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)


By now, you should have a good understanding of how face detection and face recognition work and how to implement them in Python and OpenCV 4.

Face detection and face recognition are constantly evolving branches of computer vision, with algorithms being developed continuously, and they will evolve even faster in the near future with a growing interest in robotics and the Internet of Things (IoT).

For now, the accuracy of detection and recognition algorithms heavily depends on the quality of the training data, so make sure you provide your applications with a large number of training images covering a variety of expressions, poses, and lighting conditions.

As human beings, we might be predisposed to think that human faces are particularly recognizable. We might even be overconfident in our own face recognition abilities. However, in computer vision, there is nothing very...