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

Speeded-up robust features (SURF)

Even though SIFT is nice and useful, it's computationally intensive. This means that it's slow and we will have a hard time implementing a real-time system if it uses SIFT. We need a system that's fast and has all the advantages of SIFT. If you remember, SIFT uses the Gaussian difference to build the pyramid, and this process is slow. So, to overcome this, SURF uses a simple box filter to approximate the Gaussian. The good thing is that this is really easy to compute and it's reasonably fast. There's a lot of documentation available online on SURF at So, you can go through it to see how they construct a descriptor. You can also refer to the original paper at It is important to know that SURF is also patented and it is not freely available for commercial use.

If you run the SURF keypoint...