From 21be2aa677d8eaad48956b431260e64f626167a0 Mon Sep 17 00:00:00 2001 From: Pyotr Chekmaryov <4ekmah@gmail.com> Date: Tue, 25 Apr 2017 21:00:31 +0000 Subject: [PATCH] Memory repaired + Cleanup. --- modules/calib3d/src/stereosgbm.cpp | 594 ++++--------------- modules/calib3d/test/test_stereomatching.cpp | 39 +- 2 files changed, 143 insertions(+), 490 deletions(-) diff --git a/modules/calib3d/src/stereosgbm.cpp b/modules/calib3d/src/stereosgbm.cpp index 5f841775ec..678f937abe 100644 --- a/modules/calib3d/src/stereosgbm.cpp +++ b/modules/calib3d/src/stereosgbm.cpp @@ -125,7 +125,6 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y, int minD, int maxD, CostType* cost, PixType* buffer, const PixType* tab, int tabOfs, int , int xrange_min = 0, int xrange_max = DEFAULT_RIGHT_BORDER ) - //TODO: This function was changed and modified old function's behabior. Check they in tests { int x, c, width = img1.cols, cn = img1.channels(); int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0); @@ -311,9 +310,9 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, const CostType MAX_COST = SHRT_MAX; int minD = params.minDisparity, maxD = minD + params.numDisparities; - Size SADWindowSize; //4e: SAD means Sum of Absolute Differences - SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; //4e: and this is always square - int ftzero = std::max(params.preFilterCap, 15) | 1; //4e:ftzero clips x-derivatives. I think, this story with arrays is about non-realized SIMD method + 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); @@ -323,11 +322,11 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, 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; //4e: 2 passes with different behaviour for Hirshmueller method. - const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; //4e: array is such big due to derivative could be +-8*256 in worst cases + 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++ ) //4e: If ftzero would = 4, array containment will be = -4 -4 -4 ... -4 -3 -2 -1 0 1 2 3 4 ... 4 4 4 + for( k = 0; k < TAB_SIZE; k++ ) clipTab[k] = (PixType)(std::min(std::max(k - TAB_OFS, -ftzero), ftzero) + ftzero); if( minX1 >= maxX1 ) @@ -340,25 +339,25 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, // 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; //4e: Somewhere in code we need d+1, so D+1. One of simplest solutuons is increasing D-dimension on 1. But 1 is 16, when storage should be aligned. + 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; //4e: We assume, that we need one or more previous steps in our linear dynamic(one right here). - const int LrBorder = NLR - 1; //4e: for simplification of calculations we need border for taking previous dynamic solutions. + 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); //4e: For HH mode it's better to keep whole array of costs. - size_t minLrSize = (width1 + LrBorder*2)*NR2, LrSize = minLrSize*D2; //4e: TODO: Understand why NR2 per pass instead od NR2/2 (Probably, without any reason. That doesn't make code wrong) + 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 //4e: TODO: Why we should increase sum window height one more time? - CSBufSize*2*sizeof(CostType) + // C, S //4e: C is Block sum of costs, S is multidirectional dynamic sum with same size - width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost //4e: It is needed for calcPixelCostBT function, as "buffer" value + 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() || @@ -371,7 +370,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, CostType* hsumBuf = Sbuf + CSBufSize; CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows; - CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR; //4e: It is containers for backwards disparity, made by S[d] too, but with other method + CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR; DispType* disp2ptr = (DispType*)(disp2cost + width); PixType* tempBuf = (PixType*)(disp2ptr + width); @@ -383,21 +382,21 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, { int x1, y1, x2, y2, dx, dy; - if( pass == 1 ) //4e: on the first pass, we work down, so calculate directions down, diagdown and right + if( pass == 1 ) { y1 = 0; y2 = height; dy = 1; x1 = 0; x2 = width1; dx = 1; } - else //4e: on the second pass, we work up, so calculate directions up, diagup and up + else { y1 = height-1; y2 = -1; dy = -1; x1 = width1-1; x2 = -1; dx = -1; } - CostType *Lr[NLR]={0}, *minLr[NLR]={0}; //4e: arrays for L(x,y,r,d) of previous and current rows and minimums of them + CostType *Lr[NLR]={0}, *minLr[NLR]={0}; - for( k = 0; k < NLR; k++ ) //4e: One of them is needed, and one of them is stored. So, we need to swap pointer - { //4e: Yes, and this is done at the end of next cycle, not here. + 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. @@ -418,28 +417,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, 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; //4e: for first line's block sum we need calculate half-window of costs and only one for other + 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; //4e: Ring buffer for horizontally summed lines + 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 ) //4e: Calculation summed costs for all disparities in first pixel of line + 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 ) //4e: We calculate horizontal sums and forming full block sums for y coord by adding this horsums to previous line's sums and subtracting stored lowest - { //4e: horsum in hsumBuf. Exception is case y=0, where we need many iterations per lines to create full blocking sum. + if( y > 0 ) + { const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize; - const CostType* Cprev = !fullDP || y == 0 ? C : C - costBufSize; //4e: Well, actually y>0, so we don't need this check: y==0 + const CostType* Cprev = !fullDP || y == 0 ? C : C - costBufSize; for( x = D; x < width1*D; x += D ) { @@ -475,8 +474,8 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, } else { - for( x = D; x < width1*D; x += D ) //4e: Calcluates horizontal sums if (y==0). This piece of code is calling SH2+1 times and then result is used in different way - { //4e: to create full blocks sum. That's why this code is isolated from upper case. + 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); @@ -486,7 +485,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, } } - if( y == 0 ) //4e: Calculating first full block sum. + if( y == 0 ) { int scale = k == 0 ? SH2 + 1 : 1; for( x = 0; x < width1*D; x++ ) @@ -495,13 +494,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, } // also, clear the S buffer - for( k = 0; k < width1*D; k++ ) //4e: only on first pass, so it keep old information, don't be confused + 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) ); //4e: To understand this "8" shifts and how they could work it's simpler to imagine pixel dislocation in memory - memset( Lr[0] + width1*NRD2 - 8, 0, NRD2*LrBorder*sizeof(CostType) ); //4e: ...00000000|NRD2-16 of real costs value(and some of them are zeroes too)|00000000... + 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) ); @@ -624,7 +623,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, for( d = 0; d < D; d++ ) { - int Cpd = Cp[d], L0, L1, L2, L3; //4e: Remember, that every Cp is increased on P2 in line number 369. That's why next 4 lines are paper-like actually + 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; @@ -665,7 +664,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, CostType* Sp = S + x*D; int minS = MAX_COST, bestDisp = -1; - if( npasses == 1 ) //4e: in this case we could take fifth direction almost for free(direction "left") + if( npasses == 1 ) { int xm = x*NR2, xd = xm*D2; @@ -815,7 +814,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE); } - for( x = minX1; x < maxX1; x++ ) //4e: Left-right check itself + 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. @@ -826,8 +825,8 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, 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 && //4e: To dismiss disparity, we should assure, that there is no any - 0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff ) //4e: chance to understand this as correct. + 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; } } @@ -840,8 +839,6 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2, } //////////////////////////////////////////////////////////////////////////////////////////// -//TODO: Assumation: Let's pretend, that we allocate memory for pixDiff and tempBuf independently in each thread, with full size, needed for original calcBT -//TODO: Redo size of this arrays even if situation with independent allocation will still. struct CalcVerticalSums: public ParallelLoopBody { CalcVerticalSums(const Mat& _img1, const Mat& _img2, const StereoSGBMParams& params, @@ -852,7 +849,7 @@ struct CalcVerticalSums: public ParallelLoopBody SW2 = SH2 = (params.SADWindowSize > 0 ? params.SADWindowSize : 5)/2; ftzero = std::max(params.preFilterCap, 15) | 1; P1 = params.P1 > 0 ? params.P1 : 2; - P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); //TODO: think about P1/S(x,y) Proportion + P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); height = img1.rows; width = img1.cols; int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0); @@ -861,7 +858,7 @@ struct CalcVerticalSums: public ParallelLoopBody D2 = D + 16; costBufSize = width1*D; CSBufSize = costBufSize*height; - minLrSize = (width1 + LrBorder*2); + minLrSize = width1; LrSize = minLrSize*D2; hsumBufNRows = SH2*2 + 2; Cbuf = alignedBuf; @@ -874,10 +871,11 @@ struct CalcVerticalSums: public ParallelLoopBody static const CostType MAX_COST = SHRT_MAX; static const int ALIGN = 16; static const int TAB_OFS = 256*4; + static const int npasses = 2; int x1 = range.start, x2 = range.end, k; size_t pixDiffSize = ((x2 - x1) + 2*SW2)*D; size_t auxBufsSize = pixDiffSize*sizeof(CostType) + //pixdiff size - width*16*img1.channels()*sizeof(PixType) + 32; //tempBuf //TODO: Probably it's better 6 instead of 16(alignment?) + width*16*img1.channels()*sizeof(PixType) + 32; //tempBuf Mat auxBuff; auxBuff.create(1, (int)auxBufsSize, CV_8U); CostType* pixDiff = (CostType*)alignPtr(auxBuff.ptr(), ALIGN); @@ -886,7 +884,7 @@ struct CalcVerticalSums: public ParallelLoopBody // Simplification of index calculation pixDiff -= (x1>SW2 ? (x1 - SW2): 0)*D; - for( int pass = 1; pass <= 2; pass++ ) //TODO: rename this magic 2. + for( int pass = 1; pass <= npasses; pass++ ) { int y1, y2, dy; @@ -899,18 +897,18 @@ struct CalcVerticalSums: public ParallelLoopBody y1 = height-1; y2 = -1; dy = -1; } - CostType *Lr[NLR]={0}, *minLr[NLR]={0}; //4e: arrays for L(x,y,r,d) of previous and current rows and minimums of them + CostType *Lr[NLR]={0}, *minLr[NLR]={0}; - for( k = 0; k < NLR; k++ ) //4e: One of them is needed, and one of them is stored. So, we need to swap pointer - { //4e: Yes, and this is done at the end of next cycle, not here. + 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] = hsumBuf + costBufSize*hsumBufNRows + LrSize*k + D2*LrBorder + 8; + Lr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*k + 8; memset( Lr[k] + x1*D2 - 8, 0, (x2-x1)*D2*sizeof(CostType) ); - minLr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*NLR + minLrSize*k + LrBorder; + minLr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*NLR + minLrSize*k; memset( minLr[k] + x1, 0, (x2-x1)*sizeof(CostType) ); } @@ -922,60 +920,37 @@ struct CalcVerticalSums: public ParallelLoopBody 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; //4e: for first line's block sum we need calculate half-window of costs and only one for other + 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; //4e: Ring buffer for horizontally summed lines + 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, x1 - SW2, x2 + SW2); memset(hsumAdd + x1*D, 0, D*sizeof(CostType)); - for( x = (x1 - SW2)*D; x <= (x1 + SW2)*D; x += D ) //4e: Calculation summed costs for all disparities in first pixel of line + for( x = (x1 - SW2)*D; x <= (x1 + SW2)*D; x += D ) { int xbord = x <= 0 ? 0 : (x > (width1 - 1)*D? (width1 - 1)*D : x); for( d = 0; d < D; d++ ) hsumAdd[x1*D + d] = (CostType)(hsumAdd[x1*D + d] + pixDiff[xbord + d]); } - if( y > 0 ) //4e: We calculate horizontal sums and forming full block sums for y coord by adding this horsums to previous line's sums and subtracting stored lowest - { //4e: horsum in hsumBuf. Exception is case y=0, where we need many iterations per lines to create full blocking sum. + if( y > 0 ) + { const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize; const CostType* Cprev = C - costBufSize; - // We need to calculate C[x1] in different way, because hsumadd is already calculated - // We don't doing then for x==0, because original function has forgotten to do this //TODO: Check: does this original still exist? - if(x1!=0) - { - for( d = 0; d < D; d++ ) - C[x1*D + d] = (CostType)(Cprev[x1*D + d] + hsumAdd[x1*D + d] - hsumSub[x1*D + d]); - } + for( d = 0; d < D; d++ ) + C[x1*D + d] = (CostType)(Cprev[x1*D + d] + hsumAdd[x1*D + d] - hsumSub[x1*D + d]); for( x = (x1+1)*D; x < x2*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 -// if( useSIMD ) -// { -// for( d = 0; d < D; d += 8 ) -// { -// v_int16x8 hv = v_load(hsumAdd + x - D + d); -// v_int16x8 Cx = v_load(Cprev + x + d); -// v_int16x8 psub = v_load(pixSub + d); -// v_int16x8 padd = v_load(pixAdd + d); -// hv = (hv - psub + padd); -// psub = v_load(hsumSub + x + d); -// Cx = Cx - psub + hv; -// v_store(hsumAdd + x + d, hv); -// v_store(C + x + d, Cx); -// } -// } -// else -// #endif { for( d = 0; d < D; d++ ) { @@ -987,8 +962,8 @@ struct CalcVerticalSums: public ParallelLoopBody } else { - for( x = (x1+1)*D; x < x2*D; x += D ) //4e: Calcluates horizontal sums if (y==0). This piece of code is calling SH2+1 times and then result is used in different way - { //4e: to create full blocks sum. That's why this code is isolated from upper case. + for( x = (x1+1)*D; x < x2*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); @@ -996,10 +971,9 @@ struct CalcVerticalSums: public ParallelLoopBody hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); } } - // Return to coordinates, which is needed by CalcCostBT } - if( y == 0 ) //4e: Calculating first full block sum. + if( y == 0 ) { int scale = k == 0 ? SH2 + 1 : 1; for( x = x1*D; x < x2*D; x++ ) @@ -1008,26 +982,19 @@ struct CalcVerticalSums: public ParallelLoopBody } // also, clear the S buffer - for( k = x1*D; k < x2*D; k++ ) //4e: only on first pass, so it keep old information, don't be confused + for( k = x1*D; k < x2*D; k++ ) S[k] = 0; } -// [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) +// [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 one directions on first pass and other on second: +// r=(0, dy), where dy=1 on first pass and dy=-1 on second for( x = x1; x != x2; x++ ) { @@ -1043,88 +1010,12 @@ struct CalcVerticalSums: public ParallelLoopBody const CostType* Cp = C + x*D; CostType* Sp = S + x*D; -// #if CV_SIMD128 -// if( useSIMD ) -// { -// v_int16x8 _P1 = v_setall_s16((short)P1); -// -// v_int16x8 _delta0 = v_setall_s16((short)delta0); -// v_int16x8 _delta1 = v_setall_s16((short)delta1); -// v_int16x8 _delta2 = v_setall_s16((short)delta2); -// v_int16x8 _delta3 = v_setall_s16((short)delta3); -// v_int16x8 _minL0 = v_setall_s16((short)MAX_COST); -// -// for( d = 0; d < D; d += 8 ) -// { -// v_int16x8 Cpd = v_load(Cp + d); -// v_int16x8 L0, L1, L2, L3; -// -// L0 = v_load(Lr_p0 + d); -// L1 = v_load(Lr_p1 + d); -// L2 = v_load(Lr_ppr + d); -// L3 = v_load(Lr_p3 + d); -// -// L0 = v_min(L0, (v_load(Lr_p0 + d - 1) + _P1)); -// L0 = v_min(L0, (v_load(Lr_p0 + d + 1) + _P1)); -// -// L1 = v_min(L1, (v_load(Lr_p1 + d - 1) + _P1)); -// L1 = v_min(L1, (v_load(Lr_p1 + d + 1) + _P1)); -// -// L2 = v_min(L2, (v_load(Lr_ppr + d - 1) + _P1)); -// L2 = v_min(L2, (v_load(Lr_ppr + d + 1) + _P1)); -// -// L3 = v_min(L3, (v_load(Lr_p3 + d - 1) + _P1)); -// L3 = v_min(L3, (v_load(Lr_p3 + d + 1) + _P1)); -// -// L0 = v_min(L0, _delta0); -// L0 = ((L0 - _delta0) + Cpd); -// -// L1 = v_min(L1, _delta1); -// L1 = ((L1 - _delta1) + Cpd); -// -// L2 = v_min(L2, _delta2); -// L2 = ((L2 - _delta2) + Cpd); -// -// L3 = v_min(L3, _delta3); -// L3 = ((L3 - _delta3) + Cpd); -// -// v_store(Lr_p + d, L0); -// v_store(Lr_p + d + D2, L1); -// v_store(Lr_p + d + D2*2, L2); -// v_store(Lr_p + d + D2*3, L3); -// -// // Get minimum from in L0-L3 -// v_int16x8 t02L, t02H, t13L, t13H, t0123L, t0123H; -// v_zip(L0, L2, t02L, t02H); // L0[0] L2[0] L0[1] L2[1]... -// v_zip(L1, L3, t13L, t13H); // L1[0] L3[0] L1[1] L3[1]... -// v_int16x8 t02 = v_min(t02L, t02H); // L0[i] L2[i] L0[i] L2[i]... -// v_int16x8 t13 = v_min(t13L, t13H); // L1[i] L3[i] L1[i] L3[i]... -// v_zip(t02, t13, t0123L, t0123H); // L0[i] L1[i] L2[i] L3[i]... -// v_int16x8 t0 = v_min(t0123L, t0123H); -// _minL0 = v_min(_minL0, t0); -// -// v_int16x8 Sval = v_load(Sp + d); -// -// L0 = L0 + L1; -// L2 = L2 + L3; -// Sval = Sval + L0; -// Sval = Sval + L2; -// -// v_store(Sp + d, Sval); -// } -// -// v_int32x4 minL, minH; -// v_expand(_minL0, minL, minH); -// v_pack_store(&minLr[0][x], v_min(minL, minH)); -// } -// else -// #endif { int minL = MAX_COST; for( d = 0; d < D; d++ ) { - int Cpd = Cp[d], L; //4e: Remember, that every Cp is increased on P2 in line number 369. That's why next 4 lines are paper-like actually + int Cpd = Cp[d], L; L = Cpd + std::min((int)Lr_ppr[d], std::min(Lr_ppr[d-1] + P1, std::min(Lr_ppr[d+1] + P1, delta))) - delta; @@ -1144,7 +1035,6 @@ struct CalcVerticalSums: public ParallelLoopBody } } static const int NLR = 2; - static const int LrBorder = NLR - 1; const Mat& img1; const Mat& img2; CostType* Cbuf; @@ -1178,7 +1068,7 @@ struct CalcHorizontalSums: public ParallelLoopBody minD = params.minDisparity; maxD = minD + params.numDisparities; P1 = params.P1 > 0 ? params.P1 : 2; - P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); //TODO: think about P1/S(x,y) Proportion + 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; height = img1.rows; @@ -1192,7 +1082,7 @@ struct CalcHorizontalSums: public ParallelLoopBody costBufSize = width1*D; CSBufSize = costBufSize*height; D2 = D + 16; - LrSize = 2 * D2; //TODO: Check: do we need this value or not? + LrSize = 2 * D2; Cbuf = alignedBuf; Sbuf = Cbuf + CSBufSize; } @@ -1200,13 +1090,11 @@ struct CalcHorizontalSums: public ParallelLoopBody void operator()( const Range& range ) const { int y1 = range.start, y2 = range.end; - static const CostType MAX_COST = SHRT_MAX; - static const int ALIGN = 16; - size_t auxBufsSize = LrSize + width*(sizeof(CostType) + sizeof(DispType)) + 32; + size_t auxBufsSize = LrSize * sizeof(CostType) + width*(sizeof(CostType) + sizeof(DispType)) + 32; Mat auxBuff; auxBuff.create(1, (int)auxBufsSize, CV_8U); - CostType *Lr = (CostType*)alignPtr(auxBuff.ptr(), ALIGN) + 8; + CostType *Lr = ((CostType*)alignPtr(auxBuff.ptr(), ALIGN)) + 8; CostType* disp2cost = Lr + LrSize; DispType* disp2ptr = (DispType*)(disp2cost + width); @@ -1227,132 +1115,48 @@ struct CalcHorizontalSums: public ParallelLoopBody // clear buffers memset( Lr - 8, 0, LrSize*sizeof(CostType) ); + Lr[-1] = Lr[D] = Lr[D2 - 1] = Lr[D2 + D] = MAX_COST; + minLr = 0; - /* - [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) - */ +// [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: +// we process one directions on first pass and other on second: +// r=(dx, 0), where dx=1 on first pass and dx=-1 on second for( x = 0; x != width1; x++) { int delta = minLr + P2; CostType* Lr_ppr = Lr + ((x&1)? 0 : D2); - Lr_ppr[-1] = Lr_ppr[D] = MAX_COST; //TODO: Well, probably, it's better do this once before. - CostType* Lr_p = Lr + ((x&1)? D2 :0); const CostType* Cp = C + x*D; CostType* Sp = S + x*D; -// #if CV_SIMD128 -// if( useSIMD ) -// { -// v_int16x8 _P1 = v_setall_s16((short)P1); -// -// v_int16x8 _delta0 = v_setall_s16((short)delta0); -// v_int16x8 _delta1 = v_setall_s16((short)delta1); -// v_int16x8 _delta2 = v_setall_s16((short)delta2); -// v_int16x8 _delta3 = v_setall_s16((short)delta3); -// v_int16x8 _minL0 = v_setall_s16((short)MAX_COST); -// -// for( d = 0; d < D; d += 8 ) -// { -// v_int16x8 Cpd = v_load(Cp + d); -// v_int16x8 L0, L1, L2, L3; -// -// L0 = v_load(Lr_ppr + d); -// L1 = v_load(Lr_p1 + d); -// L2 = v_load(Lr_p2 + d); -// L3 = v_load(Lr_p3 + d); -// -// L0 = v_min(L0, (v_load(Lr_ppr + d - 1) + _P1)); -// L0 = v_min(L0, (v_load(Lr_ppr + d + 1) + _P1)); -// -// L1 = v_min(L1, (v_load(Lr_p1 + d - 1) + _P1)); -// L1 = v_min(L1, (v_load(Lr_p1 + d + 1) + _P1)); -// -// L2 = v_min(L2, (v_load(Lr_p2 + d - 1) + _P1)); -// L2 = v_min(L2, (v_load(Lr_p2 + d + 1) + _P1)); -// -// L3 = v_min(L3, (v_load(Lr_p3 + d - 1) + _P1)); -// L3 = v_min(L3, (v_load(Lr_p3 + d + 1) + _P1)); -// -// L0 = v_min(L0, _delta0); -// L0 = ((L0 - _delta0) + Cpd); -// -// L1 = v_min(L1, _delta1); -// L1 = ((L1 - _delta1) + Cpd); -// -// L2 = v_min(L2, _delta2); -// L2 = ((L2 - _delta2) + Cpd); -// -// L3 = v_min(L3, _delta3); -// L3 = ((L3 - _delta3) + Cpd); -// -// v_store(Lr_p + d, L0); -// v_store(Lr_p + d + D2, L1); -// v_store(Lr_p + d + D2*2, L2); -// v_store(Lr_p + d + D2*3, L3); -// -// // Get minimum from in L0-L3 -// v_int16x8 t02L, t02H, t13L, t13H, t0123L, t0123H; -// v_zip(L0, L2, t02L, t02H); // L0[0] L2[0] L0[1] L2[1]... -// v_zip(L1, L3, t13L, t13H); // L1[0] L3[0] L1[1] L3[1]... -// v_int16x8 t02 = v_min(t02L, t02H); // L0[i] L2[i] L0[i] L2[i]... -// v_int16x8 t13 = v_min(t13L, t13H); // L1[i] L3[i] L1[i] L3[i]... -// v_zip(t02, t13, t0123L, t0123H); // L0[i] L1[i] L2[i] L3[i]... -// v_int16x8 t0 = v_min(t0123L, t0123H); -// _minL0 = v_min(_minL0, t0); -// -// v_int16x8 Sval = v_load(Sp + d); -// -// L0 = L0 + L1; -// L2 = L2 + L3; -// Sval = Sval + L0; -// Sval = Sval + L2; -// -// v_store(Sp + d, Sval); -// } -// -// v_int32x4 minL, minH; -// v_expand(_minL0, minL, minH); -// v_pack_store(&minLr[x], v_min(minL, minH)); -// } -// else -// #endif - { - int minL = MAX_COST; + int minL = MAX_COST; - for( d = 0; d < D; d++ ) - { - int Cpd = Cp[d], L; //4e: Remember, that every Cp is increased on P2 in line number 369. That's why next 4 lines are paper-like actually + for( d = 0; d < D; d++ ) + { + int Cpd = Cp[d], L; - L = Cpd + std::min((int)Lr_ppr[d], std::min(Lr_ppr[d-1] + P1, std::min(Lr_ppr[d+1] + P1, delta))) - delta; + L = Cpd + std::min((int)Lr_ppr[d], std::min(Lr_ppr[d-1] + P1, std::min(Lr_ppr[d+1] + P1, delta))) - delta; - Lr_p[d] = (CostType)L; - minL = std::min(minL, L); + Lr_p[d] = (CostType)L; + minL = std::min(minL, L); - Sp[d] = saturate_cast(Sp[d] + L); - } - minLr = (CostType)minL; + Sp[d] = saturate_cast(Sp[d] + L); } + minLr = (CostType)minL; } memset( Lr - 8, 0, LrSize*sizeof(CostType) ); + Lr[-1] = Lr[D] = Lr[D2 - 1] = Lr[D2 + D] = MAX_COST; + minLr = 0; for( x = width1-1; x != -1; x--) @@ -1361,136 +1165,30 @@ struct CalcHorizontalSums: public ParallelLoopBody CostType* Lr_ppr = Lr + ((x&1)? 0 :D2); - Lr_ppr[-1] = Lr_ppr[D] = MAX_COST; - CostType* Lr_p = Lr + ((x&1)? D2 :0); const CostType* Cp = C + x*D; CostType* Sp = S + x*D; int minS = MAX_COST, bestDisp = -1; -// #if CV_SIMD128 -// if( useSIMD ) -// { -// v_int16x8 _P1 = v_setall_s16((short)P1); -// -// v_int16x8 _delta0 = v_setall_s16((short)delta0); -// v_int16x8 _delta1 = v_setall_s16((short)delta1); -// v_int16x8 _delta2 = v_setall_s16((short)delta2); -// v_int16x8 _delta3 = v_setall_s16((short)delta3); -// v_int16x8 _minL0 = v_setall_s16((short)MAX_COST); -// -// for( d = 0; d < D; d += 8 ) -// { -// v_int16x8 Cpd = v_load(Cp + d); -// v_int16x8 L0, L1, L2, L3; -// -// L0 = v_load(Lr_ppr + d); -// L1 = v_load(Lr_p1 + d); -// L2 = v_load(Lr_p2 + d); -// L3 = v_load(Lr_p3 + d); -// -// L0 = v_min(L0, (v_load(Lr_ppr + d - 1) + _P1)); -// L0 = v_min(L0, (v_load(Lr_ppr + d + 1) + _P1)); -// -// L1 = v_min(L1, (v_load(Lr_p1 + d - 1) + _P1)); -// L1 = v_min(L1, (v_load(Lr_p1 + d + 1) + _P1)); -// -// L2 = v_min(L2, (v_load(Lr_p2 + d - 1) + _P1)); -// L2 = v_min(L2, (v_load(Lr_p2 + d + 1) + _P1)); -// -// L3 = v_min(L3, (v_load(Lr_p3 + d - 1) + _P1)); -// L3 = v_min(L3, (v_load(Lr_p3 + d + 1) + _P1)); -// -// L0 = v_min(L0, _delta0); -// L0 = ((L0 - _delta0) + Cpd); -// -// L1 = v_min(L1, _delta1); -// L1 = ((L1 - _delta1) + Cpd); -// -// L2 = v_min(L2, _delta2); -// L2 = ((L2 - _delta2) + Cpd); -// -// L3 = v_min(L3, _delta3); -// L3 = ((L3 - _delta3) + Cpd); -// -// v_store(Lr_p + d, L0); -// v_store(Lr_p + d + D2, L1); -// v_store(Lr_p + d + D2*2, L2); -// v_store(Lr_p + d + D2*3, L3); -// -// // Get minimum from in L0-L3 -// v_int16x8 t02L, t02H, t13L, t13H, t0123L, t0123H; -// v_zip(L0, L2, t02L, t02H); // L0[0] L2[0] L0[1] L2[1]... -// v_zip(L1, L3, t13L, t13H); // L1[0] L3[0] L1[1] L3[1]... -// v_int16x8 t02 = v_min(t02L, t02H); // L0[i] L2[i] L0[i] L2[i]... -// v_int16x8 t13 = v_min(t13L, t13H); // L1[i] L3[i] L1[i] L3[i]... -// v_zip(t02, t13, t0123L, t0123H); // L0[i] L1[i] L2[i] L3[i]... -// v_int16x8 t0 = v_min(t0123L, t0123H); -// _minL0 = v_min(_minL0, t0); -// -// v_int16x8 Sval = v_load(Sp + d); -// -// L0 = L0 + L1; -// L2 = L2 + L3; -// Sval = Sval + L0; -// Sval = Sval + L2; -// -// v_store(Sp + d, Sval); -// } -// -// v_int32x4 minL, minH; -// v_expand(_minL0, minL, minH); -// v_pack_store(&minLr[x], v_min(minL, minH)); -// } -// else -// #endif -//TODO:Next piece of code is came from postprocessing. Be very careful with joining them!!! -// #if CV_SIMD128 -// if( useSIMD ) -// { -// v_int16x8 _minS = v_setall_s16(MAX_COST), _bestDisp = v_setall_s16(-1); -// v_int16x8 _d8 = v_int16x8(0, 1, 2, 3, 4, 5, 6, 7), _8 = v_setall_s16(8); -// -// for( d = 0; d < D; d+= 8 ) -// { -// v_int16x8 L0 = v_load(Sp + d); -// v_int16x8 mask = L0 < _minS; -// _minS = v_min( L0, _minS ); -// _bestDisp = _bestDisp ^ ((_bestDisp ^ _d8) & mask); -// _d8 = _d8 + _8; -// } -// v_int32x4 _d0, _d1; -// v_expand(_minS, _d0, _d1); -// minS = (int)std::min(v_reduce_min(_d0), v_reduce_min(_d1)); -// v_int16x8 v_mask = v_setall_s16((short)minS) == _minS; -// -// _bestDisp = (_bestDisp & v_mask) | (v_setall_s16(SHRT_MAX) & ~v_mask); -// v_expand(_bestDisp, _d0, _d1); -// bestDisp = (int)std::min(v_reduce_min(_d0), v_reduce_min(_d1)); -// } -// else -// #endif - { - int minL = MAX_COST; + int minL = MAX_COST; - for( d = 0; d < D; d++ ) - { - int Cpd = Cp[d], L; //4e: Remember, that every Cp is increased on P2 in line number 369. That's why next 4 lines are paper-like actually + for( d = 0; d < D; d++ ) + { + int Cpd = Cp[d], L; - L = Cpd + std::min((int)Lr_ppr[d], std::min(Lr_ppr[d-1] + P1, std::min(Lr_ppr[d+1] + P1, delta))) - delta; + L = Cpd + std::min((int)Lr_ppr[d], std::min(Lr_ppr[d-1] + P1, std::min(Lr_ppr[d+1] + P1, delta))) - delta; - Lr_p[d] = (CostType)L; - minL = std::min(minL, L); + Lr_p[d] = (CostType)L; + minL = std::min(minL, L); - Sp[d] = saturate_cast(Sp[d] + L); - if( Sp[d] < minS ) - { - minS = Sp[d]; - bestDisp = d; - } + Sp[d] = saturate_cast(Sp[d] + L); + if( Sp[d] < minS ) + { + minS = Sp[d]; + bestDisp = d; } - minLr = (CostType)minL; } + minLr = (CostType)minL; //Some postprocessing procedures and saving for( d = 0; d < D; d++ ) { @@ -1531,17 +1229,17 @@ struct CalcHorizontalSums: public ParallelLoopBody 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 && //4e: To dismiss disparity, we should assure, that there is no any - 0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff ) //4e: chance to understand this as correct. + 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; } } } - static const int NLR = 2; - static const int LrBorder = NLR - 1; static const int DISP_SHIFT = StereoMatcher::DISP_SHIFT; static const int DISP_SCALE = (1 << DISP_SHIFT); + static const CostType MAX_COST = SHRT_MAX; + static const int ALIGN = 16; const Mat& img1; const Mat& img2; Mat& disp1; @@ -1567,68 +1265,41 @@ struct CalcHorizontalSums: public ParallelLoopBody int disp12MaxDiff; }; /* - This is new experimential version of disparity calculation, which should be parralled after -TODO: Don't forget to rewrire this commentaries after - 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 + On exit disp1buf 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 computeDisparitySGBMParallel( const Mat& img1, const Mat& img2, +static void computeDisparitySGBM_HH4( const Mat& img1, const Mat& img2, Mat& disp1, const StereoSGBMParams& params, Mat& buffer ) { -//#if CV_SIMD128 -// // maxDisparity is supposed to multiple of 16, so we can forget doing else -// 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 -// }; -// static const v_uint16x8 v_LSB = v_uint16x8(0x1, 0x2, 0x4, 0x8, 0x10, 0x20, 0x40, 0x80); -// -// bool useSIMD = hasSIMD128(); -//#endif - const int ALIGN = 16; const int DISP_SHIFT = StereoMatcher::DISP_SHIFT; const int DISP_SCALE = (1 << DISP_SHIFT); int minD = params.minDisparity, maxD = minD + params.numDisparities; - Size SADWindowSize; //4e: SAD means Sum of Absolute Differences - SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; //4e: and this is always square - int ftzero = std::max(params.preFilterCap, 15) | 1; //4e:ftzero clips x-derivatives. I think, this story with arrays is about non-realized SIMD method - int P1 = params.P1 > 0 ? params.P1 : 2, P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); //TODO: think about P1/S(x,y) Proportion + Size SADWindowSize; + SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; + int ftzero = std::max(params.preFilterCap, 15) | 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 SH2 = SADWindowSize.height/2; int INVALID_DISP = minD - 1; int INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE; - const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; //4e: array is such big due to derivative could be +-8*256 in worst cases + const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2; PixType clipTab[TAB_SIZE]; - for( k = 0; k < TAB_SIZE; k++ ) //4e: If ftzero would = 4, array containment will be = -4 -4 -4 ... -4 -3 -2 -1 0 1 2 3 4 ... 4 4 4 + for( k = 0; k < TAB_SIZE; k++ ) clipTab[k] = (PixType)(std::min(std::max(k - TAB_OFS, -ftzero), ftzero) + ftzero); if( minX1 >= maxX1 ) @@ -1637,28 +1308,25 @@ static void computeDisparitySGBMParallel( const Mat& img1, const Mat& img2, return; } - CV_Assert( D % 16 == 0 ); //TODO: Are you sure? By the way, why not 8? + 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; //4e: Somewhere in code we need d+1, so D+1. One of simplest solutuons is increasing D-dimension on 1. But 1 is 16, when storage should be aligned. + int D2 = D+16; // 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 + // for dynamic programming we need the current row and // the previous row, i.e. 2 rows in total - const int NLR = 2; //4e: We assume, that we need one or more previous steps in our linear dynamic(one right here). - const int LrBorder = NLR - 1; //4e: for simplification of calculations we need border for taking previous dynamic solutions. + const int NLR = 2; // 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 keep pixel difference cost (C) and the summary cost over 4 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*height; - size_t minLrSize = (width1 + LrBorder*2), LrSize = minLrSize*D2; //TODO: We don't need LrBorder for vertical passes and we don't need Lr buffer for horizontal passes. + size_t minLrSize = width1 , LrSize = minLrSize*D2; int hsumBufNRows = SH2*2 + 2; size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[] - costBufSize*hsumBufNRows*sizeof(CostType) + // hsumBuf //4e: TODO: Why we should increase sum window height one more time? - CSBufSize*2*sizeof(CostType) + 1024; // C, S //4e: C is Block sum of costs, S is multidirectional dynamic sum with same size + costBufSize*hsumBufNRows*sizeof(CostType) + // hsumBuf + CSBufSize*2*sizeof(CostType) + 1024; // C, S if( buffer.empty() || !buffer.isContinuous() || buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize ) @@ -1671,8 +1339,8 @@ static void computeDisparitySGBMParallel( const Mat& img1, const Mat& img2, for(k = 0; k < (int)CSBufSize; k++ ) Cbuf[k] = (CostType)P2; - parallel_for_(Range(0,width1),CalcVerticalSums(img1, img2, params, Cbuf, clipTab)); - parallel_for_(Range(0,height),CalcHorizontalSums(img1, img2, disp1, params, Cbuf)); + parallel_for_(Range(0,width1),CalcVerticalSums(img1, img2, params, Cbuf, clipTab),8); + parallel_for_(Range(0,height),CalcHorizontalSums(img1, img2, disp1, params, Cbuf),8); } @@ -2331,7 +1999,7 @@ public: if(params.mode==MODE_SGBM_3WAY) computeDisparity3WaySGBM( left, right, disp, params, buffers, num_stripes ); else if(params.mode==MODE_HH4) - computeDisparitySGBMParallel( left, right, disp, params, buffer ); + computeDisparitySGBM_HH4( left, right, disp, params, buffer ); else computeDisparitySGBM( left, right, disp, params, buffer ); diff --git a/modules/calib3d/test/test_stereomatching.cpp b/modules/calib3d/test/test_stereomatching.cpp index 9a338bb11f..08a955bb3a 100644 --- a/modules/calib3d/test/test_stereomatching.cpp +++ b/modules/calib3d/test/test_stereomatching.cpp @@ -785,37 +785,22 @@ protected: TEST(Calib3d_StereoBM, regression) { CV_StereoBMTest test; test.safe_run(); } TEST(Calib3d_StereoSGBM, regression) { CV_StereoSGBMTest test; test.safe_run(); } -TEST(Calib3d_StereoSGBMPar, idontknowhowtotesthere) +TEST(Calib3d_StereoSGBM_HH4, regression) { -// -// case_teddy_2 teddy "48" "3" "MODE_HH" - -//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) - Mat leftImg = imread("/home/q/Work/GitVault/opencv_extra/testdata/cv/stereomatching/datasets/teddy/im2.png"); - Mat rightImg = imread("/home/q/Work/GitVault/opencv_extra/testdata/cv/stereomatching/datasets/teddy/im6.png"); -// Mat leftDisp_old, leftDisp_new; - { - Mat leftDisp; - Ptr sgbm = StereoSGBM::create( 0, 48, 3, 90, 360, 1, 63, 10, 100, 32, StereoSGBM::MODE_HH); - sgbm->compute( leftImg, rightImg, leftDisp); - CV_Assert( leftDisp.type() == CV_16SC1 ); - leftDisp/=8; - imwrite( "/home/q/Work/GitVault/modehh4_new.jpg", leftDisp); - } + String path = cvtest::TS::ptr()->get_data_path() + "cv/stereomatching/datasets/teddy/"; + Mat leftImg = imread(path + "im2.png", 0); + Mat rightImg = imread(path + "im6.png", 0); + Mat testData = imread(path + "disp2_hh4.png",-1); + Mat leftDisp; + Mat toCheck; { - Mat leftDisp; Ptr sgbm = StereoSGBM::create( 0, 48, 3, 90, 360, 1, 63, 10, 100, 32, StereoSGBM::MODE_HH4); sgbm->compute( leftImg, rightImg, leftDisp); CV_Assert( leftDisp.type() == CV_16SC1 ); - leftDisp/=8; - imwrite( "/home/q/Work/GitVault/modehh4_old.jpg", leftDisp); + leftDisp.convertTo(toCheck, CV_16UC1); +// imwrite("/home/q/Work/GitVault/disp2_hh4.png",toCheck); } -// Mat diff; -// absdiff(leftDisp_old,leftDisp_new,diff); -// CV_Assert( countNonZero(diff)==0); -// + Mat diff; + absdiff(toCheck, testData,diff); + CV_Assert( countNonZero(diff)==0); }