- NVIDIA Jetson with JetPack 4.5 or later
- USB camera
Install docker-compose (You need to have docker-compose with the runtime paramter support.)
- If you already have pip installed with apt in your Jetson. Remove it.
$ sudo apt remove python3-pip
- Install pip from PyPA
$ sudo apt update
$ sudo apt install curl python3-testresources
$ curl -kL https://bootstrap.pypa.io/get-pip.py | python3
- Install docker-compose
$ python3 -m pip install --user docker-compose
- Add $HOME/.local/bin directory in your PATH.
- Comfirm docker-compose installed successfully.
$ docker-compose --version
- Add "default-runtime": "nvidia" to your /etc/docker/daemon.json configuration file to build the TensorRT plugin. And restart the docker service or reboot your system.
{
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia"
}
- Clone this repository.
$ git clone https://github.com/MACNICA-CLAVIS-NV/abandoned_object_detection
- Add the execution permission to the shell scripts.
$ cd abandoned_object_detection
$ chmod +x ./scripts/*.sh
Start the services of the application
$ ./scripts/compose-up.sh
You can see the dashboard at http://localhost:1880/ui
Stop the services of the application
$ ./scripts/compose-down.sh
- This application was inherited the TensorRT based YOLO implementation from jkjung-avt/tensorrt_demos. The software is under the MIT license.
- The jkjung-avt/tensorrt_demos referenced source code of NVIDIA/TensorRT samples to develop most of the demos in this repository. Those NVIDIA samples are under Apache License 2.0.