video_summary_keyframes — VIDEOCUBE `summary` op

• Data kinds: video → indices

• Call: import fullseye as fs; fs.ledger.video_summary_keyframes(video, k: 'int' = 5, min_gap: 'int | None' = None, bins: 'int' = 32) -> 'np.ndarray' (to call the implementation directly, import videocube; videocube.video_summary_keyframes(video, k: 'int' = 5, min_gap: 'int | None' = None, bins: 'int' = 32) -> 'np.ndarray'; from the registry, opsvideocube.get("video_summary_keyframes"))

Usage

Indices of representative frames (k,) int64, ascending (indices).

Per-frame score = amount of motion (mean of |I_t − I_{t−1}|, normalised to [0, 1]) + scene change (histogram χ² distance, normalised). In descending order of score, k frames are chosen greedily that are at least min_gap (default T // (2k)) away from frames already chosen. If T < k, all frames. These are learning-free "frames where something happened", not a semantic summary.

>>> idx = video_summary_keyframes(clip, k=4)

>>> thumbnails = clip[idx]

Detailed usage guide

• videocube family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_video_cube — py -3.11 examples/poc_video_cube.py

Ops the type connects to (they accept `indices` as input)

—

Same category (`summary`)

—

*Provenance: videocube.py — VIDEOCUBE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

video_summary_keyframes — VIDEOCUBE summary op