1. Installing scikit-image#

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.

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#

1.3.1. pip#

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 3.12. 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

Some additional dependencies are required to access all example datasets in skimage.data. Install them using:

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

To install optional scientific Python packages that expand scikit-image’s capabilities to include, e.g., parallel processing, use:

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

Warning

Do not use the command sudo and pip together as pip may overwrite critical system libraries.

1.3.2. conda#

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:

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

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

2. Installing scikit-image for contributors#

See the contributing guide for instructions on setting up a development environment and contributing to scikit-image.