I'm interested in contributing to scikit-image but I am new to scientific
programming. I studied physics in undergrad and have a minor in
bioinformatics. As a result, I'm familiar with oop, data structures, and
algorithms. I'd like some mentorship to get started on actually developing
/ contributing code. I'm interested in image processing for structural
biology applications, and sometimes in my spare time I write scripts for my
current company. However, it is not production-grade, and it is quite
hacky. I'm hoping that through contributing to scikit-image, in whatever
way possible, I can gain some skills and give back in parallel.
According to the scikit-image website, mentoring is available and it seems
like contributing to open source is encouraged. Would anyone be interested
in mentoring me?
Thank you for your time,
I wanted to get some feedback regarding some changes to the building of the
docs. Juan asked myself and a colleague Vinicius Cerutti to look after a
feature request (posted on Zulip) for a warning banner when a user is not
on the docs for the most recent release. A nice solution for this would be
https://goerz.github.io/docs_versions_menu/v0.4.1/. This builds on the
efforts of doctr, a tool that, while similar to readthedocs, provides a lot
of flexibility in the build of the documentation: https://drdoctr.github.io/.
The sphinx theme (alabaster) used by doctr is quite similar to the current
theme used for the scikit-image docs so there won't be too much of a change
to the aesthetics of the site.
How do people feel about such a change? The alternative would be to add a
quick fix if people aren't fans of such a docs-infrastructure migration,
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I am new to open source and would like to contribute to scikit image. I
couldn't find scikit methods to evaluate contrast measures. Image quality
metrics like EME, EMEE, AME etc are helpful in writing contrast enhancement
algorithms. I would like to know if it is something scikit-image interested
in and if so how should I proceed?
Specifically I am talking about Table 1 of
https://doi.org/10.1109/TCE.2013.6626251. It summarises commonly used gray
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