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])

classify_shape: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

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.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

threshold 0.50 0.50
classify_shape 0.50 0.50

▸ Load this pipeline · Load & run

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