xcv2_lap_var — 2D `features` op

• Data kinds: image → feature

• Call: fullseye.apply(img, "xcv2_lap_var", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Focus (blur) metric based on Laplacian variance (variance of `cv2.Laplacian`).

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

画像全体に Laplacian を掛けた結果の分散を計算し、`min(1.0, 分散*20)` で

[0,1] にクリップしたスカラーを返す。`a, b` は未使用。値が大きいほど

エッジ/テクスチャが豊富＝合焦、小さいほどボケている可能性が高い、という

古典的なオートフォーカス評価指標。倍率 20 は経験的なスケーリングで、

絶対的なボケ量ではなく相対比較に向く。

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 (`features`)

blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_entropy_feat · sk_blur_effect

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

xcv2_lap_var — 2D features op