The functions in this section use a socalled pinhole camera model. In this model, a scene view is formed by projecting 3D points into the image plane using a perspective transformation.
or
where:
 are the coordinates of a 3D point in the world coordinate space
 are the coordinates of the projection point in pixels
 is a camera matrix, or a matrix of intrinsic parameters
 is a principal point that is usually at the image center
 are the focal lengths expressed in pixel units.
Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled (multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not depend on the scene viewed. So, once estimated, it can be reused as long as the focal length is fixed (in case of zoom lens). The joint rotationtranslation matrix is called a matrix of extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa, rigid motion of an object in front of a still camera. That is, translates coordinates of a point to a coordinate system, fixed with respect to the camera. The transformation above is equivalent to the following (when ):
The following figure illustrates the pinhole camera model.
Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion. So, the above model is extended as:
, , , , , and are radial distortion coefficients. and are tangential distortion coefficients. Higherorder coefficients are not considered in OpenCV.
The next figure shows two common types of radial distortion: barrel distortion (typically and pincushion distortion (typically ).
In the functions below the coefficients are passed or returned as
vector. That is, if the vector contains four elements, it means that
.
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
,
,
, and
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 3D2D 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.
Note
Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
double 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, 30, DBL_EPSILON) )¶
cv2.
calibrateCamera
(objectPoints, imagePoints, imageSize[, cameraMatrix[, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]]]) → retval, cameraMatrix, distCoeffs, rvecs, tvecs¶
double cvCalibrateCamera2
(const CvMat* object_points, const CvMat* image_points, const CvMat* point_counts, CvSize image_size, CvMat* camera_matrix, CvMat* distortion_coeffs, CvMat* rotation_vectors=NULL, CvMat* translation_vectors=NULL, int flags=0, CvTermCriteria term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON) )¶
cv.
CalibrateCamera2
(objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, flags=0) → None¶Parameters: 


The function estimates the intrinsic camera
parameters and extrinsic parameters for each of the views. The algorithm is based on [Zhang2000] and [BouguetMCT]. 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 a known geometry and easily detectable feature points.
Such an object is called a calibration rig or calibration pattern,
and OpenCV has builtin support for a chessboard as a calibration
rig (see
findChessboardCorners()
). Currently, initialization
of intrinsic parameters (when CV_CALIB_USE_INTRINSIC_GUESS
is not set) is only implemented for planar calibration patterns
(where Zcoordinates 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:
CV_CALIB_FIX_K?
are specified.solvePnP()
.imagePoints
and the projected (using the current estimates for camera parameters and the poses) object points objectPoints
. See projectPoints()
for details.The function returns the final reprojection error.
Note
If you use a nonsquare (=nonNxN) grid and findChessboardCorners()
for calibration, and calibrateCamera
returns bad values (zero distortion coefficients, an image center very far from (w/20.5,h/20.5)
, and/or large differences between and (ratios of 10:1 or more)), then you have probably used patternSize=cvSize(rows,cols)
instead of using patternSize=cvSize(cols,rows)
in findChessboardCorners()
.
Computes useful camera characteristics from the camera matrix.
void calibrationMatrixValues
(InputArray cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, Point2d& principalPoint, double& aspectRatio)¶
cv2.
calibrationMatrixValues
(cameraMatrix, imageSize, apertureWidth, apertureHeight) → fovx, fovy, focalLength, principalPoint, aspectRatio¶Parameters: 


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).
Combines two rotationandshift transformations.
void composeRT
(InputArray rvec1, InputArray tvec1, InputArray rvec2, InputArray tvec2, OutputArray rvec3, OutputArray tvec3, OutputArray dr3dr1=noArray(), OutputArray dr3dt1=noArray(), OutputArray dr3dr2=noArray(), OutputArray dr3dt2=noArray(), OutputArray dt3dr1=noArray(), OutputArray dt3dt1=noArray(), OutputArray dt3dr2=noArray(), OutputArray dt3dt2=noArray() )¶
cv2.
composeRT
(rvec1, tvec1, rvec2, tvec2[, rvec3[, tvec3[, dr3dr1[, dr3dt1[, dr3dr2[, dr3dt2[, dt3dr1[, dt3dt1[, dt3dr2[, dt3dt2]]]]]]]]]]) → rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2¶Parameters: 


The functions compute:
where denotes a rotation vector to a rotation matrix transformation, and
denotes the inverse transformation. See Rodrigues()
for details.
Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see matMulDeriv()
).
The functions are used inside stereoCalibrate()
but can also be used in your own code where LevenbergMarquardt or another gradientbased solver is used to optimize a function that contains a matrix multiplication.
For points in an image of a stereo pair, computes the corresponding epilines in the other image.
void computeCorrespondEpilines
(InputArray points, int whichImage, InputArray F, OutputArray lines)¶
void cvComputeCorrespondEpilines
(const CvMat* points, int which_image, const CvMat* fundamental_matrix, CvMat* correspondent_lines)¶
cv.
ComputeCorrespondEpilines
(points, whichImage, F, lines) → None¶Parameters: 


For every point in one of the two images of a stereo pair, the function finds the equation of the corresponding epipolar line in the other image.
From the fundamental matrix definition (see
findFundamentalMat()
),
line
in the second image for the point
in the first image (when whichImage=1
) is computed as:
And vice versa, when whichImage=2
,
is computed from
as:
Line coefficients are defined up to a scale. They are normalized so that .
Converts points from Euclidean to homogeneous space.
void convertPointsToHomogeneous
(InputArray src, OutputArray dst)¶
cv2.
convertPointsToHomogeneous
(src[, dst]) → dst¶Parameters: 


The function converts points from Euclidean to homogeneous space by appending 1’s to the tuple of point coordinates. That is, each point (x1, x2, ..., xn)
is converted to (x1, x2, ..., xn, 1)
.
Converts points from homogeneous to Euclidean space.
void convertPointsFromHomogeneous
(InputArray src, OutputArray dst)¶
cv2.
convertPointsFromHomogeneous
(src[, dst]) → dst¶Parameters: 


The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, ... x(n1), xn)
is converted to (x1/xn, x2/xn, ..., x(n1)/xn)
. When xn=0
, the output point coordinates will be (0,0,0,...)
.
Converts points to/from homogeneous coordinates.
void convertPointsHomogeneous
(InputArray src, OutputArray dst)¶
void cvConvertPointsHomogeneous
(const CvMat* src, CvMat* dst)¶
cv.
ConvertPointsHomogeneous
(src, dst) → None¶Parameters: 


The function converts 2D or 3D points from/to homogeneous coordinates by calling either convertPointsToHomogeneous()
or convertPointsFromHomogeneous()
.
Note
The function is obsolete. Use one of the previous two functions instead.
Refines coordinates of corresponding points.
void correctMatches
(InputArray F, InputArray points1, InputArray points2, OutputArray newPoints1, OutputArray newPoints2)¶
cv2.
correctMatches
(F, points1, points2[, newPoints1[, newPoints2]]) → newPoints1, newPoints2¶
void cvCorrectMatches
(CvMat* F, CvMat* points1, CvMat* points2, CvMat* new_points1, CvMat* new_points2)¶Parameters: 


The function implements the Optimal Triangulation Method (see Multiple View Geometry for details). For each given point correspondence points1[i] <> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] <> newPoints2[i] that minimize the geometric error (where is the geometric distance between points and ) subject to the epipolar constraint .
Decomposes a projection matrix into a rotation matrix and a camera matrix.
void decomposeProjectionMatrix
(InputArray projMatrix, OutputArray cameraMatrix, OutputArray rotMatrix, OutputArray transVect, OutputArray rotMatrixX=noArray(), OutputArray rotMatrixY=noArray(), OutputArray rotMatrixZ=noArray(), OutputArray eulerAngles=noArray() )¶
cv2.
decomposeProjectionMatrix
(projMatrix[, cameraMatrix[, rotMatrix[, transVect[, rotMatrixX[, rotMatrixY[, rotMatrixZ[, eulerAngles]]]]]]]) → cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAngles¶
void cvDecomposeProjectionMatrix
(const CvMat* projMatr, CvMat* calibMatr, CvMat* rotMatr, CvMat* posVect, CvMat* rotMatrX=NULL, CvMat* rotMatrY=NULL, CvMat* rotMatrZ=NULL, CvPoint3D64f* eulerAngles=NULL )¶
cv.
DecomposeProjectionMatrix
(projMatrix, cameraMatrix, rotMatrix, transVect, rotMatrX=None, rotMatrY=None, rotMatrZ=None) → eulerAngles¶Parameters: 


The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.
It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principle axes that results in the same orientation of an object, eg. see [Slabaugh]. Returned tree rotation matrices and corresponding three Euler angules are only one of the possible solutions.
The function is based on
RQDecomp3x3()
.
Renders the detected chessboard corners.
void drawChessboardCorners
(InputOutputArray image, Size patternSize, InputArray corners, bool patternWasFound)¶
cv2.
drawChessboardCorners
(image, patternSize, corners, patternWasFound) → None¶
void cvDrawChessboardCorners
(CvArr* image, CvSize pattern_size, CvPoint2D32f* corners, int count, int pattern_was_found)¶
cv.
DrawChessboardCorners
(image, patternSize, corners, patternWasFound) → None¶Parameters: 


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.
Finds the positions of internal corners of the chessboard.
bool findChessboardCorners
(InputArray image, Size patternSize, OutputArray corners, int flags=CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE )¶
cv2.
findChessboardCorners
(image, patternSize[, corners[, flags]]) → retval, corners¶
int cvFindChessboardCorners
(const void* image, CvSize pattern_size, CvPoint2D32f* corners, int* corner_count=NULL, int flags=CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE )¶
cv.
FindChessboardCorners
(image, patternSize, flags=CV_CALIB_CB_ADAPTIVE_THRESH) → corners¶Parameters: 


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 nonzero
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,
CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
+ CALIB_CB_FAST_CHECK);
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 squarethick 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.
Finds centers in the grid of circles.
bool findCirclesGrid
(InputArray image, Size patternSize, OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID, const Ptr<FeatureDetector>& blobDetector=new SimpleBlobDetector() )¶
cv2.
findCirclesGridDefault
(image, patternSize[, centers[, flags]]) → retval, centers¶Parameters: 


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 nonzero 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 squarethick border, the wider the better) around the board to make the detection more robust in various environments.
Finds an object pose from 3D2D point correspondences.
bool solvePnP
(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int flags=ITERATIVE )¶
cv2.
solvePnP
(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]) → retval, rvec, tvec¶
void cvFindExtrinsicCameraParams2
(const CvMat* object_points, const CvMat* image_points, const CvMat* camera_matrix, const CvMat* distortion_coeffs, CvMat* rotation_vector, CvMat* translation_vector, int use_extrinsic_guess=0 )¶
cv.
FindExtrinsicCameraParams2
(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess=0) → None¶Parameters: 


The function estimates the object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients.
Note
Finds an object pose from 3D2D point correspondences using the RANSAC scheme.
void solvePnPRansac
(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess=false, int iterationsCount=100, float reprojectionError=8.0, int minInliersCount=100, OutputArray inliers=noArray(), int flags=ITERATIVE )¶
cv2.
solvePnPRansac
(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, minInliersCount[, inliers[, flags]]]]]]]]) → rvec, tvec, inliers¶Parameters: 


The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints
and the projected (using
projectPoints()
) objectPoints
. The use of RANSAC makes the function resistant to outliers. The function is parallelized with the TBB library.
Calculates a fundamental matrix from the corresponding points in two images.
Mat findFundamentalMat
(InputArray points1, InputArray points2, int method=FM_RANSAC, double param1=3., double param2=0.99, OutputArray mask=noArray() )¶
cv2.
findFundamentalMat
(points1, points2[, method[, param1[, param2[, mask]]]]) → retval, mask¶
int cvFindFundamentalMat
(const CvMat* points1, const CvMat* points2, CvMat* fundamental_matrix, int method=CV_FM_RANSAC, double param1=3., double param2=0.99, CvMat* status=NULL )¶
cv.
FindFundamentalMat
(points1, points2, fundamentalMatrix, method=CV_FM_RANSAC, param1=1., param2=0.99, status=None) → retval¶Parameters: 


The epipolar geometry is described by the following equation:
where is a fundamental matrix, and are corresponding points in the first and the second images, respectively.
The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7point algorithm, the function may return up to 3 solutions ( matrix that stores all 3 matrices sequentially).
The calculated fundamental matrix may be passed further to
computeCorrespondEpilines()
that finds the epipolar lines
corresponding to the specified points. It can also be passed to
stereoRectifyUncalibrated()
to compute the rectification transformation.
// Example. Estimation of fundamental matrix using the RANSAC algorithm
int point_count = 100;
vector<Point2f> points1(point_count);
vector<Point2f> points2(point_count);
// initialize the points here ... */
for( int i = 0; i < point_count; i++ )
{
points1[i] = ...;
points2[i] = ...;
}
Mat fundamental_matrix =
findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
Finds a perspective transformation between two planes.
Mat findHomography
(InputArray srcPoints, InputArray dstPoints, int method=0, double ransacReprojThreshold=3, OutputArray mask=noArray() )¶
cv2.
findHomography
(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask]]]) → retval, mask¶
int cvFindHomography
(const CvMat* src_points, const CvMat* dst_points, CvMat* homography, int method=0, double ransacReprojThreshold=3, CvMat* mask=0 )¶
cv.
FindHomography
(srcPoints, dstPoints, H, method=0, ransacReprojThreshold=3.0, status=None) → None¶Parameters: 


The functions find and return the perspective transformation between the source and the destination planes:
so that the backprojection error
is minimized. If the parameter method
is set to the default value 0, the function
uses all the point pairs to compute an initial homography estimate with a simple leastsquares scheme.
However, if not all of the point pairs (
, ) fit the rigid perspective transformation (that is, there
are some outliers), this initial estimate will be poor.
In this case, you can use one of the two robust methods. Both methods, RANSAC
and LMeDS
, try many different random subsets
of the corresponding point pairs (of four pairs each), estimate
the homography matrix using this subset and a simple leastsquare
algorithm, and then compute the quality/goodness of the computed homography
(which is the number of inliers for RANSAC or the median reprojection
error for LMeDs). The best subset is then used to produce the initial
estimate of the homography matrix and the mask of inliers/outliers.
Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the LevenbergMarquardt method to reduce the reprojection error even more.
The method RANSAC
can handle practically any ratio of outliers
but it needs a threshold to distinguish inliers from outliers.
The method LMeDS
does not need any threshold but it works
correctly only when there are more than 50% of inliers. Finally,
if there are no outliers and the noise is rather small, use the default method (method=0
).
The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. Thus, it is normalized so that . Note that whenever an H matrix cannot be estimated, an empty one will be returned.
See also
getAffineTransform()
,
getPerspectiveTransform()
,
estimateRigidTransform()
,
warpPerspective()
,
perspectiveTransform()
Note
Computes an optimal affine transformation between two 3D point sets.
int estimateAffine3D
(InputArray src, InputArray dst, OutputArray out, OutputArray inliers, double ransacThreshold=3, double confidence=0.99)¶
cv2.
estimateAffine3D
(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) → retval, out, inliers¶Parameters: 


The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.
Filters off small noise blobs (speckles) in the disparity map
void filterSpeckles
(InputOutputArray img, double newVal, int maxSpeckleSize, double maxDiff, InputOutputArray buf=noArray() )¶
cv2.
filterSpeckles
(img, newVal, maxSpeckleSize, maxDiff[, buf]) → None¶Parameters: 


Returns the new camera matrix based on the free scaling parameter.
Mat getOptimalNewCameraMatrix
(InputArray cameraMatrix, InputArray distCoeffs, Size imageSize, double alpha, Size newImgSize=Size(), Rect* validPixROI=0, bool centerPrincipalPoint=false )¶
cv2.
getOptimalNewCameraMatrix
(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) → retval, validPixROI¶
void cvGetOptimalNewCameraMatrix
(const CvMat* camera_matrix, const CvMat* dist_coeffs, CvSize image_size, double alpha, CvMat* new_camera_matrix, CvSize new_imag_size=cvSize(0,0), CvRect* valid_pixel_ROI=0, int center_principal_point=0 )¶
cv.
GetOptimalNewCameraMatrix
(cameraMatrix, distCoeffs, imageSize, alpha, newCameraMatrix, newImageSize=(0, 0), validPixROI=0, centerPrincipalPoint=0) → None¶Parameters: 


The function computes and returns
the optimal new camera matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0
, keep all the original image pixels if there is valuable information in the corners alpha=1
, or get something in between. When alpha>0
, the undistortion result is likely to have some black pixels corresponding to “virtual” pixels outside of the captured distorted image. The original camera matrix, distortion coefficients, the computed new camera matrix, and newImageSize
should be passed to
initUndistortRectifyMap()
to produce the maps for
remap()
.
Finds an initial camera matrix from 3D2D point correspondences.
Mat initCameraMatrix2D
(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size imageSize, double aspectRatio=1.)¶
cv2.
initCameraMatrix2D
(objectPoints, imagePoints, imageSize[, aspectRatio]) → retval¶
void cvInitIntrinsicParams2D
(const CvMat* object_points, const CvMat* image_points, const CvMat* npoints, CvSize image_size, CvMat* camera_matrix, double aspect_ratio=1. )¶
cv.
InitIntrinsicParams2D
(objectPoints, imagePoints, npoints, imageSize, cameraMatrix, aspectRatio=1.) → None¶Parameters: 


The function estimates and returns an initial camera matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has zcoordinate =0.
Computes partial derivatives of the matrix product for each multiplied matrix.
void matMulDeriv
(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB)¶
cv2.
matMulDeriv
(A, B[, dABdA[, dABdB]]) → dABdA, dABdB¶Parameters: 


The function computes partial derivatives of the elements of the matrix product
with regard to the elements of each of the two input matrices. The function is used to compute the Jacobian matrices in
stereoCalibrate()
but can also be used in any other similar optimization function.
Projects 3D points to an image plane.
void projectPoints
(InputArray objectPoints, InputArray rvec, InputArray tvec, InputArray cameraMatrix, InputArray distCoeffs, OutputArray imagePoints, OutputArray jacobian=noArray(), double aspectRatio=0 )¶
cv2.
projectPoints
(objectPoints, rvec, tvec, cameraMatrix, distCoeffs[, imagePoints[, jacobian[, aspectRatio]]]) → imagePoints, jacobian¶
void cvProjectPoints2
(const CvMat* object_points, const CvMat* rotation_vector, const CvMat* translation_vector, const CvMat* camera_matrix, const CvMat* distortion_coeffs, CvMat* image_points, CvMat* dpdrot=NULL, CvMat* dpdt=NULL, CvMat* dpdf=NULL, CvMat* dpdc=NULL, CvMat* dpddist=NULL, double aspect_ratio=0 )¶
cv.
ProjectPoints2
(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, dpdrot=None, dpdt=None, dpdf=None, dpdc=None, dpddist=None) → None¶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. The Jacobians are used during the global optimization
in
calibrateCamera()
,
solvePnP()
, and
stereoCalibrate()
. The
function itself can also be used to compute a reprojection error given the
current intrinsic and extrinsic parameters.
Note
By setting rvec=tvec=(0,0,0)
or by setting cameraMatrix
to a 3x3 identity matrix, or by passing zero distortion coefficients, you can get various useful partial cases of the function. This means that you can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zerodistortion setup.
Reprojects a disparity image to 3D space.
void reprojectImageTo3D
(InputArray disparity, OutputArray _3dImage, InputArray Q, bool handleMissingValues=false, int ddepth=1 )¶
cv2.
reprojectImageTo3D
(disparity, Q[, _3dImage[, handleMissingValues[, ddepth]]]) → _3dImage¶
void cvReprojectImageTo3D
(const CvArr* disparityImage, CvArr* _3dImage, const CvMat* Q, int handleMissingValues=0 )¶
cv.
ReprojectImageTo3D
(disparity, _3dImage, Q, handleMissingValues=0) → None¶Parameters: 


The function transforms a singlechannel disparity map to a 3channel image representing a 3D surface. That is, for each pixel (x,y)
andthe corresponding disparity d=disparity(x,y)
, it computes:
The matrix Q
can be an arbitrary
matrix (for example, the one computed by
stereoRectify()
). To reproject a sparse set of points {(x,y,d),...} to 3D space, use
perspectiveTransform()
.
Computes an RQ decomposition of 3x3 matrices.
Vec3d RQDecomp3x3
(InputArray src, OutputArray mtxR, OutputArray mtxQ, OutputArray Qx=noArray(), OutputArray Qy=noArray(), OutputArray Qz=noArray() )¶
cv2.
RQDecomp3x3
(src[, mtxR[, mtxQ[, Qx[, Qy[, Qz]]]]]) → retval, mtxR, mtxQ, Qx, Qy, Qz¶
void cvRQDecomp3x3
(const CvMat* matrixM, CvMat* matrixR, CvMat* matrixQ, CvMat* matrixQx=NULL, CvMat* matrixQy=NULL, CvMat* matrixQz=NULL, CvPoint3D64f* eulerAngles=NULL )¶
cv.
RQDecomp3x3
(M, R, Q, Qx=None, Qy=None, Qz=None) → eulerAngles¶Parameters: 


The function computes a RQ decomposition using the given rotations. This function is used in
decomposeProjectionMatrix()
to decompose the left 3x3 submatrix of a projection matrix into a camera and a rotation matrix.
It optionally returns three rotation matrices, one for each axis, and the three Euler angles in degrees (as the return value) that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principle axes that results in the same orientation of an object, eg. see [Slabaugh]. Returned tree rotation matrices and corresponding three Euler angules are only one of the possible solutions.
Converts a rotation matrix to a rotation vector or vice versa.
void Rodrigues
(InputArray src, OutputArray dst, OutputArray jacobian=noArray())¶
cv2.
Rodrigues
(src[, dst[, jacobian]]) → dst, jacobian¶
int cvRodrigues2
(const CvMat* src, CvMat* dst, CvMat* jacobian=0 )¶
cv.
Rodrigues2
(src, dst, jacobian=0) → None¶Parameters: 


Inverse transformation can be also done easily, since
A rotation vector is a convenient and most compact representation of a rotation matrix
(since any rotation matrix has just 3 degrees of freedom). The representation is
used in the global 3D geometry optimization procedures like
calibrateCamera()
,
stereoCalibrate()
, or
solvePnP()
.
StereoBM
¶Class for computing stereo correspondence using the block matching algorithm.
// Block matching stereo correspondence algorithm class StereoBM
{
enum { NORMALIZED_RESPONSE = CV_STEREO_BM_NORMALIZED_RESPONSE,
BASIC_PRESET=CV_STEREO_BM_BASIC,
FISH_EYE_PRESET=CV_STEREO_BM_FISH_EYE,
NARROW_PRESET=CV_STEREO_BM_NARROW };
StereoBM();
// the preset is one of ..._PRESET above.
// ndisparities is the size of disparity range,
// in which the optimal disparity at each pixel is searched for.
// SADWindowSize is the size of averaging window used to match pixel blocks
// (larger values mean better robustness to noise, but yield blurry disparity maps)
StereoBM(int preset, int ndisparities=0, int SADWindowSize=21);
// separate initialization function
void init(int preset, int ndisparities=0, int SADWindowSize=21);
// computes the disparity for the two rectified 8bit singlechannel images.
// the disparity will be 16bit signed (fixedpoint) or 32bit floatingpoint image of the same size as left.
void operator()( InputArray left, InputArray right, OutputArray disparity, int disptype=CV_16S );
Ptr<CvStereoBMState> state;
};
The class is a C++ wrapper for the associated functions. In particular, StereoBM::operator()
is the wrapper for
cvFindStereoCorrespondenceBM()
.
The constructors.
StereoBM::
StereoBM
()¶
StereoBM::
StereoBM
(int preset, int ndisparities=0, int SADWindowSize=21)¶
cv2.
StereoBM
([preset[, ndisparities[, SADWindowSize]]]) → <StereoBM object>¶
CvStereoBMState* cvCreateStereoBMState
(int preset=CV_STEREO_BM_BASIC, int numberOfDisparities=0 )¶
cv.
CreateStereoBMState
(preset=CV_STEREO_BM_BASIC, numberOfDisparities=0) → CvStereoBMState¶Parameters: 


The constructors initialize StereoBM
state. You can then call StereoBM::operator()
to compute disparity for a specific stereo pair.
Note
In the C API you need to deallocate CvStereoBM
state when it is not needed anymore using cvReleaseStereoBMState(&stereobm)
.
Computes disparity using the BM algorithm for a rectified stereo pair.
void StereoBM::
operator()
(InputArray left, InputArray right, OutputArray disparity, int disptype=CV_16S )¶
cv2.StereoBM.
compute
(left, right[, disparity[, disptype]]) → disparity¶
void cvFindStereoCorrespondenceBM
(const CvArr* left, const CvArr* right, CvArr* disparity, CvStereoBMState* state)¶
cv.
FindStereoCorrespondenceBM
(left, right, disparity, state) → None¶Parameters: 


The method executes the BM algorithm on a rectified stereo pair. See the stereo_match.cpp
OpenCV sample on how to prepare images and call the method. Note that the method is not constant, thus you should not use the same StereoBM
instance from within different threads simultaneously. The function is parallelized with the TBB library.
StereoSGBM
¶Class for computing stereo correspondence using the semiglobal block matching algorithm.
class StereoSGBM
{
StereoSGBM();
StereoSGBM(int minDisparity, int numDisparities, int SADWindowSize,
int P1=0, int P2=0, int disp12MaxDiff=0,
int preFilterCap=0, int uniquenessRatio=0,
int speckleWindowSize=0, int speckleRange=0,
bool fullDP=false);
virtual ~StereoSGBM();
virtual void operator()(InputArray left, InputArray right, OutputArray disp);
int minDisparity;
int numberOfDisparities;
int SADWindowSize;
int preFilterCap;
int uniquenessRatio;
int P1, P2;
int speckleWindowSize;
int speckleRange;
int disp12MaxDiff;
bool fullDP;
...
};
The class implements the modified H. Hirschmuller algorithm [HH08] that differs from the original one as follows:
 By default, the algorithm is singlepass, which means that you consider only 5 directions instead of 8. Set
fullDP=true
to run the full variant of the algorithm but beware that it may consume a lot of memory. The algorithm matches blocks, not individual pixels. Though, setting
SADWindowSize=1
reduces the blocks to single pixels. Mutual information cost function is not implemented. Instead, a simpler BirchfieldTomasi subpixel metric from [BT98] is used. Though, the color images are supported as well.
 Some pre and post processing steps from K. Konolige algorithm
StereoBM::operator()
are included, for example: prefiltering (CV_STEREO_BM_XSOBEL
type) and postfiltering (uniqueness check, quadratic interpolation and speckle filtering).
Note
StereoSGBM::
StereoSGBM
()¶
StereoSGBM::
StereoSGBM
(int minDisparity, int numDisparities, int SADWindowSize, int P1=0, int P2=0, int disp12MaxDiff=0, int preFilterCap=0, int uniquenessRatio=0, int speckleWindowSize=0, int speckleRange=0, bool fullDP=false)¶
cv2.
StereoSGBM
([minDisparity, numDisparities, SADWindowSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, fullDP]]]]]]]]]) → <StereoSGBM object>¶Initializes StereoSGBM
and sets parameters to custom values.??
Parameters: 


The first constructor initializes StereoSGBM
with all the default parameters. So, you only have to set StereoSGBM::numberOfDisparities
at minimum. The second constructor enables you to set each parameter to a custom value.
void StereoSGBM::
operator()
(InputArray left, InputArray right, OutputArray disp)¶
cv2.StereoSGBM.
compute
(left, right[, disp]) → disp¶Computes disparity using the SGBM algorithm for a rectified stereo pair.
Parameters: 


The method executes the SGBM algorithm on a rectified stereo pair. See stereo_match.cpp
OpenCV sample on how to prepare images and call the method.
Note
The method is not constant, so you should not use the same StereoSGBM
instance from different threads simultaneously.
Calibrates the stereo camera.
double 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, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e6), int flags=CALIB_FIX_INTRINSIC )¶
cv2.
stereoCalibrate
(objectPoints, imagePoints1, imagePoints2, imageSize[, cameraMatrix1[, distCoeffs1[, cameraMatrix2[, distCoeffs2[, R[, T[, E[, F[, criteria[, flags]]]]]]]]]]) → retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F¶
double cvStereoCalibrate
(const CvMat* object_points, const CvMat* image_points1, const CvMat* image_points2, const CvMat* npoints, CvMat* camera_matrix1, CvMat* dist_coeffs1, CvMat* camera_matrix2, CvMat* dist_coeffs2, CvSize image_size, CvMat* R, CvMat* T, CvMat* E=0, CvMat* F=0, CvTermCriteria term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e6), int flags=CV_CALIB_FIX_INTRINSIC )¶
cv.
StereoCalibrate
(objectPoints, imagePoints1, imagePoints2, pointCounts, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E=None, F=None, term_crit=(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 30, 1e6), flags=CV_CALIB_FIX_INTRINSIC) → None¶Parameters: 


The function estimates transformation between two cameras making a stereo pair. If you have a stereo camera where the relative position and orientation of two cameras is fixed, and if you computed poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2), respectively (this can be done with
solvePnP()
), then those poses definitely relate to each other. This means that, given (
,:math:T_1 ), it should be possible to compute (
,:math:T_2 ). You only need to know the position and orientation of the second camera relative to the first camera. This is what the described function does. It computes (
,:math:T ) so that:
Optionally, it computes the essential matrix E:
where are components of the translation vector : . And the function can also compute the fundamental matrix F:
Besides the stereorelated information, the function can also perform a full calibration of each of 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 CV_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 CV_CALIB_SAME_FOCAL_LENGTH
and CV_CALIB_ZERO_TANGENT_DIST
flags, which is usually a reasonable assumption.
Similarly to calibrateCamera()
, the function minimizes the total reprojection error for all the points in all the available views from both cameras. The function returns the final value of the reprojection error.
Computes rectification transforms for each head of a calibrated stereo camera.
void stereoRectify
(InputArray cameraMatrix1, InputArray distCoeffs1, InputArray cameraMatrix2, InputArray distCoeffs2, Size imageSize, InputArray R, InputArray T, OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags=CALIB_ZERO_DISPARITY, double alpha=1, Size newImageSize=Size(), Rect* validPixROI1=0, Rect* validPixROI2=0 )¶
void cvStereoRectify
(const CvMat* camera_matrix1, const CvMat* camera_matrix2, const CvMat* dist_coeffs1, const CvMat* dist_coeffs2, CvSize image_size, const CvMat* R, const CvMat* T, CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2, CvMat* Q=0, int flags=CV_CALIB_ZERO_DISPARITY, double alpha=1, CvSize new_image_size=cvSize(0,0), CvRect* valid_pix_ROI1=0, CvRect* valid_pix_ROI2=0 )¶
cv.
StereoRectify
(cameraMatrix1, cameraMatrix2, distCoeffs1, distCoeffs2, imageSize, R, T, R1, R2, P1, P2, Q=None, flags=CV_CALIB_ZERO_DISPARITY, alpha=1, newImageSize=(0, 0)) > (roi1, roi2)¶Parameters: 


The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by
stereoCalibrate()
as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:
Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same ycoordinate. P1 and P2 look like:
where
is a horizontal shift between the cameras and
if CV_CALIB_ZERO_DISPARITY
is set.
Vertical stereo: the first and the second camera views are shifted relative to each other mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same xcoordinate. P1 and P2 look like:
where
is a vertical shift between the cameras and
if CALIB_ZERO_DISPARITY
is set.
As you can see, the first three columns of P1
and P2
will effectively be the new “rectified” camera matrices.
The matrices, together with R1
and R2
, can then be passed to
initUndistortRectifyMap()
to initialize the rectification map for each camera.
See below the screenshot from the stereo_calib.cpp
sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1
and roi2
. You see that their interiors are all valid pixels.
Computes a rectification transform for an uncalibrated stereo camera.
bool stereoRectifyUncalibrated
(InputArray points1, InputArray points2, InputArray F, Size imgSize, OutputArray H1, OutputArray H2, double threshold=5 )¶
cv2.
stereoRectifyUncalibrated
(points1, points2, F, imgSize[, H1[, H2[, threshold]]]) → retval, H1, H2¶
int cvStereoRectifyUncalibrated
(const CvMat* points1, const CvMat* points2, const CvMat* F, CvSize img_size, CvMat* H1, CvMat* H2, double threshold=5 )¶
cv.
StereoRectifyUncalibrated
(points1, points2, F, imageSize, H1, H2, threshold=5) → None¶Parameters: 


The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix “uncalibrated”. Another related difference from
stereoRectify()
is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1
and H2
. The function implements the algorithm
[Hartley99].
Note
While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera()
. Then, the images can be corrected using undistort()
, or just the point coordinates can be corrected with undistortPoints()
.
Reconstructs points by triangulation.
void triangulatePoints
(InputArray projMatr1, InputArray projMatr2, InputArray projPoints1, InputArray projPoints2, OutputArray points4D)¶
cv2.
triangulatePoints
(projMatr1, projMatr2, projPoints1, projPoints2[, points4D]) → points4D¶
void cvTriangulatePoints
(CvMat* projMatr1, CvMat* projMatr2, CvMat* projPoints1, CvMat* projPoints2, CvMat* points4D)¶Parameters: 


The function reconstructs 3dimensional points (in homogeneous coordinates) by using their observations with a stereo camera. Projections matrices can be obtained from stereoRectify()
.
Note
Keep in mind that all input data should be of float type in order for this function to work.
See also
The methods in this namespace use a socalled fisheye camera model.
namespace fisheye
{
//! projects 3D points using fisheye model
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());
//! distorts 2D points using fisheye model
void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0);
//! undistorts 2D points using fisheye model
void undistortPoints(InputArray distorted, OutputArray undistorted,
InputArray K, InputArray D, InputArray R = noArray(), InputArray P = noArray());
//! computing 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 initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P,
const cv::Size& size, int m1type, OutputArray map1, OutputArray map2);
//! undistorts image, optionally changes resolution and camera matrix.
void undistortImage(InputArray distorted, OutputArray undistorted,
InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size());
//! estimates new camera matrix for undistortion or rectification
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);
//! performs camera calibaration
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));
//! stereo rectification estimation
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);
//! performs stereo calibration
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));
};
Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the matrix X) The coordinate vector of P in the camera reference frame is:
center
¶where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y and z the 3 coordinates of Xc:
center
The pinehole projection coordinates of P is [a; b] where
center
Fisheye distortion:
center
The distorted point coordinates are [x’; y’] where
..class:: center .. math:
x' = (\theta_d / r) x \\
y' = (\theta_d / r) y
Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where:
center
Projects points using fisheye model
void fisheye::
projectPoints
(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine, InputArray K, InputArray D, double alpha=0, OutputArray jacobian=noArray())¶
void fisheye::
projectPoints
(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec, InputArray K, InputArray D, double alpha=0, OutputArray jacobian=noArray())¶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.
Distorts 2D points using fisheye model.
void fisheye::
distortPoints
(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha=0)¶Parameters: 


Undistorts 2D points using fisheye model
void fisheye::
undistortPoints
(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray R=noArray(), InputArray P=noArray())¶Parameters: 


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 fisheye::
initUndistortRectifyMap
(InputArray K, InputArray D, InputArray R, InputArray P, const cv::Size& size, int m1type, OutputArray map1, OutputArray map2)¶Parameters: 


Transforms an image to compensate for fisheye lens distortion.
void fisheye::
undistortImage
(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray Knew=cv::noArray(), const Size& new_size=Size())¶Parameters: 


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.
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)fisheye::undistortImage()
of fisheye camera model (all possible coefficients (k_1, k_2, k_3, k_4) of fisheye distortion were optimized under calibration)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.
Estimates new camera matrix for undistortion or rectification.
void 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)¶Parameters: 


Stereo rectification for fisheye camera model
void fisheye::
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)¶Parameters: 


Performs camera calibaration
double fisheye::
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))¶Parameters: 


Performs stereo calibration
double fisheye::
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))¶Parameters: 


[BT98]  Birchfield, S. and Tomasi, C. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998. 
[BouguetMCT]  J.Y.Bouguet. MATLAB calibration tool. http://www.vision.caltech.edu/bouguetj/calib_doc/ 
[Hartley99]  Hartley, R.I., Theory and Practice of Projective Rectification. IJCV 35 2, pp 115127 (1999) 
[HH08]  Hirschmuller, H. Stereo Processing by Semiglobal Matching and Mutual Information, PAMI(30), No. 2, February 2008, pp. 328341. 
[Slabaugh]  (1, 2) Slabaugh, G.G. Computing Euler angles from a rotation matrix. http://www.soi.city.ac.uk/~sbbh653/publications/euler.pdf (verified: 20130415) 
[Zhang2000] 
