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base on NVIDIA DeepStream SDK 8.0 / 7.1 / 7.0 / 6.4 / 6.3 / 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 application for YOLO-Pose models # DeepStream-Yolo-Pose
NVIDIA DeepStream SDK 8.0 / 7.1 / 7.0 / 6.4 / 6.3 / 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 application for YOLO-Pose models
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### YOLO object detection models and other infos: https://github.com/marcoslucianops/DeepStream-Yolo
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### Important: Please export the ONNX model with the new export file, generate the TensorRT engine again with the updated files, and use the new config_infer_primary file according to your model
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### Getting started
* [Supported models](#supported-models)
* [Instructions](#basic-usage)
* [YOLOv7-Pose usage](docs/YOLOv7_Pose.md)
* [YOLOv8-Pose usage](docs/YOLOv8_Pose.md)
* [YOLO11-Pose usage](docs/YOLO11_Pose.md)
* [YOLO-NAS-Pose usage](docs/YOLONAS_Pose.md)
* [NMS configuration](#nms-configuration)
* [Detection threshold configuration](#detection-threshold-configuration)
##
### Supported models
* [YOLO-NAS-Pose](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS-POSE.md)
* [YOLO11-Pose](https://github.com/ultralytics/ultralytics)
* [YOLOv8-Pose](https://github.com/ultralytics/ultralytics)
* [YOLOv7-Pose](https://github.com/WongKinYiu/yolov7)
##
### Instructions
#### 1. Download the DeepStream-Yolo-Pose repo
```
git clone https://github.com/marcoslucianops/DeepStream-Yolo-Pose.git
cd DeepStream-Yolo-Pose
```
#### 2. Compile the libs
2.1. Set the `CUDA_VER` according to your DeepStream version
```
export CUDA_VER=XY.Z
```
* x86 platform
```
DeepStream 8.0 = 12.8
DeepStream 7.1 = 12.6
DeepStream 7.0 / 6.4 = 12.2
DeepStream 6.3 = 12.1
DeepStream 6.2 = 11.8
DeepStream 6.1.1 = 11.7
DeepStream 6.1 = 11.6
DeepStream 6.0.1 / 6.0 = 11.4
```
* Jetson platform
```
DeepStream 8.0 = 13.0
DeepStream 7.1 = 12.6
DeepStream 7.0 / 6.4 = 12.2
DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 = 11.4
DeepStream 6.0.1 / 6.0 = 10.2
```
2.2. Make the libs
```
make -C nvdsinfer_custom_impl_Yolo_pose clean && make -C nvdsinfer_custom_impl_Yolo_pose
make clean && make
```
**NOTE**: To use the Python code, you need to install the DeepStream Python bindings.
Reference: https://github.com/NVIDIA-AI-IOT/deepstream_python_apps
* x86 platform:
```
pip3 install https://github.com/NVIDIA-AI-IOT/deepstream_python_apps/releases/download/v1.2.2/pyds-1.2.2-cp312-cp312-linux_x86_64.whl
```
* Jetson platform:
```
pip3 install https://github.com/NVIDIA-AI-IOT/deepstream_python_apps/releases/download/v1.2.2/pyds-1.2.2-cp312-cp312-linux_aarch64.whl
```
**NOTE**: It is recommended to use Python virtualenv.
**NOTE**: The steps above only work on **DeepStream 8.0**. For previous versions, please check the files on the `NVIDIA-AI-IOT/deepstream_python_apps` repo.
#### 3. Run
* C code
```
./deepstream -s file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 -c config_infer_primary_yoloV8_pose.txt
```
* Python code
```
python3 deepstream.py -s file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4 -c config_infer_primary_yoloV8_pose.txt
```
**NOTE**: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).
**NOTE**: To change the source
```
-s file:// or rtsp:// or http://
--source file:// or rtsp:// or http://
```
**NOTE**: To change the infer config file (example for config_infer.txt file)
```
-c config_infer.txt
--infer-config config_infer.txt
```
**NOTE**: To change the nvstreammux batch-size (example for 2; default: 1)
```
-b 2
--streammux-batch-size 2
```
**NOTE**: To change the nvstreammux width (example for 1280; default: 1920)
```
-w 1280
--streammux-width 1280
```
**NOTE**: To change the nvstreammux height (example for 720; default: 1080)
```
-e 720
--streammux-height 720
```
**NOTE**: To change the GPU id (example for 1; default: 0)
```
-g 1
--gpu-id 1
```
**NOTE**: The **DeepStream-Yolo-Pose** requires
```
[property]
...
maintain-aspect-ratio=1
symmetric-padding=1
...
```
##
### NMS configuration
For now, the `nms-iou-threshold` is fixed to `0.45`.
**NOTE**: Make sure to set `cluster-mode=4` in the config_infer file.
##
### Detection threshold configuration
```
[class-attrs-all]
pre-cluster-threshold=0.25
```
##
My projects: https://www.youtube.com/MarcosLucianoTV
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