halcon_ext op• Data kinds: image → image
• Call: fullseye.apply(img, "hx_plane_deviation", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: plane_deviation (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.*
*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
*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_plane_deviation: 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).*
The deviation of the grey values from their first-order plane fit, |v − plane| (flatness / defect inspection).
Fits the gray values to a least-squares plane on the basis [1, x, y] with coordinates normalised to [-1,1] (the same regression as hx_fit_surface1), and returns the absolute residual |v - plane| clipped to [0,1]. It is not normalised (for a pure plane it becomes an almost-0 image at the level of rounding error).
• a, b are unused.
Pixels with a large deviation from the plane = structures not explained by an illumination tilt (scratches, dents, foreign matter). The output discards the sign, so it does not distinguish "brighter/darker than the plane". If you need that distinction, subtract the result of hx_fit_surface1 from the original image separately. Since it is a 1st-order surface, gently curved backgrounds show up in the residual. In that case, estimate the background with hx_fit_surface2. Downstream, turn it into a defect region with threshold, or use it as preprocessing for hx_char_threshold in place of background subtraction.
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_plane_deviation 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.