Book Image

Mastering OpenCV 3 - Second Edition

By : Jason Saragih
Book Image

Mastering OpenCV 3 - Second Edition

By: Jason Saragih

Overview of this book

As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what we can do with those self-driving cars, face recognition, and even augmented reality applications and games. This is all powered by Computer Vision. This book will put you straight to work in creating powerful and unique computer vision applications. Each chapter is structured around a central project and deep dives into an important aspect of OpenCV such as facial recognition, image target tracking, making augmented reality applications, the 3D visualization framework, and machine learning. You’ll learn how to make AI that can remember and use neural networks to help your applications learn. By the end of the book, you will have created various working prototypes with the projects in the book and will be well versed with the new features of OpenCV3.
Table of Contents (14 chapters)
Title Page
Mastering OpenCV 3 Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Face detection and initialization


The method for face tracking described thus far has assumed that the facial features in the image are located within a reasonable proximity to the current estimate. Although this assumption is reasonable during tracking, where face motion between frames is often quite small, we are still faced with the dilemma of how to initialize the model in the first frame of the sequence. An obvious choice for this is to use OpenCV's in-built cascade detector to find the face. However, the placement of the model within the detected bounding box will depend on the selection made for the facial features to track. In keeping with the data-driven paradigm we have followed so far in this chapter, a simple solution is to learn the geometrical relationship between the face detection's bounding box and the facial features.

The face_detector class implements exactly this solution. A snippet of its declaration that highlights its functionality is given as follows:

    class face_detector...