Book Image

Mastering OpenCV 4 - Third Edition

By : Roy Shilkrot, David Millán Escrivá
Book Image

Mastering OpenCV 4 - Third Edition

By: Roy Shilkrot, David Millán Escrivá

Overview of this book

Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4.
Table of Contents (12 chapters)

Technical requirements

The following technologies and installations are required to build the code in this chapter:

  • OpenCV v4 (compiled with the face contrib module)
  • Boost v1.66+

Build instructions for the preceding components listed, as well as the code to implement the concepts presented in this chapter, will be provided in the accompanying code repository.

To run the facemark detector, a pre-trained model is required. Although training the detector model is certainly possible with the APIs provided in OpenCV, some pre-trained models are offered for download. One such model can be obtained from https://raw.githubusercontent.com/kurnianggoro/GSOC2017/master/data/lbfmodel.yaml, supplied by the contributor of the algorithm implementation to OpenCV (during the 2017 Google Summer of Code (GSoC)).

The facemark detector can work with any image; however, we can use a prescribed dataset...