car_moto_tracking_Jetson_Nano_Yolov8_TensorRT

Detect, track and count cars and motorcycles using yolov8 and TensorRT on Jetson Nano

video_presentation.1.1.1.mp4

video_presentation.mp4

To deploy this work on a Jetson Nano, you should do it in two steps:

1- On your PC:

1-1- Clone this repo on your PC:

On the car_moto_tracking_Jetson_Nano_Yolov8_TensorRT repository, create your own repository by clicking "Use this template" then "Create a new repository".

Put a name for your own repository then click "Create repository from template"

Then clone your own repository on your PC:

git clone "URL to your own repository"
cd "repository_name"
export root=${PWD}
cd ${root}/train

1-2- Install the required packages:

python -m pip install -r requirements.txt

1-3- Create a folder for the dataset:

mkdir datasets
cd datasets
mkdir Train 
mkdir Validation

1-4- 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.

1-5- 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.

References:

https://github.com/triple-Mu/YOLOv8-TensorRT

https://gitee.com/codemaxi-opensource/ByteTrack/tree/main

https://github.com/ultralytics/ultralytics

Contributing:

Contributions are welcome! Please feel free to submit pull requests to improve the project.

License:

This project is licensed under the MIT License - see the LICENSE.md file for details.

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