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

Identifying a pizza with a slice taken out

The title might be slightly misleading, because we will not be talking about pizza slices. But let's say you are in a situation where you have an image containing different types of pizzas with different shapes. Now, somebody has taken a slice out of one of those pizzas. How would we automatically identify this?

We cannot take the approach we took earlier because we don't know what the shape looks like, so we don't have any template. We are not even sure what shape we are looking for, so we cannot build a template based on any prior information. All we know is the fact that a slice has been taken from one of the pizzas. Let's consider the following image:

It's not exactly a real image, but you get the idea. You know what shape we are talking about. Since we don't know what we are looking for, we need to use some of the properties of these shapes to identify the sliced pizza. If you notice, all the other shapes are nicely closed; that is, you can take...