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
http://scikit-image.org/docs/dev/auto_examples/
For this release, we merged over 200 pull requests with bug fixes, cleanups, improved documentation and new features. Highlights include:
Sourcedask:skimage.util.apply_parallel
(#1493)filters.laplace (#1763)imageio library (#1575)measure.label: 0-valued
pixels are considered as background by default, and the label of
background pixels is 0.skimage.measure.regionprops
(#1505)morphology.remove_small_holes now complements
morphology.remove_small_objects (#1689)morphology.skeletonizerestoration.denoise_tv_chambolle and feature.peak_local_maxmeasure.regionprops.color.gray2rgbmeasure.compare_ssim) is now
n-dimensional and supports color channels as well.segmentation.watershedDocumentation:
equalize_adapthist now takes a kernel_size keyword argument,
replacing the ntiles_* arguments.blob_dog, blob_log and blob_doh now return
float arrays instead of integer arrays.transform.integrate now takes lists of tuples instead of integers
to define the window over which to integrate.True.filters.gaussian_filter has been renamed filters.gaussianfilters.gabor_filter has been renamed filters.gaborrestoration.nl_means_denoising has been renamed
restoration.denoise_nl_meansmeasure.LineModel was deprecated in favor of measure.LineModelNDmeasure.structural_similarity has been renamed
measure.compare_ssimdata.lena has been deprecated, and gallery examples use instead the
data.astronaut() picture.(Listed alphabetically by last name)
From the shell/command prompt, execute:
conda install scikit-image
scikit-image comes pre-installed with several Python
distributions, including Anaconda, Enthought Canopy,
Python(x,y) and WinPython.
If you are using the distribution from python.org, you’ll need to manually download a few packages: numpy, scipy and scikit-image from Christoph Gohlke’s website. Python wheels are installed 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
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
example at C:\Python26\Lib\distutils\distutils.cfg) to contain:
[build]
compiler=mingw32
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
Get compilers:
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 .
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.
(or PIL)
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:
The qt plugin that provides imshow(x, fancy=True) and skivi.
The freeimage plugin provides support for reading various types of
image file formats, including multi-page TIFFs.
The pyamg module is used for the fast cg_mg mode of random
walker segmentation.
Astropy provides FITS io capability.
SimpleITK Optional io plugin providing a wide variety of formats. including specialized formats using in medical imaging.
A Python Unit Testing Framework
A tool that generates a unit test code coverage report
sphinx >= 1.3 is required to build the documentation.