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 pose estimation?

Before we proceed, we need to understand how to estimate the camera pose. This is a very critical step in an augmented reality system and we need to get it right if we want our experience to be seamless. In the world of augmented reality, we overlay graphics on top of an object in real time. In order to do that, we need to know the location and orientation of the camera, and we need to do it quickly. This is where pose estimation becomes very important. If you don't track the pose correctly, the overlaid graphics will look unnatural.

Consider the following image:

The arrow indiactes that the surface is normal. Let's say the object changes its orientation:

Now, even though the location is the same, the orientation has changed. We need to have this information so that the overlaid graphics look natural. We need to make sure that the graphic is aligned with this orientation and position.