A lesson teaching foundational image processing skills with Python and scikit-image.
This lesson introduces fundamental concepts in image handling and processing. Learners will gain the skills needed to load images into Python, to select, summarise, and modify specific regions in these image, and to identify and extract objects within an image for further analysis.
The lesson assumes a working knowledge of Python and some previous exposure to the Bash shell.
A detailed list of prerequisites can be found in learners/prereqs.md.
There is an extra episode covering analysis of multidimensional data (3D stacks and timelapses) including use of the Napari image viewer. This additional episode is not part of the official Carpentries Image Processing with Python lesson. It was developed by Jeremy Pike from the Research Software Group and the Institute for Interdisciplinary Data Science and AI at the University of Birmingham.
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All participants should agree to abide by the The Carpentries Code of Conduct.
The Image Processing with Python lesson is currently being maintained by:
The lesson is built on content originally developed by Mark Meysenburg, Tessa Durham Brooks, Dominik Kutra, Constantin Pape, and Erin Becker.