halcon_ext op• Data kinds: image → image
• Call: fullseye.apply(img, "hx_gen_derivative_filter", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: gen_derivative_filter (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_gen_derivative_filter: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ hx_gen_derivative_filter: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ hx_gen_derivative_filter: 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).*
A derivative filter in the frequency domain. a is the order of the derivative, b its direction.
HALCON's gen_derivative_filter takes a Derivative (x / y / xx / yy / xy) and an order, but for a long time this op merely normalised and returned |f|, looking at neither the input content nor the knobs at all -- measured on 2026-09-02: for the same shape, changing a/b or passing a different picture gave a byte-identical result (the siblings hx_gen_lowpass / hx_gen_highpass / hx_gen_bandfilter change with their knobs, so this 1 op alone stood out).
• a : order 1 to 2 (|f| or |f|^2). Order 2 corresponds to the Laplacian.
• b : orientation. 0 gives the x direction |f_x|, 1 the y direction |f_y|, and in between it blends smoothly toward the isotropic |f| (exactly isotropic at 0.5).
The result is the filter itself in the frequency domain (the same convention as HALCON's gen_*), so the input is used only to determine the shape. However, it now actually changes with the knobs, not just with the shape, so evolution can choose the order and orientation.
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.
• 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_gen_derivative_filter 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.