CImg VS OpenCV

It looks promising.

I like the way each algorithm is explained with minimal example and demo images.

I'm looking for such a lightweight vision lib to embed simple image manipulation programs on tiny ESP32-CAM boards.

OpenCV seems too heavy to integrate on such small devices.

So far, I've been able to develop simple image processing programs with CImg[1] (simple filtering, image cropping, adding text).

I'll try to spend some time exploring this SOD lib, thank you for sharing.

[1] https://cimg.eu/

You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++.

wget -O opencv.zip https://github.com/opencv/opencv/archive/4.1.0.zip

Another option is using the OpenCV library with Python to extract features from images and compare them using feature matching algorithms like SIFT, SURF, or ORB. This approach is more computationally intensive but might be more accurate in identifying near-duplicates. You can find more information about OpenCV here: https://opencv.org/

OpenCV (GitHub: https://github.com/opencv/opencv) is a widely-used computer vision library with real-time image/video processing, feature extraction, and ML algorithms, supporting multiple languages.

Sau aici: https://github.com/opencv/opencv

here they are

OpenCV? Open.

You first import the OpenCV library. It will pre-process the selected flower image to ensure it has the same dimensions as the images that trained the deep learning model.

If that’s of interest, OpenCV provides a detailed stitching example, although my revision is likely a fair amount easier to follow and understand (but it’s been optimised for video, so if you’re wanting to use it with minimal modification you’ll need to provide your images in a sequence where each image has overlap with the one before it, not in a random order).