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

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.