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• Cover ; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface;
• Chapter 1: Playing with Images ; Introduction; Installing the OpenCV library; Getting ready; How to do it ... ; How it works ... ; There's more ... ; The Visualization Toolkit and the cv::viz module; The OpenCV developer site; See also; Loading, displaying, and saving images; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Clicking on images; Drawing on images; See also; Exploring the cv::Mat data structure; How to do it ... ; How it works ... ; There's more ...

• The input and output arraysManipulating small matrices; See also; Defining regions of interest; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Using image masks; See also;
• Chapter 2 : Manipulating Pixels; Introduction; Accessing pixel values; Getting ready; How to do it ... ; How it works ... ; There's more ... ; The cv::Mat_ template class; See also; Scanning an image with pointers; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Other color reduction formulas; Having input and output arguments; Efficient scanning of continuous images; Low-level pointer arithmetic; See also.

• Scanning an image with iteratorsGetting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Writing efficient image-scanning loops; How to do it ... ; How it works ... ; There's more ... ; See also; Scanning an image with neighbor access; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Performing simple image arithmetic; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Overloaded image operators; Splitting the image channels; Remapping an image; How to do it ... ; How it works ... ; See also;
• Chapter 3: Processing the Colors of an Image ; Introduction.

• Comparing colors using the Strategy design patternHow to do it ... ; How it works ... ; There's more ... ; Computing the distance between two color vectors; Using OpenCV functions; The floodFill function; Functor or function object; The OpenCV base class for algorithms; See also; Segmenting an image with the GrabCut algorithm; How to do it ... ; How it works ... ; See also; Converting color representations; How to do it ... ; How it works ... ; See also; Representing colors with hue, saturation, and brightness; How to do it ... ; How it works ... ; There's more ... ; Using colors for detection
• skin tone detection; See also.

• Chapter
• 4: Counting the Pixels with Histograms Introduction; Computing an image histogram; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Computing histograms of color images; See also; Applying look-up tables to modify the image's appearance; How to do it ... ; How it works ... ; There's more ... ; Stretching a histogram to improve the image contrast; Applying a look-up table to color images; See also; Equalizing the image histogram; How to do it ... ; How it works ... ; Backprojecting a histogram to detect specific image content; How to do it ... ; How it works ... ; There's more ...

Recipes to help you build computer vision applications that make the most of the popular C++ library OpenCV 3 About This Book * Written to the latest, gold-standard specification of OpenCV 3 * Master OpenCV, the open source library of the computer vision community * Master fundamental concepts in computer vision and image processing * Learn about the important classes and functions of OpenCV with complete working examples applied to real images Who This Book Is For OpenCV 3 Computer Vision Application Programming Cookbook Third Edition is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming. It can also be used as a companion book for university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision. What You Will Learn * Install and create a program using the OpenCV library * Process an image by manipulating its pixels * Analyze an image using histograms * Segment images into homogenous regions and extract meaningful objects * Apply image filters to enhance image content * Exploit the image geometry in order to relay different views of a pictured scene * Calibrate the camera from different image observations * Detect people and objects in images using machine learning techniques * Reconstruct a 3D scene from images In Detail Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you'll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you'll also get acquainted with recent approaches in machine learning and object classification. Style and approach This book will arm you with the basics you need to start writing world-aware applications right from a pixel level all the way through to processing video sequences.
(source: Nielsen Book Data)

• Setting up OpenCV
• Handling files, cameras, and GUIs
• Filtering images
• Tracking faces with Haar cascades
• Detecting foreground/background regions and depth
• Integrating with Pygame
• Generating Haar cascades for custom targets.

A practical, project-based tutorial for Python developers and hobbyists who want to get started with computer vision with OpenCV and Python. OpenCV Computer Vision with Python is written for Python developers who are new to computer vision and want a practical guide to teach them the essentials. Some understanding of image data (for example, pixels and color channels) would be beneficial. At a minimum you will need access to at least one webcam. Certain exercises require additional hardware like a second webcam, a Microsoft Kinect or an OpenNI-compliant depth sensor such as the Asus Xtion PRO.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Clusters and robots
• 3. Cluster programming
• 4. Robot motion
• 5. Sensors
• 6. Mapping and localization
• 7. Vision and tracking
• 8. Learning landmarks
• 9. Robot architectures
• Appendix I: Summary of OpenMPI man page for mpirun
• Appendix II: MPI datatypes
• Appendix III: MPI reduction operations
• Appendix IV: MPI application programmer interface.

In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, target tracking, action selection and landmark learning, are reviewed and cluster implementations for these are presented.The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.
(source: Nielsen Book Data)

Recipes to help you build computer vision applications that make the most of the popular C++ library OpenCV 3 About This Book * Written to the latest, gold-standard specification of OpenCV 3 * Master OpenCV, the open source library of the computer vision community * Master fundamental concepts in computer vision and image processing * Learn about the important classes and functions of OpenCV with complete working examples applied to real images Who This Book Is For OpenCV 3 Computer Vision Application Programming Cookbook Third Edition is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers who wish to be introduced to the concepts of computer vision programming. It can also be used as a companion book for university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision. What You Will Learn * Install and create a program using the OpenCV library * Process an image by manipulating its pixels * Analyze an image using histograms * Segment images into homogenous regions and extract meaningful objects * Apply image filters to enhance image content * Exploit the image geometry in order to relay different views of a pictured scene * Calibrate the camera from different image observations * Detect people and objects in images using machine learning techniques * Reconstruct a 3D scene from images In Detail Making your applications see has never been easier with OpenCV. With it, you can teach your robot how to follow your cat, write a program to correctly identify the members of One Direction, or even help you find the right colors for your redecoration. OpenCV 3 Computer Vision Application Programming Cookbook Third Edition provides a complete introduction to the OpenCV library and explains how to build your first computer vision program. You will be presented with a variety of computer vision algorithms and exposed to important concepts in image and video analysis that will enable you to build your own computer vision applications. This book helps you to get started with the library, and shows you how to install and deploy the OpenCV library to write effective computer vision applications following good programming practices. You will learn how to read and write images and manipulate their pixels. Different techniques for image enhancement and shape analysis will be presented. You will learn how to detect specific image features such as lines, circles or corners. You will be introduced to the concepts of mathematical morphology and image filtering. The most recent methods for image matching and object recognition are described, and you'll discover how to process video from files or cameras, as well as how to detect and track moving objects. Techniques to achieve camera calibration and perform multiple-view analysis will also be explained. Finally, you'll also get acquainted with recent approaches in machine learning and object classification. Style and approach This book will arm you with the basics you need to start writing world-aware applications right from a pixel level all the way through to processing video sequences.
(source: Nielsen Book Data)

• Cover
• Title Page
• Copyright and Credits
• Contributors
• Table of Contents
• Preface
• Part 1: Introduction to CV on AWS and Amazon Rekognition
• Chapter 1: Computer Vision Applications and AWS AI/ML Services Overview
• Technical requirements
• Understanding CV
• CV architecture and applications
• Data processing and feature engineering
• Data labeling
• Solving business challenges with CV
• Contactless check-in and checkout
• Video analysis
• Content moderation
• CV at the edge
• Exploring AWS AI/ML services
• AWS AI services
• Amazon SageMaker

• Setting up your AWS environment
• Creating an Amazon SageMaker Jupyter notebook instance
• Summary
• Chapter 2: Interacting with Amazon Rekognition
• Technical requirements
• The Amazon Rekognition console
• Using the Label detection demo
• Examining the API request
• Examining the API response
• Other demos
• Monitoring Amazon Rekognition
• Quick recap
• Detecting Labels using the API
• Uploading the images to S3
• Initializing the boto3 client
• Detect the Labels
• Using the Label information
• Using bounding boxes
• Quick recap
• Cleanup
• Summary

• Chapter 3: Creating Custom Models with Amazon Rekognition Custom Labels
• Technical requirements
• Introducing Amazon Rekognition Custom Labels
• Benefits of Amazon Rekognition Custom Labels
• Creating a model using Rekognition Custom Labels
• Deciding the model type based on your business goal
• Creating a model
• Improving the model
• Starting your model
• Analyzing an image
• Stopping your model
• Building a model to identify Packt's logo
• Step 1
• Collecting your images
• Step 2
• Creating a project
• Step 3
• Creating training and test datasets

• Step 4
• Adding labels to the project
• Step 5
• Drawing bounding boxes on your training and test datasets
• Step 6
• Training your model
• Validating that the model works
• Step 1
• Starting your model
• Step 2
• Analyzing an image with your model
• Step 3
• Stopping your model
• Summary
• Part 2: Applying CV to Real-World Use Cases
• Chapter 4: Using Identity Verification to Build a Contactless Hotel Check-In System
• Technical requirements
• Prerequisites
• Creating the image bucket
• Uploading the sample images
• Creating the profile table
• Introducing collections

• Creating a collection
• Describing a collection
• Deleting a collection
• Quick recap
• Describing the user journeys
• Registering a new user
• Authenticating a user
• Registering a new user with an ID card
• Updating the user profile
• Implementing the solution
• Checking image quality
• Indexing face information
• Search existing faces
• Quick recap
• Supporting ID cards
• Reading an ID card
• Using the CompareFaces API
• Quick recap
• Guidance for identity verification on AWS
• Solution overview
• Deployment process
• Cleanup
• Summary

Develop scalable computer vision solutions for real-world business problems and discover scaling, cost reduction, security, and bias mitigation best practices with AWS AI/ML services Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to quickly deploy and automate end-to-end CV pipelines on AWS Implement design principles to mitigate bias and scale production of CV workloads Work with code examples to master CV concepts using AWS AI/ML services Book Description Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models. You'll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that'll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads. By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services. What you will learn Apply CV across industries, including e-commerce, logistics, and media Build custom image classifiers with Amazon Rekognition Custom Labels Create automated end-to-end CV workflows on AWS Detect product defects on edge devices using Amazon Lookout for Vision Build, deploy, and monitor CV models using Amazon SageMaker Discover best practices for designing and evaluating CV workloads Develop an AI governance strategy across the entire machine learning life cycle Who this book is for If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.

• Machine generated contents note: 1. Introduction
• 1.1. A Difficult Problem
• 1.2. The Human Vision System
• 1.3. Practical Applications of Computer Vision
• 1.4. The Future of Computer Vision
• 1.5. Material in This Textbook
• 1.6. Going Further with Computer Vision
• 2. Images
• 2.1. Cameras
• 2.1.1. The Simple Pinhole Camera Model
• 2.2. Images
• 2.2.1. Sampling
• 2.2.2. Quantisation
• 2.3. Colour Images
• 2.3.1. Red-Green -- Blue (RGB) Images
• 2.5.2. Cyan-Magenta -- Yellow (CMY) Images
• 2.5.3. YUV Images
• 2.5.4. Hue Luminance Saturation (HLS) Images
• 2.5.5. Other Colour Spaces
• 2.5.6. Some Colour Applications
• 2.4. Noise
• 2.4.1. Types of Noise
• 2.4.2. Noise Models
• 2.4.3. Noise Generation
• 2.4.4. Noise Evaluation
• 2.5. Smoothing
• 2.5.1. Image Averaging
• 2.5.2. Local Averaging and Gaussian Smoothing
• 2.5.3. Rotating Mask
• 2.5.4. Median Filter
• 3. Histograms
• 3.1. 1D Histograms
• 3.1.1. Histogram Smoothing
• 3.1.2. Colour Histograms
• 3.2. 3D Histograms
• 3.3. Histogram/Image Equalisation
• 3.4. Histogram Comparison
• 3.5. Back-projection
• 3.6. k-means Clustering
• 4. Binary Vision
• 4.1. Thresholding
• 4.1.1. Thresholding Problems
• 4.2. Threshold Detection Methods
• 4.2.1. Bimodal Histogram Analysis
• 4.2.2. Optimal Thresholding
• 4.2.3. Otsu Thresholding
• 4.3. Variations on Thresholding
• 4.3.1. Adaptive Thresholding
• 4.3.2. Band Thresholding
• 4.3.3. Semi-thresholding
• 4.3.4. Multispectral Thresholding
• 4.4. Mathematical Morphology
• 4.4.1. Dilation
• 4.4.2. Erosion
• 4.4.3. Opening and Closing
• 4.4.4. Grey-scale and Colour Morphology
• 4.5. Connectivity
• 4.5.1. Connectedness: Paradoxes and Solutions
• 4.5.2. Connected Components Analysis
• 5. Geometric Transformations
• 5.1. Problem Specification and Algorithm
• 5.2. Affine Transformations
• 5.2.1. Known Affine Transformations
• 5.2.2. Unknown Affine Transformations
• 5.3. Perspective Transformations
• 5.4. Specification of More Complex Transformations
• 5.5. Interpolation
• 5.5.1. Nearest Neighbour Interpolation
• 5.5.2. Bilinear Interpolation
• 5.5.3. Bi-Cubic Interpolation
• 5.6. Modelling and Removing Distortion from Cameras
• 5.6.7. Camera Distortions
• 5.6.2. Camera Calibration and Removing Distortion
• 6. Edges
• 6.1. Edge Detection
• 6.1.1. First Derivative Edge Detectors
• 6.1.2. Second Derivative Edge Detectors
• 6.1.3. Multispectral Edge Detection
• 6.1.4. Image Sharpening
• 6.2. Contour Segmentation
• 6.2.1. Basic Representations of Edge Data
• 6.2.2. Border Detection
• 6.2.3. Extracting Line Segment Representations of Edge Contours
• 6.3. Hough Transform
• 6.3.1. Hough for Lines
• 6.3.2. Hough for Circles
• 6.3.3. Generalised Hough
• 7. Features
• 7.1. Moravec Corner Detection
• 7.2. Harris Corner Detection
• 7.3. FAST Corner Detection
• 7.4. SIFT
• 7.4.1. Scale Space Extrema Detection
• 7.4.2. Accurate Keypoint Location
• 7.4.3. Keypoint Orientation Assignment
• 7.4.4. Keypoint Descriptor
• 7.4.5. Matching Keypoints
• 7.4.6. Recognition
• 7.5. Other Detectors
• 7.5.1. Minimum Eigenvalues
• 7.5.2. SURF
• 8. Recognition
• 8.1. Template Matching
• 8.1.1. Applications
• 8.1.2. Template Matching Algorithm
• 8.1.3. Matching Metrics
• 8.1.4. Finding Local Maxima or Minima
• 8.1.5. Control Strategies for Matching
• 8.2. Chamfer Matching
• 8.2.1. Chamfering Algorithm
• 8.2.2. Chamfer Matching Algorithm
• 8.3. Statistical Pattern Recognition
• 8.3.1. Probability Review
• 8.3.2. Sample Features
• 8.3.3. Statistical Pattern Recognition Technique
• 8.4. Cascade of Haar Classifiers
• 8.4.1. Features
• 8.4.2. Training
• 8.4.3. Classifiers
• 8.4.4. Recognition
• 8.5. Other Recognition Techniques
• 8.5.1. Support Vector Machines (SVM)
• 8.5.2. Histogram of Oriented Gradients (HoG)
• 8.6. Performance
• 8.6.1. Image and Video Datasets
• 8.6.2. Ground Truth
• 8.6.3. Metrics for Assessing Classification Performance
• 8.6.4. Improving Computation Time
• 9. Video
• 9.1. Moving Object Detection
• 9.1.1. Object of Interest
• 9.1.2. Common Problems
• 9.1.3. Difference Images
• 9.1.4. Background Models
• 9.1.5. Shadow Detection
• 9.2. Tracking
• 9.2.1. Exhaustive Search
• 9.2.2. Mean Shift
• 9.2.3. Dense Optical Flow
• 9.2.4. Feature Based Optical Flow
• 9.3. Performance
• 9.3.1. Video Datasets (and Formats)
• 9.3.2. Metrics for Assessing Video Tracking Performance
• 10. Vision Problems
• 10.1. Baby Food
• 10.2. Labels on Glue
• 10.3. O-rings
• 10.4. Staying in Lane
• 10.5. Reading Notices
• 10.6. Mailboxes
• 10.7. Abandoned and Removed Object Detection
• 10.8. Surveillance
• 10.9. Traffic Lights
• 10.10. Real Time Face Tracking
• 10.11. Playing Pool
• 10.12. Open Windows
• 10.13. Modelling Doors
• 10.14. Determining the Time from Analogue Clocks
• 10.15. Which Page
• 10.16. Nut/Bolt/Washer Classification
• 10.17. Road Sign Recognition
• 10.18. License Plates
• 10.19. Counting Bicycles
• 10.20. Recognise Paintings.

Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways. * Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries * Offers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issues * Provides enough material for a one semester course in computer vision at senior undergraduate and Masters levels * Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in images * Contains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook.
(source: Nielsen Book Data)

• Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface;
• Chapter 1: Building an Image Viewer; Technical requirements; Designing the user interface; Starting the project from scratch; Setting up the full user interface; Implementing the functions for the actions; The Exit action; Opening an image; Zooming in and out; Saving a copy; Navigating in the folder; Responding to hotkeys; Summary; Questions;
• Chapter 2: Editing Images Like a Pro; Technical requirements; The ImageEditor application; Blurring images using OpenCV; Adding the blur action

• Building and installing OpenCV from the sourceBlurring images; QPixmap, QImage, and Mat; QPixmap; QImage; Mat; Adding features using Qt's plugin mechanism; The plugin interface; Eroding images with ErodePlugin; Loading the plugin into our application; Editing images like a pro; Sharpening images; Cartoon effect; Rotating images; Affine transformation; Summary; Questions;
• Chapter 3: Home Security Applications; Technical requirements; The Gazer application; Starting the project and setting up the UI; Accessing cameras; Listing cameras with Qt; Capturing and playing

• Threading and the performance of real-time video processingCapturing and playing with Qt; Calculating the FPS; Saving videos; Motion analysis with OpenCV; Motion detection with OpenCV; Sending notifications to our mobile phone; Summary; Questions;
• Chapter 4: Fun with Faces; Technical requirements; The Facetious application; From Gazer to Facetious; Taking photos; Detecting faces using cascade classifiers; Detecting facial landmarks; Applying masks to faces; Loading images with the Qt resource system; Drawing masks on the faces; Selecting masks on the UI; Summary; Questions

• Chapter 5: Optical Character RecognitionTechnical requirements; Creating Literacy; Designing the UI; Setting up the UI; OCR with Tesseract; Building Tesseract from the source; Recognizing characters in Literacy; Detecting text areas with OpenCV; Recognizing characters on the screen; Summary; Questions;
• Chapter 6: Object Detection in Real Time; Technical requirements; Detecting objects using OpenCV; Detecting objects using a cascade classifier; Training a cascade classifier; The no-entry traffic sign; The faces of Boston Bulls; Detecting objects using deep learning models; About real time

Questions; Chapter 7: Real-Time Car Detection and Distance Measurement; Technical requirements; Car detection in real time; Distance measurement; Measuring the distance between cars or between the car and the camera; Measuring the distance between a car and the camera in the eye-level view; Switching between view modes; Summary; Questions; Chapter 8: Using OpenGL for the High-Speed Filtering of Images; Technical requirements; Hello OpenGL; OpenGL in Qt; Filtering images with OpenGL; Drawing images with OpenGL; Filtering images in the fragment shader; Saving the filtered images
We are entering the age of artificial intelligence, and Computer Vision plays an important role in the AI field. This book combines OpenCV 4 and Qt 5 as well as many deep learning models to develop many complete, practical, and functional applications through which the readers can learn a lot in CV, GUI, and AI domains.