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#include "precomp.hpp"

#ifndef HAVE_CUDA


void cv::gpu::warpAffine(const GpuMat&, GpuMat&, const Mat&, Size, int, int, Scalar, Stream&) { throw_nogpu(); }
void cv::gpu::buildWarpAffineMaps(const Mat&, bool, Size, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }

void cv::gpu::warpPerspective(const GpuMat&, GpuMat&, const Mat&, Size, int, int, Scalar, Stream&) { throw_nogpu(); }
void cv::gpu::buildWarpPerspectiveMaps(const Mat&, bool, Size, GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }

#else // HAVE_CUDA

namespace cv { namespace gpu { namespace device
{
    namespace imgproc
    {
        void buildWarpAffineMaps_gpu(float coeffs[2 * 3], DevMem2Df xmap, DevMem2Df ymap, cudaStream_t stream);

        template <typename T>
        void warpAffine_gpu(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation,
                            int borderMode, const float* borderValue, cudaStream_t stream, int cc);

        void buildWarpPerspectiveMaps_gpu(float coeffs[3 * 3], DevMem2Df xmap, DevMem2Df ymap, cudaStream_t stream);

        template <typename T>
        void warpPerspective_gpu(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[3 * 3], DevMem2Db dst, int interpolation,
                            int borderMode, const float* borderValue, cudaStream_t stream, int cc);
    }
}}}

void cv::gpu::buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream)
{
    using namespace cv::gpu::device::imgproc;

    CV_Assert(M.rows == 2 && M.cols == 3);

    xmap.create(dsize, CV_32FC1);
    ymap.create(dsize, CV_32FC1);

    float coeffs[2 * 3];
    Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);

    if (inverse)
        M.convertTo(coeffsMat, coeffsMat.type());
    else
    {
        cv::Mat iM;
        invertAffineTransform(M, iM);
        iM.convertTo(coeffsMat, coeffsMat.type());
    }

    buildWarpAffineMaps_gpu(coeffs, xmap, ymap, StreamAccessor::getStream(stream));
}

void cv::gpu::buildWarpPerspectiveMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream)
{
    using namespace cv::gpu::device::imgproc;

    CV_Assert(M.rows == 3 && M.cols == 3);

    xmap.create(dsize, CV_32FC1);
    ymap.create(dsize, CV_32FC1);

    float coeffs[3 * 3];
    Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);

    if (inverse)
        M.convertTo(coeffsMat, coeffsMat.type());
    else
    {
        cv::Mat iM;
        invert(M, iM);
        iM.convertTo(coeffsMat, coeffsMat.type());
    }

    buildWarpPerspectiveMaps_gpu(coeffs, xmap, ymap, StreamAccessor::getStream(stream));
}

namespace
{
    template<int DEPTH> struct NppTypeTraits;
    template<> struct NppTypeTraits<CV_8U>  { typedef Npp8u npp_t; };
    template<> struct NppTypeTraits<CV_8S>  { typedef Npp8s npp_t; };
    template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
    template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; typedef Npp16sc npp_complex_type; };
    template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; typedef Npp32sc npp_complex_type; };
    template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; typedef Npp32fc npp_complex_type; };
    template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; typedef Npp64fc npp_complex_type; };

    template <int DEPTH> struct NppWarpFunc
    {
        typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;

        typedef NppStatus (*func_t)(const npp_t* pSrc, NppiSize srcSize, int srcStep, NppiRect srcRoi, npp_t* pDst,
                                    int dstStep, NppiRect dstRoi, const double coeffs[][3],
                                    int interpolation);
    };

    template <int DEPTH, typename NppWarpFunc<DEPTH>::func_t func> struct NppWarp
    {
        typedef typename NppWarpFunc<DEPTH>::npp_t npp_t;

        static void call(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst,
                         double coeffs[][3], cv::Size dsize, int interpolation, cudaStream_t stream)
        {
            static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC};

            dst.create(dsize, src.type());
            dst.setTo(cv::Scalar::all(0));

            NppiSize srcsz;
            srcsz.height = wholeSize.height;
            srcsz.width = wholeSize.width;

            NppiRect srcroi;
            srcroi.x = ofs.x;
            srcroi.y = ofs.y;
            srcroi.height = src.rows;
            srcroi.width = src.cols;

            NppiRect dstroi;
            dstroi.x = dstroi.y = 0;
            dstroi.height = dst.rows;
            dstroi.width = dst.cols;

            cv::gpu::NppStreamHandler h(stream);

            nppSafeCall( func((npp_t*)src.datastart, srcsz, static_cast<int>(src.step), srcroi,
                              dst.ptr<npp_t>(), static_cast<int>(dst.step), dstroi, coeffs, npp_inter[interpolation]) );

            if (stream == 0)
                cudaSafeCall( cudaDeviceSynchronize() );
        }
    };
}

void cv::gpu::warpAffine(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s)
{
    CV_Assert(M.rows == 2 && M.cols == 3);

    int interpolation = flags & INTER_MAX;

    CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
    CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
    CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);

    Size wholeSize;
    Point ofs;
    src.locateROI(wholeSize, ofs);

    static const bool useNppTab[6][4][3] =
    {
        {
            {false, false, true},
            {false, false, false},
            {false, true, true},
            {false, false, false}
        },
        {
            {false, false, false},
            {false, false, false},
            {false, false, false},
            {false, false, false}
        },
        {
            {false, true, true},
            {false, false, false},
            {false, true, true},
            {false, false, false}
        },
        {
            {false, false, false},
            {false, false, false},
            {false, false, false},
            {false, false, false}
        },
        {
            {false, true, true},
            {false, false, false},
            {false, true, true},
            {false, false, true}
        },
        {
            {false, true, true},
            {false, false, false},
            {false, true, true},
            {false, false, true}
        }
    };

    bool useNpp = borderMode == BORDER_CONSTANT;
    useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation];
    #ifdef linux
        // NPP bug on float data
        useNpp = useNpp && src.depth() != CV_32F;
    #endif

    if (useNpp)
    {
        typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, int flags, cudaStream_t stream);

        static const func_t funcs[2][6][4] =
        {
            {
                {NppWarp<CV_8U, nppiWarpAffine_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpAffine_8u_C3R>::call, NppWarp<CV_8U, nppiWarpAffine_8u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_16U, nppiWarpAffine_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpAffine_16u_C3R>::call, NppWarp<CV_16U, nppiWarpAffine_16u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_32S, nppiWarpAffine_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpAffine_32s_C3R>::call, NppWarp<CV_32S, nppiWarpAffine_32s_C4R>::call},
                {NppWarp<CV_32F, nppiWarpAffine_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpAffine_32f_C3R>::call, NppWarp<CV_32F, nppiWarpAffine_32f_C4R>::call}
            },
            {
                {NppWarp<CV_8U, nppiWarpAffineBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpAffineBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpAffineBack_8u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_16U, nppiWarpAffineBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpAffineBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpAffineBack_16u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_32S, nppiWarpAffineBack_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpAffineBack_32s_C3R>::call, NppWarp<CV_32S, nppiWarpAffineBack_32s_C4R>::call},
                {NppWarp<CV_32F, nppiWarpAffineBack_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpAffineBack_32f_C3R>::call, NppWarp<CV_32F, nppiWarpAffineBack_32f_C4R>::call}
            }
        };

        double coeffs[2][3];
        Mat coeffsMat(2, 3, CV_64F, (void*)coeffs);
        M.convertTo(coeffsMat, coeffsMat.type());

        const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
        CV_Assert(func != 0);

        func(src, wholeSize, ofs, dst, coeffs, dsize, interpolation, StreamAccessor::getStream(s));
    }
    else
    {
        using namespace cv::gpu::device::imgproc;

        typedef void (*func_t)(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation,
            int borderMode, const float* borderValue, cudaStream_t stream, int cc);

        static const func_t funcs[6][4] =
        {
            {warpAffine_gpu<uchar>      , 0 /*warpAffine_gpu<uchar2>*/ , warpAffine_gpu<uchar3>     , warpAffine_gpu<uchar4>     },
            {0 /*warpAffine_gpu<schar>*/, 0 /*warpAffine_gpu<char2>*/  , 0 /*warpAffine_gpu<char3>*/, 0 /*warpAffine_gpu<char4>*/},
            {warpAffine_gpu<ushort>     , 0 /*warpAffine_gpu<ushort2>*/, warpAffine_gpu<ushort3>    , warpAffine_gpu<ushort4>    },
            {warpAffine_gpu<short>      , 0 /*warpAffine_gpu<short2>*/ , warpAffine_gpu<short3>     , warpAffine_gpu<short4>     },
            {0 /*warpAffine_gpu<int>*/  , 0 /*warpAffine_gpu<int2>*/   , 0 /*warpAffine_gpu<int3>*/ , 0 /*warpAffine_gpu<int4>*/ },
            {warpAffine_gpu<float>      , 0 /*warpAffine_gpu<float2>*/ , warpAffine_gpu<float3>     , warpAffine_gpu<float4>     }
        };

        const func_t func = funcs[src.depth()][src.channels() - 1];
        CV_Assert(func != 0);

        int gpuBorderType;
        CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));

        dst.create(dsize, src.type());

        float coeffs[2 * 3];
        Mat coeffsMat(2, 3, CV_32F, (void*)coeffs);

        if (flags & WARP_INVERSE_MAP)
            M.convertTo(coeffsMat, coeffsMat.type());
        else
        {
            cv::Mat iM;
            invertAffineTransform(M, iM);
            iM.convertTo(coeffsMat, coeffsMat.type());
        }

        Scalar_<float> borderValueFloat;
        borderValueFloat = borderValue;

        DeviceInfo info;
        int cc = info.majorVersion() * 10 + info.minorVersion();

        func(src, DevMem2Db(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
            dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), cc);
    }
}

void cv::gpu::warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s)
{
    CV_Assert(M.rows == 3 && M.cols == 3);

    int interpolation = flags & INTER_MAX;

    CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
    CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC);
    CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);

    Size wholeSize;
    Point ofs;
    src.locateROI(wholeSize, ofs);

    static const bool useNppTab[6][4][3] =
    {
        {
            {false, false, true},
            {false, false, false},
            {false, true, true},
            {false, false, false}
        },
        {
            {false, false, false},
            {false, false, false},
            {false, false, false},
            {false, false, false}
        },
        {
            {false, true, true},
            {false, false, false},
            {false, true, true},
            {false, false, false}
        },
        {
            {false, false, false},
            {false, false, false},
            {false, false, false},
            {false, false, false}
        },
        {
            {false, true, true},
            {false, false, false},
            {false, true, true},
            {false, false, true}
        },
        {
            {false, true, true},
            {false, false, false},
            {false, true, true},
            {false, false, true}
        }
    };

    bool useNpp = borderMode == BORDER_CONSTANT;
    useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation];
    #ifdef linux
        // NPP bug on float data
        useNpp = useNpp && src.depth() != CV_32F;
    #endif

    if (useNpp)
    {
        typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, int flags, cudaStream_t stream);

        static const func_t funcs[2][6][4] =
        {
            {
                {NppWarp<CV_8U, nppiWarpPerspective_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspective_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspective_8u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_16U, nppiWarpPerspective_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspective_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspective_16u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_32S, nppiWarpPerspective_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspective_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspective_32s_C4R>::call},
                {NppWarp<CV_32F, nppiWarpPerspective_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspective_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspective_32f_C4R>::call}
            },
            {
                {NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C4R>::call},
                {0, 0, 0, 0},
                {NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C4R>::call},
                {NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C4R>::call}
            }
        };

        double coeffs[3][3];
        Mat coeffsMat(3, 3, CV_64F, (void*)coeffs);
        M.convertTo(coeffsMat, coeffsMat.type());

        const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
        CV_Assert(func != 0);

        func(src, wholeSize, ofs, dst, coeffs, dsize, interpolation, StreamAccessor::getStream(s));
    }
    else
    {
        using namespace cv::gpu::device::imgproc;

        typedef void (*func_t)(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation,
            int borderMode, const float* borderValue, cudaStream_t stream, int cc);

        static const func_t funcs[6][4] =
        {
            {warpPerspective_gpu<uchar>      , 0 /*warpPerspective_gpu<uchar2>*/ , warpPerspective_gpu<uchar3>     , warpPerspective_gpu<uchar4>     },
            {0 /*warpPerspective_gpu<schar>*/, 0 /*warpPerspective_gpu<char2>*/  , 0 /*warpPerspective_gpu<char3>*/, 0 /*warpPerspective_gpu<char4>*/},
            {warpPerspective_gpu<ushort>     , 0 /*warpPerspective_gpu<ushort2>*/, warpPerspective_gpu<ushort3>    , warpPerspective_gpu<ushort4>    },
            {warpPerspective_gpu<short>      , 0 /*warpPerspective_gpu<short2>*/ , warpPerspective_gpu<short3>     , warpPerspective_gpu<short4>     },
            {0 /*warpPerspective_gpu<int>*/  , 0 /*warpPerspective_gpu<int2>*/   , 0 /*warpPerspective_gpu<int3>*/ , 0 /*warpPerspective_gpu<int4>*/ },
            {warpPerspective_gpu<float>      , 0 /*warpPerspective_gpu<float2>*/ , warpPerspective_gpu<float3>     , warpPerspective_gpu<float4>     }
        };

        const func_t func = funcs[src.depth()][src.channels() - 1];
        CV_Assert(func != 0);

        int gpuBorderType;
        CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));

        dst.create(dsize, src.type());

        float coeffs[3 * 3];
        Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);

        if (flags & WARP_INVERSE_MAP)
            M.convertTo(coeffsMat, coeffsMat.type());
        else
        {
            cv::Mat iM;
            invert(M, iM);
            iM.convertTo(coeffsMat, coeffsMat.type());
        }

        Scalar_<float> borderValueFloat;
        borderValueFloat = borderValue;

        DeviceInfo info;
        int cc = info.majorVersion() * 10 + info.minorVersion();

        func(src, DevMem2Db(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
            dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), cc);
    }
}

#endif // HAVE_CUDA
