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transform-registration

transform-registration

Geometric transforms, warps, pyramids, Radon/Hough helpers, phase cross-correlation, and optical flow for scikit-image.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: transform-registration description: "Geometric transforms, warps, pyramids, Radon/Hough helpers, phase cross-correlation, and optical flow for scikit-image." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Transform and Registration

Use this sub-skill when you need to align images, warp coordinates, change scale, build pyramids, reconstruct from projections, or estimate dense motion with scikit-image. Keep point-feature extraction, descriptor matching, and segmentation in other routes.

Route by Task

Core Rules

  • warp takes an inverse map. Pass a transform object, its .inverse, a 3×3 matrix, a callable inverse map, or a coordinate array. For 2-D homographies, SimilarityTransform, AffineTransform, and ProjectiveTransform use a faster matrix path.
  • resize, rescale, resize_local_mean, warp_polar, and pyramid_* accept channel_axis; warp and rotate assume a normal image layout, so move channels before calling them if needed.
  • Use preserve_range=True when the input intensities must stay on their native scale. Interpolated warps usually return floating output; use order=0 for discrete masks or edge maps.
  • phase_cross_correlation returns the correction shift in input axis order, not the original motion. If the images may differ by more than half the frame, use disambiguate=True.
  • optical_flow_ilk and optical_flow_tvl1 require grayscale images and return one flow component per axis.
  • radon and iradon use projection angles in degrees. hough_line and probabilistic_hough_line use theta in radians.
  • If from_estimate or estimate_transform fails, check bool(tform) before warping.
  • Use matrix_transform for point arrays and warp_coords for reusable custom coordinate grids.

What Stays Out

  • If you need corners, descriptors, or matched keypoints first, use analysis.
  • If you need thresholds, morphology, masks, or region cleanup first, use segmentation-and-shapes.
  • If you only need intensity normalization or denoising, use enhancement.

Reference Files

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