OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
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Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#2When 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 However, I believe that using YOLO and other methods is outdated.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 AI image models.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#3When 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 However, I believe that using YOLO and other methods is outdated.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…
do you realize how many edge or unconnected nodes do OpenCV work?
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.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#4> LLMs and VLMs, Running Inside OpenCV…Qwen 2.5, Gemma 3, PaliGemma, and the GPT-2 / GPT-4 family
Why these specific models / versions?
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#5When 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 However, I believe that using YOLO and other methods is outdated.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…
Whenever you can run a model like Nano Banana or other vision-LLM with the same compute and time performance/restrictions as an OpenCV or YOLO call, you can make that comparison. Until then, I would not call YOLO and OpenCV outdated, it's simply wrong. There's a time and place for big V-LLMs just as there is a time and place for more "traditional" computer vision methods.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#6When 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?
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#7When 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…
That is a very uninformed view. Real time CV is not going to be doing that anytime soon.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#8does 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.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#9When 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've built hardware with a pi zero 2 + pi cam running a mildly fine-tuned YOLO doing local-only object detection as a USB-OTG device, in a use case where any off-device API calls would have been totally unacceptable, and where the object detection was part of the human interaction loop with a hard ceiling of 300ms on the total interaction time of which the object detection was only one process among many.
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.
Re: OpenCV 5 Is Here: The Biggest Leap in Years for Computer Vision
#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!