dynamic-object-aware-egomotion-sampling-and-indexing（动态对象感知自我运动采样和索引）

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.