Book Image

Building Computer Vision Projects with OpenCV 4 and C++

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

Building Computer Vision Projects with OpenCV 4 and C++

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

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. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

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 for Segmentation

To enable this user selection, we are going to use the command line parser class with the following keys:

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