OpenCV  3.4.15 Open Source Computer Vision
Camera calibration with square chessboard

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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.

• Compile OpenCV with samples by setting BUILD_EXAMPLES to ON in cmake configuration.
• Go to bin folder and use imagelist_creator to create an XML/YAML list of your images.
• Then, run calibration sample to get camera parameters. Use square size equal to 3cm.

## Pose estimation

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.

• Create an empty console project. Load a test image :
Mat img = imread(argv[1], IMREAD_GRAYSCALE);

• Detect a chessboard in this image using findChessboard function :
bool found = findChessboardCorners( img, boardSize, ptvec, CALIB_CB_ADAPTIVE_THRESH );

• Now, write a function that generates a vector<Point3f> array of 3d coordinates of a chessboard in any coordinate system. For simplicity, let us choose a system such that one of the chessboard corners is in the origin and the board is in the plane z = 0
• Read camera parameters from XML/YAML file :
FileStorage fs( filename, FileStorage::READ );
Mat intrinsics, distortion;
fs["camera_matrix"] >> intrinsics;
fs["distortion_coefficients"] >> distortion;

• Now we are ready to find a chessboard pose by running solvePnP :
vector<Point3f> boardPoints;
// fill the array
...

solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
distCoeffs, rvec, tvec, false);

• Calculate reprojection error like it is done in calibration sample (see opencv/samples/cpp/calibration.cpp, function computeReprojectionErrors).

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).