cv_hough_circles — 2D `features` op

• Data kinds: image → feature

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

• HALCON equivalent: hough_circles (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Circle detection via the Hough transform (a single scalar feature, OpenCV implementation). Detects circle centers and radii by voting, using the HOUGH_GRADIENT method that relies on edge gradient information - here, only the number of detected circles is returned (0 if none).

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

HALCON の hough_circles(Detect centers of circles for a specific radius using the Hough transform.)に相当(近似。中心座標ではなく本数のみ)。実装は `cv2.HoughCircles(_u8(v), HOUGH_GRADIENT, dp=1, minDist=10+int(a*20), param1=100, param2=20+int(b*20), minRadius=3, maxRadius=20)` —— a は検出する円同士の最小中心間距離を 10〜30 に、b は中心検出の投票しきい値 param2(小さいほど誤検出が増える)を 20〜40 に振る。param1(内部の Canny 高しきい値)と半径範囲(3〜20)は固定。

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).*

cv_hough_circles — 2D features op