Computer Vision and Augmented Reality in iOS : OpenCV and ARKit Applications, 1st Edition

Learn how computer vision works, how augmented reality renders digital graphics into the physical world via an iPhone’s camera, and how to incorporate these technologies into your own apps. This book shows you how to take full advantage of computer vision technologies. Interacting with other people online usually involves user-generated images and videos; whether it be “memes”, short videos, or heavily-modified images. Before smart phones, generating this content required a professional using high-level image and video editing software. Not any more. This book will teach you to use computer vision in the most popular ways, such as for facial recognition, image to text analysis and, of course, recording a video of a dancing hot dog in your living room. Starting with the history of computer vision, image and video processing fundamentals, and an introduction to developing augmented reality applications, you’ll learn to incorporate computer vision both in the content you create and the apps you develop for end users. Computer Vision and Augmented Reality in iOS reveals how every user with access to the Internet and a smart phone can easily generate heavily-modified images and videos. What You'll Learn Incorporate mathematics related to computer vision into your apps Host computer vision models remotely for mobile use Implement visual-inertial state estimation algorithms for mobile augmented reality Who This Book Is For Professionals or post graduate students in software development or engineering who have a basic understanding of how software development works and are interested in implementing computer vision into their development. It's recommended that readers already have a working knowledge of C++ and Swift. Table of Contents About the Author About the Technical Reviewer Acknowledgments Chapter 1: Introduction to Computer Vision What Is Computer Vision? An Overview on How Computer Vision Works Why Do We Need Computer Vision? The Evolution of Computer Vision Traditional vs. Deep Learning–Based Computer Vision Computer Vision in Augmented Reality Summary Chapter 2: Introduction to Augmented Reality What Is Augmented Reality? Different Types of Augmented Reality Summary Chapter 3: Image and Video Processing Fundamentals Mathematics Overview Image Processing Basics Standard Algorithms Object Detection Summary Chapter 4: Computer Vision Applications Overview Applications of Computer Vision Read, Write, and Display Images Change the Color Space Resize the Images Rotate the Image Bitwise Operation Detect the Edges Image Filtering Detect Face Computer Vision Algorithms Image Classification in Deep Learning Object Detection Object Detection Types Importance of Object Detection Detection of Anomaly Object Tracking Semantic Segmentation Vehicles That Are Automatic Medical Diagnostic Picture Instance Segmentation Requirements Work on Image Reconstruction Implementing Vision Applications A) Face Detection/Recognition Implementation B) Body Detection/Tracking OpenCV Tracking API BOOSTING Tracker MIL Tracker Kernelized Correlation Filter (KCF) MedianFlow Tracker GOTURN Tracker MOSSE Tracker CSRT Tracker C) Image-to-Text Analysis Using the C++ OCR Library, Convert an Image to Text Using C++ to Convert an Image Page to Text Using C++, Perform Single-Line OCR on an Image In C++, Convert a Specific Area of an Image into Text Chapter 5: Custom Object Tracking Overview Real-Time Object Tracking Background Utilizing Code Libraries Conclusions Points of Concern Custom Barcode Generator/Reader Utilizing C++, Build and Read Barcodes API for Producing and Reading Barcodes in C++ Generating a Barcode Using C++ Utilizing C++, Produce a 2D Barcode (QR or Other) With C++, Create and Customize Barcodes Utilizing C++, Create Barcodes with Subtitles Utilizing C++, Scanning and Reading Barcodes C++ Is Used to Read Barcodes with Several Symbologies Summary Chapter 6: Augmented Reality Using OpenCV Overview Marker-Based AR Marker-Less AR Location-Based AR Marker-Based Augmented Reality Using ArUco Markers in OpenCV (C++) to Create Augmented Reality What Are ArUco Markers? Using OpenCV to Create ArUco Markers An Augmented Reality Application Marker-Less Augmented Reality Using Visual-Inertial Estimation on a Phone Augmented Reality (AR) Subclass of VR Estimation ORB Function Detector SLAM Process Monocular Camera Key Problem Proposed Adaptive Visual-Inertial Approach Real-Time Motion Tracking Summary Chapter 7: Augmented Reality Using ARKit and OpenCV Why Do You Use OpenCV instead of Core ML? Multiple Platforms Are Supported ARKit and OpenCV Integration To Sum Up Prerequisite Hardware Software Body Tracking Step by Step Step 1: Set Up the Primary Scenario Step 2: Set Up the Skeleton Step 3: Locate the Followed Bodies Step 4: Show the Skeleton Step 5: Create and Deploy Face Tracking in ARKit 3 User Level: Basic Setup and Specifications for ARKit 3 Session Objects in Augmented Reality Prefab AR Glasses ARGlassesController Class ARFace Data and Face Detection in ARKit 3 Color Selector Summary Index