People Counter App at the Edge - Intel Edge AI for IoT Developers Nanodegree

Details
Programming Language:	Python 3.5 or 3.6

What it Does

The people counter application will demonstrate how to create a smart video IoT solution using Intel® hardware and software tools. The app will detect people in a designated area, providing the number of people in the frame, average duration of people in frame, and total count.

How it Works

The counter will use the Inference Engine included in the Intel® Distribution of OpenVINO™ Toolkit. The model used should be able to identify people in a video frame. The app should count the number of people in the current frame, the duration that a person is in the frame (time elapsed between entering and exiting a frame) and the total count of people. It then sends the data to a local web server using the Paho MQTT Python package.

You will choose a model to use and convert it with the Model Optimizer.

Requirements

Hardware

• 6th to 10th generation Intel® Core™ processor with Iris® Pro graphics or Intel® HD Graphics.
• OR use of Intel® Neural Compute Stick 2 (NCS2)

Software

• Intel® Distribution of OpenVINO™ toolkit 2019 R3 release
• Node v6.17.1
• Npm v3.10.10
• CMake
• MQTT Mosca server

Setup

Install Intel® Distribution of OpenVINO™ toolkit

https://docs.openvinotoolkit.org/latest/index.html

Add a Person Detection Model

You could download any model of your selection and then convert it to an OpenVino Intermediate Representation Follow this link for extra information https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Converting_Model.html

Or you can use any of the already pretained models offered by Intel. As a recommendation I used: https://docs.openvinotoolkit.org/latest/_models_intel_person_detection_retail_0013_description_person_detection_retail_0013.html

cd /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader
sudo ./downloader.py --name person-detection-retail-0013 -o /home/workspace

Install Nodejs and its dependencies

Utilize the classroom workspace, or refer to the relevant instructions for your operating system for this step.

• Linux/Ubuntu
• Mac
• Windows

Install npm

There are three components that need to be running in separate terminals for this application to work:

• MQTT Mosca server
• Node.js* Web server
• FFmpeg server

From the main directory:

• For MQTT/Mosca server:

  cd webservice/server
  npm install
  

• For Web server:

  cd ../ui
  npm install
  

  Note: If any configuration errors occur in mosca server or Web server while using npm install, use the below commands:

  sudo npm install npm -g 
  rm -rf node_modules
  npm cache clean
  npm config set registry "http://registry.npmjs.org"
  npm install
  

Run the application

From the main directory:

Step 1 - Start the Mosca server

cd webservice/server/node-server
node ./server.js

You should see the following message, if successful:

Mosca server started.

Step 2 - Start the GUI

Open new terminal and run below commands.

cd webservice/ui
npm run dev

You should see the following message in the terminal.

webpack: Compiled successfully

Step 3 - FFmpeg Server

Open new terminal and run the below commands.

sudo ffserver -f ./ffmpeg/server.conf

Step 4 - Run the code

Open a new terminal to run the code.

Setup the environment

You must configure the environment to use the Intel® Distribution of OpenVINO™ toolkit one time per session by running the following command:

source /opt/intel/openvino/bin/setupvars.sh -pyver 3.5

You should also be able to run the application with Python 3.6, although newer versions of Python will not work with the app.

Running on the CPU

When running Intel® Distribution of OpenVINO™ toolkit Python applications on the CPU, the CPU extension library is required. This can be found at:

/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/

Depending on whether you are using Linux or Mac, the filename will be either libcpu_extension_sse4.so or libcpu_extension.dylib, respectively. (The Linux filename may be different if you are using a AVX architecture)

Though by default application runs on CPU, this can also be explicitly specified by -d CPU command-line argument:

On Video

python main.py -i resources/Pedestrian_Detect_2_1_1.mp4 -m model/person-detection-retail-0013.xml -l /opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so -d CPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://0.0.0.0:3004/fac.ffm

On Image

python main.py -i images/people-counter-image.png -m model/person-detection-retail-0013.xml -l /opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so -d CPU -pt 0.6

On Webcam

python main.py -i CAM -m model/person-detection-retail-0013.xml -l /opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so -d CPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://0.0.0.0:3004/fac.ffm