halcon_ext op• Data kinds: contour → feature
• Call: fullseye.apply(img, "hx_dist_ellipse_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_ellipse_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_ellipse_points: stages (docs site)
The maximum distance of the contour points from the fitted ellipse boundary (the per-point distances aggregated by max; a feature).
Follows the same procedure as hx_dist_ellipse_contour (pool all points, fit an ellipse with semi-axes 2*sqrt(λ) from the centroid and the eigendecomposition of the covariance, compute each point's normalised radius rad), but returns the maximum of |rad - 1| instead of the mean, capped at 1, as np.float64.
• a, b are unused.
• 0.0 if there are fewer than 4 points.
Being a maximum, 1 outlier decides it (sensitive to noise). The value is a difference in the ellipse's normalised coordinates, not a pixel distance. As with the mean version, the 2σ semi-axes are sqrt(2) times too large for a boundary-only point set, so even a perfect ellipse outline carries an offset of about 0.29. The mean version is hx_dist_ellipse_contour; to reduce outliers, put smooth_contours_xld in front.
• 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_ellipse_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.