halcon_ext op• Data kinds: contour → feature
• Call: fullseye.apply(img, "hx_dist_rect2_points", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: dist_rectangle2_contour_points_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_dist_rect2_points: stages (docs site)
The mean normalised distance from the contour points to the sides of the minimum-area bounding rectangle (a feature).
Pools the points of all contours, finds the exact minimum-area bounding rectangle (rotating calipers), and returns the mean Euclidean distance from each point to the nearest side of that rectangle, divided by max(H, W) and capped at 1, as np.float64. Because the rectangle encloses all points, the distance of a point (u, w) (coordinates along the rectangle axes, origin at the centre) is min(h1 - |u|, h2 - |w|) (h1/h2 are the half side lengths).
• a, b are unused.
• 0.0 if there are fewer than 3 points; also 0.0 when all points lie on one line (a rectangle of width 0).
The value says how closely the outline follows the rectangle's edges: 0 for a rectangle outline itself, about 0.0997 times the radius for a circle (1 - (4/π)·sin(π/4)), and larger the more points lie inside. It does not depend on the rectangle's orientation (rotation-invariant). HALCON's dist_rectangle2_contour_points_xld takes the rectangle as an argument and returns per-point distances; this op uses the minimum-area rectangle fitted to the contour itself and folds the result into a single mean.
Until 2026-10-11 it contradicted its name: it computed no rectangle and returned the mean distance from the point centroid (not 0 even for a rectangle outline; about 0.57·side/max(H, W) for a square outline). The rectangle itself comes from hx_smallest_rect2_xld / hx_fit_rectangle2_contour; the spread about the centroid from hx_moments_any_xld.
• 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_dist_rect2_points 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.