Calibration¶

class Calibration(*args, **kwargs)¶

Bases: pybind11_object

Tools for perspective camera calibration.

Overloaded function.

__init__(self: visp._visp.vision.Calibration) -> None

Default constructor.

__init__(self: visp._visp.vision.Calibration, c: visp._visp.vision.Calibration) -> None

Copy constructor.

Methods

`__init__`	Overloaded function.
`addPoint`	Add a new point in the array of points.
`clearPoint`	Suppress all the point in the array of point.
`computeCalibration`	Compute the calibration according to the desired method using one pose.
`computeCalibrationMulti`	Compute the multi-images calibration according to the desired method using many poses.
`computeStdDeviation`	Overloaded function.
`computeStdDeviation_dist`	Compute and return the standard deviation expressed in pixel for pose matrix and camera intrinsic parameters with pixel to meter model.
`displayData`	Display the data of the calibration (center of the tracked dots).
`displayGrid`	Display estimated centers of dots using intrinsic camera parameters with model with distortion and the computed pose.
`getLambda`	Get the gain of the virtual visual servoing algorithm.
`getResidual`	Get the residual in pixels.
`getResidual_dist`	Get the residual for perspective projection with distortion (in pixels).
`get_npt`	Get the number of points.
`init`	Basic initialisation (called by the constructors).
`readData`	Read data from disk : data are organized as follow oX oY oZ u v
`readGrid`	Read calibration grid coordinates from disk.
`setAspectRatio`	Set pixel aspect ratio px/py.
`setLambda`	Set the gain for the virtual visual servoing algorithm.
`writeData`	Write data into a file.

Inherited Methods

Operators

`__doc__`
`__init__`	Overloaded function.
`__module__`

Attributes

`CALIB_LAGRANGE`
`CALIB_LAGRANGE_VIRTUAL_VS`
`CALIB_LAGRANGE_VIRTUAL_VS_DIST`
`CALIB_VIRTUAL_VS`
`CALIB_VIRTUAL_VS_DIST`
`__annotations__`
`aspect_ratio`
`cMo`
`cMo_dist`
`cam`
`cam_dist`
`eMc`
`eMc_dist`
`rMe`

class CalibrationMethodType(self, value: int)¶

Bases: pybind11_object

Minimization algorithm use to estimate the camera parameters.

Values:

CALIB_LAGRANGE: Lagrange approach without estimation of the distortion.
CALIB_VIRTUAL_VS: Virtual visual servoing approach without estimation of the distortion (results are similar to Lowe approach).
CALIB_VIRTUAL_VS_DIST: Virtual visual servoing approach with estimation of the distortion.
CALIB_LAGRANGE_VIRTUAL_VS: Lagrange approach first, than virtual visual servoing approach, without estimation of the distortion.
CALIB_LAGRANGE_VIRTUAL_VS_DIST: Lagrange approach first, than virtual visual servoing approach, with estimation of the distortion.

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: visp._visp.vision.Calibration) -> None

Default constructor.

__init__(self: visp._visp.vision.Calibration, c: visp._visp.vision.Calibration) -> None

Copy constructor.

addPoint(self, X: float, Y: float, Z: float, ip: visp._visp.core.ImagePoint) → int¶

Add a new point in the array of points.

Parameters:

X: float¶: 3D coordinates of a point in the object frame
Y: float¶: 3D coordinates of a point in the object frame
Z: float¶: 3D coordinates of a point in the object frame
ip: visp._visp.core.ImagePoint¶: 2D Coordinates of the point in the camera frame.

clearPoint(self) → int¶: Suppress all the point in the array of point.

computeCalibration(self, method: visp._visp.vision.Calibration.CalibrationMethodType, cMo_est: visp._visp.core.HomogeneousMatrix, cam_est: visp._visp.core.CameraParameters, verbose: bool = false) → int¶

Compute the calibration according to the desired method using one pose.

Parameters:

method: visp._visp.vision.Calibration.CalibrationMethodType¶: Method that will be used to estimate the parameters.
cMo_est: visp._visp.core.HomogeneousMatrix¶: estimated homogeneous matrix that defines the pose.
cam_est: visp._visp.core.CameraParameters¶: estimated intrinsic camera parameters.
verbose: bool = false¶: set at true if information about the residual at each loop of the algorithm is hoped.

Returns:

EXIT_SUCCESS if the calibration succeed, EXIT_FAILURE otherwise.

static computeCalibrationMulti(method: visp._visp.vision.Calibration.CalibrationMethodType, table_cal: list[visp._visp.vision.Calibration], cam_est: visp._visp.core.CameraParameters, globalReprojectionError: float, verbose: bool = false) → tuple[int, list[visp._visp.vision.Calibration], float]¶

Compute the multi-images calibration according to the desired method using many poses.

Parameters:

method: visp._visp.vision.Calibration.CalibrationMethodType¶: Method used to estimate the camera parameters.
table_cal: list[visp._visp.vision.Calibration]¶: Vector of vpCalibration .
cam_est: visp._visp.core.CameraParameters¶: Estimated intrinsic camera parameters.
globalReprojectionError: float¶: Global reprojection error or global residual.
verbose: bool = false¶: Set at true if information about the residual at each loop of the algorithm is hoped.

Returns:

A tuple containing:

EXIT_SUCCESS if the calibration succeed, EXIT_FAILURE otherwise.
table_cal: Vector of vpCalibration .
globalReprojectionError: Global reprojection error or global residual.

computeStdDeviation(*args, **kwargs)¶

Overloaded function.

computeStdDeviation(self: visp._visp.vision.Calibration, deviation: float, deviation_dist: float) -> tuple[float, float]

Compute and return the standard deviation expressed in pixel for pose matrix and camera intrinsic parameters.

Parameters:

deviation: the standard deviation computed for the model without distortion.
deviation_dist: the standard deviation computed for the model with distortion.

Returns:

A tuple containing:

deviation: the standard deviation computed for the model without distortion.
deviation_dist: the standard deviation computed for the model with distortion.

computeStdDeviation(self: visp._visp.vision.Calibration, cMo_est: visp._visp.core.HomogeneousMatrix, camera: visp._visp.core.CameraParameters) -> float

Compute and return the standard deviation expressed in pixel for pose matrix and camera intrinsic parameters for model without distortion.

Parameters:

cMo_est: the matrix that defines the pose to be tested.
camera: camera intrinsic parameters to be tested.

Returns:

the standard deviation by point of the error in pixel .

computeStdDeviation_dist(self, cMo_est: visp._visp.core.HomogeneousMatrix, camera: visp._visp.core.CameraParameters) → float¶

Compute and return the standard deviation expressed in pixel for pose matrix and camera intrinsic parameters with pixel to meter model.

Parameters:

cMo_est: visp._visp.core.HomogeneousMatrix¶: the matrix that defines the pose to be tested.
camera: visp._visp.core.CameraParameters¶: camera intrinsic parameters to be tested.

Returns:

the standard deviation by point of the error in pixel .

displayData(self, I: visp._visp.core.ImageGray, color: visp._visp.core.Color, thickness: int = 1, subsampling_factor: int = 1) → int¶

Display the data of the calibration (center of the tracked dots).

Parameters:

I: visp._visp.core.ImageGray¶: Image where to display data.
color: visp._visp.core.Color¶: Color of the data.
thickness: int = 1¶: Thickness of the displayed data.
subsampling_factor: int = 1¶: Subsampling factor. Default value is 1. Admissible values are multiple of 2. Divide by this parameter the coordinates of the data points resulting from image processing.

displayGrid(self, I: visp._visp.core.ImageGray, color: visp._visp.core.Color, thickness: int = 1, subsampling_factor: int = 1) → int¶

Display estimated centers of dots using intrinsic camera parameters with model with distortion and the computed pose.

Parameters:

I: visp._visp.core.ImageGray¶: Image where to display grid data.
color: visp._visp.core.Color¶: Color of the data.
thickness: int = 1¶: Thickness of the displayed data.
subsampling_factor: int = 1¶: Subsampling factor. Default value is 1. Admissible values are multiple of 2. Divide by this parameter the values of the camera parameters.

static getLambda() → float¶: Get the gain of the virtual visual servoing algorithm.

getResidual(self) → float¶: Get the residual in pixels.

getResidual_dist(self) → float¶: Get the residual for perspective projection with distortion (in pixels).

get_npt(self) → int¶: Get the number of points.

init(self) → int¶: Basic initialisation (called by the constructors).

readData(self, filename: str) → int¶

Read data from disk : data are organized as follow oX oY oZ u v

Parameters:

filename: str¶: Name of the file.

static readGrid(filename: str, n: int, oX: list[float], oY: list[float], oZ: list[float], verbose: bool = false) → tuple[int, int, list[float], list[float], list[float]]¶

Read calibration grid coordinates from disk. Data are organized as follow oX oY oZ

Parameters:

filename: str¶: Name of the file.
n: int¶: Number of points in the calibration grid.
oX: list[float]¶: List of oX coordinates.
oY: list[float]¶: List of oY coordinates.
oZ: list[float]¶: List of oZ coordinates.
verbose: bool = false¶: Additional printings if true (number of points on the calibration grid and their respective coordinates in the object frame).

Returns:

A tuple containing:

0 if success, -1 if an error occurs.
n: Number of points in the calibration grid.
oX: List of oX coordinates.
oY: List of oY coordinates.
oZ: List of oZ coordinates.

setAspectRatio(self, aspect_ratio: float) → None¶

Set pixel aspect ratio px/py.

Parameters:

aspect_ratio: float¶: px/py aspect ratio. Value need to be positive. To estimate a model where px=py set 1 as aspect ratio.

static setLambda(lambda: float) → None¶: Set the gain for the virtual visual servoing algorithm.

writeData(self, filename: str) → int¶

Write data into a file.

Data are organized as follow oX oY oZ u v

Parameters:

filename: str¶: Name of the file.