edges op• Data kinds: image → image
• Call: fullseye.apply(img, "cv_scharr", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: edges_image (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):
▸ cv_scharr: 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).*
Scharr gradient magnitude (OpenCV implementation). Sums the absolute values of the horizontal and vertical Scharr derivatives to obtain the gradient magnitude - the same kind of edge detection as sk_scharr (the skimage version), but the combination method (sum of absolute values instead of the norm of the sum of squares) differs, so the results are not identical.
> The detailed description below is the original text — the summary and the headings are translated.
HALCON の edges_image に相当(近似)。実装は `|cv2.Scharr(v,CV_64F,1,0)| + |cv2.Scharr(v,CV_64F,0,1)|` を正規化したもの。a, b は未使用。
Border handling: mirror without repeating the edge pixel (d c b | a b c d — OpenCV's `BORDER_REFLECT_101) (measured; tools/impl2/border_probe.py`).
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.
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_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.
cv_scharr 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.