Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

opencv.org

Post reply on HN
Print view View original

21–30 of 160 posts

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

#21

Post by IanCal »

wongarsu wrote: post #12
globalnode wrote: post #8

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.

YOLO has basically solved that for my use cases for a couple years now. If you want labels that are not in the pretrained labels it's also easy to fine-tune, provided you're willing to label 200 or so images 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

> provided you're willing to label 200 or so images

A quick note to say that this is also a task you can hand to things like gemini.

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

#22

Post by taneq »

TZubiri wrote: post #6
leoncos wrote: post #2

When I use Codex/Claude to complete a computer vision task, such as extracting assets from an image, OpenCV is their default solution. However, I believe that using YOLO and other methods is outdated. The best solution now is to directly use Nano Banana or other AI image models. A paper has proven that image generation models can perform most CV tasks well. I believe the new OpenCV should become a wrapper for VLM or…

I am confused, how can functions that output images help with functions that should take images as input?

They’re multimodal LLMs trained for image generation. Turns out that if you want to generate images you gotta know what things look like.

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

#23

Post by gunalx »

nnevatie wrote: post #17
ftchd wrote: post #10

> One practical detail is worth knowing. The new engine is CPU-only at the moment, so if you select a non-CPU backend and target (for example CUDA or OpenVINO through setPreferableBackend and setPreferableTarget), you will want the classic engine. So there's room for even better performance!

No one uses ONNXRuntime (nor the new engine in OpenCV 5) in production. For anything performance-sensitive, one would run models under TensorRT, as an example.

Production dosent have to be performance sensitive, so devex may still outcompete the performance differences in some scenarios.

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

#24

Post by VadimPR »

wongarsu wrote: post #13
ftchd wrote: post #10

> One practical detail is worth knowing. The new engine is CPU-only at the moment, so if you select a non-CPU backend and target (for example CUDA or OpenVINO through setPreferableBackend and setPreferableTarget), you will want the classic engine. So there's room for even better performance!

It's certainly a choice to make your headline feature a new ONNX engine, feature a bunch of comparisons how it's better than ONNXRuntime, while casually mentioning on the side that the cool new much faster engine is CPU-only 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 worl…

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

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

#25

Post by oliveiracwb »

Computer vision was the formative school for many autodidacts. Although I acquired substantial knowledge from articles translated via Power Translator and Babylon (whose outputs closely mirror those of any 2-million-parameter SLM), it was OpenCV that made concepts like convolutions, softmax, minmax, and others finally click for me. I have consistently viewed OpenCV as an intrinsically open, educational, and adaptable library. Any developer can dissect its codebase to extract a specific filter or algorithmic implementation and tailor it to their requirements. It is certainly not cruising at the velocity of trillion-dollar capital. But it holds its altitude. And it will always be there.

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

#26

Post by snovv_crash »

nnevatie wrote: post #17
ftchd wrote: post #10

> One practical detail is worth knowing. The new engine is CPU-only at the moment, so if you select a non-CPU backend and target (for example CUDA or OpenVINO through setPreferableBackend and setPreferableTarget), you will want the classic engine. So there's room for even better performance!

No one uses ONNXRuntime (nor the new engine in OpenCV 5) in production. For anything performance-sensitive, one would run models under TensorRT, as an example.

Strong statement to make when I have at least 2 datapoints contradicting it, in SaaS and embedded/robotics.

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

#27

Post by amorroxic »

nnevatie wrote: post #17
ftchd wrote: post #10

> One practical detail is worth knowing. The new engine is CPU-only at the moment, so if you select a non-CPU backend and target (for example CUDA or OpenVINO through setPreferableBackend and setPreferableTarget), you will want the classic engine. So there's room for even better performance!

No one uses ONNXRuntime (nor the new engine in OpenCV 5) in production. For anything performance-sensitive, one would run models under TensorRT, as an example.

Curious on what backs this assertion. As a counterpoint we’ve been running 200+ models in production for more than 5 years - language models, embedding, classifiers, low tens to hundred M params. Traffic in the order of 1-2M requests/day and everything is enabled by onnx with some cgo (or Rust) plumbing on top. What’s your SLA?
Post reply on HN