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)

Formulating a curve

Our first step toward curve-based filters is to convert control points into a function. Most of this work is done for us by a SciPy function called scipy.interp1d, which takes two arrays (x and y coordinates) and returns a function that interpolates the points. As an optional argument to scipy.interp1d, we may specify the kind interpolation; supported options include 'linear', 'nearest', 'zero', 'slinear' (spherical linear), 'quadratic', and 'cubic'. Another optional argument, bounds_error, may be set to False to permit extrapolation as well as interpolation.

Let's edit the script that we use with our Cameo demo and add a function that wraps scipy.interp1d with a slightly simpler interface:

def createCurveFunc(points):
"""Return a function derived from control points...