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Discussion (119 Comments)Read Original on HackerNews
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 subtle permutations. Downstream projects tend to version lock the lib for really practical reasons.
5. A shift away from core C libraries like ImageMagick & V4l, and into C++ abstractions with legacy Swig wrapper libraries in Java or Python.
6. Perpetual-Beta culture means the library will unlikely ever really fully stabilize.
It is a fun library, until people actually try to deploy something serious. As users will often simply suggest using an old version release if there is a bug.
Everything from Build flags to the API documentation has never fully stabilized. ymmv =3
- 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 ;-)
Indeed, if your library dependency constellation works, some will static link to stabilize/freeze their project for more than a few months.
It wasn't that v3 was particularly good, but rather v4 was a mess. I predict v5 inherited that mess, and improved it... lol =3
To enable Intel TBB, CUDA, and CPU specific compiler optimizations... one will almost certainly need to re-build the library, and customize your application build.
Some tasks degrade in performance on a GPU, and others are 740 times faster... ymmv. =3
But I can’t really complain because it’s open source and added to by contributors.
> 1. Patented algorithms that are effectively impossible to license in a commercial setting.
then does anyone know how "OpenCV has been the foundation of countless production systems" is possible, as the OP article claims?
However, until each code area turns 17/21 no one knows for sure. It just looks normal at first, and $12k cheaper than MatLab server host licenses. =3
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.
I'm happy they added option for ONNXRuntime. I wish their cv.dnn was mostly that unified wrapper around many different backends (ONNXRuntime, Executorch, LiteRT, CoreAI) and maybe just some tooling around it (performance metrics tools, model downloads etc). Transformers(.js) approach looks better for me.
Wish they also invested more time into better production ready Camera I/O (for mobiles, device/format discovery, manual settings, depthmap support, etc) and better Highgui that could use different backends (skia, webgpu) and on mobiles.
So there's room for even better performance!
Sure, running models on the CPU is very much a thing in computer vision (the benchmarked YOLOv8n has 37M params). But this whole announcement feels more like OpenCV catching up to the modern world, not "The Biggest Leap in Years for Computer Vision"
Still great, needing fewer libraries is a good thing, but maybe a bit oversold
If a human can't be bothered to write a piece, I can't be bothered to read it.
Where is the human creativity in writing release notes gone?
https://docs.rs/onnxruntime/latest/onnxruntime/
It’s a Rust wrapper around ONNX Runtime. We currently serve 5+ million inference requests per day for a highly performance-sensitive application, for a long list of major enterprise clients. We don’t use GPUs for inference, because it would be cost-prohibitive. We launch tens of thousands of VMs per day to run these workloads.
I personally don't mind AI generated content when it's properly reviewed, but unfortunately more often than not the author just glances at the result and decides it's good enough.
Example: https://opencv.org/wp-content/uploads/2026/06/image-1.jpeg
I'm not knowledgable enough to determine whether this diagram is 100% accurate, but some things look off - the arrows in the bottom left seem superficial, some arrows are connected in weird ways, the mini diagram in AttentionLayer block doesn't look right (it has two Softmax icons and one MatMul icon, while the "before" diagram is the opposite).
> This is not just another incremental release. OpenCV 5 is a major step forward.
If someone slapped together an article from an LLM and a few internal documents, that tells me exactly how much they cared about it.
As for being well-written, does that refer to correct use of grammar and no typos, or do you mean that you find that bots write better than humans in any other way?
Opencv 4.11 : ~255ms Opencv 5.0.0 : ~185ms
with the same code.
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?
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.
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.
OpenCV was so easy and smooth to set up for doing tasks like generating thumbnails from uploads from arbitrary photo uploads regardless of format (including funky new formats like webp, avif, or heic).
Why these specific models / versions?
Am I the only one that finds this sentence very cheesey?
If you need something less restricted to existing labels (say wanting all the red apples, or all cardboard signs) SAM3 is great, as the sibling comment says
A quick note to say that this is also a task you can hand to things like gemini.
Large general models have taken over in NLP, and (outside of embedded/low latency applications) it seems like they are coming for CV next.
So you should soon be able to have large generic model that can detect whatever for you.
It's already pretty much possible with open-vocabulary detectors like SAM3, where you could just prompt it with "Apple": https://ai.meta.com/research/sam3/
gptzero.me rates it 91% AI, 9% mixed, and 0% human. (I've only pasted a portion of the text to fit in the 10000 character free limit.)
I might be on board about LLMs being the future of OCR (though many would disagree), but for general CV they are very inefficient for very limited benefit
Also if they are better then you can also have a flow that’s cheap model -> marginal cases go to more complex thing (and a chain of these).
The yolo models are really shockingly good for their cost and how well they can work with not much training data as well.
Due to how simple they are to work with they will become popular. Compare NLP before and after GPT-3. GPT-3 majorly brought down the complexity and skill needed for doing NLP tasks even if traditional NLP is much much faster. Ultimately ease of development will win out and the industry will work towards optimizing running such LLMs to make it cheap enough to run.
We're not going to fit Nano Banana or anything like it on a device with 512MB RAM and a GPU old enough to be irrelevant, and again, API calls just aren't on the menu.
Even if they were an option, your 300ms latency requirement would exclude them anyway.
some SBC w/ an industrial camera that is doing pick-place or go/no-go operations on a conveyor belt against a singular object type doesn't need a huge image-gen/llm model governing it.
I mean have you even considered the kind of performance an opencv function can get w/ just mask-matching? I mean even with a fancy YOLO model these answers get thrown out in 1.5-50ms ; this is just a wholly different time scaling.
Its a lot better, faster, cheaper to use LLMs for initial labeling together with hand finetuning and then training YOLO with this.
Training YOLO takes a few hours and is then very fast.
Like, the AI model tools already exist, all that would be accomplished if OpenCV pivoted would be to take it away for people who want to do low-level vision programming. It wouldn't add anything useful to the world, just destroy an excellent library.
Dude, in business we think in terms of large numbers, internationally easily in billion times processing images. This wouldn't cut it.
Also, do you buy the mega expensive super individually designed shoes from the best shoemaker there is to march along though some dirt or simply stick to gumboots?
OpenCV is used behind the scenes for many of the fancy stuff those major AI provider pretend to do. Claude is a huge system and not a LLM anymore.
Is the image(text) function reversible? Or are they brute force searching a nearest neighbor like word2vec/hash brute forcing.