We’re happy to announce scikit-image v0.20.0!
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
Highlights:
- With this release, many of the functions in
skimage.measurenow support anisotropic images with different voxel spacings. - Many performance improvements were made, such as support for footprint decomposition in
skimage.morphology. - Five 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_axisparameter 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
distutilsin 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.
Before upgrading, we recommend that users check that their own code does not use deprecated functionality. For a complete and detailed list of changes, please check out the full list of release notes for v0.20.0.
This release is the culmination of 9 months of work by at least 71 code authors, 42 reviewers and many more who contributed through other means. Special thanks to @jarrodmillman who helped get this release done with our new build system!