This PR adds sample support of YOLOv9 and YOLOv10) in OpenCV. Models for this test are located in this PR.

Running YOLOv10 using OpenCV.

1. In oder to run YOLOv10 one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is this forked branch from official YOLOv10. Particularty follow this proceduce.

git clone [email protected]:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>

By default model="yolov10s" and imgsz=(480,640). This will generate file yolov10s.onnx, which can be use for inference in OpenCV

2. For inference part on OpenCV. one can use yolo_detector.cpp sample. If you have followed above exporting procedure, then you can use following command to run the model.

build opencv from source 
cd build 
./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114

If you do not specify --input argument, OpenCV will grab first camera that is avaliable on your platform.
For more deatils on how to run the yolo_detector.cpp file see this guide

Running YOLOv9 using OpenCV

1. Export model following official guideof the YOLOv9 repository. Particularly you can do following for converting.

git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
conda create -n yolov9 python=3.9
conda activate yolov9
pip install -r requirements.txt
wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt
python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640) 

This will generate <yolov9-t-converted.onnx> file.

2. Inference on OpenCV.

build opencv from source 
cd build 
./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image>

Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under Apache 2 License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
• The PR is proposed to the proper branch
• There is a reference to the original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake