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Home > Data > Computer Vision > OpenCV Computer Vision Application Programming Cookbook Second Edition
OpenCV Computer Vision Application Programming Cookbook Second Edition
OpenCV Computer Vision Application Programming Cookbook Second Edition

OpenCV Computer Vision Application Programming Cookbook Second Edition: Over 50 recipes to help you build computer vision applications in C++ using the OpenCV library

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OpenCV Computer Vision Application Programming Cookbook Second Edition

Chapter 2. Manipulating Pixels

In this chapter, we will cover the following recipes:

  • Accessing pixel values
  • Scanning an image with pointers
  • Scanning an image with iterators
  • Writing efficient image-scanning loops
  • Scanning an image with neighbor access
  • Performing simple image arithmetic
  • Remapping an image

Introduction

In order to build computer vision applications, you need to be able to access the image content and eventually modify or create images. This chapter will teach you how to manipulate the picture elements (also known as pixels). You will learn how to scan an image and process each of its pixels. You will also learn how to do this efficiently, since even images of modest dimensions can contain hundreds of thousands of pixels.

Fundamentally, an image is a matrix of numerical values. This is why, as we learned in Chapter 1, Playing with Images, OpenCV 2 manipulates them using the cv::Mat data structure. Each element of the matrix represents one pixel. For a gray-level image (a black-and-white image), pixels are unsigned 8-bit values where 0 corresponds to black and 255 corresponds to white. In the case of color images, three primary color values are required in order to reproduce the different visible colors. This is a consequence of the fact that our human visual system is trichromatic...

Accessing pixel values

In order to access each individual element of a matrix, you just need to specify its row and column numbers. The corresponding element, which can be a single numerical value or a vector of values in the case of a multi-channel image, will be returned.

Getting ready

To illustrate the direct access to pixel values, we will create a simple function that adds salt-and-pepper noise to an image. As the name suggests, salt-and-pepper noise is a particular type of noise in which some randomly selected pixels are replaced by a white or a black pixel. This type of noise can occur in faulty communications when the value of some pixels is lost during the transmission. In our case, we will simply randomly select a few pixels and assign them a white color.

How to do it...

We create a function that receives an input image. This is the image that will be modified by our function. The second parameter is the number of pixels on which we want to overwrite white values:

void salt(cv::Mat...

Scanning an image with pointers

In most image-processing tasks, you need to scan all pixels of the image in order to perform a computation. Considering the large number of pixels that will need to be visited, it is essential that you perform this task in an efficient way. This recipe, and the next one, will show you different ways of implementing efficient scanning loops. This recipe uses the pointer arithmetic.

Getting ready

We will illustrate the image-scanning process by accomplishing a simple task: reducing the number of colors in an image.

Color images are composed of 3-channel pixels. Each of these channels corresponds to the intensity value of one of the three primary colors, red, green, and blue. Since each of these values is an 8-bit unsigned character, the total number of colors is 256x256x256, which is more than 16 million colors. Consequently, to reduce the complexity of an analysis, it is sometimes useful to reduce the number of colors in an image. One way to achieve this goal...

Scanning an image with iterators

In object-oriented programming, looping over a data collection is usually done using iterators. Iterators are specialized classes that are built to go over each element of a collection, hiding how the iteration over each element is specifically done for a given collection. This application of the information-hiding principle makes scanning a collection easier and safer. In addition, it makes it similar in form no matter what type of collection is used. The Standard Template Library (STL) has an iterator class associated with each of its collection classes. OpenCV then offers a cv::Mat iterator class that is compatible with the standard iterators found in the C++ STL.

Getting ready

In this recipe, we again use the color reduction example described in the previous recipe.

How to do it...

An iterator object for a cv::Mat instance can be obtained by first creating a cv::MatIterator_ object. As is the case with cv::Mat_, the underscore indicates that this is a template...

Writing efficient image-scanning loops

In the previous recipes of this chapter, we presented different ways of scanning an image in order to process its pixels. In this recipe, we will compare the efficiency of these different approaches.

When you write an image-processing function, efficiency is often a concern. When you design your function, you will frequently need to check the computational efficiency of your code in order to detect any bottleneck in your processing that might slow down your program.

However, it is important to note that unless necessary, optimization should not be done at the price of reducing the program clarity. Simple code is indeed always easier to debug and maintain. Only code portions that are critical to a program's efficiency should be heavily optimized.

How to do it...

In order to measure the execution time of a function or a portion of code, there exists a very convenient OpenCV function called cv::getTickCount(). This function gives you the number of clock...

Introduction

In order to build computer vision applications, you need to be able to access the image content and eventually modify or create images. This chapter will teach you how to manipulate the picture elements (also known as pixels). You will learn how to scan an image and process each of its pixels. You will also learn how to do this efficiently, since even images of modest dimensions can contain hundreds of thousands of pixels.

Fundamentally, an image is a matrix of numerical values. This is why, as we learned in Chapter 1, Playing with Images, OpenCV 2 manipulates them using the cv::Mat data structure. Each element of the matrix represents one pixel. For a gray-level image (a black-and-white image), pixels are unsigned 8-bit values where 0 corresponds to black and 255 corresponds to white. In the case of color images, three primary color values are required in order to reproduce the different visible colors. This is a consequence of the fact that our human visual system is trichromatic...

Accessing pixel values

In order to access each individual element of a matrix, you just need to specify its row and column numbers. The corresponding element, which can be a single numerical value or a vector of values in the case of a multi-channel image, will be returned.

Getting ready

To illustrate the direct access to pixel values, we will create a simple function that adds salt-and-pepper noise to an image. As the name suggests, salt-and-pepper noise is a particular type of noise in which some randomly selected pixels are replaced by a white or a black pixel. This type of noise can occur in faulty communications when the value of some pixels is lost during the transmission. In our case, we will simply randomly select a few pixels and assign them a white color.

How to do it...

We create a function that receives an input image. This is the image that will be modified by our function. The second parameter is the number of pixels on which we want to overwrite white values:

void salt(cv::Mat...

Scanning an image with pointers

In most image-processing tasks, you need to scan all pixels of the image in order to perform a computation. Considering the large number of pixels that will need to be visited, it is essential that you perform this task in an efficient way. This recipe, and the next one, will show you different ways of implementing efficient scanning loops. This recipe uses the pointer arithmetic.

Getting ready

We will illustrate the image-scanning process by accomplishing a simple task: reducing the number of colors in an image.

Color images are composed of 3-channel pixels. Each of these channels corresponds to the intensity value of one of the three primary colors, red, green, and blue. Since each of these values is an 8-bit unsigned character, the total number of colors is 256x256x256, which is more than 16 million colors. Consequently, to reduce the complexity of an analysis, it is sometimes useful to reduce the number of colors in an image. One way to achieve this goal...

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Description

OpenCV 3 Computer Vision Application Programming 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 wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.

What you will learn

  • Install and create a program using the OpenCV library
  • Process an image by manipulating its pixels
  • Analyze an image using histograms
  • Segment images into homogenous regions and extract meaningful objects
  • Apply image filters to enhance image content
  • Exploit image geometry in order to relate different views of a pictured scene
  • Calibrate the camera from different image observations
  • Detect faces and people in images using machine learning techniques

Product Details

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Publication date : Aug 26, 2014
Length: 374 pages
Edition : 1st
Language : English
ISBN-13 : 9781782161493
Category :
Data
Languages :
Python
Concepts :
Computer Vision
Tools :
OpenCV

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Publication date : Aug 26, 2014
Length: 374 pages
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ISBN-13 : 9781782161493
Category :
Data
Languages :
Python
Concepts :
Computer Vision
Tools :
OpenCV

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Table of Contents

12 Chapters
1. Playing with Images Chevron down icon Chevron up icon
1. Playing with Images
Introduction
Installing the OpenCV library
Loading, displaying, and saving images
Exploring the cv::Mat data structure
Defining regions of interest
2. Manipulating Pixels Chevron down icon Chevron up icon
2. Manipulating Pixels
Introduction
Accessing pixel values
Scanning an image with pointers
Scanning an image with iterators
Writing efficient image-scanning loops
Scanning an image with neighbor access
Performing simple image arithmetic
Remapping an image
3. Processing Color Images with Classes Chevron down icon Chevron up icon
3. Processing Color Images with Classes
Introduction
Using the Strategy pattern in an algorithm design
Using a Controller design pattern to communicate with processing modules
Converting color representations
Representing colors with hue, saturation, and brightness
4. Counting the Pixels with Histograms Chevron down icon Chevron up icon
4. Counting the Pixels with Histograms
Introduction
Computing the image histogram
Applying look-up tables to modify the image appearance
Equalizing the image histogram
Backprojecting a histogram to detect specific image content
Using the mean shift algorithm to find an object
Retrieving similar images using the histogram comparison
Counting pixels with integral images
5. Transforming Images with Morphological Operations Chevron down icon Chevron up icon
5. Transforming Images with Morphological Operations
Introduction
Eroding and dilating images using morphological filters
Opening and closing images using morphological filters
Detecting edges and corners using morphological filters
Segmenting images using watersheds
Extracting distinctive regions using MSER
Extracting foreground objects with the GrabCut algorithm
6. Filtering the Images Chevron down icon Chevron up icon
6. Filtering the Images
Introduction
Filtering images using low-pass filters
Filtering images using a median filter
Applying directional filters to detect edges
Computing the Laplacian of an image
7. Extracting Lines, Contours, and Components Chevron down icon Chevron up icon
7. Extracting Lines, Contours, and Components
Introduction
Detecting image contours with the Canny operator
Detecting lines in images with the Hough transform
Fitting a line to a set of points
Extracting the components' contours
Computing components' shape descriptors
8. Detecting Interest Points Chevron down icon Chevron up icon
8. Detecting Interest Points
Introduction
Detecting corners in an image
Detecting features quickly
Detecting scale-invariant features
Detecting FAST features at multiple scales
9. Describing and Matching Interest Points Chevron down icon Chevron up icon
9. Describing and Matching Interest Points
Introduction
Matching local templates
Describing local intensity patterns
Describing keypoints with binary features
10. Estimating Projective Relations in Images Chevron down icon Chevron up icon
10. Estimating Projective Relations in Images
Introduction
Calibrating a camera
Computing the fundamental matrix of an image pair
Matching images using a random sample consensus
Computing a homography between two images
11. Processing Video Sequences Chevron down icon Chevron up icon
11. Processing Video Sequences
Introduction
Reading video sequences
Processing the video frames
Writing video sequences
Tracking feature points in a video
Extracting the foreground objects in a video
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 3.7
(3 Ratings)
5 star 33.3%
4 star 33.3%
3 star 0%
2 star 33.3%
1 star 0%
Jan Pedersen Apr 02, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Having read the negative reviews of this book I was impressed at it when I got it (Kindle edition). Nicely structured into what to do and how it works the book carries you through a lot of case studies in computer vision with openCV. I plan to use the book as an inspiration for a hobby robot project and need something to get me started fast and easy. I think the book does the job. Mingling around with the code, reading the explanations and some of the research papers should produce the wanted results I hope.The only critique I have is that the font of the code examples should be smaller in the Kindle edition.
Amazon Verified review Amazon
Maurizio Jun 18, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Molto bella ed esplicativa la parte iniziale dedicata all'installazione. Ho apprezzato molto il paragrafo su QT.Gli altri capitoli trattano gli aspetti basilari della libreria, spiegandoli in maniera chiara senza troppe dimostrazioni matematiche, con molto codice ben commentato. L'unico appunto che posso fare riguarda la mancanza di codice QT, mi aspettavo che, dopo aver inserito la parte dell'installazione, aggiungesse anche qualche aiuto per adattare il codice C++ alle librerie QT.In ogni caso è un ottimo libro per cominciare ad utilizzare la libreria OpenCV con C++.
Amazon Verified review Amazon
Cardale Goddard Sep 18, 2014
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
The first thing I disliked about this book was the description every where. For some reason it was listed as an OpenCV 3 book. It isn't it only talks about OpenCV 2.The second thing I dislike about this book is the fact it doesn't have color, or even half decent images. It is kind of important to have good images in a computer vision book.The third thing I disliked about this book was the way everything was explained. It was all not in context. This very well could of just been a big listing of code and a few sentences. Hardly anything is in context at least in terms of other relative functions. The writing was very dull and lacked any sort of humor or interesting nuggets of information.On the bright side. This book does have a good amount of information on some of the most popular computer vision algorithms and opencv functions.I say don't buy this book though. Better of getting another book. I am in the process of reading another book at the moment and will update my review if it turns out to be better.
Amazon Verified review Amazon

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About the author

Profile icon Robert Laganiere
Robert Laganiere
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 start-up that was later acquired by iwatchlife.com in 2009. He is also a consultant in computer vision and has assumed the role of Chief Scientist in a number of start-up 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). You can visit the author's website at laganiere.name.
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  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.