/* Copyright (c) 2012 Patrick Ruoff * * Permission to use, copy, modify, and/or distribute this software for any * purpose with or without fee is hereby granted, provided that the above * copyright notice and this permission notice appear in all copies. */ #include "point_tracker.h" #include <vector> #include <algorithm> #include <cmath> #include <QDebug> using namespace cv; using namespace std; const float PI = 3.14159265358979323846f; // ---------------------------------------------------------------------------- static void get_row(const Matx33f& m, int i, Vec3f& v) { v[0] = m(i,0); v[1] = m(i,1); v[2] = m(i,2); } static void set_row(Matx33f& m, int i, const Vec3f& v) { m(i,0) = v[0]; m(i,1) = v[1]; m(i,2) = v[2]; } PointModel::PointModel() : M01 { 0, 0, 0 }, M02 { 0, 0, 0 } { } PointModel::PointModel(Vec3f M01, Vec3f M02) : M01(M01), M02(M02) { // calculate u u = M01.cross(M02); u /= norm(u); // calculate projection matrix on M01,M02 plane float s11 = M01.dot(M01); float s12 = M01.dot(M02); float s22 = M02.dot(M02); P = 1.0/(s11*s22-s12*s12) * Matx22f(s22, -s12, -s12, s11); // calculate d and d_order for simple freetrack-like point correspondence vector<Vec2f> points; points.push_back(Vec2f(0,0)); points.push_back(Vec2f(M01[0], M01[1])); points.push_back(Vec2f(M02[0], M02[1])); } #ifdef OPENTRACK_API static bool d_vals_sort(const pair<float,int> a, const pair<float,int> b) { return a.first < b.first; } #endif void PointModel::get_d_order(const std::vector<cv::Vec2f>& points, int d_order[]) const { // fit line to orthographically projected points Vec2f d(M01[0]-M02[0], M01[1]-M02[1]); // get sort indices with respect to d scalar product vector< pair<float,int> > d_vals; for (unsigned i = 0; i<points.size(); ++i) d_vals.push_back(pair<float, int>(d.dot(points[i]), i)); std::sort(d_vals.begin(), d_vals.end(), #ifdef OPENTRACK_API d_vals_sort #else comp #endif ); for (unsigned i = 0; i<points.size(); ++i) d_order[i] = d_vals[i].second; } // ---------------------------------------------------------------------------- PointTracker::PointTracker() { X_CM.t[2] = 1000; // default position: 1 m away from cam; } void PointTracker::track(const vector<Vec2f>& points, const PointModel& model) { const PointOrder& order = find_correspondences(points, model); POSIT(model, order); } PointTracker::PointOrder PointTracker::find_correspondences(const std::vector<cv::Vec2f>& points, const PointModel& model) { // We do a simple freetrack-like sorting in the init phase... // sort points int point_d_order[PointModel::N_POINTS]; model.get_d_order(points, point_d_order); // set correspondences PointOrder p; for (int i=0; i<PointModel::N_POINTS; ++i) p.points[i] = points[point_d_order[i]]; return p; } int PointTracker::POSIT(const PointModel& model, const PointOrder& order_) { // POSIT algorithm for coplanar points as presented in // [Denis Oberkampf, Daniel F. DeMenthon, Larry S. Davis: "Iterative Pose Estimation Using Coplanar Feature Points"] // we use the same notation as in the paper here // The expected rotation used for resolving the ambiguity in POSIT: // In every iteration step the rotation closer to R_expected is taken Matx33f R_expected = Matx33f::eye(); // initial pose = last (predicted) pose Vec3f k; get_row(R_expected, 2, k); float Z0 = 1000.f; float old_epsilon_1 = 0; float old_epsilon_2 = 0; float epsilon_1 = 1; float epsilon_2 = 1; Vec3f I0, J0; Vec2f I0_coeff, J0_coeff; Vec3f I_1, J_1, I_2, J_2; Matx33f R_1, R_2; Matx33f* R_current; const int MAX_ITER = 100; const float EPS_THRESHOLD = 1e-4; const cv::Vec2f* order = order_.points; int i=1; for (; i<MAX_ITER; ++i) { epsilon_1 = k.dot(model.M01)/Z0; epsilon_2 = k.dot(model.M02)/Z0; // vector of scalar products <I0, M0i> and <J0, M0i> Vec2f I0_M0i(order[1][0]*(1.0 + epsilon_1) - order[0][0], order[2][0]*(1.0 + epsilon_2) - order[0][0]); Vec2f J0_M0i(order[1][1]*(1.0 + epsilon_1) - order[0][1], order[2][1]*(1.0 + epsilon_2) - order[0][1]); // construct projection of I, J onto M0i plane: I0 and J0 I0_coeff = model.P * I0_M0i; J0_coeff = model.P * J0_M0i; I0 = I0_coeff[0]*model.M01 + I0_coeff[1]*model.M02; J0 = J0_coeff[0]*model.M01 + J0_coeff[1]*model.M02; // calculate u component of I, J float II0 = I0.dot(I0); float IJ0 = I0.dot(J0); float JJ0 = J0.dot(J0); float rho, theta; if (JJ0 == II0) { rho = sqrt(abs(2*IJ0)); theta = -PI/4; if (IJ0<0) theta *= -1; } else { rho = sqrt(sqrt( (JJ0-II0)*(JJ0-II0) + 4*IJ0*IJ0 )); theta = atan( -2*IJ0 / (JJ0-II0) ); if (JJ0 - II0 < 0) theta += PI; theta /= 2; } // construct the two solutions I_1 = I0 + rho*cos(theta)*model.u; I_2 = I0 - rho*cos(theta)*model.u; J_1 = J0 + rho*sin(theta)*model.u; J_2 = J0 - rho*sin(theta)*model.u; float norm_const = 1.0/norm(I_1); // all have the same norm // create rotation matrices I_1 *= norm_const; J_1 *= norm_const; I_2 *= norm_const; J_2 *= norm_const; set_row(R_1, 0, I_1); set_row(R_1, 1, J_1); set_row(R_1, 2, I_1.cross(J_1)); set_row(R_2, 0, I_2); set_row(R_2, 1, J_2); set_row(R_2, 2, I_2.cross(J_2)); // the single translation solution Z0 = norm_const * focal_length; // pick the rotation solution closer to the expected one // in simple metric d(A,B) = || I - A * B^T || float R_1_deviation = norm(Matx33f::eye() - R_expected * R_1.t()); float R_2_deviation = norm(Matx33f::eye() - R_expected * R_2.t()); if (R_1_deviation < R_2_deviation) R_current = &R_1; else R_current = &R_2; get_row(*R_current, 2, k); // check for convergence condition if (abs(epsilon_1 - old_epsilon_1) + abs(epsilon_2 - old_epsilon_2) < EPS_THRESHOLD) break; old_epsilon_1 = epsilon_1; old_epsilon_2 = epsilon_2; } // apply results X_CM.R = *R_current; X_CM.t[0] = order[0][0] * Z0/focal_length; X_CM.t[1] = order[0][1] * Z0/focal_length; X_CM.t[2] = Z0; return i; //Rodrigues(X_CM.R, r); //qDebug()<<"iter: "<<i; //qDebug()<<"t: "<<X_CM.t[0]<<' '<<X_CM.t[1]<<' '<<X_CM.t[2]; //Vec3f r; // //qDebug()<<"r: "<<r[0]<<' '<<r[1]<<' '<<r[2]<<'\n'; }