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)

Creating a mask from a disparity map

Let's assume that a user's face, or some other object of interest, occupies most of the depth camera's field of view. However, the image also contains some other content that is not of interest. By analyzing the disparity map, we can tell that some pixels within the rectangle are outliers—too near or too far to really be a part of the face or another object of interest. We can make a mask to exclude these outliers. However, we should only apply this test where the data is valid, as indicated by the valid depth mask.

Let's write a function to generate a mask whose values are 0 for the rejected regions of the image and 255 for the accepted regions. This function should take a disparity map, valid depth mask, and optionally a rectangle as arguments. If a rectangle is specified, we will make a mask that is just the size...