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, CPU-intensive 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.

Who this book is for

This cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who want to be introduced to the concepts of computer vision programming. It can be used as a companion book for university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.

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 object-oriented 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.

To get the most out of this book

This cookbook is based on the C++ API of the OpenCV library. It is therefore assumed that you have some experience with the C++ language. In order to run the examples presented in the recipes and experiment with them, you need a good C++ development environment. Microsoft Visual Studio and Qt are two popular choices.

Download the example code files

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Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789340723_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The cv::Mat variable's image refers to the input image, while result refers to the binary output image."

A block of code is set as follows:

// compute distance from target color 
if (getDistanceToTargetColor(*it)<=maxDist) { 
        *itout= 255; 

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classIdPoint;  
double confidence;  // Get the value and location of the maximum score

Any command-line input or output is written as follows:

cd llvm_root

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "You then click on Build Solution in Visual Studio."

Warnings or important notes appear like this.

Tips and tricks appear like this.

Sections

In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).

To give clear instructions on how to complete a recipe, use these sections as follows:

Getting ready

This section tells you what to expect in the recipe and describes how to set up any software or any preliminary settings required for the recipe.

How to do it...

This section contains the steps required to follow the recipe.

How it works...

This section usually consists of a detailed explanation of what happened in the previous section.

There's more...

This section consists of additional information about the recipe in order to make you more knowledgeable about the recipe.

See also

This section provides helpful links to other useful information for the recipe.

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