
Chinese: 
欢迎回到OpenVINO频道，
现在我们可以使用OpenVINO在配备有Movidius VPU视觉处理单元的树莓派微型电脑（Raspberry-pi）上执行推论，
这期视频我们就来介绍它的硬件，安装，以及如何使用。
在之前的视频中，我们已经使用OpenVINO在开发系统上创建了一个应用程序,并介绍了所有相关流程。
现在，我们想在一个目标物联网（IOT）平台上执行它，
我这里有一个树莓派微型电脑和一个NCS，这是一个通过USB连接Movidius VPU的神经计算棒。
它可以是配有Myriad 2的NCS一代，或者是配有myriad–x 的NCS二代。
这期视频，我们将介绍如何获取OpenVINO工具包，
如何设置OpenVINO的环境变量并运行,
由于NCS是USB装置，所以我们还将介绍如何添加USB规则，

English: 
Welcome back to the OpenVINO Channel
Now you can use OpenVINO to run inference on a Raspberry-pi with a Movidius VPU
Let's talk about the Hardware, the installation, and how to use it..
You have created an application on your development system using OpenVINO
We have talked about all the process in the previous videos..
Now, we want to run it on a target IOT platform
I have here a Raspberry Pi and and an  NCS, A Neural Compute Stick with a MVD VPU connected via USB
It could be NCS 1 with Myriad 2 or NCS2, which has a myriad - x
Now let's talk about How to get the OpenVINO package
How to set the environment variables and get  it to work
How to add USB rules, as the NCS is a USB device

Chinese: 
以及如何使用OpenVINO执行随附的人脸检测范例，当然你也可以执行自己的应用程序。
链接中详细描述了所有阶段，你也可以利用副本复制粘贴。
获取OpenVINO工具包很简单，
我们现在仅获取最小的工具包，其中仅包含推论引擎，OpenCV和一组范例应用程序。
来到下载目录（cd〜/ OpenVINO /）
获取OpenVINO工具包（wget--no-check-certificate https://download.01.org/openvinotoolkit/2018_R5/packages/l_openvino_toolkit_ie_p_2018.5.445.tgz）
然后解压 (tar -xf l_openvino_toolkit_ie_p_2018.5.445.tgz)
setupvars.sh脚本的变量为
使用此SED命令将其替换为安装目录，
( sed -i "s||$(pwd)/inference_engine_vpu_arm|"
inference_engine_vpu_arm/bin/setupvars.sh )
现在，执行此脚本来设置环境变量，
(source inference_engine_vpu_arm/bin/setupvars.sh)

English: 
and how to run the face detection sample supplied with
OpenVINO, but you can run your own application..
All the stages are described in detail in
the link.. And you can use the transcript to copy paste them..
Getting the OpenVINO package is easy
We are getting only the minimal package which includes only the inference-engine, The OpenCV, and a set of sample applications.
Go to Downloads directory
(cd ~/OpenVINO/)
Get the OpenVINO Package
( wget --no-check-certificate https://download.01.org/openvinotoolkit/2018_R5/packages/l_openvino_toolkit_ie_p_2018.5.445.tgz )
And Un-Tar
(tar -xf l_openvino_toolkit_ie_p_2018.5.445.tgz)
the setupvars.sh script has a variable of

Let's replace it with our installation directory
using this SED command..
( sed -i "s||$(pwd)/inference_engine_vpu_arm|"
inference_engine_vpu_arm/bin/setupvars.sh )
Now let's run this script to set our variables..
(source inference_engine_vpu_arm/bin/setupvars.sh)

Chinese: 
然后得到一个指示，表明环境变量已准备就绪。
我们需要把当前的Linux用户添加到用户组：
(sudo usermod -a -G users "$(whoami)") )
这需要注销并登录才能生效，
我这里就跳过这一步。
现在，我们需要更新USB规则，神经计算棒才可以正常运行。
这个脚本可以解决此问题：Install NCS_Udev_Rules
(sh inference_engine_vpu_arm/install_dependencies/install_NCS_udev_rules.sh)
现在一切准备就绪。
现在我们已经准备好利用OpenVINO推论引擎来执行应用程序，
为了方便，我们执行工具包里提供的范例，
我们需要构建此范例，并获取所需的IR文档和模型。
转到范例目录
(cd inference_engine_vpu_arm/deployment_tools/inference_engine/samples/ )
这里有很多范例，

English: 
And you get an indication that the environment variables are ready..
We need to add the current Linux user to the
users group:
(sudo usermod -a -G users "$(whoami)") )
You will need to Log out and log in for it
to take effect.
I will just skip it..
Now We need to update the USB rules to allow the compute stick to work..
This script will do the trick..
Install NCS_Udev_Rules..
(sh inference_engine_vpu_arm/install_dependencies/install_NCS_udev_rules.sh)
And we are all set..
Now you are all set to run your application
with OpenVINO inference-engine..
As an example, let's run one of the supplied samples..
We will need to build the sample and get the required IR files, the model..
Let's go to the samples directory (cd inference_engine_vpu_arm/deployment_tools/inference_engine/samples/ )
Many samples are here..

English: 
Create a "build" directory
(mkdir build && cd build)
And build the interactive face detection demo
CMAKE..
(cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a")
And Make
(make -j2 interactive_face_detection_demo )
The binaries will be under Arm/Release
Now let's get the face detection model,
the IR files
Get the bin file
(wget --no-check-certificate https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/face-detection-adas-0001/FP16/face-detection-adas-0001.bin)
And the xml file
(wget --no-check-certificate https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/face-detection-adas-0001/FP16/face-detection-adas-0001.xml)
And they are here..
And now let's run the sample..
Running the interactive face detection demo
Input model is the face detection
Device is MYRIAD

Chinese: 
创建一个“build”目录 (mkdir build && cd build)
然后构建交互式人脸检测范例演示，
输入CMAKE指令，
(cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a")
然后输入Make指令，(make -j2 interactive_face_detection_demo )
二进制文档将在Arm/Release目录底下，
现在我们来获取人脸检测模型和IR文档，
先获取BIN文档，
(wget --no-check-certificate https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/face-detection-adas-0001/FP16/face-detection-adas-0001.bin)
然后是XML文档
(wget --no-check-certificate https://download.01.org/openvinotoolkit/2018_R4/open_model_zoo/face-detection-adas-0001/FP16/face-detection-adas-0001.xml)
它们都在这里了。
现在我们来执行范例，
执行交互式人脸检测范例演示，
输入模型是人脸检测模型，
装置是MYRIAD，

Chinese: 
我将使用我的网络摄像头作为输入，所以这里的指令是 /dev/video0，
但是你可以使用任何你想要的视频文件，
(./interactive_face_detection_demo -m face-detection-adas-0001.xml
-d MYRIAD -I /dev/video0)
可以看到，这就是我，
这是通过VNC(虚拟网络控制台)并使用Myriad-2录制下来的，
所以它看起来很慢，但是运行得很好，
我的脸被树莓派微型电脑和Movidius神经计算棒检测到了。
好了，现在你可以在树莓派微型电脑上使用OpenVINO工具包了，很棒吧！
期待看到你的项目。
订阅我们的频道将获得更多类似视频，
谢谢！

English: 
I will use my web cam as an input,
so /dev/video0
but you can use any video file you want
(./interactive_face_detection_demo -m face-detection-adas-0001.xml
-d MYRIAD -I /dev/video0)
And this is me
This is recorded via VNC and using Myriad-2 so it's pretty slow, but working very
nice..
My face detected with a Raspberry-PI..and a Movidius Neural Compute Stick
So you can now use OpenVINO on your Raspberry-PI.
How cool is that? Waiting to see your projects..
Subscribe to out channel and we'll send
you more videos like this
Thank you.
