halcon_ext op• Data kinds: image → image
• Call: fullseye.apply(img, "hx_mean_shape", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: mean_image_shape (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):
▸ hx_mean_shape: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ hx_mean_shape: other inputs (docs site)
*The 4th column is a colour (H,W,3) input. This op handles colour without crossing channels (it does not convolve the colour channel as a third spatial axis).*
Mean smoothing with an arbitrary mask (a disc). The radius r varies with a (a different op from the rectangular mean).
Convolves with a disc of radius r (pixels satisfying xx^2+yy^2 <= r^2 set to 1, normalised by dividing by the sum) via ndimage.convolve(mode="reflect"). The output is a float array of the same shape as the input, and its values do not leave the input's range (the weights sum to 1).
• a → radius r = 1 + int(a*4) (1 to 5 pixels; it is 5 only when a=1). The kernel is a disc inside a (2r+1) square.
• b is unused.
Unlike mean_image with a rectangular window, it has less directional weighting bias and isotropically blurs structures up to the disc radius. The border is extended by mirroring, so no darkening occurs at the edges. There is no input validation; any NaN propagates as-is.
• 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.
hx_mean_shape 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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.