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

How do we define interesting?

Before we start computing the seams, we need to find out what metric we will be using to compute them. We need a way to assign importance to each pixel so that we can identify the paths that are least important. In computer vision terminology, we say that we need to assign an energy value to each pixel so that we can find the path of minimum energy. Coming up with a good way to assign the energy value is very important because it will affect the quality of the output.

One of the metrics that we can use is the value of the derivative at each point. This is a good indicator of the level of activity in that neighborhood. If there is some activity, then the pixel values will change rapidly, hence the value of the derivative at that point will be high. On the other hand, if regions are plain and uninteresting, then pixel values won't change as rapidly, so the value of the derivative at that point in the grayscale image will be low.

For each pixel location, we compute...