/*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. // Copyright (C) 2013, OpenCV Foundation, 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*/ /* This is a variation of "Stereo Processing by Semiglobal Matching and Mutual Information" by Heiko Hirschmuller. We match blocks rather than individual pixels, thus the algorithm is called SGBM (Semi-global block matching) */ #include "precomp.hpp" #include #include "opencv2/core/hal/intrin.hpp" namespace cv { typedef uchar PixType; typedef short CostType; typedef short DispType; enum { NR = 16, NR2 = NR/2 }; struct StereoSGBMParams { StereoSGBMParams() { minDisparity = numDisparities = 0; SADWindowSize = 0; P1 = P2 = 0; disp12MaxDiff = 0; preFilterCap = 0; uniquenessRatio = 0; speckleWindowSize = 0; speckleRange = 0; mode = StereoSGBM::MODE_SGBM; } StereoSGBMParams( int _minDisparity, int _numDisparities, int _SADWindowSize, int _P1, int _P2, int _disp12MaxDiff, int _preFilterCap, int _uniquenessRatio, int _speckleWindowSize, int _speckleRange, int _mode ) { minDisparity = _minDisparity; numDisparities = _numDisparities; SADWindowSize = _SADWindowSize; P1 = _P1; P2 = _P2; disp12MaxDiff = _disp12MaxDiff; preFilterCap = _preFilterCap; uniquenessRatio = _uniquenessRatio; speckleWindowSize = _speckleWindowSize; speckleRange = _speckleRange; mode = _mode; } int minDisparity; int numDisparities; int SADWindowSize; int preFilterCap; int uniquenessRatio; int P1; int P2; int speckleWindowSize; int speckleRange; int disp12MaxDiff; int mode; }; /* For each pixel row1[x], max(maxD, 0) <= minX <= x < maxX <= width - max(0, -minD), and for each disparity minD<=d(y), *row2 = img2.ptr(y); PixType *prow1 = buffer + width2*2, *prow2 = prow1 + width*cn*2; tab += tabOfs; for( c = 0; c < cn*2; c++ ) { prow1[width*c] = prow1[width*c + width-1] = prow2[width*c] = prow2[width*c + width-1] = tab[0]; } int n1 = y > 0 ? -(int)img1.step : 0, s1 = y < img1.rows-1 ? (int)img1.step : 0; int n2 = y > 0 ? -(int)img2.step : 0, s2 = y < img2.rows-1 ? (int)img2.step : 0; if( cn == 1 ) { for( x = 1; x < width-1; x++ ) { prow1[x] = tab[(row1[x+1] - row1[x-1])*2 + row1[x+n1+1] - row1[x+n1-1] + row1[x+s1+1] - row1[x+s1-1]]; prow2[width-1-x] = tab[(row2[x+1] - row2[x-1])*2 + row2[x+n2+1] - row2[x+n2-1] + row2[x+s2+1] - row2[x+s2-1]]; prow1[x+width] = row1[x]; prow2[width-1-x+width] = row2[x]; } } else { for( x = 1; x < width-1; x++ ) { prow1[x] = tab[(row1[x*3+3] - row1[x*3-3])*2 + row1[x*3+n1+3] - row1[x*3+n1-3] + row1[x*3+s1+3] - row1[x*3+s1-3]]; prow1[x+width] = tab[(row1[x*3+4] - row1[x*3-2])*2 + row1[x*3+n1+4] - row1[x*3+n1-2] + row1[x*3+s1+4] - row1[x*3+s1-2]]; prow1[x+width*2] = tab[(row1[x*3+5] - row1[x*3-1])*2 + row1[x*3+n1+5] - row1[x*3+n1-1] + row1[x*3+s1+5] - row1[x*3+s1-1]]; prow2[width-1-x] = tab[(row2[x*3+3] - row2[x*3-3])*2 + row2[x*3+n2+3] - row2[x*3+n2-3] + row2[x*3+s2+3] - row2[x*3+s2-3]]; prow2[width-1-x+width] = tab[(row2[x*3+4] - row2[x*3-2])*2 + row2[x*3+n2+4] - row2[x*3+n2-2] + row2[x*3+s2+4] - row2[x*3+s2-2]]; prow2[width-1-x+width*2] = tab[(row2[x*3+5] - row2[x*3-1])*2 + row2[x*3+n2+5] - row2[x*3+n2-1] + row2[x*3+s2+5] - row2[x*3+s2-1]]; prow1[x+width*3] = row1[x*3]; prow1[x+width*4] = row1[x*3+1]; prow1[x+width*5] = row1[x*3+2]; prow2[width-1-x+width*3] = row2[x*3]; prow2[width-1-x+width*4] = row2[x*3+1]; prow2[width-1-x+width*5] = row2[x*3+2]; } } memset( cost, 0, width1*D*sizeof(cost[0]) ); buffer -= minX2; cost -= minX1*D + minD; // simplify the cost indices inside the loop #if 1 for( c = 0; c < cn*2; c++, prow1 += width, prow2 += width ) { int diff_scale = c < cn ? 0 : 2; // precompute // v0 = min(row2[x-1/2], row2[x], row2[x+1/2]) and // v1 = max(row2[x-1/2], row2[x], row2[x+1/2]) and for( x = minX2; x < maxX2; x++ ) { int v = prow2[x]; int vl = x > 0 ? (v + prow2[x-1])/2 : v; int vr = x < width-1 ? (v + prow2[x+1])/2 : v; int v0 = std::min(vl, vr); v0 = std::min(v0, v); int v1 = std::max(vl, vr); v1 = std::max(v1, v); buffer[x] = (PixType)v0; buffer[x + width2] = (PixType)v1; } for( x = minX1; x < maxX1; x++ ) { int u = prow1[x]; int ul = x > 0 ? (u + prow1[x-1])/2 : u; int ur = x < width-1 ? (u + prow1[x+1])/2 : u; int u0 = std::min(ul, ur); u0 = std::min(u0, u); int u1 = std::max(ul, ur); u1 = std::max(u1, u); #if CV_SIMD128 v_uint8x16 _u = v_setall_u8((uchar)u), _u0 = v_setall_u8((uchar)u0); v_uint8x16 _u1 = v_setall_u8((uchar)u1); for( int d = minD; d < maxD; d += 16 ) { v_uint8x16 _v = v_load(prow2 + width-x-1 + d); v_uint8x16 _v0 = v_load(buffer + width-x-1 + d); v_uint8x16 _v1 = v_load(buffer + width-x-1 + d + width2); v_uint8x16 c0 = v_max(_u - _v1, _v0 - _u); v_uint8x16 c1 = v_max(_v - _u1, _u0 - _v); v_uint8x16 diff = v_min(c0, c1); v_int16x8 _c0 = v_load_aligned(cost + x*D + d); v_int16x8 _c1 = v_load_aligned(cost + x*D + d + 8); v_uint16x8 diff1,diff2; v_expand(diff,diff1,diff2); v_store_aligned(cost + x*D + d, _c0 + v_reinterpret_as_s16(diff1 >> diff_scale)); v_store_aligned(cost + x*D + d + 8, _c1 + v_reinterpret_as_s16(diff2 >> diff_scale)); } #else for( int d = minD; d < maxD; d++ ) { int v = prow2[width-x-1 + d]; int v0 = buffer[width-x-1 + d]; int v1 = buffer[width-x-1 + d + width2]; int c0 = std::max(0, u - v1); c0 = std::max(c0, v0 - u); int c1 = std::max(0, v - u1); c1 = std::max(c1, u0 - v); cost[x*D + d] = (CostType)(cost[x*D+d] + (std::min(c0, c1) >> diff_scale)); } #endif } } #else for( c = 0; c < cn*2; c++, prow1 += width, prow2 += width ) { for( x = minX1; x < maxX1; x++ ) { int u = prow1[x]; #if CV_SSE2 if( useSIMD ) { __m128i _u = _mm_set1_epi8(u), z = _mm_setzero_si128(); for( int d = minD; d < maxD; d += 16 ) { __m128i _v = _mm_loadu_si128((const __m128i*)(prow2 + width-1-x + d)); __m128i diff = _mm_adds_epu8(_mm_subs_epu8(_u,_v), _mm_subs_epu8(_v,_u)); __m128i c0 = _mm_load_si128((__m128i*)(cost + x*D + d)); __m128i c1 = _mm_load_si128((__m128i*)(cost + x*D + d + 8)); _mm_store_si128((__m128i*)(cost + x*D + d), _mm_adds_epi16(c0, _mm_unpacklo_epi8(diff,z))); _mm_store_si128((__m128i*)(cost + x*D + d + 8), _mm_adds_epi16(c1, _mm_unpackhi_epi8(diff,z))); } } else #endif { for( int d = minD; d < maxD; d++ ) { int v = prow2[width-1-x + d]; cost[x*D + d] = (CostType)(cost[x*D + d] + (CostType)std::abs(u - v)); } } } } #endif } /* computes disparity for "roi" in img1 w.r.t. img2 and write it to disp1buf. that is, disp1buf(x, y)=d means that img1(x+roi.x, y+roi.y) ~ img2(x+roi.x-d, y+roi.y). minD <= d < maxD. disp2full is the reverse disparity map, that is: disp2full(x+roi.x,y+roi.y)=d means that img2(x+roi.x, y+roi.y) ~ img1(x+roi.x+d, y+roi.y) note that disp1buf will have the same size as the roi and disp2full will have the same size as img1 (or img2). On exit disp2buf is not the final disparity, it is an intermediate result that becomes final after all the tiles are processed. the disparity in disp1buf is written with sub-pixel accuracy (4 fractional bits, see StereoSGBM::DISP_SCALE), using quadratic interpolation, while the disparity in disp2buf is written as is, without interpolation. disp2cost also has the same size as img1 (or img2). It contains the minimum current cost, used to find the best disparity, corresponding to the minimal cost. */ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, Mat& disp1, const StereoSGBMParams& params, Mat& buffer ) { #if CV_SSE2 static const uchar LSBTab[] = { 0, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 6, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 7, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 6, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 5, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0, 4, 0, 1, 0, 2, 0, 1, 0, 3, 0, 1, 0, 2, 0, 1, 0 }; volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2); #endif const int ALIGN = 16; const int DISP_SHIFT = StereoMatcher::DISP_SHIFT; const int DISP_SCALE = (1 << DISP_SHIFT); const CostType MAX_COST = SHRT_MAX; int minD = params.minDisparity, maxD = minD + params.numDisparities; Size SADWindowSize; SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; int ftzero = std::max(params.preFilterCap, 15) | 1; int uniquenessRatio = params.uniquenessRatio >= 0 ? params.uniquenessRatio : 10; int disp12MaxDiff = params.disp12MaxDiff > 0 ? params.disp12MaxDiff : 1; int P1 = params.P1 > 0 ? params.P1 : 2, P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); int k, width = disp1.cols, height = disp1.rows; int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0); int D = maxD - minD, width1 = maxX1 - minX1; int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE; int SW2 = SADWindowSize.width/2, SH2 = SADWindowSize.height/2; bool fullDP = params.mode == StereoSGBM::MODE_HH; int npasses = fullDP ? 2 : 1; const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; PixType clipTab[TAB_SIZE]; for( k = 0; k < TAB_SIZE; k++ ) clipTab[k] = (PixType)(std::min(std::max(k - TAB_OFS, -ftzero), ftzero) + ftzero); if( minX1 >= maxX1 ) { disp1 = Scalar::all(INVALID_DISP_SCALED); return; } CV_Assert( D % 16 == 0 ); // NR - the number of directions. the loop on x below that computes Lr assumes that NR == 8. // if you change NR, please, modify the loop as well. int D2 = D+16, NRD2 = NR2*D2; // the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer: // for 8-way dynamic programming we need the current row and // the previous row, i.e. 2 rows in total const int NLR = 2; const int LrBorder = NLR - 1; // for each possible stereo match (img1(x,y) <=> img2(x-d,y)) // we keep pixel difference cost (C) and the summary cost over NR directions (S). // we also keep all the partial costs for the previous line L_r(x,d) and also min_k L_r(x, k) size_t costBufSize = width1*D; size_t CSBufSize = costBufSize*(fullDP ? height : 1); size_t minLrSize = (width1 + LrBorder*2)*NR2, LrSize = minLrSize*D2; int hsumBufNRows = SH2*2 + 2; size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[] costBufSize*(hsumBufNRows + 1)*sizeof(CostType) + // hsumBuf, pixdiff CSBufSize*2*sizeof(CostType) + // C, S width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost width*(sizeof(CostType) + sizeof(DispType)) + 1024; // disp2cost + disp2 if( buffer.empty() || !buffer.isContinuous() || buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize ) buffer.create(1, (int)totalBufSize, CV_8U); // summary cost over different (nDirs) directions CostType* Cbuf = (CostType*)alignPtr(buffer.ptr(), ALIGN); CostType* Sbuf = Cbuf + CSBufSize; CostType* hsumBuf = Sbuf + CSBufSize; CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows; CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR; DispType* disp2ptr = (DispType*)(disp2cost + width); PixType* tempBuf = (PixType*)(disp2ptr + width); // add P2 to every C(x,y). it saves a few operations in the inner loops for( k = 0; k < width1*D; k++ ) Cbuf[k] = (CostType)P2; for( int pass = 1; pass <= npasses; pass++ ) { int x1, y1, x2, y2, dx, dy; if( pass == 1 ) { y1 = 0; y2 = height; dy = 1; x1 = 0; x2 = width1; dx = 1; } else { y1 = height-1; y2 = -1; dy = -1; x1 = width1-1; x2 = -1; dx = -1; } CostType *Lr[NLR]={0}, *minLr[NLR]={0}; for( k = 0; k < NLR; k++ ) { // shift Lr[k] and minLr[k] pointers, because we allocated them with the borders, // and will occasionally use negative indices with the arrays // we need to shift Lr[k] pointers by 1, to give the space for d=-1. // however, then the alignment will be imperfect, i.e. bad for SSE, // thus we shift the pointers by 8 (8*sizeof(short) == 16 - ideal alignment) Lr[k] = pixDiff + costBufSize + LrSize*k + NRD2*LrBorder + 8; memset( Lr[k] - LrBorder*NRD2 - 8, 0, LrSize*sizeof(CostType) ); minLr[k] = pixDiff + costBufSize + LrSize*NLR + minLrSize*k + NR2*LrBorder; memset( minLr[k] - LrBorder*NR2, 0, minLrSize*sizeof(CostType) ); } for( int y = y1; y != y2; y += dy ) { int x, d; DispType* disp1ptr = disp1.ptr(y); CostType* C = Cbuf + (!fullDP ? 0 : y*costBufSize); CostType* S = Sbuf + (!fullDP ? 0 : y*costBufSize); if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any. { int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1; for( k = dy1; k <= dy2; k++ ) { CostType* hsumAdd = hsumBuf + (std::min(k, height-1) % hsumBufNRows)*costBufSize; if( k < height ) { calcPixelCostBT( img1, img2, k, minD, maxD, pixDiff, tempBuf, clipTab, TAB_OFS, ftzero ); memset(hsumAdd, 0, D*sizeof(CostType)); for( x = 0; x <= SW2*D; x += D ) { int scale = x == 0 ? SW2 + 1 : 1; for( d = 0; d < D; d++ ) hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale); } if( y > 0 ) { const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize; const CostType* Cprev = !fullDP || y == 0 ? C : C - costBufSize; for( x = D; x < width1*D; x += D ) { const CostType* pixAdd = pixDiff + std::min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + std::max(x - (SW2+1)*D, 0); #if CV_SSE2 if( useSIMD ) { for( d = 0; d < D; d += 8 ) { __m128i hv = _mm_load_si128((const __m128i*)(hsumAdd + x - D + d)); __m128i Cx = _mm_load_si128((__m128i*)(Cprev + x + d)); hv = _mm_adds_epi16(_mm_subs_epi16(hv, _mm_load_si128((const __m128i*)(pixSub + d))), _mm_load_si128((const __m128i*)(pixAdd + d))); Cx = _mm_adds_epi16(_mm_subs_epi16(Cx, _mm_load_si128((const __m128i*)(hsumSub + x + d))), hv); _mm_store_si128((__m128i*)(hsumAdd + x + d), hv); _mm_store_si128((__m128i*)(C + x + d), Cx); } } else #endif { for( d = 0; d < D; d++ ) { int hv = hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); C[x + d] = (CostType)(Cprev[x + d] + hv - hsumSub[x + d]); } } } } else { for( x = D; x < width1*D; x += D ) { const CostType* pixAdd = pixDiff + std::min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + std::max(x - (SW2+1)*D, 0); for( d = 0; d < D; d++ ) hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); } } } if( y == 0 ) { int scale = k == 0 ? SH2 + 1 : 1; for( x = 0; x < width1*D; x++ ) C[x] = (CostType)(C[x] + hsumAdd[x]*scale); } } // also, clear the S buffer for( k = 0; k < width1*D; k++ ) S[k] = 0; } // clear the left and the right borders memset( Lr[0] - NRD2*LrBorder - 8, 0, NRD2*LrBorder*sizeof(CostType) ); memset( Lr[0] + width1*NRD2 - 8, 0, NRD2*LrBorder*sizeof(CostType) ); memset( minLr[0] - NR2*LrBorder, 0, NR2*LrBorder*sizeof(CostType) ); memset( minLr[0] + width1*NR2, 0, NR2*LrBorder*sizeof(CostType) ); /* [formula 13 in the paper] compute L_r(p, d) = C(p, d) + min(L_r(p-r, d), L_r(p-r, d-1) + P1, L_r(p-r, d+1) + P1, min_k L_r(p-r, k) + P2) - min_k L_r(p-r, k) where p = (x,y), r is one of the directions. we process all the directions at once: 0: r=(-dx, 0) 1: r=(-1, -dy) 2: r=(0, -dy) 3: r=(1, -dy) 4: r=(-2, -dy) 5: r=(-1, -dy*2) 6: r=(1, -dy*2) 7: r=(2, -dy) */ for( x = x1; x != x2; x += dx ) { int xm = x*NR2, xd = xm*D2; int delta0 = minLr[0][xm - dx*NR2] + P2, delta1 = minLr[1][xm - NR2 + 1] + P2; int delta2 = minLr[1][xm + 2] + P2, delta3 = minLr[1][xm + NR2 + 3] + P2; CostType* Lr_p0 = Lr[0] + xd - dx*NRD2; CostType* Lr_p1 = Lr[1] + xd - NRD2 + D2; CostType* Lr_p2 = Lr[1] + xd + D2*2; CostType* Lr_p3 = Lr[1] + xd + NRD2 + D2*3; Lr_p0[-1] = Lr_p0[D] = Lr_p1[-1] = Lr_p1[D] = Lr_p2[-1] = Lr_p2[D] = Lr_p3[-1] = Lr_p3[D] = MAX_COST; CostType* Lr_p = Lr[0] + xd; const CostType* Cp = C + x*D; CostType* Sp = S + x*D; #if CV_SSE2 if( useSIMD ) { __m128i _P1 = _mm_set1_epi16((short)P1); __m128i _delta0 = _mm_set1_epi16((short)delta0); __m128i _delta1 = _mm_set1_epi16((short)delta1); __m128i _delta2 = _mm_set1_epi16((short)delta2); __m128i _delta3 = _mm_set1_epi16((short)delta3); __m128i _minL0 = _mm_set1_epi16((short)MAX_COST); for( d = 0; d < D; d += 8 ) { __m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)); __m128i L0, L1, L2, L3; L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d)); L1 = _mm_load_si128((const __m128i*)(Lr_p1 + d)); L2 = _mm_load_si128((const __m128i*)(Lr_p2 + d)); L3 = _mm_load_si128((const __m128i*)(Lr_p3 + d)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1)); L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d - 1)), _P1)); L1 = _mm_min_epi16(L1, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p1 + d + 1)), _P1)); L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d - 1)), _P1)); L2 = _mm_min_epi16(L2, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p2 + d + 1)), _P1)); L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d - 1)), _P1)); L3 = _mm_min_epi16(L3, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p3 + d + 1)), _P1)); L0 = _mm_min_epi16(L0, _delta0); L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd); L1 = _mm_min_epi16(L1, _delta1); L1 = _mm_adds_epi16(_mm_subs_epi16(L1, _delta1), Cpd); L2 = _mm_min_epi16(L2, _delta2); L2 = _mm_adds_epi16(_mm_subs_epi16(L2, _delta2), Cpd); L3 = _mm_min_epi16(L3, _delta3); L3 = _mm_adds_epi16(_mm_subs_epi16(L3, _delta3), Cpd); _mm_store_si128( (__m128i*)(Lr_p + d), L0); _mm_store_si128( (__m128i*)(Lr_p + d + D2), L1); _mm_store_si128( (__m128i*)(Lr_p + d + D2*2), L2); _mm_store_si128( (__m128i*)(Lr_p + d + D2*3), L3); __m128i t0 = _mm_min_epi16(_mm_unpacklo_epi16(L0, L2), _mm_unpackhi_epi16(L0, L2)); __m128i t1 = _mm_min_epi16(_mm_unpacklo_epi16(L1, L3), _mm_unpackhi_epi16(L1, L3)); t0 = _mm_min_epi16(_mm_unpacklo_epi16(t0, t1), _mm_unpackhi_epi16(t0, t1)); _minL0 = _mm_min_epi16(_minL0, t0); __m128i Sval = _mm_load_si128((const __m128i*)(Sp + d)); L0 = _mm_adds_epi16(L0, L1); L2 = _mm_adds_epi16(L2, L3); Sval = _mm_adds_epi16(Sval, L0); Sval = _mm_adds_epi16(Sval, L2); _mm_store_si128((__m128i*)(Sp + d), Sval); } _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8)); _mm_storel_epi64((__m128i*)&minLr[0][xm], _minL0); } else #endif { int minL0 = MAX_COST, minL1 = MAX_COST, minL2 = MAX_COST, minL3 = MAX_COST; for( d = 0; d < D; d++ ) { int Cpd = Cp[d], L0, L1, L2, L3; L0 = Cpd + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0; L1 = Cpd + std::min((int)Lr_p1[d], std::min(Lr_p1[d-1] + P1, std::min(Lr_p1[d+1] + P1, delta1))) - delta1; L2 = Cpd + std::min((int)Lr_p2[d], std::min(Lr_p2[d-1] + P1, std::min(Lr_p2[d+1] + P1, delta2))) - delta2; L3 = Cpd + std::min((int)Lr_p3[d], std::min(Lr_p3[d-1] + P1, std::min(Lr_p3[d+1] + P1, delta3))) - delta3; Lr_p[d] = (CostType)L0; minL0 = std::min(minL0, L0); Lr_p[d + D2] = (CostType)L1; minL1 = std::min(minL1, L1); Lr_p[d + D2*2] = (CostType)L2; minL2 = std::min(minL2, L2); Lr_p[d + D2*3] = (CostType)L3; minL3 = std::min(minL3, L3); Sp[d] = saturate_cast(Sp[d] + L0 + L1 + L2 + L3); } minLr[0][xm] = (CostType)minL0; minLr[0][xm+1] = (CostType)minL1; minLr[0][xm+2] = (CostType)minL2; minLr[0][xm+3] = (CostType)minL3; } } if( pass == npasses ) { for( x = 0; x < width; x++ ) { disp1ptr[x] = disp2ptr[x] = (DispType)INVALID_DISP_SCALED; disp2cost[x] = MAX_COST; } for( x = width1 - 1; x >= 0; x-- ) { CostType* Sp = S + x*D; int minS = MAX_COST, bestDisp = -1; if( npasses == 1 ) { int xm = x*NR2, xd = xm*D2; int minL0 = MAX_COST; int delta0 = minLr[0][xm + NR2] + P2; CostType* Lr_p0 = Lr[0] + xd + NRD2; Lr_p0[-1] = Lr_p0[D] = MAX_COST; CostType* Lr_p = Lr[0] + xd; const CostType* Cp = C + x*D; #if CV_SSE2 if( useSIMD ) { __m128i _P1 = _mm_set1_epi16((short)P1); __m128i _delta0 = _mm_set1_epi16((short)delta0); __m128i _minL0 = _mm_set1_epi16((short)minL0); __m128i _minS = _mm_set1_epi16(MAX_COST), _bestDisp = _mm_set1_epi16(-1); __m128i _d8 = _mm_setr_epi16(0, 1, 2, 3, 4, 5, 6, 7), _8 = _mm_set1_epi16(8); for( d = 0; d < D; d += 8 ) { __m128i Cpd = _mm_load_si128((const __m128i*)(Cp + d)), L0; L0 = _mm_load_si128((const __m128i*)(Lr_p0 + d)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d - 1)), _P1)); L0 = _mm_min_epi16(L0, _mm_adds_epi16(_mm_loadu_si128((const __m128i*)(Lr_p0 + d + 1)), _P1)); L0 = _mm_min_epi16(L0, _delta0); L0 = _mm_adds_epi16(_mm_subs_epi16(L0, _delta0), Cpd); _mm_store_si128((__m128i*)(Lr_p + d), L0); _minL0 = _mm_min_epi16(_minL0, L0); L0 = _mm_adds_epi16(L0, *(__m128i*)(Sp + d)); _mm_store_si128((__m128i*)(Sp + d), L0); __m128i mask = _mm_cmpgt_epi16(_minS, L0); _minS = _mm_min_epi16(_minS, L0); _bestDisp = _mm_xor_si128(_bestDisp, _mm_and_si128(_mm_xor_si128(_bestDisp,_d8), mask)); _d8 = _mm_adds_epi16(_d8, _8); } short CV_DECL_ALIGNED(16) bestDispBuf[8]; _mm_store_si128((__m128i*)bestDispBuf, _bestDisp); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 8)); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 4)); _minL0 = _mm_min_epi16(_minL0, _mm_srli_si128(_minL0, 2)); __m128i qS = _mm_min_epi16(_minS, _mm_srli_si128(_minS, 8)); qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 4)); qS = _mm_min_epi16(qS, _mm_srli_si128(qS, 2)); minLr[0][xm] = (CostType)_mm_cvtsi128_si32(_minL0); minS = (CostType)_mm_cvtsi128_si32(qS); qS = _mm_shuffle_epi32(_mm_unpacklo_epi16(qS, qS), 0); qS = _mm_cmpeq_epi16(_minS, qS); int idx = _mm_movemask_epi8(_mm_packs_epi16(qS, qS)) & 255; bestDisp = bestDispBuf[LSBTab[idx]]; } else #endif { for( d = 0; d < D; d++ ) { int L0 = Cp[d] + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0; Lr_p[d] = (CostType)L0; minL0 = std::min(minL0, L0); int Sval = Sp[d] = saturate_cast(Sp[d] + L0); if( Sval < minS ) { minS = Sval; bestDisp = d; } } minLr[0][xm] = (CostType)minL0; } } else { for( d = 0; d < D; d++ ) { int Sval = Sp[d]; if( Sval < minS ) { minS = Sval; bestDisp = d; } } } for( d = 0; d < D; d++ ) { if( Sp[d]*(100 - uniquenessRatio) < minS*100 && std::abs(bestDisp - d) > 1 ) break; } if( d < D ) continue; d = bestDisp; int _x2 = x + minX1 - d - minD; if( disp2cost[_x2] > minS ) { disp2cost[_x2] = (CostType)minS; disp2ptr[_x2] = (DispType)(d + minD); } if( 0 < d && d < D-1 ) { // do subpixel quadratic interpolation: // fit parabola into (x1=d-1, y1=Sp[d-1]), (x2=d, y2=Sp[d]), (x3=d+1, y3=Sp[d+1]) // then find minimum of the parabola. int denom2 = std::max(Sp[d-1] + Sp[d+1] - 2*Sp[d], 1); d = d*DISP_SCALE + ((Sp[d-1] - Sp[d+1])*DISP_SCALE + denom2)/(denom2*2); } else d *= DISP_SCALE; disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE); } for( x = minX1; x < maxX1; x++ ) { // we round the computed disparity both towards -inf and +inf and check // if either of the corresponding disparities in disp2 is consistent. // This is to give the computed disparity a chance to look valid if it is. int d1 = disp1ptr[x]; if( d1 == INVALID_DISP_SCALED ) continue; int _d = d1 >> DISP_SHIFT; int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT; int _x = x - _d, x_ = x - d_; if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff && 0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff ) disp1ptr[x] = (DispType)INVALID_DISP_SCALED; } } // now shift the cyclic buffers std::swap( Lr[0], Lr[1] ); std::swap( minLr[0], minLr[1] ); } } } ////////////////////////////////////////////////////////////////////////////////////////////////////// void getBufferPointers(Mat& buffer, int width, int width1, int D, int num_ch, int SH2, int P2, CostType*& curCostVolumeLine, CostType*& hsumBuf, CostType*& pixDiff, PixType*& tmpBuf, CostType*& horPassCostVolume, CostType*& vertPassCostVolume, CostType*& vertPassMin, CostType*& rightPassBuf, CostType*& disp2CostBuf, short*& disp2Buf); struct SGBM3WayMainLoop : public ParallelLoopBody { Mat* buffers; const Mat *img1, *img2; Mat* dst_disp; int nstripes, stripe_sz; int stripe_overlap; int width,height; int minD, maxD, D; int minX1, maxX1, width1; int SW2, SH2; int P1, P2; int uniquenessRatio, disp12MaxDiff; int costBufSize, hsumBufNRows; int TAB_OFS, ftzero; PixType* clipTab; SGBM3WayMainLoop(Mat *_buffers, const Mat& _img1, const Mat& _img2, Mat* _dst_disp, const StereoSGBMParams& params, PixType* _clipTab, int _nstripes, int _stripe_overlap); void getRawMatchingCost(CostType* C, CostType* hsumBuf, CostType* pixDiff, PixType* tmpBuf, int y, int src_start_idx) const; void operator () (const Range& range) const; }; SGBM3WayMainLoop::SGBM3WayMainLoop(Mat *_buffers, const Mat& _img1, const Mat& _img2, Mat* _dst_disp, const StereoSGBMParams& params, PixType* _clipTab, int _nstripes, int _stripe_overlap): buffers(_buffers), img1(&_img1), img2(&_img2), dst_disp(_dst_disp), clipTab(_clipTab) { nstripes = _nstripes; stripe_overlap = _stripe_overlap; stripe_sz = (int)ceil(img1->rows/(double)nstripes); width = img1->cols; height = img1->rows; minD = params.minDisparity; maxD = minD + params.numDisparities; D = maxD - minD; minX1 = std::max(maxD, 0); maxX1 = width + std::min(minD, 0); width1 = maxX1 - minX1; CV_Assert( D % 16 == 0 ); SW2 = SH2 = params.SADWindowSize > 0 ? params.SADWindowSize/2 : 1; P1 = params.P1 > 0 ? params.P1 : 2; P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); uniquenessRatio = params.uniquenessRatio >= 0 ? params.uniquenessRatio : 10; disp12MaxDiff = params.disp12MaxDiff > 0 ? params.disp12MaxDiff : 1; costBufSize = width1*D; hsumBufNRows = SH2*2 + 2; TAB_OFS = 256*4; ftzero = std::max(params.preFilterCap, 15) | 1; } void getBufferPointers(Mat& buffer, int width, int width1, int D, int num_ch, int SH2, int P2, CostType*& curCostVolumeLine, CostType*& hsumBuf, CostType*& pixDiff, PixType*& tmpBuf, CostType*& horPassCostVolume, CostType*& vertPassCostVolume, CostType*& vertPassMin, CostType*& rightPassBuf, CostType*& disp2CostBuf, short*& disp2Buf) { // allocating all the required memory: int costVolumeLineSize = width1*D; int width1_ext = width1+2; int costVolumeLineSize_ext = width1_ext*D; int hsumBufNRows = SH2*2 + 2; // main buffer to store matching costs for the current line: int curCostVolumeLineSize = costVolumeLineSize*sizeof(CostType); // auxiliary buffers for the raw matching cost computation: int hsumBufSize = costVolumeLineSize*hsumBufNRows*sizeof(CostType); int pixDiffSize = costVolumeLineSize*sizeof(CostType); int tmpBufSize = width*16*num_ch*sizeof(PixType); // auxiliary buffers for the matching cost aggregation: int horPassCostVolumeSize = costVolumeLineSize_ext*sizeof(CostType); // buffer for the 2-pass horizontal cost aggregation int vertPassCostVolumeSize = costVolumeLineSize_ext*sizeof(CostType); // buffer for the vertical cost aggregation int vertPassMinSize = width1_ext*sizeof(CostType); // buffer for storing minimum costs from the previous line int rightPassBufSize = D*sizeof(CostType); // additional small buffer for the right-to-left pass // buffers for the pseudo-LRC check: int disp2CostBufSize = width*sizeof(CostType); int disp2BufSize = width*sizeof(short); // sum up the sizes of all the buffers: size_t totalBufSize = curCostVolumeLineSize + hsumBufSize + pixDiffSize + tmpBufSize + horPassCostVolumeSize + vertPassCostVolumeSize + vertPassMinSize + rightPassBufSize + disp2CostBufSize + disp2BufSize + 16; //to compensate for the alignPtr shifts if( buffer.empty() || !buffer.isContinuous() || buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize ) buffer.create(1, (int)totalBufSize, CV_8U); // set up all the pointers: curCostVolumeLine = (CostType*)alignPtr(buffer.ptr(), 16); hsumBuf = curCostVolumeLine + costVolumeLineSize; pixDiff = hsumBuf + costVolumeLineSize*hsumBufNRows; tmpBuf = (PixType*)(pixDiff + costVolumeLineSize); horPassCostVolume = (CostType*)(tmpBuf + width*16*num_ch); vertPassCostVolume = horPassCostVolume + costVolumeLineSize_ext; rightPassBuf = vertPassCostVolume + costVolumeLineSize_ext; vertPassMin = rightPassBuf + D; disp2CostBuf = vertPassMin + width1_ext; disp2Buf = disp2CostBuf + width; // initialize memory: memset(buffer.ptr(),0,totalBufSize); for(int i=0;i src_start_idx ) { const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, src_start_idx) % hsumBufNRows)*costBufSize; for( x = D; x < width1*D; x += D ) { const CostType* pixAdd = pixDiff + std::min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + std::max(x - (SW2+1)*D, 0); #if CV_SIMD128 v_int16x8 hv_reg; for( d = 0; d < D; d+=8 ) { hv_reg = v_load_aligned(hsumAdd+x-D+d) + (v_load_aligned(pixAdd+d) - v_load_aligned(pixSub+d)); v_store_aligned(hsumAdd+x+d,hv_reg); v_store_aligned(C+x+d,v_load_aligned(C+x+d)+(hv_reg-v_load_aligned(hsumSub+x+d))); } #else for( d = 0; d < D; d++ ) { int hv = hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); C[x + d] = (CostType)(C[x + d] + hv - hsumSub[x + d]); } #endif } } else { for( x = D; x < width1*D; x += D ) { const CostType* pixAdd = pixDiff + std::min(x + SW2*D, (width1-1)*D); const CostType* pixSub = pixDiff + std::max(x - (SW2+1)*D, 0); for( d = 0; d < D; d++ ) hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); } } } if( y == src_start_idx ) { int scale = k == src_start_idx ? SH2 + 1 : 1; for( x = 0; x < width1*D; x++ ) C[x] = (CostType)(C[x] + hsumAdd[x]*scale); } } } #if CV_SIMD128 // define some additional reduce operations: inline short min(const v_int16x8& a) { short CV_DECL_ALIGNED(16) buf[8]; v_store_aligned(buf, a); short s0 = std::min(buf[0], buf[1]); short s1 = std::min(buf[2], buf[3]); short s2 = std::min(buf[4], buf[5]); short s3 = std::min(buf[6], buf[7]); return std::min(std::min(s0, s1),std::min(s2, s3)); } inline short min_pos(const v_int16x8& val,const v_int16x8& pos) { short CV_DECL_ALIGNED(16) val_buf[8]; v_store_aligned(val_buf, val); short CV_DECL_ALIGNED(16) pos_buf[8]; v_store_aligned(pos_buf, pos); short res_pos = 0; short min_val = SHRT_MAX; if(val_buf[0](P1)); v_int16x8 leftMinCostP2_reg = v_setall_s16(cv::saturate_cast(leftMinCost+P2)); v_int16x8 leftMinCost_new_reg = v_setall_s16(SHRT_MAX); v_int16x8 src0_leftBuf = v_setall_s16(SHRT_MAX); v_int16x8 src1_leftBuf = v_load_aligned(leftBuf_prev); v_int16x8 topMinCostP2_reg = v_setall_s16(cv::saturate_cast(topMinCost+P2)); v_int16x8 topMinCost_new_reg = v_setall_s16(SHRT_MAX); v_int16x8 src0_topBuf = v_setall_s16(SHRT_MAX); v_int16x8 src1_topBuf = v_load_aligned(topBuf); v_int16x8 src2; v_int16x8 src_shifted_left,src_shifted_right; v_int16x8 res; for(int i=0;i (src0_leftBuf,src1_leftBuf) + P1_reg; src_shifted_right = v_extract<1> (src1_leftBuf,src2 ) + P1_reg; // process and save current block: res = v_load_aligned(costs+i) + (v_min(v_min(src_shifted_left,src_shifted_right),v_min(src1_leftBuf,leftMinCostP2_reg))-leftMinCostP2_reg); leftMinCost_new_reg = v_min(leftMinCost_new_reg,res); v_store_aligned(leftBuf+i, res); //update src buffers: src0_leftBuf = src1_leftBuf; src1_leftBuf = src2; //process topBuf: //lookahead load: src2 = v_load_aligned(topBuf+i+8); //get shifted versions of the current block and add P1: src_shifted_left = v_extract<7> (src0_topBuf,src1_topBuf) + P1_reg; src_shifted_right = v_extract<1> (src1_topBuf,src2 ) + P1_reg; // process and save current block: res = v_load_aligned(costs+i) + (v_min(v_min(src_shifted_left,src_shifted_right),v_min(src1_topBuf,topMinCostP2_reg))-topMinCostP2_reg); topMinCost_new_reg = v_min(topMinCost_new_reg,res); v_store_aligned(topBuf+i, res); //update src buffers: src0_topBuf = src1_topBuf; src1_topBuf = src2; } // a bit different processing for the last cycle of the loop: //process leftBuf: src2 = v_setall_s16(SHRT_MAX); src_shifted_left = v_extract<7> (src0_leftBuf,src1_leftBuf) + P1_reg; src_shifted_right = v_extract<1> (src1_leftBuf,src2 ) + P1_reg; res = v_load_aligned(costs+D-8) + (v_min(v_min(src_shifted_left,src_shifted_right),v_min(src1_leftBuf,leftMinCostP2_reg))-leftMinCostP2_reg); leftMinCost = min(v_min(leftMinCost_new_reg,res)); v_store_aligned(leftBuf+D-8, res); //process topBuf: src2 = v_setall_s16(SHRT_MAX); src_shifted_left = v_extract<7> (src0_topBuf,src1_topBuf) + P1_reg; src_shifted_right = v_extract<1> (src1_topBuf,src2 ) + P1_reg; res = v_load_aligned(costs+D-8) + (v_min(v_min(src_shifted_left,src_shifted_right),v_min(src1_topBuf,topMinCostP2_reg))-topMinCostP2_reg); topMinCost = min(v_min(topMinCost_new_reg,res)); v_store_aligned(topBuf+D-8, res); #else CostType leftMinCost_new = SHRT_MAX; CostType topMinCost_new = SHRT_MAX; int leftMinCost_P2 = leftMinCost + P2; int topMinCost_P2 = topMinCost + P2; CostType leftBuf_prev_i_minus_1 = SHRT_MAX; CostType topBuf_i_minus_1 = SHRT_MAX; CostType tmp; for(int i=0;i(costs[i] + std::min(std::min(leftBuf_prev_i_minus_1+P1,leftBuf_prev[i+1]+P1),std::min((int)leftBuf_prev[i],leftMinCost_P2))-leftMinCost_P2); leftBuf_prev_i_minus_1 = leftBuf_prev[i]; leftMinCost_new = std::min(leftMinCost_new,leftBuf[i]); tmp = topBuf[i]; topBuf[i] = cv::saturate_cast(costs[i] + std::min(std::min(topBuf_i_minus_1+P1,topBuf[i+1]+P1),std::min((int)topBuf[i],topMinCost_P2))-topMinCost_P2); topBuf_i_minus_1 = tmp; topMinCost_new = std::min(topMinCost_new,topBuf[i]); } leftBuf[D-1] = cv::saturate_cast(costs[D-1] + std::min(leftBuf_prev_i_minus_1+P1,std::min((int)leftBuf_prev[D-1],leftMinCost_P2))-leftMinCost_P2); leftMinCost = std::min(leftMinCost_new,leftBuf[D-1]); topBuf[D-1] = cv::saturate_cast(costs[D-1] + std::min(topBuf_i_minus_1+P1,std::min((int)topBuf[D-1],topMinCost_P2))-topMinCost_P2); topMinCost = std::min(topMinCost_new,topBuf[D-1]); #endif } // performing in-place SGM cost accumulation from right to left (the result is stored in rightBuf) and // summing rightBuf, topBuf, leftBuf together (the result is stored in leftBuf), as well as finding the // optimal disparity value with minimum accumulated cost inline void accumulateCostsRight(CostType* rightBuf, CostType* topBuf, CostType* leftBuf, CostType* costs, CostType& rightMinCost, int D, int P1, int P2, int& optimal_disp, CostType& min_cost) { #if CV_SIMD128 v_int16x8 P1_reg = v_setall_s16(cv::saturate_cast(P1)); v_int16x8 rightMinCostP2_reg = v_setall_s16(cv::saturate_cast(rightMinCost+P2)); v_int16x8 rightMinCost_new_reg = v_setall_s16(SHRT_MAX); v_int16x8 src0_rightBuf = v_setall_s16(SHRT_MAX); v_int16x8 src1_rightBuf = v_load(rightBuf); v_int16x8 src2; v_int16x8 src_shifted_left,src_shifted_right; v_int16x8 res; v_int16x8 min_sum_cost_reg = v_setall_s16(SHRT_MAX); v_int16x8 min_sum_pos_reg = v_setall_s16(0); v_int16x8 loop_idx(0,1,2,3,4,5,6,7); v_int16x8 eight_reg = v_setall_s16(8); for(int i=0;i (src0_rightBuf,src1_rightBuf) + P1_reg; src_shifted_right = v_extract<1> (src1_rightBuf,src2 ) + P1_reg; // process and save current block: res = v_load_aligned(costs+i) + (v_min(v_min(src_shifted_left,src_shifted_right),v_min(src1_rightBuf,rightMinCostP2_reg))-rightMinCostP2_reg); rightMinCost_new_reg = v_min(rightMinCost_new_reg,res); v_store_aligned(rightBuf+i, res); // compute and save total cost: res = res + v_load_aligned(leftBuf+i) + v_load_aligned(topBuf+i); v_store_aligned(leftBuf+i, res); // track disparity value with the minimum cost: min_sum_cost_reg = v_min(min_sum_cost_reg,res); min_sum_pos_reg = min_sum_pos_reg + ((min_sum_cost_reg == res) & (loop_idx - min_sum_pos_reg)); loop_idx = loop_idx+eight_reg; //update src: src0_rightBuf = src1_rightBuf; src1_rightBuf = src2; } // a bit different processing for the last cycle of the loop: src2 = v_setall_s16(SHRT_MAX); src_shifted_left = v_extract<7> (src0_rightBuf,src1_rightBuf) + P1_reg; src_shifted_right = v_extract<1> (src1_rightBuf,src2 ) + P1_reg; res = v_load_aligned(costs+D-8) + (v_min(v_min(src_shifted_left,src_shifted_right),v_min(src1_rightBuf,rightMinCostP2_reg))-rightMinCostP2_reg); rightMinCost = min(v_min(rightMinCost_new_reg,res)); v_store_aligned(rightBuf+D-8, res); res = res + v_load_aligned(leftBuf+D-8) + v_load_aligned(topBuf+D-8); v_store_aligned(leftBuf+D-8, res); min_sum_cost_reg = v_min(min_sum_cost_reg,res); min_cost = min(min_sum_cost_reg); min_sum_pos_reg = min_sum_pos_reg + ((min_sum_cost_reg == res) & (loop_idx - min_sum_pos_reg)); optimal_disp = min_pos(min_sum_cost_reg,min_sum_pos_reg); #else CostType rightMinCost_new = SHRT_MAX; int rightMinCost_P2 = rightMinCost + P2; CostType rightBuf_i_minus_1 = SHRT_MAX; CostType tmp; min_cost = SHRT_MAX; for(int i=0;i(costs[i] + std::min(std::min(rightBuf_i_minus_1+P1,rightBuf[i+1]+P1),std::min((int)rightBuf[i],rightMinCost_P2))-rightMinCost_P2); rightBuf_i_minus_1 = tmp; rightMinCost_new = std::min(rightMinCost_new,rightBuf[i]); leftBuf[i] = cv::saturate_cast((int)leftBuf[i]+rightBuf[i]+topBuf[i]); if(leftBuf[i](costs[D-1] + std::min(rightBuf_i_minus_1+P1,std::min((int)rightBuf[D-1],rightMinCost_P2))-rightMinCost_P2); rightMinCost = std::min(rightMinCost_new,rightBuf[D-1]); leftBuf[D-1] = cv::saturate_cast((int)leftBuf[D-1]+rightBuf[D-1]+topBuf[D-1]); if(leftBuf[D-1]range.start+1) { for(int n=range.start;nchannels(),SH2,P2, curCostVolumeLine,hsumBuf,pixDiff,tmpBuf,horPassCostVolume, vertPassCostVolume,vertPassMin,rightPassBuf,disp2CostBuf,disp2Buf); // start real processing: for(int y=src_start_idx;y=D;x-=D) { accumulateCostsRight(rightPassBuf,vertPassCostVolume+x,horPassCostVolume+x,C+x,prev_min,D,P1,P2,best_d,min_cost); if(uniquenessRatio>0) { #if CV_SIMD128 horPassCostVolume+=x; int thresh = (100*min_cost)/(100-uniquenessRatio); v_int16x8 thresh_reg = v_setall_s16((short)(thresh+1)); v_int16x8 d1 = v_setall_s16((short)(best_d-1)); v_int16x8 d2 = v_setall_s16((short)(best_d+1)); v_int16x8 eight_reg = v_setall_s16(8); v_int16x8 cur_d(0,1,2,3,4,5,6,7); v_int16x8 mask,cost1,cost2; for( d = 0; d < D; d+=16 ) { cost1 = v_load_aligned(horPassCostVolume+d); cost2 = v_load_aligned(horPassCostVolume+d+8); mask = cost1 < thresh_reg; mask = mask & ( (cur_dd2) ); if( v_signmask(mask) ) break; cur_d = cur_d+eight_reg; mask = cost2 < thresh_reg; mask = mask & ( (cur_dd2) ); if( v_signmask(mask) ) break; cur_d = cur_d+eight_reg; } horPassCostVolume-=x; #else for( d = 0; d < D; d++ ) { if( horPassCostVolume[x+d]*(100 - uniquenessRatio) < min_cost*100 && std::abs(d - best_d) > 1 ) break; } #endif if( d < D ) continue; } d = best_d; int _x2 = x/D - 1 + minX1 - d - minD; if( _x2>=0 && _x2 min_cost ) { disp2CostBuf[_x2] = min_cost; disp2Buf[_x2] = (short)(d + minD); } if( 0 < d && d < D-1 ) { // do subpixel quadratic interpolation: // fit parabola into (x1=d-1, y1=Sp[d-1]), (x2=d, y2=Sp[d]), (x3=d+1, y3=Sp[d+1]) // then find minimum of the parabola. int denom2 = std::max(horPassCostVolume[x+d-1] + horPassCostVolume[x+d+1] - 2*horPassCostVolume[x+d], 1); d = d*DISP_SCALE + ((horPassCostVolume[x+d-1] - horPassCostVolume[x+d+1])*DISP_SCALE + denom2)/(denom2*2); } else d *= DISP_SCALE; disp_row[(x/D)-1 + minX1] = (DispType)(d + minD*DISP_SCALE); } for(int x = minX1; x < maxX1; x++ ) { // pseudo LRC consistency check using only one disparity map; // pixels with difference more than disp12MaxDiff are invalidated int d1 = disp_row[x]; if( d1 == INVALID_DISP_SCALED ) continue; int _d = d1 >> StereoMatcher::DISP_SHIFT; int d_ = (d1 + DISP_SCALE-1) >> StereoMatcher::DISP_SHIFT; int _x = x - _d, x_ = x - d_; if( 0 <= _x && _x < width && disp2Buf[_x] >= minD && std::abs(disp2Buf[_x] - _d) > disp12MaxDiff && 0 <= x_ && x_ < width && disp2Buf[x_] >= minD && std::abs(disp2Buf[x_] - d_) > disp12MaxDiff ) disp_row[x] = (short)INVALID_DISP_SCALED; } } } static void computeDisparity3WaySGBM( const Mat& img1, const Mat& img2, Mat& disp1, const StereoSGBMParams& params, Mat* buffers, int nstripes ) { // precompute a lookup table for the raw matching cost computation: const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; PixType* clipTab = new PixType[TAB_SIZE]; int ftzero = std::max(params.preFilterCap, 15) | 1; for(int k = 0; k < TAB_SIZE; k++ ) clipTab[k] = (PixType)(std::min(std::max(k - TAB_OFS, -ftzero), ftzero) + ftzero); // allocate separate dst_disp arrays to avoid conflicts due to stripe overlap: int stripe_sz = (int)ceil(img1.rows/(double)nstripes); int stripe_overlap = (params.SADWindowSize/2+1) + (int)ceil(0.1*stripe_sz); Mat* dst_disp = new Mat[nstripes]; for(int i=0;i 0 ) filterSpeckles(disp, (params.minDisparity - 1)*StereoMatcher::DISP_SCALE, params.speckleWindowSize, StereoMatcher::DISP_SCALE*params.speckleRange, buffer); } int getMinDisparity() const { return params.minDisparity; } void setMinDisparity(int minDisparity) { params.minDisparity = minDisparity; } int getNumDisparities() const { return params.numDisparities; } void setNumDisparities(int numDisparities) { params.numDisparities = numDisparities; } int getBlockSize() const { return params.SADWindowSize; } void setBlockSize(int blockSize) { params.SADWindowSize = blockSize; } int getSpeckleWindowSize() const { return params.speckleWindowSize; } void setSpeckleWindowSize(int speckleWindowSize) { params.speckleWindowSize = speckleWindowSize; } int getSpeckleRange() const { return params.speckleRange; } void setSpeckleRange(int speckleRange) { params.speckleRange = speckleRange; } int getDisp12MaxDiff() const { return params.disp12MaxDiff; } void setDisp12MaxDiff(int disp12MaxDiff) { params.disp12MaxDiff = disp12MaxDiff; } int getPreFilterCap() const { return params.preFilterCap; } void setPreFilterCap(int preFilterCap) { params.preFilterCap = preFilterCap; } int getUniquenessRatio() const { return params.uniquenessRatio; } void setUniquenessRatio(int uniquenessRatio) { params.uniquenessRatio = uniquenessRatio; } int getP1() const { return params.P1; } void setP1(int P1) { params.P1 = P1; } int getP2() const { return params.P2; } void setP2(int P2) { params.P2 = P2; } int getMode() const { return params.mode; } void setMode(int mode) { params.mode = mode; } void write(FileStorage& fs) const { fs << "name" << name_ << "minDisparity" << params.minDisparity << "numDisparities" << params.numDisparities << "blockSize" << params.SADWindowSize << "speckleWindowSize" << params.speckleWindowSize << "speckleRange" << params.speckleRange << "disp12MaxDiff" << params.disp12MaxDiff << "preFilterCap" << params.preFilterCap << "uniquenessRatio" << params.uniquenessRatio << "P1" << params.P1 << "P2" << params.P2 << "mode" << params.mode; } void read(const FileNode& fn) { FileNode n = fn["name"]; CV_Assert( n.isString() && String(n) == name_ ); params.minDisparity = (int)fn["minDisparity"]; params.numDisparities = (int)fn["numDisparities"]; params.SADWindowSize = (int)fn["blockSize"]; params.speckleWindowSize = (int)fn["speckleWindowSize"]; params.speckleRange = (int)fn["speckleRange"]; params.disp12MaxDiff = (int)fn["disp12MaxDiff"]; params.preFilterCap = (int)fn["preFilterCap"]; params.uniquenessRatio = (int)fn["uniquenessRatio"]; params.P1 = (int)fn["P1"]; params.P2 = (int)fn["P2"]; params.mode = (int)fn["mode"]; } StereoSGBMParams params; Mat buffer; // the number of stripes is fixed, disregarding the number of threads/processors // to make the results fully reproducible: static const int num_stripes = 4; Mat buffers[num_stripes]; static const char* name_; }; const char* StereoSGBMImpl::name_ = "StereoMatcher.SGBM"; Ptr StereoSGBM::create(int minDisparity, int numDisparities, int SADWindowSize, int P1, int P2, int disp12MaxDiff, int preFilterCap, int uniquenessRatio, int speckleWindowSize, int speckleRange, int mode) { return Ptr( new StereoSGBMImpl(minDisparity, numDisparities, SADWindowSize, P1, P2, disp12MaxDiff, preFilterCap, uniquenessRatio, speckleWindowSize, speckleRange, mode)); } Rect getValidDisparityROI( Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int SADWindowSize ) { int SW2 = SADWindowSize/2; int minD = minDisparity, maxD = minDisparity + numberOfDisparities - 1; int xmin = std::max(roi1.x, roi2.x + maxD) + SW2; int xmax = std::min(roi1.x + roi1.width, roi2.x + roi2.width - minD) - SW2; int ymin = std::max(roi1.y, roi2.y) + SW2; int ymax = std::min(roi1.y + roi1.height, roi2.y + roi2.height) - SW2; Rect r(xmin, ymin, xmax - xmin, ymax - ymin); return r.width > 0 && r.height > 0 ? r : Rect(); } typedef cv::Point_ Point2s; template void filterSpecklesImpl(cv::Mat& img, int newVal, int maxSpeckleSize, int maxDiff, cv::Mat& _buf) { using namespace cv; int width = img.cols, height = img.rows, npixels = width*height; size_t bufSize = npixels*(int)(sizeof(Point2s) + sizeof(int) + sizeof(uchar)); if( !_buf.isContinuous() || _buf.empty() || _buf.cols*_buf.rows*_buf.elemSize() < bufSize ) _buf.create(1, (int)bufSize, CV_8U); uchar* buf = _buf.ptr(); int i, j, dstep = (int)(img.step/sizeof(T)); int* labels = (int*)buf; buf += npixels*sizeof(labels[0]); Point2s* wbuf = (Point2s*)buf; buf += npixels*sizeof(wbuf[0]); uchar* rtype = (uchar*)buf; int curlabel = 0; // clear out label assignments memset(labels, 0, npixels*sizeof(labels[0])); for( i = 0; i < height; i++ ) { T* ds = img.ptr(i); int* ls = labels + width*i; for( j = 0; j < width; j++ ) { if( ds[j] != newVal ) // not a bad disparity { if( ls[j] ) // has a label, check for bad label { if( rtype[ls[j]] ) // small region, zero out disparity ds[j] = (T)newVal; } // no label, assign and propagate else { Point2s* ws = wbuf; // initialize wavefront Point2s p((short)j, (short)i); // current pixel curlabel++; // next label int count = 0; // current region size ls[j] = curlabel; // wavefront propagation while( ws >= wbuf ) // wavefront not empty { count++; // put neighbors onto wavefront T* dpp = &img.at(p.y, p.x); T dp = *dpp; int* lpp = labels + width*p.y + p.x; if( p.y < height-1 && !lpp[+width] && dpp[+dstep] != newVal && std::abs(dp - dpp[+dstep]) <= maxDiff ) { lpp[+width] = curlabel; *ws++ = Point2s(p.x, p.y+1); } if( p.y > 0 && !lpp[-width] && dpp[-dstep] != newVal && std::abs(dp - dpp[-dstep]) <= maxDiff ) { lpp[-width] = curlabel; *ws++ = Point2s(p.x, p.y-1); } if( p.x < width-1 && !lpp[+1] && dpp[+1] != newVal && std::abs(dp - dpp[+1]) <= maxDiff ) { lpp[+1] = curlabel; *ws++ = Point2s(p.x+1, p.y); } if( p.x > 0 && !lpp[-1] && dpp[-1] != newVal && std::abs(dp - dpp[-1]) <= maxDiff ) { lpp[-1] = curlabel; *ws++ = Point2s(p.x-1, p.y); } // pop most recent and propagate // NB: could try least recent, maybe better convergence p = *--ws; } // assign label type if( count <= maxSpeckleSize ) // speckle region { rtype[ls[j]] = 1; // small region label ds[j] = (T)newVal; } else rtype[ls[j]] = 0; // large region label } } } } } #ifdef HAVE_IPP static bool ipp_filterSpeckles(Mat &img, int maxSpeckleSize, int newVal, int maxDiff) { #if IPP_VERSION_X100 >= 810 int type = img.type(); Ipp32s bufsize = 0; IppiSize roisize = { img.cols, img.rows }; IppDataType datatype = type == CV_8UC1 ? ipp8u : ipp16s; Ipp8u *pBuffer = NULL; IppStatus status = ippStsNoErr; if(ippiMarkSpecklesGetBufferSize(roisize, datatype, CV_MAT_CN(type), &bufsize) < 0) return false; pBuffer = (Ipp8u*)ippMalloc(bufsize); if(!pBuffer && bufsize) return false; if (type == CV_8UC1) { status = ippiMarkSpeckles_8u_C1IR(img.ptr(), (int)img.step, roisize, (Ipp8u)newVal, maxSpeckleSize, (Ipp8u)maxDiff, ippiNormL1, pBuffer); } else { status = ippiMarkSpeckles_16s_C1IR(img.ptr(), (int)img.step, roisize, (Ipp16s)newVal, maxSpeckleSize, (Ipp16s)maxDiff, ippiNormL1, pBuffer); } if(pBuffer) ippFree(pBuffer); if (status >= 0) return true; #else CV_UNUSED(img); CV_UNUSED(maxSpeckleSize); CV_UNUSED(newVal); CV_UNUSED(maxDiff); #endif return false; } #endif } void cv::filterSpeckles( InputOutputArray _img, double _newval, int maxSpeckleSize, double _maxDiff, InputOutputArray __buf ) { Mat img = _img.getMat(); int type = img.type(); Mat temp, &_buf = __buf.needed() ? __buf.getMatRef() : temp; CV_Assert( type == CV_8UC1 || type == CV_16SC1 ); int newVal = cvRound(_newval), maxDiff = cvRound(_maxDiff); CV_IPP_RUN(IPP_VERSION_X100 >= 810 && !__buf.needed() && (type == CV_8UC1 || type == CV_16SC1), ipp_filterSpeckles(img, maxSpeckleSize, newVal, maxDiff)); if (type == CV_8UC1) filterSpecklesImpl(img, newVal, maxSpeckleSize, maxDiff, _buf); else filterSpecklesImpl(img, newVal, maxSpeckleSize, maxDiff, _buf); } void cv::validateDisparity( InputOutputArray _disp, InputArray _cost, int minDisparity, int numberOfDisparities, int disp12MaxDiff ) { Mat disp = _disp.getMat(), cost = _cost.getMat(); int cols = disp.cols, rows = disp.rows; int minD = minDisparity, maxD = minDisparity + numberOfDisparities; int x, minX1 = std::max(maxD, 0), maxX1 = cols + std::min(minD, 0); AutoBuffer _disp2buf(cols*2); int* disp2buf = _disp2buf; int* disp2cost = disp2buf + cols; const int DISP_SHIFT = 4, DISP_SCALE = 1 << DISP_SHIFT; int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE; int costType = cost.type(); disp12MaxDiff *= DISP_SCALE; CV_Assert( numberOfDisparities > 0 && disp.type() == CV_16S && (costType == CV_16S || costType == CV_32S) && disp.size() == cost.size() ); for( int y = 0; y < rows; y++ ) { short* dptr = disp.ptr(y); for( x = 0; x < cols; x++ ) { disp2buf[x] = INVALID_DISP_SCALED; disp2cost[x] = INT_MAX; } if( costType == CV_16S ) { const short* cptr = cost.ptr(y); for( x = minX1; x < maxX1; x++ ) { int d = dptr[x], c = cptr[x]; if( d == INVALID_DISP_SCALED ) continue; int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT); if( disp2cost[x2] > c ) { disp2cost[x2] = c; disp2buf[x2] = d; } } } else { const int* cptr = cost.ptr(y); for( x = minX1; x < maxX1; x++ ) { int d = dptr[x], c = cptr[x]; if( d == INVALID_DISP_SCALED ) continue; int x2 = x - ((d + DISP_SCALE/2) >> DISP_SHIFT); if( disp2cost[x2] > c ) { disp2cost[x2] = c; disp2buf[x2] = d; } } } for( x = minX1; x < maxX1; x++ ) { // we round the computed disparity both towards -inf and +inf and check // if either of the corresponding disparities in disp2 is consistent. // This is to give the computed disparity a chance to look valid if it is. int d = dptr[x]; if( d == INVALID_DISP_SCALED ) continue; int d0 = d >> DISP_SHIFT; int d1 = (d + DISP_SCALE-1) >> DISP_SHIFT; int x0 = x - d0, x1 = x - d1; if( (0 <= x0 && x0 < cols && disp2buf[x0] > INVALID_DISP_SCALED && std::abs(disp2buf[x0] - d) > disp12MaxDiff) && (0 <= x1 && x1 < cols && disp2buf[x1] > INVALID_DISP_SCALED && std::abs(disp2buf[x1] - d) > disp12MaxDiff) ) dptr[x] = (short)INVALID_DISP_SCALED; } } }