gabor — 2D texture op

• Data kinds: image → image

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

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

gabor: 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.*

*The output is shown in a viridis-like pseudo-colour (dark purple = low, yellow = high) so that a field of quantities — distance, phase, orientation, depth — can be read.*

Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):

▸ gabor: knob a sweep (docs site)

Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):

▸ gabor: knob b sweep (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

▸ gabor: 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).*

Usage

Gabor energy |v * g| — an oriented band-pass texture response.

• a — orientation θ = π·a [rad]. The kernel's cosine runs along the x direction after rotation, so a=0 (θ=0) responds most strongly to vertical stripes (a pattern whose brightness changes along the column direction), and a=0.5 (θ=90°) responds to horizontal stripes. a=1 is θ=180°, the same orientation as a=0.

• b — spatial frequency 0.1 + 0.3·b [cycles/px].

★Normalisation (fix of 2026-09-02): a fixed scale dividing by the L1 norm of the kernel. Previously it divided by _norm = the maximum absolute value in that image, so the magnitude of the response itself was lost. Measured (horizontal stripes 96×96, frequency 0.25): the mean amplitude of the raw convolution was 0.0165 at θ=0° and 0.9077 at θ=90°, a difference of 54.9 times, yet after _norm the means were squashed to 0.3554 vs 0.4790 = 1.35 times — a feature meant to tell orientations apart had lost its discriminative power (naturally, since each orientation was divided by a different divisor). Since |v| <= 1 implies |v * g| <= sum|g|, dividing by L1 keeps the range [0,1] and gives an absolute scale comparable across ops.

Border handling: mirror repeating the edge pixel (d c b a | a b c d — scipy's default reflect) (measured; tools/impl2/border_probe.py).

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.

Detailed usage guide

• gallery2d_texture_freq 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.

gabor 0.50 0.50

▸ Load this pipeline  ·  Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_texture_freq — py -3.11 examples/gallery2d_texture_freq.py

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (texture)

std_filter · local_bimodality · local_std · scale_select_std · bootstrap_std_error · structure_tensor_orientation · structure_tensor_coherence · sk_frangi


*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.