Mastering OpenCV with Practical Computer Vision Projects电子书

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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Free Access for Packt account holders

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