opencv/modules/gpu/src/cuda/surf.cu

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// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
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// The original code was written by Paul Furgale and Chi Hay Tong 
// and later optimized and prepared for integration into OpenCV by Itseez.
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//M*/

#include "internal_shared.hpp"
#include "opencv2/gpu/device/limits_gpu.hpp"

using namespace cv::gpu;
using namespace cv::gpu::device;

#define CV_PI 3.1415926535897932384626433832795f

namespace cv { namespace gpu { namespace surf
{
    ////////////////////////////////////////////////////////////////////////
    // Global parameters

    // The maximum number of features (before subpixel interpolation) that memory is reserved for.
    __constant__ int c_max_candidates;
    // The maximum number of features that memory is reserved for.
    __constant__ int c_max_features;
    // The maximum number of keypoints that memory is reserved for.
    __constant__ int c_max_keypoints;
    // The image size.
    __constant__ int c_img_rows;
    __constant__ int c_img_cols;
    // The number of layers.
    __constant__ int c_nOctaveLayers;
    // The hessian threshold.
    __constant__ float c_hessianThreshold;

    // The current octave.
    __constant__ int c_octave;
    // The current layer size.
    __constant__ int c_layer_rows;
    __constant__ int c_layer_cols;

    ////////////////////////////////////////////////////////////////////////
    // Integral image texture

    texture<unsigned int, 2, cudaReadModeElementType> sumTex(0, cudaFilterModePoint, cudaAddressModeClamp);
    texture<unsigned int, 2, cudaReadModeElementType> maskSumTex(0, cudaFilterModePoint, cudaAddressModeClamp);

    template <int N> __device__ float icvCalcHaarPatternSum(const float src[][5], int oldSize, int newSize, int y, int x)
	{
		#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
        typedef double real_t;        
        #else
        typedef float  real_t;
        #endif

		float ratio = (float)newSize / oldSize;
        
        real_t d = 0;

        #pragma unroll
        for (int k = 0; k < N; ++k)
        {
            int dx1 = __float2int_rn(ratio * src[k][0]);
            int dy1 = __float2int_rn(ratio * src[k][1]);
            int dx2 = __float2int_rn(ratio * src[k][2]);
            int dy2 = __float2int_rn(ratio * src[k][3]);

            real_t t = 0;
            t += tex2D(sumTex, x + dx1, y + dy1);
            t -= tex2D(sumTex, x + dx1, y + dy2);
            t -= tex2D(sumTex, x + dx2, y + dy1);
            t += tex2D(sumTex, x + dx2, y + dy2);

            d += t * src[k][4] / ((dx2 - dx1) * (dy2 - dy1));
        }

        return (float)d;
    }

    ////////////////////////////////////////////////////////////////////////
    // Hessian

    __constant__ float c_DX [3][5] = { {0, 2, 3, 7, 1}, {3, 2, 6, 7, -2}, {6, 2, 9, 7, 1} };
    __constant__ float c_DY [3][5] = { {2, 0, 7, 3, 1}, {2, 3, 7, 6, -2}, {2, 6, 7, 9, 1} };
    __constant__ float c_DXY[4][5] = { {1, 1, 4, 4, 1}, {5, 1, 8, 4, -1}, {1, 5, 4, 8, -1}, {5, 5, 8, 8, 1} };

    __host__ __device__ int calcSize(int octave, int layer)
    {
        /* Wavelet size at first layer of first octave. */
        const int HAAR_SIZE0 = 9;

        /* Wavelet size increment between layers. This should be an even number,
         such that the wavelet sizes in an octave are either all even or all odd.
         This ensures that when looking for the neighbours of a sample, the layers
         above and below are aligned correctly. */
        const int HAAR_SIZE_INC = 6;

        return (HAAR_SIZE0 + HAAR_SIZE_INC * layer) << octave;
    }

    __global__ void icvCalcLayerDetAndTrace(PtrStepf det, PtrStepf trace)
    {
        // Determine the indices
        const int gridDim_y = gridDim.y / (c_nOctaveLayers + 2);
        const int blockIdx_y = blockIdx.y % gridDim_y;
        const int blockIdx_z = blockIdx.y / gridDim_y;

        const int j = threadIdx.x + blockIdx.x * blockDim.x;
        const int i = threadIdx.y + blockIdx_y * blockDim.y;
        const int layer = blockIdx_z;

        const int size = calcSize(c_octave, layer);

        const int samples_i = 1 + ((c_img_rows - size) >> c_octave);
        const int samples_j = 1 + ((c_img_cols - size) >> c_octave);

        // Ignore pixels where some of the kernel is outside the image
        const int margin = (size >> 1) >> c_octave;

        if (size <= c_img_rows && size <= c_img_cols && i < samples_i && j < samples_j)
        {
            const float dx  = icvCalcHaarPatternSum<3>(c_DX , 9, size, i << c_octave, j << c_octave);
            const float dy  = icvCalcHaarPatternSum<3>(c_DY , 9, size, i << c_octave, j << c_octave);
            const float dxy = icvCalcHaarPatternSum<4>(c_DXY, 9, size, i << c_octave, j << c_octave);

            det.ptr(layer * c_layer_rows + i + margin)[j + margin] = dx * dy - 0.81f * dxy * dxy;
            trace.ptr(layer * c_layer_rows + i + margin)[j + margin] = dx + dy;
        }
    }

    void icvCalcLayerDetAndTrace_gpu(const PtrStepf& det, const PtrStepf& trace, int img_rows, int img_cols, int octave, int nOctaveLayers)
    {
        const int min_size = calcSize(octave, 0);
        const int max_samples_i = 1 + ((img_rows - min_size) >> octave);
        const int max_samples_j = 1 + ((img_cols - min_size) >> octave);

        dim3 threads(16, 16);

        dim3 grid;
        grid.x = divUp(max_samples_j, threads.x);
        grid.y = divUp(max_samples_i, threads.y) * (nOctaveLayers + 2);

        icvCalcLayerDetAndTrace<<<grid, threads>>>(det, trace);
        cudaSafeCall( cudaGetLastError() );

        cudaSafeCall( cudaThreadSynchronize() );
    }

    ////////////////////////////////////////////////////////////////////////
    // NONMAX
    
    struct WithOutMask
    {
        static __device__ bool check(int, int, int)
        {
            return true;
        }
    };

    __constant__ float c_DM[5] = {0, 0, 9, 9, 1};

    struct WithMask
    {
        static __device__ bool check(int sum_i, int sum_j, int size)
        {
			#if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 200
			typedef double real_t;        
			#else
			typedef float  real_t;
			#endif

			float ratio = (float)size / 9.0f;
	        
			real_t d = 0;

			int dx1 = __float2int_rn(ratio * c_DM[0]);
			int dy1 = __float2int_rn(ratio * c_DM[1]);
			int dx2 = __float2int_rn(ratio * c_DM[2]);
			int dy2 = __float2int_rn(ratio * c_DM[3]);

			real_t t = 0;
			t += tex2D(maskSumTex, sum_j + dx1, sum_i + dy1);
			t -= tex2D(maskSumTex, sum_j + dx1, sum_i + dy2);
			t -= tex2D(maskSumTex, sum_j + dx2, sum_i + dy1);
			t += tex2D(maskSumTex, sum_j + dx2, sum_i + dy2);

			d += t * c_DM[4] / ((dx2 - dx1) * (dy2 - dy1));

            return (d >= 0.5);
        }
    };

    template <typename Mask>
    __global__ void icvFindMaximaInLayer(PtrStepf det, PtrStepf trace, int4* maxPosBuffer, unsigned int* maxCounter)
    {
        #if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 110

        extern __shared__ float N9[];

        // The hidx variables are the indices to the hessian buffer.
        const int gridDim_y = gridDim.y / c_nOctaveLayers;
        const int blockIdx_y = blockIdx.y % gridDim_y;
        const int blockIdx_z = blockIdx.y / gridDim_y;

        const int layer = blockIdx_z + 1;

        const int size = calcSize(c_octave, layer);

        // Ignore pixels without a 3x3x3 neighbourhood in the layer above
        const int margin = ((calcSize(c_octave, layer + 1) >> 1) >> c_octave) + 1;

        const int j = threadIdx.x + blockIdx.x * (blockDim.x - 2) + margin - 1;
        const int i = threadIdx.y + blockIdx_y * (blockDim.y - 2) + margin - 1;

        // Is this thread within the hessian buffer?
        const int zoff = blockDim.x * blockDim.y;
        const int localLin = threadIdx.x + threadIdx.y * blockDim.x + zoff;
        N9[localLin - zoff] = det.ptr(c_layer_rows * (layer - 1) + i)[j];
        N9[localLin       ] = det.ptr(c_layer_rows * (layer    ) + i)[j];
        N9[localLin + zoff] = det.ptr(c_layer_rows * (layer + 1) + i)[j];
        __syncthreads();

        if (i < c_layer_rows - margin && j < c_layer_cols - margin && threadIdx.x > 0 && threadIdx.x < blockDim.x - 1 && threadIdx.y > 0 && threadIdx.y < blockDim.y - 1)
        {
            float val0 = N9[localLin];

            if (val0 > c_hessianThreshold)
            {
                // Coordinates for the start of the wavelet in the sum image. There
                // is some integer division involved, so don't try to simplify this
                // (cancel out sampleStep) without checking the result is the same
                const int sum_i = (i - ((size >> 1) >> c_octave)) << c_octave;
                const int sum_j = (j - ((size >> 1) >> c_octave)) << c_octave;

                if (Mask::check(sum_i, sum_j, size))
                {
                    // Check to see if we have a max (in its 26 neighbours)
                    const bool condmax = val0 > N9[localLin - 1 - blockDim.x - zoff]
                    &&                   val0 > N9[localLin     - blockDim.x - zoff]
                    &&                   val0 > N9[localLin + 1 - blockDim.x - zoff]
                    &&                   val0 > N9[localLin - 1              - zoff]
                    &&                   val0 > N9[localLin                  - zoff]
                    &&                   val0 > N9[localLin + 1              - zoff]
                    &&                   val0 > N9[localLin - 1 + blockDim.x - zoff]
                    &&                   val0 > N9[localLin     + blockDim.x - zoff]
                    &&                   val0 > N9[localLin + 1 + blockDim.x - zoff]

                    &&                   val0 > N9[localLin - 1 - blockDim.x]
                    &&                   val0 > N9[localLin     - blockDim.x]
                    &&                   val0 > N9[localLin + 1 - blockDim.x]
                    &&                   val0 > N9[localLin - 1             ]
                    &&                   val0 > N9[localLin + 1             ]
                    &&                   val0 > N9[localLin - 1 + blockDim.x]
                    &&                   val0 > N9[localLin     + blockDim.x]
                    &&                   val0 > N9[localLin + 1 + blockDim.x]

                    &&                   val0 > N9[localLin - 1 - blockDim.x + zoff]
                    &&                   val0 > N9[localLin     - blockDim.x + zoff]
                    &&                   val0 > N9[localLin + 1 - blockDim.x + zoff]
                    &&                   val0 > N9[localLin - 1              + zoff]
                    &&                   val0 > N9[localLin                  + zoff]
                    &&                   val0 > N9[localLin + 1              + zoff]
                    &&                   val0 > N9[localLin - 1 + blockDim.x + zoff]
                    &&                   val0 > N9[localLin     + blockDim.x + zoff]
                    &&                   val0 > N9[localLin + 1 + blockDim.x + zoff]
                    ;

                    if(condmax)
                    {
                        unsigned int ind = atomicInc(maxCounter,(unsigned int) -1);

                        if (ind < c_max_candidates)
                        {
                            const int laplacian = (int) copysignf(1.0f, trace.ptr(layer * c_layer_rows + i)[j]);

                            maxPosBuffer[ind] = make_int4(j, i, layer, laplacian);
                        }
                    }
                }
            }
        }

        #endif
    }

    void icvFindMaximaInLayer_gpu(const PtrStepf& det, const PtrStepf& trace, int4* maxPosBuffer, unsigned int* maxCounter,
        int img_rows, int img_cols, int octave, bool use_mask, int nOctaveLayers)
    {
        const int layer_rows = img_rows >> octave;
        const int layer_cols = img_cols >> octave;

        int min_margin = ((calcSize(octave, 2) >> 1) >> octave) + 1;

        dim3 threads(16, 16);

        dim3 grid;
        grid.x = divUp(layer_cols - 2 * min_margin, threads.x - 2);
        grid.y = divUp(layer_rows - 2 * min_margin, threads.y - 2) * nOctaveLayers;

        const size_t smem_size = threads.x * threads.y * 3 * sizeof(float);

        if (use_mask)
            icvFindMaximaInLayer<WithMask><<<grid, threads, smem_size>>>(det, trace, maxPosBuffer, maxCounter);
        else
            icvFindMaximaInLayer<WithOutMask><<<grid, threads, smem_size>>>(det, trace, maxPosBuffer, maxCounter);

        cudaSafeCall( cudaGetLastError() );

        cudaSafeCall( cudaThreadSynchronize() );
    }

    ////////////////////////////////////////////////////////////////////////
    // INTERPOLATION
    
    __global__ void icvInterpolateKeypoint(PtrStepf det, const int4* maxPosBuffer, KeyPoint_GPU* featuresBuffer, unsigned int* featureCounter)
    {
        #if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 110

        const int4 maxPos = maxPosBuffer[blockIdx.x];

        const int j = maxPos.x - 1 + threadIdx.x;
        const int i = maxPos.y - 1 + threadIdx.y;
        const int layer = maxPos.z - 1 + threadIdx.z;

        __shared__ float N9[3][3][3];
        __shared__ KeyPoint_GPU p;

        N9[threadIdx.z][threadIdx.y][threadIdx.x] = det.ptr(c_layer_rows * layer + i)[j];
        __syncthreads();

        if (threadIdx.x == 0 && threadIdx.y == 0 && threadIdx.z == 0)
        {
            __shared__ float dD[3];

            //dx
            dD[0] = -0.5f * (N9[1][1][2] - N9[1][1][0]);
            //dy
            dD[1] = -0.5f * (N9[1][2][1] - N9[1][0][1]);
            //ds
            dD[2] = -0.5f * (N9[2][1][1] - N9[0][1][1]);

            __shared__ float H[3][3];

            //dxx
            H[0][0] = N9[1][1][0] - 2.0f * N9[1][1][1] + N9[1][1][2];
            //dxy
            H[0][1]= 0.25f * (N9[1][2][2] - N9[1][2][0] - N9[1][0][2] + N9[1][0][0]);
            //dxs
            H[0][2]= 0.25f * (N9[2][1][2] - N9[2][1][0] - N9[0][1][2] + N9[0][1][0]);
            //dyx = dxy
            H[1][0] = H[0][1];
            //dyy
            H[1][1] = N9[1][0][1] - 2.0f * N9[1][1][1] + N9[1][2][1];
            //dys
            H[1][2]= 0.25f * (N9[2][2][1] - N9[2][0][1] - N9[0][2][1] + N9[0][0][1]);
            //dsx = dxs
            H[2][0] = H[0][2];
            //dsy = dys
            H[2][1] = H[1][2];
            //dss
            H[2][2] = N9[0][1][1] - 2.0f * N9[1][1][1] + N9[2][1][1];

            float det = H[0][0] * (H[1][1] * H[2][2] - H[1][2] * H[2][1])
              - H[0][1] * (H[1][0] * H[2][2] - H[1][2] * H[2][0])
              + H[0][2] * (H[1][0] * H[2][1] - H[1][1] * H[2][0]);

            if (det != 0.0f)
            {
                float invdet = 1.0f / det;

                __shared__ float x[3];

                x[0] = invdet * 
                    (dD[0] * (H[1][1] * H[2][2] - H[1][2] * H[2][1]) -
                     H[0][1] * (dD[1] * H[2][2] - H[1][2] * dD[2]) +
                     H[0][2] * (dD[1] * H[2][1] - H[1][1] * dD[2]));

                x[1] = invdet * 
                    (H[0][0] * (dD[1] * H[2][2] - H[1][2] * dD[2]) -
                     dD[0] * (H[1][0] * H[2][2] - H[1][2] * H[2][0]) +
                     H[0][2] * (H[1][0] * dD[2] - dD[1] * H[2][0]));

                x[2] = invdet * 
                    (H[0][0] * (H[1][1] * dD[2] - dD[1] * H[2][1]) -
                     H[0][1] * (H[1][0] * dD[2] - dD[1] * H[2][0]) +
                     dD[0] * (H[1][0] * H[2][1] - H[1][1] * H[2][0]));

                if (fabs(x[0]) <= 1.f && fabs(x[1]) <= 1.f && fabs(x[2]) <= 1.f)
                {
                    // if the step is within the interpolation region, perform it

                    // Get a new feature index.
                    unsigned int ind = atomicInc(featureCounter, (unsigned int)-1);

                    if (ind < c_max_features)
                    {
                        const int size = calcSize(c_octave, maxPos.z);

                        const int sum_i = (maxPos.y - ((size >> 1) >> c_octave)) << c_octave;
                        const int sum_j = (maxPos.x - ((size >> 1) >> c_octave)) << c_octave;
                        
                        const float center_i = sum_i + (float)(size - 1) / 2;
                        const float center_j = sum_j + (float)(size - 1) / 2;

                        p.x = center_j + x[0] * (1 << c_octave);
                        p.y = center_i + x[1] * (1 << c_octave);

                        int ds = size - calcSize(c_octave, maxPos.z - 1);
                        p.size = roundf(size + x[2] * ds);

                        p.laplacian = maxPos.w;
                        p.dir = 0.0f;
                        p.hessian = N9[1][1][1];

                        // Should we split up this transfer over many threads?
                        featuresBuffer[ind] = p;
                    }
                } // If the subpixel interpolation worked
            }
        } // If this is thread 0.

        #endif
    }

    void icvInterpolateKeypoint_gpu(const PtrStepf& det, const int4* maxPosBuffer, unsigned int maxCounter, KeyPoint_GPU* featuresBuffer, unsigned int* featureCounter)
    {
        dim3 threads;
        threads.x = 3;
        threads.y = 3;
        threads.z = 3;

        dim3 grid;
        grid.x = maxCounter;

        icvInterpolateKeypoint<<<grid, threads>>>(det, maxPosBuffer, featuresBuffer, featureCounter);
        cudaSafeCall( cudaGetLastError() );

        cudaSafeCall( cudaThreadSynchronize() );
    }

    ////////////////////////////////////////////////////////////////////////
    // Orientation

    #define ORI_SEARCH_INC 5
    #define ORI_WIN        60
    #define ORI_SAMPLES    113

    __constant__ float c_aptX[ORI_SAMPLES] = {-6, -5, -5, -5, -5, -5, -5, -5, -4, -4, -4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -3, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6};
    __constant__ float c_aptY[ORI_SAMPLES] = {0, -3, -2, -1, 0, 1, 2, 3, -4, -3, -2, -1, 0, 1, 2, 3, 4, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -4, -3, -2, -1, 0, 1, 2, 3, 4, -3, -2, -1, 0, 1, 2, 3, 0};
    __constant__ float c_aptW[ORI_SAMPLES] = {0.001455130288377404f, 0.001707611023448408f, 0.002547456417232752f, 0.003238451667129993f, 0.0035081731621176f, 0.003238451667129993f, 0.002547456417232752f, 0.001707611023448408f, 0.002003900473937392f, 0.0035081731621176f, 0.005233579315245152f, 0.00665318313986063f, 0.00720730796456337f, 0.00665318313986063f, 0.005233579315245152f, 0.0035081731621176f, 0.002003900473937392f, 0.001707611023448408f, 0.0035081731621176f, 0.006141661666333675f, 0.009162282571196556f, 0.01164754293859005f, 0.01261763460934162f, 0.01164754293859005f, 0.009162282571196556f, 0.006141661666333675f, 0.0035081731621176f, 0.001707611023448408f, 0.002547456417232752f, 0.005233579315245152f, 0.009162282571196556f, 0.01366852037608624f, 0.01737609319388866f, 0.0188232995569706f, 0.01737609319388866f, 0.01366852037608624f, 0.009162282571196556f, 0.005233579315245152f, 0.002547456417232752f, 0.003238451667129993f, 0.00665318313986063f, 0.01164754293859005f, 0.01737609319388866f, 0.02208934165537357f, 0.02392910048365593f, 0.02208934165537357f, 0.01737609319388866f, 0.01164754293859005f, 0.00665318313986063f, 0.003238451667129993f, 0.001455130288377404f, 0.0035081731621176f, 0.00720730796456337f, 0.01261763460934162f, 0.0188232995569706f, 0.02392910048365593f, 0.02592208795249462f, 0.02392910048365593f, 0.0188232995569706f, 0.01261763460934162f, 0.00720730796456337f, 0.0035081731621176f, 0.001455130288377404f, 0.003238451667129993f, 0.00665318313986063f, 0.01164754293859005f, 0.01737609319388866f, 0.02208934165537357f, 0.02392910048365593f, 0.02208934165537357f, 0.01737609319388866f, 0.01164754293859005f, 0.00665318313986063f, 0.003238451667129993f, 0.002547456417232752f, 0.005233579315245152f, 0.009162282571196556f, 0.01366852037608624f, 0.01737609319388866f, 0.0188232995569706f, 0.01737609319388866f, 0.01366852037608624f, 0.009162282571196556f, 0.005233579315245152f, 0.002547456417232752f, 0.001707611023448408f, 0.0035081731621176f, 0.006141661666333675f, 0.009162282571196556f, 0.01164754293859005f, 0.01261763460934162f, 0.01164754293859005f, 0.009162282571196556f, 0.006141661666333675f, 0.0035081731621176f, 0.001707611023448408f, 0.002003900473937392f, 0.0035081731621176f, 0.005233579315245152f, 0.00665318313986063f, 0.00720730796456337f, 0.00665318313986063f, 0.005233579315245152f, 0.0035081731621176f, 0.002003900473937392f, 0.001707611023448408f, 0.002547456417232752f, 0.003238451667129993f, 0.0035081731621176f, 0.003238451667129993f, 0.002547456417232752f, 0.001707611023448408f, 0.001455130288377404f};
    
    __constant__ float c_NX[2][5] = {{0, 0, 2, 4, -1}, {2, 0, 4, 4, 1}};
    __constant__ float c_NY[2][5] = {{0, 0, 4, 2, 1}, {0, 2, 4, 4, -1}};

    __global__ void icvCalcOrientation(const KeyPoint_GPU* featureBuffer, KeyPoint_GPU* keypoints, unsigned int* keypointCounter)
    {        
        #if defined (__CUDA_ARCH__) && __CUDA_ARCH__ >= 110

        __shared__ float s_X[128];
        __shared__ float s_Y[128];
        __shared__ float s_angle[128];

        __shared__ float s_sumx[64 * 4];
        __shared__ float s_sumy[64 * 4];

        __shared__ float s_feature[6];

        if (threadIdx.x < 6 && threadIdx.y == 0)
            s_feature[threadIdx.x] = ((float*)(&featureBuffer[blockIdx.x]))[threadIdx.x];
        __syncthreads();


        /* The sampling intervals and wavelet sized for selecting an orientation
         and building the keypoint descriptor are defined relative to 's' */
        const float s = s_feature[SF_SIZE] * 1.2f / 9.0f;

        /* To find the dominant orientation, the gradients in x and y are
         sampled in a circle of radius 6s using wavelets of size 4s.
         We ensure the gradient wavelet size is even to ensure the
         wavelet pattern is balanced and symmetric around its center */
        const int grad_wav_size = 2 * __float2int_rn(2.0f * s);

        // check when grad_wav_size is too big
        if ((c_img_rows + 1) >= grad_wav_size && (c_img_cols + 1) >= grad_wav_size)
        {
            // Calc X, Y, angle and store it to shared memory
            {
                const int tid = threadIdx.y * blockDim.x + threadIdx.x;

                float X = 0.0f, Y = 0.0f, angle = 0.0f;

                if (tid < ORI_SAMPLES)
                {
                    const float margin = (float)(grad_wav_size - 1) / 2.0f;
                    const int x = __float2int_rn(s_feature[SF_X] + c_aptX[tid] * s - margin);
                    const int y = __float2int_rn(s_feature[SF_Y] + c_aptY[tid] * s - margin);

                    if ((unsigned)y < (unsigned)((c_img_rows + 1) - grad_wav_size) && (unsigned)x < (unsigned)((c_img_cols + 1) - grad_wav_size))
                    {
                        X = c_aptW[tid] * icvCalcHaarPatternSum<2>(c_NX, 4, grad_wav_size, y, x);
                        Y = c_aptW[tid] * icvCalcHaarPatternSum<2>(c_NY, 4, grad_wav_size, y, x);
                    
                        angle = atan2f(Y, X);
                        if (angle < 0)
                            angle += 2.0f * CV_PI;
                        angle *= 180.0f / CV_PI;
                    }
                }
                if (tid < 128)
                {
                    s_X[tid] = X;
                    s_Y[tid] = Y;
                    s_angle[tid] = angle;
                }
            }
            __syncthreads();

            float bestx = 0, besty = 0, best_mod = 0;

            #pragma unroll
            for (int i = 0; i < 18; ++i)
            {
                const int dir = (i * 4 + threadIdx.y) * ORI_SEARCH_INC;

                float sumx = 0.0f, sumy = 0.0f;
                int d = abs(__float2int_rn(s_angle[threadIdx.x]) - dir);
                if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
                {
                    sumx = s_X[threadIdx.x];
                    sumy = s_Y[threadIdx.x];
                }
                d = abs(__float2int_rn(s_angle[threadIdx.x + 64]) - dir);
                if (d < ORI_WIN / 2 || d > 360 - ORI_WIN / 2)
                {
                    sumx += s_X[threadIdx.x + 64];
                    sumy += s_Y[threadIdx.x + 64];
                }

                float* s_sumx_row = s_sumx + threadIdx.y * 64;
                float* s_sumy_row = s_sumy + threadIdx.y * 64;

                s_sumx_row[threadIdx.x] = sumx;
                s_sumy_row[threadIdx.x] = sumy;
                __syncthreads();

                if (threadIdx.x < 32)
                {
                    volatile float* v_sumx_row = s_sumx_row;
                    volatile float* v_sumy_row = s_sumy_row;

                    v_sumx_row[threadIdx.x] = sumx += v_sumx_row[threadIdx.x + 32];
                    v_sumy_row[threadIdx.x] = sumy += v_sumy_row[threadIdx.x + 32];

                    v_sumx_row[threadIdx.x] = sumx += v_sumx_row[threadIdx.x + 16];
                    v_sumy_row[threadIdx.x] = sumy += v_sumy_row[threadIdx.x + 16];

                    v_sumx_row[threadIdx.x] = sumx += v_sumx_row[threadIdx.x + 8];
                    v_sumy_row[threadIdx.x] = sumy += v_sumy_row[threadIdx.x + 8];

                    v_sumx_row[threadIdx.x] = sumx += v_sumx_row[threadIdx.x + 4];
                    v_sumy_row[threadIdx.x] = sumy += v_sumy_row[threadIdx.x + 4];

                    v_sumx_row[threadIdx.x] = sumx += v_sumx_row[threadIdx.x + 2];
                    v_sumy_row[threadIdx.x] = sumy += v_sumy_row[threadIdx.x + 2];

                    v_sumx_row[threadIdx.x] = sumx += v_sumx_row[threadIdx.x + 1];
                    v_sumy_row[threadIdx.x] = sumy += v_sumy_row[threadIdx.x + 1];
                }

                const float temp_mod = sumx * sumx + sumy * sumy;
                if (temp_mod > best_mod)
                {
                    best_mod = temp_mod;
                    bestx = sumx;
                    besty = sumy;
                }
                __syncthreads();
            }

            if (threadIdx.x == 0)
            {
                s_X[threadIdx.y] = bestx;
                s_Y[threadIdx.y] = besty;
                s_angle[threadIdx.y] = best_mod;
            }
            __syncthreads();

            if (threadIdx.x < 2 && threadIdx.y == 0)
            {
                volatile float* v_x = s_X;
                volatile float* v_y = s_Y;
                volatile float* v_mod = s_angle;

                bestx = v_x[threadIdx.x];
                besty = v_y[threadIdx.x];
                best_mod = v_mod[threadIdx.x];

                float temp_mod = v_mod[threadIdx.x + 2];
                if (temp_mod > best_mod)
                {
                    v_x[threadIdx.x] = bestx = v_x[threadIdx.x + 2];
                    v_y[threadIdx.x] = besty = v_y[threadIdx.x + 2];
                    v_mod[threadIdx.x] = best_mod = temp_mod;
                }
                temp_mod = v_mod[threadIdx.x + 1];
                if (temp_mod > best_mod)
                {
                    v_x[threadIdx.x] = bestx = v_x[threadIdx.x + 1];
                    v_y[threadIdx.x] = besty = v_y[threadIdx.x + 1];
                }
            }

            if (threadIdx.x == 0 && threadIdx.y == 0 && best_mod != 0)
            {
                // Get a new feature index.
                unsigned int ind = atomicInc(keypointCounter, (unsigned int)-1);

                if (ind < c_max_keypoints)
                {
                    float kp_dir = atan2f(besty, bestx);
                    if (kp_dir < 0)
                        kp_dir += 2.0f * CV_PI;
                    kp_dir *= 180.0f / CV_PI;
                    __shared__ KeyPoint_GPU kp;

                    kp.x = s_feature[SF_X];
                    kp.y = s_feature[SF_Y];
                    kp.laplacian = s_feature[SF_LAPLACIAN];
                    kp.size = s_feature[SF_SIZE];
                    kp.dir = kp_dir;
                    kp.hessian = s_feature[SF_HESSIAN];

                    keypoints[ind] = kp;
                }
            }
        }

        #endif
    }

    #undef ORI_SEARCH_INC
    #undef ORI_WIN
    #undef ORI_SAMPLES

    void icvCalcOrientation_gpu(const KeyPoint_GPU* featureBuffer, int nFeatures, KeyPoint_GPU* keypoints, unsigned int* keypointCounter) 
    {
        dim3 threads;
        threads.x = 64;
        threads.y = 4;

        dim3 grid;
        grid.x = nFeatures;

        icvCalcOrientation<<<grid, threads>>>(featureBuffer, keypoints, keypointCounter);
        cudaSafeCall( cudaGetLastError() );

        cudaSafeCall( cudaThreadSynchronize() );
    }

    ////////////////////////////////////////////////////////////////////////
    // Descriptors

    #define PATCH_SZ 20

    texture<unsigned char, 2, cudaReadModeElementType> imgTex(0, cudaFilterModePoint, cudaAddressModeClamp);

    __constant__ float c_DW[PATCH_SZ * PATCH_SZ] = 
    {
        3.695352233989979e-006f, 8.444558261544444e-006f, 1.760426494001877e-005f, 3.34794785885606e-005f, 5.808438800158911e-005f, 9.193058212986216e-005f, 0.0001327334757661447f, 0.0001748319627949968f, 0.0002100782439811155f, 0.0002302826324012131f, 0.0002302826324012131f, 0.0002100782439811155f, 0.0001748319627949968f, 0.0001327334757661447f, 9.193058212986216e-005f, 5.808438800158911e-005f, 3.34794785885606e-005f, 1.760426494001877e-005f, 8.444558261544444e-006f, 3.695352233989979e-006f, 
        8.444558261544444e-006f, 1.929736572492402e-005f, 4.022897701361217e-005f, 7.650675252079964e-005f, 0.0001327334903180599f, 0.0002100782585330308f, 0.0003033203829545528f, 0.0003995231236331165f, 0.0004800673632416874f, 0.0005262381164357066f, 0.0005262381164357066f, 0.0004800673632416874f, 0.0003995231236331165f, 0.0003033203829545528f, 0.0002100782585330308f, 0.0001327334903180599f, 7.650675252079964e-005f, 4.022897701361217e-005f, 1.929736572492402e-005f, 8.444558261544444e-006f, 
        1.760426494001877e-005f, 4.022897701361217e-005f, 8.386484114453197e-005f, 0.0001594926579855382f, 0.0002767078403849155f, 0.0004379475140012801f, 0.0006323281559161842f, 0.0008328808471560478f, 0.001000790391117334f, 0.001097041997127235f, 0.001097041997127235f, 0.001000790391117334f, 0.0008328808471560478f, 0.0006323281559161842f, 0.0004379475140012801f, 0.0002767078403849155f, 0.0001594926579855382f, 8.386484114453197e-005f, 4.022897701361217e-005f, 1.760426494001877e-005f, 
        3.34794785885606e-005f, 7.650675252079964e-005f, 0.0001594926579855382f, 0.0003033203247468919f, 0.0005262380582280457f, 0.0008328807889483869f, 0.001202550483867526f, 0.001583957928232849f, 0.001903285388834775f, 0.002086334861814976f, 0.002086334861814976f, 0.001903285388834775f, 0.001583957928232849f, 0.001202550483867526f, 0.0008328807889483869f, 0.0005262380582280457f, 0.0003033203247468919f, 0.0001594926579855382f, 7.650675252079964e-005f, 3.34794785885606e-005f, 
        5.808438800158911e-005f, 0.0001327334903180599f, 0.0002767078403849155f, 0.0005262380582280457f, 0.0009129836107604206f, 0.001444985857233405f, 0.002086335094645619f, 0.002748048631474376f, 0.00330205773934722f, 0.003619635012000799f, 0.003619635012000799f, 0.00330205773934722f, 0.002748048631474376f, 0.002086335094645619f, 0.001444985857233405f, 0.0009129836107604206f, 0.0005262380582280457f, 0.0002767078403849155f, 0.0001327334903180599f, 5.808438800158911e-005f, 
        9.193058212986216e-005f, 0.0002100782585330308f, 0.0004379475140012801f, 0.0008328807889483869f, 0.001444985857233405f, 0.002286989474669099f, 0.00330205773934722f, 0.004349356517195702f, 0.00522619066759944f, 0.005728822201490402f, 0.005728822201490402f, 0.00522619066759944f, 0.004349356517195702f, 0.00330205773934722f, 0.002286989474669099f, 0.001444985857233405f, 0.0008328807889483869f, 0.0004379475140012801f, 0.0002100782585330308f, 9.193058212986216e-005f, 
        0.0001327334757661447f, 0.0003033203829545528f, 0.0006323281559161842f, 0.001202550483867526f, 0.002086335094645619f, 0.00330205773934722f, 0.004767658654600382f, 0.006279794964939356f, 0.007545807864516974f, 0.008271530270576477f, 0.008271530270576477f, 0.007545807864516974f, 0.006279794964939356f, 0.004767658654600382f, 0.00330205773934722f, 0.002086335094645619f, 0.001202550483867526f, 0.0006323281559161842f, 0.0003033203829545528f, 0.0001327334757661447f, 
        0.0001748319627949968f, 0.0003995231236331165f, 0.0008328808471560478f, 0.001583957928232849f, 0.002748048631474376f, 0.004349356517195702f, 0.006279794964939356f, 0.008271529339253902f, 0.009939077310264111f, 0.01089497376233339f, 0.01089497376233339f, 0.009939077310264111f, 0.008271529339253902f, 0.006279794964939356f, 0.004349356517195702f, 0.002748048631474376f, 0.001583957928232849f, 0.0008328808471560478f, 0.0003995231236331165f, 0.0001748319627949968f, 
        0.0002100782439811155f, 0.0004800673632416874f, 0.001000790391117334f, 0.001903285388834775f, 0.00330205773934722f, 0.00522619066759944f, 0.007545807864516974f, 0.009939077310264111f, 0.01194280479103327f, 0.01309141051024199f, 0.01309141051024199f, 0.01194280479103327f, 0.009939077310264111f, 0.007545807864516974f, 0.00522619066759944f, 0.00330205773934722f, 0.001903285388834775f, 0.001000790391117334f, 0.0004800673632416874f, 0.0002100782439811155f, 
        0.0002302826324012131f, 0.0005262381164357066f, 0.001097041997127235f, 0.002086334861814976f, 0.003619635012000799f, 0.005728822201490402f, 0.008271530270576477f, 0.01089497376233339f, 0.01309141051024199f, 0.01435048412531614f, 0.01435048412531614f, 0.01309141051024199f, 0.01089497376233339f, 0.008271530270576477f, 0.005728822201490402f, 0.003619635012000799f, 0.002086334861814976f, 0.001097041997127235f, 0.0005262381164357066f, 0.0002302826324012131f, 
        0.0002302826324012131f, 0.0005262381164357066f, 0.001097041997127235f, 0.002086334861814976f, 0.003619635012000799f, 0.005728822201490402f, 0.008271530270576477f, 0.01089497376233339f, 0.01309141051024199f, 0.01435048412531614f, 0.01435048412531614f, 0.01309141051024199f, 0.01089497376233339f, 0.008271530270576477f, 0.005728822201490402f, 0.003619635012000799f, 0.002086334861814976f, 0.001097041997127235f, 0.0005262381164357066f, 0.0002302826324012131f, 
        0.0002100782439811155f, 0.0004800673632416874f, 0.001000790391117334f, 0.001903285388834775f, 0.00330205773934722f, 0.00522619066759944f, 0.007545807864516974f, 0.009939077310264111f, 0.01194280479103327f, 0.01309141051024199f, 0.01309141051024199f, 0.01194280479103327f, 0.009939077310264111f, 0.007545807864516974f, 0.00522619066759944f, 0.00330205773934722f, 0.001903285388834775f, 0.001000790391117334f, 0.0004800673632416874f, 0.0002100782439811155f, 
        0.0001748319627949968f, 0.0003995231236331165f, 0.0008328808471560478f, 0.001583957928232849f, 0.002748048631474376f, 0.004349356517195702f, 0.006279794964939356f, 0.008271529339253902f, 0.009939077310264111f, 0.01089497376233339f, 0.01089497376233339f, 0.009939077310264111f, 0.008271529339253902f, 0.006279794964939356f, 0.004349356517195702f, 0.002748048631474376f, 0.001583957928232849f, 0.0008328808471560478f, 0.0003995231236331165f, 0.0001748319627949968f, 
        0.0001327334757661447f, 0.0003033203829545528f, 0.0006323281559161842f, 0.001202550483867526f, 0.002086335094645619f, 0.00330205773934722f, 0.004767658654600382f, 0.006279794964939356f, 0.007545807864516974f, 0.008271530270576477f, 0.008271530270576477f, 0.007545807864516974f, 0.006279794964939356f, 0.004767658654600382f, 0.00330205773934722f, 0.002086335094645619f, 0.001202550483867526f, 0.0006323281559161842f, 0.0003033203829545528f, 0.0001327334757661447f, 
        9.193058212986216e-005f, 0.0002100782585330308f, 0.0004379475140012801f, 0.0008328807889483869f, 0.001444985857233405f, 0.002286989474669099f, 0.00330205773934722f, 0.004349356517195702f, 0.00522619066759944f, 0.005728822201490402f, 0.005728822201490402f, 0.00522619066759944f, 0.004349356517195702f, 0.00330205773934722f, 0.002286989474669099f, 0.001444985857233405f, 0.0008328807889483869f, 0.0004379475140012801f, 0.0002100782585330308f, 9.193058212986216e-005f, 
        5.808438800158911e-005f, 0.0001327334903180599f, 0.0002767078403849155f, 0.0005262380582280457f, 0.0009129836107604206f, 0.001444985857233405f, 0.002086335094645619f, 0.002748048631474376f, 0.00330205773934722f, 0.003619635012000799f, 0.003619635012000799f, 0.00330205773934722f, 0.002748048631474376f, 0.002086335094645619f, 0.001444985857233405f, 0.0009129836107604206f, 0.0005262380582280457f, 0.0002767078403849155f, 0.0001327334903180599f, 5.808438800158911e-005f, 
        3.34794785885606e-005f, 7.650675252079964e-005f, 0.0001594926579855382f, 0.0003033203247468919f, 0.0005262380582280457f, 0.0008328807889483869f, 0.001202550483867526f, 0.001583957928232849f, 0.001903285388834775f, 0.002086334861814976f, 0.002086334861814976f, 0.001903285388834775f, 0.001583957928232849f, 0.001202550483867526f, 0.0008328807889483869f, 0.0005262380582280457f, 0.0003033203247468919f, 0.0001594926579855382f, 7.650675252079964e-005f, 3.34794785885606e-005f, 
        1.760426494001877e-005f, 4.022897701361217e-005f, 8.386484114453197e-005f, 0.0001594926579855382f, 0.0002767078403849155f, 0.0004379475140012801f, 0.0006323281559161842f, 0.0008328808471560478f, 0.001000790391117334f, 0.001097041997127235f, 0.001097041997127235f, 0.001000790391117334f, 0.0008328808471560478f, 0.0006323281559161842f, 0.0004379475140012801f, 0.0002767078403849155f, 0.0001594926579855382f, 8.386484114453197e-005f, 4.022897701361217e-005f, 1.760426494001877e-005f, 
        8.444558261544444e-006f, 1.929736572492402e-005f, 4.022897701361217e-005f, 7.650675252079964e-005f, 0.0001327334903180599f, 0.0002100782585330308f, 0.0003033203829545528f, 0.0003995231236331165f, 0.0004800673632416874f, 0.0005262381164357066f, 0.0005262381164357066f, 0.0004800673632416874f, 0.0003995231236331165f, 0.0003033203829545528f, 0.0002100782585330308f, 0.0001327334903180599f, 7.650675252079964e-005f, 4.022897701361217e-005f, 1.929736572492402e-005f, 8.444558261544444e-006f, 
        3.695352233989979e-006f, 8.444558261544444e-006f, 1.760426494001877e-005f, 3.34794785885606e-005f, 5.808438800158911e-005f, 9.193058212986216e-005f, 0.0001327334757661447f, 0.0001748319627949968f, 0.0002100782439811155f, 0.0002302826324012131f, 0.0002302826324012131f, 0.0002100782439811155f, 0.0001748319627949968f, 0.0001327334757661447f, 9.193058212986216e-005f, 5.808438800158911e-005f, 3.34794785885606e-005f, 1.760426494001877e-005f, 8.444558261544444e-006f, 3.695352233989979e-006f
    };

    __device__ void calcPATCH(float s_PATCH[6][6], float s_pt[5], int i1, int j1, int i2, int j2)
    {
        const float centerX = s_pt[SF_X];
        const float centerY = s_pt[SF_Y];
        const float size = s_pt[SF_SIZE];
        const float descriptor_dir = s_pt[SF_DIR] * (float)(CV_PI / 180);

        /* The sampling intervals and wavelet sized for selecting an orientation
         and building the keypoint descriptor are defined relative to 's' */
        const float s = size * 1.2f / 9.0f;

        /* Extract a window of pixels around the keypoint of size 20s */
        const int win_size = (int)((PATCH_SZ + 1) * s);

        float sin_dir;
        float cos_dir;
        sincosf(descriptor_dir, &sin_dir, &cos_dir);

        /* Nearest neighbour version (faster) */
        const float win_offset = -(float)(win_size - 1) / 2; 

        /* Scale the window to size PATCH_SZ so each pixel's size is s. This
           makes calculating the gradients with wavelets of size 2s easy */
        const float icoo = ((float)i1 / (PATCH_SZ + 1)) * win_size;
        const float jcoo = ((float)j1 / (PATCH_SZ + 1)) * win_size;

        const int i = __float2int_rd(icoo);
        const int j = __float2int_rd(jcoo);

        float pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir;
        float pixel_y = centerY - (win_offset + j) * sin_dir + (win_offset + i) * cos_dir;

        float res = tex2D(imgTex, pixel_x, pixel_y) * (i + 1 - icoo) * (j + 1 - jcoo);

        pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i + 1) * sin_dir;
        pixel_y = centerY - (win_offset + j) * sin_dir + (win_offset + i + 1) * cos_dir;

        res += tex2D(imgTex, pixel_x, pixel_y) * (icoo - i) * (j + 1 - jcoo);

        pixel_x = centerX + (win_offset + j + 1) * cos_dir + (win_offset + i + 1) * sin_dir;
        pixel_y = centerY - (win_offset + j + 1) * sin_dir + (win_offset + i + 1) * cos_dir;

        res += tex2D(imgTex, pixel_x, pixel_y) * (icoo - i) * (jcoo - j);
        
        pixel_x = centerX + (win_offset + j + 1) * cos_dir + (win_offset + i) * sin_dir;
        pixel_y = centerY - (win_offset + j + 1) * sin_dir + (win_offset + i) * cos_dir;

        res += tex2D(imgTex, pixel_x, pixel_y) * (i + 1 - icoo) * (jcoo - j);

        s_PATCH[i2][j2] = (unsigned char)res;
    }

    __device__ void calc_dx_dy(float s_PATCH[6][6], float s_dx_bin[25], float s_dy_bin[25], const KeyPoint_GPU* keypoints, int tid)
    {
        // get the interest point parameters (x, y, size, response, angle)
        __shared__ float s_pt[5];
        if (tid < 5)
        {
            s_pt[tid] = ((float*)(&keypoints[blockIdx.x]))[tid];
        }
        __syncthreads();

        // Compute sampling points
        // since grids are 2D, need to compute xBlock and yBlock indices
        const int xBlock = (blockIdx.y & 3);  // blockIdx.y % 4
        const int yBlock = (blockIdx.y >> 2); // floor(blockIdx.y/4)
        const int xIndex = xBlock * blockDim.x + threadIdx.x;
        const int yIndex = yBlock * blockDim.y + threadIdx.y;

        calcPATCH(s_PATCH, s_pt, yIndex, xIndex, threadIdx.y, threadIdx.x);
        if (threadIdx.x == 0)
            calcPATCH(s_PATCH, s_pt, yIndex, xBlock * blockDim.x + 5, threadIdx.y, 5);
        if (threadIdx.y == 0)
            calcPATCH(s_PATCH, s_pt, yBlock * blockDim.y + 5, xIndex, 5, threadIdx.x);
        if (threadIdx.x == 0 && threadIdx.y == 0)
            calcPATCH(s_PATCH, s_pt, xBlock * blockDim.x + 5, yBlock * blockDim.y + 5, 5, 5);
        __syncthreads();

        const float dw = c_DW[yIndex * PATCH_SZ + xIndex];

        const float vx = (s_PATCH[threadIdx.y    ][threadIdx.x + 1] - s_PATCH[threadIdx.y][threadIdx.x] + s_PATCH[threadIdx.y + 1][threadIdx.x + 1] - s_PATCH[threadIdx.y + 1][threadIdx.x    ]) * dw;
        const float vy = (s_PATCH[threadIdx.y + 1][threadIdx.x    ] - s_PATCH[threadIdx.y][threadIdx.x] + s_PATCH[threadIdx.y + 1][threadIdx.x + 1] - s_PATCH[threadIdx.y    ][threadIdx.x + 1]) * dw;

        s_dx_bin[tid] = vx;
        s_dy_bin[tid] = vy;
    }

    __device__ void reduce_sum25(volatile float* sdata1, volatile float* sdata2,
                                 volatile float* sdata3, volatile float* sdata4, int tid)
    {
        // first step is to reduce from 25 to 16
        if (tid < 9) // use 9 threads
        {
            sdata1[tid] += sdata1[tid + 16];
            sdata2[tid] += sdata2[tid + 16];
            sdata3[tid] += sdata3[tid + 16];
            sdata4[tid] += sdata4[tid + 16];
        }

        // sum (reduce) from 16 to 1 (unrolled - aligned to a half-warp)
        if (tid < 16)
        {
            sdata1[tid] += sdata1[tid + 8];
            sdata1[tid] += sdata1[tid + 4];
            sdata1[tid] += sdata1[tid + 2];
            sdata1[tid] += sdata1[tid + 1];

            sdata2[tid] += sdata2[tid + 8];
            sdata2[tid] += sdata2[tid + 4];
            sdata2[tid] += sdata2[tid + 2];
            sdata2[tid] += sdata2[tid + 1];

            sdata3[tid] += sdata3[tid + 8];
            sdata3[tid] += sdata3[tid + 4];
            sdata3[tid] += sdata3[tid + 2];
            sdata3[tid] += sdata3[tid + 1];

            sdata4[tid] += sdata4[tid + 8];
            sdata4[tid] += sdata4[tid + 4];
            sdata4[tid] += sdata4[tid + 2];
            sdata4[tid] += sdata4[tid + 1];
        }
    }

    // Spawn 16 blocks per interest point
    // - computes unnormalized 64 dimensional descriptor, puts it into d_descriptors in the correct location
    __global__ void compute_descriptors64(PtrStepf descriptors, const KeyPoint_GPU* features)
    {
        // 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
        __shared__ float s_PATCH[6][6];
        __shared__ float sdx[25];
        __shared__ float sdy[25];
        __shared__ float sdxabs[25];
        __shared__ float sdyabs[25];

        const int tid = threadIdx.y * blockDim.x + threadIdx.x;

        calc_dx_dy(s_PATCH, sdx, sdy, features, tid);
        __syncthreads();

        sdxabs[tid] = fabs(sdx[tid]); // |dx| array
        sdyabs[tid] = fabs(sdy[tid]); // |dy| array
        __syncthreads();

        reduce_sum25(sdx, sdy, sdxabs, sdyabs, tid);
        __syncthreads();

        float* descriptors_block = descriptors.ptr(blockIdx.x) + (blockIdx.y << 2);

        // write dx, dy, |dx|, |dy|
        if (tid == 0)
        {
            descriptors_block[0] = sdx[0];
            descriptors_block[1] = sdy[0];
            descriptors_block[2] = sdxabs[0];
            descriptors_block[3] = sdyabs[0];
        }
    }

    // Spawn 16 blocks per interest point
    // - computes unnormalized 128 dimensional descriptor, puts it into d_descriptors in the correct location
    __global__ void compute_descriptors128(PtrStepf descriptors, const KeyPoint_GPU* features)
    {
        // 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
        __shared__ float s_PATCH[6][6];
        __shared__ float sdx[25];
        __shared__ float sdy[25];

        // sum (reduce) 5x5 area response
        __shared__ float sd1[25];
        __shared__ float sd2[25];
        __shared__ float sdabs1[25];
        __shared__ float sdabs2[25];

        const int tid = threadIdx.y * blockDim.x + threadIdx.x;

        calc_dx_dy(s_PATCH, sdx, sdy, features, tid);
        __syncthreads();

        if (sdy[tid] >= 0)
        {
            sd1[tid] = sdx[tid];
            sdabs1[tid] = fabs(sdx[tid]);
            sd2[tid] = 0;
            sdabs2[tid] = 0;
        }
        else
        {
            sd1[tid] = 0;
            sdabs1[tid] = 0;
            sd2[tid] = sdx[tid];
            sdabs2[tid] = fabs(sdx[tid]);
        }
        __syncthreads();

        reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
        __syncthreads();

        float* descriptors_block = descriptors.ptr(blockIdx.x) + (blockIdx.y << 3);

        // write dx (dy >= 0), |dx| (dy >= 0), dx (dy < 0), |dx| (dy < 0)
        if (tid == 0)
        {
            descriptors_block[0] = sd1[0];
            descriptors_block[1] = sdabs1[0];
            descriptors_block[2] = sd2[0];
            descriptors_block[3] = sdabs2[0];
        }
        __syncthreads();

        if (sdx[tid] >= 0)
        {
            sd1[tid] = sdy[tid];
            sdabs1[tid] = fabs(sdy[tid]);
            sd2[tid] = 0;
            sdabs2[tid] = 0;
        }
        else
        {
            sd1[tid] = 0;
            sdabs1[tid] = 0;
            sd2[tid] = sdy[tid];
            sdabs2[tid] = fabs(sdy[tid]);
        }
        __syncthreads();

        reduce_sum25(sd1, sd2, sdabs1, sdabs2, tid);
        __syncthreads();

        // write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
        if (tid == 0)
        {
            descriptors_block[4] = sd1[0];
            descriptors_block[5] = sdabs1[0];
            descriptors_block[6] = sd2[0];
            descriptors_block[7] = sdabs2[0];
        }
    }

    template <int BLOCK_DIM_X> __global__ void normalize_descriptors(PtrStepf descriptors)
    {
        // no need for thread ID
        float* descriptor_base = descriptors.ptr(blockIdx.x);

        // read in the unnormalized descriptor values (squared)
        __shared__ float sqDesc[BLOCK_DIM_X];
        const float lookup = descriptor_base[threadIdx.x];
        sqDesc[threadIdx.x] = lookup * lookup;
        __syncthreads();

        if (BLOCK_DIM_X >= 128)
        {
            if (threadIdx.x < 64)
                sqDesc[threadIdx.x] += sqDesc[threadIdx.x + 64];
            __syncthreads();
        }

        // reduction to get total
        if (threadIdx.x < 32)
        {
            volatile float* smem = sqDesc;

            smem[threadIdx.x] += smem[threadIdx.x + 32];
            smem[threadIdx.x] += smem[threadIdx.x + 16];
            smem[threadIdx.x] += smem[threadIdx.x + 8];
            smem[threadIdx.x] += smem[threadIdx.x + 4];
            smem[threadIdx.x] += smem[threadIdx.x + 2];
            smem[threadIdx.x] += smem[threadIdx.x + 1];
        }

        // compute length (square root)
        __shared__ float len;
        if (threadIdx.x == 0)
        {
            len = sqrtf(sqDesc[0]);
        }
        __syncthreads();

        // normalize and store in output
        descriptor_base[threadIdx.x] = lookup / len;
    }

    void compute_descriptors_gpu(const DevMem2Df& descriptors, const KeyPoint_GPU* features, int nFeatures)
    {
        // compute unnormalized descriptors, then normalize them - odd indexing since grid must be 2D
        
        if (descriptors.cols == 64)
        {
            compute_descriptors64<<<dim3(nFeatures, 16, 1), dim3(5, 5, 1)>>>(descriptors, features);
            cudaSafeCall( cudaGetLastError() );

            cudaSafeCall( cudaThreadSynchronize() );

            normalize_descriptors<64><<<dim3(nFeatures, 1, 1), dim3(64, 1, 1)>>>(descriptors);
            cudaSafeCall( cudaGetLastError() );

            cudaSafeCall( cudaThreadSynchronize() );
        }
        else
        {
            compute_descriptors128<<<dim3(nFeatures, 16, 1), dim3(5, 5, 1)>>>(descriptors, features);            
            cudaSafeCall( cudaGetLastError() );

            cudaSafeCall( cudaThreadSynchronize() );

            normalize_descriptors<128><<<dim3(nFeatures, 1, 1), dim3(128, 1, 1)>>>(descriptors);            
            cudaSafeCall( cudaGetLastError() );

            cudaSafeCall( cudaThreadSynchronize() );
        }
    }
}}}