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


Blurring refers to averaging the pixel values within a neighborhood. This is also called a low pass filter. A low pass filter is a filter that allows low frequencies, and blocks higher frequencies. Now, the next question that comes to our mind is: what does frequency mean in an image? Well, in this context, frequency refers to the rate of change of pixel values. So we can say that the sharp edges would be high-frequency content because the pixel values change rapidly in that region. Going by that logic, plain areas would be low-frequency content. Going by this definition, a low pass filter would try to smooth the edges.

A simple way to build a low pass filter is by uniformly averaging the values in the neighborhood of a pixel. We can choose the size of the kernel depending on how much we want to smooth the image, and it will correspondingly have different effects. If you choose a bigger size, then you will be averaging over a larger area. This tends to increase the smoothing effect...