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OpenCV By Example电子书

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30人正在读 | 0人评论 9.8

作       者:Prateek Joshi

出  版  社:Packt Publishing

出版时间:2016-01-22

字       数:88.8万

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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.
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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

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