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temporarily disabled compute descriptor kernel for new cards (some problems with threads synchronization), old version of kernels is used.

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This commit is contained in:
Vladislav Vinogradov 2011-02-22 09:27:42 +00:00
parent 5b3d786e30
commit 32a2fde8ac
2 changed files with 341 additions and 383 deletions
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354
modules/gpu/src/cuda/surf.cu
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@ -122,7 +122,7 @@ namespace cv { namespace gpu { namespace surf
__constant__ float c_dxy_scale;
// The scale associated with the first interval of the first octave
__constant__ float c_initialScale;
//! The interest operator threshold
// The interest operator threshold
__constant__ float c_threshold;
// Ther octave
@ -685,7 +685,6 @@ namespace cv { namespace gpu { namespace surf
// - SURF says to only use a circle, but the branching logic would slow it down
// - Gaussian weighting should reduce the effects of the outer points anyway
if (tid2 < 169)
{
dx -= texLookups[threadIdx.x ][threadIdx.y ];
dx += 2.f*texLookups[threadIdx.x + 2][threadIdx.y ];
@ -835,24 +834,12 @@ namespace cv { namespace gpu { namespace surf
descriptor_base[threadIdx.x] = lookup / len;
}
__device__ void calc_dx_dy(float sdx[4][4][25], float sdy[4][4][25], const KeyPoint_GPU* features)
__device__ void calc_dx_dy(float* sdx_bin, float* sdy_bin, const float* ipt,
int xIndex, int yIndex, int tid)
{
// get the interest point parameters (x, y, size, response, angle)
__shared__ float ipt[5];
if (threadIdx.x < 5 && threadIdx.y == 0 && threadIdx.z == 0)
{
ipt[threadIdx.x] = ((float*)(&features[blockIdx.x]))[threadIdx.x];
}
__syncthreads();
float sin_theta, cos_theta;
sincosf(ipt[SF_ANGLE] * (CV_PI / 180.0f), &sin_theta, &cos_theta);
// Compute sampling points
// since grids are 2D, need to compute xBlock and yBlock indices
const int xIndex = threadIdx.y * 5 + threadIdx.x % 5;
const int yIndex = threadIdx.z * 5 + threadIdx.x / 5;
// Compute rotated sampling points
// (clockwise rotation since we are rotating the lattice)
// (subtract 9.5f to start sampling at the top left of the lattice, 0.5f is to space points out properly - there is no center pixel)
@ -865,7 +852,6 @@ namespace cv { namespace gpu { namespace surf
// a b c
// d f
// g h i
const float a = tex2D(sumTex, sample_x - ipt[SF_SIZE], sample_y - ipt[SF_SIZE]);
const float b = tex2D(sumTex, sample_x, sample_y - ipt[SF_SIZE]);
const float c = tex2D(sumTex, sample_x + ipt[SF_SIZE], sample_y - ipt[SF_SIZE]);
@ -883,53 +869,64 @@ namespace cv { namespace gpu { namespace surf
// rotate responses (store all dxs then all dys)
// - counterclockwise rotation to rotate back to zero orientation
sdx[threadIdx.z][threadIdx.y][threadIdx.x] = aa_dx * cos_theta - aa_dy * sin_theta; // rotated dx
sdy[threadIdx.z][threadIdx.y][threadIdx.x] = aa_dx * sin_theta + aa_dy * cos_theta; // rotated dy
sdx_bin[tid] = aa_dx * cos_theta - aa_dy * sin_theta; // rotated dx
sdy_bin[tid] = aa_dx * sin_theta + aa_dy * cos_theta; // rotated dy
}
__device__ void reduce_sum(float sdata1[4][4][25], float sdata2[4][4][25], float sdata3[4][4][25],
float sdata4[4][4][25])
__device__ void calc_dx_dy(float* sdx_bin, float* sdy_bin, const KeyPoint_GPU* features)//(float sdx[4][4][25], float sdy[4][4][25], const KeyPoint_GPU* features)
{
// first step is to reduce from 25 to 16
if (threadIdx.x < 9) // use 9 threads
// get the interest point parameters (x, y, size, response, angle)
__shared__ float ipt[5];
if (threadIdx.x < 5 && threadIdx.y == 0)
{
sdata1[threadIdx.z][threadIdx.y][threadIdx.x] += sdata1[threadIdx.z][threadIdx.y][threadIdx.x + 16];
sdata2[threadIdx.z][threadIdx.y][threadIdx.x] += sdata2[threadIdx.z][threadIdx.y][threadIdx.x + 16];
sdata3[threadIdx.z][threadIdx.y][threadIdx.x] += sdata3[threadIdx.z][threadIdx.y][threadIdx.x + 16];
sdata4[threadIdx.z][threadIdx.y][threadIdx.x] += sdata4[threadIdx.z][threadIdx.y][threadIdx.x + 16];
ipt[threadIdx.x] = ((float*)(&features[blockIdx.x]))[threadIdx.x];
}
__syncthreads();
// sum (reduce) from 16 to 1 (unrolled - aligned to a half-warp)
if (threadIdx.x < 16)
// Compute sampling points
// since grids are 2D, need to compute xBlock and yBlock indices
const int xBlock = (threadIdx.y & 3); // threadIdx.y % 4
const int yBlock = (threadIdx.y >> 2); // floor(threadIdx.y / 4)
const int xIndex = (xBlock * 5) + (threadIdx.x % 5);
const int yIndex = (yBlock * 5) + (threadIdx.x / 5);
calc_dx_dy(sdx_bin, sdy_bin, ipt, xIndex, yIndex, threadIdx.x);
}
__device__ void reduce_sum25(volatile float* sdata1, volatile float* sdata2,
volatile float* sdata3, volatile float* sdata4, int tid)
{
volatile float* smem = sdata1[threadIdx.z][threadIdx.y];
// 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];
}
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];
// 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];
smem = sdata2[threadIdx.z][threadIdx.y];
sdata2[tid] += sdata2[tid + 8];
sdata2[tid] += sdata2[tid + 4];
sdata2[tid] += sdata2[tid + 2];
sdata2[tid] += sdata2[tid + 1];
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];
sdata3[tid] += sdata3[tid + 8];
sdata3[tid] += sdata3[tid + 4];
sdata3[tid] += sdata3[tid + 2];
sdata3[tid] += sdata3[tid + 1];
smem = sdata3[threadIdx.z][threadIdx.y];
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];
smem = sdata4[threadIdx.z][threadIdx.y];
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];
sdata4[tid] += sdata4[tid + 8];
sdata4[tid] += sdata4[tid + 4];
sdata4[tid] += sdata4[tid + 2];
sdata4[tid] += sdata4[tid + 1];
}
}
@ -938,31 +935,43 @@ namespace cv { namespace gpu { namespace surf
__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 sdx[4][4][25];
__shared__ float sdy[4][4][25];
__shared__ float sdx [16 * 25];
__shared__ float sdy [16 * 25];
__shared__ float sdxabs[16 * 25];
__shared__ float sdyabs[16 * 25];
calc_dx_dy(sdx, sdy, features);
__shared__ float sdesc[64];
float* sdx_bin = sdx + (threadIdx.y * 25);
float* sdy_bin = sdy + (threadIdx.y * 25);
float* sdxabs_bin = sdxabs + (threadIdx.y * 25);
float* sdyabs_bin = sdyabs + (threadIdx.y * 25);
calc_dx_dy(sdx_bin, sdy_bin, features);
__syncthreads();
__shared__ float sdxabs[4][4][25];
__shared__ float sdyabs[4][4][25];
sdxabs[threadIdx.z][threadIdx.y][threadIdx.x] = fabs(sdx[threadIdx.z][threadIdx.y][threadIdx.x]); // |dx| array
sdyabs[threadIdx.z][threadIdx.y][threadIdx.x] = fabs(sdy[threadIdx.z][threadIdx.y][threadIdx.x]); // |dy| array
sdxabs_bin[threadIdx.x] = fabs(sdx_bin[threadIdx.x]); // |dx| array
sdyabs_bin[threadIdx.x] = fabs(sdy_bin[threadIdx.x]); // |dy| array
__syncthreads();
reduce_sum(sdx, sdy, sdxabs, sdyabs);
reduce_sum25(sdx_bin, sdy_bin, sdxabs_bin, sdyabs_bin, threadIdx.x);
__syncthreads();
float* descriptors_block = descriptors.ptr(blockIdx.x) + threadIdx.z * 16 + threadIdx.y * 4;
float* sdesc_bin = sdesc + (threadIdx.y << 2);
// write dx, dy, |dx|, |dy|
if (threadIdx.x == 0)
{
descriptors_block[0] = sdx[threadIdx.z][threadIdx.y][0];
descriptors_block[1] = sdy[threadIdx.z][threadIdx.y][0];
descriptors_block[2] = sdxabs[threadIdx.z][threadIdx.y][0];
descriptors_block[3] = sdyabs[threadIdx.z][threadIdx.y][0];
sdesc_bin[0] = sdx_bin[0];
sdesc_bin[1] = sdy_bin[0];
sdesc_bin[2] = sdxabs_bin[0];
sdesc_bin[3] = sdyabs_bin[0];
}
__syncthreads();
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
if (tid < 64)
descriptors.ptr(blockIdx.x)[tid] = sdesc[tid];
}
// Spawn 16 blocks per interest point
@ -970,74 +979,90 @@ namespace cv { namespace gpu { namespace surf
__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 sdx[4][4][25];
__shared__ float sdy[4][4][25];
calc_dx_dy(sdx, sdy, features);
__syncthreads();
__shared__ float sdx[16 * 25];
__shared__ float sdy[16 * 25];
// sum (reduce) 5x5 area response
__shared__ float sd1[4][4][25];
__shared__ float sd2[4][4][25];
__shared__ float sdabs1[4][4][25];
__shared__ float sdabs2[4][4][25];
__shared__ float sd1[16 * 25];
__shared__ float sd2[16 * 25];
__shared__ float sdabs1[16 * 25];
__shared__ float sdabs2[16 * 25];
if (sdy[threadIdx.z][threadIdx.y][threadIdx.x] >= 0)
__shared__ float sdesc[128];
float* sdx_bin = sdx + (threadIdx.y * 25);
float* sdy_bin = sdy + (threadIdx.y * 25);
float* sd1_bin = sd1 + (threadIdx.y * 25);
float* sd2_bin = sd2 + (threadIdx.y * 25);
float* sdabs1_bin = sdabs1 + (threadIdx.y * 25);
float* sdabs2_bin = sdabs2 + (threadIdx.y * 25);
calc_dx_dy(sdx_bin, sdy_bin, features);
__syncthreads();
if (sdy_bin[threadIdx.x] >= 0)
{
sd1[threadIdx.z][threadIdx.y][threadIdx.x] = sdx[threadIdx.z][threadIdx.y][threadIdx.x];
sdabs1[threadIdx.z][threadIdx.y][threadIdx.x] = fabs(sdx[threadIdx.z][threadIdx.y][threadIdx.x]);
sd2[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sdabs2[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sd1_bin[threadIdx.x] = sdx_bin[threadIdx.x];
sdabs1_bin[threadIdx.x] = fabs(sdx_bin[threadIdx.x]);
sd2_bin[threadIdx.x] = 0;
sdabs2_bin[threadIdx.x] = 0;
}
else
{
sd1[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sdabs1[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sd2[threadIdx.z][threadIdx.y][threadIdx.x] = sdx[threadIdx.z][threadIdx.y][threadIdx.x];
sdabs2[threadIdx.z][threadIdx.y][threadIdx.x] = fabs(sdx[threadIdx.z][threadIdx.y][threadIdx.x]);
sd1_bin[threadIdx.x] = 0;
sdabs1_bin[threadIdx.x] = 0;
sd2_bin[threadIdx.x] = sdx_bin[threadIdx.x];
sdabs2_bin[threadIdx.x] = fabs(sdx[threadIdx.x]);
}
__syncthreads();
reduce_sum(sd1, sd2, sdabs1, sdabs2);
reduce_sum25(sd1_bin, sd2_bin, sdabs1_bin, sdabs2_bin, threadIdx.x);
__syncthreads();
float* descriptors_block = descriptors.ptr(blockIdx.x) + threadIdx.z * 32 + threadIdx.y * 8;
float* sdesc_bin = sdesc + (threadIdx.y << 3);
// write dx (dy >= 0), |dx| (dy >= 0), dx (dy < 0), |dx| (dy < 0)
if (threadIdx.x == 0)
{
descriptors_block[0] = sd1[threadIdx.z][threadIdx.y][0];
descriptors_block[1] = sdabs1[threadIdx.z][threadIdx.y][0];
descriptors_block[2] = sd2[threadIdx.z][threadIdx.y][0];
descriptors_block[3] = sdabs2[threadIdx.z][threadIdx.y][0];
sdesc_bin[0] = sd1_bin[0];
sdesc_bin[1] = sdabs1_bin[0];
sdesc_bin[2] = sd2_bin[0];
sdesc_bin[3] = sdabs2_bin[0];
}
__syncthreads();
if (sdx[threadIdx.z][threadIdx.y][threadIdx.x] >= 0)
if (sdx_bin[threadIdx.x] >= 0)
{
sd1[threadIdx.z][threadIdx.y][threadIdx.x] = sdy[threadIdx.z][threadIdx.y][threadIdx.x];
sdabs1[threadIdx.z][threadIdx.y][threadIdx.x] = fabs(sdy[threadIdx.z][threadIdx.y][threadIdx.x]);
sd2[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sdabs2[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sd1_bin[threadIdx.x] = sdy_bin[threadIdx.x];
sdabs1_bin[threadIdx.x] = fabs(sdy_bin[threadIdx.x]);
sd2_bin[threadIdx.x] = 0;
sdabs2_bin[threadIdx.x] = 0;
}
else
{
sd1[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sdabs1[threadIdx.z][threadIdx.y][threadIdx.x] = 0;
sd2[threadIdx.z][threadIdx.y][threadIdx.x] = sdy[threadIdx.z][threadIdx.y][threadIdx.x];
sdabs2[threadIdx.z][threadIdx.y][threadIdx.x] = fabs(sdy[threadIdx.z][threadIdx.y][threadIdx.x]);
sd1_bin[threadIdx.x] = 0;
sdabs1_bin[threadIdx.x] = 0;
sd2_bin[threadIdx.x] = sdy_bin[threadIdx.x];
sdabs2_bin[threadIdx.x] = fabs(sdy_bin[threadIdx.x]);
}
__syncthreads();
reduce_sum(sd1, sd2, sdabs1, sdabs2);
reduce_sum25(sd1_bin, sd2_bin, sdabs1_bin, sdabs2_bin, threadIdx.x);
__syncthreads();
// write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
if (threadIdx.x == 0)
{
descriptors_block[4] = sd1[threadIdx.z][threadIdx.y][0];
descriptors_block[5] = sdabs1[threadIdx.z][threadIdx.y][0];
descriptors_block[6] = sd2[threadIdx.z][threadIdx.y][0];
descriptors_block[7] = sdabs2[threadIdx.z][threadIdx.y][0];
sdesc_bin[4] = sd1_bin[0];
sdesc_bin[5] = sdabs1_bin[0];
sdesc_bin[6] = sd2_bin[0];
sdesc_bin[7] = sdabs2_bin[0];
}
__syncthreads();
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
if (tid < 128)
descriptors.ptr(blockIdx.x)[tid] = sdesc[tid];
}
void compute_descriptors_gpu(const DevMem2Df& descriptors, const KeyPoint_GPU* features, int nFeatures)
@ -1046,7 +1071,7 @@ namespace cv { namespace gpu { namespace surf
if (descriptors.cols == 64)
{
compute_descriptors64<<<dim3(nFeatures, 1, 1), dim3(25, 4, 4)>>>(descriptors, features);
compute_descriptors64<<<dim3(nFeatures, 1, 1), dim3(25, 16, 1)>>>(descriptors, features);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaThreadSynchronize() );
@ -1058,7 +1083,7 @@ namespace cv { namespace gpu { namespace surf
}
else
{
compute_descriptors128<<<dim3(nFeatures, 1, 1), dim3(25, 4, 4)>>>(descriptors, features);
compute_descriptors128<<<dim3(nFeatures, 1, 1), dim3(25, 16, 1)>>>(descriptors, features);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaThreadSynchronize() );
@ -1080,9 +1105,6 @@ namespace cv { namespace gpu { namespace surf
}
__syncthreads();
float sin_theta, cos_theta;
sincosf(ipt[SF_ANGLE] * (CV_PI / 180.0f), &sin_theta, &cos_theta);
// Compute sampling points
// since grids are 2D, need to compute xBlock and yBlock indices
const int xBlock = (blockIdx.y & 3); // blockIdx.y % 4
@ -1090,100 +1112,40 @@ namespace cv { namespace gpu { namespace surf
const int xIndex = xBlock * blockDim.x + threadIdx.x;
const int yIndex = yBlock * blockDim.y + threadIdx.y;
// Compute rotated sampling points
// (clockwise rotation since we are rotating the lattice)
// (subtract 9.5f to start sampling at the top left of the lattice, 0.5f is to space points out properly - there is no center pixel)
const float sample_x = ipt[SF_X] + (cos_theta * ((float) (xIndex-9.5f)) * ipt[SF_SIZE]
+ sin_theta * ((float) (yIndex-9.5f)) * ipt[SF_SIZE]);
const float sample_y = ipt[SF_Y] + (-sin_theta * ((float) (xIndex-9.5f)) * ipt[SF_SIZE]
+ cos_theta * ((float) (yIndex-9.5f)) * ipt[SF_SIZE]);
// gather integral image lookups for Haar wavelets at each point (some lookups are shared between dx and dy)
// a b c
// d f
// g h i
const float a = tex2D(sumTex, sample_x - ipt[SF_SIZE], sample_y - ipt[SF_SIZE]);
const float b = tex2D(sumTex, sample_x, sample_y - ipt[SF_SIZE]);
const float c = tex2D(sumTex, sample_x + ipt[SF_SIZE], sample_y - ipt[SF_SIZE]);
const float d = tex2D(sumTex, sample_x - ipt[SF_SIZE], sample_y);
const float f = tex2D(sumTex, sample_x + ipt[SF_SIZE], sample_y);
const float g = tex2D(sumTex, sample_x - ipt[SF_SIZE], sample_y + ipt[SF_SIZE]);
const float h = tex2D(sumTex, sample_x, sample_y + ipt[SF_SIZE]);
const float i = tex2D(sumTex, sample_x + ipt[SF_SIZE], sample_y + ipt[SF_SIZE]);
// compute axis-aligned HaarX, HaarY
// (could group the additions together into multiplications)
const float gauss = c_3p3gauss1D[xIndex] * c_3p3gauss1D[yIndex]; // separable because independent (circular)
const float aa_dx = gauss * (-(a-b-g+h) + (b-c-h+i)); // unrotated dx
const float aa_dy = gauss * (-(a-c-d+f) + (d-f-g+i)); // unrotated dy
// rotate responses (store all dxs then all dys)
// - counterclockwise rotation to rotate back to zero orientation
sdx[tid] = aa_dx * cos_theta - aa_dy * sin_theta; // rotated dx
sdy[tid] = aa_dx * sin_theta + aa_dy * cos_theta; // rotated dy
}
__device__ void reduce_sum_old(float sdata[25], int tid)
{
// first step is to reduce from 25 to 16
if (tid < 9) // use 9 threads
sdata[tid] += sdata[tid + 16];
__syncthreads();
// sum (reduce) from 16 to 1 (unrolled - aligned to a half-warp)
if (tid < 16)
{
volatile float* smem = sdata;
smem[tid] += smem[tid + 8];
smem[tid] += smem[tid + 4];
smem[tid] += smem[tid + 2];
smem[tid] += smem[tid + 1];
}
calc_dx_dy(sdx, sdy, ipt, xIndex, yIndex, tid);
}
// Spawn 16 blocks per interest point
// - computes unnormalized 64 dimensional descriptor, puts it into d_descriptors in the correct location
__global__ void compute_descriptors64_old(PtrStepf descriptors, const KeyPoint_GPU* features)
{
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
float* descriptors_block = descriptors.ptr(blockIdx.x) + (blockIdx.y << 2);
// 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
__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_old(sdx, sdy, features, tid);
__syncthreads();
__shared__ float sabs[25];
sabs[tid] = fabs(sdx[tid]); // |dx| array
sdxabs[tid] = fabs(sdx[tid]); // |dx| array
sdyabs[tid] = fabs(sdy[tid]); // |dy| array
__syncthreads();
reduce_sum_old(sdx, tid);
reduce_sum_old(sdy, tid);
reduce_sum_old(sabs, tid);
reduce_sum25(sdx, sdy, sdxabs, sdyabs, tid);
__syncthreads();
// write dx, dy, |dx|
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] = sabs[0];
}
__syncthreads();
sabs[tid] = fabs(sdy[tid]); // |dy| array
__syncthreads();
reduce_sum_old(sabs, tid);
// write |dy|
if (tid == 0)
{
descriptors_block[3] = sabs[0];
descriptors_block[2] = sdxabs[0];
descriptors_block[3] = sdyabs[0];
}
}
@ -1191,23 +1153,21 @@ namespace cv { namespace gpu { namespace surf
// - computes unnormalized 128 dimensional descriptor, puts it into d_descriptors in the correct location
__global__ void compute_descriptors128_old(PtrStepf descriptors, const KeyPoint_GPU* features)
{
float* descriptors_block = descriptors.ptr(blockIdx.x) + (blockIdx.y << 3);
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
// 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
__shared__ float sdx[25];
__shared__ float sdy[25];
calc_dx_dy_old(sdx, sdy, features, tid);
__syncthreads();
// 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_old(sdx, sdy, features, tid);
__syncthreads();
if (sdy[tid] >= 0)
{
sd1[tid] = sdx[tid];
@ -1224,10 +1184,10 @@ namespace cv { namespace gpu { namespace surf
}
__syncthreads();
reduce_sum_old(sd1, tid);
reduce_sum_old(sd2, tid);
reduce_sum_old(sdabs1, tid);
reduce_sum_old(sdabs2, tid);
reduce_sum25(sd1, sd1, 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)
@ -1255,10 +1215,8 @@ namespace cv { namespace gpu { namespace surf
}
__syncthreads();
reduce_sum_old(sd1, tid);
reduce_sum_old(sd2, tid);
reduce_sum_old(sdabs1, tid);
reduce_sum_old(sdabs2, tid);
reduce_sum25(sd1, sd1, sdabs1, sdabs2, tid);
__syncthreads();
// write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
if (tid == 0)

4
modules/gpu/src/surf.cpp
View File

@ -233,8 +233,8 @@ namespace
typedef void (*compute_descriptors_t)(const DevMem2Df& descriptors,
const KeyPoint_GPU* features, int nFeatures);
const compute_descriptors_t compute_descriptors =
DeviceInfo().supports(FEATURE_SET_COMPUTE_13) ? compute_descriptors_gpu : compute_descriptors_gpu_old;
const compute_descriptors_t compute_descriptors = compute_descriptors_gpu_old;
//DeviceInfo().supports(FEATURE_SET_COMPUTE_13) ? compute_descriptors_gpu : compute_descriptors_gpu_old;
if (keypoints.cols > 0)
{