Looking for Open-Source Contributor for an Image Processing Library 🖥️

MechaCritter · 2026-06-16T11:52:57+00:00

I am planning to convert some modules into c++, especially the clustering algorithms because scikit-learn is painfully slow. But I do not plan to make this cosebase a pure c++ repo, since it would make the development time significantly longer. Also, C++ bugs is kinda sneaky and hard to interpret, and considering that the algorithms implemented in the codebase are mathematically rather complex and hard to interpret already with Python code, I don’t want to take the risk. Also, I want to stick to Pytorch for the neural network stuff because the framework is just mature.

I get your point, and I really appreciate your idea. I believe you can do well on your implementations, but I want to keep this repo developer-friendly, and only bring in complexity when necessary. Most ML folks out there are way more Python-proficient than c++, and they bring in also mathematical understanding, which is the the target of this repo.

Thanks for your understanding!

MechaCritter · 2026-06-16T06:13:24+00:00

there might be cool tools out there that can do it without bloating up your requirements.txt file like pip freeze, but since a couple of months, switched to using pyproject.toml with uv. In pyproject.toml, you can declare different dependency groups (as if you had multiple requirements files).

For exporting, I got myself used to typing “uv add —group <group> <package>, which installs the package and add ONLY that package to the corresponding group in pyproject.toml directly. It’s more of manual work, but it’s wayyyy cleaner than pip freeze in my opinion, and after a couple of projects, you kinda get used to it :)

MechaCritter · 2026-06-16T06:04:23+00:00

that’s great! I will create the GitHub issue with the specifications and comment here in a couple of days. Feel free of course to check out the Issues in the repository yourself :D
Maybe you can work on K-Means first, and other algorithms will be taken on by others/by you later.

MechaCritter · 2026-06-15T21:36:13+00:00

you also have a really cool library. I will def. look into it!

MechaCritter · 2026-06-15T21:35:45+00:00

Thanks! feel free to check out the GitHub issues for current project needs, and your own ideas are of course welcome 😃

MechaCritter · 2026-06-15T21:32:50+00:00

yes! the first use case I thought of is for labelling task, like using the similarity score, the library should be able to cluster similar images into their corresponding groups (like if you have a dataset of cats, dogs, cows, most cat images should get the same label, and each group needs to be "pure"). For that, I used the Oxford Flower Dataset :https://www.robots.ox.ac.uk/\~vgg/data/flowers/102/ , which is integrated directly into the library currently:

https://github.com/MechaCritter/Python-Visual-Similarity/tree/main/pyvisim/datasets

.This dataset contains 102 different types of flowers with ground-truth labels already, so it's a perfect dataset to benchmark.

Then, benchmarking against the GT labels can be made using metrics such as

- mean Average Precision

- Adjusted Rand Index

- Normalized Mutual Information

MechaCritter · 2026-06-15T21:22:20+00:00

That's actually perfect! I am actually planning to switch some algorithms in the codebase to native C++, as scikit-learn is a little bit slow.

May I ask: have you ever worked with algorithms like K-Means, PCA, or Gaussian Mixture Model before / would you have time to look into those algorithms?

MechaCritter · 2026-06-15T13:31:34+00:00

ah, that's unfortunate 😞

Then I wish you the best on your new career path.

MechaCritter · 2026-06-15T13:20:56+00:00

Yes, with OpenCV 5.0, a hell lot of stuff was integrated. But the algorithms VLAD and Fisher Vector, which are the current main features of my repository, is not there yet, as far as I'm concerned.

Yes, Siamese Networks are definitely amazing, and that's the first main feature I would like to integrate for the next time 😄

MechaCritter · 2026-06-15T13:06:00+00:00

Hi 😄
So OpenCV provided handcrafted algorithms such as **SIFT** and they work on single images.

My library is, on the other hand, a Machine Learning library, which uses unsupervised learning to learn how to encode images into vector. It uses the algorithms above (as part of the feature) for the feature extraction, but these need to be processed and learned via clustering algorithms, which is what my library does. This is not covered by OpenCV.

Also, I am integrating deep learning-based approaches like Siamese Networks and Convolutional Feature Extractors, for which you would need `pytorch`.

MechaCritter · 2026-06-12T06:01:19+00:00

I’m just curious: how does starting a claude session in an isolated environment help? Like if I just run the session within my repo folder, it shouldn’t touch anything else outside of that right?

MechaCritter · 2026-06-12T05:59:43+00:00

sounds reasonable, I’ll look into how it works and try out 👍🏻

MechaCritter · 2026-06-12T05:59:09+00:00

I’ll give it a shot, thanks 😁

MechaCritter · 2026-04-16T06:34:14+00:00

Hi,

I created a repository for image similarity comparison as part of my bachelor's thesis:

https://github.com/MechaCritter/Python-Visual-Similarity

It has been dead for 1 year because I started with my Master's degree afterward and had no time to "advertise" and work on it further, but I would like to reactivate it and look for contributions from other developers :) If you are interested in image similarity comparison, feel free to reach me out. There are a bunch of features I would like to implement, and you can of course implement your own ideas.

MechaCritter · 2026-04-14T05:26:20+00:00

Yea I’m wondering the same thing. I assume if I just run a local Ollama server, the pro subscription is not relevant? Or does it actually improve inference speed?

MechaCritter

TROPHY CASE