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Learning OpenCV 3 无水印pdf.pdf

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    Cover
    Copyright
    About the Author
    Table of Contents
    Preface
    1: Laying the Foundation
    Digital image basics
    Pixel intensities
    Color depth and color spaces
    Color channels
    Introduction to the Mat class
    Exploring the Mat class: loading images
    Exploring the Mat class – declaring Mat objects
    Spatial dimensions of an image
    Color space or color depth
    Color channels
    Image size
    Default initialization value
    Digging inside Mat objects
    Traversing Mat objects
    Continuity of the Mat data matrix
    Image traversals
    Image enhancement
    Lookup tables
    Linear transformations
    Identity transformation
    Negative transformation
    Logarithmic transformations
    Log transformation
    Exponential or inverse-log transformation
    Summary
    2: Image Filtering
    Neighborhood of a pixel
    Image averaging
    Image filters
    Image averaging in OpenCV
    Blurring an image in OpenCV
    Gaussian smoothing
    Gaussian function and Gaussian filtering
    Gaussian filtering in OpenCV
    Using your own filters in OpenCV
    Image noise
    Vignetting
    Implementing Vignetting in OpenCV
    Summary
    3: Image Thresholding
    Binary images
    Image thresholding basics
    Image thresholding in OpenCV
    Types of simple image thresholding
    Binary threshold
    Inverted binary threshold
    Truncate
    Threshold-to-zero
    Inverted threshold-to-zero
    Adaptive thresholding
    Morphological operations
    Erosion and dilation
    Erosion and dilation in OpenCV
    Summary
    4: Image Histograms
    The basics of histograms
    Histograms in OpenCV
    Plotting histograms in OpenCV
    Color histograms in OpenCV
    Multidimensional histograms in OpenCV
    Summary
    5: Image Derivatives and Edge Detection
    Image derivatives
    Image derivatives in two dimensions
    Visualizing image derivatives with OpenCV
    The Sobel derivative filter
    From derivatives to edges
    The Sobel detector – a basic framework for edge detection
    The Canny edge detector
    Image noise and edge detection
    Laplacian – yet another edge detection technique
    Blur detection using OpenCV
    Summary
    6: Face Detection Using OpenCV
    Image classification systems
    Face detection
    Haar features
    Integral image
    Integral image in OpenCV
    AdaBoost learning
    Cascaded classifiers
    Face detection in OpenCV
    Controlling the quality of detected faces
    Gender classification
    Working with real datasets
    Summary
    7: Affine Transformations and Face Alignment
    Exploring the dataset
    Running face detection on the dataset
    Face alignment – the first step in facial analysis
    Rotating faces
    Image cropping — basics
    Image cropping for face alignment
    Face alignment – the complete pipeline
    Summary
    8: Feature Descriptors in OpenCV
    Introduction to the local binary pattern
    A basic implementation of LBP
    Variants of LBP
    What does LBP capture?
    Applying LBP to aligned facial images
    A complete implementation of LBP
    Putting it all together – the main() function
    Summary
    9: Machine Learning with OpenCV
    What is machine learning
    Supervised and unsupervised learning
    Revisiting the image classification framework
    k-means clustering – the basics
    k-means clustering – the algorithm
    k-means clustering in OpenCV
    k-nearest neighbors classifier – introduction
    k-nearest neighbors classifier – algorithm
    What k to use
    k-nearest neighbors classifier in OpenCV
    Some problems with kNN
    Some enhancements to kNN
    Support vector machines (SVMs) – introduction
    Intuition into the workings of SVMs
    Non-linear SVMs
    SVM in OpenCV
    Using an SVM as a gender classifier
    Overfitting
    Cross-validation
    Common evaluation metrics
    The P-R curve
    Some qualitative results
    Summary
    Appendix: Command-line Arguments in C++
    Introduction to command-line arguments
    Parsing command-line arguments
    Summary
    Index
    Learning OpenCV 3 Application Development Build, create, and deploy your own computer vision applications with the power of OpenCV Samyak Datta BIRMINGHAM - MUMBAI
    Learning OpenCV 3 Application Development Copyright © 2016 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: December 2016 Production reference: 1131216 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78439-145-4 www.packtpub.com
    Credits Author Samyak Datta Copy Editor Safis Editing Reviewer Nikolaus Gradwohl Project Coordinator Sheejal Shah Commissioning Editor Kunal Parikh Acquisition Editor Sonali Vernekar Proofreader Safis Editing Indexer Rekha Nair Content Development Editor Nikhil Borkar Graphics Abhinash Sahu Technical Editor Hussain Kanchwala Production Coordinator Shraddha Falebhai
    About the Author Samyak Datta has a bachelor's and a master's degree in Computer Science from the Indian Institute of Technology, Roorkee. He is a computer vision and machine learning enthusiast. His first contact with OpenCV was in 2013 when he was working on his master's thesis, and since then, there has been no looking back. He has contributed to OpenCV's GitHub repository. Over the course of his undergraduate and master's degrees, Samyak has had the opportunity to engage with both the industry and research. He worked with Google India and Media.net (Directi) as a software engineering intern, where he was involved with projects ranging from machine learning and natural language processing to computer vision. As of 2016, he is working at the Center for Visual Information Technology (CVIT) at the Indian Institute of Information Technology, Hyderabad.
    About the Reviewer Nikolaus Gradwohl was born 1976 in Vienna, Austria and always wanted to become an inventor like Gyro Gearloose. When he got his first Atari, he figured out that being a computer programmer is the closest he could get to that dream. He has written programs for nearly anything that can be programmed, ranging from 8-bit microcontrollers to mainframes, for a living. In his free time, he collects programming languages and operating systems. He is the author of the book Processing 2: Creative Coding Hotshot. You can see some of his work on his blog at h t t p ://w w w . l o c a l - g u r u . n e t /.
    www.PacktPub.com For support files and downloads related to your book, please visit www.PacktPub.com. Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.PacktPub.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at service@packtpub.com for more details. At www.PacktPub.com, you can also read a collection of free technical articles, sign up for a range of free newsletters and receive exclusive discounts and offers on Packt books and eBooks. h t t p s ://w w w . p a c k t p u b . c o m /m a p t Get the most in-demand software skills with Mapt. Mapt gives you full access to all Packt books and video courses, as well as industry-leading tools to help you plan your personal development and advance your career. Why subscribe? Fully searchable across every book published by Packt Copy and paste, print, and bookmark content On demand and accessible via a web browser
    Table of Contents Preface Chapter 1: Laying the Foundation Digital image basics Pixel intensities Color depth and color spaces Color channels Introduction to the Mat class Exploring the Mat class: loading images Exploring the Mat class – declaring Mat objects Spatial dimensions of an image Color space or color depth Color channels Image size Default initialization value Digging inside Mat objects Traversing Mat objects Continuity of the Mat data matrix Image traversals Image enhancement Lookup tables Linear transformations Identity transformation Negative transformation Logarithmic transformations Log transformation Exponential or inverse-log transformation Summary Chapter 2: Image Filtering Neighborhood of a pixel Image averaging Image filters Image averaging in OpenCV Blurring an image in OpenCV Gaussian smoothing 1 7 8 9 9 11 11 13 15 15 16 16 17 17 19 22 23 24 28 30 31 32 33 38 38 46 50 51 52 54 55 58 65 66
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