Learning OpenCV 3 Application Development

Build, create, and deploy your own computer vision applications with the power of OpenCV

Learning OpenCV 3 Application Development

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Samyak Datta

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Build, create, and deploy your own computer vision applications with the power of OpenCV
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Book Details

ISBN 139781784391454
Paperback310 pages

Book Description

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++.

At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations.

Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code!

The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!

Table of Contents

Chapter 1: Laying the Foundation
Digital image basics
Introduction to the Mat class
Exploring the Mat class: loading images
Exploring the Mat class - declaring Mat objects
Digging inside Mat objects
Traversing Mat objects
Image enhancement
Lookup tables
Linear transformations
Logarithmic transformations
Summary
Chapter 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
Chapter 3: Image Thresholding
Binary images
Image thresholding basics
Image thresholding in OpenCV
Types of simple image thresholding
Adaptive thresholding
Morphological operations
Erosion and dilation
Erosion and dilation in OpenCV
Summary
Chapter 4: Image Histograms
The basics of histograms
Histograms in OpenCV
Plotting histograms in OpenCV
Color histograms in OpenCV
Multidimensional histograms in OpenCV
Summary
Chapter 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
Chapter 6: Face Detection Using OpenCV
Image classification systems
Face detection
Haar features
Integral image
AdaBoost learning
Cascaded classifiers
Face detection in OpenCV
Gender classification
Working with real datasets
Summary
Chapter 7: Affine Transformations and Face Alignment
Exploring 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
Chapter 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
Chapter 9: Machine Learning with OpenCV
What is machine learning
Supervised and unsupervised learning
Revisiting the image classification framework
k-means clustering - the basics
k-nearest neighbors classifier - introduction
Support vector machines (SVMs) - introduction
Non-linear SVMs
Using an SVM as a gender classifier
Overfitting
Cross-validation
Common evaluation metrics
The P-R curve
Some qualitative results
Summary

What You Will Learn

  • Explore the steps involved in building a typical computer vision/machine learning application
  • Understand the relevance of OpenCV at every stage of building an application
  • Harness the vast amount of information that lies hidden in images into the apps you build
  • Incorporate visual information in your apps to create more appealing software
  • Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV
  • Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings
  • Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition

Authors

Table of Contents

Chapter 1: Laying the Foundation
Digital image basics
Introduction to the Mat class
Exploring the Mat class: loading images
Exploring the Mat class - declaring Mat objects
Digging inside Mat objects
Traversing Mat objects
Image enhancement
Lookup tables
Linear transformations
Logarithmic transformations
Summary
Chapter 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
Chapter 3: Image Thresholding
Binary images
Image thresholding basics
Image thresholding in OpenCV
Types of simple image thresholding
Adaptive thresholding
Morphological operations
Erosion and dilation
Erosion and dilation in OpenCV
Summary
Chapter 4: Image Histograms
The basics of histograms
Histograms in OpenCV
Plotting histograms in OpenCV
Color histograms in OpenCV
Multidimensional histograms in OpenCV
Summary
Chapter 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
Chapter 6: Face Detection Using OpenCV
Image classification systems
Face detection
Haar features
Integral image
AdaBoost learning
Cascaded classifiers
Face detection in OpenCV
Gender classification
Working with real datasets
Summary
Chapter 7: Affine Transformations and Face Alignment
Exploring 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
Chapter 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
Chapter 9: Machine Learning with OpenCV
What is machine learning
Supervised and unsupervised learning
Revisiting the image classification framework
k-means clustering - the basics
k-nearest neighbors classifier - introduction
Support vector machines (SVMs) - introduction
Non-linear SVMs
Using an SVM as a gender classifier
Overfitting
Cross-validation
Common evaluation metrics
The P-R curve
Some qualitative results
Summary

Book Details

ISBN 139781784391454
Paperback310 pages
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