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DLS performance modifications

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This commit is contained in:
edgarriba 2014-08-12 16:15:19 +02:00
parent dccc10ff12
commit d1f4f6f4b8
4 changed files with 60 additions and 27 deletions
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56
modules/calib3d/src/dls.cpp
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@ -20,6 +20,7 @@ dls::dls(const cv::Mat& opoints, const cv::Mat& ipoints)
N = std::max(opoints.checkVector(3, CV_32F), opoints.checkVector(3, CV_64F));
p = cv::Mat(3, N, CV_64F);
z = cv::Mat(3, N, CV_64F);
mn = cv::Mat::zeros(3, 1, CV_64F);
cost__ = 9999;
@ -39,7 +40,7 @@ dls::dls(const cv::Mat& opoints, const cv::Mat& ipoints)
else
init_points<cv::Point3d, cv::Point2f>(opoints, ipoints);
norm_z_vector();
//norm_z_vector();
}
@ -48,7 +49,7 @@ dls::~dls()
// TODO Auto-generated destructor stub
}
void dls::norm_z_vector()
/*void dls::norm_z_vector()
{
// make z into unit vectors from normalized pixel coords
for (int i = 0; i < N; ++i)
@ -63,7 +64,7 @@ void dls::norm_z_vector()
z.at<double>(1, i) /= sr;
z.at<double>(2, i) /= sr;
}
}
}*/
bool dls::compute_pose(cv::Mat& R, cv::Mat& t)
{
@ -73,13 +74,14 @@ bool dls::compute_pose(cv::Mat& R, cv::Mat& t)
R_.push_back(roty(CV_PI/2));
R_.push_back(rotz(CV_PI/2));
cv::Mat t_mean = this->mean(p);
//cv::Mat t_mean = this->mean(p);
// version that calls dls 3 times, to avoid Cayley singularity
for (int i = 0; i < 3; ++i)
{
// Make a random rotation
cv::Mat pp = R_[i] * ( p - cv::repeat(t_mean, 1, p.cols) );
//cv::Mat pp = R_[i] * ( p - cv::repeat(t_mean, 1, p.cols) );
cv::Mat pp = R_[i] * ( p - cv::repeat(mn, 1, p.cols) );
// clear for new data
C_est_.clear();
@ -93,7 +95,8 @@ bool dls::compute_pose(cv::Mat& R, cv::Mat& t)
{
if( cost_[j] < cost__ )
{
t_est__ = t_est_[j] - C_est_[j] * R_[i] * t_mean;
//t_est__ = t_est_[j] - C_est_[j] * R_[i] * t_mean;
t_est__ = t_est_[j] - C_est_[j] * R_[i] * mn;
C_est__ = C_est_[j] * R_[i];
cost__ = cost_[j];
}
@ -236,21 +239,28 @@ void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D)
cv::Mat H = cv::Mat::zeros(3, 3, CV_64F);
cv::Mat A = cv::Mat::zeros(3, 9, CV_64F);
cv::Mat z_i(3, 1, CV_64F);
for (int i = 0; i < N; ++i)
{
cv::Mat z_dot = z.col(i)*z.col(i).t();
H += eye - z_dot;
A += ( z_dot - eye ) * LeftMultVec(pp.col(i));
z.col(i).copyTo(z_i);
A += ( z_i*z_i.t() - eye ) * LeftMultVec(pp.col(i));
}
H = eye.mul(N) - z * z.t();
// A\B
cv::solve(H, A, A);
cv::solve(H, A, A, cv::DECOMP_NORMAL);
H.release();
cv::Mat ppi_A(3, 1, CV_64F);
for (int i = 0; i < N; ++i)
{
cv::Mat z_dot = z.col(i)*z.col(i).t();
D += cv::Mat( LeftMultVec(pp.col(i)) + A ).t() * (eye-z_dot) * ( LeftMultVec(pp.col(i)) + A );
//z_i = z.col(i);
z.col(i).copyTo(z_i);
ppi_A = LeftMultVec(pp.col(i)) + A;
D += ppi_A.t() * ( eye - z_i*z_i.t() ) * ppi_A;
}
A.release();
// fill the coefficients
fill_coeff(&D);
@ -265,9 +275,11 @@ void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D)
cv::Mat M2_2 = M2(cv::Range(0,27), cv::Range(27,120));
cv::Mat M2_3 = M2(cv::Range(27,120), cv::Range(27,120));
cv::Mat M2_4 = M2(cv::Range(27,120), cv::Range(0,27));
M2.release();
// A/B = B'\A'
cv::Mat M2_5; cv::solve(M2_3.t(), M2_2.t(), M2_5);
M2_2.release(); M2_3.release();
// construct the multiplication matrix via schur compliment of the Macaulay
// matrix
@ -398,20 +410,28 @@ void dls::fill_coeff(const cv::Mat * D_mat)
cv::Mat dls::LeftMultVec(const cv::Mat& v)
{
cv::Mat mat, row1, row2, row3;
cv::Mat zeros16 = cv::Mat::zeros(1, 6, CV_64F);
cv::Mat zeros13 = cv::Mat::zeros(1, 3, CV_64F);
//cv::Mat mat, row1, row2, row3;
//cv::Mat zeros16 = cv::Mat::zeros(1, 6, CV_64F);
//cv::Mat zeros13 = cv::Mat::zeros(1, 3, CV_64F);
cv::Mat mat_ = cv::Mat::zeros(3, 9, CV_64F);
cv::hconcat(v.clone().t(), zeros16, row1); // first row
for (int i = 0; i < 3; ++i)
{
mat_.at<double>(i, 3*i + 0) = v.at<double>(0);
mat_.at<double>(i, 3*i + 1) = v.at<double>(1);
mat_.at<double>(i, 3*i + 2) = v.at<double>(2);
}
/*cv::hconcat(v.clone().t(), zeros16, row1); // first row
cv::hconcat(zeros13, v.clone().t(), row2); // second row
cv::hconcat(row2, zeros13, row2); // second row
cv::hconcat(zeros16, v.clone().t(), row3); // third row
mat.push_back(row1);
mat.push_back(row2);
mat.push_back(row3);
mat.push_back(row3);*/
return mat;
return mat_;
}
cv::Mat dls::cayley_LS_M(const std::vector<double>& a, const std::vector<double>& b, const std::vector<double>& c, const std::vector<double>& u)

27
modules/calib3d/src/dls.h
View File

@ -26,15 +26,30 @@ private:
p.at<double>(1,i) = opoints.at<OpointType>(0,i).y;
p.at<double>(2,i) = opoints.at<OpointType>(0,i).z;
z.at<double>(0,i) = ipoints.at<IpointType>(0,i).x;
z.at<double>(1,i) = ipoints.at<IpointType>(0,i).y;
z.at<double>(2,i) = (double)1;
// compute mean of object points
mn.at<double>(0) += p.at<double>(0,i);
mn.at<double>(1) += p.at<double>(1,i);
mn.at<double>(2) += p.at<double>(2,i);
// make z into unit vectors from normalized pixel coords
double sr = std::pow(ipoints.at<IpointType>(0,i).x, 2) +
std::pow(ipoints.at<IpointType>(0,i).y, 2) + (double)1;
sr = std::sqrt(sr);
z.at<double>(0,i) = ipoints.at<IpointType>(0,i).x / sr;
z.at<double>(1,i) = ipoints.at<IpointType>(0,i).y / sr;
z.at<double>(2,i) = (double)1 / sr;
}
mn.at<double>(0) /= (double)N;
mn.at<double>(1) /= (double)N;
mn.at<double>(2) /= (double)N;
}
void norm_z_vector();
//void norm_z_vector();
// main algorithm
cv::Mat LeftMultVec(const cv::Mat& v);
void run_kernel(const cv::Mat& pp);
void build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D);
void compute_eigenvec(const cv::Mat& Mtilde, cv::Mat& eigenval_real, cv::Mat& eigenval_imag,
@ -42,7 +57,6 @@ private:
void fill_coeff(const cv::Mat * D);
// useful functions
cv::Mat LeftMultVec(const cv::Mat& v);
cv::Mat cayley_LS_M(const std::vector<double>& a, const std::vector<double>& b,
const std::vector<double>& c, const std::vector<double>& u);
cv::Mat Hessian(const double s[]);
@ -57,8 +71,7 @@ private:
bool is_empty(const cv::Mat * v);
bool positive_eigenvalues(const cv::Mat * eigenvalues);
cv::Mat p, z; // object-image points
cv::Mat p, z, mn; // object-image points
int N; // number of input points
std::vector<double> f1coeff, f2coeff, f3coeff, cost_; // coefficient for coefficients matrix
std::vector<cv::Mat> C_est_, t_est_; // optimal candidates

2
modules/calib3d/src/solvepnp.cpp
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@ -108,7 +108,7 @@ bool cv::solvePnP( InputArray _opoints, InputArray _ipoints,
return result;
}
else
CV_Error(CV_StsBadArg, "The flags argument must be one of CV_ITERATIVE, CV_P3P, CV_EPNP or CV_DLS");
CV_Error(CV_StsBadArg, "The flags argument must be one of SOLVEPNP_ITERATIVE, SOLVEPNP_P3P, SOLVEPNP_EPNP or SOLVEPNP_DLS");
return false;
}

2
modules/calib3d/test/test_solvepnp_ransac.cpp
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@ -196,7 +196,7 @@ public:
eps[SOLVEPNP_ITERATIVE] = 1.0e-6;
eps[SOLVEPNP_EPNP] = 1.0e-6;
eps[SOLVEPNP_P3P] = 1.0e-4;
eps[SOLVEPNP_DLS] = 1.0e-6;
eps[SOLVEPNP_DLS] = 1.0e-4;
totalTestsCount = 1000;
}