OpenCV 4 Computer Vision Application Programming Cookbook Fourth Edition

Build complex computer vision applications with OpenCV and C++

David Millan Escriva , Robert Laganiere 지음

Packt(GCO Science) 2019년 05월 03일 출간

작품소개

책소개

▶Book Description
OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs.

This book begins by setting up OpenCV, and explains how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection.

By the end of the book, you'll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects

▶What You Will Learn
- Install and create a program using the OpenCV library
- Segment images into homogenous regions and extract meaningful objects
- Apply image filters to enhance image content
- Exploit image geometry to relay different views of a pictured scene
- Calibrate the camera from different image observations
- Detect people and objects in images using machine learning techniques
- Reconstruct a 3D scene from images
- Explore face detection using deep learning

▶Key Features
- Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms
- Develop effective, robust, and fail-safe vision for your applications
- Build computer vision algorithms with machine learning capabilities

▶Who This Book Is For
If you're a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You'll also find this book useful if you're a C++ programmer looking to extend your computer vision skillset by learning OpenCV.

목차

▶TABLE of CONTENTS
1. Playing with Images
2. Manipulating the Pixels
3. Processing Color Images with Classes
4. Counting the Pixels with Histograms
5. Transforming Images with Morphological Operations
6. Filtering the Images
7. Extracting Lines, Contours, and Components
8. Detecting Interest Points
9. Describing and Matching Interest Points
10. Estimating Projective Relations in Images
11. Reconstructing 3D Scenes
12. Processing Video Sequences
13. Tracking Visual Motion
14. Learning from Examples
15. OpenCV Advanced Features

책 속으로

▶What this book covers
- Chapter 1, Playing with Images, introduces the OpenCV library and shows you how to build simple applications that can read and display images. It also introduces basic OpenCV data structures.

- Chapter 2, Manipulating the Pixels, explains how an image can be read. It describes different methods for scanning an image in order to perform an operation on each of its pixels.

- Chapter 3, Processing Color Images with Classes, consists of recipes presenting various objectoriented design patterns that can help you to build better computer vision applications. It also discusses the concept of colors in images.

- Chapter 4, Counting the Pixels with Histograms, shows you how to compute image histograms and how they can be used to modify an image. Different applications based on histograms are presented that achieve image segmentation, object detection, and image retrieval.

- Chapter 5, Transforming Images with Morphological Operations, explores the concept of mathematical morphology. It presents different operators and how they can be used to detect edges, corners, and segments in images.

- Chapter 6, Filtering the Images, teaches you the principles of frequency analysis and image filtering. It shows how low-pass and high-pass filters can be applied to images, and presents the concept of derivative operators.

- Chapter 7, Extracting Lines, Contours, and Components, focuses on the detection of geometric image features. It explains how to extract contours, lines, and connected components in an image.

- Chapter 8, Detecting Interest Points, describes various feature point detectors in images.

- Chapter 9, Describing and Matching Interest Points, explains how descriptors of interest points can be computed and used to match points between images.

- Chapter 10, Estimating Projective Relations in Images, explores the projective relations that exist between two images in the same scene. It also describes how to detect specific targets in an image.

- Chapter 11, Reconstructing 3D Scenes, allows you to reconstruct the 3D elements of a scene from multiple images and recover the camera pose. It also includes a description of the camera calibration process.

- Chapter 12, Processing Video Sequences, provides a framework to read and write a video sequence and to process its frames. It also shows you how it is possible to extract foreground objects moving in front of a camera.

- Chapter 13, Tracking Visual Motion, addresses the visual tracking problem. It also shows you how to compute apparent motion in videos, and explains how to track moving objects in an image sequence.

- Chapter 14, Learning from Examples, introduces basic concepts in machine learning. It shows how object classifiers can be built from image samples.

- Chapter 15, OpenCV Advanced Features, covers the most advanced and newest features of OpenCV. This chapter introduces the reader to state-of-the-art deep learning models in artificial intelligence and machine learning. Deep learning is applied to object detection, autonomous cars, and facial recognition. This chapter will introduce you to OpenCV.js, a new binding that ports web technology directly from OpenCV.

출판사 서평

▶ Preface
Augmented reality, driving assistance, video monitoring; more and more applications are now using computer vision and image analysis technologies, and yet we are still in the infancy of the development of new computerized systems capable of understanding our world through the sense of vision. And with the advent of powerful and affordable computing devices and visual sensors, it has never been easier to create sophisticated imaging applications.

A multitude of software tools and libraries manipulating images and videos are available, but for anyone who wishes to develop smart vision-based applications, the OpenCV library is the tool to use. OpenCV is an open source library containing more than 500 optimized algorithms for image and video analysis. Since its introduction in 1999, it has been largely adopted as the primary development tool by the community of researchers and developers in computer vision.

OpenCV was originally developed at Intel by a team led by Gary Bradski as an initiative to advance research in vision and promote the development of rich vision-based, CPUintensive applications. After a series of beta releases, version 1.0 was launched in 2006. A second major release occurred in 2009 with the launch of OpenCV 2, which proposed important changes, especially the new C++ interface, which we use in this book. In 2012, OpenCV reshaped itself as a non-profit foundation (https:/ / opencv. org/ ) relying on crowdfunding for its future development.

OpenCV 3 was introduced in 2013; changes were made mainly to improve the usability of the library. Its structure has been revised to remove the unnecessary dependencies, large modules have been split into smaller ones, and the API has been refined. This book is the fourth edition of OpenCV Computer Vision Application Programming Cookbook, and the first one that covers OpenCV 4. All the programming recipes of the previous editions have been reviewed and updated. We have also added new content and new chapters to provide readers with even better coverage of the essential functionalities of the library.

This book covers many of the library's features and explains how to use them to accomplish specific tasks. Our objective is not to provide detailed coverage of every option offered by the OpenCV functions and classes, but rather to give you the elements you need to build your applications from the ground up. We also explore fundamental concepts in image analysis, and describe some of the important algorithms in computer vision.

This book is an opportunity for you to get introduced to the world of image and video analysis―but this is just the beginning. The good news is that OpenCV continues to evolve and expand. Just consult the OpenCV online documentation at https://opencv.org/ to stay updated about what the library can do for you. You can also visit the author’s website at http://www.laganiere.name/ for updated information about this cookbook.

작가정보

저자(글) David Millan Escriva

David Mill？n Escriv？ was 8 years old when he wrote his first program on an 8086 PC in Basic, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT with honors, through the Universitat Polit？cnica de Valencia, in human-computer interaction supported by computer vision with OpenCV (v0.96). He has worked with Blender, an open source, 3D software project, and on its first commercial movie, Plumiferos, as a computer graphics software developer. David has more than 10 years' experience in IT, with experience in computer vision, computer graphics, pattern recognition, and machine learning, working on different projects, and at different start-ups, and companies. He currently works as a researcher in computer vision.

저자(글) Robert Laganiere

Robert Laganiere is a professor at the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the coauthor of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. He cofounded Visual Cortek, a video analytics start-up, which was later acquired by iWatchLife. He is also a consultant in computer vision and has assumed the role of chief scientist in a number of start-ups companies, including Cognivue Corp, iWatchLife, and Tempo Analytics. Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987), and M.Sc. and Ph.D. degrees from INRS-Telecommunications, Montreal (1996).