OpenCV VS libvips

OpenCV

Google's Gemini and other multimodal models also fit here, especially for mixed-input apps. James Allsopp, Founder of Ask Zyro, suggests, "For anything involving images or mixed inputs, tools like Claude 3 Opus (great for handling long context) or Google's Gemini can work well, depending on what you need for your user interface." These frameworks excel in scenarios requiring visual understanding, such as augmented reality apps or image-based search tools.

cd C:\sources git clone https://github.com/opencv/opencv.git // create the opencv directory git clone https://github.com/opencv/opencv_contrib.git // create the opencv_contrib directory

git clone https://github.com/opencv/opencv.git cd opencv git checkout 4.x

To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but contextualise them with human-like reasoning. Start building, and you’ll shape how tomorrow’s machines perceive reality.

Ideal For: Computer vision, NLP, deep learning, and machine learning

Note that the 'Jump Flood Algorithm' is O(N log N) where N is the number of pixels. There is a better O(N) algorithm which can be parallelized over the number of rows/columns of an image:

https://news.ycombinator.com/item?id=36809404

Unfortunately, it requires random access writes (compute shaders) if you want to run it on the GPU. But if CPU is fine, here are a few implementations:

JavaScript: https://parmanoir.com/distance/

C: https://github.com/983/df

C++: https://github.com/opencv/opencv/blob/4.x/modules/imgproc/sr...

Python: https://github.com/pymatting/pymatting/blob/afd2dec073cb08b8...

OpenCV is an open-source computer vision and machine learning software library that allows users to perform various ML tasks, from processing images and videos to identifying objects, faces, or handwriting. Besides object detection, this platform can also be used for complex computer vision tasks like Geometry-based monocular or stereo computer vision.

Yep, imgproxy seems to use libvips, that recommends jemalloc. I was checking and this is a funny (not) bug report:

https://github.com/libvips/libvips/discussions/3019

Was pretty surprised myself, as I consider myself decently knowledgeable in the field. Learn of it via https://github.com/libvips/libvips/issues/4089.

For most things you would use this for, or ImageMagick for that matter, you are almost certainly better of using libvips[0]

0. https://github.com/libvips/libvips

Read through the comments and was surprised no one mentioned libvips - https://github.com/libvips/libvips. At my current small company we were trying to allow image uploads and started with imagemagick but certain images took too long to process and we were looking for faster alternatives. It's a great tool with minimum overhead. For video thumbnails, we use ffmpeg which is really heavy. We off-load video thumbnail generation to a queue. We've had great luck with these tools.

After some research, I found libvips, a demand-driven, horizontally threaded image processing library. It is designed to run quickly while using as little as memory as possible.

Sharp is a fast and efficient image optimization Node.js module that makes use of the native libvips library.

h2non/bimg can handle both if the underlying libvips is compiled with support for both formats.

if you're using linux (which it doesn't seem so) there's also vispdisp https://github.com/jcupitt/vipsdisp which is based on https://github.com/libvips/libvips which will likely take over how images are decoded in the future for everything, at least methodology wise.

The magic happens by way of a library called Libvips, which contains an ultra-high-speed low-memory image resizer.