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Memory repaired + Cleanup.
This commit is contained in:
Pyotr Chekmaryov
parent
7ffa49e02c
commit
21be2aa677
@ -125,7 +125,6 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
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int minD, int maxD, CostType* cost,
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PixType* buffer, const PixType* tab,
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int tabOfs, int , int xrange_min = 0, int xrange_max = DEFAULT_RIGHT_BORDER )
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//TODO: This function was changed and modified old function's behabior. Check they in tests
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{
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int x, c, width = img1.cols, cn = img1.channels();
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int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0);
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@ -311,9 +310,9 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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const CostType MAX_COST = SHRT_MAX;
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int minD = params.minDisparity, maxD = minD + params.numDisparities;
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Size SADWindowSize; //4e: SAD means Sum of Absolute Differences
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SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5; //4e: and this is always square
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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
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Size SADWindowSize;
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SADWindowSize.width = SADWindowSize.height = params.SADWindowSize > 0 ? params.SADWindowSize : 5;
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int ftzero = std::max(params.preFilterCap, 15) | 1;
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int uniquenessRatio = params.uniquenessRatio >= 0 ? params.uniquenessRatio : 10;
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int disp12MaxDiff = params.disp12MaxDiff > 0 ? params.disp12MaxDiff : 1;
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int P1 = params.P1 > 0 ? params.P1 : 2, P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1);
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@ -323,11 +322,11 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE;
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int SW2 = SADWindowSize.width/2, SH2 = SADWindowSize.height/2;
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bool fullDP = params.mode == StereoSGBM::MODE_HH;
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int npasses = fullDP ? 2 : 1; //4e: 2 passes with different behaviour for Hirshmueller method.
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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
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int npasses = fullDP ? 2 : 1;
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const int TAB_OFS = 256*4, TAB_SIZE = 256 + TAB_OFS*2;
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PixType clipTab[TAB_SIZE];
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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
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for( k = 0; k < TAB_SIZE; k++ )
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clipTab[k] = (PixType)(std::min(std::max(k - TAB_OFS, -ftzero), ftzero) + ftzero);
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if( minX1 >= maxX1 )
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@ -340,25 +339,25 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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// NR - the number of directions. the loop on x below that computes Lr assumes that NR == 8.
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// if you change NR, please, modify the loop as well.
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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.
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int D2 = D+16, NRD2 = NR2*D2;
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// the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer:
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// for 8-way dynamic programming we need the current row and
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// the previous row, i.e. 2 rows in total
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const int NLR = 2; //4e: We assume, that we need one or more previous steps in our linear dynamic(one right here).
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const int LrBorder = NLR - 1; //4e: for simplification of calculations we need border for taking previous dynamic solutions.
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const int NLR = 2;
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const int LrBorder = NLR - 1;
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// for each possible stereo match (img1(x,y) <=> img2(x-d,y))
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// we keep pixel difference cost (C) and the summary cost over NR directions (S).
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// we also keep all the partial costs for the previous line L_r(x,d) and also min_k L_r(x, k)
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size_t costBufSize = width1*D;
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size_t CSBufSize = costBufSize*(fullDP ? height : 1); //4e: For HH mode it's better to keep whole array of costs.
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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)
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size_t CSBufSize = costBufSize*(fullDP ? height : 1);
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size_t minLrSize = (width1 + LrBorder*2)*NR2, LrSize = minLrSize*D2;
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int hsumBufNRows = SH2*2 + 2;
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size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[]
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costBufSize*(hsumBufNRows + 1)*sizeof(CostType) + // hsumBuf, pixdiff //4e: TODO: Why we should increase sum window height one more time?
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CSBufSize*2*sizeof(CostType) + // C, S //4e: C is Block sum of costs, S is multidirectional dynamic sum with same size
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width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost //4e: It is needed for calcPixelCostBT function, as "buffer" value
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costBufSize*(hsumBufNRows + 1)*sizeof(CostType) + // hsumBuf, pixdiff
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CSBufSize*2*sizeof(CostType) + // C, S
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width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost
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width*(sizeof(CostType) + sizeof(DispType)) + 1024; // disp2cost + disp2
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if( buffer.empty() || !buffer.isContinuous() ||
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@ -371,7 +370,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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CostType* hsumBuf = Sbuf + CSBufSize;
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CostType* pixDiff = hsumBuf + costBufSize*hsumBufNRows;
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CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR; //4e: It is containers for backwards disparity, made by S[d] too, but with other method
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CostType* disp2cost = pixDiff + costBufSize + (LrSize + minLrSize)*NLR;
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DispType* disp2ptr = (DispType*)(disp2cost + width);
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PixType* tempBuf = (PixType*)(disp2ptr + width);
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@ -383,21 +382,21 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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{
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int x1, y1, x2, y2, dx, dy;
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if( pass == 1 ) //4e: on the first pass, we work down, so calculate directions down, diagdown and right
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if( pass == 1 )
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{
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y1 = 0; y2 = height; dy = 1;
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x1 = 0; x2 = width1; dx = 1;
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}
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else //4e: on the second pass, we work up, so calculate directions up, diagup and up
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else
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{
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y1 = height-1; y2 = -1; dy = -1;
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x1 = width1-1; x2 = -1; dx = -1;
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}
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CostType *Lr[NLR]={0}, *minLr[NLR]={0}; //4e: arrays for L(x,y,r,d) of previous and current rows and minimums of them
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CostType *Lr[NLR]={0}, *minLr[NLR]={0};
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for( k = 0; k < NLR; k++ ) //4e: One of them is needed, and one of them is stored. So, we need to swap pointer
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{ //4e: Yes, and this is done at the end of next cycle, not here.
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for( k = 0; k < NLR; k++ )
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{
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// shift Lr[k] and minLr[k] pointers, because we allocated them with the borders,
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// and will occasionally use negative indices with the arrays
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// we need to shift Lr[k] pointers by 1, to give the space for d=-1.
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@ -418,28 +417,28 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any.
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{
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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
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int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1;
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for( k = dy1; k <= dy2; k++ )
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{
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CostType* hsumAdd = hsumBuf + (std::min(k, height-1) % hsumBufNRows)*costBufSize; //4e: Ring buffer for horizontally summed lines
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CostType* hsumAdd = hsumBuf + (std::min(k, height-1) % hsumBufNRows)*costBufSize;
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if( k < height )
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{
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calcPixelCostBT( img1, img2, k, minD, maxD, pixDiff, tempBuf, clipTab, TAB_OFS, ftzero );
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memset(hsumAdd, 0, D*sizeof(CostType));
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for( x = 0; x <= SW2*D; x += D ) //4e: Calculation summed costs for all disparities in first pixel of line
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for( x = 0; x <= SW2*D; x += D )
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{
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int scale = x == 0 ? SW2 + 1 : 1;
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for( d = 0; d < D; d++ )
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hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale);
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}
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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
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{ //4e: horsum in hsumBuf. Exception is case y=0, where we need many iterations per lines to create full blocking sum.
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if( y > 0 )
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{
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const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize;
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const CostType* Cprev = !fullDP || y == 0 ? C : C - costBufSize; //4e: Well, actually y>0, so we don't need this check: y==0
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const CostType* Cprev = !fullDP || y == 0 ? C : C - costBufSize;
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for( x = D; x < width1*D; x += D )
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{
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@ -475,8 +474,8 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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}
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else
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{
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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
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{ //4e: to create full blocks sum. That's why this code is isolated from upper case.
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for( x = D; x < width1*D; x += D )
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{
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const CostType* pixAdd = pixDiff + std::min(x + SW2*D, (width1-1)*D);
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const CostType* pixSub = pixDiff + std::max(x - (SW2+1)*D, 0);
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@ -486,7 +485,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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}
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}
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if( y == 0 ) //4e: Calculating first full block sum.
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if( y == 0 )
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{
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int scale = k == 0 ? SH2 + 1 : 1;
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for( x = 0; x < width1*D; x++ )
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@ -495,13 +494,13 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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}
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// also, clear the S buffer
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for( k = 0; k < width1*D; k++ ) //4e: only on first pass, so it keep old information, don't be confused
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for( k = 0; k < width1*D; k++ )
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S[k] = 0;
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}
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// clear the left and the right borders
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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
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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...
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memset( Lr[0] - NRD2*LrBorder - 8, 0, NRD2*LrBorder*sizeof(CostType) );
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memset( Lr[0] + width1*NRD2 - 8, 0, NRD2*LrBorder*sizeof(CostType) );
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memset( minLr[0] - NR2*LrBorder, 0, NR2*LrBorder*sizeof(CostType) );
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memset( minLr[0] + width1*NR2, 0, NR2*LrBorder*sizeof(CostType) );
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@ -624,7 +623,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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for( d = 0; d < D; d++ )
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{
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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
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int Cpd = Cp[d], L0, L1, L2, L3;
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L0 = Cpd + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0;
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L1 = Cpd + std::min((int)Lr_p1[d], std::min(Lr_p1[d-1] + P1, std::min(Lr_p1[d+1] + P1, delta1))) - delta1;
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@ -665,7 +664,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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CostType* Sp = S + x*D;
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int minS = MAX_COST, bestDisp = -1;
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if( npasses == 1 ) //4e: in this case we could take fifth direction almost for free(direction "left")
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if( npasses == 1 )
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{
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int xm = x*NR2, xd = xm*D2;
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@ -815,7 +814,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE);
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}
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for( x = minX1; x < maxX1; x++ ) //4e: Left-right check itself
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for( x = minX1; x < maxX1; x++ )
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{
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// we round the computed disparity both towards -inf and +inf and check
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// if either of the corresponding disparities in disp2 is consistent.
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@ -826,8 +825,8 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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int _d = d1 >> DISP_SHIFT;
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int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT;
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int _x = x - _d, x_ = x - d_;
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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
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0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff ) //4e: chance to understand this as correct.
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if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff &&
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0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff )
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disp1ptr[x] = (DispType)INVALID_DISP_SCALED;
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}
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}
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@ -840,8 +839,6 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
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}
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////////////////////////////////////////////////////////////////////////////////////////////
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//TODO: Assumation: Let's pretend, that we allocate memory for pixDiff and tempBuf independently in each thread, with full size, needed for original calcBT
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//TODO: Redo size of this arrays even if situation with independent allocation will still.
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struct CalcVerticalSums: public ParallelLoopBody
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{
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CalcVerticalSums(const Mat& _img1, const Mat& _img2, const StereoSGBMParams& params,
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@ -852,7 +849,7 @@ struct CalcVerticalSums: public ParallelLoopBody
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SW2 = SH2 = (params.SADWindowSize > 0 ? params.SADWindowSize : 5)/2;
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ftzero = std::max(params.preFilterCap, 15) | 1;
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P1 = params.P1 > 0 ? params.P1 : 2;
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P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1); //TODO: think about P1/S(x,y) Proportion
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P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1);
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height = img1.rows;
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width = img1.cols;
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int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0);
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@ -861,7 +858,7 @@ struct CalcVerticalSums: public ParallelLoopBody
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D2 = D + 16;
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costBufSize = width1*D;
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CSBufSize = costBufSize*height;
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minLrSize = (width1 + LrBorder*2);
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minLrSize = width1;
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LrSize = minLrSize*D2;
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hsumBufNRows = SH2*2 + 2;
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Cbuf = alignedBuf;
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@ -874,10 +871,11 @@ struct CalcVerticalSums: public ParallelLoopBody
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static const CostType MAX_COST = SHRT_MAX;
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static const int ALIGN = 16;
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static const int TAB_OFS = 256*4;
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static const int npasses = 2;
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int x1 = range.start, x2 = range.end, k;
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size_t pixDiffSize = ((x2 - x1) + 2*SW2)*D;
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size_t auxBufsSize = pixDiffSize*sizeof(CostType) + //pixdiff size
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width*16*img1.channels()*sizeof(PixType) + 32; //tempBuf //TODO: Probably it's better 6 instead of 16(alignment?)
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width*16*img1.channels()*sizeof(PixType) + 32; //tempBuf
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Mat auxBuff;
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auxBuff.create(1, (int)auxBufsSize, CV_8U);
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CostType* pixDiff = (CostType*)alignPtr(auxBuff.ptr(), ALIGN);
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@ -886,7 +884,7 @@ struct CalcVerticalSums: public ParallelLoopBody
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// Simplification of index calculation
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pixDiff -= (x1>SW2 ? (x1 - SW2): 0)*D;
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for( int pass = 1; pass <= 2; pass++ ) //TODO: rename this magic 2.
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for( int pass = 1; pass <= npasses; pass++ )
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{
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int y1, y2, dy;
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@ -899,18 +897,18 @@ struct CalcVerticalSums: public ParallelLoopBody
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y1 = height-1; y2 = -1; dy = -1;
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}
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CostType *Lr[NLR]={0}, *minLr[NLR]={0}; //4e: arrays for L(x,y,r,d) of previous and current rows and minimums of them
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CostType *Lr[NLR]={0}, *minLr[NLR]={0};
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for( k = 0; k < NLR; k++ ) //4e: One of them is needed, and one of them is stored. So, we need to swap pointer
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{ //4e: Yes, and this is done at the end of next cycle, not here.
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for( k = 0; k < NLR; k++ )
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{
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// shift Lr[k] and minLr[k] pointers, because we allocated them with the borders,
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// and will occasionally use negative indices with the arrays
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// we need to shift Lr[k] pointers by 1, to give the space for d=-1.
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// however, then the alignment will be imperfect, i.e. bad for SSE,
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// thus we shift the pointers by 8 (8*sizeof(short) == 16 - ideal alignment)
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Lr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*k + D2*LrBorder + 8;
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Lr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*k + 8;
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memset( Lr[k] + x1*D2 - 8, 0, (x2-x1)*D2*sizeof(CostType) );
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minLr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*NLR + minLrSize*k + LrBorder;
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minLr[k] = hsumBuf + costBufSize*hsumBufNRows + LrSize*NLR + minLrSize*k;
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memset( minLr[k] + x1, 0, (x2-x1)*sizeof(CostType) );
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}
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@ -922,60 +920,37 @@ struct CalcVerticalSums: public ParallelLoopBody
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if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any.
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{
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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;
|
||||
|
||||
for( d = 0; d < D; d++ )
|
||||
{
|
||||
int minL = MAX_COST;
|
||||
int Cpd = Cp[d], L;
|
||||
|
||||
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
|
||||
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<CostType>(Sp[d] + L);
|
||||
}
|
||||
minLr = (CostType)minL;
|
||||
Sp[d] = saturate_cast<CostType>(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;
|
||||
|
||||
for( d = 0; d < D; d++ )
|
||||
{
|
||||
int minL = MAX_COST;
|
||||
int Cpd = Cp[d], L;
|
||||
|
||||
for( d = 0; d < D; d++ )
|
||||
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);
|
||||
|
||||
Sp[d] = saturate_cast<CostType>(Sp[d] + L);
|
||||
if( Sp[d] < minS )
|
||||
{
|
||||
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
|
||||
|
||||
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);
|
||||
|
||||
Sp[d] = saturate_cast<CostType>(Sp[d] + L);
|
||||
if( Sp[d] < minS )
|
||||
{
|
||||
minS = Sp[d];
|
||||
bestDisp = d;
|
||||
}
|
||||
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.
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const int NLR = 2;
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// for each possible stereo match (img1(x,y) <=> img2(x-d,y))
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// we keep pixel difference cost (C) and the summary cost over NR directions (S).
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// we keep pixel difference cost (C) and the summary cost over 4 directions (S).
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// we also keep all the partial costs for the previous line L_r(x,d) and also min_k L_r(x, k)
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size_t costBufSize = width1*D;
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size_t CSBufSize = costBufSize*height;
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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.
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size_t minLrSize = width1 , LrSize = minLrSize*D2;
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int hsumBufNRows = SH2*2 + 2;
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size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[]
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costBufSize*hsumBufNRows*sizeof(CostType) + // hsumBuf //4e: TODO: Why we should increase sum window height one more time?
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CSBufSize*2*sizeof(CostType) + 1024; // C, S //4e: C is Block sum of costs, S is multidirectional dynamic sum with same size
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costBufSize*hsumBufNRows*sizeof(CostType) + // hsumBuf
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CSBufSize*2*sizeof(CostType) + 1024; // C, S
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if( buffer.empty() || !buffer.isContinuous() ||
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buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize )
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@ -1671,8 +1339,8 @@ static void computeDisparitySGBMParallel( const Mat& img1, const Mat& img2,
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for(k = 0; k < (int)CSBufSize; k++ )
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Cbuf[k] = (CostType)P2;
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parallel_for_(Range(0,width1),CalcVerticalSums(img1, img2, params, Cbuf, clipTab));
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parallel_for_(Range(0,height),CalcHorizontalSums(img1, img2, disp1, params, Cbuf));
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parallel_for_(Range(0,width1),CalcVerticalSums(img1, img2, params, Cbuf, clipTab),8);
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parallel_for_(Range(0,height),CalcHorizontalSums(img1, img2, disp1, params, Cbuf),8);
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||||
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}
|
||||
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||||
@ -2331,7 +1999,7 @@ public:
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if(params.mode==MODE_SGBM_3WAY)
|
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computeDisparity3WaySGBM( left, right, disp, params, buffers, num_stripes );
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||||
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 );
|
||||
|
||||
|
||||
@ -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)
|
||||
{
|
||||
// <!-- caseName, datasetName, numDisp, winSize, mode -->
|
||||
// case_teddy_2 teddy "48" "3" "MODE_HH"
|
||||
|
||||
//Ptr<StereoSGBM> 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;
|
||||
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<StereoSGBM> 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);
|
||||
}
|
||||
{
|
||||
Mat leftDisp;
|
||||
Ptr<StereoSGBM> 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);
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user