[boredemployee]

How can I learn the practical side of computer vision in 2026?

I'm not interested in understanding papers or the math behind it, but rather in how to put a system into production, whether it's object detection, running 20 cameras in parallel on a single computer, like sizing hardware for a specific task, and so on.

Any tips?

[bonoboTP]

By doing it. Decide on a small project, like tracking your cat, detecting food items in your fridge, then take it step by step.

Then do a slightly more ambitious project. Start with something very simple.

It also heavily depends on what you already know regarding programming, image processing etc.

[kelvinjps10]

Just start cooking , python is easy and the bindings are not that hard

[yayitswei]

Try a coding agent for writing and tuning the OpenCV part, and have it explain its choices. That's probably the most practical path to shipping a working system.

Speaking from experience: never used OpenCV before, recently vibe coded a tool that makes supercuts of pool videos, trimming each clip from the cue ball's first strike to when the motion stops.

[eastof]

One of the great things about OpenCV is how ubiquitous it is, there's a ton samples online and well represented in frontier model training data. I recently vibe-coded an object detector for my own personal photo library so I could separate out my pictures with humans in them. Very approachable with Codex + feeding it a sample from Github.