OpenCV
3.4.19
Open Source Computer Vision
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Prev Tutorial: Create calibration pattern
Next Tutorial: Camera calibration With OpenCV
The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
Test data: use images in your data/chess folder.
Now, let us write code that detects a chessboard in an image and finds its distance from the camera. You can apply this method to any object with known 3D geometry; which you detect in an image.
Test data: use chess_test*.jpg images from your data folder.
Mat img = imread(argv[1], IMREAD_GRAYSCALE);
bool found = findChessboardCorners( img, boardSize, ptvec, CALIB_CB_ADAPTIVE_THRESH );
FileStorage fs( filename, FileStorage::READ ); Mat intrinsics, distortion; fs["camera_matrix"] >> intrinsics; fs["distortion_coefficients"] >> distortion;
vector<Point3f> boardPoints; // fill the array ... solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix, distCoeffs, rvec, tvec, false);
Question: how would you calculate distance from the camera origin to any one of the corners? Answer: As our image lies in a 3D space, firstly we would calculate the relative camera pose. This would give us 3D to 2D correspondences. Next, we can apply a simple L2 norm to calculate distance between any point (end point for corners).