Book Image

OpenCV By Example

By : Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça
Book Image

OpenCV By Example

By: Prateek Joshi, David Millán Escrivá, Vinícius G. Mendonça

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (18 chapters)
OpenCV By Example
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Creating an application for AOI


To create our new application, we require a few input parameters when the user executes them; all of them are optional, excluding the input image to be processed:

  • An input image to be processed

  • The light image pattern

  • The light operation, where the user can choose between difference or division operations:

    • If the input value of the user is set to 0, then a difference operation is applied

    • If the input value of the user is set to 1, then a division operation is applied

  • Segmentation, where the user can choose between connected components with or without statistics and findContours methods:

    • If the input value of the user is set to 1, then the connected components method for the segment is applied

    • If the input value of the user is set to 2, then the connected components with the statistics area is applied

    • If the input value of the user is set to 3, then the findContours method is applied to the segmentation

To enable this user selection, we will use the command line parser...