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#ifndef __OPENCV_OCL_HPP__
#define __OPENCV_OCL_HPP__

#include <memory>
#include <vector>

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

namespace cv
{
    namespace ocl
    {
        using std::auto_ptr;
        enum
        {
            CVCL_DEVICE_TYPE_DEFAULT     = (1 << 0),
            CVCL_DEVICE_TYPE_CPU         = (1 << 1),
            CVCL_DEVICE_TYPE_GPU         = (1 << 2),
            CVCL_DEVICE_TYPE_ACCELERATOR = (1 << 3),
            //CVCL_DEVICE_TYPE_CUSTOM      = (1 << 4)
            CVCL_DEVICE_TYPE_ALL         = 0xFFFFFFFF
        };

        enum DevMemRW
        {
            DEVICE_MEM_R_W = 0,
            DEVICE_MEM_R_ONLY,
            DEVICE_MEM_W_ONLY
        };

        enum DevMemType
        {
            DEVICE_MEM_DEFAULT = 0,
            DEVICE_MEM_AHP,         //alloc host pointer
            DEVICE_MEM_UHP,         //use host pointer
            DEVICE_MEM_CHP,         //copy host pointer
            DEVICE_MEM_PM           //persistent memory
        };

        //Get the global device memory and read/write type
        //return 1 if unified memory system supported, otherwise return 0
        CV_EXPORTS int getDevMemType(DevMemRW& rw_type, DevMemType& mem_type);

        //Set the global device memory and read/write type,
        //the newly generated oclMat will all use this type
        //return -1 if the target type is unsupported, otherwise return 0
        CV_EXPORTS int setDevMemType(DevMemRW rw_type = DEVICE_MEM_R_W, DevMemType mem_type = DEVICE_MEM_DEFAULT);

        //this class contains ocl runtime information
        class CV_EXPORTS Info
        {
        public:
            struct Impl;
            Impl *impl;

            Info();
            Info(const Info &m);
            ~Info();
            void release();
            Info &operator = (const Info &m);
            std::vector<string> DeviceName;
        };
        //////////////////////////////// Initialization & Info ////////////////////////
        //this function may be obsoleted
        //CV_EXPORTS cl_device_id getDevice();
        //the function must be called before any other cv::ocl::functions, it initialize ocl runtime
        //each Info relates to an OpenCL platform
        //there is one or more devices in each platform, each one has a separate name
        CV_EXPORTS int getDevice(std::vector<Info> &oclinfo, int devicetype = CVCL_DEVICE_TYPE_GPU);

        //set device you want to use, optional function after getDevice be called
        //the devnum is the index of the selected device in DeviceName vector of INfo
        CV_EXPORTS void setDevice(Info &oclinfo, int devnum = 0);

        //The two functions below enable other opencl program to use ocl module's cl_context and cl_command_queue
        //returns cl_context * 
        CV_EXPORTS void* getoclContext();
        //returns cl_command_queue *
        CV_EXPORTS void* getoclCommandQueue();

        //explicit call clFinish. The global command queue will be used.
        CV_EXPORTS void finish();

        //this function enable ocl module to use customized cl_context and cl_command_queue
        //getDevice also need to be called before this function
        CV_EXPORTS void setDeviceEx(Info &oclinfo, void *ctx, void *qu, int devnum = 0);

        //returns true when global OpenCL context is initialized
        CV_EXPORTS bool initialized();

        //////////////////////////////// Error handling ////////////////////////
        CV_EXPORTS void error(const char *error_string, const char *file, const int line, const char *func);

        //////////////////////////////// OpenCL context ////////////////////////
        //This is a global singleton class used to represent a OpenCL context.
        class CV_EXPORTS Context
        {
        protected:
            Context();
            friend class auto_ptr<Context>;
            friend bool initialized();
        private:
            static auto_ptr<Context> clCxt;
            static int val;
        public:
            ~Context();
            void release();
            Info::Impl* impl;

            static Context* getContext();
            static void setContext(Info &oclinfo);

            enum {CL_DOUBLE, CL_UNIFIED_MEM, CL_VER_1_2};
            bool supportsFeature(int ftype);
            size_t computeUnits();
            void* oclContext();
            void* oclCommandQueue();
        };

        //! Calls a kernel, by string. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
        CV_EXPORTS double openCLExecuteKernelInterop(Context *clCxt ,
                                                        const char **source, string kernelName,
                                                        size_t globalThreads[3], size_t localThreads[3],
                                                        std::vector< std::pair<size_t, const void *> > &args,
                                                        int channels, int depth, const char *build_options,
                                                        bool finish = true, bool measureKernelTime = false,
                                                        bool cleanUp = true);

        //! Calls a kernel, by file. Pass globalThreads = NULL, and cleanUp = true, to finally clean-up without executing.
        CV_EXPORTS double openCLExecuteKernelInterop(Context *clCxt ,
                                                        const char **fileName, const int numFiles, string kernelName,
                                                        size_t globalThreads[3], size_t localThreads[3],
                                                        std::vector< std::pair<size_t, const void *> > &args,
                                                        int channels, int depth, const char *build_options,
                                                        bool finish = true, bool measureKernelTime = false,
                                                        bool cleanUp = true);

        //! Enable or disable OpenCL program binary caching onto local disk
        // After a program (*.cl files in opencl/ folder) is built at runtime, we allow the
        // compiled OpenCL program to be cached to the path automatically as "path/*.clb" 
        // binary file, which will be reused when the OpenCV executable is started again. 
        //
        // Caching mode is controlled by the following enums
        // Notes
        //   1. the feature is by default enabled when OpenCV is built in release mode.
        //   2. the CACHE_DEBUG / CACHE_RELEASE flags only effectively work with MSVC compiler;
        //      for GNU compilers, the function always treats the build as release mode (enabled by default).
        enum
        {
            CACHE_NONE    = 0,        // do not cache OpenCL binary
            CACHE_DEBUG   = 0x1 << 0, // cache OpenCL binary when built in debug mode (only work with MSVC)
            CACHE_RELEASE = 0x1 << 1, // default behavior, only cache when built in release mode (only work with MSVC)
            CACHE_ALL     = CACHE_DEBUG | CACHE_RELEASE, // always cache opencl binary
            CACHE_UPDATE  = 0x1 << 2  // if the binary cache file with the same name is already on the disk, it will be updated.
        };
        CV_EXPORTS void setBinaryDiskCache(int mode = CACHE_RELEASE, cv::String path = "./");

        //! set where binary cache to be saved to 
        CV_EXPORTS void setBinpath(const char *path);

        class CV_EXPORTS oclMatExpr;
        //////////////////////////////// oclMat ////////////////////////////////
        class CV_EXPORTS oclMat
        {
        public:
            //! default constructor
            oclMat();
            //! constructs oclMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
            oclMat(int rows, int cols, int type);
            oclMat(Size size, int type);
            //! constucts oclMatrix and fills it with the specified value _s.
            oclMat(int rows, int cols, int type, const Scalar &s);
            oclMat(Size size, int type, const Scalar &s);
            //! copy constructor
            oclMat(const oclMat &m);

            //! constructor for oclMatrix headers pointing to user-allocated data
            oclMat(int rows, int cols, int type, void *data, size_t step = Mat::AUTO_STEP);
            oclMat(Size size, int type, void *data, size_t step = Mat::AUTO_STEP);

            //! creates a matrix header for a part of the bigger matrix
            oclMat(const oclMat &m, const Range &rowRange, const Range &colRange);
            oclMat(const oclMat &m, const Rect &roi);

            //! builds oclMat from Mat. Perfom blocking upload to device.
            explicit oclMat (const Mat &m);

            //! destructor - calls release()
            ~oclMat();

            //! assignment operators
            oclMat &operator = (const oclMat &m);
            //! assignment operator. Perfom blocking upload to device.
            oclMat &operator = (const Mat &m);
            oclMat &operator = (const oclMatExpr& expr);

            //! pefroms blocking upload data to oclMat.
            void upload(const cv::Mat &m);


            //! downloads data from device to host memory. Blocking calls.
            operator Mat() const;
            void download(cv::Mat &m) const;

            //! convert to _InputArray
            operator _InputArray();

            //! convert to _OutputArray
            operator _OutputArray();

            //! returns a new oclMatrix header for the specified row
            oclMat row(int y) const;
            //! returns a new oclMatrix header for the specified column
            oclMat col(int x) const;
            //! ... for the specified row span
            oclMat rowRange(int startrow, int endrow) const;
            oclMat rowRange(const Range &r) const;
            //! ... for the specified column span
            oclMat colRange(int startcol, int endcol) const;
            oclMat colRange(const Range &r) const;

            //! returns deep copy of the oclMatrix, i.e. the data is copied
            oclMat clone() const;
            //! copies the oclMatrix content to "m".
            // It calls m.create(this->size(), this->type()).
            // It supports any data type
            void copyTo( oclMat &m ) const;
            //! copies those oclMatrix elements to "m" that are marked with non-zero mask elements.
            //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
            void copyTo( oclMat &m, const oclMat &mask ) const;
            //! converts oclMatrix to another datatype with optional scalng. See cvConvertScale.
            //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
            void convertTo( oclMat &m, int rtype, double alpha = 1, double beta = 0 ) const;

            void assignTo( oclMat &m, int type = -1 ) const;

            //! sets every oclMatrix element to s
            //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
            oclMat& operator = (const Scalar &s);
            //! sets some of the oclMatrix elements to s, according to the mask
            //It supports 8UC1 8UC4 32SC1 32SC4 32FC1 32FC4
            oclMat& setTo(const Scalar &s, const oclMat &mask = oclMat());
            //! creates alternative oclMatrix header for the same data, with different
            // number of channels and/or different number of rows. see cvReshape.
            oclMat reshape(int cn, int rows = 0) const;

            //! allocates new oclMatrix data unless the oclMatrix already has specified size and type.
            // previous data is unreferenced if needed.
            void create(int rows, int cols, int type);
            void create(Size size, int type);

            //! allocates new oclMatrix with specified device memory type.
            void createEx(int rows, int cols, int type, DevMemRW rw_type, DevMemType mem_type);
            void createEx(Size size, int type, DevMemRW rw_type, DevMemType mem_type);

            //! decreases reference counter;
            // deallocate the data when reference counter reaches 0.
            void release();

            //! swaps with other smart pointer
            void swap(oclMat &mat);

            //! locates oclMatrix header within a parent oclMatrix. See below
            void locateROI( Size &wholeSize, Point &ofs ) const;
            //! moves/resizes the current oclMatrix ROI inside the parent oclMatrix.
            oclMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
            //! extracts a rectangular sub-oclMatrix
            // (this is a generalized form of row, rowRange etc.)
            oclMat operator()( Range rowRange, Range colRange ) const;
            oclMat operator()( const Rect &roi ) const;

            oclMat& operator+=( const oclMat& m );
            oclMat& operator-=( const oclMat& m );
            oclMat& operator*=( const oclMat& m );
            oclMat& operator/=( const oclMat& m );

            //! returns true if the oclMatrix data is continuous
            // (i.e. when there are no gaps between successive rows).
            // similar to CV_IS_oclMat_CONT(cvoclMat->type)
            bool isContinuous() const;
            //! returns element size in bytes,
            // similar to CV_ELEM_SIZE(cvMat->type)
            size_t elemSize() const;
            //! returns the size of element channel in bytes.
            size_t elemSize1() const;
            //! returns element type, similar to CV_MAT_TYPE(cvMat->type)
            int type() const;
            //! returns element type, i.e. 8UC3 returns 8UC4 because in ocl
            //! 3 channels element actually use 4 channel space
            int ocltype() const;
            //! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
            int depth() const;
            //! returns element type, similar to CV_MAT_CN(cvMat->type)
            int channels() const;
            //! returns element type, return 4 for 3 channels element,
            //!becuase 3 channels element actually use 4 channel space
            int oclchannels() const;
            //! returns step/elemSize1()
            size_t step1() const;
            //! returns oclMatrix size:
            // width == number of columns, height == number of rows
            Size size() const;
            //! returns true if oclMatrix data is NULL
            bool empty() const;

            //! returns pointer to y-th row
            uchar* ptr(int y = 0);
            const uchar *ptr(int y = 0) const;

            //! template version of the above method
            template<typename _Tp> _Tp *ptr(int y = 0);
            template<typename _Tp> const _Tp *ptr(int y = 0) const;

            //! matrix transposition
            oclMat t() const;

            /*! includes several bit-fields:
              - the magic signature
              - continuity flag
              - depth
              - number of channels
              */
            int flags;
            //! the number of rows and columns
            int rows, cols;
            //! a distance between successive rows in bytes; includes the gap if any
            size_t step;
            //! pointer to the data(OCL memory object)
            uchar *data;

            //! pointer to the reference counter;
            // when oclMatrix points to user-allocated data, the pointer is NULL
            int *refcount;

            //! helper fields used in locateROI and adjustROI
            //datastart and dataend are not used in current version
            uchar *datastart;
            uchar *dataend;

            //! OpenCL context associated with the oclMat object.
            Context *clCxt;
            //add offset for handle ROI, calculated in byte
            int offset;
            //add wholerows and wholecols for the whole matrix, datastart and dataend are no longer used
            int wholerows;
            int wholecols;
        };

        // convert InputArray/OutputArray to oclMat references
        CV_EXPORTS oclMat& getOclMatRef(InputArray src);
        CV_EXPORTS oclMat& getOclMatRef(OutputArray src);

        ///////////////////// mat split and merge /////////////////////////////////
        //! Compose a multi-channel array from several single-channel arrays
        // Support all types
        CV_EXPORTS void merge(const oclMat *src, size_t n, oclMat &dst);
        CV_EXPORTS void merge(const vector<oclMat> &src, oclMat &dst);

        //! Divides multi-channel array into several single-channel arrays
        // Support all types
        CV_EXPORTS void split(const oclMat &src, oclMat *dst);
        CV_EXPORTS void split(const oclMat &src, vector<oclMat> &dst);

        ////////////////////////////// Arithmetics ///////////////////////////////////
        //#if defined DOUBLE_SUPPORT
        //typedef double F;
        //#else
        //typedef float F;
        //#endif
        //	CV_EXPORTS void addWeighted(const oclMat& a,F  alpha, const oclMat& b,F beta,F gama, oclMat& c);
        CV_EXPORTS void addWeighted(const oclMat &a, double  alpha, const oclMat &b, double beta, double gama, oclMat &c);
        //! adds one matrix to another (c = a + b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void add(const oclMat &a, const oclMat &b, oclMat &c);
        //! adds one matrix to another (c = a + b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void add(const oclMat &a, const oclMat &b, oclMat &c, const oclMat &mask);
        //! adds scalar to a matrix (c = a + s)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void add(const oclMat &a, const Scalar &sc, oclMat &c, const oclMat &mask = oclMat());
        //! subtracts one matrix from another (c = a - b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void subtract(const oclMat &a, const oclMat &b, oclMat &c);
        //! subtracts one matrix from another (c = a - b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void subtract(const oclMat &a, const oclMat &b, oclMat &c, const oclMat &mask);
        //! subtracts scalar from a matrix (c = a - s)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void subtract(const oclMat &a, const Scalar &sc, oclMat &c, const oclMat &mask = oclMat());
        //! subtracts scalar from a matrix (c = a - s)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void subtract(const Scalar &sc, const oclMat &a, oclMat &c, const oclMat &mask = oclMat());
        //! computes element-wise product of the two arrays (c = a * b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void multiply(const oclMat &a, const oclMat &b, oclMat &c, double scale = 1);
        //! multiplies matrix to a number (dst = scalar * src)
        // supports CV_32FC1 only
        CV_EXPORTS void multiply(double scalar, const oclMat &src, oclMat &dst);
        //! computes element-wise quotient of the two arrays (c = a / b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void divide(const oclMat &a, const oclMat &b, oclMat &c, double scale = 1);
        //! computes element-wise quotient of the two arrays (c = a / b)
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void divide(double scale, const oclMat &b, oclMat &c);

        //! compares elements of two arrays (c = a <cmpop> b)
        // supports except CV_8SC1,CV_8SC2,CV8SC3,CV_8SC4 types
        CV_EXPORTS void compare(const oclMat &a, const oclMat &b, oclMat &c, int cmpop);

        //! transposes the matrix
        // supports  CV_8UC1, 8UC4, 8SC4, 16UC2, 16SC2, 32SC1 and 32FC1.(the same as cuda)
        CV_EXPORTS void transpose(const oclMat &src, oclMat &dst);

        //! computes element-wise absolute difference of two arrays (c = abs(a - b))
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void absdiff(const oclMat &a, const oclMat &b, oclMat &c);
        //! computes element-wise absolute difference of array and scalar (c = abs(a - s))
        // supports all types except CV_8SC1,CV_8SC2,CV8SC3 and CV_8SC4
        CV_EXPORTS void absdiff(const oclMat &a, const Scalar &s, oclMat &c);

        //! computes mean value and standard deviation of all or selected array elements
        // supports except CV_32F,CV_64F
        CV_EXPORTS void meanStdDev(const oclMat &mtx, Scalar &mean, Scalar &stddev);

        //! computes norm of array
        // supports NORM_INF, NORM_L1, NORM_L2
        // supports only CV_8UC1 type
        CV_EXPORTS double norm(const oclMat &src1, int normType = NORM_L2);

        //! computes norm of the difference between two arrays
        // supports NORM_INF, NORM_L1, NORM_L2
        // supports only CV_8UC1 type
        CV_EXPORTS double norm(const oclMat &src1, const oclMat &src2, int normType = NORM_L2);

        //! reverses the order of the rows, columns or both in a matrix
        // supports all types
        CV_EXPORTS void flip(const oclMat &a, oclMat &b, int flipCode);

        //! computes sum of array elements
        // disabled until fix crash
        // support all types
        CV_EXPORTS Scalar sum(const oclMat &m);
        CV_EXPORTS Scalar absSum(const oclMat &m);
        CV_EXPORTS Scalar sqrSum(const oclMat &m);

        //! finds global minimum and maximum array elements and returns their values
        // support all C1 types

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

        //! finds global minimum and maximum array elements and returns their values with locations
        // support all C1 types

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

        //! counts non-zero array elements
        // support all types
        CV_EXPORTS int countNonZero(const oclMat &src);

        //! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
        // destination array will have the depth type as lut and the same channels number as source
        //It supports 8UC1 8UC4 only
        CV_EXPORTS void LUT(const oclMat &src, const oclMat &lut, oclMat &dst);

        //! only 8UC1 and 256 bins is supported now
        CV_EXPORTS void calcHist(const oclMat &mat_src, oclMat &mat_hist);
        //! only 8UC1 and 256 bins is supported now
        CV_EXPORTS void equalizeHist(const oclMat &mat_src, oclMat &mat_dst);
        
        //! only 8UC1 is supported now
        CV_EXPORTS Ptr<cv::CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
        
        //! bilateralFilter
        // supports 8UC1 8UC4
        CV_EXPORTS void bilateralFilter(const oclMat& src, oclMat& dst, int d, double sigmaColor, double sigmaSpave, int borderType=BORDER_DEFAULT);
        //! computes exponent of each matrix element (b = e**a)
        // supports only CV_32FC1 type
        CV_EXPORTS void exp(const oclMat &a, oclMat &b);

        //! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
        // supports only CV_32FC1 type
        CV_EXPORTS void log(const oclMat &a, oclMat &b);

        //! computes magnitude of each (x(i), y(i)) vector
        // supports only CV_32F CV_64F type
        CV_EXPORTS void magnitude(const oclMat &x, const oclMat &y, oclMat &magnitude);
        CV_EXPORTS void magnitudeSqr(const oclMat &x, const oclMat &y, oclMat &magnitude);

        CV_EXPORTS void magnitudeSqr(const oclMat &x, oclMat &magnitude);

        //! computes angle (angle(i)) of each (x(i), y(i)) vector
        // supports only CV_32F CV_64F type
        CV_EXPORTS void phase(const oclMat &x, const oclMat &y, oclMat &angle, bool angleInDegrees = false);

        //! the function raises every element of tne input array to p
        //! support only CV_32F CV_64F type
        CV_EXPORTS void pow(const oclMat &x, double p, oclMat &y);

        //! converts Cartesian coordinates to polar
        // supports only CV_32F CV_64F type
        CV_EXPORTS void cartToPolar(const oclMat &x, const oclMat &y, oclMat &magnitude, oclMat &angle, bool angleInDegrees = false);

        //! converts polar coordinates to Cartesian
        // supports only CV_32F CV_64F type
        CV_EXPORTS void polarToCart(const oclMat &magnitude, const oclMat &angle, oclMat &x, oclMat &y, bool angleInDegrees = false);

        //! perfroms per-elements bit-wise inversion
        // supports all types
        CV_EXPORTS void bitwise_not(const oclMat &src, oclMat &dst);
        //! calculates per-element bit-wise disjunction of two arrays
        // supports all types
        CV_EXPORTS void bitwise_or(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
        CV_EXPORTS void bitwise_or(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
        //! calculates per-element bit-wise conjunction of two arrays
        // supports all types
        CV_EXPORTS void bitwise_and(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
        CV_EXPORTS void bitwise_and(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());
        //! calculates per-element bit-wise "exclusive or" operation
        // supports all types
        CV_EXPORTS void bitwise_xor(const oclMat &src1, const oclMat &src2, oclMat &dst, const oclMat &mask = oclMat());
        CV_EXPORTS void bitwise_xor(const oclMat &src1, const Scalar &s, oclMat &dst, const oclMat &mask = oclMat());

        //! Logical operators
        CV_EXPORTS oclMat operator ~ (const oclMat &);
        CV_EXPORTS oclMat operator | (const oclMat &, const oclMat &);
        CV_EXPORTS oclMat operator & (const oclMat &, const oclMat &);
        CV_EXPORTS oclMat operator ^ (const oclMat &, const oclMat &);


        //! Mathematics operators
        CV_EXPORTS oclMatExpr operator + (const oclMat &src1, const oclMat &src2);
        CV_EXPORTS oclMatExpr operator - (const oclMat &src1, const oclMat &src2);
        CV_EXPORTS oclMatExpr operator * (const oclMat &src1, const oclMat &src2);
        CV_EXPORTS oclMatExpr operator / (const oclMat &src1, const oclMat &src2);

        //! computes convolution of two images
        //! support only CV_32FC1 type
        CV_EXPORTS void convolve(const oclMat &image, const oclMat &temp1, oclMat &result);

        CV_EXPORTS void cvtColor(const oclMat &src, oclMat &dst, int code , int dcn = 0);

        //////////////////////////////// Filter Engine ////////////////////////////////

        /*!
          The Base Class for 1D or Row-wise Filters

          This is the base class for linear or non-linear filters that process 1D data.
          In particular, such filters are used for the "horizontal" filtering parts in separable filters.
          */
        class CV_EXPORTS BaseRowFilter_GPU
        {
        public:
            BaseRowFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
            virtual ~BaseRowFilter_GPU() {}
            virtual void operator()(const oclMat &src, oclMat &dst) = 0;
            int ksize, anchor, bordertype;
        };

        /*!
          The Base Class for Column-wise Filters

          This is the base class for linear or non-linear filters that process columns of 2D arrays.
          Such filters are used for the "vertical" filtering parts in separable filters.
          */
        class CV_EXPORTS BaseColumnFilter_GPU
        {
        public:
            BaseColumnFilter_GPU(int ksize_, int anchor_, int bordertype_) : ksize(ksize_), anchor(anchor_), bordertype(bordertype_) {}
            virtual ~BaseColumnFilter_GPU() {}
            virtual void operator()(const oclMat &src, oclMat &dst) = 0;
            int ksize, anchor, bordertype;
        };

        /*!
          The Base Class for Non-Separable 2D Filters.

          This is the base class for linear or non-linear 2D filters.
          */
        class CV_EXPORTS BaseFilter_GPU
        {
        public:
            BaseFilter_GPU(const Size &ksize_, const Point &anchor_, const int &borderType_)
                : ksize(ksize_), anchor(anchor_), borderType(borderType_) {}
            virtual ~BaseFilter_GPU() {}
            virtual void operator()(const oclMat &src, oclMat &dst) = 0;
            Size ksize;
            Point anchor;
            int borderType;
        };

        /*!
          The Base Class for Filter Engine.

          The class can be used to apply an arbitrary filtering operation to an image.
          It contains all the necessary intermediate buffers.
          */
        class CV_EXPORTS FilterEngine_GPU
        {
        public:
            virtual ~FilterEngine_GPU() {}

            virtual void apply(const oclMat &src, oclMat &dst, Rect roi = Rect(0, 0, -1, -1)) = 0;
        };

        //! returns the non-separable filter engine with the specified filter
        CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU> filter2D);

        //! returns the primitive row filter with the specified kernel
        CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat &rowKernel,
                int anchor = -1, int bordertype = BORDER_DEFAULT);

        //! returns the primitive column filter with the specified kernel
        CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat &columnKernel,
                int anchor = -1, int bordertype = BORDER_DEFAULT, double delta = 0.0);

        //! returns the separable linear filter engine
        CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat &rowKernel,
                const Mat &columnKernel, const Point &anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);

        //! returns the separable filter engine with the specified filters
        CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU> &rowFilter,
                const Ptr<BaseColumnFilter_GPU> &columnFilter);

        //! returns the Gaussian filter engine
        CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);

        //! returns filter engine for the generalized Sobel operator
        CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU( int srcType, int dstType, int dx, int dy, int ksize, int borderType = BORDER_DEFAULT );

        //! applies Laplacian operator to the image
        // supports only ksize = 1 and ksize = 3 8UC1 8UC4 32FC1 32FC4 data type
        CV_EXPORTS void Laplacian(const oclMat &src, oclMat &dst, int ddepth, int ksize = 1, double scale = 1);

        //! returns 2D box filter
        // supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
        CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType,
                const Size &ksize, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

        //! returns box filter engine
        CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size &ksize,
                const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

        //! returns 2D filter with the specified kernel
        // supports CV_8UC1 and CV_8UC4 types
        CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat &kernel, const Size &ksize,
                Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

        //! returns the non-separable linear filter engine
        CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat &kernel,
                const Point &anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

        //! smooths the image using the normalized box filter
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101,BORDER_WRAP
        CV_EXPORTS void boxFilter(const oclMat &src, oclMat &dst, int ddepth, Size ksize,
                                  Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

        //! returns 2D morphological filter
        //! only MORPH_ERODE and MORPH_DILATE are supported
        // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
        // kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
        CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat &kernel, const Size &ksize,
                Point anchor = Point(-1, -1));

        //! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
        CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat &kernel,
                const Point &anchor = Point(-1, -1), int iterations = 1);

        //! a synonym for normalized box filter
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
        static inline void blur(const oclMat &src, oclMat &dst, Size ksize, Point anchor = Point(-1, -1),
                                int borderType = BORDER_CONSTANT)
        {
            boxFilter(src, dst, -1, ksize, anchor, borderType);
        }

        //! applies non-separable 2D linear filter to the image
        //  Note, at the moment this function only works when anchor point is in the kernel center
        //  and kernel size supported is either 3x3 or 5x5; otherwise the function will fail to output valid result
        CV_EXPORTS void filter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernel,
                                 Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);

        //! applies separable 2D linear filter to the image
        CV_EXPORTS void sepFilter2D(const oclMat &src, oclMat &dst, int ddepth, const Mat &kernelX, const Mat &kernelY,
                                    Point anchor = Point(-1, -1), double delta = 0.0, int bordertype = BORDER_DEFAULT);

        //! applies generalized Sobel operator to the image
        // dst.type must equalize src.type
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
        CV_EXPORTS void Sobel(const oclMat &src, oclMat &dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0.0, int bordertype = BORDER_DEFAULT);

        //! applies the vertical or horizontal Scharr operator to the image
        // dst.type must equalize src.type
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
        CV_EXPORTS void Scharr(const oclMat &src, oclMat &dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0.0, int bordertype = BORDER_DEFAULT);

        //! smooths the image using Gaussian filter.
        // dst.type must equalize src.type
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        // supports border type: BORDER_CONSTANT, BORDER_REPLICATE, BORDER_REFLECT,BORDER_REFLECT_101
        CV_EXPORTS void GaussianBlur(const oclMat &src, oclMat &dst, Size ksize, double sigma1, double sigma2 = 0, int bordertype = BORDER_DEFAULT);

        //! erodes the image (applies the local minimum operator)
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        CV_EXPORTS void erode( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,

                               int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());


        //! dilates the image (applies the local maximum operator)
        // supports data type: CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4
        CV_EXPORTS void dilate( const oclMat &src, oclMat &dst, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,

                                int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());


        //! applies an advanced morphological operation to the image
        CV_EXPORTS void morphologyEx( const oclMat &src, oclMat &dst, int op, const Mat &kernel, Point anchor = Point(-1, -1), int iterations = 1,

                                      int borderType = BORDER_CONSTANT, const Scalar &borderValue = morphologyDefaultBorderValue());


        ////////////////////////////// Image processing //////////////////////////////
        //! Does mean shift filtering on GPU.
        CV_EXPORTS void meanShiftFiltering(const oclMat &src, oclMat &dst, int sp, int sr,
                                           TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));

        //! Does mean shift procedure on GPU.
        CV_EXPORTS void meanShiftProc(const oclMat &src, oclMat &dstr, oclMat &dstsp, int sp, int sr,
                                      TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));

        //! Does mean shift segmentation with elimiation of small regions.
        CV_EXPORTS void meanShiftSegmentation(const oclMat &src, Mat &dst, int sp, int sr, int minsize,
                                              TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));

        //! applies fixed threshold to the image.
        // supports CV_8UC1 and CV_32FC1 data type
        // supports threshold type: THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV
        CV_EXPORTS double threshold(const oclMat &src, oclMat &dst, double thresh, double maxVal, int type = THRESH_TRUNC);

        //! resizes the image
        // Supports INTER_NEAREST, INTER_LINEAR
        // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
        CV_EXPORTS void resize(const oclMat &src, oclMat &dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR);

        //! Applies a generic geometrical transformation to an image.

        // Supports INTER_NEAREST, INTER_LINEAR.

        // Map1 supports CV_16SC2, CV_32FC2  types.

        // Src supports CV_8UC1, CV_8UC2, CV_8UC4.

        CV_EXPORTS void remap(const oclMat &src, oclMat &dst, oclMat &map1, oclMat &map2, int interpolation, int bordertype, const Scalar &value = Scalar());

        //! copies 2D array to a larger destination array and pads borders with user-specifiable constant
        // supports CV_8UC1, CV_8UC4, CV_32SC1 types
        CV_EXPORTS void copyMakeBorder(const oclMat &src, oclMat &dst, int top, int bottom, int left, int right, int boardtype, const Scalar &value = Scalar());

        //! Smoothes image using median filter
        // The source 1- or 4-channel image. When m is 3 or 5, the image depth should be CV 8U or CV 32F.
        CV_EXPORTS void medianFilter(const oclMat &src, oclMat &dst, int m);

        //! warps the image using affine transformation
        // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
        // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
        CV_EXPORTS void warpAffine(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);

        //! warps the image using perspective transformation
        // Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
        // supports CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 types
        CV_EXPORTS void warpPerspective(const oclMat &src, oclMat &dst, const Mat &M, Size dsize, int flags = INTER_LINEAR);

        //! computes the integral image and integral for the squared image
        // sum will have CV_32S type, sqsum - CV32F type
        // supports only CV_8UC1 source type
        CV_EXPORTS void integral(const oclMat &src, oclMat &sum, oclMat &sqsum);
        CV_EXPORTS void integral(const oclMat &src, oclMat &sum);
        CV_EXPORTS void cornerHarris(const oclMat &src, oclMat &dst, int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
        CV_EXPORTS void cornerHarris_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
            int blockSize, int ksize, double k, int bordertype = cv::BORDER_DEFAULT);
        CV_EXPORTS void cornerMinEigenVal(const oclMat &src, oclMat &dst, int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);
        CV_EXPORTS void cornerMinEigenVal_dxdy(const oclMat &src, oclMat &dst, oclMat &Dx, oclMat &Dy,
            int blockSize, int ksize, int bordertype = cv::BORDER_DEFAULT);

        ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
        ///////////////////////////////////////////CascadeClassifier//////////////////////////////////////////////////////////////////
        ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

        class CV_EXPORTS_W OclCascadeClassifier : public  cv::CascadeClassifier
        {
        public:
            OclCascadeClassifier() {};
            ~OclCascadeClassifier() {};

            CvSeq* oclHaarDetectObjects(oclMat &gimg, CvMemStorage *storage, double scaleFactor,
                                        int minNeighbors, int flags, CvSize minSize = cvSize(0, 0), CvSize maxSize = cvSize(0, 0));
        };

        class CV_EXPORTS OclCascadeClassifierBuf : public  cv::CascadeClassifier
        {
        public:
            OclCascadeClassifierBuf() :
                m_flags(0), initialized(false), m_scaleFactor(0), buffers(NULL) {}

            ~OclCascadeClassifierBuf() { release(); }

            void detectMultiScale(oclMat &image, CV_OUT std::vector<cv::Rect>& faces,
                                  double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0,
                                  Size minSize = Size(), Size maxSize = Size());
            void release();

        private:
            void Init(const int rows, const int cols, double scaleFactor, int flags,
                      const int outputsz, const size_t localThreads[],
                      CvSize minSize, CvSize maxSize);
            void CreateBaseBufs(const int datasize, const int totalclassifier, const int flags, const int outputsz);
            void CreateFactorRelatedBufs(const int rows, const int cols, const int flags,
                                         const double scaleFactor, const size_t localThreads[],
                                         CvSize minSize, CvSize maxSize);
            void GenResult(CV_OUT std::vector<cv::Rect>& faces, const std::vector<cv::Rect> &rectList, const std::vector<int> &rweights);

            int m_rows;
            int m_cols;
            int m_flags;
            int m_loopcount;
            int m_nodenum;
            bool findBiggestObject;
            bool initialized;
            double m_scaleFactor;
            Size m_minSize;
            Size m_maxSize;
            vector<CvSize> sizev;
            vector<float> scalev;
            oclMat gimg1, gsum, gsqsum;
            void * buffers;
        };


        /////////////////////////////// Pyramid /////////////////////////////////////
        CV_EXPORTS void pyrDown(const oclMat &src, oclMat &dst);

        //! upsamples the source image and then smoothes it
        CV_EXPORTS void pyrUp(const oclMat &src, oclMat &dst);

        //! performs linear blending of two images
        //! to avoid accuracy errors sum of weigths shouldn't be very close to zero
        // supports only CV_8UC1 source type
        CV_EXPORTS void blendLinear(const oclMat &img1, const oclMat &img2, const oclMat &weights1, const oclMat &weights2, oclMat &result);

        //! computes vertical sum, supports only CV_32FC1 images
        CV_EXPORTS void columnSum(const oclMat &src, oclMat &sum);

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

        //! computes the proximity map for the raster template and the image where the template is searched for
        // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
        // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
        CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method);

        //! computes the proximity map for the raster template and the image where the template is searched for
        // Supports TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED for type 8UC1 and 8UC4
        // Supports TM_SQDIFF, TM_CCORR for type 32FC1 and 32FC4
        CV_EXPORTS void matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf);

        ///////////////////////////////////////////// Canny /////////////////////////////////////////////
        struct CV_EXPORTS CannyBuf;
        //! compute edges of the input image using Canny operator
        // Support CV_8UC1 only
        CV_EXPORTS void Canny(const oclMat &image, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
        CV_EXPORTS void Canny(const oclMat &image, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);
        CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);
        CV_EXPORTS void Canny(const oclMat &dx, const oclMat &dy, CannyBuf &buf, oclMat &edges, double low_thresh, double high_thresh, bool L2gradient = false);

        struct CV_EXPORTS CannyBuf
        {
            CannyBuf() : counter(NULL) {}
            ~CannyBuf()
            {
                release();
            }
            explicit CannyBuf(const Size &image_size, int apperture_size = 3) : counter(NULL)
            {
                create(image_size, apperture_size);
            }
            CannyBuf(const oclMat &dx_, const oclMat &dy_);

            void create(const Size &image_size, int apperture_size = 3);
            void release();
            oclMat dx, dy;
            oclMat dx_buf, dy_buf;
            oclMat edgeBuf;
            oclMat trackBuf1, trackBuf2;
            void *counter;
            Ptr<FilterEngine_GPU> filterDX, filterDY;
        };

        ///////////////////////////////////////// clAmdFft related /////////////////////////////////////////
        //! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
        //! Param dft_size is the size of DFT transform.
        //!
        //! For complex-to-real transform it is assumed that the source matrix is packed in CLFFT's format.
        // support src type of CV32FC1, CV32FC2
        // support flags: DFT_INVERSE, DFT_REAL_OUTPUT, DFT_COMPLEX_OUTPUT, DFT_ROWS
        // dft_size is the size of original input, which is used for transformation from complex to real.
        // dft_size must be powers of 2, 3 and 5
        // real to complex dft requires at least v1.8 clAmdFft
        // real to complex dft output is not the same with cpu version
        // real to complex and complex to real does not support DFT_ROWS
        CV_EXPORTS void dft(const oclMat &src, oclMat &dst, Size dft_size = Size(0, 0), int flags = 0);

        //! implements generalized matrix product algorithm GEMM from BLAS
        // The functionality requires clAmdBlas library
        // only support type CV_32FC1
        // flag GEMM_3_T is not supported
        CV_EXPORTS void gemm(const oclMat &src1, const oclMat &src2, double alpha,
                             const oclMat &src3, double beta, oclMat &dst, int flags = 0);

        //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
        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 oclMat &img, vector<Point> &found_locations,
                        double hit_threshold = 0, Size win_stride = Size(),
                        Size padding = Size());
            void detectMultiScale(const oclMat &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 getDescriptors(const oclMat &img, Size win_stride,
                                oclMat &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:
            // initialize buffers; only need to do once in case of multiscale detection
            void init_buffer(const oclMat &img, Size win_stride);
            void computeBlockHistograms(const oclMat &img);
            void computeGradient(const oclMat &img, oclMat &grad, oclMat &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;
            oclMat detector;
            // Results of the last classification step
            oclMat labels;
            Mat labels_host;
            // Results of the last histogram evaluation step
            oclMat block_hists;
            // Gradients conputation results
            oclMat grad, qangle;
            // scaled image
            oclMat image_scale;
            // effect size of input image (might be different from original size after scaling)
            Size effect_size;
        };


        ////////////////////////feature2d_ocl/////////////////
        /****************************************************************************************\
        *                                      Distance                                          *
        \****************************************************************************************/
        template<typename T>
        struct CV_EXPORTS Accumulator
        {
            typedef T Type;
        };
        template<> struct Accumulator<unsigned char>
        {
            typedef float Type;
        };
        template<> struct Accumulator<unsigned short>
        {
            typedef float Type;
        };
        template<> struct Accumulator<char>
        {
            typedef float Type;
        };
        template<> struct Accumulator<short>
        {
            typedef float Type;
        };

        /*
         * Manhattan distance (city block distance) functor
         */
        template<class T>
        struct CV_EXPORTS L1
        {
            enum { normType = NORM_L1 };
            typedef T ValueType;
            typedef typename Accumulator<T>::Type ResultType;

            ResultType operator()( const T *a, const T *b, int size ) const
            {
                return normL1<ValueType, ResultType>(a, b, size);
            }
        };

        /*
         * Euclidean distance functor
         */
        template<class T>
        struct CV_EXPORTS L2
        {
            enum { normType = NORM_L2 };
            typedef T ValueType;
            typedef typename Accumulator<T>::Type ResultType;

            ResultType operator()( const T *a, const T *b, int size ) const
            {
                return (ResultType)sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
            }
        };

        /*
         * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
         * bit count of A exclusive XOR'ed with B
         */
        struct CV_EXPORTS Hamming
        {
            enum { normType = NORM_HAMMING };
            typedef unsigned char ValueType;
            typedef int ResultType;

            /** this will count the bits in a ^ b
             */
            ResultType operator()( const unsigned char *a, const unsigned char *b, int size ) const
            {
                return normHamming(a, b, size);
            }
        };

        ////////////////////////////////// BruteForceMatcher //////////////////////////////////

        class CV_EXPORTS BruteForceMatcher_OCL_base
        {
        public:
            enum DistType {L1Dist = 0, L2Dist, HammingDist};
            explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
            // Add descriptors to train descriptor collection
            void add(const std::vector<oclMat> &descCollection);
            // Get train descriptors collection
            const std::vector<oclMat> &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 oclMat &query, const oclMat &train,
                             oclMat &trainIdx, oclMat &distance,
                             const oclMat &mask = oclMat());

            // Download trainIdx and distance and convert it to CPU vector with DMatch
            static void matchDownload(const oclMat &trainIdx, const oclMat &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 oclMat &query, const oclMat &train, std::vector<DMatch> &matches, const oclMat &mask = oclMat());

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


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

            // Download trainIdx, imgIdx and distance and convert it to vector with DMatch
            static void matchDownload(const oclMat &trainIdx, const oclMat &imgIdx, const oclMat &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 oclMat &query, std::vector<DMatch> &matches, const std::vector<oclMat> &masks = std::vector<oclMat>());

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

            // 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 oclMat &trainIdx, const oclMat &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 oclMat &query, const oclMat &train,
                          std::vector< std::vector<DMatch> > &matches, int k, const oclMat &mask = oclMat(),
                          bool compactResult = false);

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

            // 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 oclMat &trainIdx, const oclMat &imgIdx, const oclMat &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 oclMat &query, std::vector< std::vector<DMatch> > &matches, int k,
                          const std::vector<oclMat> &masks = std::vector<oclMat>(), 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 oclMat &query, const oclMat &train,
                                   oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
                                   const oclMat &mask = oclMat());

            // 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 oclMat &trainIdx, const oclMat &distance, const oclMat &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 oclMat &query, const oclMat &train,
                             std::vector< std::vector<DMatch> > &matches, float maxDistance,
                             const oclMat &mask = oclMat(), 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 oclMat &query, oclMat &trainIdx, oclMat &imgIdx, oclMat &distance, oclMat &nMatches, float maxDistance,
                                       const std::vector<oclMat> &masks = std::vector<oclMat>());
            // 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 oclMat &trainIdx, const oclMat &imgIdx, const oclMat &distance, const oclMat &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 oclMat &query, std::vector< std::vector<DMatch> > &matches, float maxDistance,
                             const std::vector<oclMat> &masks = std::vector<oclMat>(), bool compactResult = false);
            DistType distType;
        private:
            std::vector<oclMat> trainDescCollection;
        };

        template <class Distance>
        class CV_EXPORTS BruteForceMatcher_OCL;

        template <typename T>
        class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
        {
        public:
            explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
            explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
        };

        template <typename T>
        class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
        {
        public:
            explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
            explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
        };

        template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
        {
        public:
            explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
            explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
        };

        class CV_EXPORTS BFMatcher_OCL : public BruteForceMatcher_OCL_base
        {
        public:
            explicit BFMatcher_OCL(int norm = NORM_L2) : BruteForceMatcher_OCL_base(norm == NORM_L1 ? L1Dist : norm == NORM_L2 ? L2Dist : HammingDist) {}
        };

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

            //! return 1 rows matrix with CV_32FC2 type
            void operator ()(const oclMat& image, oclMat& corners, const oclMat& mask = oclMat());
            //! download points of type Point2f to a vector. the vector's content will be erased
            void downloadPoints(const oclMat &points, vector<Point2f> &points_v);

            int maxCorners;
            double qualityLevel;
            double minDistance;

            int blockSize;
            bool useHarrisDetector;
            double harrisK;
            void releaseMemory()
            {
                Dx_.release();
                Dy_.release();
                eig_.release();
                minMaxbuf_.release();
                tmpCorners_.release();
            }
        private:
            oclMat Dx_;
            oclMat Dy_;
            oclMat eig_;
            oclMat minMaxbuf_;
            oclMat tmpCorners_;
        };

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

        /////////////////////////////// PyrLKOpticalFlow /////////////////////////////////////
        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_ = false;
            }

            void sparse(const oclMat &prevImg, const oclMat &nextImg, const oclMat &prevPts, oclMat &nextPts,
                        oclMat &status, oclMat *err = 0);
            void dense(const oclMat &prevImg, const oclMat &nextImg, oclMat &u, oclMat &v, oclMat *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();
            }
        private:
            void calcSharrDeriv(const oclMat &src, oclMat &dx, oclMat &dy);
            void buildImagePyramid(const oclMat &img0, vector<oclMat> &pyr, bool withBorder);

            oclMat dx_calcBuf_;
            oclMat dy_calcBuf_;

            vector<oclMat> prevPyr_;
            vector<oclMat> nextPyr_;

            oclMat dx_buf_;
            oclMat dy_buf_;
            oclMat uPyr_[2];
            oclMat vPyr_[2];
            bool isDeviceArch11_;
        };

        class CV_EXPORTS FarnebackOpticalFlow
        {
        public:
            FarnebackOpticalFlow();

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

            void operator ()(const oclMat &frame0, const oclMat &frame1, oclMat &flowx, oclMat &flowy);

            void releaseMemory();

        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 oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat &flowy,
                oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);

            void updateFlow_gaussianBlur(
                const oclMat& R0, const oclMat& R1, oclMat& flowx, oclMat& flowy,
                oclMat& M, oclMat &bufM, int blockSize, bool updateMatrices);

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

        //////////////// build warping maps ////////////////////
        //! builds plane warping maps
        CV_EXPORTS void buildWarpPlaneMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, const Mat &T, float scale, oclMat &map_x, oclMat &map_y);
        //! builds cylindrical warping maps
        CV_EXPORTS void buildWarpCylindricalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
        //! builds spherical warping maps
        CV_EXPORTS void buildWarpSphericalMaps(Size src_size, Rect dst_roi, const Mat &K, const Mat &R, float scale, oclMat &map_x, oclMat &map_y);
        //! builds Affine warping maps
        CV_EXPORTS void buildWarpAffineMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);

        //! builds Perspective warping maps
        CV_EXPORTS void buildWarpPerspectiveMaps(const Mat &M, bool inverse, Size dsize, oclMat &xmap, oclMat &ymap);

        ///////////////////////////////////// interpolate frames //////////////////////////////////////////////
        //! Interpolate frames (images) using provided optical flow (displacement field).
        //! frame0   - frame 0 (32-bit floating point images, single channel)
        //! frame1   - frame 1 (the same type and size)
        //! fu       - forward horizontal displacement
        //! fv       - forward vertical displacement
        //! bu       - backward horizontal displacement
        //! bv       - backward vertical displacement
        //! pos      - new frame position
        //! newFrame - new frame
        //! buf      - temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 oclMat;
        //!            occlusion masks            0, occlusion masks            1,
        //!            interpolated forward flow  0, interpolated forward flow  1,
        //!            interpolated backward flow 0, interpolated backward flow 1
        //!
        CV_EXPORTS void interpolateFrames(const oclMat &frame0, const oclMat &frame1,
                                          const oclMat &fu, const oclMat &fv,
                                          const oclMat &bu, const oclMat &bv,
                                          float pos, oclMat &newFrame, oclMat &buf);

        //! computes moments of the rasterized shape or a vector of points
        CV_EXPORTS Moments ocl_moments(InputArray _array, bool binaryImage);

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

            enum { DEFAULT_NDISP = 64, DEFAULT_WINSZ = 19 };

            //! the default constructor
            StereoBM_OCL();
            //! the full constructor taking the camera-specific preset, number of disparities and the SAD window size. ndisparities must be multiple of 8.
            StereoBM_OCL(int preset, int ndisparities = DEFAULT_NDISP, int winSize = DEFAULT_WINSZ);

            //! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
            //! Output disparity has CV_8U type.
            void operator() ( const oclMat &left, const oclMat &right, oclMat &disparity);

            //! Some heuristics that tries to estmate
            // if current GPU will be faster then 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:
            oclMat minSSD, leBuf, riBuf;
        };

        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_16S);
            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 oclMat &left, const oclMat &right, oclMat &disparity);
            void operator()(const oclMat &data, oclMat &disparity);
            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:
            oclMat u, d, l, r, u2, d2, l2, r2;
            std::vector<oclMat> datas;
            oclMat out;
        };

        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 oclMat &left, const oclMat &right, oclMat &disparity);
            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:
            oclMat u[2], d[2], l[2], r[2];
            oclMat disp_selected_pyr[2];
            oclMat data_cost;
            oclMat data_cost_selected;
            oclMat temp;
            oclMat out;
        };

        // Implementation of the Zach, Pock and Bischof Dual TV-L1 Optical Flow method
        //
        // see reference:
        //   [1] C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
        //   [2] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
        class CV_EXPORTS OpticalFlowDual_TVL1_OCL
        {
        public:
            OpticalFlowDual_TVL1_OCL();

            void operator ()(const oclMat& I0, const oclMat& I1, oclMat& flowx, oclMat& flowy);

            void collectGarbage();

            /**
            * Time step of the numerical scheme.
            */
            double tau;

            /**
            * Weight parameter for the data term, attachment parameter.
            * This is the most relevant parameter, which determines the smoothness of the output.
            * The smaller this parameter is, the smoother the solutions we obtain.
            * It depends on the range of motions of the images, so its value should be adapted to each image sequence.
            */
            double lambda;

            /**
            * Weight parameter for (u - v)^2, tightness parameter.
            * It serves as a link between the attachment and the regularization terms.
            * In theory, it should have a small value in order to maintain both parts in correspondence.
            * The method is stable for a large range of values of this parameter.
            */
            double theta;

            /**
            * Number of scales used to create the pyramid of images.
            */
            int nscales;

            /**
            * Number of warpings per scale.
            * Represents the number of times that I1(x+u0) and grad( I1(x+u0) ) are computed per scale.
            * This is a parameter that assures the stability of the method.
            * It also affects the running time, so it is a compromise between speed and accuracy.
            */
            int warps;

            /**
            * Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time.
            * A small value will yield more accurate solutions at the expense of a slower convergence.
            */
            double epsilon;

            /**
            * Stopping criterion iterations number used in the numerical scheme.
            */
            int iterations;

            bool useInitialFlow;

        private:
            void procOneScale(const oclMat& I0, const oclMat& I1, oclMat& u1, oclMat& u2);

            std::vector<oclMat> I0s;
            std::vector<oclMat> I1s;
            std::vector<oclMat> u1s;
            std::vector<oclMat> u2s;

            oclMat I1x_buf;
            oclMat I1y_buf;

            oclMat I1w_buf;
            oclMat I1wx_buf;
            oclMat I1wy_buf;

            oclMat grad_buf;
            oclMat rho_c_buf;

            oclMat p11_buf;
            oclMat p12_buf;
            oclMat p21_buf;
            oclMat p22_buf;

            oclMat diff_buf;
            oclMat norm_buf;
        };
    }
}
#if defined _MSC_VER && _MSC_VER >= 1200
#  pragma warning( push)
#  pragma warning( disable: 4267)
#endif
#include "opencv2/ocl/matrix_operations.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#  pragma warning( pop)
#endif

#endif /* __OPENCV_GPU_HPP__ */
