DLS performance modifications

modules/calib3d/src/dls.cpp

@@ -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();
+        z.col(i).copyTo(z_i);
-        H += eye - z_dot;
+        A += ( z_i*z_i.t() - eye ) * LeftMultVec(pp.col(i));
-        A += ( z_dot - 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();
+        //z_i = z.col(i);
-        D += cv::Mat( LeftMultVec(pp.col(i)) + A ).t() * (eye-z_dot) * ( LeftMultVec(pp.col(i)) + A );
+        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 mat, row1, row2, row3;
-    cv::Mat zeros16 = cv::Mat::zeros(1, 6, CV_64F);
+    //cv::Mat zeros16 = cv::Mat::zeros(1, 6, CV_64F);
-    cv::Mat zeros13 = cv::Mat::zeros(1, 3, 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)

modules/calib3d/src/dls.h

@@ -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;
+            // compute mean of object points
-            z.at<double>(1,i) = ipoints.at<IpointType>(0,i).y;
+            mn.at<double>(0) += p.at<double>(0,i);
-            z.at<double>(2,i) = (double)1;
+            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, mn;        // object-image points
-    cv::Mat p, z;        // 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

modules/calib3d/src/solvepnp.cpp

@@ -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;
 }
 

modules/calib3d/test/test_solvepnp_ransac.cpp

@@ -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;
     }