halcon_ext op• Data kinds: image → image
• Call: fullseye.apply(img, "hx_close_edges", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: close_edges (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_close_edges: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ hx_close_edges: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ hx_close_edges: 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).*
Close the gaps in an edge-amplitude image: threshold at a, then morphological closing (radius b).
After binarising with v > a, applies scipy.ndimage.binary_closing with it iterations of a 3×3 fully connected (8-neighbourhood) structuring element = 1 closing (dilation→erosion) with a (2*it+1) square, and returns a 0/1 float image.
• a → binarisation threshold (assumes an edge amplitude image; at a=0, every pixel greater than 0 is foreground).
• b → number of structuring-element iterations it = 1 + int(b*3) (1 to 4; square side 3 to 9 pixels). Gaps that can be closed are roughly
2*it pixels or less.
Caution: binary_closing erodes treating outside the image as 0, so a band of it pixels from the image border always becomes 0 (applying it=1 to an all-1 20×20 leaves 18×18). When handling edges touching the border, add a margin beforehand or dilate the result again. The input is an image (edge amplitude) and the output is also of image sort, but its content is binary. Place it after hx_nonmax_dir or sobel_amp, and pass the connected edges on to hx_region_to_label or hx_detect_edge_segments.
Blank frame (measured): feeding an image of uniform brightness gives an empty result — every pixel is background (0) — regardless of the brightness. Pure white and pure black behave alike; brightness by itself detects nothing.
• 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_close_edges 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py
• poc_metal_grain_size — py -3.11 examples/poc_metal_grain_size.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.