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

Project that you will be Developing:

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.

Course Content

  • Introduction –> 1 lecture • 3min.
  • Setting Up Project –> 3 lectures • 10min.
  • Data Preparation & Preprocessing –> 18 lectures • 1hr 5min.
  • Face Recognition Model for Mask Identification with Deep Learning –> 8 lectures • 35min.
  • Predictions with Face Recognition model for Face Mask –> 8 lectures • 43min.
  • PyQt Basics –> 17 lectures • 1hr 9min.
  • Desktop App with PyQt –> 8 lectures • 39min.
  • BONUS –> 1 lecture • 1min.

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Requirements

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

Project that you will be Developing:

Prerequisite of Project: OpenCV

  1. Image Processing with OpenCV

Section -0 : Setting Up Project

  1. Install Python
  2. Install Dependencies

Section -1 : Data Preprocessing

  1. Gather Images
  2. Extract Faces only from Images
  3. Labeling (Target output) Images
  4. Data Preprocessing
    1. RGB mean subtraction image

Section – 2: Develop Deep Learning Model

  1. Training Face Recognition with OWN Deep Learning Model.
    1. Convolutional Neural Network
  2. 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.