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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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Camera Models and Augmented Reality

If you like geometry, photography, or 3D graphics, then this chapter's topics should especially appeal to you. We will learn about the relationship between 3D space and a 2D projection. We will model this relationship in terms of the basic optical parameters of a camera and lens. Finally, we will apply the same relationship to the task of drawing 3D shapes in an accurate perspective projection. Throughout all of this, we will integrate our previous knowledge of image matching and object tracking in order to track 3D motion of a real-world object whose 2D projection is captured by a camera in real time.

On a practical level, we will build an augmented reality application that uses information about a camera, an object, and motion in order to superimpose 3D graphics on top of a tracked object in real time. To achieve this, we will conquer...

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