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Image analysis in Python with SciPy and scikit-image
To participate, please follow the preparation instructions at
https://github.com/scikit-image/skimage-tutorials/
(click on **preparation.md**).
TL;DR: Install Python 3.6, scikit-image, and the Jupyter notebook. Then clone this repo:
If you cloned it before today, use git pull origin to get the latest changes.
scikit-image is a collection of image processing algorithms for the SciPy ecosystem. It aims to have a Pythonic API (read: does what you'd expect), is well documented, and provides researchers and practitioners with well-tested, fundamental building blocks for rapidly constructing sophisticated image processing pipelines.
In this tutorial, we provide an interactive overview of the library, where participants have the opportunity to try their hand at various image processing challenges.
Attendees are expected to have a working knowledge of NumPy, SciPy, and Matplotlib.
Across domains, modalities, and scales of exploration, images form an integral subset of scientific measurements. Despite a deep appeal to human intuition, gaining understanding of image content remains challenging, and often relies on heuristics. Even so, the wealth of knowledge contained inside of images cannot be understated, and scikit-image, along with SciPy, provides a strong foundation upon which to build algorithms and applications for exploring this domain.
Prerequisites
Please see the preparation instructions.
Schedule
1:30–2:20: Introduction & images as NumPy arrays
2:30–3:20: Filters
3:30–4:20: Segmentation
4:30–5:00: Advanced workflow example
5:00–5:20: Tour of scikit-image
5:20–5:30: Q&A
Note: Snacks are available 2:15-4:00; coffee & tea until 5.
For later
Check out the other lectures
Check out a 3D segmentation workflow
Some real world use cases
After the tutorial
Stay in touch!
Come to the sprint!
Follow the project's progress on GitHub.
Ask the team questions on the mailing list
Read our paper (or this other paper, for skimage in microtomography)