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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

Understanding 3D point cloud visualization

The last step is visualizing the triangulated 3D real-world points. An easy way of creating 3D scatterplots is by using Matplotlib. However, if you are looking for more professional visualization tools, you may be interested in Mayavi (http://docs.enthought.com/mayavi/mayavi), VisPy (http://vispy.org), or the Point Cloud Library (http://pointclouds.org).

Although the last one does not have Python support for point cloud visualization yet, it is an excellent tool for point cloud segmentation, filtering, and sample consensus model fitting. For more information, head over to Strawlab's GitHub repository at https://github.com/strawlab/python-pcl.

Before we can plot our 3D point cloud, we obviously have to extract the [R | t] matrix and perform the triangulation as explained earlier:


def plot_point_cloud(self, feat_mode="SIFT...

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