equ_histo_image — 2D `gray` op

• Data kinds: image → image

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

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

Usage

Global histogram equalization of the whole image (tone curve generated from the cumulative distribution function, 256 bins). A classic technique that flattens a tonal distribution skewed toward shadows or highlights to increase apparent contrast. Equivalent to HALCON's `equ_histo_image` (Histogram linearization of images).

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

`a, b` は未使用。局所版(ブロックごとに平坦化)が欲しい場合は

`equ_histo_image_rect` を使う。

Detailed usage guide

• gallery2d_gray_arith 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_gray_arith — py -3.11 examples/gallery2d_gray_arith.py

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

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

Same category (`gray`)

gamma · invert · scale_clip · equalize · sigmoid · clahe · sk_adapthist · sk_enhance_contrast

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

equ_histo_image — 2D gray op