OpenCV By Example
图书信息
| 作者 | Prateek Joshi |
| 出版社 | Packt Publishing |
| ISBN | 9781785287077 |
| 出版时间 | 2016-01-22 |
| 字数 | 88.8万 |
| 分类 | 进口书,外文原版书,电脑,网络 |
读书简介
Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3About This BookGet to grips with the basics of Computer Vision and image processingThis is a step-by-step guide to developing several real-world Computer Vision projects using OpenCV 3This book takes a special focus on working with Tesseract OCR, a free, open-source library to recognize text in imagesWho This Book Is ForIf you are a software developer with a basic understanding of Computer Vision and image processing and want to develop interesting Computer Vision applications with Open CV, this is the book for you. Knowledge of C++ is required.What You Will LearnInstall OpenCV 3 on your operating systemCreate the required CMake *s to compile the C++ application and manage its dependenciesGet to grips with the Computer Vision workflows and understand the basic image matrix format and filtersUnderstand the segmentation and feature extraction techniquesRemove backgrounds from a static scene to identify moving objects for video surveillanceTrack different objects in a live video using various techniquesUse the new OpenCV functions for text detection and recognition with TesseractIn DetailOpen CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation.Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects.Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch.By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.Style and approachThis book is a practical guide with lots of tips, and is closely focused on developing Computer vision applications with OpenCV. Beginning with the fundamentals, the complexity increases with each chapter. Sample applications are developed throughout the book that you can execute and use in your own projects.
目录
OpenCV By Example
Table of Contents
OpenCV By Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Getting Started with OpenCV
Understanding the human visual system
How do humans understand image content?
Why is it difficult for machines to understand image content?
What can you do with OpenCV?
In-built data structures and input/output
Image processing operations
Building GUI
Video analysis
3D reconstruction
Feature extraction
Object detection
Machine learning
Computational photography
Shape analysis
Optical flow algorithms
Face and object recognition
Surface matching
Text detection and recognition
Installing OpenCV
Windows
Mac OS X
Linux
Summary
2. An Introduction to the Basics of OpenCV
Basic CMake configuration files
Creating a library
Managing dependencies
Making the script more complex
Images and matrices
Reading/writing images
Reading videos and cameras
Other basic object types
The vec object type
The Scalar object type
The Point object type
The Size object type
The Rect object type
RotatedRect object type
Basic matrix operations
Basic data persistence and storage
Writing to a file storage
Summary
3. Learning the Graphical User Interface and Basic Filtering
Introducing the OpenCV user interface
A basic graphical user interface with OpenCV
The graphical user interface with QT
Adding slider and mouse events to our interfaces
Adding buttons to a user interface
OpenGL support
Summary
4. Delving into Histograms and Filters
Generating a CMake script file
Creating the Graphical User Interface
Drawing a histogram
Image color equalization
Lomography effect
The cartoonize effect
Summary
5. Automated Optical Inspection, Object Segmentation, and Detection
Isolating objects in a scene
Creating an application for AOI
Preprocessing the input image
Noise removal
Removing the background using the light pattern for segmentation
The thresholding operation
Segmenting our input image
The connected component algorithm
The findContours algorithm
Summary
6. Learning Object Classification
Introducing machine learning concepts
Computer Vision and the machine learning workflow
Automatic object inspection classification example
Feature extraction
Training an SVM model
Input image prediction
Summary
7. Detecting Face Parts and Overlaying Masks
Understanding Haar cascades
What are integral images?
Overlaying a facemask in a live video
What happened in the code?
Get your sunglasses on
Looking inside the code
Tracking your nose, mouth, and ears
Summary
8. Video Surveillance, Background Modeling, and Morphological Operations
Understanding background subtraction
Naive background subtraction
Does it work well?
Frame differencing
How well does it work?
The Mixture of Gaussians approach
What happened in the code?
Morphological image processing
What's the underlying principle?
Slimming the shapes
Thickening the shapes
Other morphological operators
Morphological opening
Morphological closing
Drawing the boundary
White Top-Hat transform
Black Top-Hat transform
Summary
9. Learning Object Tracking
Tracking objects of a specific color
Building an interactive object tracker
Detecting points using the Harris corner detector
Shi-Tomasi Corner Detector
Feature-based tracking
The Lucas-Kanade method
The Farneback algorithm
Summary
10. Developing Segmentation Algorithms for Text Recognition
Introducing optical character recognition
The preprocessing step
Thresholding the image
Text segmentation
Creating connected areas
Identifying paragraph blocks
Text extraction and skew adjustment
Installing Tesseract OCR on your operating system
Installing Tesseract on Windows
Setting up Tesseract in Visual Studio
Setting the import and library paths
Configuring the linker
Adding the libraries to the windows path
Installing Tesseract on Mac
Using Tesseract OCR library
Creating a OCR function
Sending the output to a file
Summary
11. Text Recognition with Tesseract
How the text API works
The scene detection problem
Extremal regions
Extremal region filtering
Using the text API
Text detection
Text extraction
Text recognition
Summary
Index
- 经济数学-微积分习题解答（安徽财经大学大学数学教学研究中心）
- 中国资本市场:重塑生态链（吴晓求 等）
- Desperate Sons（Standiford, Les）
- Daughters of the Puritans: A Group of Brief Biographies（Seth Curtis Beach）
- 内部审计工作指南:穿透实务核心（郭长水,纪新伟 主编）
- 女性排毒与食补（元秀 编著）
- 简单易学的基金投资（杨天南,孙振曦,贾泽亮 等）
- 漫画素描技法5：分镜头篇（CG动漫社）