region op• Data kinds: region → region
• Call: fullseye.apply(img, "r2_partition_rectangle", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: partition_rectangle (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_partition_rectangle: 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_partition_rectangle: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ r2_partition_rectangle: other inputs (docs site)
Split the region bbox into an NxN grid; keep cells overlapping the region.
Divides the region's circumscribed axis-parallel rectangle evenly into an N x N grid and returns a mask in which every cell that overlaps the region by even 1 pixel is painted entirely with 1.0. N = 2 + int(a*4): a=0 → 2, a=1 → 6 divisions (a is clipped to [0,1]). The cell boundaries are numpy.linspace(top, bottom+1, N+1) converted to integers; if the bbox is smaller than the number of cells, cells of width 0 arise and are skipped. b is unused.
The return value is a float64 0/1 mask of the same shape as the input. All zeros if there is no foreground. The output is "the union of cells covering the region", so it always contains the input region, and nothing outside the bbox is painted. For a convex region filling the bbox completely, every cell qualifies, and whatever a is, the output is the bbox itself. The coarseness of the grid only becomes visible in regions with large holes or concavities. Uses:
coarse tiling (choosing windows for local processing) and coarse-graining occupancy patterns. Adjacent cells merge into 1 blob, so it is unsuitable for handling cells separately.
• 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_partition_rectangle 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.