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 implement an ANN-MLP classifier? 

After all that theoretical explanation on how to implement an ANN, we will implement it ourself. For that, and as we did also in the SVM classifier, we will download the training images from the same source Caltech256 We will start with a few items, easily extendable to many other, creating a folder, images, with a subfolder for each of the categories that we will classify: dresses, footwear, and bagpack. We will take a bunch of images for each of them; around 20-25 images should be enough for the training, and on top of that we will include another set of sample images, which we will use for evaluating the accuracy of our network after the training.

As we discussed earlier, we need to align the number of descriptors for each of the images using a Bag of Words (BOW). For that, we will first extract the feature vectors for each of the images using dense feature detectors for the keypoints of...