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

Segmenting images using watersheds


The watershed transformation is a popular image processing algorithm that is used to quickly segment an image into homogenous regions. It relies on the idea that when the image is seen as a topological relief, the homogeneous regions correspond to relatively flat basins delimited by steep edges. With the watershed algorithm, segmentation is achieved by flooding this relief by gradually increasing the level of water in this one. As a result of its simplicity, the original version of this algorithm tends to over-segment the image, which produces multiple small regions. This is why OpenCV proposes a variant of this algorithm that uses a set of predefined markers to guide the definition of the image segments.

How to do it...

The watershed segmentation is obtained through the use of the cv::watershed function. The input for this function is a 32-bit signed integer marker image in which each nonzero pixel represents a label. The idea is to mark some pixels of the...