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# Start DeepStream Using the engine build from [`YOLOv8-TensorRT`](https://github.com/triple-Mu/YOLOv8-TensorRT) |
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## 1. Build you own TensorRT engine from `trtexec` or [`build.py`](https://github.com/triple-Mu/YOLOv8-TensorRT/blob/main/build.py) |
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For example, if you have built an engine named `yolov8s.engine`. |
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## 2. Compile deepstream plugin |
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First, modify the [`CMakeLists.txt`](https://github.com/triple-Mu/YOLOv8-TensorRT/blob/main/csrc/deepstream/CMakeLists.txt) |
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```cmake |
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# Set your own TensorRT path |
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set(TensorRT_INCLUDE_DIRS /usr/include/x86_64-linux-gnu) |
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set(TensorRT_LIBRARIES /usr/lib/x86_64-linux-gnu) |
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# Set your own DeepStream path |
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set(DEEPSTREAM /opt/nvidia/deepstream/deepstream) |
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``` |
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Second, build deepstream plugin |
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```shell |
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mkdir build |
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cd build |
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cmake .. |
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make |
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``` |
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You will get a lib `libnvdsinfer_custom_bbox_yoloV8.so` in [`build`]. |
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## 3. Modify the deepstream config |
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The net config is [`config_yoloV8.txt`](config_yoloV8.txt). Please modify by your own model. |
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```text |
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net-scale-factor=0.0039215697906911373 # the normalize param == 1/255 |
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model-engine-file=./yolov8s.engine # the engine path you build |
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labelfile-path=./labels.txt # the class name path |
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num-detected-classes=80 # the number of classes |
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output-blob-names=num_dets;bboxes;scores;labels # the model output names |
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custom-lib-path=./build/libnvdsinfer_custom_bbox_yoloV8.so # the deepstream plugin you build |
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``` |
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The deepstream config is [`deepstream_app_config.txt`](deepstream_app_config.txt). |
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```text |
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config-file=config_yoloV8.txt # the net config path |
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``` |
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You can get more information from [`deepstream offical`](https://developer.nvidia.com/deepstream-sdk). |
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## 4. Runing detector ! |
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```shell |
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deepstream-app -c deepstream_app_config.txt |
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``` |
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