Vsevolod Glumov
13 years ago
commit
e578259a34
12 changed files with 726 additions and 36 deletions
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/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or bpied warranties, including, but not limited to, the bpied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include <thrust/sort.h> |
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#include "opencv2/gpu/device/common.hpp" |
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#include "opencv2/gpu/device/emulation.hpp" |
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namespace cv { namespace gpu { namespace device |
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{ |
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namespace hough |
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{ |
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__device__ int g_counter; |
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//////////////////////////////////////////////////////////////////////// |
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// buildPointList |
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const int PIXELS_PER_THREAD = 16; |
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__global__ void buildPointList(const DevMem2Db src, unsigned int* list) |
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{ |
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__shared__ int s_queues[4][32 * PIXELS_PER_THREAD]; |
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__shared__ int s_qsize[4]; |
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__shared__ int s_start[4]; |
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const int x = blockIdx.x * blockDim.x * PIXELS_PER_THREAD + threadIdx.x; |
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const int y = blockIdx.y * blockDim.y + threadIdx.y; |
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if (y >= src.rows) |
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return; |
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if (threadIdx.x == 0) |
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s_qsize[threadIdx.y] = 0; |
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__syncthreads(); |
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// fill the queue |
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for (int i = 0, xx = x; i < PIXELS_PER_THREAD && xx < src.cols; ++i, xx += blockDim.x) |
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{ |
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if (src(y, xx)) |
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{ |
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const unsigned int val = (y << 16) | xx; |
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const int qidx = Emulation::smem::atomicAdd(&s_qsize[threadIdx.y], 1); |
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s_queues[threadIdx.y][qidx] = val; |
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} |
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} |
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__syncthreads(); |
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// let one thread reserve the space required in the global list |
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if (threadIdx.x == 0 && threadIdx.y == 0) |
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{ |
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// find how many items are stored in each list |
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int total_size = 0; |
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for (int i = 0; i < blockDim.y; ++i) |
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{ |
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s_start[i] = total_size; |
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total_size += s_qsize[i]; |
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} |
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// calculate the offset in the global list |
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const int global_offset = atomicAdd(&g_counter, total_size); |
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for (int i = 0; i < blockDim.y; ++i) |
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s_start[i] += global_offset; |
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} |
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__syncthreads(); |
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// copy local queues to global queue |
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const int qsize = s_qsize[threadIdx.y]; |
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for(int i = threadIdx.x; i < qsize; i += blockDim.x) |
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{ |
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const unsigned int val = s_queues[threadIdx.y][i]; |
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list[s_start[threadIdx.y] + i] = val; |
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} |
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} |
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int buildPointList_gpu(DevMem2Db src, unsigned int* list) |
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{ |
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void* counter_ptr; |
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cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, g_counter) ); |
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cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(int)) ); |
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const dim3 block(32, 4); |
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const dim3 grid(divUp(src.cols, block.x * PIXELS_PER_THREAD), divUp(src.rows, block.y)); |
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cudaSafeCall( cudaFuncSetCacheConfig(buildPointList, cudaFuncCachePreferShared) ); |
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buildPointList<<<grid, block>>>(src, list); |
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cudaSafeCall( cudaGetLastError() ); |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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int total_count; |
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cudaSafeCall( cudaMemcpy(&total_count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) ); |
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return total_count; |
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} |
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//////////////////////////////////////////////////////////////////////// |
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// linesAccum |
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__global__ void linesAccumGlobal(const unsigned int* list, const int count, PtrStepi accum, const float irho, const float theta, const int numrho) |
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{ |
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const int n = blockIdx.x; |
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const float ang = n * theta; |
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float sin_ang; |
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float cos_ang; |
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sincosf(ang, &sin_ang, &cos_ang); |
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const float tabSin = sin_ang * irho; |
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const float tabCos = cos_ang * irho; |
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for (int i = threadIdx.x; i < count; i += blockDim.x) |
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{ |
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const unsigned int qvalue = list[i]; |
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const int x = (qvalue & 0x0000FFFF); |
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const int y = (qvalue >> 16) & 0x0000FFFF; |
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int r = __float2int_rn(x * tabCos + y * tabSin); |
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r += (numrho - 1) / 2; |
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::atomicAdd(accum.ptr(n + 1) + r + 1, 1); |
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} |
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} |
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__global__ void linesAccumShared(const unsigned int* list, const int count, PtrStepi accum, const float irho, const float theta, const int numrho) |
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{ |
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extern __shared__ int smem[]; |
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for (int i = threadIdx.x; i < numrho + 1; i += blockDim.x) |
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smem[i] = 0; |
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__syncthreads(); |
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const int n = blockIdx.x; |
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const float ang = n * theta; |
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float sin_ang; |
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float cos_ang; |
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sincosf(ang, &sin_ang, &cos_ang); |
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const float tabSin = sin_ang * irho; |
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const float tabCos = cos_ang * irho; |
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for (int i = threadIdx.x; i < count; i += blockDim.x) |
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{ |
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const unsigned int qvalue = list[i]; |
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const int x = (qvalue & 0x0000FFFF); |
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const int y = (qvalue >> 16) & 0x0000FFFF; |
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int r = __float2int_rn(x * tabCos + y * tabSin); |
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r += (numrho - 1) / 2; |
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Emulation::smem::atomicAdd(&smem[r + 1], 1); |
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} |
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__syncthreads(); |
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for (int i = threadIdx.x; i < numrho; i += blockDim.x) |
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accum(n + 1, i) = smem[i]; |
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} |
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void linesAccum_gpu(const unsigned int* list, int count, DevMem2Di accum, float rho, float theta, size_t sharedMemPerBlock) |
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{ |
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const dim3 block(1024); |
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const dim3 grid(accum.rows - 2); |
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cudaSafeCall( cudaFuncSetCacheConfig(linesAccumShared, cudaFuncCachePreferShared) ); |
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size_t smemSize = (accum.cols - 1) * sizeof(int); |
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if (smemSize < sharedMemPerBlock - 1000) |
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linesAccumShared<<<grid, block, smemSize>>>(list, count, accum, 1.0f / rho, theta, accum.cols - 2); |
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else |
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linesAccumGlobal<<<grid, block>>>(list, count, accum, 1.0f / rho, theta, accum.cols - 2); |
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cudaSafeCall( cudaGetLastError() ); |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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} |
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//////////////////////////////////////////////////////////////////////// |
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// linesGetResult |
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__global__ void linesGetResult(const DevMem2Di accum, float2* out, int* votes, const int maxSize, const float threshold, const float theta, const float rho, const int numrho) |
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{ |
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__shared__ int smem[8][32]; |
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int r = blockIdx.x * (blockDim.x - 2) + threadIdx.x; |
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int n = blockIdx.y * (blockDim.y - 2) + threadIdx.y; |
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if (r >= accum.cols || n >= accum.rows) |
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return; |
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smem[threadIdx.y][threadIdx.x] = accum(n, r); |
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__syncthreads(); |
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r -= 1; |
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n -= 1; |
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if (threadIdx.x == 0 || threadIdx.x == blockDim.x - 1 || threadIdx.y == 0 || threadIdx.y == blockDim.y - 1 || r >= accum.cols - 2 || n >= accum.rows - 2) |
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return; |
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if (smem[threadIdx.y][threadIdx.x] > threshold && |
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smem[threadIdx.y][threadIdx.x] > smem[threadIdx.y - 1][threadIdx.x] && |
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smem[threadIdx.y][threadIdx.x] >= smem[threadIdx.y + 1][threadIdx.x] && |
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smem[threadIdx.y][threadIdx.x] > smem[threadIdx.y][threadIdx.x - 1] && |
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smem[threadIdx.y][threadIdx.x] >= smem[threadIdx.y][threadIdx.x + 1]) |
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{ |
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const float radius = (r - (numrho - 1) * 0.5f) * rho; |
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const float angle = n * theta; |
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const int ind = ::atomicAdd(&g_counter, 1); |
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if (ind < maxSize) |
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{ |
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out[ind] = make_float2(radius, angle); |
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votes[ind] = smem[threadIdx.y][threadIdx.x]; |
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} |
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} |
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} |
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int linesGetResult_gpu(DevMem2Di accum, float2* out, int* votes, int maxSize, float rho, float theta, float threshold, bool doSort) |
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{ |
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void* counter_ptr; |
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cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, g_counter) ); |
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cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(int)) ); |
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const dim3 block(32, 8); |
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const dim3 grid(divUp(accum.cols, block.x - 2), divUp(accum.rows, block.y - 2)); |
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linesGetResult<<<grid, block>>>(accum, out, votes, maxSize, threshold, theta, rho, accum.cols - 2); |
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cudaSafeCall( cudaGetLastError() ); |
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cudaSafeCall( cudaDeviceSynchronize() ); |
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int total_count; |
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cudaSafeCall( cudaMemcpy(&total_count, counter_ptr, sizeof(int), cudaMemcpyDeviceToHost) ); |
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total_count = ::min(total_count, maxSize); |
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if (doSort && total_count > 0) |
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{ |
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thrust::device_ptr<float2> out_ptr(out); |
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thrust::device_ptr<int> votes_ptr(votes); |
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thrust::sort_by_key(votes_ptr, votes_ptr + total_count, out_ptr, thrust::greater<int>()); |
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} |
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return total_count; |
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} |
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} |
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}}} |
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@ -0,0 +1,144 @@ |
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
|
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||||
|
//
|
||||||
|
// By downloading, copying, installing or using the software you agree to this license.
|
||||||
|
// If you do not agree to this license, do not download, install,
|
||||||
|
// copy or use the software.
|
||||||
|
//
|
||||||
|
//
|
||||||
|
// License Agreement
|
||||||
|
// For Open Source Computer Vision Library
|
||||||
|
//
|
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|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
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|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
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|
// Third party copyrights are property of their respective owners.
|
||||||
|
//
|
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|
// Redistribution and use in source and binary forms, with or without modification,
|
||||||
|
// are permitted provided that the following conditions are met:
|
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|
//
|
||||||
|
// * Redistribution's of source code must retain the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer.
|
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|
//
|
||||||
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer in the documentation
|
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|
// and/or other materials provided with the distribution.
|
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|
//
|
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// * The name of the copyright holders may not be used to endorse or promote products
|
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|
// derived from this software without specific prior written permission.
|
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|
//
|
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|
// This software is provided by the copyright holders and contributors "as is" and
|
||||||
|
// any express or implied warranties, including, but not limited to, the implied
|
||||||
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||||
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||||
|
// indirect, incidental, special, exemplary, or consequential damages
|
||||||
|
// (including, but not limited to, procurement of substitute goods or services;
|
||||||
|
// loss of use, data, or profits; or business interruption) however caused
|
||||||
|
// and on any theory of liability, whether in contract, strict liability,
|
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|
// or tort (including negligence or otherwise) arising in any way out of
|
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|
// the use of this software, even if advised of the possibility of such damage.
|
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|
//
|
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//M*/
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#include "precomp.hpp" |
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#if !defined (HAVE_CUDA) |
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void cv::gpu::HoughLinesTransform(const GpuMat&, GpuMat&, GpuMat&, float, float) { throw_nogpu(); } |
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void cv::gpu::HoughLinesGet(const GpuMat&, GpuMat&, float, float, int, bool, int) { throw_nogpu(); } |
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void cv::gpu::HoughLines(const GpuMat&, GpuMat&, float, float, int, bool, int) { throw_nogpu(); } |
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void cv::gpu::HoughLines(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, float, int, bool, int) { throw_nogpu(); } |
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void cv::gpu::HoughLinesDownload(const GpuMat&, OutputArray, OutputArray) { throw_nogpu(); } |
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#else /* !defined (HAVE_CUDA) */ |
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namespace cv { namespace gpu { namespace device |
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{ |
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namespace hough |
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{ |
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int buildPointList_gpu(DevMem2Db src, unsigned int* list); |
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void linesAccum_gpu(const unsigned int* list, int count, DevMem2Di accum, float rho, float theta, size_t sharedMemPerBlock); |
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int linesGetResult_gpu(DevMem2Di accum, float2* out, int* votes, int maxSize, float rho, float theta, float threshold, bool doSort); |
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} |
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}}} |
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void cv::gpu::HoughLinesTransform(const GpuMat& src, GpuMat& accum, GpuMat& buf, float rho, float theta) |
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{ |
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using namespace cv::gpu::device::hough; |
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CV_Assert(src.type() == CV_8UC1); |
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CV_Assert(src.cols < std::numeric_limits<unsigned short>::max()); |
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CV_Assert(src.rows < std::numeric_limits<unsigned short>::max()); |
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ensureSizeIsEnough(1, src.size().area(), CV_32SC1, buf); |
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const int count = buildPointList_gpu(src, buf.ptr<unsigned int>()); |
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const int numangle = cvRound(CV_PI / theta); |
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const int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho); |
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CV_Assert(numangle > 0 && numrho > 0); |
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ensureSizeIsEnough(numangle + 2, numrho + 2, CV_32SC1, accum); |
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accum.setTo(cv::Scalar::all(0)); |
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cv::gpu::DeviceInfo devInfo; |
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if (count > 0) |
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linesAccum_gpu(buf.ptr<unsigned int>(), count, accum, rho, theta, devInfo.sharedMemPerBlock()); |
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} |
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void cv::gpu::HoughLinesGet(const GpuMat& accum, GpuMat& lines, float rho, float theta, int threshold, bool doSort, int maxLines) |
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{ |
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using namespace cv::gpu::device; |
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CV_Assert(accum.type() == CV_32SC1); |
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ensureSizeIsEnough(2, maxLines, CV_32FC2, lines); |
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int count = hough::linesGetResult_gpu(accum, lines.ptr<float2>(0), lines.ptr<int>(1), maxLines, rho, theta, threshold, doSort); |
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if (count > 0) |
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lines.cols = count; |
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else |
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lines.release(); |
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} |
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void cv::gpu::HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort, int maxLines) |
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{ |
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cv::gpu::GpuMat accum, buf; |
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HoughLines(src, lines, accum, buf, rho, theta, threshold, doSort, maxLines); |
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} |
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|
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void cv::gpu::HoughLines(const GpuMat& src, GpuMat& lines, GpuMat& accum, GpuMat& buf, float rho, float theta, int threshold, bool doSort, int maxLines) |
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{ |
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|
HoughLinesTransform(src, accum, buf, rho, theta); |
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HoughLinesGet(accum, lines, rho, theta, threshold, doSort, maxLines); |
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|
} |
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|
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void cv::gpu::HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines_, OutputArray h_votes_) |
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|
{ |
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|
if (d_lines.empty()) |
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|
{ |
||||||
|
h_lines_.release(); |
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|
if (h_votes_.needed()) |
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|
h_votes_.release(); |
||||||
|
return; |
||||||
|
} |
||||||
|
|
||||||
|
CV_Assert(d_lines.rows == 2 && d_lines.type() == CV_32FC2); |
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|
|
||||||
|
h_lines_.create(1, d_lines.cols, CV_32FC2); |
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|
cv::Mat h_lines = h_lines_.getMat(); |
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|
d_lines.row(0).download(h_lines); |
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|
|
||||||
|
if (h_votes_.needed()) |
||||||
|
{ |
||||||
|
h_votes_.create(1, d_lines.cols, CV_32SC1); |
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|
cv::Mat h_votes = h_votes_.getMat(); |
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|
cv::gpu::GpuMat d_votes(1, d_lines.cols, CV_32SC1, const_cast<int*>(d_lines.ptr<int>(1))); |
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|
d_votes.download(h_votes); |
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|
} |
||||||
|
} |
||||||
|
|
||||||
|
#endif /* !defined (HAVE_CUDA) */ |
||||||
Loading…
Reference in new issue