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)


This chapter introduced AR, along with a robust set of approaches to the problem of tracking an image in 3D space.

We began by learning the concept of 6DOF tracking. We recognized that familiar tools such as ORB descriptors, FLANN-based matching, and Kalman filtering are useful in this kind of tracking, but that we also needed to work with camera and lens parameters in order to solve the PnP problem.

Next, we addressed practical considerations of how best to represent a reference object (such as a book cover or a photo print) in the form of a grayscale image, a set of 2D keypoints, and a set of 3D keypoints.

We proceeded to implement a class that encapsulated a demo of image tracking in 3D space, with a 3D highlighting effect as a basic form of AR. Our implementation dealt with real-time considerations, such as the need to update the Kalman filter's transition matrix...