transform op• Data kinds: image → image
• Call: fullseye.apply(img, "xmh_haar", 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 real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
Haar wavelet transform (`mahotas.haar`). Truncates the image to an even size before transforming, then displays the resulting coefficient array -- a Matlab-style in-place layout where the approximation/horizontal/vertical/diagonal subbands are packed into an array the same size as the original image -- directly as an image after min-max normalization.
> The detailed description below is the original text — the summary and the headings are translated.
分解レベルは 1 段固定。`a, b` は未使用。値そのものは復元可能な画像ではなく係数の可視化であることに注意。
• gallery2d_geometry 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.
xmh_haar 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_geometry — py -3.11 examples/gallery2d_geometry.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
transform)xmh_daubechies · tf_radon_sinogram
*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.