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#ifndef __OPENCV_CUDAIMGPROC_HPP__
#define __OPENCV_CUDAIMGPROC_HPP__

#ifndef __cplusplus
#  error cudaimgproc.hpp header must be compiled as C++
#endif

#include "opencv2/core/cuda.hpp"
#include "opencv2/imgproc.hpp"

/**
  @addtogroup cuda
  @{
    @defgroup cudaimgproc Image Processing
    @{
      @defgroup cudaimgproc_color Color space processing
      @defgroup cudaimgproc_hist Histogram Calculation
      @defgroup cudaimgproc_hough Hough Transform
      @defgroup cudaimgproc_feature Feature Detection
    @}
  @}
*/

namespace cv { namespace cuda {

//! @addtogroup cudaimgproc
//! @{

/////////////////////////// Color Processing ///////////////////////////

//! @addtogroup cudaimgproc_color
//! @{

/** @brief Converts an image from one color space to another.

@param src Source image with CV_8U , CV_16U , or CV_32F depth and 1, 3, or 4 channels.
@param dst Destination image.
@param code Color space conversion code. For details, see cvtColor .
@param dcn Number of channels in the destination image. If the parameter is 0, the number of the
channels is derived automatically from src and the code .
@param stream Stream for the asynchronous version.

3-channel color spaces (like HSV, XYZ, and so on) can be stored in a 4-channel image for better
performance.

@sa cvtColor
 */
CV_EXPORTS void cvtColor(InputArray src, OutputArray dst, int code, int dcn = 0, Stream& stream = Stream::Null());

enum
{
    //! Bayer Demosaicing (Malvar, He, and Cutler)
    COLOR_BayerBG2BGR_MHT = 256,
    COLOR_BayerGB2BGR_MHT = 257,
    COLOR_BayerRG2BGR_MHT = 258,
    COLOR_BayerGR2BGR_MHT = 259,

    COLOR_BayerBG2RGB_MHT = COLOR_BayerRG2BGR_MHT,
    COLOR_BayerGB2RGB_MHT = COLOR_BayerGR2BGR_MHT,
    COLOR_BayerRG2RGB_MHT = COLOR_BayerBG2BGR_MHT,
    COLOR_BayerGR2RGB_MHT = COLOR_BayerGB2BGR_MHT,

    COLOR_BayerBG2GRAY_MHT = 260,
    COLOR_BayerGB2GRAY_MHT = 261,
    COLOR_BayerRG2GRAY_MHT = 262,
    COLOR_BayerGR2GRAY_MHT = 263
};

/** @brief Converts an image from Bayer pattern to RGB or grayscale.

@param src Source image (8-bit or 16-bit single channel).
@param dst Destination image.
@param code Color space conversion code (see the description below).
@param dcn Number of channels in the destination image. If the parameter is 0, the number of the
channels is derived automatically from src and the code .
@param stream Stream for the asynchronous version.

The function can do the following transformations:

-   Demosaicing using bilinear interpolation

    > -   COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
    > -   COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR

-   Demosaicing using Malvar-He-Cutler algorithm (@cite MHT2011)

    > -   COLOR_BayerBG2GRAY_MHT , COLOR_BayerGB2GRAY_MHT , COLOR_BayerRG2GRAY_MHT ,
    >     COLOR_BayerGR2GRAY_MHT
    > -   COLOR_BayerBG2BGR_MHT , COLOR_BayerGB2BGR_MHT , COLOR_BayerRG2BGR_MHT ,
    >     COLOR_BayerGR2BGR_MHT

@sa cvtColor
 */
CV_EXPORTS void demosaicing(InputArray src, OutputArray dst, int code, int dcn = -1, Stream& stream = Stream::Null());

/** @brief Exchanges the color channels of an image in-place.

@param image Source image. Supports only CV_8UC4 type.
@param dstOrder Integer array describing how channel values are permutated. The n-th entry of the
array contains the number of the channel that is stored in the n-th channel of the output image.
E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR channel order.
@param stream Stream for the asynchronous version.

The methods support arbitrary permutations of the original channels, including replication.
 */
CV_EXPORTS void swapChannels(InputOutputArray image, const int dstOrder[4], Stream& stream = Stream::Null());

/** @brief Routines for correcting image color gamma.

@param src Source image (3- or 4-channel 8 bit).
@param dst Destination image.
@param forward true for forward gamma correction or false for inverse gamma correction.
@param stream Stream for the asynchronous version.
 */
CV_EXPORTS void gammaCorrection(InputArray src, OutputArray dst, bool forward = true, Stream& stream = Stream::Null());

enum { ALPHA_OVER, ALPHA_IN, ALPHA_OUT, ALPHA_ATOP, ALPHA_XOR, ALPHA_PLUS, ALPHA_OVER_PREMUL, ALPHA_IN_PREMUL, ALPHA_OUT_PREMUL,
       ALPHA_ATOP_PREMUL, ALPHA_XOR_PREMUL, ALPHA_PLUS_PREMUL, ALPHA_PREMUL};

/** @brief Composites two images using alpha opacity values contained in each image.

@param img1 First image. Supports CV_8UC4 , CV_16UC4 , CV_32SC4 and CV_32FC4 types.
@param img2 Second image. Must have the same size and the same type as img1 .
@param dst Destination image.
@param alpha_op Flag specifying the alpha-blending operation:
-   **ALPHA_OVER**
-   **ALPHA_IN**
-   **ALPHA_OUT**
-   **ALPHA_ATOP**
-   **ALPHA_XOR**
-   **ALPHA_PLUS**
-   **ALPHA_OVER_PREMUL**
-   **ALPHA_IN_PREMUL**
-   **ALPHA_OUT_PREMUL**
-   **ALPHA_ATOP_PREMUL**
-   **ALPHA_XOR_PREMUL**
-   **ALPHA_PLUS_PREMUL**
-   **ALPHA_PREMUL**
@param stream Stream for the asynchronous version.

@note
   -   An example demonstrating the use of alphaComp can be found at
        opencv_source_code/samples/gpu/alpha_comp.cpp
 */
CV_EXPORTS void alphaComp(InputArray img1, InputArray img2, OutputArray dst, int alpha_op, Stream& stream = Stream::Null());

//! @} cudaimgproc_color

////////////////////////////// Histogram ///////////////////////////////

//! @addtogroup cudaimgproc_hist
//! @{

/** @brief Calculates histogram for one channel 8-bit image.

@param src Source image with CV_8UC1 type.
@param hist Destination histogram with one row, 256 columns, and the CV_32SC1 type.
@param stream Stream for the asynchronous version.
 */
CV_EXPORTS void calcHist(InputArray src, OutputArray hist, Stream& stream = Stream::Null());

/** @brief Equalizes the histogram of a grayscale image.

@param src Source image with CV_8UC1 type.
@param dst Destination image.
@param buf Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
@param stream Stream for the asynchronous version.

@sa equalizeHist
 */
CV_EXPORTS void equalizeHist(InputArray src, OutputArray dst, InputOutputArray buf, Stream& stream = Stream::Null());

/** @overload */
static inline void equalizeHist(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
{
    GpuMat buf;
    cuda::equalizeHist(src, dst, buf, stream);
}

/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. :
 */
class CV_EXPORTS CLAHE : public cv::CLAHE
{
public:
    using cv::CLAHE::apply;
    /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization.

    @param src Source image with CV_8UC1 type.
    @param dst Destination image.
    @param stream Stream for the asynchronous version.
     */
    virtual void apply(InputArray src, OutputArray dst, Stream& stream) = 0;
};

/** @brief Creates implementation for cuda::CLAHE .

@param clipLimit Threshold for contrast limiting.
@param tileGridSize Size of grid for histogram equalization. Input image will be divided into
equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
 */
CV_EXPORTS Ptr<cuda::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));

/** @brief Computes levels with even distribution.

@param levels Destination array. levels has 1 row, nLevels columns, and the CV_32SC1 type.
@param nLevels Number of computed levels. nLevels must be at least 2.
@param lowerLevel Lower boundary value of the lowest level.
@param upperLevel Upper boundary value of the greatest level.
 */
CV_EXPORTS void evenLevels(OutputArray levels, int nLevels, int lowerLevel, int upperLevel);

/** @brief Calculates a histogram with evenly distributed bins.

@param src Source image. CV_8U, CV_16U, or CV_16S depth and 1 or 4 channels are supported. For
a four-channel image, all channels are processed separately.
@param hist Destination histogram with one row, histSize columns, and the CV_32S type.
@param histSize Size of the histogram.
@param lowerLevel Lower boundary of lowest-level bin.
@param upperLevel Upper boundary of highest-level bin.
@param buf Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
@param stream Stream for the asynchronous version.
 */
CV_EXPORTS void histEven(InputArray src, OutputArray hist, InputOutputArray buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());

/** @overload */
static inline void histEven(InputArray src, OutputArray hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null())
{
    GpuMat buf;
    cuda::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
}

/** @overload */
CV_EXPORTS void histEven(InputArray src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());

/** @overload */
static inline void histEven(InputArray src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null())
{
    GpuMat buf;
    cuda::histEven(src, hist, buf, histSize, lowerLevel, upperLevel, stream);
}

/** @brief Calculates a histogram with bins determined by the levels array.

@param src Source image. CV_8U , CV_16U , or CV_16S depth and 1 or 4 channels are supported.
For a four-channel image, all channels are processed separately.
@param hist Destination histogram with one row, (levels.cols-1) columns, and the CV_32SC1 type.
@param levels Number of levels in the histogram.
@param buf Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
@param stream Stream for the asynchronous version.
 */
CV_EXPORTS void histRange(InputArray src, OutputArray hist, InputArray levels, InputOutputArray buf, Stream& stream = Stream::Null());

/** @overload */
static inline void histRange(InputArray src, OutputArray hist, InputArray levels, Stream& stream = Stream::Null())
{
    GpuMat buf;
    cuda::histRange(src, hist, levels, buf, stream);
}

/** @overload */
CV_EXPORTS void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], InputOutputArray buf, Stream& stream = Stream::Null());

/** @overload */
static inline void histRange(InputArray src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null())
{
    GpuMat buf;
    cuda::histRange(src, hist, levels, buf, stream);
}

//! @} cudaimgproc_hist

//////////////////////////////// Canny ////////////////////////////////

/** @brief Base class for Canny Edge Detector. :
 */
class CV_EXPORTS CannyEdgeDetector : public Algorithm
{
public:
    /** @brief Finds edges in an image using the @cite Canny86 algorithm.

    @param image Single-channel 8-bit input image.
    @param edges Output edge map. It has the same size and type as image .
     */
    virtual void detect(InputArray image, OutputArray edges) = 0;
    /** @overload
    @param dx First derivative of image in the vertical direction. Support only CV_32S type.
    @param dy First derivative of image in the horizontal direction. Support only CV_32S type.
    @param edges Output edge map. It has the same size and type as image .
    */
    virtual void detect(InputArray dx, InputArray dy, OutputArray edges) = 0;

    virtual void setLowThreshold(double low_thresh) = 0;
    virtual double getLowThreshold() const = 0;

    virtual void setHighThreshold(double high_thresh) = 0;
    virtual double getHighThreshold() const = 0;

    virtual void setAppertureSize(int apperture_size) = 0;
    virtual int getAppertureSize() const = 0;

    virtual void setL2Gradient(bool L2gradient) = 0;
    virtual bool getL2Gradient() const = 0;
};

/** @brief Creates implementation for cuda::CannyEdgeDetector .

@param low_thresh First threshold for the hysteresis procedure.
@param high_thresh Second threshold for the hysteresis procedure.
@param apperture_size Aperture size for the Sobel operator.
@param L2gradient Flag indicating whether a more accurate \f$L_2\f$ norm
\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to compute the image gradient magnitude (
L2gradient=true ), or a faster default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( L2gradient=false
).
 */
CV_EXPORTS Ptr<CannyEdgeDetector> createCannyEdgeDetector(double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);

/////////////////////////// Hough Transform ////////////////////////////

//////////////////////////////////////
// HoughLines

//! @addtogroup cudaimgproc_hough
//! @{

/** @brief Base class for lines detector algorithm. :
 */
class CV_EXPORTS HoughLinesDetector : public Algorithm
{
public:
    /** @brief Finds lines in a binary image using the classical Hough transform.

    @param src 8-bit, single-channel binary source image.
    @param lines Output vector of lines. Each line is represented by a two-element vector
    \f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
    the image). \f$\theta\f$ is the line rotation angle in radians (
    \f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).

    @sa HoughLines
     */
    virtual void detect(InputArray src, OutputArray lines) = 0;

    /** @brief Downloads results from cuda::HoughLinesDetector::detect to host memory.

    @param d_lines Result of cuda::HoughLinesDetector::detect .
    @param h_lines Output host array.
    @param h_votes Optional output array for line's votes.
     */
    virtual void downloadResults(InputArray d_lines, OutputArray h_lines, OutputArray h_votes = noArray()) = 0;

    virtual void setRho(float rho) = 0;
    virtual float getRho() const = 0;

    virtual void setTheta(float theta) = 0;
    virtual float getTheta() const = 0;

    virtual void setThreshold(int threshold) = 0;
    virtual int getThreshold() const = 0;

    virtual void setDoSort(bool doSort) = 0;
    virtual bool getDoSort() const = 0;

    virtual void setMaxLines(int maxLines) = 0;
    virtual int getMaxLines() const = 0;
};

/** @brief Creates implementation for cuda::HoughLinesDetector .

@param rho Distance resolution of the accumulator in pixels.
@param theta Angle resolution of the accumulator in radians.
@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
votes ( \f$>\texttt{threshold}\f$ ).
@param doSort Performs lines sort by votes.
@param maxLines Maximum number of output lines.
 */
CV_EXPORTS Ptr<HoughLinesDetector> createHoughLinesDetector(float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);


//////////////////////////////////////
// HoughLinesP

/** @brief Base class for line segments detector algorithm. :
 */
class CV_EXPORTS HoughSegmentDetector : public Algorithm
{
public:
    /** @brief Finds line segments in a binary image using the probabilistic Hough transform.

    @param src 8-bit, single-channel binary source image.
    @param lines Output vector of lines. Each line is represented by a 4-element vector
    \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected
    line segment.

    @sa HoughLinesP
     */
    virtual void detect(InputArray src, OutputArray lines) = 0;

    virtual void setRho(float rho) = 0;
    virtual float getRho() const = 0;

    virtual void setTheta(float theta) = 0;
    virtual float getTheta() const = 0;

    virtual void setMinLineLength(int minLineLength) = 0;
    virtual int getMinLineLength() const = 0;

    virtual void setMaxLineGap(int maxLineGap) = 0;
    virtual int getMaxLineGap() const = 0;

    virtual void setMaxLines(int maxLines) = 0;
    virtual int getMaxLines() const = 0;
};

/** @brief Creates implementation for cuda::HoughSegmentDetector .

@param rho Distance resolution of the accumulator in pixels.
@param theta Angle resolution of the accumulator in radians.
@param minLineLength Minimum line length. Line segments shorter than that are rejected.
@param maxLineGap Maximum allowed gap between points on the same line to link them.
@param maxLines Maximum number of output lines.
 */
CV_EXPORTS Ptr<HoughSegmentDetector> createHoughSegmentDetector(float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096);

//////////////////////////////////////
// HoughCircles

/** @brief Base class for circles detector algorithm. :
 */
class CV_EXPORTS HoughCirclesDetector : public Algorithm
{
public:
    /** @brief Finds circles in a grayscale image using the Hough transform.

    @param src 8-bit, single-channel grayscale input image.
    @param circles Output vector of found circles. Each vector is encoded as a 3-element
    floating-point vector \f$(x, y, radius)\f$ .

    @sa HoughCircles
     */
    virtual void detect(InputArray src, OutputArray circles) = 0;

    virtual void setDp(float dp) = 0;
    virtual float getDp() const = 0;

    virtual void setMinDist(float minDist) = 0;
    virtual float getMinDist() const = 0;

    virtual void setCannyThreshold(int cannyThreshold) = 0;
    virtual int getCannyThreshold() const = 0;

    virtual void setVotesThreshold(int votesThreshold) = 0;
    virtual int getVotesThreshold() const = 0;

    virtual void setMinRadius(int minRadius) = 0;
    virtual int getMinRadius() const = 0;

    virtual void setMaxRadius(int maxRadius) = 0;
    virtual int getMaxRadius() const = 0;

    virtual void setMaxCircles(int maxCircles) = 0;
    virtual int getMaxCircles() const = 0;
};

/** @brief Creates implementation for cuda::HoughCirclesDetector .

@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
half as big width and height.
@param minDist Minimum distance between the centers of the detected circles. If the parameter is
too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
too large, some circles may be missed.
@param cannyThreshold The higher threshold of the two passed to Canny edge detector (the lower one
is twice smaller).
@param votesThreshold The accumulator threshold for the circle centers at the detection stage. The
smaller it is, the more false circles may be detected.
@param minRadius Minimum circle radius.
@param maxRadius Maximum circle radius.
@param maxCircles Maximum number of output circles.
 */
CV_EXPORTS Ptr<HoughCirclesDetector> createHoughCirclesDetector(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);

//////////////////////////////////////
// GeneralizedHough

/** @brief Creates implementation for generalized hough transform from @cite Ballard1981 .
 */
CV_EXPORTS Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();

/** @brief Creates implementation for generalized hough transform from @cite Guil1999 .
 */
CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();

//! @} cudaimgproc_hough

////////////////////////// Corners Detection ///////////////////////////

//! @addtogroup cudaimgproc_feature
//! @{

/** @brief Base class for Cornerness Criteria computation. :
 */
class CV_EXPORTS CornernessCriteria : public Algorithm
{
public:
    /** @brief Computes the cornerness criteria at each image pixel.

    @param src Source image.
    @param dst Destination image containing cornerness values. It will have the same size as src and
    CV_32FC1 type.
    @param stream Stream for the asynchronous version.
     */
    virtual void compute(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};

/** @brief Creates implementation for Harris cornerness criteria.

@param srcType Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
@param blockSize Neighborhood size.
@param ksize Aperture parameter for the Sobel operator.
@param k Harris detector free parameter.
@param borderType Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are
supported for now.

@sa cornerHarris
 */
CV_EXPORTS Ptr<CornernessCriteria> createHarrisCorner(int srcType, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);

/** @brief Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the
cornerness criteria).

@param srcType Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
@param blockSize Neighborhood size.
@param ksize Aperture parameter for the Sobel operator.
@param borderType Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are
supported for now.

@sa cornerMinEigenVal
 */
CV_EXPORTS Ptr<CornernessCriteria> createMinEigenValCorner(int srcType, int blockSize, int ksize, int borderType = BORDER_REFLECT101);

////////////////////////// Corners Detection ///////////////////////////

/** @brief Base class for Corners Detector. :
 */
class CV_EXPORTS CornersDetector : public Algorithm
{
public:
    /** @brief Determines strong corners on an image.

    @param image Input 8-bit or floating-point 32-bit, single-channel image.
    @param corners Output vector of detected corners (1-row matrix with CV_32FC2 type with corners
    positions).
    @param mask Optional region of interest. If the image is not empty (it needs to have the type
    CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
     */
    virtual void detect(InputArray image, OutputArray corners, InputArray mask = noArray()) = 0;
};

/** @brief Creates implementation for cuda::CornersDetector .

@param srcType Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
@param maxCorners Maximum number of corners to return. If there are more corners than are found,
the strongest of them is returned.
@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
(see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the
quality measure less than the product are rejected. For example, if the best corner has the
quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
less than 15 are rejected.
@param minDistance Minimum possible Euclidean distance between the returned corners.
@param blockSize Size of an average block for computing a derivative covariation matrix over each
pixel neighborhood. See cornerEigenValsAndVecs .
@param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris)
or cornerMinEigenVal.
@param harrisK Free parameter of the Harris detector.
 */
CV_EXPORTS Ptr<CornersDetector> createGoodFeaturesToTrackDetector(int srcType, int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
                                                                  int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);

//! @} cudaimgproc_feature

///////////////////////////// Mean Shift //////////////////////////////

/** @brief Performs mean-shift filtering for each point of the source image.

@param src Source image. Only CV_8UC4 images are supported for now.
@param dst Destination image containing the color of mapped points. It has the same size and type
as src .
@param sp Spatial window radius.
@param sr Color window radius.
@param criteria Termination criteria. See TermCriteria.
@param stream

It maps each point of the source image into another point. As a result, you have a new color and new
position of each point.
 */
CV_EXPORTS void meanShiftFiltering(InputArray src, OutputArray dst, int sp, int sr,
                                   TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
                                   Stream& stream = Stream::Null());

/** @brief Performs a mean-shift procedure and stores information about processed points (their colors and
positions) in two images.

@param src Source image. Only CV_8UC4 images are supported for now.
@param dstr Destination image containing the color of mapped points. The size and type is the same
as src .
@param dstsp Destination image containing the position of mapped points. The size is the same as
src size. The type is CV_16SC2 .
@param sp Spatial window radius.
@param sr Color window radius.
@param criteria Termination criteria. See TermCriteria.
@param stream

@sa cuda::meanShiftFiltering
 */
CV_EXPORTS void meanShiftProc(InputArray src, OutputArray dstr, OutputArray dstsp, int sp, int sr,
                              TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
                              Stream& stream = Stream::Null());

/** @brief Performs a mean-shift segmentation of the source image and eliminates small segments.

@param src Source image. Only CV_8UC4 images are supported for now.
@param dst Segmented image with the same size and type as src (host memory).
@param sp Spatial window radius.
@param sr Color window radius.
@param minsize Minimum segment size. Smaller segments are merged.
@param criteria Termination criteria. See TermCriteria.
 */
CV_EXPORTS void meanShiftSegmentation(InputArray src, OutputArray dst, int sp, int sr, int minsize,
                                      TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));

/////////////////////////// Match Template ////////////////////////////

/** @brief Base class for Template Matching. :
 */
class CV_EXPORTS TemplateMatching : public Algorithm
{
public:
    /** @brief Computes a proximity map for a raster template and an image where the template is searched for.

    @param image Source image.
    @param templ Template image with the size and type the same as image .
    @param result Map containing comparison results ( CV_32FC1 ). If image is *W x H* and templ is *w
    x h*, then result must be *W-w+1 x H-h+1*.
    @param stream Stream for the asynchronous version.
     */
    virtual void match(InputArray image, InputArray templ, OutputArray result, Stream& stream = Stream::Null()) = 0;
};

/** @brief Creates implementation for cuda::TemplateMatching .

@param srcType Input source type. CV_32F and CV_8U depth images (1..4 channels) are supported
for now.
@param method Specifies the way to compare the template with the image.
@param user_block_size You can use field user_block_size to set specific block size. If you
leave its default value Size(0,0) then automatic estimation of block size will be used (which is
optimized for speed). By varying user_block_size you can reduce memory requirements at the cost
of speed.

The following methods are supported for the CV_8U depth images for now:

-   CV_TM_SQDIFF
-   CV_TM_SQDIFF_NORMED
-   CV_TM_CCORR
-   CV_TM_CCORR_NORMED
-   CV_TM_CCOEFF
-   CV_TM_CCOEFF_NORMED

The following methods are supported for the CV_32F images for now:

-   CV_TM_SQDIFF
-   CV_TM_CCORR

@sa matchTemplate
 */
CV_EXPORTS Ptr<TemplateMatching> createTemplateMatching(int srcType, int method, Size user_block_size = Size());

////////////////////////// Bilateral Filter ///////////////////////////

/** @brief Performs bilateral filtering of passed image

@param src Source image. Supports only (channles != 2 && depth() != CV_8S && depth() != CV_32S
&& depth() != CV_64F).
@param dst Destination imagwe.
@param kernel_size Kernel window size.
@param sigma_color Filter sigma in the color space.
@param sigma_spatial Filter sigma in the coordinate space.
@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
@param stream Stream for the asynchronous version.

@sa bilateralFilter
 */
CV_EXPORTS void bilateralFilter(InputArray src, OutputArray dst, int kernel_size, float sigma_color, float sigma_spatial,
                                int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());

///////////////////////////// Blending ////////////////////////////////

/** @brief Performs linear blending of two images.

@param img1 First image. Supports only CV_8U and CV_32F depth.
@param img2 Second image. Must have the same size and the same type as img1 .
@param weights1 Weights for first image. Must have tha same size as img1 . Supports only CV_32F
type.
@param weights2 Weights for second image. Must have tha same size as img2 . Supports only CV_32F
type.
@param result Destination image.
@param stream Stream for the asynchronous version.
 */
CV_EXPORTS void blendLinear(InputArray img1, InputArray img2, InputArray weights1, InputArray weights2,
                            OutputArray result, Stream& stream = Stream::Null());

//! @}

}} // namespace cv { namespace cuda {

#endif /* __OPENCV_CUDAIMGPROC_HPP__ */