Mastering OpenCV With Practical Computer Vision Projects by Emami, Shervin -- Read -- Imperial Library of Trantor

Index

Mastering OpenCV with Practical Computer Vision Projects

Table of Contents Mastering OpenCV with Practical Computer Vision Projects Credits About the Authors About the Reviewers www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

1. Cartoonifier and Skin Changer for Android

Accessing the webcam Main camera processing loop for a desktop app Generating a black-and-white sketch Generating a color painting and a cartoon Generating an "evil" mode using edge filters Generating an "alien" mode using skin detection

Skin-detection algorithm Showing the user where to put their face Implementation of the skin-color changer

Porting from desktop to Android

Setting up an Android project that uses OpenCV

Color formats used for image processing on Android Input color format from the camera Output color format for display

Adding the cartoonifier code to the Android NDK app

Reviewing the Android app Cartoonifying the image when the user taps the screen Saving the image to a file and to the Android picture gallery

Showing an Android notification message about a saved image

Changing cartoon modes through the Android menu bar

Reducing the random pepper noise from the sketch image

Showing the FPS of the app Using a different camera resolution Customizing the app

Summary

2. Marker-based Augmented Reality on iPhone or iPad

Creating an iOS project that uses OpenCV

Adding OpenCV framework Including OpenCV headers

Application architecture

Accessing the camera

Marker detection

Marker identification

Grayscale conversion Image binarization Contours detection Candidates search

Marker code recognition

Reading marker code Marker location refinement

Placing a marker in 3D

Camera calibration Marker pose estimation

Rendering the 3D virtual object

Creating the OpenGL rendering layer Rendering an AR scene

Summary References

3. Marker-less Augmented Reality

Marker-based versus marker-less AR Using feature descriptors to find an arbitrary image on video

Feature extraction Definition of a pattern object Matching of feature points

PatternDetector.cpp

Outlier removal

Cross-match filter Ratio test

PatternDetector.cpp

Homography estimation

PatternDetector.cpp

Homography refinement

PatternDetector.cpp

Putting it all together

Pattern pose estimation

PatternDetector.cpp Obtaining the camera-intrinsic matrix

Pattern.cpp

Application infrastructure

ARPipeline.hpp ARPipeline.cpp Enabling support for 3D visualization in OpenCV Creating OpenGL windows using OpenCV Video capture using OpenCV Rendering augmented reality

ARDrawingContext.hpp ARDrawingContext.cpp

Demonstration

main.cpp

Summary References

4. Exploring Structure from Motion Using OpenCV

Structure from Motion concepts Estimating the camera motion from a pair of images

Point matching using rich feature descriptors Point matching using optical flow Finding camera matrices

Reconstructing the scene Reconstruction from many views Refinement of the reconstruction Visualizing 3D point clouds with PCL Using the example code Summary References

5. Number Plate Recognition Using SVM and Neural Networks

Introduction to ANPR ANPR algorithm Plate detection

Segmentation Classification

Plate recognition

OCR segmentation Feature extraction OCR classification Evaluation

Summary

6. Non-rigid Face Tracking

Overview Utilities

Object-oriented design Data collection: Image and video annotation

Training data types Annotation tool Pre-annotated data (The MUCT dataset)

Geometrical constraints

Procrustes analysis Linear shape models A combined local-global representation Training and visualization

Facial feature detectors

Correlation-based patch models

Learning discriminative patch models Generative versus discriminative patch models

Accounting for global geometric transformations Training and visualization

Face detection and initialization Face tracking

Face tracker implementation Training and visualization Generic versus person-specific models

Summary References

7. 3D Head Pose Estimation Using AAM and POSIT

Active Appearance Models overview Active Shape Models

Getting the feel of PCA Triangulation Triangle texture warping

Model Instantiation – playing with the Active Appearance Model AAM search and fitting POSIT

Diving into POSIT POSIT and head model Tracking from webcam or video file

Summary References

8. Face Recognition using Eigenfaces or Fisherfaces

Introduction to face recognition and face detection

Step 1: Face detection

Implementing face detection using OpenCV Loading a Haar or LBP detector for object or face detection Accessing the webcam Detecting an object using the Haar or LBP Classifier

Grayscale color conversion Shrinking the camera image Histogram equalization

Detecting the face Step 2: Face preprocessing

Eye detection Eye search regions

Geometrical transformation Separate histogram equalization for left and right sides Smoothing Elliptical mask

Step 3: Collecting faces and learning from them

Collecting preprocessed faces for training Training the face recognition system from collected faces Viewing the learned knowledge Average face Eigenvalues, Eigenfaces, and Fisherfaces

Step 4: Face recognition

Face identification: Recognizing people from their face Face verification: Validating that it is the claimed person

Finishing touches: Saving and loading files Finishing touches: Making a nice and interactive GUI

Drawing the GUI elements

Startup mode Detection mode Collection mode Training mode Recognition mode

Checking and handling mouse clicks

Summary References

Index

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