What to read after Learn OpenCV 4.5 with Python 3.7 by Examples?

What This Book is About

When you searched for this book, you have already known the importance of the OpenCV/Python in the fields of computer vision, image processing and machine learning. This book begins with step-by-step instructions of installation as well as a simple Hello World, then gets into the OpenCV Basics, Image Processing, Object Detection and finally Machine Learning.

Key Features

Example for every topic, all the source codes are available in Github.

Line by line explanation of the source codes.

Focus mainly on implementation of algorithms, rather than mathematical theories.

Whom This Book Is For

This book is for people with a variety of computer programming levels, from those with very limited knowledge of computer vision to the experienced ones. The readers do not need to have previous experiences of Python/OpenCV. No matter you are a beginner or experienced programmer, as long as you want to learn OpenCV with Python, you will benefit from this book.

Table of Contents

1. Introduction

1.1 What Is OpenCV

1.2 Whom This Book Is For

1.3 How to Get the Source Codes for This Book

1.4 Hardware Requirements and Software Versions

1.5 How This Book Is Organized

2. Installation

2.1 Install on Windows

2.2 Install Python on Ubuntu

2.3 Configure PyCharm and Install OpenCV

3. OpenCV Basics

3.1 Load and Display Images

3.2 Load and Display Videos

3.3 Display Webcam

3.4 Play Youtube Video

3.5 Image Fundamentals

3.6 Draw Shapes

3.7 Draw Texts

3.8 Draw an OpenCV-like Icon

4. User Interaction

4.1 Mouse Operations

4.2 Draw Circles with Mouse

4.3 Draw Polygon with Mouse

4.4 Crop an Image with Mouse

4.5 Input Values with Trackbars

5. Image Processing

5.1 Change Color Spaces

5.2 Resize, Crop and Rotate an Image

5.3 Adjust Contrast and Brightness of an Image

5.4 Adjust Hue, Saturation and Value

5.5 Blend Image

5.6 Bitwise Operation

5.7 Warp Image

5.8 Blur Image

5.9 Histogram

6. Object Detection

6.1 Canny Edge Detection

6.2 Dilation and Erosion

6.3 Shape Detection

6.4 Color Detection

6.5 Text Recognition with Tesseract

6.6 Human Detection

6.7 Face and Eye Detection

6.8 Remove Background

6.9 Blur Background

7. Machine Learning

7.1 K-Means Clustering

7.2 K-Nearest Neighbors

7.3 Support Vector Machine

7.4 Artificial Neural Network (ANN)

About the Author

Index