Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases 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 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, 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. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
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Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

Detecting Fast Hessian features and extracting SURF descriptors

Computer vision is a relatively young branch of computer science, so many famous algorithms and techniques have only been invented recently. SIFT is, in fact, only 23 years old, having been published by David Lowe in 1999.

SURF is a feature detection algorithm that was published in 2006 by Herbert Bay. SURF is several times faster than SIFT, and it is partially inspired by it.

Note that SURF is a patented algorithm and, for this reason, is made available only in builds of opencv_contrib where the OPENCV_ENABLE_NONFREE CMake flag is used. SIFT was formerly a patented algorithm but its patent expired and now SIFT is available in standard builds of OpenCV.

It is not particularly relevant to this book to understand how SURF works under the hood, inasmuch as we can use it in our applications and make the best of it. What is important to understand is that cv2.SURF is an OpenCV class that performs keypoint detection with the...