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 lines, circles, and other shapes

Detecting edges and finding contours are not only common and important tasks in their own right; they also form the basis of other complex operations. Line and shape detection walk hand-in-hand with edge and contour detection, so let's examine how OpenCV implements these.

The theory behind line and shape detection has its foundation in a technique called the Hough transform, invented by Richard Duda and Peter Hart, who extended and generalized the work that was done by Paul Hough in the early 1960s. Let's take a look at OpenCV's API for Hough transforms.

Detecting lines

First of all, let's detect some lines. We can do this with either the HoughLines function or the HoughLinesP function. The former uses the standard Hough transform, while the latter uses the probabilistic Hough transform (hence the P in the name). The probabilistic version is so-called because it only analyzes a subset of the image's points and estimates...