1. Docker Install

   #Uninstall Old Version
   $ sudo apt-get remove docker docker-engine docker.io containerd runc
   
   # Install Docker
   $ sudo apt-get update
   $ sudo apt-get install \\
       ca-certificates \\
       curl \\
       gnupg \\
       lsb-release
   $ curl -fsSL <https://download.docker.com/linux/ubuntu/gpg> | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
   $ echo \\
     "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] <https://download.docker.com/linux/ubuntu> \\
     $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
   $ sudo apt-get update
   $ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin
   

2. Pull the Image ( Image Size : 18.4 GB )

   $ sudo docker pull tlsdusrb123/nuvo_trt:first_integration_220513
   

3. Docker GPU Setting

   Docker GPU[0000] ERROR

   $ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
   
   $ curl -s -L <https://nvidia.github.io/nvidia-docker/gpgkey> | sudo apt-key add -
   
   curl -s -L <https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list> | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
   -
   $ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
   
   $ sudo systemctl restart docker
   

4. Execute the Container

   xhost + && sudo docker run -it --net=host --gpus all --privileged --name VISION3 -v /dev/video*:/dev/video* -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix/ -v ~/docker_ws:/workspace tlsdusrb123/all_trts:latest
   

<record bagfile>

1. device 실행시키기

2. 모든 topic record

   $cd 원하는경로
   $rosbag record -a 
   

3. Play

   $ cd 아까 그 경로
   $ rosbag play 파일이름
   $ rviz
   
   하고 원하는 토픽추가해서 보면 끝
   

<Execute YOLOv4_TRT IN DOCKER>

**1.Docker 실행**
# alias = sudo docker exec -it GIGACHA_VISION bash
$ Terminal → doc ( alias = sudo docker exec -it GIGACHA_VISION bash )

**2.Source
#** alias = cd /root/caktin_ws/ && source devel/setup.bash
$ cw

**3.경로 이동**
# alias = cd /root/caktin_ws/src/tensorrt_demos/yolo
$ go

4. yolo실행 
1) 신호등 모델 실행
# alias = python3 trt_yolo.py --usb 0 -m yolov4-traffic
$ yolo_light

2) 정지표지판 이지만 truck으로 되는 모델 실행
# alias = python3 trt_yolo.py --usb 0 -m yolov4-custom
$ yolo_truck

3)COCO dataset 모델 실행
# alias = python3 trt_yolo.py --usb 0 -m yolov4-416
$ yolo_coco

220514 Check List

• trigger_vision.py 17번째 줄에서 truck아니고 해당하는 class int 찾아서 적어주기

trt_yolo_yk.py

trigger_vision.py

<GPU Compute Capability>

헌 누보 : TITANX ⇒ 52 ( 사이트에는 61)

새 누보 : RTX 3050 ⇒ 사이트기준 86

자비어 : 사이트기준 72