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 supervised and unsupervised learning?

If you are familiar with the basics of machine learning, you will certainly know what supervised and unsupervised learning is all about.

To give a quick refresher, supervised learning refers to building a function based on labeled samples. For example, if we are building a system to separate dress images from footwear images, we first need to build a database and label it. We need to tell our algorithm what images correspond to dresses and what images correspond to footwear. Based on this data, the algorithm will learn how to identify dresses and footwear so that when an unknown image comes in, it can recognize what's inside that image.

Unsupervised learning is the opposite of what we just discussed. There is no labeled data available here. Let's say we have a bunch of images, and we just want to separate them into three groups. We don't know what the criteria will be. So, an unsupervised learning algorithm will try to separate the given set of...