\section{Image Processing}


\cvCppFunc{gpu::meanShiftFiltering}
Performs mean-shift filtering.

\cvdefCpp{void meanShiftFiltering(const GpuMat\& src, GpuMat\& dst,\par
  int sp, int sr,\par
  TermCriteria criteria = TermCriteria(TermCriteria::MAX\_ITER\par
  + TermCriteria::EPS, 5, 1));}

\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC4} images are supported for now.}
\cvarg{dst}{Destination image. Will have the same size and type as \texttt{src}. Each pixel \texttt{(x,y)} of the destination image will contain color of the converged point started from \texttt{(x,y)} pixel of the source image.}
\cvarg{sp}{Spatial window radius.}
\cvarg{sr}{Color window radius.}
\cvarg{criteria}{Termination criteria. See \hyperref[TermCriteria]{cv::TermCriteria}.}
\end{description}


\cvCppFunc{gpu::meanShiftProc}
Performs mean-shift procedure and stores information about converged points in two images.

\cvdefCpp{void meanShiftProc(const GpuMat\& src, GpuMat\& dstr, GpuMat\& dstsp,\par
  int sp, int sr,\par
  TermCriteria criteria = TermCriteria(TermCriteria::MAX\_ITER\par
  + TermCriteria::EPS, 5, 1));}

\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC4} images are supported for now.}
\cvarg{dstr}{Destination image. Will have the same size and type as \texttt{src}. Each pixel \texttt{(x,y)} of the destination image will contain color of converged point started from \texttt{(x,y)} pixel of the source image.}
\cvarg{dstsp}{\texttt{CV\_16SC2} matrix, which will contain coordinates of converged points and have the same size as \texttt{src}.}
\cvarg{sp}{Spatial window radius.}
\cvarg{sr}{Color window radius.}
\cvarg{criteria}{Termination criteria. See \hyperref[TermCriteria]{cv::TermCriteria}.}
\end{description}


\cvCppFunc{gpu::meanShiftSegmentation}
Performs mean-shift segmentation of the source image and eleminates small segments.

\cvdefCpp{void meanShiftSegmentation(const GpuMat\& src, Mat\& dst,\par
  int sp, int sr, int minsize,\par
  TermCriteria criteria = TermCriteria(TermCriteria::MAX\_ITER\par
  + TermCriteria::EPS, 5, 1));}

\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC4} images are supported for now.}
\cvarg{dst}{Segmented image. Will have the same size and type as \texttt{src}.}
\cvarg{sp}{Spatial window radius.}
\cvarg{sr}{Color window radius.}
\cvarg{minsize}{Minimum segment size. Smaller segements will be merged.}
\cvarg{criteria}{Termination criteria. See \hyperref[TermCriteria]{cv::TermCriteria}.}
\end{description}


\cvCppFunc{gpu::integral}
Computes integral image and squared integral image.

\cvdefCpp{void integral(const GpuMat\& src, GpuMat\& sum);\newline
void integral(const GpuMat\& src, GpuMat\& sum, GpuMat\& sqsum);}

\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC1} images are supported for now.}
\cvarg{sum}{Integral image. Will contain 32-bit unsigned integer values packed into \texttt{CV\_32SC1}.}
\cvarg{sqsum}{Squared integral image. Will have \texttt{CV\_32FC1} type.}
\end{description}

See also: \cvCppCross{integral}.


\cvCppFunc{gpu::sqrIntegral}
Computes squared integral image.

\cvdefCpp{void sqrIntegral(const GpuMat\& src, GpuMat\& sqsum);}
\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC1} images are supported for now.}
\cvarg{sqsum}{Squared integral image. Will contain 64-bit floating point values packed into 64U.}
\end{description}


\cvCppFunc{gpu::columnSum}
Computes vertical (column) sum.

\cvdefCpp{void columnSum(const GpuMat\& src, GpuMat\& sum);}
\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_32FC1} images are supported for now.}
\cvarg{sum}{Destination image. Will have \texttt{CV\_32FC1} type.}
\end{description}


\cvCppFunc{gpu::cornerHarris}
Computes Harris cornerness criteria at each image pixel.

\cvdefCpp{void cornerHarris(const GpuMat\& src, GpuMat\& dst,\par
  int blockSize, int ksize, double k,\par
  int borderType=BORDER\_REFLECT101);}

\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC1} and \texttt{CV\_32FC1} images are supported for now.}
\cvarg{dst}{Destination image. Will have the same size and \texttt{CV\_32FC1} type and contain cornerness values.}
\cvarg{blockSize}{Neighborhood size.}
\cvarg{ksize}{Aperture parameter for the Sobel operator.}
\cvarg{k}{Harris detector free parameter.}
\cvarg{borderType}{Pixel extrapolation method. Only \texttt{BORDER\_REFLECT101} and \texttt{BORDER\_REPLICATE} are supported for now.}
\end{description}

See also: \cvCppCross{cornerHarris}.


\cvCppFunc{gpu::cornerMinEigenVal}
Computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria.

\cvdefCpp{void cornerMinEigenVal(const GpuMat\& src, GpuMat\& dst,\par
  int blockSize, int ksize,\par
  int borderType=BORDER\_REFLECT101);}

\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC1} and \texttt{CV\_32FC1} images are supported for now.}
\cvarg{dst}{Destination image. Will have the same size and \texttt{CV\_32FC1} type and contain cornerness values.}
\cvarg{blockSize}{Neighborhood size.}
\cvarg{ksize}{Aperture parameter for the Sobel operator.}
\cvarg{k}{Harris detector free parameter.}
\cvarg{borderType}{Pixel extrapolation method. Only \texttt{BORDER\_REFLECT101} and \texttt{BORDER\_REPLICATE} are supported for now.}
\end{description}

See also: \cvCppCross{cornerMinEigenValue}.


\cvCppFunc{gpu::mulSpectrums}
Performs per-element multiplication of two Fourier spectrums.

\cvdefCpp{void mulSpectrums(const GpuMat\& a, const GpuMat\& b,\par
  GpuMat\& c, int flags, bool conjB=false);}

\begin{description}
\cvarg{a}{First spectrum.}
\cvarg{b}{Second spectrum. Must have the same size and type as \texttt{a}.}
\cvarg{c}{Destination spectrum.}
\cvarg{flags}{Mock paramter is kept for CPU/GPU interfaces similarity.}
\cvarg{conjB}{Optional flag which indicates the second spectrum must be conjugated before the multiplcation.}
\end{description}

Only full (i.e. not packed) \texttt{CV\_32FC2} complex spectrums in the interleaved format are supported for now.

See also: \cvCppCross{mulSpectrums}.


\cvCppFunc{gpu::mulAndScaleSpectrums}
Performs per-element multiplication of two Fourier spectrums and scales the result.

\cvdefCpp{void mulAndScaleSpectrums(const GpuMat\& a, const GpuMat\& b,\par
  GpuMat\& c, int flags, float scale, bool conjB=false);}

\begin{description}
\cvarg{a}{First spectrum.}
\cvarg{b}{Second spectrum. Must have the same size and type as \texttt{a}.}
\cvarg{c}{Destination spectrum.}
\cvarg{flags}{Mock paramter is kept for CPU/GPU interfaces similarity.}
\cvarg{scale}{Scale constant.}
\cvarg{conjB}{Optional flag which indicates the second spectrum must be conjugated before the multiplcation.}
\end{description}

Only full (i.e. not packed) \texttt{CV\_32FC2} complex spectrums in the interleaved format are supported for now.

See also: \cvCppCross{mulSpectrums}.


\cvCppFunc{gpu::dft}
Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.

\cvdefCpp{void dft(const GpuMat\& src, GpuMat\& dst, Size dft\_size, int flags=0);}

\begin{description}
\cvarg{src}{Source matrix (real or complex).}
\cvarg{dst}{Destination matrix (real or complex).}
\cvarg{dft\_size}{Size of discrete Fourier transform.}
\cvarg{flags}{Optional flags:
\begin{description}
  \cvarg{DFT\_ROWS}{Transform each individual row of the source matrix.}
  \cvarg{DFT\_SCALE}{Scale the result: divide it by the number of elements in the transform (it's obtained from \texttt{dft\_size}).
  \cvarg{DFT\_INVERSE}{Inverse DFT must be perfromed for complex-complex case (real-complex and complex-real cases are respectively forward and inverse always).}}
  \cvarg{DFT\_REAL\_OUTPUT}{The source matrix is the result of real-complex transform, so the destination matrix must be real.}
\end{description}}
\end{description}

The source matrix should be continuous, otherwise reallocation and data copying will be performed. Function chooses the operation mode depending on the flags, size and channel count of the source matrix:
\begin{itemize}
  \item If the source matrix is complex and the output isn't specified as real then the destination matrix will be complex, will have \texttt{dft\_size} size and \texttt{CV\_32FC2} type. It will contain full result of the DFT (forward or inverse).
  \item If the source matrix is complex and the output is specified as real then function assumes that its input is the result of the forward transform (see next item). The destionation matrix will have \texttt{dft\_size} size and \texttt{CV\_32FC1} type. It will contain result of the inverse DFT.
  \item If the source matrix is real (i.e. its type is \texttt{CV\_32FC1}) then forward DFT will be performed. The result of the DFT will be packed into complex (\texttt{CV\_32FC2}) matrix so its width will be \texttt{dft\_size.width / 2 + 1}, but if the source is a single column then height will be reduced.
\end{itemize}

See also: \cvCppCross{dft}.


\cvCppFunc{gpu::convolve}
Computes convolution (or cross-correlation) of two images.

\cvdefCpp{void convolve(const GpuMat\& image, const GpuMat\& templ, GpuMat\& result,\par
  bool ccorr=false);\newline
void convolve(const GpuMat\& image, const GpuMat\& templ, GpuMat\& result,\par
  bool ccorr, ConvolveBuf\& buf);}

\begin{description}
\cvarg{image}{Source image. Only \texttt{CV\_32FC1} images are supported for now.}
\cvarg{templ}{Template image. Must have size not greater then \texttt{image} size and be the same type as \texttt{image}.}
\cvarg{result}{Result image. Will have the same size and type as \texttt{image}.}
\cvarg{ccorr}{Flags which indicates cross-correlation must be evaluated instead of convolution.}
\cvarg{buf}{Optional buffer to decrease memory reallocation count (for many calls with the same sizes).}
\end{description}


\cvclass{gpu::ConvolveBuf}
Memory buffer for the \cvCppCross{gpu::convolve} function.

\begin{lstlisting}
struct CV_EXPORTS ConvolveBuf
{
    ConvolveBuf() {}
    ConvolveBuf(Size image_size, Size templ_size) 
        { create(image_size, templ_size); }
    void create(Size image_size, Size templ_size);

private:
    // Hidden
};
\end{lstlisting}


\cvCppFunc{gpu::ConvolveBuf::ConvolveBuf}

\cvdefCpp{ConvolveBuf();}
Constructs an empty buffer which will be properly resized after first call of the convolve function.

\cvdefCpp{ConvolveBuf(Size image\_size, Size templ\_size);}
Constructs a buffer for the convolve function with respectively arguments.


\cvCppFunc{gpu::matchTemplate}
Computes a proximity map for a raster template and an image where the template is searched for.

\cvdefCpp{void matchTemplate(const GpuMat\& image, const GpuMat\& templ,\par
  GpuMat\& result, int method);}

\begin{description}
\cvarg{image}{Source image. 32F and 8U images (1..4 channels) are supported for now.}
\cvarg{templ}{Template image. Must have the same size and type as \texttt{image}.}
\cvarg{result}{Map containing comparison results (32FC1). If \texttt{image} is $W \times H$ and
\texttt{templ} is $w \times h$ then \texttt{result} must be $(W-w+1) \times (H-h+1)$.}
\cvarg{method}{Specifies the way which the template must be compared with the image.}
\end{description}
Following methods are supported for the 8U images for now:
\begin{itemize}
\item CV\_TM\_SQDIFF \item CV\_TM\_SQDIFF\_NORMED \item CV\_TM\_CCORR \item CV\_TM\_CCORR\_NORMED \item CV\_TM\_CCOEFF \item CV\_TM\_CCOEFF\_NORMED 
\end{itemize}
Following methods are supported for the 32F images for now:
\begin{itemize}
\item CV\_TM\_SQDIFF \item CV\_TM\_CCORR
\end{itemize}

See also: \cvCppCross{matchTemplate}. 

\cvCppFunc{gpu::remap}
Applies a generic geometrical transformation to an image.
\cvdefCpp{
void remap(const GpuMat\& src, GpuMat\& dst, \par const GpuMat\& xmap, const GpuMat\& ymap);
}
\begin{description}
\cvarg{src}{Source image. Only \texttt{CV\_8UC1} and \texttt{CV\_8UC3} source types are supported.}
\cvarg{dst}{Destination image. It will have the same size as \texttt{xmap} and the same type as \texttt{src}.}
\cvarg{xmap}{The x values. Only \texttt{CV\_32FC1} type are supported.}
\cvarg{ymap}{The y values. Only \texttt{CV\_32FC1} type are supported.}
\end{description}
The function remap transforms the source image using the specified map:
\[
\texttt{dst}(x,y) = \texttt{src}(xmap(x,y), ymap(x,y))
\]
Where values of pixels with non-integer coordinates are computed using bilinear interpolation.\newline
See also: \cvCppCross{remap}.

\cvCppFunc{gpu::drawColorDisp}
Does coloring of disparity image.
\cvdefCpp{
void drawColorDisp(const GpuMat\& src\_disp, GpuMat\& dst\_disp, int ndisp);
}
\cvdefCpp{
void drawColorDisp(const GpuMat\& src\_disp, GpuMat\& dst\_disp, int ndisp, \par const Stream\& stream);
}
\begin{description}
\cvarg{src\_disp}{Source disparity image. Supports \texttt{CV\_8UC1} and \texttt{CV\_16SC1} types.}
\cvarg{dst\_disp}{Output disparity image. Will have the same size as \texttt{src\_disp} and \texttt{CV\_8UC4} type in \texttt{BGRA} format (alpha = 255).}
\cvarg{ndisp}{Number of disparities.}
\cvarg{stream}{Stream fo async version.}
\end{description}
This function converts $[0..ndisp)$ interval to $[0..240, 1, 1]$ in \texttt{HSV} color space, than convert \texttt{HSV} color space to \texttt{RGB}.

\cvCppFunc{gpu::reprojectImageTo3D}
Reprojects disparity image to 3D space.
\cvdefCpp{
void reprojectImageTo3D(const GpuMat\& disp, GpuMat\& xyzw, \par const Mat\& Q);
}
\cvdefCpp{
void reprojectImageTo3D(const GpuMat\& disp, GpuMat\& xyzw, \par const Mat\& Q, const Stream\& stream);
}
\begin{description}
\cvarg{disp}{The input single-channel 8-bit unsigned ot 16-bit signed integer disparity image.}
\cvarg{xyzw}{The output 4-channel floating-point image of the same size as \texttt{disp}. Each element of \texttt{xyzw(x,y)} will contain the 3D coordinates \texttt{(x,y,z,1)} of the point \texttt{(x,y)}, computed from the disparity map.}
\cvarg{Q}{The $4 \times 4$ perspective transformation matrix that can be obtained with \cvCross{StereoRectify}{stereoRectify}.}
\cvarg{stream}{Stream fo async version.}
\end{description}
See also: \cvCppCross{reprojectImageTo3D}.

\cvCppFunc{gpu::cvtColor}
Converts image from one color space to another.
\cvdefCpp{
void cvtColor(const GpuMat\& src, GpuMat\& dst, int code, int dcn = 0);
}
\cvdefCpp{
void cvtColor(const GpuMat\& src, GpuMat\& dst, int code, int dcn, \par const Stream\& stream);
}
\begin{description}
\cvarg{src}{The source image, 8-bit unsigned, 16-bit unsigned (\texttt{CV\_16UC...}) or single-precision floating-point.}
\cvarg{dst}{The destination image; will have the same size and the same depth as \texttt{src}.}
\cvarg{code}{The color space conversion code.}
\cvarg{dcn}{The number of channels in the destination image; if the parameter is 0, the number of the channels will be derived automatically from \texttt{src} and the \texttt{code}.}
\cvarg{stream}{Stream fo async version.}
\end{description}
3-channels color spaces (like \texttt{HSV}, \texttt{XYZ}, etc) can be stored to 4-channels image for better perfomance.\newline
See also: \cvCppCross{cvtColor}.

\cvCppFunc{gpu::threshold}
Applies a fixed-level threshold to each array element.
\cvdefCpp{
double threshold(const GpuMat\& src, GpuMat\& dst, double thresh);
}
\begin{description}
\cvarg{src}{Source array. Supports only \texttt{CV\_32FC1} type.}
\cvarg{dst}{Destination array; will have the same size and the same type as \texttt{src}.}
\cvarg{thresh}{Threshold value.}
\end{description}
Does only \texttt{THRESH\_TRUNC} threshold.\newline
See also: \cvCppCross{threshold}.

\cvCppFunc{gpu::resize}
Resizes the image.
\cvdefCpp{
void resize(const GpuMat\& src, GpuMat\& dst, Size dsize, \par double fx=0, double fy=0, \par int interpolation = INTER\_LINEAR);
}
\begin{description}
\cvarg{src}{Source image. Supports \texttt{CV\_8UC1}, \texttt{CV\_8UC4} types.}
\cvarg{dst}{Destination image. It will have size \texttt{dsize} (when it is non-zero) or the size computed from \texttt{src.size()} and \texttt{fx} and \texttt{fy}. The type of \texttt{dst} will be the same as of \texttt{src}.}
\cvarg{dsize}{The destination image size. If it is zero, then it is computed as: \[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\] Either \texttt{dsize} or both \texttt{fx} or \texttt{fy} must be non-zero.}
\cvarg{fx}{The scale factor along the horizontal axis. When 0, it is computed as \[\texttt{(double)dsize.width/src.cols}\]} 
\cvarg{fy}{The scale factor along the vertical axis. When 0, it is computed as \[\texttt{(double)dsize.height/src.rows}\]}
\cvarg{interpolation}{The interpolation method. Supports only \texttt{INTER\_NEAREST} and \texttt{INTER\_LINEAR}.}
\end{description}
See also: \cvCppCross{resize}.

\cvCppFunc{gpu::warpAffine}
Applies an affine transformation to an image.
\cvdefCpp{
void warpAffine(const GpuMat\& src, GpuMat\& dst, const Mat\& M, \par Size dsize, int flags = INTER\_LINEAR);
}
\begin{description}
\cvarg{src}{Source image. Supports 8-bit unsigned, 16-bit unsigned, 32-bit signed amd 32-bit floating one, three and four channels images.}
\cvarg{dst}{Destination image; will have size \texttt{dsize} and the same type as \texttt{src}.}
\cvarg{M}{$2\times 3$ transformation matrix.}
\cvarg{dsize}{Size of the destination image.}
\cvarg{flags}{A combination of interpolation methods, see \cvCppCross{resize}, and the optional flag \texttt{WARP\_INVERSE\_MAP} that means that \texttt{M} is the inverse transformation ($\texttt{dst}\rightarrow\texttt{src}$). Supports only \texttt{INTER\_NEAREST}, \texttt{INTER\_LINEAR} and \texttt{INTER\_CUBIC} interpolation methods.}
\end{description}
See also: \cvCppCross{warpAffine}.

\cvCppFunc{gpu::warpPerspective}
Applies a perspective transformation to an image.
\cvdefCpp{
void warpPerspective(const GpuMat\& src, GpuMat\& dst, const Mat\& M, \par Size dsize, int flags = INTER\_LINEAR);
}
\begin{description}
\cvarg{src}{Source image. Supports 8-bit unsigned, 16-bit unsigned, 32-bit signed amd 32-bit floating one, three and four channels images.}
\cvarg{dst}{Destination image; will have size \texttt{dsize} and the same type as \texttt{src}.}
\cvarg{M}{$2\times 3$ transformation matrix.}
\cvarg{dsize}{Size of the destination image.}
\cvarg{flags}{A combination of interpolation methods, see \cvCppCross{resize}, and the optional flag \texttt{WARP\_INVERSE\_MAP} that means that \texttt{M} is the inverse transformation ($\texttt{dst}\rightarrow\texttt{src}$). Supports only \texttt{INTER\_NEAREST}, \texttt{INTER\_LINEAR} and \texttt{INTER\_CUBIC} interpolation methods.}
\end{description}
See also: \cvCppCross{warpPerspective}.

\cvCppFunc{gpu::rotate}
Rotates an image around the origin (0,0) and then shifts it.
\cvdefCpp{
void rotate(const GpuMat\& src, GpuMat\& dst, Size dsize, \par double angle, double xShift = 0, double yShift = 0, \par int interpolation = INTER\_LINEAR);
}
\begin{description}
\cvarg{src}{Source image. Supports \texttt{CV\_8UC1}, \texttt{CV\_8UC4} types.}
\cvarg{dst}{Destination image; will have size \texttt{dsize} and the same type as \texttt{src}.}
\cvarg{dsize}{Size of the destination image.}
\cvarg{angle}{The angle of rotation in degrees.}
\cvarg{xShift}{Shift along horizontal axis.}
\cvarg{yShift}{Shift along vertical axis.}
\cvarg{interpolation}{The interpolation method. Supports only \texttt{INTER\_NEAREST}, \texttt{INTER\_LINEAR} and \texttt{INTER\_CUBIC}.}
\end{description}
See also: \cvCppCross{gpu::warpAffine}.

\cvCppFunc{gpu::copyMakeBorder}
Copies 2D array to a larger destination array and pads borders with user-specifiable constant.
\cvdefCpp{
void copyMakeBorder(const GpuMat\& src, GpuMat\& dst, \par int top, int bottom, int left, int right, \par const Scalar\& value = Scalar());
}
\begin{description}
\cvarg{src}{The source image. Supports \texttt{CV\_8UC1}, \texttt{CV\_8UC4}, \texttt{CV\_32SC1} and \texttt{CV\_32FC1} types.}
\cvarg{dst}{The destination image; will have the same type as \texttt{src} and the size \texttt{Size(src.cols+left+right, src.rows+top+bottom)}.}
\cvarg{top, bottom, left, right}{Specify how much pixels in each direction from the source image rectangle one needs to extrapolate, e.g. \texttt{top=1, bottom=1, left=1, right=1} mean that 1 pixel-wide border needs to be built.}
\cvarg{value}{The border value.}
\end{description}
See also: \cvCppCross{copyMakeBorder}

\cvCppFunc{gpu::rectStdDev}
Computes the standard deviation of integral images. 
\cvdefCpp{
void rectStdDev(const GpuMat\& src, const GpuMat\& sqr, GpuMat\& dst, \par const Rect\& rect);
}
\begin{description}
\cvarg{src}{The source image. Supports only \texttt{CV\_32SC1} type.}
\cvarg{sqr}{The squared source image. Supports only \texttt{CV\_32FC1} type.}
\cvarg{dst}{The destination image; will have the same type and the same size as \texttt{src}.}
\cvarg{rect}{Rectangular window.}
\end{description}

\cvCppFunc{gpu::evenLevels}
Compute levels with even distribution.
\cvdefCpp{
void evenLevels(GpuMat\& levels, int nLevels, \par int lowerLevel, int upperLevel);
}
\begin{description}
\cvarg{levels}{The destination array. \texttt{levels} will have 1 row and \texttt{nLevels} cols and \texttt{CV\_32SC1} type.}
\cvarg{nLevels}{The number of levels being computed. \texttt{nLevels} must be at least 2}
\cvarg{lowerLevel}{Lower boundary value of the lowest level.}
\cvarg{upperLevel}{Upper boundary value of the greatest level.}
\end{description}

\cvCppFunc{gpu::histEven}
Calculates histogram with evenly distributed bins.
\cvdefCpp{
void histEven(const GpuMat\& src, GpuMat\& hist, \par int histSize, int lowerLevel, int upperLevel);
}
\cvdefCpp{
void histEven(const GpuMat\& src, GpuMat hist[4], \par int histSize[4], int lowerLevel[4], int upperLevel[4]);
}
\begin{description}
\cvarg{src}{The source image. Supports 8-bit unsigned, 16-bit unsigned and 16-bit one or four channel images. For four channels image all channels are processed separately.}
\cvarg{hist}{Destination histogram. Will have one row, \texttt{histSize} cols and \texttt{CV\_32S} type.}
\cvarg{histSize}{Size of histogram.}
\cvarg{lowerLevel}{Lower boundary of lowest level bin.}
\cvarg{upperLevel}{Upper boundary of highest level bin.}
\end{description}

\cvCppFunc{gpu::histRange}
Calculates histogram with bins determined by levels array.
\cvdefCpp{
void histRange(const GpuMat\& src, GpuMat\& hist, const GpuMat\& levels);
}
\cvdefCpp{
void histRange(const GpuMat\& src, GpuMat hist[4], \par const GpuMat levels[4]);
}
\begin{description}
\cvarg{src}{The source image. Supports 8-bit unsigned, 16-bit unsigned and 16-bit one or four channel images. For four channels image all channels are processed separately.}
\cvarg{hist}{Destination histogram. Will have one row, \texttt{(levels.cols-1)} cols and \texttt{CV\_32SC1} type.}
\cvarg{levels}{Number of levels in histogram.}
\end{description}