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Merge pull request #8660 from 4ekmah:making_sgbm_parallel

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This commit is contained in:
Vadim Pisarevsky
2017-05-03 13:47:36 +00:00
parent 5950431e73 d6bc6895a6
commit dea5eaca30
3 changed files with 545 additions and 7 deletions
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modules/calib3d/include/opencv2/calib3d.hpp
+2 -1
View File
@@ -1862,7 +1862,8 @@ public:
{
MODE_SGBM = 0,
MODE_HH = 1,
MODE_SGBM_3WAY = 2
MODE_SGBM_3WAY = 2,
MODE_HH4 = 3
};
CV_WRAP virtual int getPreFilterCap() const = 0;
modules/calib3d/src/stereosgbm.cpp
+524 -6
View File
@@ -110,6 +110,7 @@ struct StereoSGBMParams
int mode;
};
static const int DEFAULT_RIGHT_BORDER = -1;
/*
For each pixel row1[x], max(maxD, 0) <= minX <= x < maxX <= width - max(0, -minD),
and for each disparity minD<=d<maxD the function
@@ -123,12 +124,20 @@ struct StereoSGBMParams
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 tabOfs, int , int xrange_min = 0, int xrange_max = DEFAULT_RIGHT_BORDER )
{
int x, c, width = img1.cols, cn = img1.channels();
int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0);
int D = maxD - minD, width1 = maxX1 - minX1;
//This minX1 & maxX2 correction is defining which part of calculatable line must be calculated
//That is needs of parallel algorithm
xrange_min = (xrange_min < 0) ? 0: xrange_min;
xrange_max = (xrange_max == DEFAULT_RIGHT_BORDER) || (xrange_max > width1) ? width1 : xrange_max;
maxX1 = minX1 + xrange_max;
minX1 += xrange_min;
width1 = maxX1 - minX1;
int minX2 = std::max(minX1 - maxD, 0), maxX2 = std::min(maxX1 - minD, width);
int D = maxD - minD, width1 = maxX1 - minX1, width2 = maxX2 - minX2;
int width2 = maxX2 - minX2;
const PixType *row1 = img1.ptr<PixType>(y), *row2 = img2.ptr<PixType>(y);
PixType *prow1 = buffer + width2*2, *prow2 = prow1 + width*cn*2;
#if CV_SIMD128
@@ -179,10 +188,10 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
}
}
memset( cost, 0, width1*D*sizeof(cost[0]) );
memset( cost + xrange_min*D, 0, width1*D*sizeof(cost[0]) );
buffer -= minX2;
cost -= minX1*D + minD; // simplify the cost indices inside the loop
buffer -= width-1-maxX2;
cost -= (minX1-xrange_min)*D + minD; // simplify the cost indices inside the loop
for( c = 0; c < cn*2; c++, prow1 += width, prow2 += width )
{
@@ -191,7 +200,7 @@ static void calcPixelCostBT( const Mat& img1, const Mat& img2, int y,
// precompute
// v0 = min(row2[x-1/2], row2[x], row2[x+1/2]) and
// v1 = max(row2[x-1/2], row2[x], row2[x+1/2]) and
for( x = minX2; x < maxX2; x++ )
for( x = width-1-maxX2; x < width-1- minX2; x++ )
{
int v = prow2[x];
int vl = x > 0 ? (v + prow2[x-1])/2 : v;
@@ -513,6 +522,7 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
6: r=(1, -dy*2)
7: r=(2, -dy)
*/
for( x = x1; x != x2; x += dx )
{
int xm = x*NR2, xd = xm*D2;
@@ -828,6 +838,512 @@ static void computeDisparitySGBM( const Mat& img1, const Mat& img2,
}
}
////////////////////////////////////////////////////////////////////////////////////////////
struct CalcVerticalSums: public ParallelLoopBody
{
CalcVerticalSums(const Mat& _img1, const Mat& _img2, const StereoSGBMParams& params,
CostType* alignedBuf, PixType* _clipTab): img1(_img1), img2(_img2), clipTab(_clipTab)
{
minD = params.minDisparity;
maxD = minD + params.numDisparities;
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);
height = img1.rows;
width = img1.cols;
int minX1 = std::max(maxD, 0), maxX1 = width + std::min(minD, 0);
D = maxD - minD;
width1 = maxX1 - minX1;
D2 = D + 16;
costBufSize = width1*D;
CSBufSize = costBufSize*height;
minLrSize = width1;
LrSize = minLrSize*D2;
hsumBufNRows = SH2*2 + 2;
Cbuf = alignedBuf;
Sbuf = Cbuf + CSBufSize;
hsumBuf = Sbuf + CSBufSize;
}
void operator()( const Range& range ) const
{
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
Mat auxBuff;
auxBuff.create(1, (int)auxBufsSize, CV_8U);
CostType* pixDiff = (CostType*)alignPtr(auxBuff.ptr(), ALIGN);
PixType* tempBuf = (PixType*)(pixDiff + pixDiffSize);
// Simplification of index calculation
pixDiff -= (x1>SW2 ? (x1 - SW2): 0)*D;
for( int pass = 1; pass <= npasses; pass++ )
{
int y1, y2, dy;
if( pass == 1 )
{
y1 = 0; y2 = height; dy = 1;
}
else
{
y1 = height-1; y2 = -1; dy = -1;
}
CostType *Lr[NLR]={0}, *minLr[NLR]={0};
for( k = 0; k < NLR; k++ )
{
// shift Lr[k] and minLr[k] pointers, because we allocated them with the borders,
// and will occasionally use negative indices with the arrays
// we need to shift Lr[k] pointers by 1, to give the space for d=-1.
// however, then the alignment will be imperfect, i.e. bad for SSE,
// thus we shift the pointers by 8 (8*sizeof(short) == 16 - ideal alignment)
Lr[k] = 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;
memset( minLr[k] + x1, 0, (x2-x1)*sizeof(CostType) );
}
for( int y = y1; y != y2; y += dy )
{
int x, d;
CostType* C = Cbuf + y*costBufSize;
CostType* S = Sbuf + y*costBufSize;
if( pass == 1 ) // compute C on the first pass, and reuse it on the second pass, if any.
{
int dy1 = y == 0 ? 0 : y + SH2, dy2 = y == 0 ? SH2 : dy1;
for( k = dy1; k <= dy2; k++ )
{
CostType* hsumAdd = hsumBuf + (std::min(k, height-1) % hsumBufNRows)*costBufSize;
if( k < height )
{
calcPixelCostBT( img1, img2, k, minD, maxD, pixDiff, tempBuf, clipTab, TAB_OFS, ftzero, x1 - SW2, x2 + SW2);
memset(hsumAdd + x1*D, 0, D*sizeof(CostType));
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 )
{
const CostType* hsumSub = hsumBuf + (std::max(y - SH2 - 1, 0) % hsumBufNRows)*costBufSize;
const CostType* Cprev = C - costBufSize;
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);
{
for( d = 0; d < D; d++ )
{
int hv = hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]);
C[x + d] = (CostType)(Cprev[x + d] + hv - hsumSub[x + d]);
}
}
}
}
else
{
for( x = (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);
for( d = 0; d < D; d++ )
hsumAdd[x + d] = (CostType)(hsumAdd[x - D + d] + pixAdd[d] - pixSub[d]);
}
}
}
if( y == 0 )
{
int scale = k == 0 ? SH2 + 1 : 1;
for( x = x1*D; x < x2*D; x++ )
C[x] = (CostType)(C[x] + hsumAdd[x]*scale);
}
}
// also, clear the S buffer
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 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++ )
{
int xd = x*D2;
int delta = minLr[1][x] + P2;
CostType* Lr_ppr = Lr[1] + xd;
Lr_ppr[-1] = Lr_ppr[D] = MAX_COST;
CostType* Lr_p = Lr[0] + xd;
const CostType* Cp = C + x*D;
CostType* Sp = S + x*D;
{
int minL = MAX_COST;
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;
Lr_p[d] = (CostType)L;
minL = std::min(minL, L);
Sp[d] = saturate_cast<CostType>(Sp[d] + L);
}
minLr[0][x] = (CostType)minL;
}
}
// now shift the cyclic buffers
std::swap( Lr[0], Lr[1] );
std::swap( minLr[0], minLr[1] );
}
}
}
static const int NLR = 2;
const Mat& img1;
const Mat& img2;
CostType* Cbuf;
CostType* Sbuf;
CostType* hsumBuf;
PixType* clipTab;
int minD;
int maxD;
int D;
int D2;
int SH2;
int SW2;
int width;
int width1;
int height;
int P1;
int P2;
size_t costBufSize;
size_t CSBufSize;
size_t minLrSize;
size_t LrSize;
size_t hsumBufNRows;
int ftzero;
};
struct CalcHorizontalSums: public ParallelLoopBody
{
CalcHorizontalSums(const Mat& _img1, const Mat& _img2, Mat& _disp1, const StereoSGBMParams& params,
CostType* alignedBuf): img1(_img1), img2(_img2), disp1(_disp1)
{
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);
uniquenessRatio = params.uniquenessRatio >= 0 ? params.uniquenessRatio : 10;
disp12MaxDiff = params.disp12MaxDiff > 0 ? params.disp12MaxDiff : 1;
height = img1.rows;
width = img1.cols;
minX1 = std::max(maxD, 0);
maxX1 = width + std::min(minD, 0);
INVALID_DISP = minD - 1;
INVALID_DISP_SCALED = INVALID_DISP*DISP_SCALE;
D = maxD - minD;
width1 = maxX1 - minX1;
costBufSize = width1*D;
CSBufSize = costBufSize*height;
D2 = D + 16;
LrSize = 2 * D2;
Cbuf = alignedBuf;
Sbuf = Cbuf + CSBufSize;
}
void operator()( const Range& range ) const
{
int y1 = range.start, y2 = range.end;
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* disp2cost = Lr + LrSize;
DispType* disp2ptr = (DispType*)(disp2cost + width);
CostType minLr;
for( int y = y1; y != y2; y++)
{
int x, d;
DispType* disp1ptr = disp1.ptr<DispType>(y);
CostType* C = Cbuf + y*costBufSize;
CostType* S = Sbuf + y*costBufSize;
for( x = 0; x < width; x++ )
{
disp1ptr[x] = disp2ptr[x] = (DispType)INVALID_DISP_SCALED;
disp2cost[x] = MAX_COST;
}
// 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:
// 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);
CostType* Lr_p = Lr + ((x&1)? D2 :0);
const CostType* Cp = C + x*D;
CostType* Sp = S + x*D;
int minL = MAX_COST;
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;
Lr_p[d] = (CostType)L;
minL = std::min(minL, L);
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--)
{
int delta = minLr + P2;
CostType* Lr_ppr = Lr + ((x&1)? 0 :D2);
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;
int minL = MAX_COST;
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;
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;
}
}
minLr = (CostType)minL;
//Some postprocessing procedures and saving
for( d = 0; d < D; d++ )
{
if( Sp[d]*(100 - uniquenessRatio) < minS*100 && std::abs(bestDisp - d) > 1 )
break;
}
if( d < D )
continue;
d = bestDisp;
int _x2 = x + minX1 - d - minD;
if( disp2cost[_x2] > minS )
{
disp2cost[_x2] = (CostType)minS;
disp2ptr[_x2] = (DispType)(d + minD);
}
if( 0 < d && d < D-1 )
{
// do subpixel quadratic interpolation:
// fit parabola into (x1=d-1, y1=Sp[d-1]), (x2=d, y2=Sp[d]), (x3=d+1, y3=Sp[d+1])
// then find minimum of the parabola.
int denom2 = std::max(Sp[d-1] + Sp[d+1] - 2*Sp[d], 1);
d = d*DISP_SCALE + ((Sp[d-1] - Sp[d+1])*DISP_SCALE + denom2)/(denom2*2);
}
else
d *= DISP_SCALE;
disp1ptr[x + minX1] = (DispType)(d + minD*DISP_SCALE);
}
//Left-right check sanity procedure
for( x = minX1; x < maxX1; x++ )
{
// we round the computed disparity both towards -inf and +inf and check
// if either of the corresponding disparities in disp2 is consistent.
// This is to give the computed disparity a chance to look valid if it is.
int d1 = disp1ptr[x];
if( d1 == INVALID_DISP_SCALED )
continue;
int _d = d1 >> DISP_SHIFT;
int d_ = (d1 + DISP_SCALE-1) >> DISP_SHIFT;
int _x = x - _d, x_ = x - d_;
if( 0 <= _x && _x < width && disp2ptr[_x] >= minD && std::abs(disp2ptr[_x] - _d) > disp12MaxDiff &&
0 <= x_ && x_ < width && disp2ptr[x_] >= minD && std::abs(disp2ptr[x_] - d_) > disp12MaxDiff )
disp1ptr[x] = (DispType)INVALID_DISP_SCALED;
}
}
}
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;
CostType* Cbuf;
CostType* Sbuf;
int minD;
int maxD;
int D;
int D2;
int width;
int width1;
int height;
int P1;
int P2;
int minX1;
int maxX1;
size_t costBufSize;
size_t CSBufSize;
size_t LrSize;
int INVALID_DISP;
int INVALID_DISP_SCALED;
int uniquenessRatio;
int disp12MaxDiff;
};
/*
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.
note that disp1buf will have the same size as the roi and
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.
*/
static void computeDisparitySGBM_HH4( const Mat& img1, const Mat& img2,
Mat& disp1, const StereoSGBMParams& params,
Mat& buffer )
{
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;
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;
PixType clipTab[TAB_SIZE];
for( k = 0; k < TAB_SIZE; k++ )
clipTab[k] = (PixType)(std::min(std::max(k - TAB_OFS, -ftzero), ftzero) + ftzero);
if( minX1 >= maxX1 )
{
disp1 = Scalar::all(INVALID_DISP_SCALED);
return;
}
CV_Assert( D % 16 == 0 );
int D2 = D+16;
// the number of L_r(.,.) and min_k L_r(.,.) lines in the buffer:
// for dynamic programming we need the current row and
// the previous row, i.e. 2 rows in total
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 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 , LrSize = minLrSize*D2;
int hsumBufNRows = SH2*2 + 2;
size_t totalBufSize = (LrSize + minLrSize)*NLR*sizeof(CostType) + // minLr[] and Lr[]
costBufSize*hsumBufNRows*sizeof(CostType) + // hsumBuf
CSBufSize*2*sizeof(CostType) + 1024; // C, S
if( buffer.empty() || !buffer.isContinuous() ||
buffer.cols*buffer.rows*buffer.elemSize() < totalBufSize )
buffer.create(1, (int)totalBufSize, CV_8U);
// summary cost over different (nDirs) directions
CostType* Cbuf = (CostType*)alignPtr(buffer.ptr(), ALIGN);
// add P2 to every C(x,y). it saves a few operations in the inner loops
for(k = 0; k < (int)CSBufSize; k++ )
Cbuf[k] = (CostType)P2;
parallel_for_(Range(0,width1),CalcVerticalSums(img1, img2, params, Cbuf, clipTab),8);
parallel_for_(Range(0,height),CalcHorizontalSums(img1, img2, disp1, params, Cbuf),8);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////
void getBufferPointers(Mat& buffer, int width, int width1, int D, int num_ch, int SH2, int P2,
@@ -1482,6 +1998,8 @@ public:
if(params.mode==MODE_SGBM_3WAY)
computeDisparity3WaySGBM( left, right, disp, params, buffers, num_stripes );
else if(params.mode==MODE_HH4)
computeDisparitySGBM_HH4( left, right, disp, params, buffer );
else
computeDisparitySGBM( left, right, disp, params, buffer );
modules/calib3d/test/test_stereomatching.cpp
+19
View File
@@ -784,3 +784,22 @@ protected:
TEST(Calib3d_StereoBM, regression) { CV_StereoBMTest test; test.safe_run(); }
TEST(Calib3d_StereoSGBM, regression) { CV_StereoSGBMTest test; test.safe_run(); }
TEST(Calib3d_StereoSGBM_HH4, regression)
{
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;
{
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.convertTo(toCheck, CV_16UC1,1,16);
}
Mat diff;
absdiff(toCheck, testData,diff);
CV_Assert( countNonZero(diff)==0);
}