OpenCV is one of the most popular libraries used to develop Computer Vision applications. It enables us to run many different Computer Vision algorithms in real time. It has been around for many years, and it has become the standard library in this field. One of the main advantages of OpenCV is that it is highly optimized and available on almost all the platforms.
This book starts off by giving a brief introduction of various fields in Computer Vision and the associated OpenCV functionalities in C++. Each chapter contains real-world examples and code samples to demonstrate the use cases. This helps you to easily grasp the topics and understand how they can be applied in real life. To sum it up, this is a practical guide on how to use OpenCV in C++ and build various applications using this library.
Chapter 1, Getting Started with OpenCV, covers installation steps on various operating systems and provides an introduction to the human visual system as well as various topics in Computer Vision.
Chapter 2, An Introduction to the Basics of OpenCV, discusses how to read/write images and videos in OpenCV, and also explains how to build a project using CMake.
Chapter 3, Learning the Graphical User Interface and Basic Filtering, covers how to build a graphical user interface and mouse event detector to build interactive applications.
Chapter 4, Delving into Histograms and Filters, explores histograms and filters and also shows how we can cartoonize an image.
Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection, describes various image preprocessing techniques, such as noise removal, thresholding, and contour analysis.
Chapter 6, Learning Object Classification, deals with object recognition and machine learning, and how to use Support Vector Machines to build an object classification system.
Chapter 7, Detecting Face Parts and Overlaying Masks, discusses face detection and Haar Cascades, and then explains how these methods can be used to detect various parts of the human face.
Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations, explores background subtraction, video surveillance, and morphological image processing and describes how they are connected to each other.
Chapter 9, Learning Object Tracking, covers how to track objects in a live video using different techniques, such as color-based and feature-based tracking.
Chapter 10, Developing Segmentation Algorithms for Text Recognition, covers optical character recognition, text segmentation, and provides an introduction to the Tesseract OCR engine.
Chapter 11, Text Recognition with Tesseract, delves deeper into the Tesseract OCR Engine to explain how it can be used for text detection, extraction, and recognition.
The examples are built using the following technologies:
OpenCV 3.0 or newer
CMake 3.3.x or newer
Tesseract
Leptonica (dependency of Tesseract)
QT (optional)
OpenGL (optional)
Detailed installation instructions are provided in the relevant chapters.
This book is for developers who are new to OpenCV and want to develop Computer Vision applications with OpenCV in C++. A basic knowledge of C++ would be helpful to understand this book. This book is also useful for people who want to get started with Computer Vision and understand the underlying concepts. They should be aware of basic mathematical concepts, such as vectors, matrices, matrix multiplication, and so on, to make the most out of this book. During the course of this book, you will learn how to build various Computer Vision applications from scratch using OpenCV.
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: "For a basic project based on an executable build from one source code file, a two line CMakeLists.txt file is all that is needed ."
A block of code is set as follows:
#include "opencv2/opencv.hpp"
using namespace cv;
int main(int, char** argv)
{
FileStorage fs2("test.yml", FileStorage::READ);
Mat r;
fs2["Result"] >> r;
std::cout << r << std::endl;
fs2.release();
return 0;
}When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
@Path("departments")
@Produces(MediaType.APPLICATION_JSON)
public class DepartmentResource{
//Class implementation goes here...
}Any command-line input or output is written as follows:
C:\> setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc11
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: "To show the control panel we can push the last tool bar button, right click in any part of QT Window and select Display properties window."
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Instructions for running examples are available in the README.md file present in the root folder of each project.
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