kirsch_dir — 2D `edges` op

• Data kinds: image → image

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

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

Usage

Returns the index of the direction with the strongest response from the Kirsch compass filter (`argmax among the 8 directions), normalized to [0,1]. The directional component paired with kirsch_amp. Equivalent to HALCON's kirsch_dir` (Detect edges (amplitude and direction) using the Kirsch operator.).

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

`a, b` は未使用。方向は 8 段階の離散値しか取れない(Sobel/Prewitt の

連続角度とは分解能が異なる)。

Detailed usage guide

• gallery2d_edges 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_edges — py -3.11 examples/gallery2d_edges.py

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

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

Same category (`edges`)

sobel_mag · prewitt_mag · roberts_mag · dog · grad_dir · log · corner_response · sk_scharr

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

kirsch_dir — 2D edges op