artistic op• Data kinds: image → image
• Call: fullseye.apply(img, "xcv_stylization", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*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):
▸ xcv_stylization: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ xcv_stylization: knob b sweep (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ xcv_stylization: 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).*
OpenCV's stylization (painterly/cartoon-like non-photorealistic rendering).
Uses edge-preserving smoothing to even out fine detail while keeping the main contours, producing an illustration-style image of flat colour areas and prominent contours (internally converted to 3ch for processing, with the result converted back to grayscale).
a sweeps the spatial smoothing range sigma_s over 20-120 (the larger, the wider the area evened out). b sweeps the colour (luminance) difference tolerance sigma_r over 0.1-0.5 (the larger, the more easily smoothing crosses edges, and the stronger the flattening).
Precision (measured): internally quantised to 8 bit (256 levels) — the output lands exactly on the k/255 grid. Passing float64 does not preserve that precision: a 16-bit camera's step (about 1.5e-5) is rounded to 1/255 = about 3.9e-3, and two images that differ by less than 1/255 return the same answer. Where faint intensity differences are being measured, or the output is fed on to differentiation or regression, the quantisation steps become visible — choose an op that does not pass through 8 bit.
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_color_artistic 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.
xcv_stylization 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_color_artistic — py -3.11 examples/gallery2d_color_artistic.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
artistic)xcv_pencil_sketch · xpil_emboss
*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.