Book Image

OpenCV 3 Computer Vision with Python Cookbook

By : Aleksei Spizhevoi, Aleksandr Rybnikov
Book Image

OpenCV 3 Computer Vision with Python Cookbook

By: Aleksei Spizhevoi, Aleksandr Rybnikov

Overview of this book

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications. In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis. Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks. By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.
Table of Contents (11 chapters)

Image segmentation using the k-means algorithm

Sometimes, the color of pixels in an image can help determine where semantically close areas are. For example, road surfaces, in some circumstances, may have almost the same color. By color, we can find all road pixels. But what if we don't know the color of the road? Here, the k-means clustering algorithm comes into play. This algorithm only needs to know how many clusters are in an image, or, in other words, how many clusters we want an image to have. With this information, it can automatically find the best clusters. In this recipe, we will consider how k-means image segmentation can be applied using OpenCV.

Getting ready

Install the OpenCV 3.x Python API package and the...