Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By : Robert Laganiere
Book Image

OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition

By: Robert Laganiere

Overview of this book

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.
Table of Contents (21 chapters)
OpenCV 3 Computer Vision Application Programming Cookbook - Third Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Applying directional filters to detect edges


The first recipe of this chapter introduced the idea of linear filtering using kernel matrices. The filters that were used had the effect of blurring an image by removing or attenuating its high-frequency components. In this recipe, we will perform the opposite transformation, that is, amplifying the high-frequency content of an image. As a result, the high-pass filters introduced in this recipe will perform edge detection.

How to do it...

The filter that we will use here is called the Sobel filter. It is said to be a directional filter, because it only affects the vertical or the horizontal image frequencies depending on which kernel of the filter is used. OpenCV has a function that applies the Sobel operator on an image. The horizontal filter is called as follows:

    cv::Sobel(image,     // input 
              sobelX,    // output 
              CV_8U,     // image type 
              1, 0,      // kernel specification 
 ...