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Home > Data > Computer Vision > Learning OpenCV 3 Application Development
Learning OpenCV 3 Application Development
Learning OpenCV 3 Application Development

Learning OpenCV 3 Application Development:

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Learning OpenCV 3 Application Development

Chapter 2. Image Filtering

In the previous chapter, we started off on our journey into the world of computer vision and image processing by familiarizing ourselves with some terms that occur frequently when we talk about images. We also had our first contact with OpenCV when we learnt about how the library provides us with efficient data structures to store and process image data. The chapter also helped us get acquainted with some basic algorithms within the realm of image processing by expounding the details of linear and logarithmic transformations. We familiarized ourselves with how these enhancement techniques essentially work to improve the image contrast (hence the name image enhancement), and saw them in action as they modified (stretched or compressed) the grayscale range of an image, thereby revealing details hidden in the darker and lighter regions of the image.

This chapter will take our journey forward. We will learn about more techniques that act on images, perform...

Neighborhood of a pixel

We have seen image processing operations where the value of a pixel at the output is dependent only on the value of the corresponding pixel at the input. By corresponding, we mean pixels at the same locations (row and column) in the input and output image. Such transformations were represented in mathematical form as follows:

s = T(r)

Here, s and r are the intensity values of a pixel in the output and input respectively. Since we are always dealing with pixels at the same locations, there is no mention of pixel coordinates in the preceding formula. That is to say, the grayscale value of the pixel at the 40th row and 30th column in the output depends on the grayscale value of the pixel at the same coordinates (the 40th row and 30th column) at the input.

This section will introduce you to a slightly more advanced form of image transformation. In the operations that we'll discuss now, the output value at a particular pixel (x, y) is not only dependent on the intensity...

Image averaging

Now that we are familiar with the notion of a neighborhood, we are ready to delve into the details of an operation called image averaging. As the name suggests, image averaging involves taking the mean of pixel intensity values. More specifically, each pixel is replaced by the mean of all pixels in its neighborhood. The size of the neighborhood is one of the parameters that is usually passed to the function that implements this sort of an averaging procedure. For illustration purposes, we consider a neighborhood of 3 x 3 around the pixel (this would include the pixel and its eight immediate neighbors). For example, consider the next image (you can take it to be a small sub-section within the entire image). Let's say that we wish to compute the output intensity value corresponding to the pixel with an intensity of 6 in the input image. We take the 3 x 3 neighborhood of that pixel and calculate the mean of all those values (the values have been marked in bold-face...

Image filters

If you carefully observe the example that we discussed in the previous section, you will notice that during the process of computing the output intensity at (x, y), we basically multiplied the intensity values of all the 3 x 3 neighbors by 1/9 and added them all up. Let's create a small matrix of dimensions 3 x 3 (the size of the neighborhood under consideration) and fill all the cells with the value 1/9 as shown in the following image:

Image filters

We'll call this a filter or a kernel. Now, we'll make use of this filter to calculate the output intensity value corresponding to any arbitrary input pixel (x, y), say the pixel having an intensity value of 6 (see the following image). How do we go about doing that? Well, we place the filter over the image in such a manner that the central grid in the filter lies right on top of the pixel at position (x, y)-(2, 2) in our case (I have assumed 1-based indexing for both rows and columns). Once we place the filter in this manner...

Image averaging in OpenCV

While implementing the image transforms that we discussed in the previous chapter, we adopted an approach that was based on the fundamentals and involved quite a bit of reinventing the wheel. We could afford to do that because the traversals that we performed over the data matrix in our implementations were conceptually pretty straightforward. However, we will no longer do that for a couple of reasons:

  • The kind of transformations that we are discussing at the moment (averaging using a filtering-based approach) no longer involves a simple pixel-by-pixel traversal of the data matrix. Rather, they involve a two-tiered approach where we have to traverse the neighborhood for each pixel that we encounter in our usual traversal of the data matrix. Implementing such a non-trivial traversal every single time can become time-consuming and error-prone.
  • As we progress through the book, we want you to rely more and more on the functions and APIs provided by the OpenCV developers...

Neighborhood of a pixel

We have seen image processing operations where the value of a pixel at the output is dependent only on the value of the corresponding pixel at the input. By corresponding, we mean pixels at the same locations (row and column) in the input and output image. Such transformations were represented in mathematical form as follows:

s = T(r)

Here, s and r are the intensity values of a pixel in the output and input respectively. Since we are always dealing with pixels at the same locations, there is no mention of pixel coordinates in the preceding formula. That is to say, the grayscale value of the pixel at the 40th row and 30th column in the output depends on the grayscale value of the pixel at the same coordinates (the 40th row and 30th column) at the input.

This section will introduce you to a slightly more advanced form of image transformation. In the operations that we'll discuss now, the output value at a particular pixel (x, y) is not only dependent on the intensity...

Image averaging

Now that we are familiar with the notion of a neighborhood, we are ready to delve into the details of an operation called image averaging. As the name suggests, image averaging involves taking the mean of pixel intensity values. More specifically, each pixel is replaced by the mean of all pixels in its neighborhood. The size of the neighborhood is one of the parameters that is usually passed to the function that implements this sort of an averaging procedure. For illustration purposes, we consider a neighborhood of 3 x 3 around the pixel (this would include the pixel and its eight immediate neighbors). For example, consider the next image (you can take it to be a small sub-section within the entire image). Let's say that we wish to compute the output intensity value corresponding to the pixel with an intensity of 6 in the input image. We take the 3 x 3 neighborhood of that pixel and calculate the mean of all those values (the values have been marked in bold-face). Hence,...

Image filters

If you carefully observe the example that we discussed in the previous section, you will notice that during the process of computing the output intensity at (x, y), we basically multiplied the intensity values of all the 3 x 3 neighbors by 1/9 and added them all up. Let's create a small matrix of dimensions 3 x 3 (the size of the neighborhood under consideration) and fill all the cells with the value 1/9 as shown in the following image:

We'll call this a filter or a kernel. Now, we'll make use of this filter to calculate the output intensity value corresponding to any arbitrary input pixel (x, y), say the pixel having an intensity value of 6 (see the following image). How do we go about doing that? Well, we place the filter over the image in such a manner that the central grid in the filter lies right on top of the pixel at position (x, y)-(2, 2) in our case (I have assumed 1-based indexing for both rows and columns). Once we place the filter in this manner, it will completely...

Image averaging in OpenCV

While implementing the image transforms that we discussed in the previous chapter, we adopted an approach that was based on the fundamentals and involved quite a bit of reinventing the wheel. We could afford to do that because the traversals that we performed over the data matrix in our implementations were conceptually pretty straightforward. However, we will no longer do that for a couple of reasons:

  • The kind of transformations that we are discussing at the moment (averaging using a filtering-based approach) no longer involves a simple pixel-by-pixel traversal of the data matrix. Rather, they involve a two-tiered approach where we have to traverse the neighborhood for each pixel that we encounter in our usual traversal of the data matrix. Implementing such a non-trivial traversal every single time can become time-consuming and error-prone.

  • As we progress through the book, we want you to rely more and more on the functions and APIs provided by the OpenCV developers...

Blurring an image in OpenCV

Since we are already familiar with the basics, let's jump right into the code. I am skipping the header declarations because they remain the same as we saw in our previous code:

int main() 
{ 
  Mat input_image = imread("lena.png", IMREAD_GRAYSCALE); 
  Mat filtered_image; 
  blur(input_image, filtered_image, Size(3, 3), Point(-1, -1), BORDER_REPLICATE); 
   
  imshow("Original Image", input_image); 
  imshow("Filtered Image", filtered_image); 
  waitKey(0); 
  return 0; 
}

The first thing that you notice about the blur() function is that the number of arguments is less than its counterpart. Upon a closer inspection, you'll find that the following two arguments are missing:

  • Depth of the output image: According to OpenCV's documentation for blur(), the output image has the same size and type as the source image. Since the equality between the input and output image types is already enforced by the implementation...

Gaussian smoothing

In the image smoothing operation that we introduced in the last section, we used a 3 x 3 filter where every value was 1/9. When we discussed the working of a filter, we explained that every element in a filter multiplies itself with the intensity value of a pixel in the neighborhood and the result is added up (this was one way to visualize the averaging operation). Now, we will present one more technique to visualize the same!

I am guessing that you are aware of the concept of weighted averages. For those who are not, we reiterate the same here. Given a sequence of n values and their corresponding weights , the weighted average of these n values is given by the following relation:

In the special case where all of the weights sum up to 1, our equation reduces to the following:

This form is starting to feel a little familiar now, isn't it? What if the weights  correspond to the values in our filter and the sequence  is our pixel intensity values? Well, in that case the...

Gaussian function and Gaussian filtering

Very shortly, we will be embarking on a long, but rather interesting discussion as we build our mathematical intuitions on Gaussian filtering. In order to motivate the same, we present to you some sample images that have been generated by applying the Gaussian filtering operation on Lena's image. A detailed description of each of the succeeding four output images will be presented at the end of this section. Right now, this is to motivate you and give you a glimpse of what to expect as we move ahead!

For the purpose of explaining the theoretical underpinnings and working of the Gaussian filtering method, we take a step back and consider 1D images. Just as regular 2D images are represented using a 2D grid of values, in the same manner (hypothetical) 1D images are nothing but a 1D array of pixel values.

The preceding diagram attempts to visualize the workings of a simple 1x3 box filter that replaces each pixel with the average of itself and its immediate...

Gaussian filtering in OpenCV

We have spent a considerable amount of time understanding the theory behind Gaussian filtering. It is now time to jump into the implementation. The headers will remain the same as in the case of boxFilter(). The functions implementing Gaussian filtering also reside within the imgproc module:

#include <iostream> 
#include <opencv2/core/core.hpp> 
#include <opencv2/highgui/highgui.hpp> 
#include <opencv2/imgproc/imgproc.hpp> 
 
using namespace std; 
using namespace cv;

Here is the code snippet that actually accomplishes the task of Gaussian filtering:

int main() { 
    Mat input_image = imread("lena.jpg", IMREAD_GRAYSCALE); 
    Mat filtered_image; 
 
    GaussianBlur(input_image, filtered_image, Size(7, 7), 1.0, 1.0, BORDER_REPLICATE); 
 
    imshow("Filtered Image", filtered_image); 
    waitKey(0); 
    return 0; 
}

One of the first things that you probably...

Using your own filters in OpenCV

So far, we have talked about a couple of different filtering techniques: box filtering and Gaussian filtering. Both of them had their own set of rules for defining a filter and also had a dedicated set of functions to help you apply the filters to images. When we introduced the concept of filtering, we said that different operations can be performed on our images by simply changing the value of the filter. So, if we design our own custom filter, how do we apply that to our image? There needs to exist a function that is more generic than boxFilter(), blur(), or GaussianBlur() and that will help us in applying the filter that we have designed to our input image. And OpenCV has the answer for you--the filter2D() function.

We have already hinted at what the filter2D() hopes to accomplish, so let's jump right into the code! I think at this point, I really don't need to say what the first few lines should look like:

#include <iostream> 
#include <opencv2...
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Key benefits

  • This book provides hands-on examples that cover the major features that are part of any important Computer Vision application
  • It explores important algorithms that allow you to recognize faces, identify objects, extract features from images, help your system make meaningful predictions from visual data, and much more
  • All the code examples in the book are based on OpenCV 3.1 – the latest version

Description

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code! The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!

Who is this book for?

This is the perfect book for anyone who wants to dive into the exciting world of image processing and computer vision. This book is aimed at programmers with a working knowledge of C++. Prior knowledge of OpenCV or Computer Vision/Machine Learning is not required.

What you will learn

  • * Explore the steps involved in building a typical computer vision/machine learning application
  • * Understand the relevance of OpenCV at every stage of building an application
  • * Harness the vast amount of information that lies hidden in images into the apps you build
  • * Incorporate visual information in your apps to create more appealing software
  • * Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV
  • * Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings
  • * Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition
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Table of Contents

10 Chapters
1. Laying the Foundation Chevron down icon Chevron up icon
1. Laying the Foundation
Digital image basics
Introduction to the Mat class
Exploring the Mat class: loading images
Exploring the Mat class - declaring Mat objects
Digging inside Mat objects
Traversing Mat objects
Image enhancement
Lookup tables
Linear transformations
Logarithmic transformations
Summary
2. Image Filtering Chevron down icon Chevron up icon
2. Image Filtering
Neighborhood of a pixel
Image averaging
Image filters
Image averaging in OpenCV
Blurring an image in OpenCV
Gaussian smoothing
Gaussian function and Gaussian filtering
Gaussian filtering in OpenCV
Using your own filters in OpenCV
Image noise
Vignetting
Implementing Vignetting in OpenCV
Summary
3. Image Thresholding Chevron down icon Chevron up icon
3. Image Thresholding
Binary images
Image thresholding basics
Image thresholding in OpenCV
Types of simple image thresholding
Adaptive thresholding
Morphological operations
Erosion and dilation
Erosion and dilation in OpenCV
Summary
4. Image Histograms Chevron down icon Chevron up icon
4. Image Histograms
The basics of histograms
Histograms in OpenCV
Plotting histograms in OpenCV
Color histograms in OpenCV
Multidimensional histograms in OpenCV
Summary
5. Image Derivatives and Edge Detection Chevron down icon Chevron up icon
5. Image Derivatives and Edge Detection
Image derivatives
Image derivatives in two dimensions
Visualizing image derivatives with OpenCV
The Sobel derivative filter
From derivatives to edges
The Sobel detector - a basic framework for edge detection
The Canny edge detector
Image noise and edge detection
Laplacian - yet another edge detection technique
Blur detection using OpenCV
Summary
6. Face Detection Using OpenCV Chevron down icon Chevron up icon
6. Face Detection Using OpenCV
Image classification systems
Face detection
Haar features
Integral image
AdaBoost learning
Cascaded classifiers
Face detection in OpenCV
Gender classification
Working with real datasets
Summary
7. Affine Transformations and Face Alignment Chevron down icon Chevron up icon
7. Affine Transformations and Face Alignment
Exploring the dataset
Face alignment - the first step in facial analysis
Rotating faces
Image cropping -- basics
Image cropping for face alignment
Face alignment - the complete pipeline
Summary
8. Feature Descriptors in OpenCV Chevron down icon Chevron up icon
8. Feature Descriptors in OpenCV
Introduction to the local binary pattern
A basic implementation of LBP
Variants of LBP
What does LBP capture?
Applying LBP to aligned facial images
A complete implementation of LBP
Putting it all together - the main() function
Summary
9. Machine Learning with OpenCV Chevron down icon Chevron up icon
9. Machine Learning with OpenCV
What is machine learning
Supervised and unsupervised learning
Revisiting the image classification framework
k-means clustering - the basics
k-nearest neighbors classifier - introduction
Support vector machines (SVMs) - introduction
Non-linear SVMs
Using an SVM as a gender classifier
Overfitting
Cross-validation
Common evaluation metrics
The P-R curve
Some qualitative results
Summary
A. Command-line Arguments in C++ Chevron down icon Chevron up icon
A. Command-line Arguments in C++
Introduction to command-line arguments
Summary

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Customer reviews

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1 star 20%
Pete May 28, 2017
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I am not a frequent reviewer but need to this time. I am new to OpenCV and can say that I have seldom read computer literature that was so well written and understandable. Samyak explains the theory and follows up with concise and logical code examples. A true pleasure to read!
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V. Sani Dec 30, 2016
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Best explanations on Computer Vision subjects. He explains the concept first and then shows how to do it with OpenCV. A very well written book especially for beginners of Computer Vision and OpenCV.
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Steve Wallace Mar 08, 2019
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Just a great book. Great for casual reading. Discovered that OpenCV has serialization which I put to quick use.
Amazon Verified review Amazon
Shu Feb 09, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is not a college textbook. It provides enough material to learn machine learning and with hands on code. You may feel it's been rushed. But as technology changes everyday, you have to sacrifice something to get to the reader asap. I like this book. I think the authors did a great job.
Amazon Verified review Amazon
Sarthak Gupta Apr 07, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Book contains many errors. Explanation of tougher concepts is poor. Better to avoid this book. Amazon delivery was excellent as always.
Amazon Verified review Amazon

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About the author

Profile icon Datta
Datta
Samyak Datta has a bachelor's and a master's degree in Computer Science from the Indian Institute of Technology, Roorkee. He is a computer vision and machine learning enthusiast. His first contact with OpenCV was in 2013 when he was working on his master's thesis, and since then, there has been no looking back. He has contributed to OpenCV's GitHub repository. Over the course of his undergraduate and master's degrees, Samyak has had the opportunity to engage with both the industry and research. He worked with Google India and Media.net (Directi) as a software engineering intern, where he was involved with projects ranging from machine learning and natural language processing to computer vision. As of 2016, he is working at the Center for Visual Information Technology (CVIT) at the Indian Institute of Information Technology, Hyderabad.
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