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Installing scikit-image

How you should install scikit-image depends on your needs and skills:

Supported platforms

  • Windows 64-bit on x86 processors
  • macOS on x86 and M (ARM) processors
  • Linux 64-bit on x86 processors

While we do not officially support other platforms, you could still try building from source.

Version check

To see whether scikit-image is already installed or to check if an install has worked, run the following in a Python shell or Jupyter notebook:

import skimage as ski
print(ski.__version__)

or, from the command line:

python -c "import skimage; print(skimage.__version__)"

(Try python3 if python is unsuccessful.)

You'll see the version number if scikit-image is installed and an error message otherwise.

Installation via pip and conda

These install only scikit-image and its dependencies; pip has an option to include related packages.

pip

Prerequisites to a pip install: You're able to use your system's command line to install packages and are using a virtual environment (any of several).

While it is possible to use pip without a virtual environment, it is not advised: virtual environments create a clean Python environment that does not interfere with any existing system installation, can be easily removed, and contain only the package versions your application needs. They help avoid a common challenge known as dependency hell.

To install the current scikit-image you'll need at least Python 3.10. If your Python is older, pip will find the most recent compatible version.

# Update pip
python -m pip install -U pip
# Install scikit-image
python -m pip install -U scikit-image

To access the full selection of demo datasets, use scikit-image[data]. To include a selection of other scientific Python packages that expand scikit-image's capabilities to include, e.g., parallel processing, you can install the package scikit-image[optional]:

python -m pip install -U scikit-image[optional]

Warning

Please do not use the command sudo and pip together as pip may overwrite critical system libraries which may require you to reinstall your operating system.

conda

Miniconda is a bare-essentials version of the Anaconda package; you'll need to install packages like scikit-image yourself. Like Anaconda, it installs Python and provides virtual environments.

Once you have your conda environment set up, you can install scikit-image with the command:

conda install scikit-image

System package managers

Using a package manager (yum, apt-get, etc.) to install scikit-image or other Python packages is not your best option:

  • You're likely to get an older version.
  • You'll probably want to make updates and add new packages outside of the package manager, leaving you with the same kind of dependency conflicts you see when using pip without a virtual environment.
  • There's an added risk because operating systems use Python, so if you make system-wide Python changes (installing as root or using sudo), you can break the operating system.

Downloading all demo datasets

Some of the data used in our examples is hosted online and is not installed by default by the procedures explained above. Data are downloaded once, at the first call, but this requires an internet connection. If you prefer downloading all the demo datasets to be able to work offline, ensure that package pooch is installed and then run this command:

python -c 'import skimage as ski; ski.data.download_all()'

or call ski.data.download_all() in your favourite interactive Python environment (IPython, Jupyter notebook, ...).

Additional help

If you still have questions, reach out through

To suggest a change in these instructions, please open an issue on GitHub.

Installing scikit-image for contributors

We are assuming that you have a default Python environment already configured on your computer and that you intend to install scikit-image inside of it.

We also make a few more assumptions about your system:

  • You have a C compiler set up.
  • You have a C++ compiler set up.
  • You are running a version of Python compatible with our system as listed in our pyproject.toml.
  • You've cloned the git repository into a directory called scikit-image. You have set up the upstream remote to point to our repository and origin to point to your fork.

This directory contains the following files:

scikit-image
├── asv.conf.json
├── azure-pipelines.yml
├── benchmarks/
├── CITATION.bib
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.rst
├── CONTRIBUTORS.txt
├── doc/
├── INSTALL.rst
├── LICENSE.txt
├── MANIFEST.in
├── meson.build
├── meson.md
├── pyproject.toml
├── README.md
├── RELEASE.txt
├── requirements/
├── requirements.txt
├── skimage/
├── TODO.txt
└── tools/

All commands below are assumed to be running from the scikit-image directory containing the files above.

Build environment setup

Once you've cloned your fork of the scikit-image repository, you should set up a Python development environment tailored for scikit-image. You may choose the environment manager of your choice. Here we provide instructions for two popular environment managers: venv (pip based) and conda (Anaconda or Miniconda).

venv

# Create a virtualenv named ``skimage-dev`` that lives outside of the repository.
# One common convention is to place it inside an ``envs`` directory under your home directory:
mkdir ~/envs
python -m venv ~/envs/skimage-dev
# Activate it
# (On Windows, please use ``skimage-dev\Scripts\activate``)
source ~/envs/skimage-dev/bin/activate
# Install main development and runtime dependencies
pip install -r requirements.txt
# Install build dependencies of scikit-image
pip install -r requirements/build.txt
# Build scikit-image from source
spin build
# The new version lives under `${PWD}/build-install/.../site-packages`.
# Test your installation
spin test
# Build docs
spin docs
# Try the new version in IPython
spin ipython

conda

When using conda for development, we recommend adding the conda-forge channel for the most up-to-date version of many dependencies. Some dependencies we use (for testing and documentation) are not available from the default Anaconda channel. Please follow the official conda-forge installation instructions before you get started.

# Create a conda environment named ``skimage-dev``
conda create --name skimage-dev
# Activate it
conda activate skimage-dev
# Install main development and runtime dependencies
conda install -c conda-forge --file requirements/default.txt
conda install -c conda-forge --file requirements/test.txt
conda install -c conda-forge pre-commit
# Install build dependencies of scikit-image
pip install -r requirements/build.txt
# Build scikit-image from source
spin build
# The new version lives under `${PWD}/build-install/.../site-packages`.
# Test your installation
spin test
# Build docs
spin docs
# Try the new version in IPython
spin ipython

For more information about building and using the spin package, see meson.md.

Testing

Test your installation for correct behavior using:

pytest skimage

Updating the installation

Before updating your installation, you typically want to grab the latest source:

git checkout main
git pull upstream main

And you likely want to create a feature branch from there. As you work on this branch, you can re-build scikit-image using:

spin build

Repeated, incremental builds usually work just fine, but if you notice build problems, rebuild from scratch using:

spin build --clean

Platform-specific notes

Windows

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

Debian and Ubuntu

Install suitable compilers:

sudo apt-get install build-essential

Full requirements list

Build Requirements

Runtime Requirements

Test Requirements

Documentation Requirements

Developer Requirements

Data Requirements

The full selection of demo datasets is only available with the following installed:

Optional Requirements

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

  • SimpleITK
    Optional I/O plugin providing a wide variety of formats. including specialized formats using in medical imaging.
  • Astropy
    Provides FITS I/O capability.
  • PyAMG
    The pyamg module is used for the fast cg_mg mode of random walker segmentation.
  • Dask
    The dask module is used to speed up certain functions.
  • Matplotlib
    Used in various functions, e.g., for drawing, segmenting, reading images.

Help with contributor installation

See Additional help above.