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Face Mask Recognition Desktop App with Deep Learning & PyQT

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MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 61 lectures (3h 59m) | Size: 958.6 MB

Learn Face Recognition for Face Mask Detection using Python, TensorFlow 2, OpenCV, PyQT, Qt


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

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: Flask API

Setting Up Visual Studio Code

Install all Dependencies of VS Code

Setting Virtual Environment

Freeze Requirements

Build Flask API

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 Flask API and make prediction to live video streaming.

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

Who this course is for
Anyone who want to develop face recognition application


Password/解压密码0daydown

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