Mastering OpenCV with Practical Computer Vision Projects contains nine chapters, where each chapter is a tutorial for an entire project from start to finish, based on OpenCV's C++ interface including full source code. The author of each chapter was chosen for their well-regarded online contributions to the OpenCV community on that topic, and the book was reviewed by one of the main OpenCV developers. Rather than explaining the basics of OpenCV functions, this is the first book that shows how to apply OpenCV to solve whole problems, including several 3D camera projects (augmented reality, 3D Structure from Motion, Kinect interaction) and several facial analysis projects (such as, skin detection, simple face and eye detection, complex facial feature tracking, 3D head orientation estimation, and face recognition), therefore it makes a great companion to existing OpenCV books.
Chapter 1, Cartoonifier and Skin Changer for Android, contains a complete tutorial and source code for both a desktop application and an Android app that automatically generates a cartoon or painting from a real camera image, with several possible types of cartoons including a skin color changer.
Chapter 2, Marker-based Augmented Reality on iPhone or iPad, contains a complete tutorial on how to build a marker-based augmented reality (AR) application for iPad and iPhone devices with an explanation of each step and source code.
Chapter 3, Marker-less Augmented Reality, contains a complete tutorial on how to develop a marker-less augmented reality desktop application with an explanation of what marker-less AR is and source code.
Chapter 4, Exploring Structure from Motion Using OpenCV, contains an introduction to Structure from Motion (SfM) via an implementation of SfM concepts in OpenCV. The reader will learn how to reconstruct 3D geometry from multiple 2D images and estimate camera positions.
Chapter 5, Number Plate Recognition Using SVM and Neural Networks, contains a complete tutorial and source code to build an automatic number plate recognition application using pattern recognition algorithms using a support vector machine and Artificial Neural Networks. The reader will learn how to train and predict pattern-recognition algorithms to decide if an image is a number plate or not. It will also help classify a set of features into a character.
Chapter 6, Non-rigid Face Tracking, contains a complete tutorial and source code to build a dynamic face tracking system that can model and track the many complex parts of a person's face.
Chapter 7, 3D Head Pose Estimation Using AAM and POSIT, contains all the background required to understand what Active Appearance Models (AAMs) are and how to create them with OpenCV using a set of face frames with different facial expressions. Besides, this chapter explains how to match a given frame through fitting capabilities offered by AAMs. Then, by applying the POSIT algorithm, one can find the 3D head pose.
Chapter 8, Face Recognition using Eigenfaces or Fisherfaces, contains a complete tutorial and source code for a real-time face-recognition application that includes basic face and eye detection to handle the rotation of faces and varying lighting conditions in the images.
Chapter 9, Developing Fluid Wall Using the Microsoft Kinect, covers the complete development of an interactive fluid simulation called the Fluid Wall, which uses the Kinect sensor. The chapter will explain how to use Kinect data with OpenCV's optical flow methods and integrating it into a fluid solver.
You can download this chapter from: http://www.packtpub.com/sites/default/files/downloads/7829OS_Chapter9_Developing_Fluid_Wall_Using_the_Microsoft_Kinect.pdf.
You don't need to have special knowledge in computer vision to read this book, but you should have good C/C++ programming skills and basic experience with OpenCV before reading this book. Readers without experience in OpenCV may wish to read the book Learning OpenCV for an introduction to the OpenCV features, or read OpenCV 2 Cookbook for examples on how to use OpenCV with recommended C/C++ patterns, because Mastering OpenCV with Practical Computer Vision Projects will show you how to solve real problems, assuming you are already familiar with the basics of OpenCV and C/C++ development.
In addition to C/C++ and OpenCV experience, you will also need a computer, and IDE of your choice (such as Visual Studio, XCode, Eclipse, or QtCreator, running on Windows, Mac or Linux). Some chapters have further requirements, in particular:
To develop the Android app, you will need an Android device, Android development tools, and basic Android development experience.
To develop the iOS app, you will need an iPhone, iPad, or iPod Touch device, iOS development tools (including an Apple computer, XCode IDE, and an Apple Developer Certificate), and basic iOS and Objective-C development experience.
Several desktop projects require a webcam connected to your computer. Any common USB webcam should suffice, but a webcam of at least 1 megapixel may be desirable.
CMake is used in some projects, including OpenCV itself, to build across operating systems and compilers. A basic understanding of build systems is required, and knowledge of cross-platform building is recommended.
An understanding of linear algebra is expected, such as basic vector and matrix operations and eigen decomposition.
Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with basic OpenCV knowledge to create practical computer vision projects, as well as for seasoned OpenCV experts who want to add more computer vision topics to their skill set. It is aimed at senior computer science university students, graduates, researchers, and computer vision experts who wish to solve real problems using the OpenCV C++ interface, through practical step-by-step tutorials.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "You should put most of the code of this chapter into the cartoonifyImage() function."
A block of code is set as follows:
int cameraNumber = 0;
if (argc > 1)
cameraNumber = atoi(argv[1]);
// Get access to the camera.
cv::VideoCapture capture;When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
// Get access to the camera.
cv::VideoCapture capture;
camera.open(cameraNumber);
if (!camera.isOpened()) {
std::cerr << "ERROR: Could not access the camera or video!" <<New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "clicking the Next button moves you to the next screen".
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