Computer Pointer Controller

This project is made using the Intel's OpenVINO toolkit. This project enables the user to move the mouse pointer according to the gaze of the user.

Project Set Up and Installation

Requirements:

Intel's OpenVINO toolkit
PyAutoGUI

After installing OpenVINO

Step 1: Install the requirements

pip install -r requirements.txt

Step 2: Setup OpenVINO for running inference.

source /opt/intel/openvino/bin/setupvars.sh -pyver {PYTHON_VERSION}

Workflow

The following workflow is used to run inference on the input stream.

Demo

To run demo video

python main.py -input_type video -input_stream bin/demo.mp4

To run demo cam

python main.py -input_type cam

Running inferences on separate models

1. Face Detection

python main.py -input_type video -input_stream bin/demo.mp4 -flag fd

2. Face Landmark Detection

python main.py -input_type video -input_stream bin/demo.mp4 -flag ld

3. Head Pose Detection

python main.py -input_type video -input_stream bin/demo.mp4 -flag hd

4. Gaze Estimation

python main.py -input_type video -input_stream bin/demo.mp4 -flag ge

Face Detection	Facial Landmarks Detection

Head Pose Detection	Gaze Estimation

Documentation

Project Structure

.
├── bin
│   └── demo.mp4
├── LICENSE
├── main.py
├── models
│   └── intel
│       ├── face-detection-adas-binary-0001
│       ├── gaze-estimation-adas-0002
│       ├── head-pose-estimation-adas-0001
│       └── landmarks-regression-retail-0009
├── README.md
├── requirements.txt
├── resources
├── src
│   ├── input_feeder.py
│   ├── models.py
│   └── mouse_controller.py
└── utils
    └── ModelBase.py

Important Files

input_feeder.py: This file contains a InputFeeder Class which handles the input stream.
model.py: Classes for various models have been written in this file
mouse_controller.py: Contains the MouseController Class which moves the mouse pointer accordingly to the gaze.
ModelBase.py: Contains base Model Class from which the other model class in models.py have been inherited.
main.py: This is the main driver file for the entire application.
requirements.txt: All python library dependencies can be installed from this file.

Command-Line Arguments

usage: main.py [-h] [-input_stream INPUT_STREAM]
               [-device {CPU,GPU,HETERO:FPGA,CPU,HETERO:MYRIAD,CPU}]
               [-prob_threshold PROB_THRESHOLD] [-flag {fd,ld,hd,ge}]
               -input_type {cam,video,img}

optional arguments:
  -h, --help            show this help message and exit
  -input_stream INPUT_STREAM
                        Give path of input stream if input_type is 'video' or 'img'
  -device {CPU,GPU,HETERO:FPGA,CPU,HETERO:MYRIAD,CPU}
                        State the device on which inference should happen
  -prob_threshold PROB_THRESHOLD
                        Probability threshold for face detections filtering
  -flag {fd,ld,hd,ge}   Choose a particular model only to run inference 
                         fd: Only Face Detection 
                         ld: Only Face Landmark Detection 
                         hd: Only Head Pose Detection 
                         ge: Only Gaze Estimation

required arguments:
  -input_type {cam,video,img}
                        Give the type of input stream

Results

Loading times of Models

CPU used: Intel(R) Core(TM) i3-6006U CPU @ 2.00GHz

	FP32 (in ms)	FP16 (in ms)	FP16-INT8 (in ms)
Face Detection Model (Same Model for all scenarios)	0.2433936595916748	0.26357316970825195	0.2504093647003174
Face Landmark Detection Model	0.1449739933013916	0.6543600559234619	0.14608454704284668
Headpose Detection Model	0.14208269119262695	0.6201651096343994	0.271759033203125
Gaze Estimation Model	0.21468305587768555	0.3256380558013916	0.3227040767669678

Edge Cases

The face which is closest to camera will be taken into consideration and the gaze of that particular face will be calculated.
Even if there is no one in the frame the application will be running.

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