opencv/doc/opencv1/py/imgproc_geometric_image_transformations.rst

Geometric Image Transformations
===============================

.. highlight:: python


The functions in this section perform various geometrical transformations of 2D images. That is, 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 
:math:`(x, y)`
of the destination image, the functions compute coordinates of the corresponding "donor" pixel in the source image and copy the pixel value, that is:



.. math::

    \texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y)) 


In the case when the user specifies the forward mapping: 
:math:`\left<g_x, g_y\right>: \texttt{src} \rightarrow \texttt{dst}`
, the OpenCV functions first compute the corresponding inverse mapping: 
:math:`\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}`
and then use the above formula.

The actual implementations of the geometrical transformations, from the most generic 
:ref:`Remap`
and to the simplest and the fastest 
:ref:`Resize`
, need to solve the 2 main problems with the above formula:


    

#.
    extrapolation of non-existing pixels. Similarly to the filtering functions, described in the previous section, for some 
    :math:`(x,y)`
    one of 
    :math:`f_x(x,y)`
    or 
    :math:`f_y(x,y)`
    , or they both, may fall outside of the image, in which case some extrapolation method needs to be used. OpenCV provides the same selection of the extrapolation methods as in the filtering functions, but also an additional method 
    ``BORDER_TRANSPARENT``
    , which means that the corresponding pixels in the destination image will not be modified at all.
        
    

#.
    interpolation of pixel values. Usually 
    :math:`f_x(x,y)`
    and 
    :math:`f_y(x,y)`
    are floating-point numbers (i.e. 
    :math:`\left<f_x, f_y\right>`
    can be an affine or perspective transformation, or radial lens distortion correction etc.), so a pixel values 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 used, which is called nearest-neighbor interpolation. However, a better result can be achieved by using more sophisticated 
    `interpolation methods <http://en.wikipedia.org/wiki/Multivariate_interpolation>`_
    , where a polynomial function is fit into some neighborhood of the computed pixel 
    :math:`(f_x(x,y), f_y(x,y))`
    and then the value of the polynomial at 
    :math:`(f_x(x,y), f_y(x,y))`
    is taken as the interpolated pixel value. In OpenCV you can choose between several interpolation methods, see 
    :ref:`Resize`
    . 
    
    

.. index:: GetRotationMatrix2D

.. _GetRotationMatrix2D:

GetRotationMatrix2D
-------------------

`id=0.155746043393 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/GetRotationMatrix2D>`__


.. function:: GetRotationMatrix2D(center,angle,scale,mapMatrix)-> None

    Calculates the affine matrix of 2d rotation.





    
    :param center: Center of the rotation in the source image 
    
    :type center: :class:`CvPoint2D32f`
    
    
    :param angle: The rotation angle in degrees. Positive values mean counter-clockwise rotation (the coordinate origin is assumed to be the top-left corner) 
    
    :type angle: float
    
    
    :param scale: Isotropic scale factor 
    
    :type scale: float
    
    
    :param mapMatrix: Pointer to the destination  :math:`2\times 3`  matrix 
    
    :type mapMatrix: :class:`CvMat`
    
    
    
The function 
``cv2DRotationMatrix``
calculates the following matrix:



.. math::

    \begin{bmatrix} \alpha &  \beta & (1- \alpha )  \cdot \texttt{center.x} -  \beta \cdot \texttt{center.y} \\ - \beta &  \alpha &  \beta \cdot \texttt{center.x} - (1- \alpha )  \cdot \texttt{center.y} \end{bmatrix} 


where



.. math::

    \alpha =  \texttt{scale} \cdot cos( \texttt{angle} ),  \beta =  \texttt{scale} \cdot sin( \texttt{angle} ) 


The transformation maps the rotation center to itself. If this is not the purpose, the shift should be adjusted.


.. index:: GetAffineTransform

.. _GetAffineTransform:

GetAffineTransform
------------------

`id=0.131853152013 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/GetAffineTransform>`__


.. function:: GetAffineTransform(src,dst,mapMatrix)-> None

    Calculates the affine transform from 3 corresponding points.





    
    :param src:  Coordinates of 3 triangle vertices in the source image 
    
    :type src: :class:`CvPoint2D32f`
    
    
    :param dst:  Coordinates of the 3 corresponding triangle vertices in the destination image 
    
    :type dst: :class:`CvPoint2D32f`
    
    
    :param mapMatrix:  Pointer to the destination  :math:`2 \times 3`  matrix 
    
    :type mapMatrix: :class:`CvMat`
    
    
    
The function cvGetAffineTransform calculates the matrix of an affine transform such that:



.. math::

    \begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{mapMatrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix} 


where



.. math::

    dst(i)=(x'_i,y'_i),
    src(i)=(x_i, y_i),
    i=0,1,2 



.. index:: GetPerspectiveTransform

.. _GetPerspectiveTransform:

GetPerspectiveTransform
-----------------------

`id=0.411609579387 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/GetPerspectiveTransform>`__


.. function:: GetPerspectiveTransform(src,dst,mapMatrix)-> None

    Calculates the perspective transform from 4 corresponding points.





    
    :param src: Coordinates of 4 quadrangle vertices in the source image 
    
    :type src: :class:`CvPoint2D32f`
    
    
    :param dst: Coordinates of the 4 corresponding quadrangle vertices in the destination image 
    
    :type dst: :class:`CvPoint2D32f`
    
    
    :param mapMatrix: Pointer to the destination  :math:`3\times 3`  matrix 
    
    :type mapMatrix: :class:`CvMat`
    
    
    
The function 
``cvGetPerspectiveTransform``
calculates a matrix of perspective transforms such that:



.. math::

    \begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{mapMatrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix} 


where



.. math::

    dst(i)=(x'_i,y'_i),
    src(i)=(x_i, y_i),
    i=0,1,2,3 



.. index:: GetQuadrangleSubPix

.. _GetQuadrangleSubPix:

GetQuadrangleSubPix
-------------------

`id=0.522776327076 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/GetQuadrangleSubPix>`__


.. function:: GetQuadrangleSubPix(src,dst,mapMatrix)-> None

    Retrieves the pixel quadrangle from an image with sub-pixel accuracy.





    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Extracted quadrangle 
    
    :type dst: :class:`CvArr`
    
    
    :param mapMatrix: The transformation  :math:`2 \times 3`  matrix  :math:`[A|b]`  (see the discussion) 
    
    :type mapMatrix: :class:`CvMat`
    
    
    
The function 
``cvGetQuadrangleSubPix``
extracts pixels from 
``src``
at sub-pixel accuracy and stores them to 
``dst``
as follows:



.. math::

    dst(x, y)= src( A_{11} x' + A_{12} y' + b_1, A_{21} x' + A_{22} y' + b_2) 


where



.. math::

    x'=x- \frac{(width(dst)-1)}{2} , 
    y'=y- \frac{(height(dst)-1)}{2} 


and



.. math::

    \texttt{mapMatrix} =  \begin{bmatrix} A_{11} & A_{12} & b_1 \\ A_{21} & A_{22} & b_2 \end{bmatrix} 


The values of pixels at non-integer coordinates are retrieved using bilinear interpolation. When the function needs pixels outside of the image, it uses replication border mode to reconstruct the values. Every channel of multiple-channel images is processed independently.



.. index:: GetRectSubPix

.. _GetRectSubPix:

GetRectSubPix
-------------

`id=0.947278759693 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/GetRectSubPix>`__


.. function:: GetRectSubPix(src,dst,center)-> None

    Retrieves the pixel rectangle from an image with sub-pixel accuracy.
 




    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Extracted rectangle 
    
    :type dst: :class:`CvArr`
    
    
    :param center: Floating point coordinates of the extracted rectangle center within the source image. The center must be inside the image 
    
    :type center: :class:`CvPoint2D32f`
    
    
    
The function 
``cvGetRectSubPix``
extracts pixels from 
``src``
:



.. math::

    dst(x, y) = src(x +  \texttt{center.x} - (width( \texttt{dst} )-1)*0.5, y +  \texttt{center.y} - (height( \texttt{dst} )-1)*0.5) 


where the values of the pixels at non-integer coordinates are retrieved
using bilinear interpolation. Every channel of multiple-channel
images is processed independently. While the rectangle center
must be inside the image, parts of the rectangle may be
outside. In this case, the replication border mode is used to get
pixel values beyond the image boundaries.



.. index:: LogPolar

.. _LogPolar:

LogPolar
--------

`id=0.991629705277 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/LogPolar>`__


.. function:: LogPolar(src,dst,center,M,flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS)-> None

    Remaps an image to log-polar space.





    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Destination image 
    
    :type dst: :class:`CvArr`
    
    
    :param center: The transformation center; where the output precision is maximal 
    
    :type center: :class:`CvPoint2D32f`
    
    
    :param M: Magnitude scale parameter. See below 
    
    :type M: float
    
    
    :param flags: A combination of interpolation methods and the following optional flags: 
        
                
            * **CV_WARP_FILL_OUTLIERS** fills all of the destination image pixels. If some of them correspond to outliers in the source image, they are set to zero 
            
               
            * **CV_WARP_INVERSE_MAP** See below 
            
            
    
    :type flags: int
    
    
    
The function 
``cvLogPolar``
transforms the source image using the following transformation:

Forward transformation (
``CV_WARP_INVERSE_MAP``
is not set):



.. math::

    dst( \phi , \rho ) = src(x,y) 


Inverse transformation (
``CV_WARP_INVERSE_MAP``
is set):



.. math::

    dst(x,y) = src( \phi , \rho ) 


where



.. math::

    \rho = M  \cdot \log{\sqrt{x^2 + y^2}} , \phi =atan(y/x) 


The function emulates the human "foveal" vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth.
The function can not operate in-place.


.. index:: Remap

.. _Remap:

Remap
-----

`id=0.129979785029 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/Remap>`__


.. function:: Remap(src,dst,mapx,mapy,flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS,fillval=(0,0,0,0))-> None

    Applies a generic geometrical transformation to the image.





    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Destination image 
    
    :type dst: :class:`CvArr`
    
    
    :param mapx: The map of x-coordinates (CV _ 32FC1 image) 
    
    :type mapx: :class:`CvArr`
    
    
    :param mapy: The map of y-coordinates (CV _ 32FC1 image) 
    
    :type mapy: :class:`CvArr`
    
    
    :param flags: A combination of interpolation method and the following optional flag(s): 
        
                
            * **CV_WARP_FILL_OUTLIERS** fills all of the destination image pixels. If some of them correspond to outliers in the source image, they are set to  ``fillval`` 
            
            
    
    :type flags: int
    
    
    :param fillval: A value used to fill outliers 
    
    :type fillval: :class:`CvScalar`
    
    
    
The function 
``cvRemap``
transforms the source image using the specified map:



.. math::

    \texttt{dst} (x,y) =  \texttt{src} ( \texttt{mapx} (x,y), \texttt{mapy} (x,y)) 


Similar to other geometrical transformations, some interpolation method (specified by user) is used to extract pixels with non-integer coordinates.
Note that the function can not operate in-place.


.. index:: Resize

.. _Resize:

Resize
------

`id=0.923811087592 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/Resize>`__


.. function:: Resize(src,dst,interpolation=CV_INTER_LINEAR)-> None

    Resizes an image.





    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Destination image 
    
    :type dst: :class:`CvArr`
    
    
    :param interpolation: Interpolation method: 
         
            * **CV_INTER_NN** nearest-neigbor interpolation 
            
            * **CV_INTER_LINEAR** bilinear interpolation (used by default) 
            
            * **CV_INTER_AREA** resampling using pixel area relation. It is the preferred method for image decimation that gives moire-free results. In terms of zooming it is similar to the  ``CV_INTER_NN``  method 
            
            * **CV_INTER_CUBIC** bicubic interpolation 
            
            
    
    :type interpolation: int
    
    
    
The function 
``cvResize``
resizes an image 
``src``
so that it fits exactly into 
``dst``
. If ROI is set, the function considers the ROI as supported.



.. index:: WarpAffine

.. _WarpAffine:

WarpAffine
----------

`id=0.314654704506 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/WarpAffine>`__


.. function:: WarpAffine(src,dst,mapMatrix,flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,fillval=(0,0,0,0))-> None

    Applies an affine transformation to an image.





    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Destination image 
    
    :type dst: :class:`CvArr`
    
    
    :param mapMatrix: :math:`2\times 3`  transformation matrix 
    
    :type mapMatrix: :class:`CvMat`
    
    
    :param flags: A combination of interpolation methods and the following optional flags: 
        
                
            * **CV_WARP_FILL_OUTLIERS** fills all of the destination image pixels; if some of them correspond to outliers in the source image, they are set to  ``fillval`` 
            
               
            * **CV_WARP_INVERSE_MAP** indicates that  ``matrix``  is inversely
                  transformed from the destination image to the source and, thus, can be used
                  directly for pixel interpolation. Otherwise, the function finds
                  the inverse transform from  ``mapMatrix`` 
            
        
        :type flags: int
        
        
        
    
    :param fillval: A value used to fill outliers 
    
    :type fillval: :class:`CvScalar`
    
    
    
The function 
``cvWarpAffine``
transforms the source image using the specified matrix:



.. math::

    dst(x',y') = src(x,y) 


where



.. math::

    \begin{matrix} \begin{bmatrix} x' \\ y' \end{bmatrix} =  \texttt{mapMatrix} \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix} &  \mbox{if CV\_WARP\_INVERSE\_MAP is not set} \\ \begin{bmatrix} x \\ y \end{bmatrix} =  \texttt{mapMatrix} \cdot \begin{bmatrix} x' \\ y' \\ 1 \end{bmatrix} &  \mbox{otherwise} \end{matrix} 


The function is similar to 
:ref:`GetQuadrangleSubPix`
but they are not exactly the same. 
:ref:`WarpAffine`
requires input and output image have the same data type, has larger overhead (so it is not quite suitable for small images) and can leave part of destination image unchanged. While 
:ref:`GetQuadrangleSubPix`
may extract quadrangles from 8-bit images into floating-point buffer, has smaller overhead and always changes the whole destination image content.
Note that the function can not operate in-place.

To transform a sparse set of points, use the 
:ref:`Transform`
function from cxcore.


.. index:: WarpPerspective

.. _WarpPerspective:

WarpPerspective
---------------

`id=0.554206520217 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/WarpPerspective>`__


.. function:: WarpPerspective(src,dst,mapMatrix,flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS,fillval=(0,0,0,0))-> None

    Applies a perspective transformation to an image.





    
    :param src: Source image 
    
    :type src: :class:`CvArr`
    
    
    :param dst: Destination image 
    
    :type dst: :class:`CvArr`
    
    
    :param mapMatrix: :math:`3\times 3`  transformation matrix 
    
    :type mapMatrix: :class:`CvMat`
    
    
    :param flags: A combination of interpolation methods and the following optional flags: 
        
                
            * **CV_WARP_FILL_OUTLIERS** fills all of the destination image pixels; if some of them correspond to outliers in the source image, they are set to  ``fillval`` 
            
               
            * **CV_WARP_INVERSE_MAP** indicates that  ``matrix``  is inversely transformed from the destination image to the source and, thus, can be used directly for pixel interpolation. Otherwise, the function finds the inverse transform from  ``mapMatrix`` 
            
            
    
    :type flags: int
    
    
    :param fillval: A value used to fill outliers 
    
    :type fillval: :class:`CvScalar`
    
    
    
The function 
``cvWarpPerspective``
transforms the source image using the specified matrix:



.. math::

    \begin{matrix} \begin{bmatrix} x' \\ y' \end{bmatrix} =  \texttt{mapMatrix} \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix} &  \mbox{if CV\_WARP\_INVERSE\_MAP is not set} \\ \begin{bmatrix} x \\ y \end{bmatrix} =  \texttt{mapMatrix} \cdot \begin{bmatrix} x' \\ y' \\ 1 \end{bmatrix} &  \mbox{otherwise} \end{matrix} 


Note that the function can not operate in-place.
For a sparse set of points use the 
:ref:`PerspectiveTransform`
function from CxCore.