classify_shape — 2D `classification` op

• Data kinds: region → feature

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

Usage

A basic shape-classification feature that computes the circularity of the largest connected region. No single corresponding HALCON op is specified.

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

`a, b は未使用。最大面積の連結成分について 4π×面積 / 周長² を計算し、理想円で 1 になるよう min(1.0, ...) で頭打ちにする（数値誤差で 1 をわずかに超えるのを防ぐ）。周長は領域からその侵食を引いた境界画素数（_region_boundary` と同じ考え方）で近似するため、輪郭ベースの周長より粗い。前景が無ければ 0 を返す。コード中のコメントの通り、OCR・良否判定など「形状で分類する」処理の土台として使うことを想定している。

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 (`classification`)

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

classify_shape — 2D classification op