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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

Setting up the app

Going forward, we will be using a famous open source dataset called fountain-P11. It depicts a Swiss fountain viewed from various angles:

The dataset consists of 11 high-resolution images and can be downloaded from https://icwww.epfl.ch/multiview/denseMVS.html. Had we taken the pictures ourselves, we would have had to go through the entire camera calibration procedure to recover the intrinsic camera matrix and the distortion coefficients. Luckily, these parameters are known for the camera that took the fountain dataset, so we can go ahead and hardcode these values in our code.

Let's prepare the main routine function in the next section.

Understanding the main routine function

Our main routine function...

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