doc/source/release_notes/release_0.17.rst
We're happy to announce the release of scikit-image v0.17.2, which is a bug-fix release.
mask, shift_x, and shift_y from skimage.filters.medianbeta1 and beta2 from skimage.filters.frangibeta1 and beta2 from skimage.filters.hessiandtype from skimage.io.imreadimg from skimage.morphology.skeletonize_3d.We're happy to announce the release of scikit-image v0.17.1!
scikit-image is an image processing toolbox for 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:
Many thanks to the 54 authors who contributed the amazing number of 213 merged pull requests! scikit-image is a community-based project and we are happy that this number includes first-time contributors to scikit-image.
Special thanks for the release to the Cython team, who helped us make our code compatible with their coming Cython 3.0 release.
restoration.calibrate_denoiser (#3824), with corresponding
gallery example and tutorial.measure.profile_line has a new reduce_func parameter to accept a
reduction operation to be computed on pixel values along the profile (#4206)filters.window (#4252) (with new gallery example)util.map_array was introduced to map a set of pixel values to another one
(for example to map region labels to the size of regions in an image of
labels) #4612 and #4646data submodule, thanks to the
pooch library. data.download_all fetches all datasets. (#3945)New doc tutorials and gallery examples have been added to the use of regionprops_table (#4348) geometrical transformations (#4385), and the registration of rotation and scaling with no shared center (#4515). A new section on registration has been added to the gallery (#4575).
exposure.equalize_adapthist algorithm (#4598)
and to the Sobel, Scharr, and Prewitt filters (#4347).segmentation.relabel_sequential has been reduced in
the case of labels much larger than the number of labels (#4612)draw.random_shapes (#4493)segmentation.random_walker new Jacobi preconditioned conjugate gradient mode
(#4359) and minor corrections #4630We have also improved the consistency of several functions regarding the way they handle data types
denoise_wavelet no longer rescales
the range of the data or clips the output to the range [0, 1] or [-1, 1].
For non-float inputs, rescaling and clipping still occurs as in prior
releases (although with a bugfix related to the scaling of sigma).scipy.ndimage.convolve is now
'reflect', which allows meaningful values at the borders for these
filters. To retain the old behavior, pass
mask=np.ones(image.shape, dtype=bool) (#4347)out_range is a range of numbers and not a dtype in
:func:skimage.exposure.rescale_intensity, the output data type will always
be float (#4585)skimage.exposure.equalize_adapthist will be
slightly different from previous versions due to different rounding behavior
(#4585)skimage.segmentation.relabel_sequential
are no longer NumPy arrays, but more memory-efficient ArrayMap objects that behave
the same way for mapping. See the relabel_sequential documentation for more details.
To get NumPy arrays back, cast it as a NumPy array: np.asarray(forward_map) (#4612)denoise_wavelet: For user-supplied sigma, if the input image gets
rescaled via img_as_float, the same scaling will be applied to sigma to
preserve the relative scale of the noise estimate. To restore the old,
behaviour, the user can manually specify rescale_sigma=False.(other small bug fixes are part of the list of other pull requests at the end)
The minimal supported Python version by this release is 3.6.
inplace in skimage.morphology.flood_fill has been deprecated
in favor of in_place and will be removed in version scikit-image 0.19.0
(#4250).skimage.segmentation.circle_level_set has been deprecated and will be
removed in 0.19. Use skimage.segmentation.disk_level_set instead.skimage.draw.circle has been deprecated and will be removed in 0.19.
Use skimage.draw.disk instead.The list of other pull requests is given at the end of this document, after the list of authors and reviewers.
Other Pull Requests
scipy.sparse.linalg.eigsh to ensure reproducibility in graph_cut (#4251)hog docstring (#4302)simple_metrics docstring (#4323)measure.block_reduce to accept explicit func_kwargs kwd (#4341)measure.block_reduce (#4355)segmentation/plot_label.py to make the background transparent (#4527)exposure.rescale_intensity for constant input. (#4615)