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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

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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Designing object-oriented curve filters

Since we cache a lookup array for each curve, our curve-based filters have data associated with them. Thus, we will implement them as classes, not just functions. Let's make a pair of curve filter classes, along with some corresponding higher-level classes that can apply any function, not just a curve function:

  • VFuncFilter: This is a class that is instantiated with a function, which it can then apply to an image using apply. The function is applied to the V (value) channel of a grayscale image or to all the channels of a color image.
  • VCurveFilter: This is a subclass of VFuncFilter. Instead of being instantiated with a function, it is instantiated with a set of control points, which it uses internally to create a curve function.
  • BGRFuncFilter: This is a class that is instantiated with up to four functions, which it can then apply to...

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