halcon_ext op• Data kinds: image → image
• Call: fullseye.apply(img, "hx_nonmax_dir", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: nonmax_suppression_dir (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_nonmax_dir: 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_nonmax_dir: 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).*
Non-maximum suppression along the gradient direction (Canny's NMS stage). Thins edges to one pixel.
Takes gx (column direction) and gy (row direction) with Sobel and computes the amplitude hypot(gx, gy) and the direction arctan2(gy, gx) mod 180°. The direction is quantized into 4 sectors in 45° steps, each pixel is compared with the 2 neighbouring pixels corresponding to its sector, and only pixels with mag >= both neighbours are kept (others become 0). The remaining amplitude is normalised to [0,1] by min-max, and values below a*0.3 are set to 0.
• a → weak-edge threshold a*0.3 (applied to the normalised value; no threshold at a=0).
• b is unused.
Points to note:
• Neighbour comparison uses np.roll, so at the image border pixels are compared with those on the opposite side (do not trust the 1-pixel border).
• Because the comparison is >=, an ideal step edge with equal amplitudes side by side remains 2 pixels wide (it does not become 1 pixel).
• In the current implementation, the pairs of neighbouring pixels compared for the 45° and 135° sectors are swapped, so diagonal edges are compared not along the gradient direction but
along the edge. Hence horizontal and vertical edges are thinned, but diagonal edges are hardly thinned at all (measured: around 1400 pixels remain as-is on a blurred diagonal step edge). If diagonal edges need thinning, consider canny or skeleton.
Pass the original gray image, not an amplitude image like sobel_amp (it differentiates internally). Downstream: hx_close_edges / hx_detect_edge_segments / threshold.
Value comparability (measured): the output is normalised by that image's own maximum (the output maximum is always 1.0, and scaling the input leaves the output unchanged). Values therefore cannot be compared across images — a feature of the same strength takes a different value depending on what the strongest feature in that image happens to be, and in an image holding only weak features the noise is lifted to 1.0. To compare across images, rescale by a common reference.
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_nonmax_dir 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.