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@ -53,7 +53,7 @@ namespace cv { namespace |
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double dalpha; |
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}; |
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void subMatrix(const Mat& src, Mat& dst, const std::vector<int>& cols, const std::vector<int>& rows); |
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void subMatrix(const Mat& src, Mat& dst, const std::vector<uchar>& cols, const std::vector<uchar>& rows); |
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}} |
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////
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@ -762,12 +762,12 @@ double cv::fisheye::calibrate(InputArrayOfArrays objectPoints, InputArrayOfArray |
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double alpha_smooth2 = 1 - std::pow(1 - alpha_smooth, iter + 1.0); |
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Mat JJ2_inv, ex3; |
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ComputeJacobians(objectPoints, imagePoints, finalParam, omc, Tc, check_cond,thresh_cond, JJ2_inv, ex3); |
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Mat JJ2, ex3; |
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ComputeJacobians(objectPoints, imagePoints, finalParam, omc, Tc, check_cond,thresh_cond, JJ2, ex3); |
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Mat G = alpha_smooth2 * JJ2_inv * ex3; |
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currentParam = finalParam + G; |
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Mat G; |
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solve(JJ2, ex3, G); |
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currentParam = finalParam + alpha_smooth2*G; |
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change = norm(Vec4d(currentParam.f[0], currentParam.f[1], currentParam.c[0], currentParam.c[1]) - |
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Vec4d(finalParam.f[0], finalParam.f[1], finalParam.c[0], finalParam.c[1])) |
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@ -899,7 +899,7 @@ double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayO |
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intrinsicLeft_errors.isEstimate = intrinsicLeft.isEstimate; |
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intrinsicRight_errors.isEstimate = intrinsicRight.isEstimate; |
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std::vector<int> selectedParams; |
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std::vector<uchar> selectedParams; |
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std::vector<int> tmp(6 * (n_images + 1), 1); |
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selectedParams.insert(selectedParams.end(), intrinsicLeft.isEstimate.begin(), intrinsicLeft.isEstimate.end()); |
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selectedParams.insert(selectedParams.end(), intrinsicRight.isEstimate.begin(), intrinsicRight.isEstimate.end()); |
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@ -923,7 +923,6 @@ double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayO |
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cv::Mat J = cv::Mat::zeros(4 * n_points * n_images, 18 + 6 * (n_images + 1), CV_64FC1), |
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e = cv::Mat::zeros(4 * n_points * n_images, 1, CV_64FC1), Jkk, ekk; |
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cv::Mat J2_inv; |
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for(int iter = 0; ; ++iter) |
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{ |
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@ -1000,12 +999,11 @@ double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayO |
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cv::Vec6d oldTom(Tcur[0], Tcur[1], Tcur[2], omcur[0], omcur[1], omcur[2]); |
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//update all parameters
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cv::subMatrix(J, J, selectedParams, std::vector<int>(J.rows, 1)); |
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cv::Mat J2 = J.t() * J; |
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J2_inv = J2.inv(); |
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cv::subMatrix(J, J, selectedParams, std::vector<uchar>(J.rows, 1)); |
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int a = cv::countNonZero(intrinsicLeft.isEstimate); |
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int b = cv::countNonZero(intrinsicRight.isEstimate); |
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cv::Mat deltas = J2_inv * J.t() * e; |
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cv::Mat deltas; |
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solve(J.t() * J, J.t()*e, deltas); |
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intrinsicLeft = intrinsicLeft + deltas.rowRange(0, a); |
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intrinsicRight = intrinsicRight + deltas.rowRange(a, a + b); |
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omcur = omcur + cv::Vec3d(deltas.rowRange(a + b, a + b + 3)); |
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@ -1052,12 +1050,12 @@ double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayO |
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} |
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namespace cv{ namespace { |
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void subMatrix(const Mat& src, Mat& dst, const std::vector<int>& cols, const std::vector<int>& rows) |
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void subMatrix(const Mat& src, Mat& dst, const std::vector<uchar>& cols, const std::vector<uchar>& rows) |
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{ |
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CV_Assert(src.type() == CV_64FC1); |
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CV_Assert(src.channels() == 1); |
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int nonzeros_cols = cv::countNonZero(cols); |
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Mat tmp(src.rows, nonzeros_cols, CV_64FC1); |
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Mat tmp(src.rows, nonzeros_cols, CV_64F); |
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for (int i = 0, j = 0; i < (int)cols.size(); i++) |
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{ |
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@ -1068,16 +1066,14 @@ void subMatrix(const Mat& src, Mat& dst, const std::vector<int>& cols, const std |
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} |
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int nonzeros_rows = cv::countNonZero(rows); |
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Mat tmp1(nonzeros_rows, nonzeros_cols, CV_64FC1); |
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dst.create(nonzeros_rows, nonzeros_cols, CV_64F); |
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for (int i = 0, j = 0; i < (int)rows.size(); i++) |
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{ |
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if (rows[i]) |
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{ |
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tmp.row(i).copyTo(tmp1.row(j++)); |
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tmp.row(i).copyTo(dst.row(j++)); |
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} |
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} |
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dst = tmp1.clone(); |
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} |
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}} |
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@ -1386,7 +1382,7 @@ void cv::internal::CalibrateExtrinsics(InputArrayOfArrays objectPoints, InputArr |
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void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, |
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const IntrinsicParams& param, InputArray omc, InputArray Tc, |
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const int& check_cond, const double& thresh_cond, Mat& JJ2_inv, Mat& ex3) |
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const int& check_cond, const double& thresh_cond, Mat& JJ2, Mat& ex3) |
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{ |
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CV_Assert(!objectPoints.empty() && (objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3)); |
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CV_Assert(!imagePoints.empty() && (imagePoints.type() == CV_32FC2 || imagePoints.type() == CV_64FC2)); |
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@ -1396,7 +1392,7 @@ void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayO |
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int n = (int)objectPoints.total(); |
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Mat JJ3 = Mat::zeros(9 + 6 * n, 9 + 6 * n, CV_64FC1); |
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JJ2 = Mat::zeros(9 + 6 * n, 9 + 6 * n, CV_64FC1); |
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ex3 = Mat::zeros(9 + 6 * n, 1, CV_64FC1 ); |
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for (int image_idx = 0; image_idx < n; ++image_idx) |
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@ -1422,16 +1418,14 @@ void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayO |
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Mat B = jacobians.colRange(8, 14).clone(); |
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B = B.t(); |
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JJ3(Rect(0, 0, 9, 9)) = JJ3(Rect(0, 0, 9, 9)) + A * A.t(); |
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JJ3(Rect(9 + 6 * image_idx, 9 + 6 * image_idx, 6, 6)) = B * B.t(); |
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Mat AB = A * B.t(); |
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AB.copyTo(JJ3(Rect(9 + 6 * image_idx, 0, 6, 9))); |
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JJ2(Rect(0, 0, 9, 9)) += A * A.t(); |
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JJ2(Rect(9 + 6 * image_idx, 9 + 6 * image_idx, 6, 6)) = B * B.t(); |
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JJ3(Rect(0, 9 + 6 * image_idx, 9, 6)) = AB.t(); |
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ex3(Rect(0,0,1,9)) = ex3(Rect(0,0,1,9)) + A * exkk.reshape(1, 2 * exkk.rows); |
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JJ2(Rect(9 + 6 * image_idx, 0, 6, 9)) = A * B.t(); |
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JJ2(Rect(0, 9 + 6 * image_idx, 9, 6)) = JJ2(Rect(9 + 6 * image_idx, 0, 6, 9)).t(); |
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ex3(Rect(0, 9 + 6 * image_idx, 1, 6)) = B * exkk.reshape(1, 2 * exkk.rows); |
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ex3.rowRange(0, 9) += A * exkk.reshape(1, 2 * exkk.rows); |
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ex3.rowRange(9 + 6 * image_idx, 9 + 6 * (image_idx + 1)) = B * exkk.reshape(1, 2 * exkk.rows); |
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if (check_cond) |
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{ |
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@ -1441,12 +1435,11 @@ void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayO |
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} |
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} |
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std::vector<int> idxs(param.isEstimate); |
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std::vector<uchar> idxs(param.isEstimate); |
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idxs.insert(idxs.end(), 6 * n, 1); |
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subMatrix(JJ3, JJ3, idxs, idxs); |
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subMatrix(ex3, ex3, std::vector<int>(1, 1), idxs); |
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JJ2_inv = JJ3.inv(); |
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subMatrix(JJ2, JJ2, idxs, idxs); |
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subMatrix(ex3, ex3, std::vector<uchar>(1, 1), idxs); |
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} |
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void cv::internal::EstimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, |
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@ -1478,30 +1471,17 @@ void cv::internal::EstimateUncertainties(InputArrayOfArrays objectPoints, InputA |
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meanStdDev(ex, noArray(), std_err); |
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std_err *= sqrt((double)ex.total()/((double)ex.total() - 1.0)); |
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Mat sigma_x; |
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Vec<double, 1> sigma_x; |
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meanStdDev(ex.reshape(1, 1), noArray(), sigma_x); |
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sigma_x *= sqrt(2.0 * (double)ex.total()/(2.0 * (double)ex.total() - 1.0)); |
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Mat _JJ2_inv, ex3; |
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ComputeJacobians(objectPoints, imagePoints, params, omc, Tc, check_cond, thresh_cond, _JJ2_inv, ex3); |
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Mat_<double>& JJ2_inv = (Mat_<double>&)_JJ2_inv; |
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sqrt(JJ2_inv, JJ2_inv); |
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Mat JJ2, ex3; |
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ComputeJacobians(objectPoints, imagePoints, params, omc, Tc, check_cond, thresh_cond, JJ2, ex3); |
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double s = sigma_x.at<double>(0); |
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Mat r = 3 * s * JJ2_inv.diag(); |
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errors = r; |
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sqrt(JJ2.inv(), JJ2); |
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rms = 0; |
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const Vec2d* ptr_ex = ex.ptr<Vec2d>(); |
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for (size_t i = 0; i < ex.total(); i++) |
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{ |
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rms += ptr_ex[i][0] * ptr_ex[i][0] + ptr_ex[i][1] * ptr_ex[i][1]; |
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} |
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rms /= (double)ex.total(); |
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rms = sqrt(rms); |
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errors = 3 * sigma_x(0) * JJ2.diag(); |
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rms = sqrt(norm(ex, NORM_L2SQR)/ex.total()); |
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} |
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void cv::internal::dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB) |
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