Tag:
Branch:
Tree:
a6ec12f58b
2.4
3.4
4.x
5.x
master
next
2.2
2.3.0
2.3.1
2.4.0
2.4.1
2.4.10
2.4.10.1
2.4.10.2
2.4.10.3
2.4.10.4
2.4.11
2.4.12
2.4.12.1
2.4.12.2
2.4.12.3
2.4.13
2.4.13.1
2.4.13.2
2.4.13.3
2.4.13.4
2.4.13.5
2.4.13.6
2.4.13.7
2.4.2
2.4.3
2.4.3-rc
2.4.3.1
2.4.3.2
2.4.4
2.4.4-beta
2.4.5
2.4.6
2.4.6.1
2.4.6.2
2.4.6.2-rc1
2.4.6.2r2
2.4.6.2r3
2.4.7
2.4.7-rc1
2.4.7.1
2.4.7.2
2.4.8
2.4.8.1
2.4.8.2
2.4.8.3
2.4.9
2.4.9.1
3.0-ocl-tech-preview
3.0-ocl-tp2
3.0.0
3.0.0-alpha
3.0.0-beta
3.0.0-rc1
3.1.0
3.2.0
3.2.0-rc
3.3.0
3.3.0-cvsdk
3.3.0-rc
3.3.1
3.3.1-cvsdk
3.4.0
3.4.0-rc
3.4.1
3.4.1-cvsdk
3.4.10
3.4.11
3.4.12
3.4.13
3.4.14
3.4.15
3.4.16
3.4.17
3.4.18
3.4.19
3.4.2
3.4.2-openvino
3.4.20
3.4.3
3.4.3-openvino
3.4.4
3.4.5
3.4.6
3.4.7
3.4.8
3.4.9
4.0.0
4.0.0-alpha
4.0.0-beta
4.0.0-openvino
4.0.0-rc
4.0.1
4.0.1-openvino
4.1.0
4.1.0-openvino
4.1.1
4.1.1-openvino
4.1.2
4.1.2-openvino
4.10.0
4.10.0-kleidicv
4.11.0
4.2.0
4.2.0-openvino
4.3.0
4.3.0-openvino
4.3.0-openvino-2020.3.0
4.4.0
4.4.0-openvino
4.5.0
4.5.0-openvino
4.5.1
4.5.1-openvino
4.5.2
4.5.2-openvino
4.5.3
4.5.3-openvino
4.5.3-openvino-2021.4.1
4.5.3-openvino-2021.4.2
4.5.4
4.5.5
4.5.5-openvino-2022.1.0
4.6.0
4.7.0
4.8.0
4.8.1
4.9.0
5.0.0-alpha
${ noResults }
2 Commits (a6ec12f58b88f8384ecebb87b60153ee4912e3c7)
| Author | SHA1 | Message | Date |
|---|---|---|---|
Abduragim Shtanchaev
|
a8d1373919
|
Merge pull request #25794 from Abdurrahheem:ash/yolov10-support
Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794 This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186). **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](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce. ```bash git clone git@github.com: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](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model. ``` bash 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](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443) **Running YOLOv9 using OpenCV** 1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting. ```bash 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. ```bash 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 - [x] I agree to contribute to the project under Apache 2 License. - [x] 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 - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake |
10 months ago |
|
Abduragim Shtanchaev
|
372b36c1d3
|
Merge pull request #24898 from Abdurrahheem:ash/yolo_ducumentation
Documentation for Yolo usage in Opencv #24898 This PR introduces documentation for the usage of yolo detection model family in open CV. This is not to be merge before #24691, as the sample will need to be changed. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] 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 - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake |
1 year ago |