JuliaImages: image processing and machine vision for Julia
JuliaImages (source code) hosts the major Julia packages for image processing. Julia is well-suited to image processing because it is a modern and elegant high-level language that is a pleasure to use, while also allowing you to write "inner loops" that compile to efficient machine code (i.e., it is as fast as C). Julia supports multithreading and, through add-on packages, GPU processing.
JuliaImages is a collection of packages specifically focused on image processing. It is not yet as complete as some toolkits for other programming languages, but it has many useful algorithms. It is focused on clean architecture and is designed to unify "machine vision" and "biomedical 3d image processing" communities.
These pages are designed to help you get started with image analysis in Julia.
Please help improve this documentation–if something confuses you, chances are you're not alone. It's easy to do as you read along: just click on the "Edit on GitHub" link above, and then edit the files directly in your browser. Your changes will be vetted by developers before becoming permanent, so don't worry about whether you might say something wrong.
This documentation is a collection of several parts:
- "Getting started" covers installation, loading images from files, and viewing images. New users should start here.
- The "Tutorials" part contains a list of tutorials that help you gain better understanding of the JuliaImages ecosystem and its underlying concepts.
- The "Packages" part contains information about specific components (themselves Julia packages) that together comprise JuliaImages and address specific subfields of image processing.
- The "Demos" part gives you demonstrations of how to carry out specific tasks with JuliaImages.
- The "References" part is a collection of function references provided by JuliaImages. The recommended way to use the references is by the searching function of your browser
- The "Comparison with other image processing frameworks" may be helpful if you've used other frameworks previously.
Johnny Chen(@johnnychen94)'s work is partially supported by Tongyuan since 2022. Portions of Tim Holy's work on JuliaImages has been funded by grants from the National Institutes of Health.