Book Image

Learn OpenCV 4 By Building Projects - Second Edition

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

Learn OpenCV 4 By Building Projects - Second Edition

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

Overview of this book

OpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module. By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Table of Contents (14 chapters)

Creating an application for AOI

To create our new application, we require a few input parameters. When a user executes the application, all of them are optional, excluding the input image to process. The input parameters are as follows:

  • Input image to process
  • Light image pattern
  • Light operation, where a user can choose between difference or divide operations
  • If the user sets 0 as a value, the difference operation is applied
  • If the user set 1 as a value, the division operation is applied
  • Segmentation, where the user can choose between connected components with or without statistics and find contour methods
  • If the user sets 1 as the input value, the connected components method for segment is applied
  • If the user sets 2 as the input value, the connected components method with the statistics area is applied
  • If the user sets 3 as the input value, the find contours method is applied...