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.

Store Traffic Monitor

Details
Target OS:	Ubuntu* 18.04 LTS
Programming Language:	Python* 3.6
Time to Complete:	50-70min

What it does

The store traffic monitor reference implementation gives the total number of people currently present and total number of people visited the facility. It also counts the product inventory. The application is capable of processing the inputs from multiple cameras and video files. It is capable of detecting objects on any number of screens.

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
  

• OpenCL™ Runtime Package

• Intel® Distribution of OpenVINO™ toolkit 2020 R3 Release

How It works

The counter uses the Inference Engine included in the Intel® Distribution of OpenVINO™ toolkit. A trained neural network detects objects within a designated area by displaying a green bounding box over them. This reference implementation identifies multiple objects entering the frame and identifies their class, count, and time entered.

Architectural Diagram

Setup

Get the code

Steps to clone the reference implementation: (store-traffic-monitor)

sudo apt-get update && sudo apt-get install git
git clone https://github.com/intel-iot-devkit/store-traffic-monitor-python.git

Install the Intel® Distribution of OpenVINO™ toolkit

Refer to Install Intel® Distribution of OpenVINO™ toolkit for Linux* on 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 dependencies

FFmpeg*

FFmpeg is a free and open-source project capable of recording, converting and streaming digital audio and video in various formats. It can be used to do most of our multimedia tasks quickly and easily say, audio compression, audio/video format conversion, extract images from a video and a lot more.

Which model to use

This application uses the mobilenet-ssd model, that can be accessed using the model downloader. The model downloader downloads the model as Caffe* model files. These need to be passed through the model optimizer to generate the IR (the .xml and .bin files) that will be used by the application.

The application also works with any object-detection model, provided it has the same input and output format of the SSD model. The model can be any object detection model:

• Downloaded using the model downloader, provided by Intel® Distribution of OpenVINO™ toolkit.

• Built by the user.
  

To install the dependencies of the RI and to download the mobilenet-ssd Intel® model, run the following command:

cd <path_to_the_store-traffic-monitor-python_directory>
./setup.sh 

The Labels file

This application requires a labels file associated with the model being used for detection. All detection models work with integer labels and not string labels (e.g. for the ssd300 and mobilenet-ssd models, the number 15 represents the class "person"), that is why each model must have a labels file, which associates an integer (the label the algorithm detects) with a string (denoting the human-readable label).

The labels file is a text file containing all the classes/labels that the model can recognize, in the order that it was trained to recognize them (one class per line).

For mobilenet-ssd model, labels.txt file is provided in the resources directory.

The Config file

The resources/config.json contains the videos that will be used by the application.

The config.json file is of the form name/value pair, "video": <path/to/video> and "label": <label>
For example:

{
    
    "inputs": [

	    {
            "video": "videos/video1.mp4",
            "label": "person"
        }
    ]
    
}

The path/to/video is the path, on the local system, to a video to use as input and the label is of the class (person, bottle, etc.) to be detected on that video. The labels used in the config.json file must be present in the labels from the labels.txt file.

The application can use any number of videos for detection (i.e. the config.json file can have any number of item), but the more videos the application uses in parallel, the more the frame rate of each video scales down. This can be solved by adding more computation power to the machine on which the application is running.

Which Input video to use

The application works with any input video. Sample videos are provided here.

For first-use, we recommend using the people-detection, one-by-one-person-detection, bottle-detection videos. The videos are automatically downloaded to the resources/ folder by setup.sh. For example:

The config.json would be:

{
    
    "inputs": [

	    {
            "video": "sample-videos/people-detection.mp4",
            "label": "person"
        },
        {
            "video": "sample-videos/one-by-one-person-detection.mp4",
            "label": "person"
        },
        {
            "video": "sample-videos/bottle-detection.mp4",
            "label": "bottle"
        }
    ]
    
}

If the user wants to use any other video, specify the video path in config.json file

Using the Camera instead of video

Replace path/to/video with the camera ID in config.json and the label to be found, where the ID is taken from the video device (the number X in /dev/videoX). On Ubuntu, to list all available video devices use 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",
            "label": "person"
        }
    ]
    
}

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_store-traffic-monitor-python_directory>/application

To see a list of the various options:

python3 store_traffic_monitor.py -h

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

To run with multiple devices use -d MULTI:device1,device2. For example: -d MULTI:CPU,GPU,HDDL
If no target device is specified the application will run on the CPU by default.

Running on the CPU

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

python3 store_traffic_monitor.py -d CPU -m ../resources/FP32/mobilenet-ssd.xml -l ../resources/labels.txt

To run the application on sync mode, use -f sync as command line argument. By default, the application runs on async mode.

Running on the integrated GPU

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

python3 store_traffic_monitor.py -d GPU -m ../resources/FP32/mobilenet-ssd.xml -l ../resources/labels.txt

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), use the following command:

python3 store_traffic_monitor.py -d GPU -m ../resources/FP16/mobilenet-ssd.xml -l ../resources/labels.txt

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 store_traffic_monitor.py -d MYRIAD -m ../resources/FP16/mobilenet-ssd.xml -l ../resources/labels.txt

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.

Run on the Intel® Movidius™ VPU

To run on the Intel® Movidius™ VPU, use the -d HDDL command-line argument:

python3 store_traffic_monitor.py -d HDDL -m ../resources/FP16/mobilenet-ssd.xml -l ../resources/labels.txt

Note: The Intel® Movidius™ VPU 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.

Loop the input video

By default, the application reads the input videos only once, and ends when the videos ends. In order to not have the sample videos end, thereby ending the application, the option to continuously loop the videos is provided.
This is done by running the application with the -lp true command-line argument:

./store_traffic_monitor.py -lp true -d CPU -m ../resources/FP32/mobilenet-ssd.xml -l ../resources/labels.txt

This looping does not affect live camera streams, as camera video streams are continuous and do not end.

Using the browser UI

The default application uses a simple user interface created with OpenCV. A web based UI, with more features is also provided with this application.
From the working directory, run the application with -ui true command line argument. For example:

./store_traffic_monitor.py -ui true -d CPU -m ../resources/FP32/mobilenet-ssd.xml -l ../resources/labels.txt

Follow the readme provided here to run the web based UI.