opencv/modules/gpuarithm/src/core.cpp

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#include "precomp.hpp"

using namespace cv;
using namespace cv::gpu;

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)

void cv::gpu::merge(const GpuMat*, size_t, OutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::merge(const std::vector<GpuMat>&, OutputArray, Stream&) { throw_no_cuda(); }

void cv::gpu::split(InputArray, GpuMat*, Stream&) { throw_no_cuda(); }
void cv::gpu::split(InputArray, std::vector<GpuMat>&, Stream&) { throw_no_cuda(); }

void cv::gpu::transpose(InputArray, OutputArray, Stream&) { throw_no_cuda(); }

void cv::gpu::flip(InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }

Ptr<LookUpTable> cv::gpu::createLookUpTable(InputArray) { throw_no_cuda(); return Ptr<LookUpTable>(); }

void cv::gpu::copyMakeBorder(InputArray, OutputArray, int, int, int, int, int, Scalar, Stream&) { throw_no_cuda(); }

#else /* !defined (HAVE_CUDA) */

////////////////////////////////////////////////////////////////////////
// merge/split

namespace cv { namespace gpu { namespace cudev
{
    namespace split_merge
    {
        void merge(const PtrStepSzb* src, PtrStepSzb& dst, int total_channels, size_t elem_size, const cudaStream_t& stream);
        void split(const PtrStepSzb& src, PtrStepSzb* dst, int num_channels, size_t elem_size1, const cudaStream_t& stream);
    }
}}}

namespace
{
    void merge_caller(const GpuMat* src, size_t n, OutputArray _dst, Stream& stream)
    {
        CV_Assert( src != 0 );
        CV_Assert( n > 0 && n <= 4 );

        const int depth = src[0].depth();
        const Size size = src[0].size();

        for (size_t i = 0; i < n; ++i)
        {
            CV_Assert( src[i].size() == size );
            CV_Assert( src[i].depth() == depth );
            CV_Assert( src[i].channels() == 1 );
        }

        if (depth == CV_64F)
        {
            if (!deviceSupports(NATIVE_DOUBLE))
                CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
        }

        if (n == 1)
        {
            src[0].copyTo(_dst, stream);
        }
        else
        {
            _dst.create(size, CV_MAKE_TYPE(depth, (int)n));
            GpuMat dst = _dst.getGpuMat();

            PtrStepSzb src_as_devmem[4];
            for(size_t i = 0; i < n; ++i)
                src_as_devmem[i] = src[i];

            PtrStepSzb dst_as_devmem(dst);
            cv::gpu::cudev::split_merge::merge(src_as_devmem, dst_as_devmem, (int)n, CV_ELEM_SIZE(depth), StreamAccessor::getStream(stream));
        }
    }

    void split_caller(const GpuMat& src, GpuMat* dst, Stream& stream)
    {
        CV_Assert( dst != 0 );

        const int depth = src.depth();
        const int num_channels = src.channels();

        CV_Assert( num_channels <= 4 );

        if (depth == CV_64F)
        {
            if (!deviceSupports(NATIVE_DOUBLE))
                CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
        }

        if (num_channels == 1)
        {
            src.copyTo(dst[0], stream);
            return;
        }

        for (int i = 0; i < num_channels; ++i)
            dst[i].create(src.size(), depth);

        PtrStepSzb dst_as_devmem[4];
        for (int i = 0; i < num_channels; ++i)
            dst_as_devmem[i] = dst[i];

        PtrStepSzb src_as_devmem(src);
        cv::gpu::cudev::split_merge::split(src_as_devmem, dst_as_devmem, num_channels, src.elemSize1(), StreamAccessor::getStream(stream));
    }
}

void cv::gpu::merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream)
{
    merge_caller(src, n, dst, stream);
}


void cv::gpu::merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream)
{
    merge_caller(&src[0], src.size(), dst, stream);
}

void cv::gpu::split(InputArray _src, GpuMat* dst, Stream& stream)
{
    GpuMat src = _src.getGpuMat();
    split_caller(src, dst, stream);
}

void cv::gpu::split(InputArray _src, std::vector<GpuMat>& dst, Stream& stream)
{
    GpuMat src = _src.getGpuMat();
    dst.resize(src.channels());
    if(src.channels() > 0)
        split_caller(src, &dst[0], stream);
}

////////////////////////////////////////////////////////////////////////
// transpose

namespace arithm
{
    template <typename T> void transpose(PtrStepSz<T> src, PtrStepSz<T> dst, cudaStream_t stream);
}

void cv::gpu::transpose(InputArray _src, OutputArray _dst, Stream& _stream)
{
    GpuMat src = _src.getGpuMat();

    CV_Assert( src.elemSize() == 1 || src.elemSize() == 4 || src.elemSize() == 8 );

    _dst.create( src.cols, src.rows, src.type() );
    GpuMat dst = _dst.getGpuMat();

    cudaStream_t stream = StreamAccessor::getStream(_stream);

    if (src.elemSize() == 1)
    {
        NppStreamHandler h(stream);

        NppiSize sz;
        sz.width  = src.cols;
        sz.height = src.rows;

        nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
            dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );

        if (stream == 0)
            cudaSafeCall( cudaDeviceSynchronize() );
    }
    else if (src.elemSize() == 4)
    {
        arithm::transpose<int>(src, dst, stream);
    }
    else // if (src.elemSize() == 8)
    {
        if (!deviceSupports(NATIVE_DOUBLE))
            CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");

        arithm::transpose<double>(src, dst, stream);
    }
}

////////////////////////////////////////////////////////////////////////
// flip

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; };
    template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; };
    template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; };
    template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; };

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

        typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oROI, NppiAxis flip);
    };

    template <int DEPTH, typename NppMirrorFunc<DEPTH>::func_t func> struct NppMirror
    {
        typedef typename NppMirrorFunc<DEPTH>::npp_t npp_t;

        static void call(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream)
        {
            NppStreamHandler h(stream);

            NppiSize sz;
            sz.width  = src.cols;
            sz.height = src.rows;

            nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step),
                dst.ptr<npp_t>(), static_cast<int>(dst.step), sz,
                (flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );

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

void cv::gpu::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& stream)
{
    typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
    static const func_t funcs[6][4] =
    {
        {NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
        {0,0,0,0},
        {NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call},
        {0,0,0,0},
        {NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call},
        {NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
    };

    GpuMat src = _src.getGpuMat();

    CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
    CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);

    _dst.create(src.size(), src.type());
    GpuMat dst = _dst.getGpuMat();

    funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
}

////////////////////////////////////////////////////////////////////////
// LUT

#if (CUDA_VERSION >= 5000)

namespace
{
    class LookUpTableImpl : public LookUpTable
    {
    public:
        LookUpTableImpl(InputArray lut);

        void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null());

    private:
        int lut_cn;

        int nValues3[3];
        const Npp32s* pValues3[3];
        const Npp32s* pLevels3[3];

        GpuMat d_pLevels;
        GpuMat d_nppLut;
        GpuMat d_nppLut3[3];
    };

    LookUpTableImpl::LookUpTableImpl(InputArray _lut)
    {
        nValues3[0] = nValues3[1] = nValues3[2] = 256;

        Npp32s pLevels[256];
        for (int i = 0; i < 256; ++i)
            pLevels[i] = i;

        d_pLevels.upload(Mat(1, 256, CV_32S, pLevels));
        pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>();

        GpuMat lut;
        if (_lut.kind() == _InputArray::GPU_MAT)
        {
            lut = _lut.getGpuMat();
        }
        else
        {
            Mat hLut = _lut.getMat();
            CV_Assert( hLut.total() == 256 && hLut.isContinuous() );
            lut.upload(Mat(1, 256, hLut.type(), hLut.data));
        }

        lut_cn = lut.channels();

        CV_Assert( lut.depth() == CV_8U );
        CV_Assert( lut.rows == 1 && lut.cols == 256 );

        lut.convertTo(d_nppLut, CV_32S);

        if (lut_cn == 1)
        {
            pValues3[0] = pValues3[1] = pValues3[2] = d_nppLut.ptr<Npp32s>();
        }
        else
        {
            gpu::split(d_nppLut, d_nppLut3);

            pValues3[0] = d_nppLut3[0].ptr<Npp32s>();
            pValues3[1] = d_nppLut3[1].ptr<Npp32s>();
            pValues3[2] = d_nppLut3[2].ptr<Npp32s>();
        }
    }

    void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& _stream)
    {
        GpuMat src = _src.getGpuMat();

        const int cn = src.channels();

        CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
        CV_Assert( lut_cn == 1 || lut_cn == cn );

        _dst.create(src.size(), src.type());
        GpuMat dst = _dst.getGpuMat();

        cudaStream_t stream = StreamAccessor::getStream(_stream);

        NppStreamHandler h(stream);

        NppiSize sz;
        sz.height = src.rows;
        sz.width = src.cols;

        if (src.type() == CV_8UC1)
        {
            nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
                dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), d_pLevels.ptr<Npp32s>(), 256) );
        }
        else
        {
            nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
                dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) );
        }

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

#else //  (CUDA_VERSION >= 5000)

namespace
{
    class LookUpTableImpl : public LookUpTable
    {
    public:
        LookUpTableImpl(InputArray lut);

        void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null());

    private:
        int lut_cn;

        Npp32s pLevels[256];
        int nValues3[3];
        const Npp32s* pValues3[3];
        const Npp32s* pLevels3[3];

        Mat nppLut;
        Mat nppLut3[3];
    };

    LookUpTableImpl::LookUpTableImpl(InputArray _lut)
    {
        nValues3[0] = nValues3[1] = nValues3[2] = 256;

        for (int i = 0; i < 256; ++i)
            pLevels[i] = i;
        pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;

        Mat lut;
        if (_lut.kind() == _InputArray::GPU_MAT)
        {
            lut = Mat(_lut.getGpuMat());
        }
        else
        {
            Mat hLut = _lut.getMat();
            CV_Assert( hLut.total() == 256 && hLut.isContinuous() );
            lut = hLut;
        }

        lut_cn = lut.channels();

        CV_Assert( lut.depth() == CV_8U );
        CV_Assert( lut.rows == 1 && lut.cols == 256 );

        lut.convertTo(nppLut, CV_32S);

        if (lut_cn == 1)
        {
            pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
        }
        else
        {
            cv::split(nppLut, nppLut3);

            pValues3[0] = nppLut3[0].ptr<Npp32s>();
            pValues3[1] = nppLut3[1].ptr<Npp32s>();
            pValues3[2] = nppLut3[2].ptr<Npp32s>();
        }
    }

    void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& _stream)
    {
        GpuMat src = _src.getGpuMat();

        const int cn = src.channels();

        CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
        CV_Assert( lut_cn == 1 || lut_cn == cn );

        _dst.create(src.size(), src.type());
        GpuMat dst = _dst.getGpuMat();

        cudaStream_t stream = StreamAccessor::getStream(_stream);

        NppStreamHandler h(stream);

        NppiSize sz;
        sz.height = src.rows;
        sz.width = src.cols;

        if (src.type() == CV_8UC1)
        {
            nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
                dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), pLevels, 256) );
        }
        else
        {
            nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
                dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) );
        }

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

#endif //  (CUDA_VERSION >= 5000)

Ptr<LookUpTable> cv::gpu::createLookUpTable(InputArray lut)
{
    return new LookUpTableImpl(lut);
}

////////////////////////////////////////////////////////////////////////
// copyMakeBorder

namespace cv { namespace gpu { namespace cudev
{
    namespace imgproc
    {
        template <typename T, int cn> void copyMakeBorder_gpu(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const T* borderValue, cudaStream_t stream);
    }
}}}

namespace
{
    template <typename T, int cn> void copyMakeBorder_caller(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderType, const Scalar& value, cudaStream_t stream)
    {
        using namespace ::cv::gpu::cudev::imgproc;

        Scalar_<T> val(saturate_cast<T>(value[0]), saturate_cast<T>(value[1]), saturate_cast<T>(value[2]), saturate_cast<T>(value[3]));

        copyMakeBorder_gpu<T, cn>(src, dst, top, left, borderType, val.val, stream);
    }
}

#if defined __GNUC__ && __GNUC__ > 2 && __GNUC_MINOR__  > 4
typedef Npp32s __attribute__((__may_alias__)) Npp32s_a;
#else
typedef Npp32s Npp32s_a;
#endif

void cv::gpu::copyMakeBorder(InputArray _src, OutputArray _dst, int top, int bottom, int left, int right, int borderType, Scalar value, Stream& _stream)
{
    GpuMat src = _src.getGpuMat();

    CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
    CV_Assert( borderType == BORDER_REFLECT_101 || borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT || borderType == BORDER_REFLECT || borderType == BORDER_WRAP );

    _dst.create(src.rows + top + bottom, src.cols + left + right, src.type());
    GpuMat dst = _dst.getGpuMat();

    cudaStream_t stream = StreamAccessor::getStream(_stream);

    if (borderType == BORDER_CONSTANT && (src.type() == CV_8UC1 || src.type() == CV_8UC4 || src.type() == CV_32SC1 || src.type() == CV_32FC1))
    {
        NppiSize srcsz;
        srcsz.width  = src.cols;
        srcsz.height = src.rows;

        NppiSize dstsz;
        dstsz.width  = dst.cols;
        dstsz.height = dst.rows;

        NppStreamHandler h(stream);

        switch (src.type())
        {
        case CV_8UC1:
            {
                Npp8u nVal = saturate_cast<Npp8u>(value[0]);
                nppSafeCall( nppiCopyConstBorder_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz,
                    dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
                break;
            }
        case CV_8UC4:
            {
                Npp8u nVal[] = {saturate_cast<Npp8u>(value[0]), saturate_cast<Npp8u>(value[1]), saturate_cast<Npp8u>(value[2]), saturate_cast<Npp8u>(value[3])};
                nppSafeCall( nppiCopyConstBorder_8u_C4R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz,
                    dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
                break;
            }
        case CV_32SC1:
            {
                Npp32s nVal = saturate_cast<Npp32s>(value[0]);
                nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz,
                    dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
                break;
            }
        case CV_32FC1:
            {
                Npp32f val = saturate_cast<Npp32f>(value[0]);
                Npp32s nVal = *(reinterpret_cast<Npp32s_a*>(&val));
                nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz,
                    dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
                break;
            }
        }

        if (stream == 0)
            cudaSafeCall( cudaDeviceSynchronize() );
    }
    else
    {
        typedef void (*caller_t)(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderType, const Scalar& value, cudaStream_t stream);
        static const caller_t callers[6][4] =
        {
            {   copyMakeBorder_caller<uchar, 1>  ,    copyMakeBorder_caller<uchar, 2>   ,    copyMakeBorder_caller<uchar, 3>  ,    copyMakeBorder_caller<uchar, 4>},
            {0/*copyMakeBorder_caller<schar, 1>*/, 0/*copyMakeBorder_caller<schar, 2>*/ , 0/*copyMakeBorder_caller<schar, 3>*/, 0/*copyMakeBorder_caller<schar, 4>*/},
            {   copyMakeBorder_caller<ushort, 1> , 0/*copyMakeBorder_caller<ushort, 2>*/,    copyMakeBorder_caller<ushort, 3> ,    copyMakeBorder_caller<ushort, 4>},
            {   copyMakeBorder_caller<short, 1>  , 0/*copyMakeBorder_caller<short, 2>*/ ,    copyMakeBorder_caller<short, 3>  ,    copyMakeBorder_caller<short, 4>},
            {0/*copyMakeBorder_caller<int,   1>*/, 0/*copyMakeBorder_caller<int,   2>*/ , 0/*copyMakeBorder_caller<int,   3>*/, 0/*copyMakeBorder_caller<int  , 4>*/},
            {   copyMakeBorder_caller<float, 1>  , 0/*copyMakeBorder_caller<float, 2>*/ ,    copyMakeBorder_caller<float, 3>  ,    copyMakeBorder_caller<float ,4>}
        };

        caller_t func = callers[src.depth()][src.channels() - 1];
        CV_Assert(func != 0);

        func(src, dst, top, left, borderType, value, stream);
    }
}

#endif /* !defined (HAVE_CUDA) */