From 3e2a57ff3561dea07a772250b2dc54dcd8352a09 Mon Sep 17 00:00:00 2001 From: edgarriba Date: Fri, 1 Aug 2014 10:48:39 +0200 Subject: [PATCH] Update for non Eigen users --- modules/calib3d/src/dls.cpp | 17 +- modules/calib3d/src/dls.h | 694 ++++++++++++++++++++++++++++++++++++++- modules/calib3d/src/solvepnp.cpp | 8 +- 3 files changed, 702 insertions(+), 17 deletions(-) diff --git a/modules/calib3d/src/dls.cpp b/modules/calib3d/src/dls.cpp index c8d9260..ef1ad58 100644 --- a/modules/calib3d/src/dls.cpp +++ b/modules/calib3d/src/dls.cpp @@ -12,15 +12,12 @@ # include "opencv2/core/eigen.hpp" #endif - -//#include -//#include - using namespace std; 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); @@ -136,8 +133,6 @@ void dls::run_kernel(const cv::Mat& pp) int count = 0; for (int k = 0; k < 27; ++k) { - // TODO: solve implementation for complex numbers - // V(:,k) = V(:,k)/V(1,k); cv::Mat V_kA = eigenvec_r.col(k); // 27x1 cv::Mat V_kB = cv::Mat(1, 1, z.depth(), V_kA.at(0)); // 1x1 @@ -145,9 +140,11 @@ void dls::run_kernel(const cv::Mat& pp) cv::Mat( V_k.t()).copyTo( eigenvec_r.col(k) ); //if (imag(V(2,k)) == 0) +#ifdef HAVE_EIGEN const double epsilon = 1e-4; if( eigenval_i.at(k,0) >= -epsilon && eigenval_i.at(k,0) <= epsilon ) - { // it should work without checking imaginari part +#endif + { double stmp[3]; stmp[0] = eigenvec_r.at(9, k); @@ -282,8 +279,6 @@ void dls::build_coeff_matrix(const cv::Mat& pp, cv::Mat& Mtilde, cv::Mat& D) void dls::compute_eigenvec(const cv::Mat& Mtilde, cv::Mat& eigenval_real, cv::Mat& eigenval_imag, cv::Mat& eigenvec_real, cv::Mat& eigenvec_imag) { - // EIGENVALUES AND EIGENVECTORS - #ifdef HAVE_EIGEN Eigen::MatrixXd Mtilde_eig, zeros_eig; cv::cv2eigen(Mtilde, Mtilde_eig); @@ -305,6 +300,10 @@ void dls::compute_eigenvec(const cv::Mat& Mtilde, cv::Mat& eigenval_real, cv::Ma cv::eigen2cv(eigval_imag, eigenval_imag); cv::eigen2cv(eigvec_real, eigenvec_real); cv::eigen2cv(eigvec_imag, eigenvec_imag); +#else + EigenvalueDecomposition es(Mtilde); + eigenval_real = es.eigenvalues(); + eigenvec_real = es.eigenvectors(); #endif } diff --git a/modules/calib3d/src/dls.h b/modules/calib3d/src/dls.h index a8250fa..83f014f 100644 --- a/modules/calib3d/src/dls.h +++ b/modules/calib3d/src/dls.h @@ -4,7 +4,7 @@ #include "precomp.hpp" using namespace std; - +using namespace cv; class dls { @@ -66,4 +66,696 @@ private: double cost__; // optimal found solution }; + +class EigenvalueDecomposition { +private: + + // Holds the data dimension. + int n; + + // Stores real/imag part of a complex division. + double cdivr, cdivi; + + // Pointer to internal memory. + double *d, *e, *ort; + double **V, **H; + + // Holds the computed eigenvalues. + Mat _eigenvalues; + + // Holds the computed eigenvectors. + Mat _eigenvectors; + + // Allocates memory. + template + _Tp *alloc_1d(int m) { + return new _Tp[m]; + } + + // Allocates memory. + template + _Tp *alloc_1d(int m, _Tp val) { + _Tp *arr = alloc_1d<_Tp> (m); + for (int i = 0; i < m; i++) + arr[i] = val; + return arr; + } + + // Allocates memory. + template + _Tp **alloc_2d(int m, int _n) { + _Tp **arr = new _Tp*[m]; + for (int i = 0; i < m; i++) + arr[i] = new _Tp[_n]; + return arr; + } + + // Allocates memory. + template + _Tp **alloc_2d(int m, int _n, _Tp val) { + _Tp **arr = alloc_2d<_Tp> (m, _n); + for (int i = 0; i < m; i++) { + for (int j = 0; j < _n; j++) { + arr[i][j] = val; + } + } + return arr; + } + + void cdiv(double xr, double xi, double yr, double yi) { + double r, dv; + if (std::abs(yr) > std::abs(yi)) { + r = yi / yr; + dv = yr + r * yi; + cdivr = (xr + r * xi) / dv; + cdivi = (xi - r * xr) / dv; + } else { + r = yr / yi; + dv = yi + r * yr; + cdivr = (r * xr + xi) / dv; + cdivi = (r * xi - xr) / dv; + } + } + + // Nonsymmetric reduction from Hessenberg to real Schur form. + + void hqr2() { + + // This is derived from the Algol procedure hqr2, + // by Martin and Wilkinson, Handbook for Auto. Comp., + // Vol.ii-Linear Algebra, and the corresponding + // Fortran subroutine in EISPACK. + + // Initialize + int nn = this->n; + int n1 = nn - 1; + int low = 0; + int high = nn - 1; + double eps = std::pow(2.0, -52.0); + double exshift = 0.0; + double p = 0, q = 0, r = 0, s = 0, z = 0, t, w, x, y; + + // Store roots isolated by balanc and compute matrix norm + + double norm = 0.0; + for (int i = 0; i < nn; i++) { + if (i < low || i > high) { + d[i] = H[i][i]; + e[i] = 0.0; + } + for (int j = std::max(i - 1, 0); j < nn; j++) { + norm = norm + std::abs(H[i][j]); + } + } + + // Outer loop over eigenvalue index + int iter = 0; + while (n1 >= low) { + + // Look for single small sub-diagonal element + int l = n1; + while (l > low) { + s = std::abs(H[l - 1][l - 1]) + std::abs(H[l][l]); + if (s == 0.0) { + s = norm; + } + if (std::abs(H[l][l - 1]) < eps * s) { + break; + } + l--; + } + + // Check for convergence + // One root found + + if (l == n1) { + H[n1][n1] = H[n1][n1] + exshift; + d[n1] = H[n1][n1]; + e[n1] = 0.0; + n1--; + iter = 0; + + // Two roots found + + } else if (l == n1 - 1) { + w = H[n1][n1 - 1] * H[n1 - 1][n1]; + p = (H[n1 - 1][n1 - 1] - H[n1][n1]) / 2.0; + q = p * p + w; + z = std::sqrt(std::abs(q)); + H[n1][n1] = H[n1][n1] + exshift; + H[n1 - 1][n1 - 1] = H[n1 - 1][n1 - 1] + exshift; + x = H[n1][n1]; + + // Real pair + + if (q >= 0) { + if (p >= 0) { + z = p + z; + } else { + z = p - z; + } + d[n1 - 1] = x + z; + d[n1] = d[n1 - 1]; + if (z != 0.0) { + d[n1] = x - w / z; + } + e[n1 - 1] = 0.0; + e[n1] = 0.0; + x = H[n1][n1 - 1]; + s = std::abs(x) + std::abs(z); + p = x / s; + q = z / s; + r = std::sqrt(p * p + q * q); + p = p / r; + q = q / r; + + // Row modification + + for (int j = n1 - 1; j < nn; j++) { + z = H[n1 - 1][j]; + H[n1 - 1][j] = q * z + p * H[n1][j]; + H[n1][j] = q * H[n1][j] - p * z; + } + + // Column modification + + for (int i = 0; i <= n1; i++) { + z = H[i][n1 - 1]; + H[i][n1 - 1] = q * z + p * H[i][n1]; + H[i][n1] = q * H[i][n1] - p * z; + } + + // Accumulate transformations + + for (int i = low; i <= high; i++) { + z = V[i][n1 - 1]; + V[i][n1 - 1] = q * z + p * V[i][n1]; + V[i][n1] = q * V[i][n1] - p * z; + } + + // Complex pair + + } else { + d[n1 - 1] = x + p; + d[n1] = x + p; + e[n1 - 1] = z; + e[n1] = -z; + } + n1 = n1 - 2; + iter = 0; + + // No convergence yet + + } else { + + // Form shift + + x = H[n1][n1]; + y = 0.0; + w = 0.0; + if (l < n1) { + y = H[n1 - 1][n1 - 1]; + w = H[n1][n1 - 1] * H[n1 - 1][n1]; + } + + // Wilkinson's original ad hoc shift + + if (iter == 10) { + exshift += x; + for (int i = low; i <= n1; i++) { + H[i][i] -= x; + } + s = std::abs(H[n1][n1 - 1]) + std::abs(H[n1 - 1][n1 - 2]); + x = y = 0.75 * s; + w = -0.4375 * s * s; + } + + // MATLAB's new ad hoc shift + + if (iter == 30) { + s = (y - x) / 2.0; + s = s * s + w; + if (s > 0) { + s = std::sqrt(s); + if (y < x) { + s = -s; + } + s = x - w / ((y - x) / 2.0 + s); + for (int i = low; i <= n1; i++) { + H[i][i] -= s; + } + exshift += s; + x = y = w = 0.964; + } + } + + iter = iter + 1; // (Could check iteration count here.) + + // Look for two consecutive small sub-diagonal elements + int m = n1 - 2; + while (m >= l) { + z = H[m][m]; + r = x - z; + s = y - z; + p = (r * s - w) / H[m + 1][m] + H[m][m + 1]; + q = H[m + 1][m + 1] - z - r - s; + r = H[m + 2][m + 1]; + s = std::abs(p) + std::abs(q) + std::abs(r); + p = p / s; + q = q / s; + r = r / s; + if (m == l) { + break; + } + if (std::abs(H[m][m - 1]) * (std::abs(q) + std::abs(r)) < eps * (std::abs(p) + * (std::abs(H[m - 1][m - 1]) + std::abs(z) + std::abs( + H[m + 1][m + 1])))) { + break; + } + m--; + } + + for (int i = m + 2; i <= n1; i++) { + H[i][i - 2] = 0.0; + if (i > m + 2) { + H[i][i - 3] = 0.0; + } + } + + // Double QR step involving rows l:n and columns m:n + + for (int k = m; k <= n1 - 1; k++) { + bool notlast = (k != n1 - 1); + if (k != m) { + p = H[k][k - 1]; + q = H[k + 1][k - 1]; + r = (notlast ? H[k + 2][k - 1] : 0.0); + x = std::abs(p) + std::abs(q) + std::abs(r); + if (x != 0.0) { + p = p / x; + q = q / x; + r = r / x; + } + } + if (x == 0.0) { + break; + } + s = std::sqrt(p * p + q * q + r * r); + if (p < 0) { + s = -s; + } + if (s != 0) { + if (k != m) { + H[k][k - 1] = -s * x; + } else if (l != m) { + H[k][k - 1] = -H[k][k - 1]; + } + p = p + s; + x = p / s; + y = q / s; + z = r / s; + q = q / p; + r = r / p; + + // Row modification + + for (int j = k; j < nn; j++) { + p = H[k][j] + q * H[k + 1][j]; + if (notlast) { + p = p + r * H[k + 2][j]; + H[k + 2][j] = H[k + 2][j] - p * z; + } + H[k][j] = H[k][j] - p * x; + H[k + 1][j] = H[k + 1][j] - p * y; + } + + // Column modification + + for (int i = 0; i <= std::min(n1, k + 3); i++) { + p = x * H[i][k] + y * H[i][k + 1]; + if (notlast) { + p = p + z * H[i][k + 2]; + H[i][k + 2] = H[i][k + 2] - p * r; + } + H[i][k] = H[i][k] - p; + H[i][k + 1] = H[i][k + 1] - p * q; + } + + // Accumulate transformations + + for (int i = low; i <= high; i++) { + p = x * V[i][k] + y * V[i][k + 1]; + if (notlast) { + p = p + z * V[i][k + 2]; + V[i][k + 2] = V[i][k + 2] - p * r; + } + V[i][k] = V[i][k] - p; + V[i][k + 1] = V[i][k + 1] - p * q; + } + } // (s != 0) + } // k loop + } // check convergence + } // while (n1 >= low) + + // Backsubstitute to find vectors of upper triangular form + + if (norm == 0.0) { + return; + } + + for (n1 = nn - 1; n1 >= 0; n1--) { + p = d[n1]; + q = e[n1]; + + // Real vector + + if (q == 0) { + int l = n1; + H[n1][n1] = 1.0; + for (int i = n1 - 1; i >= 0; i--) { + w = H[i][i] - p; + r = 0.0; + for (int j = l; j <= n1; j++) { + r = r + H[i][j] * H[j][n1]; + } + if (e[i] < 0.0) { + z = w; + s = r; + } else { + l = i; + if (e[i] == 0.0) { + if (w != 0.0) { + H[i][n1] = -r / w; + } else { + H[i][n1] = -r / (eps * norm); + } + + // Solve real equations + + } else { + x = H[i][i + 1]; + y = H[i + 1][i]; + q = (d[i] - p) * (d[i] - p) + e[i] * e[i]; + t = (x * s - z * r) / q; + H[i][n1] = t; + if (std::abs(x) > std::abs(z)) { + H[i + 1][n1] = (-r - w * t) / x; + } else { + H[i + 1][n1] = (-s - y * t) / z; + } + } + + // Overflow control + + t = std::abs(H[i][n1]); + if ((eps * t) * t > 1) { + for (int j = i; j <= n1; j++) { + H[j][n1] = H[j][n1] / t; + } + } + } + } + // Complex vector + } else if (q < 0) { + int l = n1 - 1; + + // Last vector component imaginary so matrix is triangular + + if (std::abs(H[n1][n1 - 1]) > std::abs(H[n1 - 1][n1])) { + H[n1 - 1][n1 - 1] = q / H[n1][n1 - 1]; + H[n1 - 1][n1] = -(H[n1][n1] - p) / H[n1][n1 - 1]; + } else { + cdiv(0.0, -H[n1 - 1][n1], H[n1 - 1][n1 - 1] - p, q); + H[n1 - 1][n1 - 1] = cdivr; + H[n1 - 1][n1] = cdivi; + } + H[n1][n1 - 1] = 0.0; + H[n1][n1] = 1.0; + for (int i = n1 - 2; i >= 0; i--) { + double ra, sa, vr, vi; + ra = 0.0; + sa = 0.0; + for (int j = l; j <= n1; j++) { + ra = ra + H[i][j] * H[j][n1 - 1]; + sa = sa + H[i][j] * H[j][n1]; + } + w = H[i][i] - p; + + if (e[i] < 0.0) { + z = w; + r = ra; + s = sa; + } else { + l = i; + if (e[i] == 0) { + cdiv(-ra, -sa, w, q); + H[i][n1 - 1] = cdivr; + H[i][n1] = cdivi; + } else { + + // Solve complex equations + + x = H[i][i + 1]; + y = H[i + 1][i]; + vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q; + vi = (d[i] - p) * 2.0 * q; + if (vr == 0.0 && vi == 0.0) { + vr = eps * norm * (std::abs(w) + std::abs(q) + std::abs(x) + + std::abs(y) + std::abs(z)); + } + cdiv(x * r - z * ra + q * sa, + x * s - z * sa - q * ra, vr, vi); + H[i][n1 - 1] = cdivr; + H[i][n1] = cdivi; + if (std::abs(x) > (std::abs(z) + std::abs(q))) { + H[i + 1][n1 - 1] = (-ra - w * H[i][n1 - 1] + q + * H[i][n1]) / x; + H[i + 1][n1] = (-sa - w * H[i][n1] - q * H[i][n1 + - 1]) / x; + } else { + cdiv(-r - y * H[i][n1 - 1], -s - y * H[i][n1], z, + q); + H[i + 1][n1 - 1] = cdivr; + H[i + 1][n1] = cdivi; + } + } + + // Overflow control + + t = std::max(std::abs(H[i][n1 - 1]), std::abs(H[i][n1])); + if ((eps * t) * t > 1) { + for (int j = i; j <= n1; j++) { + H[j][n1 - 1] = H[j][n1 - 1] / t; + H[j][n1] = H[j][n1] / t; + } + } + } + } + } + } + + // Vectors of isolated roots + + for (int i = 0; i < nn; i++) { + if (i < low || i > high) { + for (int j = i; j < nn; j++) { + V[i][j] = H[i][j]; + } + } + } + + // Back transformation to get eigenvectors of original matrix + + for (int j = nn - 1; j >= low; j--) { + for (int i = low; i <= high; i++) { + z = 0.0; + for (int k = low; k <= std::min(j, high); k++) { + z = z + V[i][k] * H[k][j]; + } + V[i][j] = z; + } + } + } + + // Nonsymmetric reduction to Hessenberg form. + void orthes() { + // This is derived from the Algol procedures orthes and ortran, + // by Martin and Wilkinson, Handbook for Auto. Comp., + // Vol.ii-Linear Algebra, and the corresponding + // Fortran subroutines in EISPACK. + int low = 0; + int high = n - 1; + + for (int m = low + 1; m <= high - 1; m++) { + + // Scale column. + + double scale = 0.0; + for (int i = m; i <= high; i++) { + scale = scale + std::abs(H[i][m - 1]); + } + if (scale != 0.0) { + + // Compute Householder transformation. + + double h = 0.0; + for (int i = high; i >= m; i--) { + ort[i] = H[i][m - 1] / scale; + h += ort[i] * ort[i]; + } + double g = std::sqrt(h); + if (ort[m] > 0) { + g = -g; + } + h = h - ort[m] * g; + ort[m] = ort[m] - g; + + // Apply Householder similarity transformation + // H = (I-u*u'/h)*H*(I-u*u')/h) + + for (int j = m; j < n; j++) { + double f = 0.0; + for (int i = high; i >= m; i--) { + f += ort[i] * H[i][j]; + } + f = f / h; + for (int i = m; i <= high; i++) { + H[i][j] -= f * ort[i]; + } + } + + for (int i = 0; i <= high; i++) { + double f = 0.0; + for (int j = high; j >= m; j--) { + f += ort[j] * H[i][j]; + } + f = f / h; + for (int j = m; j <= high; j++) { + H[i][j] -= f * ort[j]; + } + } + ort[m] = scale * ort[m]; + H[m][m - 1] = scale * g; + } + } + + // Accumulate transformations (Algol's ortran). + + for (int i = 0; i < n; i++) { + for (int j = 0; j < n; j++) { + V[i][j] = (i == j ? 1.0 : 0.0); + } + } + + for (int m = high - 1; m >= low + 1; m--) { + if (H[m][m - 1] != 0.0) { + for (int i = m + 1; i <= high; i++) { + ort[i] = H[i][m - 1]; + } + for (int j = m; j <= high; j++) { + double g = 0.0; + for (int i = m; i <= high; i++) { + g += ort[i] * V[i][j]; + } + // Double division avoids possible underflow + g = (g / ort[m]) / H[m][m - 1]; + for (int i = m; i <= high; i++) { + V[i][j] += g * ort[i]; + } + } + } + } + } + + // Releases all internal working memory. + void release() { + // releases the working data + delete[] d; + delete[] e; + delete[] ort; + for (int i = 0; i < n; i++) { + delete[] H[i]; + delete[] V[i]; + } + delete[] H; + delete[] V; + } + + // Computes the Eigenvalue Decomposition for a matrix given in H. + void compute() { + // Allocate memory for the working data. + V = alloc_2d (n, n, 0.0); + d = alloc_1d (n); + e = alloc_1d (n); + ort = alloc_1d (n); + // Reduce to Hessenberg form. + orthes(); + // Reduce Hessenberg to real Schur form. + hqr2(); + // Copy eigenvalues to OpenCV Matrix. + _eigenvalues.create(1, n, CV_64FC1); + for (int i = 0; i < n; i++) { + _eigenvalues.at (0, i) = d[i]; + } + // Copy eigenvectors to OpenCV Matrix. + _eigenvectors.create(n, n, CV_64FC1); + for (int i = 0; i < n; i++) + for (int j = 0; j < n; j++) + _eigenvectors.at (i, j) = V[i][j]; + // Deallocate the memory by releasing all internal working data. + release(); + } + +public: + EigenvalueDecomposition() + : n(0) { } + + // Initializes & computes the Eigenvalue Decomposition for a general matrix + // given in src. This function is a port of the EigenvalueSolver in JAMA, + // which has been released to public domain by The MathWorks and the + // National Institute of Standards and Technology (NIST). + EigenvalueDecomposition(InputArray src) { + compute(src); + } + + // This function computes the Eigenvalue Decomposition for a general matrix + // given in src. This function is a port of the EigenvalueSolver in JAMA, + // which has been released to public domain by The MathWorks and the + // National Institute of Standards and Technology (NIST). + void compute(InputArray src) + { + /*if(isSymmetric(src)) { + // Fall back to OpenCV for a symmetric matrix! + cv::eigen(src, _eigenvalues, _eigenvectors); + } else {*/ + Mat tmp; + // Convert the given input matrix to double. Is there any way to + // prevent allocating the temporary memory? Only used for copying + // into working memory and deallocated after. + src.getMat().convertTo(tmp, CV_64FC1); + // Get dimension of the matrix. + this->n = tmp.cols; + // Allocate the matrix data to work on. + this->H = alloc_2d (n, n); + // Now safely copy the data. + for (int i = 0; i < tmp.rows; i++) { + for (int j = 0; j < tmp.cols; j++) { + this->H[i][j] = tmp.at(i, j); + } + } + // Deallocates the temporary matrix before computing. + tmp.release(); + // Performs the eigenvalue decomposition of H. + compute(); + // } + } + + ~EigenvalueDecomposition() {} + + // Returns the eigenvalues of the Eigenvalue Decomposition. + Mat eigenvalues() { return _eigenvalues; } + // Returns the eigenvectors of the Eigenvalue Decomposition. + Mat eigenvectors() { return _eigenvectors; } +}; + #endif // DLS_H diff --git a/modules/calib3d/src/solvepnp.cpp b/modules/calib3d/src/solvepnp.cpp index 73eda92..4c1e80b 100644 --- a/modules/calib3d/src/solvepnp.cpp +++ b/modules/calib3d/src/solvepnp.cpp @@ -96,21 +96,15 @@ bool cv::solvePnP( InputArray _opoints, InputArray _ipoints, } else if (flags == DLS) { - bool result = false; -#ifdef HAVE_EIGEN - cv::Mat undistortedPoints; cv::undistortPoints(ipoints, undistortedPoints, cameraMatrix, distCoeffs); dls PnP(opoints, undistortedPoints); cv::Mat R, rvec = _rvec.getMat(), tvec = _tvec.getMat(); - result = PnP.compute_pose(R, tvec); + bool result = PnP.compute_pose(R, tvec); if (result) cv::Rodrigues(R, rvec); -#else - std::cout << "EIGEN library needed for DLS" << std::endl; -#endif return result; } else -- 2.7.4