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opencv_contrib/modules/stereo/src/stereo_binary_bm.cpp

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//M*//////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// 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.
//
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// 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
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// and on any theory of liability, whether in contract, strict liability,
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//M*/
/****************************************************************************************\
* Very fast SAD-based (Sum-of-Absolute-Diffrences) stereo correspondence algorithm. *
* Contributed by Kurt Konolige *
\****************************************************************************************/
#include "precomp.hpp"
#include <stdio.h>
#include <limits>
namespace cv
{
struct StereoBinaryBMParams
{
StereoBinaryBMParams(int _numDisparities = 64, int _SADWindowSize = 9)
{
preFilterType = StereoBinaryBM::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;
};
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];
}
}
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_SSE2
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2);
#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_SSE2
if (useSIMD)
{
__m128i z = _mm_setzero_si128(), ftz = _mm_set1_epi16((short)ftzero),
ftz2 = _mm_set1_epi8(cv::saturate_cast<uchar>(ftzero * 2));
for (; x <= size.width - 9; x += 8)
{
__m128i c0 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow0 + x - 1)), z);
__m128i c1 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow1 + x - 1)), z);
__m128i d0 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow0 + x + 1)), z);
__m128i d1 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow1 + x + 1)), z);
d0 = _mm_sub_epi16(d0, c0);
d1 = _mm_sub_epi16(d1, c1);
__m128i c2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x - 1)), z);
__m128i c3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow3 + x - 1)), z);
__m128i d2 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow2 + x + 1)), z);
__m128i d3 = _mm_unpacklo_epi8(_mm_loadl_epi64((__m128i*)(srow3 + x + 1)), z);
d2 = _mm_sub_epi16(d2, c2);
d3 = _mm_sub_epi16(d3, c3);
__m128i v0 = _mm_add_epi16(d0, _mm_add_epi16(d2, _mm_add_epi16(d1, d1)));
__m128i v1 = _mm_add_epi16(d1, _mm_add_epi16(d3, _mm_add_epi16(d2, d2)));
v0 = _mm_packus_epi16(_mm_add_epi16(v0, ftz), _mm_add_epi16(v1, ftz));
v0 = _mm_min_epu8(v0, ftz2);
_mm_storel_epi64((__m128i*)(dptr0 + x), v0);
_mm_storel_epi64((__m128i*)(dptr1 + x), _mm_unpackhi_epi64(v0, v0));
}
}
#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);
for (x = 0; x < size.width; x++)
dptr[x] = val0;
}
}
static const int DISPARITY_SHIFT = 4;
static void
findStereoCorrespondenceBM(const Mat& left, const Mat& right,
Mat& disp, Mat& cost, const StereoBinaryBMParams& 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);
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;
short* dptr = disp.ptr<short>();
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 - 1) - dy0*sstep;
for (y = -dy0; y < height + dy1; y++, hsad += ndisp, cbuf += ndisp, lptr += sstep, rptr += sstep)
{
int lval = lptr[0];
for (d = 0; 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 - 1 - 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];
for (d = 0; 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
int tsum = 0;
{
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)
for (d = 0; d < ndisp; d++)
sad[d] = (int)(sad[d] + hsad[d]);
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;
for (d = 0; 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] = (short)(((ndisp - mind - 1 + mindisp) * 256 + (d != 0 ? (p - n) * 256 / d : 0) + 15) >> 4);
costptr[y*coststep] = sad[mind];
}
}
}
}
}
struct PrefilterInvoker : public ParallelLoopBody
{
PrefilterInvoker(const Mat& left0, const Mat& right0, Mat& left, Mat& right,
uchar* buf0, uchar* buf1, StereoBinaryBMParams* _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 == StereoBinaryBM::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];
StereoBinaryBMParams* state;
};
struct FindStereoCorrespInvoker : public ParallelLoopBody
{
FindStereoCorrespInvoker(const Mat& _left, const Mat& _right,
Mat& _disp, StereoBinaryBMParams* _state,
int _nstripes, size_t _stripeBufSize,
bool _useShorts, Rect _validDisparityRect,
Mat& _slidingSumBuf, Mat& _cost)
{
left = &_left; right = &_right;
disp = &_disp; state = _state;
nstripes = _nstripes; stripeBufSize = _stripeBufSize;
useShorts = _useShorts;
validDisparityRect = _validDisparityRect;
slidingSumBuf = &_slidingSumBuf;
cost = &_cost;
}
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();
findStereoCorrespondenceBM(left_i, right_i, disp_i, cost_i, *state, ptr, row0, rows - row1);
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;
StereoBinaryBMParams *state;
int nstripes;
size_t stripeBufSize;
bool useShorts;
Rect validDisparityRect;
};
class StereoBinaryBMImpl : public StereoBinaryBM
{
public:
StereoBinaryBMImpl()
{
params = StereoBinaryBMParams();
}
StereoBinaryBMImpl(int _numDisparities, int _SADWindowSize)
{
params = StereoBinaryBMParams(_numDisparities, _SADWindowSize);
}
void compute(InputArray leftarr, InputArray rightarr, OutputArray disparr)
{
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 FILTERED = (params.minDisparity - 1) << DISPARITY_SHIFT;
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 << DISPARITY_SHIFT)));
return;
}
Mat disp = disp0;
if (dtype == CV_32F)
{
dispbuf.create(disp0.size(), CV_16S);
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));
#if CV_SSE2
bool useShorts = params.preFilterCap <= 31 && params.SADWindowSize <= 21 && checkHardwareSupport(CV_CPU_SSE2);
#else
const bool useShorts = false;
#endif
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 ? Rect(0, 0, width, height) : validDisparityRect,
R2.area() > 0 ? Rect(0, 0, width, height) : 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 << DISPARITY_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
{
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();
}
StereoBinaryBMParams params;
Mat preFilteredImg0, preFilteredImg1, cost, dispbuf;
Mat slidingSumBuf;
static const char* name_;
};
const char* StereoBinaryBMImpl::name_ = "StereoMatcher.BM";
Ptr<StereoBinaryBM> StereoBinaryBM::create(int _numDisparities, int _SADWindowSize)
{
return makePtr<StereoBinaryBMImpl>(_numDisparities, _SADWindowSize);
}
}
/* End of file. */