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
Contributors
Packt Upsell
Preface

Object detection versus object recognition


Before we proceed, we need to understand what we are going to discuss in this chapter. You must have frequently heard the terms object detection and object recognition, and they are often mistaken to be the same thing. There is a very distinct difference between the two.

Object detection refers to detecting the presence of a particular object in a given scene. We don't know what the object might be. For instance, we discussed face detection in Chapter 4, Detecting and Tracking Different Body Parts. During the discussion, we only detected whether or not a face was present in the given image. We didn't recognize the person! The reason we didn't recognize the person is because we didn't care about that in our discussion. Our goal was to find the location of the face in the given image. Commercial face recognition systems employ both face detection and face recognition to identify a person. First, we need to locate the face, and then run the face recognizer...