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)

Foreground detection with the GrabCut algorithm

Calculating a disparity map is a useful way to segment the foreground and background of an image, but StereoSGBM is not the only algorithm that can accomplish this and, in fact, StereoSGBM is more about gathering three-dimensional information from two-dimensional pictures than anything else. GrabCut, however, is a perfect tool for foreground/background segmentation. The GrabCut algorithm consists of the following steps:

  1. A rectangle including the subject(s) of the picture is defined.
  2. The area lying outside the rectangle is automatically defined as a background.
  3. The data contained in the background is used as a reference to distinguish background areas from foreground areas within the user-defined rectangle.
  4. A Gaussian Mixture Model (GMM) models the foreground and background, and labels undefined pixels as probable background and...