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])

xcv2_lap_var: 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

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.

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.

xcv2_lap_var 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

• poc_focus_stacking — py -3.11 examples/poc_focus_stacking.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