int PoseEstimator3d::estimate(const Frame& f0, const Frame& f1,
const std::vector<cv::DMatch> &matches)
{
// convert keypoints in match to 3d points
std::vector<Vector4d, aligned_allocator<Vector4d> > p0; // homogeneous coordinates
std::vector<Vector4d, aligned_allocator<Vector4d> > p1;
int nmatch = matches.size();
// set up data structures for fast processing
// indices to good matches
vector<int> m0, m1;
for (int i=0; i<nmatch; i++)
{
if (f0.disps[matches[i].queryIdx] > minMatchDisp &&
f1.disps[matches[i].trainIdx] > minMatchDisp)
{
m0.push_back(matches[i].queryIdx);
m1.push_back(matches[i].trainIdx);
}
}
nmatch = m0.size();
if (nmatch < 3) return 0; // can't do it...
int bestinl = 0;
// RANSAC loop
#pragma omp parallel for shared( bestinl )
for (int i=0; i<numRansac; i++)
{
// find a candidate
int a=rand()%nmatch;
int b = a;
while (a==b)
b=rand()%nmatch;
int c = a;
while (a==c || b==c)
c=rand()%nmatch;
int i0a = m0[a];
int i0b = m0[b];
int i0c = m0[c];
int i1a = m1[a];
int i1b = m1[b];
int i1c = m1[c];
if (i0a == i0b || i0a == i0c || i0b == i0c ||
i1a == i1b || i1a == i1c || i1b == i1c)
continue;
// get centroids
Vector3d p0a = f0.pts[i0a].head<3>();
Vector3d p0b = f0.pts[i0b].head<3>();
Vector3d p0c = f0.pts[i0c].head<3>();
Vector3d p1a = f1.pts[i1a].head<3>();
Vector3d p1b = f1.pts[i1b].head<3>();
Vector3d p1c = f1.pts[i1c].head<3>();
Vector3d c0 = (p0a+p0b+p0c)*(1.0/3.0);
Vector3d c1 = (p1a+p1b+p1c)*(1.0/3.0);
// subtract out
p0a -= c0;
p0b -= c0;
p0c -= c0;
p1a -= c1;
p1b -= c1;
p1c -= c1;
Matrix3d H = p1a*p0a.transpose() + p1b*p0b.transpose() +
p1c*p0c.transpose();
#if 0
cout << p0a.transpose() << endl;
cout << p0b.transpose() << endl;
cout << p0c.transpose() << endl;
#endif
// do the SVD thang
JacobiSVD<Matrix3d> svd(H, ComputeFullU | ComputeFullV);
Matrix3d V = svd.matrixV();
Matrix3d R = V * svd.matrixU().transpose();
double det = R.determinant();
//ntot++;
if (det < 0.0)
{
//nneg++;
V.col(2) = V.col(2)*-1.0;
R = V * svd.matrixU().transpose();
}
Vector3d tr = c0-R*c1; // translation
// cout << "[PE test] R: " << endl << R << endl;
// cout << "[PE test] t: " << tr.transpose() << endl;
#if 0
R << 0.9997, 0.002515, 0.02418,
-0.002474, .9999, -0.001698,
-0.02418, 0.001638, 0.9997;
tr << -0.005697, -0.01753, 0.05666;
R = R.transpose();
tr = -R*tr;
#endif
// transformation matrix, 3x4
Matrix<double,3,4> tfm;
// tfm.block<3,3>(0,0) = R.transpose();
// tfm.col(3) = -R.transpose()*tr;
tfm.block<3,3>(0,0) = R;
tfm.col(3) = tr;
// cout << "[PE test] T: " << endl << tfm << endl;
// find inliers, based on image reprojection
int inl = 0;
for (int i=0; i<nmatch; i++)
{
Vector3d pt = tfm*f1.pts[m1[i]];
Vector3d ipt = f0.cam2pix(pt);
const cv::KeyPoint &kp = f0.kpts[m0[i]];
double dx = kp.pt.x - ipt.x();
double dy = kp.pt.y - ipt.y();
double dd = f0.disps[m0[i]] - ipt.z();
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
inl+=(int)sqrt(ipt.z()); // clever way to weight closer points
// inl++;
}
#pragma omp critical
if (inl > bestinl)
{
bestinl = inl;
rot = R;
trans = tr;
}
}
// printf("Best inliers: %d\n", bestinl);
// printf("Total ransac: %d Neg det: %d\n", ntot, nneg);
// reduce matches to inliers
vector<cv::DMatch> inls; // temporary for current inliers
inliers.clear();
Matrix<double,3,4> tfm;
tfm.block<3,3>(0,0) = rot;
tfm.col(3) = trans;
nmatch = matches.size();
for (int i=0; i<nmatch; i++)
{
if (!f0.goodPts[matches[i].queryIdx] || !f1.goodPts[matches[i].trainIdx])
continue;
Vector3d pt = tfm*f1.pts[matches[i].trainIdx];
Vector3d ipt = f0.cam2pix(pt);
const cv::KeyPoint &kp = f0.kpts[matches[i].queryIdx];
double dx = kp.pt.x - ipt.x();
double dy = kp.pt.y - ipt.y();
double dd = f0.disps[matches[i].queryIdx] - ipt.z();
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
inls.push_back(matches[i]);
}
#if 0
cout << endl << trans.transpose().head(3) << endl << endl;
cout << rot << endl;
#endif
// polish with stereo sba
if (polish)
{
// system
SysSBA sba;
sba.verbose = 0;
// set up nodes
// should have a frame => node function
Vector4d v0 = Vector4d(0,0,0,1);
Quaterniond q0 = Quaternion<double>(Vector4d(0,0,0,1));
sba.addNode(v0, q0, f0.cam, true);
Quaterniond qr1(rot); // from rotation matrix
Vector4d temptrans = Vector4d(trans(0), trans(1), trans(2), 1.0);
// sba.addNode(temptrans, qr1.normalized(), f1.cam, false);
qr1.normalize();
sba.addNode(temptrans, qr1, f1.cam, false);
int in = 3;
if (in > (int)inls.size())
in = inls.size();
// set up projections
for (int i=0; i<(int)inls.size(); i++)
{
// add point
int i0 = inls[i].queryIdx;
int i1 = inls[i].trainIdx;
Vector4d pt = f0.pts[i0];
sba.addPoint(pt);
// projected point, ul,vl,ur
Vector3d ipt;
ipt(0) = f0.kpts[i0].pt.x;
ipt(1) = f0.kpts[i0].pt.y;
ipt(2) = ipt(0)-f0.disps[i0];
sba.addStereoProj(0, i, ipt);
// projected point, ul,vl,ur
ipt(0) = f1.kpts[i1].pt.x;
ipt(1) = f1.kpts[i1].pt.y;
ipt(2) = ipt(0)-f1.disps[i1];
sba.addStereoProj(1, i, ipt);
}
sba.huber = 2.0;
sba.doSBA(5,10e-4,SBA_DENSE_CHOLESKY);
int nbad = sba.removeBad(2.0);
// cout << endl << "Removed " << nbad << " projections > 2 pixels error" << endl;
sba.doSBA(5,10e-5,SBA_DENSE_CHOLESKY);
// cout << endl << sba.nodes[1].trans.transpose().head(3) << endl;
// get the updated transform
trans = sba.nodes[1].trans.head(3);
Quaterniond q1;
q1 = sba.nodes[1].qrot;
rot = q1.toRotationMatrix();
// set up inliers
inliers.clear();
for (int i=0; i<(int)inls.size(); i++)
{
ProjMap &prjs = sba.tracks[i].projections;
if (prjs[0].isValid && prjs[1].isValid) // valid track
inliers.push_back(inls[i]);
}
#if 0
printf("Inliers: %d After polish: %d\n", (int)inls.size(), (int)inliers.size());
#endif
}
return (int)inliers.size();
}
// assumes cv::Mats are of type <float>
int PoseEstimator::estimate(const matches_t &matches,
const cv::Mat &train_kpts, const cv::Mat &query_kpts,
const cv::Mat &train_pts, const cv::Mat &query_pts,
const cv::Mat &K, const double baseline)
{
// make sure we're using floats
if (train_kpts.depth() != CV_32F ||
query_kpts.depth() != CV_32F ||
train_pts.depth() != CV_32F ||
query_pts.depth() != CV_32F)
throw std::runtime_error("Expected input to be of floating point (CV_32F)");
int nmatch = matches.size();
cout << endl << "[pe3d] Matches: " << nmatch << endl;
// set up data structures for fast processing
// indices to good matches
std::vector<Eigen::Vector3f> t3d, q3d;
std::vector<Eigen::Vector2f> t2d, q2d;
std::vector<int> m; // keep track of matches
for (int i=0; i<nmatch; i++)
{
const float* ti = train_pts.ptr<float>(matches[i].trainIdx);
const float* qi = query_pts.ptr<float>(matches[i].queryIdx);
const float* ti2 = train_kpts.ptr<float>(matches[i].trainIdx);
const float* qi2 = query_kpts.ptr<float>(matches[i].queryIdx);
// make sure we have points within range; eliminates NaNs as well
if (matches[i].distance < 60 && // TODO: get this out of the estimator
ti[2] < maxMatchRange &&
qi[2] < maxMatchRange)
{
m.push_back(i);
t2d.push_back(Eigen::Vector2f(ti2[0],ti2[1]));
q2d.push_back(Eigen::Vector2f(qi2[0],qi2[1]));
t3d.push_back(Eigen::Vector3f(ti[0],ti[1],ti[2]));
q3d.push_back(Eigen::Vector3f(qi[0],qi[1],qi[2]));
}
}
nmatch = m.size();
cout << "[pe3d] Filtered matches: " << nmatch << endl;
if (nmatch < 3) return 0; // can't do it...
int bestinl = 0;
Matrix3f Kf;
cv::cv2eigen(K,Kf); // camera matrix
float fb = Kf(0,0)*baseline; // focal length times baseline
float maxInlierXDist2 = maxInlierXDist*maxInlierXDist;
float maxInlierDDist2 = maxInlierDDist*maxInlierDDist;
// set up minimum hyp pixel distance
float minpdist = minPDist * Kf(0,2) * 2.0;
// RANSAC loop
int numchoices = 1 + numRansac / 10;
for (int ii=0; ii<numRansac; ii++)
{
// find a candidate
int k;
int a=rand()%nmatch;
int b;
for (k=0; k<numchoices; k++)
{
b=rand()%nmatch;
if (a!=b && (t2d[a]-t2d[b]).norm() > minpdist
&& (q2d[a]-q2d[b]).norm() > minpdist)
// TODO: add distance check
break;
}
if (k >= numchoices) continue;
int c;
for (k=0; k<numchoices+numchoices; k++)
{
c=rand()%nmatch;
if (c!=b && c!=a && (t2d[a]-t2d[c]).norm() > minpdist
&& (t2d[b]-t2d[c]).norm() > minpdist
&& (q2d[a]-q2d[c]).norm() > minpdist
&& (q2d[b]-q2d[c]).norm() > minpdist)
// TODO: add distance check
break;
}
if (k >= numchoices+numchoices) continue;
// get centroids
Vector3f p0a = t3d[a];
Vector3f p0b = t3d[b];
Vector3f p0c = t3d[c];
Vector3f p1a = q3d[a];
Vector3f p1b = q3d[b];
Vector3f p1c = q3d[c];
Vector3f c0 = (p0a+p0b+p0c)*(1.0/3.0);
Vector3f c1 = (p1a+p1b+p1c)*(1.0/3.0);
// subtract out
p0a -= c0;
p0b -= c0;
p0c -= c0;
p1a -= c1;
p1b -= c1;
p1c -= c1;
Matrix3f Hf = p1a*p0a.transpose() + p1b*p0b.transpose() +
p1c*p0c.transpose();
Matrix3d H = Hf.cast<double>();
#if 0
cout << p0a.transpose() << endl;
cout << p0b.transpose() << endl;
cout << p0c.transpose() << endl;
#endif
// do the SVD thang
JacobiSVD<Matrix3d> svd(H, ComputeFullU | ComputeFullV);
Matrix3d V = svd.matrixV();
Matrix3d R = V * svd.matrixU().transpose();
double det = R.determinant();
//ntot++;
if (det < 0.0)
{
//nneg++;
V.col(2) = V.col(2)*-1.0;
R = V * svd.matrixU().transpose();
}
Vector3d cd0 = c0.cast<double>();
Vector3d cd1 = c1.cast<double>();
Vector3d tr = cd0-R*cd1; // translation
// cout << "[PE test] R: " << endl << R << endl;
// cout << "[PE test] t: " << tr.transpose() << endl;
Vector3f trf = tr.cast<float>(); // convert to floats
Matrix3f Rf = R.cast<float>();
Rf = Kf*Rf;
trf = Kf*trf;
// find inliers, based on image reprojection
int inl = 0;
for (int i=0; i<nmatch; i++)
{
const Vector3f &pt1 = q3d[i];
Vector3f ipt = Rf*pt1+trf; // transform query point
float iz = 1.0/ipt.z();
Vector2f &kp = t2d[i];
// cout << kp.transpose() << " " << pt1.transpose() << " " << ipt.transpose() << endl;
float dx = kp.x() - ipt.x()*iz;
float dy = kp.y() - ipt.y()*iz;
float dd = fb/t3d[i].z() - fb/ipt.z(); // diff of disparities, could pre-compute t3d
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2
&& dd*dd < maxInlierDDist2)
// inl+=(int)fsqrt(ipt.z()); // clever way to weight closer points
inl++;
}
if (inl > bestinl)
{
bestinl = inl;
trans = tr.cast<float>(); // convert to floats
rot = R.cast<float>();
}
}
cout << "[pe3d] Best inliers: " << bestinl << endl;
// printf("Total ransac: %d Neg det: %d\n", ntot, nneg);
// reduce matches to inliers
matches_t inls; // temporary for current inliers
inliers.clear();
Matrix3f Rf = Kf*rot;
Vector3f trf = Kf*trans;
// cout << "[pe3e] R: " << endl << rot << endl;
cout << "[pe3d] t: " << trans.transpose() << endl;
AngleAxisf aa;
aa.fromRotationMatrix(rot);
cout << "[pe3d] AA: " << aa.angle()*180.0/M_PI << " " << aa.axis().transpose() << endl;
for (int i=0; i<nmatch; i++)
{
Vector3f &pt1 = q3d[i];
Vector3f ipt = Rf*pt1+trf; // transform query point
float iz = 1.0/ipt.z();
Vector2f &kp = t2d[i];
// cout << kp.transpose() << " " << pt1.transpose() << " " << ipt.transpose() << endl;
float dx = kp.x() - ipt.x()*iz;
float dy = kp.y() - ipt.y()*iz;
float dd = fb/t3d[i].z() - fb/ipt.z(); // diff of disparities, could pre-compute t3d
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
inls.push_back(matches[m[i]]);
}
cout << "[pe3d] Final inliers: " << inls.size() << endl;
// polish with SVD
if (polish)
{
Matrix3d Rd = rot.cast<double>();
Vector3d trd = trans.cast<double>();
StereoPolish pol(5,false);
pol.polish(inls,train_kpts,query_kpts,train_pts,query_pts,K,baseline,
Rd,trd);
AngleAxisd aa;
aa.fromRotationMatrix(Rd);
cout << "[pe3d] Polished t: " << trd.transpose() << endl;
cout << "[pe3d] Polished AA: " << aa.angle()*180.0/M_PI << " " << aa.axis().transpose() << endl;
int num = inls.size();
// get centroids
Vector3f c0(0,0,0);
Vector3f c1(0,0,0);
for (int i=0; i<num; i++)
{
c0 += t3d[i];
c1 += q3d[i];
}
c0 = c0 / (float)num;
c1 = c1 / (float)num;
// subtract out and create H matrix
Matrix3f Hf;
Hf.setZero();
for (int i=0; i<num; i++)
{
Vector3f p0 = t3d[i]-c0;
Vector3f p1 = q3d[i]-c1;
Hf += p1*p0.transpose();
}
Matrix3d H = Hf.cast<double>();
// do the SVD thang
JacobiSVD<Matrix3d> svd(H, ComputeFullU | ComputeFullV);
Matrix3d V = svd.matrixV();
Matrix3d R = V * svd.matrixU().transpose();
double det = R.determinant();
//ntot++;
if (det < 0.0)
{
//nneg++;
V.col(2) = V.col(2)*-1.0;
R = V * svd.matrixU().transpose();
}
Vector3d cd0 = c0.cast<double>();
Vector3d cd1 = c1.cast<double>();
Vector3d tr = cd0-R*cd1; // translation
aa.fromRotationMatrix(R);
cout << "[pe3d] t: " << tr.transpose() << endl;
cout << "[pe3d] AA: " << aa.angle()*180.0/M_PI << " " << aa.axis().transpose() << endl;
#if 0
// system
SysSBA sba;
sba.verbose = 0;
// set up nodes
// should have a frame => node function
Vector4d v0 = Vector4d(0,0,0,1);
Quaterniond q0 = Quaternion<double>(Vector4d(0,0,0,1));
sba.addNode(v0, q0, f0.cam, true);
Quaterniond qr1(rot); // from rotation matrix
Vector4d temptrans = Vector4d(trans(0), trans(1), trans(2), 1.0);
// sba.addNode(temptrans, qr1.normalized(), f1.cam, false);
qr1.normalize();
sba.addNode(temptrans, qr1, f1.cam, false);
int in = 3;
if (in > (int)inls.size())
in = inls.size();
// set up projections
for (int i=0; i<(int)inls.size(); i++)
{
// add point
int i0 = inls[i].queryIdx;
int i1 = inls[i].trainIdx;
Vector4d pt = query_pts[i0];
sba.addPoint(pt);
// projected point, ul,vl,ur
Vector3d ipt;
ipt(0) = f0.kpts[i0].pt.x;
ipt(1) = f0.kpts[i0].pt.y;
ipt(2) = ipt(0)-f0.disps[i0];
sba.addStereoProj(0, i, ipt);
// projected point, ul,vl,ur
ipt(0) = f1.kpts[i1].pt.x;
ipt(1) = f1.kpts[i1].pt.y;
ipt(2) = ipt(0)-f1.disps[i1];
sba.addStereoProj(1, i, ipt);
}
sba.huber = 2.0;
sba.doSBA(5,10e-4,SBA_DENSE_CHOLESKY);
int nbad = sba.removeBad(2.0);
// cout << endl << "Removed " << nbad << " projections > 2 pixels error" << endl;
sba.doSBA(5,10e-5,SBA_DENSE_CHOLESKY);
// cout << endl << sba.nodes[1].trans.transpose().head(3) << endl;
// get the updated transform
trans = sba.nodes[1].trans.head(3);
Quaterniond q1;
q1 = sba.nodes[1].qrot;
rot = q1.toRotationMatrix();
// set up inliers
inliers.clear();
for (int i=0; i<(int)inls.size(); i++)
{
ProjMap &prjs = sba.tracks[i].projections;
if (prjs[0].isValid && prjs[1].isValid) // valid track
inliers.push_back(inls[i]);
}
#if 0
printf("Inliers: %d After polish: %d\n", (int)inls.size(), (int)inliers.size());
#endif
#endif
}
inliers = inls;
return (int)inls.size();
}
size_t PoseEstimator<PointSource,PointTarget>::estimate(const NDTFrame<PointSource>& f0, const NDTFrame<PointTarget>& f1,
const std::vector<cv::DMatch> &matches)
{
// convert keypoints in match to 3d points
std::vector<Eigen::Vector4d, Eigen::aligned_allocator<Eigen::Vector4d> > p0; // homogeneous coordinates
std::vector<Eigen::Vector4d, Eigen::aligned_allocator<Eigen::Vector4d> > p1;
int nmatch = matches.size();
//srand(getDoubleTime());
// set up data structures for fast processing
// indices to good matches
std::vector<int> m0, m1;
for (int i=0; i<nmatch; i++)
{
m0.push_back(matches[i].queryIdx);
m1.push_back(matches[i].trainIdx);
//std::cout<<m0[i]<<" "<<m1[i]<<std::endl;
}
nmatch = m0.size();
if (nmatch < 3) return 0; // can't do it...
int bestinl = 0;
// RANSAC loop
//#pragma omp parallel for shared( bestinl )
for (int i=0; i<numRansac; i++)
{
//std::cout << "ransac loop : " << i << std::endl;
// find a candidate
int a=rand()%nmatch;
int b = a;
while (a==b)
b=rand()%nmatch;
int c = a;
while (a==c || b==c)
c=rand()%nmatch;
int i0a = m0[a];
int i0b = m0[b];
int i0c = m0[c];
int i1a = m1[a];
int i1b = m1[b];
int i1c = m1[c];
if (i0a == i0b || i0a == i0c || i0b == i0c ||
i1a == i1b || i1a == i1c || i1b == i1c)
continue;
//std::cout<<a<<" "<<b<<" "<<c<<std::endl;
//std::cout<<i0a<<" "<<i0b<<" "<<i0c<<std::endl;
//std::cout<<i1a<<" "<<i1b<<" "<<i1c<<std::endl;
// get centroids
Eigen::Vector3d p0a = f0.pts[i0a].head(3);
Eigen::Vector3d p0b = f0.pts[i0b].head(3);
Eigen::Vector3d p0c = f0.pts[i0c].head(3);
Eigen::Vector3d p1a = f1.pts[i1a].head(3);
Eigen::Vector3d p1b = f1.pts[i1b].head(3);
Eigen::Vector3d p1c = f1.pts[i1c].head(3);
Eigen::Vector3d c0 = (p0a+p0b+p0c)*(1.0/3.0);
Eigen::Vector3d c1 = (p1a+p1b+p1c)*(1.0/3.0);
//std::cout<<c0.transpose()<<std::endl;
//std::cout<<c1.transpose()<<std::endl;
// subtract out
p0a -= c0;
p0b -= c0;
p0c -= c0;
p1a -= c1;
p1b -= c1;
p1c -= c1;
Eigen::Matrix3d H = p1a*p0a.transpose() + p1b*p0b.transpose() +
p1c*p0c.transpose();
// do the SVD thang
Eigen::JacobiSVD<Eigen::Matrix3d> svd(H, Eigen::ComputeFullU | Eigen::ComputeFullV);
Eigen::Matrix3d V = svd.matrixV();
Eigen::Matrix3d R = V * svd.matrixU().transpose();
double det = R.determinant();
//ntot++;
if (det < 0.0)
{
//nneg++;
V.col(2) = V.col(2)*-1.0;
R = V * svd.matrixU().transpose();
}
Eigen::Vector3d tr = c0-R*c1; // translation
// transformation matrix, 3x4
Eigen::Matrix<double,3,4> tfm;
// tfm.block<3,3>(0,0) = R.transpose();
// tfm.col(3) = -R.transpose()*tr;
tfm.block<3,3>(0,0) = R;
tfm.col(3) = tr;
#if 0
// find inliers, based on image reprojection
int inl = 0;
for (int i=0; i<nmatch; i++)
{
Vector3d pt = tfm*f1.pts[m1[i]];
Vector3d ipt = f0.cam2pix(pt);
const cv::KeyPoint &kp = f0.kpts[m0[i]];
double dx = kp.pt.x - ipt.x();
double dy = kp.pt.y - ipt.y();
double dd = f0.disps[m0[i]] - ipt.z();
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
{
inl+=(int)sqrt(ipt.z()); // clever way to weight closer points
// inl+=(int)sqrt(ipt.z()/matches[i].distance);
// cout << "matches[i].distance : " << matches[i].distance << endl;
// inl++;
}
}
#endif
int inl = 0;
for (int i=0; i<nmatch; i++)
{
Eigen::Vector3d pt1 = tfm*f1.pts[m1[i]];
Eigen::Vector3d pt0 = f0.pts[m0[i]].head(3);
// double z = fabs(pt1.z() - pt0.z())*0.5;
double z = pt1.z();
double dx = pt1.x() - pt0.x();
double dy = pt1.y() - pt0.y();
double dd = pt1.z() - pt0.z();
if (projectMatches)
{
// The central idea here is to divide by the distance (this is essentially what cam2pix does).
dx = dx / z;
dy = dy / z;
}
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
{
//---- inl+=(int)sqrt(pt0.z()); // clever way to weight closer points
// inl+=(int)sqrt(ipt.z()/matches[i].distance);
// cout << "matches[i].distance : " << matches[i].distance << endl;
inl++;
}
}
//#pragma omp critical
if (inl > bestinl)
{
bestinl = inl;
rot = R;
trans = tr;
// std::cout << "bestinl : " << bestinl << std::endl;
}
}
//printf("Best inliers: %d\n", bestinl);
//printf("Total ransac: %d Neg det: %d\n", ntot, nneg);
// reduce matches to inliers
std::vector<cv::DMatch> inls; // temporary for current inliers
inliers.clear();
Eigen::Matrix<double,3,4> tfm;
tfm.block<3,3>(0,0) = rot;
tfm.col(3) = trans;
//std::cout<<"f0: "<<f0.pts.size()<<" "<<f0.kpts.size()<<" "<<f0.pc_kpts.size()<<std::endl;
//std::cout<<"f1: "<<f1.pts.size()<<" "<<f1.kpts.size()<<" "<<f1.pc_kpts.size()<<std::endl;
nmatch = matches.size();
for (int i=0; i<nmatch; i++)
{
Eigen::Vector3d pt1 = tfm*f1.pts[matches[i].trainIdx];
//Eigen::Vector3d pt1_unchanged = f1.pts[matches[i].trainIdx].head(3);
//Vector3d pt1 = pt1_unchanged;
#if 0
Vector3d ipt = f0.cam2pix(pt);
const cv::KeyPoint &kp = f0.kpts[matches[i].queryIdx];
double dx = kp.pt.x - ipt.x();
double dy = kp.pt.y - ipt.y();
double dd = f0.disps[matches[i].queryIdx] - ipt.z();
#endif
Eigen::Vector3d pt0 = f0.pts[matches[i].queryIdx].head(3);
//double z = fabs(pt1.z() - pt0.z())*0.5;
double z = pt1.z();
double dx = pt1.x() - pt0.x();
double dy = pt1.y() - pt0.y();
double dd = pt1.z() - pt0.z();
if (projectMatches)
{
// The central idea here is to divide by the distance (this is essentially what cam2pix does).
dx = dx / z;
dy = dy / z;
}
if (dx*dx < maxInlierXDist2 && dy*dy < maxInlierXDist2 &&
dd*dd < maxInlierDDist2)
{
if (z < maxDist && z > minDist)
// if (fabs(f0.kpts[matches[i].queryIdx].pt.y - f1.kpts[matches[i].trainIdx].pt.y) > 300)
{
// std::cout << " ---------- " << dx << "," << dy << "," << dd << ",\npt0 " << pt0.transpose() << "\npt1 " << pt1.transpose() << f0.kpts[matches[i].queryIdx].pt << "," <<
// f1.kpts[matches[i].trainIdx].pt << "\n unchanged pt1 " << pt1_unchanged.transpose() << std::endl;
inliers.push_back(matches[i]);
}
}
}
#if 0
// Test with the SBA...
{
// system
SysSBA sba;
sba.verbose = 0;
#if 0
// set up nodes
// should have a frame => node function
Vector4d v0 = Vector4d(0,0,0,1);
Quaterniond q0 = Quaternion<double>(Vector4d(0,0,0,1));
sba.addNode(v0, q0, f0.cam, true);
Quaterniond qr1(rot); // from rotation matrix
Vector4d temptrans = Vector4d(trans(0), trans(1), trans(2), 1.0);
// sba.addNode(temptrans, qr1.normalized(), f1.cam, false);
qr1.normalize();
sba.addNode(temptrans, qr1, f1.cam, false);
int in = 3;
if (in > (int)inls.size())
in = inls.size();
// set up projections
for (int i=0; i<(int)inls.size(); i++)
{
// add point
int i0 = inls[i].queryIdx;
int i1 = inls[i].trainIdx;
Vector4d pt = f0.pts[i0];
sba.addPoint(pt);
// projected point, ul,vl,ur
Vector3d ipt;
ipt(0) = f0.kpts[i0].pt.x;
ipt(1) = f0.kpts[i0].pt.y;
ipt(2) = ipt(0)-f0.disps[i0];
sba.addStereoProj(0, i, ipt);
// projected point, ul,vl,ur
ipt(0) = f1.kpts[i1].pt.x;
ipt(1) = f1.kpts[i1].pt.y;
ipt(2) = ipt(0)-f1.disps[i1];
sba.addStereoProj(1, i, ipt);
}
sba.huber = 2.0;
sba.doSBA(5,10e-4,SBA_DENSE_CHOLESKY);
int nbad = sba.removeBad(2.0); // 2.0
cout << endl << "Removed " << nbad << " projections > 2 pixels error" << endl;
sba.doSBA(5,10e-5,SBA_DENSE_CHOLESKY);
// cout << endl << sba.nodes[1].trans.transpose().head(3) << endl;
// get the updated transform
trans = sba.nodes[1].trans.head(3);
Quaterniond q1;
q1 = sba.nodes[1].qrot;
quat = q1;
rot = q1.toRotationMatrix();
// set up inliers
inliers.clear();
for (int i=0; i<(int)inls.size(); i++)
{
ProjMap &prjs = sba.tracks[i].projections;
if (prjs[0].isValid && prjs[1].isValid) // valid track
inliers.push_back(inls[i]);
}
printf("Inliers: %d After polish: %d\n", (int)inls.size(), (int)inliers.size());
#endif
}
#endif
// std::cout << std::endl << trans.transpose().head(3) << std::endl << std::endl;
// std::cout << rot << std::endl;
return inliers.size();
}
