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.
Table of Contents (15 chapters)
Mastering OpenCV with Practical Computer Vision Projects
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Pattern pose estimation


The pose estimation is done in a similar manner to marker pose estimation from the previous chapter. As usual we need 2D-3D correspondences to estimate the camera-extrinsic parameters. We assign four 3D points to coordinate with the corners of the unit rectangle that lies in the XY plane (the Z axis is up), and 2D points correspond to the corners of the image bitmap.

PatternDetector.cpp

The buildPatternFromImage class creates a Pattern object from the input image as follows:

void PatternDetector::buildPatternFromImage(const cv::Mat& image, Pattern& pattern) const
{
    int numImages = 4;
    float step = sqrtf(2.0f);

    // Store original image in pattern structure
    pattern.size = cv::Size(image.cols, image.rows);
    pattern.frame = image.clone();
    getGray(image, pattern.grayImg);
    
    // Build 2d and 3d contours (3d contour lie in XY plane since // it's planar)
    pattern.points2d.resize(4);
    pattern.points3d.resize(4);

    // Image dimensions...