Retail Pandemic Reference Implementation - Capacity Limit

Details
Target OS:	Ubuntu* 18.04 LTS
Programming Language:	Python* 3.5
Time to Complete:	1 hour

What it does

This reference implementation showcases a retail capacity limit application where a people counter keeps track of how many people are in the store based on the detected people entering and exiting the store. Virtual lines are configured in entrance and exit areas to indicate the limits of entering and exiting the area of interest.

Requirements

Hardware

• 6th gen or greater Intel® Core™ processors or Intel® Xeon® processor, with 8Gb of RAM

Software

• Ubuntu 18.04

• Intel® Distribution of OpenVINO™ toolkit 2020.3 Release

How It works

The application uses the Inference Engine and Model Downloader included in the Intel® Distribution of OpenVINO Toolkit doing the following steps:

1. Ingests video from a file, processing frame by frame.
2. Detects people in the frame using a pre-trained DNN model.
3. Extracts features from people detected using a second pre-trained DNN in order to do a tracking of the people.
4. Check if people cross the predefined virtual gates based on the coordinates and identify in which direction the virtual gate was closed to determine if it is an entry or exit event.
5. People counter gets updated based on the entry and exit data.

The DNN models are optimized for Intel® Architecture and are included with Intel® Distribution of OpenVINO™ toolkit.

Setup

Get the code

Clone the reference implementation:

sudo apt-get update && sudo apt-get install git
git clone github.com:intel-iot-devkit/capacity-limit.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.

Installing the requirements

To install the dependencies of the Reference Implementation, run the following commands:

  cd <path_to_capacity-limit-directory>
  pip3 install -r requirements.txt

Which model to use

This application uses the person-detection-retail-0013 and person-reidentification-retail-0300 Intel® pre-trained models, that can be downloaded using the model downloader. The model downloader downloads the .xml and .bin files that is used by the application.

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

mkdir models
cd models
python3 /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name person-detection-retail-0013 --precisions FP32
python3 /opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name person-reidentification-retail-0300 --precisions FP32

The models will be downloaded inside the following directories:

- models/intel/person-detection-retail-0013/FP32/
- models/intel/person-reidentification-retail-0300/FP32/

The Config File

The config.json contains the path to the videos and models that will be used by the application and also the coordinates of a virtual line.

The config.json file is of the form name/value pair. Find below an example of the _config.json_file:

{
  "video": "path/to/video/myvideo.mp4",
  "pedestrian_model_weights": "models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.bin",
  "pedestrian_model_description": "models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml",
  "reidentification_model_weights": "models/intel/person-reidentification-retail-0300/FP32/person-reidentification-retail-0300.bin",
  "reidentification_model_description": "models/intel/person-reidentification-retail-0300/FP32/person-reidentification-retail-0300.xml",
  "coords": [[50, 45], [80, 45]]
}

Note that coords represents a virtual gate defined by two points with x,y coordinates.

Which Input video to use

The application works with any input video format supported by OpenCV.

Sample video: https://www.pexels.com/video/black-and-white-video-of-people-853889/

Data set subject to license https://www.pexels.com/license. The terms and conditions of the data set license apply. Intel does not grant any rights to the data files.

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

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

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 project location on your system:

    cd <path-to-capacity-limit-directory>

Run the python script.

python3 capacitylimit.py