Bump scikit-image from 0.19.3 to 0.20.0 in /requirements

[dependabot]

Bumps scikit-image from 0.19.3 to 0.20.0.

Release notes

Sourced from scikit-image's releases.

v0.20.0

Announcement: scikit-image 0.20.0

scikit-image is an image processing toolbox built on SciPy that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more.

For more information, examples, and documentation, please visit our website: https://scikit-image.org

With this release, many of the functions in skimage.measure now support anisotropic images with different voxel spacings.

Many performance improvements were made, such as support for footprint decomposition in skimage.morphology

Four new gallery examples were added to the documentation, including the new interactive example "Track solidification of a metallic alloy".

This release completes the transition to a more flexible channel_axis parameter for indicating multi-channel images, and includes several other deprecations that make the API more consistent and expressive.

Finally, in preparation for the removal of distutils in the upcoming Python 3.12 release, we replaced our build system with meson and a static pyproject.toml specification.

This release supports Python 3.8--3.11.

New features and improvements

• Support footprint decomposition to several footprint generating and consuming functions in skimage.morphology. By decomposing a footprint into several smaller ones, morphological operations can potentially be sped up. The decomposed footprint can be generated with the new decomposition parameter of the functions rectangle, diamond, disk, cube, octahedron, ball, and octagon in skimage.morphology. The footprint parameter of the functions binary_erosion, binary_dilation, binary_opening, binary_closing, erosion, dilation, opening, closing, white_tophat, and black_tophat in skimage.morphology now accepts a sequence of 2-element tuples (footprint_i, num_iter_i) where each entry, i, of the sequence contains a footprint and the number of times it should be iteratively applied. This is the form produced by the footprint decompositions mentioned above (#5482, #6151).
• Support anisotropic images with different voxel spacings. Spacings can be defined with the new parameter spacing of the following functions in skimage.measure: regionprops, regionprops_table, moments, moments_central, moments_normalized, centroid, inertia_tensor, and inertia_tensor_eigvals. Voxel spacing is taken into account for the following existing properties in skimage.measure.regionprops: area, area_bbox, centroid, area_convex, extent, feret_diameter_max, area_filled, inertia_tensor, moments, moments_central, moments_hu, moments_normalized, perimeter, perimeter_crofton, solidity, moments_weighted_central, and moments_weighted_hu. The new properties num_pixels and coords_scaled are available as well. See the respective docstrings for more details (#6296).
• Add isotropic binary morphological operators isotropic_closing, isotropic_dilation, isotropic_erosion, and isotropic_opening in skimage.morphology. These functions return the same results as their non-isotropic counterparts but perform faster for large circular structuring elements (#6492).
• Add new colocalization metrics pearson_corr_coeff, manders_coloc_coeff, manders_overlap_coeff and intersection_coeff to skimage.measure (#6189).
• Support the Modified Hausdorff Distance (MHD) metric in skimage.metrics.hausdorff_distance via the new parameter method. The MHD can be more robust against outliers than the directed Hausdorff Distance (HD) (#5581).
• Add two datasets skimage.data.protein_transport and skimage.data.nickel_solidification (#6087).
• Add new parameter use_gaussian_derivatives to skimage.feature.hessian_matrix which allows the computation of the Hessian matrix by convolving with Gaussian derivatives (#6149).
• Add new parameters squared_butterworth and npad to skimage.filters.butterworth, which support traditional or squared filtering and edge padding, respectively (#6251).
• Support construction of a skimage.io.ImageCollection from a load_pattern with an arbitrary sequence as long as a matching load_func is provided (#6276).
• Add new parameter alpha to skimage.metrics.adapted_rand_error allowing control over the weight given to precision and recall (#6472).
• Add new parameter binarize to skimage.measure.grid_points_in_poly to optionally return labels that tell whether a pixel is inside, outside, or on the border of the polygon (#6515).
• Add new parameter include_borders to skimage.measure.convex_hull_image to optionally exclude vertices or edges from the final hull mask (#6515).
• Add new parameter offsets to skimage.measure.regionprops that optionally allows specifying the coordinates of the origin and affects the properties coords_scaled and coords (#3706).
• Add new parameter disambiguate to skimage.registration.phase_cross_correlation to optionally disambiguate periodic shifts (#6617).
• Support n-dimensional images in skimage.filters.farid (Farid & Simoncelli filter) (#6257).
• Support n-dimensional images in skimage.restoration.wiener (#6454).
• Support three dimensions for the properties rotation and translation in skimage.transform.EuclideanTransform as well as for skimage.transform.SimilarityTransform.scale (#6367).
• Allow footprints with non-adjacent pixels as neighbors in skimage.morphology.flood_fill (#6236).
• Support array-likes consistently in AffineTransform, EssentialMatrixTransform, EuclideanTransform, FundamentalMatrixTransform, GeometricTransform, PiecewiseAffineTransform, PolynomialTransform, ProjectiveTransform, SimilarityTransform, estimate_transform, and matrix_transform in skimage.transform (#6270).

Performance

• Improve performance (~2x speedup) of skimage.feature.canny by porting a part of its implementation to Cython (#6387).
• Improve performance (~2x speedup) of skimage.feature.hessian_matrix_eigvals and 2D skimage.feature.structure_tensor_eigenvalues (#6441).
• Improve performance of skimage.measure.moments_central by avoiding redundant computations (#6188).
• Reduce import time of skimage.io by loading the matplotlib plugin only when required (#6550).
• Incorporate RANSAC improvements from scikit-learn into skimage.measure.ransac which decrease the number of iterations (#6046).
• Improve histogram matching performance on unsigned integer data with skimage.exposure.match_histograms. (#6209, #6354).
• Reduce memory consumption of the ridge filters meijering, sato, frangi, and hessian in skimage.filters (#6509).

... (truncated)

Changelog

Sourced from scikit-image's changelog.

How to make a new release of skimage

While following this guide, note down all the times that you need to consult a previous release manager, or that you find an instruction unclear. You will, of course, make a PR to update these notes after you are done with the release! ;-)

Before you start, make sure you have all the required write permissions (if not, you will need to ask an owner to grant you access), specifically to:

• https://pypi.org/project/scikit-image/

• https://github.com/scikit-image/scikit-image-web

• Check TODO.txt for any outstanding tasks.

We use a variant of "semantic versioning", where version numbers are classified as v... By default, releases are made from the main branch as part of a linear release history and, as described below, are triggered by pushing a git tag to the scikit-image repository on github. If a patch release is required for an older version, a branch can be created from the appropriate point in main and the following instructions are still apt.

• Update the release notes (note this will soon change with the addition of Towncrier integrated directly into the CI):

  1. Review and cleanup doc/release/release_dev.rst.

  2. Make a list of merges, contributors, and reviewers by running tools/generate_release_notes.py -h and following that file's usage.

  3. Paste this list at the end of the release_dev.txt.

  4. Scan the PR titles for highlights, deprecations, API changes, and bugfixes, and mention these in the relevant sections of the notes. Try to present the information in an expressive way by mentioning the affected functions, elaborating on the changes and their consequences. If possible, organize semantically close PRs in groups.

  5. Check for duplicate names in the automatically generated list of contributors and reviewers

  6. Rename the file to doc/release/release_<major>.<minor>.txt

  7. Copy doc/release/release_template.txt to doc/release/release_dev.txt for the next release.

  8. Copy relevant deprecations from release_<major>_<minor>.txt

... (truncated)

Commits

• 5e74a4a Designate 0.20.0 release
• 0d09ff1 Merge pull request #6766 from jarrodmillman/release-notes-0.20.0
• 1895495 Re-add missing sections and update style
• 2571da1 Edit release notes summary
• 38b0849 Add recently merged PR 6768
• 6182c93 Update author section
• 6655762 Hide PRs that were backported to 0.19.x
• b13988e Finish sorting new PRs into categories
• a2289f6 Continue sorting new PRs into categories
• 0bd0ca4 Remove duplicate classifier in pyproject.toml
• Additional commits viewable in compare view

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[adamjstewart]

This is failing because of scikit-image/scikit-image#6567

[adamjstewart]

Not really sure what to do here. I don't really want to change the entire test procedure just to support different versions of scipy based on python version. We could wait for the next skimage release, but idk when that is. We could also pin to an older version of scipy now since it's stable and doesn't need to be tested as recently as skimage.

[adamjstewart]

Once this PR makes it into a release we can remove this test the latest version of scipy again.