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SURF

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This commit is contained in:
Vladislav Vinogradov 2012-11-12 14:20:43 +04:00
parent 19c87d1c9d
commit fbf3de43a2
3 changed files with 156 additions and 231 deletions
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382
modules/gpu/src/cuda/surf.cu
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@ -47,13 +47,13 @@
#if !defined CUDA_DISABLER
#include "internal_shared.hpp"
#include "opencv2/gpu/device/common.hpp"
#include "opencv2/gpu/device/limits.hpp"
#include "opencv2/gpu/device/saturate_cast.hpp"
#include "opencv2/gpu/device/reduce.hpp"
#include "opencv2/gpu/device/utility.hpp"
#include "opencv2/gpu/device/functional.hpp"
#include "opencv2/gpu/device/filters.hpp"
#include <float.h>
namespace cv { namespace gpu { namespace device
{
@ -599,8 +599,9 @@ namespace cv { namespace gpu { namespace device
sumy += s_Y[threadIdx.x + 96];
}
device::reduce_old<32>(s_sumx + threadIdx.y * 32, sumx, threadIdx.x, plus<volatile float>());
device::reduce_old<32>(s_sumy + threadIdx.y * 32, sumy, threadIdx.x, plus<volatile float>());
plus<float> op;
device::reduce<32>(smem_tuple(s_sumx + threadIdx.y * 32, s_sumy + threadIdx.y * 32),
thrust::tie(sumx, sumy), threadIdx.x, thrust::make_tuple(op, op));
const float temp_mod = sumx * sumx + sumy * sumy;
if (temp_mod > best_mod)
@ -638,7 +639,7 @@ namespace cv { namespace gpu { namespace device
kp_dir *= 180.0f / CV_PI_F;
kp_dir = 360.0f - kp_dir;
if (::fabsf(kp_dir - 360.f) < FLT_EPSILON)
if (::fabsf(kp_dir - 360.f) < numeric_limits<float>::epsilon())
kp_dir = 0.f;
featureDir[blockIdx.x] = kp_dir;
@ -697,11 +698,6 @@ namespace cv { namespace gpu { namespace device
{
typedef uchar elem_type;
__device__ __forceinline__ WinReader(float centerX_, float centerY_, float win_offset_, float cos_dir_, float sin_dir_) :
centerX(centerX_), centerY(centerY_), win_offset(win_offset_), cos_dir(cos_dir_), sin_dir(sin_dir_)
{
}
__device__ __forceinline__ uchar operator ()(int i, int j) const
{
float pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir;
@ -715,285 +711,215 @@ namespace cv { namespace gpu { namespace device
float win_offset;
float cos_dir;
float sin_dir;
int width;
int height;
};
__device__ void calc_dx_dy(float s_dx_bin[25], float s_dy_bin[25],
const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
__device__ void calc_dx_dy(const float* featureX, const float* featureY, const float* featureSize, const float* featureDir,
float& dx, float& dy)
{
__shared__ float s_PATCH[6][6];
__shared__ float s_PATCH[PATCH_SZ + 1][PATCH_SZ + 1];
const float centerX = featureX[blockIdx.x];
const float centerY = featureY[blockIdx.x];
const float size = featureSize[blockIdx.x];
float descriptor_dir = 360.0f - featureDir[blockIdx.x];
if (std::abs(descriptor_dir - 360.f) < FLT_EPSILON)
descriptor_dir = 0.f;
descriptor_dir *= (float)(CV_PI_F / 180.0f);
dx = dy = 0.0f;
/* 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;
WinReader win;
/* Extract a window of pixels around the keypoint of size 20s */
win.centerX = featureX[blockIdx.x];
win.centerY = featureY[blockIdx.x];
// The sampling intervals and wavelet sized for selecting an orientation
// and building the keypoint descriptor are defined relative to 's'
const float s = featureSize[blockIdx.x] * 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);
win.width = win.height = win_size;
/* Nearest neighbour version (faster) */
const float win_offset = -(float)(win_size - 1) / 2;
// 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 * 5 + threadIdx.x;
const int yIndex = yBlock * 5 + threadIdx.y;
const float icoo = ((float)yIndex / (PATCH_SZ + 1)) * win_size;
const float jcoo = ((float)xIndex / (PATCH_SZ + 1)) * win_size;
LinearFilter<WinReader> filter(WinReader(centerX, centerY, win_offset, cos_dir, sin_dir));
s_PATCH[threadIdx.y][threadIdx.x] = filter(icoo, jcoo);
__syncthreads();
if (threadIdx.x < 5 && threadIdx.y < 5)
{
const int tid = threadIdx.y * 5 + threadIdx.x;
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 < 8)
{
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];
}
}
__global__ void compute_descriptors64(PtrStepf descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
{
// 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];
calc_dx_dy(sdx, sdy, featureX, featureY, featureSize, featureDir);
__syncthreads();
// Nearest neighbour version (faster)
win.win_offset = -(win_size - 1.0f) / 2.0f;
float descriptor_dir = 360.0f - featureDir[blockIdx.x];
if (::fabsf(descriptor_dir - 360.f) < numeric_limits<float>::epsilon())
descriptor_dir = 0.f;
descriptor_dir *= CV_PI_F / 180.0f;
sincosf(descriptor_dir, &win.sin_dir, &win.cos_dir);
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
if (tid < 25)
const int xLoadInd = tid % (PATCH_SZ + 1);
const int yLoadInd = tid / (PATCH_SZ + 1);
if (yLoadInd < (PATCH_SZ + 1))
{
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)
if (s > 1)
{
descriptors_block[0] = sdx[0];
descriptors_block[1] = sdy[0];
descriptors_block[2] = sdxabs[0];
descriptors_block[3] = sdyabs[0];
AreaFilter<WinReader> filter(win, s, s);
s_PATCH[yLoadInd][xLoadInd] = filter(yLoadInd, xLoadInd);
}
else
{
LinearFilter<WinReader> filter(win);
s_PATCH[yLoadInd][xLoadInd] = filter(yLoadInd * s, xLoadInd * s);
}
}
__syncthreads();
const int xPatchInd = threadIdx.x % 5;
const int yPatchInd = threadIdx.x / 5;
if (yPatchInd < 5)
{
const int xBlockInd = threadIdx.y % 4;
const int yBlockInd = threadIdx.y / 4;
const int xInd = xBlockInd * 5 + xPatchInd;
const int yInd = yBlockInd * 5 + yPatchInd;
const float dw = c_DW[yInd * PATCH_SZ + xInd];
dx = (s_PATCH[yInd ][xInd + 1] - s_PATCH[yInd][xInd] + s_PATCH[yInd + 1][xInd + 1] - s_PATCH[yInd + 1][xInd ]) * dw;
dy = (s_PATCH[yInd + 1][xInd ] - s_PATCH[yInd][xInd] + s_PATCH[yInd + 1][xInd + 1] - s_PATCH[yInd ][xInd + 1]) * dw;
}
}
__global__ void compute_descriptors128(PtrStepf descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
__global__ void compute_descriptors_64(PtrStep<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
{
// 2 floats (dx,dy) for each thread (5x5 sample points in each sub-region)
__shared__ float sdx[25];
__shared__ float sdy[25];
__shared__ float smem[32 * 16];
// sum (reduce) 5x5 area response
__shared__ float sd1[25];
__shared__ float sd2[25];
__shared__ float sdabs1[25];
__shared__ float sdabs2[25];
float* sRow = smem + threadIdx.y * 32;
calc_dx_dy(sdx, sdy, featureX, featureY, featureSize, featureDir);
__syncthreads();
float dx, dy;
calc_dx_dy(featureX, featureY, featureSize, featureDir, dx, dy);
const int tid = threadIdx.y * blockDim.x + threadIdx.x;
float dxabs = ::fabsf(dx);
float dyabs = ::fabsf(dy);
if (tid < 25)
plus<float> op;
reduce<32>(sRow, dx, threadIdx.x, op);
reduce<32>(sRow, dy, threadIdx.x, op);
reduce<32>(sRow, dxabs, threadIdx.x, op);
reduce<32>(sRow, dyabs, threadIdx.x, op);
float4* descriptors_block = descriptors.ptr(blockIdx.x) + threadIdx.y;
// write dx, dy, |dx|, |dy|
if (threadIdx.x == 0)
*descriptors_block = make_float4(dx, dy, dxabs, dyabs);
}
__global__ void compute_descriptors_128(PtrStep<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir)
{
__shared__ float smem[32 * 16];
float* sRow = smem + threadIdx.y * 32;
float dx, dy;
calc_dx_dy(featureX, featureY, featureSize, featureDir, dx, dy);
float4* descriptors_block = descriptors.ptr(blockIdx.x) + threadIdx.y * 2;
plus<float> op;
float d1 = 0.0f;
float d2 = 0.0f;
float abs1 = 0.0f;
float abs2 = 0.0f;
if (dy >= 0)
{
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];
}
d1 = dx;
abs1 = ::fabsf(dx);
}
else
{
d2 = dx;
abs2 = ::fabsf(dx);
}
reduce<32>(sRow, d1, threadIdx.x, op);
reduce<32>(sRow, d2, threadIdx.x, op);
reduce<32>(sRow, abs1, threadIdx.x, op);
reduce<32>(sRow, abs2, threadIdx.x, op);
// write dx (dy >= 0), |dx| (dy >= 0), dx (dy < 0), |dx| (dy < 0)
if (threadIdx.x == 0)
descriptors_block[0] = make_float4(d1, abs1, d2, abs2);
if (dx >= 0)
{
d1 = dy;
abs1 = ::fabsf(dy);
d2 = 0.0f;
abs2 = 0.0f;
}
else
{
d1 = 0.0f;
abs1 = 0.0f;
d2 = dy;
abs2 = ::fabsf(dy);
}
reduce<32>(sRow, d1, threadIdx.x, op);
reduce<32>(sRow, d2, threadIdx.x, op);
reduce<32>(sRow, abs1, threadIdx.x, op);
reduce<32>(sRow, abs2, threadIdx.x, op);
// write dy (dx >= 0), |dy| (dx >= 0), dy (dx < 0), |dy| (dx < 0)
if (threadIdx.x == 0)
descriptors_block[1] = make_float4(d1, abs1, d2, abs2);
}
template <int BLOCK_DIM_X> __global__ void normalize_descriptors(PtrStepf descriptors)
{
__shared__ float smem[BLOCK_DIM_X];
__shared__ float s_len;
// 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();
const float val = descriptor_base[threadIdx.x];
if (BLOCK_DIM_X >= 128)
{
if (threadIdx.x < 64)
sqDesc[threadIdx.x] += sqDesc[threadIdx.x + 64];
__syncthreads();
}
float len = val * val;
reduce<BLOCK_DIM_X>(smem, len, threadIdx.x, plus<float>());
// 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]);
}
s_len = ::sqrtf(len);
__syncthreads();
// normalize and store in output
descriptor_base[threadIdx.x] = lookup / len;
descriptor_base[threadIdx.x] = val / s_len;
}
void compute_descriptors_gpu(const PtrStepSzf& descriptors,
const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures)
void compute_descriptors_gpu(PtrStepSz<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, 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(6, 6, 1)>>>(descriptors, featureX, featureY, featureSize, featureDir);
compute_descriptors_64<<<nFeatures, dim3(32, 16)>>>(descriptors, featureX, featureY, featureSize, featureDir);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
normalize_descriptors<64><<<dim3(nFeatures, 1, 1), dim3(64, 1, 1)>>>(descriptors);
normalize_descriptors<64><<<nFeatures, 64>>>((PtrStepSzf) descriptors);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
else
{
compute_descriptors128<<<dim3(nFeatures, 16, 1), dim3(6, 6, 1)>>>(descriptors, featureX, featureY, featureSize, featureDir);
compute_descriptors_128<<<nFeatures, dim3(32, 16)>>>(descriptors, featureX, featureY, featureSize, featureDir);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
normalize_descriptors<128><<<dim3(nFeatures, 1, 1), dim3(128, 1, 1)>>>(descriptors);
normalize_descriptors<128><<<nFeatures, 128>>>((PtrStepSzf) descriptors);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );

3
modules/gpu/src/surf.cpp
View File

@ -86,8 +86,7 @@ namespace cv { namespace gpu { namespace device
void icvCalcOrientation_gpu(const float* featureX, const float* featureY, const float* featureSize, float* featureDir, int nFeatures);
void compute_descriptors_gpu(const PtrStepSzf& descriptors,
const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures);
void compute_descriptors_gpu(PtrStepSz<float4> descriptors, const float* featureX, const float* featureY, const float* featureSize, const float* featureDir, int nFeatures);
}
}}}

2
modules/gpu/test/test_features2d.cpp
View File

@ -328,7 +328,7 @@ TEST_P(SURF, Descriptor)
int matchedCount = getMatchedPointsCount(keypoints, keypoints, matches);
double matchedRatio = static_cast<double>(matchedCount) / keypoints.size();
EXPECT_GT(matchedRatio, 0.35);
EXPECT_GT(matchedRatio, 0.6);
}
}