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 does ANN work?

In this section, we will see which are the elements taking part in an ANN-MLP. First, we will represent a regular ANN-MLP shape with one layer each of input, output, and hidden, and how the information flows across them:

An MLP network is formed by at least three layers:

  • Input layer: Every MLP always has one of these layers. It is a passive layer, which means that it does not modify the data. It receives information from the outside world and sends it out to the network. The number of nodes (neurons) in this layer will depend on the amount of features or descriptive information we want to extract from the images. For example, in case of using feature vectors, there will be one node for each of the columns within the vector.
  • Hidden layers: This layer is where all the groundwork happens. It transforms the inputs into something that the output layer or another hidden layer can use (there can be more than one). This layer works as a black box, sensing patterns within received...