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Face Mask Recognition Desktop App with Deep Learning & PyQT

Face Mask Recognition Desktop App with Deep Learning & PyQT

Learn and Build Face Recognition for Face Mask Detection Desktop App using Python, TensorFlow 2, OpenCV, PyQT, Qt. Face Mask Recognition Desktop App with Deep Learning & PyQT

What you'll learn

  • Face Recognition for Mask detection with Deep Learning
  • Develop Convolutional Network Network for Face Mask from Scratch using TensorFlow
  • Preprocess the big data of image
  • OpenCV for Face Detection
  • Computer Vision Desktop Application with PyQt
  • PyQt Essential Concepts

Requirements

  • Basic Python Knowledge
  • Familiar with Tensor Flow and Deep Learning
  • Familiar with Numpy and Pandas

Description

Project that you will be Developing:

Prerequisite of Project: OpenCV

Image Processing with OpenCV

  • Section -0 : Setting Up Project

Install Python

Install Dependencies

  • Section -1 : Data Preprocessing

Gather Images

Extract Faces only from Images

Labeling (Target output) Images

Data Preprocessing

RGB mean subtraction image

  • Section - 2: Develop Deep Learning Model

Training Face Recognition with OWN Deep Learning Model.

Convolutional Neural Network

Model Evaluation

  • Section - 3: Prediction with CNN Model

1. Putting All together

  • Section - 4: PyQT Basics
  • Section -5: PyQt based Desktop Application

Overview:

I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks.

With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Deep learning models like Convolutional Neural Network.  I will teach you the model selection and hyperparameter tuning for face recognition models

Once our Deep learning model is ready, will we move to Section-3, and write the code for preforming predictions with CNN model.

Finally, we will develop the desktop application and make prediction to live video streaming.

What are you waiting for? Start the course develop your own Computer Vision Flask Desktop Application Project using Machine Learning, Python and Deploy it in Cloud with your own hands.

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Views > The Complete Machine Learning 2021 : 10 Real World Projects

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