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Home > Data > Computer Vision > OpenCV By Example
OpenCV By Example
OpenCV By Example

OpenCV By Example: Enhance your understanding of Computer Vision and image processing by developing real-world projects in OpenCV 3

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Profile Icon Millán Escrivá Profile Icon Joshi Profile Icon Vinícius G. Mendonça
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Arrow left icon
Profile Icon Millán Escrivá Profile Icon Joshi Profile Icon Vinícius G. Mendonça
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OpenCV By Example

Chapter 2. An Introduction to the Basics of OpenCV

After covering the installation of OpenCV on different operating systems in Chapter 1, Getting Started with OpenCV, we are going to introduce the basics of OpenCV development in this chapter.

In this chapter, you will learn how to create your project using CMake.

We will also introduce the image basic data structures, matrices, and other structures that are required in our projects.

We will learn how to save our variables and data in files using the XML/YAML persistence OpenCV functions.

In this chapter, we will cover the following topics:

  • Configuring projects with CMake
  • Reading/writing images from/to disk
  • Reading videos and accessing camera devices
  • The main image structures (matrices)
  • Other important and basic structures (vectors, scalars, and so on)
  • An introduction to basic matrix operations
  • File storage operations with the XML/YAML persistence OpenCV API

Basic CMake configuration files

To configure and check all the required dependencies of our project, we are going to use CMake; but it is not mandatory, so we can configure our project in any other tool or IDE such as Makefiles or Visual Studio. However, CMake is the most portable way to configure multiplatform C++ projects.

CMake uses configuration files called CMakeLists.txt, where the compilation and dependency processes are defined. 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. The file looks like this:

cmake_minimum_required (VERSION 2.6)
project (CMakeTest)
add_executable(${PROJECT_NAME} main.cpp)

The first line defines the minimum version of CMake required. This line is mandatory in our CMakeLists.txt file and allows you to use the cmake functionality defined from a given version defined in the second line; it defines the project name. This name is saved in a variable called PROJECT_NAME.

The last line...

Creating a library

CMake allows you to create libraries, which are indeed used by the OpenCV build system. Factorizing the shared code among multiple applications is a common and useful practice in software development. In big applications or when the common code is shared in multiple applications, this practice is very useful.

In this case, we do not create a binary executable; instead, we create a compiled file that includes all the functions, classes, and so on, developed. We can then share this library file with the other applications without sharing our source code.

CMake includes the add_library function for this purpose:

# Create our hello library
add_library(Hello hello.cpp hello.h)

# Create our application that uses our new library
add_executable(executable main.cpp)

# Link our executable with the new library
target_link_libraries( executable Hello )

The lines starting with # add comments and are ignored by CMake.

The add_library(Hello hello.cpp hello.h) command defines our new library...

Managing dependencies

CMake has the ability to search our dependencies and external libraries, giving us the facility to build complex projects depending on external components in our projects and by adding some requirements.

In this book, the most important dependency is, of course, OpenCV, and we will add it to all our projects:

cmake_minimum_required (VERSION 2.6)
cmake_policy(SET CMP0012 NEW)
PROJECT(Chapter2)
# Requires OpenCV
FIND_PACKAGE( OpenCV 3.0.0 REQUIRED )
# Show a message with the opencv version detected
MESSAGE("OpenCV version : ${OpenCV_VERSION}")
include_directories(${OpenCV_INCLUDE_DIRS})
link_directories(${OpenCV_LIB_DIR})
# Create a variable called SRC
SET(SRC main.cpp )
# Create our executable
ADD_EXECUTABLE( ${PROJECT_NAME} ${SRC} )
# Link our library
TARGET_LINK_LIBRARIES( ${PROJECT_NAME} ${OpenCV_LIBS} )

Now, let's understand the working of the script:

cmake_minimum_required (VERSION 2.6)
cmake_policy(SET CMP0012 NEW)
PROJECT(Chapter2)

The first line defines...

Making the script more complex

In this section, we will show you a more complex script that includes subfolders, libraries, and executables, all in only two files and a few lines, as shown in this script.

It's not mandatory to create multiple CMakeLists.txt files because we can specify everything in the main CMakeLists.txt file. It is more common to use different CMakeLists.txt files for each project subfolder, making it more flexible and portable.

This example has a code structure folder that contains one folder for the utils library and the other for the root folder, which contains the main executable:

CMakeLists.txt
main.cpp
utils/
  CMakeLists.txt
  computeTime.cpp
  computeTime.h
  logger.cpp
  logger.h
  plotting.cpp
  plotting.h

Then, we need to define two CMakeLists.txt files: one in the root folder and the other in the utils folder. The CMakeLists.txt root folder file has the following contents:

cmake_minimum_required (VERSION 2.6)
project (Chapter2)

# Opencv Package required...

Images and matrices

The most important structure in a Computer Vision is without any doubt the images. The image in Computer Vision is a representation of the physical world captured with a digital device. This picture is only a sequence of numbers stored in a matrix format, as shown in the following image. Each number is a measurement of the light intensity for the considered wavelength (for example, red, green, or blue in color images) or for a wavelength range (for panchromatic devices). Each point in an image is called a pixel (for a picture element), and each pixel can store one or more values depending on whether it is a gray, black, or white image (called a binary image as well) that stores only one value, such as 0 or 1, a gray-scale-level image that can store only one value, or a color image that can store three values. These values are usually integer numbers between 0 and 255, but you can use the other range. For example, 0 to 1 in a floating point numbers such as HDRI (High...

Basic CMake configuration files

To configure and check all the required dependencies of our project, we are going to use CMake; but it is not mandatory, so we can configure our project in any other tool or IDE such as Makefiles or Visual Studio. However, CMake is the most portable way to configure multiplatform C++ projects.

CMake uses configuration files called CMakeLists.txt, where the compilation and dependency processes are defined. 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. The file looks like this:

cmake_minimum_required (VERSION 2.6)
project (CMakeTest)
add_executable(${PROJECT_NAME} main.cpp)

The first line defines the minimum version of CMake required. This line is mandatory in our CMakeLists.txt file and allows you to use the cmake functionality defined from a given version defined in the second line; it defines the project name. This name is saved in a variable called PROJECT_NAME.

The last line...

Creating a library

CMake allows you to create libraries, which are indeed used by the OpenCV build system. Factorizing the shared code among multiple applications is a common and useful practice in software development. In big applications or when the common code is shared in multiple applications, this practice is very useful.

In this case, we do not create a binary executable; instead, we create a compiled file that includes all the functions, classes, and so on, developed. We can then share this library file with the other applications without sharing our source code.

CMake includes the add_library function for this purpose:

# Create our hello library
add_library(Hello hello.cpp hello.h)

# Create our application that uses our new library
add_executable(executable main.cpp)

# Link our executable with the new library
target_link_libraries( executable Hello )

The lines starting with # add comments and are ignored by CMake.

The add_library(Hello hello.cpp hello.h) command defines our new library...

Managing dependencies

CMake has the ability to search our dependencies and external libraries, giving us the facility to build complex projects depending on external components in our projects and by adding some requirements.

In this book, the most important dependency is, of course, OpenCV, and we will add it to all our projects:

cmake_minimum_required (VERSION 2.6)
cmake_policy(SET CMP0012 NEW)
PROJECT(Chapter2)
# Requires OpenCV
FIND_PACKAGE( OpenCV 3.0.0 REQUIRED )
# Show a message with the opencv version detected
MESSAGE("OpenCV version : ${OpenCV_VERSION}")
include_directories(${OpenCV_INCLUDE_DIRS})
link_directories(${OpenCV_LIB_DIR})
# Create a variable called SRC
SET(SRC main.cpp )
# Create our executable
ADD_EXECUTABLE( ${PROJECT_NAME} ${SRC} )
# Link our library
TARGET_LINK_LIBRARIES( ${PROJECT_NAME} ${OpenCV_LIBS} )

Now, let's understand the working of the script:

cmake_minimum_required (VERSION 2.6)
cmake_policy(SET CMP0012 NEW)
PROJECT(Chapter2)

The first line defines the minimum...

Making the script more complex

In this section, we will show you a more complex script that includes subfolders, libraries, and executables, all in only two files and a few lines, as shown in this script.

It's not mandatory to create multiple CMakeLists.txt files because we can specify everything in the main CMakeLists.txt file. It is more common to use different CMakeLists.txt files for each project subfolder, making it more flexible and portable.

This example has a code structure folder that contains one folder for the utils library and the other for the root folder, which contains the main executable:

CMakeLists.txt
main.cpp
utils/
  CMakeLists.txt
  computeTime.cpp
  computeTime.h
  logger.cpp
  logger.h
  plotting.cpp
  plotting.h

Then, we need to define two CMakeLists.txt files: one in the root folder and the other in the utils folder. The CMakeLists.txt root folder file has the following contents:

cmake_minimum_required (VERSION 2.6)
project (Chapter2)

# Opencv Package required
FIND_PACKAGE...

Images and matrices

The most important structure in a Computer Vision is without any doubt the images. The image in Computer Vision is a representation of the physical world captured with a digital device. This picture is only a sequence of numbers stored in a matrix format, as shown in the following image. Each number is a measurement of the light intensity for the considered wavelength (for example, red, green, or blue in color images) or for a wavelength range (for panchromatic devices). Each point in an image is called a pixel (for a picture element), and each pixel can store one or more values depending on whether it is a gray, black, or white image (called a binary image as well) that stores only one value, such as 0 or 1, a gray-scale-level image that can store only one value, or a color image that can store three values. These values are usually integer numbers between 0 and 255, but you can use the other range. For example, 0 to 1 in a floating point numbers such as HDRI (High...

Reading/writing images

After the introduction of this matrix, we are going to start with the basics of the OpenCV code. Firstly, we need to learn how to read and write images:

#include <iostream>
#include <string>
#include <sstream>
using namespace std;

// OpenCV includes
#include "opencv2/core.hpp"
#include "opencv2/highgui.hpp"
using namespace cv;

int main( int argc, const char** argv )
{
  // Read images
  Mat color= imread("../lena.jpg");
  Mat gray= imread("../lena.jpg", 0);
  
  // Write images
  imwrite("lenaGray.jpg", gray);
  
  // Get same pixel with opencv function
  int myRow=color.cols-1;
  int myCol=color.rows-1;
  Vec3b pixel= color.at<Vec3b>(myRow, myCol);
  cout << "Pixel value (B,G,R): (" << (int)pixel[0] << "," << (int)pixel[1] << "," << (int)pixel[2] << ")" << endl;
  
  // show images
  imshow("Lena BGR", color);
  imshow("Lena Gray", gray);
  // wait for any key press
  waitKey(0);
  return 0;...
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Key benefits

  • Get to grips with the basics of Computer Vision and image processing
  • This is a step-by-step guide to developing several real-world Computer Vision projects using OpenCV 3
  • This book takes a special focus on working with Tesseract OCR, a free, open-source library to recognize text in images

Description

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.

Who is this book for?

If you are a software developer with a basic understanding of Computer Vision and image processing and want to develop interesting Computer Vision applications with Open CV, this is the book for you. Knowledge of C++ is required.

What you will learn

  • Install OpenCV 3 on your operating system
  • Create the required CMake scripts to compile the C++ application and manage its dependencies
  • Get to grips with the Computer Vision workflows and understand the basic image matrix format and filters
  • Understand the segmentation and feature extraction techniques
  • Remove backgrounds from a static scene to identify moving objects for video surveillance
  • Track different objects in a live video using various techniques
  • Use the new OpenCV functions for text detection and recognition with Tesseract
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 22, 2016
Length: 296 pages
Edition : 1st
Language : English
ISBN-13 : 9781785280948
Category :
Data
Languages :
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Concepts :
Computer Vision
Tools :
OpenCV

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Estimated delivery fee Deliver to Denmark

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Product Details

Publication date : Jan 22, 2016
Length: 296 pages
Edition : 1st
Language : English
ISBN-13 : 9781785280948
Category :
Data
Languages :
C++
Concepts :
Computer Vision
Tools :
OpenCV

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Table of Contents

12 Chapters
1. Getting Started with OpenCV Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
11. Text Recognition with Tesseract
How the text API works
Using the text API
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(5 Ratings)
5 star 40%
4 star 40%
3 star 0%
2 star 0%
1 star 20%
Placeholder Apr 27, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a good book for beginners to explore both - OpenCV and Image Processing. I am a 9 years experienced .Net developer but I had no working knowledge of OpenCV - this book helped me a lot.
Amazon Verified review Amazon
Amazon Customer Mar 15, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I had the opportunity to review the book "OpenCV by Example" written by Prateek Joshi, David Millan Escriva and Vinicius Godoy for Packt Publishing. The books helps you to integrate OpenCV 3 into your projects in the beginning by providing step by step instructions but also provides detailed background infor mations on various areas of computer vision. If you need to digg even deeper you also get links to the original scientific research papers. the areas covered range from computer vision basics like filtering or image manipulation to complex areas like object tracking or text recognition. So if you need to add vision to your robotics project, mobile application, media art project, desktop application, ... and always wondered how to do it this is a book you should read.
Amazon Verified review Amazon
Sandy Feb 09, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good book for beginner & excellent code & example.Book print quality is good,But why Images are Black & White, it should be in colors.
Amazon Verified review Amazon
Pourya Hoseini Dec 10, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Useful book to get an initial hands on how OpenCV works, and how are its data types used.
Amazon Verified review Amazon
Keith Sloan Sep 26, 2016
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Do not buy this book . The example install information does not work and to get support you have to buy from packtpub.
Amazon Verified review Amazon

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Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
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May 2023 286 pages
Full star icon 4.2 (34)
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Generative AI with LangChain
Generative AI with LangChain
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Dec 2023 368 pages
Full star icon 4 (34)
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Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
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May 2023 456 pages
Full star icon 4.5 (49)
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About the 3 authors

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Profile icon Millán Escrivá
Millán Escrivá
David Millán Escrivá 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 Politécnica 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.
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Profile icon Joshi
Joshi
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Vijay Joshi is a full stack web developer having more than a decade of experience in working with PHP and JavaScript.
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Profile icon Vinícius G. Mendonça
Vinícius G. Mendonça
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Vinícius G. Mendonça is a professor at PUCPR and a mentor at Apple Developer Academy. He has a master's degree in Computer Vision and Image Processing (PUCPR) and a specialization degree in Game Development (Universidade Positivo). He is also one of the authors of the book Learn OpenCV 4 by Building Projects, also by Packt Publishing. He has been in this field since 1996. His former experience includes designing and programming a multithreaded framework for PBX tests at Siemens, coordination of Aurélio Dictionary software (including its apps for Android, IOS, and Windows phones), and coordination of an augmented reality educational activity for Positivo's Mesa Alfabeto, presented at CEBIT. Currently, he works with server-side Node.js at a company called Tenet Tech.
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Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

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Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela