Shamshad Ansari - Building Computer Vision Applications Using Artificial Neural Networks: With Step-by-Step Examples in OpenCV and TensorFlow with Python

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-5886-6 . For more detailed information, please visit www.apress.com/source-code .

For more than 20 years I have had the pleasure of working with some of the greatest data scientists and computer vision experts. Along the way I have learned a lot, especially the best practices of building large-scale computer vision systems. In this book I present the learnings from my own personal experience and the experience of people I have had opportunities to work with. I also present the work of some of the greatest contributors and thought leaders of computer vision, even though I have not had a chance to work with them. I have provided references to their work at appropriate places throughout the book.

When I hire new engineers and scientists, one of my biggest challenges has been to provide them with systematic training so that they can start contributing to the development of vision systems in the shortest possible time. There are a large number of online resources and books available on various topics related to computer vision, and it is easy to get lost in the piles of information they present, given that the field of computer vision is vast and complex. In this book, I attempted to provide a structured and systematic approach of building the key concepts and working through example code to develop real-world computer vision systems. I hope this helps you connect the dots as you read through the chapters. My goal is to keep this book as practical and hands-on as possible.

This book starts with the introduction of core concepts of computer vision and provides code examples to aid in the learning of those concepts. The code examples in the early part of the book are mainly based on OpenCV with Python.

This book also covers the basic concepts of machine learning and gradually develops the advanced-level concepts of artificial neural networks or deep learning. Every single concept is followed by working code examples of real-world use cases. All machine learningrelated code examples are written in TensorFlow with Python.

In this book, there are eight real-world use cases of computer vision with working code. These use cases are from various industries, such as healthcare, security, surveillance, and manufacturing. I have provided line-by-line explanations to help you understand the code. There are three chapters dedicated to practical use cases. These chapters demonstrate how to build the vision systems from the ground up, starting from image/video acquisition to building a data pipeline, model training, and deployment.

Training state-of-the-art computer vision models requires a lot of hardware resources. It is desirable and economically beneficial to train computer vision models on a cloud infrastructure to leverage the latest hardware resources, such as GPUs, and pay-as-you-go cost models. The last chapter, Chapter 10, provides step-by-step instructions for building machine learningbased computer vision applications on the three popular cloud infrastructures: Google Cloud Platform, Amazon AWS, and Microsoft Azure.

Though the book develops the concepts from one pixel all the way to model training on the cloud, it has certain prerequisites. You should have a working knowledge of the Python programming language. This book is intended to help working professionals, programmers, data scientists, and undergraduate and graduate students gain practical knowledge of building computer vision applications using artificial neural networks.

I decided to write this book because I wanted to achieve two objectives: build the computer vision concepts from the ground up to an advanced level, and provide a guide to apply the concepts in building real-world vision systems. I will demonstrate every single concept with use cases and code examples. I have organized the topics, connected the contents to meaningful and practical use cases, and made sure the code was working and fully tested. It all required my undivided attention, and I could not have done it without the support of my family. I cant thank my wife enough for taking care of our two daughters and keeping them occupied while I was busy writing this book. She turned this into a positive experience for them and for me: The kids started keeping track of my progress and celebrated every time I finished a section, subsection, or chapter. In turn, this gave me tremendous energy and motivation that I thoroughly enjoyed while working on this book. I just dont know what magic my wife used to do this.

My life is indebted to Anumati Bhagi and Ashok Bhagi, who are no less than parents to me; their love and support always motivate me.

This book is a collection of my lifetime experiences that I gained by working with some of the greatest engineers, data scientists, and business professionals. I would like to thank all my colleagues at Accure and all the past companies I have worked at. I sincerely thank all my teachers, professors, and mentors who enlightened me with their knowledge and wisdom.

It has been a great experience working with the Apress editorial team. Aditee Marashi, the coordinating editor, has been prompt with her responses to any question I have had. She has also been instrumental in keeping track of the schedule. Hats off to her. Its been awesome working with Mathew Moodie, the development editor. Thank you, Aditee and Matt.