Book Image

OpenCV 4 with Python Blueprints - Second Edition

By : Dr. Menua Gevorgyan, Arsen Mamikonyan, Michael Beyeler
Book Image

OpenCV 4 with Python Blueprints - Second Edition

By: Dr. Menua Gevorgyan, Arsen 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)
11
Profiling and Accelerating Your Apps
12
Setting Up a Docker Container

Summary

Throughout this chapter, we have used an object detection network and combined it with a tracker to track and count objects over time. After reading through the chapter, you should now understand how detection networks work and understand their training mechanisms.

You have learned how you can import models built with other frameworks into OpenCV and bind them into an application that processes a video or uses other video streams such as your camera or a remote IP camera. You have implemented a simple, yet robust, algorithm for tracking, which, in combination with a robust detector network, allows the answering of multiple statistical questions related to video data.

You can now use and train object detection networks of your choice in order to create your own highly accurate applications that implement their functionality around object detection and tracking.

Throughout...