Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases 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 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, 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. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
Free Chapter
1
Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

Swapping faces in 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 the 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's modify Cameo to do something that exercises our previous skills in depth segmentation and our new skills in...