OpenCV 3 Blueprints.pdf

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OpenCV 3 Blueprints

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Basic installation guides

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Getting the Most out of Your Camera System

Coloring the light

Capturing the subject in the moment

Rounding up the unusual suspects

Supercharging the PlayStation Eye

Supercharging the ASUS Xtion PRO Live and other OpenNI-compliant depth cameras

Supercharging the GS3-U3-23S6M-C and other Point Grey Research cameras

Shopping for glass

Summary

2. Photographing Nature and Wildlife with an Automated Camera

Planning the camera trap

Controlling a photo camera with gPhoto2

Writing a shell script to unmount camera drives

Setting up and testing gPhoto2

Writing a shell script for exposure bracketing

Writing a Python script to wrap gPhoto2

Finding libgphoto2 and wrappers

Detecting the presence of a photogenic subject

Detecting a moving subject

Detecting a colorful subject

Detecting the face of a mammal

Processing images to show subtle colors and motion

Creating HDR images

Creating time-lapse videos

Further study

Summary

3. Recognizing Facial Expressions with Machine Learning

Introducing facial expression recognition

Facial expression dataset

Finding the face region in the image

Extracting the face region using a face detection algorithm

Extracting facial landmarks from the face region

Introducing the flandmark library

Downloading and compiling the flandmark library

Detecting facial landmarks with flandmark

Visualizing the landmarks in an image

Extracting the face region

Software usage guide

Feature extraction

Extracting image features from facial component regions

Contributed features

Advanced features

Visualizing key points for each feature type

Computing the distribution of feature representation over k clusters

Clustering image features space into k clusters

Computing a final feature for each image

Dimensionality reduction

Software usage guide

Classification

Classification process

Splitting the dataset into a training set and testing set

Support vector machines

Training stage

Testing stage

Multi-layer perceptron

Training stage

Define the network

Train the network

Testing stage

K-Nearest Neighbors (KNN)

Training stage

The testing stage

Normal Bayes classifier

Training stage

Testing stage

Software usage guide

Evaluation

Evaluation with different learning algorithms

Evaluation with different features

Evaluation with a different number of clusters

System overview

Further reading

Compiling the opencv_contrib module

Kaggle facial expression dataset

Facial landmarks

What are facial landmarks?

How do you detect facial landmarks?

How do you use facial landmarks?

Improving feature extraction

K-fold cross validation

Summary

4. Panoramic Image Stitching Application Using Android Studio and NDK

Introducing the concept of panorama

The Android section – an application user interface

The setup activity layout

Capturing the camera frame

Using the Camera API to get the camera frame

Implementing the Capture button

Implementing the Save button

Integrating OpenCV into the Android Studio

Compiling OpenCV Android SDK to the Android Studio project

Setting up the Android Studio to work with OpenCV

Importing the OpenCV Android SDK

Creating a Java and C++ interaction with Java Native Interface (JNI)

Compiling OpenCV C++ with NDK/JNI

Implementing the OpenCV Java code

Implementing the OpenCV C++ code

Application showcase

Further improvement

Summary

5. Generic Object Detection for Industrial Applications

Difference between recognition, detection, and categorization

Smartly selecting and preparing application specific training data

The amount of training data

Creating object annotation files for the positive samples

Parsing your positive dataset into the OpenCV data vector

Parameter selection when training an object model

Training parameters involved in training an object model

The cascade classification process in detail

Step 1 – grabbing positive and negative samples

Step 2 – precalculation of integral image and all possible features from the training data

Step 3 – firing up the boosting process

Step 4 – saving the temporary result to a stage file

The resulting object model explained in detail

HAAR-like wavelet feature models

Local binary pattern models

Visualization tool for object models

Using cross-validation to achieve the best model possible

Using scene specific knowledge and constraints to optimize the detection result

Using the parameters of the detection command to influence your detection result

Increasing object instance detection and reducing false positive detections

Obtaining rotation invariance object detection

2D scale space relation

Performance evaluation and GPU optimizations

Object detection performance testing

Optimizations using GPU code

Practical applications

Summary

6. Efficient Person Identification Using Biometric Properties

Biometrics, a general approach

Step 1 – getting a good training dataset and applying application-specific normalization

Step 2 – creating a descriptor of the recorded biometric

Step 3 – using machine learning to match the retrieved feature vector

Step 4 – think about your authentication process

Face detection and recognition

Face detection using the Viola and Jones boosted cascade classifier algorithm

Data normalization on the detected face regions

Various face recognition approaches and their corresponding feature space

Eigenface decomposition through PCA

Linear discriminant analysis using the Fisher criterion

Local binary pattern histograms

The problems with facial recognition in its current OpenCV 3 based implementation

Fingerprint identification, how is it done?

Implementing the approach in OpenCV 3

Iris identification, how is it done?

Implementing the approach in OpenCV 3

Combining the techniques to create an efficient people-registration system

Summary

7. Gyroscopic Video Stabilization

Stabilization with images

Stabilization with hardware

A hybrid of hardware and software

The math

The camera model

The Camera motion

Rolling shutter compensation

Image warping

Project overview

Capturing data

Recording video

Recording gyro signals

Android specifics

Threaded overlay

Reading media files

Calibration

Data structures

Reading the gyroscope trace

The training video

Handling rotations

Rotating an image

Accumulated rotations

The calibration class

Undistorting images

Testing calibration results

Rolling shutter compensation

Calibrating the rolling shutter

Warping with grid points

Unwarping with calibration

What's next?

Identifying gyroscope axes

Estimating the rolling shutter direction

Smoother timelapses

Repository of calibration parameters

Incorporating translations

Additional tips

Use the Python pickle module

Write out single images

Testing without the delta

Summary

Index

OpenCV 3 Blueprints

Table of Contents OpenCV 3 Blueprints Credits About the Authors About the Reviewers www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Basic installation guides Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Getting the Most out of Your Camera System Coloring the light Capturing the subject in the moment Rounding up the unusual suspects Supercharging the PlayStation Eye Supercharging the ASUS Xtion PRO Live and other OpenNI-compliant depth cameras Supercharging the GS3-U3-23S6M-C and other Point Grey Research cameras Shopping for glass

Summary 2. Photographing Nature and Wildlife with an Automated Camera Planning the camera trap Controlling a photo camera with gPhoto2 Writing a shell script to unmount camera drives Setting up and testing gPhoto2 Writing a shell script for exposure bracketing Writing a Python script to wrap gPhoto2 Finding libgphoto2 and wrappers Detecting the presence of a photogenic subject Detecting a moving subject Detecting a colorful subject Detecting the face of a mammal Processing images to show subtle colors and motion Creating HDR images Creating time-lapse videos Further study Summary 3. Recognizing Facial Expressions with Machine Learning Introducing facial expression recognition Facial expression dataset Finding the face region in the image Extracting the face region using a face detection algorithm Extracting facial landmarks from the face region Introducing the flandmark library Downloading and compiling the flandmark library Detecting facial landmarks with flandmark Visualizing the landmarks in an image Extracting the face region Software usage guide Feature extraction

Extracting image features from facial component regions Contributed features Advanced features Visualizing key points for each feature type Computing the distribution of feature representation over k clusters Clustering image features space into k clusters Computing a final feature for each image Dimensionality reduction Software usage guide Classification Classification process Splitting the dataset into a training set and testing set Support vector machines Training stage Testing stage Multi-layer perceptron Training stage Define the network Train the network Testing stage K-Nearest Neighbors (KNN) Training stage The testing stage Normal Bayes classifier Training stage Testing stage Software usage guide Evaluation Evaluation with different learning algorithms Evaluation with different features Evaluation with a different number of clusters

System overview Further reading Compiling the opencv_contrib module Kaggle facial expression dataset Facial landmarks What are facial landmarks? How do you detect facial landmarks? How do you use facial landmarks? Improving feature extraction K-fold cross validation Summary 4. Panoramic Image Stitching Application Using Android Studio and NDK Introducing the concept of panorama The Android section – an application user interface The setup activity layout Capturing the camera frame Using the Camera API to get the camera frame Implementing the Capture button Implementing the Save button Integrating OpenCV into the Android Studio Compiling OpenCV Android SDK to the Android Studio project Setting up the Android Studio to work with OpenCV Importing the OpenCV Android SDK Creating a Java and C++ interaction with Java Native Interface (JNI) Compiling OpenCV C++ with NDK/JNI Implementing the OpenCV Java code Implementing the OpenCV C++ code Application showcase Further improvement Summary 5. Generic Object Detection for Industrial Applications

Difference between recognition, detection, and categorization Smartly selecting and preparing application specific training data The amount of training data Creating object annotation files for the positive samples Parsing your positive dataset into the OpenCV data vector Parameter selection when training an object model Training parameters involved in training an object model The cascade classification process in detail Step 1 – grabbing positive and negative samples Step 2 – precalculation of integral image and all possible features from the training data Step 3 – firing up the boosting process Step 4 – saving the temporary result to a stage file The resulting object model explained in detail HAAR-like wavelet feature models Local binary pattern models Visualization tool for object models Using cross-validation to achieve the best model possible Using scene specific knowledge and constraints to optimize the detection result Using the parameters of the detection command to influence your detection result Increasing object instance detection and reducing false positive detections Obtaining rotation invariance object detection 2D scale space relation Performance evaluation and GPU optimizations Object detection performance testing Optimizations using GPU code Practical applications Summary 6. Efficient Person Identification Using Biometric Properties Biometrics, a general approach Step 1 – getting a good training dataset and applying application-specific normalization

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