contour op• Data kinds: color → contour
• Call: fullseye.apply(img, "lines_color", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: lines_color (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.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ lines_color: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ lines_color: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ lines_color: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ lines_color: other inputs (docs site)
Detects lines (ridges) in a color image. Claimed to correspond to HALCON's lines_color (detects color lines and their width), but it does not estimate line width -- it only thresholds the Laplacian-of-Gaussian response on the luminance image into a binary image and returns the connected components as contours; it has no output corresponding to the line width that HALCON returns.
After converting to luminance with _rgb_to_gray, applies scipy.ndimage.gaussian_laplace with sigma 0.5 + 2.5 * a, normalises the absolute value by its maximum, binarises with the threshold 0.2 + 0.4 * b and performs 8-connected labelling. a sweeps the ridge thickness (the LoG scale), b the detection threshold. Components smaller than 3 pixels are discarded.
• gallery2d_contour_measure 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.
cfa_to_rgb 0.50 0.50 lines_color 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_contour_measure — py -3.11 examples/gallery2d_contour_measure.py
contour as input)identity · select_contours · smooth_contours · fit_line_contours · contours_to_region · count_contours · total_length · select_contours_xld
contour)select_contours · smooth_contours · fit_line_contours · contours_to_region · sk_find_contours · edges_sub_pix · lines_gauss · select_contours_xld
*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.