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OpenCV By Example

By : Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça

3.8 (5)

OpenCV By Example

3.8 (5)

By: Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça

Overview of this book

Open 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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with OpenCV

1. Getting Started with OpenCV

Understanding the human visual system

How do humans understand image content?

What can you do with OpenCV?

Installing OpenCV

Summary

2. An Introduction to the Basics of OpenCV

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

Basic matrix operations

Basic data persistence and storage

Summary

3. Learning the Graphical User Interface and Basic Filtering

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

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

5. Automated Optical Inspection, Object Segmentation, and Detection

Isolating objects in a scene

Creating an application for AOI

Preprocessing the input image

Segmenting our input image

Summary

6. Learning Object Classification

6. Learning Object Classification

Introducing machine learning concepts

Computer Vision and the machine learning workflow

Automatic object inspection classification example

Feature extraction

Summary

7. Detecting Face Parts and Overlaying Masks

7. Detecting Face Parts and Overlaying Masks

Understanding Haar cascades

What are integral images?

Overlaying a facemask in a live video

Get your sunglasses on

Tracking your nose, mouth, and ears

Summary

8. Video Surveillance, Background Modeling, and Morphological Operations

8. Video Surveillance, Background Modeling, and Morphological Operations

Understanding background subtraction

Naive background subtraction

Frame differencing

The Mixture of Gaussians approach

Morphological image processing

Slimming the shapes

Thickening the shapes

Other morphological operators

Summary

9. Learning Object Tracking

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

Summary

10. Developing Segmentation Algorithms for Text Recognition

10. Developing Segmentation Algorithms for Text Recognition

Introducing optical character recognition

The preprocessing step

Installing Tesseract OCR on your operating system

Using Tesseract OCR library

Summary

11. Text Recognition with Tesseract

11. Text Recognition with Tesseract

How the text API works

Using the text API

Summary

Index

Index

Using the text API

Enough of theory. It's time to see how the text module works in practice. Let's study how to use it to perform text detection, extraction, and identification.

Text detection

Let's start with creating a simple program to perform text segmentation using ERFilters. In this program, we will use the trained classifiers from text API samples. You can download them from the OpenCV repository, but they are also available in the book's companion code.

First, we start with including all the necessary libs and using:

#include  "opencv2/highgui.hpp"
#include  "opencv2/imgproc.hpp"
#include  "opencv2/text.hpp"

#include  <vector>
#include  <iostream>

using namespace std;
using namespace cv;
using namespace cv::text;

Recall from our previous section that the ERFilter works separately in each image channel. So, we must provide a way to separate each desired channel in a different single cv::Mat channel. This is done by the separateChannels...

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