DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel. This project has been identified as having known security escapes. 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.

People Counter

Details
Target OS:	Ubuntu* 18.04 LTS
Programming Language:	Python* 3.6
Time to Complete:	45 min

What it Does

The people counter application is one of a series of IoT reference implementations aimed at instructing users on how to develop a working solution for a particular problem. It demonstrates how to create a smart video IoT solution using Intel� hardware and software tools. This solution detects people in a designated area, providing the number of people in the frame, average duration of people in frame, and total count.

Requirements

Hardware

• 6th to 8th generation Intel� Core� processor with Iris� Pro graphics or Intel� HD Graphics.

Software

• Ubuntu 18.04 LTS

  Note: We recommend using a 4.14+ Linux kernel with this software. Run the following command to determine your kernel version:

  uname -a
  

• Intel� Distribution of OpenVINO� toolkit 2020 R3 Release

• OpenCL� Runtime Package

• Node v6.17.1

• Npm v3.10.10

• MQTT Mosca* server

Which model to use

This application uses the person-detection-retail-0013 Intel® model, that can be accessed using the model downloader. The model downloader downloads the .xml and .bin files that will be used by the application.

Install the dependencies

To install the dependencies of the RI and to download the person-detection-retail-0013 Intel® model, run the following command:

cd <path_to_the_people-counter-python_directory>
./setup.sh

Make sure the npm and node versions are exact, using the commands given below:

node -v

The version should be v6.17.1

npm -v

The version should be v3.10.10

Note: If the Node and Npm versions are different, run the following commands:

sudo npm install -g n
sudo n 6.17.1

Note: After running the above commands, please open a new terminal to proceed further. Also, verify the node and npm versions from the new terminal.

How it Works

The counter uses the Inference Engine included in the Intel� Distribution of OpenVINO� toolkit and the Intel� Deep Learning Deployment Toolkit. A pre-trained, SSD neural network detects people within a designated area by displaying a bounding box over them. It counts 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.

The DNN model used in this application is an Intel� optimized model that is part of the Intel� Distribution of OpenVINO� toolkit. You can find it here:

/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/

Setup

Get the code

Steps to clone the reference implementation:

sudo apt-get update && sudo apt-get install git
git clone https://github.com/intel-iot-devkit/people-counter-python.git 

Install Intel� Distribution of OpenVINO� toolkit

Refer to https://software.intel.com/en-us/articles/OpenVINO-Install-Linux for more information about how to install and setup the Intel� Distribution of OpenVINO� toolkit.

You will need the OpenCL� Runtime Package if you plan to run inference on the GPU. It is not mandatory for CPU inference.

Other Dependecies Installation

Install npm

Go to people-counter-python directory

cd <path_to_people-counter-python_directory>

• For mosca server:

  cd webservice/server
  npm install
  npm i jsonschema@1.2.6
  

• For Web server:

  cd ../ui
  npm install
  

The Config File

The resources/config.json contains the path to the videos that will be used by the application. The config.json file is of the form name/value pair, video: <path/to/video>

Example of the config.json file:

{

    "inputs": [
	    {
            "video": "videos/video1.mp4"
        }
    ]
}

Which Input video to use

The application works with any input video. Find sample videos for object detection here.

For first-use, we recommend using the one-by-one-person-detection video.The video is automatically downloaded to the resources/ folder. For example:
The config.json would be:

{

    "inputs": [
	    {
            "video": "sample-videos/one-by-one-person-detection.mp4"
        }
    ]
}

To use any other video, specify the path in config.json file.

Using the Camera instead of video

Replace the path/to/video in the resources/config.json file with the camera ID, where the ID is taken from the video device (the number X in /dev/videoX).

On Ubuntu, list all available video devices with the following command:

ls /dev/video*

For example, if the output of above command is /dev/video0, then config.json would be::

{

    "inputs": [
	    {
            "video": "0"
        }
    ]
}

Run the application

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

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

Go to people-counter-python directory:

cd <path_to_people-counter-python_directory>

Step 1 - Start the Mosca server

Ensure that no process is running at port address 3000 / 8000, using the following command:

sudo lsof -i:3000

Navigate to the node-server path and run the server using following commands:

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

If successful, this message will appear in the terminal:

connected to ./db/data.db
Mosca server started.

Step 2 - Start the GUI

Open new terminal and run below commands.

cd ../../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.

cd ../..
sudo ffserver -f ./ffmpeg/server.conf

Step 4 - Run the application

Open a new terminal to run the application.

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

Note: This command needs to be executed only once in the terminal where the application will be executed. If the terminal is closed, the command needs to be executed again.

Run the application

Change the current directory to the git-cloned application code location on your system:

cd <path_to_the_people-counter-python_directory>/application

To see a list of the various options:

python3 people_counter.py --help

Running on the CPU

A user can specify a target device to run on by using the device command-line argument -d followed by one of the values CPU, GPU,MYRIAD or HDDL.

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

python3 people_counter.py -m /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml -d CPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://localhost:8090/fac.ffm

To see the output on a web based interface, open the link http://localhost:8080 in a browser.

Running on the GPU

• To run on the integrated Intel� GPU with floating point precision 32 (FP32), use the -d GPU command-line argument:

  python3 people_counter.py -m /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml -d GPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://localhost:8090/fac.ffm
  

  To see the output on a web based interface, open the link http://localhost:8080 in a browser.
  
  

  FP32: FP32 is single-precision floating-point arithmetic uses 32 bits to represent numbers. 8 bits for the magnitude and 23 bits for the precision. For more information, click here
  
  

• To run on the integrated Intel� GPU with floating point precision 16 (FP16):

  python3 people_counter.py -m /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml -d GPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://localhost:8090/fac.ffm
  

  To see the output on a web based interface, open the link http://localhost:8080 in a browser. FP16: FP16 is half-precision floating-point arithmetic uses 16 bits. 5 bits for the magnitude and 10 bits for the precision. For more information, click here

Running on the Intel� Neural Compute Stick

To run on the Intel� Neural Compute Stick, use the -d MYRIAD command-line argument:

python3 people_counter.py -d MYRIAD -m /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://localhost:8090/fac.ffm

To see the output on a web based interface, open the link http://localhost:8080 in a browser.
Note: The Intel� Neural Compute Stick can only run FP16 models. The model that is passed to the application, through the -m <path_to_model> command-line argument, must be of data type FP16.