Docs for 0.15.dev0
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Installing scikit-image

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.

  1. Standard Installation
  2. Development Installation

1. Standard Installation:

scikit-image comes pre-installed with several Python distributions, including Anaconda, Enthought Canopy, Python(x,y) and WinPython.

On all other systems, install it via shell/command prompt:

pip install scikit-image

If you are running Anaconda or miniconda, use:

conda install -c conda-forge scikit-image

2. Development Installation:

You can install the scikit-image development version if either your distribution ships an outdated version or you want to develop and work on new features before the package is released officially.

First, uninstall any existing installations:

pip uninstall scikit-image

or, on conda-based systems:

conda uninstall scikit-image

Now, clone scikit-image on your local computer, and install:

git clone https://github.com/scikit-image/scikit-image.git
cd scikit-image
pip install -e .

To update the installation:

git pull  # Grab latest source
pip install -e .  # Reinstall

Platform-specific notes follow below.

a. Windows

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

A run-through of the compilation process for Windows is included in our setup of AppVeyor (a continuous integration service).

b. Debian and Ubuntu

Install all the required dependencies:

sudo apt-get install python3-matplotlib python3-numpy python3-pil python3-scipy python3-tk

Install suitable compilers:

sudo apt-get install build-essential cython3

Complete the general development installation instructions above.

Build Requirements (docs)

# Sphinx 1.7.8 has a bug when building on cached envs
# https://github.com/sphinx-doc/sphinx/issues/5361
sphinx>=1.3,!=1.7.8
numpydoc>=0.6
sphinx-gallery
sphinx-copybutton
pytest-runner
scikit-learn

Runtime requirements

numpy>=1.11
scipy>=0.17.0
matplotlib>=2.0.0,!=3.0.0
networkx>=2.0
pillow>=4.3.0
imageio>=2.0.1
PyWavelets>=0.4.0
dask[array]>=0.9.0
cloudpickle>=0.2.1

Optional Requirements

You can use scikit-image with the basic requirements listed above, but some functionality is only available with the following installed:

  • PyQt5 or PySide2 through qtpy
    A Qt plugin will provide imshow(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.
  • SimpleITK
    Optional I/O plugin providing a wide variety of formats. including specialized formats using in medical imaging.
  • imread
    Optional I/O plugin providing most standard formats.

Testing requirements

# pytest 3.7.3 is broken on macOS, see:
# https://github.com/pytest-dev/pytest/issues/3888
pytest!=3.7.3
pytest-cov
flake8
codecov

Documentation requirements

# Sphinx 1.7.8 has a bug when building on cached envs
# https://github.com/sphinx-doc/sphinx/issues/5361
sphinx>=1.3,!=1.7.8
numpydoc>=0.6
sphinx-gallery
sphinx-copybutton
pytest-runner
scikit-learn