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)

Swapping faces in the infrared

Face detection and recognition are not limited to the visible spectrum of light. With a Near-Infrared (NIR) camera and NIR light source, face detection and recognition are possible even when a scene appears totally dark to the human eye. This capability is quite useful in security and surveillance applications.

We studied basic usage of NIR depth cameras, such as the Asus Xtion PRO, in Chapter 4, Depth Estimation and Segmentation. We extended the object-oriented code of our interactive application, Cameo. We captured frames from a depth camera. Based on depth, we segmented each frame into a main layer (such as the user's face) and other layers. We painted the other layers black. This achieved the effect of hiding the background so that only the main layer (the user's face) appeared on-screen in the interactive video feed.

Now, let&apos...