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OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

By : Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler
5 (4)
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OpenCV 4 with Python Blueprints

OpenCV 4 with Python Blueprints

5 (4)
By: Dr. Menua Gevorgyan , Michael Beyeler (USD), Mamikonyan, Michael Beyeler

Overview of this book

OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You’ll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you’ll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you’ll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you’ll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs.
Table of Contents (14 chapters)
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11
Profiling and Accelerating Your Apps
12
Setting Up a Docker Container

Estimating the camera motion from a pair of images

Now that we have loaded two images (self.img1 and self.img2) of the same scene, such as two examples from the fountain dataset, we find ourselves in a similar situation as in the previous chapter. We are given two images that supposedly show the same rigid object or static scene but from different viewpoints.

However, this time we want to go a step further—if the only thing that changes between taking the two pictures is the location of the camera, can we infer the relative camera motion by looking at the matching features?

Well, of course we can. Otherwise, this chapter would not make much sense, would it? We will take the location and orientation of the camera in the first image as a given and then find out how much we have to reorient and relocate the camera so that its viewpoint matches that from the second image.

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