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/*M///////////////////////////////////////////////////////////////////////////////////////
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#ifndef __OPENCV_GPU_UTILITY_HPP__
#define __OPENCV_GPU_UTILITY_HPP__

#include "saturate_cast.hpp"
#include "datamov_utils.hpp"

namespace cv { namespace gpu { namespace device
{
    #define OPENCV_GPU_LOG_WARP_SIZE        (5)
    #define OPENCV_GPU_WARP_SIZE            (1 << OPENCV_GPU_LOG_WARP_SIZE)
    #define OPENCV_GPU_LOG_MEM_BANKS        ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla
    #define OPENCV_GPU_MEM_BANKS            (1 << OPENCV_GPU_LOG_MEM_BANKS)

    ///////////////////////////////////////////////////////////////////////////////
    // swap

    template  void __device__ __host__ __forceinline__ swap(T& a, T& b)
    {
        const T temp = a;
        a = b;
        b = temp;
    }

    ///////////////////////////////////////////////////////////////////////////////
    // Mask Reader

    struct SingleMask
    {
        explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {}
        __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){}

        __device__ __forceinline__ bool operator()(int y, int x) const
        {
            return mask.ptr(y)[x] != 0;
        }

        PtrStepb mask;
    };

    struct SingleMaskChannels
    {
        __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_)
        : mask(mask_), channels(channels_) {}
        __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_)
            :mask(mask_.mask), channels(mask_.channels){}

        __device__ __forceinline__ bool operator()(int y, int x) const
        {
            return mask.ptr(y)[x / channels] != 0;
        }

        PtrStepb mask;
        int channels;
    };

    struct MaskCollection
    {
        explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_)
            : maskCollection(maskCollection_) {}

        __device__ __forceinline__ MaskCollection(const MaskCollection& masks_)
            : maskCollection(masks_.maskCollection), curMask(masks_.curMask){}

        __device__ __forceinline__ void next()
        {
            curMask = *maskCollection++;
        }
        __device__ __forceinline__ void setMask(int z)
        {
            curMask = maskCollection[z];
        }

        __device__ __forceinline__ bool operator()(int y, int x) const
        {
            uchar val;
            return curMask.data == 0 || (ForceGlob::Load(curMask.ptr(y), x, val), (val != 0));
        }

        const PtrStepb* maskCollection;
        PtrStepb curMask;
    };

    struct WithOutMask
    {
        __host__ __device__ __forceinline__ WithOutMask(){}
        __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){}

        __device__ __forceinline__ void next() const
        {
        }
        __device__ __forceinline__ void setMask(int) const
        {
        }

        __device__ __forceinline__ bool operator()(int, int) const
        {
            return true;
        }

        __device__ __forceinline__ bool operator()(int, int, int) const
        {
            return true;
        }

        static __device__ __forceinline__ bool check(int, int)
        {
            return true;
        }

        static __device__ __forceinline__ bool check(int, int, int)
        {
            return true;
        }
    };

    ///////////////////////////////////////////////////////////////////////////////
    // Solve linear system

    // solve 2x2 linear system Ax=b
    template  __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2])
    {
        T det = A[0][0] * A[1][1] - A[1][0] * A[0][1];

        if (det != 0)
        {
            double invdet = 1.0 / det;

            x[0] = saturate_cast(invdet * (b[0] * A[1][1] - b[1] * A[0][1]));

            x[1] = saturate_cast(invdet * (A[0][0] * b[1] - A[1][0] * b[0]));

            return true;
        }

        return false;
    }

    // solve 3x3 linear system Ax=b
    template  __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3])
    {
        T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1])
              - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0])
              + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]);

        if (det != 0)
        {
            double invdet = 1.0 / det;

            x[0] = saturate_cast(invdet *
                (b[0]    * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) -
                 A[0][1] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) +
                 A[0][2] * (b[1]    * A[2][1] - A[1][1] * b[2]   )));

            x[1] = saturate_cast(invdet *
                (A[0][0] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) -
                 b[0]    * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) +
                 A[0][2] * (A[1][0] * b[2]    - b[1]    * A[2][0])));

            x[2] = saturate_cast(invdet *
                (A[0][0] * (A[1][1] * b[2]    - b[1]    * A[2][1]) -
                 A[0][1] * (A[1][0] * b[2]    - b[1]    * A[2][0]) +
                 b[0]    * (A[1][0] * A[2][1] - A[1][1] * A[2][0])));

            return true;
        }

        return false;
    }
}}} // namespace cv { namespace gpu { namespace device

#endif // __OPENCV_GPU_UTILITY_HPP__