halcon_ext op• Data kinds: image → image
• Call: fullseye.apply(img, "hx_region_to_mean", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: region_to_mean (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_region_to_mean: 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_region_to_mean: other inputs (docs site)
*No colour (H,W,3) input is shown: this op treats the colour channel as a third spatial axis (it crosses channels). Call it per channel.*
Paint each connected region with its mean grey value (image -> image). Foreground/background split at threshold a, then labelled.
Takes v > a as foreground, splits it into connected components with ndimage.label (4-neighbourhood), and fills the pixels of each component with the mean gray value within the component obtained by ndimage.mean. The background (v <= a) is filled with the 1 mean value of all background pixels (0.0 if there is no background). The output is a float image of the same shape as the input, with values within the input's range (not normalised).
• a → threshold separating foreground/background. At a=0, every pixel greater than 0 is foreground.
• b is unused.
Since the result is constant per component, it can be used, for example, with a downstream threshold to select "only parts whose mean is bright". With 4-neighbourhood labels, blobs touching diagonally get separate means. There is no input validation.
• 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_region_to_mean 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.