算法部署-在Jetson-Nano上使用TensorRT部署YOLOv8车辆+摩托车跟踪计数算法-附项目源码-优质项目实战

# car_moto_tracking_Jetson_Nano_Yolov8_TensorRT Detect, track and count cars and motorcycles using yolov8 and TensorRT on Jetson Nano To deploy this work on a Jetson Nano, you should do it in two steps: ## 1- On your PC: ### Install the required packages: python -m pip install -r requirements.txt ### Create a folder for the dataset: mkdir datasets cd datasets mkdir Train mkdir Validation ### Copy your dataset: Copy your train dataset (images + .txt files) into datasets/Train cp -r path_to_your_train_dataset/. ${root}/train/datasets/Train Copy your validation dataset (images + .txt files) into datasets/Validation cp -r path_to_your_validation_dataset/. ${root}/train/datasets/Validation If you have more than car and motorcycle classes, modify the data.yaml to add the other classes. ### Train the yolov8n model: python main.py When the training is finished, your custom yolov8n model will be saved in ${root}/train/runs/detect/train/weights/best.pt ### 1-6- Push your custom model to GitHub: git add runs/detect/train/weights/best.onnx git commit -am "Add the trained yolov8n model" git push ### Remark: If you want to use an already pretrained model: python export_onnx.py --weights path_to_your_pretrained_model git add path_to_the_onnx_model git commit -am "Add the trained yolov8n model" git push ## 2- On the Jetson Nano: ### 2-1- Clone this repo on the Jetson Nano: git clone "URL to your own repository" cd "repository_name" export root=${PWD} ### 2-2- Export the engine from the onnx model /usr/src/tensorrt/bin/trtexec \ --onnx=train/runs/detect/train/weights/best.onnx \ --saveEngine=best.engine After executing the above command, you will get an engine named best.engine . ### 2-3- For Detection: cd ${root}/detect mkdir build cd build cmake .. make cd ${root} #### 2-3-1- Launch Detection: for video: ${root}/detect/build/yolov8_detect ${root}/best.engine video ${root}/src/test.mp4 1 show #### Description of all arguments - 1st argument : path to the maked file - 2nd argument : path to the engine - 3rd argument : video for saved video - 4rth argument: path to video file - 5th argument : if inference capacity of the Jetson is more then 30 fps, put 1, otherwise put 2, 3, 4 depending on the inference capacity of the Jetson - 6th argument : show or save for camera: ${root}/detect/build/yolov8_detect ${root}/best.engine camera 1 show #### Description of all arguments - 1st argument : path to the maked file - 2nd argument : path to the engine - 3rd argument : camera for using embedded camera - 4rth argument: if inference capacity of the Jetson is more then 30 fps, put 1, otherwise put 2, 3, 4 depending on the inference capacity of the Jetson - 5th argument : show or save ### 2-4- For Tracking and Counting: cd ${root}/track_count mkdir build cd build cmake .. make cd ${root} #### 2-4-1- Launch Tracking and Counting: If you want to count only in one direction, put 1 as 7th argument. Otherwise, for 2 directions counting, put 2 as 7th argument. Before displaying the processed video, the first frame of the video will be displayed. You should click on this frame to indicate the position of the line(s). For one direction counting, click twice and for 2 directions counting, click four time. for video: ${root}/track_count/build/yolov8_track_count ${root}/best.engine video ${root}/src/test.mp4 1 show #### Description of all arguments - 1st argument : path to the maked file - 2nd argument : path to the engine - 3rd argument : video for saved video - 4rth argument: path to video file - 5th argument : if inference capacity of the Jetson is more then 30 fps, put 1, otherwise put 2, 3, 4 depending on the inference capacity of the Jetson - 6th argument : show or save for camera: ${root}/track_count/build/yolov8_track_count ${root}/best.engine camera 1 show #### Description of all arguments - 1st argument : path to the maked file - 2nd argument : path to the engine - 3rd argument : camera for using embedded camera - 4rth argument: if inference capacity of the Jetson is more then 30 fps, put 1, otherwise put 2, 3, 4 depending on the inference capacity of the Jetson - 5th argument : show or save #### Remark: If you are using the Jetson with SSH, you can not see the first frame of the video to draw the line. With SSH connection, follow these steps: 1- Add argument to the command line with ssh for video: ${root}/track_count/build/yolov8_track_count ${root}/best.engine video ${root}/src/test.mp4 1 show ssh for camera ${root}/track_count/build/yolov8_track_count ${root}/best.engine camera 1 show ssh 2- The first frame of the video will be saved 3- Copie this frame to you PC via SSH (in PC's terminal): scp jetson_name@jetson_server:path_to_frame_in_jetson/frame_for_line.jpg ${root}/utils/frame_for_line.jpg 4- On your PC, launch the python script draw_line.py to draw the line and get the points. python3 ${root}/utils/draw_line.py 5- Give the points value to Jetson in the Jetson's terminal.