CV_CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization.Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. CV_CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further.tvecs – Output vector of translation vectors estimated for each pattern view.ĭifferent flags that may be zero or a combination of the following values:.That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the calibration pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the calibration pattern in the k-th pattern view (k=0. rvecs – Output vector of rotation vectors (see Rodrigues() ) estimated for each pattern view.distCoeffs – Output vector of distortion coefficients of 4, 5, or 8 elements.If CV_CALIB_USE_INTRINSIC_GUESS and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. cameraMatrix – Output 3x3 floating-point camera matrix.imageSize – Size of the image used only to initialize the intrinsic camera matrix.Usually, all the elements are the same and equal to the number of feature points on the calibration pattern. Each element is the number of points in each view. pointCounts – In the old interface this is a vector of integers, containing as many elements, as the number of views of the calibration pattern.imagePoints.size() and objectPoints.size() and imagePoints.size() must be equal to objectPoints.size() for each i. In the new interface it is a vector of vectors of the projections of calibration pattern points. In the old interface all the vectors of object points from different views are concatenated together. The points are 3D, but since they are in a pattern coordinate system, then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that Z-coordinate of each input object point is 0. Although, it is possible to use partially occluded patterns, or even different patterns in different views.
If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same.
The outer vector contains as many elements as the number of the pattern views.
In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space. CalibrateCamera2 (objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, flags=0 ) → None ¶ Parameters: calibrateCamera (objectPoints, imagePoints, imageSize ] ] ] ] ) → retval, cameraMatrix, distCoeffs, rvecs, tvecs ¶ C: double cvCalibrateCamera2 (const CvMat* objectPoints, const CvMat* imagePoints, const CvMat* pointCounts, CvSize imageSize, CvMat* cameraMatrix, CvMat* distCoeffs, CvMat* rvecs=NULL, CvMat* tvecs=NULL, int flags=0 ) ¶ Python: cv. C++: double calibrateCamera (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags=0 ) ¶ Python: cv2. Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.