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

Image thresholding in OpenCV


We'll start with simple thresholding in this section and move on to adaptive thresholding in the next. OpenCV has a threshold() function in its imgproc module which implements different variants of simple thresholding. Where do these variants come from?

If you remember the working of thresholding operations, we map output intensity values to each of the two outcomes of the threshold comparison. The type of mapping that we perform gives rise to several different variants of simple thresholding. Before going into the details of these variants one by one, we will learn how to implement image thresholding in OpenCV. As in the case of image averaging, the implementation essentially boils down to a single line of code. However, a close inspection of the different parameters of the function call is imperative to enjoy a thorough understanding of image thresholding using OpenCV:

#include <iostream> 
#include <opencv2/core/core.hpp> 
#include <opencv2...