis) + + top_3 = analysis.get("top_3_activities", []) + n_events = analysis.get("total_events", 0) + n_cams = analysis.get("num_cameras", 0) + n_person = analysis.get("person_event_count", 0) + n_vehicle = analysis.get("vehicle_event_count", 0) + + # Build correct answer based on dominant activity pattern + if n_person > n_vehicle * 2: + scene_type = "pedestrian-dominant" + elif n_vehicle > n_person * 2: + scene_type = "vehicle-dominant" + else: + scene_type = "mixed activity" + + _cam_word = 'camera' if n_cams == 1 else 'cameras' + + if top_3: + top_act = humanize_activity_gerund(top_3[0][0]).lower() + correct_desc = ( + f"A {scene_type} scene across {n_cams} {_cam_word}, " + f"primarily featuring {top_act}" + ) + else: + correct_desc = f"A {scene_type} scene across {n_cams} {_cam_word}" + + # Generate plausible but wrong descriptions + wrong_descs = [] + if scene_type == "pedestrian-dominant": + wrong_descs.append( + f"A vehicle-focused scene with mostly parking and driving activity" + ) + else: + wrong_descs.append( + f"A scene dominated by people entering and exiting buildings" + ) + + _empty_cam_count = max(1, n_cams - 3) + wrong_descs.append( + f"An empty scene with minimal activity, captured on {_empty_cam_count} {'camera' if _empty_cam_count == 1 else 'cameras'}" + ) + wrong_descs.append( + f"A single-camera scene showing only indoor activities" + ) + + options = [correct_desc] + wrong_descs[:3] + rng.shuffle(options) + correct_idx = options.index(correct_desc) + + question = ( + f"Considering all {n_cams} {_cam_word} in this slot, " + f"which description best characterizes the overall scene?" + ) + + # Collect all clip_files across all cameras + all_clip_files = sorted(set( + e.video_file.replace(".avi", ".mp4") + for e in sg.events if e.video_file + )) + + debug_info = { + "question_type": "scene_characterization", + "scene_analysis": analysis, + "scene_description": description, + "scene_type": scene_type, + "clip_files": all_clip_files, + } + + qa = { + "question_id": f"v8_summary_{len(qa_pairs)+1:03d}", + "category": "scene_summary", + "difficulty": "hard", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted(sg.cameras.keys()), + "requires_multi_camera": True, + "verification": { + "question_type": "scene_characterization", + "total_events": n_events, + "num_cameras": n_cams, + "person_events": n_person, + "vehicle_events": n_vehicle, + "scene_type": scene_type, + "top_activity": top_3[0][0] if top_3 else None, + "top_activity_count": top_3[0][1] if top_3 else 0, + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + + # Type 2: Busiest camera (if count > 1) + if count > 1 and analysis.get("busiest_camera"): + busiest = analysis["busiest_camera"] + busiest_count = analysis["busiest_camera_count"] + + other_cameras = [c for c in sg.cameras.keys() if c != busiest] + distractors = rng.sample(other_cameras, min(3, len(other_cameras))) + + options = [busiest] + distractors + rng.shuffle(options) + correct_idx = options.index(busiest) + + question = ( + f"Across all cameras in this scene, which camera captures " + f"the most activity events?" + ) + + qa2 = { + "question_id": f"v8_summary_{len(qa_pairs)+1:03d}", + "category": "scene_summary", + "difficulty": "hard", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted(sg.cameras.keys()), + "requires_multi_camera": True, + "verification": { + "question_type": "busiest_camera", + "correct_camera": busiest, + "event_count": busiest_count, + "all_camera_counts": analysis["camera_event_counts"], + }, + "debug_info": { + "question_type": "busiest_camera", + "camera_event_counts": analysis["camera_event_counts"], + "clip_files": all_clip_files, + }, + } + qa_pairs.append(qa2) + + if verbose: + print(f" Scene Summary: {len(qa_pairs)} questions generated " + f"(scene_type={analysis.get('scene_type', scene_type)}, " + f"{n_events} events, {n_cams} cameras)") + + return qa_pairs[:count] diff --git a/meva/scripts/v10/generate_spatial.py b/meva/scripts/v10/generate_spatial.py new file mode 100644 index 0000000..2dd6375 --- /dev/null +++ b/meva/scripts/v10/generate_spatial.py @@ -0,0 +1,677 @@ +""" +generate_spatial.py — Closest-approach spatial questions. + +Asks: "How close do two people come to each other in this scene?" + +Instead of a single snapshot distance, computes the MINIMUM distance +(closest approach) over the overlapping time window when both entities +are visible on the same camera. This handles moving actors correctly. + +Answer options: close together / moderate distance / far apart / cross paths +Distance buckets: <=5m = near, 5-15m = moderate, >15m = far +"Cross paths" = entities whose minimum distance is <=2m (they physically +pass through the same location at overlapping times). +""" + +import random +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple + +import numpy as np + +from .parse_annotations import Event, find_clips_for_slot, DEFAULT_FRAMERATE +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .activity_hierarchy import humanize_activity, humanize_activity_gerund +from .utils.krtd import ( + load_camera_model, CameraModel, + classify_proximity, INDOOR_CAMERAS, +) +from .utils.yaml_stream import stream_geom_records + +DEFAULT_FPS = 30.0 +FRAME_WIDTH = 1920 +FRAME_HEIGHT = 1080 +FRAME_EDGE_MARGIN = 10 # pixels + +# Sampling: sample every N frames for distance computation +SAMPLE_EVERY = 15 # ~0.5 second at 30fps (finer than 30 to catch brief encounters) + +# Cross-paths threshold: if minimum distance <= this, entities "cross paths" +CROSS_PATHS_THRESHOLD_M = 2.0 + +# Same-person dedup thresholds +SAME_PERSON_MIN_DIST_M = 0.5 # closest approach < this = likely same person +SAME_PERSON_BBOX_IOU = 0.3 # bbox IoU above this = same person +SAME_PERSON_TEMPORAL_OVERLAP = 0.5 # frame overlap ratio threshold + +# Maximum distance to consider (skip absurd projections) +MAX_REASONABLE_DISTANCE_M = 500.0 + + +def _compute_bbox_iou(bbox_a: List[int], bbox_b: List[int]) -> float: + """Compute Intersection over Union of two bounding boxes [x1, y1, x2, y2].""" + if not bbox_a or not bbox_b or len(bbox_a) < 4 or len(bbox_b) < 4: + return 0.0 + x1 = max(bbox_a[0], bbox_b[0]) + y1 = max(bbox_a[1], bbox_b[1]) + x2 = min(bbox_a[2], bbox_b[2]) + y2 = min(bbox_a[3], bbox_b[3]) + inter = max(0, x2 - x1) * max(0, y2 - y1) + if inter == 0: + return 0.0 + area_a = (bbox_a[2] - bbox_a[0]) * (bbox_a[3] - bbox_a[1]) + area_b = (bbox_b[2] - bbox_b[0]) * (bbox_b[3] - bbox_b[1]) + union = area_a + area_b - inter + return inter / union if union > 0 else 0.0 + + +def _is_bbox_clipping_frame(bbox: List[int], margin: int = FRAME_EDGE_MARGIN) -> bool: + """Check if a bounding box is clipping the frame edge.""" + if not bbox or len(bbox) < 4: + return False + x1, y1, x2, y2 = bbox[:4] + return (x1 <= margin or y1 <= margin or + x2 >= FRAME_WIDTH - margin or y2 >= FRAME_HEIGHT - margin) + + +def _disambiguate_description(desc: str, entity: Entity, sg: SceneGraph, + other_desc: str) -> str: + """Add disambiguating context when two entities share the same description.""" + primary_activity = None + for evt in sg.events: + if evt.camera_id == entity.camera_id: + for actor in evt.actors: + if actor["actor_id"] == entity.actor_id: + primary_activity = evt.activity + break + if primary_activity: + break + if primary_activity: + act_gerund = humanize_activity_gerund(primary_activity) + return f"{desc} ({act_gerund.lower()})" + return desc + + +# ============================================================================ +# Geom-based closest approach computation +# ============================================================================ + +def _load_geom_bboxes_for_actors(geom_path: Path, + actor_ids: Set[int], + sample_every: int = SAMPLE_EVERY + ) -> Dict[int, Dict[int, List[int]]]: + """Load sampled bounding boxes for specific actors from geom.yml. + + Returns {actor_id: {frame: [x1,y1,x2,y2], ...}, ...} + Samples every `sample_every` frames for efficiency. + """ + result: Dict[int, Dict[int, List[int]]] = defaultdict(dict) + for rec in stream_geom_records(geom_path): + aid = rec['id1'] + if aid not in actor_ids: + continue + frame = rec['ts0'] + if frame % sample_every != 0: + continue + result[aid][frame] = rec['g0'] + return dict(result) + + +def _compute_closest_approach( + bboxes_a: Dict[int, List[int]], + bboxes_b: Dict[int, List[int]], + camera_model: CameraModel, +) -> Tuple[Optional[float], Optional[int], Optional[List[int]], Optional[List[int]], + Optional[List[Tuple[int, float, List[int], List[int]]]]]: + """Compute closest approach between two entities on the same camera. + + Finds the frame where the 3D distance between the two entities is minimized + across their overlapping time window. + + Returns: (min_distance_m, closest_frame, bbox_a_at_frame, bbox_b_at_frame, + distance_trajectory) + distance_trajectory is a list of (frame, distance_m, bbox_a, bbox_b) tuples + in chronological order, used for cross-paths verification. + Returns (None, None, None, None, None) if no valid overlap. + """ + # Collect all frame pairs (exact or nearest-neighbor matched) + frames_a = set(bboxes_a.keys()) + frames_b = set(bboxes_b.keys()) + common_frames = sorted(frames_a & frames_b) + + frame_pairs: List[Tuple[int, int]] = [] + if common_frames: + frame_pairs = [(f, f) for f in common_frames] + else: + # No exact frame matches - try nearest-neighbor matching + tolerance = 2 * SAMPLE_EVERY + sorted_b = sorted(frames_b) + for fa in sorted(frames_a): + best_fb = None + best_dist = tolerance + 1 + for fb in sorted_b: + d = abs(fa - fb) + if d < best_dist: + best_dist = d + best_fb = fb + if fb > fa + tolerance: + break + if best_fb is not None and best_dist <= tolerance: + frame_pairs.append((fa, best_fb)) + + if not frame_pairs: + return None, None, None, None, None + + min_dist = float('inf') + closest_frame = None + closest_bbox_a = None + closest_bbox_b = None + trajectory: List[Tuple[int, float, List[int], List[int]]] = [] + + for fa, fb in sorted(frame_pairs): + ba = bboxes_a[fa] + bb = bboxes_b[fb] + if _is_bbox_clipping_frame(ba) or _is_bbox_clipping_frame(bb): + continue + pos_a = camera_model.bbox_foot_to_world(ba) + pos_b = camera_model.bbox_foot_to_world(bb) + if pos_a is None or pos_b is None: + continue + dist = float(np.linalg.norm(pos_a - pos_b)) + if dist > MAX_REASONABLE_DISTANCE_M: + continue + trajectory.append((fa, dist, ba, bb)) + if dist < min_dist: + min_dist = dist + closest_frame = fa + closest_bbox_a = ba + closest_bbox_b = bb + + if closest_frame is None: + return None, None, None, None, None + return min_dist, closest_frame, closest_bbox_a, closest_bbox_b, trajectory + + +# Threshold for "far enough" that the entities are clearly separated +# before/after the close encounter (used for cross-paths verification) +_CROSS_PATHS_FAR_THRESHOLD_M = 5.0 + + +def _is_true_crossing( + trajectory: List[Tuple[int, float, List[int], List[int]]], + min_dist: float, +) -> bool: + """Verify that a cross-paths encounter is a true crossing: + entities approach from separate positions, pass very close (<=2m), + and separate again — with their bbox horizontal centers swapping + relative position (A was left of B, then A is right of B, or vice versa). + + Requirements: + 1. min_dist <= CROSS_PATHS_THRESHOLD_M (already checked by caller) + 2. Distance goes from >5m to <=2m and back to >5m (far→close→far) + 3. Bbox horizontal centers swap relative order across the encounter + """ + if not trajectory or len(trajectory) < 3: + return False + + # Find the index of the closest approach + min_idx = min(range(len(trajectory)), key=lambda i: trajectory[i][1]) + + # Check far→close: is there a frame BEFORE the closest approach where + # distance > _CROSS_PATHS_FAR_THRESHOLD_M? + far_before = any(trajectory[i][1] > _CROSS_PATHS_FAR_THRESHOLD_M + for i in range(min_idx)) + # Check close→far: is there a frame AFTER the closest approach where + # distance > _CROSS_PATHS_FAR_THRESHOLD_M? + far_after = any(trajectory[i][1] > _CROSS_PATHS_FAR_THRESHOLD_M + for i in range(min_idx + 1, len(trajectory))) + + if not (far_before and far_after): + return False + + # Check bbox horizontal center swap: + # Before the encounter, A is left/right of B. + # After the encounter, A should be on the opposite side. + def _bbox_center_x(bbox): + return (bbox[0] + bbox[2]) / 2.0 + + # Use the first frame where distance > far threshold (before encounter) + pre_frame = None + for i in range(min_idx): + if trajectory[i][1] > _CROSS_PATHS_FAR_THRESHOLD_M: + pre_frame = trajectory[i] + # Use the last frame where distance > far threshold (after encounter) + post_frame = None + for i in range(len(trajectory) - 1, min_idx, -1): + if trajectory[i][1] > _CROSS_PATHS_FAR_THRESHOLD_M: + post_frame = trajectory[i] + + if pre_frame is None or post_frame is None: + return False + + # Compare horizontal positions: did A and B swap sides? + pre_diff = _bbox_center_x(pre_frame[2]) - _bbox_center_x(pre_frame[3]) # A_x - B_x + post_diff = _bbox_center_x(post_frame[2]) - _bbox_center_x(post_frame[3]) # A_x - B_x + + # Sign change = they swapped horizontal position + return (pre_diff > 0) != (post_diff > 0) + + +# ============================================================================ +# Candidate Finding (closest-approach version) +# ============================================================================ + +def _find_spatial_candidates(sg: SceneGraph, verbose: bool = False) -> List[Dict]: + """Find entity pairs with valid closest-approach distances. + + For each pair of person entities on the same camera: + 1. Load sampled bboxes from geom.yml + 2. Compute closest approach distance over co-visible frames + 3. Record minimum distance, proximity, and reference frame + """ + # Load camera models (skip indoor) + camera_models: Dict[str, CameraModel] = {} + for cam_id in sg.cameras: + if cam_id in INDOOR_CAMERAS: + continue + model = load_camera_model(cam_id) + if model is not None: + camera_models[cam_id] = model + + if verbose: + print(f" Spatial: {len(camera_models)} cameras with KRTD models") + + if not camera_models: + return [] + + # Find geom.yml paths per camera + clips = find_clips_for_slot(sg.slot) + geom_paths: Dict[str, Path] = {} + for clip in clips: + cam = clip["camera_id"] + if cam not in camera_models: + continue + act_path = Path(clip["activities_file"]) + geom_path = act_path.with_name( + act_path.name.replace(".activities.yml", ".geom.yml") + ) + if geom_path.exists() and geom_path.stat().st_size > 100: + geom_paths[cam] = geom_path + + if verbose: + print(f" Spatial: {len(geom_paths)} cameras with geom.yml files") + + # Group person entities by camera + cam_entities: Dict[str, List[Entity]] = defaultdict(list) + for eid, ent in sg.entities.items(): + if ent.entity_type != "person": + continue + if ent.camera_id in camera_models and ent.camera_id in geom_paths: + cam_entities[ent.camera_id].append(ent) + + candidates = [] + + for cam_id, entities in cam_entities.items(): + if len(entities) < 2: + continue + + model = camera_models[cam_id] + geom_path = geom_paths[cam_id] + + # Collect actor IDs we need + actor_ids = {ent.actor_id for ent in entities} + + # Load sampled bboxes for all actors on this camera + all_bboxes = _load_geom_bboxes_for_actors(geom_path, actor_ids) + + if verbose: + print(f" Camera {cam_id}: {len(entities)} entities, " + f"{len(all_bboxes)} with geom bboxes") + + # Compare all pairs + for i in range(len(entities)): + for j in range(i + 1, len(entities)): + ent_a = entities[i] + ent_b = entities[j] + + bboxes_a = all_bboxes.get(ent_a.actor_id, {}) + bboxes_b = all_bboxes.get(ent_b.actor_id, {}) + + if not bboxes_a or not bboxes_b: + continue + + # Check temporal overlap + overlap_start = max(ent_a.first_frame, ent_b.first_frame) + overlap_end = min(ent_a.last_frame, ent_b.last_frame) + if overlap_end <= overlap_start: + continue # no temporal overlap + + # Compute closest approach + trajectory + result = _compute_closest_approach( + bboxes_a, bboxes_b, model + ) + min_dist, closest_frame, bbox_a, bbox_b, trajectory = result + + if min_dist is None: + continue + + # Classify proximity + proximity = classify_proximity(min_dist) + + # Check if entities truly cross paths: + # min_dist <= 2m AND far→close→far AND bbox positions swap + crosses_paths = ( + min_dist <= CROSS_PATHS_THRESHOLD_M + and trajectory is not None + and _is_true_crossing(trajectory, min_dist) + ) + + candidates.append({ + "entity_a": ent_a.entity_id, + "entity_b": ent_b.entity_id, + "camera": cam_id, + "min_distance_m": round(min_dist, 2), + "closest_frame": closest_frame, + "bbox_a": bbox_a, + "bbox_b": bbox_b, + "proximity": proximity, + "crosses_paths": crosses_paths, + "overlap_frames": (overlap_start, overlap_end), + "entity_a_obj": ent_a, + "entity_b_obj": ent_b, + }) + + return candidates + + +# ============================================================================ +# Same-person filtering +# ============================================================================ + +def _is_likely_same_person(cand: Dict, resolved: ResolvedGraph) -> bool: + """Check if two entities are likely the same person.""" + eid_a = cand["entity_a"] + eid_b = cand["entity_b"] + + # Check 1: Same entity cluster + for cluster in resolved.entity_clusters: + if eid_a in cluster.entities and eid_b in cluster.entities: + return True + + # Check 2: Bbox IoU at closest frame + bbox_iou = _compute_bbox_iou(cand["bbox_a"], cand["bbox_b"]) + if bbox_iou > SAME_PERSON_BBOX_IOU: + return True + + # Check 3: Very close proximity with high temporal overlap + if cand["min_distance_m"] < SAME_PERSON_MIN_DIST_M: + ent_a = cand["entity_a_obj"] + ent_b = cand["entity_b_obj"] + start_a, end_a = ent_a.first_frame, ent_a.last_frame + start_b, end_b = ent_b.first_frame, ent_b.last_frame + overlap_start = max(start_a, start_b) + overlap_end = min(end_a, end_b) + if overlap_end > overlap_start: + duration_a = max(end_a - start_a, 1) + duration_b = max(end_b - start_b, 1) + overlap_ratio = (overlap_end - overlap_start) / min(duration_a, duration_b) + if overlap_ratio > SAME_PERSON_TEMPORAL_OVERLAP: + return True + + return False + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_spatial_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 3, + verbose: bool = False, + fallback_eids: Optional[Set[str]] = None) -> List[Dict]: + """ + Generate spatial closest-approach questions. + + Asks how close two people come to each other in the scene. + Uses minimum distance over co-visible time window (not a single snapshot). + """ + candidates = _find_spatial_candidates(sg, verbose) + + if verbose: + print(f" Spatial: {len(candidates)} candidate pairs") + + if not candidates: + return [] + + # Filter same-person pairs + before = len(candidates) + candidates = [c for c in candidates if not _is_likely_same_person(c, resolved)] + if verbose and before != len(candidates): + print(f" Filtered {before - len(candidates)} same-person pairs " + f"-> {len(candidates)} remaining") + + if not candidates: + return [] + + # Filter out pairs with fallback (non-visual) descriptions + if fallback_eids: + before = len(candidates) + candidates = [ + c for c in candidates + if c["entity_a"] not in fallback_eids and c["entity_b"] not in fallback_eids + ] + if verbose and before != len(candidates): + print(f" Filtered {before - len(candidates)} fallback-description pairs") + + # Try to disambiguate identical descriptions + filtered = [] + for c in candidates: + desc_a = entity_descs.get(c["entity_a"], "") + desc_b = entity_descs.get(c["entity_b"], "") + if desc_a and desc_b and desc_a == desc_b: + ent_a = c["entity_a_obj"] + ent_b = c["entity_b_obj"] + new_a = _disambiguate_description(desc_a, ent_a, sg, desc_b) + new_b = _disambiguate_description(desc_b, ent_b, sg, desc_a) + if new_a != new_b: + c["disambiguated_a"] = new_a + c["disambiguated_b"] = new_b + filtered.append(c) + elif verbose: + print(f" Skipping: cannot disambiguate '{desc_a}'") + continue + filtered.append(c) + candidates = filtered + + if not candidates: + return [] + + # Sort by distance diversity - pick from each proximity bucket + near = [c for c in candidates if c["proximity"] == "near"] + moderate = [c for c in candidates if c["proximity"] == "moderate"] + far = [c for c in candidates if c["proximity"] == "far"] + cross = [c for c in candidates if c["crosses_paths"]] + + rng.shuffle(near) + rng.shuffle(moderate) + rng.shuffle(far) + rng.shuffle(cross) + + # Dedup: track (desc_a, desc_b, camera) tuples + def _dedup_key(c): + da = c.get("disambiguated_a") or entity_descs.get(c["entity_a"], "") + db = c.get("disambiguated_b") or entity_descs.get(c["entity_b"], "") + cam = c["camera"] + pair = tuple(sorted([da, db])) + return (pair, cam) + + seen_keys = set() + def _try_add(c, selected_list): + key = _dedup_key(c) + if key in seen_keys: + return False + seen_keys.add(key) + selected_list.append(c) + return True + + selected = [] + + # Priority: cross-paths first (most interesting), then one from each bucket + for c in cross: + if len(selected) >= count: + break + _try_add(c, selected) + + for bucket in [near, moderate, far]: + if len(selected) >= count: + break + for b in bucket: + if _try_add(b, selected): + break + + # Fill remaining + remaining = near + moderate + far + rng.shuffle(remaining) + for c in remaining: + if len(selected) >= count: + break + _try_add(c, selected) + + # Generate questions + qa_pairs = [] + + for idx, cand in enumerate(selected[:count]): + ent_a = cand["entity_a_obj"] + ent_b = cand["entity_b_obj"] + proximity = cand["proximity"] + min_dist = cand["min_distance_m"] + crosses = cand["crosses_paths"] + + desc_a = cand.get("disambiguated_a") or entity_descs.get(cand["entity_a"], "a person") + desc_b = cand.get("disambiguated_b") or entity_descs.get(cand["entity_b"], "a person") + + # Skip if descriptions are still identical + if desc_a == desc_b: + if verbose: + print(f" Skipping: identical descriptions '{desc_a}'") + continue + + question = ( + f"How do {desc_a} and {desc_b} move relative to each other in the scene?" + ) + + # Build options — A/D are clearly distinct: + # A = they end up near each other (approach/converge) + # D = they walk past each other and swap positions (true crossing) + options = [ + "They approach and stay near each other (within a few meters)", + "They stay at a moderate distance (5-15 meters apart)", + "They remain far apart (more than 15 meters)", + "They walk past each other, swapping positions (cross paths)", + ] + + if crosses: + correct_idx = 3 # true crossing: far→close→far with position swap + elif proximity == "near": + correct_idx = 0 + elif proximity == "moderate": + correct_idx = 1 + else: + correct_idx = 2 + + # Build reasoning + if crosses: + reasoning = ( + f"{desc_a.capitalize()} and {desc_b} come within " + f"{min_dist:.1f} meters of each other, crossing paths " + f"during the scene." + ) + else: + proximity_text = { + "near": "close together", + "moderate": "at a moderate distance", + "far": "far apart", + } + reasoning = ( + f"The closest {desc_a} and {desc_b} come to each other is " + f"approximately {min_dist:.1f} meters, placing them " + f"{proximity_text.get(proximity, proximity)}." + ) + + # Find clip_file for the camera + def _entity_clip_file(entity_id): + ent = sg.entities.get(entity_id) + if not ent: + return "" + event_map = {e.event_id: e for e in sg.events} + for evid in ent.events: + evt = event_map.get(evid) + if evt and evt.video_file: + return evt.video_file.replace(".avi", ".mp4") + return "" + + clip_file = _entity_clip_file(cand["entity_a"]) + if not clip_file: + clip_file = _entity_clip_file(cand["entity_b"]) + + overlap_start, overlap_end = cand["overlap_frames"] + + debug_info = { + "entity_a": { + "entity_id": cand["entity_a"], + "camera": cand["camera"], + "description": desc_a, + "bbox_at_closest": cand["bbox_a"], + "timestamp": f"{ent_a.first_sec:.2f}-{ent_a.last_sec:.2f}s", + }, + "entity_b": { + "entity_id": cand["entity_b"], + "camera": cand["camera"], + "description": desc_b, + "bbox_at_closest": cand["bbox_b"], + "timestamp": f"{ent_b.first_sec:.2f}-{ent_b.last_sec:.2f}s", + }, + "min_distance_meters": min_dist, + "closest_frame": cand["closest_frame"], + "proximity": proximity, + "crosses_paths": crosses, + "overlap_frames": f"{overlap_start}-{overlap_end}", + "projection_method": "krtd_bbox_foot_closest_approach", + "clip_file": clip_file, + } + if clip_file: + debug_info["clip_files"] = [clip_file] + + qa = { + "question_id": f"v10_spatial_{idx+1:03d}", + "category": "spatial", + "difficulty": "easy" if crosses or proximity == "near" else "medium", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "reasoning": reasoning, + "requires_cameras": [cand["camera"]], + "requires_multi_camera": False, + "verification": { + "entity_a": cand["entity_a"], + "entity_b": cand["entity_b"], + "entity_a_desc": desc_a, + "entity_b_desc": desc_b, + "min_distance_meters": min_dist, + "closest_frame": cand["closest_frame"], + "proximity": proximity, + "crosses_paths": crosses, + "projection_method": "krtd_bbox_foot_closest_approach", + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + + if verbose: + print(f" Spatial: {len(qa_pairs)} questions generated") + + return qa_pairs diff --git a/meva/scripts/v10/generate_temporal.py b/meva/scripts/v10/generate_temporal.py new file mode 100644 index 0000000..c3021a3 --- /dev/null +++ b/meva/scripts/v10/generate_temporal.py @@ -0,0 +1,818 @@ +""" +V10 generate_temporal.py — Multi-camera temporal cross-camera questions. + +V10 CHANGES from V8: +- REMOVED entity-cluster linkage as scoring factor — + deliberately pairs unrelated events across different cameras so VLMs + can't "cheat" by inferring causal/narrative answers. +- Scoring now driven by CAMERA PROXIMITY: adjacent cameras at the same + site get highest priority (events *require* multi-camera reasoning). +- Added "What happened before/after X?" question format (alongside + "which occurred first?") to match ego-exo4d/agibot breadth. +- Uses new camera_proximity utility for indoor-aware spatial reasoning, + including cameras without KRTD (admin, school hallways, bus indoor). +- Connection type metadata preserved for debug but not used for selection. +""" + +import random +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple + +import numpy as np + +from .parse_annotations import Event, find_clips_for_slot +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .person_descriptions import enrich_entities, get_mevid_persons_with_cameras +from .distractor_bank import get_distractors +from .activity_hierarchy import ( + are_related, get_relationship, get_relationship_strength, humanize_activity, + get_activity_entity_type, +) +from .utils.mevid import find_mevid_persons_for_slot +from .utils.krtd import load_camera_model, CameraModel, INDOOR_CAMERAS +from .utils.yaml_stream import get_bbox_at_frame + +# Camera proximity (new V10) — graceful degradation +try: + from .utils.camera_proximity import ( + score_camera_pair_for_temporal, get_proximity_tier, + ) + _HAS_PROXIMITY = True +except ImportError: + _HAS_PROXIMITY = False + +# Scene context (optional — graceful degradation) +try: + from .scene_context import get_scene_context, enrich_description_with_location + _HAS_SCENE_CONTEXT = True +except ImportError: + _HAS_SCENE_CONTEXT = False + +# ============================================================================ +# Constants +# ============================================================================ + +MIN_GAP = 1.0 +MAX_GAP = 10.0 +FALLBACK_MAX_GAP = 15.0 +DEFAULT_FPS = 30.0 +# Cross-camera duplicate detection: if two events have same activity, +# 3D positions within this distance AND time within this window, +# they are likely the same real-world event seen by different cameras. +CROSS_CAM_DEDUP_DISTANCE_M = 5.0 # meters +CROSS_CAM_DEDUP_TIME_SEC = 8.0 # seconds +# Frame boundary margin for bbox-in-frame validation +FRAME_WIDTH = 1920 +FRAME_HEIGHT = 1080 +FRAME_EDGE_MARGIN = 10 # pixels from edge to consider "clipping" + + +# ============================================================================ +# Cross-Camera Event Deduplication +# ============================================================================ + +def _is_bbox_clipping_frame(bbox: List[int], margin: int = FRAME_EDGE_MARGIN) -> bool: + """Check if a bounding box is clipping the frame edge (entity likely out of view).""" + if not bbox or len(bbox) < 4: + return False + x1, y1, x2, y2 = bbox[:4] + return (x1 <= margin or y1 <= margin or + x2 >= FRAME_WIDTH - margin or y2 >= FRAME_HEIGHT - margin) + + +def _get_event_3d_position(event: Event, sg: SceneGraph) -> Optional[np.ndarray]: + """Get approximate 3D world position for an event using KRTD projection. + + Uses the first person actor's bbox at the event's mid-frame. + Returns ENU coordinates or None if projection fails. + """ + if event.camera_id in INDOOR_CAMERAS: + return None + model = load_camera_model(event.camera_id) + if model is None: + return None + + # Find bbox for first person actor + mid_frame = (event.start_frame + event.end_frame) // 2 + for eid, entity in sg.entities.items(): + if entity.camera_id != event.camera_id: + continue + for actor in event.actors: + if actor["actor_id"] == entity.actor_id and entity.entity_type == "person": + # Look for bbox near mid_frame + if entity.keyframe_bboxes: + closest = min(entity.keyframe_bboxes.keys(), + key=lambda f: abs(int(f) - mid_frame)) + if abs(int(closest) - mid_frame) <= 30: # within 1 second + bbox = entity.keyframe_bboxes[closest] + pos = model.bbox_foot_to_world(bbox) + return pos + return None + + +# Camera overlap detection for aggressive dedup on overlapping FOVs +try: + from .utils.camera_overlap import cameras_overlap as _cameras_overlap + _HAS_OVERLAP = True +except ImportError: + _HAS_OVERLAP = False + + +def _is_likely_duplicate_event(ea: Event, eb: Event, sg: SceneGraph) -> bool: + """Check if two cross-camera events are likely the same real-world event. + + Two events are duplicates if they: + 1. Have the same activity type + 2. Are temporally close (within CROSS_CAM_DEDUP_TIME_SEC) + 3. Have similar 3D positions (within CROSS_CAM_DEDUP_DISTANCE_M) + + For cameras with known FOV overlap, the time window is applied more + aggressively (any temporal proximity = likely duplicate). + """ + # Must be same activity for duplicate detection + if ea.activity != eb.activity: + return False + + # Must be on different cameras (same-camera = not duplicates) + if ea.camera_id == eb.camera_id: + return False + + # Check if cameras have known overlap + overlap = _HAS_OVERLAP and _cameras_overlap(ea.camera_id, eb.camera_id) + + # Check temporal proximity + time_gap = abs(ea.start_sec - eb.start_sec) + if overlap: + # Overlapping cameras: wider time window, skip 3D check + if time_gap <= CROSS_CAM_DEDUP_TIME_SEC: + return True + return False + + if time_gap > CROSS_CAM_DEDUP_TIME_SEC: + return False + + # If we can get 3D positions, check spatial proximity + pos_a = _get_event_3d_position(ea, sg) + pos_b = _get_event_3d_position(eb, sg) + + if pos_a is not None and pos_b is not None: + dist = float(np.linalg.norm(pos_a - pos_b)) + if dist < CROSS_CAM_DEDUP_DISTANCE_M: + return True # Same place, same time, same activity = duplicate + # If they're far apart, they're distinct events even with same activity + return False + + # Without 3D positions, use heuristic: same activity + close time = likely dup + # Be conservative — only flag if very close temporally + if time_gap <= 3.0: + return True + + return False + + +def _event_has_visible_bbox(event: Event, sg: SceneGraph) -> bool: + """Check if the event's primary actor has a bbox that is fully within the frame. + + Rejects actors whose bounding boxes clip the frame edge, since they + may not be visually identifiable from the camera angle. + """ + mid_frame = (event.start_frame + event.end_frame) // 2 + for eid, entity in sg.entities.items(): + if entity.camera_id != event.camera_id: + continue + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + if entity.keyframe_bboxes: + closest = min(entity.keyframe_bboxes.keys(), + key=lambda f: abs(int(f) - mid_frame)) + if abs(int(closest) - mid_frame) <= 30: + bbox = entity.keyframe_bboxes[closest] + if not _is_bbox_clipping_frame(bbox): + return True + return False # bbox clips edge + return True # No bbox data — assume visible (don't block) + + +# ============================================================================ +# Connection Scoring (V10: camera-proximity-driven, entity-cluster removed) +# ============================================================================ + +def _score_connection(event_a: Event, event_b: Event, + sg: SceneGraph, resolved: ResolvedGraph, + mevid_person_cameras: Dict[int, Set[str]]) -> Dict: + """Score connection strength between two cross-camera events. + + V10 philosophy: Score is driven by CAMERA PROXIMITY, not entity linkage. + Adjacent cameras at the same site produce the best multi-camera temporal + questions. Entity-cluster and activity-relationship info is recorded in + metadata for debugging but does NOT influence the score — we deliberately + want unrelated event pairs so VLMs can't shortcut the answer via causal + reasoning. + """ + score = 0.0 + connection_type = "unrelated" + connection_strength = "weak" + mevid_validated = False + mevid_person_id = None + relationship = None + cluster_id = None + proximity_tier = None + + # Primary scoring signal: camera spatial proximity + if _HAS_PROXIMITY: + proximity_score = score_camera_pair_for_temporal( + event_a.camera_id, event_b.camera_id + ) + proximity_tier = get_proximity_tier(event_a.camera_id, event_b.camera_id) + score += proximity_score + else: + # Fallback: any cross-camera pair on same site gets base score + score += 1.0 + + # Bonus: different activities = more interesting question (harder to guess) + if event_a.activity != event_b.activity: + score += 1.0 + + # Record (but do NOT score) entity cluster linkage — metadata only + for cluster_obj in resolved.entity_clusters: + a_entities = set() + b_entities = set() + for eid in cluster_obj.entities: + entity = sg.entities.get(eid) + if not entity: + continue + if entity.camera_id == event_a.camera_id: + for actor in event_a.actors: + if actor["actor_id"] == entity.actor_id: + a_entities.add(eid) + if entity.camera_id == event_b.camera_id: + for actor in event_b.actors: + if actor["actor_id"] == entity.actor_id: + b_entities.add(eid) + if a_entities and b_entities: + connection_type = "same_entity_cluster" + cluster_id = cluster_obj.cluster_id + break + + # Record (but do NOT score) activity relationships — metadata only + rel = get_relationship(event_a.activity, event_b.activity) + if rel: + relationship = rel + if connection_type == "unrelated": + connection_type = f"related_activities_{rel}" + + # Record MEVID validation — metadata only (no score bonus) + for pid, cameras in mevid_person_cameras.items(): + if event_a.camera_id in cameras and event_b.camera_id in cameras: + mevid_validated = True + mevid_person_id = pid + break + + # Derive connection_strength from proximity tier (for debug display) + if proximity_tier == "adjacent": + connection_strength = "strong" + elif proximity_tier == "same_site": + connection_strength = "medium" + else: + connection_strength = "weak" + + return { + "connection_type": connection_type, + "connection_strength": connection_strength, + "score": score, + "mevid_validated": mevid_validated, + "mevid_person_id": mevid_person_id, + "relationship": relationship, + "cluster_id": cluster_id, + "proximity_tier": proximity_tier, + } + + +def _build_debug_info(event: Event, sg: SceneGraph, + entity_descs: Dict[str, str]) -> Dict: + """Build debug info for one event, using MEVID descriptions.""" + clip_file = event.video_file + if clip_file.endswith(".avi"): + clip_file = clip_file.replace(".avi", ".mp4") + + desc = None + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + desc = entity_descs.get(eid, entity.alias) + break + if desc: + break + + return { + "camera": event.camera_id, + "activity": event.activity, + "actor_ids": [a["actor_id"] for a in event.actors], + "frame_range": [event.start_frame, event.end_frame], + "timestamp": f"{event.start_sec:.2f}-{event.end_sec:.2f}s", + "fps": DEFAULT_FPS, + "clip_file": clip_file, + "entity_description": desc, + } + + +def _get_event_description(event: Event, sg: SceneGraph, + entity_descs: Dict[str, str], + fallback_eids: Optional[Set[str]] = None) -> str: + """Get a human-readable description for an event, using MEVID if available. + + Prefers visual (non-fallback) descriptions. Falls back to 'someone {activity}' + if only fallback descriptions are available. + """ + from .person_descriptions import is_visual_description + short_act = humanize_activity(event.activity) + + # Try to get entity description — prefer visual descriptions + best_desc = None + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + desc = entity_descs.get(eid) + if desc: + if fallback_eids and eid in fallback_eids: + # Record fallback but keep searching for visual + if best_desc is None: + best_desc = desc + continue + # Found a visual description — use it + if short_act in desc: + return desc + return f"{desc} {short_act}" + + # If only fallback was found, use it (better than "someone") + if best_desc: + if short_act in best_desc: + return best_desc + return f"{best_desc} {short_act}" + + # Last resort: use any available description from entity_descs + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + desc = entity_descs.get(eid) + if desc and desc not in ("a person", "a vehicle", "someone"): + if short_act in desc: + return desc + return f"{desc} {short_act}" + + # Absolute fallback: use correct entity type (person vs vehicle) + entity_type = get_activity_entity_type(event.activity) + return f"a {entity_type} {short_act}" + + +def _enrich_with_location(desc: str, event: Event, sg: SceneGraph) -> str: + """Optionally append location context to an event description using 3D scene models. + + e.g. "a person in navy top opens vehicle door" → + "a person in navy top opens vehicle door near the school" + """ + if not _HAS_SCENE_CONTEXT: + return desc + # Extract site from slot name + parts = sg.slot.split(".") + if len(parts) < 3: + return desc + site = parts[2] + # Get entity bbox for 3D projection + cam_model = load_camera_model(event.camera_id) + if cam_model is None: + return desc + # Use midpoint frame bbox + mid_frame = int((event.start_frame + event.end_frame) / 2) + bbox = None + for actor in event.actors: + eid = f"{event.camera_id}_actor_{actor['actor_id']}" + entity = sg.entities.get(eid) + if entity and entity.keyframe_bboxes: + # Find nearest keyframe + nearest = min(entity.keyframe_bboxes.keys(), + key=lambda f: abs(f - mid_frame)) + bbox = entity.keyframe_bboxes[nearest] + break + if bbox is None: + return desc + point_3d = cam_model.bbox_foot_to_world(bbox) + if point_3d is None: + return desc + return enrich_description_with_location(desc, point_3d, site) + + +def _short_option_label(desc: str, activity: str) -> str: + """Build a short label for an option from the event description. + + E.g. 'a person in gray top entering through a structure' -> + 'The person in gray top entering through a structure' + """ + label = desc.strip() + # Capitalize first letter for option text + if label.startswith("a "): + label = "The " + label[2:] + elif label.startswith("someone "): + label = "Someone " + label[8:] + else: + label = label[0].upper() + label[1:] + return label + + +# ============================================================================ +# Uniqueness Gate +# ============================================================================ + +DESC_UNIQUENESS_LONG_EVENT_SEC = 30.0 # align with event_ordering first-instance threshold + + +def _build_uniqueness_index( + events: List[Event], sg: SceneGraph, entity_descs: Dict[str, str], + fallback_eids: Optional[Set[str]] = None, +) -> Dict[Tuple[str, str], int]: + """Build index of how many visually-indistinguishable entities share the + same (camera, activity). Returns {(camera_id, activity): count_of_entities} + where count > 1 means ambiguity. + + Two entities on the same camera doing the same activity are ambiguous if + their descriptions are identical (or both missing). + """ + # (camera, activity) -> set of descriptions seen + cam_act_descs: Dict[Tuple[str, str], Dict[str, int]] = {} + + for evt in events: + key = (evt.camera_id, evt.activity) + if key not in cam_act_descs: + cam_act_descs[key] = {} + for actor in evt.actors: + eid = f"{evt.camera_id}_actor_{actor['actor_id']}" + desc = entity_descs.get(eid, "") + if not desc or desc in ("a person", "a vehicle", "someone"): + desc = "__generic__" + cam_act_descs[key][desc] = cam_act_descs[key].get(desc, 0) + 1 + + # For each (cam, activity), record the max entity count sharing one description + ambiguity: Dict[Tuple[str, str], int] = {} + for key, desc_counts in cam_act_descs.items(): + ambiguity[key] = max(desc_counts.values()) if desc_counts else 0 + return ambiguity + + +def _event_is_unique( + event: Event, sg: SceneGraph, entity_descs: Dict[str, str], + uniqueness_index: Dict[Tuple[str, str], int], +) -> bool: + """Return True if this event's (camera, activity, description) combination + is unambiguous — no other entity on the same camera does the same activity + with an indistinguishable description.""" + key = (event.camera_id, event.activity) + return uniqueness_index.get(key, 0) <= 1 + + +# ============================================================================ +# Candidate Selection +# ============================================================================ + +def _find_temporal_candidates(events: List[Event], sg: SceneGraph, + resolved: ResolvedGraph, + mevid_person_cameras: Dict[int, Set[str]], + max_gap: float = MAX_GAP) -> List[Dict]: + """Find cross-camera event pairs within temporal gap constraints. + + V10 additions: + - Rejects pairs that are likely the same real-world event (cross-camera dedup) + - Rejects events whose actors have bbox clipping the frame edge + - First-instance filter only for long events (>30s), aligning with event_ordering + """ + # First-instance filter: only for long-duration events (>30s). + # Short discrete actions (<30s) may be genuinely different occurrences + # by different people, so they're preserved — the uniqueness gate + # handles ambiguity at selection time. + first_instance: Dict[Tuple[str, str], Event] = {} + short_events: List[Event] = [] + for evt in events: + if evt.duration_sec > DESC_UNIQUENESS_LONG_EVENT_SEC: + key = (evt.activity, evt.camera_id) + if key not in first_instance or evt.start_sec < first_instance[key].start_sec: + first_instance[key] = evt + else: + short_events.append(evt) + # Merge: first instances of long events + all short events + merged = list(first_instance.values()) + short_events + # Skip events in the first 5 seconds (camera stabilization period) + events = [e for e in merged if e.start_sec >= 5.0] + events.sort(key=lambda e: e.start_sec) + + candidates = [] + seen = set() + + for i, ea in enumerate(events): + for j in range(i + 1, len(events)): + eb = events[j] + if ea.camera_id == eb.camera_id: + continue + + gap = eb.start_sec - ea.end_sec + gap_rev = ea.start_sec - eb.end_sec + + first, second, actual_gap = None, None, None + + if gap >= MIN_GAP and gap <= max_gap: + first, second, actual_gap = ea, eb, gap + elif gap_rev >= MIN_GAP and gap_rev <= max_gap: + first, second, actual_gap = eb, ea, gap_rev + else: + continue + + key = (first.activity, first.camera_id, second.activity, second.camera_id) + if key in seen: + continue + seen.add(key) + + # V10: Cross-camera event dedup — reject duplicate events + if _is_likely_duplicate_event(first, second, sg): + continue + + # V10: Bbox-in-frame validation — reject actors clipping frame edge + if not _event_has_visible_bbox(first, sg) or not _event_has_visible_bbox(second, sg): + continue + + conn = _score_connection(first, second, sg, resolved, mevid_person_cameras) + candidates.append({ + "event_a": first, + "event_b": second, + "gap_sec": round(actual_gap, 2), + **conn, + }) + + candidates.sort(key=lambda c: (-c["score"], c["gap_sec"])) + return candidates + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def _event_has_visual_desc(event: Event, sg: SceneGraph, + fallback_eids: Set[str]) -> bool: + """Check if at least one actor in this event has a visual description.""" + for eid, entity in sg.entities.items(): + if entity.camera_id == event.camera_id: + for actor in event.actors: + if actor["actor_id"] == entity.actor_id: + if eid not in fallback_eids: + return True + return False + + +# ============================================================================ +# Question Format Builders (V10) +# ============================================================================ + +def _build_which_first_question( + desc_a: str, desc_b: str, short_a: str, short_b: str, + rng: random.Random, +) -> Tuple[str, List[str], int]: + """Build a 'which occurred first?' question (original V8 format). + + Returns (question_text, options_list, correct_answer_index). + event_a is always the one that occurred first. + """ + question = f"{desc_a} and {desc_b} -- which occurred first?" + options = [ + f"{short_a} occurred first", + f"{short_b} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + correct_idx = 0 + + if rng.random() < 0.5: + question = f"{desc_b} and {desc_a} -- which occurred first?" + options = [ + f"{short_b} occurred first", + f"{short_a} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + correct_idx = 1 + + return question, options, correct_idx + + +def generate_temporal_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False, + fallback_eids: Optional[Set[str]] = None) -> List[Dict]: + """ + Generate temporal cross-camera questions. + + V10 changes: + - Entity-cluster linkage removed from scoring — camera proximity drives + pair selection instead (adjacent cams at same site = best). + - Deliberately pairs unrelated events across cameras to prevent VLMs + from shortcutting via causal/narrative reasoning. + - All questions use "which occurred first?" format. + - Uniqueness gate: rejects pairs where the activity+description is + ambiguous on that camera (prevents indistinguishable-entity confusion). + - Same_area relaxation: 2-camera sites (admin) allowed since sparse. + - Max gap 10s (FALLBACK_MAX_GAP 15s). + """ + slot_cameras = list(sg.cameras.keys()) + mevid_person_cameras = find_mevid_persons_for_slot(sg.slot, slot_cameras) + + # Build uniqueness index for the gate + uniqueness_index = _build_uniqueness_index( + sg.events, sg, entity_descs, fallback_eids + ) + + candidates = _find_temporal_candidates( + sg.events, sg, resolved, mevid_person_cameras, MAX_GAP + ) + + if len(candidates) < count: + candidates = _find_temporal_candidates( + sg.events, sg, resolved, mevid_person_cameras, FALLBACK_MAX_GAP + ) + + if verbose: + print(f" Temporal: {len(candidates)} candidate pairs") + + if not candidates: + return [] + + # Filter out candidates where EITHER event uses fallback (non-visual) descriptions + if fallback_eids: + visual_candidates = [ + c for c in candidates + if _event_has_visual_desc(c["event_a"], sg, fallback_eids) + and _event_has_visual_desc(c["event_b"], sg, fallback_eids) + ] + if verbose and len(visual_candidates) < len(candidates): + print(f" Filtered {len(candidates) - len(visual_candidates)} " + f"fallback-only pairs → {len(visual_candidates)} remaining") + candidates = visual_candidates + + # Uniqueness gate: reject pairs where either event's (cam, activity) + # has multiple entities with indistinguishable descriptions + pre_uniq = len(candidates) + candidates = [ + c for c in candidates + if _event_is_unique(c["event_a"], sg, entity_descs, uniqueness_index) + and _event_is_unique(c["event_b"], sg, entity_descs, uniqueness_index) + ] + if verbose and len(candidates) < pre_uniq: + print(f" Uniqueness gate: {pre_uniq - len(candidates)} ambiguous " + f"pairs rejected → {len(candidates)} remaining") + + if not candidates: + return [] + + # Determine if this site has only 2 cameras (sparse site like admin) + # If so, allow same_area pairs as fallback since there's no alternative + n_cameras = len(slot_cameras) + allow_same_area = (n_cameras <= 2) + + # ---------------------------------------------------------------- + # Selection: camera-proximity-based, single pass (no tiered passes) + # Prefer: adjacent cameras > same-site > same-area, diverse activities + # ---------------------------------------------------------------- + used_event_ids: Set[str] = set() + used_activity_names: Set[str] = set() + selected = [] + + for c in candidates: + if len(selected) >= count: + break + ea_id = c["event_a"].event_id + eb_id = c["event_b"].event_id + if ea_id in used_event_ids or eb_id in used_event_ids: + continue + if (c["event_a"].activity in used_activity_names + or c["event_b"].activity in used_activity_names): + continue + # Skip same_area pairs unless sparse site (≤2 cameras) + if _HAS_PROXIMITY and not allow_same_area: + tier = get_proximity_tier(c["event_a"].camera_id, c["event_b"].camera_id) + if tier == "same_area": + continue + selected.append(c) + used_event_ids.add(ea_id) + used_event_ids.add(eb_id) + used_activity_names.add(c["event_a"].activity) + used_activity_names.add(c["event_b"].activity) + + # If still short, relax the activity-uniqueness constraint + if len(selected) < count: + for c in candidates: + if len(selected) >= count: + break + if c in selected: + continue + ea_id = c["event_a"].event_id + eb_id = c["event_b"].event_id + if ea_id in used_event_ids or eb_id in used_event_ids: + continue + if _HAS_PROXIMITY and not allow_same_area: + tier = get_proximity_tier(c["event_a"].camera_id, c["event_b"].camera_id) + if tier == "same_area": + continue + selected.append(c) + used_event_ids.add(ea_id) + used_event_ids.add(eb_id) + + # ---------------------------------------------------------------- + # Generate QA pairs — always "which occurred first?" format + # ---------------------------------------------------------------- + qa_pairs = [] + + for idx, cand in enumerate(selected[:count]): + ea = cand["event_a"] + eb = cand["event_b"] + gap = cand["gap_sec"] + + desc_a = _get_event_description(ea, sg, entity_descs, fallback_eids) + desc_b = _get_event_description(eb, sg, entity_descs, fallback_eids) + + # Enrich with spatial location context (e.g. "near the school") + desc_a = _enrich_with_location(desc_a, ea, sg) + desc_b = _enrich_with_location(desc_b, eb, sg) + + # If descriptions identical after enrichment, skip (can't distinguish) + if desc_a == desc_b: + if verbose: + print(f" Skipping temporal pair: identical descriptions '{desc_a}'") + continue + + short_a = _short_option_label(desc_a, ea.activity) + short_b = _short_option_label(desc_b, eb.activity) + + q_format = "which_first" + question, options, correct_idx = _build_which_first_question( + desc_a, desc_b, short_a, short_b, rng + ) + + debug_info = { + "event_a": _build_debug_info(ea, sg, entity_descs), + "event_b": _build_debug_info(eb, sg, entity_descs), + "gap_sec": gap, + "connection_type": cand["connection_type"], + "connection_strength": cand["connection_strength"], + "connection_score": cand["score"], + "relationship": cand["relationship"], + "cluster_id": cand.get("cluster_id"), + "proximity_tier": cand.get("proximity_tier"), + "mevid_validated": cand["mevid_validated"], + "mevid_person_id": cand["mevid_person_id"], + "question_format": "which_first", + } + + qa = { + "question_id": f"v10_temporal_{idx+1:03d}", + "category": "temporal", + "difficulty": "easy", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted(set([ea.camera_id, eb.camera_id])), + "requires_multi_camera": True, + "verification": { + "event_a": { + "activity": ea.activity, + "camera": ea.camera_id, + "start_sec": ea.start_sec, + "end_sec": ea.end_sec, + "actor_ids": [a["actor_id"] for a in ea.actors], + "description": desc_a, + }, + "event_b": { + "activity": eb.activity, + "camera": eb.camera_id, + "start_sec": eb.start_sec, + "end_sec": eb.end_sec, + "actor_ids": [a["actor_id"] for a in eb.actors], + "description": desc_b, + }, + "gap_sec": gap, + "entity_link": "mevid_validated" if cand["mevid_validated"] else "heuristic", + "same_person": cand["connection_type"] == "same_entity_cluster", + }, + "debug_info": debug_info, + } + qa_pairs.append(qa) + + if verbose: + prox_counts = {} + for q in qa_pairs: + tier = q["debug_info"].get("proximity_tier", "unknown") + prox_counts[tier] = prox_counts.get(tier, 0) + 1 + print(f" Temporal: {len(qa_pairs)} questions " + f"(proximity: {prox_counts})") + + return qa_pairs diff --git a/meva/scripts/v10/naturalize.py b/meva/scripts/v10/naturalize.py new file mode 100644 index 0000000..fed0c5e --- /dev/null +++ b/meva/scripts/v10/naturalize.py @@ -0,0 +1,1220 @@ +#!/usr/bin/env python3 +""" +Naturalize — Pre-processes and naturalizes template QA pairs via GPT. + +Clean 3-stage architecture: + Stage 1 — Structural pre-processing (Python, FREE): + - Reconstruct questions from structured verification data + - Description simplification: "blue upper body garment" -> "blue top" + - Camera reference removal from temporal/spatial question text + - MEVA ontology vocabulary normalization + - NO grammar fixes — all language rewriting delegated to GPT + + Stage 2 — Language rewrite (GPT, 1 API call per question): + - Receives plain text question + context (never raw JSON) + - Handles ALL grammar, phrasing, style variation, article agreement + - Returns rewritten question + reasoning sentence + - Options are frozen (never sent to GPT for rewriting) + + Stage 3 — JSON assembly (Python): + - Inserts GPT's returned text back into QA structure + - Saves output file + GPT log + +Modes: + --preprocess-only: Just run Stage 1 (free, instant) + --dry-run: Show what would be sent to GPT + (default): Full pipeline (Stage 1 + 2 + 3) + +Usage: + python3 -m meva.scripts.v10.naturalize --input data/qa_pairs/SLOT.final.raw.json + python3 -m meva.scripts.v10.naturalize --input data/qa_pairs/SLOT.final.raw.json --preprocess-only + python3 -m meva.scripts.v10.naturalize --input data/qa_pairs/SLOT.final.raw.json --dry-run +""" + +import json +import re +import time +import argparse +import os +import sys +from pathlib import Path +from typing import List, Dict, Any, Optional, Tuple + +# ============================================================================ +# Paths & Constants +# ============================================================================ + +# User output directory — override with MEVA_OUTPUT_DIR env var +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) + +QA_DIR = _OUTPUT / "qa_pairs" +LOG_DIR = _OUTPUT / "gpt_logs" + +DEFAULT_MODEL = "gpt-4o-mini" +DEFAULT_TEMPERATURE = 0.3 # Low: naturalize only, no creative drift +MAX_RETRIES = 3 +RETRY_DELAY = 2.0 +CLIP_DURATION = 300.0 # 5-minute clips + + +# ============================================================================ +# Description Simplification +# ============================================================================ + +# Garment type simplifications +_GARMENT_SUBS = [ + (r"upper body garment with a hood", "hoodie"), + (r"upper body garment", "top"), + (r"lower body clothing", "pants"), + (r"lower body garment", "pants"), + (r"lower body pants", "pants"), + (r"lower body shorts", "shorts"), + (r"hooded jacket", "hoodie"), +] + +# Phrasing cleanups +_PHRASING_SUBS = [ + (r",\s*and they are\s+", ", "), + (r",\s*and is\s+", ", "), + (r"\.\s*The person appears to be [^\.]+\.?", ""), + (r",?\s*and they may be carrying personal belongings", ""), + (r",?\s*and they appear to have [^,\.]+", ""), + (r"\.\s*Their hair appears short[^\.]*\.?", ""), +] + +# Posture/action context to strip (not useful for identification) +_STRIP_PATTERNS = [ + r",?\s*sitting on a chair[^,\.]*", + r",?\s*with their back turned[^,\.]*", + r",?\s*while ascending a staircase[^,\.]*", + r",?\s*while holding a clipboard or some papers", + r",?\s*sitting\b[^,\.]*", + r",?\s*items on the table[^,\.]*", + r",?\s*appears to be looking[^,\.]*", +] + + +def simplify_description(desc: str) -> str: + """ + Simplify verbose GPT descriptions into natural short form. + + "wearing a blue upper body garment and blue lower body clothing, + with a black hoodie featuring a graphic design on the back." + -> + "wearing a blue top and blue pants, with a black hoodie featuring + a graphic design on the back" + """ + if not desc: + return desc + + desc = desc.rstrip(". ") + + for pattern, replacement in _GARMENT_SUBS: + desc = re.sub(pattern, replacement, desc, flags=re.IGNORECASE) + + for pattern, replacement in _PHRASING_SUBS: + desc = re.sub(pattern, replacement, desc, flags=re.IGNORECASE) + + for pattern in _STRIP_PATTERNS: + desc = re.sub(pattern, "", desc, flags=re.IGNORECASE) + + # Clean up orphaned commas / double spaces + desc = re.sub(r",\s*,", ",", desc) + desc = re.sub(r"\s{2,}", " ", desc) + desc = desc.strip(", ") + desc = desc.rstrip(".") + + return desc + + +# ============================================================================ +# Activity Humanization +# ============================================================================ + +_GERUND_MAP = { + "opens": "opening", "closes": "closing", "enters": "entering", + "exits": "exiting", "reads": "reading", "carries": "carrying", + "picks": "picking", "puts": "putting", "sets": "setting", + "rides": "riding", "loads": "loading", "unloads": "unloading", + "talks": "talking", "stands": "standing", "walks": "walking", + "runs": "running", "sits": "sitting", "texts": "texting", + "pulls": "pulling", "pushes": "pushing", "interacts": "interacting", + "drops": "dropping", "embraces": "embracing", "uses": "using", + "makes": "making", "steals": "stealing", "starts": "starting", + "stops": "stopping", "turns": "turning", "transfers": "transferring", + "reverses": "reversing", "abandons": "abandoning", + "leaves": "leaving", "purchases": "purchasing", +} + + +def _conjugate_gerund(verb: str) -> str: + """Smart fallback: conjugate an unknown verb to its -ing form.""" + if verb in _GERUND_MAP: + return _GERUND_MAP[verb] + if verb.endswith("es"): + base = verb[:-2] + elif verb.endswith("s") and not verb.endswith("ss"): + base = verb[:-1] + else: + base = verb + if base.endswith("ie"): + return base[:-2] + "ying" + if base.endswith("ee"): + return base + "ing" + if base.endswith("e"): + return base[:-1] + "ing" + if (len(base) >= 3 + and base[-1] not in "aeiouwxy" + and base[-2] in "aeiou" + and base[-3] not in "aeiou"): + return base + base[-1] + "ing" + return base + "ing" + +_NO_ARTICLE = frozenset({ + "up", "down", "on", "off", "out", "in", "to", "from", + "through", "with", "around", "right", "left", "a", + "an", "the", "into", "onto", "over", "away", +}) + + +def _humanize_activity(activity: str) -> str: + """person_opens_facility_door -> opens facility door""" + for prefix in ("person_", "vehicle_", "hand_"): + if activity.startswith(prefix): + activity = activity[len(prefix):] + break + return activity.replace("_", " ") + + +def _humanize_gerund(activity: str) -> str: + """person_opens_facility_door -> Opening a facility door""" + base = _humanize_activity(activity) + words = base.split() + if not words: + return base.capitalize() + + first = words[0] + gerund = _conjugate_gerund(first) + rest = " ".join(words[1:]) + + if rest: + first_rest = rest.split()[0] + if first_rest not in _NO_ARTICLE: + rest = "a " + rest + + result = f"{gerund} {rest}".strip() if rest else gerund + + # Fix missing articles in common phrases + result = re.sub(r'\bwith person\b', 'with a person', result) + result = re.sub(r'\bto person\b', 'to a person', result) + result = re.sub(r'\bthrough structure\b', 'through a structure', result) + result = re.sub(r'\bthrough door\b', 'through a door', result) + result = re.sub(r'\bin vehicle\b', 'in a vehicle', result) + result = re.sub(r'\bwith object\b', 'with an object', result) + result = re.sub(r'\bon phone\b', 'on a phone', result) + + return result.capitalize() + + +def _short_activity_label(activity: str) -> str: + """Short gerund label: person_opens_facility_door -> opening a facility door""" + result = _humanize_gerund(activity) + return result[0].lower() + result[1:] if result else result + + +# ============================================================================ +# Ontology Rewrites (module-level, used by preprocess_all) +# ============================================================================ + +_ONTOLOGY_REWRITES = [ + (re.compile(r'(enter(?:s|ing))(?: a)? scene through structure', re.IGNORECASE), + lambda m: m.group(1) + " the camera's view through a doorway/gate"), + (re.compile(r'(exit(?:s|ing)|leav(?:es|ing))(?: a)? scene through structure', re.IGNORECASE), + lambda m: ('leaving' if m.group(1).lower().startswith(('exit', 'leav')) and m.group(1)[0].islower() + else 'Leaving' if m.group(1)[0].isupper() + else m.group(1)) + " the camera's view through a doorway/gate"), +] + + +def _apply_ontology_rewrites(text: str) -> str: + """Apply MEVA ontology clarifications via case-insensitive regex.""" + for pattern, repl in _ONTOLOGY_REWRITES: + text = pattern.sub(repl, text) + return text + + +# ============================================================================ +# Temporal Anchoring +# ============================================================================ + +def _temporal_anchor(sec: float, clip_duration: float = CLIP_DURATION) -> str: + """Generate a temporal anchor for event disambiguation within a 5-min clip.""" + if sec < 5: + return "at the very start" + elif sec < 30: + return f"about {int(round(sec))} seconds in" + elif sec < 60: + return f"roughly {int(round(sec))} seconds in" + elif sec < 120: + return f"around the {int(round(sec))}-second mark" + elif sec < 250: + return f"around {int(round(sec / 10)) * 10} seconds in" + else: + return "near the end of the clip" + + +# ============================================================================ +# Appearance Detection +# ============================================================================ + +_APPEARANCE_WORDS = frozenset({ + "wearing", "shirt", "top", "pants", "jacket", "hoodie", "backpack", + "blue", "red", "green", "gray", "grey", "black", "white", "dark", "light", + "brown", "orange", "yellow", "purple", "pink", "navy", "beige", "tan", + "khaki", "camouflage", "striped", "patterned", "logo", "graphic", + "garment", "clothing", "jeans", "shorts", "dress", "skirt", "hat", "cap", + "glasses", "sunglasses", "scarf", "vest", "coat", "boots", "sneakers", + "bottle", "bag", "purse", "suitcase", "umbrella", +}) + + +def _has_appearance_info(desc: str) -> bool: + """Check if description contains visual appearance info (clothing/colors).""" + if not desc: + return False + words = set(desc.lower().split()) + return len(words & _APPEARANCE_WORDS) >= 2 + + +# ============================================================================ +# Person Description Extraction +# ============================================================================ + +def _extract_person_desc(entity_description: str, activity: str = "") -> str: + """ + Extract just the entity appearance, stripping any embedded activity text. + + Input: "the person wearing a gray upper body garment... enters scene" + Output: "a person wearing a gray top and green pants, carrying a black backpack" + + Input: "a person interacts with person" (fallback, no appearance) + Output: "a person" + + Input: "a vehicle" (vehicle entity) + Output: "a vehicle" + """ + if not entity_description: + # Use activity prefix to determine entity type + if activity.startswith("vehicle_"): + return "a vehicle" + return "a person" + + desc = entity_description.strip() + + # Preserve vehicle descriptions as-is + if desc.lower().startswith("a vehicle") or desc.lower() == "vehicle": + return desc if desc.lower().startswith("a ") else "a " + desc + + # Check if this is a real appearance description vs. activity fallback + if not _has_appearance_info(desc): + # Use activity prefix to determine entity type + if activity.startswith("vehicle_"): + return "a vehicle" + return "a person" + + # Remove embedded activity text after the description + activity_verbs = { + "enters", "exits", "opens", "closes", "picks", "puts", "carries", + "talks", "sits", "stands", "reads", "texts", "interacts", "embraces", + "rides", "loads", "unloads", "transfers", "drops", "pulls", "pushes", + "walks", "runs", "stops", "turns", "starts", + } + + # Split on periods; keep appearance parts, drop activity parts + parts = desc.split(".") + appearance_parts = [] + for part in parts: + part_stripped = part.strip() + if not part_stripped: + continue + first_word = part_stripped.split()[0].lower() if part_stripped.split() else "" + if first_word in activity_verbs: + break # Rest is activity description + appearance_parts.append(part_stripped) + + if appearance_parts: + desc = ". ".join(appearance_parts) + + # Strip "on camera GXXX" + desc = re.sub(r"\s*on camera G\d+", "", desc) + + # Simplify garment terminology and strip clutter + desc = simplify_description(desc) + + # Normalize prefix: "the person" -> "a person" + for prefix in ["The person ", "the person "]: + if desc.startswith(prefix): + desc = "a person " + desc[len(prefix):] + break + + # Ensure starts with "a person" or "A person" + if not desc.lower().startswith(("a ", "the ")): + desc = "a person " + desc + + return desc + + +# ============================================================================ +# Per-Category Pre-processing +# ============================================================================ + +def _preprocess_temporal(qa: Dict, strip_camera_refs: bool = True) -> Dict: + """ + Reconstruct temporal question from structured verification data. + + Fixes: camera refs in question, description verbosity, activity doubling, + temporal ambiguity (adds timestamp anchors), capitalization. + + Camera IDs are removed from the question text by default — person + descriptions (clothing colors, carried objects) serve as the primary + disambiguator instead. Camera refs are still kept in answer options. + """ + result = {k: v for k, v in qa.items()} + v = qa.get("verification", {}) + d = qa.get("debug_info", {}) + + if not v or "event_a" not in v or "event_b" not in v: + return result + + ev_a = v["event_a"] + ev_b = v["event_b"] + da = d.get("event_a", {}) + db_ = d.get("event_b", {}) + + # Get person descriptions from debug_info (enriched with MEVID) + desc_a = _extract_person_desc( + da.get("entity_description", ""), ev_a.get("activity", "") + ) + desc_b = _extract_person_desc( + db_.get("entity_description", ""), ev_b.get("activity", "") + ) + + # Get activities in gerund form + act_a = _short_activity_label(ev_a.get("activity", "")) + act_b = _short_activity_label(ev_b.get("activity", "")) + + cam_a = ev_a.get("camera", da.get("camera", "")) + cam_b = ev_b.get("camera", db_.get("camera", "")) + + # Build clean event descriptions + include_cam = not strip_camera_refs + + def _fmt_event(desc, act, cam, with_camera=True): + d = desc.rstrip('.') + cam_ref = f" on camera {cam}" if cam and with_camera else "" + if d.lower() in ("a person", "someone"): + return f"{d} {act}{cam_ref}" + if d.lower() in ("a vehicle",): + return f"{d} {act}{cam_ref}" + return f"{d}, {act}{cam_ref}" + + clean_a = _fmt_event(desc_a, act_a, cam_a, with_camera=include_cam) + clean_b = _fmt_event(desc_b, act_b, cam_b, with_camera=include_cam) + + # Capitalize first letter + clean_a = clean_a[0].upper() + clean_a[1:] + clean_b = clean_b[0].upper() + clean_b[1:] + + # Determine mention order (preserve original answer randomization) + ci = qa["correct_answer_index"] + if ci == 0: + desc_1, desc_2 = clean_a, clean_b + ev_1, ev_2 = ev_a, ev_b + else: + desc_1, desc_2 = clean_b, clean_a + ev_1, ev_2 = ev_b, ev_a + + # Build clean question + question = ( + f"Consider two events in this multi-camera scene: " + f"(1) {desc_1}. (2) {desc_2}. " + f"Which event occurred first?" + ) + + # Build options using person descriptions (no camera IDs in options) + def _option_label(desc, act): + d = desc.strip().rstrip('.') + if d.startswith("A "): + d = "The " + d[2:] + elif d.startswith("a "): + d = "The " + d[2:] + return d + + opt_1 = _option_label(desc_1, None) + opt_2 = _option_label(desc_2, None) + + options = [ + f"{opt_1} occurred first", + f"{opt_2} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + + # Article agreement is applied globally in preprocess_all; skip here + result["question_template"] = question + result["options"] = options + result["correct_answer"] = options[ci] + + return result + + +def _preprocess_spatial(qa: Dict) -> Dict: + """Clean spatial question: simplify descriptions, remove camera refs.""" + result = {k: v for k, v in qa.items()} + v = qa.get("verification", {}) + + if not v: + return result + + # Get and simplify entity descriptions + desc_a = simplify_description(v.get("entity_a_desc", "a person")) + desc_b = simplify_description(v.get("entity_b_desc", "another person")) + + # Normalize prefixes + for prefix in ["The person ", "the person "]: + if desc_a.startswith(prefix): + desc_a = "the person " + desc_a[len(prefix):] + if desc_b.startswith(prefix): + desc_b = "the person " + desc_b[len(prefix):] + + desc_a = desc_a.rstrip(".") + desc_b = desc_b.rstrip(".") + + # Build question without camera reference + question = f"How close are {desc_a} and {desc_b} in the scene?" + question = question[0].upper() + question[1:] + + result["question_template"] = question + return result + + +def _preprocess_perception(qa: Dict) -> Dict: + """ + Clean perception question: simplify any embedded descriptions, fix grammar. + Camera refs kept since they're inherent to perception question types. + """ + result = {k: v for k, v in qa.items()} + v = qa.get("verification", {}) + q_type = v.get("question_type", "") + + template = qa.get("question_template", "") + + if q_type == "attribute_verification": + person_desc = v.get("person_description", "") + if person_desc: + template = re.sub( + re.escape(person_desc), + simplify_description(person_desc).rstrip("."), + template, + count=1, + ) + elif q_type == "which_camera": + alias = v.get("activity_alias", "") + if alias: + gerund = _humanize_gerund(v.get("activity", alias)) + template = f"Which camera captures {gerund.lower().lstrip('a ')}?" + template = template[0].upper() + template[1:] + + # General cleanup + template = simplify_description(template) + if template: + template = template[0].upper() + template[1:] + if not template.endswith("?"): + template += "?" + + result["question_template"] = template + return result + + +def _preprocess_reid(qa: Dict) -> Dict: + """ + Clean re-ID question: simplify descriptions, fix grammar. + Camera refs kept since they're fundamental to re-identification. + """ + result = {k: v for k, v in qa.items()} + v = qa.get("verification", {}) + d = qa.get("debug_info", {}) + + if not v: + return result + + q_type = v.get("question_type", "") + desc = simplify_description(v.get("person_description", "a person")) + desc = desc.rstrip(".") + + # Normalize prefix + for prefix in ["the person ", "The person "]: + if desc.startswith(prefix): + desc = "a person " + desc[len(prefix):] + break + + if q_type == "which_camera_reid": + cam = v.get("source_camera", "") + question = ( + f"On camera {cam}, {desc} is visible. " + f"Which other camera also shows this same person?" + ) + elif q_type == "same_person_confirmation": + cam_a = v.get("camera_a", d.get("camera_a", "")) + cam_b = v.get("camera_b", d.get("camera_b", "")) + capitalized_desc = desc[0].upper() + desc[1:] if desc else desc + question = ( + f"{capitalized_desc} is observed on camera {cam_a}. " + f"Is this the same person visible on camera {cam_b}?" + ) + else: + question = simplify_description(qa.get("question_template", "")) + if question: + question = question[0].upper() + question[1:] + if question and not question.endswith("?"): + question += "?" + + result["question_template"] = question + return result + + +def _preprocess_scene_summary(qa: Dict) -> Dict: + """Clean scene summary question: minimal changes.""" + result = {k: v for k, v in qa.items()} + template = qa.get("question_template", "") + if template: + template = template[0].upper() + template[1:] + result["question_template"] = template + return result + + +# ============================================================================ +# Main Pre-processing Pipeline +# ============================================================================ + +def preprocess_all(input_data: Dict, verbose: bool = False, + strip_camera_refs: bool = True) -> Dict: + """ + Pre-process all QA pairs: simplify descriptions, remove camera refs, + add temporal anchors, fix grammar. FREE (no API call). + + strip_camera_refs: If True (default), strips camera IDs from temporal + question text. Person descriptions disambiguate instead. + """ + output = {k: v for k, v in input_data.items() if k != "qa_pairs"} + output["version"] = "preprocessed" + output["preprocessor"] = "naturalize.py" + + preprocessed = [] + changes = {"temporal": 0, "spatial": 0, "perception": 0, + "re_identification": 0, "scene_summary": 0} + + for qa in input_data.get("qa_pairs", []): + cat = qa.get("category", "") + + if cat == "temporal": + cleaned = _preprocess_temporal(qa, strip_camera_refs=strip_camera_refs) + elif cat == "spatial": + cleaned = _preprocess_spatial(qa) + elif cat == "perception": + cleaned = _preprocess_perception(qa) + elif cat == "re_identification": + cleaned = _preprocess_reid(qa) + elif cat in ("scene_summary", "summarization"): + cleaned = _preprocess_scene_summary(qa) + else: + cleaned = qa.copy() + + # Track original template for comparison + if cleaned.get("question_template") != qa.get("question_template"): + cleaned["original_template"] = qa["question_template"] + changes[cat] = changes.get(cat, 0) + 1 + if cleaned.get("options") != qa.get("options"): + cleaned["original_options"] = qa["options"] + + preprocessed.append(cleaned) + + # --------------------------------------------------------------- + # Global text fixes (applied to ALL categories, all text fields) + # Ontology vocabulary normalization only — all grammar/article + # fixes are delegated to GPT to avoid double-transformation + # --------------------------------------------------------------- + for qa in preprocessed: + for field in ("question_template", "correct_answer"): + if field in qa: + qa[field] = _apply_ontology_rewrites(str(qa[field])) + if "options" in qa: + qa["options"] = [ + _apply_ontology_rewrites(str(o)) + for o in qa["options"] + ] + + output["qa_pairs"] = preprocessed + + if verbose: + total_changed = sum(changes.values()) + print(f"\n Pre-processing: {total_changed}/{len(preprocessed)} questions modified") + for cat, cnt in sorted(changes.items()): + if cnt > 0: + print(f" {cat}: {cnt} changed") + + return output + + +# ============================================================================ +# GPT System Prompt +# ============================================================================ + +SYSTEM_PROMPT = """\ +You are a skilled question writer AND meticulous copy editor for a multi-camera \ +surveillance video QA benchmark. + +Your task: rewrite each template question into varied, natural English with \ +perfect grammar, punctuation, and phrasing — all in a single step. Each \ +question should sound like a DIFFERENT person wrote it. + +IMPORTANT: You rewrite ONLY the question text and provide a reasoning sentence. \ +You do NOT rewrite the answer options — those are deterministically generated \ +and must not be changed. + +Priority order (resolve conflicts by rank): +1. Preserve factual meaning exactly — never alter facts, person descriptions \ +(clothing colors, carried objects), spatial terms, or answer options +2. Ensure flawless grammar, punctuation, and natural phrasing — FIX any \ +garbled verb forms (e.g. "leavesing" → "leaving", "transfersing" → \ +"transferring", "purchasesing" → "purchasing"). These are template bugs, \ +not intentional text. +3. Apply creative stylistic variation — use different sentence openings, \ +structures, and vocabulary each time. Avoid formulaic patterns like always \ +starting with "In this scene..." or "Looking at the cameras..." +4. Add one concise reasoning sentence for why the correct answer is right + +## Constraints +- Do NOT change the meaning of the question. +- Do NOT add new facts or details not present in the original. +- Do NOT remove constraints or simplify the logical requirement. +- Do NOT alter person descriptions (clothing colors, carried objects) — but \ +DO fix obvious grammar errors within them (broken verb conjugations, garbled \ +words, missing articles). +- Do NOT change "a vehicle" to "a person" or vice versa — entity type is \ +factually significant. Vehicles and persons are different entities. +- Do not change answer options. +- Camera identifiers (e.g., G421) in question text are acceptable ONLY for \ +perception and re-identification questions where cameras are inherent. +- For PERCEPTION questions ("What activity..." / "Which camera..."), maintain \ +the direct question form but you may vary surrounding wording naturally. +- You MAY rephrase robotic activity descriptions into natural English \ +(e.g. "entering scene through structure" → "walking in through a doorway"). +- Fix all grammar, spelling, and conjugation errors. + +## Ontology Translation +Translate robotic activity labels into natural human prose. Examples: +- "enters scene through structure" → "walks into the building" +- "person_opens_facility_door" → "opens a door" +Smooth out awkward clothing lists into natural descriptions. Only rephrase \ +what is given — do not invent new details. + +## Grammar & Polish +Fix grammatical errors, run-on sentences, punctuation, capitalization, \ +awkward phrasing, redundancy, and unclear references. + +Bad → Good transformation example: +- BAD: "Throughout all the cameras in this time frame, how many instances of \ +stopping can be observed?" +- GOOD: "Across all cameras during this time period, how many stopping events \ +occur?" + +Phrasing variety examples (do NOT copy verbatim — invent your own): +- "A man in a gray hoodie appears near the entrance..." +- "Which of these events took place first?" +- "Based on the footage, what happened after..." +- Direct question without preamble: "Who was spotted on more than one camera?" +- Vary active/passive voice, question-first vs. description-first +- Sometimes be brief and direct, sometimes more descriptive + +Output format — respond with ONLY a JSON object: +{ + "question": "The creatively rephrased and grammar-polished question", + "reasoning": "Brief explanation of why the correct answer is right" +} +""" + + + +# ============================================================================ +# Category-specific prompt examples (few-shot) +# ============================================================================ + +CATEGORY_EXAMPLES = { + "temporal": { + "hint": "This is a temporal ordering question about two events. CRITICAL: Do NOT mention camera IDs (like G421, G339), timestamps (like 'at 45 seconds'), or locations (like 'near the parking lot'). Person descriptions (clothing, objects) identify who is who. VARY your phrasing creatively. Return ONLY {question, reasoning}.", + "example_input": 'Consider two events in this multi-camera scene: (1) A person wearing a gray top and green pants, carrying a black backpack, entering a scene through a structure. (2) A person in a blue top and green pants, interacting with a person. Which event occurred first?', + "example_output": '{"question": "Which of these happened first: a person in gray with green pants and a black backpack walking in through a structure, or a person in a blue top and green pants interacting with someone?", "reasoning": "The person in gray entered through the structure before the blue-topped person interacted with anyone."}', + }, + "spatial": { + "hint": "Spatial closest-approach question about how close two people come to each other. CRITICAL: Do NOT mention camera IDs (like G421), timestamps (like 'at 45 seconds', 'around the 2:10 mark'), or raw time references. Use only visual appearance descriptions (clothing, hair, objects) to identify people. VARY phrasing. Return ONLY {question, reasoning}.", + "example_input": 'How close do a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back and a person wearing a white hoodie with a Puma logo, camouflage pants, and a camouflage cap come to each other in the scene?', + "example_output": '{"question": "How close do the person in blue with the black graphic hoodie and the one in a white Puma hoodie with camo pants get to each other?", "reasoning": "Their closest approach places them approximately 6 meters apart, keeping them at a moderate distance throughout."}', + }, + "perception": { + "hint": "Perception question about activities or visual attributes. Camera references are part of the question — keep them. IMPORTANT: Preserve 'What activity...' and 'Which camera...' question structures EXACTLY — do NOT rephrase to 'Can you identify...' or 'Identify the camera...'. Only fix grammar and smooth descriptions. Return ONLY {question, reasoning}.", + "example_input": 'What activity is occurring on camera G423?', + "example_output": '{"question": "What activity is occurring on Camera G423?", "reasoning": "The activity visible on Camera G423 is a person opening a door."}', + }, + "re_identification": { + "hint": "Person re-identification across cameras. Camera refs are essential (use 'Camera Gxxx' format). Appearance descriptions must be precise. VARY phrasing. Return ONLY {question, reasoning}.", + "example_input": 'On Camera G419, a person wearing a blue top and blue pants, with a black hoodie featuring a graphic design on the back, is visible. Which other camera also shows this same person?', + "example_output": '{"question": "There is someone on Camera G419 in a blue top, blue pants, and a black hoodie with a graphic on the back. Can you spot the same person on any other camera?", "reasoning": "This person with the distinctive graphic hoodie shows up on both Camera G419 and Camera G423."}', + }, + "scene_summary": { + "hint": "Scene-level summary question. Keep camera counts and activity references. VARY phrasing. Return ONLY {question, reasoning}.", + "example_input": 'Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?', + "example_output": '{"question": "What would you say best describes what is going on across all 8 camera feeds?", "reasoning": "Pedestrian activities dominate across all 8 feeds, with putting down objects being the most common."}', + }, + "event_ordering": { + "hint": "Event ordering question about chronological sequence. CRITICAL: Do NOT mention camera IDs (like G421, G339), timestamps, or time references. Rephrase the event list naturally using visual descriptions and activities only. Vary how you introduce the events and ask for the order. Do NOT add letter prefixes (A, B, C, D) to options. Return ONLY {question, reasoning}.", + "example_input": "Identify the correct chronological order of the following events: I. Someone opening a door II. A person walking through the courtyard. Which is the correct chronological order?", + "example_output": '{"question": "Several activities were captured across different camera feeds. Place these events in the order they occurred: I. A door being opened near the entrance II. Someone strolling through the courtyard What is the right sequence?", "reasoning": "The door was opened before the person walked through the courtyard."}', + }, + "causality": { + "hint": "Cause-effect reasoning question. Rephrase naturally — for forward causal, vary how you ask 'what happened next'; for backward, vary how you ask 'why did this happen'. Keep the causal logic intact. Return ONLY {question, reasoning}.", + "example_input": "After a person picks up object, what activity most likely followed?", + "example_output": '{"question": "Once the individual grabbed an item from the ground, what did they most likely do next?", "reasoning": "Picking up an object is commonly followed by putting it down in another location."}', + }, + "numerical": { + "hint": "Counting question about activities across cameras. CRITICAL: Do NOT mention specific camera IDs (like G421) in the question. Rephrase the counting query naturally — vary sentence structure but preserve the exact scope. IMPORTANT: The reasoning will be provided separately — you ONLY need to return {question, reasoning} where reasoning preserves ALL numbers from the original reasoning EXACTLY. Do NOT change any count or number in the reasoning.", + "example_input": "How many times does someone perform the action of opening a vehicle door across all cameras in this slot?", + "example_output": '{"question": "Across the available camera feeds, how many times can you observe someone opening a vehicle door?", "reasoning": "Opening a vehicle door was observed 8 times across 2 cameras."}', + }, + + "best_camera": { + "hint": "Question about which camera first/last captures a person entering the scene. VARY phrasing. Keep camera identifiers in options. Return ONLY {question, reasoning}.", + "example_input": "Which camera first captures the entrance of a person in a blue top and gray pants into the scene?", + "example_output": '{"question": "On which camera does a person wearing a blue top and gray pants first appear?", "reasoning": "Camera G419 is the first to capture this person entering the scene."}', + }, +} + + +# ============================================================================ +# GPT Client & Category Aliases +# ============================================================================ + +# Alias categories that share the same few-shot examples +_CAT_ALIASES = {"summarization": "scene_summary", "counting": "numerical"} + + +def _create_client(): + """Create OpenAI client.""" + import openai + return openai.OpenAI() + + +# ============================================================================ +# GPT Naturalization (1 API call per question) +# ============================================================================ + +def _naturalize_question( + client, + question: Dict, + model: str, + temperature: float, +) -> Optional[Dict]: + """ + Single GPT call: send plain text question + context, get back rewritten + question + reasoning. Options are never sent to GPT for rewriting. + + Architecture per colleague review: + - Input: labeled plaintext fields (never raw JSON structure) + - Output: JSON with 2 fields {question, reasoning} + - Python handles all JSON assembly + """ + category = question["category"] + template = question["question_template"] + options = question["options"] + verification = question.get("verification", {}) + + # Select category-specific few-shot examples (with aliases) + lookup_cat = question.get("subcategory", category) + lookup_cat = _CAT_ALIASES.get(lookup_cat, lookup_cat) + cat_info = CATEGORY_EXAMPLES.get( + lookup_cat, CATEGORY_EXAMPLES.get( + _CAT_ALIASES.get(category, category), {}) + ) + hint = cat_info.get("hint", "Rephrase this question naturally with perfect grammar.") + example_in = cat_info.get("example_input", "") + example_out = cat_info.get("example_output", "") + + # Build user message as labeled plaintext (never send raw JSON) + parts = [f"CATEGORY: {category}", hint, ""] + + if example_in and example_out: + parts.append(f"EXAMPLE INPUT:\n{example_in}") + parts.append(f"EXAMPLE OUTPUT:\n{example_out}") + parts.append("") + + parts.append(f"QUESTION TO REWRITE:\n{template}") + parts.append("") + + # Options as context only + opt_lines = [f" {chr(65 + i)}) {opt}" for i, opt in enumerate(options)] + parts.append("OPTIONS (context only — do NOT modify):\n" + "\n".join(opt_lines)) + + # Verification context for reasoning (as plain English) + if category == "temporal" and "gap_sec" in verification: + parts.append(f"\nCONTEXT: The gap between events is {verification['gap_sec']} seconds.") + elif category == "spatial" and "min_distance_meters" in verification: + parts.append(f"\nCONTEXT: Closest approach distance between entities is {verification['min_distance_meters']} meters.") + elif category == "best_camera": + correct_cam = verification.get("correct_camera", "") + entrance_time = verification.get("entrance_time_sec", 0) + if correct_cam: + parts.append(f"\nCONTEXT: First entrance on {correct_cam} at {entrance_time}s.") + + # For counting questions: pass the deterministic reasoning so GPT preserves numbers + original_reasoning = question.get("reasoning", "") + if category in ("numerical", "counting") and original_reasoning: + parts.append(f"\nORIGINAL REASONING (preserve all numbers exactly): {original_reasoning}") + + user_message = "\n".join(parts) + + for attempt in range(MAX_RETRIES): + try: + request_args = { + "model": model, + "temperature": temperature, + "response_format": {"type": "json_object"}, + "messages": [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": user_message}, + ], + } + if model.startswith("gpt-5"): + request_args["max_completion_tokens"] = 400 + else: + request_args["max_tokens"] = 400 + response = client.chat.completions.create(**request_args) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result: + print(f" WARNING: Missing 'question' field, retry {attempt + 1}") + continue + + return { + "naturalized_question": result["question"], + "naturalized_options": options, # frozen, no GPT rewriting + "reasoning": result.get("reasoning", ""), + "usage": { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + }, + } + + except json.JSONDecodeError: + print(f" WARNING: Invalid JSON response, retry {attempt + 1}") + time.sleep(RETRY_DELAY) + except Exception as e: + print(f" WARNING: API error: {e}, retry {attempt + 1}") + time.sleep(RETRY_DELAY * (attempt + 1)) + + return None + + +# ============================================================================ +# Batch Processing +# ============================================================================ + +def naturalize_batch( + input_data: Dict, + model: str, + temperature: float, + verbose: bool = False, +) -> Dict: + """Stage 1 (pre-process) + Stage 2 (GPT) + Stage 3 (assemble). + + Architecture: 1 API call per question. Options are never sent to GPT + for rewriting — only the question text is naturalized. + """ + # Stage 1: Pre-process (free) + preprocessed = preprocess_all(input_data, verbose=verbose, + strip_camera_refs=True) + + # Stage 2: GPT naturalize (1 call per question) + client = _create_client() + qa_pairs = preprocessed["qa_pairs"] + total = len(qa_pairs) + + print(f"\n Naturalizing {total} questions with {model} (temp={temperature})...") + + naturalized_pairs = [] + total_tokens = 0 + failures = 0 + + for i, q in enumerate(qa_pairs): + if verbose: + print(f" [{i + 1}/{total}] {q['category']}: " + f"{q['question_template'][:60]}...") + + result = _naturalize_question(client, q, model, temperature) + + # Stage 3: JSON assembly + nat_q = q.copy() + + if result is None: + failures += 1 + nat_q["naturalized_question"] = q["question_template"] + nat_q["naturalized_options"] = q["options"] + nat_q["reasoning"] = q.get("reasoning", "") # preserve raw reasoning on failure + nat_q["naturalization_failed"] = True + else: + nat_q["naturalized_question"] = result["naturalized_question"] + nat_q["naturalized_options"] = result["naturalized_options"] + nat_q["reasoning"] = result["reasoning"] + total_tokens += result["usage"]["total_tokens"] + + # Post-naturalization safety: for counting questions, verify the + # correct answer number appears in the reasoning. If GPT changed + # it, fall back to the raw deterministic reasoning. + if q["category"] in ("numerical", "counting"): + correct_answer = q.get("correct_answer", "") + raw_reasoning = q.get("reasoning", "") + nat_reasoning = nat_q["reasoning"] + if correct_answer and nat_reasoning: + # Check if the correct count appears in naturalized reasoning + if correct_answer not in nat_reasoning and raw_reasoning: + if verbose: + print(f" WARNING: Counting reasoning corrupted " + f"(correct={correct_answer}, not found in " + f"'{nat_reasoning[:80]}...'). Using raw reasoning.") + nat_q["reasoning"] = raw_reasoning + nat_q["reasoning_restored_from_raw"] = True + + naturalized_pairs.append(nat_q) + + if (i + 1) % 5 == 0: + print(f" Progress: {i + 1}/{total} ({total_tokens} tokens)") + + output = { + "slot": input_data["slot"], + "version": "final_naturalized", + "generator": "naturalize.py", + "model": model, + "temperature": temperature, + "total_tokens": total_tokens, + "total_questions": len(naturalized_pairs), + "failures": failures, + "cameras": input_data.get("cameras", []), + "mevid_supported": input_data.get("mevid_supported", False), + "mevid_persons_in_slot": input_data.get("mevid_persons_in_slot", 0), + "category_counts": input_data.get("category_counts", {}), + "stats": input_data.get("stats", input_data.get("v8_stats", {})), + "qa_pairs": naturalized_pairs, + } + + return output + + +# ============================================================================ +# Dry Run +# ============================================================================ + +def dry_run(input_data: Dict): + """Show pre-processed templates and what would be sent to GPT.""" + preprocessed = preprocess_all(input_data, verbose=True) + qa_pairs = preprocessed["qa_pairs"] + + print(f"\n === DRY RUN — {len(qa_pairs)} pre-processed questions ===\n") + + for q in qa_pairs: + cat = q["category"] + subcat = q.get("subcategory", "") + original = q.get("original_template", q["question_template"]) + + print(f" [{cat}{' / ' + subcat if subcat else ''}]") + + if "original_template" in q: + print(f" BEFORE: {original[:100]}...") + print(f" AFTER: {q['question_template'][:100]}...") + else: + print(f" (no change): {q['question_template'][:100]}...") + + # Show options comparison + original_opts = q.get("original_options", q["options"]) + if original_opts != q["options"]: + print(f" OPTIONS BEFORE: {original_opts[0][:60]}...") + print(f" OPTIONS AFTER: {q['options'][0][:60]}...") + + for i, opt in enumerate(q["options"]): + marker = " *" if i == q.get("correct_answer_index") else "" + print(f" {chr(65 + i)}) {opt}{marker}") + print() + + # Cost estimate + calls = len(qa_pairs) + est_tokens = len(qa_pairs) * 450 + + est_cost_mini = est_tokens * 0.4e-6 + est_cost_4o = est_tokens * 6e-6 + + print(f" === Cost Estimate (1 API call per question) ===") + print(f" Questions: {len(qa_pairs)}") + print(f" API calls: {calls}") + print(f" Est. tokens: ~{est_tokens}") + print(f" gpt-4o-mini: ~${est_cost_mini:.4f}") + print(f" gpt-4o: ~${est_cost_4o:.4f}") + print() + print(f" TIP: Use --preprocess-only to get the pre-processed output for free.") + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Naturalize — Pre-process + GPT naturalize QA pairs", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + parser.add_argument("--input", "-i", required=True, + help="Path to QA JSON file (e.g., SLOT.final.raw.json)") + parser.add_argument("--output", "-o", + help="Output path (default: auto-generated .naturalized.json)") + parser.add_argument("--model", "-m", default=DEFAULT_MODEL, + help=f"GPT model (default: {DEFAULT_MODEL})") + parser.add_argument("--temperature", "-t", type=float, default=DEFAULT_TEMPERATURE, + help=f"Temperature (default: {DEFAULT_TEMPERATURE})") + parser.add_argument("--preprocess-only", action="store_true", + help="Only pre-process templates (no GPT call, FREE)") + parser.add_argument("--dry-run", action="store_true", + help="Show pre-processed templates and cost estimate") + parser.add_argument("--verbose", "-v", action="store_true") + parser.add_argument("--yes", "-y", action="store_true", + help="Skip confirmation prompt") + + args = parser.parse_args() + temperature = args.temperature + + input_path = Path(args.input) + if not input_path.exists(): + print(f"ERROR: Input not found: {input_path}") + return + + print(f"Loading: {input_path} (temp: {temperature})") + with open(input_path) as f: + input_data = json.load(f) + + total = len(input_data.get("qa_pairs", [])) + print(f" Slot: {input_data.get('slot', 'N/A')}") + print(f" Version: {input_data.get('version', 'N/A')}") + print(f" Questions: {total}") + + # Mode 1: Pre-process only (free) + if args.preprocess_only: + result = preprocess_all(input_data, verbose=True) + + out_path = args.output or str(input_path).replace( + ".json", ".preprocessed.json") + with open(out_path, "w") as f: + json.dump(result, f, indent=2, default=str) + + print(f"\n Pre-processed output -> {out_path}") + + # Show before/after for each question + print(f"\n === Before -> After ===") + for q in result["qa_pairs"]: + if "original_template" in q: + print(f"\n [{q['category']}]") + print(f" BEFORE: {q['original_template'][:120]}") + print(f" AFTER: {q['question_template'][:120]}") + if "original_options" in q: + print(f" OPTS BEFORE: {q['original_options'][0][:80]}") + print(f" OPTS AFTER: {q['options'][0][:80]}") + + return + + # Mode 2: Dry run + if args.dry_run: + dry_run(input_data) + return + + # Mode 3: Full pipeline (pre-process + GPT) + if not os.environ.get("OPENAI_API_KEY"): + print("ERROR: OPENAI_API_KEY not set") + print(" TIP: Use --preprocess-only for free pre-processing without GPT.") + return + + if not args.yes: + print(f"\n Will pre-process + naturalize {total} questions " + f"with {args.model} (temp={temperature})") + print(f" API calls: {total}") + resp = input(" Continue? [y/N] ").strip().lower() + if resp != "y": + print(" Aborted.") + return + + result = naturalize_batch( + input_data, args.model, temperature, + verbose=args.verbose, + ) + + print(f"\n === Results ===") + print(f" Naturalized: {total - result['failures']}/{total}") + print(f" Failures: {result['failures']}") + print(f" Total tokens: {result['total_tokens']}") + print(f" API calls: {total}") + + # Derive output path + if args.output: + out_path = args.output + elif ".final.raw.json" in str(input_path): + out_path = str(input_path).replace(".final.raw.json", ".final.naturalized.json") + elif ".v9.raw.json" in str(input_path): + out_path = str(input_path).replace(".v9.raw.json", ".v9.naturalized.json") + else: + out_path = str(input_path).replace(".json", ".naturalized.json") + with open(out_path, "w") as f: + json.dump(result, f, indent=2, default=str) + print(f" Output: {out_path}") + + # Save GPT log + slot = input_data.get("slot", "unknown") + log_dir = LOG_DIR / slot + log_dir.mkdir(parents=True, exist_ok=True) + log_path = log_dir / f"naturalize_{args.model}.json" + with open(log_path, "w") as f: + json.dump({ + "model": args.model, + "temperature": temperature, + "total_tokens": result["total_tokens"], + "questions_processed": total, + "failures": result["failures"], + "api_calls": total, + }, f, indent=2) + print(f" Log: {log_path}") + + # Show samples + print(f"\n === Sample Naturalized Questions ===") + seen_cats = set() + for q in result["qa_pairs"]: + if q["category"] in seen_cats: + continue + seen_cats.add(q["category"]) + + print(f"\n [{q['category']}]") + orig = q.get("original_template", q.get("question_template", "")) + print(f" RAW: {orig[:80]}...") + print(f" PREPROC: {q.get('question_template', '')[:80]}...") + print(f" NATURAL: {q.get('naturalized_question', '')[:80]}...") + if q.get("reasoning"): + print(f" REASON: {q['reasoning'][:80]}...") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/parse_annotations.py b/meva/scripts/v10/parse_annotations.py new file mode 100644 index 0000000..6411034 --- /dev/null +++ b/meva/scripts/v10/parse_annotations.py @@ -0,0 +1,267 @@ +""" +V6 parse_annotations.py — Step 1: Parse Kitware YAML annotations into raw events. + +Input: Slot name (e.g., "2018-03-11.11-25.school") +Output: List of Event dicts with activity, camera, frame range, actors. +""" + +import json +import os +import re +from pathlib import Path +from typing import Any, Dict, List, Optional +from dataclasses import dataclass, asdict, field + +# ============================================================================ +# Paths +# ============================================================================ + +MEVA_ROOT = Path("/nas/mars/dataset/MEVA") +ANNOTATION_BASE = MEVA_ROOT / "meva-data-repo" / "annotation" / "DIVA-phase-2" / "MEVA" +KITWARE_ROOT = ANNOTATION_BASE / "kitware" +KITWARE_TRAINING_ROOT = ANNOTATION_BASE / "kitware-meva-training" +# Repo-relative data directory (meva/data/) — works for any clone location +_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data" +SLOT_INDEX_PATH = _REPO_DATA / "slot_index.json" + +DEFAULT_FRAMERATE = 30.0 + + +# ============================================================================ +# Data Structures +# ============================================================================ + +@dataclass +class Event: + """A single annotated activity event.""" + event_id: str + activity: str + camera_id: str + site: str + start_frame: int + end_frame: int + start_sec: float + end_sec: float + duration_sec: float + actors: List[Dict[str, Any]] # [{actor_id, entity_type}] + video_file: str + annotation_source: str + + def to_dict(self) -> dict: + return asdict(self) + + +# ============================================================================ +# YAML Parsing +# ============================================================================ + +def _load_yaml_fast(path: Path) -> list: + """Load YAML using CSafeLoader for speed.""" + import yaml + try: + Loader = yaml.CSafeLoader + except AttributeError: + Loader = yaml.SafeLoader + with open(path) as f: + return yaml.load(f, Loader=Loader) or [] + + +def _parse_types_yml(path: Path) -> Dict[int, str]: + """Parse types.yml to get actor_id → entity_type mapping.""" + if not path.exists(): + return {} + type_map = {} + for entry in _load_yaml_fast(path): + t = entry.get("types", {}) + if t: + aid = t.get("id1") + cset = t.get("cset3", {}) + etype = next(iter(cset.keys()), "unknown") if cset else "unknown" + if aid is not None: + type_map[aid] = etype + return type_map + + +def _parse_activities_yml(path: Path, camera_id: str, site: str, + framerate: float, source: str) -> List[Event]: + """Parse a Kitware activities.yml file into Event objects.""" + if not path.exists(): + return [] + entries = _load_yaml_fast(path) + events = [] + + # Load types.yml for actor type resolution + types_path = path.with_name(path.name.replace(".activities.yml", ".types.yml")) + type_map = _parse_types_yml(types_path) + + for entry in entries: + act = entry.get("act", {}) + if not act: + continue + act2 = act.get("act2", {}) + activity_name = next(iter(act2.keys()), "unknown") + activity_id = act.get("id2", -1) + timespan = act.get("timespan", []) + if not timespan: + continue + tsr = timespan[0].get("tsr0", []) + if len(tsr) < 2: + continue + start_frame, end_frame = int(tsr[0]), int(tsr[1]) + start_sec = round(start_frame / framerate, 2) + end_sec = round(end_frame / framerate, 2) + + actors = [] + for actor_entry in act.get("actors", []): + aid = actor_entry.get("id1") + if aid is not None: + actors.append({ + "actor_id": aid, + "entity_type": type_map.get(aid, "unknown"), + }) + + clip_name = path.stem.replace(".activities", "") + event_id = f"{camera_id}_evt_{activity_id}" + events.append(Event( + event_id=event_id, + activity=activity_name, + camera_id=camera_id, + site=site, + start_frame=start_frame, + end_frame=end_frame, + start_sec=start_sec, + end_sec=end_sec, + duration_sec=round(end_sec - start_sec, 2), + actors=actors, + video_file=f"{clip_name}.r13.avi", + annotation_source=source, + )) + + return events + + +# ============================================================================ +# Slot-Level Annotation Discovery +# ============================================================================ + +CANONICAL_SLOTS_PATH = _REPO_DATA / "canonical_slots.json" + +def _resolve_to_raw_slots(slot: str) -> List[str]: + """Resolve a slot name to raw slot names. + + With HH-MM slot format (no seconds), each slot is already canonical. + No indirection needed. + """ + return [slot] + + +def find_clips_for_slot(slot: str) -> List[Dict]: + """ + Find all annotation clips for a given slot using slot_index.json. + + Handles both raw slots and canonical slots (auto-resolves via canonical_slots.json). + Returns list of clip metadata dicts with paths to activities.yml files. + Priority: kitware > kitware-training (skip camera if already found in higher-priority source). + """ + if not SLOT_INDEX_PATH.exists(): + raise FileNotFoundError( + "slot_index.json not found. Run: python3 scripts/extract_logic_tuples.py --build-index" + ) + + with open(SLOT_INDEX_PATH) as f: + index = json.load(f) + + # Resolve canonical → raw slots if needed + if slot not in index: + raw_slots = _resolve_to_raw_slots(slot) + if not any(rs in index for rs in raw_slots): + raise ValueError(f"Slot '{slot}' not found in index ({len(index)} total slots)") + else: + raw_slots = [slot] + + # Merge clips from all raw slots, deduplicating by camera + clips = [] + cameras_seen = set() + + for raw_slot in raw_slots: + if raw_slot not in index: + continue + info = index[raw_slot] + + # Parse date/time/site from slot name: "2018-03-11.11-25.school" + slot_parts = raw_slot.split(".") + date = slot_parts[0] + slot_time = slot_parts[1] # HH-MM (no seconds) + site = slot_parts[2] if len(slot_parts) > 2 else "school" + hour = slot_time[:2] + + # Priority order + source_dirs = { + "kitware": KITWARE_ROOT, + "kitware-training": KITWARE_TRAINING_ROOT, + } + + for source_name, source_dir in source_dirs.items(): + if source_name not in info.get("sources", {}): + continue + ann_dir = source_dir / date / hour + if not ann_dir.exists(): + continue + + for cam_id in sorted(info["cameras"]): + if cam_id in cameras_seen: + continue + if cam_id not in info["sources"].get(source_name, []): + continue + + # Find matching activities.yml (minute-level match) + pattern = f"{date}.{slot_time}*{cam_id}*.activities.yml" + matches = sorted(ann_dir.glob(pattern)) + if matches: + act_file = matches[0] + clip_name = act_file.stem.replace(".activities", "") + cameras_seen.add(cam_id) + clips.append({ + "clip_name": clip_name, + "camera_id": cam_id, + "site": site, + "annotation_dir": str(ann_dir), + "annotation_source": source_name, + "framerate": DEFAULT_FRAMERATE, + "activities_file": str(act_file), + }) + + return clips + + +def parse_slot_events(slot: str, verbose: bool = False) -> List[Event]: + """ + Parse all annotation events for a slot. + + Args: + slot: Slot name e.g. "2018-03-11.11-25.school" + verbose: Print progress info + + Returns: + Sorted list of Event objects (chronological). + """ + clips = find_clips_for_slot(slot) + if verbose: + print(f" Found {len(clips)} clips: {[c['camera_id'] for c in clips]}") + + all_events = [] + for clip in clips: + events = _parse_activities_yml( + Path(clip["activities_file"]), + clip["camera_id"], + clip["site"], + clip["framerate"], + clip["annotation_source"], + ) + all_events.extend(events) + if verbose: + print(f" {clip['camera_id']}: {len(events)} events ({clip['annotation_source']})") + + # Sort chronologically + all_events.sort(key=lambda e: (e.start_sec, e.camera_id)) + return all_events diff --git a/meva/scripts/v10/person_descriptions.py b/meva/scripts/v10/person_descriptions.py new file mode 100644 index 0000000..2b4379b --- /dev/null +++ b/meva/scripts/v10/person_descriptions.py @@ -0,0 +1,1175 @@ +""" +V8 person_descriptions.py — MEVID person description loading and entity enrichment. + +Loads the YOLO+GPT person database and injects natural-language person +descriptions into scene graph entities. This is the key V8 addition over V7. + +Description priority: + 1. GPT description (richest, from person_database_yolo.json) — simplified + 2. YOLO color summary (structured fallback) + 3. Activity-verb description (V7 style, no MEVID) +""" + +import os +import re +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple + +from .build_scene_graph import SceneGraph, Entity +from .activity_hierarchy import humanize_activity + +# ============================================================================ +# Paths +# ============================================================================ + +# Repo-relative data directory (meva/data/) — works for any clone location +_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data" +# User output directory — override with MEVA_OUTPUT_DIR env var +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) +# Entity descriptions directory — override with MEVA_ENTITY_DESC_DIR env var +_ENTITY_DESC_DIR = Path(os.environ.get("MEVA_ENTITY_DESC_DIR") or "/nas/mars/dataset/MEVA/entity_descriptions") +# VLM description directory (InternVL2.5-8B output) +_VLM_DESC_DIR = _ENTITY_DESC_DIR / "vlm" + +PERSON_DB_PATH = _REPO_DATA / "person_database_yolo.json" +PERSON_DB_ORIG_PATH = _REPO_DATA / "person_database.json" +MEVID_SLOTS_PATH = _REPO_DATA / "mevid_supported_slots.json" + + +# ============================================================================ +# Person Database Loading +# ============================================================================ + +_person_db_cache: Optional[Dict] = None +_person_db_orig_cache: Optional[Dict] = None + +def load_person_database() -> Dict: + """Load the YOLO+GPT person attribute database (cached).""" + global _person_db_cache + if _person_db_cache is not None: + return _person_db_cache + + if not PERSON_DB_PATH.exists(): + return {"persons": {}, "metadata": {}} + + with open(PERSON_DB_PATH) as f: + _person_db_cache = json.load(f) + return _person_db_cache + + +def _load_person_db_orig() -> Dict: + """Load the original person database with camera mappings (cached).""" + global _person_db_orig_cache + if _person_db_orig_cache is not None: + return _person_db_orig_cache + + if not PERSON_DB_ORIG_PATH.exists(): + return {"persons": {}} + + with open(PERSON_DB_ORIG_PATH) as f: + _person_db_orig_cache = json.load(f) + return _person_db_orig_cache + + +# ============================================================================ +# Description Simplification — Clean GPT's verbose formal language +# ============================================================================ + +# Garment type simplifications: "upper body garment" → "top", etc. +_GARMENT_SUBS = [ + (r"upper body garment with a hood", "hoodie"), + (r"upper body garment", "top"), + (r"lower body clothing", "pants"), + (r"lower body garment", "pants"), + (r"lower body pants", "pants"), + (r"lower body shorts", "shorts"), + (r"hooded jacket", "hoodie"), +] + +# Posture/action context to strip (not useful for identification) +_STRIP_PATTERNS = [ + r",?\s*sitting on a chair[^,\.]*", + r",?\s*with their back turned[^,\.]*", + r",?\s*while ascending a staircase[^,\.]*", + r",?\s*while holding a clipboard or some papers", + r",?\s*and they appear to have\s+", + r",?\s*sitting\b[^,\.]*", +] + +def simplify_description(desc: str) -> str: + """ + Simplify a GPT description into natural short form. + + "wearing a blue upper body garment and blue lower body clothing, + with a black hoodie featuring a graphic design on the back." + → + "wearing a blue top and blue pants, with a black hoodie featuring + a graphic design on the back" + """ + if not desc: + return desc + + # 1. Remove trailing period + desc = desc.rstrip(". ") + + # 2. Simplify garment types + for pattern, replacement in _GARMENT_SUBS: + desc = re.sub(pattern, replacement, desc, flags=re.IGNORECASE) + + # 3. Strip posture/action context + for pattern in _STRIP_PATTERNS: + desc = re.sub(pattern, "", desc, flags=re.IGNORECASE) + + # 4. Clean up orphaned commas / double spaces + desc = re.sub(r",\s*,", ",", desc) + desc = re.sub(r"\s{2,}", " ", desc) + desc = desc.strip(", ") + + # 5. Remove trailing period again (after stripping may leave one) + desc = desc.rstrip(".") + + return desc + + +def get_person_description(person_id: str, outfit_id: str = None) -> str: + """ + Get the best available description for a MEVID person. + + Priority: + 1. GPT description (natural language, simplified) + 2. YOLO color summary + 3. Generic "a person" + + Args: + person_id: MEVID person ID (e.g., "0008") + outfit_id: Optional outfit ID for outfit-specific colors + + Returns: + Description string like "a person in a dark hoodie and dark pants" + """ + db = load_person_database() + persons = db.get("persons", {}) + + if person_id not in persons: + return "a person" + + person = persons[person_id] + + # Priority 1: GPT description (simplified) + gpt_desc = person.get("gpt_description") + if gpt_desc: + desc = gpt_desc.strip() + # Remove leading boilerplate + for prefix in ["The person is ", "the person is ", "The person ", "the person "]: + if desc.startswith(prefix): + desc = desc[len(prefix):] + break + # Simplify formal language + desc = simplify_description(desc) + return f"a person {desc}" + + # Priority 2: YOLO color summary + upper = person.get("primary_upper_color", "unknown") + lower = person.get("primary_lower_color", "unknown") + + # Try outfit-specific colors if specified + if outfit_id and outfit_id in person.get("outfits", {}): + outfit = person["outfits"][outfit_id] + upper = outfit.get("upper_body_color", upper) + lower = outfit.get("lower_body_color", lower) + + if upper != "unknown" or lower != "unknown": + parts = [] + if upper != "unknown": + parts.append(f"{upper} top") + if lower != "unknown": + parts.append(f"{lower} pants") + return f"a person in {' and '.join(parts)}" + + # Priority 3: Generic + return "a person" + + +# ============================================================================ +# Visual Description Detection +# ============================================================================ + +_VISUAL_KEYWORDS = frozenset([ + "top", "pants", "wearing", "shirt", "jacket", "hoodie", "shorts", + "color", "blue", "red", "black", "white", "green", "gray", "grey", + "yellow", "brown", "orange", "purple", "pink", "navy", "dark", "light", + "backpack", "bag", "suitcase", "hat", "cap", "dress", "coat", +]) + + +def is_visual_description(desc: str) -> bool: + """Check if a description is based on visual appearance (clothing/color). + + Returns True for MEVID and geom-color descriptions like: + - "a person in a blue top and black pants" + - "a person wearing a dark hoodie" + + Returns False for activity-verb fallbacks like: + - "a person opens facility door" + - "a person carries object" + - "a person" + - "a vehicle" + - "someone walking" + """ + if not desc: + return False + desc_lower = desc.lower() + if desc_lower in ("a person", "a vehicle", "someone"): + return False + return any(kw in desc_lower for kw in _VISUAL_KEYWORDS) + + +# ============================================================================ +# Color Match Scoring — Cross-validate MEVID ↔ geom-extracted colors (Issue 1) +# ============================================================================ + +# Color similarity groups: colors in the same group are considered "matching" +_COLOR_GROUPS = { + "black": {"black", "charcoal", "dark"}, + "dark_gray": {"charcoal", "dark gray", "dark"}, + "gray": {"gray", "grey", "silver", "dark gray"}, + "white": {"white", "ivory", "light"}, + "red": {"red", "crimson", "maroon", "rust"}, + "orange": {"orange", "rust"}, + "yellow": {"yellow", "gold", "khaki"}, + "green": {"green", "olive", "teal"}, + "blue": {"blue", "navy", "indigo", "teal"}, + "purple": {"purple", "plum", "indigo", "mauve"}, + "pink": {"pink", "mauve"}, + "brown": {"brown", "khaki", "beige"}, + "beige": {"beige", "khaki", "ivory"}, + "navy": {"navy", "blue", "dark blue", "indigo"}, + "olive": {"olive", "green", "khaki"}, +} + +def _normalize_color(color: str) -> str: + """Normalize a color string for matching (lowercase, strip prefixes).""" + if not color: + return "" + color = color.lower().strip() + # Strip 'dark ' / 'light ' prefixes for fuzzy matching + for prefix in ("dark ", "light ", "bright "): + if color.startswith(prefix): + return color[len(prefix):] + return color + +def _colors_similar(color_a: str, color_b: str) -> bool: + """Check if two color names are semantically similar.""" + a = _normalize_color(color_a) + b = _normalize_color(color_b) + if not a or not b: + return False + if a == b: + return True + # Check if they share a color group + for group_colors in _COLOR_GROUPS.values(): + if a in group_colors and b in group_colors: + return True + return False + +def _color_match_score(mevid_person_data: Dict, geom_desc: str) -> float: + """ + Score how well a MEVID person's colors match a geom-extracted description. + + Returns 0.0-2.0: + - 1.0 per matching region (upper, lower) + - 0.0 for unknown/missing data + - -1.0 penalty for clear mismatch (known colors that don't match) + + Args: + mevid_person_data: Dict with primary_upper_color, primary_lower_color + geom_desc: e.g. "a person in a blue top and black pants" + """ + if not geom_desc or geom_desc in ("a person", "a vehicle", "someone"): + return 0.0 # No geom data to compare — neutral score + + mevid_upper = mevid_person_data.get("primary_upper_color", "unknown") + mevid_lower = mevid_person_data.get("primary_lower_color", "unknown") + + # Parse geom description for colors: "a person in a {color} top and {color} pants" + geom_upper = "" + geom_lower = "" + desc_lower = geom_desc.lower() + + # Extract upper color: look for "{color} top" or "wearing a {color} top" + import re as _re_color + upper_match = _re_color.search(r'(?:in\s+(?:a\s+)?|wearing\s+(?:a\s+)?)(\w+)\s+top', desc_lower) + if upper_match: + geom_upper = upper_match.group(1) + + # Extract lower color: look for "{color} pants/shorts" + lower_match = _re_color.search(r'(\w+)\s+(?:pants|shorts|skirt)', desc_lower) + if lower_match: + geom_lower = lower_match.group(1) + + score = 0.0 + + # Score upper body + if mevid_upper != "unknown" and geom_upper: + if _colors_similar(mevid_upper, geom_upper): + score += 1.0 + else: + score -= 1.0 # Clear mismatch penalty + + # Score lower body + if mevid_lower != "unknown" and geom_lower: + if _colors_similar(mevid_lower, geom_lower): + score += 1.0 + else: + score -= 1.0 # Clear mismatch penalty + + return score + + +def get_person_short_label(person_id: str) -> str: + """ + Get a short label for a person (for options / distractor text). + + Returns things like "person in blue jacket" (shorter than full GPT description). + """ + db = load_person_database() + persons = db.get("persons", {}) + + if person_id not in persons: + return f"Person #{person_id}" + + person = persons[person_id] + upper = person.get("primary_upper_color", "unknown") + lower = person.get("primary_lower_color", "unknown") + + objects = person.get("all_carried_objects", []) + + parts = [] + if upper != "unknown": + parts.append(f"{upper} top") + if lower != "unknown": + parts.append(f"{lower} bottom") + if objects: + parts.append(f"carrying {objects[0]}") + + if parts: + return f"person with {', '.join(parts)}" + return f"Person #{person_id}" + + +# ============================================================================ +# Slot Filtering — Only MEVID-Supported Slots +# ============================================================================ + +_mevid_slots_cache: Optional[Dict] = None + +def load_mevid_slots() -> Dict: + """Load the MEVID-supported slots data (cached).""" + global _mevid_slots_cache + if _mevid_slots_cache is not None: + return _mevid_slots_cache + + if not MEVID_SLOTS_PATH.exists(): + return {"slots": {}} + + with open(MEVID_SLOTS_PATH) as f: + _mevid_slots_cache = json.load(f) + return _mevid_slots_cache + + +def _resolve_mevid_slot(slot: str) -> Optional[str]: + """Resolve a slot name to its key in mevid_supported_slots.json. + + The MEVID index uses HH-MM-SS format while the pipeline uses HH-MM. + This bridges the gap by trying both forms. + """ + data = load_mevid_slots() + slots = data.get("slots", {}) + if slot in slots: + return slot + # Try appending -00 to get HH-MM-SS from HH-MM + parts = slot.split(".") + if len(parts) >= 2: + time_part = parts[1] + if len(time_part) == 5: # HH-MM + expanded = f"{parts[0]}.{time_part}-00.{'.' .join(parts[2:])}" + if expanded in slots: + return expanded + return None + + +def _resolve_all_mevid_slots(slot: str) -> List[str]: + """Return all MEVID slot keys matching a canonical HH-MM slot. + + A canonical HH-MM slot may map to multiple HH-MM-SS raw slots in the + MEVID index (e.g., 2018-03-11.11-25.school → 11-25-00, 11-25-01, etc.). + """ + data = load_mevid_slots() + slots = data.get("slots", {}) + if slot in slots: + return [slot] + parts = slot.split(".") + if len(parts) < 3 or len(parts[1]) != 5: + return [] + prefix = f"{parts[0]}.{parts[1]}" + site = parts[2] + return [k for k in slots if k.startswith(prefix) and k.endswith(f".{site}")] + + +def is_mevid_supported(slot: str) -> bool: + """Check if a slot has MEVID person support.""" + return len(_resolve_all_mevid_slots(slot)) > 0 + + +def get_mevid_persons_for_slot(slot: str) -> List[str]: + """ + Get list of MEVID person IDs available for a slot. + + Reads from mevid_supported_slots.json which maps each slot to its + MEVID persons (built by aggregate_mevid_slots.py). + Merges across all matching raw slots for canonical HH-MM lookups. + """ + data = load_mevid_slots() + slots = data.get("slots", {}) + matching = _resolve_all_mevid_slots(slot) + all_persons = set() + for m in matching: + slot_info = slots.get(m, {}) + all_persons.update(slot_info.get("mevid_persons", [])) + return sorted(all_persons) + + +def get_mevid_persons_with_cameras(slot: str) -> Dict[str, List[str]]: + """ + Get MEVID person IDs mapped to their cameras for this specific slot. + + Cross-references: + - mevid_supported_slots.json → which persons and cameras are in this slot + - person_database.json → which cameras each person globally appears on + + Merges across all matching raw slots for canonical HH-MM lookups. + Returns: {person_id: [camera_ids_in_this_slot]} + """ + # Get slot info (merge across all matching raw slots) + slot_data = load_mevid_slots() + slots = slot_data.get("slots", {}) + matching = _resolve_all_mevid_slots(slot) + + mevid_persons = set() + mevid_cameras = set() + for m in matching: + slot_info = slots.get(m, {}) + mevid_persons.update(slot_info.get("mevid_persons", [])) + mevid_cameras.update(slot_info.get("mevid_cameras", [])) + + if not mevid_persons or not mevid_cameras: + return {} + + # Get per-person camera lists from original person database + orig_db = _load_person_db_orig() + orig_persons = orig_db.get("persons", {}) + + result = {} + for pid in sorted(mevid_persons): + person_data = orig_persons.get(pid, {}) + person_cameras = set(person_data.get("cameras", {}).keys()) + # Intersect with this slot's MEVID cameras + cameras_in_slot = sorted(person_cameras & mevid_cameras) + if cameras_in_slot: + result[pid] = cameras_in_slot + + return result + + +# ============================================================================ +# Entity Enrichment — Inject MEVID Descriptions into Scene Graph +# ============================================================================ + +# Geom-extracted description bank directory +_GEOM_DESC_DIR = _ENTITY_DESC_DIR + + +def _load_geom_descriptions(slot: str) -> Dict[str, str]: + """ + Load pre-extracted visual descriptions from extract_entity_descriptions.py. + These are HSV color-based descriptions from raw AVI + geom.yml bounding boxes. + Returns Dict[entity_id → description], e.g. "G330_actor_123" → "a person in a blue top and black pants" + """ + desc_path = _GEOM_DESC_DIR / f"{slot}.json" + if not desc_path.exists(): + return {} + try: + with open(desc_path) as f: + data = json.load(f) + descs = {eid: info["description"] for eid, info in data.get("actors", {}).items() + if info.get("description") and info["description"] != "a person"} + # Clean up geom descriptions: remove "unknown" qualifiers, fix articles + return {eid: _clean_geom_description(desc) for eid, desc in descs.items()} + except (json.JSONDecodeError, KeyError): + return {} + + +def _clean_geom_description(desc: str) -> str: + """Clean a pre-formatted geom description string. + + Removes "unknown" qualifier words and fixes article agreement (a→an + before vowels). Does NOT consolidate colors — specific colors like + indigo, navy, teal are kept for better entity differentiation. + """ + import re + result = desc + # Remove "unknown" qualifier + result = re.sub(r'\bunknown\s+', '', result, flags=re.IGNORECASE) + # Fix article agreement: "a indigo" → "an indigo", "a olive" → "an olive" + result = re.sub(r'\ba\s+([aeiou])', r'an \1', result, flags=re.IGNORECASE) + return result + + +def _load_vlm_descriptions(slot: str) -> Dict[str, str]: + """ + Load VLM-generated descriptions (InternVL2.5-8B) for a slot. + These are rich natural-language descriptions from video crops. + Returns Dict[entity_id → description], e.g. "G330_actor_123" → "a man in a dark blue jacket..." + """ + vlm_path = _VLM_DESC_DIR / f"{slot}.vlm.json" + if not vlm_path.exists(): + return {} + try: + with open(vlm_path) as f: + data = json.load(f) + descs = data.get("descriptions", {}) + # Filter out empty/generic descriptions + return {eid: desc for eid, desc in descs.items() + if desc and len(desc) > 10 and desc.lower() != "a person"} + except (json.JSONDecodeError, KeyError): + return {} + + +def enrich_entities(sg: SceneGraph, verbose: bool = False) -> Dict[str, str]: + """ + Enrich scene graph entities with visual descriptions. + + Priority: + 1. MEVID GPT description (natural language, richest — if available) + 2. Geom-extracted descriptions (SegFormer color from raw AVI + bbox) + 3. VLM descriptions (InternVL2.5-8B from video crops — rich NL) + 4. MEVID YOLO color summary (crude upper/lower color — better than nothing) + 5. Activity-verb fallback ("a person walking") + + NOTE: SegFormer descriptions are preferred over MEVID YOLO colors because + SegFormer produces rich multi-attribute descriptions (hair, clothing, shoes, + accessories) while MEVID YOLO only provides crude "blue top and blue pants" + for 82% of persons. MEVID GPT descriptions (when populated) are still + highest priority since they are natural language from crop analysis. + + The geom-extracted layer covers ALL annotated actors (not just MEVID's ~10%), + giving every entity a color-based description for disambiguation. + + Args: + sg: Scene graph to enrich + verbose: Print enrichment details + + Returns: + Dict mapping entity_id → description string + """ + slot = sg.slot + person_cameras = get_mevid_persons_with_cameras(slot) + geom_descs = _load_geom_descriptions(slot) + vlm_descs = _load_vlm_descriptions(slot) + + # Build reverse map: camera_id → [person_ids on this camera] + camera_persons: Dict[str, List[str]] = {} + for pid, cams in person_cameras.items(): + for cam in cams: + if cam not in camera_persons: + camera_persons[cam] = [] + camera_persons[cam].append(pid) + + # Pre-check: does the MEVID database have any GPT descriptions? + db = load_person_database() + persons_db = db.get("persons", {}) + + entity_descriptions: Dict[str, str] = {} + mevid_count = 0 + vlm_count = 0 + geom_count = 0 + mevid_yolo_count = 0 + fallback_count = 0 + + # Track which MEVID persons have been assigned to avoid reuse + assigned_persons: Dict[str, Set[str]] = {} # camera → set of used person_ids + + for eid, entity in sg.entities.items(): + # Determine effective entity type: if entity is tagged as "vehicle" + # but participates ONLY in person_* activities, treat it as a person. + # This fixes annotation artifacts where types.yml misclassifies actors. + effective_type = entity.entity_type + if effective_type == "vehicle": + # Check if ALL activities for this entity start with 'person_' + entity_activities = [] + for evt in sg.events: + if evt.camera_id == entity.camera_id: + for actor in evt.actors: + if actor["actor_id"] == entity.actor_id: + entity_activities.append(evt.activity) + # If entity has person activities, treat as person + has_person_acts = any(a.startswith("person_") for a in entity_activities) + has_vehicle_acts = any(a.startswith("vehicle_") for a in entity_activities) + if has_person_acts and not has_vehicle_acts: + effective_type = "person" # reclassify + + if effective_type != "person": + entity_descriptions[eid] = "a vehicle" + continue + + cam = entity.camera_id + available_persons = camera_persons.get(cam, []) + + # Priority 1: MEVID GPT description (if available — richest source) + # Only use MEVID here if the person has a real GPT description. + # MEVID YOLO-only colors ("blue top and blue pants") are too crude + # and would override richer SegFormer descriptions, so they are + # deferred to Priority 4 below. + if available_persons: + used = assigned_persons.get(cam, set()) + unused = [p for p in available_persons if p not in used] + + # Filter to candidates that actually have GPT descriptions + gpt_candidates = [p for p in unused + if persons_db.get(p, {}).get("gpt_description")] + + if gpt_candidates: + geom_desc = geom_descs.get(eid, "") + + best_pid = None + best_score = -999.0 + for candidate_pid in gpt_candidates: + pdata = persons_db.get(candidate_pid, {}) + score = _color_match_score(pdata, geom_desc) + if score > best_score: + best_score = score + best_pid = candidate_pid + + if best_pid is not None and best_score >= -0.5: + pid = best_pid + desc = get_person_description(pid) + entity_descriptions[eid] = desc + + if cam not in assigned_persons: + assigned_persons[cam] = set() + assigned_persons[cam].add(pid) + entity._mevid_person_id = pid + mevid_count += 1 + + if verbose: + print(f" {eid}: MEVID-GPT → {desc[:60]}... (color_score={best_score:.1f})") + continue + + # Priority 2: Geom-extracted color description (SegFormer + bbox) + # These are rich multi-attribute descriptions (hair, upper, lower, + # shoes, accessories) extracted by SegFormer from raw video frames. + if eid in geom_descs: + desc = geom_descs[eid] + entity_descriptions[eid] = desc + geom_count += 1 + + # Still try to assign a MEVID person_id for re-ID tracking, + # even though we're using the geom description text + if available_persons: + used = assigned_persons.get(cam, set()) + unused = [p for p in available_persons if p not in used] + if unused: + best_pid = None + best_score = -999.0 + for candidate_pid in unused: + pdata = persons_db.get(candidate_pid, {}) + score = _color_match_score(pdata, desc) + if score > best_score: + best_score = score + best_pid = candidate_pid + if best_pid is not None and best_score >= -0.5: + if cam not in assigned_persons: + assigned_persons[cam] = set() + assigned_persons[cam].add(best_pid) + entity._mevid_person_id = best_pid + + if verbose: + pid_tag = f" [MEVID:{entity._mevid_person_id}]" if hasattr(entity, '_mevid_person_id') and entity._mevid_person_id else "" + print(f" {eid}: geom → {desc[:60]}{pid_tag}") + continue + + # Priority 3: VLM description (InternVL2.5-8B from video crops) + if eid in vlm_descs: + desc = vlm_descs[eid] + entity_descriptions[eid] = desc + vlm_count += 1 + if verbose: + print(f" {eid}: VLM → {desc[:60]}...") + continue + + # Priority 4: MEVID YOLO color summary (crude but better than nothing) + # Only used when no SegFormer/VLM description is available. + if available_persons: + used = assigned_persons.get(cam, set()) + unused = [p for p in available_persons if p not in used] + + if unused: + geom_desc = geom_descs.get(eid, "") + + best_pid = None + best_score = -999.0 + for candidate_pid in unused: + pdata = persons_db.get(candidate_pid, {}) + score = _color_match_score(pdata, geom_desc) + if score > best_score: + best_score = score + best_pid = candidate_pid + + if best_pid is not None and best_score >= -0.5: + pid = best_pid + desc = get_person_description(pid) + entity_descriptions[eid] = desc + + if cam not in assigned_persons: + assigned_persons[cam] = set() + assigned_persons[cam].add(pid) + entity._mevid_person_id = pid + mevid_yolo_count += 1 + + if verbose: + print(f" {eid}: MEVID-YOLO → {desc[:60]}... (color_score={best_score:.1f})") + continue + elif verbose and best_pid: + print(f" {eid}: MEVID-YOLO rejected (color_score={best_score:.1f} < -0.5)") + + # Priority 5: Activity-verb fallback (V7 style) + primary_activity = None + for evt in sg.events: + if evt.camera_id == entity.camera_id: + for actor in evt.actors: + if actor["actor_id"] == entity.actor_id: + primary_activity = evt.activity + break + if primary_activity: + break + + if primary_activity: + short_act = humanize_activity(primary_activity) + desc = f"a person {short_act}" + else: + desc = "a person" + + entity_descriptions[eid] = desc + fallback_count += 1 + + if verbose: + total = mevid_count + vlm_count + geom_count + mevid_yolo_count + fallback_count + print(f" Entity enrichment: {mevid_count} MEVID-GPT, {geom_count} geom, {vlm_count} VLM, " + f"{mevid_yolo_count} MEVID-YOLO, {fallback_count} fallback ({total} total)") + + # Build set of PERSON entity IDs that got fallback (non-visual) descriptions. + # Non-person entities (vehicles, objects) always get generic descriptions like + # "a vehicle" — that's correct and complete, not a quality failure. + # Only person entities with generic fallbacks ("a person", "someone walking") + # degrade question quality, so only they are flagged here. + fallback_eids = set() + for eid, desc in entity_descriptions.items(): + entity = sg.entities.get(eid) + if entity and entity.entity_type != "person": + continue # vehicles/objects: "a vehicle" is acceptable, not fallback + if not is_visual_description(desc): + fallback_eids.add(eid) + + return (entity_descriptions, + {"mevid": mevid_count, "vlm": vlm_count, "geom": geom_count, + "mevid_yolo": mevid_yolo_count, "fallback": fallback_count}, + fallback_eids) + + +# ============================================================================ +# Cross-Camera Clustering — Unify descriptions for cross-camera entities +# ============================================================================ + +# Standard color palette: map exotic HSV names → standard ~12 colors +_COLOR_CONSOLIDATION = { + "navy": "dark blue", "indigo": "dark blue", + "teal": "teal", "olive": "olive", + "charcoal": "dark gray", "rust": "brown", + "plum": "purple", "mauve": "pink", + "gold": "yellow", "khaki": "tan", + "ivory": "white", "beige": "tan", + "crimson": "red", "maroon": "dark red", + "silver": "gray", +} + + +def _consolidate_color(color: str) -> str: + """Map exotic color names to standard palette for better matching.""" + if not color or color == "unknown": + return color + return _COLOR_CONSOLIDATION.get(color.lower(), color.lower()) + + +def _height_category(avg_crop_height: float) -> str: + """Categorize entity height from average crop pixel height. + + Height thresholds calibrated for MEVA surveillance cameras: + - Tall: > 200px (close to camera or genuinely tall) + - Short: < 100px (far from camera or genuinely short) + - Average: in between (majority) + + Returns empty string if height doesn't meaningfully differentiate. + """ + if avg_crop_height >= 200: + return "tall" + elif avg_crop_height <= 80: + return "short" + return "" + + +def _majority_vote_attr(values: List[str]) -> str: + """Return the most common non-unknown value, or 'unknown'. + + For color attributes, groups similar colors (e.g., navy/dark blue/indigo) + before voting, but returns the RAW most-common color (not consolidated) + to preserve display richness. + """ + valid = [v for v in values if v and v != "unknown"] + if not valid: + return "unknown" + from collections import Counter + + # Group by consolidated color for voting strength, but return raw winner + consolidated_groups = {} # consolidated_color → [raw_colors] + for v in valid: + c = _consolidate_color(v) + if c not in consolidated_groups: + consolidated_groups[c] = [] + consolidated_groups[c].append(v) + + # Find the consolidated group with most votes + best_group = max(consolidated_groups.values(), key=len) + # Return the most common raw color within that group + return Counter(best_group).most_common(1)[0][0] + + +def _merge_accessories(acc_lists: List[List[str]]) -> List[str]: + """Merge accessory lists — keep items appearing in 2+ sources.""" + from collections import Counter + all_items = Counter() + for acc in acc_lists: + for item in set(acc): # deduplicate within each source + all_items[item] += 1 + # Keep items appearing in at least 1 source (any evidence is useful) + return sorted(set(all_items.keys())) + + +def _build_description(attrs: dict) -> str: + """Build a natural description string from merged attributes. + + Uses the same format as extract_entity_descriptions.py for consistency. + Includes 68b fields: texture (striped/patterned) and brightness (dark/light). + """ + parts = [] + + # Hair + hair = attrs.get("hair_color", "unknown") + if hair != "unknown": + parts.append(f"with {hair} hair") + + # Clothing — keep specific colors (indigo, navy, teal etc.) for differentiation + upper = attrs.get("upper_color", "unknown") + lower = attrs.get("lower_color", "unknown") + lower_type = attrs.get("lower_type", "pants") + upper_brightness = attrs.get("upper_brightness", "") + lower_brightness = attrs.get("lower_brightness", "") + upper_texture = attrs.get("upper_texture", "") + lower_texture = attrs.get("lower_texture", "") + + clothing = [] + if upper != "unknown": + # Build qualifier: "dark patterned navy" or just "navy" + upper_quals = [] + # Skip brightness qualifier if the consolidated color already includes it + # (e.g., "dark blue" already implies "dark", so don't say "dark dark blue") + if (upper_brightness and upper_brightness not in ("", "medium", "unknown") + and not upper.startswith(upper_brightness)): + upper_quals.append(upper_brightness) + if upper_texture and upper_texture not in ("", "solid", "unknown"): + upper_quals.append(upper_texture) + qualifier = " ".join(upper_quals) + if qualifier: + article = "an" if qualifier[0].lower() in "aeiou" else "a" + clothing.append(f"{article} {qualifier} {upper} top") + else: + article = "an" if upper[0].lower() in "aeiou" else "a" + clothing.append(f"{article} {upper} top") + if lower != "unknown": + lower_quals = [] + if (lower_brightness and lower_brightness not in ("", "medium", "unknown") + and not lower.startswith(lower_brightness)): + lower_quals.append(lower_brightness) + if lower_texture and lower_texture not in ("", "solid", "unknown"): + lower_quals.append(lower_texture) + qualifier = " ".join(lower_quals) + if qualifier: + clothing.append(f"{qualifier} {lower} {lower_type}") + else: + clothing.append(f"{lower} {lower_type}") + + if clothing: + parts.append("wearing " + " and ".join(clothing)) + + # Shoes + shoe = attrs.get("shoe_color", "unknown") + if shoe != "unknown": + parts.append(f"{shoe} shoes") + + # Accessories + accessories = attrs.get("accessories", []) + if accessories: + parts.append("with " + ", ".join(accessories)) + + if not parts: + return "a person" + + return "a person " + ", ".join(parts) + + +def merge_cross_camera_descriptions( + entity_descs: Dict[str, str], + resolved, # ResolvedGraph + sg, # SceneGraph + verbose: bool = False, +) -> Dict[str, str]: + """ + Post-processing: unify descriptions for cross-camera entity clusters. + + For each entity cluster (same person seen on multiple cameras): + 1. Collect raw SegFormer attributes from all entities in the cluster + 2. Majority-vote on each attribute across cameras + 3. Merge accessories/carried objects (union) + 4. Build a single unified description + 5. Assign it to ALL entities in the cluster + + Also adds height hints to differentiate same-description entities + within a single camera. + + Args: + entity_descs: Current entity_id → description mapping + resolved: ResolvedGraph from entity_resolution + sg: SceneGraph with entity data + verbose: Print progress + + Returns: + Updated entity_descs with unified cross-camera descriptions + """ + if not resolved.entity_clusters: + if verbose: + print(" Cross-camera clustering: no clusters to merge") + return entity_descs + + # Load raw SegFormer actor data for attribute-level merging + desc_path = _ENTITY_DESC_DIR / f"{sg.slot}.json" + raw_actors = {} + if desc_path.exists(): + try: + with open(desc_path) as f: + raw_data = json.load(f) + raw_actors = raw_data.get("actors", {}) + except (json.JSONDecodeError, KeyError): + pass + + merged_count = 0 + enriched_count = 0 + + for cluster in resolved.entity_clusters: + entity_ids = cluster.entities + if len(entity_ids) < 2: + continue + + # Collect attributes from all entities in cluster + hair_colors = [] + upper_colors = [] + lower_colors = [] + lower_types = [] + shoe_colors = [] + upper_textures = [] + lower_textures = [] + upper_brightnesses = [] + lower_brightnesses = [] + all_accessories = [] + all_carried = [] + heights = [] + + for eid in entity_ids: + actor_data = raw_actors.get(eid, {}) + if not actor_data: + continue + + hair_colors.append(actor_data.get("hair_color", "unknown")) + # Use RAW colors for majority vote (display) — NOT consolidated + # Color consolidation is only for cross-camera matching similarity + upper_colors.append(actor_data.get("upper_color", "unknown")) + lower_colors.append(actor_data.get("lower_color", "unknown")) + lower_types.append(actor_data.get("lower_type", "pants")) + shoe_colors.append(actor_data.get("shoe_color", "unknown")) + upper_textures.append(actor_data.get("upper_texture", "")) + lower_textures.append(actor_data.get("lower_texture", "")) + upper_brightnesses.append(actor_data.get("upper_brightness", "")) + lower_brightnesses.append(actor_data.get("lower_brightness", "")) + all_accessories.append(actor_data.get("accessories", [])) + all_carried.append(actor_data.get("carried_objects", [])) + h = actor_data.get("avg_crop_height", 0) + if h > 0: + heights.append(h) + + if not upper_colors: + continue # No raw data available for this cluster + + # Majority vote on each attribute + merged_attrs = { + "hair_color": _majority_vote_attr(hair_colors), + "upper_color": _majority_vote_attr(upper_colors), + "lower_color": _majority_vote_attr(lower_colors), + "lower_type": _majority_vote_attr(lower_types), + "shoe_color": _majority_vote_attr(shoe_colors), + "upper_texture": _majority_vote_attr(upper_textures), + "lower_texture": _majority_vote_attr(lower_textures), + "upper_brightness": _majority_vote_attr(upper_brightnesses), + "lower_brightness": _majority_vote_attr(lower_brightnesses), + "accessories": _merge_accessories(all_accessories + all_carried), + } + + # Build unified description + unified = _build_description(merged_attrs) + + # Count how many attributes the unified version has vs individual ones + old_descs = {eid: entity_descs.get(eid, "a person") for eid in entity_ids} + + # Assign to all entities in cluster + for eid in entity_ids: + old = entity_descs.get(eid, "a person") + # Only upgrade — don't replace a richer MEVID description with a simpler one + if unified != "a person" and ( + not is_visual_description(old) or + len(unified) >= len(old) + ): + entity_descs[eid] = unified + if unified != old: + enriched_count += 1 + + merged_count += 1 + + if verbose and merged_count <= 3: + print(f" Cluster {cluster.cluster_id}: {len(entity_ids)} entities " + f"across {cluster.cameras}") + for eid in entity_ids[:2]: + print(f" {eid}: {old_descs.get(eid, '?')[:50]} → {unified[:50]}") + + if verbose: + print(f" Cross-camera clustering: {merged_count} clusters merged, " + f"{enriched_count} descriptions unified") + + return entity_descs + + +def differentiate_within_camera( + entity_descs: Dict[str, str], + sg, # SceneGraph + verbose: bool = False, +) -> Dict[str, str]: + """ + Add differentiating attributes for entities with identical descriptions + on the same camera. + + Strategy: + - Group entities by (camera, description) + - For groups with >1 entity, add height category if available + - This makes "a person wearing a navy top and black pants" into + "a tall person wearing a navy top and black pants" + + Args: + entity_descs: entity_id → description mapping + sg: SceneGraph with entity data + verbose: Print stats + + Returns: + Updated entity_descs with differentiated descriptions + """ + # Load raw actor data for height info + desc_path = _ENTITY_DESC_DIR / f"{sg.slot}.json" + raw_actors = {} + if desc_path.exists(): + try: + with open(desc_path) as f: + raw_data = json.load(f) + raw_actors = raw_data.get("actors", {}) + except (json.JSONDecodeError, KeyError): + pass + + if not raw_actors: + return entity_descs + + # Group entities by (camera, description) + from collections import defaultdict + cam_desc_groups: Dict[tuple, list] = defaultdict(list) + for eid, desc in entity_descs.items(): + entity = sg.entities.get(eid) + if not entity or entity.entity_type != "person": + continue + cam_desc_groups[(entity.camera_id, desc)].append(eid) + + differentiated = 0 + for (cam, desc), eids in cam_desc_groups.items(): + if len(eids) < 2: + continue + + # Get heights for entities in this group + eid_heights = {} + for eid in eids: + actor_data = raw_actors.get(eid, {}) + h = actor_data.get("avg_crop_height", 0) + if h > 0: + eid_heights[eid] = h + + if not eid_heights: + continue + + # Compute relative height categories within this group + heights = sorted(eid_heights.values()) + if len(heights) < 2: + continue + + median_h = heights[len(heights) // 2] + spread = max(heights) - min(heights) + + # Only differentiate if there's meaningful height spread (>30% of median) + if spread < median_h * 0.3: + continue + + for eid in eids: + h = eid_heights.get(eid) + if h is None: + continue + + # Relative categorization within the group + if h > median_h * 1.2: + prefix = "tall" + elif h < median_h * 0.8: + prefix = "short" + else: + continue # Near median — don't label + + old_desc = entity_descs[eid] + # Insert height after "a " — "a person..." → "a tall person..." + if old_desc.startswith("a person"): + new_desc = f"a {prefix} person" + old_desc[len("a person"):] + entity_descs[eid] = new_desc + differentiated += 1 + + if verbose: + print(f" Height differentiation: {differentiated} entities labeled tall/short") + + return entity_descs \ No newline at end of file diff --git a/meva/scripts/v10/reextract_missing_cameras.py b/meva/scripts/v10/reextract_missing_cameras.py new file mode 100644 index 0000000..d67edfb --- /dev/null +++ b/meva/scripts/v10/reextract_missing_cameras.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +""" +Re-extract entity descriptions for slots with missing cameras. + +The original batch extraction missed cameras from kitware-meva-training/. +This script identifies affected slots and re-extracts them with the current +find_slot_files() which searches BOTH kitware/ and kitware-meva-training/. + +Usage: + python3 scripts/v10/reextract_missing_cameras.py --dry-run # Show affected slots + python3 scripts/v10/reextract_missing_cameras.py -v # Re-extract all + python3 scripts/v10/reextract_missing_cameras.py --slot "2018-03-09.10-40.bus" -v # Single slot +""" + +import argparse +import json +import os +import re +import shutil +import sys +import time +from pathlib import Path + +# Use venv python for subprocess calls — prefer activated venv, else system python3 +VENV_PYTHON = shutil.which("python3") or "python3" + +KITWARE_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware") +KITWARE_TRAINING = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware-meva-training") +DESC_DIR = Path("/nas/mars/dataset/MEVA/entity_descriptions") +SLOT_INDEX = Path(__file__).resolve().parent.parent.parent / "data" / "slot_index.json" + + +def find_geom_cameras(slot: str) -> set: + """Find all cameras with geom.yml files for a slot.""" + parts = slot.split(".") + date, time_part, site = parts[0], parts[1], parts[2] + hour = time_part.split("-")[0] + prefix = f"{date}.{time_part}" + + cameras = set() + for base in [KITWARE_BASE, KITWARE_TRAINING]: + d = base / date / hour + if d.is_dir(): + for gf in d.glob(f"{prefix}*.{site}.*.geom.yml"): + m = re.search(rf'\.{site}\.(G\d+)\.geom\.yml$', gf.name) + if m: + cameras.add(m.group(1)) + return cameras + + +def find_desc_cameras(slot: str) -> set: + """Get cameras already in the entity description file.""" + desc_file = DESC_DIR / f"{slot}.json" + if not desc_file.exists(): + return set() + with open(desc_file) as f: + data = json.load(f) + return set(data.get("cameras", {}).keys()) + + +def find_affected_slots() -> list: + """Find all slots with cameras missing from entity descriptions.""" + with open(SLOT_INDEX) as f: + slot_index = json.load(f) + + affected = [] + for slot in sorted(slot_index.keys()): + geom_cams = find_geom_cameras(slot) + desc_cams = find_desc_cameras(slot) + missing = geom_cams - desc_cams + + if missing: + # Also check slots with no desc file at all but geom exists + affected.append({ + "slot": slot, + "missing_cameras": sorted(missing), + "existing_cameras": sorted(desc_cams), + "all_geom_cameras": sorted(geom_cams), + }) + + # Also find slots with no desc file but geom cameras exist + for slot in sorted(slot_index.keys()): + desc_file = DESC_DIR / f"{slot}.json" + if not desc_file.exists(): + geom_cams = find_geom_cameras(slot) + if geom_cams: + affected.append({ + "slot": slot, + "missing_cameras": sorted(geom_cams), + "existing_cameras": [], + "all_geom_cameras": sorted(geom_cams), + }) + + # Deduplicate + seen = set() + deduped = [] + for a in affected: + if a["slot"] not in seen: + seen.add(a["slot"]) + deduped.append(a) + + return deduped + + +def reextract_slot(slot: str, verbose: bool = False) -> dict: + """Re-extract entity descriptions for a slot (overwrites existing file).""" + import subprocess + + output_path = DESC_DIR / f"{slot}.json" + cmd = [ + VENV_PYTHON, + "-m", "scripts.v10.extract_entity_descriptions", + "--slot", slot, + "--output", str(output_path), + "--method", "segformer", + ] + if verbose: + cmd.append("-v") + + try: + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=600, + cwd=str(Path(__file__).resolve().parent.parent.parent), + ) + + if result.returncode != 0: + return { + "success": False, + "entities": 0, + "error": f"Exit {result.returncode}: {result.stderr[:300]}", + } + + if output_path.exists(): + with open(output_path) as f: + data = json.load(f) + return { + "success": True, + "entities": len(data.get("actors", {})), + "cameras": list(data.get("cameras", {}).keys()), + } + return {"success": False, "entities": 0, "error": "No output file"} + + except subprocess.TimeoutExpired: + return {"success": False, "entities": 0, "error": "Timeout >10min"} + except Exception as e: + return {"success": False, "entities": 0, "error": str(e)[:200]} + + +def main(): + parser = argparse.ArgumentParser(description="Re-extract slots with missing cameras") + parser.add_argument("--dry-run", action="store_true", help="Show affected slots without processing") + parser.add_argument("--slot", type=str, help="Process a single slot") + parser.add_argument("-v", "--verbose", action="store_true") + args = parser.parse_args() + + if args.slot: + # Single slot mode + geom_cams = find_geom_cameras(args.slot) + desc_cams = find_desc_cameras(args.slot) + missing = geom_cams - desc_cams + print(f"Slot: {args.slot}") + print(f" Geom cameras: {sorted(geom_cams)}") + print(f" Desc cameras: {sorted(desc_cams)}") + print(f" Missing: {sorted(missing)}") + + if not missing and desc_cams == geom_cams: + print(" All cameras already extracted!") + return + + if args.dry_run: + return + + print(f"\n Re-extracting...") + result = reextract_slot(args.slot, verbose=args.verbose) + print(f" Result: {result}") + return + + # Batch mode + affected = find_affected_slots() + print(f"\nFound {len(affected)} slots with missing camera descriptions") + total_missing = sum(len(a["missing_cameras"]) for a in affected) + print(f"Total missing cameras: {total_missing}") + + if args.dry_run: + print("\n--- Affected Slots ---") + for a in affected: + print(f" {a['slot']}: missing {a['missing_cameras']} (has {a['existing_cameras']})") + return + + # Process all affected slots + print(f"\nProcessing {len(affected)} slots...") + success = 0 + failed = 0 + total_new_actors = 0 + + for i, a in enumerate(affected): + slot = a["slot"] + print(f"\n[{i+1}/{len(affected)}] {slot} (missing: {a['missing_cameras']})") + + # Get actor count before + desc_file = DESC_DIR / f"{slot}.json" + before_actors = 0 + if desc_file.exists(): + with open(desc_file) as f: + before_actors = len(json.load(f).get("actors", {})) + + result = reextract_slot(slot, verbose=args.verbose) + + if result["success"]: + after_actors = result["entities"] + delta = after_actors - before_actors + total_new_actors += max(0, delta) + print(f" OK: {before_actors} → {after_actors} actors (+{delta}), cameras: {result.get('cameras', [])}") + success += 1 + else: + print(f" FAILED: {result['error']}") + failed += 1 + + print(f"\n{'='*60}") + print(f"DONE: {success} succeeded, {failed} failed") + print(f"Total new actors: +{total_new_actors}") + print(f"{'='*60}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/render_question_validation.py b/meva/scripts/v10/render_question_validation.py new file mode 100644 index 0000000..95d7e90 --- /dev/null +++ b/meva/scripts/v10/render_question_validation.py @@ -0,0 +1,931 @@ +#!/usr/bin/env python3 +""" +Render question validation videos: self-contained verification with activity highlighting. + +Creates a single video per question showing EVERYTHING needed to verify: +- Full question text with all options (correct answer marked in green) +- Multi-camera grid with bounding box overlays +- BLUE highlighted boxes on actors involved in the question's activities +- Green boxes on background actors (other people/objects) +- Activity labels on each camera during event frames +- 5-second context before first activity, 5 seconds after last +- Timeline progress bar showing event regions + playback cursor + +Usage: + python3 render_question_validation.py --slot 2018-03-07.17-05.school --question-id 0 + python3 render_question_validation.py --slot 2018-03-07.17-05.school --all + python3 render_question_validation.py --slot 2018-03-07.17-05.school --natural --all +""" + +import argparse +import json +import math +import os +import re +import cv2 +import numpy as np +from pathlib import Path +from collections import defaultdict +import subprocess +import shutil +from typing import Dict, List, Optional, Tuple, Set + + +def _find_ffmpeg() -> Optional[str]: + """Find ffmpeg binary -- system PATH first, then imageio-ffmpeg fallback.""" + path = shutil.which("ffmpeg") + if path: + return path + try: + import imageio_ffmpeg + return imageio_ffmpeg.get_ffmpeg_exe() + except ImportError: + return None + +FFMPEG_BIN = _find_ffmpeg() + +# ============================================================================ +# Paths +# ============================================================================ +MEVA_MP4_ROOT = Path("/nas/mars/dataset/MEVA/mp4s") +KITWARE_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware") +KITWARE_TRAINING_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware-meva-training") +# User output directory — override with MEVA_OUTPUT_DIR env var +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) +QA_OUTPUT_DIR = _OUTPUT / "qa_pairs" + + +def _get_video_output_dir(slot: str) -> Path: + """Return per-slot validation video directory.""" + d = QA_OUTPUT_DIR / slot / "validation_videos" + d.mkdir(parents=True, exist_ok=True) + return d + +# ============================================================================ +# Display constants +# ============================================================================ +CELL_W, CELL_H = 640, 360 # Each camera cell +PAD = 12 # Padding between cells +FONT = cv2.FONT_HERSHEY_SIMPLEX +FONT_AA = cv2.LINE_AA + +# Colors (BGR format) +COLOR_BG = (30, 30, 30) # Dark canvas background +COLOR_TEXT = (220, 220, 220) # Light gray text +COLOR_TITLE = (100, 200, 255) # Orange-yellow title +COLOR_CORRECT = (80, 255, 80) # Green for correct answer +COLOR_OPTION = (170, 170, 170) # Gray for regular options +COLOR_BOX_ACTIVE = (255, 180, 0) # BLUE highlight for relevant actors (BGR) +COLOR_BOX_BG = (0, 160, 0) # Green for background actors +COLOR_ACTIVITY_TXT = (255, 200, 50) # Bright blue for activity label text +COLOR_CONTEXT_TXT = (100, 100, 100) # Dim gray for context label +COLOR_CAM_LABEL = (0, 200, 255) # Orange camera label +COLOR_EVENT_BORDER = (255, 180, 0) # Blue border during events +COLOR_TL_BG = (60, 60, 60) # Timeline background +COLOR_TL_EVENT = (255, 180, 0) # Timeline event region (blue) +COLOR_TL_CURSOR = (0, 0, 255) # Red playback cursor +COLOR_SEPARATOR = (80, 80, 80) # Separator lines + +CONTEXT_PADDING_SEC = 5.0 # 5 seconds before/after activities +MAX_RENDER_FRAMES = 9000 # Up to 5 minutes at 30fps +DEFAULT_CONTEXT_FRAMES = 150 # For categories with no frame range (5s) + + +# ============================================================================ +# Geom parsing (regex approach -- fast, no YAML dependency) +# ============================================================================ +_RE_ID1 = re.compile(r"['\"]?id1['\"]?\s*:\s*['\"]?(\d+)") +_RE_TS0 = re.compile(r"['\"]?ts0['\"]?\s*:\s*['\"]?(\d+)") +_RE_G0 = re.compile(r"['\"]?g0['\"]?\s*:\s*['\"]?(\d+)\s+(\d+)\s+(\d+)\s+(\d+)") + + +def parse_geom_boxes(geom_file: Path) -> Dict[int, list]: + """Extract bounding boxes from geom.yml by frame number. + + Returns {frame_number: [(x1, y1, x2, y2, actor_id), ...]} + """ + boxes_by_frame: Dict[int, list] = defaultdict(list) + if not geom_file.exists(): + return boxes_by_frame + + with open(geom_file) as f: + for line in f: + line = line.strip() + if not line or line.startswith('#'): + continue + m_id = _RE_ID1.search(line) + m_ts = _RE_TS0.search(line) + m_g0 = _RE_G0.search(line) + if m_id and m_ts and m_g0: + actor_id = int(m_id.group(1)) + frame = int(m_ts.group(1)) + x1, y1, x2, y2 = map(int, m_g0.groups()) + boxes_by_frame[frame].append((x1, y1, x2, y2, actor_id)) + + return dict(boxes_by_frame) + + +# ============================================================================ +# File finders +# ============================================================================ + +def _slot_dir_name(date: str, start_time: str, site: str) -> str: + """Build slot directory name using HH-MM format (no seconds).""" + parts = start_time.split("-") + hh_mm = f"{parts[0]}-{parts[1]}" if len(parts) >= 2 else start_time + return f"{date}.{hh_mm}.{site}" + + +def find_mp4(date: str, hour: str, start_time: str, site: str, + end_time: str, camera: str) -> Optional[Path]: + """Find MP4 file for a camera.""" + slot_name = _slot_dir_name(date, start_time, site) + slot_dir = MEVA_MP4_ROOT / date / hour / slot_name + if not slot_dir.exists(): + parent = MEVA_MP4_ROOT / date / hour + if parent.exists(): + candidates = list(parent.glob(f"{date}.*{site}")) + if candidates: + slot_dir = candidates[0] + else: + return None + else: + return None + + pattern = f"*{camera}*.r13.mp4" + matches = list(slot_dir.glob(pattern)) + if matches: + return matches[0] + matches = list(slot_dir.glob(f"*{camera}*.mp4")) + return matches[0] if matches else None + + +def find_geom_file(date: str, hour: str, start_time: str, + site: str, camera: str) -> Optional[Path]: + """Find geom.yml file for a camera.""" + prefix = f"{date}.{start_time}" + for kitware_dir in [KITWARE_BASE, KITWARE_TRAINING_BASE]: + ann_dir = kitware_dir / date / hour + if not ann_dir.exists(): + continue + matches = list(ann_dir.glob(f"{prefix}*.{site}.{camera}.geom.yml")) + if matches: + return matches[0] + return None + + +def extract_clip_timing(clip_file: str) -> Optional[Tuple[str, str, str, str, str, str]]: + """Parse clip filename -> (date, hour, start_time, end_time, site, camera).""" + name = Path(clip_file).name + for suffix in [".r13.mp4", ".r13.avi", ".mp4", ".avi"]: + if name.endswith(suffix): + name = name[: -len(suffix)] + break + parts = name.split(".") + if len(parts) >= 5: + date, start_time, end_time, site, camera = parts[0], parts[1], parts[2], parts[3], parts[4] + hour = start_time.split("-")[0] + return date, hour, start_time, end_time, site, camera + return None + + +# ============================================================================ +# Event extraction -- handles ALL debug_info formats +# ============================================================================ + +def _extract_events(q: dict, slot: str) -> List[dict]: + """Extract a unified list of event dicts from any question category. + + Each returned event dict has at minimum: + camera, clip_file, frame_range, fps, label, actor_ids + """ + debug = q.get("debug_info", {}) + category = q.get("category", "") + events: List[dict] = [] + + # --- Pattern 1: top-level event dicts (temporal, perception, spatial, best_camera) --- + for key, val in debug.items(): + if isinstance(val, dict) and "camera" in val and val.get("clip_file"): + events.append({ + "camera": val["camera"], + "clip_file": val["clip_file"], + "frame_range": val.get("frame_range"), + "fps": val.get("fps", 30.0), + "actor_ids": val.get("actor_ids", []), + "label": val.get("activity", key), + }) + + # --- Pattern 2: events LIST (event_ordering) --- + if "events" in debug and isinstance(debug["events"], list): + for ev in debug["events"]: + if isinstance(ev, dict) and "camera" in ev and ev.get("clip_file"): + events.append({ + "camera": ev["camera"], + "clip_file": ev["clip_file"], + "frame_range": ev.get("frame_range"), + "fps": ev.get("fps", 30.0), + "actor_ids": ev.get("actor_ids", []), + "label": ev.get("activity", "event"), + }) + + # --- Pattern 3: no events, but clip_files list (summarization, counting) --- + if not events and "clip_files" in debug: + for cf in debug["clip_files"]: + timing = extract_clip_timing(cf) + if timing: + _date, _hour, _st, _et, _site, _camera = timing + events.append({ + "camera": _camera, + "clip_file": cf, + "frame_range": None, + "fps": 30.0, + "actor_ids": [], + "label": category, + }) + + # --- Pattern 4: no events, no clip_files, but cameras list --- + if not events: + cameras = q.get("requires_cameras", []) + slot_parts = slot.split(".") + if len(slot_parts) >= 3: + date = slot_parts[0] + hhmm = slot_parts[1] + site = slot_parts[2] + hour = hhmm.split("-")[0] + for cam in cameras: + mp4 = find_mp4(date, hour, f"{hhmm}-00", site, "", cam) + if mp4: + events.append({ + "camera": cam, + "clip_file": mp4.name, + "frame_range": None, + "fps": 30.0, + "actor_ids": [], + "label": category, + }) + + return events + + +# ============================================================================ +# Text wrapping utilities (for full question display in header) +# ============================================================================ + +def _wrap_text(text: str, max_chars: int = 95) -> List[str]: + """Word-wrap text to max_chars width.""" + words = text.split() + lines = [] + line = "" + for w in words: + if line and len(line) + 1 + len(w) > max_chars: + lines.append(line) + line = w + else: + line = (line + " " + w) if line else w + if line: + lines.append(line) + return lines or [""] + + +def _compute_header_layout(q_text: str, options: list, + correct_idx: int, category: str, + q_idx: int, canvas_w: int + ) -> Tuple[list, int]: + """Build header text lines and compute total header height. + + Returns (lines, header_height) where each line is (style, text). + style in {"title", "question", "option", "correct", "gap"} + """ + LINE_H = 22 + # Compute max chars based on canvas width (font scale 0.5 ~ 10px/char) + max_chars = max(60, int((canvas_w - 2 * PAD) / 9.5)) + + lines = [] + # Title + lines.append(("title", f"Q{q_idx}: {category.upper()}")) + lines.append(("gap", "")) + + # Full question text (word-wrapped, never truncated) + for wl in _wrap_text(q_text, max_chars): + lines.append(("question", wl)) + lines.append(("gap", "")) + + # Options with A/B/C/D labels + labels = "ABCDEFGH" + for i, opt in enumerate(options[:len(labels)]): + suffix = " << CORRECT" if i == correct_idx else "" + opt_full = f" {labels[i]}) {opt}{suffix}" + for ol in _wrap_text(opt_full, max_chars): + style = "correct" if i == correct_idx else "option" + lines.append((style, ol)) + + header_h = (len(lines) + 2) * LINE_H # +2 for top/bottom margins + if header_h % 2: + header_h += 1 + return lines, header_h + + +def _draw_header(canvas: np.ndarray, lines: list): + """Render header text lines onto the canvas.""" + LINE_H = 22 + y = LINE_H + 6 # top margin + + for style, text in lines: + if style == "gap": + y += 8 + continue + + if style == "title": + color, scale, thick = COLOR_TITLE, 0.6, 2 + elif style == "question": + color, scale, thick = COLOR_TEXT, 0.5, 1 + elif style == "correct": + color, scale, thick = COLOR_CORRECT, 0.48, 2 + else: # "option" + color, scale, thick = COLOR_OPTION, 0.45, 1 + + cv2.putText(canvas, text, (PAD + 4, y), FONT, scale, color, thick, FONT_AA) + y += LINE_H + + +# ============================================================================ +# Frame loading +# ============================================================================ + +def load_video_segment(mp4_path: Path, + frame_start: int, + frame_end: int, + target_w: int = CELL_W, + target_h: int = CELL_H + ) -> Tuple[Optional[List[np.ndarray]], float, float, float]: + """Load a range of frames, resized to target dims. + + Returns (frames, fps, scale_x, scale_y) or (None, 0, 0, 0). + scale_x/y map original coords -> display coords for box scaling. + """ + if not mp4_path or not mp4_path.exists(): + return None, 0, 0, 0 + + cap = cv2.VideoCapture(str(mp4_path)) + if not cap.isOpened(): + return None, 0, 0, 0 + + fps = cap.get(cv2.CAP_PROP_FPS) or 30.0 + orig_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH) or 1920 + orig_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) or 1072 + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + frame_start = max(0, frame_start) + frame_end = min(frame_end, total_frames - 1) + n_frames = min(frame_end - frame_start + 1, MAX_RENDER_FRAMES) + + scale_x = target_w / orig_w + scale_y = target_h / orig_h + + frames = [] + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start) + for _ in range(n_frames): + ret, frame = cap.read() + if not ret: + break + frames.append(cv2.resize(frame, (target_w, target_h))) + + cap.release() + return (frames, fps, scale_x, scale_y) if frames else (None, 0, 0, 0) + + +# ============================================================================ +# Bounding box overlay with activity highlighting +# ============================================================================ + +def overlay_boxes(frame: np.ndarray, boxes: list, + scale_x: float, scale_y: float, + highlight_ids: Optional[Set[int]] = None, + is_event_frame: bool = False) -> np.ndarray: + """Draw scaled bounding boxes on a frame. + + During event frames: + - Actors in highlight_ids (or ALL if highlight_ids empty): BLUE, thick + - Other actors: green, thin + During context frames: + - All actors: green, thin + """ + out = frame.copy() + for (x1, y1, x2, y2, actor_id) in boxes: + sx1 = max(0, int(x1 * scale_x)) + sy1 = max(0, int(y1 * scale_y)) + sx2 = min(out.shape[1] - 1, int(x2 * scale_x)) + sy2 = min(out.shape[0] - 1, int(y2 * scale_y)) + + if is_event_frame: + # Highlight if actor is relevant (or if we have no specific IDs, highlight all) + is_relevant = (not highlight_ids) or (actor_id in highlight_ids) + if is_relevant: + color = COLOR_BOX_ACTIVE + thickness = 3 + label = f"A{actor_id} *" + else: + color = COLOR_BOX_BG + thickness = 1 + label = f"A{actor_id}" + else: + color = COLOR_BOX_BG + thickness = 1 + label = f"A{actor_id}" + + cv2.rectangle(out, (sx1, sy1), (sx2, sy2), color, thickness) + cv2.putText(out, label, (sx1, max(sy1 - 5, 12)), + FONT, 0.4, color, 1, FONT_AA) + return out + + +# ============================================================================ +# Grid composition with header, activity labels, and timeline +# ============================================================================ + +def compose_grid(frames_dict: Dict[str, List[np.ndarray]], + cam_metadata: Dict[str, dict], + q_text: str, + options: list, + correct_idx: int, + category: str, + q_idx: int, + fps: float = 30.0) -> List[np.ndarray]: + """Compose multi-camera views into a labelled grid with: + - Dynamic word-wrapped header (question + options + correct answer) + - Camera labels with frame counters + - Activity labels during event frames, [CONTEXT] otherwise + - Blue border around camera cell during events + - Timeline progress bar at bottom with event regions + """ + cameras = sorted(frames_dict.keys()) + n_cams = len(cameras) + if n_cams == 0: + return [] + + # Grid layout + if n_cams == 1: + grid_cols, grid_rows = 1, 1 + elif n_cams == 2: + grid_cols, grid_rows = 2, 1 + elif n_cams <= 4: + grid_cols, grid_rows = 2, 2 + elif n_cams <= 6: + grid_cols, grid_rows = 3, 2 + else: + grid_cols = min(5, math.ceil(math.sqrt(n_cams))) + grid_rows = math.ceil(n_cams / grid_cols) + + # Canvas dimensions + grid_w = grid_cols * CELL_W + PAD * (grid_cols + 1) + grid_h = grid_rows * CELL_H + PAD * (grid_rows + 1) + TIMELINE_H = 32 + + # Compute header + canvas_w = grid_w + header_lines, header_h = _compute_header_layout( + q_text, options, correct_idx, category, q_idx, canvas_w) + + canvas_h = header_h + grid_h + TIMELINE_H + if canvas_w % 2: + canvas_w += 1 + if canvas_h % 2: + canvas_h += 1 + + # Pad shorter camera feeds with last-frame freeze + max_frames = max(len(v) for v in frames_dict.values()) + for cam in cameras: + deficit = max_frames - len(frames_dict[cam]) + if deficit > 0: + frames_dict[cam].extend([frames_dict[cam][-1]] * deficit) + + # Precompute per-camera event local frame ranges (relative to loaded segment) + cam_event_locals: Dict[str, List[Tuple[int, int, str]]] = {} + for cam in cameras: + meta = cam_metadata.get(cam, {}) + f_offset = meta.get("f_start", 0) + event_ranges = meta.get("event_ranges", []) + local_ranges = [] + for er in event_ranges: + fr = er.get("frame_range") + if fr: + local_s = fr[0] - f_offset + local_e = fr[1] - f_offset + local_ranges.append((local_s, local_e, er.get("activity", ""))) + cam_event_locals[cam] = local_ranges + + composed = [] + for fi in range(max_frames): + canvas = np.full((canvas_h, canvas_w, 3), COLOR_BG, dtype=np.uint8) + + # --- Header --- + _draw_header(canvas, header_lines) + + # Separator line below header + cv2.line(canvas, (PAD, header_h - 4), (canvas_w - PAD, header_h - 4), + COLOR_SEPARATOR, 1) + + # --- Camera cells --- + for ci, cam in enumerate(cameras): + row, col = ci // grid_cols, ci % grid_cols + x = PAD + col * (CELL_W + PAD) + y = header_h + PAD + row * (CELL_H + PAD) + + # Safety: skip if cell outside canvas + if y + CELL_H > canvas_h - TIMELINE_H or x + CELL_W > canvas_w: + continue + + frame = frames_dict[cam][fi] + canvas[y:y + CELL_H, x:x + CELL_W] = frame + + # Check if this frame is during an event on this camera + ev_active = False + ev_label = "" + for ls, le, act_label in cam_event_locals.get(cam, []): + if ls <= fi <= le: + ev_active = True + ev_label = act_label + break + + # Camera label (top-left) + ts_sec = fi / fps + cam_label = f"{cam} [{fi + 1}/{max_frames}] {ts_sec:.1f}s" + cv2.putText(canvas, cam_label, (x + 8, y + 22), + FONT, 0.5, COLOR_CAM_LABEL, 2, FONT_AA) + + if ev_active: + # Blue border around cell during event + cv2.rectangle(canvas, (x, y), (x + CELL_W - 1, y + CELL_H - 1), + COLOR_EVENT_BORDER, 3) + + # Activity label at bottom of cell + act_display = f">> {ev_label}" + # Dark background for readability + cv2.rectangle(canvas, (x, y + CELL_H - 28), (x + CELL_W, y + CELL_H), + (0, 0, 0), -1) + cv2.putText(canvas, act_display, (x + 8, y + CELL_H - 8), + FONT, 0.5, COLOR_ACTIVITY_TXT, 1, FONT_AA) + elif cam_event_locals.get(cam): + # Context label at bottom (only if this camera has events) + cv2.putText(canvas, "[CONTEXT]", (x + 8, y + CELL_H - 8), + FONT, 0.4, COLOR_CONTEXT_TXT, 1, FONT_AA) + + # --- Timeline bar --- + tl_y = canvas_h - TIMELINE_H + tl_left = PAD + 4 + tl_right = canvas_w - PAD - 4 + tl_width = tl_right - tl_left + + # Timeline background + cv2.rectangle(canvas, (PAD, tl_y + 4), (canvas_w - PAD, tl_y + TIMELINE_H - 4), + COLOR_TL_BG, -1) + + # Draw event regions on timeline + if max_frames > 1: + for cam, locs in cam_event_locals.items(): + for ls, le, _ in locs: + x1 = tl_left + int(max(0, ls) / (max_frames - 1) * tl_width) + x2 = tl_left + int(min(max_frames - 1, le) / (max_frames - 1) * tl_width) + x1 = max(tl_left, min(tl_right, x1)) + x2 = max(tl_left, min(tl_right, x2)) + cv2.rectangle(canvas, (x1, tl_y + 6), (x2, tl_y + TIMELINE_H - 6), + COLOR_TL_EVENT, -1) + + # Playback cursor + cursor_x = tl_left + int(fi / (max_frames - 1) * tl_width) + cv2.line(canvas, (cursor_x, tl_y + 2), (cursor_x, tl_y + TIMELINE_H - 2), + COLOR_TL_CURSOR, 2) + + # "CONTEXT" / "EVENT" text on timeline + any_active = any( + any(ls <= fi <= le for ls, le, _ in locs) + for locs in cam_event_locals.values() + ) + status_txt = "ACTIVITY" if any_active else "CONTEXT" + cv2.putText(canvas, status_txt, (tl_left + tl_width + 8, tl_y + TIMELINE_H - 10), + FONT, 0.35, COLOR_TEXT, 1, FONT_AA) + + composed.append(canvas) + + return composed + + +# ============================================================================ +# Video writer +# ============================================================================ + +def write_video(frames: List[np.ndarray], output_path: Path, fps: float = 30.0) -> bool: + """Write frames to MP4 -- uses ffmpeg if available, cv2.VideoWriter fallback.""" + if not frames: + return False + + h, w = frames[0].shape[:2] + if w % 2: + w -= 1 + if h % 2: + h -= 1 + + output_path.parent.mkdir(parents=True, exist_ok=True) + + if FFMPEG_BIN: + cmd = [ + FFMPEG_BIN, '-hide_banner', '-loglevel', 'error', + '-f', 'rawvideo', '-pix_fmt', 'bgr24', + '-s', f'{w}x{h}', '-r', str(fps), + '-i', '-', + '-c:v', 'libx264', '-crf', '26', '-preset', 'fast', + '-pix_fmt', 'yuv420p', '-y', str(output_path), + ] + proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE) + for frame in frames: + proc.stdin.write(frame[:h, :w].tobytes()) + proc.stdin.close() + proc.wait() + if proc.returncode != 0: + stderr = proc.stderr.read().decode() + print(f" ffmpeg error: {stderr[:200]}") + return False + return output_path.exists() + else: + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + writer = cv2.VideoWriter(str(output_path), fourcc, fps, (w, h)) + if not writer.isOpened(): + print(f" cv2.VideoWriter failed to open") + return False + for frame in frames: + writer.write(frame[:h, :w]) + writer.release() + return output_path.exists() + + +# ============================================================================ +# Main render function +# ============================================================================ + +def render_question(qa_data: dict, question_idx: int, + output_path: Path = None, verbose: bool = False) -> Optional[Path]: + """Render a single question into a self-contained validation video. + + Features: + - Full question text + all options + correct answer highlighted + - Blue bounding boxes on actors involved in the activity + - 5-second context before/after events + - Activity labels and timeline bar + """ + qa_pairs = qa_data.get("qa_pairs", []) + if question_idx >= len(qa_pairs): + print(f" ERROR: Q{question_idx} out of range (max {len(qa_pairs) - 1})") + return None + + q = qa_pairs[question_idx] + slot = qa_data.get("slot", "unknown") + category = q.get("category", "unknown") + q_text = q.get("naturalized_question") or q.get("question_template", "") + answer_text = q.get("correct_answer") or q.get("answer", "") + options = q.get("options", []) + correct_idx = q.get("correct_answer_index", -1) + + print(f"\n{'=' * 60}") + print(f"Q{question_idx}: {category.upper()}") + print(f" {q_text[:120]}...") + print(f" Answer: {answer_text[:80]}") + + # Extract events from debug_info + events = _extract_events(q, slot) + if not events: + print(f" SKIP: No video events found in debug_info") + return None + + # Group events by camera + cam_events: Dict[str, list] = defaultdict(list) + for ev in events: + cam_events[ev["camera"]].append(ev) + + # Build per-camera metadata with 5-second padding + cam_info: Dict[str, dict] = {} + for cam, evts in cam_events.items(): + clip_file = evts[0]["clip_file"] + fps = evts[0]["fps"] + timing = extract_clip_timing(clip_file) + if not timing: + print(f" WARN: Cannot parse clip_file for {cam}: {clip_file}") + continue + + date, hour, start_time, end_time, site, _ = timing + + # Individual event frame ranges for this camera + event_ranges = [] + for e in evts: + if e.get("frame_range"): + event_ranges.append({ + "frame_range": tuple(e["frame_range"]), + "actor_ids": set(e.get("actor_ids", [])), + "activity": e.get("label", "activity"), + }) + + # Compute loading range: union of all events + 5s padding + ranges = [er["frame_range"] for er in event_ranges] + if ranges: + padding_frames = int(fps * CONTEXT_PADDING_SEC) + f_start = max(0, min(r[0] for r in ranges) - padding_frames) + f_end = max(r[1] for r in ranges) + padding_frames + else: + # No frame range (summarization/counting): show first 10 seconds + f_start = 0 + f_end = int(fps * DEFAULT_CONTEXT_FRAMES / fps * 2) + + # Union of all highlight actor IDs for this camera + all_highlight_ids: Set[int] = set() + for er in event_ranges: + all_highlight_ids.update(er["actor_ids"]) + + cam_info[cam] = { + "clip_file": clip_file, + "fps": fps, + "date": date, + "hour": hour, + "start_time": start_time, + "end_time": end_time, + "site": site, + "f_start": f_start, + "f_end": f_end, + "event_ranges": event_ranges, + "highlight_ids": all_highlight_ids, + } + + if not cam_info: + print(f" SKIP: No parseable camera info") + return None + + # Load frames + geom for each camera + frames_dict: Dict[str, List[np.ndarray]] = {} + scales: Dict[str, Tuple[float, float]] = {} + geom_dict: Dict[str, Dict[int, list]] = {} + + for cam, info in cam_info.items(): + mp4_path = find_mp4(info["date"], info["hour"], info["start_time"], + info["site"], info["end_time"], cam) + if not mp4_path: + if verbose: + print(f" {cam}: MP4 NOT FOUND") + continue + + frames, fps, sx, sy = load_video_segment( + mp4_path, info["f_start"], info["f_end"]) + if not frames: + if verbose: + print(f" {cam}: FAILED TO LOAD") + continue + + frames_dict[cam] = frames + scales[cam] = (sx, sy) + + # Load geom + geom_path = find_geom_file(info["date"], info["hour"], + info["start_time"], info["site"], cam) + if geom_path: + geom_dict[cam] = parse_geom_boxes(geom_path) + + n_geom = len(geom_dict.get(cam, {})) + print(f" {cam}: {len(frames)} frames " + f"(f{info['f_start']}-{info['f_end']}) | " + f"geom={n_geom} frames | {mp4_path.name}") + + if not frames_dict: + print(f" ERROR: No frames loaded for any camera") + return None + + # ---- Overlay bounding boxes with event-aware highlighting ---- + for cam in frames_dict: + boxes_by_frame = geom_dict.get(cam, {}) + if not boxes_by_frame: + continue + + sx, sy = scales[cam] + info = cam_info[cam] + f_offset = info["f_start"] + event_ranges = info["event_ranges"] + highlight_ids = info["highlight_ids"] + + for fi in range(len(frames_dict[cam])): + actual_frame = f_offset + fi + if actual_frame not in boxes_by_frame: + continue + + # Check if this frame is within any event range on this camera + is_event = any( + er["frame_range"][0] <= actual_frame <= er["frame_range"][1] + for er in event_ranges + ) + + frames_dict[cam][fi] = overlay_boxes( + frames_dict[cam][fi], + boxes_by_frame[actual_frame], + sx, sy, + highlight_ids=highlight_ids if is_event else None, + is_event_frame=is_event, + ) + + # ---- Compose grid with header, labels, timeline ---- + composed = compose_grid( + frames_dict, cam_info, + q_text, options, correct_idx, + category, question_idx, fps=30.0 + ) + if not composed: + print(f" ERROR: Grid composition produced no frames") + return None + + # Output path + if not output_path: + vid_dir = _get_video_output_dir(slot) + output_path = vid_dir / f"{slot}_q{question_idx}_{category}.mp4" + + print(f" Writing {len(composed)} frames -> {output_path.name} ...", end="", flush=True) + if write_video(composed, output_path, 30.0): + sz_mb = output_path.stat().st_size / (1024 * 1024) + duration_s = len(composed) / 30.0 + print(f" OK ({sz_mb:.1f} MB, {duration_s:.1f}s)") + return output_path + else: + print(f" FAILED") + return None + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Render question validation videos with activity highlighting") + parser.add_argument("--slot", help="Slot name (e.g., 2018-03-07.17-05.school)") + parser.add_argument("--qa-file", help="QA JSON file path") + parser.add_argument("--question-id", type=int, help="Question index to render (0-based)") + parser.add_argument("--all", action="store_true", help="Render ALL questions in the file") + parser.add_argument("--categories", nargs="+", + help="Only render these categories (e.g., temporal spatial)") + parser.add_argument("--output", "-o", help="Output video path (single question only)") + parser.add_argument("--natural", action="store_true", + help="Use .final.naturalized.json instead of .final.raw.json") + parser.add_argument("-v", "--verbose", action="store_true") + args = parser.parse_args() + + # Resolve QA file + if args.qa_file: + qa_path = Path(args.qa_file) + elif args.slot: + suffix = ".final.naturalized.json" if args.natural else ".final.raw.json" + qa_path = QA_OUTPUT_DIR / f"{args.slot}{suffix}" + if not qa_path.exists() and args.natural: + qa_path = QA_OUTPUT_DIR / f"{args.slot}.final.raw.json" + print(f" (naturalized not found, falling back to raw)") + else: + parser.error("Must provide --slot or --qa-file") + + if not qa_path.exists(): + print(f"ERROR: {qa_path} not found") + return + + with open(qa_path) as f: + qa_data = json.load(f) + + slot = qa_data.get("slot", "unknown") + qa_pairs = qa_data.get("qa_pairs", []) + print(f"Loaded: {qa_path.name}") + print(f"Slot: {slot}") + print(f"Questions: {len(qa_pairs)}") + + # Determine which questions to render + if args.all: + indices = list(range(len(qa_pairs))) + elif args.question_id is not None: + indices = [args.question_id] + else: + indices = [0] + + if args.categories: + cats = set(c.lower() for c in args.categories) + indices = [i for i in indices if qa_pairs[i].get("category", "").lower() in cats] + + print(f"Rendering: {len(indices)} question(s)") + + successes, failures = 0, 0 + for idx in indices: + out = Path(args.output) if (args.output and len(indices) == 1) else None + result = render_question(qa_data, idx, out, verbose=args.verbose) + if result: + successes += 1 + else: + failures += 1 + + vid_dir = _get_video_output_dir(slot) + print(f"\n{'=' * 60}") + print(f"Done: {successes} rendered, {failures} skipped/failed") + print(f"Output dir: {vid_dir}") + if successes: + print(f"Play: ffplay {vid_dir}/{slot}_q*") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/render_question_validation.py.bak b/meva/scripts/v10/render_question_validation.py.bak new file mode 100644 index 0000000..3efeac5 --- /dev/null +++ b/meva/scripts/v10/render_question_validation.py.bak @@ -0,0 +1,678 @@ +#!/usr/bin/env python3 +""" +Render question validation videos: multi-camera grid with geom overlays. + +Creates a video showing all cameras in a question side-by-side/grid, with: +- Bounding boxes from geom.yml overlayed (scaled to display resolution) +- Time span limited to the answer frame range +- Camera labels and timestamps +- Question text overlay at the bottom + +Handles ALL question categories: + temporal → event_a / event_b dicts + event_ordering→ debug_info["events"] list + perception → representative_event dict + spatial → entity_a / entity_b dicts + summarization → clip_files list (no events) + counting → clip_files or cameras_involved (no events) + best_camera → representative_event dict + +Usage: + python3 render_question_validation.py --slot 2018-03-07.17-05.school --question-id 0 + python3 render_question_validation.py --slot 2018-03-07.17-05.school --all + python3 render_question_validation.py --qa-file qa.json --question-id 5 -v +""" + +import argparse +import json +import re +import cv2 +import numpy as np +from pathlib import Path +from collections import defaultdict +import subprocess +import shutil +from typing import Dict, List, Optional, Tuple + + +def _find_ffmpeg() -> Optional[str]: + """Find ffmpeg binary — system PATH first, then imageio-ffmpeg fallback.""" + path = shutil.which("ffmpeg") + if path: + return path + try: + import imageio_ffmpeg + return imageio_ffmpeg.get_ffmpeg_exe() + except ImportError: + return None + +FFMPEG_BIN = _find_ffmpeg() + +# ============================================================================ +# Paths +# ============================================================================ +MEVA_MP4_ROOT = Path("/nas/mars/dataset/MEVA/mp4s") +KITWARE_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware") +KITWARE_TRAINING_BASE = Path("/nas/mars/dataset/MEVA/meva-data-repo/annotation/DIVA-phase-2/MEVA/kitware-meva-training") +QA_OUTPUT_DIR = Path("/home/ah66742/data/qa_pairs") +VIDEO_OUTPUT_DIR = Path("/home/ah66742/output/validation_videos") + +VIDEO_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + +# ============================================================================ +# Display constants +# ============================================================================ +CELL_W, CELL_H = 640, 360 # Each camera cell +PAD = 20 # Padding between cells +HEADER_H = 60 # Question text header height +COLOR_BOX = (0, 255, 0) # Green bounding boxes +COLOR_TEXT = (255, 255, 255) # White labels +COLOR_BGND = (40, 40, 40) # Dark gray canvas +COLOR_CAM = (0, 200, 255) # Orange camera label +MAX_RENDER_FRAMES = 300 # Cap at 10s @ 30fps (avoid huge files) +DEFAULT_CONTEXT_SEC = 5.0 # For categories with no frame range + +# ============================================================================ +# Geom parsing (regex approach — fast, no YAML dependency) +# ============================================================================ +_RE_ID1 = re.compile(r"['\"]?id1['\"]?\s*:\s*['\"]?(\d+)") +_RE_TS0 = re.compile(r"['\"]?ts0['\"]?\s*:\s*['\"]?(\d+)") +_RE_G0 = re.compile(r"['\"]?g0['\"]?\s*:\s*['\"]?(\d+)\s+(\d+)\s+(\d+)\s+(\d+)") + + +def parse_geom_boxes(geom_file: Path) -> Dict[int, list]: + """Extract bounding boxes from geom.yml by frame number. + + Returns {frame_number: [(x1, y1, x2, y2, actor_id), ...]} + """ + boxes_by_frame: Dict[int, list] = defaultdict(list) + if not geom_file.exists(): + return boxes_by_frame + + with open(geom_file) as f: + for line in f: + line = line.strip() + if not line or line.startswith('#'): + continue + m_id = _RE_ID1.search(line) + m_ts = _RE_TS0.search(line) + m_g0 = _RE_G0.search(line) + if m_id and m_ts and m_g0: + actor_id = int(m_id.group(1)) + frame = int(m_ts.group(1)) + x1, y1, x2, y2 = map(int, m_g0.groups()) + boxes_by_frame[frame].append((x1, y1, x2, y2, actor_id)) + + return dict(boxes_by_frame) + + +# ============================================================================ +# File finders +# ============================================================================ + +def _slot_dir_name(date: str, start_time: str, site: str) -> str: + """Build slot directory name using HH-MM format (no seconds). + + Clip filenames use HH-MM-SS, but slot directories use HH-MM. + Example: start_time='17-05-00' → dir '2018-03-07.17-05.school' + """ + parts = start_time.split("-") + hh_mm = f"{parts[0]}-{parts[1]}" if len(parts) >= 2 else start_time + return f"{date}.{hh_mm}.{site}" + + +def find_mp4(date: str, hour: str, start_time: str, site: str, + end_time: str, camera: str) -> Optional[Path]: + """Find MP4 file for a camera, using slot-grouped directory structure. + + Directory: /mp4s/{date}/{hour}/{date}.{HH-MM}.{site}/ + File: {date}.{start_time}.{end_time}.{site}.{camera}.r13.mp4 + """ + slot_name = _slot_dir_name(date, start_time, site) + slot_dir = MEVA_MP4_ROOT / date / hour / slot_name + if not slot_dir.exists(): + # Fallback: try globbing for the slot directory + parent = MEVA_MP4_ROOT / date / hour + if parent.exists(): + candidates = list(parent.glob(f"{date}.*{site}")) + if candidates: + slot_dir = candidates[0] + else: + return None + else: + return None + + # Try exact glob + pattern = f"*{camera}*.r13.mp4" + matches = list(slot_dir.glob(pattern)) + if matches: + return matches[0] + # Broader fallback + matches = list(slot_dir.glob(f"*{camera}*.mp4")) + return matches[0] if matches else None + + +def find_geom_file(date: str, hour: str, start_time: str, + site: str, camera: str) -> Optional[Path]: + """Find geom.yml file for a camera.""" + prefix = f"{date}.{start_time}" + for kitware_dir in [KITWARE_BASE, KITWARE_TRAINING_BASE]: + ann_dir = kitware_dir / date / hour + if not ann_dir.exists(): + continue + matches = list(ann_dir.glob(f"{prefix}*.{site}.{camera}.geom.yml")) + if matches: + return matches[0] + return None + + +def extract_clip_timing(clip_file: str) -> Optional[Tuple[str, str, str, str, str, str]]: + """Parse clip filename → (date, hour, start_time, end_time, site, camera). + + Input: '2018-03-07.17-05-00.17-10-00.school.G339.r13.mp4' + """ + name = Path(clip_file).name + # Strip common suffixes + for suffix in [".r13.mp4", ".r13.avi", ".mp4", ".avi"]: + if name.endswith(suffix): + name = name[: -len(suffix)] + break + parts = name.split(".") + if len(parts) >= 5: + date, start_time, end_time, site, camera = parts[0], parts[1], parts[2], parts[3], parts[4] + hour = start_time.split("-")[0] + return date, hour, start_time, end_time, site, camera + return None + + +# ============================================================================ +# Event extraction — handles ALL debug_info formats +# ============================================================================ + +def _extract_events(q: dict, slot: str) -> List[dict]: + """Extract a unified list of event dicts from any question category. + + Each returned event dict has at minimum: + camera, clip_file, frame_range, fps, label + """ + debug = q.get("debug_info", {}) + category = q.get("category", "") + events: List[dict] = [] + + # --- Pattern 1: top-level event dicts (temporal, perception, spatial, best_camera) --- + for key, val in debug.items(): + if isinstance(val, dict) and "camera" in val and val.get("clip_file"): + events.append({ + "camera": val["camera"], + "clip_file": val["clip_file"], + "frame_range": val.get("frame_range"), + "fps": val.get("fps", 30.0), + "actor_ids": val.get("actor_ids", []), + "label": val.get("activity", key), + }) + + # --- Pattern 2: events LIST (event_ordering) --- + if "events" in debug and isinstance(debug["events"], list): + for ev in debug["events"]: + if isinstance(ev, dict) and "camera" in ev and ev.get("clip_file"): + events.append({ + "camera": ev["camera"], + "clip_file": ev["clip_file"], + "frame_range": ev.get("frame_range"), + "fps": ev.get("fps", 30.0), + "actor_ids": ev.get("actor_ids", []), + "label": ev.get("activity", "event"), + }) + + # --- Pattern 3: no events, but clip_files list (summarization, counting) --- + if not events and "clip_files" in debug: + for cf in debug["clip_files"]: + timing = extract_clip_timing(cf) + if timing: + _date, _hour, _st, _et, _site, _camera = timing + events.append({ + "camera": _camera, + "clip_file": cf, + "frame_range": None, + "fps": 30.0, + "actor_ids": [], + "label": category, + }) + + # --- Pattern 4: no events, no clip_files, but cameras list --- + if not events: + cameras = q.get("requires_cameras", []) + # Try to derive clip_files from slot name + # slot format: 2018-03-07.17-05.school + slot_parts = slot.split(".") + if len(slot_parts) >= 3: + date = slot_parts[0] + hhmm = slot_parts[1] # e.g. 17-05 + site = slot_parts[2] + hour = hhmm.split("-")[0] + for cam in cameras: + mp4 = find_mp4(date, hour, f"{hhmm}-00", site, "", cam) + if mp4: + events.append({ + "camera": cam, + "clip_file": mp4.name, + "frame_range": None, + "fps": 30.0, + "actor_ids": [], + "label": category, + }) + + return events + + +# ============================================================================ +# Frame loading with proper scaling +# ============================================================================ + +def load_video_segment(mp4_path: Path, + frame_start: int, + frame_end: int, + target_w: int = CELL_W, + target_h: int = CELL_H + ) -> Tuple[Optional[List[np.ndarray]], float, float, float]: + """Load a range of frames, resized to target dims. + + Returns (frames, fps, scale_x, scale_y) or (None, 0, 0, 0). + scale_x/y map original coords → display coords for box scaling. + """ + if not mp4_path or not mp4_path.exists(): + return None, 0, 0, 0 + + cap = cv2.VideoCapture(str(mp4_path)) + if not cap.isOpened(): + return None, 0, 0, 0 + + fps = cap.get(cv2.CAP_PROP_FPS) or 30.0 + orig_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH) or 1920 + orig_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) or 1072 + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + # Clamp frame range + frame_start = max(0, frame_start) + frame_end = min(frame_end, total_frames - 1) + n_frames = min(frame_end - frame_start + 1, MAX_RENDER_FRAMES) + + scale_x = target_w / orig_w + scale_y = target_h / orig_h + + frames = [] + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start) + for _ in range(n_frames): + ret, frame = cap.read() + if not ret: + break + frames.append(cv2.resize(frame, (target_w, target_h))) + + cap.release() + return (frames, fps, scale_x, scale_y) if frames else (None, 0, 0, 0) + + +def overlay_boxes(frame: np.ndarray, boxes: list, + scale_x: float, scale_y: float) -> np.ndarray: + """Draw scaled bounding boxes on a (resized) frame.""" + out = frame.copy() + for (x1, y1, x2, y2, actor_id) in boxes: + sx1, sy1 = int(x1 * scale_x), int(y1 * scale_y) + sx2, sy2 = int(x2 * scale_x), int(y2 * scale_y) + cv2.rectangle(out, (sx1, sy1), (sx2, sy2), COLOR_BOX, 2) + label = f"A{actor_id}" + cv2.putText(out, label, (sx1, max(sy1 - 5, 12)), + cv2.FONT_HERSHEY_SIMPLEX, 0.45, COLOR_TEXT, 1, cv2.LINE_AA) + return out + + +# ============================================================================ +# Grid composition +# ============================================================================ + +def compose_grid(frames_dict: Dict[str, List[np.ndarray]], + question_text: str, + answer_text: str = "", + fps: float = 30.0) -> List[np.ndarray]: + """Compose multi-camera views into a labelled grid with question header. + + Uses cameras present in frames_dict (not requires_cameras) so only + cameras that were successfully loaded appear. + """ + cameras = sorted(frames_dict.keys()) + n_cams = len(cameras) + if n_cams == 0: + return [] + + # Grid layout — compute rows/cols dynamically + import math + if n_cams == 1: + grid_rows, grid_cols = 1, 1 + elif n_cams == 2: + grid_rows, grid_cols = 1, 2 + elif n_cams <= 4: + grid_rows, grid_cols = 2, 2 + elif n_cams <= 6: + grid_rows, grid_cols = 2, 3 + else: + grid_cols = min(4, math.ceil(math.sqrt(n_cams))) + grid_rows = math.ceil(n_cams / grid_cols) + + comp_w = grid_cols * CELL_W + PAD * (grid_cols + 1) + comp_h = HEADER_H + grid_rows * CELL_H + PAD * (grid_rows + 1) + + # Pad shorter camera feeds with last-frame freeze + max_frames = max(len(v) for v in frames_dict.values()) + for cam in cameras: + deficit = max_frames - len(frames_dict[cam]) + if deficit > 0: + frames_dict[cam].extend([frames_dict[cam][-1]] * deficit) + + composed = [] + for fi in range(max_frames): + canvas = np.full((comp_h, comp_w, 3), COLOR_BGND, dtype=np.uint8) + + # Draw question text header + q_short = question_text[:120] + ("..." if len(question_text) > 120 else "") + cv2.putText(canvas, q_short, (PAD, 35), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_TEXT, 1, cv2.LINE_AA) + if answer_text: + a_short = f"Answer: {answer_text[:80]}" + cv2.putText(canvas, a_short, (PAD, 52), + cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 100), 1, cv2.LINE_AA) + + for ci, cam in enumerate(cameras): + row, col = ci // grid_cols, ci % grid_cols + x = PAD + col * (CELL_W + PAD) + y = HEADER_H + PAD + row * (CELL_H + PAD) + + # Safety check — skip if cell falls outside canvas + if y + CELL_H > comp_h or x + CELL_W > comp_w: + continue + + frame = frames_dict[cam][fi] + canvas[y:y + CELL_H, x:x + CELL_W] = frame + + # Camera label with frame counter + label = f"{cam} [{fi + 1}/{max_frames}]" + cv2.putText(canvas, label, (x + 8, y + 22), + cv2.FONT_HERSHEY_SIMPLEX, 0.55, COLOR_CAM, 2, cv2.LINE_AA) + + composed.append(canvas) + + return composed + + +# ============================================================================ +# Video writer +# ============================================================================ + +def write_video(frames: List[np.ndarray], output_path: Path, fps: float = 30.0) -> bool: + """Write frames to MP4 — uses ffmpeg if available, cv2.VideoWriter fallback.""" + if not frames: + return False + + h, w = frames[0].shape[:2] + # Ensure dimensions are even (required by libx264) + if w % 2: w -= 1 + if h % 2: h -= 1 + + output_path.parent.mkdir(parents=True, exist_ok=True) + + if FFMPEG_BIN: + cmd = [ + FFMPEG_BIN, '-hide_banner', '-loglevel', 'error', + '-f', 'rawvideo', '-pix_fmt', 'bgr24', + '-s', f'{w}x{h}', '-r', str(fps), + '-i', '-', + '-c:v', 'libx264', '-crf', '26', '-preset', 'fast', + '-pix_fmt', 'yuv420p', '-y', str(output_path), + ] + proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE) + for frame in frames: + proc.stdin.write(frame[:h, :w].tobytes()) + proc.stdin.close() + proc.wait() + if proc.returncode != 0: + stderr = proc.stderr.read().decode() + print(f" ffmpeg error: {stderr[:200]}") + return False + return output_path.exists() + else: + # Fallback: cv2.VideoWriter with mp4v codec + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + writer = cv2.VideoWriter(str(output_path), fourcc, fps, (w, h)) + if not writer.isOpened(): + print(f" cv2.VideoWriter failed to open") + return False + for frame in frames: + writer.write(frame[:h, :w]) + writer.release() + return output_path.exists() + + +# ============================================================================ +# Main render function +# ============================================================================ + +def render_question(qa_data: dict, question_idx: int, + output_path: Path = None, verbose: bool = False) -> Optional[Path]: + """Render a single question into a validation video. + + Works for ALL question categories by using _extract_events(). + """ + qa_pairs = qa_data.get("qa_pairs", []) + if question_idx >= len(qa_pairs): + print(f" ERROR: Q{question_idx} out of range (max {len(qa_pairs) - 1})") + return None + + q = qa_pairs[question_idx] + slot = qa_data.get("slot", "unknown") + category = q.get("category", "unknown") + q_text = q.get("naturalized_question") or q.get("question_template", "") + answer_text = q.get("correct_answer") or q.get("answer", "") + + print(f"\n{'='*60}") + print(f"Q{question_idx}: {category.upper()}") + print(f" {q_text[:100]}...") + print(f" Answer: {answer_text[:80]}") + + # Extract events from debug_info (handles all category formats) + events = _extract_events(q, slot) + if not events: + print(f" SKIP: No video events found in debug_info") + return None + + # Group events by camera + cam_events: Dict[str, list] = defaultdict(list) + for ev in events: + cam_events[ev["camera"]].append(ev) + + # Compute frame range per camera (union of all events on that camera) + cam_info: Dict[str, dict] = {} + for cam, evts in cam_events.items(): + # Pick clip_file from first event (all events on same camera share clip) + clip_file = evts[0]["clip_file"] + fps = evts[0]["fps"] + timing = extract_clip_timing(clip_file) + if not timing: + print(f" WARN: Cannot parse clip_file for {cam}: {clip_file}") + continue + + date, hour, start_time, end_time, site, _ = timing + + # Compute frame range — union of all events, with context padding + ranges = [e["frame_range"] for e in evts if e.get("frame_range")] + if ranges: + f_start = max(0, min(r[0] for r in ranges) - int(fps * 1.0)) # 1s before + f_end = max(r[1] for r in ranges) + int(fps * 1.0) # 1s after + else: + # No frame range (summarization/counting) — show middle 5 seconds + f_start = 0 + f_end = int(fps * DEFAULT_CONTEXT_SEC) + + cam_info[cam] = { + "clip_file": clip_file, + "fps": fps, + "date": date, + "hour": hour, + "start_time": start_time, + "end_time": end_time, + "site": site, + "f_start": f_start, + "f_end": f_end, + "actor_ids": list({aid for e in evts for aid in e.get("actor_ids", [])}), + } + + if not cam_info: + print(f" SKIP: No parseable camera info") + return None + + # Load frames + geom for each camera + frames_dict: Dict[str, List[np.ndarray]] = {} + scales: Dict[str, Tuple[float, float]] = {} + geom_dict: Dict[str, Dict[int, list]] = {} + + for cam, info in cam_info.items(): + mp4_path = find_mp4(info["date"], info["hour"], info["start_time"], + info["site"], info["end_time"], cam) + if not mp4_path: + if verbose: + print(f" {cam}: MP4 NOT FOUND") + continue + + frames, fps, sx, sy = load_video_segment( + mp4_path, info["f_start"], info["f_end"]) + if not frames: + if verbose: + print(f" {cam}: FAILED TO LOAD") + continue + + frames_dict[cam] = frames + scales[cam] = (sx, sy) + + # Load geom + geom_path = find_geom_file(info["date"], info["hour"], + info["start_time"], info["site"], cam) + if geom_path: + geom_dict[cam] = parse_geom_boxes(geom_path) + + n_geom = len(geom_dict.get(cam, {})) + print(f" {cam}: {len(frames)} frames " + f"(f{info['f_start']}-{info['f_end']}) | " + f"geom={n_geom} frames | {mp4_path.name}") + + if not frames_dict: + print(f" ERROR: No frames loaded for any camera") + return None + + # Overlay geom bounding boxes (scaled) + for cam in frames_dict: + boxes_by_frame = geom_dict.get(cam, {}) + if not boxes_by_frame: + continue + sx, sy = scales[cam] + f_offset = cam_info[cam]["f_start"] + for fi in range(len(frames_dict[cam])): + actual_frame = f_offset + fi + if actual_frame in boxes_by_frame: + frames_dict[cam][fi] = overlay_boxes( + frames_dict[cam][fi], boxes_by_frame[actual_frame], sx, sy) + + # Compose grid + composed = compose_grid(frames_dict, q_text, answer_text, 30.0) + if not composed: + print(f" ERROR: Grid composition produced no frames") + return None + + # Output path + if not output_path: + output_path = VIDEO_OUTPUT_DIR / f"{slot}_q{question_idx}_{category}.mp4" + + print(f" Writing {len(composed)} frames → {output_path.name} ...", end="", flush=True) + if write_video(composed, output_path, 30.0): + sz_mb = output_path.stat().st_size / (1024 * 1024) + print(f" OK ({sz_mb:.1f} MB)") + return output_path + else: + print(f" FAILED") + return None + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Render question validation videos with multi-camera grids") + parser.add_argument("--slot", help="Slot name (e.g., 2018-03-07.17-05.school)") + parser.add_argument("--qa-file", help="QA JSON file path") + parser.add_argument("--question-id", type=int, help="Question index to render (0-based)") + parser.add_argument("--all", action="store_true", help="Render ALL questions in the file") + parser.add_argument("--categories", nargs="+", + help="Only render these categories (e.g., temporal spatial)") + parser.add_argument("--output", "-o", help="Output video path (single question only)") + parser.add_argument("--natural", action="store_true", + help="Use .final.naturalized.json instead of .final.raw.json") + parser.add_argument("-v", "--verbose", action="store_true") + args = parser.parse_args() + + # Resolve QA file + if args.qa_file: + qa_path = Path(args.qa_file) + elif args.slot: + suffix = ".final.naturalized.json" if args.natural else ".final.raw.json" + qa_path = QA_OUTPUT_DIR / f"{args.slot}{suffix}" + if not qa_path.exists() and args.natural: + qa_path = QA_OUTPUT_DIR / f"{args.slot}.final.raw.json" + print(f" (naturalized not found, falling back to raw)") + else: + parser.error("Must provide --slot or --qa-file") + + if not qa_path.exists(): + print(f"ERROR: {qa_path} not found") + return + + with open(qa_path) as f: + qa_data = json.load(f) + + slot = qa_data.get("slot", "unknown") + qa_pairs = qa_data.get("qa_pairs", []) + print(f"Loaded: {qa_path.name}") + print(f"Slot: {slot}") + print(f"Questions: {len(qa_pairs)}") + + # Determine which questions to render + if args.all: + indices = list(range(len(qa_pairs))) + elif args.question_id is not None: + indices = [args.question_id] + else: + indices = [0] + + # Filter by category if requested + if args.categories: + cats = set(c.lower() for c in args.categories) + indices = [i for i in indices if qa_pairs[i].get("category", "").lower() in cats] + + print(f"Rendering: {len(indices)} question(s)") + + successes, failures = 0, 0 + for idx in indices: + out = Path(args.output) if (args.output and len(indices) == 1) else None + result = render_question(qa_data, idx, out, verbose=args.verbose) + if result: + successes += 1 + else: + failures += 1 + + print(f"\n{'='*60}") + print(f"Done: {successes} rendered, {failures} skipped/failed") + print(f"Output dir: {VIDEO_OUTPUT_DIR}") + if successes: + print(f"Play: ffplay {VIDEO_OUTPUT_DIR}/{slot}_q*") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/review_qa.py b/meva/scripts/v10/review_qa.py new file mode 100644 index 0000000..3483c23 --- /dev/null +++ b/meva/scripts/v10/review_qa.py @@ -0,0 +1,456 @@ +#!/usr/bin/env python3 +""" +Interactive QA Review CLI — manual verification of generated questions. + +For each question in a slot, displays: + - Category, cameras, question text, answer, options + - Debug info summary (frame ranges, connection strength, etc.) + - Path to validation video (for playback) + - Checklist of things to verify + +Usage: + # Review a slot (auto-finds raw JSON) + python3 -m scripts.final.review_qa --slot 2018-03-07.17-05.school + + # Review naturalized version + python3 -m scripts.final.review_qa --slot 2018-03-07.17-05.school --natural + + # Review specific QA file + python3 -m scripts.final.review_qa --qa-file data/qa_pairs/SLOT.final.raw.json + + # Generate audit report without interactive prompts + python3 -m scripts.final.review_qa --slot 2018-03-07.17-05.school --report +""" + +import argparse +import json +import os +import sys +import textwrap +from pathlib import Path +from datetime import datetime + +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) + +QA_DIR = _OUTPUT / "qa_pairs" +VIDEO_DIR = _OUTPUT / "validation_videos" +AUDIT_DIR = _OUTPUT / "qa_audits" +AUDIT_DIR.mkdir(parents=True, exist_ok=True) + +# ANSI colors +C_RESET = "\033[0m" +C_BOLD = "\033[1m" +C_GREEN = "\033[32m" +C_RED = "\033[31m" +C_YELLOW = "\033[33m" +C_CYAN = "\033[36m" +C_DIM = "\033[2m" + +CATEGORY_ORDER = ["temporal", "event_ordering", "perception", "spatial", + "summarization", "counting", "best_camera"] + +CHECKLIST = [ + "Answer observable in video", + "Cameras minimal & sufficient", + "Geom boxes track actors", + "Question clear & unambiguous", + "Frame range covers activity", +] + + +def fmt_debug(q: dict) -> str: + """Format debug_info into a readable summary.""" + debug = q.get("debug_info", {}) + cat = q.get("category", "") + lines = [] + + # Temporal-specific + if cat == "temporal": + lines.append(f" Connection: {debug.get('connection_strength', '?')} " + f"(score={debug.get('connection_score', '?')})") + lines.append(f" Gap: {debug.get('gap_sec', '?')}s | " + f"MEVID: {debug.get('mevid_validated', False)}") + for key in ["event_a", "event_b"]: + ev = debug.get(key, {}) + if ev: + lines.append(f" {key}: {ev.get('activity','?')} on {ev.get('camera','?')} " + f"frames={ev.get('frame_range','?')}") + + # Event ordering + elif cat == "event_ordering": + events = debug.get("events", []) + lines.append(f" Events: {len(events)} | " + f"Group score: {debug.get('group_score', '?')} | " + f"MEVID: {debug.get('mevid_validated', False)}") + for i, ev in enumerate(events): + lines.append(f" [{i+1}] {ev.get('activity','?')} on {ev.get('camera','?')} " + f"frames={ev.get('frame_range','?')}") + + # Perception + elif cat == "perception": + lines.append(f" Type: {debug.get('question_type', '?')}") + rep = debug.get("representative_event", {}) + if rep: + lines.append(f" Event: {rep.get('activity','?')} on {rep.get('camera','?')} " + f"frames={rep.get('frame_range','?')}") + + # Spatial + elif cat == "spatial": + lines.append(f" Distance: {debug.get('distance_meters', '?')}m " + f"({debug.get('proximity', '?')})") + lines.append(f" Method: {debug.get('projection_method', '?')}") + for key in ["entity_a", "entity_b"]: + ent = debug.get(key, {}) + if ent: + lines.append(f" {key}: actor={ent.get('actor_ids','?')} on " + f"{ent.get('camera','?')} frames={ent.get('frame_range','?')}") + + # Summarization + elif cat == "summarization": + lines.append(f" Type: {debug.get('question_type', '?')}") + lines.append(f" Scene: {debug.get('scene_type', '?')}") + cf = debug.get("clip_files", []) + lines.append(f" Clip files: {len(cf)}") + + # Counting + elif cat == "counting": + lines.append(f" Subtype: {debug.get('subtype', '?')}") + lines.append(f" Correct count: {debug.get('correct_count', '?')}") + lines.append(f" Cross-camera: {debug.get('cross_camera', '?')}") + lines.append(f" Cameras: {debug.get('cameras_involved', '?')}") + + # Best camera + elif cat == "best_camera": + rep = debug.get("representative_event", {}) + if rep: + lines.append(f" Event: {rep.get('activity','?')} on {rep.get('camera','?')} " + f"frames={rep.get('frame_range','?')}") + + return "\n".join(lines) if lines else " (no debug info)" + + +def display_question(q: dict, idx: int, total: int, slot: str, show_video_path: bool = True): + """Display a single question for review.""" + cat = q.get("category", "unknown") + cameras = q.get("requires_cameras", []) + q_text = q.get("naturalized_question") or q.get("question_template", "") + answer = q.get("correct_answer") or q.get("answer", "N/A") + correct_idx = q.get("correct_answer_index") + options = q.get("options", []) + + print(f"\n{C_BOLD}{'='*72}{C_RESET}") + print(f"{C_BOLD}Q{idx}/{total-1} [{cat.upper()}] Cameras: {', '.join(cameras)}{C_RESET}") + print(f"{'='*72}") + + # Question text (wrapped) + print(f"\n{C_CYAN}Question:{C_RESET}") + for line in textwrap.wrap(q_text, width=70): + print(f" {line}") + + # Options + if options: + print(f"\n{C_CYAN}Options:{C_RESET}") + for i, opt in enumerate(options): + is_correct = (i == correct_idx) if correct_idx is not None else (opt.strip() == str(answer).strip()) + marker = f" {C_GREEN}← ANSWER{C_RESET}" if is_correct else "" + print(f" {chr(65+i)}) {opt[:80]}{marker}") + else: + print(f"\n{C_CYAN}Answer:{C_RESET} {answer}") + + # Debug summary + print(f"\n{C_CYAN}Debug:{C_RESET}") + print(fmt_debug(q)) + + # Video path + if show_video_path: + video = VIDEO_DIR / f"{slot}_q{idx}_{cat}.mp4" + if video.exists(): + sz = video.stat().st_size / 1024 + print(f"\n{C_DIM}Video: {video} ({sz:.0f}KB){C_RESET}") + else: + print(f"\n{C_YELLOW}Video: NOT RENDERED{C_RESET}") + + +def interactive_review(qa_data: dict): + """Interactive review loop — walk through each question.""" + slot = qa_data.get("slot", "unknown") + qa_pairs = qa_data.get("qa_pairs", []) + total = len(qa_pairs) + + verdicts = {} # idx -> {"verdict": "pass"/"fail"/"skip", "notes": "..."} + + print(f"\n{C_BOLD}╔══════════════════════════════════════════════════╗{C_RESET}") + print(f"{C_BOLD}║ Interactive QA Review — {slot:<24s}║{C_RESET}") + print(f"{C_BOLD}║ {total} questions to review{' '*26}║{C_RESET}") + print(f"{C_BOLD}╚══════════════════════════════════════════════════╝{C_RESET}") + + idx = 0 + while idx < total: + q = qa_pairs[idx] + display_question(q, idx, total, slot) + + # Checklist + print(f"\n{C_CYAN}Checklist:{C_RESET}") + for item in CHECKLIST: + print(f" □ {item}") + + # Prompt + print(f"\n{C_BOLD}Actions:{C_RESET}") + print(f" {C_GREEN}[p]ass{C_RESET} {C_RED}[f]ail{C_RESET} " + f"[s]kip [n]otes [v]ideo [b]ack [q]uit [j]ump #") + try: + resp = input(f"\n Q{idx} verdict > ").strip().lower() + except (EOFError, KeyboardInterrupt): + print("\n Interrupted.") + break + + if resp == 'q': + break + elif resp == 'b': + idx = max(0, idx - 1) + continue + elif resp.startswith('j'): + try: + target = int(resp[1:].strip()) + if 0 <= target < total: + idx = target + else: + print(f" Invalid index (0-{total-1})") + except ValueError: + print(f" Usage: j5 (jump to Q5)") + continue + elif resp == 'v': + video = VIDEO_DIR / f"{slot}_q{idx}_{q['category']}.mp4" + if video.exists(): + print(f" Opening: {video}") + os.system(f"nohup xdg-open '{video}' >/dev/null 2>&1 &") + else: + print(f" {C_RED}Video not found. Run render_question_validation.py --slot {slot} --all{C_RESET}") + continue + elif resp == 'n': + notes = input(" Notes > ").strip() + verdicts.setdefault(idx, {})["notes"] = notes + print(f" {C_DIM}Notes saved for Q{idx}{C_RESET}") + continue + elif resp in ('p', 'pass', ''): + verdicts[idx] = {**verdicts.get(idx, {}), "verdict": "pass"} + print(f" {C_GREEN}✓ Q{idx} PASS{C_RESET}") + elif resp in ('f', 'fail'): + notes = input(" Failure reason > ").strip() + verdicts[idx] = {"verdict": "fail", "notes": notes} + print(f" {C_RED}✗ Q{idx} FAIL: {notes}{C_RESET}") + elif resp == 's': + verdicts[idx] = {**verdicts.get(idx, {}), "verdict": "skip"} + print(f" {C_YELLOW}— Q{idx} SKIPPED{C_RESET}") + else: + print(f" Unknown command: {resp}") + continue + + idx += 1 + + # Summary + return print_summary(qa_data, verdicts) + + +def print_summary(qa_data: dict, verdicts: dict) -> dict: + """Print review summary and return audit dict.""" + slot = qa_data.get("slot", "unknown") + qa_pairs = qa_data.get("qa_pairs", []) + total = len(qa_pairs) + + passes = sum(1 for v in verdicts.values() if v.get("verdict") == "pass") + fails = sum(1 for v in verdicts.values() if v.get("verdict") == "fail") + skips = sum(1 for v in verdicts.values() if v.get("verdict") == "skip") + unreviewed = total - len(verdicts) + + print(f"\n{C_BOLD}{'='*72}{C_RESET}") + print(f"{C_BOLD}REVIEW SUMMARY — {slot}{C_RESET}") + print(f"{'='*72}") + print(f" {C_GREEN}Pass: {passes}{C_RESET} | {C_RED}Fail: {fails}{C_RESET} | " + f"{C_YELLOW}Skip: {skips}{C_RESET} | Unreviewed: {unreviewed}") + pct = (passes / total * 100) if total else 0 + print(f" Pass rate: {pct:.0f}% ({passes}/{total})") + + if pct >= 80: + print(f"\n {C_GREEN}✓ READY FOR BATCH PROCESSING{C_RESET}") + elif pct >= 50: + print(f"\n {C_YELLOW}⚠ NEEDS TARGETED FIXES{C_RESET}") + else: + print(f"\n {C_RED}✗ SIGNIFICANT ISSUES — DO NOT BATCH{C_RESET}") + + # Show failures + if fails: + print(f"\n{C_RED}FAILURES:{C_RESET}") + for idx, v in sorted(verdicts.items()): + if v.get("verdict") == "fail": + cat = qa_pairs[idx]["category"] + print(f" Q{idx} [{cat}]: {v.get('notes', 'no reason')}") + + # Per-category breakdown + print(f"\n{C_CYAN}Per-Category:{C_RESET}") + cat_stats = {} + for idx, q in enumerate(qa_pairs): + cat = q["category"] + v = verdicts.get(idx, {}).get("verdict", "unreviewed") + cat_stats.setdefault(cat, []).append(v) + for cat in CATEGORY_ORDER: + if cat in cat_stats: + results = cat_stats[cat] + p = results.count("pass") + f = results.count("fail") + icon = C_GREEN + "✓" if f == 0 else C_RED + "✗" + print(f" {icon} {cat:<18s}{C_RESET} pass={p} fail={f} total={len(results)}") + + # Build audit record + audit = { + "slot": slot, + "date": datetime.now().isoformat(), + "total": total, + "pass": passes, + "fail": fails, + "skip": skips, + "pass_rate": round(pct, 1), + "verdicts": {str(k): v for k, v in verdicts.items()}, + "version": qa_data.get("version", "unknown"), + } + return audit + + +def generate_report(qa_data: dict) -> dict: + """Non-interactive report — auto-checks for common issues.""" + slot = qa_data.get("slot", "unknown") + qa_pairs = qa_data.get("qa_pairs", []) + total = len(qa_pairs) + + print(f"\n{C_BOLD}AUTO-REPORT — {slot}{C_RESET}") + print(f"{'='*72}") + + issues = [] + verdicts = {} + + for idx, q in enumerate(qa_pairs): + cat = q.get("category", "") + debug = q.get("debug_info", {}) + cameras = q.get("requires_cameras", []) + q_text = q.get("question_template", "") + options = q.get("options", []) + q_issues = [] + + correct_answer = q.get("correct_answer") or q.get("answer", "") + correct_idx = q.get("correct_answer_index") + + # Check 1: Empty answer + if not correct_answer and correct_idx is None: + q_issues.append("EMPTY ANSWER") + + # Check 2: Answer not in options + if options and correct_answer and correct_answer not in options: + # Also check by index + if correct_idx is None or correct_idx >= len(options): + q_issues.append(f"ANSWER NOT IN OPTIONS") + + # Check 3: Too many cameras for simple categories + # best_camera inherently needs all cameras (it picks the best one) + if cat == "perception" and len(cameras) > 10: + q_issues.append(f"TOO MANY CAMERAS ({len(cameras)})") + + # Check 4: No debug_info + if not debug: + q_issues.append("NO DEBUG INFO") + + # Check 5: Missing frame range (for event-based categories) + if cat == "temporal": + for key in ["event_a", "event_b"]: + ev = debug.get(key, {}) + if ev and not ev.get("frame_range"): + q_issues.append(f"MISSING FRAME RANGE in {key}") + # Spatial entities use camera-center distance; frame_range is optional + # (entity may only have actor_ids + camera, no specific frames) + + # Check 6: Temporal — weak connection + if cat == "temporal": + strength = debug.get("connection_strength", "") + if strength == "weak": + q_issues.append(f"WEAK CONNECTION (low quality)") + + # Check 7: No clip_file in events + if cat in ("temporal", "spatial"): + for key in ["event_a", "event_b", "entity_a", "entity_b"]: + ev = debug.get(key, {}) + if ev and not ev.get("clip_file"): + q_issues.append(f"NO CLIP FILE in {key}") + + # Check 8: Video file exists for rendering + video = VIDEO_DIR / f"{slot}_q{idx}_{cat}.mp4" + has_video = video.exists() + + # Verdict + if q_issues: + verdict = "fail" + icon = C_RED + "✗" + else: + verdict = "pass" + icon = C_GREEN + "✓" + + verdicts[idx] = {"verdict": verdict, "notes": "; ".join(q_issues) if q_issues else ""} + + vid_icon = "📹" if has_video else " " + print(f" {icon} Q{idx:<2d} [{cat:<16s}]{C_RESET} {vid_icon} " + f"cams={len(cameras):<3d} " + f"{' '.join(q_issues) if q_issues else 'OK'}") + issues.extend([(idx, cat, iss) for iss in q_issues]) + + audit = print_summary(qa_data, verdicts) + audit["mode"] = "auto-report" + return audit + + +def main(): + parser = argparse.ArgumentParser(description="Interactive QA Review CLI") + parser.add_argument("--slot", help="Slot name") + parser.add_argument("--qa-file", help="QA JSON file") + parser.add_argument("--natural", action="store_true", + help="Use naturalized version") + parser.add_argument("--report", action="store_true", + help="Auto-report (non-interactive)") + parser.add_argument("--save", action="store_true", + help="Save audit to file") + args = parser.parse_args() + + # Resolve input file + if args.qa_file: + qa_path = Path(args.qa_file) + elif args.slot: + suffix = ".final.naturalized.json" if args.natural else ".final.raw.json" + qa_path = QA_DIR / f"{args.slot}{suffix}" + if not qa_path.exists() and args.natural: + qa_path = QA_DIR / f"{args.slot}.final.raw.json" + print(f" (naturalized not found, using raw)") + else: + parser.error("Must provide --slot or --qa-file") + + if not qa_path.exists(): + print(f"ERROR: {qa_path} not found") + return + + with open(qa_path) as f: + qa_data = json.load(f) + + print(f"Loaded: {qa_path.name}") + + if args.report: + audit = generate_report(qa_data) + else: + audit = interactive_review(qa_data) + + # Save audit + if args.save or args.report: + slot = qa_data.get("slot", "unknown") + audit_path = AUDIT_DIR / f"{slot}.audit.json" + with open(audit_path, "w") as f: + json.dump(audit, f, indent=2) + print(f"\n Audit saved: {audit_path}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/run_pipeline.py b/meva/scripts/v10/run_pipeline.py new file mode 100644 index 0000000..e048d8d --- /dev/null +++ b/meva/scripts/v10/run_pipeline.py @@ -0,0 +1,837 @@ +#!/usr/bin/env python3 +""" +FINAL run_pipeline.py — Main orchestrator for FINAL QA generation pipeline. + +6 categories (matching paper taxonomy): + temporal(2) + event_ordering(2) + spatial(3) + + summarization(1) + counting(1) + best_camera(3) = ~12 Qs/slot + +REMOVED from V9: re_identification, causality +REMOVED: perception (killed — not useful for benchmark) +ADDED: best_camera (Camera Transition Logic) + +Setup (run from the meva/ directory inside the repo): + cd /path/to/repo/meva + export PYTHONPATH=$PYTHONPATH:$(pwd) + export OPENAI_API_KEY=sk-... # required only for naturalization step + export OUTPUT_DIR=~/data # optional: where QA output is saved (default: ~/data) + +Usage: + # Step 1 — generate raw QA (free, ~5s/slot) + python3 -m scripts.v10.run_pipeline --slot "2018-03-11.11-25.school" -v + + # Step 2 — naturalize with GPT (costs tokens, requires OPENAI_API_KEY) + python3 -m scripts.v10.naturalize \\ + --input $MEVA_OUTPUT_DIR/qa_pairs/2018-03-11.11-25.school/2018-03-11.11-25.school.final.raw.json \\ + -v --yes + + # Step 3 — export to multi-cam-dataset repo format + python3 -m scripts.v10.export_to_multicam_format --slot "2018-03-11.11-25.school" + + # List all available slots + python3 -m scripts.v10.run_pipeline --list-slots +""" + +import json +import argparse +import os +import random +import sys +import time +from pathlib import Path +from typing import Dict, List, Any + +# Handle both direct execution and module execution +try: + from .parse_annotations import parse_slot_events, find_clips_for_slot + from .build_scene_graph import build_scene_graph + from .entity_resolution import resolve_entities + from .person_descriptions import ( + enrich_entities, is_mevid_supported, get_mevid_persons_for_slot, + get_mevid_persons_with_cameras, load_person_database, + merge_cross_camera_descriptions, differentiate_within_camera, + ) + from .generate_temporal import generate_temporal_qa + from .generate_spatial import generate_spatial_qa + # from .generate_perception import generate_perception_qa # KILLED: perception category removed + from .generate_scene_summary import generate_scene_summary_qa + from .generate_event_ordering import generate_event_ordering_qa + from .generate_numerical import generate_numerical_qa + from .generate_best_camera import generate_best_camera_qa +except ImportError: + sys.path.insert(0, str(Path(__file__).resolve().parent)) + from parse_annotations import parse_slot_events, find_clips_for_slot + from build_scene_graph import build_scene_graph + from entity_resolution import resolve_entities + from person_descriptions import ( + enrich_entities, is_mevid_supported, get_mevid_persons_for_slot, + get_mevid_persons_with_cameras, load_person_database, + merge_cross_camera_descriptions, differentiate_within_camera, + ) + from generate_temporal import generate_temporal_qa + from generate_spatial import generate_spatial_qa + # from generate_perception import generate_perception_qa # KILLED: perception category removed + from generate_scene_summary import generate_scene_summary_qa + from generate_event_ordering import generate_event_ordering_qa + from generate_numerical import generate_numerical_qa + from generate_best_camera import generate_best_camera_qa + + +# ============================================================================ +# Constants +# ============================================================================ + +# Repo-relative data directory (meva/data/) — works for any clone location +_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data" +# User output directory — override with MEVA_OUTPUT_DIR env var +_OUTPUT = Path(os.environ.get("OUTPUT_DIR") or os.environ.get("MEVA_OUTPUT_DIR") or str(Path.home() / "data")) + +OUTPUT_DIR = _OUTPUT / "qa_pairs" / "raw" +MEVA_MP4_BASE = Path("/nas/mars/dataset/MEVA/mp4s") +# Entity descriptions directory — override with MEVA_ENTITY_DESC_DIR env var +_ENTITY_DESC_DIR = Path(os.environ.get("MEVA_ENTITY_DESC_DIR") or "/nas/mars/dataset/MEVA/entity_descriptions") +CANONICAL_SLOTS_PATH = _REPO_DATA / "canonical_slots.json" +RANDOM_SEED = 42 + +# 6 categories — max question counts per slot (soft ceilings, not rigid targets) +# Generators produce all valid candidates and cap at MAX. +MAX_TEMPORAL = 2 +MAX_EVENT_ORDERING = 2 +# MAX_PERCEPTION = 2 # KILLED: perception category removed +MAX_SPATIAL = 3 # ~70% slot hit rate requires 3/slot for 500 total +MAX_SUMMARIZATION = 1 # scene_summary (renamed for paper alignment) +MAX_COUNTING = 1 # activity-counting only (entity-counting removed) +MAX_BEST_CAMERA = 3 # Camera Transition Logic, ~70% hit rate +# Maximum total: ~14 questions per slot (actual count may be lower) + + +# ============================================================================ +# Text Post-Processing +# ============================================================================ + +import re as _re + +def fix_articles(text: str) -> str: + """Fix 'a' → 'an' before vowel sounds (a orange → an orange).""" + return _re.sub(r'\ba ([aeiouAEIOU])', r'an \1', text) + + +# ============================================================================ +# Video Path Construction +# ============================================================================ + +def _build_video_paths(q: dict, slot: str) -> List[str]: + """Build absolute MP4 paths for each camera in a QA pair. + + Uses slot-grouped MP4 directory structure: + slot = "2018-03-11.11-25.school" + clip_file = "2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4" + → /nas/mars/dataset/MEVA/mp4s/2018-03-11/11/2018-03-11.11-25.school/ + 2018-03-11.11-25-00.11-30-00.school.G330.r13.mp4 + """ + date = slot.split(".")[0] # "2018-03-11" + hour = slot.split(".")[1].split("-")[0] # "11" + slot_dir = MEVA_MP4_BASE / date / hour / slot + + # Prefer including all videos for the slot, if available. + if slot_dir.exists(): + return sorted(str(p) for p in slot_dir.glob("*.mp4")) + + paths = [] + seen = set() + + def _add_clip(clip: str): + """Normalize a clip filename and add its MP4 path (deduped).""" + if not clip or clip in seen: + return + seen.add(clip) + # Normalize: strip any extension, ensure single .r13.mp4 + stem = clip + for ext in (".mp4", ".avi"): + if stem.endswith(ext): + stem = stem[:-len(ext)] + # Strip trailing .r13 if present (avoid double .r13) + if stem.endswith(".r13"): + stem = stem[:-4] + mp4_name = f"{stem}.r13.mp4" + mp4_path = str(slot_dir / mp4_name) + if mp4_path not in paths: + paths.append(mp4_path) + + # Collect clip files from debug_info + debug = q.get("debug_info", {}) + + # 1. Direct clip_files list (counting, best_camera, summarization, spatial) + for cf in debug.get("clip_files", []): + _add_clip(cf) + + # 2. temporal: event_a, event_b (dict with clip_file) + for key in ["event_a", "event_b"]: + info = debug.get(key, {}) + if isinstance(info, dict) and info.get("clip_file"): + _add_clip(info["clip_file"]) + + # 3. perception / best_camera: representative_event (dict with clip_file) + rep = debug.get("representative_event", {}) + if isinstance(rep, dict) and rep.get("clip_file"): + _add_clip(rep["clip_file"]) + + # 4. perception multi-cam: camera_1_event, camera_2_event, etc. + for key in debug: + if key.startswith("camera_") and key.endswith("_event"): + info = debug[key] + if isinstance(info, dict) and info.get("clip_file"): + _add_clip(info["clip_file"]) + + # 5. event_ordering: events (list of dicts with clip_file) + events_list = debug.get("events", []) + if isinstance(events_list, list): + for ev in events_list: + if isinstance(ev, dict) and ev.get("clip_file"): + _add_clip(ev["clip_file"]) + + # 6. Spatial: entity_a, entity_b (dict with clip_file) + for key in ["entity_a", "entity_b"]: + info = debug.get(key, {}) + if isinstance(info, dict) and info.get("clip_file"): + _add_clip(info["clip_file"]) + + # Fallback: match only this slot's cameras in the slot directory + if not paths: + cameras = q.get("requires_cameras", []) + if slot_dir.exists() and cameras: + for cam in cameras: + for f in sorted(slot_dir.glob(f"*{cam}*.r13.mp4")): + p = str(f) + if p not in paths: + paths.append(p) + + return sorted(set(paths)) + + +# ============================================================================ +# Canonical Slot Resolution +# ============================================================================ + +def resolve_canonical_slot(slot: str) -> List[str]: + """Given a slot name, return the list of slots to process. + + With HH-MM slot format (no seconds), each slot name is already canonical. + The old canonical_slots.json indirection is no longer needed. + """ + return [slot] + + +# ============================================================================ +# Deduplication +# ============================================================================ + +def is_duplicate_within_slot(new_q: dict, existing_qs: list) -> bool: + """Prevent asking similar questions within the same slot.""" + for eq in existing_qs: + if eq["category"] != new_q["category"]: + continue + v_new = new_q.get("verification", {}) + v_old = eq.get("verification", {}) + + cat = new_q["category"] + + if cat == "temporal": + ea_new = v_new.get("event_a", {}).get("activity") + eb_new = v_new.get("event_b", {}).get("activity") + ea_old = v_old.get("event_a", {}).get("activity") + eb_old = v_old.get("event_b", {}).get("activity") + if ea_new and eb_new and ea_old and eb_old: + if ea_new == ea_old and eb_new == eb_old: + return True + + elif cat == "spatial": + if (v_new.get("entity_a") and + v_new.get("entity_a") == v_old.get("entity_a") and + v_new.get("entity_b") == v_old.get("entity_b")): + return True + + elif cat == "perception": + if v_new.get("question_type") == v_old.get("question_type"): + qt = v_new.get("question_type") + if qt == "which_camera" and v_new.get("activity") == v_old.get("activity"): + return True + elif qt == "activity_identification" and v_new.get("camera") == v_old.get("camera"): + return True + elif qt == "multi_camera_confirmation" and v_new.get("activity") == v_old.get("activity"): + return True + elif qt == "attribute_verification" and v_new.get("mevid_person_id") == v_old.get("mevid_person_id"): + return True + + elif cat == "summarization": + if v_new.get("question_type") == v_old.get("question_type"): + return True + + elif cat == "event_ordering": + new_events = v_new.get("ordered_events", []) + old_events = v_old.get("ordered_events", []) + if new_events and old_events: + new_acts = {e.get("activity") for e in new_events} + old_acts = {e.get("activity") for e in old_events} + if len(new_acts & old_acts) >= 3: + return True + + elif cat == "counting": + if (v_new.get("question_type") == v_old.get("question_type") and + v_new.get("activity") == v_old.get("activity")): + return True + + elif cat == "best_camera": + if (v_new.get("cluster_id") == v_old.get("cluster_id") and + v_new.get("question_type") == v_old.get("question_type")): + return True + # V10: Also dedup by question text / entity description + if (v_new.get("question_type") == v_old.get("question_type") and + v_new.get("entity_description") == v_old.get("entity_description")): + return True + + return False + + +# ============================================================================ +# Validation +# ============================================================================ + +def validate_temporal(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + ea = v.get("event_a", {}) + eb = v.get("event_b", {}) + if not ea or not eb: + errors.append("Missing event_a or event_b in verification") + return errors + if ea.get("camera") == eb.get("camera"): + errors.append(f"Same camera: {ea.get('camera')}") + gap = v.get("gap_sec", 0) + if gap < 3.0: + errors.append(f"Gap too small: {gap}s (min 3s)") + if gap > 20.0: + errors.append(f"Gap too large: {gap}s (max 20s)") + if ea.get("start_sec", 0) >= eb.get("start_sec", 0): + errors.append("Event A does not precede Event B") + return errors + + +def validate_spatial(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + d = v.get("min_distance_meters") + proximity = v.get("proximity") + if d is None: + errors.append("Missing min_distance_meters") + return errors + if proximity == "near" and d > 5.0: + errors.append(f"Near but distance={d}m (should be <=5m)") + elif proximity == "moderate" and (d <= 5.0 or d > 15.0): + errors.append(f"Moderate but distance={d}m (should be 5-15m)") + elif proximity == "far" and d <= 15.0: + errors.append(f"Far but distance={d}m (should be >15m)") + # Validate cross-paths flag consistency + crosses = v.get("crosses_paths", False) + if crosses and d > 2.0: + errors.append(f"crosses_paths=True but distance={d}m (should be <=2m)") + return errors + + +def validate_perception(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("question_type"): + errors.append("Missing question_type in verification") + return errors + + +def validate_summarization(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("question_type"): + errors.append("Missing question_type") + return errors + + +def validate_event_ordering(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + ordered = v.get("ordered_events", []) + if len(ordered) < 3: + errors.append(f"Too few events: {len(ordered)} (min 3)") + min_gap = v.get("min_gap_sec", 0) + if min_gap < 3.0: + errors.append(f"Min gap too small: {min_gap}s (min 3s)") + for i in range(len(ordered) - 1): + if ordered[i].get("start_sec", 0) >= ordered[i+1].get("start_sec", 0): + errors.append(f"Events not in chronological order at position {i}") + return errors + + +def validate_counting(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("question_type"): + errors.append("Missing question_type") + cnt = v.get("correct_count") + if cnt is not None and (cnt < 2 or cnt > 20): + errors.append(f"Count out of range: {cnt} (should be 2-20)") + return errors + + +def validate_best_camera(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("question_type"): + errors.append("Missing question_type") + if not v.get("correct_camera"): + errors.append("Missing correct_camera") + return errors + + +def validate_all(qa_pairs: List[dict]) -> Dict[str, List[str]]: + validators = { + "temporal": validate_temporal, + "spatial": validate_spatial, + # "perception": validate_perception, # KILLED + "summarization": validate_summarization, + "event_ordering": validate_event_ordering, + "counting": validate_counting, + "best_camera": validate_best_camera, + } + issues = {} + for q in qa_pairs: + cat = q.get("category", "") + if cat in validators: + errors = validators[cat](q) + if errors: + issues[q["question_id"]] = errors + return issues + + +# ============================================================================ +# Category Renaming (paper alignment) +# ============================================================================ + +def _rename_category(q: dict) -> dict: + """Rename internal category names to match paper taxonomy. + + scene_summary → summarization + numerical → counting + """ + cat = q.get("category", "") + if cat == "scene_summary": + q["category"] = "summarization" + elif cat == "numerical": + q["category"] = "counting" + return q + + +def _compute_question_ambiguity(qa_pairs: List[Dict], + entity_descs: Dict[str, str], + sg) -> Dict[str, Any]: + """ + Compute per-question ambiguity: for each entity referenced in a question, + how many other entities on the same camera share the same description? + + Returns: + Dict with: + - per_question: list of {question_id, max_ambiguity, entities} + - avg_ambiguity: average max_ambiguity across questions + - worst_questions: top 5 most ambiguous questions + - fully_unique: count of questions where all refs are unique + """ + # Build camera → description → count map + cam_desc_counts: Dict[str, Dict[str, int]] = {} + cam_desc_eids: Dict[str, Dict[str, List[str]]] = {} + for eid, desc in entity_descs.items(): + entity = sg.entities.get(eid) + if not entity: + continue + cam = entity.camera_id + if cam not in cam_desc_counts: + cam_desc_counts[cam] = {} + cam_desc_eids[cam] = {} + cam_desc_counts[cam][desc] = cam_desc_counts[cam].get(desc, 0) + 1 + if desc not in cam_desc_eids[cam]: + cam_desc_eids[cam][desc] = [] + cam_desc_eids[cam][desc].append(eid) + + per_question = [] + for q in qa_pairs: + verification = q.get("verification", {}) + max_ambiguity = 0 + entity_ambiguities = [] + + # Collect all entity descriptions referenced in this question + for evt_key in ("event_a", "event_b", "event"): + evt_info = verification.get(evt_key, {}) + cam = evt_info.get("camera", "") + desc = evt_info.get("description", "") + if cam and desc and cam in cam_desc_counts: + count = cam_desc_counts[cam].get(desc, 1) + ambiguity = count - 1 # 0 = unique, N = N other entities share desc + max_ambiguity = max(max_ambiguity, ambiguity) + entity_ambiguities.append({ + "camera": cam, + "description_preview": desc[:50], + "same_desc_count": count, + "ambiguity": ambiguity, + }) + + per_question.append({ + "question_id": q.get("question_id", ""), + "category": q.get("category", ""), + "max_ambiguity": max_ambiguity, + "entities": entity_ambiguities, + }) + + # Aggregate stats + ambiguities = [pq["max_ambiguity"] for pq in per_question] + if not ambiguities: + return {"per_question": [], "avg_ambiguity": 0, "worst_questions": [], + "fully_unique": 0, "total": 0, "pct_unique": 100.0} + + fully_unique = sum(1 for a in ambiguities if a == 0) + avg_amb = sum(ambiguities) / len(ambiguities) if ambiguities else 0 + worst = sorted(per_question, key=lambda x: x["max_ambiguity"], reverse=True)[:5] + + return { + "per_question": per_question, + "avg_ambiguity": round(avg_amb, 2), + "worst_questions": worst, + "fully_unique": fully_unique, + "total": len(per_question), + "pct_unique": round(100 * fully_unique / len(per_question), 1) if per_question else 0, + } + + +# ============================================================================ +# Main Pipeline +# ============================================================================ + +def run_pipeline(slot: str, verbose: bool = False, + seed: int = RANDOM_SEED, + require_mevid: bool = True) -> Dict[str, Any]: + """ + Run the complete FINAL QA generation pipeline on one slot. + + Steps: + 1. Parse annotations (Kitware YAML) + 2. Build entity-based scene graph + 3. Resolve cross-camera entities (MEVID + heuristic) + 3.5. Auto-extract entity descriptions if not yet done + 4. Enrich entities with visual descriptions + 5. Generate temporal questions (2) + 6. Generate event ordering questions (2) + 7. Generate perception questions (2) + 8. Generate spatial questions (2) + 9. Generate summarization questions (1) + 10. Generate counting questions (1) + 11. Generate best_camera questions (2) + 12. Validate and output + """ + t0 = time.time() + rng = random.Random(seed) + + if verbose: + print(f"{'=' * 60}") + print(f"FINAL Pipeline: {slot}") + print(f"{'=' * 60}") + + # Resolve canonical slot → raw slots + raw_slots = resolve_canonical_slot(slot) + if verbose and len(raw_slots) > 1: + print(f" Canonical slot → {len(raw_slots)} raw variants: {raw_slots}") + + # Check MEVID support + mevid_supported = is_mevid_supported(slot) + mevid_persons = get_mevid_persons_for_slot(slot) + + if require_mevid and not mevid_supported and not mevid_persons: + if verbose: + print(f" WARNING: Slot {slot} has no MEVID support") + print(f" Running anyway (descriptions will fallback to geom-color or activity-verb)") + + if verbose: + print(f" MEVID persons in slot: {len(mevid_persons)}") + + # Step 1: Parse annotations (merge across raw slots if canonical) + if verbose: + print(f"\nStep 1: Parsing annotations...") + events = [] + for raw_slot in raw_slots: + slot_events = parse_slot_events(raw_slot, verbose=verbose) + events.extend(slot_events) + if not events: + raise ValueError(f"No events found for slot {slot}") + + # Step 2: Build scene graph + if verbose: + print(f"\nStep 2: Building scene graph...") + sg = build_scene_graph(slot, events, verbose=verbose) + + # Step 3: Entity resolution + if verbose: + print(f"\nStep 3: Resolving entities...") + resolved = resolve_entities(sg, verbose=verbose) + + # Step 3.5: Auto-extract entity descriptions if not yet done + desc_path = _ENTITY_DESC_DIR / f"{slot}.json" + if not desc_path.exists(): + if verbose: + print(f"\nStep 3.5: Extracting entity descriptions (YOLO+HSV)...") + try: + from scripts.final.extract_entity_descriptions import process_slot as extract_descs + result = extract_descs(slot, use_yolo=True, verbose=verbose) + desc_path.parent.mkdir(parents=True, exist_ok=True) + with open(desc_path, "w") as f: + json.dump(result, f, indent=2, default=str) + if verbose: + n_actors = len(result.get("actors", {})) + n_described = sum(1 for a in result.get("actors", {}).values() + if a.get("description", "a person") != "a person") + print(f" Extracted {n_described}/{n_actors} entity descriptions → {desc_path.name}") + except Exception as e: + if verbose: + print(f" WARNING: Entity description extraction failed: {e}") + print(f" Continuing with MEVID + activity-verb fallback only") + elif verbose: + print(f"\nStep 3.5: Entity descriptions already exist → {desc_path.name}") + + # Step 4: Enrich entities with visual descriptions + if verbose: + print(f"\nStep 4: Enriching entities with visual descriptions...") + entity_descs, desc_counts, fallback_eids = enrich_entities(sg, verbose=verbose) + + mevid_cnt = desc_counts["mevid"] + vlm_cnt = desc_counts.get("vlm", 0) + geom_cnt = desc_counts["geom"] + mevid_yolo_cnt = desc_counts.get("mevid_yolo", 0) + fallback_cnt = desc_counts["fallback"] + + if verbose: + print(f" {mevid_cnt} MEVID-GPT + {geom_cnt} geom + {vlm_cnt} VLM + " + f"{mevid_yolo_cnt} MEVID-YOLO + {fallback_cnt} fallback / {len(entity_descs)} total") + + # Step 4b: Cross-camera clustering + height differentiation + if verbose: + print(f"\nStep 4b: Cross-camera clustering + differentiation...") + entity_descs = merge_cross_camera_descriptions( + entity_descs, resolved, sg, verbose=verbose) + entity_descs = differentiate_within_camera( + entity_descs, sg, verbose=verbose) + + # Step 5-11: Generate QA pairs (6 categories) + if verbose: + print(f"\nStep 5-11: Generating questions (6 categories)...") + + temporal_qa = generate_temporal_qa(sg, resolved, entity_descs, rng, + count=MAX_TEMPORAL, verbose=verbose, + fallback_eids=fallback_eids) + ordering_qa = generate_event_ordering_qa(sg, resolved, entity_descs, rng, + count=MAX_EVENT_ORDERING, verbose=verbose, + fallback_eids=fallback_eids) + # KILLED: perception category removed + # perception_qa = generate_perception_qa(sg, resolved, entity_descs, rng, + # count=MAX_PERCEPTION, verbose=verbose) + perception_qa = [] + spatial_qa = generate_spatial_qa(sg, resolved, entity_descs, rng, + count=MAX_SPATIAL, verbose=verbose, + fallback_eids=fallback_eids) + summary_qa = generate_scene_summary_qa(sg, resolved, entity_descs, rng, + count=MAX_SUMMARIZATION, verbose=verbose) + counting_qa = generate_numerical_qa(sg, resolved, entity_descs, rng, + count=MAX_COUNTING, verbose=verbose) + best_camera_qa = generate_best_camera_qa(sg, resolved, entity_descs, rng, + count=MAX_BEST_CAMERA, verbose=verbose) + + # Rename categories for paper alignment + for q in summary_qa: + _rename_category(q) + for q in counting_qa: + _rename_category(q) + + all_qa = (temporal_qa + ordering_qa + perception_qa + spatial_qa + + summary_qa + counting_qa + best_camera_qa) + + # Deduplication + unique_qa = [] + for q in all_qa: + if not is_duplicate_within_slot(q, unique_qa): + unique_qa.append(q) + + # Renumber question IDs sequentially + for i, q in enumerate(unique_qa): + q["question_id"] = f"final_{q['category']}_{i+1:03d}" + + # NOTE: article fixes (a→an) and text polish happen in naturalization.py, + # not here. Generators produce raw mechanical text; naturalization owns all + # wording/grammar changes. + + # Add video paths to each QA pair + for q in unique_qa: + q["video_paths"] = _build_video_paths(q, slot) + + # Step 12: Validation + if verbose: + print(f"\nStep 12: Validating...") + issues = validate_all(unique_qa) + if verbose: + if issues: + print(f" Validation issues:") + for qid, errs in issues.items(): + for e in errs: + print(f" {qid}: {e}") + else: + print(f" All questions passed validation") + + # Step 13: Per-question ambiguity analysis + ambiguity = _compute_question_ambiguity(unique_qa, entity_descs, sg) + if verbose: + print(f"\nStep 13: Ambiguity analysis...") + print(f" {ambiguity['fully_unique']}/{ambiguity['total']} questions fully unique " + f"({ambiguity['pct_unique']}%)") + print(f" Avg max-ambiguity: {ambiguity['avg_ambiguity']}") + if ambiguity['worst_questions']: + print(f" Worst: {ambiguity['worst_questions'][0]['question_id']} " + f"(ambiguity={ambiguity['worst_questions'][0]['max_ambiguity']})") + + # Build output + cameras_in_slot = sorted(sg.cameras.keys()) + person_cameras = get_mevid_persons_with_cameras(slot) + + # Category counts + cat_counts = {} + for q in unique_qa: + cat = q["category"] + cat_counts[cat] = cat_counts.get(cat, 0) + 1 + + # Stats + attr_verification = sum( + 1 for q in unique_qa + if q.get("subcategory") == "attribute_verification" + ) + + output = { + "slot": slot, + "version": "final", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "geom_segformer+mevid_gpt+vlm+mevid_yolo", + "generator": "final_pipeline", + "seed": seed, + "cameras": cameras_in_slot, + "mevid_supported": mevid_supported or len(mevid_persons) > 0, + "mevid_persons_in_slot": len(mevid_persons), + "mevid_person_ids": sorted(mevid_persons), + "mevid_person_cameras": { + pid: sorted(cams) for pid, cams in person_cameras.items() + }, + "total_events": len(events), + "total_entities": len(sg.entities), + "cross_camera_clusters": len(resolved.entity_clusters), + "total_questions": len(unique_qa), + "category_counts": cat_counts, + "stats": { + "entities_with_mevid_gpt_descriptions": mevid_cnt, + "entities_with_vlm_descriptions": vlm_cnt, + "entities_with_geom_descriptions": geom_cnt, + "entities_with_mevid_yolo_descriptions": mevid_yolo_cnt, + "entities_with_fallback_descriptions": fallback_cnt, + "attribute_verification_questions": attr_verification, + "best_camera_questions": cat_counts.get("best_camera", 0), + "questions_with_debug_info": sum(1 for q in unique_qa if "debug_info" in q), + }, + "ambiguity": { + "avg_ambiguity": ambiguity["avg_ambiguity"], + "pct_unique": ambiguity["pct_unique"], + "fully_unique": ambiguity["fully_unique"], + "total_questions": ambiguity["total"], + "worst_questions": ambiguity["worst_questions"], + }, + "validation_issues": len(issues), + "generation_time_sec": round(time.time() - t0, 2), + "qa_pairs": unique_qa, + } + + if verbose: + print(f"\n{'=' * 60}") + print(f"DONE: {len(unique_qa)} questions generated in {output['generation_time_sec']}s") + for cat, cnt in sorted(cat_counts.items()): + print(f" {cat:25s}: {cnt}") + print(f" ---") + print(f" Cameras: {cameras_in_slot}") + print(f" Events: {len(events)}") + print(f" Entities: {len(sg.entities)} ({mevid_cnt} MEVID-GPT + {geom_cnt} geom + {vlm_cnt} VLM + {mevid_yolo_cnt} MEVID-YOLO)") + print(f" MEVID persons: {sorted(mevid_persons)}") + print(f" Cross-cam clusters: {len(resolved.entity_clusters)}") + print(f" Validation issues: {len(issues)}") + print(f"{'=' * 60}") + + return output + + +def list_canonical_slots(): + """List all canonical slots from canonical_slots.json.""" + if not CANONICAL_SLOTS_PATH.exists(): + print(f"ERROR: {CANONICAL_SLOTS_PATH} not found. Run slot audit first.") + sys.exit(1) + + with open(CANONICAL_SLOTS_PATH) as f: + canonical = json.load(f) + + multi = sum(1 for v in canonical.values() if v.get("multi_camera")) + cam_counts = [len(v.get("cameras", [])) for v in canonical.values()] + + print(f"Canonical slots: {len(canonical)} ({multi} multi-camera)") + print(f"Cameras: min={min(cam_counts)}, max={max(cam_counts)}, avg={sum(cam_counts)/len(cam_counts):.1f}") + print(f"\n{'Slot':45s} {'Cams':>5s} {'Clips':>6s} {'Variants':>9s}") + print("-" * 70) + + for slot in sorted(canonical.keys()): + info = canonical[slot] + n_cams = len(info.get("cameras", [])) + n_clips = info.get("clip_count", 0) + n_vars = len(info.get("raw_slots", [])) + cam_str = ",".join(info.get("cameras", [])) + print(f"{slot:45s} {n_cams:5d} {n_clips:6d} {n_vars:9d} [{cam_str}]") + + +def main(): + parser = argparse.ArgumentParser(description="FINAL QA Pipeline (6 categories)") + parser.add_argument("--slot", help="Slot name (e.g., 2018-03-11.11-25.school)") + parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output") + parser.add_argument("--seed", type=int, default=RANDOM_SEED, help="Random seed") + parser.add_argument("--output", help="Output file path (default: data/qa_pairs/raw/{slot}.raw.json)") + parser.add_argument("--no-save", action="store_true", help="Don't save to file, just print") + parser.add_argument("--list-slots", action="store_true", + help="List all canonical slots") + parser.add_argument("--no-require-mevid", action="store_true", + help="Process slot even without MEVID support") + args = parser.parse_args() + + if args.list_slots: + list_canonical_slots() + return + + if not args.slot: + parser.error("--slot is required (or use --list-slots)") + + output = run_pipeline(args.slot, verbose=args.verbose, seed=args.seed, + require_mevid=not args.no_require_mevid) + + # Save output + if not args.no_save: + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + out_path = Path(args.output) if args.output else OUTPUT_DIR / f"{args.slot}.raw.json" + with open(out_path, "w") as f: + json.dump(output, f, indent=2, default=str) + print(f"\nSaved: {out_path}") + else: + print(json.dumps(output, indent=2, default=str)) + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/scene_context.py b/meva/scripts/v10/scene_context.py new file mode 100644 index 0000000..0086a28 --- /dev/null +++ b/meva/scripts/v10/scene_context.py @@ -0,0 +1,447 @@ +#!/usr/bin/env python3 +""" +scene_context.py — Camera-aware spatial context using KRTD + building PLYs. + +Provides: + - Named building proximity for entity 3D positions ("near the school entrance") + - Cardinal direction from camera to entity ("to the north") + - Semantic location labels ("in the parking lot", "by the gas station") + - Cross-camera entity matching boost via 3D proximity + +Uses: + - KRTD camera models (ENU coordinate system) from utils/krtd.py + - Building segmentation PLYs from /nas/mars/dataset/MEVA/model_segmentations/ + - trimesh for PLY loading and nearest-point queries + +Coordinate system: ENU (East-North-Up) — shared across all outdoor cameras. + x = East, y = North, z = Up (meters) +""" + +import math +import numpy as np +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +try: + import trimesh +except ImportError: + trimesh = None # Graceful degradation — scene context disabled + +try: + from .utils.krtd import load_camera_model, load_all_camera_models, CameraModel +except ImportError: + import sys + sys.path.insert(0, str(Path(__file__).resolve().parent)) + from utils.krtd import load_camera_model, load_all_camera_models, CameraModel + + +# ============================================================================ +# Constants +# ============================================================================ + +MODEL_SEG_DIR = Path("/nas/mars/dataset/MEVA/model_segmentations") + +# Human-friendly building names for each PLY file +BUILDING_NAMES = { + "school": "the school", + "building1": "Building 1 (east annex)", + "building2": "Building 2 (southeast lab)", + "building3": "Building 3 (south wing)", + "building4": "Building 4 (west annex)", + "building5": "Building 5 (northwest housing)", + "gas_station": "the gas station", + "background": None, # "background" mesh = ground/trees, no label +} + +# Short labels for question text (avoid verbosity) +BUILDING_SHORT_LABELS = { + "school": "the school", + "building1": "the east building", + "building2": "the southeast building", + "building3": "the south building", + "building4": "the west building", + "building5": "the northwest building", + "gas_station": "the gas station", +} + +# Site → which buildings are relevant (cameras only see nearby structures) +SITE_BUILDINGS = { + "school": ["school", "building1", "building2", "gas_station"], + "admin": ["building3", "building4", "building5"], + "bus": ["building4", "building5", "gas_station"], + "hospital": ["building3", "building4"], +} + +# Distance thresholds for proximity labels (meters) +# Note: Building centroids are center-of-mass, so even "adjacent" entities may +# be 20-30m from centroid. Use generous thresholds. +NEAR_THRESHOLD = 25.0 # "near X" +MODERATE_THRESHOLD = 60.0 # "in the area of X" +# Beyond moderate = "away from X" / use cardinal direction instead + +# Cardinal direction names +_CARDINAL_NAMES = [ + "north", "northeast", "east", "southeast", + "south", "southwest", "west", "northwest" +] + + +# ============================================================================ +# Scene Geometry Cache +# ============================================================================ + +_scene_cache: Dict[str, 'SceneContext'] = {} + + +def get_scene_context(site: str) -> Optional['SceneContext']: + """Get or create SceneContext for a site (cached).""" + if trimesh is None: + return None + if site not in _scene_cache: + try: + _scene_cache[site] = SceneContext(site) + except Exception: + _scene_cache[site] = None + return _scene_cache[site] + + +# ============================================================================ +# SceneContext Class +# ============================================================================ + +class SceneContext: + """ + Scene geometry for a MEVA site: buildings, cameras, spatial queries. + + Loads PLY meshes and precomputes centroids for fast nearest-building lookup. + """ + + def __init__(self, site: str): + """ + Args: + site: 'school', 'bus', 'admin', or 'hospital' + """ + self.site = site + self.buildings: Dict[str, trimesh.Trimesh] = {} + self.centroids: Dict[str, np.ndarray] = {} + self._load_buildings() + + def _load_buildings(self): + """Load PLY meshes for buildings relevant to this site.""" + building_ids = SITE_BUILDINGS.get(self.site, list(BUILDING_NAMES.keys())) + + for bid in building_ids: + ply_path = MODEL_SEG_DIR / f"{bid}.ply" + if not ply_path.exists(): + continue + try: + mesh = trimesh.load(str(ply_path)) + self.buildings[bid] = mesh + self.centroids[bid] = np.array(mesh.centroid) + except Exception: + continue + + def nearest_building(self, point_3d: np.ndarray) -> Optional[Tuple[str, float]]: + """ + Find the nearest named building to a 3D point. + + Args: + point_3d: [east, north, up] in ENU meters + + Returns: + (building_id, distance_meters) or None if no buildings loaded. + """ + if not self.buildings: + return None + + point = np.array(point_3d[:2]) # Use 2D (east, north) for horizontal distance + + best_id = None + best_dist = float('inf') + + for bid, mesh in self.buildings.items(): + if BUILDING_NAMES.get(bid) is None: + continue # Skip "background" + + # Use centroid distance as fast approximation + centroid_2d = self.centroids[bid][:2] + dist = np.linalg.norm(point - centroid_2d) + + if dist < best_dist: + best_dist = dist + best_id = bid + + if best_id is None: + return None + return (best_id, float(best_dist)) + + def get_location_label(self, point_3d: np.ndarray) -> Optional[str]: + """ + Generate a semantic location label for a 3D point. + + Returns labels like: + "near the school" + "in the parking lot area" + "by the gas station" + None if no meaningful label can be generated + """ + result = self.nearest_building(point_3d) + if result is None: + return None + + bid, dist = result + short_name = BUILDING_SHORT_LABELS.get(bid, bid) + + if dist < NEAR_THRESHOLD: + return f"near {short_name}" + elif dist < MODERATE_THRESHOLD: + return f"in the area near {short_name}" + else: + return None # Too far for meaningful label + + def get_cardinal_direction(self, from_point: np.ndarray, + to_point: np.ndarray) -> str: + """ + Cardinal direction from one point to another. + + Args: + from_point: [east, north, up] - observer position (e.g., camera) + to_point: [east, north, up] - target position (e.g., entity) + + Returns: + One of: "north", "northeast", "east", "southeast", + "south", "southwest", "west", "northwest" + """ + delta_east = to_point[0] - from_point[0] + delta_north = to_point[1] - from_point[1] + + # atan2(east, north) gives angle from north, clockwise + angle_rad = math.atan2(delta_east, delta_north) + angle_deg = math.degrees(angle_rad) % 360 + + # Quantize to 8 directions (each 45°, centered on 0°=North) + idx = round(angle_deg / 45.0) % 8 + return _CARDINAL_NAMES[idx] + + def get_camera_relative_direction(self, camera_id: str, + entity_point: np.ndarray) -> Optional[str]: + """ + Cardinal direction from camera center to entity world position. + + Returns: "to the north", "to the southeast", etc. + None if camera model not available. + """ + cam_model = load_camera_model(camera_id) + if cam_model is None: + return None + + cam_center = cam_model.camera_center + direction = self.get_cardinal_direction(cam_center, entity_point) + return f"to the {direction}" + + def annotate_entity(self, point_3d: np.ndarray, + camera_id: Optional[str] = None) -> Dict[str, str]: + """ + Generate spatial context annotations for an entity at a 3D position. + + Returns dict with: + location_label: "near the school" or None + cardinal_from_camera: "to the north" or None + nearest_building: "school" or None + building_distance_m: float or None + """ + annotations = {} + + # Location label + annotations["location_label"] = self.get_location_label(point_3d) + + # Cardinal direction from camera + if camera_id: + annotations["cardinal_from_camera"] = \ + self.get_camera_relative_direction(camera_id, point_3d) + + # Nearest building ID + distance + result = self.nearest_building(point_3d) + if result: + bid, dist = result + annotations["nearest_building"] = bid + annotations["building_distance_m"] = round(dist, 1) + + return annotations + + def entity_3d_distance(self, point_a: np.ndarray, + point_b: np.ndarray) -> float: + """3D Euclidean distance between two entity world positions (meters).""" + return float(np.linalg.norm(np.array(point_a) - np.array(point_b))) + + def entity_2d_distance(self, point_a: np.ndarray, + point_b: np.ndarray) -> float: + """2D horizontal distance (East-North plane) in meters.""" + return float(np.linalg.norm( + np.array(point_a[:2]) - np.array(point_b[:2]) + )) + + +# ============================================================================ +# Cross-Camera Entity Matching with 3D Proximity +# ============================================================================ + +def compute_3d_matching_score( + cam_a: str, bbox_a: List[float], + cam_b: str, bbox_b: List[float], + time_gap_sec: float, + description_overlap: float = 0.0, +) -> Optional[Dict]: + """ + Compute cross-camera entity matching score using 3D + temporal + visual. + + Projects bbox footpoints to 3D world coords, checks proximity. + + Args: + cam_a, cam_b: Camera IDs + bbox_a, bbox_b: [x1, y1, x2, y2] bounding boxes + time_gap_sec: Temporal gap between entity A (last frame) and B (first frame) + description_overlap: Token overlap ratio of entity descriptions (0-1) + + Returns: + { + "spatial_score": float, # 0-1, based on 3D proximity + "temporal_score": float, # 0-1, based on time gap + "description_score": float,# 0-1, based on visual similarity + "combined_score": float, # weighted combination + "distance_3d_m": float, # 3D distance in meters + "point_a": list, # [e, n, u] + "point_b": list, # [e, n, u] + } + None if cameras don't have KRTD models or projection fails. + """ + model_a = load_camera_model(cam_a) + model_b = load_camera_model(cam_b) + if model_a is None or model_b is None: + return None + + # Project bbox footpoints to world + point_a = model_a.bbox_foot_to_world(bbox_a) + point_b = model_b.bbox_foot_to_world(bbox_b) + if point_a is None or point_b is None: + return None + + # 3D distance + dist = float(np.linalg.norm(point_a - point_b)) + + # Spatial score: closer = higher score + # 0-5m → 1.0, 5-20m → linear decay, >20m → 0.0 + if dist < 5.0: + spatial_score = 1.0 + elif dist < 20.0: + spatial_score = max(0.0, 1.0 - (dist - 5.0) / 15.0) + else: + spatial_score = 0.0 + + # Temporal score: smaller gap = higher score + # 0-2s → 1.0, 2-10s → linear decay, >10s → 0.4 minimum + if time_gap_sec < 2.0: + temporal_score = 1.0 + elif time_gap_sec < 10.0: + temporal_score = max(0.4, 1.0 - (time_gap_sec - 2.0) / 8.0 * 0.6) + else: + temporal_score = 0.4 + + # Description score is passed in directly + desc_score = max(0.0, min(1.0, description_overlap)) + + # Combined: 0.4 * temporal + 0.3 * spatial + 0.3 * description + combined = 0.4 * temporal_score + 0.3 * spatial_score + 0.3 * desc_score + + return { + "spatial_score": round(spatial_score, 3), + "temporal_score": round(temporal_score, 3), + "description_score": round(desc_score, 3), + "combined_score": round(combined, 3), + "distance_3d_m": round(dist, 2), + "point_a": point_a.tolist(), + "point_b": point_b.tolist(), + } + + +# ============================================================================ +# Utility: Enrich Question Text with Spatial Context +# ============================================================================ + +def enrich_description_with_location( + description: str, + point_3d: Optional[np.ndarray], + site: str, +) -> str: + """ + Append spatial context to an entity description for question text. + + "a person in navy top and khaki pants opens a vehicle door" + → "a person in navy top and khaki pants opens a vehicle door near the school parking lot" + + Only adds context if a meaningful location label exists. + Does NOT reveal camera ID (important for perception questions). + """ + if point_3d is None: + return description + + ctx = get_scene_context(site) + if ctx is None: + return description + + label = ctx.get_location_label(point_3d) + if label is None: + return description + + # Avoid repeating location if already mentioned + if label.lower() in description.lower(): + return description + + return f"{description} {label}" + + +# ============================================================================ +# CLI for testing / debugging +# ============================================================================ + +def main(): + """Quick test: show scene context for the school site.""" + import argparse + parser = argparse.ArgumentParser(description="Scene context test") + parser.add_argument("--site", default="school", help="Site name") + parser.add_argument("--camera", default="G339", help="Camera ID to test") + args = parser.parse_args() + + if trimesh is None: + print("ERROR: trimesh not installed. Run: pip install trimesh") + return + + ctx = get_scene_context(args.site) + if ctx is None: + print(f"Failed to create scene context for {args.site}") + return + + print(f"Scene Context: {args.site}") + print(f" Buildings loaded: {list(ctx.buildings.keys())}") + for bid, centroid in ctx.centroids.items(): + print(f" {bid:15s}: centroid=({centroid[0]:8.1f}, {centroid[1]:8.1f}, {centroid[2]:8.1f})") + + # Test with camera center + cam = load_camera_model(args.camera) + if cam: + cc = cam.camera_center + print(f"\n Camera {args.camera} center: ({cc[0]:.1f}, {cc[1]:.1f}, {cc[2]:.1f})") + ann = ctx.annotate_entity(cc, args.camera) + print(f" Annotations: {ann}") + + # Test a point slightly offset from camera + test_point = cc + np.array([10, 0, 0]) # 10m east + ann2 = ctx.annotate_entity(test_point, args.camera) + label = ctx.get_location_label(test_point) + print(f"\n Test point (10m east of camera): ({test_point[0]:.1f}, {test_point[1]:.1f})") + print(f" Location label: {label}") + print(f" Annotations: {ann2}") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/utils/__init__.py b/meva/scripts/v10/utils/__init__.py new file mode 100644 index 0000000..a3fa321 --- /dev/null +++ b/meva/scripts/v10/utils/__init__.py @@ -0,0 +1 @@ +"""V6 utility modules.""" diff --git a/meva/scripts/v10/utils/camera_overlap.py b/meva/scripts/v10/utils/camera_overlap.py new file mode 100644 index 0000000..770ab63 --- /dev/null +++ b/meva/scripts/v10/utils/camera_overlap.py @@ -0,0 +1,136 @@ +""" +Camera overlap detection — identifies camera pairs with significant FOV overlap. + +Used by dedup logic to avoid double-counting events seen by overlapping cameras. +Combines: + 1. KRTD-derived camera positions + viewing directions (bus, hospital, school) + 2. Manual/hardcoded overlap for sites without KRTD (admin) + +An overlapping pair means the same physical event could appear in both cameras, +so cross-camera dedup should be MORE aggressive (wider time window, no 3D req). +""" + +import numpy as np +from pathlib import Path +from typing import Dict, FrozenSet, Optional, Set, Tuple + +from .krtd import KRTD_DIR, INDOOR_CAMERAS + +# ============================================================================ +# Hardcoded overlaps for sites without KRTD calibration +# ============================================================================ + +# Admin site: G326 and G329 point at the same building entrance. +# No KRTD models exist for admin. Confirmed via visual inspection. +_MANUAL_OVERLAPS: Dict[str, Set[FrozenSet[str]]] = { + "admin": {frozenset({"G326", "G329"})}, +} + +# ============================================================================ +# KRTD-based overlap computation +# ============================================================================ + +# Thresholds for automatic overlap detection +_CLOSE_DIST_M = 15.0 # cameras within 15m are "close" +_MEDIUM_DIST_M = 50.0 # cameras within 50m might overlap +_HIGH_COS_SIM = 0.5 # cosine similarity of viewing dirs for "similar view" +_MEDIUM_COS_SIM = 0.3 # lower threshold for medium-distance cameras + + +def _compute_krtd_overlaps() -> Set[FrozenSet[str]]: + """Compute overlapping camera pairs from KRTD calibration files. + + Two cameras are considered overlapping if: + - Within 15m of each other (any viewing direction), OR + - Within 50m AND viewing directions have cosine similarity > 0.3, OR + - Within 30m AND viewing directions have cosine similarity > 0.5 + """ + overlaps: Set[FrozenSet[str]] = set() + + # Parse all KRTD files + cameras: Dict[str, Tuple[np.ndarray, np.ndarray]] = {} # cam_id -> (position, view_dir) + for f in KRTD_DIR.glob("*.krtd"): + cam_id = f.stem.split(".")[-1] + if cam_id in INDOOR_CAMERAS: + continue + try: + lines = [l.strip() for l in open(f) if l.strip()] + K = np.array([[float(x) for x in lines[i].split()] for i in range(3)]) + R = np.array([[float(x) for x in lines[i].split()] for i in range(3, 6)]) + T = np.array([float(x) for x in lines[6].split()]) + C = -R.T @ T + view_dir = R.T @ np.array([0.0, 0.0, 1.0]) + view_dir = view_dir / np.linalg.norm(view_dir) + cameras[cam_id] = (C, view_dir) + except Exception: + continue + + cam_list = sorted(cameras.keys()) + for i in range(len(cam_list)): + for j in range(i + 1, len(cam_list)): + ca, cb = cam_list[i], cam_list[j] + pos_a, dir_a = cameras[ca] + pos_b, dir_b = cameras[cb] + dist = float(np.linalg.norm(pos_a - pos_b)) + cos_sim = float(np.dot(dir_a, dir_b)) + + if dist < _CLOSE_DIST_M: + overlaps.add(frozenset({ca, cb})) + elif dist < 30.0 and cos_sim > _HIGH_COS_SIM: + overlaps.add(frozenset({ca, cb})) + elif dist < _MEDIUM_DIST_M and cos_sim > _MEDIUM_COS_SIM: + overlaps.add(frozenset({ca, cb})) + + return overlaps + + +# ============================================================================ +# Cached overlap map +# ============================================================================ + +_OVERLAP_CACHE: Optional[Set[FrozenSet[str]]] = None + + +def _get_all_overlaps() -> Set[FrozenSet[str]]: + """Get all overlapping camera pairs (cached).""" + global _OVERLAP_CACHE + if _OVERLAP_CACHE is None: + _OVERLAP_CACHE = _compute_krtd_overlaps() + for pairs in _MANUAL_OVERLAPS.values(): + _OVERLAP_CACHE |= pairs + return _OVERLAP_CACHE + + +def cameras_overlap(cam_a: str, cam_b: str) -> bool: + """Check if two cameras have significant FOV overlap. + + When True, events on these cameras with similar timing are likely + the same real-world event and should be deduped aggressively. + """ + if cam_a == cam_b: + return True + return frozenset({cam_a, cam_b}) in _get_all_overlaps() + + +def get_overlapping_cameras(camera_id: str) -> Set[str]: + """Get all cameras that overlap with the given camera.""" + result = set() + for pair in _get_all_overlaps(): + if camera_id in pair: + result |= pair + result.discard(camera_id) + return result + + +def get_overlap_pairs_for_cameras(camera_ids: list) -> list: + """Get all overlap pairs among a set of cameras. + + Returns list of (cam_a, cam_b) tuples. + """ + cam_set = set(camera_ids) + result = [] + for pair in _get_all_overlaps(): + if pair <= cam_set: + a, b = sorted(pair) + result.append((a, b)) + return sorted(result) diff --git a/meva/scripts/v10/utils/camera_proximity.py b/meva/scripts/v10/utils/camera_proximity.py new file mode 100644 index 0000000..d1d55df --- /dev/null +++ b/meva/scripts/v10/utils/camera_proximity.py @@ -0,0 +1,262 @@ +""" +Camera proximity detection — identifies spatial relationships between cameras. + +Unlike camera_overlap.py (which flags duplicate-event risk for dedup), this module +identifies cameras that cover ADJACENT areas — ideal for multi-camera temporal +questions because events on nearby but non-overlapping cameras are likely part +of the same scene narrative. + +Combines: + 1. KRTD-derived camera positions + viewing directions (outdoor cameras) + 2. Camera-set metadata from the MEVA clip table (groups cameras by shared FOV) + 3. Manual indoor camera knowledge (admin, school hallways, bus indoor) + +Camera proximity tiers: + - "same_area": Cameras that likely observe the same physical space + (overlapping FOV, <15m apart, or same camera-set) + - "adjacent": Cameras covering adjacent areas — a person walking between + them is plausible in 10-120s (15-80m apart, same site) + - "same_site": Same site but far apart (>80m or different indoor/outdoor zones) + - "different_site": Different sites (should never be paired) +""" + +import numpy as np +from pathlib import Path +from typing import Dict, FrozenSet, List, Optional, Set, Tuple + +from .krtd import KRTD_DIR, INDOOR_CAMERAS, load_camera_model + +# ============================================================================ +# Camera-Set Groups (from MEVA clip table metadata) +# ============================================================================ + +# Camera sets indicate cameras that share sufficient FOV for frame-level sync. +# Source: meva-data-repo/metadata/meva-clip-camera-and-time-table.txt +CAMERA_SETS: Dict[str, List[str]] = { + # School + "3-300": ["G300"], + "3-328": ["G328"], + "3-330": ["G299", "G330"], # Indoor gym pair — both see basketball court + "3-336": ["G336"], + "3-420": ["G419", "G420"], # Indoor hallway close-up cameras + "3-421": ["G421"], # Indoor close-up + "3-423": ["G423"], # Indoor close-up + "3-424": ["G424"], + "skip": ["G339"], # PTZ / not yet assigned + "IR-school": ["G474"], # Infrared + "3-638": ["G638"], + "3-639": ["G639"], + # Bus + "3-331": ["G331"], # Indoor + "3-340": ["G340"], + "3-508": ["G508", "G509"], # G508 indoor, G509 outdoor — adjacent + "IR-bus": ["G475"], # Infrared + "3-505": ["G505"], + "3-506": ["G506"], + # Hospital + "3-301": ["G301"], + "3-341": ["G341"], + "3-436": ["G436"], + "3-476": ["G476"], + "3-479": ["G479"], + # Admin + "admin": ["G326", "G329"], # Both point at same building entrance +} + +# Which site each camera belongs to +CAMERA_SITE: Dict[str, str] = { + # School + "G299": "school", "G300": "school", "G328": "school", "G330": "school", + "G336": "school", "G339": "school", "G419": "school", "G420": "school", + "G421": "school", "G423": "school", "G424": "school", "G474": "school", + "G638": "school", "G639": "school", + # Bus + "G331": "bus", "G340": "bus", "G475": "bus", "G505": "bus", + "G506": "bus", "G508": "bus", "G509": "bus", + # Hospital + "G301": "hospital", "G341": "hospital", "G436": "hospital", + "G476": "hospital", "G479": "hospital", + # Admin + "G326": "admin", "G329": "admin", +} + +# Interior vs exterior classification +# Source: meva-data-repo/metadata/meva-camera-daily-status.txt +INDOOR_CAMERAS_FULL: Set[str] = { + "G299", "G326", "G329", "G330", "G331", + "G419", "G420", "G421", "G423", "G508", +} + +OUTDOOR_CAMERAS: Set[str] = { + "G300", "G301", "G328", "G336", "G339", "G340", "G341", + "G424", "G436", "G474", "G475", "G476", "G479", + "G505", "G506", "G509", "G638", "G639", +} + +# ============================================================================ +# Manual Indoor Camera Adjacency +# ============================================================================ + +# Indoor cameras without KRTD: manually specify which outdoor cameras are +# adjacent (a person exiting the indoor area would appear on these cameras). +# Based on camera-set analysis, site layout, and annotation patterns. + +INDOOR_ADJACENT_OUTDOOR: Dict[str, List[str]] = { + # School gym cameras → nearby school outdoor cameras + "G299": ["G300", "G328", "G336", "G424", "G638", "G639"], + "G330": ["G300", "G328", "G336", "G424", "G638", "G639"], + # School hallway close-up cameras → nearby outdoor + gym + "G419": ["G300", "G328", "G424", "G299", "G330"], + "G420": ["G300", "G328", "G424", "G299", "G330"], + "G421": ["G300", "G328", "G424"], + "G423": ["G300", "G328", "G424"], + # Admin indoor → both see same entrance (overlap, not adjacent) + "G326": ["G329"], + "G329": ["G326"], + # Bus indoor cameras → bus outdoor cameras + "G331": ["G340", "G505", "G506", "G509", "G508"], + "G508": ["G340", "G505", "G506", "G509", "G331"], +} + + +# ============================================================================ +# KRTD-based Distance Cache +# ============================================================================ + +_DISTANCE_CACHE: Optional[Dict[FrozenSet[str], float]] = None + + +def _compute_krtd_distances() -> Dict[FrozenSet[str], float]: + """Compute pairwise distances between all cameras with KRTD calibration.""" + distances: Dict[FrozenSet[str], float] = {} + + # Parse all KRTD files for camera centers + positions: Dict[str, np.ndarray] = {} + for f in KRTD_DIR.glob("*.krtd"): + cam_id = f.stem.split(".")[-1] + if cam_id in INDOOR_CAMERAS: + continue + try: + lines = [l.strip() for l in open(f) if l.strip()] + R = np.array([[float(x) for x in lines[i].split()] for i in range(3, 6)]) + T = np.array([float(x) for x in lines[6].split()]) + C = -R.T @ T + positions[cam_id] = C + except Exception: + continue + + cam_list = sorted(positions.keys()) + for i in range(len(cam_list)): + for j in range(i + 1, len(cam_list)): + key = frozenset({cam_list[i], cam_list[j]}) + dist = float(np.linalg.norm(positions[cam_list[i]] - positions[cam_list[j]])) + distances[key] = dist + + return distances + + +def _get_distances() -> Dict[FrozenSet[str], float]: + global _DISTANCE_CACHE + if _DISTANCE_CACHE is None: + _DISTANCE_CACHE = _compute_krtd_distances() + return _DISTANCE_CACHE + + +# ============================================================================ +# Public API +# ============================================================================ + +def get_camera_distance(cam_a: str, cam_b: str) -> Optional[float]: + """Get distance in meters between two cameras (KRTD-based). None if unavailable.""" + if cam_a == cam_b: + return 0.0 + key = frozenset({cam_a, cam_b}) + return _get_distances().get(key) + + +def are_cameras_same_set(cam_a: str, cam_b: str) -> bool: + """Check if two cameras belong to the same camera-set (shared FOV).""" + for cameras in CAMERA_SETS.values(): + if cam_a in cameras and cam_b in cameras: + return True + return False + + +def get_camera_site(cam_id: str) -> Optional[str]: + """Get the site a camera belongs to.""" + return CAMERA_SITE.get(cam_id) + + +def get_proximity_tier(cam_a: str, cam_b: str) -> str: + """ + Classify the spatial relationship between two cameras. + + Returns one of: + "same_area" — Overlapping FOV / same camera-set + "adjacent" — Nearby, non-overlapping (ideal for temporal Q) + "same_site" — Same site, but far apart + "different_site" — Different sites + """ + if cam_a == cam_b: + return "same_area" + + site_a = CAMERA_SITE.get(cam_a) + site_b = CAMERA_SITE.get(cam_b) + + # Different sites → never pair + if site_a and site_b and site_a != site_b: + return "different_site" + + # Same camera-set → same area (overlapping, use for dedup not questions) + if are_cameras_same_set(cam_a, cam_b): + return "same_area" + + # Check indoor adjacency map + if cam_a in INDOOR_ADJACENT_OUTDOOR and cam_b in INDOOR_ADJACENT_OUTDOOR[cam_a]: + return "adjacent" + if cam_b in INDOOR_ADJACENT_OUTDOOR and cam_a in INDOOR_ADJACENT_OUTDOOR[cam_b]: + return "adjacent" + + # Use KRTD distance if available + dist = get_camera_distance(cam_a, cam_b) + if dist is not None: + if dist < 15.0: + return "same_area" + elif dist < 80.0: + return "adjacent" + else: + return "same_site" + + # Fallback: both at same site but no distance info + if site_a == site_b: + # Indoor+outdoor at same site = adjacent (person can walk between) + if (cam_a in INDOOR_CAMERAS_FULL) != (cam_b in INDOOR_CAMERAS_FULL): + return "adjacent" + return "same_site" + + return "different_site" + + +def score_camera_pair_for_temporal(cam_a: str, cam_b: str) -> float: + """ + Score how good a camera pair is for temporal cross-camera questions. + + Higher = better. Ideal pairs are adjacent cameras at the same site + (close enough that events are related, far enough that they require + multi-camera reasoning). + + Returns: + 3.0 — adjacent cameras (best: events are likely related) + 2.0 — same_site but far (ok: events could be part of same scene) + 0.5 — same_area / overlapping (bad: same event seen twice) + 0.0 — different_site (invalid) + """ + tier = get_proximity_tier(cam_a, cam_b) + if tier == "adjacent": + return 3.0 + elif tier == "same_site": + return 2.0 + elif tier == "same_area": + return 0.5 + else: + return 0.0 diff --git a/meva/scripts/v10/utils/iou.py b/meva/scripts/v10/utils/iou.py new file mode 100644 index 0000000..aaf670c --- /dev/null +++ b/meva/scripts/v10/utils/iou.py @@ -0,0 +1,35 @@ +""" +V6 utils/iou.py — IoU (Intersection over Union) utilities for bounding box matching. +""" + +from typing import List, Optional + + +def compute_iou(bbox_a: List[int], bbox_b: List[int]) -> float: + """ + Compute IoU between two bounding boxes. + + Args: + bbox_a: [x1, y1, x2, y2] + bbox_b: [x1, y1, x2, y2] + + Returns: + IoU value [0.0, 1.0] + """ + x1 = max(bbox_a[0], bbox_b[0]) + y1 = max(bbox_a[1], bbox_b[1]) + x2 = min(bbox_a[2], bbox_b[2]) + y2 = min(bbox_a[3], bbox_b[3]) + + if x2 <= x1 or y2 <= y1: + return 0.0 + + inter = (x2 - x1) * (y2 - y1) + area_a = (bbox_a[2] - bbox_a[0]) * (bbox_a[3] - bbox_a[1]) + area_b = (bbox_b[2] - bbox_b[0]) * (bbox_b[3] - bbox_b[1]) + union = area_a + area_b - inter + + if union <= 0: + return 0.0 + + return inter / union diff --git a/meva/scripts/v10/utils/krtd.py b/meva/scripts/v10/utils/krtd.py new file mode 100644 index 0000000..65e8b69 --- /dev/null +++ b/meva/scripts/v10/utils/krtd.py @@ -0,0 +1,124 @@ +""" +V6 utils/krtd.py — KRTD camera calibration parsing and 3D projection. + +Ported from V4's CameraModel class with improvements for V6 pipeline. +""" + +import numpy as np +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +KRTD_DIR = Path("/nas/mars/dataset/MEVA/meva-data-repo/metadata/camera-models/krtd") + +# Indoor cameras (local coordinate frame, NOT in shared ENU) +INDOOR_CAMERAS = {"G299", "G330"} + + +class CameraModel: + """ + KRTD camera calibration model. + + Projects 2D pixel coordinates to 3D world coordinates on the ground plane. + Uses the ENU (East-North-Up) coordinate system shared across outdoor cameras. + """ + + def __init__(self, krtd_path: Path): + self.path = krtd_path + self.K: np.ndarray = None # 3x3 intrinsic + self.R: np.ndarray = None # 3x3 rotation + self.T: np.ndarray = None # 3x1 translation + self.D: np.ndarray = None # distortion coefficients + self._parse(krtd_path) + + def _parse(self, path: Path): + with open(path) as f: + lines = [l.strip() for l in f if l.strip()] + self.K = np.array([[float(x) for x in lines[i].split()] for i in range(3)]) + self.R = np.array([[float(x) for x in lines[i].split()] for i in range(3, 6)]) + self.T = np.array([float(x) for x in lines[6].split()]) + if len(lines) > 7: + self.D = np.array([float(x) for x in lines[7].split()]) + + @property + def camera_center(self) -> np.ndarray: + """Camera center in world (ENU) coordinates: C = -R^T * T""" + return -self.R.T @ self.T + + def project_to_ground(self, u: float, v: float, ground_z: float = 0.0) -> Optional[np.ndarray]: + """ + Back-project pixel (u, v) onto the ground plane (ENU z = ground_z). + + Returns [east, north, up] in meters, or None if ray is parallel to ground. + """ + K_inv = np.linalg.inv(self.K) + d_cam = K_inv @ np.array([u, v, 1.0]) + d_world = self.R.T @ d_cam + C = self.camera_center + + if abs(d_world[2]) < 1e-10: + return None # ray parallel to ground + + t = (ground_z - C[2]) / d_world[2] + if t < 0: + return None # behind camera + + return C + t * d_world + + def bbox_foot_to_world(self, bbox: List[float], ground_z: float = 0.0) -> Optional[np.ndarray]: + """ + Project the bottom-center of a bounding box to world coordinates. + + bbox = [x1, y1, x2, y2] in pixel coordinates. + Returns [east, north, up] or None. + """ + x1, y1, x2, y2 = bbox + foot_u = (x1 + x2) / 2.0 # horizontal center + foot_v = max(y1, y2) # bottom of bbox + return self.project_to_ground(foot_u, foot_v, ground_z) + + +def load_camera_model(camera_id: str) -> Optional[CameraModel]: + """Load KRTD calibration for a camera. Returns None if not available.""" + if camera_id in INDOOR_CAMERAS: + return None + krtd_files = list(KRTD_DIR.glob(f"*.{camera_id}.krtd")) + if not krtd_files: + return None + try: + return CameraModel(krtd_files[0]) + except Exception: + return None + + +def load_all_camera_models(camera_ids: List[str]) -> Dict[str, CameraModel]: + """Load KRTD models for a list of cameras. Skips indoor / unavailable.""" + models = {} + for cam_id in camera_ids: + model = load_camera_model(cam_id) + if model is not None: + models[cam_id] = model + return models + + +def compute_entity_distance(model_a: CameraModel, bbox_a: List[float], + model_b: CameraModel, bbox_b: List[float]) -> Optional[float]: + """ + Compute 3D distance between two entities given their bounding boxes and camera models. + + Returns distance in meters, or None if projection fails. + """ + pos_a = model_a.bbox_foot_to_world(bbox_a) + pos_b = model_b.bbox_foot_to_world(bbox_b) + if pos_a is None or pos_b is None: + return None + return float(np.linalg.norm(pos_a - pos_b)) + + +def classify_proximity(distance_m: float) -> str: + """Classify distance into proximity buckets.""" + if distance_m <= 5.0: + return "near" + elif distance_m <= 15.0: + return "moderate" + else: + return "far" diff --git a/meva/scripts/v10/utils/mevid.py b/meva/scripts/v10/utils/mevid.py new file mode 100644 index 0000000..cc3000d --- /dev/null +++ b/meva/scripts/v10/utils/mevid.py @@ -0,0 +1,176 @@ +""" +V6 utils/mevid.py — MEVID tracklet parsing and cross-camera person mapping. + +Parses MEVID annotation data to find: +1. All (person_id, camera_id) pairs in the dataset +2. Cross-camera person links (same person on 2+ cameras) +3. MEVID-supported slots (slots where cross-camera persons exist) +""" + +import os +import re +from pathlib import Path +from collections import defaultdict +from typing import Dict, List, Set, Tuple, Optional + +MEVID_DATA_DIR = Path(os.environ.get("MEVA_MEVID_DATA_DIR") or "/nas/mars/dataset/MEVA/mevid_data/mevid-v1-annotation-data") +MEVID_URLS = Path(os.environ.get("MEVA_MEVID_URLS") or "/nas/mars/dataset/MEVA/mevid_data/mevid-v1-video-URLS.txt") + +# Regex for MEVID image filename: {PersonID}O{OutfitID}C{CameraID}T{TrackletID}F{Frame}.jpg +MEVID_NAME_RE = re.compile(r'^(\d{4})O(\d{3})C(\d+)T(\d{3})F(\d{5})\.jpg$') + +# Module-level cache for MEVID parsing (avoids re-reading files on every call) +_MEVID_PERSON_CAMERAS_CACHE: Optional[Dict[int, Set[str]]] = None + + +def parse_mevid_person_cameras() -> Dict[int, Set[str]]: + """ + Parse MEVID train_name.txt and test_name.txt to extract all (person_id, camera_id) pairs. + Results are cached after first call. + + Returns: + {person_id: {camera_id, ...}, ...} + Camera IDs are in MEVA format: "G424" (not "C424"). + """ + global _MEVID_PERSON_CAMERAS_CACHE + if _MEVID_PERSON_CAMERAS_CACHE is not None: + return _MEVID_PERSON_CAMERAS_CACHE + + person_cameras: Dict[int, Set[str]] = defaultdict(set) + + for fname in ["train_name.txt", "test_name.txt"]: + fpath = MEVID_DATA_DIR / fname + if not fpath.exists(): + continue + # Use set to avoid re-parsing the same (person, camera) pair + seen_keys = set() + with open(fpath) as f: + for line in f: + line = line.strip() + if not line: + continue + m = MEVID_NAME_RE.match(line) + if not m: + continue + person_id = int(m.group(1)) + camera_id = f"G{m.group(3)}" + key = (person_id, camera_id) + if key not in seen_keys: + seen_keys.add(key) + person_cameras[person_id].add(camera_id) + + _MEVID_PERSON_CAMERAS_CACHE = dict(person_cameras) + return _MEVID_PERSON_CAMERAS_CACHE + + +def parse_mevid_clips() -> List[Dict[str, str]]: + """ + Parse MEVID video URLs to get the list of MEVA clips used by MEVID. + + Returns list of dicts with keys: clip_name, date, start_time, end_time, site, camera_id + """ + if not MEVID_URLS.exists(): + return [] + + clips = [] + clip_re = re.compile( + r'(\d{4}-\d{2}-\d{2})\.(\d{2}-\d{2}-\d{2})\.(\d{2}-\d{2}-\d{2})\.(\w+)\.(G\d+)' + ) + + with open(MEVID_URLS) as f: + for line in f: + line = line.strip() + m = clip_re.search(line) + if m: + clips.append({ + "clip_name": f"{m.group(1)}.{m.group(2)}.{m.group(3)}.{m.group(4)}.{m.group(5)}", + "date": m.group(1), + "start_time": m.group(2), + "end_time": m.group(3), + "site": m.group(4), + "camera_id": m.group(5), + }) + return clips + + +def find_mevid_persons_for_slot(slot: str, slot_cameras: List[str]) -> Dict[int, Set[str]]: + """ + Find MEVID persons who appear on 2+ cameras within this slot's camera set. + + This is an approximation: MEVID tells us person X appears on camera Y globally, + but we can't confirm the specific time slot without extracted tracklet images. + For March dates within a single session day, this mapping is reliable. + + Args: + slot: Slot name e.g. "2018-03-11.11-25-00.school" + slot_cameras: List of camera IDs in this slot + + Returns: + {person_id: {camera_ids in this slot}, ...} + Only persons appearing on 2+ slot cameras are included. + """ + all_person_cameras = parse_mevid_person_cameras() + slot_camera_set = set(slot_cameras) + + result = {} + for person_id, cameras in all_person_cameras.items(): + overlap = cameras & slot_camera_set + if len(overlap) >= 2: + result[person_id] = overlap + + return result + + +def find_mevid_supported_slots(slot_index: Dict) -> List[Dict]: + """ + Find all slots in the index that have MEVID cross-camera person coverage. + + Returns list of {slot, cameras, mevid_persons_count, mevid_camera_overlap}. + """ + all_person_cameras = parse_mevid_person_cameras() + + supported = [] + for slot, info in slot_index.items(): + # Only March dates are on disk + if not slot.startswith("2018-03"): + continue + + slot_cameras = set(info.get("cameras", [])) + cross_persons = 0 + for person_id, cameras in all_person_cameras.items(): + if len(cameras & slot_cameras) >= 2: + cross_persons += 1 + + if cross_persons > 0: + supported.append({ + "slot": slot, + "cameras": info["cameras"], + "mevid_persons_count": cross_persons, + "mevid_camera_overlap": len(slot_cameras), + "sources": list(info.get("sources", {}).keys()), + }) + + supported.sort(key=lambda x: x["mevid_persons_count"], reverse=True) + return supported + + +def get_mevid_stats() -> Dict: + """Get summary statistics of MEVID data.""" + person_cameras = parse_mevid_person_cameras() + clips = parse_mevid_clips() + + cross_camera = sum(1 for cams in person_cameras.values() if len(cams) >= 2) + all_cameras = set() + for cams in person_cameras.values(): + all_cameras.update(cams) + + march_clips = [c for c in clips if c["date"].startswith("2018-03")] + + return { + "total_persons": len(person_cameras), + "cross_camera_persons": cross_camera, + "total_cameras": len(all_cameras), + "cameras": sorted(all_cameras), + "total_clips": len(clips), + "march_clips": len(march_clips), + } diff --git a/meva/scripts/v10/utils/yaml_stream.py b/meva/scripts/v10/utils/yaml_stream.py new file mode 100644 index 0000000..f4b2626 --- /dev/null +++ b/meva/scripts/v10/utils/yaml_stream.py @@ -0,0 +1,158 @@ +""" +V6 utils/yaml_stream.py — Memory-efficient geom.yml streaming parser. + +Parses geom.yml files line-by-line using regex, yielding {id1, ts0, g0} dicts +without loading the entire file into memory. This avoids OOM on large files (50MB+). +""" + +import re +from pathlib import Path +from typing import Dict, Generator, List, Optional, Set + + +def stream_geom_records(path: Path) -> Generator[Dict, None, None]: + """ + Stream-parse a geom.yml file line-by-line. + Yields dicts: {"id1": int, "ts0": int, "g0": [x1, y1, x2, y2]} + + Handles TWO Kitware geom.yml formats: + 1. kitware format (unquoted keys, fixed field order): + - { geom: {id1: NNN, id0: M, ts0: FFF, ts1: T.T, g0: X1 Y1 X2 Y2, ...} } + 2. kitware-meva-training format (Python dict notation, quoted keys, arbitrary order): + - {'geom': {'g0': '886 418 929 504', 'id0': 1, 'id1': 10, 'ts0': 1475}} + Also handles multi-line format (fallback). + """ + # Format 1: kitware inline (unquoted, id1 before ts0 before g0) + kitware_re = re.compile( + r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)' + ) + + # Format 2: kitware-meva-training (quoted keys, any order) + # Extract fields individually since order varies + training_id1_re = re.compile(r"'id1':\s*(\d+)") + training_ts0_re = re.compile(r"'ts0':\s*(\d+)") + training_g0_re = re.compile(r"'g0':\s*'(\d+\s+\d+\s+\d+\s+\d+)'") + + # Fallback: multi-line parsing + current: Dict = {} + + with open(path) as f: + for line in f: + line = line.strip() + if not line: + continue + + # Try kitware inline format first (most common) + m = kitware_re.search(line) + if m: + yield { + 'id1': int(m.group(1)), + 'ts0': int(m.group(2)), + 'g0': [int(x) for x in m.group(3).split()], + } + continue + + # Try kitware-meva-training format (quoted keys, any field order) + m_id1 = training_id1_re.search(line) + m_ts0 = training_ts0_re.search(line) + m_g0 = training_g0_re.search(line) + if m_id1 and m_ts0 and m_g0: + yield { + 'id1': int(m_id1.group(1)), + 'ts0': int(m_ts0.group(1)), + 'g0': [int(x) for x in m_g0.group(1).split()], + } + continue + + # Fallback: multi-line format + m = re.match(r"'?id1'?:\s*(\d+)", line) + if m: + current['id1'] = int(m.group(1)) + continue + m = re.match(r"'?ts0'?:\s*(\d+)", line) + if m: + current['ts0'] = int(m.group(1)) + continue + m = re.match(r"'?g0'?:\s*['\"]?(\d+\s+\d+\s+\d+\s+\d+)", line) + if m: + current['g0'] = [int(x) for x in m.group(1).split()] + if 'id1' in current and 'ts0' in current: + yield dict(current) + current = {} + + +def get_actor_keyframe_bboxes(path: Path, actor_ids: Optional[Set[int]] = None, + sample_every: int = 1) -> Dict[int, Dict[int, List[int]]]: + """ + Extract bounding boxes for specific actors (or all actors). + + Args: + path: Path to geom.yml + actor_ids: Set of actor IDs to extract (None = all) + sample_every: Sample every Nth frame (default 1 = all frames) + + Returns: + {actor_id: {frame: [x1, y1, x2, y2], ...}, ...} + """ + result: Dict[int, Dict[int, List[int]]] = {} + for rec in stream_geom_records(path): + aid = rec['id1'] + if actor_ids is not None and aid not in actor_ids: + continue + frame = rec['ts0'] + if frame % sample_every != 0: + continue + if aid not in result: + result[aid] = {} + result[aid][frame] = rec['g0'] + return result + + +def get_actor_frame_range(path: Path) -> Dict[int, tuple]: + """ + Get (min_frame, max_frame) for each actor in a geom.yml. + Memory-efficient: only tracks frame extremes. + + Returns: + {actor_id: (first_frame, last_frame), ...} + """ + ranges: Dict[int, list] = {} + for rec in stream_geom_records(path): + aid = rec['id1'] + frame = rec['ts0'] + if aid not in ranges: + ranges[aid] = [frame, frame] + else: + if frame < ranges[aid][0]: + ranges[aid][0] = frame + if frame > ranges[aid][1]: + ranges[aid][1] = frame + return {aid: tuple(r) for aid, r in ranges.items()} + + +def get_bbox_at_frame(path: Path, actor_id: int, target_frame: int, + tolerance: int = 5) -> Optional[List[int]]: + """ + Get a single bbox for a specific actor at (or near) a target frame. + + Args: + path: Path to geom.yml + actor_id: Actor to look for + target_frame: Frame number to find + tolerance: Accept frames within ±tolerance + + Returns: + [x1, y1, x2, y2] or None + """ + best = None + best_dist = tolerance + 1 + for rec in stream_geom_records(path): + if rec['id1'] != actor_id: + continue + dist = abs(rec['ts0'] - target_frame) + if dist < best_dist: + best_dist = dist + best = rec['g0'] + if dist == 0: + break + return best diff --git a/meva/scripts/v10/validate_qa.py b/meva/scripts/v10/validate_qa.py new file mode 100644 index 0000000..4b626d4 --- /dev/null +++ b/meva/scripts/v10/validate_qa.py @@ -0,0 +1,1021 @@ +#!/usr/bin/env python3 +""" +validate_qa.py — Automated QA validator for MEVA V10 pipeline. + +Runs 6 structural checks on raw or naturalized QA JSON without watching video: + 1. Reasoning ↔ answer consistency + 2. Raw token leak detection + 3. Duplicate / near-duplicate detection + 4. Generic description detection + 5. Multi-correct-answer ambiguity + 6. Grammar / conjugation checking + +Usage: + python3 -m scripts.v10.validate_qa --input path/to/slot.raw.json + python3 -m scripts.v10.validate_qa --input path/to/slot.naturalized.json -v + python3 -m scripts.v10.validate_qa --input path/to/slot.raw.json --json + +Output: + Structured report: {slot, total_questions, total_issues, issues: [...]} + Each issue: {question_id, check_name, severity, message, suggestion} + Severity: error (must fix), warning (should fix), info (cosmetic) +""" + +import argparse +import json +import re +import sys +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple + + +# ============================================================================ +# Constants +# ============================================================================ + +# All 37 MEVA ActEV activity IDs (raw Kitware names) +KITWARE_ACTIVITY_IDS = { + "hand_interacts_with_person", + "person_abandons_package", + "person_carries_heavy_object", + "person_closes_facility_door", + "person_closes_trunk", + "person_closes_vehicle_door", + "person_embraces_person", + "person_enters_scene_through_structure", + "person_enters_vehicle", + "person_exits_scene_through_structure", + "person_exits_vehicle", + "person_interacts_with_laptop", + "person_loads_vehicle", + "person_opens_facility_door", + "person_opens_trunk", + "person_opens_vehicle_door", + "person_picks_up_object", + "person_purchases", + "person_puts_down_object", + "person_reads_document", + "person_rides_bicycle", + "person_sits_down", + "person_stands_up", + "person_steals_object", + "person_talks_on_phone", + "person_talks_to_person", + "person_texts_on_phone", + "person_transfers_object", + "person_unloads_vehicle", + "vehicle_drops_off_person", + "vehicle_makes_u_turn", + "vehicle_picks_up_person", + "vehicle_reverses", + "vehicle_starts", + "vehicle_stops", + "vehicle_turns_left", + "vehicle_turns_right", +} + +# Severity levels +ERROR = "error" # Must fix — answer is wrong or question is invalid +WARNING = "warning" # Should fix — degrades quality +INFO = "info" # Cosmetic — minor wording issue + + +# ============================================================================ +# Issue dataclass +# ============================================================================ + +class Issue: + """Single validation issue.""" + __slots__ = ("question_id", "check_name", "severity", "message", "suggestion") + + def __init__(self, question_id: str, check_name: str, severity: str, + message: str, suggestion: str = ""): + self.question_id = question_id + self.check_name = check_name + self.severity = severity + self.message = message + self.suggestion = suggestion + + def to_dict(self) -> dict: + d = { + "question_id": self.question_id, + "check_name": self.check_name, + "severity": self.severity, + "message": self.message, + } + if self.suggestion: + d["suggestion"] = self.suggestion + return d + + +# ============================================================================ +# Helper: get question text fields (works on both raw and naturalized) +# ============================================================================ + +def _get_question_text(q: dict) -> str: + """Get the question text (naturalized preferred, raw fallback).""" + return q.get("naturalized_question") or q.get("question_template") or "" + +def _get_options(q: dict) -> List[str]: + """Get options list (naturalized preferred, raw fallback).""" + return q.get("naturalized_options") or q.get("options") or [] + +def _get_answer_text(q: dict) -> str: + """Get correct answer text.""" + return q.get("correct_answer") or "" + +def _get_reasoning(q: dict) -> str: + """Get reasoning text if available (naturalized may have it).""" + return q.get("reasoning") or q.get("naturalized_reasoning") or "" + + +# ============================================================================ +# Check 1: Reasoning ↔ Answer Consistency +# ============================================================================ + +def check_reasoning_consistency(qa_pairs: List[dict]) -> List[Issue]: + """ + Parse reasoning text + correct_answer for each question. + - For temporal: if reasoning says "X happened first" but answer says Y, flag. + - For counting: extract number from reasoning, compare to correct_answer. + - For event_ordering: check if reasoning's described sequence matches answer. + - For perception: check if camera mentioned in reasoning matches answer. + """ + issues = [] + + for q in qa_pairs: + qid = q.get("question_id", "?") + cat = q.get("category", "") + answer = _get_answer_text(q).lower() + reasoning = _get_reasoning(q).lower() + verification = q.get("verification", {}) + correct_idx = q.get("correct_answer_index", -1) + options = _get_options(q) + + if not reasoning: + # No reasoning field — skip this check (raw files often lack it) + continue + + # --- Temporal: "first" keyword matching --- + if cat == "temporal": + # Check if reasoning mentions which event was first + first_match = re.search(r'(\w[\w\s]*?)\s+(?:occurred|happened|took place)\s+first', reasoning) + if first_match and answer: + reasoning_first = first_match.group(1).strip() + # Cross-check with answer + if reasoning_first not in answer and len(reasoning_first) > 5: + issues.append(Issue( + qid, "reasoning_consistency", ERROR, + f"Reasoning says '{reasoning_first}' occurred first, " + f"but correct_answer is: '{answer[:80]}'", + "Regenerate reasoning with correct event order" + )) + + # --- Counting: number extraction --- + elif cat == "counting": + # Extract the primary count from reasoning (the one after "observed X times") + correct_count = verification.get("correct_count") + if correct_count is not None: + # Look for the stated count: "observed N time(s)" + observed_match = re.search(r'observed\s+(\d+)\s+time', reasoning) + if observed_match: + stated_count = int(observed_match.group(1)) + if stated_count != correct_count: + issues.append(Issue( + qid, "reasoning_consistency", ERROR, + f"Reasoning says 'observed {stated_count} times', " + f"but correct_count is {correct_count}", + "Regenerate reasoning with correct count" + )) + + # --- Event ordering: sequence match --- + elif cat == "event_ordering": + ordered_events = verification.get("ordered_events", []) + if len(ordered_events) >= 3 and correct_idx >= 0 and correct_idx < len(options): + correct_opt = options[correct_idx].lower() + # Extract activity verbs from ordered events + activities_in_order = [e.get("activity", "") for e in ordered_events] + # Check if the correct option's described sequence contradicts verification + # Simple check: does the answer option mention the last event first? + if activities_in_order: + last_act = activities_in_order[-1].replace("_", " ") + first_act = activities_in_order[0].replace("_", " ") + # If answer says last event happened first, that's wrong + if (last_act in correct_opt and + correct_opt.index(last_act) < correct_opt.index(first_act) + if first_act in correct_opt else False): + issues.append(Issue( + qid, "reasoning_consistency", ERROR, + f"Correct answer mentions '{last_act}' before '{first_act}', " + f"contradicting event ordering in verification", + "Regenerate with correct chronological order" + )) + + # --- Perception: camera consistency --- + elif cat == "perception": + qt = verification.get("question_type") + if qt == "which_camera": + correct_cam = verification.get("correct_camera", "") + if correct_cam and reasoning: + # Check if reasoning mentions a different camera as "correct" + cam_mentions = re.findall(r'G\d{3}', reasoning) + if cam_mentions: + last_cam = cam_mentions[-1] + if last_cam != correct_cam and f"camera {correct_cam}" not in reasoning: + issues.append(Issue( + qid, "reasoning_consistency", WARNING, + f"Reasoning mentions camera {last_cam} but correct is {correct_cam}", + "Check reasoning references correct camera" + )) + + # --- Structural: correct_answer_index vs correct_answer text match --- + if correct_idx >= 0 and correct_idx < len(options): + expected = options[correct_idx].lower().strip() + actual = answer.lower().strip() + # They should match (or be very close) + if actual and expected and actual != expected: + # Check if one is substring of the other (GPT may have rephrased) + overlap = len(set(actual.split()) & set(expected.split())) + total = max(len(actual.split()), len(expected.split())) + if total > 0 and overlap / total < 0.5: + issues.append(Issue( + qid, "reasoning_consistency", ERROR, + f"correct_answer text doesn't match options[correct_answer_index]: " + f"'{actual[:60]}' vs '{expected[:60]}'", + "Sync correct_answer with options[correct_answer_index]" + )) + + return issues + + +# ============================================================================ +# Check 2: Raw Token Leak Detection +# ============================================================================ + +# Pre-compiled patterns for speed +_RAW_TIMESTAMP_PATTERNS = [ + re.compile(r'\b\d{2,3}\s*seconds?\b', re.I), # "127 seconds", "42 second" + re.compile(r'\b\d+\.\d+s\b'), # "127.13s" + re.compile(r'\bat\s+\d+s\b'), # "at 127s" + re.compile(r'\b\d+\.\d+-\d+\.\d+s\b'), # "127.13-133.43s" + re.compile(r'\bframe\s*\d+', re.I), # "frame 3814" + re.compile(r'\bframes?\s*\d+-\d+', re.I), # "frames 3814-4003" + re.compile(r'\b\d+\.\d{2}\s*-\s*\d+\.\d{2}\b'), # "127.13 - 133.43" (timestamp ranges) +] + +_RAW_FIELD_PATTERNS = [ + re.compile(r'\bstart_sec\b'), + re.compile(r'\bend_sec\b'), + re.compile(r'\bframe_range\b'), + re.compile(r'\bactor_id\b'), + re.compile(r'\bclip_file\b'), + re.compile(r'\bentity_\w+\b'), # entity_description, entity_a, etc. +] + +_TEMPLATE_ARTIFACT_PATTERNS = [ + re.compile(r'\{[a-z_]+\}'), # {variable_name} template placeholders + re.compile(r'__\w+__'), # __PLACEHOLDER__ + re.compile(r'\bevt_\w+\b'), # evt_prefix internal IDs + re.compile(r'\bcluster_\d+\b'), # cluster_123 internal IDs +] + +# Build regex alternation for activity IDs (match as whole words) +_ACTIVITY_ID_PATTERN = re.compile( + r'\b(' + '|'.join(re.escape(a) for a in sorted(KITWARE_ACTIVITY_IDS)) + r')\b' +) + + +def check_raw_token_leaks(qa_pairs: List[dict]) -> List[Issue]: + """ + Regex scan question text + options for raw pipeline tokens that should + have been cleaned/naturalized. + """ + issues = [] + + for q in qa_pairs: + qid = q.get("question_id", "?") + question_text = _get_question_text(q) + options = _get_options(q) + all_text = question_text + " " + " ".join(options) + + # Only check naturalized text (raw text is allowed to have these) + is_naturalized = "naturalized_question" in q + + if not is_naturalized: + # For raw files, only check for the most egregious leaks + # (template artifacts still shouldn't be in raw text) + for pat in _TEMPLATE_ARTIFACT_PATTERNS: + m = pat.search(all_text) + if m: + issues.append(Issue( + qid, "raw_token_leak", WARNING, + f"Template artifact in raw text: '{m.group()}'", + "Check template formatting" + )) + continue + + # --- Naturalized text checks --- + + # Raw timestamps + for pat in _RAW_TIMESTAMP_PATTERNS: + m = pat.search(all_text) + if m: + issues.append(Issue( + qid, "raw_token_leak", WARNING, + f"Raw timestamp in naturalized text: '{m.group()}'", + "Re-run naturalization to remove timestamps" + )) + break # One timestamp leak per question is enough + + # Raw activity names (Kitware IDs with underscores) + m = _ACTIVITY_ID_PATTERN.search(all_text) + if m: + issues.append(Issue( + qid, "raw_token_leak", WARNING, + f"Raw activity ID in naturalized text: '{m.group()}'", + "Re-run simplify_description() on this question" + )) + + # Raw field names + for pat in _RAW_FIELD_PATTERNS: + m = pat.search(all_text) + if m: + issues.append(Issue( + qid, "raw_token_leak", INFO, + f"Raw field name in naturalized text: '{m.group()}'", + "Remove technical field references" + )) + break + + # Template artifacts + for pat in _TEMPLATE_ARTIFACT_PATTERNS: + m = pat.search(all_text) + if m: + issues.append(Issue( + qid, "raw_token_leak", WARNING, + f"Template artifact in naturalized text: '{m.group()}'", + "Re-run naturalization to fill template" + )) + break + + return issues + + +# ============================================================================ +# Check 3: Duplicate / Near-Duplicate Detection +# ============================================================================ + +def _normalize_tokens(text: str) -> Set[str]: + """Normalize text to lowercase token set for overlap comparison.""" + text = re.sub(r'[^\w\s]', ' ', text.lower()) + return set(text.split()) + +def _token_overlap_ratio(tokens_a: Set[str], tokens_b: Set[str]) -> float: + """Compute Jaccard-like overlap ratio between two token sets.""" + if not tokens_a or not tokens_b: + return 0.0 + intersection = tokens_a & tokens_b + union = tokens_a | tokens_b + return len(intersection) / len(union) + + +def check_duplicates(qa_pairs: List[dict]) -> List[Issue]: + """ + Detect duplicate/near-duplicate questions within a slot: + - Exact text match + - Fuzzy match: >90% token overlap + - Same entity pair (verification.entity_a + entity_b) + - Same category + same correct_answer + """ + issues = [] + n = len(qa_pairs) + + for i in range(n): + qi = qa_pairs[i] + qi_id = qi.get("question_id", f"q{i}") + qi_text = _get_question_text(qi) + qi_tokens = _normalize_tokens(qi_text) + qi_cat = qi.get("category", "") + qi_answer = _get_answer_text(qi) + qi_ver = qi.get("verification", {}) + + for j in range(i + 1, n): + qj = qa_pairs[j] + qj_id = qj.get("question_id", f"q{j}") + qj_text = _get_question_text(qj) + qj_tokens = _normalize_tokens(qj_text) + qj_cat = qj.get("category", "") + qj_answer = _get_answer_text(qj) + qj_ver = qj.get("verification", {}) + + # 1. Exact text match + if qi_text and qi_text == qj_text: + issues.append(Issue( + qi_id, "duplicate", ERROR, + f"Exact duplicate of {qj_id}", + f"Remove one of {qi_id} or {qj_id}" + )) + continue + + # 2. Fuzzy match: >90% token overlap + # Skip for categories with structurally similar templates + # (spatial, best_camera) — these naturally share template words + # but reference different entities. True duplicates are caught + # by the entity pair check (#3) instead. + _TEMPLATE_CATS = {"spatial", "best_camera"} + if qi_cat not in _TEMPLATE_CATS or qj_cat not in _TEMPLATE_CATS: + overlap = _token_overlap_ratio(qi_tokens, qj_tokens) + if overlap > 0.90: + issues.append(Issue( + qi_id, "near_duplicate", WARNING, + f"Near-duplicate of {qj_id} ({overlap:.0%} token overlap)", + "Rephrase or replace one of the questions" + )) + + # 3. Same entity pair check + ea_i = qi_ver.get("entity_a", qi_ver.get("event_a", {}).get("description", "")) + eb_i = qi_ver.get("entity_b", qi_ver.get("event_b", {}).get("description", "")) + ea_j = qj_ver.get("entity_a", qj_ver.get("event_a", {}).get("description", "")) + eb_j = qj_ver.get("entity_b", qj_ver.get("event_b", {}).get("description", "")) + + if ea_i and eb_i and ea_j and eb_j: + if ({ea_i, eb_i} == {ea_j, eb_j}): + issues.append(Issue( + qi_id, "same_entity_pair", WARNING, + f"Same entity pair as {qj_id}: ({ea_i[:40]}, {eb_i[:40]})", + "Use different entity pair for one question" + )) + + # 4. Same category + same correct answer + if qi_cat == qj_cat and qi_answer and qi_answer == qj_answer: + issues.append(Issue( + qi_id, "same_category_answer", INFO, + f"Same category '{qi_cat}' and identical correct_answer as {qj_id}", + "Consider diversifying answers within category" + )) + + return issues + + +# ============================================================================ +# Check 4: Generic Description Detection +# ============================================================================ + +def check_generic_descriptions(qa_pairs: List[dict]) -> List[Issue]: + """ + Flag questions with generic entity references: + - Description is just "a person" / "someone" / "an individual" + - Entity description is null + - Multiple entities on same camera share exact description + """ + issues = [] + _GENERIC_DESCS = {"a person", "someone", "an individual", "a vehicle", + "the person", "the individual", "a man", "a woman"} + + # Build per-camera description frequency map from all questions + camera_descriptions: Dict[str, List[str]] = defaultdict(list) + + for q in qa_pairs: + qid = q.get("question_id", "?") + cat = q.get("category", "") + verification = q.get("verification", {}) + debug = q.get("debug_info", {}) + question_text = _get_question_text(q) + + # Collect entity descriptions from verification/debug + descs_in_q = [] + + # Temporal/spatial: event_a, event_b + for key in ("event_a", "event_b", "entity_a", "entity_b"): + info = verification.get(key, {}) + if isinstance(info, dict): + desc = info.get("description") or info.get("entity_description", "") + cam = info.get("camera", "") + if desc: + descs_in_q.append((desc, cam)) + camera_descriptions[cam].append(desc) + + # Debug entity_description + for key in ("event_a", "event_b", "entity_a", "entity_b"): + info = debug.get(key, {}) + if isinstance(info, dict): + desc = info.get("entity_description", "") + cam = info.get("camera", "") + if desc: + descs_in_q.append((desc, cam)) + camera_descriptions[cam].append(desc) + + # Check for generic descriptions in question text + for generic in _GENERIC_DESCS: + if generic in question_text.lower(): + # Only flag if it's a truly generic reference (no clothing/appearance follows) + # Skip if followed by clothing descriptors ("a person in blue", "a person wearing") + # or comma+clothing ("a person, wearing a ...") + clothing_pattern = re.compile( + r'(?:^|\s)' + re.escape(generic) + + r'(?:[,\s]+(?:wearing|in\s+(?:a\s+)?(?:blue|red|green|black|white|gray|grey|dark|light|navy|teal|indigo|brown|beige|olive|pink|purple|plum|maroon|khaki|camo|charcoal)\b|with\s+(?:a\s+)?(?:blue|red|green|black|white|gray|dark|hat|bag|backpack|hoodie|jacket)\b))', + re.I + ) + if clothing_pattern.search(question_text): + continue # Has clothing descriptor — not generic + + # Check by looking at surrounding context for activity verbs + activity_pattern = re.compile( + r'(?:^|\s)' + re.escape(generic) + r'(?:\s+(?:who|that|opens|closes|exits|enters|walks|sits|stands|talks|carries|picks|puts|rides|reads|loads|unloads)\b)', + re.I + ) + if activity_pattern.search(question_text): + issues.append(Issue( + qid, "generic_description", WARNING, + f"Generic entity reference '{generic}' used in question text", + "Add clothing/visual description to disambiguate" + )) + break + + # Check entity descriptions in verification + for desc, cam in descs_in_q: + desc_lower = desc.strip().lower() + if desc_lower in _GENERIC_DESCS or not desc_lower: + issues.append(Issue( + qid, "generic_description", WARNING, + f"Generic entity description '{desc}' on camera {cam}", + "Re-extract visual description or use spatial context" + )) + + # Cross-question check: same description on same camera + for cam, descs in camera_descriptions.items(): + if not cam: + continue + desc_counts = Counter(descs) + for desc, count in desc_counts.items(): + if count >= 2 and desc.lower().strip() not in _GENERIC_DESCS: + # Same non-generic description appears multiple times on same camera + issues.append(Issue( + f"slot-level:{cam}", "shared_description", INFO, + f"Description '{desc[:50]}' used {count} times on camera {cam}", + "Use spatial position or activity to disambiguate" + )) + + return issues + + +# ============================================================================ +# Check 5: Multi-Correct-Answer Ambiguity +# ============================================================================ + +def check_multi_correct_ambiguity(qa_pairs: List[dict]) -> List[Issue]: + """ + Detect questions where multiple answer options could be correct: + - which_camera perception: activity visible on >1 camera in options + - spatial proximity: distance near bucket boundary (borderline classification) + """ + issues = [] + + for q in qa_pairs: + qid = q.get("question_id", "?") + cat = q.get("category", "") + verification = q.get("verification", {}) + options = _get_options(q) + + # --- Perception: which_camera ambiguity --- + if cat == "perception": + qt = verification.get("question_type") + if qt == "which_camera": + cameras_with = verification.get("cameras_with_activity", []) + if len(cameras_with) > 1: + # Check how many of those cameras are among the options + option_cams = set() + for opt in options: + for cam in cameras_with: + if cam in opt: + option_cams.add(cam) + if len(option_cams) > 1: + issues.append(Issue( + qid, "multi_correct_ambiguity", ERROR, + f"Activity appears on cameras {cameras_with}, " + f"and {len(option_cams)} of them are in the options", + "Use activity that appears on exactly 1 camera" + )) + + # --- Spatial: borderline proximity --- + elif cat == "spatial": + distance = verification.get("distance_meters") + proximity = verification.get("proximity") + if distance is not None and proximity: + # Check if distance is within 20% of bucket boundary + # near/moderate boundary: 5m (check 4-6m) + # moderate/far boundary: 15m (check 12-18m) + if proximity == "near" and 4.0 <= distance <= 6.0: + issues.append(Issue( + qid, "multi_correct_ambiguity", WARNING, + f"Borderline near/moderate: distance={distance:.1f}m " + f"(boundary at 5m, ±20% = 4-6m)", + "Use entity pair with clearer distance separation" + )) + elif proximity == "moderate" and ( + (4.0 <= distance <= 6.0) or (12.0 <= distance <= 18.0) + ): + issues.append(Issue( + qid, "multi_correct_ambiguity", WARNING, + f"Borderline proximity: distance={distance:.1f}m " + f"(boundaries at 5m and 15m, ±20%)", + "Use entity pair with clearer distance separation" + )) + elif proximity == "far" and 12.0 <= distance <= 18.0: + issues.append(Issue( + qid, "multi_correct_ambiguity", WARNING, + f"Borderline moderate/far: distance={distance:.1f}m " + f"(boundary at 15m, ±20% = 12-18m)", + "Use entity pair with clearer distance separation" + )) + + return issues + + +# ============================================================================ +# Check 6: Grammar / Conjugation Checking +# ============================================================================ + +# Words that start with vowel sound but use "a" (not "an") +_A_EXCEPTIONS = {"uniform", "university", "unique", "united", "union", + "european", "euclidean", "eulerian", "user", "useful", + "usual", "utensil", "utility", "uranium", "one", "once"} + +_GRAMMAR_PATTERNS = [ + # a [vowel] → should be an (with exceptions) + (re.compile(r'\ba\s+([aeiou]\w*)\b', re.I), "article_a_an"), + # Double articles + (re.compile(r'\b(a|an|the)\s+\1\b', re.I), "double_article"), + # Common gerund errors: *sing pattern (e.g., "leavesing") + (re.compile(r'\b\w+(?:es|[^s]s)ing\b', re.I), "gerund_error"), + # Subject-verb: "a person close" (should be "closes") + (re.compile(r'\ba\s+person\s+(?:close|open|exit|enter|pick|put|sit|stand|walk|talk|read|ride|carry|load|unload|steal|transfer|abandon|embrace|purchase|text)\b', re.I), "subject_verb"), +] + + +def check_grammar(qa_pairs: List[dict]) -> List[Issue]: + """ + Regex-based grammar checking for common errors in generated text. + """ + issues = [] + + for q in qa_pairs: + qid = q.get("question_id", "?") + question_text = _get_question_text(q) + options = _get_options(q) + answer = _get_answer_text(q) + all_texts = [question_text] + options + [answer] + + for text in all_texts: + if not text: + continue + + # Check a/an + for m in re.finditer(r'\ba\s+([aeiou]\w*)\b', text, re.I): + word = m.group(1).lower() + # Check exceptions (words starting with vowel letter but consonant sound) + if word not in _A_EXCEPTIONS and not word.startswith("uni"): + issues.append(Issue( + qid, "grammar", INFO, + f"'a {word}' should be 'an {word}' in: '...{text[max(0,m.start()-10):m.end()+10]}...'", + f"Change 'a {word}' to 'an {word}'" + )) + break # One article issue per text segment + + # Double articles + m = re.search(r'\b(a|an|the)\s+\1\b', text, re.I) + if m: + issues.append(Issue( + qid, "grammar", WARNING, + f"Double article: '{m.group()}' in text", + f"Remove duplicate article" + )) + + # Gerund errors (e.g., "closesing", "opensing", "leavesing") + for m in re.finditer(r'\b(\w+(?:es|[^s]s)ing)\b', text, re.I): + word = m.group(1).lower() + # Skip legitimate words + legit = {"missing", "passing", "crossing", "dressing", "pressing", + "blessing", "guessing", "assessing", "processing", + "accessing", "addressing", "expressing", "possessing", + "discussing", "bussing", "kissing", "tossing", + "fussing", "messing", "stressing", "confessing", + "obsessing", "caressing", "reassessing", "progressing", + "impressive", "using", "housing", "causing", "pausing", + "refusing", "abusing", "amusing", "bruising", + "choosing", "closing", "composing", "losing", + "opposing", "proposing", "raising", "praising", + "rising", "surprising", "advising", "exercising", + "promising", "comprising", "disguising", "revising", + "supervising", "nursing", "purchasing", "reversing", + "licensing", "sensing", "rinsing", "dispensing", + "conversing", "rehearsing", "endorsing", "immersing", + "coercing", "cursing"} + if word not in legit: + issues.append(Issue( + qid, "grammar", WARNING, + f"Possible gerund error: '{word}'", + f"Check conjugation of '{word}'" + )) + + # Subject-verb disagreement + m = re.search( + r'\ba\s+person\s+(close|open|exit|enter|pick|put|sit|stand|walk|talk|read|ride|carry|load|unload|text)\b', + text, re.I + ) + if m: + verb = m.group(1).lower() + issues.append(Issue( + qid, "grammar", INFO, + f"Subject-verb disagreement: 'a person {verb}' → 'a person {verb}s'", + f"Change '{verb}' to '{verb}s'" + )) + + return issues + + +# ============================================================================ +# Main Validator +# ============================================================================ + +# ============================================================================ +# Check 7: Forbidden Pattern Detection (Issue 12) +# ============================================================================ + +# Categories where camera IDs, raw timestamps, and location context are FORBIDDEN +_NO_CAMERA_CATEGORIES = {"temporal", "event_ordering", "spatial", "counting"} + +# Regex patterns that should NOT appear in question text for restricted categories +_FORBIDDEN_PATTERNS = [ + (re.compile(r'\bcamera\s+G?\d+', re.IGNORECASE), "camera ID reference"), + (re.compile(r'\bon camera\b', re.IGNORECASE), "camera reference"), + (re.compile(r'\bat\s+\d+s\b', re.IGNORECASE), "raw timestamp (e.g. 'at 45s')"), + (re.compile(r'\b\d+s[-–]\d+s\b', re.IGNORECASE), "timestamp range (e.g. '12s-180s')"), + (re.compile(r'\baround the \d+', re.IGNORECASE), "temporal marker"), + (re.compile(r'\bat time\s+\d+', re.IGNORECASE), "timestamp reference"), + (re.compile(r'\b\d+ seconds?\b', re.IGNORECASE), "raw seconds in question"), +] + + +def check_forbidden_patterns(qa_pairs: List[dict]) -> List[Issue]: + """ + Issue 12: Enforce no-camera/no-timestamp policy for restricted categories. + + temporal, event_ordering, spatial, counting questions must rely ONLY on + visual appearance + activity verbs. Camera IDs, raw timestamps, and + spatial/location markers are forbidden in question text and options. + """ + issues: List[Issue] = [] + + for q in qa_pairs: + cat = q.get("category", "") + if cat not in _NO_CAMERA_CATEGORIES: + continue + + qid = q.get("question_id", "?") + + # Check question text + for field_name in ("question_template", "naturalized_question"): + text = q.get(field_name, "") + if not text: + continue + for pattern, label in _FORBIDDEN_PATTERNS: + m = pattern.search(text) + if m: + issues.append(Issue(qid, "forbidden_pattern", ERROR, + f"[{cat}] {field_name} contains {label}: '{m.group()}'" + )) + + # Check options + for i, opt in enumerate(q.get("options", [])): + for pattern, label in _FORBIDDEN_PATTERNS: + m = pattern.search(opt) + if m: + issues.append(Issue(qid, "forbidden_pattern", WARNING, + f"[{cat}] option[{i}] contains {label}: '{m.group()}'" + )) + + return issues + + +# ============================================================================ +# Check 8: Annotation-Based Verification (Issue 11) +# ============================================================================ + +def check_annotation_verification(qa_pairs: List[dict]) -> List[Issue]: + """ + Lightweight sanity check: verify numerical counts and temporal ordering + match the verification data. NOT circular — checks internal consistency + between question text/answer and verification metadata. + + Catches: + - Counting: reasoning mentions wrong number vs correct_answer + - Temporal: event_a start_sec >= event_b start_sec (wrong ordering) + - Event ordering: events in verification not chronologically sorted + - All: correct_answer_index out of range or pointing to wrong option + """ + issues: List[Issue] = [] + + for q in qa_pairs: + qid = q.get("question_id", "unknown") + cat = q.get("category", "") + v = q.get("verification", {}) + options = q.get("options", []) + correct_idx = q.get("correct_answer_index", -1) + correct_answer = q.get("correct_answer", "") + + # Universal: correct_answer_index in range and matches correct_answer + if correct_idx < 0 or correct_idx >= len(options): + issues.append(Issue(qid, "annotation_verify", ERROR, + f"correct_answer_index={correct_idx} out of range (options has {len(options)} items)", + "Fix correct_answer_index")) + elif options[correct_idx] != correct_answer: + issues.append(Issue(qid, "annotation_verify", ERROR, + f"correct_answer '{correct_answer}' != options[{correct_idx}] '{options[correct_idx]}'", + "Sync correct_answer with correct_answer_index")) + + # Counting: verify reasoning numbers match correct_answer + if cat in ("numerical", "counting"): + reasoning = q.get("reasoning", "") + correct_count = v.get("correct_count") + if correct_count is not None: + # Check correct_answer matches correct_count + try: + if str(correct_count) != correct_answer: + issues.append(Issue(qid, "annotation_verify", ERROR, + f"correct_count={correct_count} but correct_answer='{correct_answer}'", + "Sync correct_count with correct_answer")) + except (ValueError, TypeError): + pass + + # Check reasoning mentions the correct number + if reasoning: + import re as _re_verify + nums_in_reasoning = [int(n) for n in _re_verify.findall(r'\b(\d+)\b', reasoning)] + if correct_count not in nums_in_reasoning and str(correct_count) not in reasoning: + issues.append(Issue(qid, "annotation_verify", WARNING, + f"Reasoning doesn't mention correct count {correct_count}", + "Regenerate reasoning with correct count")) + + # Temporal: verify event ordering matches + elif cat == "temporal": + ea = v.get("event_a", {}) + eb = v.get("event_b", {}) + ea_start = ea.get("start_sec", 0) + eb_start = eb.get("start_sec", 0) + if ea_start and eb_start and ea_start >= eb_start: + issues.append(Issue(qid, "annotation_verify", ERROR, + f"Event A (start={ea_start:.2f}s) does not precede Event B (start={eb_start:.2f}s)", + "Fix temporal ordering — event_a must come first")) + + # Event ordering: verify events are chronologically sorted + elif cat == "event_ordering": + ordered = v.get("ordered_events", []) + for i in range(len(ordered) - 1): + t_curr = ordered[i].get("start_sec", 0) + t_next = ordered[i + 1].get("start_sec", 0) + if t_curr >= t_next: + issues.append(Issue(qid, "annotation_verify", ERROR, + f"Verification events not chronological at position {i}: " + f"{t_curr:.2f}s >= {t_next:.2f}s", + "Fix event ordering in verification")) + + return issues + + +def validate(data: Dict[str, Any], verbose: bool = False) -> Dict[str, Any]: + """ + Run all 6 checks on QA data. + + Args: + data: Full QA JSON (with "qa_pairs" key) + verbose: Print detailed output + + Returns: + Structured report dict. + """ + qa_pairs = data.get("qa_pairs", []) + slot = data.get("slot", "unknown") + + if verbose: + print(f"\n{'=' * 60}") + print(f"QA Validator — {slot}") + print(f"{'=' * 60}") + print(f"Total questions: {len(qa_pairs)}") + is_nat = any("naturalized_question" in q for q in qa_pairs) + print(f"Format: {'naturalized' if is_nat else 'raw'}") + + all_issues: List[Issue] = [] + + # Run all 8 checks + checks = [ + ("reasoning_consistency", check_reasoning_consistency), + ("raw_token_leak", check_raw_token_leaks), + ("duplicate_detection", check_duplicates), + ("generic_description", check_generic_descriptions), + ("multi_correct_ambiguity", check_multi_correct_ambiguity), + ("grammar", check_grammar), + ("forbidden_pattern", check_forbidden_patterns), + ("annotation_verify", check_annotation_verification), + ] + + for check_name, check_fn in checks: + found = check_fn(qa_pairs) + all_issues.extend(found) + if verbose: + severity_counts = Counter(i.severity for i in found) + status = "PASS" if not found else f"FAIL ({len(found)} issues)" + detail = "" + if found: + parts = [] + for sev in [ERROR, WARNING, INFO]: + if severity_counts[sev]: + parts.append(f"{severity_counts[sev]} {sev}") + detail = f" [{', '.join(parts)}]" + print(f" {check_name:30s}: {status}{detail}") + + # Compute quality score + score = 100 + for issue in all_issues: + if issue.severity == ERROR: + score -= 10 + elif issue.severity == WARNING: + score -= 5 + elif issue.severity == INFO: + score -= 1 + score = max(0, score) + + # Build report + severity_counts = Counter(i.severity for i in all_issues) + report = { + "slot": slot, + "total_questions": len(qa_pairs), + "total_issues": len(all_issues), + "quality_score": score, + "severity_counts": { + "error": severity_counts[ERROR], + "warning": severity_counts[WARNING], + "info": severity_counts[INFO], + }, + "issues": [i.to_dict() for i in all_issues], + } + + if verbose: + print(f"\n Quality Score: {score}/100") + print(f" Total Issues: {len(all_issues)} " + f"({severity_counts[ERROR]} error, " + f"{severity_counts[WARNING]} warning, " + f"{severity_counts[INFO]} info)") + if all_issues: + print(f"\n Issues:") + for issue in all_issues: + sev_symbol = {"error": "E", "warning": "W", "info": "I"}[issue.severity] + print(f" [{sev_symbol}] {issue.question_id}: " + f"{issue.check_name} — {issue.message}") + if issue.suggestion: + print(f" → {issue.suggestion}") + + return report + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Automated QA validator for MEVA V10 pipeline" + ) + parser.add_argument("--input", "-i", required=True, + help="Path to raw or naturalized QA JSON file") + parser.add_argument("-v", "--verbose", action="store_true", + help="Print detailed validation report") + parser.add_argument("--json", action="store_true", + help="Output report as JSON") + parser.add_argument("--min-score", type=int, default=0, + help="Exit with code 1 if quality score < this threshold") + args = parser.parse_args() + + input_path = Path(args.input) + if not input_path.exists(): + print(f"ERROR: File not found: {input_path}") + sys.exit(1) + + with open(input_path) as f: + data = json.load(f) + + report = validate(data, verbose=args.verbose) + + if args.json: + print(json.dumps(report, indent=2)) + + if args.min_score and report["quality_score"] < args.min_score: + print(f"\nFAIL: Quality score {report['quality_score']} < {args.min_score}") + sys.exit(1) + + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v10/vlm_describe_entities.py b/meva/scripts/v10/vlm_describe_entities.py new file mode 100644 index 0000000..a0ac2e4 --- /dev/null +++ b/meva/scripts/v10/vlm_describe_entities.py @@ -0,0 +1,597 @@ +#!/usr/bin/env python3 +""" +VLM Entity Description Enrichment — Use InternVL2.5-8B to generate rich +natural-language descriptions of MEVA person entities. + +Takes entity crops already extracted by extract_entity_descriptions.py, +sends them to a local vLLM server, and produces rich descriptions like: + "a middle-aged man in a dark blue jacket and khaki pants, carrying a + black backpack, walking briskly" + +This is a POST-PROCESSING step that enriches existing SegFormer descriptions. +It does NOT replace SegFormer — it adds a `vlm_description` field alongside +the existing color-based `description`. + +Prerequisites: + - vLLM server running (launched automatically or manually) + - Entity descriptions already extracted (SegFormer batch) + - Crops cached or re-extractable from video + +Usage: + # Process a single slot + python3 -m meva.scripts.v10.vlm_describe_entities --slot 2018-03-11.11-25.school -v + + # Batch process all slots with existing entity descriptions + python3 -m meva.scripts.v10.vlm_describe_entities --batch -v + + # Use existing vLLM server + python3 -m meva.scripts.v10.vlm_describe_entities --slot ... --api-url http://localhost:8001/v1 + +Cost: $0 (local model, no API calls) +GPU: 1x RTX A5000 24GB (InternVL2.5-8B ~16GB VRAM) +Time: ~1-2 sec per entity, ~3-5 min per slot +""" + +import argparse +import base64 +import json +import os +import re +import sys +import time +import subprocess +import signal +from pathlib import Path +from typing import Dict, List, Optional, Tuple +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor, as_completed + +import cv2 +import numpy as np + +# ============================================================================ +# Paths +# ============================================================================ + +_REPO_DATA = Path(__file__).resolve().parent.parent.parent / "data" +ENTITY_DESC_DIR = Path(os.environ.get("MEVA_ENTITY_DESC_DIR") or "/nas/mars/dataset/MEVA/entity_descriptions") +VLM_DESC_DIR = Path(os.environ.get("MEVA_VLM_DESC_DIR") or "/nas/mars/dataset/MEVA/entity_descriptions/vlm") + +# Reuse the crop extraction infrastructure +try: + from .extract_entity_descriptions import parse_geom, extract_crops, find_slot_files +except ImportError: + sys.path.insert(0, str(Path(__file__).resolve().parent)) + from extract_entity_descriptions import parse_geom, extract_crops, find_slot_files + +# ============================================================================ +# Constants +# ============================================================================ + +VLM_MODEL = "OpenGVLab/InternVL2_5-8B" +DEFAULT_PORT = 8001 # Avoid conflict with SegFormer on default port +DEFAULT_GPU = "1" # GPU 1 (0 used by SegFormer re-extraction) +MAX_WORKERS = 8 # Parallel VLM requests +CROPS_FOR_VLM = 3 # Crops to send per entity (middle-of-track, best quality) +MIN_CROP_HEIGHT_VLM = 80 # Min height for meaningful VLM description +MAX_ENTITIES_PER_SLOT = 200 # Cap to avoid extremely long slots + + +# ============================================================================ +# VLM Server Management +# ============================================================================ + +_vllm_proc = None + + +def start_vllm_server(gpu: str = DEFAULT_GPU, port: int = DEFAULT_PORT, + model: str = VLM_MODEL, verbose: bool = False) -> str: + """Start a vLLM server if not already running. Returns API URL.""" + global _vllm_proc + api_url = f"http://localhost:{port}/v1" + + # Check if server is already responsive + if _check_server(api_url): + if verbose: + print(f" vLLM server already running at {api_url}") + return api_url + + if verbose: + print(f" Starting vLLM server on GPU {gpu}, port {port}...") + print(f" Model: {model}") + + env = os.environ.copy() + env["CUDA_VISIBLE_DEVICES"] = gpu + env["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + + cmd = [ + "vllm", "serve", model, + "--tensor-parallel-size", "1", + "--port", str(port), + "--trust-remote-code", + "--max-model-len", "4096", + "--gpu-memory-utilization", "0.85", + ] + + log_path = Path.home() / "data" / "extraction_logs" / f"vllm_server_{port}.log" + log_path.parent.mkdir(parents=True, exist_ok=True) + + _vllm_proc = subprocess.Popen( + cmd, + env=env, + stdout=open(log_path, "w"), + stderr=subprocess.STDOUT, + preexec_fn=os.setsid, + ) + + if verbose: + print(f" Waiting for vLLM server to start (PID {_vllm_proc.pid})...") + + # Wait up to 120s for server to become healthy + for i in range(120): + time.sleep(1) + if _check_server(api_url): + if verbose: + print(f" vLLM server ready after {i+1}s") + return api_url + # Check if process died + if _vllm_proc.poll() is not None: + print(f" ERROR: vLLM server died (exit code {_vllm_proc.returncode})") + print(f" Check log: {log_path}") + sys.exit(1) + + print(f" ERROR: vLLM server did not become ready in 120s") + print(f" Check log: {log_path}") + sys.exit(1) + + +def _check_server(api_url: str) -> bool: + """Check if vLLM server is healthy.""" + try: + import urllib.request + req = urllib.request.urlopen(f"{api_url}/models", timeout=3) + return req.status == 200 + except Exception: + return False + + +def stop_vllm_server(): + """Stop the vLLM server if we started it.""" + global _vllm_proc + if _vllm_proc is not None: + os.killpg(os.getpgid(_vllm_proc.pid), signal.SIGTERM) + _vllm_proc.wait(timeout=10) + _vllm_proc = None + + +# ============================================================================ +# VLM Description Generation +# ============================================================================ + +def _encode_image(image: np.ndarray) -> str: + """Encode BGR image to base64 JPEG string.""" + ret, buffer = cv2.imencode(".jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85]) + if not ret: + raise ValueError("Could not encode image") + return base64.b64encode(buffer).decode("utf-8") + + +def _build_prompt(num_images: int, segformer_desc: str = "") -> str: + """Build the VLM prompt for person description.""" + context = "" + if segformer_desc and segformer_desc != "a person": + context = f"\nA computer vision system described this person as: \"{segformer_desc}\"\n" + + return f"""You are viewing {num_images} cropped image(s) of the SAME person from a surveillance camera at different moments. +{context} +Describe this person's appearance in ONE concise sentence (max 25 words). Focus on: +- Clothing colors and types (shirt, jacket, hoodie, pants, shorts, dress) +- Distinguishing features (hat, backpack, bag, glasses, beard, hair style/color) +- Apparent build (tall/short/heavyset if clearly visible) +- DO NOT mention actions, activities, or what the person is doing +- DO NOT mention image quality or camera angle +- Use natural casual language, e.g. "a tall man in a dark blue jacket and tan pants, carrying a gray backpack" + +Respond with ONLY the description sentence, starting with "a" or "an". Nothing else.""" + + +def describe_entity_vlm( + crops: List[np.ndarray], + api_url: str, + model: str = VLM_MODEL, + segformer_desc: str = "", +) -> Optional[str]: + """ + Send entity crops to VLM and get a rich description. + + Args: + crops: List of BGR crop images (1-3) + api_url: vLLM server URL + model: Model name + segformer_desc: Existing SegFormer description for context + + Returns: + Description string or None on failure + """ + try: + from openai import OpenAI + except ImportError: + # Fallback to raw HTTP + return _describe_entity_vlm_http(crops, api_url, model, segformer_desc) + + client = OpenAI(api_key="EMPTY", base_url=api_url) + + # Select best crops (largest, middle-of-track already selected by extractor) + use_crops = crops[:CROPS_FOR_VLM] + + # Build content + prompt = _build_prompt(len(use_crops), segformer_desc) + content = [{"type": "text", "text": prompt}] + + for crop in use_crops: + encoded = _encode_image(crop) + content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{encoded}"} + }) + + try: + response = client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": content}], + max_tokens=100, + temperature=0.1, + ) + result = response.choices[0].message.content.strip() + + # Clean up: ensure it starts with "a " or "an " + result = result.strip('"\'') + if not result.lower().startswith(("a ", "an ")): + result = "a " + result + + # Truncate if too long + if len(result) > 200: + result = result[:197] + "..." + + return result + + except Exception as e: + return None + + +def _describe_entity_vlm_http( + crops: List[np.ndarray], + api_url: str, + model: str, + segformer_desc: str = "", +) -> Optional[str]: + """Fallback HTTP-based VLM call if openai package not available.""" + import urllib.request + import json as _json + + use_crops = crops[:CROPS_FOR_VLM] + prompt = _build_prompt(len(use_crops), segformer_desc) + + content = [{"type": "text", "text": prompt}] + for crop in use_crops: + encoded = _encode_image(crop) + content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{encoded}"} + }) + + payload = { + "model": model, + "messages": [{"role": "user", "content": content}], + "max_tokens": 100, + "temperature": 0.1, + } + + try: + data = _json.dumps(payload).encode("utf-8") + req = urllib.request.Request( + f"{api_url}/chat/completions", + data=data, + headers={"Content-Type": "application/json"}, + ) + resp = urllib.request.urlopen(req, timeout=30) + result = _json.loads(resp.read()) + text = result["choices"][0]["message"]["content"].strip().strip('"\'') + if not text.lower().startswith(("a ", "an ")): + text = "a " + text + return text[:200] + except Exception: + return None + + +# ============================================================================ +# Slot Processing +# ============================================================================ + +def process_slot_vlm( + slot: str, + api_url: str, + model: str = VLM_MODEL, + verbose: bool = False, + max_entities: int = MAX_ENTITIES_PER_SLOT, + max_workers: int = MAX_WORKERS, +) -> Dict: + """ + Enrich entity descriptions for a slot with VLM captioning. + + Reads existing SegFormer descriptions, extracts crops for entities + that are large enough, queries VLM for rich descriptions, and saves + the result alongside the SegFormer data. + + Returns stats dict. + """ + t0 = time.time() + + # Load existing SegFormer descriptions + segformer_path = ENTITY_DESC_DIR / f"{slot}.json" + if not segformer_path.exists(): + if verbose: + print(f" No SegFormer descriptions for {slot}, skipping") + return {"error": "no_segformer_data", "slot": slot} + + with open(segformer_path) as f: + segformer_data = json.load(f) + + actors = segformer_data.get("actors", {}) + if not actors: + return {"error": "no_actors", "slot": slot} + + # Find entities worth sending to VLM (large enough crops) + vlm_candidates = [] + for eid, edata in actors.items(): + h = edata.get("avg_crop_height", 0) + if h >= MIN_CROP_HEIGHT_VLM: + vlm_candidates.append((eid, edata)) + + if not vlm_candidates: + if verbose: + print(f" No entities large enough for VLM in {slot}") + return {"slot": slot, "total": len(actors), "vlm_described": 0} + + # Cap entities + if len(vlm_candidates) > max_entities: + vlm_candidates = vlm_candidates[:max_entities] + + if verbose: + print(f" {len(vlm_candidates)}/{len(actors)} entities qualify for VLM") + + # Extract crops from video + files = find_slot_files(slot) + if not files: + return {"error": "no_video_files", "slot": slot} + + # Group candidates by camera + cam_entities = defaultdict(list) + for eid, edata in vlm_candidates: + cam_entities[edata["camera"]].append((eid, edata)) + + # Extract crops per camera + all_crops = {} # eid → [crop, ...] + for cam, entities in cam_entities.items(): + # Find matching camera file + cam_file = None + for cf in files: + if cf["camera"] == cam: + cam_file = cf + break + if cam_file is None or cam_file.get("video_path") is None: + continue + + # Parse geom for this camera + geom_actors = parse_geom(cam_file["geom_path"]) + + # Filter to only our candidates + target_actors = {} + for eid, edata in entities: + aid = edata.get("actor_id") + if aid is not None and aid in geom_actors: + target_actors[aid] = geom_actors[aid] + + if not target_actors: + continue + + # Extract crops (reuse existing function, but with VLM-specific min height) + cam_crops = extract_crops( + cam_file["video_path"], + target_actors, + max_crops=CROPS_FOR_VLM, + min_h=MIN_CROP_HEIGHT_VLM, + min_w=48, + ) + + # Map back to entity IDs + for eid, edata in entities: + aid = edata.get("actor_id") + if aid in cam_crops and cam_crops[aid]: + all_crops[eid] = cam_crops[aid] + + if verbose: + print(f" Extracted crops for {len(all_crops)} entities") + + if not all_crops: + return {"slot": slot, "total": len(actors), "vlm_described": 0} + + # Query VLM in parallel + vlm_descriptions = {} + failed = 0 + + def _vlm_worker(eid_crops): + eid, crops = eid_crops + segformer_desc = actors[eid].get("description", "") + desc = describe_entity_vlm(crops, api_url, model, segformer_desc) + return eid, desc + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = { + executor.submit(_vlm_worker, (eid, crops)): eid + for eid, crops in all_crops.items() + } + for future in as_completed(futures): + eid = futures[future] + try: + eid_result, desc = future.result() + if desc: + vlm_descriptions[eid_result] = desc + else: + failed += 1 + except Exception: + failed += 1 + + if verbose: + print(f" VLM: {len(vlm_descriptions)} described, {failed} failed") + # Show a few examples + for eid, desc in list(vlm_descriptions.items())[:3]: + old = actors[eid].get("description", "") + print(f" {eid}:") + print(f" SegFormer: {old}") + print(f" VLM: {desc}") + + # Save VLM descriptions alongside SegFormer data + VLM_DESC_DIR.mkdir(parents=True, exist_ok=True) + output = { + "slot": slot, + "model": model, + "total_actors": len(actors), + "vlm_described": len(vlm_descriptions), + "vlm_failed": failed, + "processing_time_sec": round(time.time() - t0, 1), + "descriptions": vlm_descriptions, + } + + output_path = VLM_DESC_DIR / f"{slot}.vlm.json" + with open(output_path, "w") as f: + json.dump(output, f, indent=2) + + if verbose: + print(f" Saved → {output_path.name} ({time.time() - t0:.1f}s)") + + return { + "slot": slot, + "total": len(actors), + "vlm_described": len(vlm_descriptions), + "vlm_failed": failed, + "time_sec": round(time.time() - t0, 1), + } + + +# ============================================================================ +# Batch Processing +# ============================================================================ + +def process_batch( + api_url: str, + model: str = VLM_MODEL, + verbose: bool = False, + force: bool = False, + max_workers: int = MAX_WORKERS, +) -> Dict: + """Process all slots that have SegFormer descriptions.""" + existing = sorted(ENTITY_DESC_DIR.glob("*.json")) + slots = [f.stem for f in existing if not f.stem.startswith(".")] + + if verbose: + print(f"Found {len(slots)} slots with SegFormer descriptions") + + results = [] + total_described = 0 + total_failed = 0 + + for i, slot in enumerate(slots, 1): + # Skip if VLM already done (unless force) + vlm_path = VLM_DESC_DIR / f"{slot}.vlm.json" + if vlm_path.exists() and not force: + if verbose: + print(f"[{i:3d}/{len(slots)}] {slot}: already done, skipping") + continue + + if verbose: + print(f"\n[{i:3d}/{len(slots)}] {slot}") + + result = process_slot_vlm( + slot, api_url, model, + verbose=verbose, + max_workers=max_workers, + ) + results.append(result) + total_described += result.get("vlm_described", 0) + total_failed += result.get("vlm_failed", 0) + + return { + "slots_processed": len(results), + "total_described": total_described, + "total_failed": total_failed, + } + + +# ============================================================================ +# Main +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="Enrich MEVA entity descriptions with VLM captioning", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--slot", type=str, help="Process a single slot") + parser.add_argument("--batch", action="store_true", help="Process all slots") + parser.add_argument("--api-url", type=str, default=None, + help="vLLM server URL (default: auto-start on GPU 1)") + parser.add_argument("--gpu", type=str, default=DEFAULT_GPU, + help=f"GPU to use for vLLM server (default: {DEFAULT_GPU})") + parser.add_argument("--port", type=int, default=DEFAULT_PORT, + help=f"Port for vLLM server (default: {DEFAULT_PORT})") + parser.add_argument("--model", type=str, default=VLM_MODEL, + help=f"VLM model name (default: {VLM_MODEL})") + parser.add_argument("--force", action="store_true", + help="Force re-description even if VLM output exists") + parser.add_argument("--max-workers", type=int, default=MAX_WORKERS, + help=f"Max parallel VLM requests (default: {MAX_WORKERS})") + parser.add_argument("--verbose", "-v", action="store_true") + + args = parser.parse_args() + + if not args.slot and not args.batch: + parser.error("Specify --slot or --batch") + + # Start or connect to vLLM server + api_url = args.api_url + we_started_server = False + if api_url is None: + api_url = start_vllm_server( + gpu=args.gpu, port=args.port, + model=args.model, verbose=args.verbose + ) + we_started_server = True + + try: + if args.slot: + result = process_slot_vlm( + args.slot, api_url, args.model, + verbose=args.verbose, + max_workers=args.max_workers, + ) + print(f"\nResult: {json.dumps(result, indent=2)}") + + elif args.batch: + result = process_batch( + api_url, args.model, + verbose=args.verbose, + force=args.force, + max_workers=args.max_workers, + ) + print(f"\nBatch Result: {json.dumps(result, indent=2)}") + + finally: + if we_started_server: + if args.verbose: + print("Stopping vLLM server...") + stop_vllm_server() + + +if __name__ == "__main__": + main()