sk_entropy — 2D `texture` op

• Data kinds: image → image

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

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

Usage

Local entropy. Computes the Shannon entropy (base-2 logarithm) of the gray-value distribution in the neighborhood (disk-shaped) around each pixel, and images it as the minimum number of bits needed to encode that location -- a measure of texture complexity/information content.

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

HALCON の entropy_image(Calculate the entropy of gray values within a rectangular window.)に相当(近似。窓形状は矩形でなく円盤)。実装は `filters.rank.entropy(_u8s(v), disk(1+int(a*3)))` を正規化したもの —— a は円盤半径を 1〜4 に振る。b は未使用。入力は内部で 8 bit 化されるため、元画像の微妙な階調差は失われる。

Detailed usage guide

• gallery2d_texture_freq 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_texture_freq — py -3.11 examples/gallery2d_texture_freq.py

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`texture`)

std_filter · gabor · sk_frangi · sk_meijering · sk_hessian · sk_gabor · sk_lbp · sk_shape_index

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

sk_entropy — 2D texture op