edges op• Data kinds: image → image
• Call: fullseye.apply(img, "xpil_contour", 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.*
*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):
▸ xpil_contour: 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).*
Pillow's contour extraction filter. Uses the fixed kernel of PIL.ImageFilter.CONTOUR to create an effect that leaves only outlines as black lines on a white background (a pen-drawing/line-art style effect).
a, b are unused (fixed kernel).
The kernel is the 3x3 [-1,-1,-1; -1,8,-1; -1,-1,-1] (scale 1, offset 255), and the output is 255 + (8*centre - sum of the 8 neighbours) saturated to 0–255. Flat areas become 255 (white), pixels darker than their surroundings become dark, and pixels brighter than their surroundings saturate at 255. In other words, a 1-pixel-wide black line sits on the "dark side" of a step while the bright side stays white (xpil_find_edges uses the same kernel with offset 0, so both sides come out bright).
The input is clipped to [0,1] and truncated after *255 into an 8-bit L image, and the output is brought back to [0,1] with /255 (introducing 1/255 quantization). The outermost 1-pixel border of the image is not filtered by Pillow and is returned with the input values. A 0-size image is rejected with a ValueError and falls back fail-soft to the clipped input (recorded in the ledger). It also responds to noise, so placing xpil_smooth_more or median before it tidies up the lines. To use the lines as regions, brighten the black-line side with invert and then feed it to threshold.
Blank frame (measured): feeding an image of uniform brightness makes every pixel foreground (1), regardless of the brightness. A frame that is blown out, occluded, or simply has no subject comes back as “100 % defective”, so test for uniformity upstream and reject such frames.
• gallery2d_edges 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.
xpil_contour 0.40 0.50
▸ Load this pipeline · Load & run
• gallery2d_edges — py -3.11 examples/gallery2d_edges.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
edges)sobel_mag · prewitt_mag · roberts_mag · dog · edge_transition_width · grad_dir · log · corner_response
*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.