Book Image

Mastering OpenCV with Practical Computer Vision Projects

Book Image

Mastering OpenCV with Practical Computer Vision Projects

Overview of this book

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials.Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV's new C++ interface before migrating from the C API to the C++ API.Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you're most interested in.Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects.

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Mastering OpenCV with Practical Computer Vision Projects
Dec, 2012 | 340 pages
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Table of Contents (15 chapters)
Mastering OpenCV with Practical Computer Vision Projects
Mastering OpenCV with Practical Computer Vision Projects
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Cartoonifier and Skin Changer for Android
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
Porting from desktop to Android
Summary
2
Marker-based Augmented Reality on iPhone or iPad
Marker-based Augmented Reality on iPhone or iPad
Creating an iOS project that uses OpenCV
Application architecture
Marker detection
Placing a marker in 3D
Rendering the 3D virtual object
Summary
References
3
Marker-less Augmented Reality
Marker-less Augmented Reality
Marker-based versus marker-less AR
Using feature descriptors to find an arbitrary image on video
Pattern pose estimation
Application infrastructure
Summary
References
4
Exploring Structure from Motion Using OpenCV
Exploring Structure from Motion Using OpenCV
Structure from Motion concepts
Estimating the camera motion from a pair of images
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
Number Plate Recognition Using SVM and Neural Networks
Introduction to ANPR
ANPR algorithm
Plate detection
Plate recognition
Summary
6
Non-rigid Face Tracking
Non-rigid Face Tracking
Overview
Utilities
Geometrical constraints
Facial feature detectors
Face detection and initialization
Face tracking
Summary
References
7
3D Head Pose Estimation Using AAM and POSIT
3D Head Pose Estimation Using AAM and POSIT
Active Appearance Models overview
Active Shape Models
Model Instantiation – playing with the Active Appearance Model
AAM search and fitting
POSIT
Summary
References
8
Face Recognition using Eigenfaces or Fisherfaces
Face Recognition using Eigenfaces or Fisherfaces
Introduction to face recognition and face detection
Summary
References
Index
Index

Visualizing 3D point clouds with PCL

While working with 3D data, it is hard to quickly understand if a result is correct simply by looking at reprojection error measures or raw point information. On the other hand, if we look at the point cloud itself we can immediately verify whether it makes sense or there was an error. For visualization we will use an up-and-coming sister project for OpenCV, called the Point Cloud Library (PCL). It comes with many tools for visualizing and also analyzing point clouds, such as finding flat surfaces, matching point clouds, segmenting objects, and eliminating outliers. These tools are highly useful if our goal is not a point cloud but rather some higher-order information such as a 3D model.

First, we should represent our cloud (essentially a list of 3D points) in PCL's data structures. This can be done as follows:

pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud;

void PopulatePCLPointCloud(const vector<Point3d>& pointcloud,
const std::vector&lt...
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