Book Image

OpenCV 3.x with Python By Example - Second Edition

By : Gabriel Garrido Calvo, Prateek Joshi
Book Image

OpenCV 3.x with Python By Example - Second Edition

By: Gabriel Garrido Calvo, Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

What is image segmentation?

Image segmentation is the process of separating an image into its constituent parts. It is an important step in many computer vision applications in the real world. There are many different ways of segmenting an image. When we segment an image, we separate the regions based on various metrics, such as color, texture, location, and so on. All the pixels within each region have something in common, depending on the metric we are using. Let's take a look at some of the popular approaches here.

To start with, we will be looking at a technique called GrabCut. It is an image segmentation method based on a more generic approach called graph-cuts. In the graph-cuts method, we consider the entire image to be a graph, and then we segment the graph based on the strength of the edges in that graph. We construct the graph by considering each pixel to be a node, and edges are constructed between the nodes, where edge weight is a function of the pixel values of those two nodes...