sk_blur_effect — 2D features op

• Data kinds: image → feature

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

Usage

Blur estimation (a single scalar feature). Deliberately re-blurs the image slightly and compares how much edge sharpness changes between the original and re-blurred versions, returning a score from 0 (no blur) to 1 (maximally blurred) -- quantifies focus/motion blur without a reference image.

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

HALCON に直接対応するものは無い。実装は `measure.blur_effect(v)`。a, b は未使用 —— 再ぼかしフィルタのサイズは既定値(11)のまま。

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 · cv_cc_count

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

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.