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199 lines
6.0 KiB
Plaintext
199 lines
6.0 KiB
Plaintext
Installing scikit-image
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-----------------------
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If you are on Mac OS X you're lucky, open the terminal and install
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scikit-image with pip::
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pip install scikit-image
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For Python 3 use pip3 instead::
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pip3 install scikit-image
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For other systems, please read on.
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Linux, Mac and Windows
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----------------------
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An easy light weight method to get scikit-image installed on all of the most
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popular operating systems is by using miniconda_. Go over and grab the
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appropriate miniconda_ version for your operating system and install it. When
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you have miniconda_ installed, open a terminal and install scikit-image
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with conda::
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conda install scikit-image
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If you prefer *not* using miniconda, find instructions for your operating
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system below.
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Windows
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-------
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Scikit-image comes with the Python distributions Anaconda_,
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`Enthought Canopy`_ and `Python(x,y)`_. If you install any of
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them, scikit-image should already be installed.
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.. _Anaconda: https://store.continuum.io/cshop/anaconda/
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.. _Enthought Canopy: https://www.enthought.com/products/canopy/
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.. _Python(x,y): http://code.google.com/p/pythonxy/wiki/Welcome
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If you prefer the regular Python distribution from python.org_, you can
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install scikit-image manually by downloading packages. You will need numpy_,
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scipy_ and the scikit-image package. You can find the packages in `Cristoph
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Gohlke's`_ web page with compiled Python packages. Here is the direct link to
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the `scipy section`_, `numpy section`_ and `scikit-image section`_. Make sure
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you download the right version for your system. E.g. numpy for Python 3.4 64
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bit would be ``numpy‑1.9.2+mkl‑cp34‑none‑win_amd64.whl``.
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To install Goehlke's packages, use pip::
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pip install wheel
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pip install --find-links Downloads scikit-image
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Here ``--find-links Downloads`` means that pip will look for packages in the
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folder named `Downloads`. Make sure that is where you saved the packages from
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Goehlke.
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As you see, installing scikit-image with pip requires some extra manual labor,
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so using a Python distribution is recommended on Windows.
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If you have a brave soul, you can also install scikit-image on Windows by
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compiling it from source::
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pip install scikit-image
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If you experience the error ``Error:unable to find vcvarsall.bat`` it means that
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distutils is not correctly configured to use the C compiler. Modify (or create,
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if not existing) the configuration file ``distutils.cfg`` (located for
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example at ``C:\Python26\Lib\distutils\distutils.cfg``) to contain::
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[build]
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compiler=mingw32
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For more details on compiling in Windows, there is a lot of knowledge iterated
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into the `setup of appveyor`_ (a continious integration service).
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.. _miniconda: http://conda.pydata.org/miniconda.html
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.. _python.org: http://python.org/
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.. _numpy: http://www.numpy.org/
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.. _scipy: http://www.scipy.org/
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.. _Cristoph Gohlke's: http://www.lfd.uci.edu/~gohlke/pythonlibs/
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.. _numpy section: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
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.. _scipy section: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
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.. _scikit-image section: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-image
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.. _setup of appveyor: https://github.com/scikit-image/scikit-image/blob/master/appveyor.yml
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Debian and Ubuntu
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-----------------
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On Debian and Ubuntu install scikit-image with::
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sudo apt-get install python-skimage
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Or if you use Python 3::
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sudo apt-get install python3-skimage
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On Ubuntu scikit-image is found in the `universe repo`_, and python-skimage can
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also be found in the `Neurodebian repository`_. For using the repository
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follow the `Neurodebian instructions`_ to add Neurodebian to your system
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package manager.
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Ubuntu 14.04 LTS ships with version 0.9.3 of scikit-image, so if you need an
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up-to-date version you must compile scikit-image yourself. First install the
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dependencies::
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sudo apt-get install python-matplotlib python-numpy python-pil python-scipy
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or for Python 3::
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sudo apt-get install python3-matplotlib python3-numpy python3-pil python3-scipy
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Get compilers::
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sudo apt-get install build-essential cython
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Compile and install the latest stable version of scikit-image::
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pip install scikit-image
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.. _universe repo: https://help.ubuntu.com/community/Repositories/Ubuntu
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.. _Neurodebian repository: http://neuro.debian.net/
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.. _Neurodebian instructions: http://neuro.debian.net/#how-to-use-this-repository
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Other Unixes
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------------
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Install binary packages of cython, matplotlib, numpy, pillow and scipy if they
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are available in your operating system's package manager. Make sure you have
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a C and C++ compilers. Then install scikit-image with pip::
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pip install scikit-image
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Upgrading
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---------
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You can upgrade scikit-image by::
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pip install --upgrade --no-deps scikit-image
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pip install scikit-image # installs new dependencies, if changed
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Building with bento
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-------------------
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``scikit-image`` can also be built using `bento
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<http://cournape.github.io/Bento/>`__. Bento depends on `WAF
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<https://code.google.com/p/waf/>`__ for compilation.
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Follow the `Bento installation instructions
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<http://cournape.github.io/Bento/html/install.html>`__ and `download the WAF
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source <http://code.google.com/p/waf/downloads/list>`__.
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Tell Bento where to find WAF by setting the ``WAFDIR`` environment variable::
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export WAFDIR=<path/to/waf>
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From the ``scikit-image`` source directory::
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bentomaker configure
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bentomaker build -j # (add -i for in-place build)
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bentomaker install # (when not builing in-place)
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Depending on file permissions, the install commands may need to be run as
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sudo.
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Install bleeding edge development version
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-----------------------------------------
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Obtain the source from the git-repository at
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http://github.com/scikit-image/scikit-image by running::
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git clone http://github.com/scikit-image/scikit-image
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If you do not have git installed on your machine, you can also download a
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zipball from https://github.com/scikit-image/scikit-image/zipball/master.
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The scikit can be installed using::
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pip install .
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If you prefer, you can use a link instead by compiling extensions in-place::
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python setup.py build_ext -i
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pip install -e .
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.. include:: ../../DEPENDS.txt
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