phamdt2003/autolaparo-qna-sample

AutoLaparo QnA Sample (spec-aligned subtypes)

A small, spec-aligned QnA dataset for surgical-video VQA. Each record follows the same schema as the production file output/qna_dataset.json, and each question_subtype is one of the subtypes defined in the project's spec/*.md. Built from AutoLaparo Task 1 (workflow phase recognition, video 14) and Task 3 (instrument and anatomy segmentation).

11 records / 11 subtypes — exactly one record per supported subtype to keep the sample lightweight. Subtypes that AutoLaparo cannot ground are listed at the bottom with the data they would need.

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Subtypes in this sample

spatial (3 records — from Task 3 segmentation masks)

question_subtype	what it asks	record id
left_right_relation	Is object A to the left or right of object B?	autolaparo_task3__001001__left_right_relation
tool_organ_contact	Is the tool touching or near the organ?	autolaparo_task3__001001__tool_organ_contact
relative_size	Which of two objects is larger (by bbox area)?	autolaparo_task3__001001__relative_size

temporal (3 records — from Task 1 phase labels of video 14)

question_subtype	what it asks	record id
order_reasoning	Which of two events happens first?	autolaparo_task1__14__order_reasoning
phase_transition	When does one phase end and the next begin?	autolaparo_task1__14__phase_transition
before_after_event	Does event A happen before or after event B?	autolaparo_task1__14__before_after_event

temporal_spatial (3 records — from Task 3 multi-frame clip)

Anchored by the 6 sampled frames (t=0..5s at 1 fps) of one Task 3 clip — these records compare bbox / class presence between two timestamps in the same clip.

question_subtype	what it asks	record id
tool_movement_direction	In which direction does a tool move between two frames?	autolaparo_task3__clip006__tool_movement_direction
object_entering_exiting	Does an object enter or exit the field of view between two frames?	autolaparo_task3__clip006__object_entering_exiting
instrument_switching	Does the primary instrument change between two frames?	autolaparo_task3__clip006__instrument_switching

free_form (2 records — mixed source)

question_subtype	what it asks	record id
phase_understanding	What characterises this surgical phase?	autolaparo_task1__14__phase_understanding
tool_purpose	What is this instrument used for?	autolaparo_task3__001001__tool_purpose

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Record schema (same as output/qna_dataset.json)

field	type	notes
id	string	composite slug, unique per record
video_id	string	source clip id (14 for Task 1, 001..300 for Task 3)
frame_id	int	frame index within the source
timestamp_s	float	seconds into the source video / clip
phase	string | null	surgical phase (Task 1) or null (Task 3, no phase labels)
qna_type	string	spatial | temporal | temporal_spatial | free_form
question_subtype	string	one of the 11 subtypes above
question	string	the natural-language question
answer	string	chain-of-thought reasoning + conclusion
validation_status	string	valid
image_context / video_context	object	path → JPG snapshot in this folder (temporal_spatial adds path_t2 for the second anchor frame)

Grounding tags used inside answer (matches spec/):

• <obj>name</obj> — refer to an object by name
• <box>[x1,y1,x2,y2]</box> — refer to a bounding box (spatial only)
• <t>SECONDS</t> — refer to a timestamp (temporal only)
• Free-form answers use no grounding tags (plain prose only).

Example record

{
  "id": "autolaparo_task1__14__phase_transition",
  "source_stack": "AutoLaparo",
  "task": "task1_workflow_recognition",
  "video_id": "14",
  "frame_id": 179,
  "timestamp_s": 178.0,
  "phase": "Dividing Ligament and Peritoneum",
  "qna_type": "temporal",
  "question_subtype": "phase_transition",
  "question": "What are the phases involved in this transition and when does it occur?",
  "answer": "Step 1: Identify the phases involved.\n  - The phase before the transition is Preparation.\n  - The phase after the transition is Dividing Ligament and Peritoneum.\n\nStep 2: Locate the transition boundary.\n  - The Preparation phase ends at <t>177.0</t>s.\n  - The Dividing Ligament and Peritoneum phase begins at <t>178.0</t>s.\n\nConclusion: The transition from Preparation to Dividing Ligament and Peritoneum occurs between <t>177.0</t>s and <t>178.0</t>s.",
  "validation_status": "valid",
  "video_context": {
    "path": "video_context/task1/14.mp4",
    "frame_index_1fps": 179,
    "timestamp_s": 178.0
  }
}

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Subtypes NOT in this sample (and why)

AutoLaparo annotates phases (Task 1) and per-frame masks (Task 3), but no per-frame tools / actions / object bboxes across time. The following spec subtypes therefore cannot be grounded from AutoLaparo alone — they exist in the full output/qna_dataset.json (built from CholecT50).

qna_type	question_subtype	missing data
temporal	tool_appearance_duration	AutoLaparo Task 1 has no per-frame tool annotations
temporal	co_occurrence_timing	no tool intervals -> cannot compute co-occurrence windows
temporal_spatial	tool_target_interaction_evolution	needs action triplets (instrument, verb, target) per frame — not annotated by AutoLaparo
temporal_spatial	action_sequence_reasoning	needs action triplets per frame — not annotated by AutoLaparo
free_form	clinical_reasoning	needs richer per-frame metadata (tools+actions) to ground reasoning
free_form	surgical_knowledge	needs richer per-frame metadata (tools+actions) to ground reasoning

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File map

autolaparo_hf_sample/
├── qna.json             # 8 records, JSON array (human-readable)
├── qna.jsonl            # 8 records, line-delimited (streaming-friendly)
├── manifest.json        # source metadata + subtype coverage trace
├── README.md            # this file
└── video_context/
    ├── task1/
    │   └── 14.mp4       # full Task 1 source video — referenced by every temporal/free_form record's `video_context.path`
    └── task3/
        ├── *.jpg        # the exact annotated frames (image_context.path / path_t2)
        └── *.mp4        # the Task 2 source clips each frame was sampled from (image_context.source_clip)

Each image_context.path and video_context.path inside a record is a relative path into this folder, so you can load the JPG directly from the record.

Reproduce

python scripts/build_autolaparo_task1_task3_sample.py --extract-videos

Needs data/task1/labels.zip, data/task3/imgs.zip, data/task3/masks.zip, and videos.zip at the repo root. Tests: python -m unittest discover tests -v.

Source & licence

Derived from AutoLaparo (Wang et al., 2022, arXiv:2208.02049). Non-commercial use only. If you use this sample, cite the AutoLaparo paper.