Keynote: Learning programming and science with Scientific Python
summary
Claims that scikit-image pairs cleanly with scikit-learn for real science get demonstrated with concrete pipelines: Daisy features into a random forest for segmentation, and total-variation denoising outperforming simple filters on a tomography example. Documentation and learning infrastructure are actual deliverables: a Sphinx gallery, examples runnable on Binder (examples start in ~20s), interactive widgets, and joblib caching to speed iterative parameter tweaks. Usage and community metrics are given: 20,000 visitors per month and 5–10 core contributors, illustrating reach and maintenance load.
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chapters (10)
- 0:00Introduction
- 4:13Image processing challenges
- 9:01SciKit-Image capabilities
- 14:52Learning programming and science
- 27:47API design principles
- 28:10Documentation and learning tools
- 41:40Interactive workflows and performance
- 46:26Open source contributions
- 47:56Future directions
- 49:07Q&A
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