KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #developer-tools #ai-agent #claude-code #codex #typescript #ai-agents #deepseek-harness #mcp #cli #rust

ros_openvino

★ 56 Open GitHub ↗

A ROS package to wrap openvino inference engine and get it working with Myriad and GPU

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# **ros_openvino**
A ROS package to wrap openvino inference engine and get it working with **Myriad** and **Intel CPU/GPUs** 
The main topic is to accelerate robotics developing using ROS with OpenVINO. 
This package is designed on async api of [Intel OpenVINO](https://software.intel.com/en-us/openvino-toolkit) and allows an easy setup for **object detection**. 
Entire package is born to support Myriad X, so it works perfectly with:
- Movidius Neural Compute Stick 2
- Ai Core X
- Movidius Neural Compute Stick
- Ai Core

**object detection** node allows you to use a standard image topic and obtain a new topic with live result image. 
If you enable *depth analysis* you can add depth image topic to obtain 3d boxes, visible as markers in rviz.

## **Wiki**
[Wiki home](https://github.com/gbr1/ros_openvino/wiki)

## **Setup**
You need some steps to get ros_openvino working. 
If you are working on a Myriad based device (NCS, NCS2, AI Core, AI Core X) follow [this guide](https://github.com/gbr1/ros_openvino/wiki/Fast-setup-for-Myriad-based-devices) because is faster.

If you have already this kind of stuffs, please take care to modify steps for your case.

### **- Prerequisites**
1. Ubuntu 16.04/18.04
2. OpenVINO 2018 R5 or newer (latest officially tested is 2019 R3)
3. ROS Kinetic Kame/Melodic Morenia

### **- Setup** ***ros_openvino***
1. Clone repo in your **source folder**, assuming `catkin_ws` as your ROS workspace: 
`cd ~/catkin_ws/src` 
`git clone http://github.com/gbr1/ros_openvino.git` 
2. Compile! 
`cd ~/catkin_ws` 
`catkin_make` 
`catkin_make install`

all installation steps and a case of study configuration are described in [Setup environment wiki page](https://github.com/gbr1/ros_openvino/wiki/Setup-environment).

Remember that OpenVINO enviroment variables, ROS enviroment variables and ROS workspace need to be sourced in `bashrc`.

### **- Tests**

There are some demo launch files.
1. **Webcam and GPU**
 you need to install usb_cam node: 
`sudo apt-get install ros-kinetic-usb-cam`
 then type: 
`roslaunch ros_openvino gpu_demo_webcam.launch`
 ![gpu](https://user-images.githubusercontent.com/9216366/53649736-21db3400-3c43-11e9-9353-ee603390aedc.png)

2. **Webcam and Myriad**
 you need to install usb_cam node: 
`sudo apt-get install ros-kinetic-usb-cam`
 then type: 
`roslaunch ros_openvino myriad_demo_webcam.launch`
 ![myriad_webcam](https://user-images.githubusercontent.com/9216366/53649817-5b13a400-3c43-11e9-963c-7f41e899b72c.png)

3. **Realsense D435/D435i/D415 and Myriad**
 you need to install realsense package, follow [here](https://github.com/intel-ros/realsense) step-by-step according your environment setup. 
After that, just type: 
`roslaunch ros_openvino myriad_demo_realsense.launch`
 ![myriad](https://user-images.githubusercontent.com/9216366/53649915-98783180-3c43-11e9-81ab-2579c291af1e.png)

## **Nodes**
- [object_detection](https://github.com/gbr1/ros_openvino/wiki/Object-Detection), a node to detect objects in enviroment using a RGB or RGBD camera using MobileNet-SSD

---
## Note
Tests are done on [UP2](https://up-board.org/upsquared/specifications/) with Intel Atom, 4GB of RAM and 64GB eMMC and [AI Core X](https://up-board.org/ai-core-x/).
Results with a complete analysis with 4-5 object detected is about 30fps for rgb output and 16-18Hz for spatial calculation.
 
On GPU analysis drop down the frame rate.
 
If OpenVINO works on Ubuntu 18.04 this package should be compatible with ROS Melodic Morenia.

## Known issues
- CPU device doesn't work.  In the future yes, it is in "todo" list;
- GPU is worst than Myriad;
- resize of input image is not availble at the moment. It is in "todo" list.

## Future steps
- add face and body tracking
- improve depth analysis

***This package is distribuited under Affero GPLv3.*** 
***Copyright (c) 2019 Giovanni di Dio Bruno***

*Models:*
- *mobilenet-ssd.bin*
- *mobilenet-ssd.xml*

*are created using OpenVINO model downloader and converter.* 
*Original mobilenet-ssd model are under Intel OpenVINO licenses.*

ros_openvino

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README