We’re happy to announce the release of scikit-image v0.12!
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
For this release, we merged over 200 pull requests with bug fixes, cleanups, improved documentation and new features. Highlights include:
measure.label: 0-valued pixels are considered as background by default, and the label of background pixels is 0.
measure.compare_ssim) is now n-dimensional and supports color channels as well.
equalize_adapthistnow takes a
kernel_sizekeyword argument, replacing the
blob_dohnow return float arrays instead of integer arrays.
transform.integratenow takes lists of tuples instead of integers to define the window over which to integrate.
filters.gaussian_filterhas been renamed
filters.gabor_filterhas been renamed
restoration.nl_means_denoisinghas been renamed
measure.LineModelwas deprecated in favor of
measure.structural_similarityhas been renamed
data.lenahas been deprecated, and gallery examples use instead the
(Listed alphabetically by last name)
From the shell/command prompt, execute:
conda install scikit-image
pip install SomePackage-1.0-py2.py3-none-any.whl
On Debian and Ubuntu install scikit-image with:
sudo apt-get install python-skimage
Execute the following command from the shell:
pip install scikit-image
If you experience the error
Error:unable to find vcvarsall.bat it means that
distutils is not correctly configured to use the C compiler. Modify (or create,
if not existing) the configuration file
distutils.cfg (located for
C:\Python26\Lib\distutils\distutils.cfg) to contain:
For more details on compiling in Windows, there is a lot of knowledge iterated into the setup of appveyor (a continuous integration service).
If your distribution ships an outdated version, you may recompile from source. First install the dependencies:
sudo apt-get install python-matplotlib python-numpy python-pil python-scipy
sudo apt-get install build-essential cython
Then run the pip installation command.
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 .
git pull # Grab latest source python setup.py build_ext -i # Compile any modified extensions
Tell Bento where to find WAF by setting the
WAFDIR environment variable:
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
Astropy provides FITS io capability.