PySceneDetect VS OpenCV

thought the same thing and wrote this: https://blog.mixpeek.com/dynamic-video-chunking-scene-detect...

which uses https://github.com/Breakthrough/PySceneDetect

under the hood i'm sure it's the same ffmpeg method ;)

You could also use something like https://github.com/Breakthrough/PySceneDetect to first split the video into camera shots, and then grab a single (or average) frame per shot, leading to a cleaner result.

If you mean scene changes, this library works: https://github.com/Breakthrough/PySceneDetect

I saw this library as well https://github.com/Breakthrough/PySceneDetect and I wonder if that might be helpful?

I do this using pyscenedetect. It is a command line tool that also has python bindings you can use so that it can all be scripted. For your use-case in which it is color based scene transitions, you would want to use the content detector. It can not only detect your scene transitions, but can also use a tool like mkvmerge to split the video into individual scenes. I personally use this to detect scene transitions and then use moviepy to annotate over the top of the video using text clips all in a single script.

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.