1. Installing scikit-image — skimage 0.26.0 documentation

First, you need to have the Python language installed. Two popular routes are the pip-based Python.org installers and the conda-based miniforge.
Install scikit-image via pip or conda, as appropriate.
Or, build the package from source. Do this if you’d like to contribute to development.
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.
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:
or, from the command line:
(Try python3 if python is unsuccessful.)
You’ll see the version number if scikit-image is installed and an error message otherwise.
Prerequisites to a pip install: you must be able to use pip on your command line to install packages.
We strongly recommend the use of a virtual environment. A virtual environment creates a clean Python environment that does not interfere with the existing system installation, can be easily removed, and contains only the package versions your application needs.
To install the current scikit-image you’ll need at least Python 3.11. If your Python is older, pip will find the most recent compatible version.
Some additional dependencies are required to access all example datasets in skimage.data. Install them using:
To install optional scientific Python packages that expand scikit-image’s capabilities to include, e.g., parallel processing, use:
Do not use the command sudo and pip together as pip may overwrite critical system libraries.
We recommend miniforge, a minimal distribution that makes use of conda-forge. It installs Python and provides virtual environments.
Once you have your conda environment set up, install scikit-image with:
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.
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:
If you still have questions, reach out through
To suggest a change in these instructions, please open an issue on GitHub.
Your system needs a:
a version of Python supported by scikit-image (see pyproject.toml).
First, fork the scikit-image repository on GitHub. Then clone your fork locally and set an upstream remote to point to the original scikit-image repository:
We use git@github.com below; if you don’t have SSH keys setup, use https://github.com instead.
All commands below are run from within the cloned scikit-image directory.
Set up a Python development environment tailored for scikit-image. Here we provide instructions for two popular environment managers: venv (pip) and conda (miniforge).
The above installs scikit-image into your environment, which makes it accessible to IDEs, IPython, etc. This is not strictly necessary; you can also build with:
In that case, the library is not installed, but is accessible via spin commands, such as spin test, spin ipython, spin run, etc.
We recommend installing conda using miniforge, an alternative to Anaconda without licensing costs.
After installing miniforge:
The above installs scikit-image into your environment, which makes it accessible to IDEs, IPython, etc. This is not strictly necessary; you can also build with:
In that case, the library is not installed, but is accessible via spin commands, such as spin test, spin ipython, spin run, etc.
Run the complete test suite:
Or run a subset of tests:
When contributing a new feature, do so via a feature branch.
First, fetch the latest source:
Create your feature branch: