teachable-machine VS opencv-python

I hope that you've enjoyed the whole journey with image classification, if you're a developer trying to enhance the open-source tools, and you want to help me with developing and enhancing Teachable Machine Python package; PRs are welcomed at project repository.

Have you ever felt like your photos needed a little extra touch to stand out? Well, get ready because we're about to learn a cool Python trick! We're going to take ordinary photos and turn them into awesome pencil sketches using Python and OpenCV. This will make your pictures look like they were drawn by hand!

There are several modules that would allow you to process images in Python. Two of the perhaps most famous ones are PIL (https://pypi.org/project/Pillow/, also known as pillow), and opencv (https://pypi.org/project/opencv-python/).

Classifying images with trained model by using OpenCV and Teachable Machine packages

opencv-python: OpenCV for Python

Tool: Data collected using Python opencv, and a rudimentary OCR built from scratch. Graphic created with Microsoft Excel.

cv2: https://pypi.org/project/opencv-python/

I would try installing opencv-python on its own to see if you can learn more: https://pypi.org/project/opencv-python/

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?

This is likely to be buggy and frustrating, and it's very likely that you'll need to use OpenCV to preprocess the screenshot. Not my idea of a good time!