On the Green Pad this week arrived the Google’s Coral Computing@Edge devices: the Coral USB Accelerator and the Coral Dev Board. Both mount the Edge TPU running TensorFlow Lite framework that brings ML inferencing.
I am planning to write few articles on them… so, stay tuned! 😉
Orage Pi 3 is a really powerful development board, valid alternative of Raspberry Pi. It has a lot of different I/Os in addition respect to other dev boards clones: for example, on-board soldered eMMC (8GB), 4x full standard USB 3.0 ports, 1x USB 2.0 auxiliary port, PCI Express expansion connector, a powerful Allwinner H6 Quad-core 64-bit 1.8GHZ Cortex™-A53 and a DC input power supply connector (5V-2A).
Orange Pi 3 is available in 4 variants: with/without 8GB emmc and 1GB/2GB onboard RAM. If we decide for the version missing the 8GB soldered emmc, we’ll need to use an external slower micro-SD(HC) memory, as on Raspeberry Pi.
Further details regard Orange Pi 3 can be found on the official site: orangepi.com.
Now. The idea is to install Intel OpenVINO toolkit/framework on a non-x86-based IoT platform.
What is Intel OpenVINO? It’s a toolkit that help to deploy software solutions with vision computing capabilities (aka Computer Vision, or abbreviated simply CV), and it’s based on a Convolutional Neural Network (CNN) framework. The OpenVINO distribution includes also the Intel DLDT (Deep Learning Deployment Toolkit).
Intel provides a version of OpenVINO™ for GNU/Linux OSs, but it requires a 64bit Intel processor (6th+ generation or Xeon™) and this is not our case as we are playing with a ARM-based cpu.
Otherwise, Intel has released also a 32bit ARM-based version adequate to be installed on the Raspberry Pi: so, optimized specifically to be installed on Raspbian OS and including the OpenVINO Inference Engine, OpenCV, the MYRIAD plugin and few example applications.
Specifically, the Inference Engine is the most important component as it runs the deep learning model. It is composed by a C++ library with a subset of C++ classes to infer input data (images) and get a result; and it provides APIs to read the Intermediate Representation, to set the input and output formats and to execute the model on devices.
Few last notes before starting:
The OpenVINO toolkit was formerly known as the Intel® Computer Vision SDK.
These steps have been validated with Orange Pi 3 (2GB RAM + 8GB on-board eMMC) but they should work also for other Orange Pi development boards.
An internet connection is required to follow the steps in this guide.
These steps have been validated to work with the Intel® Movidius™ Neural Compute Stick (Intel® NCS) plugged in one of USB ports but they should work without modifications with the Intel® Neural Compute Stick 2.
1 x Orange Pi 3
1 x microSDHC class 6+ / 32+GB
1 x power supply (5V 3000mA/h) with microUSB or 4×1.7mm socket
Method 1: using the official Debian image for Orange Pi 3
Statement of the problem here: this method was harder that expected as the official Debian image released by Xunlong Software CO. (manufacturer of Orange Pi boards) is versioned as Jessie (Debian 8) and compiled for “aarch64” architecture. Unfortunately, Jessie is now labeled as “oldstable” and all its repository were moved to Debian Archive: this cause few issue that we’ll try to bypass with the following steps.
Ok. Let’s start.
First of all I’ll take for granted that you already have a working board with the official release of Debian 8 “Jessie” Server for Orange Pi 3 on it (image named “OrangePi_3_jessie_server_v1.0.img”). You can follow instructions in the manual to do it. In addition, I am also taking for granted that your Orange Pi 3 is connected to the internet: we’ll need to download few files (also, date/time need to be adjusted). If not, I suggest to use nmtui to do so.
Now. As I already stated briefly before, Debian 8 is not the best as its repository seems to be not complete anymore (for example, “binary-amr64” and others are now missing). We’ll need to upgrade from Debian 8 to Debian 9 (from “Jessie” to “Stretch”) as we’ll need to install few packages from the repositories.
deb http://ftp.debian.org/debian/ stretch main contrib non-free
#deb-src http://ftp.debian.org/debian/ stretch main contrib non-free
deb http://ftp.debian.org/debian/ stretch-backports main contrib non-free
#deb-src http://ftp.debian.org/debian/ stretch-backports main contrib non-free
deb http://security.debian.org/ stretch/updates main contrib non-free
#deb-src http://security.debian.org/ stretch/updates main contrib non-free
deb http://ftp.debian.org/debian/ stretch-updates main contrib non-free
#deb-src http://ftp.debian.org/debian/ stretch-updates main contrib non-free
[Ctrl+X], “y” and [ENTER] to save and exit from the editor. Now we can update the repositories with:
Remember: you should run the setupvars.sh script everytime you’ll open a shell and you want to play with OpenVINO. If you want to make the process automatic you can edit the bash runtime configuration file as follows:
~/OpenVINOfiles$ git clone https://github.com/fbaldassarri/opencv-sun50iw6p1.git
~/OpenVINOfiles$ cd opencv-sun50iw6p1
~/OpenVINOfiles/opencv-sun50iw6p1$ sudo cp -R lib/* /opt/intel/openvino/opencv/lib
~/OpenVINOfiles/opencv-sun50iw6p1$ cd ..
To be able to run pre-trained network (for example, Face Detection or Object Detection) using the Inference Engine you’ll need to create/navigate to a directory of which you have read/write permissions. Let’s create one as subfolder of our OpenVINOfiles:
~/OpenVINOfiles$ mkdir build
~/OpenVINOfiles$ cd build
Let’s try compiling through cmake the Inference Engine samples provided by Intel inside the OpenVINO distribution toolkit:
The application should output an image (out_0.bmp) with detected faced enclosed in rectangles.
Time to clean up our workspace:
~/OpenVINOfiles$ cd OpenVINOfiles
~/OpenVINOfiles$ rm -r l_openvino_toolkit_raspbi_p_2019.1.094.tgz
~/OpenVINOfiles$ rm -r opencv-sun50iw6p1
Method 2: using the un-official Armbian image for Orange Pi 3
Statement of the problem here: the image of Armbian is not official release of Xunlong Software CO. and it could not support completely all the features of Orange Pi 3 board; in addition, it still in “unstable” release. But, it is already updated at Debian 9 “Stretch” and it has few interesting tweaks.
The process is quite the same of the method 1, but first of all you need to use the latest nightly build of Armbian for Orange Pi 3 at the following link: https://dl.armbian.com/orangepi3/.