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OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

opencv.org

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Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#92

The thing I love about OpenCV is that it remains hands down the best library for simply loading images and video. I've never even used any of its fancy computer vision features, but if I need to load a video file and look at the pixels - which I did need to do recently for an art project - OpenCV does it in about four lines of code.

opencv file loading is crap. it will load images with the wrong gamma, it will give you floating point values that hide the limitation that it pretty much only loads colors in 8 bit, and it will not be able to save to anything useful.

Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#93

The thing I love about OpenCV is that it remains hands down the best library for simply loading images and video. I've never even used any of its fancy computer vision features, but if I need to load a video file and look at the pixels - which I did need to do recently for an art project - OpenCV does it in about four lines of code.

> best library for simply loading images and video

But not for saving video. That fourcc pile of crap doesn't open up in QuickTime player, the default Ubuntu video player, or anything anybody actually uses. I've always had to add a os.system("ffmpeg [ask llm to generate the command for you]") afterwards to fix anything that OpenCV generates.

Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#94

does this mean im actually able to try object detection in opencv now? i mean i know basic image processing techniques, and i know "in theory" how ML works but ive never really seen a case where i can just say "heres an image now detect all the apples". theres always 1. find a model that has the knowledge, 2. hook it up to an inference engine, 3. do something useful. i always get stuck at 1.

Roboflow is your friend.

Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#96

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?

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.

Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#97

The thing I love about OpenCV is that it remains hands down the best library for simply loading images and video. I've never even used any of its fancy computer vision features, but if I need to load a video file and look at the pixels - which I did need to do recently for an art project - OpenCV does it in about four lines of code.

Done a few projects with OpenCV over the years, and I agree it can be fun. However, it has a few issues: 1. Patented algorithms that are effectively impossible to license in a commercial setting. 2. Permuted API that change how identically named functions behave over versions. 3. Hardware CUDA version coupling deprecating support every major release. 4. Inconsistent and contradictory documentation in the constant sub…

Done a few projects with OpenCV myself, and your list of issues reads as if you throw OpenCV and opencv_contrib into the same bucket. Which you shouldnt. And maybe your assessment is outdated here and there and it is time to look again.

- OpenCV is Apache license. Yes, it used to be more complicated.

- The only patented algorithm I am aware of, SIFT, used to be part of opencv_contrib. And the README in opencv_contrib would greet you with a warning, that the code may not be fit commercial use for various reasons. Only when the patent expired, it was moved into OpenCV core.

- Same observation for Aruco marker detection, which was in contrib for a long time because the options to choose from were either not-well-maintained or GPL-licensed code. It is now in core OpenCV (and Apache).

- Despite its age, I think that OpenCV is still more than relevant today. And being part of modern languages like C++, Swig, Java and Python (and for years already) is part of that. Still I was surprised how long they maintained OpenCV 2 and 3.

- Over the past releases and few years, my impression was actually that core API was very much stable(izing). Cant say what happened in contrib – or what it feels like when you treat core and contribute as one and a feature progressed from contributing to core.

- I do agree, that I usually I would check that a MINOR releases wasnt actually a MAJOR release, breaking some API or behavior I was relying on. I am hoping that Version 5 is pulling the ambitions for making things differently away from Version 4. So v4 can be used stably ;-)

Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#99
I remember trying to do photo stitching myself (panoramas) then I failed miserably but it's built into opencv ha. I've used quite a bit of OpenCV features eg. laplace variance for an automatic zoom/focusing mechanical lens camera system (steppers) and contour/blob finding for crude color segmentation.

Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision

#100
post #24

Earlier quoted context omitted.

The release post is AI-written with little human oversight and it shows.

I had to stop reading after: "This is not just another incremental release. OpenCV 5 is a major step forward." If a human can't be bothered to write a piece, I can't be bothered to read it.

I think the only thing that the human did was remove the emdash between the two sentence fragments and replace with a period.
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