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Alexander Smorkalov
2024-07-16 10:11:21 +03:00
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doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown
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@@ -24,7 +24,9 @@ model, but the methodology applies to other supported models.
@note Currently, OpenCV supports the following YOLO models:
- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main),
- [YoloNas](https://github.com/Deci-AI/super-gradients/tree/master),
- [YOLONas](https://github.com/Deci-AI/super-gradients/tree/master),
- [YOLOv10](https://github.com/THU-MIG/yolov10/tree/main),
- [YOLOv9](https://github.com/WongKinYiu/yolov9),
- [YOLOv8](https://github.com/ultralytics/ultralytics/tree/main),
- [YOLOv7](https://github.com/WongKinYiu/yolov7/tree/main),
- [YOLOv6](https://github.com/meituan/YOLOv6/blob/main),
@@ -79,7 +81,7 @@ the ONNX graph, a process that we will detail further in the subsequent sections
Now that we know know the parameters of the pre-precessing we can go on and export the model from
Pytorch to ONNX graph. Since in this tutorial we are using YOLOX as our sample model, lets use its
export for demonstration purposes (the process is identical for the rest of the YOLO detectors).
export for demonstration purposes (the process is identical for the rest of the YOLO detectors except `YOLOv10` model, see details on how to export it later in the post).
To exporting YOLOX we can just use [export script](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/tools/export_onnx.py). Particularly we need following commands:
@code{.bash}
@@ -125,6 +127,20 @@ than YOLOX) in case it is needed. However, usually each YOLO repository has pred
onnx.save(model_simp, args.output_name)
@endcode
#### Exporting YOLOv10 model
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 on how to cut off the postprocessing, there is this [forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. The forked branch cuts of the postprocessing by [returning output](https://github.com/Abdurrahheem/yolov10/blob/4fdaafd912c8891642bfbe85751ea66ec20f05ad/ultralytics/nn/modules/head.py#L522) of the model before postprocessing procedure itself. To convert torch model to ONNX follow this proceduce.
@code{.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>
@endcode
By default `--model="yolov10s"` and `--imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV
### Running Yolo ONNX detector with OpenCV Sample
Once we have our ONNX graph of the model, we just simply can run with OpenCV's sample. To that we need to make sure:
@@ -144,24 +160,25 @@ Once we have our ONNX graph of the model, we just simply can run with OpenCV's s
--padvalue=<padding_value> \
--paddingmode=<padding_mode> \
--backend=<computation_backend> \
--target=<target_computation_device>
--target=<target_computation_device> \
--width=<model_input_width> \
--height=<model_input_height> \
@endcode
VIDEO DEMO:
@youtube{NHtRlndE2cg}
- --input: File path to your input image or video. If omitted, it will capture frames from a camera.
- --classes: File path to a text file containing class names for object detection.
- --thr: Confidence threshold for detection (e.g., 0.5).
- --nms: Non-maximum suppression threshold (e.g., 0.4).
- --mean: Mean normalization value (e.g., 0.0 for no mean normalization).
- --scale: Scale factor for input normalization (e.g., 1.0).
- --scale: Scale factor for input normalization (e.g., 1.0, 1/255.0, etc).
- --yolo: YOLO model version (e.g., YOLOv3, YOLOv4, etc.).
- --padvalue: Padding value used in pre-processing (e.g., 114.0).
- --paddingmode: Method for handling image resizing and padding. Options: 0 (resize without extra processing), 1 (crop after resize), 2 (resize with aspect ratio preservation).
- --backend: Selection of computation backend (0 for automatic, 1 for Halide, 2 for OpenVINO, etc.).
- --target: Selection of target computation device (0 for CPU, 1 for OpenCL, etc.).
- --device: Camera device number (0 for default camera). If `--input` is not provided camera with index 0 will used by default.
- --width: Model input width. Not to be confused with the image width. (e.g., 416, 480, 640, 1280, etc).
- --height: Model input height. Not to be confused with the image height. (e.g., 416, 480, 640, 1280, etc).
Here `mean`, `scale`, `padvalue`, `paddingmode` should exactly match those that we discussed
in pre-processing section in order for the model to match result in PyTorch
@@ -183,7 +200,8 @@ cd <build directory of OpenCV>
./bin/example_dnn_yolo_detector
@endcode
This will execute the YOLOX detector with your camera. For YOLOv8 (for instance), follow these additional steps:
This will execute the YOLOX detector with your camera.
For YOLOv8 (for instance), follow these additional steps:
@code{.sh}
cd opencv_extra/testdata/dnn
@@ -195,6 +213,23 @@ cd <build directory of OpenCV>
./bin/example_dnn_yolo_detector --model=onnx/models/yolov8n.onnx --yolo=yolov8 --mean=0.0 --scale=0.003921568627 --paddingmode=2 --padvalue=144.0 --thr=0.5 --nms=0.4 --rgb=0
@endcode
For YOLOv10, follow these steps:
@code{.sh}
cd opencv_extra/testdata/dnn
python download_models.py yolov10
cd ..
export OPENCV_TEST_DATA_PATH=$(pwd)
cd <build directory of OpenCV>
./bin/example_dnn_yolo_detector --model=onnx/models/yolov10s.onnx --yolo=yolov10 --width=640 --height=480 --scale=0.003921568627 --padvalue=114
@endcode
This will run `YOLOv10` detector on first camera found on your system. If you want to run it on a image/video file, you can use `--input` option to specify the path to the file.
VIDEO DEMO:
@youtube{NHtRlndE2cg}
### Building a Custom Pipeline
doc/tutorials/introduction/config_reference/config_reference.markdown
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@@ -217,6 +217,7 @@ Following options can be used to produce special builds with instrumentation or
| `ENABLE_BUILD_HARDENING` | GCC, Clang, MSVC | Enable compiler options which reduce possibility of code exploitation. |
| `ENABLE_LTO` | GCC, Clang, MSVC | Enable Link Time Optimization (LTO). |
| `ENABLE_THIN_LTO` | Clang | Enable thin LTO which incorporates intermediate bitcode to binaries allowing consumers optimize their applications later. |
| `OPENCV_ALGO_HINT_DEFAULT` | Any | Set default OpenCV implementation hint value: `ALGO_HINT_ACCURATE` or `ALGO_HINT_APROX`. Dangerous! The option changes behaviour globally and may affect accuracy of many algorithms. |
@see [GCC instrumentation](https://gcc.gnu.org/onlinedocs/gcc/Instrumentation-Options.html)
@see [Build hardening](https://en.wikipedia.org/wiki/Hardening_(computing))