Book Image

Mastering OpenCV 4 - Third Edition

By : Roy Shilkrot, David Millán Escrivá
Book Image

Mastering OpenCV 4 - Third Edition

By: Roy Shilkrot, David Millán Escrivá

Overview of this book

Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4.
Table of Contents (12 chapters)

Camera calibration with ArUco

To perform camera calibration as we discussed earlier, we must obtain corresponding 2D-3D point pairings. With ArUco marker detection, this task is made simple. ArUco provides a tool to create a calibration board, a grid of squares and AR markers, in which all the parameters are known: number, size, and position of markers. We can print such a board with our home or office printer, with the image for printing supplied by the ArUco API:

Ptr<aruco::Dictionary> dict = aruco::Dictionary::get(aruco::DICT_ARUCO_ORIGINAL);
Ptr<aruco::GridBoard> board = aruco::GridBoard::create(
10 /* N markers x */,
7 /* M markers y */,
14.0f /* marker width (mm) */,
9.2f /* marker separation (mm) */,
Mat boardImage;
board->draw({1000, 700}, boardImage, 25); // an image of 1000x700 pixels