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fix formatting

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Pepijn
2025-11-28 13:27:20 +01:00
parent b98c70376b
commit fa5004bd8c
1 changed files with 37 additions and 38 deletions
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examples/dataset_annotation/subtask_annotation.py
+37 -38
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@@ -97,48 +97,47 @@ def create_sarm_prompt(subtask_list: list[str]) -> str:
subtask_str = "\n".join([f" - {name}" for name in subtask_list])
return f"""# Role
You are an expert Robotics Vision System specializing in temporal action localization. Your task is to segment a video of a robot manipulation demonstration into a sequence of distinct, non-overlapping atomic actions.
You are an expert Robotics Vision System specializing in temporal action localization. Your task is to segment a video of a robot manipulation demonstration into a sequence of distinct, non-overlapping atomic actions.
# Input Data
## Allowed Subtask Vocabulary
You must strictly identify the video segments using ONLY the following labels. Do not create new labels or modify existing ones:
[
{subtask_str}
]
# Input Data
## Allowed Subtask Vocabulary
You must strictly identify the video segments using ONLY the following labels. Do not create new labels or modify existing ones:
[
{subtask_str}
]
# Constraints & Logic
1. **Continuous Coverage:** The entire video duration (from 00:00 to the final second) must be accounted for. There can be no gaps between tasks.
2. **Boundary Logic:** The `end` timestamp of one task must be the exact `start` timestamp of the next task.
3. **Linear Progression:** The video represents a single successful demonstration. Each subtask from the vocabulary appears exactly once, in logical chronological order.
4. **Format:** Timestamps must be in "MM:SS" format.
# Constraints & Logic
1. **Continuous Coverage:** The entire video duration (from 00:00 to the final second) must be accounted for. There can be no gaps between tasks.
2. **Boundary Logic:** The `end` timestamp of one task must be the exact `start` timestamp of the next task.
3. **Linear Progression:** The video represents a single successful demonstration. Each subtask from the vocabulary appears exactly once, in logical chronological order.
4. **Format:** Timestamps must be in "MM:SS" format.
# Step-by-Step Analysis Process
1. **Visual grounding:** Look for the specific visual state changes that define the transition between tasks (e.g., gripper touching object, object lifting off table).
2. **Define Boundaries:** Determine the specific frame where the motion profile changes to fit the next subtask label.
3. **Fill Gaps:** If there is a pause between meaningful actions, append that time to the *preceding* task to ensure continuous coverage.
# Step-by-Step Analysis Process
1. **Visual grounding:** Look for the specific visual state changes that define the transition between tasks (e.g., gripper touching object, object lifting off table).
2. **Define Boundaries:** Determine the specific frame where the motion profile changes to fit the next subtask label.
3. **Fill Gaps:** If there is a pause between meaningful actions, append that time to the *preceding* task to ensure continuous coverage.
# Output Format
Provide the output in valid JSON format.
Structure:
{
"subtasks": [
{
"name": "EXACT_NAME_FROM_LIST",
"timestamps": {
"start": "MM:SS",
"end": "MM:SS"
}
},
{
"name": "EXACT_NAME_FROM_LIST",
"timestamps": {
"start": "MM:SS",
"end": "MM:SS"
}
}
]
}
"""
# Output Format
Provide the output in valid JSON format.
Structure:
{{
"subtasks": [
{{
"name": "EXACT_NAME_FROM_LIST",
"timestamps": {{
"start": "MM:SS",
"end": "MM:SS"
}}
}},
{{
"name": "EXACT_NAME_FROM_LIST",
"timestamps": {{
"start": "MM:SS",
"end": "MM:SS"
}}
}}
]
}}"""
class VideoAnnotator:
"""Annotates robot manipulation videos using local Qwen3-VL model on GPU"""