Book Image

Learning OpenCV 3 Application Development

By : Samyak Datta
Book Image

Learning OpenCV 3 Application Development

By: Samyak Datta

Overview of this book

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!
Table of Contents (16 chapters)
Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Blur detection using OpenCV


Let's take a look at one of the applications of the Laplacian operator: detecting the amount of blur in images. Often, the pictures that we take in our day-to-day lives using digital cameras, DSLRs, and so on. turn out to be not that clear, sharp, and well-focused. This can arise due to a variety of factors ranging from the motion of the subject that is being captured to the sudden movement of the capturing device just before the picture was taken. The problem that we are going to solve is that given an image, can you detect whether it is blurry or not?

The approach that we are going to take here is to use the Laplacian operator to quantify the amount of blur that is present in the image. As we'll soon see, the higher the value of our metric, the less blurry our image would be.

Now, how do we arrive at such a metric? As it turns out, it has been proven (through peer-reviewed research that we are not going to get into) that the variance of Laplacian gives a sufficiently...