| OpenCV 3.4.8 Open Source Computer Vision

Camera calibration with square chessboard

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).