getting_started_with_ros2_ov2.0.md

ROS2_OpenVINO_Toolkit

NOTE: Below steps have been tested on Ubuntu 20.04 and Ubuntu 22.04. Supported ROS2 versions include foxy,galactic and humble.

1. Environment Setup

For ROS2 foxy and galactic on ubuntu 20.04:

• Install ROS2.
  Refer to: ROS_foxy_install_guide & ROS_galactic_install_guide

• Install Intel® OpenVINO™ Toolkit Version: 2022.3.
  Refer to: OpenVINO_install_guide

  • Install from an achive file. Both runtime and development tool are needed, pip is recommended for installing the development tool.
    Refer to: OpenVINO_devtool_install_guide

• Install Intel® RealSense™ SDK.
  Refer to: RealSense_install_guide

For ROS2 humble on ubuntu 22.04:

• Install ROS2.
  Refer to: ROS_humble_install_guide

• Install Intel® OpenVINO™ Toolkit Latest Version by Source.
  Refer to: OpenVINO_install_guide

• Install Intel® RealSense™ SDK by Source.
  Refer to: RealSense_install_guide

2. Building and Installation

• Install ROS2_OpenVINO_Toolkit packages

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone https://github.com/intel/ros2_openvino_toolkit -b ros2
git clone https://github.com/intel/ros2_object_msgs
git clone https://github.com/IntelRealSense/realsense-ros.git -b ros2-development
git clone https://github.com/ros-perception/vision_opencv.git -b <ROS2_VERSION>

• Install dependencies

sudo apt-get install ros-<ROS2_VERSION>-diagnostic-updater
sudo apt install python3-colcon-common-extensions

• Build package

source /opt/ros/<ROS2_VERSION>/setup.bash 
source <OpenVINO_INSTALL_DIR>/setupvars.sh
cd ~/catkin_ws
colcon build --symlink-install
source ./install/local_setup.bash

3. Running the Demo

Install OpenVINO 2022.3 by PIP

OMZ tools are provided for downloading and converting models of open_model_zoo in ov2022.
Refer to: OMZtool_guide

• See all available models

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

Install OpenVINO 2022.3 by source code

• See all available models

cd ~/openvino/thirdparty/open_model_zoo/tools/model_tools
sudo python3 downloader.py --print_all

• Download the optimized Intermediate Representation (IR) of models (execute once), for example:

cd ~/openvino/thirdparty/open_model_zoo/tools/model_tools
sudo python3 downloader.py --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 ~/openvino/thirdparty/open_model_zoo/tools/model_tools
sudo python3 converter.py --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 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
  

More Information

• ROS2 OpenVINO discription writen in Chinese: https://mp.weixin.qq.com/s/BgG3RGauv5pmHzV_hkVAdw

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