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integrated patch: HOG confidence calculation. Thanks, Wongun.

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marina.kolpakova
2012-07-25 15:26:26 +04:00
parent 4fa282e1a9
commit e1e0c46639
5 changed files with 467 additions and 1 deletions
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91
modules/gpu/src/cuda/hog.cu
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@@ -326,6 +326,97 @@ namespace cv { namespace gpu { namespace device
// Linear SVM based classification
//
// return confidence values not just positive location
template <int nthreads, // Number of threads per one histogram block
int nblocks> // Number of histogram block processed by single GPU thread block
__global__ void compute_confidence_hists_kernel_many_blocks(const int img_win_width, const int img_block_width,
const int win_block_stride_x, const int win_block_stride_y,
const float* block_hists, const float* coefs,
float free_coef, float threshold, float* confidences)
{
const int win_x = threadIdx.z;
if (blockIdx.x * blockDim.z + win_x >= img_win_width)
return;
const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width +
blockIdx.x * win_block_stride_x * blockDim.z + win_x) *
cblock_hist_size;
float product = 0.f;
for (int i = threadIdx.x; i < cdescr_size; i += nthreads)
{
int offset_y = i / cdescr_width;
int offset_x = i - offset_y * cdescr_width;
product += coefs[i] * hist[offset_y * img_block_width * cblock_hist_size + offset_x];
}
__shared__ float products[nthreads * nblocks];
const int tid = threadIdx.z * nthreads + threadIdx.x;
products[tid] = product;
__syncthreads();
if (nthreads >= 512)
{
if (threadIdx.x < 256) products[tid] = product = product + products[tid + 256];
__syncthreads();
}
if (nthreads >= 256)
{
if (threadIdx.x < 128) products[tid] = product = product + products[tid + 128];
__syncthreads();
}
if (nthreads >= 128)
{
if (threadIdx.x < 64) products[tid] = product = product + products[tid + 64];
__syncthreads();
}
if (threadIdx.x < 32)
{
volatile float* smem = products;
if (nthreads >= 64) smem[tid] = product = product + smem[tid + 32];
if (nthreads >= 32) smem[tid] = product = product + smem[tid + 16];
if (nthreads >= 16) smem[tid] = product = product + smem[tid + 8];
if (nthreads >= 8) smem[tid] = product = product + smem[tid + 4];
if (nthreads >= 4) smem[tid] = product = product + smem[tid + 2];
if (nthreads >= 2) smem[tid] = product = product + smem[tid + 1];
}
if (threadIdx.x == 0)
confidences[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x]
= (float)(product + free_coef);
}
void compute_confidence_hists(int win_height, int win_width, int block_stride_y, int block_stride_x,
int win_stride_y, int win_stride_x, int height, int width, float* block_hists,
float* coefs, float free_coef, float threshold, float *confidences)
{
const int nthreads = 256;
const int nblocks = 1;
int win_block_stride_x = win_stride_x / block_stride_x;
int win_block_stride_y = win_stride_y / block_stride_y;
int img_win_width = (width - win_width + win_stride_x) / win_stride_x;
int img_win_height = (height - win_height + win_stride_y) / win_stride_y;
dim3 threads(nthreads, 1, nblocks);
dim3 grid(divUp(img_win_width, nblocks), img_win_height);
cudaSafeCall(cudaFuncSetCacheConfig(compute_confidence_hists_kernel_many_blocks<nthreads, nblocks>,
cudaFuncCachePreferL1));
int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) /
block_stride_x;
compute_confidence_hists_kernel_many_blocks<nthreads, nblocks><<<grid, threads>>>(
img_win_width, img_block_width, win_block_stride_x, win_block_stride_y,
block_hists, coefs, free_coef, threshold, confidences);
cudaSafeCall(cudaThreadSynchronize());
}
template <int nthreads, // Number of threads per one histogram block
int nblocks> // Number of histogram block processed by single GPU thread block