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#include "precomp.hpp"
#include "opencv2/videostab/global_motion.hpp"
#include "opencv2/videostab/ring_buffer.hpp"
#include "opencv2/videostab/outlier_rejection.hpp"
#include "opencv2/opencv_modules.hpp"
#include "clp.hpp"
#include "opencv2/core/private.cuda.hpp"
#if defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW)
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
namespace cv { namespace cuda {
static void compactPoints(GpuMat&, GpuMat&, const GpuMat&) { throw_no_cuda(); }
}}
#else
namespace cv { namespace cuda { namespace device { namespace globmotion {
int compactPoints(int N, float *points0, float *points1, const uchar *mask);
}}}}
namespace cv { namespace cuda {
static void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask)
{
CV_Assert(points0.rows == 1 && points1.rows == 1 && mask.rows == 1);
CV_Assert(points0.type() == CV_32FC2 && points1.type() == CV_32FC2 && mask.type() == CV_8U);
CV_Assert(points0.cols == mask.cols && points1.cols == mask.cols);
int npoints = points0.cols;
int remaining = cv::cuda::device::globmotion::compactPoints(
npoints, (float*)points0.data, (float*)points1.data, mask.data);
points0 = points0.colRange(0, remaining);
points1 = points1.colRange(0, remaining);
}
}}
#endif
#endif
namespace cv
{
namespace videostab
{
// does isotropic normalization
static Mat normalizePoints(int npoints, Point2f *points)
{
float cx = 0.f, cy = 0.f;
for (int i = 0; i < npoints; ++i)
{
cx += points[i].x;
cy += points[i].y;
}
cx /= npoints;
cy /= npoints;
float d = 0.f;
for (int i = 0; i < npoints; ++i)
{
points[i].x -= cx;
points[i].y -= cy;
d += std::sqrt(sqr(points[i].x) + sqr(points[i].y));
}
d /= npoints;
float s = std::sqrt(2.f) / d;
for (int i = 0; i < npoints; ++i)
{
points[i].x *= s;
points[i].y *= s;
}
Mat_<float> T = Mat::eye(3, 3, CV_32F);
T(0,0) = T(1,1) = s;
T(0,2) = -cx*s;
T(1,2) = -cy*s;
return T;
}
static Mat estimateGlobMotionLeastSquaresTranslation(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Mat_<float> M = Mat::eye(3, 3, CV_32F);
for (int i = 0; i < npoints; ++i)
{
M(0,2) += points1[i].x - points0[i].x;
M(1,2) += points1[i].y - points0[i].y;
}
M(0,2) /= npoints;
M(1,2) /= npoints;
if (rmse)
{
*rmse = 0;
for (int i = 0; i < npoints; ++i)
*rmse += sqr(points1[i].x - points0[i].x - M(0,2)) +
sqr(points1[i].y - points0[i].y - M(1,2));
*rmse = std::sqrt(*rmse / npoints);
}
return M;
}
static Mat estimateGlobMotionLeastSquaresTranslationAndScale(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Mat_<float> T0 = normalizePoints(npoints, points0);
Mat_<float> T1 = normalizePoints(npoints, points1);
Mat_<float> A(2*npoints, 3), b(2*npoints, 1);
float *a0, *a1;
Point2f p0, p1;
for (int i = 0; i < npoints; ++i)
{
a0 = A[2*i];
a1 = A[2*i+1];
p0 = points0[i];
p1 = points1[i];
a0[0] = p0.x; a0[1] = 1; a0[2] = 0;
a1[0] = p0.y; a1[1] = 0; a1[2] = 1;
b(2*i,0) = p1.x;
b(2*i+1,0) = p1.y;
}
Mat_<float> sol;
solve(A, b, sol, DECOMP_NORMAL | DECOMP_LU);
if (rmse)
*rmse = static_cast<float>(norm(A*sol, b, NORM_L2) / std::sqrt(static_cast<double>(npoints)));
Mat_<float> M = Mat::eye(3, 3, CV_32F);
M(0,0) = M(1,1) = sol(0,0);
M(0,2) = sol(1,0);
M(1,2) = sol(2,0);
return T1.inv() * M * T0;
}
static Mat estimateGlobMotionLeastSquaresRotation(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Point2f p0, p1;
float A(0), B(0);
for(int i=0; i<npoints; ++i)
{
p0 = points0[i];
p1 = points1[i];
A += p0.x*p1.x + p0.y*p1.y;
B += p0.x*p1.y - p1.x*p0.y;
}
// A*sin(alpha) + B*cos(alpha) = 0
float C = std::sqrt(A*A + B*B);
Mat_<float> M = Mat::eye(3, 3, CV_32F);
if ( C != 0 )
{
float sinAlpha = - B / C;
float cosAlpha = A / C;
M(0,0) = cosAlpha;
M(1,1) = M(0,0);
M(0,1) = sinAlpha;
M(1,0) = - M(0,1);
}
if (rmse)
{
*rmse = 0;
for (int i = 0; i < npoints; ++i)
{
p0 = points0[i];
p1 = points1[i];
*rmse += sqr(p1.x - M(0,0)*p0.x - M(0,1)*p0.y) +
sqr(p1.y - M(1,0)*p0.x - M(1,1)*p0.y);
}
*rmse = std::sqrt(*rmse / npoints);
}
return M;
}
static Mat estimateGlobMotionLeastSquaresRigid(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Point2f mean0(0.f, 0.f);
Point2f mean1(0.f, 0.f);
for (int i = 0; i < npoints; ++i)
{
mean0 += points0[i];
mean1 += points1[i];
}
mean0 *= 1.f / npoints;
mean1 *= 1.f / npoints;
Mat_<float> A = Mat::zeros(2, 2, CV_32F);
Point2f pt0, pt1;
for (int i = 0; i < npoints; ++i)
{
pt0 = points0[i] - mean0;
pt1 = points1[i] - mean1;
A(0,0) += pt1.x * pt0.x;
A(0,1) += pt1.x * pt0.y;
A(1,0) += pt1.y * pt0.x;
A(1,1) += pt1.y * pt0.y;
}
Mat_<float> M = Mat::eye(3, 3, CV_32F);
SVD svd(A);
Mat_<float> R = svd.u * svd.vt;
Mat tmp(M(Rect(0,0,2,2)));
R.copyTo(tmp);
M(0,2) = mean1.x - R(0,0)*mean0.x - R(0,1)*mean0.y;
M(1,2) = mean1.y - R(1,0)*mean0.x - R(1,1)*mean0.y;
if (rmse)
{
*rmse = 0;
for (int i = 0; i < npoints; ++i)
{
pt0 = points0[i];
pt1 = points1[i];
*rmse += sqr(pt1.x - M(0,0)*pt0.x - M(0,1)*pt0.y - M(0,2)) +
sqr(pt1.y - M(1,0)*pt0.x - M(1,1)*pt0.y - M(1,2));
}
*rmse = std::sqrt(*rmse / npoints);
}
return M;
}
static Mat estimateGlobMotionLeastSquaresSimilarity(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Mat_<float> T0 = normalizePoints(npoints, points0);
Mat_<float> T1 = normalizePoints(npoints, points1);
Mat_<float> A(2*npoints, 4), b(2*npoints, 1);
float *a0, *a1;
Point2f p0, p1;
for (int i = 0; i < npoints; ++i)
{
a0 = A[2*i];
a1 = A[2*i+1];
p0 = points0[i];
p1 = points1[i];
a0[0] = p0.x; a0[1] = p0.y; a0[2] = 1; a0[3] = 0;
a1[0] = p0.y; a1[1] = -p0.x; a1[2] = 0; a1[3] = 1;
b(2*i,0) = p1.x;
b(2*i+1,0) = p1.y;
}
Mat_<float> sol;
solve(A, b, sol, DECOMP_NORMAL | DECOMP_LU);
if (rmse)
*rmse = static_cast<float>(norm(A*sol, b, NORM_L2) / std::sqrt(static_cast<double>(npoints)));
Mat_<float> M = Mat::eye(3, 3, CV_32F);
M(0,0) = M(1,1) = sol(0,0);
M(0,1) = sol(1,0);
M(1,0) = -sol(1,0);
M(0,2) = sol(2,0);
M(1,2) = sol(3,0);
return T1.inv() * M * T0;
}
static Mat estimateGlobMotionLeastSquaresAffine(
int npoints, Point2f *points0, Point2f *points1, float *rmse)
{
Mat_<float> T0 = normalizePoints(npoints, points0);
Mat_<float> T1 = normalizePoints(npoints, points1);
Mat_<float> A(2*npoints, 6), b(2*npoints, 1);
float *a0, *a1;
Point2f p0, p1;
for (int i = 0; i < npoints; ++i)
{
a0 = A[2*i];
a1 = A[2*i+1];
p0 = points0[i];
p1 = points1[i];
a0[0] = p0.x; a0[1] = p0.y; a0[2] = 1; a0[3] = a0[4] = a0[5] = 0;
a1[0] = a1[1] = a1[2] = 0; a1[3] = p0.x; a1[4] = p0.y; a1[5] = 1;
b(2*i,0) = p1.x;
b(2*i+1,0) = p1.y;
}
Mat_<float> sol;
solve(A, b, sol, DECOMP_NORMAL | DECOMP_LU);
if (rmse)
*rmse = static_cast<float>(norm(A*sol, b, NORM_L2) / std::sqrt(static_cast<double>(npoints)));
Mat_<float> M = Mat::eye(3, 3, CV_32F);
for (int i = 0, k = 0; i < 2; ++i)
for (int j = 0; j < 3; ++j, ++k)
M(i,j) = sol(k,0);
return T1.inv() * M * T0;
}
Mat estimateGlobalMotionLeastSquares(
InputOutputArray points0, InputOutputArray points1, int model, float *rmse)
{
CV_Assert(model <= MM_AFFINE);
CV_Assert(points0.type() == points1.type());
const int npoints = points0.getMat().checkVector(2);
CV_Assert(points1.getMat().checkVector(2) == npoints);
typedef Mat (*Impl)(int, Point2f*, Point2f*, float*);
static Impl impls[] = { estimateGlobMotionLeastSquaresTranslation,
estimateGlobMotionLeastSquaresTranslationAndScale,
estimateGlobMotionLeastSquaresRotation,
estimateGlobMotionLeastSquaresRigid,
estimateGlobMotionLeastSquaresSimilarity,
estimateGlobMotionLeastSquaresAffine };
Point2f *points0_ = points0.getMat().ptr<Point2f>();
Point2f *points1_ = points1.getMat().ptr<Point2f>();
return impls[model](npoints, points0_, points1_, rmse);
}
Mat estimateGlobalMotionRansac(
InputArray points0, InputArray points1, int model, const RansacParams ¶ms,
float *rmse, int *ninliers)
{
CV_Assert(model <= MM_AFFINE);
CV_Assert(points0.type() == points1.type());
const int npoints = points0.getMat().checkVector(2);
CV_Assert(points1.getMat().checkVector(2) == npoints);
if (npoints < params.size)
return Mat::eye(3, 3, CV_32F);
const Point2f *points0_ = points0.getMat().ptr<Point2f>();
const Point2f *points1_ = points1.getMat().ptr<Point2f>();
const int niters = params.niters();
// current hypothesis
std::vector<int> indices(params.size);
std::vector<Point2f> subset0(params.size);
std::vector<Point2f> subset1(params.size);
// best hypothesis
std::vector<Point2f> subset0best(params.size);
std::vector<Point2f> subset1best(params.size);
Mat_<float> bestM;
int ninliersMax = -1;
RNG rng(0);
Point2f p0, p1;
float x, y;
for (int iter = 0; iter < niters; ++iter)
{
for (int i = 0; i < params.size; ++i)
{
bool ok = false;
while (!ok)
{
ok = true;
indices[i] = static_cast<unsigned>(rng) % npoints;
for (int j = 0; j < i; ++j)
if (indices[i] == indices[j])
{ ok = false; break; }
}
}
for (int i = 0; i < params.size; ++i)
{
subset0[i] = points0_[indices[i]];
subset1[i] = points1_[indices[i]];
}
Mat_<float> M = estimateGlobalMotionLeastSquares(subset0, subset1, model, 0);
int numinliers = 0;
for (int i = 0; i < npoints; ++i)
{
p0 = points0_[i];
p1 = points1_[i];
x = M(0,0)*p0.x + M(0,1)*p0.y + M(0,2);
y = M(1,0)*p0.x + M(1,1)*p0.y + M(1,2);
if (sqr(x - p1.x) + sqr(y - p1.y) < params.thresh * params.thresh)
numinliers++;
}
if (numinliers >= ninliersMax)
{
bestM = M;
ninliersMax = numinliers;
subset0best.swap(subset0);
subset1best.swap(subset1);
}
}
if (ninliersMax < params.size)
// compute RMSE
bestM = estimateGlobalMotionLeastSquares(subset0best, subset1best, model, rmse);
else
{
subset0.resize(ninliersMax);
subset1.resize(ninliersMax);
for (int i = 0, j = 0; i < npoints && j < ninliersMax ; ++i)
{
p0 = points0_[i];
p1 = points1_[i];
x = bestM(0,0)*p0.x + bestM(0,1)*p0.y + bestM(0,2);
y = bestM(1,0)*p0.x + bestM(1,1)*p0.y + bestM(1,2);
if (sqr(x - p1.x) + sqr(y - p1.y) < params.thresh * params.thresh)
{
subset0[j] = p0;
subset1[j] = p1;
j++;
}
}
bestM = estimateGlobalMotionLeastSquares(subset0, subset1, model, rmse);
}
if (ninliers)
*ninliers = ninliersMax;
return bestM;
}
MotionEstimatorRansacL2::MotionEstimatorRansacL2(MotionModel model)
: MotionEstimatorBase(model)
{
setRansacParams(RansacParams::default2dMotion(model));
setMinInlierRatio(0.1f);
}
Mat MotionEstimatorRansacL2::estimate(InputArray points0, InputArray points1, bool *ok)
{
CV_Assert(points0.type() == points1.type());
const int npoints = points0.getMat().checkVector(2);
CV_Assert(points1.getMat().checkVector(2) == npoints);
// find motion
int ninliers = 0;
Mat_<float> M;
if (motionModel() != MM_HOMOGRAPHY)
M = estimateGlobalMotionRansac(
points0, points1, motionModel(), ransacParams_, 0, &ninliers);
else
{
std::vector<uchar> mask;
M = findHomography(points0, points1, mask, LMEDS);
for (int i = 0; i < npoints; ++i)
if (mask[i]) ninliers++;
}
// check if we're confident enough in estimated motion
if (ok) *ok = true;
if (static_cast<float>(ninliers) / npoints < minInlierRatio_)
{
M = Mat::eye(3, 3, CV_32F);
if (ok) *ok = false;
}
return M;
}
MotionEstimatorL1::MotionEstimatorL1(MotionModel model)
: MotionEstimatorBase(model)
{
}
// TODO will estimation of all motions as one LP problem be faster?
Mat MotionEstimatorL1::estimate(InputArray points0, InputArray points1, bool *ok)
{
CV_Assert(points0.type() == points1.type());
const int npoints = points0.getMat().checkVector(2);
CV_Assert(points1.getMat().checkVector(2) == npoints);
#ifndef HAVE_CLP
CV_Error(Error::StsError, "The library is built without Clp support");
if (ok) *ok = false;
return Mat::eye(3, 3, CV_32F);
#else
CV_Assert(motionModel() <= MM_AFFINE && motionModel() != MM_RIGID);
if(npoints <= 0)
return Mat::eye(3, 3, CV_32F);
// prepare LP problem
const Point2f *points0_ = points0.getMat().ptr<Point2f>();
const Point2f *points1_ = points1.getMat().ptr<Point2f>();
int ncols = 6 + 2*npoints;
int nrows = 4*npoints;
if (motionModel() == MM_SIMILARITY)
nrows += 2;
else if (motionModel() == MM_TRANSLATION_AND_SCALE)
nrows += 3;
else if (motionModel() == MM_TRANSLATION)
nrows += 4;
rows_.clear();
cols_.clear();
elems_.clear();
obj_.assign(ncols, 0);
collb_.assign(ncols, -INF);
colub_.assign(ncols, INF);
int c = 6;
for (int i = 0; i < npoints; ++i, c += 2)
{
obj_[c] = 1;
collb_[c] = 0;
obj_[c+1] = 1;
collb_[c+1] = 0;
}
elems_.clear();
rowlb_.assign(nrows, -INF);
rowub_.assign(nrows, INF);
int r = 0;
Point2f p0, p1;
for (int i = 0; i < npoints; ++i, r += 4)
{
p0 = points0_[i];
p1 = points1_[i];
set(r, 0, p0.x); set(r, 1, p0.y); set(r, 2, 1); set(r, 6+2*i, -1);
rowub_[r] = p1.x;
set(r+1, 3, p0.x); set(r+1, 4, p0.y); set(r+1, 5, 1); set(r+1, 6+2*i+1, -1);
rowub_[r+1] = p1.y;
set(r+2, 0, p0.x); set(r+2, 1, p0.y); set(r+2, 2, 1); set(r+2, 6+2*i, 1);
rowlb_[r+2] = p1.x;
set(r+3, 3, p0.x); set(r+3, 4, p0.y); set(r+3, 5, 1); set(r+3, 6+2*i+1, 1);
rowlb_[r+3] = p1.y;
}
if (motionModel() == MM_SIMILARITY)
{
set(r, 0, 1); set(r, 4, -1); rowlb_[r] = rowub_[r] = 0;
set(r+1, 1, 1); set(r+1, 3, 1); rowlb_[r+1] = rowub_[r+1] = 0;
}
else if (motionModel() == MM_TRANSLATION_AND_SCALE)
{
set(r, 0, 1); set(r, 4, -1); rowlb_[r] = rowub_[r] = 0;
set(r+1, 1, 1); rowlb_[r+1] = rowub_[r+1] = 0;
set(r+2, 3, 1); rowlb_[r+2] = rowub_[r+2] = 0;
}
else if (motionModel() == MM_TRANSLATION)
{
set(r, 0, 1); rowlb_[r] = rowub_[r] = 1;
set(r+1, 1, 1); rowlb_[r+1] = rowub_[r+1] = 0;
set(r+2, 3, 1); rowlb_[r+2] = rowub_[r+2] = 0;
set(r+3, 4, 1); rowlb_[r+3] = rowub_[r+3] = 1;
}
// solve
CoinPackedMatrix A(true, &rows_[0], &cols_[0], &elems_[0], elems_.size());
A.setDimensions(nrows, ncols);
ClpSimplex model(false);
model.loadProblem(A, &collb_[0], &colub_[0], &obj_[0], &rowlb_[0], &rowub_[0]);
ClpDualRowSteepest dualSteep(1);
model.setDualRowPivotAlgorithm(dualSteep);
model.scaling(1);
model.dual();
// extract motion
const double *sol = model.getColSolution();
Mat_<float> M = Mat::eye(3, 3, CV_32F);
M(0,0) = sol[0];
M(0,1) = sol[1];
M(0,2) = sol[2];
M(1,0) = sol[3];
M(1,1) = sol[4];
M(1,2) = sol[5];
if (ok) *ok = true;
return M;
#endif
}
FromFileMotionReader::FromFileMotionReader(const String &path)
: ImageMotionEstimatorBase(MM_UNKNOWN)
{
file_.open(path.c_str());
CV_Assert(file_.is_open());
}
Mat FromFileMotionReader::estimate(const Mat &/*frame0*/, const Mat &/*frame1*/, bool *ok)
{
Mat_<float> M(3, 3);
bool ok_;
file_ >> M(0,0) >> M(0,1) >> M(0,2)
>> M(1,0) >> M(1,1) >> M(1,2)
>> M(2,0) >> M(2,1) >> M(2,2) >> ok_;
if (ok) *ok = ok_;
return M;
}
ToFileMotionWriter::ToFileMotionWriter(const String &path, Ptr<ImageMotionEstimatorBase> estimator)
: ImageMotionEstimatorBase(estimator->motionModel()), motionEstimator_(estimator)
{
file_.open(path.c_str());
CV_Assert(file_.is_open());
}
Mat ToFileMotionWriter::estimate(const Mat &frame0, const Mat &frame1, bool *ok)
{
bool ok_;
Mat_<float> M = motionEstimator_->estimate(frame0, frame1, &ok_);
file_ << M(0,0) << " " << M(0,1) << " " << M(0,2) << " "
<< M(1,0) << " " << M(1,1) << " " << M(1,2) << " "
<< M(2,0) << " " << M(2,1) << " " << M(2,2) << " " << ok_ << std::endl;
if (ok) *ok = ok_;
return M;
}
KeypointBasedMotionEstimator::KeypointBasedMotionEstimator(Ptr<MotionEstimatorBase> estimator)
: ImageMotionEstimatorBase(estimator->motionModel()), motionEstimator_(estimator)
{
setDetector(GFTTDetector::create());
setOpticalFlowEstimator(makePtr<SparsePyrLkOptFlowEstimator>());
setOutlierRejector(makePtr<NullOutlierRejector>());
}
Mat KeypointBasedMotionEstimator::estimate(const Mat &frame0, const Mat &frame1, bool *ok)
{
// find keypoints
detector_->detect(frame0, keypointsPrev_);
if (keypointsPrev_.empty())
return Mat::eye(3, 3, CV_32F);
// extract points from keypoints
pointsPrev_.resize(keypointsPrev_.size());
for (size_t i = 0; i < keypointsPrev_.size(); ++i)
pointsPrev_[i] = keypointsPrev_[i].pt;
// find correspondences
optFlowEstimator_->run(frame0, frame1, pointsPrev_, points_, status_, noArray());
// leave good correspondences only
pointsPrevGood_.clear(); pointsPrevGood_.reserve(points_.size());
pointsGood_.clear(); pointsGood_.reserve(points_.size());
for (size_t i = 0; i < points_.size(); ++i)
{
if (status_[i])
{
pointsPrevGood_.push_back(pointsPrev_[i]);
pointsGood_.push_back(points_[i]);
}
}
// perform outlier rejection
IOutlierRejector *outlRejector = outlierRejector_.get();
if (!dynamic_cast<NullOutlierRejector*>(outlRejector))
{
pointsPrev_.swap(pointsPrevGood_);
points_.swap(pointsGood_);
outlierRejector_->process(frame0.size(), pointsPrev_, points_, status_);
pointsPrevGood_.clear();
pointsPrevGood_.reserve(points_.size());
pointsGood_.clear();
pointsGood_.reserve(points_.size());
for (size_t i = 0; i < points_.size(); ++i)
{
if (status_[i])
{
pointsPrevGood_.push_back(pointsPrev_[i]);
pointsGood_.push_back(points_[i]);
}
}
}
// estimate motion
return motionEstimator_->estimate(pointsPrevGood_, pointsGood_, ok);
}
#if defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW)
KeypointBasedMotionEstimatorGpu::KeypointBasedMotionEstimatorGpu(Ptr<MotionEstimatorBase> estimator)
: ImageMotionEstimatorBase(estimator->motionModel()), motionEstimator_(estimator)
{
detector_ = cuda::createGoodFeaturesToTrackDetector(CV_8UC1);
CV_Assert(cuda::getCudaEnabledDeviceCount() > 0);
setOutlierRejector(makePtr<NullOutlierRejector>());
}
Mat KeypointBasedMotionEstimatorGpu::estimate(const Mat &frame0, const Mat &frame1, bool *ok)
{
frame0_.upload(frame0);
frame1_.upload(frame1);
return estimate(frame0_, frame1_, ok);
}
Mat KeypointBasedMotionEstimatorGpu::estimate(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, bool *ok)
{
// convert frame to gray if it's color
cuda::GpuMat grayFrame0;
if (frame0.channels() == 1)
grayFrame0 = frame0;
else
{
cuda::cvtColor(frame0, grayFrame0_, COLOR_BGR2GRAY);
grayFrame0 = grayFrame0_;
}
// find keypoints
detector_->detect(grayFrame0, pointsPrev_);
// find correspondences
optFlowEstimator_.run(frame0, frame1, pointsPrev_, points_, status_);
// leave good correspondences only
cuda::compactPoints(pointsPrev_, points_, status_);
pointsPrev_.download(hostPointsPrev_);
points_.download(hostPoints_);
// perform outlier rejection
IOutlierRejector *rejector = outlierRejector_.get();
if (!dynamic_cast<NullOutlierRejector*>(rejector))
{
outlierRejector_->process(frame0.size(), hostPointsPrev_, hostPoints_, rejectionStatus_);
hostPointsPrevTmp_.clear();
hostPointsPrevTmp_.reserve(hostPoints_.cols);
hostPointsTmp_.clear();
hostPointsTmp_.reserve(hostPoints_.cols);
for (int i = 0; i < hostPoints_.cols; ++i)
{
if (rejectionStatus_[i])
{
hostPointsPrevTmp_.push_back(hostPointsPrev_.at<Point2f>(0,i));
hostPointsTmp_.push_back(hostPoints_.at<Point2f>(0,i));
}
}
hostPointsPrev_ = Mat(1, (int)hostPointsPrevTmp_.size(), CV_32FC2, &hostPointsPrevTmp_[0]);
hostPoints_ = Mat(1, (int)hostPointsTmp_.size(), CV_32FC2, &hostPointsTmp_[0]);
}
// estimate motion
return motionEstimator_->estimate(hostPointsPrev_, hostPoints_, ok);
}
#endif // defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW)
Mat getMotion(int from, int to, const std::vector<Mat> &motions)
{
Mat M = Mat::eye(3, 3, CV_32F);
if (to > from)
{
for (int i = from; i < to; ++i)
M = at(i, motions) * M;
}
else if (from > to)
{
for (int i = to; i < from; ++i)
M = at(i, motions) * M;
M = M.inv();
}
return M;
}
} // namespace videostab
} // namespace cv