1.1. Supported platforms

• Windows 64-bit on x86 processors
• macOS on x86 and ARM (M1, etc.) processors
• Linux 64-bit on x86 and ARM processors

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

1.2. 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.

1.3. Installation via pip and conda

# Update pip
python -m pip install -U pip

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

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

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

conda install scikit-image

1.4. System package managers

Using a package manager (apt, dnf, etc.) to install scikit-image or other Python packages is not your best option, since you’re likely to get an older version. It also becomes harder to install other Python packages not provided by the package manager.

1.5. Downloading all demo datasets

Some of our example images (in skimage.data) are hosted online and are not installed by default. These images are downloaded upon first access. If you prefer to download all demo datasets, so they can be accessed offline, ensure that pooch is installed, then run:

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

1.6. Additional help

If you still have questions, reach out through

• our user forum
• our developer forum
• our chat channel

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

2.1. Build environment setup

Set up a Python development environment tailored for scikit-image. Here we provide instructions for two popular environment managers: venv (pip) and conda (miniforge).

# 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, use ``skimage-dev\Scripts\activate``)
source ~/envs/skimage-dev/bin/activate

# Install development dependencies
pip install -r requirements.txt
pip install -r requirements/build.txt

# Install scikit-image in editable mode. In editable mode,
# scikit-image will be recompiled, as necessary, on import.
spin install -v

spin build

# Create a conda environment named ``skimage-dev``
conda create --name skimage-dev

# Activate it
conda activate skimage-dev

# Install development 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 ipython
conda install -c conda-forge --file requirements/build.txt

# Install scikit-image in editable mode. In editable mode,
# scikit-image will be recompiled, as necessary, on import.
spin install -v

spin build

2.2. Testing

Run the complete test suite:

spin test

Or run a subset of tests:

# Run tests in a given file
spin test skimage/morphology/tests/test_gray.py

# Run tests in a given directory
spin test skimage/morphology

# Run tests matching a given expression
spin test -- -k local_maxima

2.3. Adding a feature branch

When contributing a new feature, do so via a feature branch.

First, fetch the latest source:

git switch main
git pull upstream main

Create your feature branch:

git switch --create my-feature-name

Using an editable install, scikit-image will rebuild itself as necessary. If you are building manually, rebuild with:

.. code-block:: sh

spin build

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

spin build --clean

2.4. Platform-specific notes

Windows

Building scikit-image on Windows is done as part of our continuous integration testing; the steps are shown in this Azure Pipeline.

Debian and Ubuntu

Install suitable compilers prior to library compilation:

sudo apt-get install build-essential

2.5. Full requirements list

Build Requirements

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
meson-python>=0.16
setuptools>=68
ninja>=1.11.1.1
Cython>=3.0.8
pythran>=0.16
numpy>=2.0
spin==0.13
build>=1.2.1

Runtime Requirements

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
numpy>=1.24
scipy>=1.11.2
networkx>=3.0
pillow>=10.1
imageio>=2.33,!=2.35.0
tifffile>=2022.8.12
packaging>=21
lazy-loader>=0.4

Test Requirements

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
asv
numpydoc>=1.7
pooch>=1.6.0
pytest>=7.0
pytest-cov>=2.11.0
pytest-localserver
pytest-faulthandler
pytest-doctestplus

Documentation Requirements

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
sphinx>=8.0
sphinx-gallery[parallel]>=0.18
numpydoc>=1.7
sphinx-copybutton
matplotlib>=3.7
dask[array]>=2022.9.2
pandas>=2.0
seaborn>=0.11
pooch>=1.6
tifffile>=2022.8.12
myst-parser
intersphinx-registry>=0.2411.14
ipywidgets
ipykernel
plotly>=5.20
kaleido==0.2.1
scikit-learn>=1.2
sphinx_design>=0.5
pydata-sphinx-theme>=0.16
PyWavelets>=1.6
pytest-doctestplus

Developer Requirements

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
pre-commit
ipython
tomli; python_version < '3.11'

Data Requirements

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

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
pooch>=1.6.0

Optional Requirements

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

• Matplotlib Used in various functions, e.g., for drawing, segmenting, reading images.
• Dask The dask module is used to parallelize certain functions.

More rarely, you may also need:

• 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 used in biomedical imaging.

# Generated via tools/generate_requirements.py and pre-commit hook.
# Do not edit this file; modify pyproject.toml instead.
SimpleITK
astropy>=5.0
cloudpickle>=0.2.1
dask[array]>=2021.1.0,!=2024.8.0
matplotlib>=3.7
pooch>=1.6.0
pyamg>=5.2
PyWavelets>=1.6
scikit-learn>=1.2

2.6. Help with contributor installation

See Additional help above.