OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By Robert Laganiere

About This Book

• Written to the latest, gold-standard specification of OpenCV 3
• Master OpenCV, the open source library of the computer vision community
• Master fundamental concepts in computer vision and image processing
• Learn about the important classes and functions of OpenCV with complete working examples applied to real images

Who This Book Is For

OpenCV 3 Computer Vision Application Programming Cookbook Third Edition 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 wish to be introduced to the concepts of computer vision programming. It can also 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.

• Language: English
• Publisher: Packt Publishing
• Release date: Feb 9, 2017
• ISBN: 9781786469113

Book preview

Table of Contents

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

Credits

About the Author

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Getting ready

How to do it…

How it works…

There's more…

See also

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book 

Errata

Piracy

Questions

1. Playing with Images

Introduction

Installing the OpenCV library

Getting ready

How to do it...

How it works...

There's more...

The Visualization Toolkit and the cv::viz module

The OpenCV developer site

See also

Loading, displaying, and saving images

Getting ready

How to do it...

How it works...

There's more...

Clicking on images

Drawing on images

See also

Exploring the cv::Mat data structure

How to do it...

How it works...

There's more...

The input and output arrays

Manipulating small matrices

See also

Defining regions of interest

Getting ready

How to do it...

How it works...

There's more...

Using image masks

See also

2. Manipulating Pixels

Introduction

Accessing pixel values

Getting ready

How to do it...

How it works...

There's more...

The cv::Mat_ template class

See also

Scanning an image with pointers

Getting ready

How to do it...

How it works...

There's more...

Other color reduction formulas

Having input and output arguments

Efficient scanning of continuous images

Low-level pointer arithmetic

See also

Scanning an image with iterators

Getting ready

How to do it...

How it works...

There's more...

See also

Writing efficient image-scanning loops

How to do it...

How it works...

There's more...

See also

Scanning an image with neighbor access

Getting ready

How to do it...

How it works...

There's more...

See also

Performing simple image arithmetic

Getting ready

How to do it...

How it works...

There's more...

Overloaded image operators

Splitting the image channels

Remapping an image

How to do it...

How it works...

See also

3. Processing the Colors of an Image

Introduction

Comparing colors using the Strategy design pattern

How to do it…

How it works…

There's more…

Computing the distance between two color vectors

Using OpenCV functions

The floodFill function

Functor or function object

The OpenCV base class for algorithms

See also

Segmenting an image with the GrabCut algorithm

How to do it…

How it works…

See also

Converting color representations

How to do it…

How it works…

See also

Representing colors with hue, saturation, and brightness

How to do it...

How it works…

There's more…

Using colors for detection - skin tone detection

See also

4. Counting the Pixels with Histograms

Introduction

Computing an image histogram

Getting ready

How to do it...

How it works...

There's more...

Computing histograms of color images

See also

Applying look-up tables to modify the image's appearance

How to do it...

How it works...

There's more...

Stretching a histogram to improve the image contrast

Applying a look-up table to color images

See also

Equalizing the image histogram

How to do it...

How it works...

Backprojecting a histogram to detect specific image content

How to do it...

How it works...

There's more...

Backprojecting color histograms

See also

Using the mean shift algorithm to find an object

How to do it...

How it works...

See also

Retrieving similar images using the histogram comparison

How to do it...

How it works...

See also

Counting pixels with integral images

How to do it...

How it works...

There's more...

Adaptive thresholding

Visual tracking using histograms

See also

5. Transforming Images with Morphological Operations

Introduction

Eroding and dilating images using morphological filters

Getting ready

How to do it...

How it works...

There's more...

See also

Opening and closing images using morphological filters

How to do it...

How it works...

See also

Applying morphological operators on gray-level images

How to do it...

How it works...

See also

Segmenting images using watersheds

How to do it...

How it works...

There's more...

See also

Extracting distinctive regions using MSER

How to do it...

How it works...

See also

6. Filtering the Images

Introduction

Filtering images using low-pass filters

How to do it...

How it works...

See also

Downsampling images with filters

How to do it...

How it works...

There's more...

Interpolating pixel values

See also

Filtering images using a median filter

How to do it...

How it works...

Applying directional filters to detect edges

How to do it...

How it works...

There's more...

Gradient operators

Gaussian derivatives

See also

Computing the Laplacian of an image

How to do it...

How it works...

There's more...

Enhancing the contrast of an image using the Laplacian

Difference of Gaussians

See also

7. Extracting Lines, Contours, and Components

Introduction

Detecting image contours with the Canny operator

How to do it...

How it works...

See also

Detecting lines in images with the Hough transform

Getting ready

How to do it...

How it works...

There's more...

Detecting circles

See also

Fitting a line to a set of points

How to do it...

How it works...

There's more...

Extracting connected components

How to do it...

How it works...

There's more...

Computing components' shape descriptors

How to do it...

How it works...

There's more...

Quadrilateral detection

8. Detecting Interest Points

Introduction

Detecting corners in an image

How to do it...

How it works...

There's more...

Good features to track

See also

Detecting features quickly

How to do it...

How it works...

There's more...

See also

Detecting scale-invariant features

How to do it...

How it works...

There's more...

The SIFT feature-detection algorithm

See also

Detecting FAST features at multiple scales

How to do it...

How it works...

There's more...

The ORB feature-detection algorithm

See also

9. Describing and Matching Interest Points

Introduction

Matching local templates

How to do it...

How it works...

There's more...

Template matching

See also

Describing and matching local intensity patterns

How to do it...

How it works...

There's more...

Cross-checking matches

The ratio test

Distance thresholding

See also

Matching keypoints with binary descriptors

How to do it...

How it works...

There's more...

FREAK

See also

10. Estimating Projective Relations in Images

Introduction

Image formation

Computing the fundamental matrix of an image pair

Getting ready

How to do it...

How it works...

See also

Matching images using random sample consensus

How to do it...

How it works...

There's more...

Refining the fundamental matrix

Refining the matches

Computing a homography between two images

Getting ready

How to do it...

How it works...

There's more...

Generating image panoramas with the cv::Stitcher module

See also

Detecting a planar target in images

How to do it...

How it works...

See also

11. Reconstructing 3D Scenes

Introduction

Digital image formation

Calibrating a camera

How to do it...

How it works...

There's more...

Calibration with known intrinsic parameters

Using a grid of circles for calibration

See also

Recovering camera pose

How to do it...

How it works...

There's more...

cv::Viz, a 3D Visualizer module

See also

Reconstructing a 3D scene from calibrated cameras

How to do it...

How it works...

There's more...

Decomposing a homography

Bundle adjustment

See also

Computing depth from stereo image

Getting ready

How to do it...

How it works...

See also

12. Processing Video Sequences

Introduction

Reading video sequences

How to do it...

How it works...

There's more...

See also

Processing the video frames

How to do it...

How it works...

There's more...

Processing a sequence of images

Using a frame processor class

See also

Writing video sequences

How to do it...

How it works...

There's more...

The codec four-character code

See also

Extracting the foreground objects in a video

How to do it...

How it works...

There's more...

The Mixture of Gaussian method

See also

13. Tracking Visual Motion

Introduction

Tracing feature points in a video

How to do it...

How it works...

See also

Estimating the optical flow

Getting ready

How to do it...

How it works...

See also

Tracking an object in a video

How to do it...

How it works...

See also

14. Learning from Examples

Introduction

Recognizing faces using nearest neighbors of local binary patterns

How to do it...

How it works...

See also

Finding objects and faces with a cascade of Haar features

Getting ready

How to do it...

How it works...

There's more...

Face detection with a Haar cascade

See also

Detecting objects and people with Support Vector Machines and histograms of oriented gradients

Getting ready

How to do it...

How it works...

There's more...

HOG visualization

People detection

Deep learning and Convolutional Neural Networks

See also

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

---

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

Copyright © 2017 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: May 2011

Second edition: August 2014

Third edition: February 2017

Production reference: 1070217

Published by Packt Publishing Ltd.

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ISBN 978-1-78646-971-7

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Credits

About the Author

Robert Laganiere is a professor at the School of Electrical Engineering and Computer Science of the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the co-author of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. Robert authored the OpenCV2 Computer Vision Application Programming Cookbook in 2011 and co-authored Object Oriented Software Development published by McGraw Hill in 2001. He co-founded Visual Cortek in 2006, an Ottawa-based video analytics startup that was later acquired by http://iwatchlife.com/ in 2009. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of startups companies such as Cognivue Corp, iWatchlife, and Tempo Analytics. Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987) and MSc and PhD degrees from INRS-Telecommunications, Montreal (1996). Visit the author’s website at http://www.laganiere.name/.

I wish to thank all my students at the VIVA lab; I learn so much from them.

About the Reviewer

Luca Del Tongo is a computer engineer with a strong passion for algorithms, computer vision, and image processing techniques. He's the coauthor of a free e-book called Data Structures and Algorithms (DSA) with over 100k downloads so far and has published several image processing tutorials on his YouTube channel using Emgu CV. During his master's thesis, he developed an image forensic algorithm published in a scientific paper called Copy Move forgery detection and localization by means of robust clustering with J-Linkage. Currently, Luca works as a software engineer in the ophthalmology field developing corneal topography, processing algorithms, IOL calculation, and computerized chart projector. He loves to play sport and follow MOOC courses in his spare time.

You can contact him through his blog at http://blogs.ugidotnet.org/wetblog.

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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 worlds 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 (Open source Computer Vision) 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 that proposed important changes, especially the new C++ interface, which we use in this book. In 2012, OpenCV reshaped itself as a non-profit foundation (http://opencv.org/) relying on crowdfunding for its future development. OpenCV3 was introduced in 2013; changes were made mainly to improve the usability of 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 third edition of the OpenCV Computer Vision Application Programming Cookbook and the first one that covers OpenCV version 3. All the programming recipes of the previous editions have been reviewed and updated. We also have 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, in this book, fundamental concepts in image analysis and we 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 http://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.

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 the basic OpenCV data structures.

Chapter 2, Manipulating 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 the Colors of an Image, 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 principle 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 detector 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, provide a framework to read and write a video sequence and to process its frames. It shows you also how it is possible to extract the foreground objects moving in front of a camera.

Chapter 13, Tracking Visual Motion, addresses the visual tracking problem. It shows you how to compute the apparent motion in videos. It also 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.

What you need for 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. 

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 wants to be introduced to the concepts of computer vision programming. It can be used as a companion book in university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.

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, we 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 the reader more knowledgeable about the recipe.

See also

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

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: We can include other contexts through the use of the include directive.

A block of code is set as follows:

    // Compute Laplacian using LaplacianZC class

    LaplacianZC laplacian;

    laplacian.setAperture(7); // 7x7 laplacian

    cv::Mat flap= laplacian.computeLaplacian(image);

    laplace= laplacian.getLaplacianImage();

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

    // Compute Laplacian using LaplacianZC class

    LaplacianZC laplacian;

    laplacian.setAperture(7); // 7x7 laplacian

   

cv::Mat flap= laplacian.computeLaplacian(image);

 

    laplace= laplacian.getLaplacianImage();

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: Clicking the Next button moves you to the next screen.

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Chapter 1. Playing with Images

In this chapter, we will get you started with the OpenCV library. You will learn how to perform the following tasks:

Installing the OpenCV library

Loading, displaying, and saving images

Exploring the cv::Mat data structure

Defining regions of interest

Introduction

This chapter will teach you the basic elements of OpenCV and will show you how to accomplish the most fundamental image processing tasks: reading, displaying, and saving images. However, before you start with OpenCV, you need to install the library. This is a simple process that is explained in the first recipe of this chapter.

All your computer vision applications will involve the processing of images. This is why OpenCV offers you a data structure to handle images and matrices. It is a powerful data structure with many useful attributes and methods. It also incorporates an advanced memory management model that greatly facilitates the development of applications. The last two recipes of this chapter will teach you how to use this important data structure of OpenCV.

Installing the OpenCV library

OpenCV is an open source library for developing computer vision applications that can run on multiple platforms, such as Windows, Linux, Mac, Android, and iOS. It can be used in both academic and commercial applications under a BSD license that allows you to freely use, distribute, and adapt it. This recipe will show you how to install the library on your machine.

Getting ready

When you visit the OpenCV official website at http://opencv.org/ , you will find the latest release of the library, the online documentation describing the Application Programming Interface (API), and many other useful resources on OpenCV.

How to do it...

From the OpenCV website, find the latest available downloads and select the one that corresponds to the platform of your choice (Windows, Linux/Mac, or iOS). Once the OpenCV package is downloaded, run the WinZip self-extractor and select the location of your choice. An opencv directory will be created; it is a good idea to rename it in a way that will show which version you are using (for example, in Windows, your final directory could be C:\opencv-3.2). This directory will contain a collection of files and directories that constitute the library. Notably, you will find the sources directory that will contain all the source files (yes, it is open source!).

In order to complete the installation of the library and have it ready for use, you need to take an important step: generate the binary files of the library for the environment of your choice. This is indeed the point where you have to make a decision on the target platform you wish to use to create your OpenCV applications. Which operating system do you prefer to use? Which compiler should you select? Which version? 32-bit or 64-bit? As you can see, there are many possible options, and this is why you have to build the library that fits your needs.

The Integrated Development Environment (IDE) you will use in your project development will also guide you to make these choices. Note that the library package also comes with precompiled binaries that you can directly use if they correspond to your situation (check the build directory adjacent to the sources directory). If one of the precompiled binaries satisfies your requirements, then you are ready to go.

One important remark, however. Since version 3, OpenCV has been split into two major components. The first one is the main OpenCV source repository that includes the mature algorithms. This is the one you have downloaded. A separate contribution repository also exists, and it contains the new computer vision algorithm, recently added by the OpenCV contributors. If your plan is to use only the core functions of OpenCV, you do not need the contrib package. But if you want to play with the latest state-of-the-art algorithms, then there is a good chance that you will need this extra module. As a matter of fact, this cookbook will show you how to use several of these advanced algorithms. You therefore need the contrib modules to follow the recipes of this book. So you have to go to https://github.com/opencv/opencv_contrib and download OpenCV's extra modules (download the ZIP file). You can unzip the extra modules into the directory of your choice; these modules should be found at opencv_contrib-master/modules. For simplicity, you can rename this directory as contrib and copy it directly inside the sources directory of the main package. Note that you can also pick the extra modules of your choice and only save them; however, you will probably find it easier, at this point, to simply keep everything.

You are now ready to proceed with the installation. To build the OpenCV binaries, it is highly suggested that you use the CMake tool, available at http://cmake.org . CMake is another open source software tool designed to control the compilation process of a software system using platform-independent configuration files. It generates the required makefile or solution files needed for compiling a software library in your environment. Therefore, you have to download and install CMake. Also see the There's more... section of this recipe for an additional software package, the Visualization Toolkit (VTK), that you may want to install before compiling the library.

You can run cmake using a command-line interface, but it is easier to use CMake with its graphical interface (cmake-gui). In the latter case, all you need to do is specify the folder containing the OpenCV library source and the one that will contain the binaries. Now click on Configure and select the compiler of your choice:

Once this initial configuration is completed, CMake will provide you with a number of configuration options. You have to decide, for example, whether you want to have the documentation installed or whether you wish to have some additional libraries installed. Unless you know what you are doing, it is probably better to leave the default options as they are. However, since we want to include the extra modules, we have to specify the directory where they can be found:

Once the extra module path is specified, click on Configure again. You are now ready to generate the project files by clicking on the Generate button. These files will allow you to compile the library. This is the last step of the installation process, which will make the library ready to be used in your development environment. For example, if you select MS Visual Studio, then all you need to do is open the top-level solution file that CMake has created for you (the OpenCV.sln file). You then select the INSTALL project (under CMakeTargets) and issue the Build command (use right-click).

To get both a Release and Debug build, you will have to repeat the compilation process twice, one for each configuration. If everything goes well, you will have an install directory (under build) created. This directory will contain all the binary files of the OpenCV library to be linked with your application as well as the dynamic library files that your executables have to call at runtime. Make sure you set your system's PATH environment variable (from Control Panel) such that your operating system would be able to find the .dll files when you run your applications (for example, C:\opencv-3.2\build \install\x64\vc14\bin). You should also define the environment variable, OPENCV_DIR pointing to the INSTALL directory. This way, CMake will be able to find the library when configuring future projects.

In Linux environments, you can use Cmake to generate the required Makefiles; you then complete the installation by executing a sudo make install command. Alternatively, you could also use the packaging tool apt-get which can automatically perform a complete installation of the library. For Mac OS, you should use the Homebrew package manager. Once installed, you just have to type brew install opencv3 --with-contrib in order to have the complete library installed (run brew info opencv3 to view all possible options). 

How it works...

OpenCV is a library that is in constant evolution. With version 3, the library continues to expand offering a lot of new functionalities with enhanced performances. The move to having a full C++ API, which was initiated in version 2, is now almost complete, and more uniform interfaces have been implemented. One of the major changes introduced in this new version is the restructuring of the modules of the library in order to facilitate its distribution. In particular, a separate repository containing the most recent algorithms has been created. This contrib repository also contains non-free algorithms that are subject to specific licenses. The idea is for OpenCV to be able to offer state-of-the-art functionalities that developers and researchers want to share while still being able to offer a very stable and well-maintained core API. The main modules are therefore the ones you get when you download the library at http://opencv.org/. The extra modules must be downloaded directly from the development repository hosted on GitHub ( https://github.com/opencv/ ). Since these extra modules are in constant development, you should expect more frequent changes to the algorithms they contain.

The OpenCV library is divided into several modules. For example, the opencv_core module contains the core functionalities of the library; the opencv_imgproc module includes the main image processing functions; the opencv_highgui module offers the image and video reading and writing functions along with some user interface functions; and so on. To use a particular module, you have to include the corresponding top-level header file. For instance, most applications that use OpenCV start with the following declarations:

    #include

    #include

    #include

As you learn to work with OpenCV, you will discover more and more functionalities available in its numerous modules.

There's more...

The OpenCV website at http://opencv.org/ contains detailed instructions on