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