halcon_ext op• Data kinds: contour → feature
• Call: fullseye.apply(img, "hx_regress_contours", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: regress_contours_xld (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.*
*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
Stages (the ops that come before → this op, left to right):
▸ hx_regress_contours: stages (docs site)
Fit a regression line to each contour and return the mean residual (the deviation from the line; a feature). Small = line-like.
For each contour with 3 or more points, fits the total-least-squares line through the point centroid (normal = eigenvector of the smallest eigenvalue of the coordinate covariance) and computes the RMS of the point-to-line distances. The RMS values of all contours are averaged, divided by max(H, W) and capped at 1, and the result is returned as np.float64.
• a, b are unused.
• 0.0 if there is no valid contour (indistinguishable from "a perfect line").
Smaller means straighter. The slope and position of the line itself are not returned (use fit_line_contours if you need the fit). To see the number of corners as a polyline, split with hx_split_contours first and count the segments. Contours with 2 or fewer points are excluded from the evaluation. The analogous measure for only 1 contour is xg_regress_contours.
• gallery2d_halcon_ext 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.
threshold 0.50 0.50 sk_find_contours 0.50 0.50 hx_regress_contours 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py
feature as input)halcon_ext)hx_gen_circle · hx_gen_ellipse · hx_gen_rectangle2 · hx_gen_checker_region · hx_gen_grid_region · hx_gabor · hx_fit_surface1 · hx_fit_surface2
*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.