entropy_gray — 2D `features` op

• Data kinds: image → feature

• Call: fullseye.apply(img, "entropy_gray", 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

> This operator's description has not been translated yet. The original text follows as it is.

グレースケール階調の Shannon エントロピー(64 ビンヒストグラムから計算

し、最大値 `log2(64)=6` bit で正規化)。階調分布の「ばらつき/情報量」を

表し、一様な画像ほど値は大きく、単一階調に偏るほど 0 に近づく。HALCON の

`entropy_gray`（Determine the entropy and anisotropy of images.）は

エントロピーと異方性の組を返すが、この代役ではエントロピーのみを実装

している。

`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_entropy_feat · sk_blur_effect

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

entropy_gray — 2D features op