Computer Pointer Controller app is used to controll the mouse pointer movement by face_detection and direction of eyes also estimated pose of head and facial_landmarks_detection. the applicatio take input from vedio saved or Live streame from Camera this applicatio suppose to raise the performance of using openvino tools for combination for more medels to gether to get perfect application like this

TODO: Explain the setup procedures to run your project. For instance, this can include your project directory structure, the models you need to download and where to place them etc. Also include details about how to install the dependencies your project requires.

basic demo of your model.https://www.youtube.com/watch?v=6ZZw4IVl4Uo&t=9s [](https://www.youtube.com/watch?v=6ZZw4IVl4Uo&t=9s"Working of the Project - Click to Watch!")

• Firstly, we install the prerequisites from requirements.txt using the following command.

pip install -r requirements.txt

• Clone the repository

• Initialize the OpenVINO environment

cd <OpenVINO-Path>\IntelSWTools\openvino\bin\
setupvars.bat

Utilize the classroom workspace, or refer to the relevant instructions for your operating system for this step.

• Linux/Ubuntu
• Mac
• Windows

I've made a step by step instruction walkthrough to install Intel® Distribution of OpenVINO™ toolkit on a Windows Machine. sample image to view who is openvino

• Go to the Model Downloader directory:

cd <OpenVINO-Path>\IntelSWTools\openvino\deployment_tools\tools\model_downloader

1. Download Face Detection Model

python downloader.py --name face-detection-adas-binary-0001

2. Download Facial Landmarks Detection Model

python downloader.py --name landmarks-regression-retail-0009

3. Download Head Pose Estimation Model

python downloader.py --name head-pose-estimation-adas-0001

4. Download Gaze Estimation Model

python downloader.py --name gaze-estimation-adas-0002

this package of that project contains folders model.py by nameing model and images.

• images: Contains all the image files used for the README.md i but it into direct folder

• model : Contains the pre-trained model needed for the project.

  • Face Detection model
  • Head Pose Estimation model
  • Gaze Estimation model
  • Facial Landmarks Detection model below all breife decription for that models all of these model contain three methode repeatly todo
  • load model
  • pre-process the input frams
  • predection (inference)

• src : Contains the python script used in this project

  • face_detection.py - Contains Class with function to load the model, pre-process the input frame, perform inference to detect/ predect the face, and pre-process the output.
  • facial_landmarks_detection.py - Contains Class with function to load the model, pre-process the faces from input frame, perform inference to detect the landmarks for eye, and pre-process the output.
  • head_pose_estimation.py - Contains Class with function to load the model, pre-process the faces from input frame, perform inference to detect the head postion using the angles of yaw, pitch, and roll, and pre-process the output.
  • gaze_estimation.py - Contains Class with function to load the model, pre-process the left eye, right eye and the head pose angle from input frame, perform inference to predict the gaze vector, and pre-process the output.
  • input_feeder.py - Contains InputFeeder class to initialize Video_Capture with either 'CAM' or video_file and return the frames sequentially as the same i shared on my youtube channel .
  • mouse_controller.py - Contains sample class that you can use to control the mouse pointer.
  • main.py - Integrates and combine all the modules to run this project.

1. Initialize the OpenVINO environment

cd <OpenVINO-Path>\IntelSWTools\openvino\bin\
setupvars.bat

2. Open a new terminal and run the following commands:

cd <Project-Repo-Path>\src

3. Run the main.py

python main.py -f "Face Detection model  .xml file" -fl "Facial Landmark Detection model .xml file" -hp "Head Pose Estimation model .xml file" -g "Gaze Estimation model .xml file" -i "video file" or "CAM" -d "Target device"

"CPU" is the default Target device.i test it for own laptop intel core i5

we will recieve fram from the input then detect and predict the face and push it into head_pose_estimation and the lanmark then push all into gaze_estimation and mouse movement direction work through that :

this url direct to the models

• Face Detection Model
• Facial Landmarks Detection Model
• Head Pose Estimation Model
• Gaze Estimation Model

The main.py file is fed with the following arguments in the command line inference, where

• -f "Path to an .xml file with Face Detection model>"
• -fl "Path to an .xml file with Facial Landmark Detection model"
• -hp "Path to an .xml file with Head Pose Estimation model".
• -g "Path to an .xml file with Gaze Estimation model"
• -i "Path to image or video file or CAM"
• -d "Target device"

The benchmark results of running the model on more than one hardwareand multiple model precisions. This include model loading time, input/output processing time, model inference time which are compared like prevouse project i tested it as follows

Model Size

• face-detection-adas-binary-0001

• head-pose-estimation-adas-0001

• landmarks-regression-retail-0009

• gaze-estimation-adas-0002

(Master-Xray) C:\workbench>python main.py  -f face-detection-adas-binary-0001.xml -fl landmarks-regression-retail-0009.xml -hp head-pose-estimation-adas-0001.xml -g gaze-estimation-adas-0002.xml -i demo.mp4 -d CPU

• Total loading time: 1762.009 ms

1. Intel Core i5 vpro inside CPU

**Note that, reducing the model precision might lose some of the important information used for training which decreases themodel accuracy.

• The video feed can be either a video_file or directly from the camera feed. i.e Inference pipeline for both video file and webcam feed as allowed as input. It can be configured in the command line argument -i "Video_file" or -i "CAM"

If you see any issues in building this project, feel free to ask me. Please do suggest new projects that you want me to do next.

Share this video if you like. https://www.youtube.com/watch?v=6ZZw4IVl4Uo&t=9s Thanks for reading!