region op• Data kinds: region → region
• Call: fullseye.apply(img, "r2_sort_region", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: sort_region (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):
▸ r2_sort_region: knob a sweep (docs site)
*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):
▸ r2_sort_region: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ r2_sort_region: other inputs (docs site)
Keep the k-th largest connected component; k = round(a*(n-1)).
Splits the foreground mask (> 0.5) into connected components with scipy.ndimage.label (default 4-connectivity), sorts them in descending order of area (pixel count), and keeps only the k-th component. k is the integer round(a*(n-1)) clipped to [0, n-1]: a=0 gives the largest component, a=1 the smallest, a=0.5 a component of roughly middle rank. n is the number of components. b is unused.
The return value is a float64 0/1 mask of the same shape as the input. All zeros if there are no components. When several components have the same area, their order is not guaranteed (it depends on the order of numpy.argsort). When the number of components n changes, the same a selects a different rank (relative specification), so if you just want to reliably get "the largest component", use a=0 or select_largest. Pixels connected only diagonally are counted as separate components (4-connectivity). Cutting constrictions upstream with opening_circle makes component-wise selection more stable.
Border handling: treat everything outside the image as background (0) (measured; tools/impl2/border_probe.py).
• gallery2d_region 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 r2_sort_region 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_region — py -3.11 examples/gallery2d_region.py
region as input)identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small
region)reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small · invert_region
*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.