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])
*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).*
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。
• gallery2d_features family guide
• 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.
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.
sk_blur_effect 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_features — py -3.11 examples/gallery2d_features.py
feature as input)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.