bgslibrary VS OpenCV

I still think my suggestion should work. Please check this GitHub repo. In the example, we can see that this library can be used to separate foreground from the background.

I used this background substraction library by Andrews Sobral in a profesional context with quite nice results. There was even a GUI to visualize how the algorithms performed for easy selection.

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.