ROS Index
|
ros2_openvino_toolkit repositoryopenvino_msgs openvino_param_lib openvino_people_msgs openvino_wrapper_lib openvino_node openvino_test |
Repository Summary
| Description | |
| Checkout URI | https://github.com/intel/ros2_openvino_toolkit.git |
| VCS Type | git |
| VCS Version | master |
| Last Updated | 2024-07-23 |
| Dev Status | UNKNOWN |
| Released | UNRELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Packages
| Name | Version |
|---|---|
| openvino_msgs | 0.9.0 |
| openvino_param_lib | 0.9.0 |
| openvino_people_msgs | 0.9.0 |
| openvino_wrapper_lib | 0.9.0 |
| openvino_node | 0.9.0 |
| openvino_test | 0.9.0 |
README
ros2_openvino_toolkit
Table of Contents
- ➤ Overview
- ➤ Prerequisite
- ➤ Introduction
- ➤ Supported Features
- ➤ Installation & Launching
- ➤ Reference
- ➤ FAQ
- ➤ Feedback
- ➤ More Information
Overview
ROS2 Version Supported
| Branch Name | ROS2 Version Supported | Openvino Version | OS Version |
|---|---|---|---|
| ros2 | Galactic, Foxy, Humble | V2022.1, V2022.2, V2022.3 | Ubuntu 20.04, Ubuntu 22.04 |
| dashing | Dashing | V2022.1, V2022.2, V2022.3 | Ubuntu 18.04 |
| foxy-ov2021.4 | Foxy | V2021.4 | Ubuntu 20.04 |
| galactic-ov2021.4 | Galactic | V2021.4 | Ubuntu 20.04 |
Inference Features Supported
- Object Detection
- Face Detection
- Age Gender Recognition
- Emotion Recognition
- Head Pose Estimation
- Object Segmentation
- Person Re-Identification
- Vehicle Attribute Detection
- Vehicle License Plate Detection
Prerequisite
| Prerequisite | Mandatory? | Description |
|---|---|---|
| Processor | Mandatory | A platform with Intel processors assembled. (Refer to here for the full list of Intel processors supported.) |
| OS | Mandatory | We only tested this project under Ubuntu distros. It is recommended to install the corresponding Ubuntu Distro according to the ROS distro that you select to use. For example: Ubuntu 18.04 for dashing, Ubuntu 20.04 for Foxy and Galactic, Ubuntu 22.04 for Humble. |
| ROS2 | Mandatory | We have already supported active ROS distros (Humble, Galactic, Foxy and Dashing (deprecated)). Choose the one matching your needs. You may find the corresponding branch from the table above in section ROS2 Version Supported. |
| OpenVINO | Mandatory | The version of OpenVINO toolkit is decided by the OS and ROS2 distros you use. See the table above in Section ROS2 Version Supported. |
| Realsense Camera | Optional | Realsense Camera is optional, you may choose these alternatives as the input: Standard Camera, ROS Image Topic, Video/Image File or RTSP camera. |
Introduction
Design Architecture
From the view of hirarchical architecture design, the package is divided into different functional components, as shown in below picture.
Intel® OpenVINO™ toolkit
- **Intel® OpenVINO™ toolkit** provides a ROS-adapted runtime framework of neural network which quickly deploys applications and solutions for vision inference. By leveraging Intel® OpenVINO™ toolkit and corresponding libraries, this ROS2 runtime framework extends workloads across Intel® hardware (including accelerators) and maximizes performance.
- Increase deep learning workload performance up to 19x1 with computer vision accelerators from Intel.
- Unleash convolutional neural network (CNN)-based deep learning inference using a common API.
- Speed development using optimized OpenCV* and OpenVX* functions.
See more from [here](https://github.com/openvinotoolkit/openvino) for Intel OpenVINO™ introduction.
ROS OpenVINO Runtime Framework
- **ROS OpenVINO Runtime Framework** is the main body of this repo. It provides key logic implementation for pipeline lifecycle management, resource management and ROS system adapter, which extends Intel OpenVINO toolkit and libraries. Furthermore, this runtime framework provides ways to simplify launching, configuration, data analysis and re-use.
ROS Input & Output
- **Diversal Input resources** are data resources to be infered and analyzed with the OpenVINO framework.
- **ROS interfaces and outputs** currently include _Topic_ and _service_. Natively, RViz output and CV image window output are also supported by refactoring topic message and inferrence results.
Optimized Models
- **Optimized Models** provided by Model Optimizer component of Intel® OpenVINO™ toolkit. Imports trained models from various frameworks (Caffe*, Tensorflow*, MxNet*, ONNX*, Kaldi*) and converts them to a unified intermediate representation file. It also optimizes topologies through node merging, horizontal fusion, eliminating batch normalization, and quantization. It also supports graph freeze and graph summarize along with dynamic input freezing.
Logic Flow
From the view of logic implementation, the package introduces the definitions of parameter manager, pipeline and pipeline manager. The following picture depicts how these entities co-work together when the corresponding program is launched.
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