Static Public Member Functions
static void	filterSpeckles (Mat img, double newVal, int maxSpeckleSize, double maxDiff)
	Filters off small noise blobs (speckles) in the disparity map.

static void	filterSpeckles (Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf)
	Filters off small noise blobs (speckles) in the disparity map.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize, double balance)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in Vec2d imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in Vec2d newImageSize, double balance, double fov_scale)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize, double balance)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, in(double width, double height) imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, in(double width, double height) newImageSize, double balance, double fov_scale)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance)
	Stereo rectification for fisheye camera model.

static void	fisheye_stereoRectify (Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance, double fov_scale)
	Stereo rectification for fisheye camera model.

static Rect	getValidDisparityROI (Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int blockSize)

static int int int int height	getValidDisparityROIAsValueTuple (in(int x, int y, int width, int height) roi1,(int x, int y, int width, int height) roi2, int minDisparity, int numberOfDisparities, int blockSize)

static Vec4i	getValidDisparityROIAsVec4i (in Vec4i roi1, Vec4i roi2, int minDisparity, int numberOfDisparities, int blockSize)

static void	reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q)
	Reprojects a disparity image to 3D space.

static void	reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues)
	Reprojects a disparity image to 3D space.

static void	reprojectImageTo3D (Mat disparity, Mat _3dImage, Mat Q, bool handleMissingValues, int ddepth)
	Reprojects a disparity image to 3D space.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize, out Vec4i validPixROI1)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in Vec2d imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in Vec2d newImageSize, out Vec4i validPixROI1, out Vec4i validPixROI2)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize, out(int x, int y, int width, int height) validPixROI1)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, in(double width, double height) imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, in(double width, double height) newImageSize, out(int x, int y, int width, int height) validPixROI1, out(int x, int y, int width, int height) validPixROI2)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1)
	Computes rectification transforms for each head of a calibrated stereo camera.

static void	stereoRectify (Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1, Rect validPixROI2)
	Computes rectification transforms for each head of a calibrated stereo camera.

static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in Vec2d imgSize, Mat H1, Mat H2)
	Computes a rectification transform for an uncalibrated stereo camera.

static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in Vec2d imgSize, Mat H1, Mat H2, double threshold)
	Computes a rectification transform for an uncalibrated stereo camera.

static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in(double width, double height) imgSize, Mat H1, Mat H2)
	Computes a rectification transform for an uncalibrated stereo camera.

static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, in(double width, double height) imgSize, Mat H1, Mat H2, double threshold)
	Computes a rectification transform for an uncalibrated stereo camera.

static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2)
	Computes a rectification transform for an uncalibrated stereo camera.

static bool	stereoRectifyUncalibrated (Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2, double threshold)
	Computes a rectification transform for an uncalibrated stereo camera.

static void	validateDisparity (Mat disparity, Mat cost, int minDisparity, int numberOfDisparities)

static void	validateDisparity (Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)

Static Public Attributes
const int	STEREO_ZERO_DISPARITY = 0x00400
	C++: enum <unnamed>

static int int int	width

static int	x

static int int	y

Member Function Documentation

◆ filterSpeckles() [1/2]

static void OpenCVForUnity.StereoModule.Stereo.filterSpeckles	(	Mat	img,
		double	newVal,
		int	maxSpeckleSize,
		double	maxDiff )

static

Filters off small noise blobs (speckles) in the disparity map.

Parameters

img	The input 16-bit signed disparity image
newVal	The disparity value used to paint-off the speckles
maxSpeckleSize	The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm
maxDiff	Maximum difference between neighbor disparity pixels to put them into the same blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value.
buf	The optional temporary buffer to avoid memory allocation within the function.

◆ filterSpeckles() [2/2]

static void OpenCVForUnity.StereoModule.Stereo.filterSpeckles	(	Mat	img,
		double	newVal,
		int	maxSpeckleSize,
		double	maxDiff,
		Mat	buf )

static

Filters off small noise blobs (speckles) in the disparity map.

Parameters

img	The input 16-bit signed disparity image
newVal	The disparity value used to paint-off the speckles
maxSpeckleSize	The maximum speckle size to consider it a speckle. Larger blobs are not affected by the algorithm
maxDiff	Maximum difference between neighbor disparity pixels to put them into the same blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value.
buf	The optional temporary buffer to avoid memory allocation within the function.

◆ fisheye_stereoRectify() [1/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [2/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		in Vec2d	newImageSize )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [3/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		in Vec2d	newImageSize,
		double	balance )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [4/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		in Vec2d	newImageSize,
		double	balance,
		double	fov_scale )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [5/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [6/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		in(double width, double height)	newImageSize )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [7/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		in(double width, double height)	newImageSize,
		double	balance )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [8/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		in(double width, double height)	newImageSize,
		double	balance,
		double	fov_scale )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [9/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		Size	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [10/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		Size	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		Size	newImageSize )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [11/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		Size	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		Size	newImageSize,
		double	balance )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ fisheye_stereoRectify() [12/12]

static void OpenCVForUnity.StereoModule.Stereo.fisheye_stereoRectify	(	Mat	K1,
		Mat	D1,
		Mat	K2,
		Mat	D2,
		Size	imageSize,
		Mat	R,
		Mat	tvec,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		Size	newImageSize,
		double	balance,
		double	fov_scale )

static

Stereo rectification for fisheye camera model.

Parameters

K1	First camera intrinsic matrix.
D1	First camera distortion parameters.
K2	Second camera intrinsic matrix.
D2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix between the coordinate systems of the first and the second cameras.
tvec	Translation vector between coordinate systems of the cameras.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D ).
flags	Operation flags that may be zero or cv::CALIB_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.
balance	Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].
fov_scale	Divisor for new focal length.

◆ getValidDisparityROI()

static Rect OpenCVForUnity.StereoModule.Stereo.getValidDisparityROI	(	Rect	roi1,
		Rect	roi2,
		int	minDisparity,
		int	numberOfDisparities,
		int	blockSize )

static

◆ getValidDisparityROIAsValueTuple()

static int int int int height OpenCVForUnity.StereoModule.Stereo.getValidDisparityROIAsValueTuple	(	in(int x, int y, int width, int height)	roi1,
		(int x, int y, int width, int height)	roi2,
		int	minDisparity,
		int	numberOfDisparities,
		int	blockSize )

static

◆ getValidDisparityROIAsVec4i()

static Vec4i OpenCVForUnity.StereoModule.Stereo.getValidDisparityROIAsVec4i	(	in Vec4i	roi1,
		Vec4i	roi2,
		int	minDisparity,
		int	numberOfDisparities,
		int	blockSize )

static

◆ reprojectImageTo3D() [1/3]

static void OpenCVForUnity.StereoModule.Stereo.reprojectImageTo3D	(	Mat	disparity,
		Mat	_3dImage,
		Mat	Q )

static

Reprojects a disparity image to 3D space.

Parameters

disparity	Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by StereoBM or StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage	Output 3-channel floating-point image of the same size as disparity. Each element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by stereoRectify, then the returned points are represented in the first camera's rectified coordinate system.
Q	\(4 \times 4\) perspective transformation matrix that can be obtained with stereoRectify.
handleMissingValues	Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher.compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth	The optional output array depth. If it is -1, the output image will have CV_32F depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ 1 \end{bmatrix}.\]

See also: To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

◆ reprojectImageTo3D() [2/3]

static void OpenCVForUnity.StereoModule.Stereo.reprojectImageTo3D	(	Mat	disparity,
		Mat	_3dImage,
		Mat	Q,
		bool	handleMissingValues )

static

Reprojects a disparity image to 3D space.

Parameters

disparity	Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by StereoBM or StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage	Output 3-channel floating-point image of the same size as disparity. Each element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by stereoRectify, then the returned points are represented in the first camera's rectified coordinate system.
Q	\(4 \times 4\) perspective transformation matrix that can be obtained with stereoRectify.
handleMissingValues	Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher.compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth	The optional output array depth. If it is -1, the output image will have CV_32F depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ 1 \end{bmatrix}.\]

See also: To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

◆ reprojectImageTo3D() [3/3]

static void OpenCVForUnity.StereoModule.Stereo.reprojectImageTo3D	(	Mat	disparity,
		Mat	_3dImage,
		Mat	Q,
		bool	handleMissingValues,
		int	ddepth )

static

Reprojects a disparity image to 3D space.

Parameters

disparity	Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by StereoBM or StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage	Output 3-channel floating-point image of the same size as disparity. Each element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by stereoRectify, then the returned points are represented in the first camera's rectified coordinate system.
Q	\(4 \times 4\) perspective transformation matrix that can be obtained with stereoRectify.
handleMissingValues	Indicates, whether the function should handle missing values (i.e. points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher.compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth	The optional output array depth. If it is -1, the output image will have CV_32F depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ 1 \end{bmatrix}.\]

See also: To reproject a sparse set of points {(x,y,d),...} to 3D space, use perspectiveTransform.

◆ stereoRectify() [1/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [2/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [3/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [4/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		in Vec2d	newImageSize )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [5/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		in Vec2d	newImageSize,
		out Vec4i	validPixROI1 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [6/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in Vec2d	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		in Vec2d	newImageSize,
		out Vec4i	validPixROI1,
		out Vec4i	validPixROI2 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [7/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [8/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [9/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [10/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		in(double width, double height)	newImageSize )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [11/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		in(double width, double height)	newImageSize,
		out(int x, int y, int width, int height)	validPixROI1 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [12/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		in(double width, double height)	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		in(double width, double height)	newImageSize,
		out(int x, int y, int width, int height)	validPixROI1,
		out(int x, int y, int width, int height)	validPixROI2 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [13/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		Size	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [14/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		Size	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [15/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		Size	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [16/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		Size	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		Size	newImageSize )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [17/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		Size	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		Size	newImageSize,
		Rect	validPixROI1 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectify() [18/18]

static void OpenCVForUnity.StereoModule.Stereo.stereoRectify	(	Mat	cameraMatrix1,
		Mat	distCoeffs1,
		Mat	cameraMatrix2,
		Mat	distCoeffs2,
		Size	imageSize,
		Mat	R,
		Mat	T,
		Mat	R1,
		Mat	R2,
		Mat	P1,
		Mat	P2,
		Mat	Q,
		int	flags,
		double	alpha,
		Size	newImageSize,
		Rect	validPixROI1,
		Rect	validPixROI2 )

static

Computes rectification transforms for each head of a calibrated stereo camera.

Parameters

cameraMatrix1	First camera intrinsic matrix.
distCoeffs1	First camera distortion parameters.
cameraMatrix2	Second camera intrinsic matrix.
distCoeffs2	Second camera distortion parameters.
imageSize	Size of the image used for stereo calibration.
R	Rotation matrix from the coordinate system of the first camera to the second camera, see stereoCalibrate.
T	Translation vector from the coordinate system of the first camera to the second camera, see stereoCalibrate.
R1	Output 3x3 rectification transform (rotation matrix) for the first camera. This matrix brings points given in the unrectified first camera's coordinate system to points in the rectified first camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera's coordinate system to the rectified first camera's coordinate system.
R2	Output 3x3 rectification transform (rotation matrix) for the second camera. This matrix brings points given in the unrectified second camera's coordinate system to points in the rectified second camera's coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera's coordinate system to the rectified second camera's coordinate system.
P1	Output 3x4 projection matrix in the new (rectified) coordinate systems for the first camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera's image.
P2	Output 3x4 projection matrix in the new (rectified) coordinate systems for the second camera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera's image.
Q	Output \(4 \times 4\) disparity-to-depth mapping matrix (see reprojectImageTo3D).
flags	Operation flags that may be zero or STEREO_ZERO_DISPARITY . If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha	Free scaling parameter. If it is -1 or absent, the function performs the default scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize	New image resolution after rectification. The same size should be passed to #initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2	Optional output rectangles inside the rectified images where all the pixels are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

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 y-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if STEREO_ZERO_DISPARITY is set.

Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\]

\[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\]

\[\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if STEREO_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.

◆ stereoRectifyUncalibrated() [1/6]

static bool OpenCVForUnity.StereoModule.Stereo.stereoRectifyUncalibrated	(	Mat	points1,
		Mat	points2,
		Mat	F,
		in Vec2d	imgSize,
		Mat	H1,
		Mat	H2 )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in #findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using #findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(\|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}\|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

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 .

◆ stereoRectifyUncalibrated() [2/6]

static bool OpenCVForUnity.StereoModule.Stereo.stereoRectifyUncalibrated	(	Mat	points1,
		Mat	points2,
		Mat	F,
		in Vec2d	imgSize,
		Mat	H1,
		Mat	H2,
		double	threshold )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in #findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using #findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(\|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}\|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

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 .

◆ stereoRectifyUncalibrated() [3/6]

static bool OpenCVForUnity.StereoModule.Stereo.stereoRectifyUncalibrated	(	Mat	points1,
		Mat	points2,
		Mat	F,
		in(double width, double height)	imgSize,
		Mat	H1,
		Mat	H2 )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in #findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using #findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(\|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}\|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

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 .

◆ stereoRectifyUncalibrated() [4/6]

static bool OpenCVForUnity.StereoModule.Stereo.stereoRectifyUncalibrated	(	Mat	points1,
		Mat	points2,
		Mat	F,
		in(double width, double height)	imgSize,
		Mat	H1,
		Mat	H2,
		double	threshold )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in #findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using #findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(\|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}\|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

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 .

◆ stereoRectifyUncalibrated() [5/6]

static bool OpenCVForUnity.StereoModule.Stereo.stereoRectifyUncalibrated	(	Mat	points1,
		Mat	points2,
		Mat	F,
		Size	imgSize,
		Mat	H1,
		Mat	H2 )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in #findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using #findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(\|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}\|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

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 .

◆ stereoRectifyUncalibrated() [6/6]

static bool OpenCVForUnity.StereoModule.Stereo.stereoRectifyUncalibrated	(	Mat	points1,
		Mat	points2,
		Mat	F,
		Size	imgSize,
		Mat	H1,
		Mat	H2,
		double	threshold )

static

Computes a rectification transform for an uncalibrated stereo camera.

Parameters

points1	Array of feature points in the first image.
points2	The corresponding points in the second image. The same formats as in #findFundamentalMat are supported.
F	Input fundamental matrix. It can be computed from the same set of point pairs using #findFundamentalMat .
imgSize	Size of the image.
H1	Output rectification homography matrix for the first image.
H2	Output rectification homography matrix for the second image.
threshold	Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(\|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}\|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

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 .

◆ validateDisparity() [1/2]

static void OpenCVForUnity.StereoModule.Stereo.validateDisparity	(	Mat	disparity,
		Mat	cost,
		int	minDisparity,
		int	numberOfDisparities )

static

◆ validateDisparity() [2/2]

static void OpenCVForUnity.StereoModule.Stereo.validateDisparity	(	Mat	disparity,
		Mat	cost,
		int	minDisparity,
		int	numberOfDisparities,
		int	disp12MaxDisp )

static

Member Data Documentation

◆ STEREO_ZERO_DISPARITY

const int OpenCVForUnity.StereoModule.Stereo.STEREO_ZERO_DISPARITY = 0x00400

static

C++: enum <unnamed>

◆ width

int int int OpenCVForUnity.StereoModule.Stereo.width

static

◆ x

int OpenCVForUnity.StereoModule.Stereo.x

static

◆ y

int int OpenCVForUnity.StereoModule.Stereo.y

static

The documentation for this class was generated from the following files:

OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Core/org/opencv/stereo/Stereo.cs
OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/stereo/Stereo_Struct.cs
OpenCVForUnity/Assets/OpenCVForUnity/Scripts/Extensions/org/opencv/stereo/Stereo_ValueTuple.cs

Static Public Member Functions

Static Public Attributes

Member Function Documentation

◆ filterSpeckles() [1/2]

◆ filterSpeckles() [2/2]

◆ fisheye_stereoRectify() [1/12]

◆ fisheye_stereoRectify() [2/12]

◆ fisheye_stereoRectify() [3/12]

◆ fisheye_stereoRectify() [4/12]

◆ fisheye_stereoRectify() [5/12]

◆ fisheye_stereoRectify() [6/12]

◆ fisheye_stereoRectify() [7/12]

◆ fisheye_stereoRectify() [8/12]

◆ fisheye_stereoRectify() [9/12]

◆ fisheye_stereoRectify() [10/12]

◆ fisheye_stereoRectify() [11/12]

◆ fisheye_stereoRectify() [12/12]

◆ getValidDisparityROI()

◆ getValidDisparityROIAsValueTuple()

◆ getValidDisparityROIAsVec4i()

◆ reprojectImageTo3D() [1/3]

◆ reprojectImageTo3D() [2/3]

◆ reprojectImageTo3D() [3/3]

◆ stereoRectify() [1/18]

◆ stereoRectify() [2/18]

◆ stereoRectify() [3/18]

◆ stereoRectify() [4/18]

◆ stereoRectify() [5/18]

◆ stereoRectify() [6/18]

◆ stereoRectify() [7/18]

◆ stereoRectify() [8/18]

◆ stereoRectify() [9/18]

◆ stereoRectify() [10/18]

◆ stereoRectify() [11/18]

◆ stereoRectify() [12/18]

◆ stereoRectify() [13/18]

◆ stereoRectify() [14/18]

◆ stereoRectify() [15/18]

◆ stereoRectify() [16/18]

◆ stereoRectify() [17/18]

◆ stereoRectify() [18/18]

◆ stereoRectifyUncalibrated() [1/6]

◆ stereoRectifyUncalibrated() [2/6]

◆ stereoRectifyUncalibrated() [3/6]

◆ stereoRectifyUncalibrated() [4/6]

◆ stereoRectifyUncalibrated() [5/6]

◆ stereoRectifyUncalibrated() [6/6]

◆ validateDisparity() [1/2]

◆ validateDisparity() [2/2]

Member Data Documentation

◆ STEREO_ZERO_DISPARITY

◆ width

◆ x

◆ y