CUDA学习笔记三
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HOG行人检测算法CUDA并行实现 这个版本的封装级别还不是特别高 所以很多还是可以看懂
这个示例可以看到写并行程序的不易 首先要对算法的实现有十分清晰的理解 也展现了很多CUDA优化的方法 包括shared memory的使用 循环展开
#include "internal_shared.hpp"#ifndef CV_PI_F #ifndef CV_PI #define CV_PI_F 3.14159265f #else #define CV_PI_F ((float)CV_PI) #endif#endif// Other values are not supported#define CELL_WIDTH 8#define CELL_HEIGHT 8#define CELLS_PER_BLOCK_X 2#define CELLS_PER_BLOCK_Y 2namespace cv { namespace gpu { namespace hog {__constant__ int cnbins;__constant__ int cblock_stride_x;__constant__ int cblock_stride_y;__constant__ int cnblocks_win_x;__constant__ int cnblocks_win_y;__constant__ int cblock_hist_size;__constant__ int cblock_hist_size_2up;__constant__ int cdescr_size;__constant__ int cdescr_width;/* Returns the nearest upper power of two, works only for the typical GPU thread count (pert block) values */int power_2up(unsigned int n){ if (n < 1) return 1; else if (n < 2) return 2; else if (n < 4) return 4; else if (n < 8) return 8; else if (n < 16) return 16; else if (n < 32) return 32; else if (n < 64) return 64; else if (n < 128) return 128; else if (n < 256) return 256; else if (n < 512) return 512; else if (n < 1024) return 1024; return -1; // Input is too big}void set_up_constants(int nbins, int block_stride_x, int block_stride_y, int nblocks_win_x, int nblocks_win_y){ uploadConstant("cv::gpu::hog::cnbins", nbins); uploadConstant("cv::gpu::hog::cblock_stride_x", block_stride_x); uploadConstant("cv::gpu::hog::cblock_stride_y", block_stride_y); uploadConstant("cv::gpu::hog::cnblocks_win_x", nblocks_win_x); uploadConstant("cv::gpu::hog::cnblocks_win_y", nblocks_win_y); int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y; uploadConstant("cv::gpu::hog::cblock_hist_size", block_hist_size); int block_hist_size_2up = power_2up(block_hist_size); uploadConstant("cv::gpu::hog::cblock_hist_size_2up", block_hist_size_2up); int descr_width = nblocks_win_x * block_hist_size; uploadConstant("cv::gpu::hog::cdescr_width", descr_width); int descr_size = descr_width * nblocks_win_y; uploadConstant("cv::gpu::hog::cdescr_size", descr_size);}//----------------------------------------------------------------------------// Histogram computationtemplate <int nblocks> // Number of histogram blocks processed by single GPU thread block__global__ void compute_hists_kernel_many_blocks(const int img_block_width, const PtrElemStepf grad, const PtrElemStep qangle, float scale, float* block_hists){ const int block_x = threadIdx.z; const int cell_x = threadIdx.x / 16; const int cell_y = threadIdx.y; const int cell_thread_x = threadIdx.x & 0xF; if (blockIdx.x * blockDim.z + block_x >= img_block_width) return; extern __shared__ float smem[]; float* hists = smem; float* final_hist = smem + cnbins * 48 * nblocks; const int offset_x = (blockIdx.x * blockDim.z + block_x) * cblock_stride_x + 4 * cell_x + cell_thread_x; const int offset_y = blockIdx.y * cblock_stride_y + 4 * cell_y; const float* grad_ptr = grad.ptr(offset_y) + offset_x * 2; const unsigned char* qangle_ptr = qangle.ptr(offset_y) + offset_x * 2; // 12 means that 12 pixels affect on block's cell (in one row) if (cell_thread_x < 12) { float* hist = hists + 12 * (cell_y * blockDim.z * CELLS_PER_BLOCK_Y + cell_x + block_x * CELLS_PER_BLOCK_X) + cell_thread_x; for (int bin_id = 0; bin_id < cnbins; ++bin_id) hist[bin_id * 48 * nblocks] = 0.f; const int dist_x = -4 + (int)cell_thread_x - 4 * cell_x; const int dist_y_begin = -4 - 4 * (int)threadIdx.y; for (int dist_y = dist_y_begin; dist_y < dist_y_begin + 12; ++dist_y) { float2 vote = *(const float2*)grad_ptr; uchar2 bin = *(const uchar2*)qangle_ptr; grad_ptr += grad.step; qangle_ptr += qangle.step; int dist_center_y = dist_y - 4 * (1 - 2 * cell_y); int dist_center_x = dist_x - 4 * (1 - 2 * cell_x); float gaussian = expf(-(dist_center_y * dist_center_y + dist_center_x * dist_center_x) * scale); float interp_weight = (8.f - fabs(dist_y + 0.5f)) * (8.f - fabs(dist_x + 0.5f)) / 64.f; hist[bin.x * 48 * nblocks] += gaussian * interp_weight * vote.x; hist[bin.y * 48 * nblocks] += gaussian * interp_weight * vote.y; } volatile float* hist_ = hist; for (int bin_id = 0; bin_id < cnbins; ++bin_id, hist_ += 48 * nblocks) { if (cell_thread_x < 6) hist_[0] += hist_[6]; if (cell_thread_x < 3) hist_[0] += hist_[3]; if (cell_thread_x == 0) final_hist[((cell_x + block_x * 2) * 2 + cell_y) * cnbins + bin_id] = hist_[0] + hist_[1] + hist_[2]; } } __syncthreads(); float* block_hist = block_hists + (blockIdx.y * img_block_width + blockIdx.x * blockDim.z + block_x) * cblock_hist_size; int tid = (cell_y * CELLS_PER_BLOCK_Y + cell_x) * 16 + cell_thread_x; if (tid < cblock_hist_size) block_hist[tid] = final_hist[block_x * cblock_hist_size + tid]; }void compute_hists(int nbins, int block_stride_x, int block_stride_y, int height, int width, const DevMem2Df& grad, const DevMem2D& qangle, float sigma, float* block_hists) { const int nblocks = 1; int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x; int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) / block_stride_y; dim3 grid(divUp(img_block_width, nblocks), img_block_height); dim3 threads(32, 2, nblocks); cudaSafeCall(cudaFuncSetCacheConfig(compute_hists_kernel_many_blocks<nblocks>, cudaFuncCachePreferL1)); // Precompute gaussian spatial window parameter float scale = 1.f / (2.f * sigma * sigma); int hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * 12 * nblocks) * sizeof(float); int final_hists_size = (nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y * nblocks) * sizeof(float); int smem = hists_size + final_hists_size; compute_hists_kernel_many_blocks<nblocks><<<grid, threads, smem>>>( img_block_width, grad, qangle, scale, block_hists); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}//-------------------------------------------------------------// Normalization of histograms via L2Hys_norm//template<int size> __device__ float reduce_smem(volatile float* smem){ unsigned int tid = threadIdx.x; float sum = smem[tid]; if (size >= 512) { if (tid < 256) smem[tid] = sum = sum + smem[tid + 256]; __syncthreads(); } if (size >= 256) { if (tid < 128) smem[tid] = sum = sum + smem[tid + 128]; __syncthreads(); } if (size >= 128) { if (tid < 64) smem[tid] = sum = sum + smem[tid + 64]; __syncthreads(); } if (tid < 32) { if (size >= 64) smem[tid] = sum = sum + smem[tid + 32]; if (size >= 32) smem[tid] = sum = sum + smem[tid + 16]; if (size >= 16) smem[tid] = sum = sum + smem[tid + 8]; if (size >= 8) smem[tid] = sum = sum + smem[tid + 4]; if (size >= 4) smem[tid] = sum = sum + smem[tid + 2]; if (size >= 2) smem[tid] = sum = sum + smem[tid + 1]; } __syncthreads(); sum = smem[0]; return sum;}template <int nthreads, // Number of threads which process one block historgam int nblocks> // Number of block hisograms processed by one GPU thread block__global__ void normalize_hists_kernel_many_blocks(const int block_hist_size, const int img_block_width, float* block_hists, float threshold){ if (blockIdx.x * blockDim.z + threadIdx.z >= img_block_width) return; float* hist = block_hists + (blockIdx.y * img_block_width + blockIdx.x * blockDim.z + threadIdx.z) * block_hist_size + threadIdx.x; __shared__ float sh_squares[nthreads * nblocks]; float* squares = sh_squares + threadIdx.z * nthreads; float elem = 0.f; if (threadIdx.x < block_hist_size) elem = hist[0]; squares[threadIdx.x] = elem * elem; __syncthreads(); float sum = reduce_smem<nthreads>(squares); float scale = 1.0f / (sqrtf(sum) + 0.1f * block_hist_size); elem = min(elem * scale, threshold); __syncthreads(); squares[threadIdx.x] = elem * elem; __syncthreads(); sum = reduce_smem<nthreads>(squares); scale = 1.0f / (sqrtf(sum) + 1e-3f); if (threadIdx.x < block_hist_size) hist[0] = elem * scale;}void normalize_hists(int nbins, int block_stride_x, int block_stride_y, int height, int width, float* block_hists, float threshold){ const int nblocks = 1; int block_hist_size = nbins * CELLS_PER_BLOCK_X * CELLS_PER_BLOCK_Y; int nthreads = power_2up(block_hist_size); dim3 threads(nthreads, 1, nblocks); int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x; int img_block_height = (height - CELLS_PER_BLOCK_Y * CELL_HEIGHT + block_stride_y) / block_stride_y; dim3 grid(divUp(img_block_width, nblocks), img_block_height); if (nthreads == 32) normalize_hists_kernel_many_blocks<32, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold); else if (nthreads == 64) normalize_hists_kernel_many_blocks<64, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold); else if (nthreads == 128) normalize_hists_kernel_many_blocks<64, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold); else if (nthreads == 256) normalize_hists_kernel_many_blocks<256, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold); else if (nthreads == 512) normalize_hists_kernel_many_blocks<512, nblocks><<<grid, threads>>>(block_hist_size, img_block_width, block_hists, threshold); else cv::gpu::error("normalize_hists: histogram's size is too big, try to decrease number of bins", __FILE__, __LINE__); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}//---------------------------------------------------------------------// Linear SVM based classification//template <int nthreads, // Number of threads per one histogram block int nblocks> // Number of histogram block processed by single GPU thread block__global__ void classify_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, unsigned char* labels){ 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) labels[blockIdx.y * img_win_width + blockIdx.x * blockDim.z + win_x] = (product + free_coef >= threshold);}void classify_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, unsigned char* labels){ 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(classify_hists_kernel_many_blocks<nthreads, nblocks>, cudaFuncCachePreferL1)); int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x; classify_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, labels); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}//----------------------------------------------------------------------------// Extract descriptorstemplate <int nthreads>__global__ void extract_descrs_by_rows_kernel(const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, const float* block_hists, PtrElemStepf descriptors){ // Get left top corner of the window in src const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width + blockIdx.x * win_block_stride_x) * cblock_hist_size; // Get left top corner of the window in dst float* descriptor = descriptors.ptr(blockIdx.y * gridDim.x + blockIdx.x); // Copy elements from src to dst for (int i = threadIdx.x; i < cdescr_size; i += nthreads) { int offset_y = i / cdescr_width; int offset_x = i - offset_y * cdescr_width; descriptor[i] = hist[offset_y * img_block_width * cblock_hist_size + offset_x]; }}void extract_descrs_by_rows(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, DevMem2Df descriptors){ const int nthreads = 256; 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); dim3 grid(img_win_width, img_win_height); int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x; extract_descrs_by_rows_kernel<nthreads><<<grid, threads>>>( img_block_width, win_block_stride_x, win_block_stride_y, block_hists, descriptors); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}template <int nthreads>__global__ void extract_descrs_by_cols_kernel(const int img_block_width, const int win_block_stride_x, const int win_block_stride_y, const float* block_hists, PtrElemStepf descriptors){ // Get left top corner of the window in src const float* hist = block_hists + (blockIdx.y * win_block_stride_y * img_block_width + blockIdx.x * win_block_stride_x) * cblock_hist_size; // Get left top corner of the window in dst float* descriptor = descriptors.ptr(blockIdx.y * gridDim.x + blockIdx.x); // Copy elements from src to dst for (int i = threadIdx.x; i < cdescr_size; i += nthreads) { int block_idx = i / cblock_hist_size; int idx_in_block = i - block_idx * cblock_hist_size; int y = block_idx / cnblocks_win_x; int x = block_idx - y * cnblocks_win_x; descriptor[(x * cnblocks_win_y + y) * cblock_hist_size + idx_in_block] = hist[(y * img_block_width + x) * cblock_hist_size + idx_in_block]; }}void extract_descrs_by_cols(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, DevMem2Df descriptors){ const int nthreads = 256; 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); dim3 grid(img_win_width, img_win_height); int img_block_width = (width - CELLS_PER_BLOCK_X * CELL_WIDTH + block_stride_x) / block_stride_x; extract_descrs_by_cols_kernel<nthreads><<<grid, threads>>>( img_block_width, win_block_stride_x, win_block_stride_y, block_hists, descriptors); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}//----------------------------------------------------------------------------// Gradients computationtemplate <int nthreads, int correct_gamma>__global__ void compute_gradients_8UC4_kernel(int height, int width, const PtrElemStep img, float angle_scale, PtrElemStepf grad, PtrElemStep qangle){ const int x = blockIdx.x * blockDim.x + threadIdx.x; const uchar4* row = (const uchar4*)img.ptr(blockIdx.y); __shared__ float sh_row[(nthreads + 2) * 3]; uchar4 val; if (x < width) val = row[x]; else val = row[width - 2]; sh_row[threadIdx.x + 1] = val.x; sh_row[threadIdx.x + 1 + (nthreads + 2)] = val.y; sh_row[threadIdx.x + 1 + 2 * (nthreads + 2)] = val.z; if (threadIdx.x == 0) { val = row[max(x - 1, 1)]; sh_row[0] = val.x; sh_row[(nthreads + 2)] = val.y; sh_row[2 * (nthreads + 2)] = val.z; } if (threadIdx.x == blockDim.x - 1) { val = row[min(x + 1, width - 2)]; sh_row[blockDim.x + 1] = val.x; sh_row[blockDim.x + 1 + (nthreads + 2)] = val.y; sh_row[blockDim.x + 1 + 2 * (nthreads + 2)] = val.z; } __syncthreads(); if (x < width) { float3 a, b; b.x = sh_row[threadIdx.x + 2]; b.y = sh_row[threadIdx.x + 2 + (nthreads + 2)]; b.z = sh_row[threadIdx.x + 2 + 2 * (nthreads + 2)]; a.x = sh_row[threadIdx.x]; a.y = sh_row[threadIdx.x + (nthreads + 2)]; a.z = sh_row[threadIdx.x + 2 * (nthreads + 2)]; float3 dx; if (correct_gamma) dx = make_float3(sqrtf(b.x) - sqrtf(a.x), sqrtf(b.y) - sqrtf(a.y), sqrtf(b.z) - sqrtf(a.z)); else dx = make_float3(b.x - a.x, b.y - a.y, b.z - a.z); float3 dy = make_float3(0.f, 0.f, 0.f); if (blockIdx.y > 0 && blockIdx.y < height - 1) { val = ((const uchar4*)img.ptr(blockIdx.y - 1))[x]; a = make_float3(val.x, val.y, val.z); val = ((const uchar4*)img.ptr(blockIdx.y + 1))[x]; b = make_float3(val.x, val.y, val.z); if (correct_gamma) dy = make_float3(sqrtf(b.x) - sqrtf(a.x), sqrtf(b.y) - sqrtf(a.y), sqrtf(b.z) - sqrtf(a.z)); else dy = make_float3(b.x - a.x, b.y - a.y, b.z - a.z); } float best_dx = dx.x; float best_dy = dy.x; float mag0 = dx.x * dx.x + dy.x * dy.x; float mag1 = dx.y * dx.y + dy.y * dy.y; if (mag0 < mag1) { best_dx = dx.y; best_dy = dy.y; mag0 = mag1; } mag1 = dx.z * dx.z + dy.z * dy.z; if (mag0 < mag1) { best_dx = dx.z; best_dy = dy.z; mag0 = mag1; } mag0 = sqrtf(mag0); float ang = (atan2f(best_dy, best_dx) + CV_PI_F) * angle_scale - 0.5f; int hidx = (int)floorf(ang); ang -= hidx; hidx = (hidx + cnbins) % cnbins; ((uchar2*)qangle.ptr(blockIdx.y))[x] = make_uchar2(hidx, (hidx + 1) % cnbins); ((float2*)grad.ptr(blockIdx.y))[x] = make_float2(mag0 * (1.f - ang), mag0 * ang); }}void compute_gradients_8UC4(int nbins, int height, int width, const DevMem2D& img, float angle_scale, DevMem2Df grad, DevMem2D qangle, bool correct_gamma){ const int nthreads = 256; dim3 bdim(nthreads, 1); dim3 gdim(divUp(width, bdim.x), divUp(height, bdim.y)); if (correct_gamma) compute_gradients_8UC4_kernel<nthreads, 1><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle); else compute_gradients_8UC4_kernel<nthreads, 0><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}template <int nthreads, int correct_gamma>__global__ void compute_gradients_8UC1_kernel(int height, int width, const PtrElemStep img, float angle_scale, PtrElemStepf grad, PtrElemStep qangle){ const int x = blockIdx.x * blockDim.x + threadIdx.x; const unsigned char* row = (const unsigned char*)img.ptr(blockIdx.y); __shared__ float sh_row[nthreads + 2]; if (x < width) sh_row[threadIdx.x + 1] = row[x]; else sh_row[threadIdx.x + 1] = row[width - 2]; if (threadIdx.x == 0) sh_row[0] = row[max(x - 1, 1)]; if (threadIdx.x == blockDim.x - 1) sh_row[blockDim.x + 1] = row[min(x + 1, width - 2)]; __syncthreads(); if (x < width) { float dx; if (correct_gamma) dx = sqrtf(sh_row[threadIdx.x + 2]) - sqrtf(sh_row[threadIdx.x]); else dx = sh_row[threadIdx.x + 2] - sh_row[threadIdx.x]; float dy = 0.f; if (blockIdx.y > 0 && blockIdx.y < height - 1) { float a = ((const unsigned char*)img.ptr(blockIdx.y + 1))[x]; float b = ((const unsigned char*)img.ptr(blockIdx.y - 1))[x]; if (correct_gamma) dy = sqrtf(a) - sqrtf(b); else dy = a - b; } float mag = sqrtf(dx * dx + dy * dy); float ang = (atan2f(dy, dx) + CV_PI_F) * angle_scale - 0.5f; int hidx = (int)floorf(ang); ang -= hidx; hidx = (hidx + cnbins) % cnbins; ((uchar2*)qangle.ptr(blockIdx.y))[x] = make_uchar2(hidx, (hidx + 1) % cnbins); ((float2*) grad.ptr(blockIdx.y))[x] = make_float2(mag * (1.f - ang), mag * ang); }}void compute_gradients_8UC1(int nbins, int height, int width, const DevMem2D& img, float angle_scale, DevMem2Df grad, DevMem2D qangle, bool correct_gamma){ const int nthreads = 256; dim3 bdim(nthreads, 1); dim3 gdim(divUp(width, bdim.x), divUp(height, bdim.y)); if (correct_gamma) compute_gradients_8UC1_kernel<nthreads, 1><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle); else compute_gradients_8UC1_kernel<nthreads, 0><<<gdim, bdim>>>(height, width, img, angle_scale, grad, qangle); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() );}//-------------------------------------------------------------------// Resizetexture<uchar4, 2, cudaReadModeNormalizedFloat> resize8UC4_tex;texture<uchar, 2, cudaReadModeNormalizedFloat> resize8UC1_tex;__global__ void resize_for_hog_kernel(float sx, float sy, DevMem2D_<uchar> dst, int colOfs){ unsigned int x = blockIdx.x * blockDim.x + threadIdx.x; unsigned int y = blockIdx.y * blockDim.y + threadIdx.y; if (x < dst.cols && y < dst.rows) dst.ptr(y)[x] = tex2D(resize8UC1_tex, x * sx + colOfs, y * sy) * 255;}__global__ void resize_for_hog_kernel(float sx, float sy, DevMem2D_<uchar4> dst, int colOfs){ unsigned int x = blockIdx.x * blockDim.x + threadIdx.x; unsigned int y = blockIdx.y * blockDim.y + threadIdx.y; if (x < dst.cols && y < dst.rows){ float4 val = tex2D(resize8UC4_tex, x * sx + colOfs, y * sy); dst.ptr(y)[x] = make_uchar4(val.x * 255, val.y * 255, val.z * 255, val.w * 255);}}template<class T, class TEX> static void resize_for_hog(const DevMem2D& src, DevMem2D dst, TEX& tex){ tex.filterMode = cudaFilterModeLinear; size_t texOfs = 0; int colOfs = 0; cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>(); cudaSafeCall( cudaBindTexture2D(&texOfs, tex, src.data, desc, src.cols, src.rows, src.step) ); if (texOfs != 0) { colOfs = static_cast<int>( texOfs/sizeof(T) ); cudaSafeCall( cudaUnbindTexture(tex) ); cudaSafeCall( cudaBindTexture2D(&texOfs, tex, src.data, desc, src.cols, src.rows, src.step) ); } dim3 threads(32, 8); dim3 grid(divUp(dst.cols, threads.x), divUp(dst.rows, threads.y)); float sx = static_cast<float>(src.cols) / dst.cols; float sy = static_cast<float>(src.rows) / dst.rows; resize_for_hog_kernel<<<grid, threads>>>(sx, sy, (DevMem2D_<T>)dst, colOfs); cudaSafeCall( cudaGetLastError() ); cudaSafeCall( cudaDeviceSynchronize() ); cudaSafeCall( cudaUnbindTexture(tex) );}void resize_8UC1(const DevMem2D& src, DevMem2D dst) { resize_for_hog<uchar> (src, dst, resize8UC1_tex); }void resize_8UC4(const DevMem2D& src, DevMem2D dst) { resize_for_hog<uchar4>(src, dst, resize8UC4_tex); }}}} 0 0
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