Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)

Detecting and Recognizing Faces

Computer vision makes many futuristic-sounding tasks a reality. Two such tasks are face detection (locating faces in an image) and face recognition (identifying a face as a specific person). OpenCV implements several algorithms for face detection and recognition. These have applications in all sorts of real-world contexts, from security to entertainment.

This chapter introduces some of OpenCV's face detection and recognition functionality, along with the data files that define particular types of trackable objects. Specifically, we look at Haar cascade classifiers, which analyze the contrast between adjacent image regions to determine whether or not a given image or sub image matches a known type. We consider how to combine multiple Haar cascade classifiers in a hierarchy so that one classifier identifies a parent region (for our purposes,...