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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

Listing the tasks performed by the app

The app will analyze each captured frame to perform the following tasks:

  • Feature extraction: We will describe an object of interest with Speeded-Up Robust Features (SURF), which is an algorithm used to find distinctive keypoints in an image that are both scale-invariant and rotation invariant. These keypoints will help us to make sure that we are tracking the right object over multiple frames because the appearance of the object might change from time to time. It is important to find keypoints that do not depend on the viewing distance or viewing angle of the object (hence, the scale and rotation invariance).
  • Feature matching: We will try to establish a correspondence between keypoints using the Fast Library for Approximate Nearest Neighbors (FLANN) to see whether a frame contains keypoints similar to the keypoints from our object of interest...

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