sk_entropy_feat — 2D `features` op

• Data kinds: image → feature

• Call: fullseye.apply(img, "sk_entropy_feat", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: entropy_gray (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Shannon entropy of the whole image (a single scalar feature). Summarizes the spread/information content of the brightness histogram in one number -- a higher value indicates the brightness distribution is more evenly spread out (more information / richer contrast).

> The detailed description below is the original text — the summary and the headings are translated.

HALCON の entropy_gray(Determine the entropy and anisotropy of images.)に相当(近似。異方性は計算しない)。実装は `measure.shannon_entropy(v)`(既定の底 2、単位はビット)。a, b は未使用。

Detailed usage guide

• gallery2d_features 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)

• gallery2d_features — py -3.11 examples/gallery2d_features.py

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

identity

Same category (`features`)

blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_blur_effect · cv_cc_count

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

sk_entropy_feat — 2D features op