hx_test_region_points — 2D halcon_ext op

• Data kinds: region → feature

• Call: fullseye.apply(img, "hx_test_region_points", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: test_region_points (the HALCON reference is a useful guide to its meaning and parameters)

hx_test_region_points: input → output

*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_test_region_points: 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):

▸ hx_test_region_points: stages (docs site)

Usage

The fraction of a grid of points that lies inside the region (test_region_points).

Samples the region v > 0.5 on a grid every step pixels (reg[::step, ::step], origin at (0,0)), and returns the fraction of grid points inside the region as np.float64 (0 to 1).

• a → grid spacing step = max(2, int((0.1 + 0.3*a) * min(h, w))) (10% to 40% of the shorter side, minimum 2 pixels).

• b is unused.

• 0.0 if there are 0 grid points.

A fine grid approximates the region's area fraction; a coarse one gives a rough indicator of "how many representative points fall in the region". The grid origin is fixed, so shifting the region slightly makes the value jump. If you want the area fraction itself, taking the mean of the region is more accurate. Testing 1 point is hx_test_region_point.

Detailed usage guide

• gallery2d_halcon_ext family guide

References (sample data, literature)

• 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.

Try it in Studio

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
hx_test_region_points 0.50 0.50

▸ Load this pipeline  ·  Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py

Ops the type connects to (they accept feature as input)

identity · feature_to_img

Same category (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.