From 4d86937bb6cf854e2164683cfa4318faf27354a0 Mon Sep 17 00:00:00 2001 From: adihebbalae Date: Fri, 13 Feb 2026 19:37:40 -0600 Subject: [PATCH] meva: update V8 pipeline with entity descriptions, 2-pass naturalization, V3 variety mode Changes: - Archive previous V8 scripts as archived/v7/ - Remove V6/V7 documentation (V6_PIPELINE_EXPLAINED, V7_FAQ, multi_camera_reference) - Updated V8 pipeline: 16 core modules + 5 utils - New: extract_entity_descriptions.py (geom.yml + AVI color analysis) - New: person_descriptions.py 3-tier enrichment (MEVID -> geom-color -> fallback) - New: naturalize_v8_qa_v2.py with 2-pass (naturalizer + grammar checker) - New: --v3 mode for varied question phrasing (temp=0.95, camera refs stripped from temporal Qs) - Add pipeline data: slot_index.json, person_database_yolo.json, mevid_supported_slots.json - Add sample output: v8 raw + v3 naturalized for 2018-03-11.11-25-00.school - Update .gitignore to allow meva/data/ pipeline files --- .gitignore | 7 + meva/.gitignore | 8 +- .../2018-03-11.11-25-00.school.v8.json | 982 + ...8-03-11.11-25-00.school.v8.natural.v3.json | 840 + meva/data/mevid_supported_slots.json | 33007 +++++ meva/data/person_database_yolo.json | 114792 +++++++++++++++ meva/data/slot_index.json | 19152 +++ meva/docs/V6_PIPELINE_EXPLAINED.md | 1155 - meva/docs/V7_FAQ.md | 806 - meva/docs/multi_camera_reference.md | 429 - meva/scripts/{ => archived}/v6/__init__.py | 0 .../{ => archived}/v6/build_scene_graph.py | 0 .../{ => archived}/v6/distractor_bank.py | 0 .../{ => archived}/v6/entity_resolution.py | 0 .../{ => archived}/v6/generate_perception.py | 0 .../{ => archived}/v6/generate_spatial.py | 0 .../{ => archived}/v6/generate_temporal.py | 0 .../{ => archived}/v6/parse_annotations.py | 0 .../scripts/{ => archived}/v6/run_pipeline.py | 0 .../{ => archived}/v6/utils/__init__.py | 0 meva/scripts/{ => archived}/v6/utils/iou.py | 0 meva/scripts/{ => archived}/v6/utils/krtd.py | 0 meva/scripts/{ => archived}/v6/utils/mevid.py | 0 .../{ => archived}/v6/utils/yaml_stream.py | 0 meva/scripts/archived/v7/__init__.py | 1 + .../scripts/archived/v7/activity_hierarchy.py | 197 + meva/scripts/archived/v7/build_scene_graph.py | 260 + meva/scripts/archived/v7/distractor_bank.py | 91 + meva/scripts/archived/v7/entity_resolution.py | 298 + .../v7/extract_entity_descriptions.py | 695 + .../archived/v7/generate_perception.py | 437 + .../archived/v7/generate_reidentification.py | 215 + .../archived/v7/generate_scene_summary.py | 269 + meva/scripts/archived/v7/generate_spatial.py | 263 + meva/scripts/archived/v7/generate_temporal.py | 336 + meva/scripts/archived/v7/naturalize_v8_qa.py | 459 + .../archived/v7/naturalize_v8_qa_v2.py | 1176 + meva/scripts/archived/v7/parse_annotations.py | 245 + .../archived/v7/person_descriptions.py | 395 + meva/scripts/archived/v7/run_pipeline.py | 466 + meva/scripts/archived/v7/utils/__init__.py | 1 + meva/scripts/archived/v7/utils/iou.py | 35 + meva/scripts/archived/v7/utils/krtd.py | 124 + meva/scripts/archived/v7/utils/mevid.py | 166 + meva/scripts/archived/v7/utils/yaml_stream.py | 137 + meva/scripts/v8/__init__.py | 1 + meva/scripts/v8/activity_hierarchy.py | 197 + meva/scripts/v8/build_scene_graph.py | 260 + meva/scripts/v8/distractor_bank.py | 91 + meva/scripts/v8/entity_resolution.py | 298 + .../scripts/v8/extract_entity_descriptions.py | 695 + meva/scripts/v8/generate_perception.py | 437 + meva/scripts/v8/generate_reidentification.py | 215 + meva/scripts/v8/generate_scene_summary.py | 269 + meva/scripts/v8/generate_spatial.py | 263 + meva/scripts/v8/generate_temporal.py | 336 + meva/scripts/v8/naturalize_v8_qa.py | 459 + meva/scripts/v8/naturalize_v8_qa_v2.py | 1176 + meva/scripts/v8/parse_annotations.py | 246 + meva/scripts/v8/person_descriptions.py | 395 + meva/scripts/v8/run_pipeline.py | 466 + meva/scripts/v8/utils/__init__.py | 1 + meva/scripts/v8/utils/iou.py | 35 + meva/scripts/v8/utils/krtd.py | 124 + meva/scripts/v8/utils/mevid.py | 166 + meva/scripts/v8/utils/yaml_stream.py | 137 + 66 files changed, 181320 insertions(+), 2391 deletions(-) create mode 100644 meva/data/examples/2018-03-11.11-25-00.school.v8.json create mode 100644 meva/data/examples/2018-03-11.11-25-00.school.v8.natural.v3.json create mode 100644 meva/data/mevid_supported_slots.json create mode 100644 meva/data/person_database_yolo.json create mode 100644 meva/data/slot_index.json delete mode 100644 meva/docs/V6_PIPELINE_EXPLAINED.md delete mode 100644 meva/docs/V7_FAQ.md delete mode 100644 meva/docs/multi_camera_reference.md rename meva/scripts/{ => archived}/v6/__init__.py (100%) rename meva/scripts/{ => archived}/v6/build_scene_graph.py (100%) rename meva/scripts/{ => archived}/v6/distractor_bank.py (100%) rename meva/scripts/{ => archived}/v6/entity_resolution.py (100%) rename meva/scripts/{ => archived}/v6/generate_perception.py (100%) rename meva/scripts/{ => archived}/v6/generate_spatial.py (100%) rename meva/scripts/{ => archived}/v6/generate_temporal.py (100%) rename meva/scripts/{ => archived}/v6/parse_annotations.py (100%) rename meva/scripts/{ => archived}/v6/run_pipeline.py (100%) rename meva/scripts/{ => archived}/v6/utils/__init__.py (100%) rename meva/scripts/{ => archived}/v6/utils/iou.py (100%) rename meva/scripts/{ => archived}/v6/utils/krtd.py (100%) rename meva/scripts/{ => archived}/v6/utils/mevid.py (100%) rename meva/scripts/{ => archived}/v6/utils/yaml_stream.py (100%) create mode 100644 meva/scripts/archived/v7/__init__.py create mode 100644 meva/scripts/archived/v7/activity_hierarchy.py create mode 100644 meva/scripts/archived/v7/build_scene_graph.py create mode 100644 meva/scripts/archived/v7/distractor_bank.py create mode 100644 meva/scripts/archived/v7/entity_resolution.py create mode 100644 meva/scripts/archived/v7/extract_entity_descriptions.py create mode 100644 meva/scripts/archived/v7/generate_perception.py create mode 100644 meva/scripts/archived/v7/generate_reidentification.py create mode 100644 meva/scripts/archived/v7/generate_scene_summary.py create mode 100644 meva/scripts/archived/v7/generate_spatial.py create mode 100644 meva/scripts/archived/v7/generate_temporal.py create mode 100644 meva/scripts/archived/v7/naturalize_v8_qa.py create mode 100644 meva/scripts/archived/v7/naturalize_v8_qa_v2.py create mode 100644 meva/scripts/archived/v7/parse_annotations.py create mode 100644 meva/scripts/archived/v7/person_descriptions.py create mode 100644 meva/scripts/archived/v7/run_pipeline.py create mode 100644 meva/scripts/archived/v7/utils/__init__.py create mode 100644 meva/scripts/archived/v7/utils/iou.py create mode 100644 meva/scripts/archived/v7/utils/krtd.py create mode 100644 meva/scripts/archived/v7/utils/mevid.py create mode 100644 meva/scripts/archived/v7/utils/yaml_stream.py create mode 100644 meva/scripts/v8/__init__.py create mode 100644 meva/scripts/v8/activity_hierarchy.py create mode 100644 meva/scripts/v8/build_scene_graph.py create mode 100644 meva/scripts/v8/distractor_bank.py create mode 100644 meva/scripts/v8/entity_resolution.py create mode 100644 meva/scripts/v8/extract_entity_descriptions.py create mode 100644 meva/scripts/v8/generate_perception.py create mode 100644 meva/scripts/v8/generate_reidentification.py create mode 100644 meva/scripts/v8/generate_scene_summary.py create mode 100644 meva/scripts/v8/generate_spatial.py create mode 100644 meva/scripts/v8/generate_temporal.py create mode 100644 meva/scripts/v8/naturalize_v8_qa.py create mode 100644 meva/scripts/v8/naturalize_v8_qa_v2.py create mode 100644 meva/scripts/v8/parse_annotations.py create mode 100644 meva/scripts/v8/person_descriptions.py create mode 100644 meva/scripts/v8/run_pipeline.py create mode 100644 meva/scripts/v8/utils/__init__.py create mode 100644 meva/scripts/v8/utils/iou.py create mode 100644 meva/scripts/v8/utils/krtd.py create mode 100644 meva/scripts/v8/utils/mevid.py create mode 100644 meva/scripts/v8/utils/yaml_stream.py diff --git a/.gitignore b/.gitignore index 382416e..854434e 100644 --- a/.gitignore +++ b/.gitignore @@ -211,6 +211,13 @@ temp/ InternVL/ logs/ data/ +# Allow MEVA pipeline data files +!meva/data/ +!meva/data/slot_index.json +!meva/data/person_database_yolo.json +!meva/data/mevid_supported_slots.json +!meva/data/examples/ +!meva/data/examples/*.json llava-video/ Video-MME/ VATEX/ diff --git a/meva/.gitignore b/meva/.gitignore index 8da094f..cfeb718 100644 --- a/meva/.gitignore +++ b/meva/.gitignore @@ -27,11 +27,17 @@ 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 the pipeline !data/annotated_activity_slots.txt !data/slot_index.json +!data/person_database_yolo.json +!data/mevid_supported_slots.json +!data/examples/ +!data/examples/*.json # Jupyter .ipynb_checkpoints/ diff --git a/meva/data/examples/2018-03-11.11-25-00.school.v8.json b/meva/data/examples/2018-03-11.11-25-00.school.v8.json new file mode 100644 index 0000000..980ffbf --- /dev/null +++ b/meva/data/examples/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/data/examples/2018-03-11.11-25-00.school.v8.natural.v3.json b/meva/data/examples/2018-03-11.11-25-00.school.v8.natural.v3.json new file mode 100644 index 0000000..38f9cf2 --- /dev/null +++ b/meva/data/examples/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/data/mevid_supported_slots.json b/meva/data/mevid_supported_slots.json new file mode 100644 index 0000000..85765f5 --- /dev/null +++ b/meva/data/mevid_supported_slots.json @@ -0,0 +1,33007 @@ +{ + "metadata": { + 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"G421", + "G424", + "G638" + ], + "kitware-training": [ + "G299", + "G330", + "G339", + "G423" + ] + }, + "multi_camera": true, + "clip_count": 20 + }, + "2018-03-15.15-55-01.admin": { + "cameras": [ + "G326", + "G329" + ], + "sources": { + "contrib/CMU-v2": [ + "G326" + ], + "contrib/UMD-v1-train": [ + "G329" + ], + "kitware-training": [ + "G326", + "G329" + ] + }, + "multi_camera": true, + "clip_count": 4 + }, + "2018-03-15.15-55-01.bus": { + "cameras": [ + "G506" + ], + "sources": { + "contrib/CMU-v2": [ + "G506" + ], + "kitware-training": [ + "G506" + ] + }, + "multi_camera": true, + "clip_count": 2 + }, + "2018-03-15.15-55-01.school": { + "cameras": [ + "G328", + "G419", + "G639" + ], + "sources": { + "contrib/CMU-v2": [ + "G328", + "G419", + "G639" + ], + "contrib/IBM-person-vehicle": [ + "G639" + ], + "contrib/UMD-v1-val": [ + "G328" + ], + "kitware": [ + "G328", + "G639" + ], + "kitware-training": [ + "G419" + ] + }, + "multi_camera": true, + "clip_count": 8 + }, + "2018-03-15.15-55-03.school": { + "cameras": [ + "G300" + ], + "sources": { + "contrib/CMU-v2": [ + "G300" + ], + "kitware-training": [ + "G300" + ] + }, + "multi_camera": true, + "clip_count": 2 + }, + "2018-03-15.15-55-04.bus": { + "cameras": [ + "G340" + ], + "sources": { + "contrib/CMU-v2": [ + "G340" + ], + "contrib/IBM-person-person": [ + "G340" + ], + "kitware-training": [ + "G340" + ] + }, + "multi_camera": true, + "clip_count": 3 + }, + "2018-03-15.15-55-06.hospital": { + "cameras": [ + "G341" + ], + "sources": { + "contrib/CMU-v2": [ + "G341" + ], + "kitware-training": [ + "G341" + ] + }, + "multi_camera": true, + "clip_count": 2 + }, + "2018-03-15.15-55-07.hospital": { + "cameras": [ + "G301", + "G436" + ], + "sources": { + "contrib/CMU-v2": [ + "G301", + "G436" + ], + "kitware": [ + "G436" + ], + "kitware-training": [ + "G301" + ] + }, + "multi_camera": true, + "clip_count": 4 + } +} \ No newline at end of file diff --git a/meva/docs/V6_PIPELINE_EXPLAINED.md b/meva/docs/V6_PIPELINE_EXPLAINED.md deleted file mode 100644 index 38c1d1c..0000000 --- a/meva/docs/V6_PIPELINE_EXPLAINED.md +++ /dev/null @@ -1,1155 +0,0 @@ -# V6 Pipeline: Complete Technical Explanation - -> **Author**: GitHub Copilot -> **Date**: 2026-02-12 -> **Purpose**: Comprehensive ground-up explanation of V6 multi-camera QA generation pipeline - ---- - -## Table of Contents - -1. [Ground-Up Principles](#1-ground-up-principles) -2. [Pipeline Architecture](#2-pipeline-architecture) -3. [Data Structures](#3-data-structures) -4. [Step-by-Step Flow](#4-step-by-step-flow) -5. [Low-Level Implementation Details](#5-low-level-implementation-details) -6. [Memory Management](#6-memory-management) -7. [Question Generation Strategies](#7-question-generation-strategies) -8. [Validation & Quality Control](#8-validation--quality-control) - ---- - -## 1. Ground-Up Principles - -### 1.1 The Core Problem - -**Input**: Multi-camera surveillance video + activity annotations -**Output**: Multiple-choice questions testing temporal/spatial/perceptual reasoning - -**Why is this hard?** -1. **Multi-camera coordination**: Same person appears on different cameras at different times -2. **Entity ambiguity**: No unique person IDs across cameras (actor_id is camera-specific) -3. **Scale**: 929 slots × 5-10 cameras × 10-100 activities each = huge search space -4. **Memory**: GEOM files (bounding boxes) can be 50+ MB per camera with 100K+ bboxes -5. **Quality**: Need diverse, non-trivial questions that can't be guessed - -### 1.2 Design Philosophy - -**Modular architecture**: 8 separate steps, each with clear input/output contracts -- Easy to debug: run each step independently -- Easy to extend: swap out entity resolution or question generators -- Easy to test: validate intermediate outputs - -**Entity-centric model**: Build a scene graph where entities (people) are first-class objects -- Events are attributes of entities, not standalone facts -- Cross-camera links connect entities, not events -- Questions ask about entity activities, distances, appearances - -**Ground truth + heuristics**: Use MEVID ground truth where available, fall back to heuristics -- MEVID provides camera-level person coverage (person 4 appears on cameras A, B, C) -- Heuristics fill gaps (temporal handoff: exit camera A → enter camera B within 5 seconds) - -**Streaming for scale**: Process GEOM files line-by-line, not load-all-to-memory -- Avoids OOM on large files (50+ MB) -- Fast regex parsing (~1.5s for 100K lines) - -### 1.3 Key Assumptions - -1. **Framerate**: 30 fps (constant across all MEVA videos) -2. **Coordination window**: Cameras are synchronized within ±1 second -3. **Actor persistence**: actor_id is stable within one camera, but NOT across cameras -4. **Activity taxonomy**: 37 standardized activity names (Kitware uses these, not NIST variants) -5. **KRTD coverage**: ~80% of cameras have calibration models, rest are indoor (no KRTD) - ---- - -## 2. Pipeline Architecture - -### 2.1 High-Level Flow - -``` -INPUT: slot name (e.g., "2018-03-11.11-25-00.school") - ↓ -STEP 1: Parse Annotations → List[Event] - ↓ -STEP 2: Build Scene Graph → SceneGraph (cameras, entities, events) - ↓ -STEP 3: Entity Resolution → ResolvedGraph (cross-camera links, clusters) - ↓ -STEP 4: Generate Temporal QA → List[Dict] (temporal reasoning questions) - ↓ -STEP 5: Generate Spatial QA → List[Dict] (spatial distance questions) - ↓ -STEP 6: Generate Perception QA → List[Dict] (perception questions) - ↓ -STEP 7: Deduplication + Validation → Filtered questions - ↓ -OUTPUT: JSON file with 9 questions (3 per category) -``` - -### 2.2 Module Structure - -``` -scripts/v6/ -├── parse_annotations.py # Step 1: YAML → Event objects -├── build_scene_graph.py # Step 2: Events → Scene graph with entities -├── entity_resolution.py # Step 3: Cross-camera entity linking -├── generate_temporal.py # Step 4: Temporal reasoning questions -├── generate_spatial.py # Step 5: Spatial distance questions -├── generate_perception.py # Step 6: Perception questions -├── run_pipeline.py # Orchestrator + validation -└── utils/ - ├── yaml_stream.py # Memory-efficient GEOM parser - ├── krtd.py # Camera calibration + 3D projection - ├── iou.py # Bounding box IoU calculation - └── mevid.py # MEVID ground truth lookup -``` - -### 2.3 Data Flow Diagram - -``` -┌─────────────────────────────────────────────────────────────┐ -│ KITWARE ANNOTATIONS │ -│ activities.yml: Events with actors (camera-specific IDs) │ -│ geom.yml: Bounding boxes for each actor at each frame │ -│ types.yml: Actor type (person/vehicle) │ -└───────────────────────┬─────────────────────────────────────┘ - ↓ - ┌──────────────┐ - │ STEP 1 │ - │ Parse YAML │ → List[Event] - └──────┬───────┘ - ↓ - ┌───────────────────────────┐ - │ STEP 2 │ - │ Build Scene Graph │ → SceneGraph - │ - Load GEOM bboxes │ (entities, cameras, events) - │ - Create Entity objects │ - │ - Link events to entities│ - └───────────┬───────────────┘ - ↓ - ┌──────────────────────────────────┐ - │ STEP 3 │ - │ Entity Resolution │ → ResolvedGraph - │ - MEVID ground truth (if avail) │ (links, clusters) - │ - Temporal handoff heuristic │ - │ - Union-Find clustering │ - └───────────┬──────────────────────┘ - ↓ - ┌───────────────┴───────────────┐ - ↓ ↓ -┌────────────┐ ┌────────────┐ -│ STEP 4 │ │ STEP 5 │ -│ Temporal │ │ Spatial │ -│ Questions │ │ Questions │ -└─────┬──────┘ └──────┬─────┘ - ↓ ↓ - │ ┌──────────┐ │ - └────────→│ STEP 6 │←──────┘ - │Perception│ - └────┬─────┘ - ↓ - ┌────────────────┐ - │ STEP 7 │ - │ Dedup + Valid │ → Final JSON - └────────────────┘ -``` - ---- - -## 3. Data Structures - -### 3.1 Event (Raw Activity) - -**Purpose**: Represents one annotated activity instance on one camera - -```python -@dataclass -class Event: - event_id: str # "{camera_id}_evt_{activity_id}" - activity: str # "person_opens_facility_door" - camera_id: str # "G328" - site: str # "school" - start_frame: int # 6965 - end_frame: int # 7020 - start_sec: float # 232.17 - end_sec: float # 234.00 - duration_sec: float # 1.83 - actors: List[Dict] # [{"actor_id": 143, "entity_type": "person"}] - video_file: str # "2018-03-11.11-25-00.11-30-01.school.G421.r13.avi" - annotation_source: str # "kitware" -``` - -**Key properties**: -- **event_id**: Unique within slot (camera + activity instance ID from YAML) -- **actors**: Can have multiple actors (e.g., "person_talks_to_person" has 2 actors) -- **Frames → seconds**: `start_sec = start_frame / 30.0` (assumes 30 fps) - -### 3.2 Entity (Person or Vehicle) - -**Purpose**: Tracked individual on ONE camera with time span and bounding boxes - -```python -@dataclass -class Entity: - entity_id: str # "{camera_id}_actor_{actor_id}" - camera_id: str # "G328" - actor_id: int # 143 (from Kitware geom.yml) - entity_type: str # "person" or "vehicle" - first_frame: int # 6800 (first appearance) - last_frame: int # 8991 (last appearance) - first_sec: float # 226.67 - last_sec: float # 299.70 - keyframe_bboxes: Dict[int, List[int]] # {frame: [x1, y1, x2, y2], ...} - events: List[str] # ["G328_evt_101", "G328_evt_102"] -``` - -**Key properties**: -- **entity_id**: Format ensures uniqueness (camera + actor ensures no collisions) -- **keyframe_bboxes**: In V6, sampled every 30 frames; in V7, every frame -- **events**: List of event_ids this entity participates in (enables event lookup) -- **Temporal span**: `first_frame` to `last_frame` from geom.yml (actor's full trajectory) - -### 3.3 SceneGraph (Slot-Level Knowledge) - -**Purpose**: Complete representation of one slot (5-minute time window at one site) - -```python -@dataclass -class SceneGraph: - slot: str # "2018-03-11.11-25-00.school" - cameras: Dict[str, CameraNode] # {camera_id: CameraNode} - entities: Dict[str, Entity] # {entity_id: Entity} - events: List[Event] # All events across all cameras - events_by_camera: Dict[str, List[Event]] # {camera_id: [Event, ...]} -``` - -**Key properties**: -- **cameras**: Metadata for each camera (has KRTD? indoor? position) -- **entities**: All tracked individuals across all cameras (NOT yet linked cross-camera) -- **events_by_camera**: Fast lookup for "what happened on camera X?" - -### 3.4 ResolvedGraph (Cross-Camera Links) - -**Purpose**: Entity resolution results — which entities are the same person across cameras - -```python -@dataclass -class CrossCameraLink: - entity_a: str # "G328_actor_143" - entity_b: str # "G421_actor_122" - camera_a: str # "G328" - camera_b: str # "G421" - link_type: str # "temporal_handoff" or "mevid_ground_truth" - confidence: float # 0.85 (heuristic) or 1.0 (MEVID) - time_gap_sec: Optional[float] # 3.03 (for temporal handoff) - -@dataclass -class EntityCluster: - cluster_id: str # "cluster_001" - entities: List[str] # ["G328_actor_143", "G421_actor_122", ...] - cameras: List[str] # ["G328", "G421"] - mevid_person_id: Optional[int] # 4 (if MEVID ground truth available) - -@dataclass -class ResolvedGraph: - cross_camera_links: List[CrossCameraLink] - entity_clusters: List[EntityCluster] - mevid_persons_in_slot: int # How many MEVID persons cover 2+ cameras - heuristic_link_count: int # How many links are heuristic - mevid_link_count: int # How many links are MEVID (currently 0, see Section 5.3) -``` - -### 3.5 Question Output Format - -```json -{ - "question_id": "v6_temporal_001", - "category": "temporal", - "difficulty": "easy", - "question_template": "In the surveillance footage, which event occurred first...", - "options": ["Event A", "Event B", "Both at same time", "Cannot determine"], - "correct_answer": 0, - "distractor_reasoning": ["Plausible near-simultaneous", ...], - "requires_cameras": ["G328", "G421"], - "verification": { - "event_a": { - "camera": "G328", - "activity": "person_opens_facility_door", - "start_sec": 232.17, - "end_sec": 234.00 - }, - "event_b": {...}, - "gap_sec": 3.03, - "entity_link": "heuristic" - } -} -``` - ---- - -## 4. Step-by-Step Flow - -### STEP 1: Parse Annotations - -**Input**: Slot name (`"2018-03-11.11-25-00.school"`) -**Output**: `List[Event]` (raw activity instances) - -**Process**: - -1. **Lookup slot in index**: Read `/home/ah66742/data/slot_index.json` - - Find all cameras that have Kitware annotations for this slot - - Get paths to `.activities.yml` files - -2. **For each camera**: - - Load `{clip}.activities.yml` (YAML list of activity dicts) - - Load `{clip}.types.yml` (actor_id → entity_type mapping) - - Parse each activity: - ```yaml - - act: - id2: 101 - act2: {person_opens_facility_door: {}} - timespan: - - tsr0: [6965, 7020] # [start_frame, end_frame] - actors: - - id1: 143 # actor_id - ``` - - Convert to Event object: - - `event_id = f"{camera_id}_evt_{act_id2}"` - - `start_sec = start_frame / 30.0` - - Look up entity_type from types.yml - -3. **Aggregate**: Collect all events across all cameras into one list - -**Example output**: -```python -[ - Event(event_id="G328_evt_101", activity="person_opens_facility_door", - camera_id="G328", start_sec=232.17, end_sec=234.00, - actors=[{"actor_id": 143, "entity_type": "person"}]), - Event(event_id="G421_evt_87", activity="person_exits_scene_through_structure", - camera_id="G421", start_sec=237.20, end_sec=240.50, - actors=[{"actor_id": 122, "entity_type": "person"}]), - ... -] -``` - ---- - -### STEP 2: Build Scene Graph - -**Input**: `List[Event]`, slot name -**Output**: `SceneGraph` (entities with bboxes + cameras) - -**Process**: - -1. **Build camera nodes**: - - For each unique camera in events: - - Load KRTD model (camera calibration) if available - - Check if camera is indoor (no KRTD for indoor cameras) - - Extract camera position (ENU coordinates) from KRTD - - Create `CameraNode` object - -2. **Extract entity actors from events**: - - Iterate through all events - - For each actor in event.actors: - - Record `(camera_id, actor_id)` pair - - Record entity_type ("person" or "vehicle") - - Track which events this actor participates in - -3. **Load bounding boxes from geom.yml**: - - For each camera, locate `{clip}.geom.yml` - - Stream-parse using regex (see Section 6.2 for details): - ```python - for rec in stream_geom_records(geom_path): - # rec = {"id1": actor_id, "ts0": frame, "g0": [x1, y1, x2, y2]} - if rec["id1"] in actor_ids: - entity_bboxes[cam_id][actor_id][frame] = rec["g0"] - ``` - - Also get frame range: `(first_frame, last_frame)` for each actor - -4. **Build Entity objects**: - - For each `(camera_id, actor_id)` pair: - - `entity_id = f"{camera_id}_actor_{actor_id}"` - - Get frame range from geom.yml (or estimate from events if missing) - - Convert frames to seconds: `first_sec = first_frame / 30.0` - - Attach keyframe_bboxes and event list - -5. **Group events by camera**: - - Create `events_by_camera` dict for fast camera-specific queries - -6. **Assemble SceneGraph**: - - Package cameras, entities, events into SceneGraph dataclass - -**Example output**: -```python -SceneGraph( - slot="2018-03-11.11-25-00.school", - cameras={ - "G328": CameraNode(camera_id="G328", is_indoor=False, has_krtd=True, - position_enu=(123.4, 567.8, 2.3)), - "G421": CameraNode(camera_id="G421", is_indoor=False, has_krtd=True, - position_enu=(145.2, 580.1, 2.5)), - ... - }, - entities={ - "G328_actor_143": Entity(entity_id="G328_actor_143", camera_id="G328", - actor_id=143, first_sec=226.67, last_sec=299.70, - keyframe_bboxes={6800: [100, 200, 150, 300], ...}, - events=["G328_evt_101"]), - "G421_actor_122": Entity(...), - ... - }, - events=[...], - events_by_camera={...} -) -``` - ---- - -### STEP 3: Entity Resolution - -**Input**: `SceneGraph` -**Output**: `ResolvedGraph` (cross-camera links + clusters) - -**Process**: - -1. **MEVID Ground Truth (Camera-Level)**: - - Load MEVID annotations for slot: - ```python - # Example MEVID data - Person 4: cameras ['G299', 'G328', 'G336', 'G419', 'G420', 'G421', 'G423'] - Person 12: cameras ['G328', 'G421'] - ``` - - **Note**: Can't create entity-to-entity links yet (no MEVID person_id → Kitware actor_id mapping) - - Store for validation: if both cameras in question are in same person's camera list → plausible - -2. **Heuristic Temporal Handoff**: - - For each entity with EXIT activity (e.g., "person_exits_scene_through_structure"): - - Look for entities on OTHER cameras with ENTRY activity within 5 seconds - - Compute confidence based on: - - Time gap (smaller = higher confidence) - - Activity match (exit → entry = higher) - - Entity type match (person → person = required) - - Create `CrossCameraLink` if confidence > 0.7 - - **Anti-mega-cluster safeguards** (fixes from V6 debugging): - - `MAX_HANDOFF_GAP = 10 seconds` (prevent linking distant events) - - `MAX_LINKS_PER_ENTITY = 2` (prevent one entity linking to many) - - Only link ACTIVE entities (those with events, not background actors) - -3. **Union-Find Clustering**: - - Initialize UnionFind data structure - - For each CrossCameraLink: - - `union(entity_a, entity_b)` — merge into same cluster - - Extract connected components: `clusters = uf.clusters()` - - Create `EntityCluster` objects: - ```python - EntityCluster( - cluster_id="cluster_001", - entities=["G328_actor_143", "G421_actor_122"], - cameras=["G328", "G421"], - mevid_person_id=None, # (can't determine without image matching) - link_type="heuristic" - ) - ``` - -4. **Package results**: - - Count heuristic vs MEVID links (currently all heuristic) - - Return ResolvedGraph - -**Example output**: -```python -ResolvedGraph( - cross_camera_links=[ - CrossCameraLink(entity_a="G328_actor_143", entity_b="G421_actor_122", - camera_a="G328", camera_b="G421", - link_type="temporal_handoff", confidence=0.85, - time_gap_sec=3.03), - ... - ], - entity_clusters=[ - EntityCluster(cluster_id="cluster_001", - entities=["G328_actor_143", "G421_actor_122"], - cameras=["G328", "G421"], - link_type="heuristic"), - ... - ], - mevid_persons_in_slot=41, - heuristic_link_count=1330, - mevid_link_count=0 -) -``` - ---- - -### STEP 4: Generate Temporal Questions - -**Input**: `SceneGraph`, `ResolvedGraph`, `random.Random` (seeded) -**Output**: `List[Dict]` (3 temporal questions) - -**Goal**: Ask "Which event happened first?" across cameras - -**Process**: - -1. **Find candidate pairs**: - - For each pair of events `(event_a, event_b)`: - - **Must be on DIFFERENT cameras** (cross-camera requirement) - - Compute time gap: `gap = event_b.start_sec - event_a.end_sec` - - **Filter**: 3 ≤ gap ≤ 20 seconds (too close = ambiguous, too far = trivial) - - Check if entities are in same cluster (stronger question if yes) - -2. **Rank/select candidates**: - - Sort by: - - Same cluster (preferred) - - Related activities (using activity hierarchy, if V7) - - Spatial proximity (if KRTD available) - - Time gap (moderate gaps preferred) - - Select top 3 diverse pairs (one near 3s, one ~10s, one ~20s) - -3. **Generate question**: - ```python - question = ( - f"In the surveillance footage, which event occurred first: " - f"{event_a.activity} on camera {event_a.camera_id} or " - f"{event_b.activity} on camera {event_b.camera_id}?" - ) - ``` - -4. **Generate distractors**: - - Option 0: Event A occurred first (CORRECT if gap > 0) - - Option 1: Event B occurred first - - Option 2: Both occurred at approximately the same time - - Option 3: Cannot be determined from the footage - -5. **Add verification block**: - ```json - "verification": { - "event_a": { - "camera": "G328", - "activity": "person_opens_facility_door", - "start_sec": 232.17, - "end_sec": 234.00 - }, - "event_b": {...}, - "gap_sec": 3.03, - "entity_link": "heuristic" - } - ``` - ---- - -### STEP 5: Generate Spatial Questions - -**Input**: `SceneGraph`, `ResolvedGraph`, `random.Random` -**Output**: `List[Dict]` (3 spatial questions) - -**Goal**: Ask "How far apart are these two people?" using 3D projection - -**Process**: - -1. **Find entities with KRTD coverage**: - - Filter entities: - - Must be on camera with KRTD model - - Must be type "person" (not vehicle) - - Must have at least one bbox in keyframe_bboxes - -2. **Project entities to 3D world coordinates**: - - For each entity: - - Choose representative bbox (at mid_frame = `(first_frame + last_frame) / 2`) - - If no keyframe bbox at mid_frame, find closest frame OR stream from geom.yml - - Project bbox foot point to 3D: `pos = camera_model.bbox_foot_to_world(bbox)` - - Foot point = `(x_center, y_bottom)` in image coordinates - - Ray-cast to ground plane (Z=0 in ENU coordinates) - - Returns `[East, North, Up]` position in meters - -3. **Compute pairwise distances**: - - For all pairs of entities with 3D positions: - - `distance = || pos_a - pos_b ||` (Euclidean distance) - - Filter out unreasonable distances (> 500m = likely projection error) - - Classify proximity: - - `near`: distance ≤ 5m - - `moderate`: 5m < distance ≤ 15m - - `far`: distance > 15m - -4. **Select diverse candidates**: - - Pick one from each proximity bucket (near, moderate, far) - - Shuffle for variety - -5. **Generate question**: - ```python - if camera_a != camera_b: - question = ( - f"In the scene, are {activity_a} (camera {camera_a}) and " - f"{activity_b} (camera {camera_b}) close together or far apart?" - ) - else: - question = ( - f"How close are {activity_a} and {activity_b} " - f"in the scene visible on camera {camera_a}?" - ) - ``` - -6. **Options based on proximity**: - - "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" - -7. **Add verification block**: - ```json - "verification": { - "entity_a": "G328_actor_143", - "entity_b": "G421_actor_122", - "distance_meters": 8.06, - "proximity": "moderate", - "world_pos_enu": { - "entity_a": [123.4, 567.8, 0.0], - "entity_b": [131.2, 570.5, 0.0] - } - } - ``` - ---- - -### STEP 6: Generate Perception Questions - -**Input**: `SceneGraph`, `ResolvedGraph`, `random.Random` -**Output**: `List[Dict]` (3 perception questions) - -**Goal**: Test visual/perceptual reasoning (activity identification, camera selection) - -**Process**: - -**Type 1: "Which camera shows activity X?"** -1. Find activities that occur on multiple cameras -2. Select one instance as correct answer -3. Distractors: other cameras in slot - -**Type 2: "What is the person doing on camera X?"** -1. Select a camera with distinctive activity -2. Correct answer: actual activity (cleaned for readability) -3. Distractors: other activities from distractor bank (contextually plausible) - -**Type 3: "Is activity X visible on both cameras A and B?"** -1. Find activities visible on 2+ cameras -2. Correct: "Yes, on both cameras" -3. Distractors: "Only on A", "Only on B", "Neither camera" - -**Distractor Selection** (uses `distractor_bank.py`): -- Load distractor bank (37 MEVA activities) -- Filter by: - - **Context appropriateness**: if site=school, prefer school-like activities - - **Visual similarity**: don't use activities too similar to correct answer (IoU of semantic space) - - **Entity type match**: person activities for person questions - -**Example question**: -```json -{ - "question_id": "v6_perception_001", - "category": "perception", - "question_template": "On camera G328, what is the person doing around timestamp 232s?", - "options": [ - "Opening a door", - "Talking to another person", - "Carrying a heavy object", - "Entering through a structure" - ], - "correct_answer": 0, - "verification": { - "question_type": "activity_identification", - "camera": "G328", - "activity": "person_opens_facility_door", - "timestamp": 233.0 - } -} -``` - ---- - -### STEP 7: Deduplication + Validation - -**Input**: `List[Dict]` (9 questions from steps 4-6) -**Output**: Filtered `List[Dict]` + validation errors - -**Deduplication logic** (category-specific): - -**Temporal**: -- If two questions ask about same `(activity_a, activity_b)` pair → duplicate -- Check: `event_a.activity == other_event_a.activity AND event_b.activity == other_event_b.activity` - -**Spatial**: -- If two questions ask about same `(entity_a, entity_b)` pair → duplicate -- Check: `entity_a == other_entity_a AND entity_b == other_entity_b` - -**Perception**: -- If two questions have same `(question_type, key)` → duplicate - - Key for "which_camera": activity name - - Key for "activity_identification": camera_id - - Key for "multi_camera_confirmation": activity name - -**Validation rules**: - -**Temporal validation**: -- ✅ Must be cross-camera (camera_a ≠ camera_b) -- ✅ Gap must be 3-20 seconds -- ✅ Event A must start before Event B - -**Spatial validation**: -- ✅ Distance must match proximity label: - - "near": ≤ 5m - - "moderate": 5-15m - - "far": > 15m - -**Perception validation**: -- ✅ Correct answer index must be valid (0-3) -- ✅ All options must be non-empty strings - ---- - -## 5. Low-Level Implementation Details - -### 5.1 YAML Parsing Strategy - -**Why not use `yaml.safe_load()` directly?** -- Safe load on 50+ MB file → 5-10 seconds + 500 MB RAM -- Only need subset of data (specific actor IDs) - -**Solution: CSafeLoader for small files, streaming for large files** - -```python -# For activities.yml and types.yml (small, <1 MB) -import yaml -with open(path) as f: - data = yaml.load(f, Loader=yaml.CSafeLoader) -``` - -**For geom.yml (large, 10-100 MB): Stream-parse with regex** - -```python -def stream_geom_records(path: Path): - inline_re = re.compile( - r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\s*(\d+\s+\d+\s+\d+\s+\d+)' - ) - with open(path) as f: - for line in f: - m = inline_re.search(line) - if m: - yield { - 'id1': int(m.group(1)), # actor_id - 'ts0': int(m.group(2)), # frame - 'g0': [int(x) for x in m.group(3).split()], # bbox - } -``` - -**Why this works**: -- Kitware uses inline YAML: `- { geom: {id1: 143, ts0: 6800, g0: 100 200 150 300} }` -- One line = one bbox record -- Regex extracts 3 fields in one pass -- Yield = generator, no memory accumulation - -**Performance**: -- 100K-line geom.yml: **1.5 seconds**, **<50 MB RAM** -- Compare to full load: 8 seconds, 500 MB RAM - -### 5.2 KRTD 3D Projection - -**What is KRTD?** -- Camera calibration format: `K` (intrinsic), `R` (rotation), `T` (translation), `D` (distortion) -- Stored in `/nas/mars/dataset/MEVA/meva-data-repo/metadata/camera-models/krtd/{camera_id}.krtd` - -**Projection pipeline**: -``` -Image coordinates (bbox) → Undistorted image plane → 3D ray → Ground plane (Z=0) -``` - -**Code flow**: -```python -def bbox_foot_to_world(self, bbox: List[int]) -> np.ndarray: - # 1. Extract foot point (person's feet on ground) - x_center = (bbox[0] + bbox[2]) / 2.0 - y_bottom = bbox[3] # Bottom of bbox = feet - - # 2. Undistort (remove lens distortion) - pts = np.array([[x_center, y_bottom]], dtype=np.float32) - pts_undist = cv2.undistortPoints(pts, self.K, self.D, P=self.K) - - # 3. Convert to normalized camera coordinates - x_norm = (pts_undist[0, 0, 0] - self.K[0, 2]) / self.K[0, 0] - y_norm = (pts_undist[0, 0, 1] - self.K[1, 2]) / self.K[1, 1] - - # 4. Create 3D ray from camera center - ray_camera = np.array([x_norm, y_norm, 1.0]) - ray_world = self.R.T @ ray_camera # Rotate to world frame - - # 5. Intersect with ground plane (Z = 0) - # Ray: P = camera_center + t * ray_world - # Ground: P_z = 0 - # Solve: camera_center[2] + t * ray_world[2] = 0 - t = -self.camera_center[2] / ray_world[2] - - # 6. Compute world position - world_pos = self.camera_center + t * ray_world - - return world_pos # [East, North, Up] in meters -``` - -**Why feet, not bbox center?** -- Feet are reliably on ground plane (Z=0) -- Bbox center is at person's waist (~1m above ground) → Z ≠ 0 → projection error - -**ENU coordinate system**: -- **E**ast: +X axis (meters east of site origin) -- **N**orth: +Y axis (meters north of site origin) -- **U**p: +Z axis (meters above ground, always 0 for ground-plane projection) - -### 5.3 Why No MEVID Links in V6? - -**MEVID provides**: -``` -Person 4: cameras ['G299', 'G328', 'G336', 'G419', 'G420', 'G421', 'G423'] -``` -- Person 4 appears on 7 cameras in this slot -- Ground truth: these cameras contain the same person - -**MEVID does NOT provide**: -- Which Kitware `actor_id` on G299 corresponds to Person 4 -- Which Kitware `actor_id` on G328 corresponds to Person 4 - -**Why not?** -- MEVID uses global person IDs (1-158 across all slots) -- Kitware uses camera-local actor IDs (e.g., 143 on G328, 122 on G421) -- **No mapping file exists** - -**To create mapping, we need**: -1. Extract MEVID tracklet images (500K-800K crops from `mevid_data/images/`) -2. Extract Kitware geom.yml bboxes (same, from videos using geom.yml coordinates) -3. Match via Person ReID model (cosine similarity of embeddings) -4. Spatial-temporal validation (frame overlap + IoU) -5. **Total compute**: ~10 hours for 47 VSet7 slots, ~8 days for 929 slots - -**V6 decision**: Skip MEVID entity-level mapping, use camera-level for validation only - -**V7 enhancement**: Add `mevid_validated: true` flag if both cameras in question are in same MEVID person's camera list (plausibility check, not entity-level match) - -### 5.4 Entity Resolution Bug Fixes (V6 Debugging) - -**Bug 1: Inline YAML format** (Session 14) -- **Symptom**: 0 bboxes found for cameras G299, G419, G423 (3 of 8 cameras) -- **Root cause**: Kitware uses inline YAML, not block YAML: - ```yaml - # Kitware format (inline): - - { geom: {id1: 143, ts0: 6800, g0: 100 200 150 300} } - - # Block format (what we expected): - - geom: - id1: 143 - ts0: 6800 - g0: 100 200 150 300 - ``` -- **Fix**: Change regex to inline-first: - ```python - inline_re = re.compile(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: Mega-cluster** (Session 15) -- **Symptom**: 1 giant cluster with 1,258 entities (all entities in slot) -- **Root cause**: Too permissive heuristic linking: - - No gap limit → links across 30-second gaps - - No per-entity link limit → one entity links to 50+ others - - Low confidence threshold (0.5) → links background actors -- **Fix**: Add safeguards: - ```python - MAX_HANDOFF_GAP = 10.0 # seconds - MAX_LINKS_PER_ENTITY = 2 - MIN_CONFIDENCE = 0.7 - # Only link active entities (those with events) - ``` -- **Result**: 20 meaningful clusters (was 1 mega-cluster) - -**Bug 3: Deduplication `None == None`** (Session 15) -- **Symptom**: Only 1 spatial question and 1 perception question (should be 3 each) -- **Root cause**: Checked `event_a`/`event_b` for ALL categories, but spatial/perception lack these fields: - ```python - if v_new.get("event_a") == v_old.get("event_a"): # Both None for spatial → duplicate! - return True - ``` -- **Fix**: Category-specific dedup logic: - ```python - if cat == "temporal": - # Check event_a/event_b activities - elif cat == "spatial": - # Check entity_a/entity_b IDs - elif cat == "perception": - # Check question_type + key - ``` -- **Result**: Full 9 questions generated (3+3+3) - ---- - -## 6. Memory Management - -### 6.1 V6 Strategy: Sampled Keyframes - -**Trade-off**: Memory vs completeness -- **V6**: `sample_every=30` → load every 30th frame -- Actor with 9000 frames → 300 keyframe bboxes -- 20 actors × 300 bboxes = 6000 bboxes per camera (~240 KB) - -**Consequences**: -- Incomplete trajectories (miss activities between keyframes) -- Spatial questions use "closest frame" (could be 15 frames = 0.5s off) -- Fast: 1.5s to load + 50 MB RAM for 8 cameras - -### 6.2 V7 Strategy: Full Loading - -**Trade-off**: Better quality vs more memory -- **V7**: `sample_every=1` → load ALL frames -- Actor with 9000 frames → 9000 bboxes -- 20 actors × 9000 bboxes = 180K bboxes per camera (~7 MB) -- 8 cameras: ~56 MB total (still <0.1% of 128 GB RAM) - -**Benefits**: -- Complete trajectories (every frame has bbox) -- Exact spatial (no "closest frame" approximation) -- Enables trajectory-based questions (future: "Was person moving toward/away?") - -**Why feasible**: -- Only load bboxes for actors with events (not all 50-100 actors in geom.yml) -- Streaming parser: no peak memory spike -- Modern machines: 128 GB RAM standard - -**Recommendation**: Use V7 full loading for all future work - -### 6.3 HDF5 mmap Strategy (Future: 929-Slot Batch Processing) - -**When needed**: Batch processing 100+ slots with query reuse - -**Pipeline**: -1. **One-time indexing** (1-2 min per geom.yml): - ```python - # Convert geom.yml → HDF5 - h5py.File(output_path, 'w').create_dataset( - 'geom', - data=np.array(all_bboxes), - chunks=True, - compression='gzip' # 60-70% size reduction - ) - ``` - -2. **Query via mmap** (10 ms per query): - ```python - h5 = h5py.File(h5_path, 'r') # OS mmap's file - geom = h5['geom'][...] # Fetch matching rows (lazy load) - match = geom[(geom['actor_id'] == aid) & (geom['frame'] == frame)] - ``` - -**Pros**: -- Constant RAM (~100-200 MB mapped, OS handles paging) -- Fast random access (O(log n) with HDF5 indexing) - -**Cons**: -- Upfront cost (2 min × 500 geom files = 16 hours one-time) -- Format lock-in (can't inspect with text editor) - -**Decision**: Defer to post-V7 (after VSet7 evaluation) - ---- - -## 7. Question Generation Strategies - -### 7.1 Temporal Questions: Gap Selection - -**Goal**: Questions should be neither trivial nor ambiguous - -**Gap thresholds**: -- **< 3 seconds**: Too close → ambiguous (camera synchronization ±1s) -- **3-20 seconds**: Ideal range (clear temporal order, both events in short window) -- **> 20 seconds**: Too far apart → trivial (obvious answer) - -**Gap distribution strategy**: -- Select one question per range: 3-7s, 8-14s, 15-20s -- Ensures diversity: not all questions have same difficulty - -**Why 3-20 seconds?** -- MEVA clips are 5 minutes (300 seconds) -- Events are 2-60 seconds each -- Most handoffs happen within 10 seconds (person exits camera A → enters camera B) - -### 7.2 Spatial Questions: Proximity Buckets - -**Classification**: -```python -def classify_proximity(distance_m: float) -> str: - if distance_m <= 5.0: - return "near" - elif distance_m <= 15.0: - return "moderate" - else: - return "far" -``` - -**Rationale**: -- **Near (≤5m)**: Same immediate area (same room, same sidewalk section) -- **Moderate (5-15m)**: Nearby but distinct locations (different sides of plaza) -- **Far (>15m)**: Separate areas (different buildings, opposite ends of site) - -**Selection strategy**: -- Pick one from each bucket (ensures diverse difficulty) -- Shuffle to avoid pattern (not always near → moderate → far) - -**Error handling**: -- Distances > 500m: likely projection error (discard) -- Common causes: indoor cameras with outdoor KRTD (ground plane assumption invalid) - -### 7.3 Perception Questions: Distractor Quality - -**Distractor bank** (`distractor_bank.py`): -- 37 MEVA activities grouped by context: - - **Facility**: open_door, close_trunk, enter_vehicle - - **Object**: pick_up, put_down, carry_heavy - - **Social**: talk_to_person, hand_gesture - - **Transport**: vehicle_stops, vehicle_turns - -**Selection criteria**: -1. **Context match**: If site=school, prefer facility/social activities -2. **Type match**: Person questions get person activities (not vehicle) -3. **Visual plausibility**: Don't use activities too similar (e.g., "open_door" ≠ "close_door" as distractor) -4. **Frequency balance**: Use rare activities as distractors for common activities - -**Example**: -```python -# Correct answer: "person_opens_facility_door" -# Good distractors: -# - "person_talks_to_person" (different action, same context) -# - "person_picks_up_object" (different action, plausible) -# - "person_enters_vehicle" (different action, transportation) -# Bad distractors: -# - "person_closes_facility_door" (too similar) -# - "vehicle_starts" (wrong entity type) -``` - ---- - -## 8. Validation & Quality Control - -### 8.1 Validation Rules (Per Category) - -**Temporal**: -```python -def validate_temporal(q: dict) -> List[str]: - errors = [] - v = q["verification"] - - # Rule 1: Must be cross-camera - if v["event_a"]["camera"] == v["event_b"]["camera"]: - errors.append("Same camera (should be cross-camera)") - - # Rule 2: Gap must be reasonable - gap = v["gap_sec"] - 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)") - - # Rule 3: Event A must precede Event B - if v["event_a"]["start_sec"] >= v["event_b"]["start_sec"]: - errors.append("Event A does not precede Event B") - - return errors -``` - -**Spatial**: -```python -def validate_spatial(q: dict) -> List[str]: - errors = [] - v = q["verification"] - d = v["distance_meters"] - prox = v["proximity"] - - # Rule 1: Distance must match proximity label - if prox == "near" and d > 5.0: - errors.append(f"Near but distance={d}m (should be ≤5m)") - elif prox == "moderate" and not (5.0 < d <= 15.0): - errors.append(f"Moderate but distance={d}m (should be 5-15m)") - elif prox == "far" and d <= 15.0: - errors.append(f"Far but distance={d}m (should be >15m)") - - return errors -``` - -### 8.2 Success Criteria (Section 13 of v6_todo.md) - -**Quantitative**: -- ✅ 9 questions generated (3 per category) -- ✅ All questions pass validation (0 errors) -- ✅ Correct answer distribution (not all option 0) -- ✅ ≥2 cameras per question (cross-camera reasoning) -- ✅ Generation time <15 seconds per slot - -**Qualitative**: -- ✅ Questions are non-trivial (can't answer from single camera) -- ✅ Distractors are plausible (no obviously wrong options) -- ✅ Language is natural (no template artifacts like "{{activity}}") -- ✅ Verification contains all debug info (cameras, timestamps, distances) - -### 8.3 Output JSON Schema - -```json -{ - "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", ...], - "mevid_persons_in_slot": 41, - "total_events": 147, - "total_entities": 926, - "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": "...", - "options": [...], - "correct_answer": 0, - "distractor_reasoning": [...], - "requires_cameras": ["G328", "G421"], - "verification": {...} - }, - ... - ] -} -``` - ---- - -## Summary - -The V6 pipeline is a **modular, memory-efficient, entity-centric** system for generating multi-camera QA pairs from surveillance video annotations. Key innovations: - -1. **Streaming GEOM parser**: Regex-based line-by-line parsing avoids OOM on 50+ MB files -2. **Entity resolution**: Heuristic temporal handoff with anti-mega-cluster safeguards -3. **3D spatial reasoning**: KRTD camera calibration enables world-coordinate distance questions -4. **Category-specific deduplication**: Prevents asking same question twice within slot -5. **V7 full loading**: `sample_every=1` provides complete trajectories for better quality - -**V7 enhancements** (next steps): -- Entity aliases (human-readable labels) -- Activity hierarchy (connected events) -- Debug markers (frame ranges, timestamps for manual verification) -- MEVID camera-level validation flags - -**Performance**: 9 questions in 7.9 seconds, 0 validation errors, 100% pass rate diff --git a/meva/docs/V7_FAQ.md b/meva/docs/V7_FAQ.md deleted file mode 100644 index bccc22b..0000000 --- a/meva/docs/V7_FAQ.md +++ /dev/null @@ -1,806 +0,0 @@ -# V7 Pipeline: Frequently Asked Questions - -> **Date**: 2026-02-12 -> **Purpose**: Clarify design decisions, limitations, and technical details - ---- - -## Table of Contents - -1. [GPS Data Usage](#1-gps-data-usage) -2. [Actor ID Persistence Problem](#2-actor-id-persistence-problem) -3. [Indoor Camera Handling](#3-indoor-camera-handling) -4. [Cross-Camera Entity Linking](#4-cross-camera-entity-linking) -5. [IoU (Intersection over Union)](#5-iou-intersection-over-union) -6. [KRTD Projection (Detailed)](#6-krtd-projection-detailed) -7. [Spatial Questions Across Cameras](#7-spatial-questions-across-cameras) -8. [Full GEOM Loading in V7](#8-full-geom-loading-in-v7) - ---- - -## 1. GPS Data Usage - -### Question: Does V7 use actor GPS data to create/validate questions? - -**Short answer**: No, GPS data is **NOT** used in V6 or V7. - -**Why not?** - -GPS data in MEVA comes from **GPS-logged actors** (people wearing GPS trackers): -- 105 unique GPS logger IDs (G517-G625) -- Continuous tracks in GPX files: lat/lon/elevation every ~10 seconds -- Located in: `/nas/mars/dataset/MEVA/meva-data-repo/metadata/gps/` - -**The fundamental problem**: There is **NO mapping** between: -- GPS logger IDs (G517-G625) ← Physical GPS devices -- MEVID person IDs (1-158) ← Re-identification annotations -- Kitware actor IDs (varies per camera) ← Activity annotations - -**Example gap**: -``` -GPS: G520 logged at (lat=38.9501, lon=-77.1037) on 2018-03-07 17:05:23 -MEVID: Person 12 appears on cameras [G328, G421] -Kitware: Camera G328 has actor_id=143, Camera G421 has actor_id=122 - -Question: Is GPS logger G520 the same person as MEVID Person 12? -Answer: UNKNOWN - no mapping exists -``` - -**Potential future use**: -- If MEVID person → GPS logger mapping is inferred (via video analysis) -- GPS could validate spatial questions: "Are these two people within 5m?" → check GPS distance -- GPS could generate trajectory questions: "Was person A moving toward/away from person B?" - -**Current V7 approach**: Use MEVID camera-level coverage only (see Section 4) - ---- - -## 2. Actor ID Persistence Problem - -### Question: Is actor_id stable within one camera? What about the `id1: 1 → id1: 1000` issue? - -**Corrected understanding**: The documentation statement "actor_id is stable within one camera" is **MISLEADING**. - -**The real behavior**: -- Kitware annotations are **activity-based**, not entity-tracking-based -- `id1` (actor_id) is assigned **per activity instance**, not per physical person -- If the same person performs two separate activities, they MAY get different `id1` values - -**Example scenario**: -```yaml -# Camera G328, timestamp 100-120s -- act: - id2: 101 - act2: {person_opens_facility_door: {}} - actors: - - id1: 1 # Person A - -# Camera G328, timestamp 150-170s (same person, different activity) -- act: - id2: 102 - act2: {person_enters_vehicle: {}} - actors: - - id1: 1000 # Same physical person, but different id1! -``` - -**Why does this happen?** -- Kitware annotators labeled activities independently -- No explicit within-camera person tracking requirement -- `id1` links to geom.yml (bounding boxes), which uses the same `id1` values -- If a person leaves FOV and returns, they might get a new `id1` - -**Impact on V6/V7 pipeline**: -1. **Two Entity objects created**: `G328_actor_1` and `G328_actor_1000` -2. **These are NOT linked within-camera** (ResolvedGraph only links CROSS-camera) -3. **Questions about "Person A" may miss activities**: If we ask about actor 1's activities, we miss actor 1000's activities - -**Is this a problem?** - -**For V6/V7**: Partially mitigated -- Most questions are **cross-camera** (temporal questions require different cameras) -- Spatial questions project bbox to 3D → distance is still valid even if same person has multiple IDs -- Perception questions ask about specific activities → don't need full person history - -**Still problematic for**: -- Within-camera temporal questions: "Did person A's first activity precede their second activity?" → CAN'T ANSWER if different IDs -- Trajectory analysis: "How did person A move over 5 minutes?" → Fragmented trajectory - -**What would fix this?** - -**Option 1: Within-camera entity resolution** (not implemented in V6/V7) -```python -# Link entities on SAME camera via: -# 1. Temporal proximity (gap < 5s between activities) -# 2. Spatial proximity (bbox IoU > 0.3 for overlapping time windows) -# 3. Appearance similarity (if using Re-ID features from bboxes) -``` - -**Option 2: Use geom.yml frame ranges** -- geom.yml has continuous bbox tracks (all frames where person visible) -- If `id1: 1` frames = [100, 500] and `id1: 1000` frames = [600, 900] -- Gap of 100 frames suggests different people (or same person who left/returned) -- Overlap of frames suggests annotation error → merge - -**Current V7 approach**: Accept this limitation, document it clearly - ---- - -## 3. Indoor Camera Handling - -### Question: How does the pipeline handle indoor cameras? - -**Indoor cameras in MEVA**: -- Defined in: `scripts/v6/utils/krtd.py` → `INDOOR_CAMERAS` set -- Examples: G639, G638, G506, G508 (mostly at hospital site) -- ~20% of cameras are indoor - -**Key characteristics**: -- **No KRTD calibration models** (indoor = no outdoor ground plane reference) -- **No GPS coverage** (indoor locations) -- **Different FOV** (narrow hallways vs outdoor plazas) - -**Pipeline handling**: - -| Step | Indoor Camera Behavior | -|------|------------------------| -| Step 1: Parse annotations | ✅ Included (parse activities.yml normally) | -| Step 2: Build scene graph | ✅ Included (entities + bboxes from geom.yml) | -| Step 3: Entity resolution | ✅ Included (can be linked cross-camera via heuristic) | -| Step 4: Temporal questions | ✅ Included (temporal ordering doesn't need KRTD) | -| Step 5: Spatial questions | ❌ **EXCLUDED** (no KRTD → can't project to 3D) | -| Step 6: Perception questions | ✅ Included (visual perception doesn't need KRTD) | - -**Code check**: -```python -# scripts/v6/generate_spatial.py, line 50 -for cam_id in sg.cameras: - if cam_id in INDOOR_CAMERAS: - continue # Skip spatial questions for indoor cameras - model = load_camera_model(cam_id) - if model is not None: - camera_models[cam_id] = model -``` - -**Cross-camera linking**: Indoor ↔ Outdoor -- Temporal handoff works: Person exits indoor camera → enters outdoor camera within 10s -- Example: Person exits hospital G506 → appears on outdoor G421 -- Creates cross-camera link for temporal questions - -**Spatial questions workaround** (future): -- Could use **camera-center distances** (ENU positions from KRTD) -- "How far apart are camera G328 and G421?" → 15.3m -- Less precise than entity-level, but works for indoor cameras -- V6 uses this for camera-based fallback (see Section 7) - ---- - -## 4. Cross-Camera Entity Linking - -### Question: Can we link entities across cameras? How? - -**Yes, via two methods**: - -### Method 1: MEVID Ground Truth (Camera-Level Only) - -**What MEVID provides**: -```python -# Example from mevid_data/mevid-v1-annotation-data/person-index-files/ -Person 4: cameras ['G299', 'G328', 'G336', 'G419', 'G420', 'G421', 'G423'] -Person 12: cameras ['G328', 'G421'] -``` - -**What this means**: -- Person 4 appears on 7 cameras in this slot -- Ground truth: **some** entity on G299 is the same person as **some** entity on G328 - -**What MEVID does NOT provide**: -- Which specific `actor_id` on G299 corresponds to Person 4 -- Which specific `actor_id` on G328 corresponds to Person 4 - -**V7 use case**: Camera-level validation -```python -# When generating temporal question: -# Is event_a.camera in same MEVID person's camera list as event_b.camera? -if 'G328' in mevid_persons[4] and 'G421' in mevid_persons[4]: - question["verification"]["mevid_validated"] = True # Plausible same person -``` - -**Why not entity-level MEVID?** -- Requires image matching: Extract crops from videos → match via Person Re-ID model -- Compute-intensive: 10 hours for 47 VSet7 slots -- See: `MEVID_IMPLEMENTATION_PLAN.md` for full technical approach - -### Method 2: Heuristic Temporal Handoff - -**Core idea**: If person exits camera A and enters camera B within 10 seconds, likely same person - -**Algorithm**: -```python -def _resolve_heuristic(sg: SceneGraph): - links = [] - - # For each entity with EXIT activity - for entity_a in sg.entities.values(): - if not has_exit_activity(entity_a): - continue - - # Look for entities on OTHER cameras with ENTRY activity - for entity_b in sg.entities.values(): - if entity_b.camera_id == entity_a.camera_id: - continue # Must be cross-camera - - if not has_entry_activity(entity_b): - continue - - # Compute time gap - gap = entity_b.first_sec - entity_a.last_sec - - if 0 < gap < MAX_HANDOFF_GAP: # 10 seconds - confidence = compute_confidence(gap, activities, entity_types) - if confidence > 0.7: - links.append(CrossCameraLink( - entity_a=entity_a.entity_id, - entity_b=entity_b.entity_id, - link_type="temporal_handoff", - confidence=confidence, - time_gap_sec=gap - )) - - return links -``` - -**Confidence scoring**: -- **Time gap**: Smaller gap → higher confidence - - 0-3s: very likely same person - - 3-7s: likely same person - - 7-10s: possible same person -- **Activity match**: exit → entry = +0.2 confidence -- **Entity type**: person → person required (person → vehicle = reject) - -**Anti-mega-cluster safeguards** (learned from V6 debugging): -```python -MAX_HANDOFF_GAP = 10.0 # Don't link events >10s apart -MAX_LINKS_PER_ENTITY = 2 # Each entity links to at most 2 others -MIN_CONFIDENCE = 0.7 # Threshold for creating link -# Only link ACTIVE entities (those with annotated events, not background actors) -``` - -**Union-Find clustering**: -```python -# After creating links, merge into clusters -uf = UnionFind() -for link in links: - uf.union(link.entity_a, link.entity_b) - -clusters = uf.clusters() # Connected components -# Example: { -# "cluster_001": ["G328_actor_143", "G421_actor_122", "G299_actor_88"], -# "cluster_002": ["G330_actor_55", "G336_actor_91"], -# ... -# } -``` - -**Result**: -- V6 on `2018-03-11.11-25-00.school`: 1330 links → 20 clusters -- Average cluster size: ~3 entities (person tracked across 3 cameras) - ---- - -## 5. IoU (Intersection over Union) - -### Question: How does IoU work in more depth? - -**Purpose**: Measure overlap between two bounding boxes (0.0 = no overlap, 1.0 = perfect overlap) - -**Mathematical definition**: -``` -IoU = Area(Intersection) / Area(Union) -``` - -**Visual example**: -``` -Bbox A: [100, 200, 150, 300] → 50px wide × 100px tall = 5000px² -Bbox B: [120, 220, 170, 320] → 50px wide × 100px tall = 5000px² - -Intersection rectangle: - x1 = max(100, 120) = 120 - y1 = max(200, 220) = 220 - x2 = min(150, 170) = 150 - y2 = min(300, 320) = 300 - → [120, 220, 150, 300] = 30px × 80px = 2400px² - -Union = Area(A) + Area(B) - Area(Intersection) - = 5000 + 5000 - 2400 = 7600px² - -IoU = 2400 / 7600 = 0.316 -``` - -**Code implementation** (`scripts/v6/utils/iou.py`): -```python -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] (top-left and bottom-right corners) - bbox_b: [x1, y1, x2, y2] - - Returns: - IoU value [0.0, 1.0] - """ - # 1. Find intersection rectangle - 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]) - - # 2. Check if boxes don't overlap - if x2 <= x1 or y2 <= y1: - return 0.0 # No overlap - - # 3. Compute areas - 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 - - # 4. Handle edge case - if union <= 0: - return 0.0 - - # 5. Return ratio - return inter / union -``` - -**Use cases in V6/V7**: - -**1. Within-camera entity resolution** (not currently implemented, but proposed): -```python -# If two entities have overlapping time spans on same camera: -# Check if their bboxes overlap → likely same person with different actor_ids - -entity_a_bbox = get_bbox_at_frame(geom_path, actor_id=1, frame=300) -entity_b_bbox = get_bbox_at_frame(geom_path, actor_id=1000, frame=300) -iou = compute_iou(entity_a_bbox, entity_b_bbox) - -if iou > 0.5: - # Likely same person with annotation inconsistency → merge entities - union_entities(entity_a, entity_b) -``` - -**2. MEVID tracklet matching** (future work): -```python -# Match MEVID person crops to Kitware geom.yml bboxes -# Step 1: Extract crop at frame F from MEVID -# Step 2: Find Kitware bbox at same frame -# Step 3: Compute IoU to validate temporal alignment - -mevid_crop_bbox = [120, 220, 170, 320] # From MEVID tracklet -kitware_bbox = get_bbox_at_frame(geom_path, actor_id=143, frame=7100) -iou = compute_iou(mevid_crop_bbox, kitware_bbox) - -if iou > 0.3: # Threshold for spatial-temporal validation - # Likely match → MEVID person_id maps to Kitware actor_id -``` - -**3. Activity overlap detection**: -```python -# Are two activities happening in same spatial region? -activity_a_bbox = entity_a.keyframe_bboxes[frame] -activity_b_bbox = entity_b.keyframe_bboxes[frame] -iou = compute_iou(activity_a_bbox, activity_b_bbox) - -if iou > 0.1: - # Activities spatially overlap → "interaction" or "co-location" -``` - -**IoU thresholds in practice**: -- **0.5+**: Strong overlap (likely same object tracked continuously) -- **0.3-0.5**: Moderate overlap (same region, could be same object or nearby objects) -- **0.1-0.3**: Weak overlap (nearby objects, edges touching) -- **<0.1**: Negligible overlap (effectively separate objects) - ---- - -## 6. KRTD Projection (Detailed) - -### Question: Can you explain KRTD projection in more depth? - -**Goal**: Convert 2D image pixel coordinates (bbox) → 3D world coordinates (meters) - -### 6.1 KRTD Parameters - -**K**: Camera Intrinsic Matrix (3×3) -``` -K = [[fx, 0, cx], - [ 0, fy, cy], - [ 0, 0, 1]] -``` -- `fx, fy`: Focal length in pixels (horizontal and vertical) -- `cx, cy`: Principal point (image center) in pixels -- Example: fx=1800, fy=1800, cx=960, cy=540 (for 1920×1080 image) - -**R**: Rotation Matrix (3×3) -``` -R = camera_space → world_space rotation -``` -- Converts vectors from camera coordinates to world coordinates -- Orthogonal matrix: R.T @ R = I -- Example: camera pointing northeast at 45° tilt - -**T**: Translation Vector (3×1) -``` -T = [tx, ty, tz] # Camera position in world coordinates -``` -- Camera center position in meters (ENU coordinates) -- Example: [123.4, 567.8, 3.2] = 123.4m East, 567.8m North, 3.2m Up - -**D**: Distortion Coefficients (4×1 or 5×1) -``` -D = [k1, k2, p1, p2, (k3)] -``` -- `k1, k2, k3`: Radial distortion (barrel/pincushion) -- `p1, p2`: Tangential distortion -- Most outdoor cameras have moderate distortion (k1 ≈ -0.1 to -0.3) - -### 6.2 Projection Pipeline (6 Steps) - -**Input**: Bounding box `[x1, y1, x2, y2]` in image coordinates -**Output**: 3D position `[East, North, Up]` in meters - -**Step 1: Extract Foot Point** -```python -# Person's feet are at bottom-center of bbox -x_center = (bbox[0] + bbox[2]) / 2.0 -y_bottom = bbox[3] # Bottom edge = feet on ground - -foot_point_image = [x_center, y_bottom] # Image pixel coordinates -``` - -**Why feet?** -- Feet are reliably on ground plane (Z=0 in ENU) -- Bbox center is at person's waist (~1-1.5m above ground) → introduces Z error -- Head could be 1.7m above ground → even more error - -**Step 2: Undistort (Remove Lens Distortion)** -```python -# OpenCV undistortPoints: image → normalized camera coordinates -pts = np.array([[x_center, y_bottom]], dtype=np.float32) -pts_undist = cv2.undistortPoints(pts, K, D, P=K) - -x_undist = pts_undist[0, 0, 0] -y_undist = pts_undist[0, 0, 1] -``` - -**What undistortPoints does**: -1. Converts pixel → normalized image plane: `(x - cx) / fx`, `(y - cy) / fy` -2. Applies inverse distortion model: removes radial/tangential warping -3. Returns in pixel coordinates (if P=K) or normalized coordinates (if P=None) - -**Step 3: Convert to Normalized Camera Coordinates** -```python -# From pixel coordinates → normalized [-1, 1] range -x_norm = (x_undist - K[0, 2]) / K[0, 0] # (x - cx) / fx -y_norm = (y_undist - K[1, 2]) / K[1, 1] # (y - cy) / fy - -# Now we have a unit direction in camera space -ray_camera = np.array([x_norm, y_norm, 1.0]) # Z=1 convention -``` - -**Step 4: Rotate to World Frame** -```python -# Camera space → World space rotation -ray_world = R.T @ ray_camera # R.T = inverse rotation (since R is orthogonal) - -# ray_world is now a 3D direction vector in ENU coordinates -# Example: [0.707, 0.707, -0.1] means pointing NE at slight downward angle -``` - -**Step 5: Intersect with Ground Plane** -```python -# Ray equation: P(t) = camera_center + t * ray_world -# Ground plane: Z = 0 (everything on ground) -# Intersection: camera_center[2] + t * ray_world[2] = 0 - -camera_center = -R.T @ T # Derive from R, T -t = -camera_center[2] / ray_world[2] - -# t is the scale factor: how far along ray to reach ground -# If camera is 3m above ground and ray points down at 30°: -# t ≈ 3 / sin(30°) = 6m -``` - -**Step 6: Compute World Position** -```python -world_pos = camera_center + t * ray_world - -# Result: [East, North, 0.0] -# Example: [131.2, 570.5, 0.0] = 131.2m East, 570.5m North, on ground -``` - -### 6.3 Error Sources - -**1. Ground Plane Assumption** -- Assumes all people are on flat ground (Z=0) -- **Violation**: Hills, stairs, elevated platforms -- **Impact**: Position error proportional to Z deviation -- Example: Person on 1m platform → position error ~1-2m - -**2. Indoor Cameras** -- No outdoor ground plane reference -- KRTD models may not exist or be unreliable -- **Solution**: Exclude indoor cameras from spatial questions - -**3. Bbox Accuracy** -- geom.yml bboxes are manually adjusted or auto-tracked -- Error in bbox foot point → error in 3D position -- **Typical error**: ±10 pixels in 1920×1080 image -- **3D error**: ±0.5-1m at 30m distance - -**4. Camera Calibration Drift** -- KRTD models calibrated once, cameras may shift over time (wind, vibration) -- **Impact**: Systematic position error (all entities shifted in same direction) -- **Mitigation**: Accept small errors (<2m) as inherent limitation - -### 6.4 Validation Techniques - -**1. Distance Sanity Checks** -```python -if distance > 500: - # Likely projection error (cameras <200m apart in MEVA) - discard_candidate() -``` - -**2. Cross-Camera Consistency** -```python -# If same person on two cameras, their 3D positions should be similar -pos_a = project_bbox(camera_a, bbox_a) -pos_b = project_bbox(camera_b, bbox_b) -distance = ||pos_a - pos_b|| - -if distance > 10: # Same person shouldn't be 10m apart simultaneously - # Annotation error or projection issue -``` - -**3. GPS Ground Truth** (future) -```python -# If GPS data available, compare projected positions to GPS -gps_pos = get_gps_position(actor_gps_id, timestamp) -projected_pos = project_bbox(camera, bbox) -error = ||gps_pos - projected_pos|| - -if error > 5: # 5m threshold - # Investigate: bbox error? KRTD error? GPS error? -``` - ---- - -## 7. Spatial Questions Across Cameras - -### Question: Can spatial questions use multiple cameras? - -**Yes, most spatial questions in V6/V7 are cross-camera.** - -### 7.1 Cross-Camera Spatial Questions - -**Typical question**: -``` -"In the scene, are {activity_a} (camera {cam_a}) and {activity_b} (camera {cam_b}) -close together or far apart?" -``` - -**Requirements**: -- Both cameras must have KRTD models -- Both entities must have bounding boxes -- Projection to 3D must succeed (feet on ground plane) - -**Example**: -```python -# Entity A on camera G328 -bbox_a = entity_a.keyframe_bboxes[7000] # [100, 200, 150, 300] -model_a = load_camera_model("G328") -pos_a = model_a.bbox_foot_to_world(bbox_a) # [123.4, 567.8, 0.0] - -# Entity B on camera G421 -bbox_b = entity_b.keyframe_bboxes[7100] # [800, 400, 900, 600] -model_b = load_camera_model("G421") -pos_b = model_b.bbox_foot_to_world(bbox_b) # [131.2, 570.5, 0.0] - -# Compute distance -distance = ||pos_a - pos_b|| = √[(131.2-123.4)² + (570.5-567.8)²] - = √[7.8² + 2.7²] = √[60.84 + 7.29] = √68.13 = 8.25m - -# Classify -proximity = "moderate" # 5-15m range -``` - -**V6 actual output**: -```json -{ - "question_id": "v6_spatial_002", - "question_template": "In the scene, are person_opens_facility_door (camera G328) and person_talks_to_person (camera G421) close together or far apart?", - "verification": { - "entity_a": "G328_actor_143", - "entity_b": "G421_actor_122", - "camera_a": "G328", - "camera_b": "G421", - "distance_meters": 8.06, - "proximity": "moderate", - "world_pos_enu": { - "entity_a": [123.4, 567.8, 0.0], - "entity_b": [131.2, 570.5, 0.0] - } - } -} -``` - -### 7.2 Same-Camera Spatial Questions - -**Also supported** (less common): -``` -"How close are {activity_a} and {activity_b} in the scene visible on camera {cam}?" -``` - -**Example**: -```python -# Both entities on camera G330 -entity_a = "G330_actor_88" # Person opening door -entity_b = "G330_actor_91" # Person carrying object - -# Project both to 3D using SAME camera model -model = load_camera_model("G330") -pos_a = model.bbox_foot_to_world(bbox_a) # [140.1, 572.3, 0.0] -pos_b = model.bbox_foot_to_world(bbox_b) # [142.8, 573.1, 0.0] - -distance = ||pos_a - pos_b|| = 2.87m -proximity = "near" -``` - -**V6 output**: -```json -{ - "question_id": "v6_spatial_001", - "question_template": "How close are person_opens_facility_door and person_carries_heavy_object in the scene visible on camera G330?", - "verification": { - "camera_a": "G330", - "camera_b": "G330", # Same camera - "distance_meters": 2.87, - "proximity": "near" - } -} -``` - -### 7.3 Camera-Center Fallback (Future Enhancement) - -**For slots with NO entity projections** (all indoor, or KRTD issues): -```python -# Fallback: Use camera center positions instead of entity positions -camera_a_pos = model_a.camera_center # [120.0, 565.0, 3.2] -camera_b_pos = model_b.camera_center # [145.0, 580.0, 2.8] - -distance = ||camera_a_pos - camera_b_pos|| = 26.3m - -question = ( - f"Which cameras are closer together: {cam_a} and {cam_b}, " - f"or {cam_c} and {cam_d}?" -) -``` - -**Not implemented in V6, but documented in V7 todo** (Section 10: Future Enhancements) - ---- - -## 8. Full GEOM Loading in V7 - -### Question: Is there merit to loading the full geom file in V7? - -**Yes, absolutely. V7 already implements this.** - -### 8.1 V6 vs V7 Comparison - -| Aspect | V6 | V7 | -|--------|----|----| -| **Sampling** | `sample_every=30` | `sample_every=1` | -| **Bboxes per actor** | ~300 (5-min clip) | ~9000 (5-min clip) | -| **Memory per camera** | ~240 KB | ~7 MB | -| **Total for 8 cameras** | ~2 MB | ~56 MB | -| **Trajectory completeness** | Sparse (1 per sec) | Complete (30 per sec) | -| **Spatial accuracy** | ±0.5s approximation | Exact frame | - -### 8.2 Benefits of Full Loading - -**1. Complete Trajectories** -```python -# V6: Only have bboxes at frames [0, 30, 60, 90, ...] -# Activity spans frames 45-75 → only 2 bboxes! - -# V7: Have ALL bboxes [0, 1, 2, ..., 9000] -# Activity spans frames 45-75 → 30 bboxes (full trajectory) -``` - -**2. Exact Spatial Positioning** -```python -# V6: Need bbox at frame 7100 -# Closest keyframe: 7110 (10 frames = 0.33s off) -# Position error: person walked ~1m in 0.33s → 1m spatial error - -# V7: Have exact bbox at frame 7100 -# No approximation error -``` - -**3. Temporal Precision** -```python -# Question: "When did person A start opening the door?" -# V6: Can only say "between frame 7080 and 7110" (30-frame = 1s window) -# V7: Can pinpoint exact frame 7093 (0.03s precision) -``` - -**4. Future Trajectory Questions** -```python -# "Was person A moving toward or away from person B?" -# V6: Only 2 positions → can't determine trajectory -# V7: 30 positions → compute velocity vector, direction of motion - -# Example: -positions = [bbox_to_3d(bbox) for bbox in entity_a.keyframe_bboxes.values()] -velocities = [positions[i+1] - positions[i] for i in range(len(positions)-1)] -avg_velocity = np.mean(velocities, axis=0) # [vx, vy] in m/s - -direction_to_b = pos_b - pos_a[0] # Vector from A's start to B -dot_product = np.dot(avg_velocity, direction_to_b) - -if dot_product > 0: - answer = "Person A was moving toward person B" -else: - answer = "Person A was moving away from person B" -``` - -**5. Activity Boundary Detection** -```python -# Detect exactly when person starts/stops activity -# V6: Activity endpoints quantized to 30-frame intervals -# V7: Exact frame where bbox motion changes -``` - -### 8.3 Memory Feasibility - -**This machine**: 128 GB RAM available - -**V7 memory usage**: -- Full GEOM load: 56 MB for 8 cameras -- Scene graph: ~20 MB -- Entity resolution: ~10 MB -- Question generation: ~5 MB -- **Total peak**: ~100 MB (0.08% of 128 GB) - -**Even for 929-slot batch processing**: -- 929 slots × 100 MB = ~93 GB -- Still fits in 128 GB RAM with room for OS + other processes - -**Conclusion**: Full loading is trivially feasible, benefits far outweigh costs. - -### 8.4 V7 Implementation Status - -✅ **Already implemented** in V7: -- [scripts/v7/build_scene_graph.py](scripts/v7/build_scene_graph.py#L147): `sample_every=1` -- [scripts/v7/utils/yaml_stream.py](scripts/v7/utils/yaml_stream.py#L63): Default `sample_every=1` -- [v7_todo.md Section 6](v7_todo.md): Full documentation of rationale - ---- - -## Summary - -| Question | Answer | -|----------|--------| -| **GPS data usage?** | No, no mapping to actor_ids | -| **Actor ID persistence?** | NOT guaranteed - can change within-camera (known limitation) | -| **Indoor cameras?** | Included in temporal/perception, excluded from spatial | -| **Cross-camera linking?** | Via MEVID (camera-level) + heuristic temporal handoff | -| **IoU?** | Intersection / Union ratio for bbox overlap (0-1) | -| **KRTD projection?** | 6-step pipeline: foot point → undistort → 3D ray → ground plane | -| **Spatial multi-camera?** | Yes, most spatial questions are cross-camera | -| **Full GEOM loading?** | Yes, implemented in V7, highly beneficial | - ---- - -**Next Steps**: See v7_todo.md for V7 implementation plan (entity aliases, activity hierarchy, debug markers) diff --git a/meva/docs/multi_camera_reference.md b/meva/docs/multi_camera_reference.md deleted file mode 100644 index caed23d..0000000 --- a/meva/docs/multi_camera_reference.md +++ /dev/null @@ -1,429 +0,0 @@ -# Multi-Camera Repository Reference Guide - -> **Repository**: [UTAustin-SwarmLab/Multi-Camera](https://github.com/UTAustin-SwarmLab/Multi-Camera) -> **Last accessed**: February 6, 2026 -> **Purpose**: Extract key algorithmic patterns, prompting strategies, and architectural components for MEVA multi-camera QA work - -## Key Architectural Patterns - -### 1. QA Generator Hierarchy - -The repository uses a clean inheritance pattern for different question types: - -```python -class QAGenerator: # Base class - def __init__(self, prompts_dir, scene_graphs_dir, gpt_logs_dir, ...): - self.prompts_dir = Path(prompts_dir) - self.gpt_logs_dir = Path(gpt_logs_dir) - - def gpt(self, prompt, api_key, model="gpt-4", temperature=0.7, max_tokens=500): - """Standardized GPT call with automatic logging""" - # GPT call + automatic logging to gpt_logs_dir - - def construct_qa_sample(self, scene_token, question_type, question, answer, metadata): - return QASample(scene_token, question_type, question, answer, metadata) - -class TemporalQAGenerator(QAGenerator): - def generate_for_scene(self, scene_token, api_key): - # Load scene graph - # Extract events from timeline - # Select temporal pairs (grounding + target events) - # Generate Q&A via GPT -``` - -**Pattern for MEVA**: Our `generate_qa_v3_multicam.py` follows a similar pattern with category-specific generators inheriting from a base class. - -### 2. Event Consolidation Algorithm - -**Key insight**: Raw frame-by-frame annotations are consolidated into temporal "events" with consistent activities: - -```python -def _consolidate_events(self, raw_timelines: Dict[str, List[Dict]]) -> List[Dict[str, Any]]: - """ - Merges consecutive frames with the same activity into a single Event interval. - Logic: - - If activity changes OR frame gap > 2: close current event, start new one - - Extend event description if longer description found - - Filter out very short events (< 3 frames) as noise - """ - events = [] - for obj_id, timeline in raw_timelines.items(): - timeline.sort(key=lambda x: x["frame_idx"]) - current_event = None - for step in timeline: - if current_event is None: - current_event = { - "obj_id": obj_id, "class": step["class"], - "activity": step["activity"], "description": step["description"], - "start_frame": step["frame_idx"], "end_frame": step["frame_idx"] - } - else: - # Extend if same activity and consecutive frames - if (step["activity"] == current_event["activity"] and - step["frame_idx"] - current_event["end_frame"] <= 2): - current_event["end_frame"] = step["frame_idx"] - # Keep longest description - if len(step["description"]) > len(current_event["description"]): - current_event["description"] = step["description"] - else: - # Close old event, start new - events.append(current_event) - current_event = { ... } # New event - if current_event: - events.append(current_event) - - # Filter noise: events must span > 2 frames - return [e for e in events if (e["end_frame"] - e["start_frame"]) > 2] -``` - -**Application to MEVA**: Our `extract_logic_tuples.py` already does similar consolidation in `parse_activities_json()` when building LogicTuple intervals. - -### 3. Temporal Relationship Selection - -**Advanced algorithm** for selecting meaningful temporal pairs: - -```python -def _select_temporal_pair(self, events, target_relationship="Before"): - """ - Selects Grounding Event and Target Event for specific relationships: - - Before: Target.end < Grounding.start - - After: Target.start > Grounding.end - - During: Overlap > 50% of shorter event - - In-Between: Target occurs between end of Event A and start of Event B - """ - shuffled_events = list(events) - random.shuffle(shuffled_events) # Ensure variety - - if target_relationship == "In-Between": - # Complex logic: find triplet where Target fits between two others - for i in range(len(shuffled_events)): - for j in range(len(shuffled_events)): - e1, e2 = shuffled_events[i], shuffled_events[j] - if e1["end_frame"] < e2["start_frame"]: # Valid gap - gap_start, gap_end = e1["end_frame"], e2["start_frame"] - for e_target in shuffled_events: - if (e_target["start_frame"] >= gap_start and - e_target["end_frame"] <= gap_end): - # Found valid triplet: combine e1+e2 as grounding - combined_grounding = { - "description": [e1['description'], e2['description']], - "class": [e1['class'], e2['class']], - "start_frame": e1["start_frame"], - "end_frame": e2["end_frame"] - } - return combined_grounding, e_target, "In-Between" - - # Standard pairwise logic for Before/After/During - for e1, e2 in combinations(shuffled_events, 2): - if e1["obj_id"] == e2["obj_id"]: continue # Different objects only - - if target_relationship == "Before": - if e2["end_frame"] < e1["start_frame"]: - return e1, e2, "Before" # Target before Grounding - elif target_relationship == "After": - if e2["start_frame"] > e1["end_frame"]: - return e1, e2, "After" # Target after Grounding - elif target_relationship == "During": - overlap = max(0, min(e1["end_frame"], e2["end_frame"]) - - max(e1["start_frame"], e2["start_frame"])) - min_len = min(e1["end_frame"] - e1["start_frame"], - e2["end_frame"] - e2["start_frame"]) - if min_len > 0 and (overlap / min_len) > 0.5: - return e1, e2, "During" # Significant overlap - - return None, None, target_relationship # No valid pair found -``` - -**Application to MEVA**: Our V3 generators could use this sophisticated relationship logic instead of simple time filtering. - -### 4. Scene Graph Spatial Relationships - -**Algorithm** for computing spatial relationships from 3D coordinates: - -```python -def _compute_spatial_relationships(self, objects, distance_threshold=10.0): - """ - Computes directional relationships between objects based on relative position. - - Logic: - - near: distance < threshold - - in_front/behind/left/right: based on angle from ego perspective - """ - relationships = [] - for obj1, obj2 in combinations(objects, 2): - pos1, pos2 = np.array(obj1["position"]), np.array(obj2["position"]) - distance = np.linalg.norm(pos2 - pos1) - - if distance < distance_threshold: - relationships.append({ - "source_id": obj1["object_id"], "target_id": obj2["object_id"], - "relationship_type": "near", "distance": distance - }) - - # Compute directional relationship - delta = pos2 - pos1 # Vector from obj1 to obj2 - angle = np.arctan2(delta[1], delta[0]) * 180 / np.pi # Degrees - - if -45 <= angle <= 45: - rel_type = "in_front" - elif 45 < angle <= 135: - rel_type = "left" - elif -135 <= angle < -45: - rel_type = "right" - else: # 135 < angle or angle < -135 - rel_type = "behind" - - relationships.append({ - "source_id": obj1["object_id"], "target_id": obj2["object_id"], - "relationship_type": rel_type, "distance": distance - }) - - return relationships -``` - -**Note**: Our MEVA dataset doesn't have 3D coordinates, but this pattern could be adapted for 2D bounding box relationships. - -### 5. Spatio-Temporal QA Generation - -**Two-mode approach** combining spatial and temporal reasoning: - -```python -class SpatioTemporalQAGenerator(TemporalQAGenerator): - def _select_event_grounded_spatial_query(self, events, frames_map): - """ - Type 1: Time -> Space - Pick an Event, look at spatial relationships at its start frame - Q: "At the start of [Event], what was to the left of [Object]?" - """ - anchor_event = random.choice(events) - time_idx = anchor_event["start_frame"] - frame_data = frames_map.get(time_idx) - spatial_rels = [r for r in frame_data.get("relationships", []) - if r["type"] in ["left", "right", "in_front", "behind"]] - target_rel = random.choice(spatial_rels) - return { - "context_info": f"Anchor Event: A {anchor_event['class']} is {anchor_event['activity']}", - "target_info": f"Spatial Query: What is {target_rel['type']} the {target_rel['source_class']}?", - "answer": target_rel['target_class'] - } - - def _select_spatially_grounded_temporal_query(self, events, frames_map): - """ - Type 2: Space -> Time - Pick object defined by spatial relationship, ask what it did next - Q: "What did the [car next to the bus] do afterwards?" - """ - for target_event in events: - # Look for spatial descriptor before this event - lookback_frame = max(0, target_event["start_frame"] - 5) - frame_data = frames_map.get(lookback_frame) - - # Find spatial relationships involving this actor - actor_rels = [r for r in frame_data.get("relationships", []) - if r["source_id"] == target_event["obj_id"]] - - if actor_rels: - spatial_rel = actor_rels[0] - spatial_desc = f"The {spatial_rel['source_class']} located {spatial_rel['type']} the {spatial_rel['target_class']}" - return { - "context_info": f"Spatially Defined Actor: {spatial_desc}", - "target_info": f"Target Activity: {target_event['activity']}", - "answer": target_event['description'] - } - return None -``` - -**Application to MEVA**: Could enhance our `camera_transition` category with more sophisticated spatial reasoning if we extract relative positions from bounding boxes. - -## Scene Graph Data Structures - -### Core Data Models - -```python -@dataclass -class SceneGraphNode: - object_id: str # Unique instance token - object_class: str # Object category - position: tuple # 3D position - size: tuple # Object dimensions - velocity: Optional[tuple] # Object velocity - attributes: List[str] # Object attributes - frame_idx: int # Frame index - timestamp: int # Timestamp - num_lidar_pts: int # Number of lidar points - visibility: int # Visibility level (0-4) - - # VLM-annotated properties (filled later) - activity: Optional[str] = None - description: Optional[str] = None - caption: Optional[str] = None - -@dataclass -class SceneGraphRelationship: - source_id: str # Source object instance token - target_id: str # Target object instance token - relationship_type: str # 'near', 'behind', 'in_front', 'left', 'right' - distance: Optional[float] # Distance in meters - frame_idx: int # Frame index -``` - -**MEVA Adaptation**: Our LogicTuple structure serves a similar role, representing object states and relationships over time. - -## GPT Prompting Strategies - -### 1. Temporal Reasoning Prompts - -**Structure**: Grounding Event + Target Event + Relationship Type - -``` -You are an expert at understanding temporal relationships in multi-camera video sequences. - -Given: -Grounding Event: {grounding_input} -Target Event: {target_input} -Relationship: {rel_type} - -Generate a multiple-choice question that tests understanding of the {rel_type} relationship between these events. Format as JSON: - -{ - "question": "Based on the video, what happened to [Target] {rel_type} [Grounding Event]?", - "options": { - "A": "...", - "B": "...", - "C": "...", - "D": "..." - }, - "correct_option": "A", - "rationale": "..." -} - -Guidelines: -- Use specific object classes and activities, not generic terms -- Include plausible distractors that test temporal reasoning -- Ensure (correct) answer follows {rel_type} logic -- Keep question under 100 words -``` - -**Key insight**: Template-based prompts with explicit slot filling work better than open-ended generation. - -### 2. Event Ordering Prompts - -**Structure**: Present events in random order, ask for chronological sequence - -``` -Given these events from a multi-camera video: - -{events_list} - -Generate a question asking users to arrange these events in chronological order. Present the events in scrambled order within the question. - -JSON format: -{ - "question": "Arrange these events in chronological order from earliest to latest: [scrambled list]", - "options": { - "A": "Event A, Event B, Event C, Event D", - "B": "Event B, Event A, Event D, Event C", - "C": "...", - "D": "..." - }, - "correct_option": "C", - "rationale": "Based on the temporal sequence..." -} -``` - -**Application**: Could enhance our ordering category with more sophisticated event chain logic. - -### 3. Summarization Prompts - -**Structure**: Temporal scene graph → Comprehensive summary - -``` -Generate a comprehensive summary of the ego-actor's interactions across all camera views. - -Temporal Scene Graph: -{temporal_scene_graph} - -Requirements: -- 2-3 sentences maximum -- Focus on key activities and interactions -- Mention spatial context when relevant -- Use specific object classes, not generic terms -- Maintain temporal flow (before, during, after) - -Example: "A black sedan approaches the intersection and stops behind a white bus. While waiting, a pedestrian crosses from left to right. After the pedestrian clears, the sedan turns right and exits the camera view." -``` - -**Note**: Multi-Camera uses GPT-4o with temperature=0.5, max_tokens=300 for summaries. - -## Integration Strategies for MEVA - -### 1. Import Key Algorithms - -```python -# From Multi-Camera temporal reasoning -def extract_temporal_pairs(logic_tuples, relationship_type): - """Adapt Multi-Camera's _select_temporal_pair for LogicTuples""" - events = consolidate_logic_tuples_to_events(logic_tuples) - return select_temporal_pair(events, relationship_type) - -def consolidate_logic_tuples_to_events(logic_tuples): - """Convert MEVA LogicTuples to Multi-Camera event format""" - events = [] - for actor_id, tuples in group_by_actor(logic_tuples).items(): - for i, curr_tuple in enumerate(tuples): - if i == 0 or curr_tuple.activity != tuples[i-1].activity: - # Start new event - event = { - "obj_id": actor_id, - "class": curr_tuple.actor_type, - "activity": curr_tuple.activity, - "start_frame": curr_tuple.start_frame, - "end_frame": curr_tuple.end_frame, - "description": curr_tuple.description - } - events.append(event) - else: - # Extend current event - events[-1]["end_frame"] = curr_tuple.end_frame - return events -``` - -### 2. Enhanced Prompt Templates - -Store Multi-Camera prompt patterns in `/home/ah66742/scripts/prompts/`: - -- `temporal_advanced.txt` - Multi-event relationship templates -- `ordering_chains.txt` - Sequential event arrangement -- `spatiotemporal.txt` - Combined spatial/temporal reasoning -- `summarization.txt` - Scene synthesis prompts - -### 3. Logging and Evaluation - -Adopt Multi-Camera's GPT logging pattern: -```python -def log_gpt_call(self, input_text, output_text, model, temperature, scene_token, **metadata): - """Log all GPT interactions for evaluation and debugging""" - log_file = self.gpt_logs_dir / f"{scene_token}.json" - log_entry = { - "input": input_text, "output": output_text, - "metadata": {"model": model, "temperature": temperature, "scene_token": scene_token, **metadata} - } - with open(log_file, "w") as f: - json.dump(log_entry, f, indent=2, default=str) -``` - -## Key Takeaways for MEVA - -1. **Event Consolidation**: Multi-Camera's frame-to-event consolidation algorithm could improve our LogicTuple processing -2. **Temporal Relationship Logic**: Their sophisticated Before/After/During/In-Between selection is more nuanced than our current time-based filtering -3. **Spatio-Temporal Integration**: Two-mode approach (Time→Space, Space→Time) could enhance camera_transition questions -4. **Prompt Engineering**: Template-based prompts with explicit slot filling produce more consistent results than open-ended generation -5. **Logging Infrastructure**: Comprehensive GPT call logging enables better evaluation and prompt iteration - -## Repository Stats -- **Languages**: 68.9% Jupyter Notebook, 30.5% Python, 0.6% Shell -- **Contributors**: 3 (sahilshah379, Syzygianinfern0, harshg99) -- **Last Update**: February 5, 2026 (very recent) -- **Size**: ~15-20 Python files, comprehensive prompt library - -This reference captures the key algorithmic insights without needing to maintain the full repository locally. \ No newline at end of file diff --git a/meva/scripts/v6/__init__.py b/meva/scripts/archived/v6/__init__.py similarity index 100% rename from meva/scripts/v6/__init__.py rename to meva/scripts/archived/v6/__init__.py diff --git a/meva/scripts/v6/build_scene_graph.py b/meva/scripts/archived/v6/build_scene_graph.py similarity index 100% rename from meva/scripts/v6/build_scene_graph.py rename to meva/scripts/archived/v6/build_scene_graph.py diff --git a/meva/scripts/v6/distractor_bank.py b/meva/scripts/archived/v6/distractor_bank.py similarity index 100% rename from meva/scripts/v6/distractor_bank.py rename to meva/scripts/archived/v6/distractor_bank.py diff --git a/meva/scripts/v6/entity_resolution.py b/meva/scripts/archived/v6/entity_resolution.py similarity index 100% rename from meva/scripts/v6/entity_resolution.py rename to meva/scripts/archived/v6/entity_resolution.py diff --git a/meva/scripts/v6/generate_perception.py b/meva/scripts/archived/v6/generate_perception.py similarity index 100% rename from meva/scripts/v6/generate_perception.py rename to meva/scripts/archived/v6/generate_perception.py diff --git a/meva/scripts/v6/generate_spatial.py b/meva/scripts/archived/v6/generate_spatial.py similarity index 100% rename from meva/scripts/v6/generate_spatial.py rename to meva/scripts/archived/v6/generate_spatial.py diff --git a/meva/scripts/v6/generate_temporal.py b/meva/scripts/archived/v6/generate_temporal.py similarity index 100% rename from meva/scripts/v6/generate_temporal.py rename to meva/scripts/archived/v6/generate_temporal.py diff --git a/meva/scripts/v6/parse_annotations.py b/meva/scripts/archived/v6/parse_annotations.py similarity index 100% rename from meva/scripts/v6/parse_annotations.py rename to meva/scripts/archived/v6/parse_annotations.py diff --git a/meva/scripts/v6/run_pipeline.py b/meva/scripts/archived/v6/run_pipeline.py similarity index 100% rename from meva/scripts/v6/run_pipeline.py rename to meva/scripts/archived/v6/run_pipeline.py diff --git a/meva/scripts/v6/utils/__init__.py b/meva/scripts/archived/v6/utils/__init__.py similarity index 100% rename from meva/scripts/v6/utils/__init__.py rename to meva/scripts/archived/v6/utils/__init__.py diff --git a/meva/scripts/v6/utils/iou.py b/meva/scripts/archived/v6/utils/iou.py similarity index 100% rename from meva/scripts/v6/utils/iou.py rename to meva/scripts/archived/v6/utils/iou.py diff --git a/meva/scripts/v6/utils/krtd.py b/meva/scripts/archived/v6/utils/krtd.py similarity index 100% rename from meva/scripts/v6/utils/krtd.py rename to meva/scripts/archived/v6/utils/krtd.py diff --git a/meva/scripts/v6/utils/mevid.py b/meva/scripts/archived/v6/utils/mevid.py similarity index 100% rename from meva/scripts/v6/utils/mevid.py rename to meva/scripts/archived/v6/utils/mevid.py diff --git a/meva/scripts/v6/utils/yaml_stream.py b/meva/scripts/archived/v6/utils/yaml_stream.py similarity index 100% rename from meva/scripts/v6/utils/yaml_stream.py rename to meva/scripts/archived/v6/utils/yaml_stream.py diff --git a/meva/scripts/archived/v7/__init__.py b/meva/scripts/archived/v7/__init__.py new file mode 100644 index 0000000..f754dcd --- /dev/null +++ b/meva/scripts/archived/v7/__init__.py @@ -0,0 +1 @@ +"""V8 QA generation pipeline for MEVA multi-camera dataset.""" diff --git a/meva/scripts/archived/v7/activity_hierarchy.py b/meva/scripts/archived/v7/activity_hierarchy.py new file mode 100644 index 0000000..2c15048 --- /dev/null +++ b/meva/scripts/archived/v7/activity_hierarchy.py @@ -0,0 +1,197 @@ +""" +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.""" + # 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", +} + + +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' + """ + base = humanize_activity(activity) # e.g. 'opens facility door' + words = base.split() + if words: + first = words[0] + gerund = _GERUND_MAP.get(first, first + "ing") + 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: + rest = "a " + rest + result = f"{gerund} {rest}".strip() if rest else gerund + return result.capitalize() + return base.capitalize() diff --git a/meva/scripts/archived/v7/build_scene_graph.py b/meva/scripts/archived/v7/build_scene_graph.py new file mode 100644 index 0000000..f605caf --- /dev/null +++ b/meva/scripts/archived/v7/build_scene_graph.py @@ -0,0 +1,260 @@ +""" +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 + + +# ============================================================================ +# 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, actor_ids in entity_actor_ids.items(): + 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 + + entities[entity_id] = 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, []), + ) + + # 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/archived/v7/distractor_bank.py b/meva/scripts/archived/v7/distractor_bank.py new file mode 100644 index 0000000..a744c29 --- /dev/null +++ b/meva/scripts/archived/v7/distractor_bank.py @@ -0,0 +1,91 @@ +""" +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) + return pool[:n] diff --git a/meva/scripts/archived/v7/entity_resolution.py b/meva/scripts/archived/v7/entity_resolution.py new file mode 100644 index 0000000..3cb80a6 --- /dev/null +++ b/meva/scripts/archived/v7/entity_resolution.py @@ -0,0 +1,298 @@ +""" +V7 entity_resolution.py — Step 3: Cross-camera entity linking. + +Uses MEVID ground truth person IDs + heuristic temporal handoff 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. +""" + +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 + + +# ============================================================================ +# Combined Entity Resolution +# ============================================================================ + +def resolve_entities(sg: SceneGraph, verbose: bool = False) -> ResolvedGraph: + """ + Run entity resolution: MEVID ground truth + heuristic temporal handoff. + + Args: + sg: Scene graph from build_scene_graph + verbose: Print progress + + 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. 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/archived/v7/extract_entity_descriptions.py b/meva/scripts/archived/v7/extract_entity_descriptions.py new file mode 100644 index 0000000..36ac8d8 --- /dev/null +++ b/meva/scripts/archived/v7/extract_entity_descriptions.py @@ -0,0 +1,695 @@ +#!/usr/bin/env python3 +""" +V8 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 MP4 video + 3. Runs YOLO color analysis on each crop (upper/lower body colors, carried objects) + 4. Aggregates via majority vote across crops + 5. Generates template description: "a person in blue top and black pants carrying a backpack" + +This gives EVERY entity a visual description (not just the 10% with MEVID matches), +solving the temporal disambiguation problem where "a person enters scene" is ambiguous +when there are 100+ such events. + +Cost: $0 (all local, no API calls) +Time: ~3-4 min per slot (mostly video decode + YOLO inference) + +Usage: + python3 scripts/v8/extract_entity_descriptions.py --slot 2018-03-11.11-25-00.school -v + python3 scripts/v8/extract_entity_descriptions.py --slot 2018-03-11.11-25-00.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") +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) +OUTPUT_DIR = Path("/home/ah66742/data/entity_descriptions") + +# ============================================================================ +# Constants +# ============================================================================ + +CROPS_PER_ACTOR = 5 # Crops to extract per actor track +MIN_BBOX_HEIGHT = 25 # Min bbox height in pixels for usable crop +MIN_BBOX_WIDTH = 10 # Min bbox width +YOLO_CONF = 0.25 # YOLO detection confidence threshold +YOLO_MODEL = "yolov8n.pt" # Nano model (fast, sufficient for crops) + +# 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) +# ============================================================================ + +_GEOM_RE = re.compile( + r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\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). + """ + actors = defaultdict(dict) + with open(geom_path) as f: + for line in f: + m = _GEOM_RE.search(line) + if not m: + continue + actor_id = int(m.group(1)) + frame = int(m.group(2)) + bbox = (int(m.group(3)), int(m.group(4)), + int(m.group(5)), int(m.group(6))) + 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: + indices = np.linspace(0, len(frames) - 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 color name.""" + if s < 40: + if v < 60: + return "black" + elif v < 150: + return "gray" + else: + return "white" + if v < 40: + return "black" + if h < 10 or h > 170: + return "red" + elif h < 22: + return "orange" + elif h < 35: + return "yellow" + elif h < 78: + return "green" + elif h < 131: + return "blue" + elif h < 155: + return "purple" + elif h <= 170: + 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] + + +# ============================================================================ +# 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} + + +# ============================================================================ +# Description Generation (template-based, free) +# ============================================================================ + +def build_description(attrs: Dict) -> str: + """ + Build a natural description from structured attributes. + + Examples: + {"upper_color": "blue", "lower_color": "black", "carried_objects": ["backpack"]} + → "a person in a blue top and black pants carrying a backpack" + + {"upper_color": "gray", "lower_color": "green", "carried_objects": []} + → "a person in a gray top and green pants" + """ + parts = [] + + upper = attrs.get("upper_color", "unknown") + lower = attrs.get("lower_color", "unknown") + + if upper != "unknown" and lower != "unknown": + parts.append(f"a person in a {upper} top and {lower} pants") + elif upper != "unknown": + parts.append(f"a person in a {upper} top") + elif lower != "unknown": + parts.append(f"a person in {lower} pants") + else: + parts.append("a person") + + objects = attrs.get("carried_objects", []) + if objects: + obj_str = " and ".join(objects[:2]) # Max 2 objects + parts[0] += f" carrying a {obj_str}" + + return parts[0] + + +# ============================================================================ +# Slot Processing Pipeline +# ============================================================================ + +def find_slot_files(slot: str) -> List[Dict]: + """ + Find geom.yml + video pairs for a slot. + 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}. + """ + # Parse slot: "2018-03-11.11-25-00.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] + site = parts[2] + + hour = time_part.split("-")[0] # e.g. "11" from "11-25-00" + + kitware_dir = KITWARE_BASE / date / hour + avi_dir = AVI_BASE / date / hour + mp4_dir = MP4_BASE / date / hour + + if not kitware_dir.is_dir(): + raise FileNotFoundError(f"Kitware dir not found: {kitware_dir}") + + # Find geom files matching slot pattern + prefix = f"{date}.{time_part}" + results = [] + + 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, + }) + + return results + + +def process_slot(slot: str, use_yolo: bool = True, + verbose: bool = False) -> Dict: + """ + Full pipeline: extract descriptions for all actors in a slot. + + Returns dict ready for JSON output: + { + "slot": "...", + "cameras": {...}, + "actors": {actor_id_str: {camera, upper_color, lower_color, objects, 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") + + if use_yolo: + if verbose: + print(f" Loading YOLO model...", end="", flush=True) + _get_yolo() + 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 + t1 = time.time() + crops_by_actor = extract_crops( + video_path, actors, + max_crops=CROPS_PER_ACTOR, + min_h=MIN_BBOX_HEIGHT, min_w=MIN_BBOX_WIDTH, + ) + decode_time = time.time() - t1 + + usable = len(crops_by_actor) + total_crops = sum(len(c) for c in crops_by_actor.values()) + 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 + + if use_yolo: + attrs = analyze_crops_yolo(crops) + else: + attrs = analyze_crops_color_only(crops) + + desc = build_description(attrs) + + # Store by camera_actorID (matching V8 entity ID format) + entity_key = f"{cam}_actor_{actor_id}" + all_actors[entity_key] = { + "actor_id": actor_id, + "camera": cam, + "upper_color": attrs.get("upper_color", "unknown"), + "lower_color": attrs.get("lower_color", "unknown"), + "carried_objects": attrs.get("carried_objects", []), + "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: + print(f" Analysis: {analyze_time:.1f}s ({'YOLO' if use_yolo else 'color-only'})") + + cam_stats[cam] = { + "actors": len(actors), + "usable": usable, + "total_crops": total_crops, + "decode_sec": round(decode_time, 1), + "analyze_sec": round(analyze_time, 1), + } + + 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": "yolo" if use_yolo else "color_only", + "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="V8 Entity Description Extractor — Geom + Video → YOLO descriptions", + ) + parser.add_argument("--slot", "-s", required=True, + help="Slot to process (e.g., 2018-03-11.11-25-00.school)") + parser.add_argument("--no-yolo", action="store_true", + help="Color-only analysis (no YOLO, faster but no carried objects)") + parser.add_argument("--dry-run", action="store_true", + help="Show what would be processed without extracting") + parser.add_argument("--output", "-o", + help="Output path (default: data/entity_descriptions/{slot}.json)") + parser.add_argument("--verbose", "-v", action="store_true") + + 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 = 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)) + vp = cf["video_path"] + has_video = vp and vp.exists() + vfmt = cf.get("video_format", "none") + print(f" {cam}: {len(actors)} actors, {usable} usable, video={'YES' if has_video else 'NO'} ({vfmt})") + return + + result = process_slot(args.slot, use_yolo=not args.no_yolo, 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 ...{str(info['actor_id'])[-6:]}: {desc}") + if len(seen) >= 10: + break + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/archived/v7/generate_perception.py b/meva/scripts/archived/v7/generate_perception.py new file mode 100644 index 0000000..f65751b --- /dev/null +++ b/meva/scripts/archived/v7/generate_perception.py @@ -0,0 +1,437 @@ +""" +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, V7 logic) + # ------------------------------------------------------------------ + 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 + + correct_cam = sorted(correct_cams)[0] + distractors = get_camera_distractors([correct_cam], all_cameras, rng, n=3) + + if len(distractors) < 2: + continue + + options = [correct_cam] + distractors[:3] + rng.shuffle(options) + correct_idx = options.index(correct_cam) + + human_act = humanize_activity(act) + question = f"Which camera captures a {human_act} event?" + + rep_event = 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), + } + 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", "") + + # Frame the question: show the person on a camera, ask about attribute + if attr_opts["attribute_type"] == "upper_color": + question = ( + f"A person is visible on camera {cam}. " + 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 {sorted_cams[0]} and {sorted_cams[1]}" + option_a_only = f"{sorted_cams[0]} only" + option_b_only = f"{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/archived/v7/generate_reidentification.py b/meva/scripts/archived/v7/generate_reidentification.py new file mode 100644 index 0000000..dbc0ec8 --- /dev/null +++ b/meva/scripts/archived/v7/generate_reidentification.py @@ -0,0 +1,215 @@ +""" +V8 generate_reidentification.py — Cross-camera person re-identification questions. + +NEW in V8: Questions that test whether a model can identify the same person +appearing on different cameras within the same slot. + +Uses MEVID ground-truth person IDs to know which persons appear on which cameras, +combined with YOLO+GPT descriptions to make questions answerable from visual features. + +Question types: + 1. "Does the person in [description] on camera A also appear on camera B?" + 2. "Which camera also shows [description person]?" + 3. "A person is seen on camera A wearing [X]. On camera B, which person matches?" +""" + +import random +from typing import Any, Dict, List, Optional, Set +from collections import defaultdict + +from .build_scene_graph import SceneGraph +from .entity_resolution import ResolvedGraph +from .person_descriptions import ( + get_person_description, get_person_short_label, + get_mevid_persons_with_cameras, load_person_database, +) +from .activity_hierarchy import humanize_activity +from .distractor_bank import get_camera_distractors + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_reidentification_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate cross-camera re-identification questions. + + These require recognizing that the same person appears on multiple cameras + based on their visual appearance (clothing, carried objects, etc.). + + Selection: + - Only uses MEVID persons (known cross-camera ground truth) + - Prefers persons with distinctive descriptions + - Requires person to appear on 2+ cameras in the slot + """ + slot = sg.slot + all_cameras = sorted(sg.cameras.keys()) + + # Get MEVID persons for this slot with their cameras + person_cameras = get_mevid_persons_with_cameras(slot) + + if not person_cameras: + if verbose: + print(" Re-ID: No MEVID persons in slot — skipping") + return [] + + # Filter to persons on 2+ cameras AND with good descriptions + db = load_person_database() + persons_data = db.get("persons", {}) + + candidates = [] + for pid, cameras in person_cameras.items(): + if len(cameras) < 2: + continue + pdata = persons_data.get(pid, {}) + has_gpt = bool(pdata.get("gpt_description")) + has_colors = (pdata.get("primary_upper_color", "unknown") != "unknown") + + if has_gpt or has_colors: + candidates.append({ + "person_id": pid, + "cameras": sorted(cameras), + "has_gpt": has_gpt, + "description": get_person_description(pid), + "short_label": get_person_short_label(pid), + "upper_color": pdata.get("primary_upper_color", "unknown"), + "lower_color": pdata.get("primary_lower_color", "unknown"), + "objects": pdata.get("all_carried_objects", []), + }) + + # Sort: prefer GPT descriptions, then by number of cameras (more = better) + candidates.sort(key=lambda c: (-int(c["has_gpt"]), -len(c["cameras"]))) + + if verbose: + print(f" Re-ID: {len(candidates)} MEVID persons with 2+ cameras and descriptions") + + if not candidates: + return [] + + qa_pairs = [] + used_persons = set() + + # Type 1: "Does person X on camera A also appear on camera B?" (Yes/No with camera options) + for cand in candidates: + if len(qa_pairs) >= count: + break + if cand["person_id"] in used_persons: + continue + + pid = cand["person_id"] + cams = cand["cameras"] + desc = cand["description"] + + # Pick two cameras this person appears on + cam_pair = rng.sample(cams, min(2, len(cams))) + cam_a, cam_b = cam_pair[0], cam_pair[1] if len(cam_pair) > 1 else cam_pair[0] + + if cam_a == cam_b: + continue + + # Find cameras where this person does NOT appear (for distractors) + other_cams = [c for c in all_cameras if c not in cams] + + if len(qa_pairs) % 2 == 0: + # Type 1: "Which camera also shows this person?" + distractors = rng.sample(other_cams, min(2, len(other_cams))) if other_cams else [] + if not distractors: + # Use all cameras as options if needed + distractors = [c for c in all_cameras if c != cam_a and c != cam_b][:2] + + options = [cam_b] + distractors + ["None of these cameras"] + rng.shuffle(options[:3]) # Shuffle camera options but keep "None" at end + correct_idx = options.index(cam_b) + + question = ( + f"On camera {cam_a}, {desc} is visible. " + f"Which other camera also shows this same person?" + ) + + debug_info = { + "question_type": "which_camera_reid", + "mevid_person_id": pid, + "source_camera": cam_a, + "target_camera": cam_b, + "all_person_cameras": cams, + "person_description": desc, + } + + qa = { + "question_id": f"v8_reid_{len(qa_pairs)+1:03d}", + "category": "re_identification", + "difficulty": "medium", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted([cam_a, cam_b]), + "requires_multi_camera": True, + "verification": { + "question_type": "which_camera_reid", + "mevid_person_id": pid, + "person_description": desc, + "source_camera": cam_a, + "correct_target_camera": cam_b, + "all_person_cameras": cams, + }, + "debug_info": debug_info, + } + else: + # Type 2: "Is the person in [desc] on camera A the same as person on camera B?" + # Correct answer: Yes (same MEVID person) + question = ( + f"{desc.capitalize()} is observed on camera {cam_a}. " + f"Is this the same person visible on camera {cam_b}?" + ) + + 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_idx = 0 + + debug_info = { + "question_type": "same_person_confirmation", + "mevid_person_id": pid, + "camera_a": cam_a, + "camera_b": cam_b, + "all_person_cameras": cams, + "person_description": desc, + } + + qa = { + "question_id": f"v8_reid_{len(qa_pairs)+1:03d}", + "category": "re_identification", + "difficulty": "medium", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted([cam_a, cam_b]), + "requires_multi_camera": True, + "verification": { + "question_type": "same_person_confirmation", + "mevid_person_id": pid, + "person_description": desc, + "camera_a": cam_a, + "camera_b": cam_b, + "all_person_cameras": cams, + "ground_truth": "same_person", + }, + "debug_info": debug_info, + } + + qa_pairs.append(qa) + used_persons.add(pid) + + if verbose: + print(f" Re-ID: {len(qa_pairs)} questions generated") + + return qa_pairs[:count] diff --git a/meva/scripts/archived/v7/generate_scene_summary.py b/meva/scripts/archived/v7/generate_scene_summary.py new file mode 100644 index 0000000..b3083d5 --- /dev/null +++ b/meva/scripts/archived/v7/generate_scene_summary.py @@ -0,0 +1,269 @@ +""" +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" + + if top_3: + top_act = humanize_activity(top_3[0][0]) + correct_desc = ( + f"A {scene_type} scene across {n_cams} cameras, " + f"primarily featuring {top_act} activity" + ) + else: + correct_desc = f"A {scene_type} scene across {n_cams} cameras" + + # 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" + ) + + wrong_descs.append( + f"An empty scene with minimal activity, captured on {max(1, n_cams - 3)} 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} camera feeds in this slot, " + f"which description best characterizes the overall scene?" + ) + + debug_info = { + "question_type": "scene_characterization", + "scene_analysis": analysis, + "scene_description": description, + "scene_type": scene_type, + } + + 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"], + }, + } + 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/archived/v7/generate_spatial.py b/meva/scripts/archived/v7/generate_spatial.py new file mode 100644 index 0000000..6650d4d --- /dev/null +++ b/meva/scripts/archived/v7/generate_spatial.py @@ -0,0 +1,263 @@ +""" +V8 generate_spatial.py — Spatial entity distance questions with MEVID descriptions. + +V8 CHANGES from V7: +- Entity descriptions from MEVID (GPT/YOLO) instead of actor ID aliases +- Questions use natural person descriptions for spatial references +""" + +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, DEFAULT_FRAMERATE +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .person_descriptions import enrich_entities +from .activity_hierarchy import humanize_activity +from .utils.krtd import ( + load_camera_model, CameraModel, compute_entity_distance, + classify_proximity, INDOOR_CAMERAS, +) +from .utils.yaml_stream import get_bbox_at_frame + +DEFAULT_FPS = 30.0 + + +# ============================================================================ +# Spatial Candidate Finding (from V7, unchanged logic) +# ============================================================================ + +def _find_spatial_candidates(sg: SceneGraph, verbose: bool = False) -> List[Dict]: + """Find entity pairs with valid KRTD projections for spatial questions.""" + candidates = [] + + 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") + + clips = find_clips_for_slot(sg.slot) + clip_by_camera = {c["camera_id"]: c for c in clips} + + entity_positions: Dict[str, Dict] = {} + + for eid, entity in sg.entities.items(): + if entity.camera_id not in camera_models: + continue + if entity.entity_type != "person": + continue + + model = camera_models[entity.camera_id] + mid_frame = (entity.first_frame + entity.last_frame) // 2 + + bbox = None + if entity.keyframe_bboxes: + closest_frame = min(entity.keyframe_bboxes.keys(), + key=lambda f: abs(int(f) - mid_frame)) + bbox = entity.keyframe_bboxes[closest_frame] + + if bbox is None and entity.camera_id in clip_by_camera: + clip = clip_by_camera[entity.camera_id] + geom_path = Path(clip["activities_file"]).with_name( + Path(clip["activities_file"]).name.replace(".activities.yml", ".geom.yml") + ) + if geom_path.exists(): + bbox = get_bbox_at_frame(geom_path, entity.actor_id, mid_frame, tolerance=15) + + if bbox is None: + continue + + pos = model.bbox_foot_to_world(bbox) + if pos is None: + continue + + entity_positions[eid] = { + "position": pos, + "frame": mid_frame, + "bbox": bbox, + "camera_id": entity.camera_id, + "entity": entity, + } + + if verbose: + print(f" Spatial: {len(entity_positions)} entities with 3D positions") + + entity_ids = sorted(entity_positions.keys()) + for i in range(len(entity_ids)): + for j in range(i + 1, len(entity_ids)): + eid_a = entity_ids[i] + eid_b = entity_ids[j] + + pos_a = entity_positions[eid_a] + pos_b = entity_positions[eid_b] + + distance = float(np.linalg.norm(pos_a["position"] - pos_b["position"])) + + if distance > 500: + continue + + proximity = classify_proximity(distance) + + candidates.append({ + "entity_a": eid_a, + "entity_b": eid_b, + "camera_a": pos_a["camera_id"], + "camera_b": pos_b["camera_id"], + "position_a": pos_a["position"].tolist(), + "position_b": pos_b["position"].tolist(), + "bbox_a": pos_a["bbox"], + "bbox_b": pos_b["bbox"], + "frame_a": pos_a["frame"], + "frame_b": pos_b["frame"], + "distance_m": round(distance, 2), + "proximity": proximity, + "entity_a_obj": pos_a["entity"], + "entity_b_obj": pos_b["entity"], + }) + + return candidates + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_spatial_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate spatial entity distance questions with MEVID descriptions. + + V8: Uses entity_descs for person descriptions instead of actor ID aliases. + """ + candidates = _find_spatial_candidates(sg, verbose) + + if verbose: + print(f" Spatial: {len(candidates)} candidate pairs") + + if not candidates: + return [] + + # Sort by distance diversity + 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"] + + rng.shuffle(near) + rng.shuffle(moderate) + rng.shuffle(far) + + selected = [] + for bucket in [near, moderate, far]: + if bucket and len(selected) < count: + selected.append(bucket[0]) + + remaining = near[1:] + moderate[1:] + far[1:] + rng.shuffle(remaining) + for c in remaining: + if len(selected) >= count: + break + selected.append(c) + + qa_pairs = [] + + for idx, cand in enumerate(selected[:count]): + ent_a = cand["entity_a_obj"] + ent_b = cand["entity_b_obj"] + proximity = cand["proximity"] + distance = cand["distance_m"] + + is_cross_camera = cand["camera_a"] != cand["camera_b"] + + # V8: Use MEVID descriptions + desc_a = entity_descs.get(cand["entity_a"], f"a person on camera {cand['camera_a']}") + desc_b = entity_descs.get(cand["entity_b"], f"a person on camera {cand['camera_b']}") + + if is_cross_camera: + question = ( + f"In the scene, how far apart are {desc_a} on camera {cand['camera_a']} " + f"and {desc_b} on camera {cand['camera_b']}?" + ) + else: + question = ( + f"How close are {desc_a} and {desc_b} " + f"in the scene visible on camera {cand['camera_a']}?" + ) + + 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", + ] + + if proximity == "near": + correct_idx = 0 + elif proximity == "moderate": + correct_idx = 1 + else: + correct_idx = 2 + + debug_info = { + "entity_a": { + "entity_id": cand["entity_a"], + "camera": cand["camera_a"], + "description": desc_a, + "bbox": cand["bbox_a"], + "frame": cand["frame_a"], + "timestamp": f"{ent_a.first_sec:.2f}-{ent_a.last_sec:.2f}s", + "world_pos_enu": cand["position_a"], + }, + "entity_b": { + "entity_id": cand["entity_b"], + "camera": cand["camera_b"], + "description": desc_b, + "bbox": cand["bbox_b"], + "frame": cand["frame_b"], + "timestamp": f"{ent_b.first_sec:.2f}-{ent_b.last_sec:.2f}s", + "world_pos_enu": cand["position_b"], + }, + "distance_meters": distance, + "proximity": proximity, + "projection_method": "krtd_bbox_foot", + } + + qa = { + "question_id": f"v8_spatial_{idx+1:03d}", + "category": "spatial", + "difficulty": "easy", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted(set([cand["camera_a"], cand["camera_b"]])), + "requires_multi_camera": is_cross_camera, + "verification": { + "entity_a": cand["entity_a"], + "entity_b": cand["entity_b"], + "entity_a_desc": desc_a, + "entity_b_desc": desc_b, + "world_pos_a_enu": cand["position_a"], + "world_pos_b_enu": cand["position_b"], + "distance_meters": distance, + "proximity": proximity, + "projection_method": "krtd_bbox_foot", + }, + "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/archived/v7/generate_temporal.py b/meva/scripts/archived/v7/generate_temporal.py new file mode 100644 index 0000000..d9e68ed --- /dev/null +++ b/meva/scripts/archived/v7/generate_temporal.py @@ -0,0 +1,336 @@ +""" +V8 generate_temporal.py — Temporal cross-camera questions with MEVID person descriptions. + +V8 CHANGES from V7: +- Entity aliases replaced with MEVID person descriptions when available +- Questions prioritize events involving described persons +- Description-enriched question text for better naturalization +""" + +import random +from pathlib import Path +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 .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, +) +from .utils.mevid import find_mevid_persons_for_slot + +# ============================================================================ +# Constants +# ============================================================================ + +MIN_GAP = 3.0 +MAX_GAP = 15.0 +FALLBACK_MAX_GAP = 20.0 +DEFAULT_FPS = 30.0 + + +# ============================================================================ +# Connection Scoring (from V7, unchanged) +# ============================================================================ + +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 events.""" + score = 0.0 + connection_type = "unrelated" + connection_strength = "weak" + mevid_validated = False + mevid_person_id = None + relationship = None + cluster_id = None + + for cluster in resolved.entity_clusters: + a_entities = set() + b_entities = set() + for eid in cluster.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" + connection_strength = "strong" + score += 3.0 + cluster_id = cluster.cluster_id + break + + rel = get_relationship(event_a.activity, event_b.activity) + if rel: + relationship = rel + rel_score = get_relationship_strength(event_a.activity, event_b.activity) + score += rel_score * 2.0 + if connection_type == "unrelated": + connection_type = f"related_activities_{rel}" + connection_strength = "medium" if rel_score >= 0.7 else "weak" + + 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 + score += 1.5 + break + + if event_a.activity != event_b.activity: + score += 0.5 + + 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, + } + + +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]) -> str: + """Get a human-readable description for an event, using MEVID if available.""" + # Try to get MEVID description for the entity involved + 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: + short_act = humanize_activity(event.activity) + return f"{desc} {short_act} on camera {event.camera_id}" + + # Fallback: V7-style + short_act = humanize_activity(event.activity) + return f"someone {short_act} on camera {event.camera_id}" + + +# ============================================================================ +# 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.""" + 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) + + 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 generate_temporal_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate temporal cross-camera questions with MEVID person descriptions. + + V8 changes: + - entity_descs parameter provides MEVID descriptions + - Question text uses natural person descriptions instead of actor IDs + - Prioritizes events involving described persons + """ + slot_cameras = list(sg.cameras.keys()) + mevid_person_cameras = find_mevid_persons_for_slot(sg.slot, slot_cameras) + + 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 [] + + # Diversify selection, preferring MEVID-validated + described events + used_pairs = set() + used_activities = set() + selected = [] + + # First pass: prefer MEVID-validated + for c in candidates: + if c["mevid_validated"] and len(selected) < count: + cam_pair = (c["event_a"].camera_id, c["event_b"].camera_id) + act_pair = (c["event_a"].activity, c["event_b"].activity) + if cam_pair not in used_pairs or act_pair not in used_activities: + used_pairs.add(cam_pair) + used_activities.add(act_pair) + selected.append(c) + + # Fill remaining from any candidates + for c in candidates: + if len(selected) >= count: + break + if c not in selected: + selected.append(c) + + # Generate QA pairs + 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) + desc_b = _get_event_description(eb, sg, entity_descs) + + question = f"{desc_a} and {desc_b} -- which occurred first?" + + options = [ + f"The event on camera {ea.camera_id} occurred first", + f"The event on camera {eb.camera_id} 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"The event on camera {eb.camera_id} occurred first", + f"The event on camera {ea.camera_id} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + correct_idx = 1 + + 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"), + "mevid_validated": cand["mevid_validated"], + "mevid_person_id": cand["mevid_person_id"], + } + + qa = { + "question_id": f"v8_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: + mevid_count = sum(1 for q in qa_pairs + if q["debug_info"]["mevid_validated"]) + print(f" Temporal: {len(qa_pairs)} questions ({mevid_count} MEVID-validated)") + + return qa_pairs diff --git a/meva/scripts/archived/v7/naturalize_v8_qa.py b/meva/scripts/archived/v7/naturalize_v8_qa.py new file mode 100644 index 0000000..48f92e7 --- /dev/null +++ b/meva/scripts/archived/v7/naturalize_v8_qa.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +""" +V8 GPT Naturalization Wrapper — Converts V8 template QA pairs into natural language. + +V8 CHANGES from V7: +- New category examples: re_identification, scene_summary, attribute_verification +- Updated system prompt for MEVID person descriptions +- Updated cost estimates for 5 categories (~9 Qs/slot) +- Output version: v8_natural + +Usage: + python3 scripts/v8/naturalize_v8_qa.py --input data/qa_pairs/SLOT.v8.json --dry-run + python3 scripts/v8/naturalize_v8_qa.py --input data/qa_pairs/SLOT.v8.json +""" + +import json +import time +import argparse +import os +import sys +from pathlib import Path +from typing import List, Dict, Any, Optional + +# ============================================================================ +# Paths & Constants +# ============================================================================ + +QA_DIR = Path("/home/ah66742/data/qa_pairs") +LOG_DIR = Path("/home/ah66742/data/gpt_logs") + +DEFAULT_MODEL = "gpt-4o-mini" +DEFAULT_TEMPERATURE = 0.7 +MAX_RETRIES = 3 +RETRY_DELAY = 2.0 + +# ============================================================================ +# System Prompt — V8 with MEVID person descriptions +# ============================================================================ + +SYSTEM_PROMPT = """\ +You are a question naturalizer for a multi-camera surveillance video understanding benchmark. + +Your task is to rephrase template questions and options into fluent, natural English suitable +for a Video Question Answering (VQA) evaluation. The questions are about activities observed +across multiple synchronized surveillance cameras at the same location. + +IMPORTANT for V8: Questions now include person descriptions derived from visual analysis +(e.g., "the person in a dark hooded jacket and dark pants"). Preserve these descriptions +naturally — they are key identifiers that replace debug markers from earlier versions. + +Rules: +1. Rephrase the question to sound natural and conversational +2. Rephrase each option to sound natural, keeping the SAME meaning and order +3. REMOVE any remaining debug markers: strip actor IDs (#NNN), timestamps (@ Ns), frame refs +4. KEEP person descriptions (e.g., "the person in a blue jacket") — these are meaningful +5. Keep camera identifiers (e.g., "camera G299") — these are meaningful +6. Keep spatial terms (near, moderate, far, meters) unchanged +7. Keep "simultaneously" and "cannot be determined" as-is +8. Do NOT add information not present in the template +9. Do NOT reorder the options +10. For perception questions: keep camera IDs as-is in options +11. For re-identification questions: preserve person appearance descriptions precisely +12. For scene summary questions: keep activity type names and camera references +13. Add a brief 1-sentence "reasoning" explaining why the correct answer is right + (based ONLY on the information given, not external knowledge) + +Output format — respond with ONLY a JSON object: +{ + "question": "The naturalized question text", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "Brief explanation of why the answer is correct" +} +""" + +# ============================================================================ +# Category-specific prompt examples (few-shot) +# ============================================================================ + +CATEGORY_EXAMPLES = { + "temporal": { + "hint": "This is a temporal ordering question about which event happened first across different cameras. The entities may have appearance descriptions — preserve those naturally.", + "example_input": 'In the scene, the person in a dark hooded jacket opening the trunk on camera G328 and the person in blue jeans exiting the vehicle on camera G421 -- which occurred first?', + "example_output": '{"question": "Between the person in a dark hooded jacket opening the trunk on camera G328 and the person in blue jeans exiting the vehicle on camera G421, which event happened first?", "options": ["The person in the dark hooded jacket opening the trunk occurred first", "The person in blue jeans exiting the vehicle occurred first", "They occurred simultaneously", "Cannot be determined"], "reasoning": "The trunk opening event started earlier, with a 7-second gap before the vehicle exit."}', + }, + "spatial": { + "hint": "This is a spatial distance question about how far apart two people are. Person descriptions should be preserved naturally.", + "example_input": 'In the scene, how far apart are the person in a blue jacket on camera G328 and the person in dark pants on camera G421?', + "example_output": '{"question": "How far apart are the person in a blue jacket visible on camera G328 and the person in dark pants seen on camera G421?", "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": "Based on their positions in the scene, these two individuals are approximately 8 meters apart."}', + }, + "perception": { + "hint": "This is a perception question about activities or attributes. For attribute verification, the question asks about a person's clothing or carried objects.", + "example_input": 'A person is visible on camera G328. What color is their upper body clothing?', + "example_output": '{"question": "What color clothing is the person on camera G328 wearing on their upper body?", "options": ["Blue", "Black", "Red", "White"], "reasoning": "Visual analysis of the person on camera G328 shows they are wearing blue upper body clothing."}', + }, + "re_identification": { + "hint": "This is a person re-identification question asking whether the same person appears on multiple cameras. Preserve appearance descriptions precisely — they are the key evidence.", + "example_input": 'A person described as \"wearing a dark hooded jacket and dark pants\" appears on camera G328. Which other camera also shows this person?', + "example_output": '{"question": "The person wearing a dark hooded jacket and dark pants is seen on camera G328. Which other camera also captures this same person?", "options": ["G421", "G299", "G330", "G336"], "reasoning": "The same individual in the dark hooded jacket and dark pants is identified on both camera G328 and camera G421 based on appearance matching."}', + }, + "scene_summary": { + "hint": "This is a scene-level summary question about overall activity patterns or camera utilization. Keep statistical terms and camera references.", + "example_input": 'What best characterizes the overall scene activity across all cameras during this time period?', + "example_output": '{"question": "How would you best characterize the overall activity patterns observed across all cameras during this time period?", "options": ["Predominantly pedestrian activity", "Mixed pedestrian and vehicle activity", "Predominantly vehicle activity", "Minimal observable activity"], "reasoning": "The majority of annotated events involve pedestrian activities across the cameras, with only a small fraction being vehicle-related."}', + }, +} + + +# ============================================================================ +# GPT Client +# ============================================================================ + +def _create_client(): + """Create OpenAI client.""" + import openai + return openai.OpenAI() + + +def _naturalize_one(client, question: Dict, model: str, + temperature: float) -> Optional[Dict]: + """ + Send one question to GPT for naturalization. + Returns {naturalized_question, naturalized_options, reasoning, usage} or None. + """ + category = question["category"] + template = question["question_template"] + options = question["options"] + verification = question.get("verification", {}) + + # Use subcategory if available (e.g., attribute_verification) + lookup_cat = question.get("subcategory", category) + cat_info = CATEGORY_EXAMPLES.get(lookup_cat, CATEGORY_EXAMPLES.get(category, {})) + hint = cat_info.get("hint", "Rephrase this question naturally.") + example_in = cat_info.get("example_input", "") + example_out = cat_info.get("example_output", "") + + user_message = f"""Category: {category} +{hint} + +""" + if example_in and example_out: + user_message += f"""Example: + Input: {example_in} + Output: {example_out} + +""" + + user_message += f"""Now naturalize this question: + +Template: {template} + +Options: +""" + for i, opt in enumerate(options): + user_message += f" {chr(65+i)}) {opt}\n" + + # Add verification context for reasoning + if category == "temporal" and "gap_sec" in verification: + user_message += f"\nContext: The gap between events is {verification['gap_sec']}s.\n" + elif category == "spatial" and "distance_meters" in verification: + user_message += f"\nContext: Distance is {verification['distance_meters']}m.\n" + elif category == "re_identification": + user_message += f"\nContext: Person identified via MEVID cross-camera matching.\n" + + user_message += "\nRespond with ONLY the JSON object." + + for attempt in range(MAX_RETRIES): + try: + response = client.chat.completions.create( + model=model, + temperature=temperature, + response_format={"type": "json_object"}, + messages=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": user_message}, + ], + max_tokens=600, + ) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result or "options" not in result: + print(f" WARNING: Missing fields in GPT response, retry {attempt+1}") + continue + + if len(result["options"]) != len(options): + print(f" WARNING: Option count mismatch " + f"({len(result['options'])} vs {len(options)}), retry {attempt+1}") + continue + + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + } + + return { + "naturalized_question": result["question"], + "naturalized_options": result["options"], + "reasoning": result.get("reasoning", ""), + "usage": usage, + } + + 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: + """Naturalize all QA pairs in the input data.""" + client = _create_client() + qa_pairs = input_data["qa_pairs"] + total = len(qa_pairs) + + print(f"\n Naturalizing {total} questions with {model}...") + + naturalized_pairs = [] + total_tokens = 0 + failures = 0 + + for i, q in enumerate(qa_pairs): + if verbose: + print(f" [{i+1}/{total}] {q['category']}: {q['question_template'][:60]}...") + + result = _naturalize_one(client, q, model, temperature) + + if result is None: + failures += 1 + nat_q = q.copy() + nat_q["naturalized_question"] = q["question_template"] + nat_q["naturalized_options"] = q["options"] + nat_q["reasoning"] = "" + nat_q["naturalization_failed"] = True + naturalized_pairs.append(nat_q) + continue + + nat_q = q.copy() + nat_q["naturalized_question"] = result["naturalized_question"] + nat_q["naturalized_options"] = result["naturalized_options"] + nat_q["reasoning"] = result["reasoning"] + nat_q["original_template"] = q["question_template"] + nat_q["original_options"] = q["options"] + naturalized_pairs.append(nat_q) + + total_tokens += result["usage"]["total_tokens"] + + if (i + 1) % 5 == 0: + print(f" Progress: {i+1}/{total} ({total_tokens} tokens)") + + output = { + "slot": input_data["slot"], + "version": "v8_natural", + "generator": "naturalize_v8_qa.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", {}), + "v8_stats": input_data.get("v8_stats", {}), + "qa_pairs": naturalized_pairs, + } + + return output + + +# ============================================================================ +# Strip Debug Markers +# ============================================================================ + +def strip_debug_markers(input_data: Dict) -> Dict: + """Remove debug_info blocks from QA pairs (for VQA model input).""" + output = input_data.copy() + cleaned_pairs = [] + for q in input_data["qa_pairs"]: + cleaned = {k: v for k, v in q.items() if k != "debug_info"} + cleaned_pairs.append(cleaned) + output["qa_pairs"] = cleaned_pairs + return output + + +# ============================================================================ +# Dry Run +# ============================================================================ + +def dry_run(input_data: Dict): + """Show what would be sent to GPT without making API calls.""" + qa_pairs = input_data["qa_pairs"] + + print(f"\n === DRY RUN — {len(qa_pairs)} questions ===\n") + + categories = {} + for q in qa_pairs: + categories.setdefault(q["category"], []).append(q) + + for cat, qs in sorted(categories.items()): + q = qs[0] + cat_info = CATEGORY_EXAMPLES.get(q.get("subcategory", cat), + CATEGORY_EXAMPLES.get(cat, {})) + + print(f" [{cat}] ({len(qs)} questions)") + print(f" TEMPLATE: {q['question_template'][:100]}...") + for i, opt in enumerate(q["options"]): + marker = " *" if i == q.get("correct_answer_index") else "" + print(f" {chr(65+i)}) {opt}{marker}") + + if q.get("subcategory"): + print(f" SUBCATEGORY: {q['subcategory']}") + print(f" DIFFICULTY: {q.get('difficulty', 'N/A')}") + print(f" HINT: {cat_info.get('hint', 'N/A')[:80]}...") + print() + + # Cost estimate (V8: ~9 Qs/slot) + est_tokens = len(qa_pairs) * 350 # slightly more with person descriptions + est_cost_mini = est_tokens * 0.4e-6 + est_cost_4o = est_tokens * 6e-6 + + print(f" === Cost Estimate ===") + print(f" Questions: {len(qa_pairs)}") + print(f" Est. tokens: ~{est_tokens}") + print(f" gpt-4o-mini: ~${est_cost_mini:.4f}") + print(f" gpt-4o: ~${est_cost_4o:.4f}") + + # V8 stats + mevid = input_data.get("mevid_persons_in_slot", 0) + print(f"\n === V8 Stats ===") + print(f" MEVID persons: {mevid}") + print(f" Categories: {sorted(categories.keys())}") + v8_stats = input_data.get("v8_stats", {}) + if v8_stats: + print(f" MEVID descriptions: {v8_stats.get('entities_with_mevid_descriptions', 0)}") + print(f" Attr verification Qs: {v8_stats.get('attribute_verification_questions', 0)}") + print(f" Re-ID Qs: {v8_stats.get('reid_questions', 0)}") + print(f" Scene summary Qs: {v8_stats.get('scene_summary_questions', 0)}") + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="V8 GPT Naturalization Wrapper", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + parser.add_argument("--input", "-i", required=True, + help="Path to V8 QA JSON file") + parser.add_argument("--output", "-o", + help="Output path (default: {input}.natural.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) + parser.add_argument("--dry-run", action="store_true", + help="Show prompts without making API calls") + parser.add_argument("--strip-debug", action="store_true", + help="Only strip debug_info blocks (no GPT call, free)") + parser.add_argument("--verbose", "-v", action="store_true") + parser.add_argument("--yes", "-y", action="store_true", + help="Skip confirmation prompt") + + args = parser.parse_args() + + input_path = Path(args.input) + if not input_path.exists(): + print(f"ERROR: Input not found: {input_path}") + return + + print(f"Loading: {input_path}") + 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}") + print(f" MEVID supported: {input_data.get('mevid_supported', False)}") + + if args.strip_debug: + result = strip_debug_markers(input_data) + out_path = args.output or str(input_path).replace(".v8.json", ".v8.clean.json") + with open(out_path, "w") as f: + json.dump(result, f, indent=2, default=str) + print(f" Stripped debug_info → {out_path}") + return + + if args.dry_run: + dry_run(input_data) + return + + if not os.environ.get("OPENAI_API_KEY"): + print("ERROR: OPENAI_API_KEY not set") + return + + if not args.yes: + print(f"\n Will send {total} questions to {args.model}") + resp = input(" Continue? [y/N] ").strip().lower() + if resp != "y": + print(" Aborted.") + return + + result = naturalize_batch(input_data, args.model, args.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']}") + + out_path = args.output or str(input_path).replace(".v8.json", ".v8.natural.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_v8_{args.model}.json" + with open(log_path, "w") as f: + json.dump({ + "model": args.model, + "temperature": args.temperature, + "total_tokens": result["total_tokens"], + "questions_processed": total, + "failures": result["failures"], + }, 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']}]") + print(f" TEMPLATE: {q.get('original_template', q['question_template'])[:80]}...") + print(f" NATURAL: {q['naturalized_question'][:80]}...") + if q.get("reasoning"): + print(f" REASONING: {q['reasoning'][:80]}...") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/archived/v7/naturalize_v8_qa_v2.py b/meva/scripts/archived/v7/naturalize_v8_qa_v2.py new file mode 100644 index 0000000..313d8fe --- /dev/null +++ b/meva/scripts/archived/v7/naturalize_v8_qa_v2.py @@ -0,0 +1,1176 @@ +#!/usr/bin/env python3 +""" +V8 Naturalization V2 — Pre-processes and naturalizes V8 template QA pairs. + +Key improvements over V1: +1. Pre-processing layer (FREE, no API call): + - Description simplification: "blue upper body garment" → "blue top" + - Camera reference removal from temporal/spatial question text + - Temporal anchors for disambiguation ("about 6 seconds in") + - Activity de-duplication ("enters scene enters scene" → "enters scene") + - Grammar fixes: capitalization, mid-sentence periods + - Event descriptions reconstructed from structured verification data + +2. Updated GPT prompts (optional GPT naturalization): + - Better few-shot examples reflecting cleaned templates + - Category-specific format guidance + - Post-processing validation + +3. Three modes: + --preprocess-only: Just pre-process templates (free, instant) + --dry-run: Show what would be sent to GPT + (default): Pre-process + GPT naturalization + +Usage: + # Pre-process only (free): + python3 scripts/v8/naturalize_v8_qa_v2.py --input data/qa_pairs/SLOT.v8.json --preprocess-only + + # Full pipeline (pre-process + GPT): + python3 scripts/v8/naturalize_v8_qa_v2.py --input data/qa_pairs/SLOT.v8.json + + # Dry-run: + python3 scripts/v8/naturalize_v8_qa_v2.py --input data/qa_pairs/SLOT.v8.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 +# ============================================================================ + +QA_DIR = Path("/home/ah66742/data/qa_pairs") +LOG_DIR = Path("/home/ah66742/data/gpt_logs") + +DEFAULT_MODEL = "gpt-4o-mini" +DEFAULT_TEMPERATURE = 0.7 +MAX_RETRIES = 3 +RETRY_DELAY = 2.0 +CLIP_DURATION = 300.0 # 5-minute clips + + +# ============================================================================ +# Description Simplification (standalone, mirrors person_descriptions.py) +# ============================================================================ + +# 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 (standalone, mirrors activity_hierarchy.py) +# ============================================================================ + +_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", +} + +_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 = _GERUND_MAP.get(first, first + "ing") + 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 for options: person_opens_facility_door → opening a facility door""" + return _humanize_gerund(activity).lower() + + +# ============================================================================ +# 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 < 180: + return f"around {int(round(sec / 10)) * 10} seconds in" + 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 person 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" + """ + if not entity_description: + return "a person" + + desc = entity_description.strip() + + # Check if this is a real appearance description vs. activity fallback + if not _has_appearance_info(desc): + return "a person" + + # Remove embedded activity text after the description + # Pattern: description ends with period, then activity follows + # e.g. "...backpack. enters scene through structure on camera G421" + 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 = False) -> Dict: + """ + Reconstruct temporal question from structured verification data. + + Fixes: camera refs in question, description verbosity, activity doubling, + temporal ambiguity (adds timestamp anchors), capitalization. + + If strip_camera_refs=True (V3 mode), camera IDs are removed from the question + text — 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 + # V3 mode: strip camera refs from question text (person descriptions disambiguate) + # V2 mode: keep camera refs in question text + 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}" + 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: + # Event A mentioned first AND occurred first → option 0 correct + desc_1, desc_2 = clean_a, clean_b + ev_1, ev_2 = ev_a, ev_b + else: + # Event B mentioned first but Event A occurred first → option 1 correct + 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 camera + activity labels + act_1 = _humanize_gerund(ev_1.get("activity", "event")) + act_2 = _humanize_gerund(ev_2.get("activity", "event")) + cam_1 = ev_1.get("camera", "") + cam_2 = ev_2.get("camera", "") + + if act_1.lower() == act_2.lower(): + # Same activity on different cameras: use camera to differentiate + options = [ + f"The event on camera {cam_1} occurred first", + f"The event on camera {cam_2} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + else: + options = [ + f"{act_1} (camera {cam_1}) occurred first", + f"{act_2} (camera {cam_2}) occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + + 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": + # "A person is visible on camera G423. What color are they wearing..." + # Simplify any embedded person description + person_desc = v.get("person_description", "") + if person_desc: + simplified = simplify_description(person_desc).rstrip(".") + # Template is already clean for this type — just ensure capitalization + elif q_type == "which_camera": + # "Which camera captures a carries heavy object event?" + # Make activity name more natural + 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:] + # Re-add question mark if simplification stripped it + 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: + # Fallback: simplify in place + 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, + version: str = "v2") -> Dict: + """ + Pre-process all QA pairs: simplify descriptions, remove camera refs, + add temporal anchors, fix grammar. FREE (no API call). + + version='v3' strips camera refs from temporal question text. + """ + output = {k: v for k, v in input_data.items() if k != "qa_pairs"} + output["version"] = "v8_preprocessed" + output["preprocessor"] = "naturalize_v8_qa_v2.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=(version == "v3")) + elif cat == "spatial": + cleaned = _preprocess_spatial(qa) + elif cat == "perception": + cleaned = _preprocess_perception(qa) + elif cat == "re_identification": + cleaned = _preprocess_reid(qa) + elif cat == "scene_summary": + 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) + + 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 + + +# ============================================================================ +# Updated GPT System Prompt (V2) +# ============================================================================ + +SYSTEM_PROMPT_V2 = """\ +You are a question naturalizer for a multi-camera surveillance video QA benchmark. + +Your task is to polish pre-processed template questions into fluent, natural English +suitable for a Video Question Answering (VQA) evaluation. The templates have already +been cleaned up — your job is to make them sound conversational while preserving all +factual content. + +Rules: +1. Rephrase the question to sound natural and conversational +2. Rephrase each option to sound natural, keeping the SAME meaning and order +3. Preserve person descriptions precisely (clothing colors, carried objects, distinctive features) +4. Preserve camera identifiers (e.g., "camera G299") when present — they tell the VLM where to look +5. Preserve event numbering (Event 1, Event 2) when present +6. Keep spatial terms unchanged (near, moderate, far, meters) +7. Keep "simultaneously" and "cannot be determined" as-is +8. Do NOT add information not present in the template +9. Do NOT reorder the options +10. Add a brief 1-sentence "reasoning" explaining why the correct answer is right + +Output format — respond with ONLY a JSON object: +{ + "question": "The naturalized question text", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "Brief explanation of why the answer is correct" +} +""" + +SYSTEM_PROMPT_V3 = """\ +You are a creative question writer for a multi-camera surveillance video QA benchmark. + +Your task: rewrite template questions into varied, natural English. Each question should +sound like a DIFFERENT person wrote it. Vary sentence structure, word choice, and phrasing +aggressively — avoid formulaic patterns like always starting with "In this scene..." or +"Looking at the cameras..." or "Two events are observed...". + +Rules: +1. VARY your phrasing — use different sentence openings, structures, and vocabulary each time +2. Preserve ALL factual content: person descriptions (clothing colors, carried objects), activities +3. Rephrase options naturally but keep the SAME meaning and order +4. Camera identifiers in answer options should be preserved +5. Keep spatial terms (near, moderate, far, meters) and "simultaneously"/"cannot be determined" +6. Do NOT add information not in the template +7. Do NOT reorder options +8. Add a 1-sentence "reasoning" for why the correct answer is right + +Phrasing variety examples (do NOT copy these verbatim — invent your own): +- "Two things happen in view of the cameras..." +- "Watch for these two events..." +- "Based on what the cameras recorded..." +- "Among the people visible..." +- Direct question without preamble: "Which happened first: ..." +- "The footage shows..." / "Can you tell..." / "What do you notice about..." +- 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 question", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "Brief explanation of why the answer is correct" +} +""" + +GRAMMAR_CHECKER_PROMPT = """\ +You are a meticulous copy editor. You receive a JSON object containing a VQA question, +options, and reasoning. Your ONLY job is to fix grammar, punctuation, and awkward phrasing. + +Rules: +1. Fix grammatical errors, run-on sentences, and punctuation mistakes +2. Do NOT change meaning, add information, or remove details +3. Do NOT reorder options +4. Do NOT change camera IDs, person descriptions, or spatial/temporal terms +5. Keep the same JSON structure +6. If the text is already grammatically correct, return it unchanged +7. Be conservative — only fix clear errors + +Output format — respond with ONLY a JSON object: +{ + "question": "The grammar-checked question", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "The grammar-checked reasoning" +} +""" + +# ============================================================================ +# Category-specific prompt examples (few-shot, V2) +# ============================================================================ + +CATEGORY_EXAMPLES_V3 = { + "temporal": { + "hint": "This is a temporal ordering question about two events. Person descriptions (clothing, objects) identify who is who — there are NO camera references in the question. Cameras only appear in answer options. VARY your phrasing creatively.", + "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?", "options": ["Entering through a structure (camera G421) occurred first", "Interacting with a person (camera G330) occurred first", "They occurred simultaneously", "Cannot be determined"], "reasoning": "The person in gray entered through the structure before the blue-topped person interacted with anyone."}', + }, + "spatial": { + "hint": "Spatial distance question about two people. Use their appearance descriptions to identify them. No camera references. VARY phrasing.", + "example_input": '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 white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?', + "example_output": '{"question": "How far apart would you say the person in blue with the black graphic hoodie is from the one wearing a white Puma hoodie and camo pants?", "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": "Their positions in the scene place them approximately 6 meters apart."}', + }, + "perception": { + "hint": "Perception question about activities or visual attributes. Camera references are part of the question — keep them. VARY phrasing.", + "example_input": 'A person is visible on camera G423. What color are they wearing on their lower body?', + "example_output": '{"question": "Check camera G423 — what color is the person there wearing below the waist?", "options": ["Gray", "Navy", "Blue", "Brown"], "reasoning": "The person visible on G423 has blue pants on."}', + }, + "re_identification": { + "hint": "Person re-identification across cameras. Camera refs are essential. Appearance descriptions must be precise. VARY phrasing.", + "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?", "options": ["G423", "G299", "G328", "None of these cameras"], "reasoning": "This person with the distinctive graphic hoodie shows up on both G419 and G423."}', + }, + "scene_summary": { + "hint": "Scene-level summary question. Keep camera counts and activity references. VARY phrasing.", + "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?", "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 putting down objects"], "reasoning": "Pedestrian activities dominate across all 8 feeds, with putting down objects being the most common."}', + }, +} + +CATEGORY_EXAMPLES_V2 = { + "temporal": { + "hint": "This is a temporal ordering question with two numbered events on specific cameras. Preserve the event numbers, person descriptions, and camera references exactly.", + "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 on camera G421. (2) A person interacting with a person on camera G330. Which event occurred first?', + "example_output": '{"question": "Two events are observed across the camera feeds: (1) A person in a gray top and green pants, carrying a black backpack, enters through a structure on camera G421. (2) A person interacts with another person on camera G330. Which of these events happened 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"], "reasoning": "Based on the video evidence, the scene entry on camera G421 occurred before the interaction on camera G330."}', + }, + "spatial": { + "hint": "This is a spatial distance question about how far apart two people are. Person descriptions should be preserved naturally. No camera references in the question.", + "example_input": '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 white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?', + "example_output": '{"question": "In the scene, how far apart are the person in blue clothes with a black graphic hoodie and the person in a white Puma hoodie with camouflage pants and cap?", "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": "Based on their projected positions in the scene, these two individuals are approximately 6 meters apart, placing them at a moderate distance."}', + }, + "perception": { + "hint": "This is a perception question about activities or visual attributes. Camera references are part of the question structure — preserve them.", + "example_input": 'A person is visible on camera G423. What color are they wearing on their lower body?', + "example_output": '{"question": "Looking at camera G423, what color is the visible person wearing on their lower body?", "options": ["Gray", "Navy", "Blue", "Brown"], "reasoning": "The person on camera G423 is wearing blue pants, making Blue the correct answer."}', + }, + "re_identification": { + "hint": "This is a person re-identification question. Camera references are essential — preserve them. Preserve appearance descriptions precisely.", + "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": "A person in a blue top and blue pants with a black graphic hoodie is visible on camera G419. Which other camera also shows this same person?", "options": ["G423", "G299", "G328", "None of these cameras"], "reasoning": "The person wearing a blue top and pants with the distinctive black graphic hoodie appears on both camera G419 and camera G423."}', + }, + "scene_summary": { + "hint": "This is a scene-level summary question. Keep statistical terms, camera counts, and activity references.", + "example_input": 'Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?', + "example_output": '{"question": "Looking at all 8 camera feeds together, which description best captures the overall activity in this 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 putting down objects"], "reasoning": "The vast majority of events are pedestrian activities observed across all 8 cameras, with putting down objects being the most frequent activity."}', + }, +} + + +# ============================================================================ +# GPT Client +# ============================================================================ + +def _create_client(): + """Create OpenAI client.""" + import openai + return openai.OpenAI() + + +def _naturalize_one(client, question: Dict, model: str, + temperature: float, + system_prompt: str = None, + examples: Dict = None) -> Optional[Dict]: + """Send one pre-processed question to GPT for naturalization.""" + if system_prompt is None: + system_prompt = SYSTEM_PROMPT_V2 + if examples is None: + examples = CATEGORY_EXAMPLES_V2 + + category = question["category"] + template = question["question_template"] + options = question["options"] + verification = question.get("verification", {}) + + lookup_cat = question.get("subcategory", category) + cat_info = examples.get(lookup_cat, + examples.get(category, {})) + hint = cat_info.get("hint", "Rephrase this question naturally.") + example_in = cat_info.get("example_input", "") + example_out = cat_info.get("example_output", "") + + user_message = f"Category: {category}\n{hint}\n\n" + + if example_in and example_out: + user_message += f"Example:\n Input: {example_in}\n Output: {example_out}\n\n" + + user_message += f"Now naturalize this question:\n\nTemplate: {template}\n\nOptions:\n" + for i, opt in enumerate(options): + user_message += f" {chr(65+i)}) {opt}\n" + + # Add verification context for reasoning + if category == "temporal" and "gap_sec" in verification: + user_message += f"\nContext: The gap between events is {verification['gap_sec']}s.\n" + elif category == "spatial" and "distance_meters" in verification: + user_message += f"\nContext: Distance is {verification['distance_meters']}m.\n" + elif category == "re_identification": + user_message += "\nContext: Person identified via cross-camera appearance matching.\n" + + user_message += "\nRespond with ONLY the JSON object." + + for attempt in range(MAX_RETRIES): + try: + response = client.chat.completions.create( + model=model, + temperature=temperature, + response_format={"type": "json_object"}, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_message}, + ], + max_tokens=600, + ) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result or "options" not in result: + print(f" WARNING: Missing fields, retry {attempt+1}") + continue + + if len(result["options"]) != len(options): + print(f" WARNING: Option count mismatch, retry {attempt+1}") + continue + + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + } + + return { + "naturalized_question": result["question"], + "naturalized_options": result["options"], + "reasoning": result.get("reasoning", ""), + "usage": usage, + } + + 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 + + +def _grammar_check_one(client, naturalized: Dict, model: str) -> Optional[Dict]: + """Send one naturalized question through grammar checker (pass 2).""" + user_message = json.dumps({ + "question": naturalized.get("naturalized_question", ""), + "options": naturalized.get("naturalized_options", []), + "reasoning": naturalized.get("reasoning", ""), + }, indent=2) + + for attempt in range(MAX_RETRIES): + try: + response = client.chat.completions.create( + model=model, + temperature=0.3, # Low temperature for conservative edits + response_format={"type": "json_object"}, + messages=[ + {"role": "system", "content": GRAMMAR_CHECKER_PROMPT}, + {"role": "user", "content": user_message}, + ], + max_tokens=600, + ) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result or "options" not in result: + break # Fall back to naturalized version + + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + } + + return { + "question": result["question"], + "options": result["options"], + "reasoning": result.get("reasoning", ""), + "usage": usage, + } + + except Exception as e: + if attempt < MAX_RETRIES - 1: + time.sleep(RETRY_DELAY) + continue + + return None # Grammar check failed, caller uses naturalized version as-is + + +# ============================================================================ +# Batch Processing +# ============================================================================ + +def naturalize_batch(input_data: Dict, model: str, temperature: float, + verbose: bool = False, version: str = "v2") -> Dict: + """Pre-process + GPT naturalize all QA pairs.""" + # Step 1: Pre-process (free) — V3 strips camera refs from temporal Qs + preprocessed = preprocess_all(input_data, verbose=verbose, version=version) + + # Step 2: Select prompts based on version + if version == "v3": + sys_prompt = SYSTEM_PROMPT_V3 + cat_examples = CATEGORY_EXAMPLES_V3 + else: + sys_prompt = SYSTEM_PROMPT_V2 + cat_examples = CATEGORY_EXAMPLES_V2 + + # Step 3: GPT naturalize + client = _create_client() + qa_pairs = preprocessed["qa_pairs"] + total = len(qa_pairs) + + print(f"\n Naturalizing {total} pre-processed questions with {model} ({version})...") + + 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]}...") + + # --- Pass 1: Naturalization --- + result = _naturalize_one(client, q, model, temperature, + system_prompt=sys_prompt, examples=cat_examples) + + if result is None: + failures += 1 + nat_q = q.copy() + nat_q["naturalized_question"] = q["question_template"] + nat_q["naturalized_options"] = q["options"] + nat_q["reasoning"] = "" + nat_q["naturalization_failed"] = True + naturalized_pairs.append(nat_q) + continue + + nat_q = q.copy() + 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"] + + # --- Pass 2: Grammar check --- + gc_result = _grammar_check_one(client, nat_q, model) + + if gc_result is not None: + nat_q["naturalized_question"] = gc_result["question"] + nat_q["naturalized_options"] = gc_result["options"] + nat_q["reasoning"] = gc_result["reasoning"] + nat_q["grammar_checked"] = True + total_tokens += gc_result["usage"]["total_tokens"] + else: + nat_q["grammar_checked"] = False + + naturalized_pairs.append(nat_q) + + if (i + 1) % 5 == 0: + print(f" Progress: {i+1}/{total} ({total_tokens} tokens)") + + version_tag = "v8_natural_v3" if version == "v3" else "v8_natural_v2" + + output = { + "slot": input_data["slot"], + "version": version_tag, + "generator": "naturalize_v8_qa_v2.py", + "preprocessor": f"{version}_preprocess", + "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", {}), + "v8_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 + est_tokens = len(qa_pairs) * 400 # slightly more with examples + est_cost_mini = est_tokens * 0.4e-6 + est_cost_4o = est_tokens * 6e-6 + + print(f" === Cost Estimate (GPT naturalization) ===") + print(f" Questions: {len(qa_pairs)}") + 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="V8 Naturalization — Pre-process + GPT naturalize (V2/V3)", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + parser.add_argument("--input", "-i", required=True, + help="Path to V8 QA JSON file") + parser.add_argument("--output", "-o", + help="Output path (default: auto-generated)") + parser.add_argument("--model", "-m", default=DEFAULT_MODEL, + help=f"GPT model (default: {DEFAULT_MODEL})") + parser.add_argument("--temperature", "-t", type=float, default=None, + help="Temperature (default: 0.7 for V2, 0.95 for V3)") + parser.add_argument("--v3", action="store_true", + help="V3 mode: more question variety, strip camera refs from temporal Qs") + 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() + + # Resolve version and temperature + version = "v3" if args.v3 else "v2" + temperature = args.temperature if args.temperature is not None else ( + 0.95 if version == "v3" else DEFAULT_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} (mode: {version}, 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, version=version) + + out_path = args.output or str(input_path).replace( + ".v8.json", ".v8.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 with {args.model} ({version}, temp={temperature})") + resp = input(" Continue? [y/N] ").strip().lower() + if resp != "y": + print(" Aborted.") + return + + result = naturalize_batch(input_data, args.model, temperature, + verbose=args.verbose, version=version) + + print(f"\n === Results ===") + print(f" Naturalized: {total - result['failures']}/{total}") + print(f" Failures: {result['failures']}") + print(f" Total tokens: {result['total_tokens']}") + + suffix = ".v8.natural.v3.json" if version == "v3" else ".v8.natural.v2.json" + out_path = args.output or str(input_path).replace(".v8.json", suffix) + 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_v8_{version}_{args.model}.json" + with open(log_path, "w") as f: + json.dump({ + "model": args.model, + "temperature": args.temperature, + "total_tokens": result["total_tokens"], + "questions_processed": total, + "failures": result["failures"], + }, 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/archived/v7/parse_annotations.py b/meva/scripts/archived/v7/parse_annotations.py new file mode 100644 index 0000000..2c09737 --- /dev/null +++ b/meva/scripts/archived/v7/parse_annotations.py @@ -0,0 +1,245 @@ +""" +V6 parse_annotations.py — Step 1: Parse Kitware YAML annotations into raw events. + +Input: Slot name (e.g., "2018-03-11.11-25-00.school") +Output: List of Event dicts with activity, camera, frame range, actors. +""" + +import json +import re +from pathlib import Path +from typing import Any, Dict, List, Optional +from dataclasses import dataclass, asdict, field + +# ============================================================================ +# Paths +# ============================================================================ + +from .config import MEVA_ROOT, SLOT_INDEX_PATH # noqa: E402 +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" + +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}.avi", + annotation_source=source, + )) + + return events + + +# ============================================================================ +# Slot-Level Annotation Discovery +# ============================================================================ + +def find_clips_for_slot(slot: str) -> List[Dict]: + """ + Find all annotation clips for a given slot using slot_index.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) + + if slot not in index: + raise ValueError(f"Slot '{slot}' not found in index ({len(index)} total slots)") + + info = index[slot] + clips = [] + + # Parse date/time/site from slot name: "2018-03-11.11-25-00.school" + slot_parts = slot.split(".") + date = slot_parts[0] + slot_time = slot_parts[1] + 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, + } + + cameras_seen = set() + 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 info["cameras"]: + if cam_id in cameras_seen: + continue + if cam_id not in info["sources"].get(source_name, []): + continue + + # Find matching activities.yml + pattern = f"{date}.{slot_time}*{cam_id}*.activities.yml" + matches = list(ann_dir.glob(pattern)) + if not matches: + # Try minute-level match + pattern_alt = f"{date}.{slot_time[:5]}*{cam_id}*.activities.yml" + matches = list(ann_dir.glob(pattern_alt)) + 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. "2018-03-11.11-25-00.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/archived/v7/person_descriptions.py b/meva/scripts/archived/v7/person_descriptions.py new file mode 100644 index 0000000..a81fa89 --- /dev/null +++ b/meva/scripts/archived/v7/person_descriptions.py @@ -0,0 +1,395 @@ +""" +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 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 +# ============================================================================ + +PERSON_DB_PATH = Path("/home/ah66742/data/person_database_yolo.json") +MEVID_SLOTS_PATH = Path("/home/ah66742/data/mevid_supported_slots.json") + + +# ============================================================================ +# Person Database Loading +# ============================================================================ + +_person_db_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 + + +# ============================================================================ +# 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" + + +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 is_mevid_supported(slot: str) -> bool: + """Check if a slot has MEVID person support.""" + data = load_mevid_slots() + slots = data.get("slots", {}) + return slot in slots + + +def get_mevid_persons_for_slot(slot: str) -> List[str]: + """ + Get list of MEVID person IDs available for a slot. + + Cross-references the person database (23 persons with descriptions) + against the slot's camera set. + """ + db = load_person_database() + persons = db.get("persons", {}) + + result = [] + for pid, pdata in persons.items(): + for slot_info in pdata.get("slots", []): + if slot_info.get("slot") == slot: + result.append(pid) + break + + return sorted(result) + + +def get_mevid_persons_with_cameras(slot: str) -> Dict[str, List[str]]: + """ + Get MEVID person IDs mapped to their cameras for this specific slot. + + Returns: {person_id: [camera_ids]} + """ + db = load_person_database() + persons = db.get("persons", {}) + + result = {} + for pid, pdata in persons.items(): + for slot_info in pdata.get("slots", []): + if slot_info.get("slot") == slot: + result[pid] = slot_info.get("cameras", []) + break + + return result + + +# ============================================================================ +# Entity Enrichment — Inject MEVID Descriptions into Scene Graph +# ============================================================================ + +# Geom-extracted description bank directory +_GEOM_DESC_DIR = Path("/home/ah66742/data/entity_descriptions") + + +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) + return {eid: info["description"] for eid, info in data.get("actors", {}).items() + if info.get("description") and info["description"] != "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 descriptions (GPT/YOLO from MEVID crops — highest quality) + 2. Geom-extracted descriptions (HSV color from raw AVI + geom.yml bbox) + 3. Activity-verb fallback ("a person walking") + + 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) + + # 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) + + entity_descriptions: Dict[str, str] = {} + mevid_count = 0 + geom_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(): + if entity.entity_type != "person": + entity_descriptions[eid] = f"a vehicle on camera {entity.camera_id}" + continue + + cam = entity.camera_id + available_persons = camera_persons.get(cam, []) + + # Priority 1: MEVID person description + if available_persons: + used = assigned_persons.get(cam, set()) + unused = [p for p in available_persons if p not in used] + + if unused: + # Assign next available person + pid = unused[0] + desc = get_person_description(pid) + entity_descriptions[eid] = desc + + if cam not in assigned_persons: + assigned_persons[cam] = set() + assigned_persons[cam].add(pid) + + # Also store the MEVID person_id on the entity for re-ID questions + entity._mevid_person_id = pid + mevid_count += 1 + + if verbose: + print(f" {eid}: MEVID → {desc[:60]}...") + continue + + # Priority 2: Geom-extracted color description (from raw AVI + bbox) + if eid in geom_descs: + desc = geom_descs[eid] + entity_descriptions[eid] = desc + geom_count += 1 + if verbose: + print(f" {eid}: geom → {desc}") + continue + + # Priority 3: 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 = f"a person on camera {cam}" + + entity_descriptions[eid] = desc + fallback_count += 1 + + if verbose: + total = mevid_count + geom_count + fallback_count + print(f" Entity enrichment: {mevid_count} MEVID, {geom_count} geom-color, " + f"{fallback_count} fallback ({total} total)") + + return entity_descriptions, {"mevid": mevid_count, "geom": geom_count, "fallback": fallback_count} diff --git a/meva/scripts/archived/v7/run_pipeline.py b/meva/scripts/archived/v7/run_pipeline.py new file mode 100644 index 0000000..8bfa38a --- /dev/null +++ b/meva/scripts/archived/v7/run_pipeline.py @@ -0,0 +1,466 @@ +#!/usr/bin/env python3 +""" +V8 run_pipeline.py — Main orchestrator for V8 QA generation pipeline. + +V8 CHANGES from V7: +- MEVID person descriptions injected into all question categories +- New categories: re_identification, scene_summary +- Attribute verification added to perception +- Only processes slots with MEVID support (filterable) +- 5 categories: temporal(2) + spatial(2) + perception(2) + re_id(2) + scene_summary(1) = ~9 Qs +- version: "v8" + +Usage: + python3 -m scripts.v8.run_pipeline --slot "2018-03-11.11-25-00.school" -v + python3 scripts/v8/run_pipeline.py --slot "2018-03-11.11-25-00.school" -v + python3 scripts/v8/run_pipeline.py --list-mevid-slots +""" + +import json +import argparse +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, + ) + from .generate_temporal import generate_temporal_qa + from .generate_spatial import generate_spatial_qa + from .generate_perception import generate_perception_qa + from .generate_reidentification import generate_reidentification_qa + from .generate_scene_summary import generate_scene_summary_qa + from .utils.mevid import find_mevid_persons_for_slot as mevid_persons_check +except ImportError: + sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + from scripts.v8.parse_annotations import parse_slot_events, find_clips_for_slot + from scripts.v8.build_scene_graph import build_scene_graph + from scripts.v8.entity_resolution import resolve_entities + from scripts.v8.person_descriptions import ( + enrich_entities, is_mevid_supported, get_mevid_persons_for_slot, + get_mevid_persons_with_cameras, load_person_database, + ) + from scripts.v8.generate_temporal import generate_temporal_qa + from scripts.v8.generate_spatial import generate_spatial_qa + from scripts.v8.generate_perception import generate_perception_qa + from scripts.v8.generate_reidentification import generate_reidentification_qa + from scripts.v8.generate_scene_summary import generate_scene_summary_qa + from scripts.v8.utils.mevid import find_mevid_persons_for_slot as mevid_persons_check + + +# ============================================================================ +# Constants +# ============================================================================ + +OUTPUT_DIR = Path("/home/ah66742/data/qa_pairs") +RANDOM_SEED = 42 + +# V8: 5 categories, weighted question distribution +TARGET_TEMPORAL = 2 +TARGET_SPATIAL = 2 +TARGET_PERCEPTION = 2 # includes 1 attribute_verification if MEVID +TARGET_REIDENTIFICATION = 2 +TARGET_SCENE_SUMMARY = 1 +# Total target: ~9 questions per 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 == "re_identification": + if (v_new.get("mevid_person_id") == v_old.get("mevid_person_id") and + v_new.get("question_type") == v_old.get("question_type")): + return True + + elif cat == "scene_summary": + if v_new.get("question_type") == v_old.get("question_type"): + 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("distance_meters") + proximity = v.get("proximity") + + if d is None: + errors.append("Missing 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)") + + 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_reidentification(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("mevid_person_id"): + errors.append("Missing mevid_person_id") + if not v.get("all_person_cameras"): + errors.append("Missing all_person_cameras list") + return errors + + +def validate_scene_summary(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("question_type"): + errors.append("Missing question_type") + return errors + + +def validate_all(qa_pairs: List[dict]) -> Dict[str, List[str]]: + validators = { + "temporal": validate_temporal, + "spatial": validate_spatial, + "perception": validate_perception, + "re_identification": validate_reidentification, + "scene_summary": validate_scene_summary, + } + 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 + + +# ============================================================================ +# Main Pipeline +# ============================================================================ + +def run_pipeline(slot: str, verbose: bool = False, + seed: int = RANDOM_SEED, + require_mevid: bool = True) -> Dict[str, Any]: + """ + Run the complete V8 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) + 4. Enrich entities with MEVID person descriptions (V8 NEW) + 5. Generate temporal questions (with MEVID descriptions) + 6. Generate spatial questions (with MEVID descriptions) + 7. Generate perception questions (+ attribute verification) + 8. Generate re-identification questions (V8 NEW) + 9. Generate scene summary questions (V8 NEW) + 10. Validate and output + """ + t0 = time.time() + rng = random.Random(seed) + + if verbose: + print(f"{'=' * 60}") + print(f"V8 Pipeline: {slot}") + print(f"{'=' * 60}") + + # 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 activity-verb)") + + if verbose: + print(f" MEVID persons in slot: {len(mevid_persons)}") + + # Step 1: Parse annotations + if verbose: + print(f"\nStep 1: Parsing annotations...") + events = parse_slot_events(slot, verbose=verbose) + 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 4 (V8 NEW): Enrich entities with visual descriptions + if verbose: + print(f"\nStep 4: Enriching entities with visual descriptions...") + entity_descs, desc_counts = enrich_entities(sg, verbose=verbose) + + mevid_desc_count = desc_counts["mevid"] + geom_desc_count = desc_counts["geom"] + fallback_count = desc_counts["fallback"] + + if verbose: + print(f" {mevid_desc_count} MEVID + {geom_desc_count} geom-color + " + f"{fallback_count} fallback / {len(entity_descs)} total") + + # Step 5-9: Generate QA pairs (all categories) + if verbose: + print(f"\nStep 5-9: Generating questions (5 categories)...") + + temporal_qa = generate_temporal_qa(sg, resolved, entity_descs, rng, + count=TARGET_TEMPORAL, verbose=verbose) + spatial_qa = generate_spatial_qa(sg, resolved, entity_descs, rng, + count=TARGET_SPATIAL, verbose=verbose) + perception_qa = generate_perception_qa(sg, resolved, entity_descs, rng, + count=TARGET_PERCEPTION, verbose=verbose) + reid_qa = generate_reidentification_qa(sg, resolved, entity_descs, rng, + count=TARGET_REIDENTIFICATION, verbose=verbose) + summary_qa = generate_scene_summary_qa(sg, resolved, entity_descs, rng, + count=TARGET_SCENE_SUMMARY, verbose=verbose) + + all_qa = temporal_qa + spatial_qa + perception_qa + reid_qa + summary_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"v8_{q['category']}_{i+1:03d}" + + # Step 10: Validation + if verbose: + print(f"\nStep 10: 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") + + # 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 + + # V8 stats + desc_used = sum( + 1 for q in unique_qa + if any("description" in str(v) for k, v in q.get("verification", {}).items() + if isinstance(v, str) and "person" in v.lower()) + ) + attr_verification = sum( + 1 for q in unique_qa + if q.get("subcategory") == "attribute_verification" + ) + + output = { + "slot": slot, + "version": "v8", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo_gpt", + "generator": "v8_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), + "entities_with_mevid_descriptions": mevid_desc_count, + "cross_camera_clusters": len(resolved.entity_clusters), + "total_questions": len(unique_qa), + "category_counts": cat_counts, + "v8_stats": { + "entities_with_mevid_descriptions": mevid_desc_count, + "entities_with_geom_descriptions": geom_desc_count, + "entities_with_fallback_descriptions": fallback_count, + "attribute_verification_questions": attr_verification, + "reid_questions": cat_counts.get("re_identification", 0), + "scene_summary_questions": cat_counts.get("scene_summary", 0), + "questions_with_debug_info": sum(1 for q in unique_qa if "debug_info" in q), + }, + "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_desc_count} with MEVID descriptions)") + print(f" MEVID persons: {sorted(mevid_persons)}") + print(f" Cross-cam clusters: {len(resolved.entity_clusters)}") + print(f" Attribute verification: {attr_verification}") + print(f" Validation issues: {len(issues)}") + print(f"{'=' * 60}") + + return output + + +def list_mevid_slots(): + """List all slots with MEVID person support.""" + db = load_person_database() + persons = db.get("persons", {}) + + slot_persons: Dict[str, List[str]] = {} + for pid, pdata in persons.items(): + for slot_info in pdata.get("slots", []): + slot = slot_info.get("slot", "") + if slot: + if slot not in slot_persons: + slot_persons[slot] = [] + slot_persons[slot].append(pid) + + print(f"Slots with MEVID person support: {len(slot_persons)}") + print(f"{'Slot':40s} {'Persons':>8s} {'Person IDs'}") + print("-" * 80) + + for slot in sorted(slot_persons.keys()): + pids = sorted(slot_persons[slot]) + print(f"{slot:40s} {len(pids):8d} {', '.join(pids[:5])}" + + (f" +{len(pids)-5} more" if len(pids) > 5 else "")) + + +def main(): + parser = argparse.ArgumentParser(description="V8 QA Pipeline") + parser.add_argument("--slot", help="Slot name (e.g., 2018-03-11.11-25-00.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/{slot}.v8.json)") + parser.add_argument("--no-save", action="store_true", help="Don't save to file, just print") + parser.add_argument("--list-mevid-slots", action="store_true", + help="List all slots with MEVID support") + parser.add_argument("--no-require-mevid", action="store_true", + help="Process slot even without MEVID support") + args = parser.parse_args() + + if args.list_mevid_slots: + list_mevid_slots() + return + + if not args.slot: + parser.error("--slot is required (or use --list-mevid-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}.v8.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/archived/v7/utils/__init__.py b/meva/scripts/archived/v7/utils/__init__.py new file mode 100644 index 0000000..a3fa321 --- /dev/null +++ b/meva/scripts/archived/v7/utils/__init__.py @@ -0,0 +1 @@ +"""V6 utility modules.""" diff --git a/meva/scripts/archived/v7/utils/iou.py b/meva/scripts/archived/v7/utils/iou.py new file mode 100644 index 0000000..aaf670c --- /dev/null +++ b/meva/scripts/archived/v7/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/archived/v7/utils/krtd.py b/meva/scripts/archived/v7/utils/krtd.py new file mode 100644 index 0000000..65e8b69 --- /dev/null +++ b/meva/scripts/archived/v7/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/archived/v7/utils/mevid.py b/meva/scripts/archived/v7/utils/mevid.py new file mode 100644 index 0000000..f71d77a --- /dev/null +++ b/meva/scripts/archived/v7/utils/mevid.py @@ -0,0 +1,166 @@ +""" +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 re +from pathlib import Path +from collections import defaultdict +from typing import Dict, List, Set, Tuple, Optional + +MEVID_DATA_DIR = Path("/nas/mars/dataset/MEVA/mevid_data/mevid-v1-annotation-data") +MEVID_URLS = Path("/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$') + + +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. + + Returns: + {person_id: {camera_id, ...}, ...} + Camera IDs are in MEVA format: "G424" (not "C424"). + """ + 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) + + return dict(person_cameras) + + +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/archived/v7/utils/yaml_stream.py b/meva/scripts/archived/v7/utils/yaml_stream.py new file mode 100644 index 0000000..a71d403 --- /dev/null +++ b/meva/scripts/archived/v7/utils/yaml_stream.py @@ -0,0 +1,137 @@ +""" +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 Kitware inline format: + - { geom: {id1: NNN, id0: M, ts0: FFF, ts1: T.T, g0: X1 Y1 X2 Y2, ...} } + Also handles multi-line format (fallback). + """ + # Inline pattern: extract id1, ts0, g0 from a single line + inline_re = re.compile( + r'id1:\s*(\d+).*?ts0:\s*(\d+).*?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 inline format first (most common in Kitware geom.yml) + m = inline_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 + + # 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/v8/__init__.py b/meva/scripts/v8/__init__.py new file mode 100644 index 0000000..f9fa1b0 --- /dev/null +++ b/meva/scripts/v8/__init__.py @@ -0,0 +1 @@ +# V8 QA Pipeline — MEVID-enriched multi-camera QA generation diff --git a/meva/scripts/v8/activity_hierarchy.py b/meva/scripts/v8/activity_hierarchy.py new file mode 100644 index 0000000..2c15048 --- /dev/null +++ b/meva/scripts/v8/activity_hierarchy.py @@ -0,0 +1,197 @@ +""" +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.""" + # 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", +} + + +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' + """ + base = humanize_activity(activity) # e.g. 'opens facility door' + words = base.split() + if words: + first = words[0] + gerund = _GERUND_MAP.get(first, first + "ing") + 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: + rest = "a " + rest + result = f"{gerund} {rest}".strip() if rest else gerund + return result.capitalize() + return base.capitalize() diff --git a/meva/scripts/v8/build_scene_graph.py b/meva/scripts/v8/build_scene_graph.py new file mode 100644 index 0000000..f605caf --- /dev/null +++ b/meva/scripts/v8/build_scene_graph.py @@ -0,0 +1,260 @@ +""" +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 + + +# ============================================================================ +# 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, actor_ids in entity_actor_ids.items(): + 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 + + entities[entity_id] = 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, []), + ) + + # 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/v8/distractor_bank.py b/meva/scripts/v8/distractor_bank.py new file mode 100644 index 0000000..a744c29 --- /dev/null +++ b/meva/scripts/v8/distractor_bank.py @@ -0,0 +1,91 @@ +""" +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) + return pool[:n] diff --git a/meva/scripts/v8/entity_resolution.py b/meva/scripts/v8/entity_resolution.py new file mode 100644 index 0000000..3cb80a6 --- /dev/null +++ b/meva/scripts/v8/entity_resolution.py @@ -0,0 +1,298 @@ +""" +V7 entity_resolution.py — Step 3: Cross-camera entity linking. + +Uses MEVID ground truth person IDs + heuristic temporal handoff 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. +""" + +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 + + +# ============================================================================ +# Combined Entity Resolution +# ============================================================================ + +def resolve_entities(sg: SceneGraph, verbose: bool = False) -> ResolvedGraph: + """ + Run entity resolution: MEVID ground truth + heuristic temporal handoff. + + Args: + sg: Scene graph from build_scene_graph + verbose: Print progress + + 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. 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/v8/extract_entity_descriptions.py b/meva/scripts/v8/extract_entity_descriptions.py new file mode 100644 index 0000000..36ac8d8 --- /dev/null +++ b/meva/scripts/v8/extract_entity_descriptions.py @@ -0,0 +1,695 @@ +#!/usr/bin/env python3 +""" +V8 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 MP4 video + 3. Runs YOLO color analysis on each crop (upper/lower body colors, carried objects) + 4. Aggregates via majority vote across crops + 5. Generates template description: "a person in blue top and black pants carrying a backpack" + +This gives EVERY entity a visual description (not just the 10% with MEVID matches), +solving the temporal disambiguation problem where "a person enters scene" is ambiguous +when there are 100+ such events. + +Cost: $0 (all local, no API calls) +Time: ~3-4 min per slot (mostly video decode + YOLO inference) + +Usage: + python3 scripts/v8/extract_entity_descriptions.py --slot 2018-03-11.11-25-00.school -v + python3 scripts/v8/extract_entity_descriptions.py --slot 2018-03-11.11-25-00.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") +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) +OUTPUT_DIR = Path("/home/ah66742/data/entity_descriptions") + +# ============================================================================ +# Constants +# ============================================================================ + +CROPS_PER_ACTOR = 5 # Crops to extract per actor track +MIN_BBOX_HEIGHT = 25 # Min bbox height in pixels for usable crop +MIN_BBOX_WIDTH = 10 # Min bbox width +YOLO_CONF = 0.25 # YOLO detection confidence threshold +YOLO_MODEL = "yolov8n.pt" # Nano model (fast, sufficient for crops) + +# 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) +# ============================================================================ + +_GEOM_RE = re.compile( + r'id1:\s*(\d+).*?ts0:\s*(\d+).*?g0:\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). + """ + actors = defaultdict(dict) + with open(geom_path) as f: + for line in f: + m = _GEOM_RE.search(line) + if not m: + continue + actor_id = int(m.group(1)) + frame = int(m.group(2)) + bbox = (int(m.group(3)), int(m.group(4)), + int(m.group(5)), int(m.group(6))) + 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: + indices = np.linspace(0, len(frames) - 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 color name.""" + if s < 40: + if v < 60: + return "black" + elif v < 150: + return "gray" + else: + return "white" + if v < 40: + return "black" + if h < 10 or h > 170: + return "red" + elif h < 22: + return "orange" + elif h < 35: + return "yellow" + elif h < 78: + return "green" + elif h < 131: + return "blue" + elif h < 155: + return "purple" + elif h <= 170: + 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] + + +# ============================================================================ +# 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} + + +# ============================================================================ +# Description Generation (template-based, free) +# ============================================================================ + +def build_description(attrs: Dict) -> str: + """ + Build a natural description from structured attributes. + + Examples: + {"upper_color": "blue", "lower_color": "black", "carried_objects": ["backpack"]} + → "a person in a blue top and black pants carrying a backpack" + + {"upper_color": "gray", "lower_color": "green", "carried_objects": []} + → "a person in a gray top and green pants" + """ + parts = [] + + upper = attrs.get("upper_color", "unknown") + lower = attrs.get("lower_color", "unknown") + + if upper != "unknown" and lower != "unknown": + parts.append(f"a person in a {upper} top and {lower} pants") + elif upper != "unknown": + parts.append(f"a person in a {upper} top") + elif lower != "unknown": + parts.append(f"a person in {lower} pants") + else: + parts.append("a person") + + objects = attrs.get("carried_objects", []) + if objects: + obj_str = " and ".join(objects[:2]) # Max 2 objects + parts[0] += f" carrying a {obj_str}" + + return parts[0] + + +# ============================================================================ +# Slot Processing Pipeline +# ============================================================================ + +def find_slot_files(slot: str) -> List[Dict]: + """ + Find geom.yml + video pairs for a slot. + 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}. + """ + # Parse slot: "2018-03-11.11-25-00.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] + site = parts[2] + + hour = time_part.split("-")[0] # e.g. "11" from "11-25-00" + + kitware_dir = KITWARE_BASE / date / hour + avi_dir = AVI_BASE / date / hour + mp4_dir = MP4_BASE / date / hour + + if not kitware_dir.is_dir(): + raise FileNotFoundError(f"Kitware dir not found: {kitware_dir}") + + # Find geom files matching slot pattern + prefix = f"{date}.{time_part}" + results = [] + + 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, + }) + + return results + + +def process_slot(slot: str, use_yolo: bool = True, + verbose: bool = False) -> Dict: + """ + Full pipeline: extract descriptions for all actors in a slot. + + Returns dict ready for JSON output: + { + "slot": "...", + "cameras": {...}, + "actors": {actor_id_str: {camera, upper_color, lower_color, objects, 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") + + if use_yolo: + if verbose: + print(f" Loading YOLO model...", end="", flush=True) + _get_yolo() + 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 + t1 = time.time() + crops_by_actor = extract_crops( + video_path, actors, + max_crops=CROPS_PER_ACTOR, + min_h=MIN_BBOX_HEIGHT, min_w=MIN_BBOX_WIDTH, + ) + decode_time = time.time() - t1 + + usable = len(crops_by_actor) + total_crops = sum(len(c) for c in crops_by_actor.values()) + 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 + + if use_yolo: + attrs = analyze_crops_yolo(crops) + else: + attrs = analyze_crops_color_only(crops) + + desc = build_description(attrs) + + # Store by camera_actorID (matching V8 entity ID format) + entity_key = f"{cam}_actor_{actor_id}" + all_actors[entity_key] = { + "actor_id": actor_id, + "camera": cam, + "upper_color": attrs.get("upper_color", "unknown"), + "lower_color": attrs.get("lower_color", "unknown"), + "carried_objects": attrs.get("carried_objects", []), + "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: + print(f" Analysis: {analyze_time:.1f}s ({'YOLO' if use_yolo else 'color-only'})") + + cam_stats[cam] = { + "actors": len(actors), + "usable": usable, + "total_crops": total_crops, + "decode_sec": round(decode_time, 1), + "analyze_sec": round(analyze_time, 1), + } + + 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": "yolo" if use_yolo else "color_only", + "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="V8 Entity Description Extractor — Geom + Video → YOLO descriptions", + ) + parser.add_argument("--slot", "-s", required=True, + help="Slot to process (e.g., 2018-03-11.11-25-00.school)") + parser.add_argument("--no-yolo", action="store_true", + help="Color-only analysis (no YOLO, faster but no carried objects)") + parser.add_argument("--dry-run", action="store_true", + help="Show what would be processed without extracting") + parser.add_argument("--output", "-o", + help="Output path (default: data/entity_descriptions/{slot}.json)") + parser.add_argument("--verbose", "-v", action="store_true") + + 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 = 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)) + vp = cf["video_path"] + has_video = vp and vp.exists() + vfmt = cf.get("video_format", "none") + print(f" {cam}: {len(actors)} actors, {usable} usable, video={'YES' if has_video else 'NO'} ({vfmt})") + return + + result = process_slot(args.slot, use_yolo=not args.no_yolo, 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 ...{str(info['actor_id'])[-6:]}: {desc}") + if len(seen) >= 10: + break + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v8/generate_perception.py b/meva/scripts/v8/generate_perception.py new file mode 100644 index 0000000..f65751b --- /dev/null +++ b/meva/scripts/v8/generate_perception.py @@ -0,0 +1,437 @@ +""" +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, V7 logic) + # ------------------------------------------------------------------ + 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 + + correct_cam = sorted(correct_cams)[0] + distractors = get_camera_distractors([correct_cam], all_cameras, rng, n=3) + + if len(distractors) < 2: + continue + + options = [correct_cam] + distractors[:3] + rng.shuffle(options) + correct_idx = options.index(correct_cam) + + human_act = humanize_activity(act) + question = f"Which camera captures a {human_act} event?" + + rep_event = 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), + } + 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", "") + + # Frame the question: show the person on a camera, ask about attribute + if attr_opts["attribute_type"] == "upper_color": + question = ( + f"A person is visible on camera {cam}. " + 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 {sorted_cams[0]} and {sorted_cams[1]}" + option_a_only = f"{sorted_cams[0]} only" + option_b_only = f"{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/v8/generate_reidentification.py b/meva/scripts/v8/generate_reidentification.py new file mode 100644 index 0000000..dbc0ec8 --- /dev/null +++ b/meva/scripts/v8/generate_reidentification.py @@ -0,0 +1,215 @@ +""" +V8 generate_reidentification.py — Cross-camera person re-identification questions. + +NEW in V8: Questions that test whether a model can identify the same person +appearing on different cameras within the same slot. + +Uses MEVID ground-truth person IDs to know which persons appear on which cameras, +combined with YOLO+GPT descriptions to make questions answerable from visual features. + +Question types: + 1. "Does the person in [description] on camera A also appear on camera B?" + 2. "Which camera also shows [description person]?" + 3. "A person is seen on camera A wearing [X]. On camera B, which person matches?" +""" + +import random +from typing import Any, Dict, List, Optional, Set +from collections import defaultdict + +from .build_scene_graph import SceneGraph +from .entity_resolution import ResolvedGraph +from .person_descriptions import ( + get_person_description, get_person_short_label, + get_mevid_persons_with_cameras, load_person_database, +) +from .activity_hierarchy import humanize_activity +from .distractor_bank import get_camera_distractors + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_reidentification_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate cross-camera re-identification questions. + + These require recognizing that the same person appears on multiple cameras + based on their visual appearance (clothing, carried objects, etc.). + + Selection: + - Only uses MEVID persons (known cross-camera ground truth) + - Prefers persons with distinctive descriptions + - Requires person to appear on 2+ cameras in the slot + """ + slot = sg.slot + all_cameras = sorted(sg.cameras.keys()) + + # Get MEVID persons for this slot with their cameras + person_cameras = get_mevid_persons_with_cameras(slot) + + if not person_cameras: + if verbose: + print(" Re-ID: No MEVID persons in slot — skipping") + return [] + + # Filter to persons on 2+ cameras AND with good descriptions + db = load_person_database() + persons_data = db.get("persons", {}) + + candidates = [] + for pid, cameras in person_cameras.items(): + if len(cameras) < 2: + continue + pdata = persons_data.get(pid, {}) + has_gpt = bool(pdata.get("gpt_description")) + has_colors = (pdata.get("primary_upper_color", "unknown") != "unknown") + + if has_gpt or has_colors: + candidates.append({ + "person_id": pid, + "cameras": sorted(cameras), + "has_gpt": has_gpt, + "description": get_person_description(pid), + "short_label": get_person_short_label(pid), + "upper_color": pdata.get("primary_upper_color", "unknown"), + "lower_color": pdata.get("primary_lower_color", "unknown"), + "objects": pdata.get("all_carried_objects", []), + }) + + # Sort: prefer GPT descriptions, then by number of cameras (more = better) + candidates.sort(key=lambda c: (-int(c["has_gpt"]), -len(c["cameras"]))) + + if verbose: + print(f" Re-ID: {len(candidates)} MEVID persons with 2+ cameras and descriptions") + + if not candidates: + return [] + + qa_pairs = [] + used_persons = set() + + # Type 1: "Does person X on camera A also appear on camera B?" (Yes/No with camera options) + for cand in candidates: + if len(qa_pairs) >= count: + break + if cand["person_id"] in used_persons: + continue + + pid = cand["person_id"] + cams = cand["cameras"] + desc = cand["description"] + + # Pick two cameras this person appears on + cam_pair = rng.sample(cams, min(2, len(cams))) + cam_a, cam_b = cam_pair[0], cam_pair[1] if len(cam_pair) > 1 else cam_pair[0] + + if cam_a == cam_b: + continue + + # Find cameras where this person does NOT appear (for distractors) + other_cams = [c for c in all_cameras if c not in cams] + + if len(qa_pairs) % 2 == 0: + # Type 1: "Which camera also shows this person?" + distractors = rng.sample(other_cams, min(2, len(other_cams))) if other_cams else [] + if not distractors: + # Use all cameras as options if needed + distractors = [c for c in all_cameras if c != cam_a and c != cam_b][:2] + + options = [cam_b] + distractors + ["None of these cameras"] + rng.shuffle(options[:3]) # Shuffle camera options but keep "None" at end + correct_idx = options.index(cam_b) + + question = ( + f"On camera {cam_a}, {desc} is visible. " + f"Which other camera also shows this same person?" + ) + + debug_info = { + "question_type": "which_camera_reid", + "mevid_person_id": pid, + "source_camera": cam_a, + "target_camera": cam_b, + "all_person_cameras": cams, + "person_description": desc, + } + + qa = { + "question_id": f"v8_reid_{len(qa_pairs)+1:03d}", + "category": "re_identification", + "difficulty": "medium", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted([cam_a, cam_b]), + "requires_multi_camera": True, + "verification": { + "question_type": "which_camera_reid", + "mevid_person_id": pid, + "person_description": desc, + "source_camera": cam_a, + "correct_target_camera": cam_b, + "all_person_cameras": cams, + }, + "debug_info": debug_info, + } + else: + # Type 2: "Is the person in [desc] on camera A the same as person on camera B?" + # Correct answer: Yes (same MEVID person) + question = ( + f"{desc.capitalize()} is observed on camera {cam_a}. " + f"Is this the same person visible on camera {cam_b}?" + ) + + 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_idx = 0 + + debug_info = { + "question_type": "same_person_confirmation", + "mevid_person_id": pid, + "camera_a": cam_a, + "camera_b": cam_b, + "all_person_cameras": cams, + "person_description": desc, + } + + qa = { + "question_id": f"v8_reid_{len(qa_pairs)+1:03d}", + "category": "re_identification", + "difficulty": "medium", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted([cam_a, cam_b]), + "requires_multi_camera": True, + "verification": { + "question_type": "same_person_confirmation", + "mevid_person_id": pid, + "person_description": desc, + "camera_a": cam_a, + "camera_b": cam_b, + "all_person_cameras": cams, + "ground_truth": "same_person", + }, + "debug_info": debug_info, + } + + qa_pairs.append(qa) + used_persons.add(pid) + + if verbose: + print(f" Re-ID: {len(qa_pairs)} questions generated") + + return qa_pairs[:count] diff --git a/meva/scripts/v8/generate_scene_summary.py b/meva/scripts/v8/generate_scene_summary.py new file mode 100644 index 0000000..b3083d5 --- /dev/null +++ b/meva/scripts/v8/generate_scene_summary.py @@ -0,0 +1,269 @@ +""" +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" + + if top_3: + top_act = humanize_activity(top_3[0][0]) + correct_desc = ( + f"A {scene_type} scene across {n_cams} cameras, " + f"primarily featuring {top_act} activity" + ) + else: + correct_desc = f"A {scene_type} scene across {n_cams} cameras" + + # 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" + ) + + wrong_descs.append( + f"An empty scene with minimal activity, captured on {max(1, n_cams - 3)} 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} camera feeds in this slot, " + f"which description best characterizes the overall scene?" + ) + + debug_info = { + "question_type": "scene_characterization", + "scene_analysis": analysis, + "scene_description": description, + "scene_type": scene_type, + } + + 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"], + }, + } + 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/v8/generate_spatial.py b/meva/scripts/v8/generate_spatial.py new file mode 100644 index 0000000..6650d4d --- /dev/null +++ b/meva/scripts/v8/generate_spatial.py @@ -0,0 +1,263 @@ +""" +V8 generate_spatial.py — Spatial entity distance questions with MEVID descriptions. + +V8 CHANGES from V7: +- Entity descriptions from MEVID (GPT/YOLO) instead of actor ID aliases +- Questions use natural person descriptions for spatial references +""" + +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, DEFAULT_FRAMERATE +from .build_scene_graph import SceneGraph, Entity +from .entity_resolution import ResolvedGraph +from .person_descriptions import enrich_entities +from .activity_hierarchy import humanize_activity +from .utils.krtd import ( + load_camera_model, CameraModel, compute_entity_distance, + classify_proximity, INDOOR_CAMERAS, +) +from .utils.yaml_stream import get_bbox_at_frame + +DEFAULT_FPS = 30.0 + + +# ============================================================================ +# Spatial Candidate Finding (from V7, unchanged logic) +# ============================================================================ + +def _find_spatial_candidates(sg: SceneGraph, verbose: bool = False) -> List[Dict]: + """Find entity pairs with valid KRTD projections for spatial questions.""" + candidates = [] + + 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") + + clips = find_clips_for_slot(sg.slot) + clip_by_camera = {c["camera_id"]: c for c in clips} + + entity_positions: Dict[str, Dict] = {} + + for eid, entity in sg.entities.items(): + if entity.camera_id not in camera_models: + continue + if entity.entity_type != "person": + continue + + model = camera_models[entity.camera_id] + mid_frame = (entity.first_frame + entity.last_frame) // 2 + + bbox = None + if entity.keyframe_bboxes: + closest_frame = min(entity.keyframe_bboxes.keys(), + key=lambda f: abs(int(f) - mid_frame)) + bbox = entity.keyframe_bboxes[closest_frame] + + if bbox is None and entity.camera_id in clip_by_camera: + clip = clip_by_camera[entity.camera_id] + geom_path = Path(clip["activities_file"]).with_name( + Path(clip["activities_file"]).name.replace(".activities.yml", ".geom.yml") + ) + if geom_path.exists(): + bbox = get_bbox_at_frame(geom_path, entity.actor_id, mid_frame, tolerance=15) + + if bbox is None: + continue + + pos = model.bbox_foot_to_world(bbox) + if pos is None: + continue + + entity_positions[eid] = { + "position": pos, + "frame": mid_frame, + "bbox": bbox, + "camera_id": entity.camera_id, + "entity": entity, + } + + if verbose: + print(f" Spatial: {len(entity_positions)} entities with 3D positions") + + entity_ids = sorted(entity_positions.keys()) + for i in range(len(entity_ids)): + for j in range(i + 1, len(entity_ids)): + eid_a = entity_ids[i] + eid_b = entity_ids[j] + + pos_a = entity_positions[eid_a] + pos_b = entity_positions[eid_b] + + distance = float(np.linalg.norm(pos_a["position"] - pos_b["position"])) + + if distance > 500: + continue + + proximity = classify_proximity(distance) + + candidates.append({ + "entity_a": eid_a, + "entity_b": eid_b, + "camera_a": pos_a["camera_id"], + "camera_b": pos_b["camera_id"], + "position_a": pos_a["position"].tolist(), + "position_b": pos_b["position"].tolist(), + "bbox_a": pos_a["bbox"], + "bbox_b": pos_b["bbox"], + "frame_a": pos_a["frame"], + "frame_b": pos_b["frame"], + "distance_m": round(distance, 2), + "proximity": proximity, + "entity_a_obj": pos_a["entity"], + "entity_b_obj": pos_b["entity"], + }) + + return candidates + + +# ============================================================================ +# Question Generation +# ============================================================================ + +def generate_spatial_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate spatial entity distance questions with MEVID descriptions. + + V8: Uses entity_descs for person descriptions instead of actor ID aliases. + """ + candidates = _find_spatial_candidates(sg, verbose) + + if verbose: + print(f" Spatial: {len(candidates)} candidate pairs") + + if not candidates: + return [] + + # Sort by distance diversity + 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"] + + rng.shuffle(near) + rng.shuffle(moderate) + rng.shuffle(far) + + selected = [] + for bucket in [near, moderate, far]: + if bucket and len(selected) < count: + selected.append(bucket[0]) + + remaining = near[1:] + moderate[1:] + far[1:] + rng.shuffle(remaining) + for c in remaining: + if len(selected) >= count: + break + selected.append(c) + + qa_pairs = [] + + for idx, cand in enumerate(selected[:count]): + ent_a = cand["entity_a_obj"] + ent_b = cand["entity_b_obj"] + proximity = cand["proximity"] + distance = cand["distance_m"] + + is_cross_camera = cand["camera_a"] != cand["camera_b"] + + # V8: Use MEVID descriptions + desc_a = entity_descs.get(cand["entity_a"], f"a person on camera {cand['camera_a']}") + desc_b = entity_descs.get(cand["entity_b"], f"a person on camera {cand['camera_b']}") + + if is_cross_camera: + question = ( + f"In the scene, how far apart are {desc_a} on camera {cand['camera_a']} " + f"and {desc_b} on camera {cand['camera_b']}?" + ) + else: + question = ( + f"How close are {desc_a} and {desc_b} " + f"in the scene visible on camera {cand['camera_a']}?" + ) + + 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", + ] + + if proximity == "near": + correct_idx = 0 + elif proximity == "moderate": + correct_idx = 1 + else: + correct_idx = 2 + + debug_info = { + "entity_a": { + "entity_id": cand["entity_a"], + "camera": cand["camera_a"], + "description": desc_a, + "bbox": cand["bbox_a"], + "frame": cand["frame_a"], + "timestamp": f"{ent_a.first_sec:.2f}-{ent_a.last_sec:.2f}s", + "world_pos_enu": cand["position_a"], + }, + "entity_b": { + "entity_id": cand["entity_b"], + "camera": cand["camera_b"], + "description": desc_b, + "bbox": cand["bbox_b"], + "frame": cand["frame_b"], + "timestamp": f"{ent_b.first_sec:.2f}-{ent_b.last_sec:.2f}s", + "world_pos_enu": cand["position_b"], + }, + "distance_meters": distance, + "proximity": proximity, + "projection_method": "krtd_bbox_foot", + } + + qa = { + "question_id": f"v8_spatial_{idx+1:03d}", + "category": "spatial", + "difficulty": "easy", + "question_template": question, + "options": options, + "correct_answer_index": correct_idx, + "correct_answer": options[correct_idx], + "requires_cameras": sorted(set([cand["camera_a"], cand["camera_b"]])), + "requires_multi_camera": is_cross_camera, + "verification": { + "entity_a": cand["entity_a"], + "entity_b": cand["entity_b"], + "entity_a_desc": desc_a, + "entity_b_desc": desc_b, + "world_pos_a_enu": cand["position_a"], + "world_pos_b_enu": cand["position_b"], + "distance_meters": distance, + "proximity": proximity, + "projection_method": "krtd_bbox_foot", + }, + "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/v8/generate_temporal.py b/meva/scripts/v8/generate_temporal.py new file mode 100644 index 0000000..d9e68ed --- /dev/null +++ b/meva/scripts/v8/generate_temporal.py @@ -0,0 +1,336 @@ +""" +V8 generate_temporal.py — Temporal cross-camera questions with MEVID person descriptions. + +V8 CHANGES from V7: +- Entity aliases replaced with MEVID person descriptions when available +- Questions prioritize events involving described persons +- Description-enriched question text for better naturalization +""" + +import random +from pathlib import Path +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 .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, +) +from .utils.mevid import find_mevid_persons_for_slot + +# ============================================================================ +# Constants +# ============================================================================ + +MIN_GAP = 3.0 +MAX_GAP = 15.0 +FALLBACK_MAX_GAP = 20.0 +DEFAULT_FPS = 30.0 + + +# ============================================================================ +# Connection Scoring (from V7, unchanged) +# ============================================================================ + +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 events.""" + score = 0.0 + connection_type = "unrelated" + connection_strength = "weak" + mevid_validated = False + mevid_person_id = None + relationship = None + cluster_id = None + + for cluster in resolved.entity_clusters: + a_entities = set() + b_entities = set() + for eid in cluster.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" + connection_strength = "strong" + score += 3.0 + cluster_id = cluster.cluster_id + break + + rel = get_relationship(event_a.activity, event_b.activity) + if rel: + relationship = rel + rel_score = get_relationship_strength(event_a.activity, event_b.activity) + score += rel_score * 2.0 + if connection_type == "unrelated": + connection_type = f"related_activities_{rel}" + connection_strength = "medium" if rel_score >= 0.7 else "weak" + + 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 + score += 1.5 + break + + if event_a.activity != event_b.activity: + score += 0.5 + + 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, + } + + +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]) -> str: + """Get a human-readable description for an event, using MEVID if available.""" + # Try to get MEVID description for the entity involved + 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: + short_act = humanize_activity(event.activity) + return f"{desc} {short_act} on camera {event.camera_id}" + + # Fallback: V7-style + short_act = humanize_activity(event.activity) + return f"someone {short_act} on camera {event.camera_id}" + + +# ============================================================================ +# 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.""" + 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) + + 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 generate_temporal_qa(sg: SceneGraph, resolved: ResolvedGraph, + entity_descs: Dict[str, str], + rng: random.Random, count: int = 2, + verbose: bool = False) -> List[Dict]: + """ + Generate temporal cross-camera questions with MEVID person descriptions. + + V8 changes: + - entity_descs parameter provides MEVID descriptions + - Question text uses natural person descriptions instead of actor IDs + - Prioritizes events involving described persons + """ + slot_cameras = list(sg.cameras.keys()) + mevid_person_cameras = find_mevid_persons_for_slot(sg.slot, slot_cameras) + + 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 [] + + # Diversify selection, preferring MEVID-validated + described events + used_pairs = set() + used_activities = set() + selected = [] + + # First pass: prefer MEVID-validated + for c in candidates: + if c["mevid_validated"] and len(selected) < count: + cam_pair = (c["event_a"].camera_id, c["event_b"].camera_id) + act_pair = (c["event_a"].activity, c["event_b"].activity) + if cam_pair not in used_pairs or act_pair not in used_activities: + used_pairs.add(cam_pair) + used_activities.add(act_pair) + selected.append(c) + + # Fill remaining from any candidates + for c in candidates: + if len(selected) >= count: + break + if c not in selected: + selected.append(c) + + # Generate QA pairs + 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) + desc_b = _get_event_description(eb, sg, entity_descs) + + question = f"{desc_a} and {desc_b} -- which occurred first?" + + options = [ + f"The event on camera {ea.camera_id} occurred first", + f"The event on camera {eb.camera_id} 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"The event on camera {eb.camera_id} occurred first", + f"The event on camera {ea.camera_id} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + correct_idx = 1 + + 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"), + "mevid_validated": cand["mevid_validated"], + "mevid_person_id": cand["mevid_person_id"], + } + + qa = { + "question_id": f"v8_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: + mevid_count = sum(1 for q in qa_pairs + if q["debug_info"]["mevid_validated"]) + print(f" Temporal: {len(qa_pairs)} questions ({mevid_count} MEVID-validated)") + + return qa_pairs diff --git a/meva/scripts/v8/naturalize_v8_qa.py b/meva/scripts/v8/naturalize_v8_qa.py new file mode 100644 index 0000000..48f92e7 --- /dev/null +++ b/meva/scripts/v8/naturalize_v8_qa.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +""" +V8 GPT Naturalization Wrapper — Converts V8 template QA pairs into natural language. + +V8 CHANGES from V7: +- New category examples: re_identification, scene_summary, attribute_verification +- Updated system prompt for MEVID person descriptions +- Updated cost estimates for 5 categories (~9 Qs/slot) +- Output version: v8_natural + +Usage: + python3 scripts/v8/naturalize_v8_qa.py --input data/qa_pairs/SLOT.v8.json --dry-run + python3 scripts/v8/naturalize_v8_qa.py --input data/qa_pairs/SLOT.v8.json +""" + +import json +import time +import argparse +import os +import sys +from pathlib import Path +from typing import List, Dict, Any, Optional + +# ============================================================================ +# Paths & Constants +# ============================================================================ + +QA_DIR = Path("/home/ah66742/data/qa_pairs") +LOG_DIR = Path("/home/ah66742/data/gpt_logs") + +DEFAULT_MODEL = "gpt-4o-mini" +DEFAULT_TEMPERATURE = 0.7 +MAX_RETRIES = 3 +RETRY_DELAY = 2.0 + +# ============================================================================ +# System Prompt — V8 with MEVID person descriptions +# ============================================================================ + +SYSTEM_PROMPT = """\ +You are a question naturalizer for a multi-camera surveillance video understanding benchmark. + +Your task is to rephrase template questions and options into fluent, natural English suitable +for a Video Question Answering (VQA) evaluation. The questions are about activities observed +across multiple synchronized surveillance cameras at the same location. + +IMPORTANT for V8: Questions now include person descriptions derived from visual analysis +(e.g., "the person in a dark hooded jacket and dark pants"). Preserve these descriptions +naturally — they are key identifiers that replace debug markers from earlier versions. + +Rules: +1. Rephrase the question to sound natural and conversational +2. Rephrase each option to sound natural, keeping the SAME meaning and order +3. REMOVE any remaining debug markers: strip actor IDs (#NNN), timestamps (@ Ns), frame refs +4. KEEP person descriptions (e.g., "the person in a blue jacket") — these are meaningful +5. Keep camera identifiers (e.g., "camera G299") — these are meaningful +6. Keep spatial terms (near, moderate, far, meters) unchanged +7. Keep "simultaneously" and "cannot be determined" as-is +8. Do NOT add information not present in the template +9. Do NOT reorder the options +10. For perception questions: keep camera IDs as-is in options +11. For re-identification questions: preserve person appearance descriptions precisely +12. For scene summary questions: keep activity type names and camera references +13. Add a brief 1-sentence "reasoning" explaining why the correct answer is right + (based ONLY on the information given, not external knowledge) + +Output format — respond with ONLY a JSON object: +{ + "question": "The naturalized question text", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "Brief explanation of why the answer is correct" +} +""" + +# ============================================================================ +# Category-specific prompt examples (few-shot) +# ============================================================================ + +CATEGORY_EXAMPLES = { + "temporal": { + "hint": "This is a temporal ordering question about which event happened first across different cameras. The entities may have appearance descriptions — preserve those naturally.", + "example_input": 'In the scene, the person in a dark hooded jacket opening the trunk on camera G328 and the person in blue jeans exiting the vehicle on camera G421 -- which occurred first?', + "example_output": '{"question": "Between the person in a dark hooded jacket opening the trunk on camera G328 and the person in blue jeans exiting the vehicle on camera G421, which event happened first?", "options": ["The person in the dark hooded jacket opening the trunk occurred first", "The person in blue jeans exiting the vehicle occurred first", "They occurred simultaneously", "Cannot be determined"], "reasoning": "The trunk opening event started earlier, with a 7-second gap before the vehicle exit."}', + }, + "spatial": { + "hint": "This is a spatial distance question about how far apart two people are. Person descriptions should be preserved naturally.", + "example_input": 'In the scene, how far apart are the person in a blue jacket on camera G328 and the person in dark pants on camera G421?', + "example_output": '{"question": "How far apart are the person in a blue jacket visible on camera G328 and the person in dark pants seen on camera G421?", "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": "Based on their positions in the scene, these two individuals are approximately 8 meters apart."}', + }, + "perception": { + "hint": "This is a perception question about activities or attributes. For attribute verification, the question asks about a person's clothing or carried objects.", + "example_input": 'A person is visible on camera G328. What color is their upper body clothing?', + "example_output": '{"question": "What color clothing is the person on camera G328 wearing on their upper body?", "options": ["Blue", "Black", "Red", "White"], "reasoning": "Visual analysis of the person on camera G328 shows they are wearing blue upper body clothing."}', + }, + "re_identification": { + "hint": "This is a person re-identification question asking whether the same person appears on multiple cameras. Preserve appearance descriptions precisely — they are the key evidence.", + "example_input": 'A person described as \"wearing a dark hooded jacket and dark pants\" appears on camera G328. Which other camera also shows this person?', + "example_output": '{"question": "The person wearing a dark hooded jacket and dark pants is seen on camera G328. Which other camera also captures this same person?", "options": ["G421", "G299", "G330", "G336"], "reasoning": "The same individual in the dark hooded jacket and dark pants is identified on both camera G328 and camera G421 based on appearance matching."}', + }, + "scene_summary": { + "hint": "This is a scene-level summary question about overall activity patterns or camera utilization. Keep statistical terms and camera references.", + "example_input": 'What best characterizes the overall scene activity across all cameras during this time period?', + "example_output": '{"question": "How would you best characterize the overall activity patterns observed across all cameras during this time period?", "options": ["Predominantly pedestrian activity", "Mixed pedestrian and vehicle activity", "Predominantly vehicle activity", "Minimal observable activity"], "reasoning": "The majority of annotated events involve pedestrian activities across the cameras, with only a small fraction being vehicle-related."}', + }, +} + + +# ============================================================================ +# GPT Client +# ============================================================================ + +def _create_client(): + """Create OpenAI client.""" + import openai + return openai.OpenAI() + + +def _naturalize_one(client, question: Dict, model: str, + temperature: float) -> Optional[Dict]: + """ + Send one question to GPT for naturalization. + Returns {naturalized_question, naturalized_options, reasoning, usage} or None. + """ + category = question["category"] + template = question["question_template"] + options = question["options"] + verification = question.get("verification", {}) + + # Use subcategory if available (e.g., attribute_verification) + lookup_cat = question.get("subcategory", category) + cat_info = CATEGORY_EXAMPLES.get(lookup_cat, CATEGORY_EXAMPLES.get(category, {})) + hint = cat_info.get("hint", "Rephrase this question naturally.") + example_in = cat_info.get("example_input", "") + example_out = cat_info.get("example_output", "") + + user_message = f"""Category: {category} +{hint} + +""" + if example_in and example_out: + user_message += f"""Example: + Input: {example_in} + Output: {example_out} + +""" + + user_message += f"""Now naturalize this question: + +Template: {template} + +Options: +""" + for i, opt in enumerate(options): + user_message += f" {chr(65+i)}) {opt}\n" + + # Add verification context for reasoning + if category == "temporal" and "gap_sec" in verification: + user_message += f"\nContext: The gap between events is {verification['gap_sec']}s.\n" + elif category == "spatial" and "distance_meters" in verification: + user_message += f"\nContext: Distance is {verification['distance_meters']}m.\n" + elif category == "re_identification": + user_message += f"\nContext: Person identified via MEVID cross-camera matching.\n" + + user_message += "\nRespond with ONLY the JSON object." + + for attempt in range(MAX_RETRIES): + try: + response = client.chat.completions.create( + model=model, + temperature=temperature, + response_format={"type": "json_object"}, + messages=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": user_message}, + ], + max_tokens=600, + ) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result or "options" not in result: + print(f" WARNING: Missing fields in GPT response, retry {attempt+1}") + continue + + if len(result["options"]) != len(options): + print(f" WARNING: Option count mismatch " + f"({len(result['options'])} vs {len(options)}), retry {attempt+1}") + continue + + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + } + + return { + "naturalized_question": result["question"], + "naturalized_options": result["options"], + "reasoning": result.get("reasoning", ""), + "usage": usage, + } + + 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: + """Naturalize all QA pairs in the input data.""" + client = _create_client() + qa_pairs = input_data["qa_pairs"] + total = len(qa_pairs) + + print(f"\n Naturalizing {total} questions with {model}...") + + naturalized_pairs = [] + total_tokens = 0 + failures = 0 + + for i, q in enumerate(qa_pairs): + if verbose: + print(f" [{i+1}/{total}] {q['category']}: {q['question_template'][:60]}...") + + result = _naturalize_one(client, q, model, temperature) + + if result is None: + failures += 1 + nat_q = q.copy() + nat_q["naturalized_question"] = q["question_template"] + nat_q["naturalized_options"] = q["options"] + nat_q["reasoning"] = "" + nat_q["naturalization_failed"] = True + naturalized_pairs.append(nat_q) + continue + + nat_q = q.copy() + nat_q["naturalized_question"] = result["naturalized_question"] + nat_q["naturalized_options"] = result["naturalized_options"] + nat_q["reasoning"] = result["reasoning"] + nat_q["original_template"] = q["question_template"] + nat_q["original_options"] = q["options"] + naturalized_pairs.append(nat_q) + + total_tokens += result["usage"]["total_tokens"] + + if (i + 1) % 5 == 0: + print(f" Progress: {i+1}/{total} ({total_tokens} tokens)") + + output = { + "slot": input_data["slot"], + "version": "v8_natural", + "generator": "naturalize_v8_qa.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", {}), + "v8_stats": input_data.get("v8_stats", {}), + "qa_pairs": naturalized_pairs, + } + + return output + + +# ============================================================================ +# Strip Debug Markers +# ============================================================================ + +def strip_debug_markers(input_data: Dict) -> Dict: + """Remove debug_info blocks from QA pairs (for VQA model input).""" + output = input_data.copy() + cleaned_pairs = [] + for q in input_data["qa_pairs"]: + cleaned = {k: v for k, v in q.items() if k != "debug_info"} + cleaned_pairs.append(cleaned) + output["qa_pairs"] = cleaned_pairs + return output + + +# ============================================================================ +# Dry Run +# ============================================================================ + +def dry_run(input_data: Dict): + """Show what would be sent to GPT without making API calls.""" + qa_pairs = input_data["qa_pairs"] + + print(f"\n === DRY RUN — {len(qa_pairs)} questions ===\n") + + categories = {} + for q in qa_pairs: + categories.setdefault(q["category"], []).append(q) + + for cat, qs in sorted(categories.items()): + q = qs[0] + cat_info = CATEGORY_EXAMPLES.get(q.get("subcategory", cat), + CATEGORY_EXAMPLES.get(cat, {})) + + print(f" [{cat}] ({len(qs)} questions)") + print(f" TEMPLATE: {q['question_template'][:100]}...") + for i, opt in enumerate(q["options"]): + marker = " *" if i == q.get("correct_answer_index") else "" + print(f" {chr(65+i)}) {opt}{marker}") + + if q.get("subcategory"): + print(f" SUBCATEGORY: {q['subcategory']}") + print(f" DIFFICULTY: {q.get('difficulty', 'N/A')}") + print(f" HINT: {cat_info.get('hint', 'N/A')[:80]}...") + print() + + # Cost estimate (V8: ~9 Qs/slot) + est_tokens = len(qa_pairs) * 350 # slightly more with person descriptions + est_cost_mini = est_tokens * 0.4e-6 + est_cost_4o = est_tokens * 6e-6 + + print(f" === Cost Estimate ===") + print(f" Questions: {len(qa_pairs)}") + print(f" Est. tokens: ~{est_tokens}") + print(f" gpt-4o-mini: ~${est_cost_mini:.4f}") + print(f" gpt-4o: ~${est_cost_4o:.4f}") + + # V8 stats + mevid = input_data.get("mevid_persons_in_slot", 0) + print(f"\n === V8 Stats ===") + print(f" MEVID persons: {mevid}") + print(f" Categories: {sorted(categories.keys())}") + v8_stats = input_data.get("v8_stats", {}) + if v8_stats: + print(f" MEVID descriptions: {v8_stats.get('entities_with_mevid_descriptions', 0)}") + print(f" Attr verification Qs: {v8_stats.get('attribute_verification_questions', 0)}") + print(f" Re-ID Qs: {v8_stats.get('reid_questions', 0)}") + print(f" Scene summary Qs: {v8_stats.get('scene_summary_questions', 0)}") + + +# ============================================================================ +# CLI +# ============================================================================ + +def main(): + parser = argparse.ArgumentParser( + description="V8 GPT Naturalization Wrapper", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + parser.add_argument("--input", "-i", required=True, + help="Path to V8 QA JSON file") + parser.add_argument("--output", "-o", + help="Output path (default: {input}.natural.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) + parser.add_argument("--dry-run", action="store_true", + help="Show prompts without making API calls") + parser.add_argument("--strip-debug", action="store_true", + help="Only strip debug_info blocks (no GPT call, free)") + parser.add_argument("--verbose", "-v", action="store_true") + parser.add_argument("--yes", "-y", action="store_true", + help="Skip confirmation prompt") + + args = parser.parse_args() + + input_path = Path(args.input) + if not input_path.exists(): + print(f"ERROR: Input not found: {input_path}") + return + + print(f"Loading: {input_path}") + 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}") + print(f" MEVID supported: {input_data.get('mevid_supported', False)}") + + if args.strip_debug: + result = strip_debug_markers(input_data) + out_path = args.output or str(input_path).replace(".v8.json", ".v8.clean.json") + with open(out_path, "w") as f: + json.dump(result, f, indent=2, default=str) + print(f" Stripped debug_info → {out_path}") + return + + if args.dry_run: + dry_run(input_data) + return + + if not os.environ.get("OPENAI_API_KEY"): + print("ERROR: OPENAI_API_KEY not set") + return + + if not args.yes: + print(f"\n Will send {total} questions to {args.model}") + resp = input(" Continue? [y/N] ").strip().lower() + if resp != "y": + print(" Aborted.") + return + + result = naturalize_batch(input_data, args.model, args.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']}") + + out_path = args.output or str(input_path).replace(".v8.json", ".v8.natural.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_v8_{args.model}.json" + with open(log_path, "w") as f: + json.dump({ + "model": args.model, + "temperature": args.temperature, + "total_tokens": result["total_tokens"], + "questions_processed": total, + "failures": result["failures"], + }, 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']}]") + print(f" TEMPLATE: {q.get('original_template', q['question_template'])[:80]}...") + print(f" NATURAL: {q['naturalized_question'][:80]}...") + if q.get("reasoning"): + print(f" REASONING: {q['reasoning'][:80]}...") + + +if __name__ == "__main__": + main() diff --git a/meva/scripts/v8/naturalize_v8_qa_v2.py b/meva/scripts/v8/naturalize_v8_qa_v2.py new file mode 100644 index 0000000..313d8fe --- /dev/null +++ b/meva/scripts/v8/naturalize_v8_qa_v2.py @@ -0,0 +1,1176 @@ +#!/usr/bin/env python3 +""" +V8 Naturalization V2 — Pre-processes and naturalizes V8 template QA pairs. + +Key improvements over V1: +1. Pre-processing layer (FREE, no API call): + - Description simplification: "blue upper body garment" → "blue top" + - Camera reference removal from temporal/spatial question text + - Temporal anchors for disambiguation ("about 6 seconds in") + - Activity de-duplication ("enters scene enters scene" → "enters scene") + - Grammar fixes: capitalization, mid-sentence periods + - Event descriptions reconstructed from structured verification data + +2. Updated GPT prompts (optional GPT naturalization): + - Better few-shot examples reflecting cleaned templates + - Category-specific format guidance + - Post-processing validation + +3. Three modes: + --preprocess-only: Just pre-process templates (free, instant) + --dry-run: Show what would be sent to GPT + (default): Pre-process + GPT naturalization + +Usage: + # Pre-process only (free): + python3 scripts/v8/naturalize_v8_qa_v2.py --input data/qa_pairs/SLOT.v8.json --preprocess-only + + # Full pipeline (pre-process + GPT): + python3 scripts/v8/naturalize_v8_qa_v2.py --input data/qa_pairs/SLOT.v8.json + + # Dry-run: + python3 scripts/v8/naturalize_v8_qa_v2.py --input data/qa_pairs/SLOT.v8.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 +# ============================================================================ + +QA_DIR = Path("/home/ah66742/data/qa_pairs") +LOG_DIR = Path("/home/ah66742/data/gpt_logs") + +DEFAULT_MODEL = "gpt-4o-mini" +DEFAULT_TEMPERATURE = 0.7 +MAX_RETRIES = 3 +RETRY_DELAY = 2.0 +CLIP_DURATION = 300.0 # 5-minute clips + + +# ============================================================================ +# Description Simplification (standalone, mirrors person_descriptions.py) +# ============================================================================ + +# 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 (standalone, mirrors activity_hierarchy.py) +# ============================================================================ + +_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", +} + +_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 = _GERUND_MAP.get(first, first + "ing") + 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 for options: person_opens_facility_door → opening a facility door""" + return _humanize_gerund(activity).lower() + + +# ============================================================================ +# 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 < 180: + return f"around {int(round(sec / 10)) * 10} seconds in" + 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 person 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" + """ + if not entity_description: + return "a person" + + desc = entity_description.strip() + + # Check if this is a real appearance description vs. activity fallback + if not _has_appearance_info(desc): + return "a person" + + # Remove embedded activity text after the description + # Pattern: description ends with period, then activity follows + # e.g. "...backpack. enters scene through structure on camera G421" + 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 = False) -> Dict: + """ + Reconstruct temporal question from structured verification data. + + Fixes: camera refs in question, description verbosity, activity doubling, + temporal ambiguity (adds timestamp anchors), capitalization. + + If strip_camera_refs=True (V3 mode), camera IDs are removed from the question + text — 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 + # V3 mode: strip camera refs from question text (person descriptions disambiguate) + # V2 mode: keep camera refs in question text + 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}" + 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: + # Event A mentioned first AND occurred first → option 0 correct + desc_1, desc_2 = clean_a, clean_b + ev_1, ev_2 = ev_a, ev_b + else: + # Event B mentioned first but Event A occurred first → option 1 correct + 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 camera + activity labels + act_1 = _humanize_gerund(ev_1.get("activity", "event")) + act_2 = _humanize_gerund(ev_2.get("activity", "event")) + cam_1 = ev_1.get("camera", "") + cam_2 = ev_2.get("camera", "") + + if act_1.lower() == act_2.lower(): + # Same activity on different cameras: use camera to differentiate + options = [ + f"The event on camera {cam_1} occurred first", + f"The event on camera {cam_2} occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + else: + options = [ + f"{act_1} (camera {cam_1}) occurred first", + f"{act_2} (camera {cam_2}) occurred first", + "They occurred simultaneously", + "Cannot be determined", + ] + + 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": + # "A person is visible on camera G423. What color are they wearing..." + # Simplify any embedded person description + person_desc = v.get("person_description", "") + if person_desc: + simplified = simplify_description(person_desc).rstrip(".") + # Template is already clean for this type — just ensure capitalization + elif q_type == "which_camera": + # "Which camera captures a carries heavy object event?" + # Make activity name more natural + 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:] + # Re-add question mark if simplification stripped it + 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: + # Fallback: simplify in place + 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, + version: str = "v2") -> Dict: + """ + Pre-process all QA pairs: simplify descriptions, remove camera refs, + add temporal anchors, fix grammar. FREE (no API call). + + version='v3' strips camera refs from temporal question text. + """ + output = {k: v for k, v in input_data.items() if k != "qa_pairs"} + output["version"] = "v8_preprocessed" + output["preprocessor"] = "naturalize_v8_qa_v2.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=(version == "v3")) + elif cat == "spatial": + cleaned = _preprocess_spatial(qa) + elif cat == "perception": + cleaned = _preprocess_perception(qa) + elif cat == "re_identification": + cleaned = _preprocess_reid(qa) + elif cat == "scene_summary": + 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) + + 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 + + +# ============================================================================ +# Updated GPT System Prompt (V2) +# ============================================================================ + +SYSTEM_PROMPT_V2 = """\ +You are a question naturalizer for a multi-camera surveillance video QA benchmark. + +Your task is to polish pre-processed template questions into fluent, natural English +suitable for a Video Question Answering (VQA) evaluation. The templates have already +been cleaned up — your job is to make them sound conversational while preserving all +factual content. + +Rules: +1. Rephrase the question to sound natural and conversational +2. Rephrase each option to sound natural, keeping the SAME meaning and order +3. Preserve person descriptions precisely (clothing colors, carried objects, distinctive features) +4. Preserve camera identifiers (e.g., "camera G299") when present — they tell the VLM where to look +5. Preserve event numbering (Event 1, Event 2) when present +6. Keep spatial terms unchanged (near, moderate, far, meters) +7. Keep "simultaneously" and "cannot be determined" as-is +8. Do NOT add information not present in the template +9. Do NOT reorder the options +10. Add a brief 1-sentence "reasoning" explaining why the correct answer is right + +Output format — respond with ONLY a JSON object: +{ + "question": "The naturalized question text", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "Brief explanation of why the answer is correct" +} +""" + +SYSTEM_PROMPT_V3 = """\ +You are a creative question writer for a multi-camera surveillance video QA benchmark. + +Your task: rewrite template questions into varied, natural English. Each question should +sound like a DIFFERENT person wrote it. Vary sentence structure, word choice, and phrasing +aggressively — avoid formulaic patterns like always starting with "In this scene..." or +"Looking at the cameras..." or "Two events are observed...". + +Rules: +1. VARY your phrasing — use different sentence openings, structures, and vocabulary each time +2. Preserve ALL factual content: person descriptions (clothing colors, carried objects), activities +3. Rephrase options naturally but keep the SAME meaning and order +4. Camera identifiers in answer options should be preserved +5. Keep spatial terms (near, moderate, far, meters) and "simultaneously"/"cannot be determined" +6. Do NOT add information not in the template +7. Do NOT reorder options +8. Add a 1-sentence "reasoning" for why the correct answer is right + +Phrasing variety examples (do NOT copy these verbatim — invent your own): +- "Two things happen in view of the cameras..." +- "Watch for these two events..." +- "Based on what the cameras recorded..." +- "Among the people visible..." +- Direct question without preamble: "Which happened first: ..." +- "The footage shows..." / "Can you tell..." / "What do you notice about..." +- 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 question", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "Brief explanation of why the answer is correct" +} +""" + +GRAMMAR_CHECKER_PROMPT = """\ +You are a meticulous copy editor. You receive a JSON object containing a VQA question, +options, and reasoning. Your ONLY job is to fix grammar, punctuation, and awkward phrasing. + +Rules: +1. Fix grammatical errors, run-on sentences, and punctuation mistakes +2. Do NOT change meaning, add information, or remove details +3. Do NOT reorder options +4. Do NOT change camera IDs, person descriptions, or spatial/temporal terms +5. Keep the same JSON structure +6. If the text is already grammatically correct, return it unchanged +7. Be conservative — only fix clear errors + +Output format — respond with ONLY a JSON object: +{ + "question": "The grammar-checked question", + "options": ["Option A", "Option B", "Option C", "Option D"], + "reasoning": "The grammar-checked reasoning" +} +""" + +# ============================================================================ +# Category-specific prompt examples (few-shot, V2) +# ============================================================================ + +CATEGORY_EXAMPLES_V3 = { + "temporal": { + "hint": "This is a temporal ordering question about two events. Person descriptions (clothing, objects) identify who is who — there are NO camera references in the question. Cameras only appear in answer options. VARY your phrasing creatively.", + "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?", "options": ["Entering through a structure (camera G421) occurred first", "Interacting with a person (camera G330) occurred first", "They occurred simultaneously", "Cannot be determined"], "reasoning": "The person in gray entered through the structure before the blue-topped person interacted with anyone."}', + }, + "spatial": { + "hint": "Spatial distance question about two people. Use their appearance descriptions to identify them. No camera references. VARY phrasing.", + "example_input": '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 white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?', + "example_output": '{"question": "How far apart would you say the person in blue with the black graphic hoodie is from the one wearing a white Puma hoodie and camo pants?", "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": "Their positions in the scene place them approximately 6 meters apart."}', + }, + "perception": { + "hint": "Perception question about activities or visual attributes. Camera references are part of the question — keep them. VARY phrasing.", + "example_input": 'A person is visible on camera G423. What color are they wearing on their lower body?', + "example_output": '{"question": "Check camera G423 — what color is the person there wearing below the waist?", "options": ["Gray", "Navy", "Blue", "Brown"], "reasoning": "The person visible on G423 has blue pants on."}', + }, + "re_identification": { + "hint": "Person re-identification across cameras. Camera refs are essential. Appearance descriptions must be precise. VARY phrasing.", + "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?", "options": ["G423", "G299", "G328", "None of these cameras"], "reasoning": "This person with the distinctive graphic hoodie shows up on both G419 and G423."}', + }, + "scene_summary": { + "hint": "Scene-level summary question. Keep camera counts and activity references. VARY phrasing.", + "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?", "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 putting down objects"], "reasoning": "Pedestrian activities dominate across all 8 feeds, with putting down objects being the most common."}', + }, +} + +CATEGORY_EXAMPLES_V2 = { + "temporal": { + "hint": "This is a temporal ordering question with two numbered events on specific cameras. Preserve the event numbers, person descriptions, and camera references exactly.", + "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 on camera G421. (2) A person interacting with a person on camera G330. Which event occurred first?', + "example_output": '{"question": "Two events are observed across the camera feeds: (1) A person in a gray top and green pants, carrying a black backpack, enters through a structure on camera G421. (2) A person interacts with another person on camera G330. Which of these events happened 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"], "reasoning": "Based on the video evidence, the scene entry on camera G421 occurred before the interaction on camera G330."}', + }, + "spatial": { + "hint": "This is a spatial distance question about how far apart two people are. Person descriptions should be preserved naturally. No camera references in the question.", + "example_input": '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 white hoodie with a Puma logo, camouflage pants, and a camouflage cap in the scene?', + "example_output": '{"question": "In the scene, how far apart are the person in blue clothes with a black graphic hoodie and the person in a white Puma hoodie with camouflage pants and cap?", "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": "Based on their projected positions in the scene, these two individuals are approximately 6 meters apart, placing them at a moderate distance."}', + }, + "perception": { + "hint": "This is a perception question about activities or visual attributes. Camera references are part of the question structure — preserve them.", + "example_input": 'A person is visible on camera G423. What color are they wearing on their lower body?', + "example_output": '{"question": "Looking at camera G423, what color is the visible person wearing on their lower body?", "options": ["Gray", "Navy", "Blue", "Brown"], "reasoning": "The person on camera G423 is wearing blue pants, making Blue the correct answer."}', + }, + "re_identification": { + "hint": "This is a person re-identification question. Camera references are essential — preserve them. Preserve appearance descriptions precisely.", + "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": "A person in a blue top and blue pants with a black graphic hoodie is visible on camera G419. Which other camera also shows this same person?", "options": ["G423", "G299", "G328", "None of these cameras"], "reasoning": "The person wearing a blue top and pants with the distinctive black graphic hoodie appears on both camera G419 and camera G423."}', + }, + "scene_summary": { + "hint": "This is a scene-level summary question. Keep statistical terms, camera counts, and activity references.", + "example_input": 'Considering all 8 camera feeds in this slot, which description best characterizes the overall scene?', + "example_output": '{"question": "Looking at all 8 camera feeds together, which description best captures the overall activity in this 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 putting down objects"], "reasoning": "The vast majority of events are pedestrian activities observed across all 8 cameras, with putting down objects being the most frequent activity."}', + }, +} + + +# ============================================================================ +# GPT Client +# ============================================================================ + +def _create_client(): + """Create OpenAI client.""" + import openai + return openai.OpenAI() + + +def _naturalize_one(client, question: Dict, model: str, + temperature: float, + system_prompt: str = None, + examples: Dict = None) -> Optional[Dict]: + """Send one pre-processed question to GPT for naturalization.""" + if system_prompt is None: + system_prompt = SYSTEM_PROMPT_V2 + if examples is None: + examples = CATEGORY_EXAMPLES_V2 + + category = question["category"] + template = question["question_template"] + options = question["options"] + verification = question.get("verification", {}) + + lookup_cat = question.get("subcategory", category) + cat_info = examples.get(lookup_cat, + examples.get(category, {})) + hint = cat_info.get("hint", "Rephrase this question naturally.") + example_in = cat_info.get("example_input", "") + example_out = cat_info.get("example_output", "") + + user_message = f"Category: {category}\n{hint}\n\n" + + if example_in and example_out: + user_message += f"Example:\n Input: {example_in}\n Output: {example_out}\n\n" + + user_message += f"Now naturalize this question:\n\nTemplate: {template}\n\nOptions:\n" + for i, opt in enumerate(options): + user_message += f" {chr(65+i)}) {opt}\n" + + # Add verification context for reasoning + if category == "temporal" and "gap_sec" in verification: + user_message += f"\nContext: The gap between events is {verification['gap_sec']}s.\n" + elif category == "spatial" and "distance_meters" in verification: + user_message += f"\nContext: Distance is {verification['distance_meters']}m.\n" + elif category == "re_identification": + user_message += "\nContext: Person identified via cross-camera appearance matching.\n" + + user_message += "\nRespond with ONLY the JSON object." + + for attempt in range(MAX_RETRIES): + try: + response = client.chat.completions.create( + model=model, + temperature=temperature, + response_format={"type": "json_object"}, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_message}, + ], + max_tokens=600, + ) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result or "options" not in result: + print(f" WARNING: Missing fields, retry {attempt+1}") + continue + + if len(result["options"]) != len(options): + print(f" WARNING: Option count mismatch, retry {attempt+1}") + continue + + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + } + + return { + "naturalized_question": result["question"], + "naturalized_options": result["options"], + "reasoning": result.get("reasoning", ""), + "usage": usage, + } + + 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 + + +def _grammar_check_one(client, naturalized: Dict, model: str) -> Optional[Dict]: + """Send one naturalized question through grammar checker (pass 2).""" + user_message = json.dumps({ + "question": naturalized.get("naturalized_question", ""), + "options": naturalized.get("naturalized_options", []), + "reasoning": naturalized.get("reasoning", ""), + }, indent=2) + + for attempt in range(MAX_RETRIES): + try: + response = client.chat.completions.create( + model=model, + temperature=0.3, # Low temperature for conservative edits + response_format={"type": "json_object"}, + messages=[ + {"role": "system", "content": GRAMMAR_CHECKER_PROMPT}, + {"role": "user", "content": user_message}, + ], + max_tokens=600, + ) + + result = json.loads(response.choices[0].message.content) + + if "question" not in result or "options" not in result: + break # Fall back to naturalized version + + usage = { + "prompt_tokens": response.usage.prompt_tokens, + "completion_tokens": response.usage.completion_tokens, + "total_tokens": response.usage.total_tokens, + } + + return { + "question": result["question"], + "options": result["options"], + "reasoning": result.get("reasoning", ""), + "usage": usage, + } + + except Exception as e: + if attempt < MAX_RETRIES - 1: + time.sleep(RETRY_DELAY) + continue + + return None # Grammar check failed, caller uses naturalized version as-is + + +# ============================================================================ +# Batch Processing +# ============================================================================ + +def naturalize_batch(input_data: Dict, model: str, temperature: float, + verbose: bool = False, version: str = "v2") -> Dict: + """Pre-process + GPT naturalize all QA pairs.""" + # Step 1: Pre-process (free) — V3 strips camera refs from temporal Qs + preprocessed = preprocess_all(input_data, verbose=verbose, version=version) + + # Step 2: Select prompts based on version + if version == "v3": + sys_prompt = SYSTEM_PROMPT_V3 + cat_examples = CATEGORY_EXAMPLES_V3 + else: + sys_prompt = SYSTEM_PROMPT_V2 + cat_examples = CATEGORY_EXAMPLES_V2 + + # Step 3: GPT naturalize + client = _create_client() + qa_pairs = preprocessed["qa_pairs"] + total = len(qa_pairs) + + print(f"\n Naturalizing {total} pre-processed questions with {model} ({version})...") + + 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]}...") + + # --- Pass 1: Naturalization --- + result = _naturalize_one(client, q, model, temperature, + system_prompt=sys_prompt, examples=cat_examples) + + if result is None: + failures += 1 + nat_q = q.copy() + nat_q["naturalized_question"] = q["question_template"] + nat_q["naturalized_options"] = q["options"] + nat_q["reasoning"] = "" + nat_q["naturalization_failed"] = True + naturalized_pairs.append(nat_q) + continue + + nat_q = q.copy() + 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"] + + # --- Pass 2: Grammar check --- + gc_result = _grammar_check_one(client, nat_q, model) + + if gc_result is not None: + nat_q["naturalized_question"] = gc_result["question"] + nat_q["naturalized_options"] = gc_result["options"] + nat_q["reasoning"] = gc_result["reasoning"] + nat_q["grammar_checked"] = True + total_tokens += gc_result["usage"]["total_tokens"] + else: + nat_q["grammar_checked"] = False + + naturalized_pairs.append(nat_q) + + if (i + 1) % 5 == 0: + print(f" Progress: {i+1}/{total} ({total_tokens} tokens)") + + version_tag = "v8_natural_v3" if version == "v3" else "v8_natural_v2" + + output = { + "slot": input_data["slot"], + "version": version_tag, + "generator": "naturalize_v8_qa_v2.py", + "preprocessor": f"{version}_preprocess", + "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", {}), + "v8_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 + est_tokens = len(qa_pairs) * 400 # slightly more with examples + est_cost_mini = est_tokens * 0.4e-6 + est_cost_4o = est_tokens * 6e-6 + + print(f" === Cost Estimate (GPT naturalization) ===") + print(f" Questions: {len(qa_pairs)}") + 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="V8 Naturalization — Pre-process + GPT naturalize (V2/V3)", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + parser.add_argument("--input", "-i", required=True, + help="Path to V8 QA JSON file") + parser.add_argument("--output", "-o", + help="Output path (default: auto-generated)") + parser.add_argument("--model", "-m", default=DEFAULT_MODEL, + help=f"GPT model (default: {DEFAULT_MODEL})") + parser.add_argument("--temperature", "-t", type=float, default=None, + help="Temperature (default: 0.7 for V2, 0.95 for V3)") + parser.add_argument("--v3", action="store_true", + help="V3 mode: more question variety, strip camera refs from temporal Qs") + 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() + + # Resolve version and temperature + version = "v3" if args.v3 else "v2" + temperature = args.temperature if args.temperature is not None else ( + 0.95 if version == "v3" else DEFAULT_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} (mode: {version}, 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, version=version) + + out_path = args.output or str(input_path).replace( + ".v8.json", ".v8.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 with {args.model} ({version}, temp={temperature})") + resp = input(" Continue? [y/N] ").strip().lower() + if resp != "y": + print(" Aborted.") + return + + result = naturalize_batch(input_data, args.model, temperature, + verbose=args.verbose, version=version) + + print(f"\n === Results ===") + print(f" Naturalized: {total - result['failures']}/{total}") + print(f" Failures: {result['failures']}") + print(f" Total tokens: {result['total_tokens']}") + + suffix = ".v8.natural.v3.json" if version == "v3" else ".v8.natural.v2.json" + out_path = args.output or str(input_path).replace(".v8.json", suffix) + 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_v8_{version}_{args.model}.json" + with open(log_path, "w") as f: + json.dump({ + "model": args.model, + "temperature": args.temperature, + "total_tokens": result["total_tokens"], + "questions_processed": total, + "failures": result["failures"], + }, 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/v8/parse_annotations.py b/meva/scripts/v8/parse_annotations.py new file mode 100644 index 0000000..83f6225 --- /dev/null +++ b/meva/scripts/v8/parse_annotations.py @@ -0,0 +1,246 @@ +""" +V6 parse_annotations.py — Step 1: Parse Kitware YAML annotations into raw events. + +Input: Slot name (e.g., "2018-03-11.11-25-00.school") +Output: List of Event dicts with activity, camera, frame range, actors. +""" + +import json +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" +SLOT_INDEX_PATH = Path("/home/ah66742/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}.avi", + annotation_source=source, + )) + + return events + + +# ============================================================================ +# Slot-Level Annotation Discovery +# ============================================================================ + +def find_clips_for_slot(slot: str) -> List[Dict]: + """ + Find all annotation clips for a given slot using slot_index.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) + + if slot not in index: + raise ValueError(f"Slot '{slot}' not found in index ({len(index)} total slots)") + + info = index[slot] + clips = [] + + # Parse date/time/site from slot name: "2018-03-11.11-25-00.school" + slot_parts = slot.split(".") + date = slot_parts[0] + slot_time = slot_parts[1] + 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, + } + + cameras_seen = set() + 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 info["cameras"]: + if cam_id in cameras_seen: + continue + if cam_id not in info["sources"].get(source_name, []): + continue + + # Find matching activities.yml + pattern = f"{date}.{slot_time}*{cam_id}*.activities.yml" + matches = list(ann_dir.glob(pattern)) + if not matches: + # Try minute-level match + pattern_alt = f"{date}.{slot_time[:5]}*{cam_id}*.activities.yml" + matches = list(ann_dir.glob(pattern_alt)) + 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. "2018-03-11.11-25-00.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/v8/person_descriptions.py b/meva/scripts/v8/person_descriptions.py new file mode 100644 index 0000000..a81fa89 --- /dev/null +++ b/meva/scripts/v8/person_descriptions.py @@ -0,0 +1,395 @@ +""" +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 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 +# ============================================================================ + +PERSON_DB_PATH = Path("/home/ah66742/data/person_database_yolo.json") +MEVID_SLOTS_PATH = Path("/home/ah66742/data/mevid_supported_slots.json") + + +# ============================================================================ +# Person Database Loading +# ============================================================================ + +_person_db_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 + + +# ============================================================================ +# 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" + + +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 is_mevid_supported(slot: str) -> bool: + """Check if a slot has MEVID person support.""" + data = load_mevid_slots() + slots = data.get("slots", {}) + return slot in slots + + +def get_mevid_persons_for_slot(slot: str) -> List[str]: + """ + Get list of MEVID person IDs available for a slot. + + Cross-references the person database (23 persons with descriptions) + against the slot's camera set. + """ + db = load_person_database() + persons = db.get("persons", {}) + + result = [] + for pid, pdata in persons.items(): + for slot_info in pdata.get("slots", []): + if slot_info.get("slot") == slot: + result.append(pid) + break + + return sorted(result) + + +def get_mevid_persons_with_cameras(slot: str) -> Dict[str, List[str]]: + """ + Get MEVID person IDs mapped to their cameras for this specific slot. + + Returns: {person_id: [camera_ids]} + """ + db = load_person_database() + persons = db.get("persons", {}) + + result = {} + for pid, pdata in persons.items(): + for slot_info in pdata.get("slots", []): + if slot_info.get("slot") == slot: + result[pid] = slot_info.get("cameras", []) + break + + return result + + +# ============================================================================ +# Entity Enrichment — Inject MEVID Descriptions into Scene Graph +# ============================================================================ + +# Geom-extracted description bank directory +_GEOM_DESC_DIR = Path("/home/ah66742/data/entity_descriptions") + + +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) + return {eid: info["description"] for eid, info in data.get("actors", {}).items() + if info.get("description") and info["description"] != "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 descriptions (GPT/YOLO from MEVID crops — highest quality) + 2. Geom-extracted descriptions (HSV color from raw AVI + geom.yml bbox) + 3. Activity-verb fallback ("a person walking") + + 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) + + # 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) + + entity_descriptions: Dict[str, str] = {} + mevid_count = 0 + geom_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(): + if entity.entity_type != "person": + entity_descriptions[eid] = f"a vehicle on camera {entity.camera_id}" + continue + + cam = entity.camera_id + available_persons = camera_persons.get(cam, []) + + # Priority 1: MEVID person description + if available_persons: + used = assigned_persons.get(cam, set()) + unused = [p for p in available_persons if p not in used] + + if unused: + # Assign next available person + pid = unused[0] + desc = get_person_description(pid) + entity_descriptions[eid] = desc + + if cam not in assigned_persons: + assigned_persons[cam] = set() + assigned_persons[cam].add(pid) + + # Also store the MEVID person_id on the entity for re-ID questions + entity._mevid_person_id = pid + mevid_count += 1 + + if verbose: + print(f" {eid}: MEVID → {desc[:60]}...") + continue + + # Priority 2: Geom-extracted color description (from raw AVI + bbox) + if eid in geom_descs: + desc = geom_descs[eid] + entity_descriptions[eid] = desc + geom_count += 1 + if verbose: + print(f" {eid}: geom → {desc}") + continue + + # Priority 3: 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 = f"a person on camera {cam}" + + entity_descriptions[eid] = desc + fallback_count += 1 + + if verbose: + total = mevid_count + geom_count + fallback_count + print(f" Entity enrichment: {mevid_count} MEVID, {geom_count} geom-color, " + f"{fallback_count} fallback ({total} total)") + + return entity_descriptions, {"mevid": mevid_count, "geom": geom_count, "fallback": fallback_count} diff --git a/meva/scripts/v8/run_pipeline.py b/meva/scripts/v8/run_pipeline.py new file mode 100644 index 0000000..8bfa38a --- /dev/null +++ b/meva/scripts/v8/run_pipeline.py @@ -0,0 +1,466 @@ +#!/usr/bin/env python3 +""" +V8 run_pipeline.py — Main orchestrator for V8 QA generation pipeline. + +V8 CHANGES from V7: +- MEVID person descriptions injected into all question categories +- New categories: re_identification, scene_summary +- Attribute verification added to perception +- Only processes slots with MEVID support (filterable) +- 5 categories: temporal(2) + spatial(2) + perception(2) + re_id(2) + scene_summary(1) = ~9 Qs +- version: "v8" + +Usage: + python3 -m scripts.v8.run_pipeline --slot "2018-03-11.11-25-00.school" -v + python3 scripts/v8/run_pipeline.py --slot "2018-03-11.11-25-00.school" -v + python3 scripts/v8/run_pipeline.py --list-mevid-slots +""" + +import json +import argparse +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, + ) + from .generate_temporal import generate_temporal_qa + from .generate_spatial import generate_spatial_qa + from .generate_perception import generate_perception_qa + from .generate_reidentification import generate_reidentification_qa + from .generate_scene_summary import generate_scene_summary_qa + from .utils.mevid import find_mevid_persons_for_slot as mevid_persons_check +except ImportError: + sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + from scripts.v8.parse_annotations import parse_slot_events, find_clips_for_slot + from scripts.v8.build_scene_graph import build_scene_graph + from scripts.v8.entity_resolution import resolve_entities + from scripts.v8.person_descriptions import ( + enrich_entities, is_mevid_supported, get_mevid_persons_for_slot, + get_mevid_persons_with_cameras, load_person_database, + ) + from scripts.v8.generate_temporal import generate_temporal_qa + from scripts.v8.generate_spatial import generate_spatial_qa + from scripts.v8.generate_perception import generate_perception_qa + from scripts.v8.generate_reidentification import generate_reidentification_qa + from scripts.v8.generate_scene_summary import generate_scene_summary_qa + from scripts.v8.utils.mevid import find_mevid_persons_for_slot as mevid_persons_check + + +# ============================================================================ +# Constants +# ============================================================================ + +OUTPUT_DIR = Path("/home/ah66742/data/qa_pairs") +RANDOM_SEED = 42 + +# V8: 5 categories, weighted question distribution +TARGET_TEMPORAL = 2 +TARGET_SPATIAL = 2 +TARGET_PERCEPTION = 2 # includes 1 attribute_verification if MEVID +TARGET_REIDENTIFICATION = 2 +TARGET_SCENE_SUMMARY = 1 +# Total target: ~9 questions per 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 == "re_identification": + if (v_new.get("mevid_person_id") == v_old.get("mevid_person_id") and + v_new.get("question_type") == v_old.get("question_type")): + return True + + elif cat == "scene_summary": + if v_new.get("question_type") == v_old.get("question_type"): + 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("distance_meters") + proximity = v.get("proximity") + + if d is None: + errors.append("Missing 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)") + + 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_reidentification(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("mevid_person_id"): + errors.append("Missing mevid_person_id") + if not v.get("all_person_cameras"): + errors.append("Missing all_person_cameras list") + return errors + + +def validate_scene_summary(q: dict) -> List[str]: + errors = [] + v = q.get("verification", {}) + if not v.get("question_type"): + errors.append("Missing question_type") + return errors + + +def validate_all(qa_pairs: List[dict]) -> Dict[str, List[str]]: + validators = { + "temporal": validate_temporal, + "spatial": validate_spatial, + "perception": validate_perception, + "re_identification": validate_reidentification, + "scene_summary": validate_scene_summary, + } + 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 + + +# ============================================================================ +# Main Pipeline +# ============================================================================ + +def run_pipeline(slot: str, verbose: bool = False, + seed: int = RANDOM_SEED, + require_mevid: bool = True) -> Dict[str, Any]: + """ + Run the complete V8 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) + 4. Enrich entities with MEVID person descriptions (V8 NEW) + 5. Generate temporal questions (with MEVID descriptions) + 6. Generate spatial questions (with MEVID descriptions) + 7. Generate perception questions (+ attribute verification) + 8. Generate re-identification questions (V8 NEW) + 9. Generate scene summary questions (V8 NEW) + 10. Validate and output + """ + t0 = time.time() + rng = random.Random(seed) + + if verbose: + print(f"{'=' * 60}") + print(f"V8 Pipeline: {slot}") + print(f"{'=' * 60}") + + # 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 activity-verb)") + + if verbose: + print(f" MEVID persons in slot: {len(mevid_persons)}") + + # Step 1: Parse annotations + if verbose: + print(f"\nStep 1: Parsing annotations...") + events = parse_slot_events(slot, verbose=verbose) + 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 4 (V8 NEW): Enrich entities with visual descriptions + if verbose: + print(f"\nStep 4: Enriching entities with visual descriptions...") + entity_descs, desc_counts = enrich_entities(sg, verbose=verbose) + + mevid_desc_count = desc_counts["mevid"] + geom_desc_count = desc_counts["geom"] + fallback_count = desc_counts["fallback"] + + if verbose: + print(f" {mevid_desc_count} MEVID + {geom_desc_count} geom-color + " + f"{fallback_count} fallback / {len(entity_descs)} total") + + # Step 5-9: Generate QA pairs (all categories) + if verbose: + print(f"\nStep 5-9: Generating questions (5 categories)...") + + temporal_qa = generate_temporal_qa(sg, resolved, entity_descs, rng, + count=TARGET_TEMPORAL, verbose=verbose) + spatial_qa = generate_spatial_qa(sg, resolved, entity_descs, rng, + count=TARGET_SPATIAL, verbose=verbose) + perception_qa = generate_perception_qa(sg, resolved, entity_descs, rng, + count=TARGET_PERCEPTION, verbose=verbose) + reid_qa = generate_reidentification_qa(sg, resolved, entity_descs, rng, + count=TARGET_REIDENTIFICATION, verbose=verbose) + summary_qa = generate_scene_summary_qa(sg, resolved, entity_descs, rng, + count=TARGET_SCENE_SUMMARY, verbose=verbose) + + all_qa = temporal_qa + spatial_qa + perception_qa + reid_qa + summary_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"v8_{q['category']}_{i+1:03d}" + + # Step 10: Validation + if verbose: + print(f"\nStep 10: 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") + + # 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 + + # V8 stats + desc_used = sum( + 1 for q in unique_qa + if any("description" in str(v) for k, v in q.get("verification", {}).items() + if isinstance(v, str) and "person" in v.lower()) + ) + attr_verification = sum( + 1 for q in unique_qa + if q.get("subcategory") == "attribute_verification" + ) + + output = { + "slot": slot, + "version": "v8", + "annotation_source": "kitware", + "entity_resolution_source": "mevid+heuristic", + "description_source": "mevid_yolo_gpt", + "generator": "v8_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), + "entities_with_mevid_descriptions": mevid_desc_count, + "cross_camera_clusters": len(resolved.entity_clusters), + "total_questions": len(unique_qa), + "category_counts": cat_counts, + "v8_stats": { + "entities_with_mevid_descriptions": mevid_desc_count, + "entities_with_geom_descriptions": geom_desc_count, + "entities_with_fallback_descriptions": fallback_count, + "attribute_verification_questions": attr_verification, + "reid_questions": cat_counts.get("re_identification", 0), + "scene_summary_questions": cat_counts.get("scene_summary", 0), + "questions_with_debug_info": sum(1 for q in unique_qa if "debug_info" in q), + }, + "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_desc_count} with MEVID descriptions)") + print(f" MEVID persons: {sorted(mevid_persons)}") + print(f" Cross-cam clusters: {len(resolved.entity_clusters)}") + print(f" Attribute verification: {attr_verification}") + print(f" Validation issues: {len(issues)}") + print(f"{'=' * 60}") + + return output + + +def list_mevid_slots(): + """List all slots with MEVID person support.""" + db = load_person_database() + persons = db.get("persons", {}) + + slot_persons: Dict[str, List[str]] = {} + for pid, pdata in persons.items(): + for slot_info in pdata.get("slots", []): + slot = slot_info.get("slot", "") + if slot: + if slot not in slot_persons: + slot_persons[slot] = [] + slot_persons[slot].append(pid) + + print(f"Slots with MEVID person support: {len(slot_persons)}") + print(f"{'Slot':40s} {'Persons':>8s} {'Person IDs'}") + print("-" * 80) + + for slot in sorted(slot_persons.keys()): + pids = sorted(slot_persons[slot]) + print(f"{slot:40s} {len(pids):8d} {', '.join(pids[:5])}" + + (f" +{len(pids)-5} more" if len(pids) > 5 else "")) + + +def main(): + parser = argparse.ArgumentParser(description="V8 QA Pipeline") + parser.add_argument("--slot", help="Slot name (e.g., 2018-03-11.11-25-00.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/{slot}.v8.json)") + parser.add_argument("--no-save", action="store_true", help="Don't save to file, just print") + parser.add_argument("--list-mevid-slots", action="store_true", + help="List all slots with MEVID support") + parser.add_argument("--no-require-mevid", action="store_true", + help="Process slot even without MEVID support") + args = parser.parse_args() + + if args.list_mevid_slots: + list_mevid_slots() + return + + if not args.slot: + parser.error("--slot is required (or use --list-mevid-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}.v8.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/v8/utils/__init__.py b/meva/scripts/v8/utils/__init__.py new file mode 100644 index 0000000..a3fa321 --- /dev/null +++ b/meva/scripts/v8/utils/__init__.py @@ -0,0 +1 @@ +"""V6 utility modules.""" diff --git a/meva/scripts/v8/utils/iou.py b/meva/scripts/v8/utils/iou.py new file mode 100644 index 0000000..aaf670c --- /dev/null +++ b/meva/scripts/v8/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/v8/utils/krtd.py b/meva/scripts/v8/utils/krtd.py new file mode 100644 index 0000000..65e8b69 --- /dev/null +++ b/meva/scripts/v8/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/v8/utils/mevid.py b/meva/scripts/v8/utils/mevid.py new file mode 100644 index 0000000..f71d77a --- /dev/null +++ b/meva/scripts/v8/utils/mevid.py @@ -0,0 +1,166 @@ +""" +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 re +from pathlib import Path +from collections import defaultdict +from typing import Dict, List, Set, Tuple, Optional + +MEVID_DATA_DIR = Path("/nas/mars/dataset/MEVA/mevid_data/mevid-v1-annotation-data") +MEVID_URLS = Path("/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$') + + +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. + + Returns: + {person_id: {camera_id, ...}, ...} + Camera IDs are in MEVA format: "G424" (not "C424"). + """ + 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) + + return dict(person_cameras) + + +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/v8/utils/yaml_stream.py b/meva/scripts/v8/utils/yaml_stream.py new file mode 100644 index 0000000..a71d403 --- /dev/null +++ b/meva/scripts/v8/utils/yaml_stream.py @@ -0,0 +1,137 @@ +""" +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 Kitware inline format: + - { geom: {id1: NNN, id0: M, ts0: FFF, ts1: T.T, g0: X1 Y1 X2 Y2, ...} } + Also handles multi-line format (fallback). + """ + # Inline pattern: extract id1, ts0, g0 from a single line + inline_re = re.compile( + r'id1:\s*(\d+).*?ts0:\s*(\d+).*?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 inline format first (most common in Kitware geom.yml) + m = inline_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 + + # 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