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sampling-and-indexing

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Standardize video sampling and frame indexing so interval instructions and mask frames stay aligned with a valid key/index scheme.

tasks/dynamic-object-aware-egomotion/environment/skills/sampling-and-indexing/SKILL.md

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# When to use
- You need to decide a sampling stride/FPS and ensure *all downstream outputs* (interval instructions, per-frame artifacts, etc.) cover the same frame range with consistent indices.

# Core steps
- Read video metadata: frame count, fps, resolution.
- Choose a sampling strategy (e.g., every 10 frames or target ~10–15 fps) to produce `sample_ids`.
- Only produce instructions and masks for `sample_ids`; the max index must be `< total_frames`.
- Use a strict interval key format such as `"{start}->{end}"` (integers only). Decide (and document) whether `end` is inclusive or exclusive, and be consistent.

# Pseudocode
```python
import cv2
VIDEO_PATH = "<path/to/video>"
cap=cv2.VideoCapture(VIDEO_PATH)
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