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)

Converting 10-bit images to 8-bit

As we noted in the previous section, some of the channels of a depth camera use a range larger than 8 bits for their data. A large range tends to be useful for computations, but inconvenient for display, since most computer monitors are only capable of using an 8-bit range, [0, 255], per channel.

OpenCV's cv2.imshow function re-scales and truncates the given input data in order to convert the image for display. Specifically, if the input image's data type is unsigned 16-bit or signed 32-bit integers, cv2.imshow divides the data by 256 and truncates it to the 8-bit unsigned integer range, [0, 255]. If the input image's data type is 32-bit or 64-bit floating-point numbers, cv2.imshow assumes that the data's range is [0.0, 1.0], so it multiplies the data by 255 and truncates it to the 8-bit unsigned integer range, [0, 255]. By...