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 to censor a shape?

Let's say you are dealing with images and you want to block out a particular shape. Now, you might say that you will use shape matching to identify the shape and then just block it out, right? But the problem here is that we don't have any template available. So, how do we go about doing this? Shape analysis comes in various forms, and we need to build our algorithm depending on the situation. Let's consider the following figure:

Let's say we want to identify all the boomerang shapes and then block them out without using any template images. As you can see, there are various other weird shapes in that image and the boomerang shapes are not really smooth. We need to identify the property that's going to differentiate the boomerang shape from the other shapes present. Let's consider the convex hull. If you take the ratio of the area of each shape to the area of the convex hull, we can see that this can be a distinguishing metric. This metric is called solidity factor...