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opencv/modules/calib3d/src/stereobm.cpp

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//M*//////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/****************************************************************************************\
* Very fast SAD-based (Sum-of-Absolute-Diffrences) stereo correspondence algorithm. *
* Contributed by Kurt Konolige *
\****************************************************************************************/
#include "precomp.hpp"
#include <stdio.h>
#include <limits>
#include "opencl_kernels_calib3d.hpp"
#include "opencv2/core/hal/intrin.hpp"
namespace cv
{
struct StereoBMParams
{
StereoBMParams(int _numDisparities=64, int _SADWindowSize=21)
{
preFilterType = StereoBM::PREFILTER_XSOBEL;
preFilterSize = 9;
preFilterCap = 31;
SADWindowSize = _SADWindowSize;
minDisparity = 0;
numDisparities = _numDisparities > 0 ? _numDisparities : 64;
textureThreshold = 10;
uniquenessRatio = 15;
speckleRange = speckleWindowSize = 0;
roi1 = roi2 = Rect(0,0,0,0);
disp12MaxDiff = -1;
dispType = CV_16S;
}
int preFilterType;
int preFilterSize;
int preFilterCap;
int SADWindowSize;
int minDisparity;
int numDisparities;
int textureThreshold;
int uniquenessRatio;
int speckleRange;
int speckleWindowSize;
Rect roi1, roi2;
int disp12MaxDiff;
int dispType;
};
#ifdef HAVE_OPENCL
static bool ocl_prefilter_norm(InputArray _input, OutputArray _output, int winsize, int prefilterCap)
{
ocl::Kernel k("prefilter_norm", ocl::calib3d::stereobm_oclsrc, cv::format("-D WSZ=%d", winsize));
if(k.empty())
return false;
int scale_g = winsize*winsize/8, scale_s = (1024 + scale_g)/(scale_g*2);
scale_g *= scale_s;
UMat input = _input.getUMat(), output;
_output.create(input.size(), input.type());
output = _output.getUMat();
size_t globalThreads[3] = { (size_t)input.cols, (size_t)input.rows, 1 };
k.args(ocl::KernelArg::PtrReadOnly(input), ocl::KernelArg::PtrWriteOnly(output), input.rows, input.cols,
prefilterCap, scale_g, scale_s);
return k.run(2, globalThreads, NULL, false);
}
#endif
static void prefilterNorm( const Mat& src, Mat& dst, int winsize, int ftzero, uchar* buf )
{
int x, y, wsz2 = winsize/2;
int* vsum = (int*)alignPtr(buf + (wsz2 + 1)*sizeof(vsum[0]), 32);
int scale_g = winsize*winsize/8, scale_s = (1024 + scale_g)/(scale_g*2);
const int OFS = 256*5, TABSZ = OFS*2 + 256;
uchar tab[TABSZ];
const uchar* sptr = src.ptr();
int srcstep = (int)src.step;
Size size = src.size();
scale_g *= scale_s;
for( x = 0; x < TABSZ; x++ )
tab[x] = (uchar)(x - OFS < -ftzero ? 0 : x - OFS > ftzero ? ftzero*2 : x - OFS + ftzero);
for( x = 0; x < size.width; x++ )
vsum[x] = (ushort)(sptr[x]*(wsz2 + 2));
for( y = 1; y < wsz2; y++ )
{
for( x = 0; x < size.width; x++ )
vsum[x] = (ushort)(vsum[x] + sptr[srcstep*y + x]);
}
for( y = 0; y < size.height; y++ )
{
const uchar* top = sptr + srcstep*MAX(y-wsz2-1,0);
const uchar* bottom = sptr + srcstep*MIN(y+wsz2,size.height-1);
const uchar* prev = sptr + srcstep*MAX(y-1,0);
const uchar* curr = sptr + srcstep*y;
const uchar* next = sptr + srcstep*MIN(y+1,size.height-1);
uchar* dptr = dst.ptr<uchar>(y);
for( x = 0; x < size.width; x++ )
vsum[x] = (ushort)(vsum[x] + bottom[x] - top[x]);
for( x = 0; x <= wsz2; x++ )
{
vsum[-x-1] = vsum[0];
vsum[size.width+x] = vsum[size.width-1];
}
int sum = vsum[0]*(wsz2 + 1);
for( x = 1; x <= wsz2; x++ )
sum += vsum[x];
int val = ((curr[0]*5 + curr[1] + prev[0] + next[0])*scale_g - sum*scale_s) >> 10;
dptr[0] = tab[val + OFS];
for( x = 1; x < size.width-1; x++ )
{
sum += vsum[x+wsz2] - vsum[x-wsz2-1];
val = ((curr[x]*4 + curr[x-1] + curr[x+1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10;
dptr[x] = tab[val + OFS];
}
sum += vsum[x+wsz2] - vsum[x-wsz2-1];
val = ((curr[x]*5 + curr[x-1] + prev[x] + next[x])*scale_g - sum*scale_s) >> 10;
dptr[x] = tab[val + OFS];
}
}
#ifdef HAVE_OPENCL
static bool ocl_prefilter_xsobel(InputArray _input, OutputArray _output, int prefilterCap)
{
ocl::Kernel k("prefilter_xsobel", ocl::calib3d::stereobm_oclsrc);
if(k.empty())
return false;
UMat input = _input.getUMat(), output;
_output.create(input.size(), input.type());
output = _output.getUMat();
size_t globalThreads[3] = { (size_t)input.cols, (size_t)input.rows, 1 };
k.args(ocl::KernelArg::PtrReadOnly(input), ocl::KernelArg::PtrWriteOnly(output), input.rows, input.cols, prefilterCap);
return k.run(2, globalThreads, NULL, false);
}
#endif
static void
prefilterXSobel( const Mat& src, Mat& dst, int ftzero )
{
int x, y;
const int OFS = 256*4, TABSZ = OFS*2 + 256;
uchar tab[TABSZ];
Size size = src.size();
for( x = 0; x < TABSZ; x++ )
tab[x] = (uchar)(x - OFS < -ftzero ? 0 : x - OFS > ftzero ? ftzero*2 : x - OFS + ftzero);
uchar val0 = tab[0 + OFS];
#if CV_SIMD128
bool useSIMD = hasSIMD128();
#endif
for( y = 0; y < size.height-1; y += 2 )
{
const uchar* srow1 = src.ptr<uchar>(y);
const uchar* srow0 = y > 0 ? srow1 - src.step : size.height > 1 ? srow1 + src.step : srow1;
const uchar* srow2 = y < size.height-1 ? srow1 + src.step : size.height > 1 ? srow1 - src.step : srow1;
const uchar* srow3 = y < size.height-2 ? srow1 + src.step*2 : srow1;
uchar* dptr0 = dst.ptr<uchar>(y);
uchar* dptr1 = dptr0 + dst.step;
dptr0[0] = dptr0[size.width-1] = dptr1[0] = dptr1[size.width-1] = val0;
x = 1;
#if CV_SIMD128
if( useSIMD )
{
v_int16x8 ftz = v_setall_s16((short) ftzero);
v_int16x8 ftz2 = v_setall_s16((short)(ftzero*2));
v_int16x8 z = v_setzero_s16();
for(; x <= size.width-8; x += 8 )
{
v_int16x8 s00 = v_reinterpret_as_s16(v_load_expand(srow0 + x + 1));
v_int16x8 s01 = v_reinterpret_as_s16(v_load_expand(srow0 + x - 1));
v_int16x8 s10 = v_reinterpret_as_s16(v_load_expand(srow1 + x + 1));
v_int16x8 s11 = v_reinterpret_as_s16(v_load_expand(srow1 + x - 1));
v_int16x8 s20 = v_reinterpret_as_s16(v_load_expand(srow2 + x + 1));
v_int16x8 s21 = v_reinterpret_as_s16(v_load_expand(srow2 + x - 1));
v_int16x8 s30 = v_reinterpret_as_s16(v_load_expand(srow3 + x + 1));
v_int16x8 s31 = v_reinterpret_as_s16(v_load_expand(srow3 + x - 1));
v_int16x8 d0 = s00 - s01;
v_int16x8 d1 = s10 - s11;
v_int16x8 d2 = s20 - s21;
v_int16x8 d3 = s30 - s31;
v_uint16x8 v0 = v_reinterpret_as_u16(v_max(v_min(d0 + d1 + d1 + d2 + ftz, ftz2), z));
v_uint16x8 v1 = v_reinterpret_as_u16(v_max(v_min(d1 + d2 + d2 + d3 + ftz, ftz2), z));
v_pack_store(dptr0 + x, v0);
v_pack_store(dptr1 + x, v1);
}
}
#endif
for( ; x < size.width-1; x++ )
{
int d0 = srow0[x+1] - srow0[x-1], d1 = srow1[x+1] - srow1[x-1],
d2 = srow2[x+1] - srow2[x-1], d3 = srow3[x+1] - srow3[x-1];
int v0 = tab[d0 + d1*2 + d2 + OFS];
int v1 = tab[d1 + d2*2 + d3 + OFS];
dptr0[x] = (uchar)v0;
dptr1[x] = (uchar)v1;
}
}
for( ; y < size.height; y++ )
{
uchar* dptr = dst.ptr<uchar>(y);
x = 0;
#if CV_SIMD128
if( useSIMD )
{
v_uint8x16 val0_16 = v_setall_u8(val0);
for(; x <= size.width-16; x+=16 )
v_store(dptr + x, val0_16);
}
#endif
for(; x < size.width; x++ )
dptr[x] = val0;
}
}
static const int DISPARITY_SHIFT_16S = 4;
static const int DISPARITY_SHIFT_32S = 8;
#if CV_SIMD128
static void findStereoCorrespondenceBM_SIMD( const Mat& left, const Mat& right,
Mat& disp, Mat& cost, StereoBMParams& state,
uchar* buf, int _dy0, int _dy1 )
{
const int ALIGN = 16;
int x, y, d;
int wsz = state.SADWindowSize, wsz2 = wsz/2;
int dy0 = MIN(_dy0, wsz2+1), dy1 = MIN(_dy1, wsz2+1);
int ndisp = state.numDisparities;
int mindisp = state.minDisparity;
int lofs = MAX(ndisp - 1 + mindisp, 0);
int rofs = -MIN(ndisp - 1 + mindisp, 0);
int width = left.cols, height = left.rows;
int width1 = width - rofs - ndisp + 1;
int ftzero = state.preFilterCap;
int textureThreshold = state.textureThreshold;
int uniquenessRatio = state.uniquenessRatio;
short FILTERED = (short)((mindisp - 1) << DISPARITY_SHIFT_16S);
ushort *sad, *hsad0, *hsad, *hsad_sub;
int *htext;
uchar *cbuf0, *cbuf;
const uchar* lptr0 = left.ptr() + lofs;
const uchar* rptr0 = right.ptr() + rofs;
const uchar *lptr, *lptr_sub, *rptr;
short* dptr = disp.ptr<short>();
int sstep = (int)left.step;
int dstep = (int)(disp.step/sizeof(dptr[0]));
int cstep = (height + dy0 + dy1)*ndisp;
short costbuf = 0;
int coststep = cost.data ? (int)(cost.step/sizeof(costbuf)) : 0;
const int TABSZ = 256;
uchar tab[TABSZ];
const v_int16x8 d0_8 = v_int16x8(0,1,2,3,4,5,6,7), dd_8 = v_setall_s16(8);
sad = (ushort*)alignPtr(buf + sizeof(sad[0]), ALIGN);
hsad0 = (ushort*)alignPtr(sad + ndisp + 1 + dy0*ndisp, ALIGN);
htext = (int*)alignPtr((int*)(hsad0 + (height+dy1)*ndisp) + wsz2 + 2, ALIGN);
cbuf0 = (uchar*)alignPtr((uchar*)(htext + height + wsz2 + 2) + dy0*ndisp, ALIGN);
for( x = 0; x < TABSZ; x++ )
tab[x] = (uchar)std::abs(x - ftzero);
// initialize buffers
memset( hsad0 - dy0*ndisp, 0, (height + dy0 + dy1)*ndisp*sizeof(hsad0[0]) );
memset( htext - wsz2 - 1, 0, (height + wsz + 1)*sizeof(htext[0]) );
for( x = -wsz2-1; x < wsz2; x++ )
{
hsad = hsad0 - dy0*ndisp; cbuf = cbuf0 + (x + wsz2 + 1)*cstep - dy0*ndisp;
lptr = lptr0 + MIN(MAX(x, -lofs), width-lofs-1) - dy0*sstep;
rptr = rptr0 + MIN(MAX(x, -rofs), width-rofs-ndisp) - dy0*sstep;
for( y = -dy0; y < height + dy1; y++, hsad += ndisp, cbuf += ndisp, lptr += sstep, rptr += sstep )
{
int lval = lptr[0];
v_uint8x16 lv = v_setall_u8((uchar)lval);
for( d = 0; d < ndisp; d += 16 )
{
v_uint8x16 rv = v_load(rptr + d);
v_uint16x8 hsad_l = v_load(hsad + d);
v_uint16x8 hsad_h = v_load(hsad + d + 8);
v_uint8x16 diff = v_absdiff(lv, rv);
v_store(cbuf + d, diff);
v_uint16x8 diff0, diff1;
v_expand(diff, diff0, diff1);
hsad_l += diff0;
hsad_h += diff1;
v_store(hsad + d, hsad_l);
v_store(hsad + d + 8, hsad_h);
}
htext[y] += tab[lval];
}
}
// initialize the left and right borders of the disparity map
for( y = 0; y < height; y++ )
{
for( x = 0; x < lofs; x++ )
dptr[y*dstep + x] = FILTERED;
for( x = lofs + width1; x < width; x++ )
dptr[y*dstep + x] = FILTERED;
}
dptr += lofs;
for( x = 0; x < width1; x++, dptr++ )
{
short* costptr = cost.data ? cost.ptr<short>() + lofs + x : &costbuf;
int x0 = x - wsz2 - 1, x1 = x + wsz2;
const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
hsad = hsad0 - dy0*ndisp;
lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep;
lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep;
rptr = rptr0 + MIN(MAX(x1, -rofs), width-ndisp-rofs) - dy0*sstep;
for( y = -dy0; y < height + dy1; y++, cbuf += ndisp, cbuf_sub += ndisp,
hsad += ndisp, lptr += sstep, lptr_sub += sstep, rptr += sstep )
{
int lval = lptr[0];
v_uint8x16 lv = v_setall_u8((uchar)lval);
for( d = 0; d < ndisp; d += 16 )
{
v_uint8x16 rv = v_load(rptr + d);
v_uint16x8 hsad_l = v_load(hsad + d);
v_uint16x8 hsad_h = v_load(hsad + d + 8);
v_uint8x16 cbs = v_load(cbuf_sub + d);
v_uint8x16 diff = v_absdiff(lv, rv);
v_int16x8 diff_l, diff_h, cbs_l, cbs_h;
v_store(cbuf + d, diff);
v_expand(v_reinterpret_as_s8(diff), diff_l, diff_h);
v_expand(v_reinterpret_as_s8(cbs), cbs_l, cbs_h);
diff_l -= cbs_l;
diff_h -= cbs_h;
hsad_h = v_reinterpret_as_u16(v_reinterpret_as_s16(hsad_h) + diff_h);
hsad_l = v_reinterpret_as_u16(v_reinterpret_as_s16(hsad_l) + diff_l);
v_store(hsad + d, hsad_l);
v_store(hsad + d + 8, hsad_h);
}
htext[y] += tab[lval] - tab[lptr_sub[0]];
}
// fill borders
for( y = dy1; y <= wsz2; y++ )
htext[height+y] = htext[height+dy1-1];
for( y = -wsz2-1; y < -dy0; y++ )
htext[y] = htext[-dy0];
// initialize sums
for( d = 0; d < ndisp; d++ )
sad[d] = (ushort)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0));
hsad = hsad0 + (1 - dy0)*ndisp;
for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp )
for( d = 0; d <= ndisp-16; d += 16 )
{
v_uint16x8 s0 = v_load(sad + d);
v_uint16x8 s1 = v_load(sad + d + 8);
v_uint16x8 t0 = v_load(hsad + d);
v_uint16x8 t1 = v_load(hsad + d + 8);
s0 = s0 + t0;
s1 = s1 + t1;
v_store(sad + d, s0);
v_store(sad + d + 8, s1);
}
int tsum = 0;
for( y = -wsz2-1; y < wsz2; y++ )
tsum += htext[y];
// finally, start the real processing
for( y = 0; y < height; y++ )
{
int minsad = INT_MAX, mind = -1;
hsad = hsad0 + MIN(y + wsz2, height+dy1-1)*ndisp;
hsad_sub = hsad0 + MAX(y - wsz2 - 1, -dy0)*ndisp;
v_int16x8 minsad8 = v_setall_s16(SHRT_MAX);
v_int16x8 mind8 = v_setall_s16(0), d8 = d0_8;
for( d = 0; d < ndisp; d += 16 )
{
v_int16x8 u0 = v_reinterpret_as_s16(v_load(hsad_sub + d));
v_int16x8 u1 = v_reinterpret_as_s16(v_load(hsad + d));
v_int16x8 v0 = v_reinterpret_as_s16(v_load(hsad_sub + d + 8));
v_int16x8 v1 = v_reinterpret_as_s16(v_load(hsad + d + 8));
v_int16x8 usad8 = v_reinterpret_as_s16(v_load(sad + d));
v_int16x8 vsad8 = v_reinterpret_as_s16(v_load(sad + d + 8));
u1 -= u0;
v1 -= v0;
usad8 += u1;
vsad8 += v1;
v_int16x8 mask = minsad8 > usad8;
minsad8 = v_min(minsad8, usad8);
mind8 = v_max(mind8, (mask& d8));
v_store(sad + d, v_reinterpret_as_u16(usad8));
v_store(sad + d + 8, v_reinterpret_as_u16(vsad8));
mask = minsad8 > vsad8;
minsad8 = v_min(minsad8, vsad8);
d8 = d8 + dd_8;
mind8 = v_max(mind8, (mask & d8));
d8 = d8 + dd_8;
}
tsum += htext[y + wsz2] - htext[y - wsz2 - 1];
if( tsum < textureThreshold )
{
dptr[y*dstep] = FILTERED;
continue;
}
ushort CV_DECL_ALIGNED(16) minsad_buf[8], mind_buf[8];
v_store(minsad_buf, v_reinterpret_as_u16(minsad8));
v_store(mind_buf, v_reinterpret_as_u16(mind8));
for( d = 0; d < 8; d++ )
if(minsad > (int)minsad_buf[d] || (minsad == (int)minsad_buf[d] && mind > mind_buf[d]))
{
minsad = minsad_buf[d];
mind = mind_buf[d];
}
if( uniquenessRatio > 0 )
{
int thresh = minsad + (minsad * uniquenessRatio/100);
v_int32x4 thresh4 = v_setall_s32(thresh + 1);
v_int32x4 d1 = v_setall_s32(mind-1), d2 = v_setall_s32(mind+1);
v_int32x4 dd_4 = v_setall_s32(4);
v_int32x4 d4 = v_int32x4(0,1,2,3);
v_int32x4 mask4;
for( d = 0; d < ndisp; d += 8 )
{
v_int16x8 sad8 = v_reinterpret_as_s16(v_load(sad + d));
v_int32x4 sad4_l, sad4_h;
v_expand(sad8, sad4_l, sad4_h);
mask4 = thresh4 > sad4_l;
mask4 = mask4 & ((d1 > d4) | (d4 > d2));
if( v_signmask(mask4) )
break;
d4 += dd_4;
mask4 = thresh4 > sad4_h;
mask4 = mask4 & ((d1 > d4) | (d4 > d2));
if( v_signmask(mask4) )
break;
d4 += dd_4;
}
if( d < ndisp )
{
dptr[y*dstep] = FILTERED;
continue;
}
}
if( 0 < mind && mind < ndisp - 1 )
{
int p = sad[mind+1], n = sad[mind-1];
d = p + n - 2*sad[mind] + std::abs(p - n);
dptr[y*dstep] = (short)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15) >> 4);
}
else
dptr[y*dstep] = (short)((ndisp - mind - 1 + mindisp)*16);
costptr[y*coststep] = sad[mind];
}
}
}
#endif
template <typename mType>
static void
findStereoCorrespondenceBM( const Mat& left, const Mat& right,
Mat& disp, Mat& cost, const StereoBMParams& state,
uchar* buf, int _dy0, int _dy1, const int disp_shift )
{
const int ALIGN = 16;
int x, y, d;
int wsz = state.SADWindowSize, wsz2 = wsz/2;
int dy0 = MIN(_dy0, wsz2+1), dy1 = MIN(_dy1, wsz2+1);
int ndisp = state.numDisparities;
int mindisp = state.minDisparity;
int lofs = MAX(ndisp - 1 + mindisp, 0);
int rofs = -MIN(ndisp - 1 + mindisp, 0);
int width = left.cols, height = left.rows;
int width1 = width - rofs - ndisp + 1;
int ftzero = state.preFilterCap;
int textureThreshold = state.textureThreshold;
int uniquenessRatio = state.uniquenessRatio;
mType FILTERED = (mType)((mindisp - 1) << disp_shift);
#if CV_SIMD128
bool useSIMD = hasSIMD128();
if( useSIMD )
{
CV_Assert (ndisp % 8 == 0);
}
#endif
int *sad, *hsad0, *hsad, *hsad_sub, *htext;
uchar *cbuf0, *cbuf;
const uchar* lptr0 = left.ptr() + lofs;
const uchar* rptr0 = right.ptr() + rofs;
const uchar *lptr, *lptr_sub, *rptr;
mType* dptr = disp.ptr<mType>();
int sstep = (int)left.step;
int dstep = (int)(disp.step/sizeof(dptr[0]));
int cstep = (height+dy0+dy1)*ndisp;
int costbuf = 0;
int coststep = cost.data ? (int)(cost.step/sizeof(costbuf)) : 0;
const int TABSZ = 256;
uchar tab[TABSZ];
sad = (int*)alignPtr(buf + sizeof(sad[0]), ALIGN);
hsad0 = (int*)alignPtr(sad + ndisp + 1 + dy0*ndisp, ALIGN);
htext = (int*)alignPtr((int*)(hsad0 + (height+dy1)*ndisp) + wsz2 + 2, ALIGN);
cbuf0 = (uchar*)alignPtr((uchar*)(htext + height + wsz2 + 2) + dy0*ndisp, ALIGN);
for( x = 0; x < TABSZ; x++ )
tab[x] = (uchar)std::abs(x - ftzero);
// initialize buffers
memset( hsad0 - dy0*ndisp, 0, (height + dy0 + dy1)*ndisp*sizeof(hsad0[0]) );
memset( htext - wsz2 - 1, 0, (height + wsz + 1)*sizeof(htext[0]) );
for( x = -wsz2-1; x < wsz2; x++ )
{
hsad = hsad0 - dy0*ndisp; cbuf = cbuf0 + (x + wsz2 + 1)*cstep - dy0*ndisp;
lptr = lptr0 + std::min(std::max(x, -lofs), width-lofs-1) - dy0*sstep;
rptr = rptr0 + std::min(std::max(x, -rofs), width-rofs-ndisp) - dy0*sstep;
for( y = -dy0; y < height + dy1; y++, hsad += ndisp, cbuf += ndisp, lptr += sstep, rptr += sstep )
{
int lval = lptr[0];
d = 0;
#if CV_SIMD128
if( useSIMD )
{
v_uint8x16 lv = v_setall_u8((uchar)lval);
for( ; d <= ndisp - 16; d += 16 )
{
v_uint8x16 rv = v_load(rptr + d);
v_int32x4 hsad_0 = v_load(hsad + d);
v_int32x4 hsad_1 = v_load(hsad + d + 4);
v_int32x4 hsad_2 = v_load(hsad + d + 8);
v_int32x4 hsad_3 = v_load(hsad + d + 12);
v_uint8x16 diff = v_absdiff(lv, rv);
v_store(cbuf + d, diff);
v_uint16x8 diff0, diff1;
v_uint32x4 diff00, diff01, diff10, diff11;
v_expand(diff, diff0, diff1);
v_expand(diff0, diff00, diff01);
v_expand(diff1, diff10, diff11);
hsad_0 += v_reinterpret_as_s32(diff00);
hsad_1 += v_reinterpret_as_s32(diff01);
hsad_2 += v_reinterpret_as_s32(diff10);
hsad_3 += v_reinterpret_as_s32(diff11);
v_store(hsad + d, hsad_0);
v_store(hsad + d + 4, hsad_1);
v_store(hsad + d + 8, hsad_2);
v_store(hsad + d + 12, hsad_3);
}
}
#endif
for( ; d < ndisp; d++ )
{
int diff = std::abs(lval - rptr[d]);
cbuf[d] = (uchar)diff;
hsad[d] = (int)(hsad[d] + diff);
}
htext[y] += tab[lval];
}
}
// initialize the left and right borders of the disparity map
for( y = 0; y < height; y++ )
{
for( x = 0; x < lofs; x++ )
dptr[y*dstep + x] = FILTERED;
for( x = lofs + width1; x < width; x++ )
dptr[y*dstep + x] = FILTERED;
}
dptr += lofs;
for( x = 0; x < width1; x++, dptr++ )
{
int* costptr = cost.data ? cost.ptr<int>() + lofs + x : &costbuf;
int x0 = x - wsz2 - 1, x1 = x + wsz2;
const uchar* cbuf_sub = cbuf0 + ((x0 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
cbuf = cbuf0 + ((x1 + wsz2 + 1) % (wsz + 1))*cstep - dy0*ndisp;
hsad = hsad0 - dy0*ndisp;
lptr_sub = lptr0 + MIN(MAX(x0, -lofs), width-1-lofs) - dy0*sstep;
lptr = lptr0 + MIN(MAX(x1, -lofs), width-1-lofs) - dy0*sstep;
rptr = rptr0 + MIN(MAX(x1, -rofs), width-ndisp-rofs) - dy0*sstep;
for( y = -dy0; y < height + dy1; y++, cbuf += ndisp, cbuf_sub += ndisp,
hsad += ndisp, lptr += sstep, lptr_sub += sstep, rptr += sstep )
{
int lval = lptr[0];
d = 0;
#if CV_SIMD128
if( useSIMD )
{
v_uint8x16 lv = v_setall_u8((uchar)lval);
for( ; d <= ndisp - 16; d += 16 )
{
v_uint8x16 rv = v_load(rptr + d);
v_int32x4 hsad_0 = v_load(hsad + d);
v_int32x4 hsad_1 = v_load(hsad + d + 4);
v_int32x4 hsad_2 = v_load(hsad + d + 8);
v_int32x4 hsad_3 = v_load(hsad + d + 12);
v_uint8x16 cbs = v_load(cbuf_sub + d);
v_uint8x16 diff = v_absdiff(lv, rv);
v_store(cbuf + d, diff);
v_uint16x8 diff0, diff1, cbs0, cbs1;
v_int32x4 diff00, diff01, diff10, diff11, cbs00, cbs01, cbs10, cbs11;
v_expand(diff, diff0, diff1);
v_expand(cbs, cbs0, cbs1);
v_expand(v_reinterpret_as_s16(diff0), diff00, diff01);
v_expand(v_reinterpret_as_s16(diff1), diff10, diff11);
v_expand(v_reinterpret_as_s16(cbs0), cbs00, cbs01);
v_expand(v_reinterpret_as_s16(cbs1), cbs10, cbs11);
v_int32x4 diff_0 = diff00 - cbs00;
v_int32x4 diff_1 = diff01 - cbs01;
v_int32x4 diff_2 = diff10 - cbs10;
v_int32x4 diff_3 = diff11 - cbs11;
hsad_0 += diff_0;
hsad_1 += diff_1;
hsad_2 += diff_2;
hsad_3 += diff_3;
v_store(hsad + d, hsad_0);
v_store(hsad + d + 4, hsad_1);
v_store(hsad + d + 8, hsad_2);
v_store(hsad + d + 12, hsad_3);
}
}
#endif
for( ; d < ndisp; d++ )
{
int diff = std::abs(lval - rptr[d]);
cbuf[d] = (uchar)diff;
hsad[d] = hsad[d] + diff - cbuf_sub[d];
}
htext[y] += tab[lval] - tab[lptr_sub[0]];
}
// fill borders
for( y = dy1; y <= wsz2; y++ )
htext[height+y] = htext[height+dy1-1];
for( y = -wsz2-1; y < -dy0; y++ )
htext[y] = htext[-dy0];
// initialize sums
for( d = 0; d < ndisp; d++ )
sad[d] = (int)(hsad0[d-ndisp*dy0]*(wsz2 + 2 - dy0));
hsad = hsad0 + (1 - dy0)*ndisp;
for( y = 1 - dy0; y < wsz2; y++, hsad += ndisp )
{
d = 0;
#if CV_SIMD128
if( useSIMD )
{
for( d = 0; d <= ndisp-8; d += 8 )
{
v_int32x4 s0 = v_load(sad + d);
v_int32x4 s1 = v_load(sad + d + 4);
v_int32x4 t0 = v_load(hsad + d);
v_int32x4 t1 = v_load(hsad + d + 4);
s0 += t0;
s1 += t1;
v_store(sad + d, s0);
v_store(sad + d + 4, s1);
}
}
#endif
for( ; d < ndisp; d++ )
sad[d] = (int)(sad[d] + hsad[d]);
}
int tsum = 0;
for( y = -wsz2-1; y < wsz2; y++ )
tsum += htext[y];
// finally, start the real processing
for( y = 0; y < height; y++ )
{
int minsad = INT_MAX, mind = -1;
hsad = hsad0 + MIN(y + wsz2, height+dy1-1)*ndisp;
hsad_sub = hsad0 + MAX(y - wsz2 - 1, -dy0)*ndisp;
d = 0;
#if CV_SIMD128
if( useSIMD )
{
v_int32x4 d0_4 = v_int32x4(0, 1, 2, 3);
v_int32x4 dd_4 = v_setall_s32(4);
v_int32x4 minsad4 = v_setall_s32(INT_MAX);
v_int32x4 mind4 = v_setall_s32(0), d4 = d0_4;
for( ; d <= ndisp - 8; d += 8 )
{
v_int32x4 u0 = v_load(hsad_sub + d);
v_int32x4 u1 = v_load(hsad + d);
v_int32x4 v0 = v_load(hsad_sub + d + 4);
v_int32x4 v1 = v_load(hsad + d + 4);
v_int32x4 usad4 = v_load(sad + d);
v_int32x4 vsad4 = v_load(sad + d + 4);
u1 -= u0;
v1 -= v0;
usad4 += u1;
vsad4 += v1;
v_store(sad + d, usad4);
v_store(sad + d + 4, vsad4);
v_int32x4 mask = minsad4 > usad4;
minsad4 = v_min(minsad4, usad4);
mind4 = v_select(mask, d4, mind4);
d4 += dd_4;
mask = minsad4 > vsad4;
minsad4 = v_min(minsad4, vsad4);
mind4 = v_select(mask, d4, mind4);
d4 += dd_4;
}
int CV_DECL_ALIGNED(16) minsad_buf[4], mind_buf[4];
v_store(minsad_buf, minsad4);
v_store(mind_buf, mind4);
if(minsad_buf[0] < minsad || (minsad == minsad_buf[0] && mind_buf[0] < mind)) { minsad = minsad_buf[0]; mind = mind_buf[0]; }
if(minsad_buf[1] < minsad || (minsad == minsad_buf[1] && mind_buf[1] < mind)) { minsad = minsad_buf[1]; mind = mind_buf[1]; }
if(minsad_buf[2] < minsad || (minsad == minsad_buf[2] && mind_buf[2] < mind)) { minsad = minsad_buf[2]; mind = mind_buf[2]; }
if(minsad_buf[3] < minsad || (minsad == minsad_buf[3] && mind_buf[3] < mind)) { minsad = minsad_buf[3]; mind = mind_buf[3]; }
}
#endif
for( ; d < ndisp; d++ )
{
int currsad = sad[d] + hsad[d] - hsad_sub[d];
sad[d] = currsad;
if( currsad < minsad )
{
minsad = currsad;
mind = d;
}
}
tsum += htext[y + wsz2] - htext[y - wsz2 - 1];
if( tsum < textureThreshold )
{
dptr[y*dstep] = FILTERED;
continue;
}
if( uniquenessRatio > 0 )
{
int thresh = minsad + (minsad * uniquenessRatio/100);
for( d = 0; d < ndisp; d++ )
{
if( (d < mind-1 || d > mind+1) && sad[d] <= thresh)
break;
}
if( d < ndisp )
{
dptr[y*dstep] = FILTERED;
continue;
}
}
{
sad[-1] = sad[1];
sad[ndisp] = sad[ndisp-2];
int p = sad[mind+1], n = sad[mind-1];
d = p + n - 2*sad[mind] + std::abs(p - n);
dptr[y*dstep] = (mType)(((ndisp - mind - 1 + mindisp)*256 + (d != 0 ? (p-n)*256/d : 0) + 15)
>> (DISPARITY_SHIFT_32S - disp_shift));
costptr[y*coststep] = sad[mind];
}
}
}
}
#ifdef HAVE_OPENCL
static bool ocl_prefiltering(InputArray left0, InputArray right0, OutputArray left, OutputArray right, StereoBMParams* state)
{
if( state->preFilterType == StereoBM::PREFILTER_NORMALIZED_RESPONSE )
{
if(!ocl_prefilter_norm( left0, left, state->preFilterSize, state->preFilterCap))
return false;
if(!ocl_prefilter_norm( right0, right, state->preFilterSize, state->preFilterCap))
return false;
}
else
{
if(!ocl_prefilter_xsobel( left0, left, state->preFilterCap ))
return false;
if(!ocl_prefilter_xsobel( right0, right, state->preFilterCap))
return false;
}
return true;
}
#endif
struct PrefilterInvoker : public ParallelLoopBody
{
PrefilterInvoker(const Mat& left0, const Mat& right0, Mat& left, Mat& right,
uchar* buf0, uchar* buf1, StereoBMParams* _state)
{
imgs0[0] = &left0; imgs0[1] = &right0;
imgs[0] = &left; imgs[1] = &right;
buf[0] = buf0; buf[1] = buf1;
state = _state;
}
void operator()( const Range& range ) const
{
for( int i = range.start; i < range.end; i++ )
{
if( state->preFilterType == StereoBM::PREFILTER_NORMALIZED_RESPONSE )
prefilterNorm( *imgs0[i], *imgs[i], state->preFilterSize, state->preFilterCap, buf[i] );
else
prefilterXSobel( *imgs0[i], *imgs[i], state->preFilterCap );
}
}
const Mat* imgs0[2];
Mat* imgs[2];
uchar* buf[2];
StereoBMParams* state;
};
#ifdef HAVE_OPENCL
static bool ocl_stereobm( InputArray _left, InputArray _right,
OutputArray _disp, StereoBMParams* state)
{
int ndisp = state->numDisparities;
int mindisp = state->minDisparity;
int wsz = state->SADWindowSize;
int wsz2 = wsz/2;
ocl::Device devDef = ocl::Device::getDefault();
int sizeX = devDef.isIntel() ? 32 : std::max(11, 27 - devDef.maxComputeUnits()),
sizeY = sizeX - 1,
N = ndisp * 2;
cv::String opt = cv::format("-D DEFINE_KERNEL_STEREOBM -D MIN_DISP=%d -D NUM_DISP=%d"
" -D BLOCK_SIZE_X=%d -D BLOCK_SIZE_Y=%d -D WSZ=%d",
mindisp, ndisp,
sizeX, sizeY, wsz);
ocl::Kernel k("stereoBM", ocl::calib3d::stereobm_oclsrc, opt);
if(k.empty())
return false;
UMat left = _left.getUMat(), right = _right.getUMat();
int cols = left.cols, rows = left.rows;
_disp.create(_left.size(), CV_16S);
_disp.setTo((mindisp - 1) << 4);
Rect roi = Rect(Point(wsz2 + mindisp + ndisp - 1, wsz2), Point(cols-wsz2-mindisp, rows-wsz2) );
UMat disp = (_disp.getUMat())(roi);
int globalX = (disp.cols + sizeX - 1) / sizeX,
globalY = (disp.rows + sizeY - 1) / sizeY;
size_t globalThreads[3] = {(size_t)N, (size_t)globalX, (size_t)globalY};
size_t localThreads[3] = {(size_t)N, 1, 1};
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(left));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(right));
idx = k.set(idx, ocl::KernelArg::WriteOnlyNoSize(disp));
idx = k.set(idx, rows);
idx = k.set(idx, cols);
idx = k.set(idx, state->textureThreshold);
idx = k.set(idx, state->uniquenessRatio);
return k.run(3, globalThreads, localThreads, false);
}
#endif
struct FindStereoCorrespInvoker : public ParallelLoopBody
{
FindStereoCorrespInvoker( const Mat& _left, const Mat& _right,
Mat& _disp, StereoBMParams* _state,
int _nstripes, size_t _stripeBufSize,
bool _useShorts, Rect _validDisparityRect,
Mat& _slidingSumBuf, Mat& _cost )
{
CV_Assert( _disp.type() == CV_16S || _disp.type() == CV_32S );
left = &_left; right = &_right;
disp = &_disp; state = _state;
nstripes = _nstripes; stripeBufSize = _stripeBufSize;
useShorts = _useShorts;
validDisparityRect = _validDisparityRect;
slidingSumBuf = &_slidingSumBuf;
cost = &_cost;
#if CV_SIMD128
useSIMD = hasSIMD128();
#endif
}
void operator()( const Range& range ) const
{
int cols = left->cols, rows = left->rows;
int _row0 = std::min(cvRound(range.start * rows / nstripes), rows);
int _row1 = std::min(cvRound(range.end * rows / nstripes), rows);
uchar *ptr = slidingSumBuf->ptr() + range.start * stripeBufSize;
int FILTERED = (state->minDisparity - 1)*16;
Rect roi = validDisparityRect & Rect(0, _row0, cols, _row1 - _row0);
if( roi.height == 0 )
return;
int row0 = roi.y;
int row1 = roi.y + roi.height;
Mat part;
if( row0 > _row0 )
{
part = disp->rowRange(_row0, row0);
part = Scalar::all(FILTERED);
}
if( _row1 > row1 )
{
part = disp->rowRange(row1, _row1);
part = Scalar::all(FILTERED);
}
Mat left_i = left->rowRange(row0, row1);
Mat right_i = right->rowRange(row0, row1);
Mat disp_i = disp->rowRange(row0, row1);
Mat cost_i = state->disp12MaxDiff >= 0 ? cost->rowRange(row0, row1) : Mat();
#if CV_SIMD128
if( useSIMD && useShorts )
{
findStereoCorrespondenceBM_SIMD( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1 );
}
else
#endif
{
if( disp_i.type() == CV_16S )
findStereoCorrespondenceBM<short>( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1, DISPARITY_SHIFT_16S );
else
findStereoCorrespondenceBM<int>( left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1, DISPARITY_SHIFT_32S );
}
if( state->disp12MaxDiff >= 0 )
validateDisparity( disp_i, cost_i, state->minDisparity, state->numDisparities, state->disp12MaxDiff );
if( roi.x > 0 )
{
part = disp_i.colRange(0, roi.x);
part = Scalar::all(FILTERED);
}
if( roi.x + roi.width < cols )
{
part = disp_i.colRange(roi.x + roi.width, cols);
part = Scalar::all(FILTERED);
}
}
protected:
const Mat *left, *right;
Mat* disp, *slidingSumBuf, *cost;
StereoBMParams *state;
int nstripes;
size_t stripeBufSize;
bool useShorts;
Rect validDisparityRect;
bool useSIMD;
};
class StereoBMImpl : public StereoBM
{
public:
StereoBMImpl()
{
params = StereoBMParams();
}
StereoBMImpl( int _numDisparities, int _SADWindowSize )
{
params = StereoBMParams(_numDisparities, _SADWindowSize);
}
void compute( InputArray leftarr, InputArray rightarr, OutputArray disparr )
{
CV_INSTRUMENT_REGION()
int dtype = disparr.fixedType() ? disparr.type() : params.dispType;
Size leftsize = leftarr.size();
if (leftarr.size() != rightarr.size())
CV_Error( Error::StsUnmatchedSizes, "All the images must have the same size" );
if (leftarr.type() != CV_8UC1 || rightarr.type() != CV_8UC1)
CV_Error( Error::StsUnsupportedFormat, "Both input images must have CV_8UC1" );
if (dtype != CV_16SC1 && dtype != CV_32FC1)
CV_Error( Error::StsUnsupportedFormat, "Disparity image must have CV_16SC1 or CV_32FC1 format" );
if( params.preFilterType != PREFILTER_NORMALIZED_RESPONSE &&
params.preFilterType != PREFILTER_XSOBEL )
CV_Error( Error::StsOutOfRange, "preFilterType must be = CV_STEREO_BM_NORMALIZED_RESPONSE" );
if( params.preFilterSize < 5 || params.preFilterSize > 255 || params.preFilterSize % 2 == 0 )
CV_Error( Error::StsOutOfRange, "preFilterSize must be odd and be within 5..255" );
if( params.preFilterCap < 1 || params.preFilterCap > 63 )
CV_Error( Error::StsOutOfRange, "preFilterCap must be within 1..63" );
if( params.SADWindowSize < 5 || params.SADWindowSize > 255 || params.SADWindowSize % 2 == 0 ||
params.SADWindowSize >= std::min(leftsize.width, leftsize.height) )
CV_Error( Error::StsOutOfRange, "SADWindowSize must be odd, be within 5..255 and be not larger than image width or height" );
if( params.numDisparities <= 0 || params.numDisparities % 16 != 0 )
CV_Error( Error::StsOutOfRange, "numDisparities must be positive and divisble by 16" );
if( params.textureThreshold < 0 )
CV_Error( Error::StsOutOfRange, "texture threshold must be non-negative" );
if( params.uniquenessRatio < 0 )
CV_Error( Error::StsOutOfRange, "uniqueness ratio must be non-negative" );
int disp_shift;
if (dtype == CV_16SC1)
disp_shift = DISPARITY_SHIFT_16S;
else
disp_shift = DISPARITY_SHIFT_32S;
int FILTERED = (params.minDisparity - 1) << disp_shift;
#ifdef HAVE_OPENCL
if(ocl::useOpenCL() && disparr.isUMat() && params.textureThreshold == 0)
{
UMat left, right;
if(ocl_prefiltering(leftarr, rightarr, left, right, &params))
{
if(ocl_stereobm(left, right, disparr, &params))
{
if( params.speckleRange >= 0 && params.speckleWindowSize > 0 )
filterSpeckles(disparr.getMat(), FILTERED, params.speckleWindowSize, params.speckleRange, slidingSumBuf);
if (dtype == CV_32F)
disparr.getUMat().convertTo(disparr, CV_32FC1, 1./(1 << disp_shift), 0);
CV_IMPL_ADD(CV_IMPL_OCL);
return;
}
}
}
#endif
Mat left0 = leftarr.getMat(), right0 = rightarr.getMat();
disparr.create(left0.size(), dtype);
Mat disp0 = disparr.getMat();
preFilteredImg0.create( left0.size(), CV_8U );
preFilteredImg1.create( left0.size(), CV_8U );
cost.create( left0.size(), CV_16S );
Mat left = preFilteredImg0, right = preFilteredImg1;
int mindisp = params.minDisparity;
int ndisp = params.numDisparities;
int width = left0.cols;
int height = left0.rows;
int lofs = std::max(ndisp - 1 + mindisp, 0);
int rofs = -std::min(ndisp - 1 + mindisp, 0);
int width1 = width - rofs - ndisp + 1;
if( lofs >= width || rofs >= width || width1 < 1 )
{
disp0 = Scalar::all( FILTERED * ( disp0.type() < CV_32F ? 1 : 1./(1 << disp_shift) ) );
return;
}
Mat disp = disp0;
if( dtype == CV_32F )
{
dispbuf.create(disp0.size(), CV_32S);
disp = dispbuf;
}
int wsz = params.SADWindowSize;
int bufSize0 = (int)((ndisp + 2)*sizeof(int));
bufSize0 += (int)((height+wsz+2)*ndisp*sizeof(int));
bufSize0 += (int)((height + wsz + 2)*sizeof(int));
bufSize0 += (int)((height+wsz+2)*ndisp*(wsz+2)*sizeof(uchar) + 256);
int bufSize1 = (int)((width + params.preFilterSize + 2) * sizeof(int) + 256);
int bufSize2 = 0;
if( params.speckleRange >= 0 && params.speckleWindowSize > 0 )
bufSize2 = width*height*(sizeof(Point_<short>) + sizeof(int) + sizeof(uchar));
bool useShorts = params.preFilterCap <= 31 && params.SADWindowSize <= 21;
const double SAD_overhead_coeff = 10.0;
double N0 = 8000000 / (useShorts ? 1 : 4); // approx tbb's min number instructions reasonable for one thread
double maxStripeSize = std::min(std::max(N0 / (width * ndisp), (wsz-1) * SAD_overhead_coeff), (double)height);
int nstripes = cvCeil(height / maxStripeSize);
int bufSize = std::max(bufSize0 * nstripes, std::max(bufSize1 * 2, bufSize2));
if( slidingSumBuf.cols < bufSize )
slidingSumBuf.create( 1, bufSize, CV_8U );
uchar *_buf = slidingSumBuf.ptr();
parallel_for_(Range(0, 2), PrefilterInvoker(left0, right0, left, right, _buf, _buf + bufSize1, &params), 1);
Rect validDisparityRect(0, 0, width, height), R1 = params.roi1, R2 = params.roi2;
validDisparityRect = getValidDisparityROI(R1.area() > 0 ? R1 : validDisparityRect,
R2.area() > 0 ? R2 : validDisparityRect,
params.minDisparity, params.numDisparities,
params.SADWindowSize);
parallel_for_(Range(0, nstripes),
FindStereoCorrespInvoker(left, right, disp, &params, nstripes,
bufSize0, useShorts, validDisparityRect,
slidingSumBuf, cost));
if( params.speckleRange >= 0 && params.speckleWindowSize > 0 )
filterSpeckles(disp, FILTERED, params.speckleWindowSize, params.speckleRange, slidingSumBuf);
if (disp0.data != disp.data)
disp.convertTo(disp0, disp0.type(), 1./(1 << disp_shift), 0);
}
int getMinDisparity() const { return params.minDisparity; }
void setMinDisparity(int minDisparity) { params.minDisparity = minDisparity; }
int getNumDisparities() const { return params.numDisparities; }
void setNumDisparities(int numDisparities) { params.numDisparities = numDisparities; }
int getBlockSize() const { return params.SADWindowSize; }
void setBlockSize(int blockSize) { params.SADWindowSize = blockSize; }
int getSpeckleWindowSize() const { return params.speckleWindowSize; }
void setSpeckleWindowSize(int speckleWindowSize) { params.speckleWindowSize = speckleWindowSize; }
int getSpeckleRange() const { return params.speckleRange; }
void setSpeckleRange(int speckleRange) { params.speckleRange = speckleRange; }
int getDisp12MaxDiff() const { return params.disp12MaxDiff; }
void setDisp12MaxDiff(int disp12MaxDiff) { params.disp12MaxDiff = disp12MaxDiff; }
int getPreFilterType() const { return params.preFilterType; }
void setPreFilterType(int preFilterType) { params.preFilterType = preFilterType; }
int getPreFilterSize() const { return params.preFilterSize; }
void setPreFilterSize(int preFilterSize) { params.preFilterSize = preFilterSize; }
int getPreFilterCap() const { return params.preFilterCap; }
void setPreFilterCap(int preFilterCap) { params.preFilterCap = preFilterCap; }
int getTextureThreshold() const { return params.textureThreshold; }
void setTextureThreshold(int textureThreshold) { params.textureThreshold = textureThreshold; }
int getUniquenessRatio() const { return params.uniquenessRatio; }
void setUniquenessRatio(int uniquenessRatio) { params.uniquenessRatio = uniquenessRatio; }
int getSmallerBlockSize() const { return 0; }
void setSmallerBlockSize(int) {}
Rect getROI1() const { return params.roi1; }
void setROI1(Rect roi1) { params.roi1 = roi1; }
Rect getROI2() const { return params.roi2; }
void setROI2(Rect roi2) { params.roi2 = roi2; }
void write(FileStorage& fs) const
{
writeFormat(fs);
fs << "name" << name_
<< "minDisparity" << params.minDisparity
<< "numDisparities" << params.numDisparities
<< "blockSize" << params.SADWindowSize
<< "speckleWindowSize" << params.speckleWindowSize
<< "speckleRange" << params.speckleRange
<< "disp12MaxDiff" << params.disp12MaxDiff
<< "preFilterType" << params.preFilterType
<< "preFilterSize" << params.preFilterSize
<< "preFilterCap" << params.preFilterCap
<< "textureThreshold" << params.textureThreshold
<< "uniquenessRatio" << params.uniquenessRatio;
}
void read(const FileNode& fn)
{
FileNode n = fn["name"];
CV_Assert( n.isString() && String(n) == name_ );
params.minDisparity = (int)fn["minDisparity"];
params.numDisparities = (int)fn["numDisparities"];
params.SADWindowSize = (int)fn["blockSize"];
params.speckleWindowSize = (int)fn["speckleWindowSize"];
params.speckleRange = (int)fn["speckleRange"];
params.disp12MaxDiff = (int)fn["disp12MaxDiff"];
params.preFilterType = (int)fn["preFilterType"];
params.preFilterSize = (int)fn["preFilterSize"];
params.preFilterCap = (int)fn["preFilterCap"];
params.textureThreshold = (int)fn["textureThreshold"];
params.uniquenessRatio = (int)fn["uniquenessRatio"];
params.roi1 = params.roi2 = Rect();
}
StereoBMParams params;
Mat preFilteredImg0, preFilteredImg1, cost, dispbuf;
Mat slidingSumBuf;
static const char* name_;
};
const char* StereoBMImpl::name_ = "StereoMatcher.BM";
Ptr<StereoBM> StereoBM::create(int _numDisparities, int _SADWindowSize)
{
return makePtr<StereoBMImpl>(_numDisparities, _SADWindowSize);
}
}
/* End of file. */