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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

Planning the app

The final app will consist of the following modules and scripts:

  • gestures: This is a module that consists of an algorithm for recognizing hand gestures.
  • gestures.process: This is a function that implements the entire process flow of hand gesture recognition. It accepts a single-channel depth image (acquired from the Kinect depth sensor) and returns an annotated Blue, Green, Red (BGR) color image with an estimated number of extended fingers.
  • chapter2: This is the main script for the chapter.
  • chapter2.main: This is the main function routine that iterates over frames acquired from a depth sensor that uses .process gestures to process frames, and then illustrates results.

The end product looks like this:

No matter how many fingers of a hand are extended, the algorithm correctly segments the hand region (white), draws the corresponding convex hull (the green line surrounding the hand), finds all convexity defects that belong to the spaces between fingers (large green points) while ignoring others (small red points), and infers the correct number of extended fingers (the number in the bottom-right corner), even for a fist.

Now, let's set up the application in the next section.

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