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docker_instructions_ov2.0.md

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Run Docker Images For ROS2_OpenVINO_Toolkit

NOTE: Below steps have been tested on Ubuntu 20.04. Supported ROS2 versions include foxy and galactic.

1. Environment Setup

2. Build docker image by dockerfile

cd ~/ros2_openvino_toolkit/docker/Dockerfile
vi ~/ros2_openvino_toolkit/docker/Dockerfile
docker build --build-arg ROS_PRE_INSTALLED_PKG=<EXPECT_ROS_PRE_INSTALLED_PKG> --build-arg VERSION=<EXPECT_ROS_VERSION> --build-arg "HTTP_PROXY=set_your_proxy" -t ros2_openvino_202203 .

For example:

  • Build image for ros_galactic
cd ~/ros2_openvino_toolkit/docker/Dockerfile
vi ~/ros2_openvino_toolkit/docker/Dockerfile
docker build --build-arg ROS_PRE_INSTALLED_PKG=galactic-desktop --build-arg VERSION=galactic --build-arg "HTTP_PROXY=set_your_proxy" -t ros2_galactic_openvino_202203 .
  • Build image for ros_foxy
cd ~/ros2_openvino_toolkit/docker/Dockerfile
vi ~/ros2_openvino_toolkit/docker/Dockerfile
docker build --build-arg ROS_PRE_INSTALLED_PKG=foxy-desktop --build-arg VERSION=foxy --build-arg "HTTP_PROXY=set_your_proxy" -t ros2_foxy_openvino_202203 .

3. Download and load docker image

  • Download docker image
 # ros2_openvino_202203 for demo
 cd ~/Downloads/
 wget <DOCKER_IMAGE_PATH>
  • Load docker image
cd ~/Downloads/
docker load -i <DOCKER_IMAGE>
docker images
// (show <DOCKER_IMAGE> in the list)

4. Running the Demos

  • Install dependency
  sudo apt install x11-xserver-utils
  xhost +
  • Run docker image
  docker images
  docker run -itd  -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v /dev:/dev  --privileged=true --name <your_image_name> <IMAGE_ID>

  • In Docker Container

  • Preparation

source /opt/ros/<ROS_VERSION>/setup.bash
cd ~/catkin_ws
source ./install/local_setup.bash
  • See all available models OMZ tools are provided for downloading and converting OMZ models in ov2022.
    Refer to: OMZtool_guide
omz_downloader --print_all
  • Download the optimized Intermediate Representation (IR) of model (execute once), for example:
cd ~/catkin_ws/src/ros2_openvino_toolkit/data/model_list
omz_downloader --list download_model.lst -o /opt/openvino_toolkit/models/
  • If the model (tensorflow, caffe, MXNet, ONNX, Kaldi) need to be converted to intermediate representation (such as the model for object detection):
cd ~/catkin_ws/src/ros2_openvino_toolkit/data/model_list
omz_converter --list convert_model.lst -d /opt/openvino_toolkit/models/ -o /opt/openvino_toolkit/models/convert
  • Copy label files (execute once) Note:Need to make label_dirs if skip steps for set output_dirs above.
sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/face_detection/face-detection-adas-0001.labels /opt/openvino_toolkit/models/intel/face-detection-adas-0001/FP32/
sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/face_detection/face-detection-adas-0001.labels /opt/openvino_toolkit/models/intel/face-detection-adas-0001/FP16/
sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/emotions-recognition/FP32/emotions-recognition-retail-0003.labels /opt/openvino_toolkit/models/intel/emotions-recognition-retail-0003/FP32/
sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_segmentation/frozen_inference_graph.labels /opt/openvino_toolkit/models/intel/semantic-segmentation-adas-0001/FP32/
sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_segmentation/frozen_inference_graph.labels /opt/openvino_toolkit/models/intel/semantic-segmentation-adas-0001/FP16/
sudo cp ~/catkin_ws/src/ros2_openvino_toolkit/data/labels/object_detection/vehicle-license-plate-detection-barrier-0106.labels /opt/openvino_toolkit/models/intel/vehicle-license-plate-detection-barrier-0106/FP32
  • Check the parameter configuration in ros2_openvino_toolkit/sample/param/xxxx.yaml before lauching, make sure parameters such as model_path, label_path and input_path are set correctly. Please refer to the quick start document for yaml configuration guidance for detailed configuration guidance.
    • run face detection sample code input from StandardCamera.
    ros2 launch openvino_node pipeline_people.launch.py
    • run person reidentification sample code input from StandardCamera.
    ros2 launch openvino_node pipeline_reidentification.launch.py
    • run face detection sample code input from Image.
    ros2 launch openvino_node pipeline_image.launch.py
    • run object segmentation sample code input from RealSenseCameraTopic.
    ros2 launch openvino_node pipeline_segmentation.launch.py
    • run object segmentation sample code input from Image.
    ros2 launch openvino_node pipeline_segmentation_image.launch.py
    • run vehicle detection sample code input from StandardCamera.
    ros2 launch openvino_node pipeline_vehicle_detection.launch.py
    • run person attributes sample code input from StandardCamera.
    ros2 launch openvino_node pipeline_person_attributes.launch.py

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