Geometric Image Transformations
The functions in this section perform various geometrical transformations of 2D images. They do not change the image content but deform the pixel grid and map this deformed grid to the destination image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from destination to the source. That is, for each pixel of the destination image, the functions compute coordinates of the corresponding “donor” pixel in the source image and copy the pixel value:
In case when you specify the forward mapping
, the OpenCV functions first compute the corresponding inverse mapping
and then use the above formula.
The actual implementations of the geometrical transformations, from the most generic
remap() and to the simplest and the fastest
resize() , need to solve two main problems with the above formula:
 Extrapolation of nonexisting pixels. Similarly to the filtering functions described in the previous section, for some
, either one of
, or
, or both of them may fall outside of the image. In this case, an extrapolation method needs to be used. OpenCV provides the same selection of extrapolation methods as in the filtering functions. In addition, it provides the method BORDER_TRANSPARENT . This means that the corresponding pixels in the destination image will not be modified at all.
 Interpolation of pixel values. Usually
and
are floatingpoint numbers. This means that
can be either an affine or perspective transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the nearest integer coordinates and the corresponding pixel can be used. This is called a nearestneighbor interpolation. However, a better result can be achieved by using more sophisticated interpolation methods
, where a polynomial function is fit into some neighborhood of the computed pixel
, and then the value of the polynomial at
is taken as the interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
resize() for details.
convertMaps
Converts image transformation maps from one representation to another.

C++: void convertMaps(InputArray map1, InputArray map2, OutputArray dstmap1, OutputArray dstmap2, int dstmap1type, bool nninterpolation=false )

Python: cv2.convertMaps(map1, map2, dstmap1type[, dstmap1[, dstmap2[, nninterpolation]]]) → dstmap1, dstmap2
Parameters: 
 map1 – The first input map of type CV_16SC2 , CV_32FC1 , or CV_32FC2 .
 map2 – The second input map of type CV_16UC1 , CV_32FC1 , or none (empty matrix), respectively.
 dstmap1 – The first output map that has the type dstmap1type and the same size as src .
 dstmap2 – The second output map.
 dstmap1type – Type of the first output map that should be CV_16SC2 , CV_32FC1 , or CV_32FC2 .
 nninterpolation – Flag indicating whether the fixedpoint maps are used for the nearestneighbor or for a more complex interpolation.

The function converts a pair of maps for
remap() from one representation to another. The following options ( (map1.type(), map2.type()) (dstmap1.type(), dstmap2.type()) ) are supported:
 . This is the most frequently used conversion operation, in which the original floatingpoint maps (see
remap() ) are converted to a more compact and much faster fixedpoint representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables.
 . The same as above but the original maps are stored in one 2channel matrix.
 Reverse conversion. Obviously, the reconstructed floatingpoint maps will not be exactly the same as the originals.
getRectSubPix
Retrieves a pixel rectangle from an image with subpixel accuracy.

C++: void getRectSubPix(InputArray image, Size patchSize, Point2f center, OutputArray patch, int patchType=1 )

Python: cv2.getRectSubPix(image, patchSize, center[, patch[, patchType]]) → patch

C: void cvGetRectSubPix(const CvArr* src, CvArr* dst, CvPoint2D32f center)

Python: cv.GetRectSubPix(src, dst, center) → None
Parameters: 
 src – Source image.
 patchSize – Size of the extracted patch.
 center – Floating point coordinates of the center of the extracted rectangle within the source image. The center must be inside the image.
 dst – Extracted patch that has the size patchSize and the same number of channels as src .
 patchType – Depth of the extracted pixels. By default, they have the same depth as src .

The function getRectSubPix extracts pixels from src :
where the values of the pixels at noninteger coordinates are retrieved
using bilinear interpolation. Every channel of multichannel
images is processed independently. While the center of the rectangle
must be inside the image, parts of the rectangle may be
outside. In this case, the replication border mode (see
borderInterpolate() ) is used to extrapolate
the pixel values outside of the image.
getRotationMatrix2D
Calculates an affine matrix of 2D rotation.

C++: Mat getRotationMatrix2D(Point2f center, double angle, double scale)

Python: cv2.getRotationMatrix2D(center, angle, scale) → retval

C: CvMat* cv2DRotationMatrix(CvPoint2D32f center, double angle, double scale, CvMat* map_matrix)

Python: cv.GetRotationMatrix2D(center, angle, scale, mapMatrix) → None
Parameters: 
 center – Center of the rotation in the source image.
 angle – Rotation angle in degrees. Positive values mean counterclockwise rotation (the coordinate origin is assumed to be the topleft corner).
 scale – Isotropic scale factor.
 map_matrix – The output affine transformation, 2x3 floatingpoint matrix.

The function calculates the following matrix:
where
The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
LogPolar
Remaps an image to logpolar space.

C: void cvLogPolar(const CvArr* src, CvArr* dst, CvPoint2D32f center, double M, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS )

Python: cv.LogPolar(src, dst, center, M, flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS) → None

The function cvLogPolar transforms the source image using the following transformation:
where
The function emulates the human “foveal” vision and can be used for fast scale and rotationinvariant template matching, for object tracking and so forth. The function can not operate inplace.
Note
 An example using the geometric logpolar operation in 4 applications can be found at opencv_source_code/samples/cpp/logpolar_bsm.cpp
remap
Applies a generic geometrical transformation to an image.

C++: void remap(InputArray src, OutputArray dst, InputArray map1, InputArray map2, int interpolation, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())

Python: cv2.remap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]) → dst

C: void cvRemap(const CvArr* src, CvArr* dst, const CvArr* mapx, const CvArr* mapy, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )

Python: cv.Remap(src, dst, mapx, mapy, flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0, 0, 0, 0)) → None
Parameters: 
 src – Source image.
 dst – Destination image. It has the same size as map1 and the same type as src .
 map1 – The first map of either (x,y) points or just x values having the type CV_16SC2 , CV_32FC1 , or CV_32FC2 . See convertMaps() for details on converting a floating point representation to fixedpoint for speed.
 map2 – The second map of y values having the type CV_16UC1 , CV_32FC1 , or none (empty map if map1 is (x,y) points), respectively.
 interpolation – Interpolation method (see resize() ). The method INTER_AREA is not supported by this function.
 borderMode – Pixel extrapolation method (see borderInterpolate() ). When borderMode=BORDER_TRANSPARENT , it means that the pixels in the destination image that corresponds to the “outliers” in the source image are not modified by the function.
 borderValue – Value used in case of a constant border. By default, it is 0.

The function remap transforms the source image using the specified map:
where values of pixels with noninteger coordinates are computed using one of available interpolation methods.
and
can be encoded as separate floatingpoint maps in
and
respectively, or interleaved floatingpoint maps of
in
, or
fixedpoint maps created by using
convertMaps() . The reason you might want to convert from floating to fixedpoint
representations of a map is that they can yield much faster (~2x) remapping operations. In the converted case,
contains pairs (cvFloor(x), cvFloor(y)) and
contains indices in a table of interpolation coefficients.
This function cannot operate inplace.
resize
Resizes an image.

C++: void resize(InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR )

Python: cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) → dst

C: void cvResize(const CvArr* src, CvArr* dst, int interpolation=CV_INTER_LINEAR )

Python: cv.Resize(src, dst, interpolation=CV_INTER_LINEAR) → None
Parameters: 
 src – input image.
 dst – output image; it has the size dsize (when it is nonzero) or the size computed from src.size(), fx, and fy; the type of dst is the same as of src.
 dsize –
output image size; if it equals zero, it is computed as:
Either dsize or both fx and fy must be nonzero.
 fx –
scale factor along the horizontal axis; when it equals 0, it is computed as
 fy –
scale factor along the vertical axis; when it equals 0, it is computed as
 interpolation –
interpolation method:
 INTER_NEAREST  a nearestneighbor interpolation
 INTER_LINEAR  a bilinear interpolation (used by default)
 INTER_AREA  resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
 INTER_CUBIC  a bicubic interpolation over 4x4 pixel neighborhood
 INTER_LANCZOS4  a Lanczos interpolation over 8x8 pixel neighborhood

The function resize resizes the image src down to or up to the specified size.
Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,``dsize``,``fx`` , and fy . If you want to resize src so that it fits the precreated dst , you may call the function as follows:
// explicitly specify dsize=dst.size(); fx and fy will be computed from that.
resize(src, dst, dst.size(), 0, 0, interpolation);
If you want to decimate the image by factor of 2 in each direction, you can call the function this way:
// specify fx and fy and let the function compute the destination image size.
resize(src, dst, Size(), 0.5, 0.5, interpolation);
To shrink an image, it will generally look best with CV_INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with CV_INTER_CUBIC (slow) or CV_INTER_LINEAR (faster but still looks OK).
warpAffine
Applies an affine transformation to an image.

C++: void warpAffine(InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())

Python: cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst

C: void cvWarpAffine(const CvArr* src, CvArr* dst, const CvMat* map_matrix, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )

Python: cv.WarpAffine(src, dst, mapMatrix, flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0, 0, 0, 0)) → None

C: void cvGetQuadrangleSubPix(const CvArr* src, CvArr* dst, const CvMat* map_matrix)

Python: cv.GetQuadrangleSubPix(src, dst, mapMatrix) → None
Parameters: 
 src – input image.
 dst – output image that has the size dsize and the same type as src .
 M – transformation matrix.
 dsize – size of the output image.
 flags – combination of interpolation methods (see resize() ) and the optional flag WARP_INVERSE_MAP that means that M is the inverse transformation ( ).
 borderMode – pixel extrapolation method (see borderInterpolate()); when borderMode=BORDER_TRANSPARENT , it means that the pixels in the destination image corresponding to the “outliers” in the source image are not modified by the function.
 borderValue – value used in case of a constant border; by default, it is 0.

The function warpAffine transforms the source image using the specified matrix:
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with
invertAffineTransform() and then put in the formula above instead of M .
The function cannot operate inplace.
Note
cvGetQuadrangleSubPix is similar to cvWarpAffine, but the outliers are extrapolated using replication border mode.
warpPerspective
Applies a perspective transformation to an image.

C++: void warpPerspective(InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar())

Python: cv2.warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst

C: void cvWarpPerspective(const CvArr* src, CvArr* dst, const CvMat* map_matrix, int flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, CvScalar fillval=cvScalarAll(0) )

Python: cv.WarpPerspective(src, dst, mapMatrix, flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, fillval=(0, 0, 0, 0)) → None
Parameters: 
 src – input image.
 dst – output image that has the size dsize and the same type as src .
 M – transformation matrix.
 dsize – size of the output image.
 flags – combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation ( ).
 borderMode – pixel extrapolation method (BORDER_CONSTANT or BORDER_REPLICATE).
 borderValue – value used in case of a constant border; by default, it equals 0.

The function warpPerspective transforms the source image using the specified matrix:
when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with
invert() and then put in the formula above instead of M .
The function cannot operate inplace.
initUndistortRectifyMap
Computes the undistortion and rectification transformation map.

C++: void initUndistortRectifyMap(InputArray cameraMatrix, InputArray distCoeffs, InputArray R, InputArray newCameraMatrix, Size size, int m1type, OutputArray map1, OutputArray map2)

Python: cv2.initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) → map1, map2

C: void cvInitUndistortRectifyMap(const CvMat* camera_matrix, const CvMat* dist_coeffs, const CvMat* R, const CvMat* new_camera_matrix, CvArr* mapx, CvArr* mapy)

C: void cvInitUndistortMap(const CvMat* camera_matrix, const CvMat* distortion_coeffs, CvArr* mapx, CvArr* mapy)

Python: cv.InitUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, map1, map2) → None

Python: cv.InitUndistortMap(cameraMatrix, distCoeffs, map1, map2) → None
Parameters: 
 cameraMatrix – Input camera matrix .
 distCoeffs – Input vector of distortion coefficients of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
 R – Optional rectification transformation in the object space (3x3 matrix). R1 or R2 , computed by stereoRectify() can be passed here. If the matrix is empty, the identity transformation is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
 newCameraMatrix – New camera matrix .
 size – Undistorted image size.
 m1type – Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps() for details.
 map1 – The first output map.
 map2 – The second output map.

The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for
remap() . The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix , or it can be computed by
getOptimalNewCameraMatrix() for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by
stereoRectify() .
Also, this new camera is oriented differently in the coordinate space, according to R . That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by
remap() . That is, for each pixel
in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:
where
are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
stereoRectify() , which in its turn is called after
stereoCalibrate() . But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using
stereoRectifyUncalibrated() . For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as
where cameraMatrix can be chosen arbitrarily.
getDefaultNewCameraMatrix
Returns the default new camera matrix.

C++: Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgsize=Size(), bool centerPrincipalPoint=false )

Python: cv2.getDefaultNewCameraMatrix(cameraMatrix[, imgsize[, centerPrincipalPoint]]) → retval
Parameters: 
 cameraMatrix – Input camera matrix.
 imgsize – Camera view image size in pixels.
 centerPrincipalPoint – Location of the principal point in the new camera matrix. The parameter indicates whether this location should be at the image center or not.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
In the latter case, the new camera matrix will be:
where
and
are
and
elements of cameraMatrix , respectively.
By default, the undistortion functions in OpenCV (see
initUndistortRectifyMap(),
undistort()) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same ycoordinate (which is required by most of stereo correspondence algorithms), and may be to the same xcoordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.
undistort
Transforms an image to compensate for lens distortion.

C++: void undistort(InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, InputArray newCameraMatrix=noArray() )

Python: cv2.undistort(src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix]]) → dst

C: void cvUndistort2(const CvArr* src, CvArr* dst, const CvMat* camera_matrix, const CvMat* distortion_coeffs, const CvMat* new_camera_matrix=0 )

Python: cv.Undistort2(src, dst, cameraMatrix, distCoeffs) → None
Parameters: 
 src – Input (distorted) image.
 dst – Output (corrected) image that has the same size and type as src .
 cameraMatrix – Input camera matrix .
 distCoeffs – Input vector of distortion coefficients of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
 newCameraMatrix – Camera matrix of the distorted image. By default, it is the same as cameraMatrix but you may additionally scale and shift the result by using a different matrix.

The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of
initUndistortRectifyMap() (with unity R ) and
remap() (with bilinear interpolation). See the former function for details of the transformation being performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix . You can use
getOptimalNewCameraMatrix() to compute the appropriate newCameraMatrix depending on your requirements.
The camera matrix and the distortion parameters can be determined using
calibrateCamera() . If the resolution of images is different from the resolution used at the calibration stage,
and
need to be scaled accordingly, while the distortion coefficients remain the same.
undistortPoints
Computes the ideal point coordinates from the observed point coordinates.

C++: void undistortPoints(InputArray src, OutputArray dst, InputArray cameraMatrix, InputArray distCoeffs, InputArray R=noArray(), InputArray P=noArray())

C: void cvUndistortPoints(const CvMat* src, CvMat* dst, const CvMat* camera_matrix, const CvMat* dist_coeffs, const CvMat* R=0, const CvMat* P=0 )

Python: cv.UndistortPoints(src, dst, cameraMatrix, distCoeffs, R=None, P=None) → None
Parameters: 
 src – Observed point coordinates, 1xN or Nx1 2channel (CV_32FC2 or CV_64FC2).
 dst – Output ideal point coordinates after undistortion and reverse perspective transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
 cameraMatrix – Camera matrix .
 distCoeffs – Input vector of distortion coefficients of 4, 5, or 8 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
 R – Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by stereoRectify() can be passed here. If the matrix is empty, the identity transformation is used.
 P – New camera matrix (3x3) or new projection matrix (3x4). P1 or P2 computed by stereoRectify() can be passed here. If the matrix is empty, the identity new camera matrix is used.

The function is similar to
undistort() and
initUndistortRectifyMap() but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to
projectPoints() . In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified.
// (u,v) is the input point, (u', v') is the output point
// camera_matrix=[fx 0 cx; 0 fy cy; 0 0 1]
// P=[fx' 0 cx' tx; 0 fy' cy' ty; 0 0 1 tz]
x" = (u  cx)/fx
y" = (v  cy)/fy
(x',y') = undistort(x",y",dist_coeffs)
[X,Y,W]T = R*[x' y' 1]T
x = X/W, y = Y/W
// only performed if P=[fx' 0 cx' [tx]; 0 fy' cy' [ty]; 0 0 1 [tz]] is specified
u' = x*fx' + cx'
v' = y*fy' + cy',
where undistort() is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates (“normalized” means that the coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).