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 are keypoints?

Now that we know that keypoints refer to the interesting regions in the image, let's dig a little deeper. What are keypoints made of? Where are these points? When we say interesting, it means that something is happening in that region. If the region is just uniform, then it's not very interesting. For example, corners are interesting because there is a sharp change in intensity in two different directions. Each corner is a unique point where two edges meet. If you look at the preceding images, you will see that the interesting regions are not completely made up of interesting content. If you look closely, we can still see plain regions within busy regions. For example, consider the following image:

If you look at the preceding object, the interior parts of the interesting regions are uninteresting:

So, if we were to characterize this object, we would need to make sure that we picked the interesting points. Now, how do we define interesting points? Can we just say that anything...