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提交 21409de1 编写于 作者: V Vadim Pisarevsky
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optimized sparse LK optical flow (tickets #1062 and #1210)

上级 442f6b41
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modules/imgproc/src/utils.cpp
浏览文件 @ 21409de1
......@@ -212,11 +212,17 @@ void cv::copyMakeBorder( InputArray _src, OutputArray _dst, int top, int bottom,
top, left, (int)src.elemSize(), borderType );
else
{
double buf[4];
scalarToRawData(value, buf, src.type());
int cn = src.channels(), cn1 = cn;
AutoBuffer<double> buf(cn);
if( cn > 4 )
{
CV_Assert( value[0] == value[1] && value[0] == value[2] && value[0] == value[3] );
cn1 = 1;
}
scalarToRawData(value, buf, CV_MAKETYPE(src.depth(), cn1), cn);
copyMakeConstBorder_8u( src.data, src.step, src.size(),
dst.data, dst.step, dst.size(),
top, left, (int)src.elemSize(), (uchar*)buf );
top, left, (int)src.elemSize(), (uchar*)(double*)buf );
}
}
......
modules/video/src/lkpyramid.cpp
浏览文件 @ 21409de1
......@@ -42,168 +42,129 @@
#include <float.h>
#include <stdio.h>
void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err,
Size winSize, int maxLevel,
TermCriteria criteria,
double derivLambda,
int flags )
namespace cv
{
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err, winSize, maxLevel, criteria, derivLambda, flags))
return;
#endif
Mat prevImg = _prevImg.getMat(), nextImg = _nextImg.getMat(), prevPtsMat = _prevPts.getMat();
derivLambda = std::min(std::max(derivLambda, 0.), 1.);
double lambda1 = 1. - derivLambda, lambda2 = derivLambda;
const int derivKernelSize = 3;
const float deriv1Scale = 0.5f/4.f;
const float deriv2Scale = 0.25f/4.f;
const int derivDepth = CV_32F;
Point2f halfWin((winSize.width-1)*0.5f, (winSize.height-1)*0.5f);
CV_Assert( maxLevel >= 0 && winSize.width > 2 && winSize.height > 2 );
CV_Assert( prevImg.size() == nextImg.size() &&
prevImg.type() == nextImg.type() );
typedef short deriv_type;
int npoints;
CV_Assert( (npoints = prevPtsMat.checkVector(2, CV_32F, true)) >= 0 );
static void calcSharrDeriv(const Mat& src, Mat& dst)
{
int rows = src.rows, cols = src.cols, cn = src.channels(), colsn = cols*cn, depth = src.depth();
CV_Assert(depth == CV_8U);
dst.create(rows, cols, CV_MAKETYPE(DataType<deriv_type>::depth, cn*2));
if( npoints == 0 )
{
_nextPts.release();
_status.release();
_err.release();
return;
}
int x, y, delta = (int)alignSize((cols + 2)*cn, 16);
AutoBuffer<deriv_type> _tempBuf(delta*2 + 64);
deriv_type *trow0 = alignPtr(_tempBuf + cn, 16), *trow1 = alignPtr(trow0 + delta, 16);
if( !(flags & OPTFLOW_USE_INITIAL_FLOW) )
_nextPts.create(prevPtsMat.size(), prevPtsMat.type(), -1, true);
#if CV_SSE2
__m128i z = _mm_setzero_si128(), c3 = _mm_set1_epi16(3), c10 = _mm_set1_epi16(10);
#endif
Mat nextPtsMat = _nextPts.getMat();
CV_Assert( nextPtsMat.checkVector(2, CV_32F, true) == npoints );
for( y = 0; y < rows; y++ )
{
const uchar* srow0 = src.ptr<uchar>(y > 0 ? y-1 : rows > 1 ? 1 : 0);
const uchar* srow1 = src.ptr<uchar>(y);
const uchar* srow2 = src.ptr<uchar>(y < rows-1 ? y+1 : rows > 1 ? rows-2 : 0);
deriv_type* drow = dst.ptr<deriv_type>(y);
const Point2f* prevPts = (const Point2f*)prevPtsMat.data;
Point2f* nextPts = (Point2f*)nextPtsMat.data;
// do vertical convolution
x = 0;
#if CV_SSE2
for( ; x <= colsn - 8; x += 8 )
{
__m128i s0 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(srow0 + x)), z);
__m128i s1 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(srow1 + x)), z);
__m128i s2 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(srow2 + x)), z);
__m128i t0 = _mm_add_epi16(_mm_mullo_epi16(_mm_add_epi16(s0, s2), c3), _mm_mullo_epi16(s1, c10));
__m128i t1 = _mm_sub_epi16(s2, s0);
_mm_store_si128((__m128i*)(trow0 + x), t0);
_mm_store_si128((__m128i*)(trow1 + x), t1);
}
#endif
for( ; x < colsn; x++ )
{
int t0 = (srow0[x] + srow2[x])*3 + srow1[x]*10;
int t1 = srow2[x] - srow0[x];
trow0[x] = (deriv_type)t0;
trow1[x] = (deriv_type)t1;
}
_status.create((int)npoints, 1, CV_8U, -1, true);
Mat statusMat = _status.getMat(), errMat;
CV_Assert( statusMat.isContinuous() );
uchar* status = statusMat.data;
float* err = 0;
// make border
int x0 = (cols > 1 ? 1 : 0)*cn, x1 = (cols > 1 ? cols-2 : 0)*cn;
for( int k = 0; k < cn; k++ )
{
trow0[-cn + k] = trow0[x0 + k]; trow0[colsn + k] = trow0[x1 + k];
trow1[-cn + k] = trow1[x0 + k]; trow1[colsn + k] = trow1[x1 + k];
}
for( int i = 0; i < npoints; i++ )
status[i] = true;
// do horizontal convolution, interleave the results and store them to dst
x = 0;
#if CV_SSE2
for( ; x <= colsn - 8; x += 8 )
{
__m128i s0 = _mm_loadu_si128((const __m128i*)(trow0 + x - cn));
__m128i s1 = _mm_loadu_si128((const __m128i*)(trow0 + x + cn));
__m128i s2 = _mm_loadu_si128((const __m128i*)(trow1 + x - cn));
__m128i s3 = _mm_load_si128((const __m128i*)(trow1 + x));
__m128i s4 = _mm_loadu_si128((const __m128i*)(trow1 + x + cn));
if( _err.needed() )
__m128i t0 = _mm_sub_epi16(s1, s0);
__m128i t1 = _mm_add_epi16(_mm_mullo_epi16(_mm_add_epi16(s2, s4), c3), _mm_mullo_epi16(s3, c10));
__m128i t2 = _mm_unpacklo_epi16(t0, t1);
t0 = _mm_unpackhi_epi16(t0, t1);
// this can probably be replaced with aligned stores if we aligned dst properly.
_mm_storeu_si128((__m128i*)(drow + x*2), t2);
_mm_storeu_si128((__m128i*)(drow + x*2 + 8), t0);
}
#endif
for( ; x < colsn; x++ )
{
_err.create((int)npoints, 1, CV_32F, -1, true);
errMat = _err.getMat();
CV_Assert( errMat.isContinuous() );
err = (float*)errMat.data;
deriv_type t0 = (deriv_type)(trow0[x+cn] - trow0[x-cn]);
deriv_type t1 = (deriv_type)((trow1[x+cn] + trow1[x-cn])*3 + trow1[x]*10);
drow[x*2] = t0; drow[x*2+1] = t1;
}
}
}
vector<Mat> prevPyr, nextPyr;
int cn = prevImg.channels();
buildPyramid( prevImg, prevPyr, maxLevel );
buildPyramid( nextImg, nextPyr, maxLevel );
// I, dI/dx ~ Ix, dI/dy ~ Iy, d2I/dx2 ~ Ixx, d2I/dxdy ~ Ixy, d2I/dy2 ~ Iyy
Mat derivIBuf((prevImg.rows + winSize.height*2),
(prevImg.cols + winSize.width*2),
CV_MAKETYPE(derivDepth, cn*6));
// J, dJ/dx ~ Jx, dJ/dy ~ Jy
Mat derivJBuf((prevImg.rows + winSize.height*2),
(prevImg.cols + winSize.width*2),
CV_MAKETYPE(derivDepth, cn*3));
Mat tempDerivBuf(prevImg.size(), CV_MAKETYPE(derivIBuf.type(), cn));
Mat derivIWinBuf(winSize, derivIBuf.type());
if( (criteria.type & TermCriteria::COUNT) == 0 )
criteria.maxCount = 30;
else
criteria.maxCount = std::min(std::max(criteria.maxCount, 0), 100);
if( (criteria.type & TermCriteria::EPS) == 0 )
criteria.epsilon = 0.01;
else
criteria.epsilon = std::min(std::max(criteria.epsilon, 0.), 10.);
criteria.epsilon *= criteria.epsilon;
struct LKTrackerInvoker
{
LKTrackerInvoker( const Mat& _prevImg, const Mat& _prevDeriv, const Mat& _nextImg,
const Point2f* _prevPts, Point2f* _nextPts,
uchar* _status, float* _err,
Size _winSize, TermCriteria _criteria,
int _level, int _maxLevel, int _flags )
{
prevImg = &_prevImg;
prevDeriv = &_prevDeriv;
nextImg = &_nextImg;
prevPts = _prevPts;
nextPts = _nextPts;
status = _status;
err = _err;
winSize = _winSize;
criteria = _criteria;
level = _level;
maxLevel = _maxLevel;
flags = _flags;
}
for( int level = maxLevel; level >= 0; level-- )
void operator()(const BlockedRange& range) const
{
int k;
Size imgSize = prevPyr[level].size();
Mat tempDeriv( imgSize, tempDerivBuf.type(), tempDerivBuf.data );
Mat _derivI( imgSize.height + winSize.height*2,
imgSize.width + winSize.width*2,
derivIBuf.type(), derivIBuf.data );
Mat _derivJ( imgSize.height + winSize.height*2,
imgSize.width + winSize.width*2,
derivJBuf.type(), derivJBuf.data );
Mat derivI(_derivI, Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
Mat derivJ(_derivJ, Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
CvMat cvderivI = _derivI;
cvZero(&cvderivI);
CvMat cvderivJ = _derivJ;
cvZero(&cvderivJ);
vector<int> fromTo(cn*2);
for( k = 0; k < cn; k++ )
fromTo[k*2] = k;
prevPyr[level].convertTo(tempDeriv, derivDepth);
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*6;
mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
// compute spatial derivatives and merge them together
Sobel(prevPyr[level], tempDeriv, derivDepth, 1, 0, derivKernelSize, deriv1Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*6 + 1;
mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
Sobel(prevPyr[level], tempDeriv, derivDepth, 0, 1, derivKernelSize, deriv1Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*6 + 2;
mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
Sobel(prevPyr[level], tempDeriv, derivDepth, 2, 0, derivKernelSize, deriv2Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*6 + 3;
mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
Sobel(prevPyr[level], tempDeriv, derivDepth, 1, 1, derivKernelSize, deriv2Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*6 + 4;
mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
Sobel(prevPyr[level], tempDeriv, derivDepth, 0, 2, derivKernelSize, deriv2Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*6 + 5;
mixChannels(&tempDeriv, 1, &derivI, 1, &fromTo[0], cn);
nextPyr[level].convertTo(tempDeriv, derivDepth);
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*3;
mixChannels(&tempDeriv, 1, &derivJ, 1, &fromTo[0], cn);
Sobel(nextPyr[level], tempDeriv, derivDepth, 1, 0, derivKernelSize, deriv1Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*3 + 1;
mixChannels(&tempDeriv, 1, &derivJ, 1, &fromTo[0], cn);
Sobel(nextPyr[level], tempDeriv, derivDepth, 0, 1, derivKernelSize, deriv1Scale );
for( k = 0; k < cn; k++ )
fromTo[k*2+1] = k*3 + 2;
mixChannels(&tempDeriv, 1, &derivJ, 1, &fromTo[0], cn);
/*copyMakeBorder( derivI, _derivI, winSize.height, winSize.height,
winSize.width, winSize.width, BORDER_CONSTANT );
copyMakeBorder( derivJ, _derivJ, winSize.height, winSize.height,
winSize.width, winSize.width, BORDER_CONSTANT );*/
for( int ptidx = 0; ptidx < npoints; ptidx++ )
Point2f halfWin((winSize.width-1)*0.5f, (winSize.height-1)*0.5f);
const Mat& I = *prevImg;
const Mat& J = *nextImg;
const Mat& derivI = *prevDeriv;
int j, cn = I.channels(), cn2 = cn*2;
cv::AutoBuffer<deriv_type> _buf(winSize.area()*(cn + cn2));
int derivDepth = DataType<deriv_type>::depth;
Mat IWinBuf(winSize, CV_MAKETYPE(derivDepth, cn), (deriv_type*)_buf);
Mat derivIWinBuf(winSize, CV_MAKETYPE(derivDepth, cn2), (deriv_type*)_buf + winSize.area()*cn);
for( int ptidx = range.begin(); ptidx < range.end(); ptidx++ )
{
Point2f prevPt = prevPts[ptidx]*(float)(1./(1 << level));
Point2f nextPt;
......@@ -228,119 +189,230 @@ void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
{
if( level == 0 )
{
if( status )
status[ptidx] = false;
err[ptidx] = FLT_MAX;
if( err )
err[ptidx] = 0;
}
continue;
}
float a = prevPt.x - iprevPt.x;
float b = prevPt.y - iprevPt.y;
float w00 = (1.f - a)*(1.f - b), w01 = a*(1.f - b);
float w10 = (1.f - a)*b, w11 = a*b;
size_t stepI = derivI.step/derivI.elemSize1();
size_t stepJ = derivJ.step/derivJ.elemSize1();
int cnI = cn*6, cnJ = cn*3;
double A11 = 0, A12 = 0, A22 = 0;
double iA11 = 0, iA12 = 0, iA22 = 0;
// extract the patch from the first image
const int W_BITS = 14, W_BITS1 = 14;
const float FLT_SCALE = 1.f/(1 << 20);
int iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS));
int iw01 = cvRound(a*(1.f - b)*(1 << W_BITS));
int iw10 = cvRound((1.f - a)*b*(1 << W_BITS));
int iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
int dstep = (int)(derivI.step/derivI.elemSize1());
int step = (int)(I.step/I.elemSize1());
CV_Assert( step == (int)(J.step/J.elemSize1()) );
float A11 = 0, A12 = 0, A22 = 0;
#if CV_SSE2
__m128i qw0 = _mm_set1_epi32(iw00 + (iw01 << 16));
__m128i qw1 = _mm_set1_epi32(iw10 + (iw11 << 16));
__m128i z = _mm_setzero_si128();
__m128i qdelta_d = _mm_set1_epi32(1 << (W_BITS1-1));
__m128i qdelta = _mm_set1_epi32(1 << (W_BITS1-5-1));
__m128 qA11 = _mm_setzero_ps(), qA12 = _mm_setzero_ps(), qA22 = _mm_setzero_ps();
#endif
// extract the patch from the first image, compute covariation matrix of derivatives
int x, y;
for( y = 0; y < winSize.height; y++ )
{
const float* src = (const float*)(derivI.data +
(y + iprevPt.y)*derivI.step) + iprevPt.x*cnI;
float* dst = (float*)(derivIWinBuf.data + y*derivIWinBuf.step);
const uchar* src = (const uchar*)I.data + (y + iprevPt.y)*step + iprevPt.x*cn;
const deriv_type* dsrc = (const deriv_type*)derivI.data + (y + iprevPt.y)*dstep + iprevPt.x*cn2;
deriv_type* Iptr = (deriv_type*)(IWinBuf.data + y*IWinBuf.step);
deriv_type* dIptr = (deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);
for( x = 0; x < winSize.width*cnI; x += cnI, src += cnI )
x = 0;
#if CV_SSE2
for( ; x <= winSize.width*cn - 4; x += 4, dsrc += 4*2, dIptr += 4*2 )
{
float I = src[0]*w00 + src[cnI]*w01 + src[stepI]*w10 + src[stepI+cnI]*w11;
dst[x] = I;
__m128i v00, v01, v10, v11, t0, t1;
v00 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x)), z);
v01 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + cn)), z);
v10 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + step)), z);
v11 = _mm_unpacklo_epi8(_mm_cvtsi32_si128(*(const int*)(src + x + step + cn)), z);
t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0),
_mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1));
t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta), W_BITS1-5);
_mm_storel_epi64((__m128i*)(Iptr + x), _mm_packs_epi32(t0,t0));
v00 = _mm_loadu_si128((const __m128i*)(dsrc));
v01 = _mm_loadu_si128((const __m128i*)(dsrc + cn2));
v10 = _mm_loadu_si128((const __m128i*)(dsrc + dstep));
v11 = _mm_loadu_si128((const __m128i*)(dsrc + dstep + cn2));
t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0),
_mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1));
t1 = _mm_add_epi32(_mm_madd_epi16(_mm_unpackhi_epi16(v00, v01), qw0),
_mm_madd_epi16(_mm_unpackhi_epi16(v10, v11), qw1));
t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta_d), W_BITS1);
t1 = _mm_srai_epi32(_mm_add_epi32(t1, qdelta_d), W_BITS1);
v00 = _mm_packs_epi32(t0, t1); // Ix0 Iy0 Ix1 Iy1 ...
_mm_storeu_si128((__m128i*)dIptr, v00);
t0 = _mm_srai_epi32(v00, 16); // Iy0 Iy1 Iy2 Iy3
t1 = _mm_srai_epi32(_mm_slli_epi32(v00, 16), 16); // Ix0 Ix1 Ix2 Ix3
float Ix = src[1]*w00 + src[cnI+1]*w01 + src[stepI+1]*w10 + src[stepI+cnI+1]*w11;
float Iy = src[2]*w00 + src[cnI+2]*w01 + src[stepI+2]*w10 + src[stepI+cnI+2]*w11;
dst[x+1] = Ix; dst[x+2] = Iy;
__m128 fy = _mm_cvtepi32_ps(t0);
__m128 fx = _mm_cvtepi32_ps(t1);
float Ixx = src[3]*w00 + src[cnI+3]*w01 + src[stepI+3]*w10 + src[stepI+cnI+3]*w11;
float Ixy = src[4]*w00 + src[cnI+4]*w01 + src[stepI+4]*w10 + src[stepI+cnI+4]*w11;
float Iyy = src[5]*w00 + src[cnI+5]*w01 + src[stepI+5]*w10 + src[stepI+cnI+5]*w11;
dst[x+3] = Ixx; dst[x+4] = Ixy; dst[x+5] = Iyy;
qA22 = _mm_add_ps(qA22, _mm_mul_ps(fy, fy));
qA12 = _mm_add_ps(qA12, _mm_mul_ps(fx, fy));
qA11 = _mm_add_ps(qA11, _mm_mul_ps(fx, fx));
}
#endif
for( ; x < winSize.width*cn; x++, dsrc += 2, dIptr += 2 )
{
int ival = CV_DESCALE(src[x]*iw00 + src[x+cn]*iw01 +
src[x+step]*iw10 + src[x+step+cn]*iw11, W_BITS1-5);
int ixval = CV_DESCALE(dsrc[0]*iw00 + dsrc[cn2]*iw01 +
dsrc[dstep]*iw10 + dsrc[dstep+cn2]*iw11, W_BITS1);
int iyval = CV_DESCALE(dsrc[1]*iw00 + dsrc[cn2+1]*iw01 + dsrc[dstep+1]*iw10 +
dsrc[dstep+cn2+1]*iw11, W_BITS1);
iA11 += (double)Ix*Ix;
iA12 += (double)Ix*Iy;
iA22 += (double)Iy*Iy;
Iptr[x] = (short)ival;
dIptr[0] = (short)ixval;
dIptr[1] = (short)iyval;
A11 += (double)Ixx*Ixx + (double)Ixy*Ixy;
A12 += Ixy*((double)Ixx + Iyy);
A22 += (double)Ixy*Ixy + (double)Iyy*Iyy;
A11 += (float)(ixval*ixval);
A12 += (float)(ixval*iyval);
A22 += (float)(iyval*iyval);
}
}
A11 = lambda1*iA11 + lambda2*A11;
A12 = lambda1*iA12 + lambda2*A12;
A22 = lambda1*iA22 + lambda2*A22;
#if CV_SSE2
float CV_DECL_ALIGNED(16) A11buf[4], A12buf[4], A22buf[4];
_mm_store_ps(A11buf, qA11);
_mm_store_ps(A12buf, qA12);
_mm_store_ps(A22buf, qA22);
A11 += A11buf[0] + A11buf[1] + A11buf[2] + A11buf[3];
A12 += A12buf[0] + A12buf[1] + A12buf[2] + A12buf[3];
A22 += A22buf[0] + A22buf[1] + A22buf[2] + A22buf[3];
#endif
A11 *= FLT_SCALE;
A12 *= FLT_SCALE;
A22 *= FLT_SCALE;
double D = A11*A22 - A12*A12;
double minEig = (A22 + A11 - std::sqrt((A11-A22)*(A11-A22) +
4.*A12*A12))/(2*winSize.width*winSize.height);
float D = A11*A22 - A12*A12;
float minEig = (A22 + A11 - std::sqrt((A11-A22)*(A11-A22) +
4.f*A12*A12))/(2*winSize.width*winSize.height);
if( err )
err[ptidx] = (float)minEig;
if( D < DBL_EPSILON )
if( D < FLT_EPSILON )
{
if( level == 0 )
if( level == 0 && status )
status[ptidx] = false;
continue;
}
D = 1./D;
D = 1.f/D;
nextPt -= halfWin;
Point2f prevDelta;
for( int j = 0; j < criteria.maxCount; j++ )
for( j = 0; j < criteria.maxCount; j++ )
{
inextPt.x = cvFloor(nextPt.x);
inextPt.y = cvFloor(nextPt.y);
if( inextPt.x < -winSize.width || inextPt.x >= derivJ.cols ||
inextPt.y < -winSize.height || inextPt.y >= derivJ.rows )
if( inextPt.x < -winSize.width || inextPt.x >= J.cols ||
inextPt.y < -winSize.height || inextPt.y >= J.rows )
{
if( level == 0 )
if( level == 0 && status )
status[ptidx] = false;
break;
}
a = nextPt.x - inextPt.x;
b = nextPt.y - inextPt.y;
w00 = (1.f - a)*(1.f - b); w01 = a*(1.f - b);
w10 = (1.f - a)*b; w11 = a*b;
double b1 = 0, b2 = 0, ib1 = 0, ib2 = 0;
iw00 = cvRound((1.f - a)*(1.f - b)*(1 << W_BITS));
iw01 = cvRound(a*(1.f - b)*(1 << W_BITS));
iw10 = cvRound((1.f - a)*b*(1 << W_BITS));
iw11 = (1 << W_BITS) - iw00 - iw01 - iw10;
float b1 = 0, b2 = 0;
#if CV_SSE2
qw0 = _mm_set1_epi32(iw00 + (iw01 << 16));
qw1 = _mm_set1_epi32(iw10 + (iw11 << 16));
__m128 qb0 = _mm_setzero_ps(), qb1 = _mm_setzero_ps();
#endif
for( y = 0; y < winSize.height; y++ )
{
const float* src = (const float*)(derivJ.data +
(y + inextPt.y)*derivJ.step) + inextPt.x*cnJ;
const float* Ibuf = (float*)(derivIWinBuf.data + y*derivIWinBuf.step);
const uchar* Jptr = (const uchar*)J.data + (y + inextPt.y)*step + inextPt.x*cn;
const deriv_type* Iptr = (const deriv_type*)(IWinBuf.data + y*IWinBuf.step);
const deriv_type* dIptr = (const deriv_type*)(derivIWinBuf.data + y*derivIWinBuf.step);
x = 0;
#if CV_SSE2
for( ; x <= winSize.width*cn - 8; x += 8, dIptr += 8*2 )
{
__m128i diff0 = _mm_loadu_si128((const __m128i*)(Iptr + x)), diff1;
__m128i v00 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x)), z);
__m128i v01 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + cn)), z);
__m128i v10 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + step)), z);
__m128i v11 = _mm_unpacklo_epi8(_mm_loadl_epi64((const __m128i*)(Jptr + x + step + cn)), z);
__m128i t0 = _mm_add_epi32(_mm_madd_epi16(_mm_unpacklo_epi16(v00, v01), qw0),
_mm_madd_epi16(_mm_unpacklo_epi16(v10, v11), qw1));
__m128i t1 = _mm_add_epi32(_mm_madd_epi16(_mm_unpackhi_epi16(v00, v01), qw0),
_mm_madd_epi16(_mm_unpackhi_epi16(v10, v11), qw1));
t0 = _mm_srai_epi32(_mm_add_epi32(t0, qdelta), W_BITS1-5);
t1 = _mm_srai_epi32(_mm_add_epi32(t1, qdelta), W_BITS1-5);
diff0 = _mm_subs_epi16(_mm_packs_epi32(t0, t1), diff0);
diff1 = _mm_unpackhi_epi16(diff0, diff0);
diff0 = _mm_unpacklo_epi16(diff0, diff0); // It0 It0 It1 It1 ...
v00 = _mm_loadu_si128((const __m128i*)(dIptr)); // Ix0 Iy0 Ix1 Iy1 ...
v01 = _mm_loadu_si128((const __m128i*)(dIptr + 8));
v10 = _mm_mullo_epi16(v00, diff0);
v11 = _mm_mulhi_epi16(v00, diff0);
v00 = _mm_unpacklo_epi16(v10, v11);
v10 = _mm_unpackhi_epi16(v10, v11);
qb0 = _mm_add_ps(qb0, _mm_cvtepi32_ps(v00));
qb1 = _mm_add_ps(qb1, _mm_cvtepi32_ps(v10));
v10 = _mm_mullo_epi16(v01, diff1);
v11 = _mm_mulhi_epi16(v01, diff1);
v00 = _mm_unpacklo_epi16(v10, v11);
v10 = _mm_unpackhi_epi16(v10, v11);
qb0 = _mm_add_ps(qb0, _mm_cvtepi32_ps(v00));
qb1 = _mm_add_ps(qb1, _mm_cvtepi32_ps(v10));
}
#endif
for( x = 0; x < winSize.width; x++, src += cnJ, Ibuf += cnI )
for( ; x < winSize.width*cn; x++, dIptr += 2 )
{
double It = src[0]*w00 + src[cnJ]*w01 + src[stepJ]*w10 +
src[stepJ+cnJ]*w11 - Ibuf[0];
double Ixt = src[1]*w00 + src[cnJ+1]*w01 + src[stepJ+1]*w10 +
src[stepJ+cnJ+1]*w11 - Ibuf[1];
double Iyt = src[2]*w00 + src[cnJ+2]*w01 + src[stepJ+2]*w10 +
src[stepJ+cnJ+2]*w11 - Ibuf[2];
b1 += Ixt*Ibuf[3] + Iyt*Ibuf[4];
b2 += Ixt*Ibuf[4] + Iyt*Ibuf[5];
ib1 += It*Ibuf[1];
ib2 += It*Ibuf[2];
int diff = CV_DESCALE(Jptr[x]*iw00 + Jptr[x+cn]*iw01 +
Jptr[x+step]*iw10 + Jptr[x+step+cn]*iw11,
W_BITS1-5) - Iptr[x];
b1 += (float)(diff*dIptr[0]);
b2 += (float)(diff*dIptr[1]);
}
}
b1 = lambda1*ib1 + lambda2*b1;
b2 = lambda1*ib2 + lambda2*b2;
#if CV_SSE2
float CV_DECL_ALIGNED(16) bbuf[4];
_mm_store_ps(bbuf, _mm_add_ps(qb0, qb1));
b1 += bbuf[0] + bbuf[2];
b2 += bbuf[1] + bbuf[3];
#endif
b1 *= FLT_SCALE;
b2 *= FLT_SCALE;
Point2f delta( (float)((A12*b2 - A22*b1) * D),
(float)((A12*b1 - A11*b2) * D));
//delta = -delta;
......@@ -361,27 +433,145 @@ void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
}
}
}
const Mat* prevImg;
const Mat* nextImg;
const Mat* prevDeriv;
const Point2f* prevPts;
Point2f* nextPts;
uchar* status;
float* err;
Size winSize;
TermCriteria criteria;
int level;
int maxLevel;
int flags;
};
}
static void
intersect( CvPoint2D32f pt, CvSize win_size, CvSize imgSize,
CvPoint* min_pt, CvPoint* max_pt )
void cv::calcOpticalFlowPyrLK( InputArray _prevImg, InputArray _nextImg,
InputArray _prevPts, InputOutputArray _nextPts,
OutputArray _status, OutputArray _err,
Size winSize, int maxLevel,
TermCriteria criteria,
double derivLambda,
int flags )
{
CvPoint ipt;
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::calcOpticalFlowPyrLK(_prevImg, _nextImg, _prevPts, _nextPts, _status, _err, winSize, maxLevel, criteria, derivLambda, flags))
return;
#endif
Mat prevImg = _prevImg.getMat(), nextImg = _nextImg.getMat(), prevPtsMat = _prevPts.getMat();
derivLambda = std::min(std::max(derivLambda, 0.), 1.);
const int derivDepth = DataType<deriv_type>::depth;
CV_Assert( derivLambda >= 0 );
CV_Assert( maxLevel >= 0 && winSize.width > 2 && winSize.height > 2 );
CV_Assert( prevImg.size() == nextImg.size() &&
prevImg.type() == nextImg.type() );
int level=0, i, k, npoints, cn = prevImg.channels(), cn2 = cn*2;
CV_Assert( (npoints = prevPtsMat.checkVector(2, CV_32F, true)) >= 0 );
ipt.x = cvFloor( pt.x );
ipt.y = cvFloor( pt.y );
if( npoints == 0 )
{
_nextPts.release();
_status.release();
_err.release();
return;
}
ipt.x -= win_size.width;
ipt.y -= win_size.height;
if( !(flags & OPTFLOW_USE_INITIAL_FLOW) )
_nextPts.create(prevPtsMat.size(), prevPtsMat.type(), -1, true);
Mat nextPtsMat = _nextPts.getMat();
CV_Assert( nextPtsMat.checkVector(2, CV_32F, true) == npoints );
const Point2f* prevPts = (const Point2f*)prevPtsMat.data;
Point2f* nextPts = (Point2f*)nextPtsMat.data;
_status.create((int)npoints, 1, CV_8U, -1, true);
Mat statusMat = _status.getMat(), errMat;
CV_Assert( statusMat.isContinuous() );
uchar* status = statusMat.data;
float* err = 0;
for( i = 0; i < npoints; i++ )
status[i] = true;
if( _err.needed() )
{
_err.create((int)npoints, 1, CV_32F, -1, true);
errMat = _err.getMat();
CV_Assert( errMat.isContinuous() );
err = (float*)errMat.data;
}
vector<Mat> prevPyr(maxLevel+1), nextPyr(maxLevel+1);
// build the image pyramids.
// we pad each level with +/-winSize.{width|height}
// pixels to simplify the further patch extraction.
// Thanks to the reference counting, "temp" mat (the pyramid layer + border)
// will not be deallocated, since {prevPyr|nextPyr}[level] will be a ROI in "temp".
for( k = 0; k < 2; k++ )
{
Size sz = prevImg.size();
vector<Mat>& pyr = k == 0 ? prevPyr : nextPyr;
Mat& img0 = k == 0 ? prevImg : nextImg;
for( level = 0; level <= maxLevel; level++ )
{
Mat temp(sz.height + winSize.height*2,
sz.width + winSize.width*2,
img0.type());
pyr[level] = temp(Rect(winSize.width, winSize.height, sz.width, sz.height));
if( level == 0 )
img0.copyTo(pyr[level]);
else
pyrDown(pyr[level-1], pyr[level], pyr[level].size());
copyMakeBorder(pyr[level], temp, winSize.height, winSize.height,
winSize.width, winSize.width, BORDER_REFLECT_101);
sz = Size((sz.width+1)/2, (sz.height+1)/2);
if( sz.width <= winSize.width || sz.height <= winSize.height )
{
maxLevel = level;
break;
}
}
}
// dI/dx ~ Ix, dI/dy ~ Iy
Mat derivIBuf((prevImg.rows + winSize.height*2),
(prevImg.cols + winSize.width*2),
CV_MAKETYPE(derivDepth, cn2));
if( (criteria.type & TermCriteria::COUNT) == 0 )
criteria.maxCount = 30;
else
criteria.maxCount = std::min(std::max(criteria.maxCount, 0), 100);
if( (criteria.type & TermCriteria::EPS) == 0 )
criteria.epsilon = 0.01;
else
criteria.epsilon = std::min(std::max(criteria.epsilon, 0.), 10.);
criteria.epsilon *= criteria.epsilon;
for( level = maxLevel; level >= 0; level-- )
{
Size imgSize = prevPyr[level].size();
Mat _derivI( imgSize.height + winSize.height*2,
imgSize.width + winSize.width*2, derivIBuf.type(), derivIBuf.data );
Mat derivI = _derivI(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
calcSharrDeriv(prevPyr[level], derivI);
copyMakeBorder(derivI, _derivI, winSize.height, winSize.height, winSize.width, winSize.width, BORDER_CONSTANT);
win_size.width = win_size.width * 2 + 1;
win_size.height = win_size.height * 2 + 1;
Mat I = prevPyr[level], J = nextPyr[level];
min_pt->x = MAX( 0, -ipt.x );
min_pt->y = MAX( 0, -ipt.y );
max_pt->x = MIN( win_size.width, imgSize.width - ipt.x );
max_pt->y = MIN( win_size.height, imgSize.height - ipt.y );
parallel_for(BlockedRange(0, npoints), LKTrackerInvoker(prevPyr[level], derivI,
nextPyr[level], prevPts, nextPts,
status, err,
winSize, criteria, level, maxLevel, flags));
}
}
......@@ -815,241 +1005,6 @@ return CV_OK; \
ICV_DEF_GET_QUADRANGLE_SUB_PIX_FUNC( 8u32f, uchar, float, double, CV_CAST_32F, CV_8TO32F )
namespace cv
{
struct LKTrackerInvoker
{
LKTrackerInvoker( const CvMat* _imgI, const CvMat* _imgJ,
const CvPoint2D32f* _featuresA,
CvPoint2D32f* _featuresB,
char* _status, float* _error,
CvTermCriteria _criteria,
CvSize _winSize, int _level, int _flags )
{
imgI = _imgI;
imgJ = _imgJ;
featuresA = _featuresA;
featuresB = _featuresB;
status = _status;
error = _error;
criteria = _criteria;
winSize = _winSize;
level = _level;
flags = _flags;
}
void operator()(const BlockedRange& range) const
{
static const float smoothKernel[] = { 0.09375, 0.3125, 0.09375 }; // 3/32, 10/32, 3/32
int i, i1 = range.begin(), i2 = range.end();
CvSize patchSize = cvSize( winSize.width * 2 + 1, winSize.height * 2 + 1 );
int patchLen = patchSize.width * patchSize.height;
int srcPatchLen = (patchSize.width + 2)*(patchSize.height + 2);
AutoBuffer<float> buf(patchLen*3 + srcPatchLen);
float* patchI = buf;
float* patchJ = patchI + srcPatchLen;
float* Ix = patchJ + patchLen;
float* Iy = Ix + patchLen;
float scaleL = 1.f/(1 << level);
CvSize levelSize = cvGetMatSize(imgI);
// find flow for each given point
for( i = i1; i < i2; i++ )
{
CvPoint2D32f v;
CvPoint minI, maxI, minJ, maxJ;
CvSize isz, jsz;
int pt_status;
CvPoint2D32f u;
CvPoint prev_minJ = { -1, -1 }, prev_maxJ = { -1, -1 };
double Gxx = 0, Gxy = 0, Gyy = 0, D = 0, minEig = 0;
float prev_mx = 0, prev_my = 0;
int j, x, y;
v.x = featuresB[i].x*2;
v.y = featuresB[i].y*2;
pt_status = status[i];
if( !pt_status )
continue;
minI = maxI = minJ = maxJ = cvPoint(0, 0);
u.x = featuresA[i].x * scaleL;
u.y = featuresA[i].y * scaleL;
intersect( u, winSize, levelSize, &minI, &maxI );
isz = jsz = cvSize(maxI.x - minI.x + 2, maxI.y - minI.y + 2);
u.x += (minI.x - (patchSize.width - maxI.x + 1))*0.5f;
u.y += (minI.y - (patchSize.height - maxI.y + 1))*0.5f;
if( isz.width < 3 || isz.height < 3 ||
icvGetRectSubPix_8u32f_C1R( imgI->data.ptr, imgI->step, levelSize,
patchI, isz.width*sizeof(patchI[0]), isz, u ) < 0 )
{
// point is outside the first image. take the next
status[i] = 0;
continue;
}
icvCalcIxIy_32f( patchI, isz.width*sizeof(patchI[0]), Ix, Iy,
(isz.width-2)*sizeof(patchI[0]), isz, smoothKernel, patchJ );
for( j = 0; j < criteria.max_iter; j++ )
{
double bx = 0, by = 0;
float mx, my;
CvPoint2D32f _v;
intersect( v, winSize, levelSize, &minJ, &maxJ );
minJ.x = MAX( minJ.x, minI.x );
minJ.y = MAX( minJ.y, minI.y );
maxJ.x = MIN( maxJ.x, maxI.x );
maxJ.y = MIN( maxJ.y, maxI.y );
jsz = cvSize(maxJ.x - minJ.x, maxJ.y - minJ.y);
_v.x = v.x + (minJ.x - (patchSize.width - maxJ.x + 1))*0.5f;
_v.y = v.y + (minJ.y - (patchSize.height - maxJ.y + 1))*0.5f;
if( jsz.width < 1 || jsz.height < 1 ||
icvGetRectSubPix_8u32f_C1R( imgJ->data.ptr, imgJ->step, levelSize, patchJ,
jsz.width*sizeof(patchJ[0]), jsz, _v ) < 0 )
{
// point is outside of the second image. take the next
pt_status = 0;
break;
}
if( maxJ.x == prev_maxJ.x && maxJ.y == prev_maxJ.y &&
minJ.x == prev_minJ.x && minJ.y == prev_minJ.y )
{
for( y = 0; y < jsz.height; y++ )
{
const float* pi = patchI +
(y + minJ.y - minI.y + 1)*isz.width + minJ.x - minI.x + 1;
const float* pj = patchJ + y*jsz.width;
const float* ix = Ix +
(y + minJ.y - minI.y)*(isz.width-2) + minJ.x - minI.x;
const float* iy = Iy + (ix - Ix);
for( x = 0; x < jsz.width; x++ )
{
double t0 = pi[x] - pj[x];
bx += t0 * ix[x];
by += t0 * iy[x];
}
}
}
else
{
Gxx = Gyy = Gxy = 0;
for( y = 0; y < jsz.height; y++ )
{
const float* pi = patchI +
(y + minJ.y - minI.y + 1)*isz.width + minJ.x - minI.x + 1;
const float* pj = patchJ + y*jsz.width;
const float* ix = Ix +
(y + minJ.y - minI.y)*(isz.width-2) + minJ.x - minI.x;
const float* iy = Iy + (ix - Ix);
for( x = 0; x < jsz.width; x++ )
{
double t = pi[x] - pj[x];
bx += (double) (t * ix[x]);
by += (double) (t * iy[x]);
Gxx += ix[x] * ix[x];
Gxy += ix[x] * iy[x];
Gyy += iy[x] * iy[x];
}
}
D = Gxx * Gyy - Gxy * Gxy;
if( D < DBL_EPSILON )
{
pt_status = 0;
break;
}
// Adi Shavit - 2008.05
if( flags & CV_LKFLOW_GET_MIN_EIGENVALS )
minEig = (Gyy + Gxx - sqrt((Gxx-Gyy)*(Gxx-Gyy) + 4.*Gxy*Gxy))/(2*jsz.height*jsz.width);
D = 1. / D;
prev_minJ = minJ;
prev_maxJ = maxJ;
}
mx = (float) ((Gyy * bx - Gxy * by) * D);
my = (float) ((Gxx * by - Gxy * bx) * D);
v.x += mx;
v.y += my;
if( mx * mx + my * my < criteria.epsilon )
break;
if( j > 0 && fabs(mx + prev_mx) < 0.01 && fabs(my + prev_my) < 0.01 )
{
v.x -= mx*0.5f;
v.y -= my*0.5f;
break;
}
prev_mx = mx;
prev_my = my;
}
featuresB[i] = v;
status[i] = (char)pt_status;
if( level == 0 && error && pt_status )
{
// calc error
double err = 0;
if( flags & CV_LKFLOW_GET_MIN_EIGENVALS )
err = minEig;
else
{
for( y = 0; y < jsz.height; y++ )
{
const float* pi = patchI +
(y + minJ.y - minI.y + 1)*isz.width + minJ.x - minI.x + 1;
const float* pj = patchJ + y*jsz.width;
for( x = 0; x < jsz.width; x++ )
{
double t = pi[x] - pj[x];
err += t * t;
}
}
err = sqrt(err);
}
error[i] = (float)err;
}
} // end of point processing loop (i)
}
const CvMat* imgI;
const CvMat* imgJ;
const CvPoint2D32f* featuresA;
CvPoint2D32f* featuresB;
char* status;
float* error;
CvTermCriteria criteria;
CvSize winSize;
int level;
int flags;
};
}
CV_IMPL void
cvCalcOpticalFlowPyrLK( const void* arrA, const void* arrB,
......@@ -1060,117 +1015,21 @@ cvCalcOpticalFlowPyrLK( const void* arrA, const void* arrB,
char *status, float *error,
CvTermCriteria criteria, int flags )
{
cv::AutoBuffer<uchar> pyrBuffer;
cv::AutoBuffer<uchar> buffer;
cv::AutoBuffer<char> _status;
const int MAX_ITERS = 100;
CvMat stubA, *imgA = (CvMat*)arrA;
CvMat stubB, *imgB = (CvMat*)arrB;
CvMat pstubA, *pyrA = (CvMat*)pyrarrA;
CvMat pstubB, *pyrB = (CvMat*)pyrarrB;
CvSize imgSize;
uchar **imgI = 0;
uchar **imgJ = 0;
int *step = 0;
double *scale = 0;
CvSize* size = 0;
int i, l;
imgA = cvGetMat( imgA, &stubA );
imgB = cvGetMat( imgB, &stubB );
if( CV_MAT_TYPE( imgA->type ) != CV_8UC1 )
CV_Error( CV_StsUnsupportedFormat, "" );
if( !CV_ARE_TYPES_EQ( imgA, imgB ))
CV_Error( CV_StsUnmatchedFormats, "" );
if( !CV_ARE_SIZES_EQ( imgA, imgB ))
CV_Error( CV_StsUnmatchedSizes, "" );
if( imgA->step != imgB->step )
CV_Error( CV_StsUnmatchedSizes, "imgA and imgB must have equal steps" );
imgSize = cvGetMatSize( imgA );
if( pyrA )
{
pyrA = cvGetMat( pyrA, &pstubA );
if( pyrA->step*pyrA->height < icvMinimalPyramidSize( imgSize ) )
CV_Error( CV_StsBadArg, "pyramid A has insufficient size" );
}
else
{
pyrA = &pstubA;
pyrA->data.ptr = 0;
}
if( pyrB )
{
pyrB = cvGetMat( pyrB, &pstubB );
if( pyrB->step*pyrB->height < icvMinimalPyramidSize( imgSize ) )
CV_Error( CV_StsBadArg, "pyramid B has insufficient size" );
}
else
{
pyrB = &pstubB;
pyrB->data.ptr = 0;
}
if( count == 0 )
if( count <= 0 )
return;
CV_Assert( featuresA && featuresB );
cv::Mat A = cv::cvarrToMat(arrA), B = cv::cvarrToMat(arrB);
cv::Mat ptA(count, 1, CV_32FC2, (void*)featuresA);
cv::Mat ptB(count, 1, CV_32FC2, (void*)featuresB);
cv::Mat st, err;
if( !featuresA || !featuresB )
CV_Error( CV_StsNullPtr, "Some of arrays of point coordinates are missing" );
if( count < 0 )
CV_Error( CV_StsOutOfRange, "The number of tracked points is negative or zero" );
if( winSize.width <= 1 || winSize.height <= 1 )
CV_Error( CV_StsBadSize, "Invalid search window size" );
icvInitPyramidalAlgorithm( imgA, imgB, pyrA, pyrB,
level, &criteria, MAX_ITERS, flags,
&imgI, &imgJ, &step, &size, &scale, &pyrBuffer );
if( !status )
{
_status.allocate(count);
status = _status;
}
memset( status, 1, count );
if( status )
st = cv::Mat(count, 1, CV_8U, (void*)status);
if( error )
memset( error, 0, count*sizeof(error[0]) );
if( !(flags & CV_LKFLOW_INITIAL_GUESSES) )
memcpy( featuresB, featuresA, count*sizeof(featuresA[0]));
for( i = 0; i < count; i++ )
{
featuresB[i].x = (float)(featuresB[i].x * scale[level] * 0.5);
featuresB[i].y = (float)(featuresB[i].y * scale[level] * 0.5);
}
/* do processing from top pyramid level (smallest image)
to the bottom (original image) */
for( l = level; l >= 0; l-- )
{
CvMat imgI_l, imgJ_l;
cvInitMatHeader(&imgI_l, size[l].height, size[l].width, imgA->type, imgI[l], step[l]);
cvInitMatHeader(&imgJ_l, size[l].height, size[l].width, imgB->type, imgJ[l], step[l]);
cv::parallel_for(cv::BlockedRange(0, count),
cv::LKTrackerInvoker(&imgI_l, &imgJ_l, featuresA,
featuresB, status, error,
criteria, winSize, l, flags));
} // end of pyramid levels loop (l)
err = cv::Mat(count, 1, CV_32F, (void*)error);
cv::calcOpticalFlowPyrLK( A, B, ptA, ptB, status ? cv::_OutputArray(st) : cv::_OutputArray(),
error ? cv::_OutputArray(err) : cv::_OutputArray(),
winSize, level, criteria, flags);
}
......
modules/video/test/test_optflowpyrlk.cpp
浏览文件 @ 21409de1
......@@ -58,8 +58,8 @@ void CV_OptFlowPyrLKTest::run( int )
{
int code = cvtest::TS::OK;
const double success_error_level = 0.2;
const int bad_points_max = 2;
const double success_error_level = 0.3;
const int bad_points_max = 8;
/* test parameters */
double max_err = 0., sum_err = 0;
......@@ -139,7 +139,7 @@ void CV_OptFlowPyrLKTest::run( int )
status = (char*)cvAlloc(n*sizeof(status[0]));
/* calculate flow */
cvCalcOpticalFlowPyrLK( imgI, imgJ, 0, 0, u, v2, n, cvSize( 20, 20 ),
cvCalcOpticalFlowPyrLK( imgI, imgJ, 0, 0, u, v2, n, cvSize( 41, 41 ),
4, status, 0, cvTermCriteria( CV_TERMCRIT_ITER|
CV_TERMCRIT_EPS, 30, 0.01f ), 0 );
......@@ -163,14 +163,6 @@ void CV_OptFlowPyrLKTest::run( int )
}
pt_exceed += err > success_error_level;
if( pt_exceed > bad_points_max )
{
ts->printf( cvtest::TS::LOG,
"The number of poorly tracked points is too big (>=%d)\n", pt_exceed );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
sum_err += err;
pt_cmpd++;
}
......@@ -187,6 +179,14 @@ void CV_OptFlowPyrLKTest::run( int )
}
}
if( pt_exceed > bad_points_max )
{
ts->printf( cvtest::TS::LOG,
"The number of poorly tracked points is too big (>=%d)\n", pt_exceed );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( max_err > 1 )
{
ts->printf( cvtest::TS::LOG, "Maximum tracking error is too big (=%g)\n", max_err );
......
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