calibrate_camerasT_calibrate_camerasCalibrateCamerasCalibrateCamerascalibrate_cameras (Operator)

Name

calibrate_camerasT_calibrate_camerasCalibrateCamerasCalibrateCamerascalibrate_cameras — Determine all camera parameters by a simultaneous minimization process.

Signature

calibrate_cameras( : : CalibDataID : Error)

Herror T_calibrate_cameras(const Htuple CalibDataID, Htuple* Error)

void CalibrateCameras(const HTuple& CalibDataID, HTuple* Error)

double HCalibData::CalibrateCameras() const

static void HOperatorSet.CalibrateCameras(HTuple calibDataID, out HTuple error)

double HCalibData.CalibrateCameras()

def calibrate_cameras(calib_data_id: HHandle) -> float

Description

The operator calibrate_camerascalibrate_camerasCalibrateCamerasCalibrateCamerasCalibrateCamerascalibrate_cameras calculates the internal and external camera parameters of a calibration data model specified in CalibDataIDCalibDataIDCalibDataIDCalibDataIDcalibDataIDcalib_data_id. The calibration data model describes a setup of one or more cameras and is specified during the creation of the data model. You can find detailed information about the calibration process in the chapter reference Calibration.

The root mean square error (RMSE) of the back projection of the optimization is returned in ErrorErrorErrorErrorerrorerror (in pixels). The error gives a general indication whether the optimization was successful. You can find more details about the RMSE in the chapter reference mentioned above.

Execution Information

This operator modifies the state of the following input parameter:

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters

CalibDataIDCalibDataIDCalibDataIDCalibDataIDcalibDataIDcalib_data_id (input_control, state is modified)  calib_data HCalibData, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Handle of a calibration data model.

ErrorErrorErrorErrorerrorerror (output_control)  number HTuplefloatHTupleHtuple (real) (double) (double) (double)

Back projection root mean square error (RMSE) of the optimization.

Possible Predecessors

create_calib_datacreate_calib_dataCreateCalibDataCreateCalibDataCreateCalibDatacreate_calib_data, set_calib_data_cam_paramset_calib_data_cam_paramSetCalibDataCamParamSetCalibDataCamParamSetCalibDataCamParamset_calib_data_cam_param, set_calib_data_calib_objectset_calib_data_calib_objectSetCalibDataCalibObjectSetCalibDataCalibObjectSetCalibDataCalibObjectset_calib_data_calib_object, set_calib_data_observ_pointsset_calib_data_observ_pointsSetCalibDataObservPointsSetCalibDataObservPointsSetCalibDataObservPointsset_calib_data_observ_points, find_calib_objectfind_calib_objectFindCalibObjectFindCalibObjectFindCalibObjectfind_calib_object, set_calib_dataset_calib_dataSetCalibDataSetCalibDataSetCalibDataset_calib_data, remove_calib_data_observremove_calib_data_observRemoveCalibDataObservRemoveCalibDataObservRemoveCalibDataObservremove_calib_data_observ

Possible Successors

get_calib_dataget_calib_dataGetCalibDataGetCalibDataGetCalibDataget_calib_data

References

Carsten Steger: “A Comprehensive and Versatile Camera Model for Cameras with Tilt Lenses”; International Journal of Computer Vision, vol. 123, no. 2, pp. 121-159, 2017.
Carsten Steger, Markus Ulrich, Christian Wiedemann: “Machine Vision Algorithms and Applications”; Wiley-VCH, Weinheim, 2nd Edition, 2018.
Markus Ulrich, Carsten Steger: “A Camera Model for Cameras with Hypercentric Lenses and Some Example Applications”; Machine Vision and Applications, vol. 30, no. 6, pp. 1013-1028, 2019.
Carsten Steger, Markus Ulrich: “A Camera Model for Line-Scan Cameras with Telecentric Lenses”; International Journal of Computer Vision, vol. 129, no. 1, pp. 80-99, 2021.
Carsten Steger, Markus Ulrich: “A Multi-view Camera Model for Line-Scan Cameras with Telecentric Lenses”; Journal of Mathematical Imaging and Vision, vol. 64, no. 2, pp. 105-130, 2022.

Module

Calibration