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 the instructions on venv and virtual environments.
There are two ways you can install scikit-image
on your preferred Python environment.
- Standard Installation
- Development Installation
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development version if either your distribution ships an outdated version or you want to develop and work on new features before the package is released officially.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
- Python >= 2.7
- Numpy >= 1.11
- SciPy >= 0.17.0
- Matplotlib >= 1.3.1
- NetworkX >= 1.8
- Six >=1.7.3
- Pillow >= 2.1.0
(or PIL)
- PyWavelets>=0.4.0
- dask[array] >= 0.9.0.
For parallel computation using skimage.util.apply_parallel.
You can use pip to automatically install the runtime dependencies as follows:
$ pip install -r requirements.txt
You can use scikit-image
with the basic requirements listed above, but some functionality is only available with the following installed:
- PyQt4
The
qt
plugin that providesimshow(x, fancy=True)
and skivi.
- PyAMG
The
pyamg
module is used for the fast cg_mg mode of random walker segmentation.
- Astropy
Provides FITS I/O capability.
- pytest
A Python Unit Testing Framework. Required to execute the tests.
- pytest-cov
A tool that generates a unit test code coverage report.
- sphinx >= 1.3
Required to build the documentation.