halcon_ext op• Data kinds: contour → feature
• Call: fullseye.apply(img, "hx_dist_ellipse_contour", 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_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_contour: stages (docs site)
The mean distance of the contour points from the fitted ellipse boundary (small = close to an ellipse; a feature).
Pools the points of all contours, transforms them into the principal-axis frame via the centroid and the eigendecomposition of the coordinate covariance, fits an ellipse with semi-axes 2*sqrt(λ) (2σ of each principal axis), and returns the mean of the difference |rad - 1| between each point's normalised radius rad = sqrt((u/ax0)^2 + (v/ax1)^2) and 1, capped at 1, as np.float64.
• a, b are unused.
• 0.0 if there are fewer than 4 points. Eigenvalues are clipped from below at 1e-9.
Note: the value is a difference in the ellipse's normalised coordinates (dimensionless), not a pixel distance. Also, semi-axes of 2σ fit "a uniformly filled ellipse"; for a boundary-only point set σ = semi-axis/sqrt(2), so even a perfect ellipse outline does not give 0 but carries an offset of about 0.29 (measured 0.2938). Use it as a relative measure for comparison; do not decide "ellipse or not" by its absolute value. The maximum version is hx_dist_ellipse_points, the axis ratio is hx_fit_ellipse_contour.
• 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_contour 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.