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)

Caching and applying a curve

By now, we can get the function of a curve that interpolates arbitrary control points. However, this function might be expensive. We don't want to run it once-per-channel, per -pixel (for example, 921,600 times per frame if applied to three channels of 640 x 480 video). Fortunately, we are typically dealing with just 256 possible input values (in 8 bits per channel) and we can cheaply precompute and store that many output values. Then, our per-channel, per-pixel cost is just a lookup of the cached output value.

Let's edit the utils.py file and add a function that will create a lookup array for a given function:

def createLookupArray(func, length=256):
"""Return a lookup for whole-number inputs to a function.

The lookup values are clamped to [0, length - 1].

"""
if func is None:
return None
...