OpenCV  5.0.0-pre
Open Source Computer Vision
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Functions

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

Functions

double calibrate (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size &image_size, InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags=0, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))
 Performs camera calibration.
 
void distortPoints (InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha=0)
 Distorts 2D points using fisheye model.
 
void distortPoints (InputArray undistorted, OutputArray distorted, InputArray Kundistorted, InputArray K, InputArray D, double alpha=0)
 
void estimateNewCameraMatrixForUndistortRectify (InputArray K, InputArray D, const Size &image_size, InputArray R, OutputArray P, double balance=0.0, const Size &new_size=Size(), double fov_scale=1.0)
 Estimates new camera intrinsic matrix for undistortion or rectification.
 
void initUndistortRectifyMap (InputArray K, InputArray D, InputArray R, InputArray P, const cv::Size &size, int m1type, OutputArray map1, OutputArray map2)
 Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero distortion is used, if R or P is empty identity matrixes are used.
 
void projectPoints (InputArray objectPoints, OutputArray imagePoints, const Affine3d &affine, InputArray K, InputArray D, double alpha=0, OutputArray jacobian=noArray())
 Projects points using fisheye model.
 
void projectPoints (InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, InputArray K, InputArray D, double alpha=0, OutputArray jacobian=noArray())
 
bool solvePnP (InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int flags=SOLVEPNP_ITERATIVE, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 10, 1e-8))
 Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.
 
double stereoCalibrate (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, OutputArray R, OutputArray T, int flags=CALIB_FIX_INTRINSIC, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))
 This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
 
double stereoCalibrate (InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize, OutputArray R, OutputArray T, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags=CALIB_FIX_INTRINSIC, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, DBL_EPSILON))
 Performs stereo calibration.
 
void stereoRectify (InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize=Size(), double balance=0.0, double fov_scale=1.0)
 Stereo rectification for fisheye camera model.
 
void undistortImage (InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray Knew=cv::noArray(), const Size &new_size=Size())
 Transforms an image to compensate for fisheye lens distortion.
 
void undistortPoints (InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray R=noArray(), InputArray P=noArray(), TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 10, 1e-8))
 Undistorts 2D points using fisheye model.
 

Detailed Description

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

Function Documentation

◆ distortPoints() [1/2]

void cv::fisheye::distortPoints ( InputArray  undistorted,
OutputArray  distorted,
InputArray  K,
InputArray  D,
double  alpha = 0 
)
Python:
cv.fisheye.distortPoints(undistorted, K, D[, distorted[, alpha]]) -> distorted
cv.fisheye.distortPoints(undistorted, Kundistorted, K, D[, distorted[, alpha]]) -> distorted

Distorts 2D points using fisheye model.

Parameters
undistortedArray of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
KCamera intrinsic matrix \(cameramatrix{K}\).
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
alphaThe skew coefficient.
distortedOutput array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .

Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. This means if you want to distort image points you have to multiply them with \(K^{-1}\) or use another function overload.

◆ distortPoints() [2/2]

void cv::fisheye::distortPoints ( InputArray  undistorted,
OutputArray  distorted,
InputArray  Kundistorted,
InputArray  K,
InputArray  D,
double  alpha = 0 
)
Python:
cv.fisheye.distortPoints(undistorted, K, D[, distorted[, alpha]]) -> distorted
cv.fisheye.distortPoints(undistorted, Kundistorted, K, D[, distorted[, alpha]]) -> distorted

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. Overload of distortPoints function to handle cases when undistorted points are got with non-identity camera matrix, e.g. output of estimateNewCameraMatrixForUndistortRectify.

Parameters
undistortedArray of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
KundistortedCamera intrinsic matrix used as new camera matrix for undistortion.
KCamera intrinsic matrix \(cameramatrix{K}\).
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
alphaThe skew coefficient.
distortedOutput array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .
See also
estimateNewCameraMatrixForUndistortRectify

◆ estimateNewCameraMatrixForUndistortRectify()

void cv::fisheye::estimateNewCameraMatrixForUndistortRectify ( InputArray  K,
InputArray  D,
const Size image_size,
InputArray  R,
OutputArray  P,
double  balance = 0.0,
const Size new_size = Size(),
double  fov_scale = 1.0 
)
Python:
cv.fisheye.estimateNewCameraMatrixForUndistortRectify(K, D, image_size, R[, P[, balance[, new_size[, fov_scale]]]]) -> P

Estimates new camera intrinsic matrix for undistortion or rectification.

Parameters
KCamera intrinsic matrix \(cameramatrix{K}\).
image_sizeSize of the image
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
RRectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
PNew camera intrinsic matrix (3x3) or new projection matrix (3x4)
balanceSets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
new_sizethe new size
fov_scaleDivisor for new focal length.

◆ initUndistortRectifyMap()

void cv::fisheye::initUndistortRectifyMap ( InputArray  K,
InputArray  D,
InputArray  R,
InputArray  P,
const cv::Size size,
int  m1type,
OutputArray  map1,
OutputArray  map2 
)
Python:
cv.fisheye.initUndistortRectifyMap(K, D, R, P, size, m1type[, map1[, map2]]) -> map1, map2

Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero distortion is used, if R or P is empty identity matrixes are used.

Parameters
KCamera intrinsic matrix \(cameramatrix{K}\).
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
RRectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
PNew camera intrinsic matrix (3x3) or new projection matrix (3x4)
sizeUndistorted image size.
m1typeType of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() for details.
map1The first output map.
map2The second output map.

◆ projectPoints() [1/2]

void cv::fisheye::projectPoints ( InputArray  objectPoints,
OutputArray  imagePoints,
const Affine3d affine,
InputArray  K,
InputArray  D,
double  alpha = 0,
OutputArray  jacobian = noArray() 
)
Python:
cv.fisheye.projectPoints(objectPoints, rvec, tvec, K, D[, imagePoints[, alpha[, jacobian]]]) -> imagePoints, jacobian

Projects points using fisheye model.

Parameters
objectPointsArray of object points, 1xN/Nx1 3-channel (or vector<Point3f> ), where N is the number of points in the view.
imagePointsOutput array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or vector<Point2f>.
affine
KCamera intrinsic matrix \(cameramatrix{K}\).
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
alphaThe skew coefficient.
jacobianOptional output 2Nx15 jacobian matrix of derivatives of image points with respect to components of the focal lengths, coordinates of the principal point, distortion coefficients, rotation vector, translation vector, and the skew. In the old interface different components of the jacobian are returned via different output parameters.

The function computes projections of 3D points to the image plane given intrinsic and extrinsic camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of image points coordinates (as functions of all the input parameters) with respect to the particular parameters, intrinsic and/or extrinsic.

◆ projectPoints() [2/2]

void cv::fisheye::projectPoints ( InputArray  objectPoints,
OutputArray  imagePoints,
InputArray  rvec,
InputArray  tvec,
InputArray  K,
InputArray  D,
double  alpha = 0,
OutputArray  jacobian = noArray() 
)
Python:
cv.fisheye.projectPoints(objectPoints, rvec, tvec, K, D[, imagePoints[, alpha[, jacobian]]]) -> imagePoints, jacobian

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

◆ solvePnP()

bool cv::fisheye::solvePnP ( InputArray  objectPoints,
InputArray  imagePoints,
InputArray  cameraMatrix,
InputArray  distCoeffs,
OutputArray  rvec,
OutputArray  tvec,
bool  useExtrinsicGuess = false,
int  flags = SOLVEPNP_ITERATIVE,
TermCriteria  criteria = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 10, 1e-8) 
)
Python:
cv.fisheye.solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags[, criteria]]]]]) -> retval, rvec, tvec

Finds an object pose from 3D-2D point correspondences for fisheye camera moodel.

Parameters
objectPointsArray of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePointsArray of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel, where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrixInput camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffsInput vector of distortion coefficients (4x1/1x4).
rvecOutput rotation vector (see Rodrigues ) that, together with tvec, brings points from the model coordinate system to the camera coordinate system.
tvecOutput translation vector.
useExtrinsicGuessParameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses the provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flagsMethod for solving a PnP problem: see calib3d_solvePnP_flags This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods:
  • P3P methods (SOLVEPNP_P3P, SOLVEPNP_AP3P): need 4 input points to return a unique solution.
  • SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
  • SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
  • point 0: [-squareLength / 2, squareLength / 2, 0]
  • point 1: [ squareLength / 2, squareLength / 2, 0]
  • point 2: [ squareLength / 2, -squareLength / 2, 0]
  • point 3: [-squareLength / 2, -squareLength / 2, 0]
  • for all the other flags, number of input points must be >= 4 and object points can be in any configuration.
criteriaTermination criteria for internal undistortPoints call. The function interally undistorts points with undistortPoints and call cv::solvePnP, thus the input are very similar. Check there and Perspective-n-Points is described in Perspective-n-Point (PnP) pose computation for more information.

◆ undistortImage()

void cv::fisheye::undistortImage ( InputArray  distorted,
OutputArray  undistorted,
InputArray  K,
InputArray  D,
InputArray  Knew = cv::noArray(),
const Size new_size = Size() 
)
Python:
cv.fisheye.undistortImage(distorted, K, D[, undistorted[, Knew[, new_size]]]) -> undistorted

Transforms an image to compensate for fisheye lens distortion.

Parameters
distortedimage with fisheye lens distortion.
undistortedOutput image with compensated fisheye lens distortion.
KCamera intrinsic matrix \(cameramatrix{K}\).
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
KnewCamera intrinsic matrix of the distorted image. By default, it is the identity matrix but you may additionally scale and shift the result by using a different matrix.
new_sizethe new size

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap (with bilinear interpolation). See the former function for details of the transformation being performed.

See below the results of undistortImage.

  • a) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3, k_4, k_5, k_6) of distortion were optimized under calibration)
    • b) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)
    • c) original image was captured with fisheye lens

Pictures a) and b) almost the same. But if we consider points of image located far from the center of image, we can notice that on image a) these points are distorted.

image

◆ undistortPoints()

void cv::fisheye::undistortPoints ( InputArray  distorted,
OutputArray  undistorted,
InputArray  K,
InputArray  D,
InputArray  R = noArray(),
InputArray  P = noArray(),
TermCriteria  criteria = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 10, 1e-8) 
)
Python:
cv.fisheye.undistortPoints(distorted, K, D[, undistorted[, R[, P[, criteria]]]]) -> undistorted

Undistorts 2D points using fisheye model.

Parameters
distortedArray of object points, 1xN/Nx1 2-channel (or vector<Point2f> ), where N is the number of points in the view.
KCamera intrinsic matrix \(cameramatrix{K}\).
DInput vector of distortion coefficients \(\distcoeffsfisheye\).
RRectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3 1-channel or 1x1 3-channel
PNew camera intrinsic matrix (3x3) or new projection matrix (3x4)
criteriaTermination criteria
undistortedOutput array of image points, 1xN/Nx1 2-channel, or vector<Point2f> .