features op• Data kinds: image → image
• Call: fullseye.apply(img, "hough_circle_trans", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: hough_circle_trans (the HALCON reference is a useful guide to its meaning and parameters)
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
*The output is shown in a viridis-like pseudo-colour (dark purple = low, yellow = high) so that a field of quantities — distance, phase, orientation, depth — can be read.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ hough_circle_trans: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ hough_circle_trans: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ hough_circle_trans: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
The Hough transform for circle detection. Computes skimage.transform.hough_circle on the edge mask with a set of circle templates of radius 4 to 19 (in steps of 3), and returns the maximum response across all radii normalized to [0,1]. Corresponds to HALCON's hough_circle_trans (Return the Hough-Transform for circles with a given radius.) (HALCON explicitly specifies the radius, but this is an approximation that brute-forces a fixed range).
a sweeps the threshold for edge extraction, and b sets the upper limit of the radii searched (radii = arange(4, max(7, round(4 + 32*b)), 3); the default b=0.5 gives radii 4-19 as before). ★If only radii smaller than the object are searched, this op does not fail but returns a meaningless accumulator. Counting 24 real coins (radius 19-31 px) with non-maximum suppression gives 13 peaks at b=0.5, and even b=1.0, widened to the upper limit, gives only 19 peaks -- because the radius step is 3 and, moreover, everything is collapsed to the maximum over all radii. It is no substitute for HALCON's op of the same name, where the radius can be specified explicitly (examples/poc_real_coin_metrology.py shows this in numbers).
Value comparability (measured): the output is normalised by that image's own maximum (the output maximum is always 1.0, and scaling the input leaves the output unchanged). Values therefore cannot be compared across images — a feature of the same strength takes a different value depending on what the strongest feature in that image happens to be, and in an image holding only weak features the noise is lifted to 1.0. To compare across images, rescale by a common reference.
Blank frame (measured): feeding an image of uniform brightness gives an empty result — every pixel is background (0) — regardless of the brightness. Pure white and pure black behave alike; brightness by itself detects nothing.
• 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.
hough_circle_trans 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_features — py -3.11 examples/gallery2d_features.py
• poc_real_coin_metrology — py -3.11 examples/poc_real_coin_metrology.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
features)effective_bit_depth · blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_entropy_feat
*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.