vidgear VS OpenCV

My experience that played out over the last few weeks lead me to a similar belief, somewhat. For rather uninteresting reasons I decided I wanted to create mp4 videos of an animation programmatically, from scratch.

The first solution suggested when googling around is to just create all the frames, save them to disk, and then let ffmpeg do its thing from there. I would have just gone with that for a one-off task, but it seems like a pretty bad solution if the video is long, or high res, or both. Plus, what I really wanted was to build something more "scalable/flexible".

Maybe I didn't know the right keywords to search for, but there really didn't seem to be many options for creating frames, piping them straight to an encoder, and writing just the final video file to disk. The only one I found that seemed like it could maybe do it the way I had in mind was VidGear[1] (Python). I had figured that with the popularity of streaming, and video in general on the web, there would be so much more tooling for these sorts of things.

I ended up digging way deeper into this than I had intended, and built myself something on top of Membrane[2] (Elixir)

[1] https://abhitronix.github.io/vidgear/

I've been googling and reading for a while but I'm very unsure about which tools I need, which tools will help me achieve what I want the easiest way. What about (pylivestream)[https://pypi.org/project/pylivestream/] for example? Will this do the job for me? What about a lower level approach including (pyopencv)[https://pypi.org/project/opencv-python/]? What about a higher level approach using (vidgear)[https://github.com/abhiTronix/vidgear], which seems promising but I don't feel confident in assessing if it's the tool I really need?

Vidgear and new deffcode library are my best. I bet you don't know none of them. But they're pretty awesome when it comes to video-processing and stuff.

Yes, fortunately I already resolved it in my previous(popular) library called vidgearthrough its WriteGear API: https://abhitronix.github.io/vidgear/latest/gears/writegear/compression/overview/

Code: https://github.com/abhiTronix/vidgear

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.