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)

Exploring the Fourier transform

Much of the processing you apply to images and videos in OpenCV involves the concept of the Fourier transform in some capacity. Joseph Fourier was an 18th-century French mathematician who discovered and popularized many mathematical concepts. He studied the physics of heat, and the mathematics of all things that can be represented by waveform functions. In particular, he observed that all waveforms are just the sum of simple sinusoids of different frequencies.

In other words, the waveforms you observe all around you are the sum of other waveforms. This concept is incredibly useful when manipulating images because it allows us to identify regions in images where a signal (such as the values of image pixels) changes a lot, and also regions where the change is less dramatic. We can then arbitrarily mark these regions as noise or regions of interests...