OpenCV  5.0.0-pre Open Source Computer Vision
Camera Calibration

## Modules

Fisheye camera model

C API

## Namespaces

cv::fisheye
The methods in this namespace use a so-called fisheye camera model.

## Classes

struct  cv::CirclesGridFinderParameters

## Typedefs

typedef CirclesGridFinderParameters cv::CirclesGridFinderParameters2

## Enumerations

enum  {
cv::CALIB_CB_NORMALIZE_IMAGE = 2,
cv::CALIB_CB_FAST_CHECK = 8,
cv::CALIB_CB_EXHAUSTIVE = 16,
cv::CALIB_CB_ACCURACY = 32,
cv::CALIB_CB_LARGER = 64,
cv::CALIB_CB_MARKER = 128
}

enum  {
cv::CALIB_CB_SYMMETRIC_GRID = 1,
cv::CALIB_CB_ASYMMETRIC_GRID = 2,
cv::CALIB_CB_CLUSTERING = 4
}

enum  {
cv::CALIB_NINTRINSIC = 18,
cv::CALIB_USE_INTRINSIC_GUESS = 0x00001,
cv::CALIB_FIX_ASPECT_RATIO = 0x00002,
cv::CALIB_FIX_PRINCIPAL_POINT = 0x00004,
cv::CALIB_ZERO_TANGENT_DIST = 0x00008,
cv::CALIB_FIX_FOCAL_LENGTH = 0x00010,
cv::CALIB_FIX_K1 = 0x00020,
cv::CALIB_FIX_K2 = 0x00040,
cv::CALIB_FIX_K3 = 0x00080,
cv::CALIB_FIX_K4 = 0x00800,
cv::CALIB_FIX_K5 = 0x01000,
cv::CALIB_FIX_K6 = 0x02000,
cv::CALIB_RATIONAL_MODEL = 0x04000,
cv::CALIB_THIN_PRISM_MODEL = 0x08000,
cv::CALIB_FIX_S1_S2_S3_S4 = 0x10000,
cv::CALIB_TILTED_MODEL = 0x40000,
cv::CALIB_FIX_TAUX_TAUY = 0x80000,
cv::CALIB_USE_QR = 0x100000,
cv::CALIB_FIX_TANGENT_DIST = 0x200000,
cv::CALIB_FIX_INTRINSIC = 0x00100,
cv::CALIB_SAME_FOCAL_LENGTH = 0x00200,
cv::CALIB_ZERO_DISPARITY = 0x00400,
cv::CALIB_USE_LU = (1 << 17),
cv::CALIB_USE_EXTRINSIC_GUESS = (1 << 22)
}

enum  cv::CirclesGridFinderParameters::GridType {
cv::CirclesGridFinderParameters::SYMMETRIC_GRID,
cv::CirclesGridFinderParameters::ASYMMETRIC_GRID
}

enum  cv::HandEyeCalibrationMethod {
cv::CALIB_HAND_EYE_TSAI = 0,
cv::CALIB_HAND_EYE_PARK = 1,
cv::CALIB_HAND_EYE_HORAUD = 2,
cv::CALIB_HAND_EYE_ANDREFF = 3,
cv::CALIB_HAND_EYE_DANIILIDIS = 4
}

enum  cv::RobotWorldHandEyeCalibrationMethod {
cv::CALIB_ROBOT_WORLD_HAND_EYE_SHAH = 0,
cv::CALIB_ROBOT_WORLD_HAND_EYE_LI = 1
}

## Functions

cv::CirclesGridFinderParameters::CirclesGridFinderParameters ()

double cv::calibrateCamera (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More...

double cv::calibrateCamera (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))

double cv::calibrateCameraRO (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray newObjPoints, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray stdDeviationsObjPoints, OutputArray perViewErrors, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. More...

double cv::calibrateCameraRO (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray newObjPoints, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))

void cv::calibrateHandEye (InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base, InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam, OutputArray R_cam2gripper, OutputArray t_cam2gripper, HandEyeCalibrationMethod method=CALIB_HAND_EYE_TSAI)
Computes Hand-Eye calibration: $$_{}^{g}\textrm{T}_c$$. More...

void cv::calibrateRobotWorldHandEye (InputArrayOfArrays R_world2cam, InputArrayOfArrays t_world2cam, InputArrayOfArrays R_base2gripper, InputArrayOfArrays t_base2gripper, OutputArray R_base2world, OutputArray t_base2world, OutputArray R_gripper2cam, OutputArray t_gripper2cam, RobotWorldHandEyeCalibrationMethod method=CALIB_ROBOT_WORLD_HAND_EYE_SHAH)
Computes Robot-World/Hand-Eye calibration: $$_{}^{w}\textrm{T}_b$$ and $$_{}^{c}\textrm{T}_g$$. More...

void cv::calibrationMatrixValues (InputArray cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double &fovx, double &fovy, double &focalLength, Point2d &principalPoint, double &aspectRatio)
Computes useful camera characteristics from the camera intrinsic matrix. More...

bool cv::checkChessboard (InputArray img, Size size)

void cv::drawChessboardCorners (InputOutputArray image, Size patternSize, InputArray corners, bool patternWasFound)
Renders the detected chessboard corners. More...

Scalar cv::estimateChessboardSharpness (InputArray image, Size patternSize, InputArray corners, float rise_distance=0.8F, bool vertical=false, OutputArray sharpness=noArray())
Estimates the sharpness of a detected chessboard. More...

bool cv::find4QuadCornerSubpix (InputArray img, InputOutputArray corners, Size region_size)
finds subpixel-accurate positions of the chessboard corners More...

bool cv::findChessboardCorners (InputArray image, Size patternSize, OutputArray corners, int flags=CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE)
Finds the positions of internal corners of the chessboard. More...

bool cv::findChessboardCornersSB (InputArray image, Size patternSize, OutputArray corners, int flags, OutputArray meta)
Finds the positions of internal corners of the chessboard using a sector based approach. More...

bool cv::findChessboardCornersSB (InputArray image, Size patternSize, OutputArray corners, int flags=0)

bool cv::findCirclesGrid (InputArray image, Size patternSize, OutputArray centers, int flags, const Ptr< FeatureDetector > &blobDetector, const CirclesGridFinderParameters &parameters)
Finds centers in the grid of circles. More...

bool cv::findCirclesGrid (InputArray image, Size patternSize, OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID, const Ptr< FeatureDetector > &blobDetector=SimpleBlobDetector::create())

Mat cv::initCameraMatrix2D (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, double aspectRatio=1.0)
Finds an initial camera intrinsic matrix from 3D-2D point correspondences. More...

double cv::stereoCalibrate (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, InputOutputArray R, InputOutputArray T, OutputArray E, OutputArray F, OutputArray perViewErrors, int flags=CALIB_FIX_INTRINSIC, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6))
Calibrates a stereo camera set up. This function finds the intrinsic parameters for each of the two cameras and the extrinsic parameters between the two cameras. More...

double cv::stereoCalibrate (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, OutputArray R, OutputArray T, OutputArray E, OutputArray F, int flags=CALIB_FIX_INTRINSIC, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6))

## Variables

float cv::CirclesGridFinderParameters::convexHullFactor

cv::Size2f cv::CirclesGridFinderParameters::densityNeighborhoodSize

float cv::CirclesGridFinderParameters::edgeGain

float cv::CirclesGridFinderParameters::edgePenalty

float cv::CirclesGridFinderParameters::existingVertexGain

GridType cv::CirclesGridFinderParameters::gridType

int cv::CirclesGridFinderParameters::keypointScale

int cv::CirclesGridFinderParameters::kmeansAttempts

float cv::CirclesGridFinderParameters::maxRectifiedDistance
Max deviation from prediction. Used by CALIB_CB_CLUSTERING. More...

float cv::CirclesGridFinderParameters::minDensity

float cv::CirclesGridFinderParameters::minGraphConfidence

float cv::CirclesGridFinderParameters::minRNGEdgeSwitchDist

float cv::CirclesGridFinderParameters::squareSize
Distance between two adjacent points. Used by CALIB_CB_CLUSTERING. More...

float cv::CirclesGridFinderParameters::vertexGain

float cv::CirclesGridFinderParameters::vertexPenalty

## Detailed Description

The functions in this section use a so-called pinhole camera model. The view of a scene is obtained by projecting a scene's 3D point $$P_w$$ into the image plane using a perspective transformation which forms the corresponding pixel $$p$$. Both $$P_w$$ and $$p$$ are represented in homogeneous coordinates, i.e. as 3D and 2D homogeneous vector respectively. You will find a brief introduction to projective geometry, homogeneous vectors and homogeneous transformations at the end of this section's introduction. For more succinct notation, we often drop the 'homogeneous' and say vector instead of homogeneous vector.

The distortion-free projective transformation given by a pinhole camera model is shown below.

$s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w,$

where $$P_w$$ is a 3D point expressed with respect to the world coordinate system, $$p$$ is a 2D pixel in the image plane, $$A$$ is the camera intrinsic matrix, $$R$$ and $$t$$ are the rotation and translation that describe the change of coordinates from world to camera coordinate systems (or camera frame) and $$s$$ is the projective transformation's arbitrary scaling and not part of the camera model.

The camera intrinsic matrix $$A$$ (notation used as in [300] and also generally notated as $$K$$) projects 3D points given in the camera coordinate system to 2D pixel coordinates, i.e.

$p = A P_c.$

The camera intrinsic matrix $$A$$ is composed of the focal lengths $$f_x$$ and $$f_y$$, which are expressed in pixel units, and the principal point $$(c_x, c_y)$$, that is usually close to the image center:

$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1},$

and thus

$s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} \vecthree{X_c}{Y_c}{Z_c}.$

The matrix of intrinsic parameters does not depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is fixed (in case of a zoom lens). Thus, if an image from the camera is scaled by a factor, all of these parameters need to be scaled (multiplied/divided, respectively) by the same factor.

The joint rotation-translation matrix $$[R|t]$$ is the matrix product of a projective transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points represented in camera coordinates to 2D points in the image plane and represented in normalized camera coordinates $$x' = X_c / Z_c$$ and $$y' = Y_c / Z_c$$:

$Z_c \begin{bmatrix} x' \\ y' \\ 1 \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} \begin{bmatrix} X_c \\ Y_c \\ Z_c \\ 1 \end{bmatrix}.$

The homogeneous transformation is encoded by the extrinsic parameters $$R$$ and $$t$$ and represents the change of basis from world coordinate system $$w$$ to the camera coordinate sytem $$c$$. Thus, given the representation of the point $$P$$ in world coordinates, $$P_w$$, we obtain $$P$$'s representation in the camera coordinate system, $$P_c$$, by

$P_c = \begin{bmatrix} R & t \\ 0 & 1 \end{bmatrix} P_w,$

This homogeneous transformation is composed out of $$R$$, a 3-by-3 rotation matrix, and $$t$$, a 3-by-1 translation vector:

$\begin{bmatrix} R & t \\ 0 & 1 \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_x \\ r_{21} & r_{22} & r_{23} & t_y \\ r_{31} & r_{32} & r_{33} & t_z \\ 0 & 0 & 0 & 1 \end{bmatrix},$

and therefore

$\begin{bmatrix} X_c \\ Y_c \\ Z_c \\ 1 \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_x \\ r_{21} & r_{22} & r_{23} & t_y \\ r_{31} & r_{32} & r_{33} & t_z \\ 0 & 0 & 0 & 1 \end{bmatrix} \begin{bmatrix} X_w \\ Y_w \\ Z_w \\ 1 \end{bmatrix}.$

Combining the projective transformation and the homogeneous transformation, we obtain the projective transformation that maps 3D points in world coordinates into 2D points in the image plane and in normalized camera coordinates:

$Z_c \begin{bmatrix} x' \\ y' \\ 1 \end{bmatrix} = \begin{bmatrix} R|t \end{bmatrix} \begin{bmatrix} X_w \\ Y_w \\ Z_w \\ 1 \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_x \\ r_{21} & r_{22} & r_{23} & t_y \\ r_{31} & r_{32} & r_{33} & t_z \end{bmatrix} \begin{bmatrix} X_w \\ Y_w \\ Z_w \\ 1 \end{bmatrix},$

with $$x' = X_c / Z_c$$ and $$y' = Y_c / Z_c$$. Putting the equations for instrincs and extrinsics together, we can write out $$s \; p = A \begin{bmatrix} R|t \end{bmatrix} P_w$$ as

$s \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1} \begin{bmatrix} r_{11} & r_{12} & r_{13} & t_x \\ r_{21} & r_{22} & r_{23} & t_y \\ r_{31} & r_{32} & r_{33} & t_z \end{bmatrix} \begin{bmatrix} X_w \\ Y_w \\ Z_w \\ 1 \end{bmatrix}.$

If $$Z_c \ne 0$$, the transformation above is equivalent to the following,

$\begin{bmatrix} u \\ v \end{bmatrix} = \begin{bmatrix} f_x X_c/Z_c + c_x \\ f_y Y_c/Z_c + c_y \end{bmatrix}$

with

$\vecthree{X_c}{Y_c}{Z_c} = \begin{bmatrix} R|t \end{bmatrix} \begin{bmatrix} X_w \\ Y_w \\ Z_w \\ 1 \end{bmatrix}.$

The following figure illustrates the pinhole camera model.

Pinhole camera model

Real lenses usually have some distortion, mostly radial distortion, and slight tangential distortion. So, the above model is extended as:

$\begin{bmatrix} u \\ v \end{bmatrix} = \begin{bmatrix} f_x x'' + c_x \\ f_y y'' + c_y \end{bmatrix}$

where

$\begin{bmatrix} x'' \\ y'' \end{bmatrix} = \begin{bmatrix} x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\ y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ \end{bmatrix}$

with

$r^2 = x'^2 + y'^2$

and

$\begin{bmatrix} x'\\ y' \end{bmatrix} = \begin{bmatrix} X_c/Z_c \\ Y_c/Z_c \end{bmatrix},$

if $$Z_c \ne 0$$.

The distortion parameters are the radial coefficients $$k_1$$, $$k_2$$, $$k_3$$, $$k_4$$, $$k_5$$, and $$k_6$$ , $$p_1$$ and $$p_2$$ are the tangential distortion coefficients, and $$s_1$$, $$s_2$$, $$s_3$$, and $$s_4$$, are the thin prism distortion coefficients. Higher-order coefficients are not considered in OpenCV.

The next figures show two common types of radial distortion: barrel distortion ( $$1 + k_1 r^2 + k_2 r^4 + k_3 r^6$$ monotonically decreasing) and pincushion distortion ( $$1 + k_1 r^2 + k_2 r^4 + k_3 r^6$$ monotonically increasing). Radial distortion is always monotonic for real lenses, and if the estimator produces a non-monotonic result, this should be considered a calibration failure. More generally, radial distortion must be monotonic and the distortion function must be bijective. A failed estimation result may look deceptively good near the image center but will work poorly in e.g. AR/SFM applications. The optimization method used in OpenCV camera calibration does not include these constraints as the framework does not support the required integer programming and polynomial inequalities. See issue #15992 for additional information.

In some cases, the image sensor may be tilted in order to focus an oblique plane in front of the camera (Scheimpflug principle). This can be useful for particle image velocimetry (PIV) or triangulation with a laser fan. The tilt causes a perspective distortion of $$x''$$ and $$y''$$. This distortion can be modeled in the following way, see e.g. [156].

$\begin{bmatrix} u \\ v \end{bmatrix} = \begin{bmatrix} f_x x''' + c_x \\ f_y y''' + c_y \end{bmatrix},$

where

$s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}$

and the matrix $$R(\tau_x, \tau_y)$$ is defined by two rotations with angular parameter $$\tau_x$$ and $$\tau_y$$, respectively,

$R(\tau_x, \tau_y) = \vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)} \vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} = \vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)} {0}{\cos(\tau_x)}{\sin(\tau_x)} {\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}.$

In the functions below the coefficients are passed or returned as

$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])$

vector. That is, if the vector contains four elements, it means that $$k_3=0$$ . The distortion coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera parameters. And they remain the same regardless of the captured image resolution. If, for example, a camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion coefficients can be used for 640 x 480 images from the same camera while $$f_x$$, $$f_y$$, $$c_x$$, and $$c_y$$ need to be scaled appropriately.

The functions below use the above model to do the following:

• Project 3D points to the image plane given intrinsic and extrinsic parameters.
• Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their projections.
• Estimate intrinsic and extrinsic camera parameters from several views of a known calibration pattern (every view is described by several 3D-2D point correspondences).
• Estimate the relative position and orientation of the stereo camera "heads" and compute the rectification* transformation that makes the camera optical axes parallel.

Homogeneous Coordinates
Homogeneous Coordinates are a system of coordinates that are used in projective geometry. Their use allows to represent points at infinity by finite coordinates and simplifies formulas when compared to the cartesian counterparts, e.g. they have the advantage that affine transformations can be expressed as linear homogeneous transformation.

One obtains the homogeneous vector $$P_h$$ by appending a 1 along an n-dimensional cartesian vector $$P$$ e.g. for a 3D cartesian vector the mapping $$P \rightarrow P_h$$ is:

$\begin{bmatrix} X \\ Y \\ Z \end{bmatrix} \rightarrow \begin{bmatrix} X \\ Y \\ Z \\ 1 \end{bmatrix}.$

For the inverse mapping $$P_h \rightarrow P$$, one divides all elements of the homogeneous vector by its last element, e.g. for a 3D homogeneous vector one gets its 2D cartesian counterpart by:

$\begin{bmatrix} X \\ Y \\ W \end{bmatrix} \rightarrow \begin{bmatrix} X / W \\ Y / W \end{bmatrix},$

if $$W \ne 0$$.

Due to this mapping, all multiples $$k P_h$$, for $$k \ne 0$$, of a homogeneous point represent the same point $$P_h$$. An intuitive understanding of this property is that under a projective transformation, all multiples of $$P_h$$ are mapped to the same point. This is the physical observation one does for pinhole cameras, as all points along a ray through the camera's pinhole are projected to the same image point, e.g. all points along the red ray in the image of the pinhole camera model above would be mapped to the same image coordinate. This property is also the source for the scale ambiguity s in the equation of the pinhole camera model.

As mentioned, by using homogeneous coordinates we can express any change of basis parameterized by $$R$$ and $$t$$ as a linear transformation, e.g. for the change of basis from coordinate system 0 to coordinate system 1 becomes:

$P_1 = R P_0 + t \rightarrow P_{h_1} = \begin{bmatrix} R & t \\ 0 & 1 \end{bmatrix} P_{h_0}.$

Note
• Many functions in this module take a camera intrinsic matrix as an input parameter. Although all functions assume the same structure of this parameter, they may name it differently. The parameter's description, however, will be clear in that a camera intrinsic matrix with the structure shown above is required.
• A calibration sample for 3 cameras in a horizontal position can be found at opencv_source_code/samples/cpp/3calibration.cpp
• A calibration sample based on a sequence of images can be found at opencv_source_code/samples/cpp/calibration.cpp
• A calibration sample in order to do 3D reconstruction can be found at opencv_source_code/samples/cpp/build3dmodel.cpp
• A calibration example on stereo calibration can be found at opencv_source_code/samples/cpp/stereo_calib.cpp
• A calibration example on stereo matching can be found at opencv_source_code/samples/cpp/stereo_match.cpp
• (Python) A camera calibration sample can be found at opencv_source_code/samples/python/calibrate.py

## ◆ CirclesGridFinderParameters2

#include <opencv2/calib.hpp>

## ◆ anonymous enum

 anonymous enum

#include <opencv2/calib.hpp>

Enumerator
CALIB_CB_NORMALIZE_IMAGE
Python: cv.CALIB_CB_NORMALIZE_IMAGE
CALIB_CB_FAST_CHECK
Python: cv.CALIB_CB_FAST_CHECK
CALIB_CB_EXHAUSTIVE
Python: cv.CALIB_CB_EXHAUSTIVE
CALIB_CB_ACCURACY
Python: cv.CALIB_CB_ACCURACY
CALIB_CB_LARGER
Python: cv.CALIB_CB_LARGER
CALIB_CB_MARKER
Python: cv.CALIB_CB_MARKER

## ◆ anonymous enum

 anonymous enum

#include <opencv2/calib.hpp>

Enumerator
CALIB_CB_SYMMETRIC_GRID
Python: cv.CALIB_CB_SYMMETRIC_GRID
CALIB_CB_ASYMMETRIC_GRID
Python: cv.CALIB_CB_ASYMMETRIC_GRID
CALIB_CB_CLUSTERING
Python: cv.CALIB_CB_CLUSTERING

## ◆ anonymous enum

 anonymous enum

#include <opencv2/calib.hpp>

Enumerator
CALIB_NINTRINSIC
Python: cv.CALIB_NINTRINSIC
CALIB_USE_INTRINSIC_GUESS
Python: cv.CALIB_USE_INTRINSIC_GUESS
CALIB_FIX_ASPECT_RATIO
Python: cv.CALIB_FIX_ASPECT_RATIO
CALIB_FIX_PRINCIPAL_POINT
Python: cv.CALIB_FIX_PRINCIPAL_POINT
CALIB_ZERO_TANGENT_DIST
Python: cv.CALIB_ZERO_TANGENT_DIST
CALIB_FIX_FOCAL_LENGTH
Python: cv.CALIB_FIX_FOCAL_LENGTH
CALIB_FIX_K1
Python: cv.CALIB_FIX_K1
CALIB_FIX_K2
Python: cv.CALIB_FIX_K2
CALIB_FIX_K3
Python: cv.CALIB_FIX_K3
CALIB_FIX_K4
Python: cv.CALIB_FIX_K4
CALIB_FIX_K5
Python: cv.CALIB_FIX_K5
CALIB_FIX_K6
Python: cv.CALIB_FIX_K6
CALIB_RATIONAL_MODEL
Python: cv.CALIB_RATIONAL_MODEL
CALIB_THIN_PRISM_MODEL
Python: cv.CALIB_THIN_PRISM_MODEL
CALIB_FIX_S1_S2_S3_S4
Python: cv.CALIB_FIX_S1_S2_S3_S4
CALIB_TILTED_MODEL
Python: cv.CALIB_TILTED_MODEL
CALIB_FIX_TAUX_TAUY
Python: cv.CALIB_FIX_TAUX_TAUY
CALIB_USE_QR
Python: cv.CALIB_USE_QR

use QR instead of SVD decomposition for solving. Faster but potentially less precise

CALIB_FIX_TANGENT_DIST
Python: cv.CALIB_FIX_TANGENT_DIST
CALIB_FIX_INTRINSIC
Python: cv.CALIB_FIX_INTRINSIC
CALIB_SAME_FOCAL_LENGTH
Python: cv.CALIB_SAME_FOCAL_LENGTH
CALIB_ZERO_DISPARITY
Python: cv.CALIB_ZERO_DISPARITY
CALIB_USE_LU
Python: cv.CALIB_USE_LU

use LU instead of SVD decomposition for solving. much faster but potentially less precise

CALIB_USE_EXTRINSIC_GUESS
Python: cv.CALIB_USE_EXTRINSIC_GUESS

for stereoCalibrate

Enumerator
SYMMETRIC_GRID
ASYMMETRIC_GRID

## ◆ HandEyeCalibrationMethod

#include <opencv2/calib.hpp>

Enumerator
CALIB_HAND_EYE_TSAI
Python: cv.CALIB_HAND_EYE_TSAI

A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration [256].

CALIB_HAND_EYE_PARK
Python: cv.CALIB_HAND_EYE_PARK

Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [195].

CALIB_HAND_EYE_HORAUD
Python: cv.CALIB_HAND_EYE_HORAUD

Hand-eye Calibration [115].

CALIB_HAND_EYE_ANDREFF
Python: cv.CALIB_HAND_EYE_ANDREFF

On-line Hand-Eye Calibration [12].

CALIB_HAND_EYE_DANIILIDIS
Python: cv.CALIB_HAND_EYE_DANIILIDIS

Hand-Eye Calibration Using Dual Quaternions [54].

## ◆ RobotWorldHandEyeCalibrationMethod

#include <opencv2/calib.hpp>

Enumerator
CALIB_ROBOT_WORLD_HAND_EYE_SHAH
Python: cv.CALIB_ROBOT_WORLD_HAND_EYE_SHAH

Solving the robot-world/hand-eye calibration problem using the kronecker product [227].

CALIB_ROBOT_WORLD_HAND_EYE_LI
Python: cv.CALIB_ROBOT_WORLD_HAND_EYE_LI

Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [147].

## ◆ CirclesGridFinderParameters()

 cv::CirclesGridFinderParameters::CirclesGridFinderParameters ( )

#include <opencv2/calib.hpp>

## ◆ calibrateCamera() [1/2]

 double cv::calibrateCamera ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray perViewErrors, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON) )
Python:
cv.calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs
cv.calibrateCameraExtended(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, stdDeviationsIntrinsics=None, stdDeviationsExtrinsics=None, perViewErrors=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors

#include <opencv2/calib.hpp>

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

Parameters
 objectPoints In the new interface it is a vector of vectors of calibration pattern points in the calibration pattern coordinate space (e.g. std::vector>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern's XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together. imagePoints In the new interface it is a vector of vectors of the projections of calibration pattern points (e.g. std::vector>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together. imageSize Size of the image used only to initialize the camera intrinsic matrix. cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix $$\cameramatrix{A}$$ . If CALIB_USE_INTRINSIC_GUESS and/or CALIB_FIX_ASPECT_RATIO, CALIB_FIX_PRINCIPAL_POINT or CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function. distCoeffs Input/output vector of distortion coefficients $$\distcoeffs$$. rvecs Output vector of rotation vectors (Rodrigues ) estimated for each pattern view (e.g. std::vector>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space. tvecs Output vector of translation vectors estimated for each pattern view, see parameter describtion above. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: $$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)$$ If one of parameters is not estimated, it's deviation is equals to zero. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: $$(R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})$$ where M is the number of pattern views. $$R_i, T_i$$ are concatenated 1x3 vectors. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use solvePnP instead. CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too. CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further. CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients $$(p_1, p_2)$$ are set to zeros and stay zero. CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if CALIB_USE_INTRINSIC_GUESS is set. CALIB_FIX_K1,..., CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. criteria Termination criteria for the iterative optimization algorithm.
Returns
the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [300] and [33] . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see findChessboardCorners). Currently, initialization of intrinsic parameters (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

• Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
• Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using solvePnP .
• Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See projectPoints for details.
Note
If you use a non-square (i.e. non-N-by-N) grid and findChessboardCorners for calibration, and calibrateCamera returns bad values (zero distortion coefficients, $$c_x$$ and $$c_y$$ very far from the image center, and/or large differences between $$f_x$$ and $$f_y$$ (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in findChessboardCorners.
calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

## ◆ calibrateCamera() [2/2]

 double cv::calibrateCamera ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON) )
Python:
cv.calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs
cv.calibrateCameraExtended(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, stdDeviationsIntrinsics=None, stdDeviationsExtrinsics=None, perViewErrors=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors

#include <opencv2/calib.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

## ◆ calibrateCameraRO() [1/2]

 double cv::calibrateCameraRO ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray newObjPoints, OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics, OutputArray stdDeviationsObjPoints, OutputArray perViewErrors, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON) )
Python:
cv.calibrateCameraRO(objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, newObjPoints=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints
cv.calibrateCameraROExtended(objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, newObjPoints=None, stdDeviationsIntrinsics=None, stdDeviationsExtrinsics=None, stdDeviationsObjPoints=None, perViewErrors=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints, stdDeviationsIntrinsics, stdDeviationsExtrinsics, stdDeviationsObjPoints, perViewErrors

#include <opencv2/calib.hpp>

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of calibrateCamera with the method of releasing object which was proposed in [237]. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, calibrateCamera is a wrapper for this function.

Parameters
 objectPoints Vector of vectors of calibration pattern points in the calibration pattern coordinate space. See calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images. imagePoints Vector of vectors of the projections of calibration pattern points. See calibrateCamera for details. imageSize Size of the image used only to initialize the intrinsic camera matrix. iFixedPoint The index of the 3D object point in objectPoints[0] to be fixed. It also acts as a switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to [237], two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough. cameraMatrix Output 3x3 floating-point camera matrix. See calibrateCamera for details. distCoeffs Output vector of distortion coefficients. See calibrateCamera for details. rvecs Output vector of rotation vectors estimated for each pattern view. See calibrateCamera for details. tvecs Output vector of translation vectors estimated for each pattern view. newObjPoints The updated output vector of calibration pattern points. The coordinates might be scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method. stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. See calibrateCamera for details. stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. See calibrateCamera for details. stdDeviationsObjPoints Output vector of standard deviations estimated for refined coordinates of calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of some predefined values. See calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases. criteria Termination criteria for the iterative optimization algorithm.
Returns
the overall RMS re-projection error.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on [300], [33] and [237]. See calibrateCamera for other detailed explanations.

calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

## ◆ calibrateCameraRO() [2/2]

 double cv::calibrateCameraRO ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, int iFixedPoint, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, OutputArray newObjPoints, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON) )
Python:
cv.calibrateCameraRO(objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, newObjPoints=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints
cv.calibrateCameraROExtended(objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs=None, tvecs=None, newObjPoints=None, stdDeviationsIntrinsics=None, stdDeviationsExtrinsics=None, stdDeviationsObjPoints=None, perViewErrors=None, flags=0, criteria=TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON)]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints, stdDeviationsIntrinsics, stdDeviationsExtrinsics, stdDeviationsObjPoints, perViewErrors

#include <opencv2/calib.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

## ◆ calibrateHandEye()

 void cv::calibrateHandEye ( InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base, InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam, OutputArray R_cam2gripper, OutputArray t_cam2gripper, HandEyeCalibrationMethod method = CALIB_HAND_EYE_TSAI )

#include <opencv2/calib.hpp>

Computes Hand-Eye calibration: $$_{}^{g}\textrm{T}_c$$.

Parameters
 [in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( $$_{}^{b}\textrm{T}_g$$). This is a vector (vector) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from gripper frame to robot base frame. [in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the robot base frame ( $$_{}^{b}\textrm{T}_g$$). This is a vector (vector) that contains the (3x1) translation vectors for all the transformations from gripper frame to robot base frame. [in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the target frame to the camera frame ( $$_{}^{c}\textrm{T}_t$$). This is a vector (vector) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from calibration target frame to camera frame. [in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the target frame to the camera frame ( $$_{}^{c}\textrm{T}_t$$). This is a vector (vector) that contains the (3x1) translation vectors for all the transformations from calibration target frame to camera frame. [out] R_cam2gripper Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point expressed in the camera frame to the gripper frame ( $$_{}^{g}\textrm{T}_c$$). [out] t_cam2gripper Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point expressed in the camera frame to the gripper frame ( $$_{}^{g}\textrm{T}_c$$). [in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod

The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:

• R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration [256]
• F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group [195]
• R. Horaud, F. Dornaika Hand-Eye Calibration [115]

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:

• N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration [12]
• K. Daniilidis Hand-Eye Calibration Using Dual Quaternions [54]

The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") mounted on a robot gripper ("hand") has to be estimated. This configuration is called eye-in-hand.

The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting the suitable transformations to the function, see below.

The calibration procedure is the following:

• a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
• the robot gripper is moved in order to acquire several poses
• for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

$\begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix}$

• for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

$\begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix}$

The Hand-Eye calibration procedure returns the following homogeneous transformation

$\begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix}$

This problem is also known as solving the $$\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}$$ equation:

• for an eye-in-hand configuration

\begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*}

• for an eye-to-hand configuration

\begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*}

Note
Additional information can be found on this website.
A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.

## ◆ calibrateRobotWorldHandEye()

 void cv::calibrateRobotWorldHandEye ( InputArrayOfArrays R_world2cam, InputArrayOfArrays t_world2cam, InputArrayOfArrays R_base2gripper, InputArrayOfArrays t_base2gripper, OutputArray R_base2world, OutputArray t_base2world, OutputArray R_gripper2cam, OutputArray t_gripper2cam, RobotWorldHandEyeCalibrationMethod method = CALIB_ROBOT_WORLD_HAND_EYE_SHAH )

#include <opencv2/calib.hpp>

Computes Robot-World/Hand-Eye calibration: $$_{}^{w}\textrm{T}_b$$ and $$_{}^{c}\textrm{T}_g$$.

Parameters
 [in] R_world2cam Rotation part extracted from the homogeneous matrix that transforms a point expressed in the world frame to the camera frame ( $$_{}^{c}\textrm{T}_w$$). This is a vector (vector) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from world frame to the camera frame. [in] t_world2cam Translation part extracted from the homogeneous matrix that transforms a point expressed in the world frame to the camera frame ( $$_{}^{c}\textrm{T}_w$$). This is a vector (vector) that contains the (3x1) translation vectors for all the transformations from world frame to the camera frame. [in] R_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the gripper frame ( $$_{}^{g}\textrm{T}_b$$). This is a vector (vector) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from robot base frame to the gripper frame. [in] t_base2gripper Rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the gripper frame ( $$_{}^{g}\textrm{T}_b$$). This is a vector (vector) that contains the (3x1) translation vectors for all the transformations from robot base frame to the gripper frame. [out] R_base2world Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the world frame ( $$_{}^{w}\textrm{T}_b$$). [out] t_base2world Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point expressed in the robot base frame to the world frame ( $$_{}^{w}\textrm{T}_b$$). [out] R_gripper2cam Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the camera frame ( $$_{}^{c}\textrm{T}_g$$). [out] t_gripper2cam Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a point expressed in the gripper frame to the camera frame ( $$_{}^{c}\textrm{T}_g$$). [in] method One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod

The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):

• M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product [227]

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:

• A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product [147]

The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame and between a robot gripper ("hand") and a camera ("eye") mounted at the robot end-effector have to be estimated.

The calibration procedure is the following:

• a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
• the robot gripper is moved in order to acquire several poses
• for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

$\begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix}$

• for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

$\begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix}$

The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations

$\begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix}$

$\begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix}$

This problem is also known as solving the $$\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}$$ equation, with:

• $$\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w$$
• $$\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b$$
• $$\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g$$
• $$\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b$$
Note
At least 3 measurements are required (input vectors size must be greater or equal to 3).

## ◆ calibrationMatrixValues()

 void cv::calibrationMatrixValues ( InputArray cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double & fovx, double & fovy, double & focalLength, Point2d & principalPoint, double & aspectRatio )
Python:
cv.calibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight[, ]) -> fovx, fovy, focalLength, principalPoint, aspectRatio

#include <opencv2/calib.hpp>

Computes useful camera characteristics from the camera intrinsic matrix.

Parameters
 cameraMatrix Input camera intrinsic matrix that can be estimated by calibrateCamera or stereoCalibrate . imageSize Input image size in pixels. apertureWidth Physical width in mm of the sensor. apertureHeight Physical height in mm of the sensor. fovx Output field of view in degrees along the horizontal sensor axis. fovy Output field of view in degrees along the vertical sensor axis. focalLength Focal length of the lens in mm. principalPoint Principal point in mm. aspectRatio $$f_y/f_x$$

The function computes various useful camera characteristics from the previously estimated camera matrix.

Note
Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).

## ◆ checkChessboard()

 bool cv::checkChessboard ( InputArray img, Size size )
Python:
cv.checkChessboard(img, size[, ]) -> retval

#include <opencv2/calib.hpp>

## ◆ drawChessboardCorners()

 void cv::drawChessboardCorners ( InputOutputArray image, Size patternSize, InputArray corners, bool patternWasFound )
Python:
cv.drawChessboardCorners(image, patternSize, corners, patternWasFound[, ]) -> image

#include <opencv2/calib.hpp>

Renders the detected chessboard corners.

Parameters
 image Destination image. It must be an 8-bit color image. patternSize Number of inner corners per a chessboard row and column (patternSize = cv::Size(points_per_row,points_per_column)). corners Array of detected corners, the output of findChessboardCorners. patternWasFound Parameter indicating whether the complete board was found or not. The return value of findChessboardCorners should be passed here.

The function draws individual chessboard corners detected either as red circles if the board was not found, or as colored corners connected with lines if the board was found.

Examples:
samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp.

## ◆ estimateChessboardSharpness()

 Scalar cv::estimateChessboardSharpness ( InputArray image, Size patternSize, InputArray corners, float rise_distance = 0.8F, bool vertical = false, OutputArray sharpness = noArray() )
Python:
cv.estimateChessboardSharpness(image, patternSize, corners[, rise_distance=0.8F, vertical=false, sharpness=None]) -> retval, sharpness

#include <opencv2/calib.hpp>

Estimates the sharpness of a detected chessboard.

Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.

Parameters
 image Gray image used to find chessboard corners patternSize Size of a found chessboard pattern corners Corners found by findChessboardCornersSB rise_distance Rise distance 0.8 means 10% ... 90% of the final signal strength vertical By default edge responses for horizontal lines are calculated sharpness Optional output array with a sharpness value for calculated edge responses (see description)

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries: 0 = x coordinate of the underlying edge in the image 1 = y coordinate of the underlying edge in the image 2 = width of the transition area (sharpness) 3 = signal strength in the black cell (min brightness) 4 = signal strength in the white cell (max brightness)

Returns
Scalar(average sharpness, average min brightness, average max brightness,0)

 bool cv::find4QuadCornerSubpix ( InputArray img, InputOutputArray corners, Size region_size )
Python:
cv.find4QuadCornerSubpix(img, corners, region_size[, ]) -> retval, corners

#include <opencv2/calib.hpp>

finds subpixel-accurate positions of the chessboard corners

## ◆ findChessboardCorners()

 bool cv::findChessboardCorners ( InputArray image, Size patternSize, OutputArray corners, int flags = CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE )
Python:
cv.findChessboardCorners(image, patternSize[, corners=None, flags=CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE]) -> retval, corners

#include <opencv2/calib.hpp>

Finds the positions of internal corners of the chessboard.

Parameters
 image Source chessboard view. It must be an 8-bit grayscale or color image. patternSize Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ). corners Output array of detected corners. flags Various operation flags that can be zero or a combination of the following values: CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before applying fixed or adaptive thresholding. CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage. CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

Size patternsize(8,6); //interior number of corners
Mat gray = ....; //source image
vector<Point2f> corners; //this will be filled by the detected corners
//CALIB_CB_FAST_CHECK saves a lot of time on images
//that do not contain any chessboard corners
bool patternfound = findChessboardCorners(gray, patternsize, corners,
if(patternfound)
cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
Note
The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Use gen_pattern.py (Create calibration pattern) to create checkerboard.

Examples:
samples/cpp/tutorial_code/features2D/Homography/decompose_homography.cpp, samples/cpp/tutorial_code/features2D/Homography/homography_from_camera_displacement.cpp, and samples/cpp/tutorial_code/features2D/Homography/pose_from_homography.cpp.

 bool cv::findChessboardCornersSB ( InputArray image, Size patternSize, OutputArray corners, int flags, OutputArray meta )
Python:
cv.findChessboardCornersSB(image, patternSize[, corners=None, flags=0]) -> retval, corners
cv.findChessboardCornersSBWithMeta(image, patternSize, flags[, corners=None, meta=None]) -> retval, corners, meta

#include <opencv2/calib.hpp>

Finds the positions of internal corners of the chessboard using a sector based approach.

Parameters
 image Source chessboard view. It must be an 8-bit grayscale or color image. patternSize Number of inner corners per a chessboard row and column ( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ). corners Output array of detected corners. flags Various operation flags that can be zero or a combination of the following values: CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection. CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate. CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects. CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description). CALIB_CB_MARKER The detected pattern must have a marker (see description). This should be used if an accurate camera calibration is required. meta Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)). Each entry stands for one corner of the pattern and can have one of the following values: 0 = no meta data attached 1 = left-top corner of a black cell 2 = left-top corner of a white cell 3 = left-top corner of a black cell with a white marker dot 4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)

The function is analog to findChessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper [63] "Accurate Detection and Localization of Checkerboard Corners for Calibration" demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.

In the case, the flags CALIB_CB_LARGER or CALIB_CB_MARKER are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.

Note
The function requires a white boarder with roughly the same width as one of the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well.

Use gen_pattern.py (Create calibration pattern) to create checkerboard.

Checkerboard

 bool cv::findChessboardCornersSB ( InputArray image, Size patternSize, OutputArray corners, int flags = 0 )
inline
Python:
cv.findChessboardCornersSB(image, patternSize[, corners=None, flags=0]) -> retval, corners
cv.findChessboardCornersSBWithMeta(image, patternSize, flags[, corners=None, meta=None]) -> retval, corners, meta

#include <opencv2/calib.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

## ◆ findCirclesGrid() [1/2]

 bool cv::findCirclesGrid ( InputArray image, Size patternSize, OutputArray centers, int flags, const Ptr< FeatureDetector > & blobDetector, const CirclesGridFinderParameters & parameters )
Python:
cv.findCirclesGrid(image, patternSize, flags, blobDetector, parameters[, centers=None]) -> retval, centers
cv.findCirclesGrid(image, patternSize[, centers=None, flags=CALIB_CB_SYMMETRIC_GRID, blobDetector=SimpleBlobDetector::create()]) -> retval, centers

#include <opencv2/calib.hpp>

Finds centers in the grid of circles.

Parameters
 image grid view of input circles; it must be an 8-bit grayscale or color image. patternSize number of circles per row and column ( patternSize = Size(points_per_row, points_per_colum) ). centers output array of detected centers. flags various operation flags that can be one of the following values: CALIB_CB_SYMMETRIC_GRID uses symmetric pattern of circles. CALIB_CB_ASYMMETRIC_GRID uses asymmetric pattern of circles. CALIB_CB_CLUSTERING uses a special algorithm for grid detection. It is more robust to perspective distortions but much more sensitive to background clutter. blobDetector feature detector that finds blobs like dark circles on light background. If blobDetector is NULL then image represents Point2f array of candidates. parameters struct for finding circles in a grid pattern.

The function attempts to determine whether the input image contains a grid of circles. If it is, the function locates centers of the circles. The function returns a non-zero value if all of the centers have been found and they have been placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.

Sample usage of detecting and drawing the centers of circles: :

Size patternsize(7,7); //number of centers
Mat gray = ...; //source image
vector<Point2f> centers; //this will be filled by the detected centers
bool patternfound = findCirclesGrid(gray, patternsize, centers);
drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
Note
The function requires white space (like a square-thick border, the wider the better) around the board to make the detection more robust in various environments.

## ◆ findCirclesGrid() [2/2]

 bool cv::findCirclesGrid ( InputArray image, Size patternSize, OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID, const Ptr< FeatureDetector > & blobDetector = SimpleBlobDetector::create() )
Python:
cv.findCirclesGrid(image, patternSize, flags, blobDetector, parameters[, centers=None]) -> retval, centers
cv.findCirclesGrid(image, patternSize[, centers=None, flags=CALIB_CB_SYMMETRIC_GRID, blobDetector=SimpleBlobDetector::create()]) -> retval, centers

#include <opencv2/calib.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

## ◆ initCameraMatrix2D()

 Mat cv::initCameraMatrix2D ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, double aspectRatio = 1.0 )
Python:
cv.initCameraMatrix2D(objectPoints, imagePoints, imageSize[, aspectRatio=1.0]) -> retval

#include <opencv2/calib.hpp>

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

Parameters
 objectPoints Vector of vectors of the calibration pattern points in the calibration pattern coordinate space. In the old interface all the per-view vectors are concatenated. See calibrateCamera for details. imagePoints Vector of vectors of the projections of the calibration pattern points. In the old interface all the per-view vectors are concatenated. imageSize Image size in pixels used to initialize the principal point. aspectRatio If it is zero or negative, both $$f_x$$ and $$f_y$$ are estimated independently. Otherwise, $$f_x = f_y * \texttt{aspectRatio}$$ .

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

## ◆ stereoCalibrate() [1/2]

 double cv::stereoCalibrate ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, InputOutputArray R, InputOutputArray T, OutputArray E, OutputArray F, OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6) )
Python:
cv.stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize[, R=None, T=None, E=None, F=None, flags=CALIB_FIX_INTRINSIC, criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6)]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F
cv.stereoCalibrateExtended(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, E=None, F=None, perViewErrors=None, flags=CALIB_FIX_INTRINSIC, criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6)]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F, perViewErrors

#include <opencv2/calib.hpp>

Calibrates a stereo camera set up. This function finds the intrinsic parameters for each of the two cameras and the extrinsic parameters between the two cameras.

Parameters
 objectPoints Vector of vectors of the calibration pattern points. The same structure as in calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i. imagePoints1 Vector of vectors of the projections of the calibration pattern points, observed by the first camera. The same structure as in calibrateCamera. imagePoints2 Vector of vectors of the projections of the calibration pattern points, observed by the second camera. The same structure as in calibrateCamera. cameraMatrix1 Input/output camera intrinsic matrix for the first camera, the same as in calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below. distCoeffs1 Input/output vector of distortion coefficients, the same as in calibrateCamera. cameraMatrix2 Input/output second camera intrinsic matrix for the second camera. See description for cameraMatrix1. distCoeffs2 Input/output lens distortion coefficients for the second camera. See description for distCoeffs1. imageSize Size of the image used only to initialize the camera intrinsic matrices. R Output rotation matrix. Together with the translation vector T, this matrix brings points given in the first camera's coordinate system to points in the second camera's coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera's coordinate system to the second camera's coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system. T Output translation vector, see description above. E Output essential matrix. F Output fundamental matrix. perViewErrors Output vector of the RMS re-projection error estimated for each pattern view. flags Different flags that may be zero or a combination of the following values: CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated. CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user. CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately). CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization. CALIB_FIX_FOCAL_LENGTH Fix $$f^{(j)}_x$$ and $$f^{(j)}_y$$ . CALIB_FIX_ASPECT_RATIO Optimize $$f^{(j)}_y$$ . Fix the ratio $$f^{(j)}_x/f^{(j)}_y$$ CALIB_SAME_FOCAL_LENGTH Enforce $$f^{(0)}_x=f^{(1)}_x$$ and $$f^{(0)}_y=f^{(1)}_y$$ . CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there. CALIB_FIX_K1,..., CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients. CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0. criteria Termination criteria for the iterative optimization algorithm.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( $$R_1$$, $$T_1$$ ) and ( $$R_2$$, $$T_2$$), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation ( $$R$$, $$T$$) of the two cameras is known, it is possible to compute ( $$R_2$$, $$T_2$$) when ( $$R_1$$, $$T_1$$) is given. This is what the described function does. It computes ( $$R$$, $$T$$) such that:

$R_2=R R_1$

$T_2=R T_1 + T.$

Therefore, one can compute the coordinate representation of a 3D point for the second camera's coordinate system when given the point's coordinate representation in the first camera's coordinate system:

$\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.$

Optionally, it computes the essential matrix E:

$E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R$

where $$T_i$$ are components of the translation vector $$T$$ : $$T=[T_0, T_1, T_2]^T$$ . And the function can also compute the fundamental matrix F:

$F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}$

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

## ◆ stereoCalibrate() [2/2]

 double cv::stereoCalibrate ( InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1, InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2, Size imageSize, OutputArray R, OutputArray T, OutputArray E, OutputArray F, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6) )
Python:
cv.stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize[, R=None, T=None, E=None, F=None, flags=CALIB_FIX_INTRINSIC, criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6)]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F
cv.stereoCalibrateExtended(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, E=None, F=None, perViewErrors=None, flags=CALIB_FIX_INTRINSIC, criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-6)]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F, perViewErrors

#include <opencv2/calib.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

## ◆ convexHullFactor

 float cv::CirclesGridFinderParameters::convexHullFactor

## ◆ densityNeighborhoodSize

 cv::Size2f cv::CirclesGridFinderParameters::densityNeighborhoodSize

## ◆ edgeGain

 float cv::CirclesGridFinderParameters::edgeGain

## ◆ edgePenalty

 float cv::CirclesGridFinderParameters::edgePenalty

## ◆ existingVertexGain

 float cv::CirclesGridFinderParameters::existingVertexGain

## ◆ gridType

 GridType cv::CirclesGridFinderParameters::gridType

## ◆ keypointScale

 int cv::CirclesGridFinderParameters::keypointScale

## ◆ kmeansAttempts

 int cv::CirclesGridFinderParameters::kmeansAttempts

## ◆ maxRectifiedDistance

 float cv::CirclesGridFinderParameters::maxRectifiedDistance

Max deviation from prediction. Used by CALIB_CB_CLUSTERING.

## ◆ minDensity

 float cv::CirclesGridFinderParameters::minDensity

## ◆ minGraphConfidence

 float cv::CirclesGridFinderParameters::minGraphConfidence

## ◆ minRNGEdgeSwitchDist

 float cv::CirclesGridFinderParameters::minRNGEdgeSwitchDist

## ◆ squareSize

 float cv::CirclesGridFinderParameters::squareSize

Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.

## ◆ vertexGain

 float cv::CirclesGridFinderParameters::vertexGain

## ◆ vertexPenalty

 float cv::CirclesGridFinderParameters::vertexPenalty