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.
Download
If you find this project useful, please cite:
[BiBTeX]
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
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
- 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
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.