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Merge pull request #2638 from pemmanuelviel:pev--constify

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* Set constants as const and replace some declaration+assignment by copy ctor

* Remove useless img2 as matching scores have already been buffered earlier
pull/2645/head^2
pemmanuelviel 5 years ago committed by GitHub
parent 513c7a1513
commit bddbcd5a40
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1 changed files with 68 additions and 60 deletions
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  1. 128
      modules/stereo/src/stereo_binary_sgbm.cpp

128
modules/stereo/src/stereo_binary_sgbm.cpp
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@ -127,7 +127,7 @@ namespace cv
disp2cost also has the same size as img1 (or img2). 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. It contains the minimum current cost, used to find the best disparity, corresponding to the minimal cost.
*/ */
static void computeDisparityBinarySGBM( const Mat& img1, const Mat& img2, static void computeDisparityBinarySGBM( const Mat& img1,
Mat& disp1, const StereoBinarySGBMParams& params, Mat& disp1, const StereoBinarySGBMParams& params,
Mat& buffer,const Mat& hamDist) Mat& buffer,const Mat& hamDist)
{ {
@ -151,19 +151,23 @@ namespace cv
const int DISP_SHIFT = StereoMatcher::DISP_SHIFT; const int DISP_SHIFT = StereoMatcher::DISP_SHIFT;
const int DISP_SCALE = (1 << DISP_SHIFT); const int DISP_SCALE = (1 << DISP_SHIFT);
const CostType MAX_COST = SHRT_MAX; const CostType MAX_COST = SHRT_MAX;
int minD = params.minDisparity, maxD = minD + params.numDisparities; const int minD = params.minDisparity;
const int maxD = minD + params.numDisparities;
Size kernelSize; Size kernelSize;
kernelSize.width = kernelSize.height = params.kernelSize > 0 ? params.kernelSize : 5; kernelSize.width = kernelSize.height = params.kernelSize > 0 ? params.kernelSize : 5;
int uniquenessRatio = params.uniquenessRatio >= 0 ? params.uniquenessRatio : 10; const int uniquenessRatio = params.uniquenessRatio >= 0 ? params.uniquenessRatio : 10;
int disp12MaxDiff = params.disp12MaxDiff > 0 ? params.disp12MaxDiff : 1; const 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); const int P1 = params.P1 > 0 ? params.P1 : 2;
int k, width = disp1.cols, height = disp1.rows; const int P2 = std::max(params.P2 > 0 ? params.P2 : 5, P1+1);
int minX1 = std::max(-maxD, 0), maxX1 = width + std::min(minD, 0); const int width = disp1.cols, height = disp1.rows;
int D = maxD - minD, width1 = maxX1 - minX1; const int minX1 = std::max(-maxD, 0);
int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE; const int maxX1 = width + std::min(minD, 0);
int SW2 = kernelSize.width/2, SH2 = kernelSize.height/2; const int D = maxD - minD;
bool fullDP = params.mode == StereoBinarySGBM::MODE_HH; const int width1 = maxX1 - minX1;
int npasses = fullDP ? 2 : 1; const int INVALID_DISP = minD - 1, INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE;
const int SW2 = kernelSize.width/2, SH2 = kernelSize.height/2;
const bool fullDP = params.mode == StereoBinarySGBM::MODE_HH;
const int npasses = fullDP ? 2 : 1;
if( minX1 >= maxX1 ) if( minX1 >= maxX1 )
{ {
@ -173,23 +177,23 @@ namespace cv
CV_Assert( D % 16 == 0 ); CV_Assert( D % 16 == 0 );
// NR - the number of directions. the loop on x below that computes Lr assumes that NR == 8. // 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. // if you change NR, please, modify the loop as well.
int D2 = D+16, NRD2 = NR2*D2; const int D2 = D+16;
const int NRD2 = NR2*D2;
// the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer: // 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 8-way dynamic programming we need the current row and
// the previous row, i.e. 2 rows in total // the previous row, i.e. 2 rows in total
const int NLR = 2; const int NLR = 2;
const int LrBorder = NLR - 1; const int LrBorder = NLR - 1;
int ww = img2.cols; short *ham = (short *)hamDist.data;
short *ham;
ham = (short *)hamDist.data;
// for each possible stereo match (img1(x,y) <=> img2(x-d,y)) // 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 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) // 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; const size_t costBufSize = width1*D;
size_t CSBufSize = costBufSize*(fullDP ? height : 1); const size_t CSBufSize = costBufSize*(fullDP ? height : 1);
size_t minLrSize = (width1 + LrBorder*2)*NR2, LrSize = minLrSize*D2; const size_t minLrSize = (width1 + LrBorder*2)*NR2;
int hsumBufNRows = SH2*2 + 2; const size_t LrSize = minLrSize*D2;
size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[] const int hsumBufNRows = SH2*2 + 2;
const size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[]
costBufSize*(hsumBufNRows + 1)*sizeof(CostType) + // hsumBuf, pixdiff costBufSize*(hsumBufNRows + 1)*sizeof(CostType) + // hsumBuf, pixdiff
CSBufSize*2*sizeof(CostType) + // C, S CSBufSize*2*sizeof(CostType) + // C, S
width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost width*16*img1.channels()*sizeof(PixType) + // temp buffer for computing per-pixel cost
@ -206,7 +210,7 @@ namespace cv
DispType* disp2ptr = (DispType*)(disp2cost + width); DispType* disp2ptr = (DispType*)(disp2cost + width);
// PixType* tempBuf = (PixType*)(disp2ptr + width); // PixType* tempBuf = (PixType*)(disp2ptr + width);
// add P2 to every C(x,y). it saves a few operations in the inner loops // add P2 to every C(x,y). it saves a few operations in the inner loops
for( k = 0; k < width1*D; k++ ) for(int k = 0; k < width1*D; k++ )
Cbuf[k] = (CostType)P2; Cbuf[k] = (CostType)P2;
for( int pass = 1; pass <= npasses; pass++ ) for( int pass = 1; pass <= npasses; pass++ )
{ {
@ -222,7 +226,7 @@ namespace cv
x1 = width1-1; x2 = -1; dx = -1; x1 = width1-1; x2 = -1; dx = -1;
} }
CostType *Lr[NLR]={0}, *minLr[NLR]={0}; CostType *Lr[NLR]={0}, *minLr[NLR]={0};
for( k = 0; k < NLR; k++ ) for(int k = 0; k < NLR; k++ )
{ {
// shift Lr[k] and minLr[k] pointers, because we allocated them with the borders, // shift Lr[k] and minLr[k] pointers, because we allocated them with the borders,
// and will occasionally use negative indices with the arrays // and will occasionally use negative indices with the arrays
@ -243,22 +247,23 @@ namespace cv
if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any. 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; int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1;
for( k = dy1; k <= dy2; k++ ) for(int k = dy1; k <= dy2; k++ )
{ {
CostType* hsumAdd = hsumBuf + (std::min(k, height-1) % hsumBufNRows)*costBufSize; CostType* hsumAdd = hsumBuf + (std::min(k, height-1) % hsumBufNRows)*costBufSize;
if( k < height ) if( k < height )
{ {
for(int ii = 0; ii < ww; ii++) for(int ii = 0; ii < width; ii++)
{ {
// fill pixDiff with the hamming costs previously processed in earlier method
for(int dd = 0; dd <= params.numDisparities; dd++) for(int dd = 0; dd <= params.numDisparities; dd++)
{ {
pixDiff[ii * (params.numDisparities)+ dd] = (CostType)(ham[(k * (ww) + ii) * (params.numDisparities +1) + dd]); pixDiff[ii * (params.numDisparities)+ dd] = (CostType)(ham[(k * width + ii) * (params.numDisparities +1) + dd]);
} }
} }
memset(hsumAdd, 0, D*sizeof(CostType)); memset(hsumAdd, 0, D*sizeof(CostType));
for( x = 0; x <= SW2*D; x += D ) for( x = 0; x <= SW2*D; x += D )
{ {
int scale = x == 0 ? SW2 + 1 : 1; const int scale = x == 0 ? SW2 + 1 : 1;
for( d = 0; d < D; d++ ) for( d = 0; d < D; d++ )
hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale); hsumAdd[d] = (CostType)(hsumAdd[d] + pixDiff[x + d]*scale);
} }
@ -295,7 +300,7 @@ namespace cv
{ {
for( d = 0; d < D; d++ ) for( d = 0; d < D; d++ )
{ {
int hv = hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]); const int hv = hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]);
C[x + d] = (CostType)(Cprev[x + d] + hv - hsumSub[x + d]); C[x + d] = (CostType)(Cprev[x + d] + hv - hsumSub[x + d]);
} }
} }
@ -314,13 +319,13 @@ namespace cv
} }
if( y == 0 ) if( y == 0 )
{ {
int scale = k == 0 ? SH2 + 1 : 1; const int scale = k == 0 ? SH2 + 1 : 1;
for( x = 0; x < width1*D; x++ ) for( x = 0; x < width1*D; x++ )
C[x] = (CostType)(C[x] + hsumAdd[x]*scale); C[x] = (CostType)(C[x] + hsumAdd[x]*scale);
} }
} }
// also, clear the S buffer // also, clear the S buffer
for( k = 0; k < width1*D; k++ ) for(int k = 0; k < width1*D; k++ )
S[k] = 0; S[k] = 0;
} }
// clear the left and the right borders // clear the left and the right borders
@ -348,9 +353,12 @@ namespace cv
*/ */
for( x = x1; x != x2; x += dx ) for( x = x1; x != x2; x += dx )
{ {
int xm = x*NR2, xd = xm*D2; const int xm = x*NR2;
int delta0 = minLr[0][xm - dx*NR2] + P2, delta1 = minLr[1][xm - NR2 + 1] + P2; const int xd = xm*D2;
int delta2 = minLr[1][xm + 2] + P2, delta3 = minLr[1][xm + NR2 + 3] + P2; const int delta0 = minLr[0][xm - dx*NR2] + P2;
const int delta1 = minLr[1][xm - NR2 + 1] + P2;
const int delta2 = minLr[1][xm + 2] + P2;
const int delta3 = minLr[1][xm + NR2 + 3] + P2;
CostType* Lr_p0 = Lr[0] + xd - dx*NRD2; CostType* Lr_p0 = Lr[0] + xd - dx*NRD2;
CostType* Lr_p1 = Lr[1] + xd - NRD2 + D2; CostType* Lr_p1 = Lr[1] + xd - NRD2 + D2;
CostType* Lr_p2 = Lr[1] + xd + D2*2; CostType* Lr_p2 = Lr[1] + xd + D2*2;
@ -418,12 +426,11 @@ namespace cv
for( d = 0; d < D; d++ ) for( d = 0; d < D; d++ )
{ {
int Cpd = Cp[d], L0, L1, L2, L3; const int Cpd = Cp[d];
const int L0 = Cpd + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0;
L0 = Cpd + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0; const int L1 = Cpd + std::min((int)Lr_p1[d], std::min(Lr_p1[d-1] + P1, std::min(Lr_p1[d+1] + P1, delta1))) - delta1;
L1 = Cpd + std::min((int)Lr_p1[d], std::min(Lr_p1[d-1] + P1, std::min(Lr_p1[d+1] + P1, delta1))) - delta1; const int L2 = Cpd + std::min((int)Lr_p2[d], std::min(Lr_p2[d-1] + P1, std::min(Lr_p2[d+1] + P1, delta2))) - delta2;
L2 = Cpd + std::min((int)Lr_p2[d], std::min(Lr_p2[d-1] + P1, std::min(Lr_p2[d+1] + P1, delta2))) - delta2; const int L3 = Cpd + std::min((int)Lr_p3[d], std::min(Lr_p3[d-1] + P1, std::min(Lr_p3[d+1] + P1, delta3))) - delta3;
L3 = Cpd + std::min((int)Lr_p3[d], std::min(Lr_p3[d-1] + P1, std::min(Lr_p3[d+1] + P1, delta3))) - delta3;
Lr_p[d] = (CostType)L0; Lr_p[d] = (CostType)L0;
minL0 = std::min(minL0, L0); minL0 = std::min(minL0, L0);
@ -457,14 +464,16 @@ namespace cv
for( x = width1 - 1; x >= 0; x-- ) for( x = width1 - 1; x >= 0; x-- )
{ {
CostType* Sp = S + x*D; CostType* Sp = S + x*D;
int minS = MAX_COST, bestDisp = -1; int minS = MAX_COST;
int bestDisp = -1;
if( npasses == 1 ) if( npasses == 1 )
{ {
int xm = x*NR2, xd = xm*D2; const int xm = x*NR2;
const int xd = xm*D2;
int minL0 = MAX_COST; int minL0 = MAX_COST;
int delta0 = minLr[0][xm + NR2] + P2; const int delta0 = minLr[0][xm + NR2] + P2;
CostType* Lr_p0 = Lr[0] + xd + NRD2; CostType* Lr_p0 = Lr[0] + xd + NRD2;
Lr_p0[-1] = Lr_p0[D] = MAX_COST; Lr_p0[-1] = Lr_p0[D] = MAX_COST;
CostType* Lr_p = Lr[0] + xd; CostType* Lr_p = Lr[0] + xd;
@ -514,10 +523,10 @@ namespace cv
{ {
for( d = 0; d < D; d++ ) for( d = 0; d < D; d++ )
{ {
int L0 = Cp[d] + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0; const int L0 = Cp[d] + std::min((int)Lr_p0[d], std::min(Lr_p0[d-1] + P1, std::min(Lr_p0[d+1] + P1, delta0))) - delta0;
Lr_p[d] = (CostType)L0; Lr_p[d] = (CostType)L0;
minL0 = std::min(minL0, L0); minL0 = std::min(minL0, L0);
int Sval = Sp[d] = saturate_cast<CostType>(Sp[d] + L0); const int Sval = Sp[d] = saturate_cast<CostType>(Sp[d] + L0);
if( Sval < minS ) if( Sval < minS )
{ {
minS = Sval; minS = Sval;
@ -531,7 +540,7 @@ namespace cv
{ {
for( d = 0; d < D; d++ ) for( d = 0; d < D; d++ )
{ {
int Sval = Sp[d]; const int Sval = Sp[d];
if( Sval < minS ) if( Sval < minS )
{ {
minS = Sval; minS = Sval;
@ -547,7 +556,7 @@ namespace cv
if( d < D ) if( d < D )
continue; continue;
d = bestDisp; d = bestDisp;
int _x2 = x + minX1 - d - minD; const int _x2 = x + minX1 - d - minD;
if( disp2cost[_x2] > minS ) if( disp2cost[_x2] > minS )
{ {
disp2cost[_x2] = (CostType)minS; disp2cost[_x2] = (CostType)minS;
@ -557,16 +566,14 @@ namespace cv
{ {
if(params.subpixelInterpolationMethod == CV_SIMETRICV_INTERPOLATION) if(params.subpixelInterpolationMethod == CV_SIMETRICV_INTERPOLATION)
{ {
double m2m1, m3m1, m3, m2, m1; const double m2 = Sp[d - 1];
m2 = Sp[d - 1]; const double m3 = Sp[d + 1];
m3 = Sp[d + 1]; const double m1 = Sp[d];
m1 = Sp[d]; const double m2m1 = m2 - m1;
m2m1 = m2 - m1; const double m3m1 = m3 - m1;
m3m1 = m3 - m1;
if (!(m2m1 == 0 || m3m1 == 0)) if (!(m2m1 == 0 || m3m1 == 0))
{ {
double p; double p = 0;
p = 0;
if (m2 > m3) if (m2 > m3)
{ {
p = (0.5 - 0.25 * ((m3m1 * m3m1) / (m2m1 * m2m1) + (m3m1 / m2m1))); p = (0.5 - 0.25 * ((m3m1 * m3m1) / (m2m1 * m2m1) + (m3m1 / m2m1)));
@ -588,7 +595,7 @@ namespace cv
// do subpixel quadratic interpolation: // do subpixel quadratic interpolation:
// fit parabola into (x1=d-1, y1=Sp[d-1]), (x2=d, y2=Sp[d]), (x3=d+1, y3=Sp[d+1]) // fit parabola into (x1=d-1, y1=Sp[d-1]), (x2=d, y2=Sp[d]), (x3=d+1, y3=Sp[d+1])
// then find minimum of the parabola. // then find minimum of the parabola.
int denom2 = std::max(Sp[d-1] + Sp[d+1] - 2*Sp[d], 1); const int denom2 = std::max(Sp[d-1] + Sp[d+1] - 2*Sp[d], 1);
d = d*DISP_SCALE + ((Sp[d-1] - Sp[d+1])*DISP_SCALE + denom2)/(denom2*2); d = d*DISP_SCALE + ((Sp[d-1] - Sp[d+1])*DISP_SCALE + denom2)/(denom2*2);
} }
} }
@ -601,12 +608,13 @@ namespace cv
// we round the computed disparity both towards -inf and +inf and check // we round the computed disparity both towards -inf and +inf and check
// if either of the corresponding disparities in disp2 is consistent. // if either of the corresponding disparities in disp2 is consistent.
// This is to give the computed disparity a chance to look valid if it is. // This is to give the computed disparity a chance to look valid if it is.
int d1 = disp1ptr[x]; const int d1 = disp1ptr[x];
if( d1 == INVALID_DISP_SCALED ) if( d1 == INVALID_DISP_SCALED )
continue; continue;
int _d = d1 >> DISP_SHIFT; const int _d = d1 >> DISP_SHIFT;
int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT; const int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT;
int _x = x - _d, x_ = x - d_; const int _x = x - _d;
const int x_ = x - d_;
if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff && 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 ) 0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff )
disp1ptr[x] = (DispType)INVALID_DISP_SCALED; disp1ptr[x] = (DispType)INVALID_DISP_SCALED;
@ -681,7 +689,7 @@ namespace cv
hammingDistanceBlockMatching(censusImageLeft, censusImageRight, hamDist, params.kernelSize); hammingDistanceBlockMatching(censusImageLeft, censusImageRight, hamDist, params.kernelSize);
computeDisparityBinarySGBM( left, right, disp, params, buffer,hamDist); computeDisparityBinarySGBM( left, disp, params, buffer,hamDist);
if(params.regionRemoval == CV_SPECKLE_REMOVAL_AVG_ALGORITHM) if(params.regionRemoval == CV_SPECKLE_REMOVAL_AVG_ALGORITHM)
{ {

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