Learn OpenCV with Python by Examples

Through a series of examples, the book covers a wide range of topics including image and video processing, feature detection, object detection and recognition, machine learning, and deep neural networks. Each chapter includes detailed explanations of the concepts and techniques involved, as well as practical examples and code snippets demonstrating how to implement them in Python. Throughout the book, readers will work through hands-on examples and projects, learning how to build image-processing applications from scratch. 

Whether you are a beginner or an experienced programmer, this book provides a valuable resource for learning computer vision with OpenCV and Python. The clear and concise writing style makes it easy for readers to follow along, and the numerous examples ensure that readers can practice and apply what they have learned. By the end of the book, readers will have a solid understanding of the fundamentals of computer vision and be able to build their own computer vision applications with confidence. This book is an excellent resource for anyone looking to learn computer vision and machine learning using the OpenCV library and Python programming language.

Table of Contents

1.  Introduction

 1.1  About OpenCV

 1.2  Target Audients of This Book

 1.3  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  Image Fundamentals

 3.5  Draw Shapes

 3.6  Draw Texts

 3.7  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  Conversion of 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)

 7.5  Convolutional Neural Network (CNN)

References

About the Author