sampling-and-indexing

# 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) n=int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) fps=cap.get(cv2.CAP_PROP_FPS) step=10 # example sample_ids=list(range(0, n, step)) if sample_ids[-1] != n-1: sample_ids.append(n-1) # Generate all downstream outputs only for sample_ids ``` # Self-check list - [ ] `sample_ids` strictly increasing, all < total frame count. - [ ] Output coverage max index matches `sample_ids[-1]` (or matches your documented sampling policy). - [ ] JSON keys are plain `start->end`, no extra text. - [ ] Any per-frame artifact store (e.g., NPZ) contains exactly the sampled frames and no extras.