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

Facial landmark detection in OpenCV


Landmark detection starts with face detection, finding faces in the image and their extents (bounding boxes). Facial detection has long been considered a solved problem, and OpenCV contains one of the first robust face detectors freely available to the public. In fact, OpenCV, in its early days, was majorly known and used for its fast face detection feature, implementing the canonical Viola-Jones boosted cascade classifier algorithm (Viola et al. 2001, 2004), and providing a pre-trained model. While face detection has grown much since those early days, the fastest and easiest method for detecting faces in OpenCV is still to use the bundled cascade classifiers, by means of the cv::CascadeClassifier class provided in the core module.

We implement a simple helper function to detect faces with the cascade classifier, shown as follows:

void faceDetector(const Mat& image,
std::vector<Rect> &faces,
CascadeClassifier &face_cascade) {
Mat gray;...