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 images between different color models

OpenCV implements literally hundreds of formulas that pertain to the conversion of color models. Some color models are commonly used by input devices such as cameras, while other models are commonly used for output devices such as televisions, computer displays, and printers. In between input and output, when we apply computer vision techniques to images, we will typically work with three kinds of color models: grayscale, blue-green-red (BGR), and hue-saturation-value (HSV). Let's go over these briefly:

  • Grayscale is a model that reduces color information by translating it into shades of gray or brightness. This model is extremely useful for the intermediate processing of images in problems where brightness information alone is sufficient, such as face detection. Typically, each pixel in a grayscale image is represented by...