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)

Processing Images with OpenCV

Sooner or later, when working with images, you will find you need to alter them: be it by applying artistic filters, extrapolating certain sections, blending two images, or whatever else your mind can conjure. This chapter presents some techniques that you can use to alter images. By the end of it, you should be able to perform tasks such as sharpening an image, marking the contours of subjects, and detecting crosswalks using a line segment detector. Specifically, our discussion and code samples will cover the following topics:

  • Converting images between different color models
  • Understanding the importance of frequencies and the Fourier transform in image processing
  • Applying high-pass filters (HPFs), low-pass filters (LPFs), edge detection filters, and custom convolution filters
  • Detecting and analyzing contours, lines, circles, and other geometric...