scikit-image 0.13.1 is a bug-fix and compatibility update. See below for the many new features in 0.13.0.
The main contribution in 0.13.1 is Jarrod Millman’s valiant work to ensure scikit-image works with both NetworkX 1.11 and 2.0 (#2766). Additional updates include:
Sourceskimage.util.plot_matches and denoise_wavelet,
by Gregory Lee (#2650, #2640)We’re happy to (finally) announce the release of scikit-image v0.13.0!
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:
and our gallery of examples
http://scikit-image.org/docs/dev/auto_examples/
This release is the result of a year of work, with over 200 pull requests by 82 contributors. Highlights include:
regionprops computation for centroids (#2083)segmentation.clear_border (#2087)restoration.denoise_wavelet
(#1833, #2190, #2238, #2240, #2241, #2242, #2462)filters.threshold_sauvola and
filters.threshold_niblack (#2266, #2441)filters.try_all_threshold to rapidly see output of various thresholding
methodssegmentation.watershed (#2211)feature.shape_index (#2312)inpaint_biharmonic (#2234)filter module. Use filters instead. (#2023)skimage.filters.canny links. Use feature.canny instead. (#2024)_mode_deprecations (#2156)rescale_intensity (#2161)ntiles_x and ntiles_y have been removed from
exposure.equalize_adapthistcircle argument to radon and iradon
transforms will be True in 0.15 (#2235)multichannel for denoise_bilateral and
denoise_nl_means will be False in 0.15block_norm in feature.hog will be L2-Hysteresis in
0.15.threshold_adaptive function is deprecated. Use threshold_local
instead.mode in transform.swirl, resize, and rescale
will be “reflect” in 0.15.dask into an optional dependency (#2013)filter module (#2023)skimage.filters.canny links (#2024)skimage.measure.label references to the docstring (#2143)_mode_deprecations (#2156)segmentation.felzenszwalb (#2164)rescale_intensity (#2161)inpaint_biharmonic (#2234)setup.py egg_info without numpy installed. (#2260)transform.{pyx/pxd} (#2288)subplots_adjust (#2289)assert_ from numpy.testing over assert (#2298)compare_ssim docstring (#2314)skimage.draw (#2321)exposure.equalize_adapthist args and docstring (#2220)pep8-compliant (#2392)util/dtype.py (#2425)skimage.hog blocks normalization methods (#2040)dask[array] to optional requirements (#2494)test_ncut_stable_subgraph for Python 3.6, enable Python 3.6 in Travis (#2511)threshold_minimum return identical results on i686 and x86_64 (#2549)offset parameter from filters.threshold_sauvola docstring (#2566)requirements.txt (#2572)morphology.watershed error message (#2570)__init__ (#2588)We are assuming that you have default Python environment already configured on your computer
and you intend to install scikit-image inside of it. If you want to create and work with Python
virtual environments, please follow instructions on venv and virtual environments.
There are two ways you can install scikit-image on your preferred Python environment.
scikit-image comes pre-installed with several Python distributions, including Anaconda, Enthought Canopy,
Python(x,y) and WinPython. However, you can install or upgrade existing scikit-image via
shell/command prompt.
On Windows, you can install scikit-image using:
pip install scikit-image
For Conda-based distributions (Anaconda, Miniconda), execute:
conda install scikit-image
If you are using pure Python i.e. the distribution from python.org, you’ll need to manually download packages (such as numpy, scipy and scikit-image) using Python wheels available from Christoph Gohlke’s website. You can install Python wheels using:
pip install SomePackage-1.0-py2.py3-none-any.whl
On Debian and Ubuntu, install scikit-image with:
sudo apt-get install python-skimage
You can install scikit-image development version if either your distribution ships an outdated version
or you want to develop and work on new features before the package is released officially.
Before installing the development version, uninstall the standard version of scikit-image using pip as:
pip uninstall scikit-image
or using conda (for Anaconda users) as:
conda uninstall scikit-image
Now clone scikit-image on your local computer:
git clone https://github.com/scikit-image/scikit-image.git
Change the directory and build from source code:
cd scikit-image
python setup.py develop
If you experience the error Error:unable to find vcvarsall.bat it means that
your computer does not have recommended compilers for python. You can either download and
install Windows compilers from here or use MinGW compilers . If using MinGW, make sure to correctly
configure distutils by modifying (or create, if not existing) the configuration file
distutils.cfg (located for example at C:\Python26\Lib\distutils\distutils.cfg)
to contain:
[build]
compiler=mingw32
Once the build process is complete, run:
pip install -U -e .
Make sure to give space after -e and add dot at the end. This will install scikit-image development version
and upgrade (or install) all the required dependencies. Otherwise, you can run the following command
to skip installation of dependencies:
pip install -U[--no-deps] -e .
You can install or upgrade dependencies required for scikit-image anytime after installation using:
pip install -r requirements.txt --upgrade
For more details on compiling in Windows, there is a lot of knowledge iterated into the setup of appveyor (a continuous integration service).
Install all the required dependencies:
sudo apt-get install python-matplotlib python-numpy python-pil python-scipy
Get suitable compilers for successful installation:
sudo apt-get install build-essential cython
Obtain the source from the git repository at
http://github.com/scikit-image/scikit-image by running:
git clone https://github.com/scikit-image/scikit-image.git
After unpacking, change into the source directory and execute:
pip install -e .
To update:
git pull # Grab latest source
python setup.py build_ext -i # Compile any modified extensions
Alternatively, scikit-image can also be built using bento. Bento depends on WAF for compilation.
Follow the Bento installation instructions and download the WAF source.
Tell Bento where to find WAF by setting the WAFDIR environment variable:
export WAFDIR=<path/to/waf>
From the scikit-image source directory:
bentomaker configure
bentomaker build -j # (add -i for in-place build)
bentomaker install # (when not building in-place)
Depending on file permissions, the install commands may need to be run as sudo.
You can use pip to automatically install the runtime dependencies as follows:
$ pip install -r requirements.txt
You can use this scikit with the basic requirements listed above, but some functionality is only available with the following installed:
qt plugin that provides imshow(x, fancy=True) and skivi.freeimage plugin provides support for reading various types of
image file formats, including multi-page TIFFs.pyamg module is used for the fast cg_mg mode of random
walker segmentation.sphinx >= 1.3 is required to build the documentation.