Face Mask Recognition: Deep Learning based Desktop App

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Facing any issue with course? Here is the solution

Chapter 2: Setting Up Project

Lecture 1: Install Python

Lecture 2: Create Virtual Environment in Python

Lecture 3: Install Libraries like TensorFlow 2, OpenCV etc.

Chapter 3: Data Preparation & Preprocessing

Lecture 1: Download Resources

Lecture 2: Data

Lecture 3: Downloading Data From Resources

Lecture 4: Data Preparation Process

Lecture 5: Data Preparation: Import Required Python Libraries

Lecture 6: Data Preparation: Get all Images Path in Folder

Lecture 7: Data Preparation: Labeling

Lecture 8: Data Preparation: Get Images Path and Labelling Images in multiple Folders

Lecture 9: Step – 3, Face Detection

Lecture 10: Face Detection: Read Image

Lecture 11: Face Detection: Load Model

Lecture 12: Face Detection: Blob from Image

Lecture 13: Draw Bounding Box for Detected Face

Lecture 14: Step – 4, Crop the Detected Face

Lecture 15: Step – 5, Image Processing – Blob from Image (RGB mean subtraction image)

Lecture 16: Step – 5, Image Processing – Rotate & Flip Image

Lecture 17: Step -5, Remove Negative values and Normalize

Lecture 18: Apply Data Preparation process to All images

Lecture 19: Step – 6, Save Preprocessed Data in Numpy zip

Chapter 4: Face Recognition Model for Mask Identification with Deep Learning

Lecture 1: Load Numpy Zip Data into Notebook

Lecture 2: One Hot Encoding to target or output variable (y)

Lecture 3: Split the Data into Train and Test sets

Lecture 4: Convolutional Neural Network Architecture

Lecture 5: Develop CNN model in TensorFlow 2

Lecture 6: Compile CNN model, Setting Adam Optimizer & Loss Function

Lecture 7: Train CNN model

Lecture 8: Model Loss Evaluation

Lecture 9: Save TensorFlow model

Chapter 5: Predictions with Face Recognition model for Face Mask

Lecture 1: Load TensorFlow based CNN Model in a Notebook

Lecture 2: Defining Labels and Setting Colors

Lecture 3: Step – 1, Face Detection

Lecture 4: Step -2, Data Preprocess

Lecture 5: Step – 3, Get Predictions from CNN Model for Face Mask

Lecture 6: Generate text for Prediction info

Lecture 7: Get Face Mask Prediction to an Image

Lecture 8: Real Time Face Mask Prediction

Chapter 6: PyQt Basics

Lecture 1: What you will Develop

Lecture 2: Install Visual Studio Code

Lecture 3: Setting Up Project

Lecture 4: Install PyQt and Connect VS code to Virtual Environment

Lecture 5: PyQt Background

Lecture 6: Your First PyQt App with QtWidgets

Lecture 7: Qt Template

Lecture 8: QtWidgets

Lecture 9: QWidget

Lecture 10: QLabel

Lecture 11: QLineEdit

Lecture 12: QPushButton

Lecture 13: QComboBox

Lecture 14: Placing & Arranging Widgets

Lecture 15: Placing Widgets using QHBoxLayout and QVBoxLayout

Lecture 16: Signals and Slots

Lecture 17: Backend Operations in PyQt

Chapter 7: Desktop App with PyQt

Lecture 1: What you will develop

Lecture 2: Setting up Visual studio code

Lecture 3: Create Main Window

Lecture 4: PyQT: Front End Design of Desktop App

Lecture 5: Video Capture with OpenCV in PyQT

Lecture 6: On Click Button function

Lecture 7: Streaming Video in PyQT

Lecture 8: Connect Face Mask Deep Learning Model to Video Stream in PyQT

Lecture 9: Face Mask Desktop App with PyQt

Chapter 8: BONUS

Lecture 1: Bonus Lecture