Get to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworks

Key Features

Understand how to capture high-quality image data, detect and track objects, and process the actions of animals or humans
Implement your learning in different areas of computer vision
Explore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented reality

Book DescriptionOpenCV 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.

What you will learn

Generate real-time visual effects using filters and image manipulation techniques such as dodging and burning
Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
Learn feature extraction and feature matching to track arbitrary objects of interest
Reconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniques
Detect faces using a cascade classifier and identify emotions in human faces using multilayer perceptrons
Classify, localize, and detect objects with deep neural networks

Who this book is forThis book is for intermediate-level OpenCV users who are looking to enhance their skills by developing advanced applications. Familiarity with OpenCV concepts and Python libraries, and basic knowledge of the Python programming language are assumed.