Image processing in Python
scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers.
The scikit-image team is hard at work at bringing you scikit-image v2, a major overhaul with a cleaner and more intuitive API. We would like to thank our project sponsors,
as well as our grassroots contributors.
Reach out if you would like to join them in supporting the next generation of open source image processing in Python.
Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu and the scikit-image contributors. scikit-image: Image processing in Python. PeerJ 2:e453 (2014) https://doi.org/10.7717/peerj.453
News¶
Release! Version 0.26.0 2025-12-20
Release! Version 0.25.2 2025-02-18
Release! Version 0.24.0 2024-06-18
Release! Version 0.23.2 2024-04-20
Release! Version 0.22.0 2023-10-03
Release! Version 0.21.0 2023-06-02
Release! Version 0.20.0 2023-02-28
As part of CZI’s 5th EOSS grant cycle, scikit-image received funding to create a typed, discoverable, and extensible API! 2022-11-30
Getting Started¶
Filtering an image with scikit-image is easy! For more examples, please
visit our gallery.
import skimage as ski
image = ski.data.coins()
# ... or any other NumPy array!
edges = ski.filters.sobel(image)
ski.io.imshow(edges)
ski.io.show()
You can read more in our user guide. For an introduction to image processing using scikit-image, see this lesson by Data Carpentry.
Our Team¶
Along with a large community of contributors, scikit-image development is guided by the following core team:
Emeritus Developers¶
We thank these previously-active core developers for their contributions to scikit-image.
