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Learn and Build Face Recognition for Face Mask Detection Desktop App using Python, TensorFlow 2, OpenCV, PyQT, Qt

What you will 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

Description

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


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  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.

English
language

Content

Introduction

Introduction

Setting Up Project

Install Python
Create Virtual Environment in Python
Install Libraries like TensorFlow 2, OpenCV etc.

Data Preparation & Preprocessing

Download Resources
Data
Data Preparation Process
Data Preparation: Import Required Python Libraries
Data Preparation: Get all Images Path in Folder
Data Preparation: Labeling
Data Preparation: Get Images Path and Labelling Images in multiple Folders
Step – 3, Face Detection
Face Detection: Read Image
Face Detection: Load Model
Face Detection: Blob from Image
Draw Bounding Box for Detected Face
Step – 4, Crop the Detected Face
Step – 5, Image Processing – Blob from Image (RGB mean subtraction image)
Step – 5, Image Processing – Rotate & Flip Image
Step -5, Remove Negative values and Normalize
Apply Data Preparation process to All images
Step – 6, Save Preprocessed Data in Numpy zip

Face Recognition Model for Mask Identification with Deep Learning

Load Numpy Zip Data into Notebook
One Hot Encoding to target or output variable (y)
Split the Data into Train and Test sets
Convolutional Neural Network Architecture
Develop CNN model in TensorFlow 2
Compile CNN model, Setting Adam Optimizer & Loss Function
Train CNN model
Save Deep Learning Model in TensorFlow

Predictions with Face Recognition model for Face Mask

Load TensorFlow based CNN Model in a Notebook
Defining Labels and Setting Colors
Step – 1, Face Detection
Step -2, Data Preprocess
Step – 3, Get Predictions from CNN Model for Face Mask
Generate text for Prediction info
Get Face Mask Prediction to an Image
Real Time Face Mask Prediction

BONUS

Bonus Lecture