README.md

DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel.

Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.

Intel no longer accepts patches to this project.

If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.

Contact: [email protected]

ros_intel_movidius_ncs

Warning: This repo is deprecated. For latest ROS wrapper for Movidius NCS, please refer to our project ros_openvino_toolkit.

See: https://github.com/intel/ros_openvino_toolkit!!

1 Introduction

The Movidius™ Neural Compute Stick (NCS) is a tiny fanless deep learning device that you can use to learn AI programming at the edge. NCS is powered by the same low power high performance Movidius™ Vision Processing Unit (VPU) that can be found in millions of smart security cameras, gesture controlled drones, industrial machine vision equipment, and more.

This project is a ROS wrapper for NC API of NCSDK, providing the following features:

• A ROS service for object classification and detection of a static image file
• A ROS publisher for object classification and detection of a video stream from a RGB camera
• Demo applications to show the capabilities of ROS service and publisher
• Support multiple CNN models of Caffe and Tensorflow
• Support multiple devices in parallel to acclerate inference

There are 2 active branches in this project:

• master - stable branch
  The latest version on it is v0.6.0 which supports NCSDK v1.12.00. master branch is only updated when every milestone release ready.
• devel - default branch
  This branch is updated from time to time and maintain the latest code on it. Each pull request should be submitted based on devel branch. We will merge patches to master branch on every milestone release.

2 Prerequisite

• An x86_64 computer running Ubuntu 16.04
• ROS Kinetic
• Movidius Neural Compute Stick (NCS)
• Movidius Neural Compute (MvNC) SDK
• Movidius Neural Compute Application Zoo
• RGB Camera, e.g. RealSense D400 Series or standard USB camera

3 Environment Setup

• Install ROS Kinetic Desktop-Full (guide)
• Create a catkin workspace (guide)
• Install NCSDK v1.12.00 (github)
• Install NC APP Zoo (github)
• NCSDK should be installed in /opt/movidius by default. Create a symbol link in /opt/movidius to NC APP Zoo.

sudo ln -s <your-workspace>/ncappzoo /opt/movidius/ncappzoo

After that, make sure you can find graph data in /opt/movidius/ncappzoo/caffe or /opt/movidius/ncappzoo/tensorflow and image data in /opt/movidius/ncappzoo/data/images

• Install ROS package for different cameras as needed. e.g.
  1. Standard USB camera

  sudo apt-get install ros-kinetic-usb-cam

  2. RealSense D400 series camera
  • Install Intel® RealSense™ SDK 2.0 (tag v2.9.1)
    • Install from source code(Recommended)
    • Install from package
         Note: Create a symbol link from libusb.a to libusb-1.0.a, otherwise "libusb.a" is probably not to be found by librealsense.
         sudo ln -s /usr/lib/x86_64-linux-gnu/libusb-1.0.a /usr/lib/libusb.a
  • Install Intel® RealSense™ ROS (guide)

  cd ~/catkin_ws/src
  git clone https://github.com/intel-ros/realsense.git
  cd  realsense
  git checkout 2.0.2
  cd ~/catkin_ws
  catkin_make

4 Building and Installation

# Building
cd ~/catkin_ws/src
git clone https://github.com/intel/object_msgs
git clone https://github.com/intel/ros_intel_movidius_ncs.git
cd ros_intel_movidius_ncs
git checkout master
cd ~/catkin_ws
catkin_make
# Installation
catkin_make install
source install/setup.bash
# Copy label files from this project to the installation location of NCSDK
cp ~/catkin_ws/src/ros_intel_movidius_ncs/data/labels/* /opt/movidius/ncappzoo/data/ilsvrc12/

5 Running the Demo

5.1 Classification

5.1.1 Supported CNN Models

Table1

CNN Model	Framework	Usage
AlexNet	Caffe	Image/Video
GoogLeNet	Caffe	Image/Video
SqueezeNet	Caffe	Image/Video
Inception_v1	Tensorflow	Image/Video
Inception_v2	Tensorflow	Image/Video
Inception_v3	Tensorflow	Image/Video
Inception_v4	Tensorflow	Image/Video
MobileNet	Tensorflow	Image/Video

5.1.2 Classification Result with GoogLeNet

5.1.3 Running the Demo

• Static Image
• Video Streaming

5.2 Detection

5.1.1 Supported CNN Models

CNN Model	Framework	Usage
MobileNetSSD(Recommended)	Caffe	Image/Video
TinyYolo_v1	Caffe	Image/Video

5.1.2 Detection Result with MobileNetSSD

5.1.3 Running the Demo

• Static Image
• Video Streaming

5.3 Inference with multiple NCS devices

We introduce multiple NCS support from v0.6.0, providing x1.8 / x2.5 / x3 performance improvement with 2NCSs / 3NCSs / 4NCSs compared with 1 NCS respectively. Refer here for more details.

6 Interfaces

6.1 Topic

Classification: /movidius_ncs_nodelet/classified_objects
Detection: /movidius_ncs_nodelet/detected_objects

6.2 Service

Classification: /movidius_ncs_image/classify_object
Detection: /movidius_ncs_image/detect_object

7 Known Issues

• Only absolute path of image file supported in image inference demo
• Only test on RealSense D400 series camera and Microsoft HD-300 USB camera
• Current v0.6.0 supporting NCSDK v1.12.00 is on master branch. devel branch is the development branch for the next release.
• Unit test for movidius_ncs_lib failed due to one exception.

8 TODO

• Support more CNN models
• Support latest NCSDK
• Support results display with Rviz

Any security issue should be reported using process at https://01.org/security