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)

Depth estimation with a normal camera

A depth camera is an impressive device, but not every developer or user has one and it has some limitations. Notably, a typical depth camera does not work well outdoors because the infrared component of sunlight is much brighter than the camera's own infrared light source. Blinded by the sun, the camera cannot see the infrared pattern that it normally uses to estimate depth.

As an alternative, we can use one or more normal cameras and we can estimate relative distances to objects based on triangulation from different camera perspectives. If we use two cameras simultaneously, this approach is called stereo vision. If we use one camera, but we move it over time to obtain different perspectives, this approach is called structure from motion. Broadly, techniques for stereo vision are also helpful in SfM, but in SfM we face additional problems...