/*M/////////////////////////////////////////////////////////////////////////////////////// // // 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 // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // 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, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ //#include "cuda_shared.hpp" #include "opencv2/gpu/devmem2d.hpp" #include "safe_call.hpp" static inline int divUp(int total, int grain) { return (total + grain - 1) / grain; } using namespace cv::gpu; ////////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////// Streeo BM //////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////// #define ROWSperTHREAD 21 // the number of rows a thread will process namespace cv { namespace gpu { namespace bm { #define BLOCK_W 128 // the thread block width (464) #define N_DISPARITIES 8 #define STEREO_MIND 0 // The minimum d range to check #define STEREO_DISP_STEP N_DISPARITIES // the d step, must be <= 1 to avoid aliasing __constant__ unsigned int* cminSSDImage; __constant__ size_t cminSSD_step; __constant__ int cwidth; __constant__ int cheight; __device__ int SQ(int a) { return a * a; } template __device__ unsigned int CalcSSD(volatile unsigned int *col_ssd_cache, volatile unsigned int *col_ssd) { unsigned int cache = 0; unsigned int cache2 = 0; for(int i = 1; i <= RADIUS; i++) cache += col_ssd[i]; col_ssd_cache[0] = cache; __syncthreads(); if (threadIdx.x < BLOCK_W - RADIUS) cache2 = col_ssd_cache[RADIUS]; else for(int i = RADIUS + 1; i < (2 * RADIUS + 1); i++) cache2 += col_ssd[i]; return col_ssd[0] + cache + cache2; } template __device__ uint2 MinSSD(volatile unsigned int *col_ssd_cache, volatile unsigned int *col_ssd) { unsigned int ssd[N_DISPARITIES]; //See above: #define COL_SSD_SIZE (BLOCK_W + 2 * RADIUS) ssd[0] = CalcSSD(col_ssd_cache, col_ssd + 0 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[1] = CalcSSD(col_ssd_cache, col_ssd + 1 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[2] = CalcSSD(col_ssd_cache, col_ssd + 2 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[3] = CalcSSD(col_ssd_cache, col_ssd + 3 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[4] = CalcSSD(col_ssd_cache, col_ssd + 4 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[5] = CalcSSD(col_ssd_cache, col_ssd + 5 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[6] = CalcSSD(col_ssd_cache, col_ssd + 6 * (BLOCK_W + 2 * RADIUS)); __syncthreads(); ssd[7] = CalcSSD(col_ssd_cache, col_ssd + 7 * (BLOCK_W + 2 * RADIUS)); int mssd = min(min(min(ssd[0], ssd[1]), min(ssd[4], ssd[5])), min(min(ssd[2], ssd[3]), min(ssd[6], ssd[7]))); int bestIdx = 0; for (int i = 0; i < N_DISPARITIES; i++) { if (mssd == ssd[i]) bestIdx = i; } return make_uint2(mssd, bestIdx); } template __device__ void StepDown(int idx1, int idx2, unsigned char* imageL, unsigned char* imageR, int d, volatile unsigned int *col_ssd) { unsigned char leftPixel1; unsigned char leftPixel2; unsigned char rightPixel1[8]; unsigned char rightPixel2[8]; unsigned int diff1, diff2; leftPixel1 = imageL[idx1]; leftPixel2 = imageL[idx2]; idx1 = idx1 - d; idx2 = idx2 - d; rightPixel1[7] = imageR[idx1 - 7]; rightPixel1[0] = imageR[idx1 - 0]; rightPixel1[1] = imageR[idx1 - 1]; rightPixel1[2] = imageR[idx1 - 2]; rightPixel1[3] = imageR[idx1 - 3]; rightPixel1[4] = imageR[idx1 - 4]; rightPixel1[5] = imageR[idx1 - 5]; rightPixel1[6] = imageR[idx1 - 6]; rightPixel2[7] = imageR[idx2 - 7]; rightPixel2[0] = imageR[idx2 - 0]; rightPixel2[1] = imageR[idx2 - 1]; rightPixel2[2] = imageR[idx2 - 2]; rightPixel2[3] = imageR[idx2 - 3]; rightPixel2[4] = imageR[idx2 - 4]; rightPixel2[5] = imageR[idx2 - 5]; rightPixel2[6] = imageR[idx2 - 6]; //See above: #define COL_SSD_SIZE (BLOCK_W + 2 * RADIUS) diff1 = leftPixel1 - rightPixel1[0]; diff2 = leftPixel2 - rightPixel2[0]; col_ssd[0 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[1]; diff2 = leftPixel2 - rightPixel2[1]; col_ssd[1 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[2]; diff2 = leftPixel2 - rightPixel2[2]; col_ssd[2 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[3]; diff2 = leftPixel2 - rightPixel2[3]; col_ssd[3 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[4]; diff2 = leftPixel2 - rightPixel2[4]; col_ssd[4 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[5]; diff2 = leftPixel2 - rightPixel2[5]; col_ssd[5 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[6]; diff2 = leftPixel2 - rightPixel2[6]; col_ssd[6 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); diff1 = leftPixel1 - rightPixel1[7]; diff2 = leftPixel2 - rightPixel2[7]; col_ssd[7 * (BLOCK_W + 2 * RADIUS)] += SQ(diff2) - SQ(diff1); } template __device__ void InitColSSD(int x_tex, int y_tex, int im_pitch, unsigned char* imageL, unsigned char* imageR, int d, volatile unsigned int *col_ssd) { unsigned char leftPixel1; int idx; unsigned int diffa[] = {0, 0, 0, 0, 0, 0, 0, 0}; for(int i = 0; i < (2 * RADIUS + 1); i++) { idx = y_tex * im_pitch + x_tex; leftPixel1 = imageL[idx]; idx = idx - d; diffa[0] += SQ(leftPixel1 - imageR[idx - 0]); diffa[1] += SQ(leftPixel1 - imageR[idx - 1]); diffa[2] += SQ(leftPixel1 - imageR[idx - 2]); diffa[3] += SQ(leftPixel1 - imageR[idx - 3]); diffa[4] += SQ(leftPixel1 - imageR[idx - 4]); diffa[5] += SQ(leftPixel1 - imageR[idx - 5]); diffa[6] += SQ(leftPixel1 - imageR[idx - 6]); diffa[7] += SQ(leftPixel1 - imageR[idx - 7]); y_tex += 1; } //See above: #define COL_SSD_SIZE (BLOCK_W + 2 * RADIUS) col_ssd[0 * (BLOCK_W + 2 * RADIUS)] = diffa[0]; col_ssd[1 * (BLOCK_W + 2 * RADIUS)] = diffa[1]; col_ssd[2 * (BLOCK_W + 2 * RADIUS)] = diffa[2]; col_ssd[3 * (BLOCK_W + 2 * RADIUS)] = diffa[3]; col_ssd[4 * (BLOCK_W + 2 * RADIUS)] = diffa[4]; col_ssd[5 * (BLOCK_W + 2 * RADIUS)] = diffa[5]; col_ssd[6 * (BLOCK_W + 2 * RADIUS)] = diffa[6]; col_ssd[7 * (BLOCK_W + 2 * RADIUS)] = diffa[7]; } template __global__ void stereoKernel(unsigned char *left, unsigned char *right, size_t img_step, PtrStep disp, int maxdisp) { extern __shared__ unsigned int col_ssd_cache[]; volatile unsigned int *col_ssd = col_ssd_cache + BLOCK_W + threadIdx.x; volatile unsigned int *col_ssd_extra = threadIdx.x < (2 * RADIUS) ? col_ssd + BLOCK_W : 0; //#define N_DIRTY_PIXELS (2 * RADIUS) //#define X (blockIdx.x * BLOCK_W + threadIdx.x + STEREO_MAXD) int X = (blockIdx.x * BLOCK_W + threadIdx.x + maxdisp + RADIUS); //#define Y (__mul24(blockIdx.y, ROWSperTHREAD) + RADIUS) #define Y (blockIdx.y * ROWSperTHREAD + RADIUS) //int Y = blockIdx.y * ROWSperTHREAD + RADIUS; unsigned int* minSSDImage = cminSSDImage + X + Y * cminSSD_step; unsigned char* disparImage = disp.data + X + Y * disp.step; /* if (X < cwidth) { unsigned int *minSSDImage_end = minSSDImage + min(ROWSperTHREAD, cheight - Y) * minssd_step; for(uint *ptr = minSSDImage; ptr != minSSDImage_end; ptr += minssd_step ) *ptr = 0xFFFFFFFF; }*/ int end_row = min(ROWSperTHREAD, cheight - Y); int y_tex; int x_tex = X - RADIUS; if (x_tex >= cwidth) return; for(int d = STEREO_MIND; d < maxdisp; d += STEREO_DISP_STEP) { y_tex = Y - RADIUS; InitColSSD(x_tex, y_tex, img_step, left, right, d, col_ssd); if (col_ssd_extra > 0) if (x_tex + BLOCK_W < cwidth) InitColSSD(x_tex + BLOCK_W, y_tex, img_step, left, right, d, col_ssd_extra); __syncthreads(); //before MinSSD function if (X < cwidth - RADIUS && Y < cheight - RADIUS) { uint2 minSSD = MinSSD(col_ssd_cache + threadIdx.x, col_ssd); if (minSSD.x < minSSDImage[0]) { disparImage[0] = (unsigned char)(d + minSSD.y); minSSDImage[0] = minSSD.x; } } for(int row = 1; row < end_row; row++) { int idx1 = y_tex * img_step + x_tex; int idx2 = (y_tex + (2 * RADIUS + 1)) * img_step + x_tex; __syncthreads(); StepDown(idx1, idx2, left, right, d, col_ssd); if (col_ssd_extra) if (x_tex + BLOCK_W < cwidth) StepDown(idx1, idx2, left + BLOCK_W, right + BLOCK_W, d, col_ssd_extra); y_tex += 1; __syncthreads(); //before MinSSD function if (X < cwidth - RADIUS && row < cheight - RADIUS - Y) { int idx = row * cminSSD_step; uint2 minSSD = MinSSD(col_ssd_cache + threadIdx.x, col_ssd); if (minSSD.x < minSSDImage[idx]) { disparImage[disp.step * row] = (unsigned char)(d + minSSD.y); minSSDImage[idx] = minSSD.x; } } } // for row loop } // for d loop } template void kernel_caller(const DevMem2D& left, const DevMem2D& right, const DevMem2D& disp, int maxdisp, cudaStream_t & stream) { dim3 grid(1,1,1); dim3 threads(BLOCK_W, 1, 1); grid.x = divUp(left.cols - maxdisp - 2 * RADIUS, BLOCK_W); grid.y = divUp(left.rows - 2 * RADIUS, ROWSperTHREAD); //See above: #define COL_SSD_SIZE (BLOCK_W + 2 * RADIUS) size_t smem_size = (BLOCK_W + N_DISPARITIES * (BLOCK_W + 2 * RADIUS)) * sizeof(unsigned int); stereoKernel<<>>(left.data, right.data, left.step, disp, maxdisp); if (stream == 0) cudaSafeCall( cudaThreadSynchronize() ); }; typedef void (*kernel_caller_t)(const DevMem2D& left, const DevMem2D& right, const DevMem2D& disp, int maxdisp, cudaStream_t & stream); const static kernel_caller_t callers[] = { 0, kernel_caller< 1>, kernel_caller< 2>, kernel_caller< 3>, kernel_caller< 4>, kernel_caller< 5>, kernel_caller< 6>, kernel_caller< 7>, kernel_caller< 8>, kernel_caller< 9>, kernel_caller<10>, kernel_caller<11>, kernel_caller<12>, kernel_caller<13>, kernel_caller<15>, kernel_caller<15>, kernel_caller<16>, kernel_caller<17>, kernel_caller<18>, kernel_caller<19>, kernel_caller<20>, kernel_caller<21>, kernel_caller<22>, kernel_caller<23>, kernel_caller<24>, kernel_caller<25> //0,0,0, 0,0,0, 0,0,kernel_caller<9> }; const int calles_num = sizeof(callers)/sizeof(callers[0]); extern "C" void stereoBM_GPU(const DevMem2D& left, const DevMem2D& right, const DevMem2D& disp, int maxdisp, int winsz, const DevMem2D_& minSSD_buf, cudaStream_t& stream) { int winsz2 = winsz >> 1; if (winsz2 == 0 || winsz2 >= calles_num) cv::gpu::error("Unsupported window size", __FILE__, __LINE__); //cudaSafeCall( cudaFuncSetCacheConfig(&stereoKernel, cudaFuncCachePreferL1) ); //cudaSafeCall( cudaFuncSetCacheConfig(&stereoKernel, cudaFuncCachePreferShared) ); cudaSafeCall( cudaMemset2D(disp.data, disp.step, 0, disp.cols, disp.rows) ); cudaSafeCall( cudaMemset2D(minSSD_buf.data, minSSD_buf.step, 0xFF, minSSD_buf.cols * minSSD_buf.elemSize(), disp.rows) ); cudaSafeCall( cudaMemcpyToSymbol( cwidth, &left.cols, sizeof(left.cols) ) ); cudaSafeCall( cudaMemcpyToSymbol( cheight, &left.rows, sizeof(left.rows) ) ); cudaSafeCall( cudaMemcpyToSymbol( cminSSDImage, &minSSD_buf.data, sizeof(minSSD_buf.data) ) ); size_t minssd_step = minSSD_buf.step/minSSD_buf.elemSize(); cudaSafeCall( cudaMemcpyToSymbol( cminSSD_step, &minssd_step, sizeof(minssd_step) ) ); callers[winsz2](left, right, disp, maxdisp, stream); } ////////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////// Sobel Prefiler /////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////// texture texForSobel; extern "C" __global__ void prefilter_kernel(DevMem2D output, int prefilterCap) { int x = blockDim.x * blockIdx.x + threadIdx.x; int y = blockDim.y * blockIdx.y + threadIdx.y; if (x < output.cols && y < output.rows) { int conv = (int)tex2D(texForSobel, x - 1, y - 1) * (-1) + (int)tex2D(texForSobel, x + 1, y - 1) * (1) + (int)tex2D(texForSobel, x - 1, y ) * (-2) + (int)tex2D(texForSobel, x + 1, y ) * (2) + (int)tex2D(texForSobel, x - 1, y + 1) * (-1) + (int)tex2D(texForSobel, x + 1, y + 1) * (1); conv = min(min(max(-prefilterCap, conv), prefilterCap) + prefilterCap, 255); output.ptr(y)[x] = conv & 0xFF; } } extern "C" void prefilter_xsobel(const DevMem2D& input, const DevMem2D& output, int prefilterCap, cudaStream_t & stream) { cudaChannelFormatDesc desc = cudaCreateChannelDesc(); cudaSafeCall( cudaBindTexture2D( 0, texForSobel, input.data, desc, input.cols, input.rows, input.step ) ); dim3 threads(16, 16, 1); dim3 grid(1, 1, 1); grid.x = divUp(input.cols, threads.x); grid.y = divUp(input.rows, threads.y); prefilter_kernel<<>>(output, prefilterCap); if (stream == 0) cudaSafeCall( cudaThreadSynchronize() ); cudaSafeCall( cudaUnbindTexture (texForSobel ) ); } ////////////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////// Textureness filtering //////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////////// texture texForTF; __device__ float sobel(int x, int y) { float conv = tex2D(texForTF, x - 1, y - 1) * (-1) + tex2D(texForTF, x + 1, y - 1) * (1) + tex2D(texForTF, x - 1, y ) * (-2) + tex2D(texForTF, x + 1, y ) * (2) + tex2D(texForTF, x - 1, y + 1) * (-1) + tex2D(texForTF, x + 1, y + 1) * (1); return fabs(conv); } __device__ float CalcSums(float *cols, float *cols_cache, int winsz) { float cache = 0; float cache2 = 0; int winsz2 = winsz/2; for(int i = 1; i <= winsz2; i++) cache += cols[i]; cols_cache[0] = cache; __syncthreads(); if (threadIdx.x < blockDim.x - winsz2) cache2 = cols_cache[winsz2]; else for(int i = winsz2 + 1; i < winsz; i++) cache2 += cols[i]; return cols[0] + cache + cache2; } #define RpT (2 * ROWSperTHREAD) // got experimentally extern "C" __global__ void textureness_kernel(DevMem2D disp, int winsz, float threshold) { int winsz2 = winsz/2; int n_dirty_pixels = (winsz2) * 2; extern __shared__ float cols_cache[]; float *cols = cols_cache + blockDim.x + threadIdx.x; float *cols_extra = threadIdx.x < n_dirty_pixels ? cols + blockDim.x : 0; int x = blockIdx.x * blockDim.x + threadIdx.x; int beg_row = blockIdx.y * RpT; int end_row = min(beg_row + RpT, disp.rows); if (x < disp.cols) { int y = beg_row; float sum = 0; float sum_extra = 0; for(int i = y - winsz2; i <= y + winsz2; ++i) { sum += sobel(x - winsz2, i); if (cols_extra) sum_extra += sobel(x + blockDim.x - winsz2, i); } *cols = sum; if (cols_extra) *cols_extra = sum_extra; __syncthreads(); float sum_win = CalcSums(cols, cols_cache + threadIdx.x, winsz) * 255; if (sum_win < threshold) disp.data[y * disp.step + x] = 0; __syncthreads(); for(int y = beg_row + 1; y < end_row; ++y) { sum = sum - sobel(x - winsz2, y - winsz2 - 1) + sobel(x - winsz2, y + winsz2); *cols = sum; if (cols_extra) { sum_extra = sum_extra - sobel(x + blockDim.x - winsz2, y - winsz2 - 1) + sobel(x + blockDim.x - winsz2, y + winsz2); *cols_extra = sum_extra; } __syncthreads(); float sum_win = CalcSums(cols, cols_cache + threadIdx.x, winsz) * 255; if (sum_win < threshold) disp.data[y * disp.step + x] = 0; __syncthreads(); } } } extern "C" void postfilter_textureness(const DevMem2D& input, int winsz, float avgTexturenessThreshold, const DevMem2D& disp, cudaStream_t & stream) { avgTexturenessThreshold *= winsz * winsz; texForTF.filterMode = cudaFilterModeLinear; texForTF.addressMode[0] = cudaAddressModeWrap; texForTF.addressMode[1] = cudaAddressModeWrap; cudaChannelFormatDesc desc = cudaCreateChannelDesc(); cudaSafeCall( cudaBindTexture2D( 0, texForTF, input.data, desc, input.cols, input.rows, input.step ) ); dim3 threads(128, 1, 1); dim3 grid(1, 1, 1); grid.x = divUp(input.cols, threads.x); grid.y = divUp(input.rows, RpT); size_t smem_size = (threads.x + threads.x + (winsz/2) * 2 ) * sizeof(float); textureness_kernel<<>>(disp, winsz, avgTexturenessThreshold); if (stream == 0) cudaSafeCall( cudaThreadSynchronize() ); cudaSafeCall( cudaUnbindTexture (texForTF) ); } }}}