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.

Machine Operator Monitor

Details
Target OS:	Ubuntu* 18.04 LTS
Programming Language:	C++
Time to Complete:	45 min

Introduction

This application is designed for a machine mounted camera system that monitors if the operator is looking at the machine and if his emotional state is detected as angry. It sends an alert if the operator is not watching the machine while it is in operation or if the emotional state of the operator is angry. It also sends an alert if this combined operator state lasts for longer than a user-defined period of time.

This reference implementation illustrates an example of machine operator safety monitoring.

Requirements

Hardware

• 6th to 8th Generation Intel® Core™ processors with Intel® Iris® Pro graphics or Intel® HD Graphics

Software

• Ubuntu* 18.04 LTS
  
  Note: We recommend using a 4.14+ kernel to use 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 application uses a video source, such as a camera, to grab frames, and then uses three different Deep Neural Networks (DNNs) to process the data. The first network detects faces, and if successful, passes the result to the second neural network.

The second neural network is then used to determine if the machine operator is watching the machine (i.e., if the operator's head is facing towards the camera). If the machine operator is watching the machine, Watching: 1 is displayed on the screen; otherwise, Watching: 0 is displayed.

Finally, if the proper head position has been detected, the third neural network determines the emotion of the operator's face.

The data can then optionally be sent to a MQTT machine to machine messaging server, as part of an industrial data analytics system.

The program creates three threads for concurrency:

• Main thread that performs the video I/O
• Worker thread that processes video frames using the deep neural networks
• Worker thread that publishes any MQTT messages

Setup

Get the code

Clone the reference implementation

sudo apt-get update && sudo apt-get install git
git clone https://github.com/intel-iot-devkit/machine-operator-monitor-cpp.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 as shown by the instructions below. It is not mandatory for CPU inference.

Other dependencies

Mosquitto

Mosquitto is an open source message broker that implements the MQTT protocol. The MQTT protocol provides a lightweight method of carrying out messaging using a publish/subscribe model.

Which model to use

This application uses the face-detection-adas-0001, head-pose-estimation-adas-0001 and emotions-recognition-retail-0003 Intel® model, that can be downloaded using the model downloader. The model downloader downloads the .xml and .bin files that is used by the application.

Steps to download .xml and .bin files:

To download the models and install the dependencies of the application, run the below command in the shopper-gaze-monitor-cpp directory:

./setup.sh

The Config File

The resources/config.json contains the path of video that will be used by the application as input.

For example:

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

The path/to/video is the path to an input video file.

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 face-demographics-walking-and-pause video.

For example:

{
    "inputs": [
       {
           "video":"sample-videos/head-pose-face-detection-female.mp4",
       }
    ]
}

If the user wants to use any other video, it can be used by providing the path in the config.json file.

Using the Camera Stream instead of video

Replace path/to/video with the camera ID in the config.json file, 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"
        }
     ]
   }

Setting the build environment

Configure the environment to use the Intel® Distribution of OpenVINO™ toolkit by exporting environment variables:

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.

Build the Application

To build , go to the shopper-mood-monitor-cpp and run the following commands:

mkdir -p build
cd build
cmake ..
make

Run the Application

To see a list of the various options:

./monitor -h

A user can specify what target device to run on by using the device command-line argument -d. If no target device is specified the application will run on the CPU by default.

Run 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:

./monitor -m=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.bin -c=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.xml -pm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.bin -pc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -sm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.bin -sc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml

The user can choose different confidence levels for both face and emotion detection by using --faceconf, -fc and --moodconf, -mc command line parameters. By default both of these parameters are set to 0.5 i.e. at least 50% detection confidence is required in order for the returned inference result to be considered valid.

Running on the GPU

• To run on the GPU in 32-bit mode, use the following command:

./monitor -m=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.bin -c=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP32/face-detection-adas-0001.xml -pm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.bin -pc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -sm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.bin -sc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -b=2 -t=1

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 GPU in 16-bit mode, use the following command:

./monitor -m=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP16/face-detection-adas-0001.bin -c=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP16/face-detection-adas-0001.xml -pm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.bin -pc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.xml -sm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.bin -sc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.xml -b=2 -t=2

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

Note: The Model Loading Time for GPU is quite long. So to observe the inference for GPU, please try to use longer duration videos.

Running on the Intel® Movidius™ VPU

./monitor -m=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP16/face-detection-adas-0001.bin -c=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/face-detection-adas-0001/FP16/face-detection-adas-0001.xml -pm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.bin -pc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.xml -sm=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.bin -sc=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/head-pose-estimation-adas-0001/FP16/head-pose-estimation-adas-0001.xml -b=2 -t=3

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.

Machine to Machine Messaging with MQTT

To use a MQTT server to publish data, set the following environment variables before running the program:

export MQTT_SERVER=localhost:1883
export MQTT_CLIENT_ID=cvservice

Change the MQTT_SERVER to a value that matches the MQTT server to which you are connected.

Change the MQTT_CLIENT_ID to a unique value for each monitoring station to track the data for individual locations. For example:

export MQTT_CLIENT_ID=machine1337

To monitor the MQTT messages sent to your local server, ensure that the mosquitto client utilities are installed. Run the following command in a new terminal while the application is running:

mosquitto_sub -t 'machine/safety'