opencv-python VS PyO3

Extra stuff like type checking and annotations are definitely not the reason why python became that popular and widely adapted and used, but it certainly doesn't hurt to add and use them.

To be clear, I'm not expecting people to start adding generics to their quick hacked together Python scripts (in fact please don't do that). Instead, if you're building a library or maintaining a larger Python codebase, a lot of these start becoming very useful. A lot of the typing features I mentioned are already used by Python under the hood, and that a lot of Python developers just take for granted.

Case in point, the python-opencv (https://github.com/opencv/opencv-python) library has basically no types and it's an absolute pain to work with.

BTW thats a really good SO thread, thanks for linking it!

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!

PyO3 0.23.0 was a big release I’ve been tinkering with extensively and support for “free-threaded Python” is a headline feature and I imagine this NoGIL Python will be extremely nice for Rust interoperability so there is definitely interest in that crate. Also could be huge for queueing data for GPUs, api servers, bulk data fetching.

For whatever reason (maybe post 2to3 PTSD), Python community seems not extremely eager to jump on latest versions of Python and it often takes a long time for popular libraries to support the latest and greatest, so I’d recommend patience and baby steps

https://github.com/PyO3/pyo3/releases/tag/v0.23.0

🤖 Unlike the above languages Rust can also be used as a replacement of part of the application. Especially if there is a part that needs some speed-up or needs some memory saving. One could replace part of a Python or Node project by Rust and embed the code using PyO3 or napi respectively. This is also going to be the topic of the presentation of Aviram Hassan called Microdosing Rust to your organization.

The interop shop for Rust and Python is Pyo3. As the only game in town, Pyo3 uses the Foreign Function Interface (FFI) to allow your Rust code to make calls into the CPython binary. This works by agreeing on the Application Binary Interface (ABI), a concept I used during my career at AMD. Core software ftw!

Sorry, "FFI" was a shorthand for "mixing and matching two languages GC expectations, memory layouts, ..." and all the overhead associated with merging something opinionated, like Rust, with something dynamic, like Python. You almost certainly _can_ reduce that overhead further, but unless somebody has gone out of their way to do so, the default expectation for cross-language calls like that should be that somebody opted for maintainable code that has actually shipped instead of shaving off every last theoretical bit of overhead.

It's been a few years, so I really can't tell you exactly what the problem was (other than the general observation that you should try to do nontrivial amounts of work in your python extensions rather than trivial amounts), but PyO3 agrees with the general sentiment [0] [1], or at least did at roughly the same time I was working there.

[0] https://github.com/PyO3/pyo3/issues/679

[1] https://github.com/PyO3/pyo3/issues/1470

This blog post is meant to be an introduction to PyO3 by walking the reader through the build process of a non-trivial extension module in Rust using PyO3. Some familiarity with Python and Rust is recommended to get the most out of this post, basic understanding of Bitcoin concepts may be required to fully grasp the code samples.