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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
4.1 (14)
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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

4.1 (14)
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)
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Emulating photo films

A common use of curves is to emulate palettes that were common in pre-digital photography. Every type of photo film has its own unique rendition of color (or gray), but we can generalize some of the differences from digital sensors. Film tends to suffer a loss of detail and saturation in shadows, whereas digital tends to suffer these failings in highlights. Also, film tends to have uneven saturation across different parts of the spectrum, so each film has certain colors that pop or jump out.

Thus, when we think of good-looking film photos, we might think of scenes (or renditions) that are bright and that have certain dominant colors. At the other extreme, maybe we remember the murky look of an underexposed roll of film that couldn't be improved much by the efforts of the lab technician.

In this section, we are going to create four different film-like...

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Learning OpenCV 4 Computer Vision with Python 3
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