include/opencv2/gpu/gpu.hpp

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#ifndef __OPENCV_GPU_HPP__
#define __OPENCV_GPU_HPP__

#ifndef SKIP_INCLUDES
#include <vector>
#include <memory>
#include <iosfwd>
#endif

#include "opencv2/core/gpumat.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"

namespace cv { namespace gpu {

// CudaMem is limited cv::Mat with page locked memory allocation.
// Page locked memory is only needed for async and faster coping to GPU.
// It is convertable to cv::Mat header without reference counting
// so you can use it with other opencv functions.

// Page-locks the matrix m memory and maps it for the device(s)
CV_EXPORTS void registerPageLocked(Mat& m);
// Unmaps the memory of matrix m, and makes it pageable again.
CV_EXPORTS void unregisterPageLocked(Mat& m);

class CV_EXPORTS CudaMem
{
public:
    enum  { ALLOC_PAGE_LOCKED = 1, ALLOC_ZEROCOPY = 2, ALLOC_WRITE_COMBINED = 4 };

    CudaMem();
    CudaMem(const CudaMem& m);

    CudaMem(int rows, int cols, int type, int _alloc_type = ALLOC_PAGE_LOCKED);
    CudaMem(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);


    explicit CudaMem(const Mat& m, int alloc_type = ALLOC_PAGE_LOCKED);

    ~CudaMem();

    CudaMem& operator = (const CudaMem& m);

    CudaMem clone() const;

    void create(int rows, int cols, int type, int alloc_type = ALLOC_PAGE_LOCKED);
    void create(Size size, int type, int alloc_type = ALLOC_PAGE_LOCKED);

    void release();

    Mat createMatHeader() const;
    operator Mat() const;

    GpuMat createGpuMatHeader() const;
    operator GpuMat() const;

    //returns if host memory can be mapperd to gpu address space;
    static bool canMapHostMemory();

    // Please see cv::Mat for descriptions
    bool isContinuous() const;
    size_t elemSize() const;
    size_t elemSize1() const;
    int type() const;
    int depth() const;
    int channels() const;
    size_t step1() const;
    Size size() const;
    bool empty() const;


    // Please see cv::Mat for descriptions
    int flags;
    int rows, cols;
    size_t step;

    uchar* data;
    int* refcount;

    uchar* datastart;
    uchar* dataend;

    int alloc_type;
};

// Encapculates Cuda Stream. Provides interface for async coping.
// Passed to each function that supports async kernel execution.
// Reference counting is enabled

class CV_EXPORTS Stream
{
public:
    Stream();
    ~Stream();

    Stream(const Stream&);
    Stream& operator=(const Stream&);

    bool queryIfComplete();
    void waitForCompletion();

    // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its subMat)
    void enqueueDownload(const GpuMat& src, CudaMem& dst);
    void enqueueDownload(const GpuMat& src, Mat& dst);

    // Warning! cv::Mat must point to page locked memory (i.e. to CudaMem data or to its ROI)
    void enqueueUpload(const CudaMem& src, GpuMat& dst);
    void enqueueUpload(const Mat& src, GpuMat& dst);

    void enqueueCopy(const GpuMat& src, GpuMat& dst);

    void enqueueMemSet(GpuMat& src, Scalar val);
    void enqueueMemSet(GpuMat& src, Scalar val, const GpuMat& mask);

    // converts matrix type, ex from float to uchar depending on type
    void enqueueConvert(const GpuMat& src, GpuMat& dst, int type, double a = 1, double b = 0);

    static Stream& Null();

    operator bool() const;

private:
    void create();
    void release();

    struct Impl;
    Impl *impl;

    friend struct StreamAccessor;

    explicit Stream(Impl* impl);
};



class CV_EXPORTS BaseRowFilter_GPU
{
public:
    BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
    virtual ~BaseRowFilter_GPU() {}
    virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
    int ksize, anchor;
};

class CV_EXPORTS BaseColumnFilter_GPU
{
public:
    BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
    virtual ~BaseColumnFilter_GPU() {}
    virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
    int ksize, anchor;
};

class CV_EXPORTS BaseFilter_GPU
{
public:
    BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
    virtual ~BaseFilter_GPU() {}
    virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
    Size ksize;
    Point anchor;
};

class CV_EXPORTS FilterEngine_GPU
{
public:
    virtual ~FilterEngine_GPU() {}

    virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
};

CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);

CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
    const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
    const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf);

CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);

CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);

CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));

CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
    const Point& anchor = Point(-1,-1));

CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
    Point anchor=Point(-1,-1));

CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
    const Point& anchor = Point(-1,-1), int iterations = 1);
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf,
    const Point& anchor = Point(-1,-1), int iterations = 1);

CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
    Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT);

CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
    int anchor = -1, int borderType = BORDER_DEFAULT);

CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
    int anchor = -1, int borderType = BORDER_DEFAULT);

CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
    const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
    int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
    const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
    int columnBorderType = -1);

CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
                                                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf,
                                                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);

CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
                                                          int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
                                                          int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);

CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));

CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));

CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null());

static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
{
    boxFilter(src, dst, -1, ksize, anchor, stream);
}

CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
                      Point anchor = Point(-1, -1), int iterations = 1,
                      Stream& stream = Stream::Null());

CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
                       Point anchor = Point(-1, -1), int iterations = 1,
                       Stream& stream = Stream::Null());

CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2,
                             Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());

CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());

CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
                            Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf,
                            Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1,
                            Stream& stream = Stream::Null());

CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
                      int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1,
                      int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());

CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1,
                       int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());

CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
                             int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
                             int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());

CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());



CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
    const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());

CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());

CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());

CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null());

CV_EXPORTS void magnitude(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());

CV_EXPORTS void magnitudeSqr(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());

CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());

CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());

CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());

CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());

CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null());



CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());

CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());

CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());

CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
CV_EXPORTS void divide(double scale, const GpuMat& b, GpuMat& c, int dtype = -1, Stream& stream = Stream::Null());

CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
                            int dtype = -1, Stream& stream = Stream::Null());

static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
{
    addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
}

CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());

CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());

CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());

//    (dst(i,j) = pow(     src(i,j) , power), if src.type() is integer
//    (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());

CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());

CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, 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};

CV_EXPORTS void alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream = Stream::Null());



CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap,
                      int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(),
                      Stream& stream = Stream::Null());

CV_EXPORTS void meanShiftFiltering(const GpuMat& src, GpuMat& dst, int sp, int sr,
                                   TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
                                   Stream& stream = Stream::Null());

CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
                              TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1),
                              Stream& stream = Stream::Null());

CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
                                      TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));

CV_EXPORTS void drawColorDisp(const GpuMat& src_disp, GpuMat& dst_disp, int ndisp, Stream& stream = Stream::Null());

CV_EXPORTS void reprojectImageTo3D(const GpuMat& disp, GpuMat& xyzw, const Mat& Q, int dst_cn = 4, Stream& stream = Stream::Null());

CV_EXPORTS void cvtColor(const GpuMat& src, GpuMat& dst, int code, int dcn = 0, Stream& stream = Stream::Null());

CV_EXPORTS void swapChannels(GpuMat& image, const int dstOrder[4], Stream& stream = Stream::Null());

CV_EXPORTS void gammaCorrection(const GpuMat& src, GpuMat& dst, bool forward = true, Stream& stream = Stream::Null());

CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());

CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());

CV_EXPORTS void warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
    int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());

CV_EXPORTS void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());

CV_EXPORTS void warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags = INTER_LINEAR,
    int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar(), Stream& stream = Stream::Null());

CV_EXPORTS void buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());

CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, const Mat &T, float scale,
                                   GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());

CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
                                         GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());

CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat& R, float scale,
                                       GpuMat& map_x, GpuMat& map_y, Stream& stream = Stream::Null());

CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0,
                       int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());

CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType,
                               const Scalar& value = Scalar(), Stream& stream = Stream::Null());

CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null());
CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());

CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null());

CV_EXPORTS void columnSum(const GpuMat& src, GpuMat& sum);

CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null());

CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, double k, int borderType = BORDER_REFLECT101);
CV_EXPORTS void cornerHarris(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, double k,
                             int borderType = BORDER_REFLECT101, Stream& stream = Stream::Null());

CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize,
    int borderType=BORDER_REFLECT101, Stream& stream = Stream::Null());

CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream = Stream::Null());

CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());

CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());

struct CV_EXPORTS ConvolveBuf
{
    Size result_size;
    Size block_size;
    Size user_block_size;
    Size dft_size;
    int spect_len;

    GpuMat image_spect, templ_spect, result_spect;
    GpuMat image_block, templ_block, result_data;

    void create(Size image_size, Size templ_size);
    static Size estimateBlockSize(Size result_size, Size templ_size);
};


CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());

struct CV_EXPORTS MatchTemplateBuf
{
    Size user_block_size;
    GpuMat imagef, templf;
    std::vector<GpuMat> images;
    std::vector<GpuMat> image_sums;
    std::vector<GpuMat> image_sqsums;
};

CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null());

CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null());

CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void pyrUp(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
                            GpuMat& result, Stream& stream = Stream::Null());

CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
                                int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());

CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());

class CV_EXPORTS FastNonLocalMeansDenoising
{
public:
    void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());

    void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());

private:

    GpuMat buffer, extended_src_buffer;
    GpuMat lab, l, ab;
};


struct CV_EXPORTS CannyBuf;

CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);
CV_EXPORTS void Canny(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient = false);

struct CV_EXPORTS CannyBuf
{
    CannyBuf() {}
    explicit CannyBuf(const Size& image_size, int apperture_size = 3) {create(image_size, apperture_size);}
    CannyBuf(const GpuMat& dx_, const GpuMat& dy_);

    void create(const Size& image_size, int apperture_size = 3);

    void release();

    GpuMat dx, dy;
    GpuMat dx_buf, dy_buf;
    GpuMat edgeBuf;
    GpuMat trackBuf1, trackBuf2;
    Ptr<FilterEngine_GPU> filterDX, filterDY;
};

class CV_EXPORTS ImagePyramid
{
public:
    inline ImagePyramid() : nLayers_(0) {}
    inline ImagePyramid(const GpuMat& img, int nLayers, Stream& stream = Stream::Null())
    {
        build(img, nLayers, stream);
    }

    void build(const GpuMat& img, int nLayers, Stream& stream = Stream::Null());

    void getLayer(GpuMat& outImg, Size outRoi, Stream& stream = Stream::Null()) const;

    inline void release()
    {
        layer0_.release();
        pyramid_.clear();
        nLayers_ = 0;
    }

private:
    GpuMat layer0_;
    std::vector<GpuMat> pyramid_;
    int nLayers_;
};


struct HoughLinesBuf
{
    GpuMat accum;
    GpuMat list;
};

CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
CV_EXPORTS void HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes = noArray());


struct HoughCirclesBuf
{
    GpuMat edges;
    GpuMat accum;
    GpuMat list;
    CannyBuf cannyBuf;
};

CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
CV_EXPORTS void HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circles);

class CV_EXPORTS GeneralizedHough_GPU : public Algorithm
{
public:
    static Ptr<GeneralizedHough_GPU> create(int method);

    virtual ~GeneralizedHough_GPU();

    void setTemplate(const GpuMat& templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1));
    void setTemplate(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter = Point(-1, -1));

    void detect(const GpuMat& image, GpuMat& positions, int cannyThreshold = 100);
    void detect(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions);

    void download(const GpuMat& d_positions, OutputArray h_positions, OutputArray h_votes = noArray());

    void release();

protected:
    virtual void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter) = 0;
    virtual void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions) = 0;
    virtual void releaseImpl() = 0;

private:
    GpuMat edges_;
    CannyBuf cannyBuf_;
};


CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);

CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);

CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf);

CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);

CV_EXPORTS Scalar sum(const GpuMat& src);

CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);

CV_EXPORTS Scalar absSum(const GpuMat& src);

CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf);

CV_EXPORTS Scalar sqrSum(const GpuMat& src);

CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf);

CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());

CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);

CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
                          const GpuMat& mask=GpuMat());

CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
                          const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);

CV_EXPORTS int countNonZero(const GpuMat& src);

CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);

CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());



CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
                                GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
                              const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
                              Stream& stream = Stream::Null());

CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
                               const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
                               int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
                               std::vector<int>* inliers=NULL);


CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
                         GpuMat& buf, Stream& stream = Stream::Null());

CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
                         GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
                         GpuMat& labels,
                         GpuMat& buf, Stream& stream = Stream::Null());

CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null());

CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null());


CV_EXPORTS void evenLevels(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel);
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
CV_EXPORTS void histEven(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], Stream& stream = Stream::Null());
CV_EXPORTS void histRange(const GpuMat& src, GpuMat hist[4], const GpuMat levels[4], GpuMat& buf, Stream& stream = Stream::Null());

CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void calcHist(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());

CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream = Stream::Null());
CV_EXPORTS void equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream = Stream::Null());


class CV_EXPORTS StereoBM_GPU
{
public:
    enum { BASIC_PRESET = 0, PREFILTER_XSOBEL = 1 };

    enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };

    StereoBM_GPU();
    StereoBM_GPU(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);

    void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());

    // if current GPU will be faster than CPU in this algorithm.
    // It queries current active device.
    static bool checkIfGpuCallReasonable();

    int preset;
    int ndisp;
    int winSize;

    // If avergeTexThreshold  == 0 => post procesing is disabled
    // If avergeTexThreshold != 0 then disparity is set 0 in each point (x,y) where for left image
    // SumOfHorizontalGradiensInWindow(x, y, winSize) < (winSize * winSize) * avergeTexThreshold
    // i.e. input left image is low textured.
    float avergeTexThreshold;

private:
    GpuMat minSSD, leBuf, riBuf;
};

// "Efficient Belief Propagation for Early Vision"
// P.Felzenszwalb

class CV_EXPORTS StereoBeliefPropagation
{
public:
    enum { DEFAULT_NDISP  = 64 };
    enum { DEFAULT_ITERS  = 5  };
    enum { DEFAULT_LEVELS = 5  };

    static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels);

    explicit StereoBeliefPropagation(int ndisp  = DEFAULT_NDISP,
                                     int iters  = DEFAULT_ITERS,
                                     int levels = DEFAULT_LEVELS,
                                     int msg_type = CV_32F);

    StereoBeliefPropagation(int ndisp, int iters, int levels,
        float max_data_term, float data_weight,
        float max_disc_term, float disc_single_jump,
        int msg_type = CV_32F);

    void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());


    void operator()(const GpuMat& data, GpuMat& disparity, Stream& stream = Stream::Null());

    int ndisp;

    int iters;
    int levels;

    float max_data_term;
    float data_weight;
    float max_disc_term;
    float disc_single_jump;

    int msg_type;
private:
    GpuMat u, d, l, r, u2, d2, l2, r2;
    std::vector<GpuMat> datas;
    GpuMat out;
};

// "A Constant-Space Belief Propagation Algorithm for Stereo Matching"
// Qingxiong Yang, Liang Wang, Narendra Ahuja
// http://vision.ai.uiuc.edu/~qyang6/

class CV_EXPORTS StereoConstantSpaceBP
{
public:
    enum { DEFAULT_NDISP    = 128 };
    enum { DEFAULT_ITERS    = 8   };
    enum { DEFAULT_LEVELS   = 4   };
    enum { DEFAULT_NR_PLANE = 4   };

    static void estimateRecommendedParams(int width, int height, int& ndisp, int& iters, int& levels, int& nr_plane);

    explicit StereoConstantSpaceBP(int ndisp    = DEFAULT_NDISP,
                                   int iters    = DEFAULT_ITERS,
                                   int levels   = DEFAULT_LEVELS,
                                   int nr_plane = DEFAULT_NR_PLANE,
                                   int msg_type = CV_32F);

    StereoConstantSpaceBP(int ndisp, int iters, int levels, int nr_plane,
        float max_data_term, float data_weight, float max_disc_term, float disc_single_jump,
        int min_disp_th = 0,
        int msg_type = CV_32F);

    void operator()(const GpuMat& left, const GpuMat& right, GpuMat& disparity, Stream& stream = Stream::Null());

    int ndisp;

    int iters;
    int levels;

    int nr_plane;

    float max_data_term;
    float data_weight;
    float max_disc_term;
    float disc_single_jump;

    int min_disp_th;

    int msg_type;

    bool use_local_init_data_cost;
private:
    GpuMat messages_buffers;

    GpuMat temp;
    GpuMat out;
};

// Disparity map refinement using joint bilateral filtering given a single color image.
// Qingxiong Yang, Liang Wang, Narendra Ahuja
// http://vision.ai.uiuc.edu/~qyang6/

class CV_EXPORTS DisparityBilateralFilter
{
public:
    enum { DEFAULT_NDISP  = 64 };
    enum { DEFAULT_RADIUS = 3 };
    enum { DEFAULT_ITERS  = 1 };

    explicit DisparityBilateralFilter(int ndisp = DEFAULT_NDISP, int radius = DEFAULT_RADIUS, int iters = DEFAULT_ITERS);

    DisparityBilateralFilter(int ndisp, int radius, int iters, float edge_threshold, float max_disc_threshold, float sigma_range);

    void operator()(const GpuMat& disparity, const GpuMat& image, GpuMat& dst, Stream& stream = Stream::Null());

private:
    int ndisp;
    int radius;
    int iters;

    float edge_threshold;
    float max_disc_threshold;
    float sigma_range;

    GpuMat table_color;
    GpuMat table_space;
};


struct CV_EXPORTS HOGConfidence
{
   double scale;
   vector<Point> locations;
   vector<double> confidences;
   vector<double> part_scores[4];
};

struct CV_EXPORTS HOGDescriptor
{
    enum { DEFAULT_WIN_SIGMA = -1 };
    enum { DEFAULT_NLEVELS = 64 };
    enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };

    HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
                  Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
                  int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
                  double threshold_L2hys=0.2, bool gamma_correction=true,
                  int nlevels=DEFAULT_NLEVELS);

    size_t getDescriptorSize() const;
    size_t getBlockHistogramSize() const;

    void setSVMDetector(const vector<float>& detector);

    static vector<float> getDefaultPeopleDetector();
    static vector<float> getPeopleDetector48x96();
    static vector<float> getPeopleDetector64x128();

    void detect(const GpuMat& img, vector<Point>& found_locations,
                double hit_threshold=0, Size win_stride=Size(),
                Size padding=Size());

    void detectMultiScale(const GpuMat& img, vector<Rect>& found_locations,
                          double hit_threshold=0, Size win_stride=Size(),
                          Size padding=Size(), double scale0=1.05,
                          int group_threshold=2);

    void computeConfidence(const GpuMat& img, vector<Point>& hits, double hit_threshold,
                                                Size win_stride, Size padding, vector<Point>& locations, vector<double>& confidences);

    void computeConfidenceMultiScale(const GpuMat& img, vector<Rect>& found_locations,
                                                                    double hit_threshold, Size win_stride, Size padding,
                                                                    vector<HOGConfidence> &conf_out, int group_threshold);

    void getDescriptors(const GpuMat& img, Size win_stride,
                        GpuMat& descriptors,
                        int descr_format=DESCR_FORMAT_COL_BY_COL);

    Size win_size;
    Size block_size;
    Size block_stride;
    Size cell_size;
    int nbins;
    double win_sigma;
    double threshold_L2hys;
    bool gamma_correction;
    int nlevels;

protected:
    void computeBlockHistograms(const GpuMat& img);
    void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);

    double getWinSigma() const;
    bool checkDetectorSize() const;

    static int numPartsWithin(int size, int part_size, int stride);
    static Size numPartsWithin(Size size, Size part_size, Size stride);

    // Coefficients of the separating plane
    float free_coef;
    GpuMat detector;

    // Results of the last classification step
    GpuMat labels, labels_buf;
    Mat labels_host;

    // Results of the last histogram evaluation step
    GpuMat block_hists, block_hists_buf;

    // Gradients conputation results
    GpuMat grad, qangle, grad_buf, qangle_buf;

    // returns subbuffer with required size, reallocates buffer if nessesary.
    static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
    static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);

    std::vector<GpuMat> image_scales;
};



class CV_EXPORTS BruteForceMatcher_GPU_base
{
public:
    enum DistType {L1Dist = 0, L2Dist, HammingDist};

    explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist);

    // Add descriptors to train descriptor collection
    void add(const std::vector<GpuMat>& descCollection);

    // Get train descriptors collection
    const std::vector<GpuMat>& getTrainDescriptors() const;

    // Clear train descriptors collection
    void clear();

    // Return true if there are not train descriptors in collection
    bool empty() const;

    // Return true if the matcher supports mask in match methods
    bool isMaskSupported() const;

    // Find one best match for each query descriptor
    void matchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance,
        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());

    // Download trainIdx and distance and convert it to CPU vector with DMatch
    static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
    // Convert trainIdx and distance to vector with DMatch
    static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);

    // Find one best match for each query descriptor
    void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());

    // Make gpu collection of trains and masks in suitable format for matchCollection function
    void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());

    // Find one best match from train collection for each query descriptor
    void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
        GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
        const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());

    // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
    static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
    // Convert trainIdx, imgIdx and distance to vector with DMatch
    static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);

    // Find one best match from train collection for each query descriptor.
    void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());

    // Find k best matches for each query descriptor (in increasing order of distances)
    void knnMatchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());

    // Download trainIdx and distance and convert it to vector with DMatch
    // compactResult is used when mask is not empty. If compactResult is false matches
    // vector will have the same size as queryDescriptors rows. If compactResult is true
    // matches vector will not contain matches for fully masked out query descriptors.
    static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    // Convert trainIdx and distance to vector with DMatch
    static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    // Find k best matches for each query descriptor (in increasing order of distances).
    // compactResult is used when mask is not empty. If compactResult is false matches
    // vector will have the same size as queryDescriptors rows. If compactResult is true
    // matches vector will not contain matches for fully masked out query descriptors.
    void knnMatch(const GpuMat& query, const GpuMat& train,
        std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
        bool compactResult = false);

    // Find k best matches from train collection for each query descriptor (in increasing order of distances)
    void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
        GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
        const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());

    // Download trainIdx and distance and convert it to vector with DMatch
    // compactResult is used when mask is not empty. If compactResult is false matches
    // vector will have the same size as queryDescriptors rows. If compactResult is true
    // matches vector will not contain matches for fully masked out query descriptors.
    static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    // Convert trainIdx and distance to vector with DMatch
    static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    // Find k best matches  for each query descriptor (in increasing order of distances).
    // compactResult is used when mask is not empty. If compactResult is false matches
    // vector will have the same size as queryDescriptors rows. If compactResult is true
    // matches vector will not contain matches for fully masked out query descriptors.
    void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);

    // Find best matches for each query descriptor which have distance less than maxDistance.
    // nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
    // carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
    // because it didn't have enough memory.
    // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
    // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
    // Matches doesn't sorted.
    void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());

    // Download trainIdx, nMatches and distance and convert it to vector with DMatch.
    // matches will be sorted in increasing order of distances.
    // compactResult is used when mask is not empty. If compactResult is false matches
    // vector will have the same size as queryDescriptors rows. If compactResult is true
    // matches vector will not contain matches for fully masked out query descriptors.
    static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    // Convert trainIdx, nMatches and distance to vector with DMatch.
    static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    // Find best matches for each query descriptor which have distance less than maxDistance
    // in increasing order of distances).
    void radiusMatch(const GpuMat& query, const GpuMat& train,
        std::vector< std::vector<DMatch> >& matches, float maxDistance,
        const GpuMat& mask = GpuMat(), bool compactResult = false);

    // Find best matches for each query descriptor which have distance less than maxDistance.
    // If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
    // otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
    // Matches doesn't sorted.
    void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());

    // Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
    // matches will be sorted in increasing order of distances.
    // compactResult is used when mask is not empty. If compactResult is false matches
    // vector will have the same size as queryDescriptors rows. If compactResult is true
    // matches vector will not contain matches for fully masked out query descriptors.
    static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    // Convert trainIdx, nMatches and distance to vector with DMatch.
    static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    // Find best matches from train collection for each query descriptor which have distance less than
    // maxDistance (in increasing order of distances).
    void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);

    DistType distType;

private:
    std::vector<GpuMat> trainDescCollection;
};

template <class Distance>
class CV_EXPORTS BruteForceMatcher_GPU;

template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base
{
public:
    explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L1Dist) {}
    explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BruteForceMatcher_GPU_base(L1Dist) {}
};
template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base
{
public:
    explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(L2Dist) {}
    explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BruteForceMatcher_GPU_base(L2Dist) {}
};
template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BruteForceMatcher_GPU_base
{
public:
    explicit BruteForceMatcher_GPU() : BruteForceMatcher_GPU_base(HammingDist) {}
    explicit BruteForceMatcher_GPU(Hamming /*d*/) : BruteForceMatcher_GPU_base(HammingDist) {}
};

// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny.
class CV_EXPORTS CascadeClassifier_GPU
{
public:
    CascadeClassifier_GPU();
    CascadeClassifier_GPU(const std::string& filename);
    ~CascadeClassifier_GPU();

    bool empty() const;
    bool load(const std::string& filename);
    void release();

    /* returns number of detected objects */
    int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.1, int minNeighbors = 4, Size minSize = Size());

    bool findLargestObject;
    bool visualizeInPlace;

    Size getClassifierSize() const;

private:

    struct CascadeClassifierImpl;
    CascadeClassifierImpl* impl;
    struct HaarCascade;
    struct LbpCascade;
    friend class CascadeClassifier_GPU_LBP;
};


class CV_EXPORTS SURF_GPU
{
public:
    enum KeypointLayout
    {
        X_ROW = 0,
        Y_ROW,
        LAPLACIAN_ROW,
        OCTAVE_ROW,
        SIZE_ROW,
        ANGLE_ROW,
        HESSIAN_ROW,
        ROWS_COUNT
    };

    SURF_GPU();
    explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4,
         int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);

    int descriptorSize() const;

    void uploadKeypoints(const vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
    void downloadKeypoints(const GpuMat& keypointsGPU, vector<KeyPoint>& keypoints);

    void downloadDescriptors(const GpuMat& descriptorsGPU, vector<float>& descriptors);

    void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints);
    void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
        bool useProvidedKeypoints = false);

    void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
    void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
        bool useProvidedKeypoints = false);

    void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
        bool useProvidedKeypoints = false);

    void releaseMemory();

    // SURF parameters
    double hessianThreshold;
    int nOctaves;
    int nOctaveLayers;
    bool extended;
    bool upright;

    float keypointsRatio;

    GpuMat sum, mask1, maskSum, intBuffer;

    GpuMat det, trace;

    GpuMat maxPosBuffer;
};


class CV_EXPORTS FAST_GPU
{
public:
    enum
    {
        LOCATION_ROW = 0,
        RESPONSE_ROW,
        ROWS_COUNT
    };

    // all features have same size
    static const int FEATURE_SIZE = 7;

    explicit FAST_GPU(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);

    void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
    void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);

    void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);

    void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);

    void release();

    bool nonmaxSupression;

    int threshold;

    double keypointsRatio;

    int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);

    int getKeyPoints(GpuMat& keypoints);

private:
    GpuMat kpLoc_;
    int count_;

    GpuMat score_;

    GpuMat d_keypoints_;
};


class CV_EXPORTS ORB_GPU
{
public:
    enum
    {
        X_ROW = 0,
        Y_ROW,
        RESPONSE_ROW,
        ANGLE_ROW,
        OCTAVE_ROW,
        SIZE_ROW,
        ROWS_COUNT
    };

    enum
    {
        DEFAULT_FAST_THRESHOLD = 20
    };

    explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
                     int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);

    void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
    void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);

    void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
    void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);

    void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);

    void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);

    inline int descriptorSize() const { return kBytes; }

    inline void setFastParams(int threshold, bool nonmaxSupression = true)
    {
        fastDetector_.threshold = threshold;
        fastDetector_.nonmaxSupression = nonmaxSupression;
    }

    void release();

    bool blurForDescriptor;

private:
    enum { kBytes = 32 };

    void buildScalePyramids(const GpuMat& image, const GpuMat& mask);

    void computeKeyPointsPyramid();

    void computeDescriptors(GpuMat& descriptors);

    void mergeKeyPoints(GpuMat& keypoints);

    int nFeatures_;
    float scaleFactor_;
    int nLevels_;
    int edgeThreshold_;
    int firstLevel_;
    int WTA_K_;
    int scoreType_;
    int patchSize_;

    // The number of desired features per scale
    std::vector<size_t> n_features_per_level_;

    // Points to compute BRIEF descriptors from
    GpuMat pattern_;

    std::vector<GpuMat> imagePyr_;
    std::vector<GpuMat> maskPyr_;

    GpuMat buf_;

    std::vector<GpuMat> keyPointsPyr_;
    std::vector<int> keyPointsCount_;

    FAST_GPU fastDetector_;

    Ptr<FilterEngine_GPU> blurFilter;

    GpuMat d_keypoints_;
};


class CV_EXPORTS BroxOpticalFlow
{
public:
    BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_) :
        alpha(alpha_), gamma(gamma_), scale_factor(scale_factor_),
        inner_iterations(inner_iterations_), outer_iterations(outer_iterations_), solver_iterations(solver_iterations_)
    {
    }

    void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null());

    float alpha;

    float gamma;

    float scale_factor;

    int inner_iterations;

    int outer_iterations;

    int solver_iterations;

    GpuMat buf;
};

class CV_EXPORTS GoodFeaturesToTrackDetector_GPU
{
public:
    explicit GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0,
        int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04);

    void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat());

    int maxCorners;
    double qualityLevel;
    double minDistance;

    int blockSize;
    bool useHarrisDetector;
    double harrisK;

    void releaseMemory()
    {
        Dx_.release();
        Dy_.release();
        buf_.release();
        eig_.release();
        minMaxbuf_.release();
        tmpCorners_.release();
    }

private:
    GpuMat Dx_;
    GpuMat Dy_;
    GpuMat buf_;
    GpuMat eig_;
    GpuMat minMaxbuf_;
    GpuMat tmpCorners_;
};

inline GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners_, double qualityLevel_, double minDistance_,
        int blockSize_, bool useHarrisDetector_, double harrisK_)
{
    maxCorners = maxCorners_;
    qualityLevel = qualityLevel_;
    minDistance = minDistance_;
    blockSize = blockSize_;
    useHarrisDetector = useHarrisDetector_;
    harrisK = harrisK_;
}


class CV_EXPORTS PyrLKOpticalFlow
{
public:
    PyrLKOpticalFlow()
    {
        winSize = Size(21, 21);
        maxLevel = 3;
        iters = 30;
        derivLambda = 0.5;
        useInitialFlow = false;
        minEigThreshold = 1e-4f;
        getMinEigenVals = false;
        isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
    }

    void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts,
        GpuMat& status, GpuMat* err = 0);

    void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0);

    Size winSize;
    int maxLevel;
    int iters;
    double derivLambda;
    bool useInitialFlow;
    float minEigThreshold;
    bool getMinEigenVals;

    void releaseMemory()
    {
        dx_calcBuf_.release();
        dy_calcBuf_.release();

        prevPyr_.clear();
        nextPyr_.clear();

        dx_buf_.release();
        dy_buf_.release();

        uPyr_.clear();
        vPyr_.clear();
    }

private:
    void calcSharrDeriv(const GpuMat& src, GpuMat& dx, GpuMat& dy);

    void buildImagePyramid(const GpuMat& img0, vector<GpuMat>& pyr, bool withBorder);

    GpuMat dx_calcBuf_;
    GpuMat dy_calcBuf_;

    vector<GpuMat> prevPyr_;
    vector<GpuMat> nextPyr_;

    GpuMat dx_buf_;
    GpuMat dy_buf_;

    vector<GpuMat> uPyr_;
    vector<GpuMat> vPyr_;

    bool isDeviceArch11_;
};


class CV_EXPORTS FarnebackOpticalFlow
{
public:
    FarnebackOpticalFlow()
    {
        numLevels = 5;
        pyrScale = 0.5;
        fastPyramids = false;
        winSize = 13;
        numIters = 10;
        polyN = 5;
        polySigma = 1.1;
        flags = 0;
        isDeviceArch11_ = !DeviceInfo().supports(FEATURE_SET_COMPUTE_12);
    }

    int numLevels;
    double pyrScale;
    bool fastPyramids;
    int winSize;
    int numIters;
    int polyN;
    double polySigma;
    int flags;

    void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null());

    void releaseMemory()
    {
        frames_[0].release();
        frames_[1].release();
        pyrLevel_[0].release();
        pyrLevel_[1].release();
        M_.release();
        bufM_.release();
        R_[0].release();
        R_[1].release();
        blurredFrame_[0].release();
        blurredFrame_[1].release();
        pyramid0_.clear();
        pyramid1_.clear();
    }

private:
    void prepareGaussian(
            int n, double sigma, float *g, float *xg, float *xxg,
            double &ig11, double &ig03, double &ig33, double &ig55);

    void setPolynomialExpansionConsts(int n, double sigma);

    void updateFlow_boxFilter(
            const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat &flowy,
            GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);

    void updateFlow_gaussianBlur(
            const GpuMat& R0, const GpuMat& R1, GpuMat& flowx, GpuMat& flowy,
            GpuMat& M, GpuMat &bufM, int blockSize, bool updateMatrices, Stream streams[]);

    GpuMat frames_[2];
    GpuMat pyrLevel_[2], M_, bufM_, R_[2], blurredFrame_[2];
    std::vector<GpuMat> pyramid0_, pyramid1_;

    bool isDeviceArch11_;
};


CV_EXPORTS void interpolateFrames(const GpuMat& frame0, const GpuMat& frame1,
                                  const GpuMat& fu, const GpuMat& fv,
                                  const GpuMat& bu, const GpuMat& bv,
                                  float pos, GpuMat& newFrame, GpuMat& buf,
                                  Stream& stream = Stream::Null());

CV_EXPORTS void createOpticalFlowNeedleMap(const GpuMat& u, const GpuMat& v, GpuMat& vertex, GpuMat& colors);



// Foreground Object Detection from Videos Containing Complex Background.
// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
// ACM MM2003 9p
class CV_EXPORTS FGDStatModel
{
public:
    struct CV_EXPORTS Params
    {
        int Lc;  // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
        int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
        int N2c; // Number of color vectors retained at given pixel.  Must be > N1c, typically ~ 5/3 of N1c.
        // Used to allow the first N1c vectors to adapt over time to changing background.

        int Lcc;  // Quantized levels per 'color co-occurrence' component.  Power of two, typically 16, 32 or 64.
        int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
        int N2cc; // Number of color co-occurrence vectors retained at given pixel.  Must be > N1cc, typically ~ 5/3 of N1cc.
        // Used to allow the first N1cc vectors to adapt over time to changing background.

        bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
        int perform_morphing;     // Number of erode-dilate-erode foreground-blob cleanup iterations.
        // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.

        float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
        float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
        float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.

        float delta;   // Affects color and color co-occurrence quantization, typically set to 2.
        float T;       // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
        float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.

        // default Params
        Params();
    };

    // out_cn - channels count in output result (can be 3 or 4)
    // 4-channels require more memory, but a bit faster
    explicit FGDStatModel(int out_cn = 3);
    explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);

    ~FGDStatModel();

    void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
    void release();

    int update(const cv::gpu::GpuMat& curFrame);

    //8UC3 or 8UC4 reference background image
    cv::gpu::GpuMat background;

    //8UC1 foreground image
    cv::gpu::GpuMat foreground;

    std::vector< std::vector<cv::Point> > foreground_regions;

private:
    FGDStatModel(const FGDStatModel&);
    FGDStatModel& operator=(const FGDStatModel&);

    class Impl;
    std::auto_ptr<Impl> impl_;
};

class CV_EXPORTS MOG_GPU
{
public:
    MOG_GPU(int nmixtures = -1);

    void initialize(Size frameSize, int frameType);

    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());

    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;

    void release();

    int history;
    float varThreshold;
    float backgroundRatio;
    float noiseSigma;

private:
    int nmixtures_;

    Size frameSize_;
    int frameType_;
    int nframes_;

    GpuMat weight_;
    GpuMat sortKey_;
    GpuMat mean_;
    GpuMat var_;
};

class CV_EXPORTS MOG2_GPU
{
public:
    MOG2_GPU(int nmixtures = -1);

    void initialize(Size frameSize, int frameType);

    void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());

    void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;

    void release();

    // parameters
    // you should call initialize after parameters changes

    int history;

    float varThreshold;
    // threshold on the squared Mahalanobis distance to decide if it is well described
    // by the background model or not. Related to Cthr from the paper.
    // This does not influence the update of the background. A typical value could be 4 sigma
    // and that is varThreshold=4*4=16; Corresponds to Tb in the paper.

    // less important parameters - things you might change but be carefull

    float backgroundRatio;
    // corresponds to fTB=1-cf from the paper
    // TB - threshold when the component becomes significant enough to be included into
    // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
    // For alpha=0.001 it means that the mode should exist for approximately 105 frames before
    // it is considered foreground
    // float noiseSigma;
    float varThresholdGen;

    //correspondts to Tg - threshold on the squared Mahalan. dist. to decide
    //when a sample is close to the existing components. If it is not close
    //to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
    //Smaller Tg leads to more generated components and higher Tg might make
    //lead to small number of components but they can grow too large
    float fVarInit;
    float fVarMin;
    float fVarMax;

    //initial variance  for the newly generated components.
    //It will will influence the speed of adaptation. A good guess should be made.
    //A simple way is to estimate the typical standard deviation from the images.
    //I used here 10 as a reasonable value
    // min and max can be used to further control the variance
    float fCT; //CT - complexity reduction prior
    //this is related to the number of samples needed to accept that a component
    //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
    //the standard Stauffer&Grimson algorithm (maybe not exact but very similar)

    //shadow detection parameters
    bool bShadowDetection; //default 1 - do shadow detection
    unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
    float fTau;
    // Tau - shadow threshold. The shadow is detected if the pixel is darker
    //version of the background. Tau is a threshold on how much darker the shadow can be.
    //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
    //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.

private:
    int nmixtures_;

    Size frameSize_;
    int frameType_;
    int nframes_;

    GpuMat weight_;
    GpuMat variance_;
    GpuMat mean_;

    GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
};

class CV_EXPORTS VIBE_GPU
{
public:
    explicit VIBE_GPU(unsigned long rngSeed = 1234567);

    void initialize(const GpuMat& firstFrame, Stream& stream = Stream::Null());

    void operator()(const GpuMat& frame, GpuMat& fgmask, Stream& stream = Stream::Null());

    void release();

    int nbSamples;         // number of samples per pixel
    int reqMatches;        // #_min
    int radius;            // R
    int subsamplingFactor; // amount of random subsampling

private:
    Size frameSize_;

    unsigned long rngSeed_;
    GpuMat randStates_;

    GpuMat samples_;
};

class CV_EXPORTS GMG_GPU
{
public:
    GMG_GPU();

    void initialize(Size frameSize, float min = 0.0f, float max = 255.0f);

    void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());

    void release();

    int maxFeatures;

    float learningRate;

    int numInitializationFrames;

    int quantizationLevels;

    float backgroundPrior;

    float decisionThreshold;

    int smoothingRadius;

    bool updateBackgroundModel;

private:
    float maxVal_, minVal_;

    Size frameSize_;

    int frameNum_;

    GpuMat nfeatures_;
    GpuMat colors_;
    GpuMat weights_;

    Ptr<FilterEngine_GPU> boxFilter_;
    GpuMat buf_;
};


// Works only under Windows
// Supports olny H264 video codec and AVI files
class CV_EXPORTS VideoWriter_GPU
{
public:
    struct EncoderParams;

    // Callbacks for video encoder, use it if you want to work with raw video stream
    class EncoderCallBack;

    enum SurfaceFormat
    {
        SF_UYVY = 0,
        SF_YUY2,
        SF_YV12,
        SF_NV12,
        SF_IYUV,
        SF_BGR,
        SF_GRAY = SF_BGR
    };

    VideoWriter_GPU();
    VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
    VideoWriter_GPU(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
    VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
    VideoWriter_GPU(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
    ~VideoWriter_GPU();

    // all methods throws cv::Exception if error occurs
    void open(const std::string& fileName, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
    void open(const std::string& fileName, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
    void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, SurfaceFormat format = SF_BGR);
    void open(const cv::Ptr<EncoderCallBack>& encoderCallback, cv::Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);

    bool isOpened() const;
    void close();

    void write(const cv::gpu::GpuMat& image, bool lastFrame = false);

    struct CV_EXPORTS EncoderParams
    {
        int       P_Interval;      //    NVVE_P_INTERVAL,
        int       IDR_Period;      //    NVVE_IDR_PERIOD,
        int       DynamicGOP;      //    NVVE_DYNAMIC_GOP,
        int       RCType;          //    NVVE_RC_TYPE,
        int       AvgBitrate;      //    NVVE_AVG_BITRATE,
        int       PeakBitrate;     //    NVVE_PEAK_BITRATE,
        int       QP_Level_Intra;  //    NVVE_QP_LEVEL_INTRA,
        int       QP_Level_InterP; //    NVVE_QP_LEVEL_INTER_P,
        int       QP_Level_InterB; //    NVVE_QP_LEVEL_INTER_B,
        int       DeblockMode;     //    NVVE_DEBLOCK_MODE,
        int       ProfileLevel;    //    NVVE_PROFILE_LEVEL,
        int       ForceIntra;      //    NVVE_FORCE_INTRA,
        int       ForceIDR;        //    NVVE_FORCE_IDR,
        int       ClearStat;       //    NVVE_CLEAR_STAT,
        int       DIMode;          //    NVVE_SET_DEINTERLACE,
        int       Presets;         //    NVVE_PRESETS,
        int       DisableCabac;    //    NVVE_DISABLE_CABAC,
        int       NaluFramingType; //    NVVE_CONFIGURE_NALU_FRAMING_TYPE
        int       DisableSPSPPS;   //    NVVE_DISABLE_SPS_PPS

        EncoderParams();
        explicit EncoderParams(const std::string& configFile);

        void load(const std::string& configFile);
        void save(const std::string& configFile) const;
    };

    EncoderParams getParams() const;

    class CV_EXPORTS EncoderCallBack
    {
    public:
        enum PicType
        {
            IFRAME = 1,
            PFRAME = 2,
            BFRAME = 3
        };

        virtual ~EncoderCallBack() {}

        // callback function to signal the start of bitstream that is to be encoded
        // must return pointer to buffer
        virtual uchar* acquireBitStream(int* bufferSize) = 0;

        // callback function to signal that the encoded bitstream is ready to be written to file
        virtual void releaseBitStream(unsigned char* data, int size) = 0;

        // callback function to signal that the encoding operation on the frame has started
        virtual void onBeginFrame(int frameNumber, PicType picType) = 0;

        // callback function signals that the encoding operation on the frame has finished
        virtual void onEndFrame(int frameNumber, PicType picType) = 0;
    };

private:
    VideoWriter_GPU(const VideoWriter_GPU&);
    VideoWriter_GPU& operator=(const VideoWriter_GPU&);

    class Impl;
    std::auto_ptr<Impl> impl_;
};



namespace detail
{
    class FrameQueue;
    class VideoParser;
}

class CV_EXPORTS VideoReader_GPU
{
public:
    enum Codec
    {
        MPEG1 = 0,
        MPEG2,
        MPEG4,
        VC1,
        H264,
        JPEG,
        H264_SVC,
        H264_MVC,

        Uncompressed_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')),   // Y,U,V (4:2:0)
        Uncompressed_YV12   = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,V,U (4:2:0)
        Uncompressed_NV12   = (('N'<<24)|('V'<<16)|('1'<<8)|('2')),   // Y,UV  (4:2:0)
        Uncompressed_YUYV   = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')),   // YUYV/YUY2 (4:2:2)
        Uncompressed_UYVY   = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')),   // UYVY (4:2:2)
    };

    enum ChromaFormat
    {
        Monochrome=0,
        YUV420,
        YUV422,
        YUV444,
    };

    struct FormatInfo
    {
        Codec codec;
        ChromaFormat chromaFormat;
        int width;
        int height;
    };

    class VideoSource;

    VideoReader_GPU();
    explicit VideoReader_GPU(const std::string& filename);
    explicit VideoReader_GPU(const cv::Ptr<VideoSource>& source);

    ~VideoReader_GPU();

    void open(const std::string& filename);
    void open(const cv::Ptr<VideoSource>& source);
    bool isOpened() const;

    void close();

    bool read(GpuMat& image);

    FormatInfo format() const;
    void dumpFormat(std::ostream& st);

    class CV_EXPORTS VideoSource
    {
    public:
        VideoSource() : frameQueue_(0), videoParser_(0) {}
        virtual ~VideoSource() {}

        virtual FormatInfo format() const = 0;
        virtual void start() = 0;
        virtual void stop() = 0;
        virtual bool isStarted() const = 0;
        virtual bool hasError() const = 0;

        void setFrameQueue(detail::FrameQueue* frameQueue) { frameQueue_ = frameQueue; }
        void setVideoParser(detail::VideoParser* videoParser) { videoParser_ = videoParser; }

    protected:
        bool parseVideoData(const uchar* data, size_t size, bool endOfStream = false);

    private:
        VideoSource(const VideoSource&);
        VideoSource& operator =(const VideoSource&);

        detail::FrameQueue* frameQueue_;
        detail::VideoParser* videoParser_;
    };

private:
    VideoReader_GPU(const VideoReader_GPU&);
    VideoReader_GPU& operator =(const VideoReader_GPU&);

    class Impl;
    std::auto_ptr<Impl> impl_;
};

CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);

CV_EXPORTS void calcWobbleSuppressionMaps(
        int left, int idx, int right, Size size, const Mat &ml, const Mat &mr,
        GpuMat &mapx, GpuMat &mapy);

} // namespace gpu

} // namespace cv

#endif /* __OPENCV_GPU_HPP__ */