Mô tả

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.

Bạn sẽ học được gì

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

Yêu cầu

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

Nội dung khoá học

8 sections

Introduction

2 lectures
Introduction
03:03
Facing any issue with course? Here is the solution
06:03

Setting Up Project

3 lectures
Install Python
02:23
Create Virtual Environment in Python
02:13
Install Libraries like TensorFlow 2, OpenCV etc.
04:56

Data Preparation & Preprocessing

19 lectures
Download Resources
00:03
Data
05:41
Downloading Data From Resources
04:16
Data Preparation Process
04:00
Data Preparation: Import Required Python Libraries
03:25
Data Preparation: Get all Images Path in Folder
05:16
Data Preparation: Labeling
01:33
Data Preparation: Get Images Path and Labelling Images in multiple Folders
02:09
Step - 3, Face Detection
01:05
Face Detection: Read Image
02:04
Face Detection: Load Model
01:31
Face Detection: Blob from Image
03:13
Draw Bounding Box for Detected Face
07:04
Step - 4, Crop the Detected Face
03:53
Step - 5, Image Processing - Blob from Image (RGB mean subtraction image)
07:03
Step - 5, Image Processing - Rotate & Flip Image
03:00
Step -5, Remove Negative values and Normalize
03:36
Apply Data Preparation process to All images
07:12
Step - 6, Save Preprocessed Data in Numpy zip
02:49

Face Recognition Model for Mask Identification with Deep Learning

9 lectures
Load Numpy Zip Data into Notebook
03:56
One Hot Encoding to target or output variable (y)
04:27
Split the Data into Train and Test sets
02:22
Convolutional Neural Network Architecture
07:51
Develop CNN model in TensorFlow 2
07:09
Compile CNN model, Setting Adam Optimizer & Loss Function
03:14
Train CNN model
02:09
Model Loss Evaluation
04:21
Save TensorFlow model
01:51

Predictions with Face Recognition model for Face Mask

8 lectures
Load TensorFlow based CNN Model in a Notebook
05:18
Defining Labels and Setting Colors
04:00
Step - 1, Face Detection
10:28
Step -2, Data Preprocess
05:54
Step - 3, Get Predictions from CNN Model for Face Mask
04:53
Generate text for Prediction info
04:01
Get Face Mask Prediction to an Image
04:15
Real Time Face Mask Prediction
04:23

PyQt Basics

17 lectures
What you will Develop
01:14
Install Visual Studio Code
03:44
Setting Up Project
06:24
Install PyQt and Connect VS code to Virtual Environment
01:14
PyQt Background
02:46
Your First PyQt App with QtWidgets
05:26
Qt Template
04:19
QtWidgets
01:19
QWidget
06:32
QLabel
06:19
QLineEdit
02:28
QPushButton
02:00
QComboBox
01:58
Placing & Arranging Widgets
01:59
Placing Widgets using QHBoxLayout and QVBoxLayout
07:44
Signals and Slots
03:53
Backend Operations in PyQt
07:22

Desktop App with PyQt

9 lectures
What you will develop
01:52
Setting up Visual studio code
01:54
Create Main Window
03:00
PyQT: Front End Design of Desktop App
07:28
Video Capture with OpenCV in PyQT
06:14
On Click Button function
08:20
Streaming Video in PyQT
06:15
Connect Face Mask Deep Learning Model to Video Stream in PyQT
04:18
Face Mask Desktop App with PyQt
02:02

BONUS

1 lectures
Bonus Lecture
00:18

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