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

Technical requirements


These technologies and installations are required to build and run the code in this chapter:

  • OpenCV 4 (compiled with the sfm contrib module)
  • Eigen v3.3+ (required by the sfm module)
  • Ceres solver v2+ (required by the sfm module)
  • CMake 3.12+
  • Boost v1.66+
  • OpenMVS
  • CGAL v4.12+ (required by OpenMVS)

The build instructions for the components listed, as well as the code to implement the concepts in this chapter, will be provided in the accompanying code repository. Using OpenMVS is optional, and we may stop after getting the sparse reconstruction. However, the full MVS reconstruction is much more impressive and useful; for instance, for 3D printing replicas.

Any set of photos with sufficient overlap may be sufficient for 3D reconstruction. For example, we may use a set of photos I took of the Crazy Horse memorial head in South Dakota that is bundled with this chapter code. The requirement is that the images should be taken with sufficient movement between them, but enough to have significant...