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Home > Data > Computer Vision > OpenCV 3 Computer Vision Application Programming Cookbook
OpenCV 3 Computer Vision Application Programming Cookbook
OpenCV 3 Computer Vision Application Programming Cookbook

OpenCV 3 Computer Vision Application Programming Cookbook: Recipes to make your applications see , Third Edition

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OpenCV 3 Computer Vision Application Programming Cookbook

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 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 (that is, of type unsigned char) 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...

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...

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 code 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 cycles...

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 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 (that is, of type unsigned char) 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...

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:...

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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Page 1 of 9

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Key benefits

  • 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

Description

Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you’ll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you’ll also get acquainted with recent approaches in machine learning and object classification.

Who is this book 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.

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 the image geometry in order to relay different views of a pictured scene
  • Calibrate the camera from different image observations
  • Detect people and objects in images using machine learning techniques
  • Reconstruct a 3D scene from images

Product Details

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Publication date : Feb 09, 2017
Length: 474 pages
Edition : 3rd
Language : English
ISBN-13 : 9781786469113
Vendor :
Intel
Category :
Data
Languages :
C++
Concepts :
Computer Vision
Tools :
OpenCV

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Publication date : Feb 09, 2017
Length: 474 pages
Edition : 3rd
Language : English
ISBN-13 : 9781786469113
Vendor :
Intel
Category :
Data
Languages :
C++
Concepts :
Computer Vision
Tools :
OpenCV

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Table of Contents

14 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 the Colors of an Image Chevron down icon Chevron up icon
3. Processing the Colors of an Image
Introduction
Comparing colors using the Strategy design pattern
Segmenting an image with the GrabCut algorithm
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 an image histogram
Applying look-up tables to modify the image's 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
Applying morphological operators on gray-level images
Segmenting images using watersheds
Extracting distinctive regions using MSER
6. Filtering the Images Chevron down icon Chevron up icon
6. Filtering the Images
Introduction
Filtering images using low-pass filters
Downsampling images with 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 connected components
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 and matching local intensity patterns
Matching keypoints with binary descriptors
10. Estimating Projective Relations in Images Chevron down icon Chevron up icon
10. Estimating Projective Relations in Images
Introduction
Computing the fundamental matrix of an image pair
Matching images using random sample consensus
Computing a homography between two images
Detecting a planar target in images
11. Reconstructing 3D Scenes Chevron down icon Chevron up icon
11. Reconstructing 3D Scenes
Introduction
Calibrating a camera
Recovering camera pose
Reconstructing a 3D scene from calibrated cameras
Computing depth from stereo image
12. Processing Video Sequences Chevron down icon Chevron up icon
12. Processing Video Sequences
Introduction
Reading video sequences
Processing the video frames
Writing video sequences
Extracting the foreground objects in a video
13. Tracking Visual Motion Chevron down icon Chevron up icon
13. Tracking Visual Motion
Introduction
Tracing feature points in a video
Estimating the optical flow
Tracking an object in a video
14. Learning from Examples Chevron down icon Chevron up icon
14. Learning from Examples
Introduction
Recognizing faces using nearest neighbors of local binary patterns
Finding objects and faces with a cascade of Haar features
Detecting objects and people with Support Vector Machines and histograms of oriented gradients

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
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abdulla hsasan May 06, 2017
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The book is organized and well written a clear explanation of the code and the theory. I cover the most important part of computer vision and how to implement them in OpenCV.
Amazon Verified review Amazon
calvinnme Aug 28, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
but as it is it is just text and images about a transform with the code. I like to understand what I am doing. If I am using Photoshop then all I need to know is what button to push. If I am writing my own original code then I need to know why I am doing these individual steps.
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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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  • They are copy-paste enabled
  • They are searchable
  • 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.