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

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Starting in Chapter 3, Processing Images with OpenCV, our Cameo demo application incorporates an image processing effect called curves, which it uses to emulate the color bias of certain photo films. This appendix describes the concept of curves and their implementation using SciPy.

Curves are a technique for remapping colors. With curves, a channel’s value at a destination pixel is a function of (only) the same channel’s value at the source pixel. Moreover, we do not define the functions directly; instead, for each function, we define a set of control points from which the function is interpolated. In pseudocode, for a BGR image:

dst.b = funcB(src.b) where funcB interpolates pointsB
dst.g = funcG(src.g) where funcG interpolates pointsG
dst.r = funcR(src.r) where funcR interpolates pointsR

The type of interpolation may vary between implementations, though it should avoid discontinuous slopes at control...