// Copyright (c) 2007, 2008 libmv authors. // // Permission is hereby granted, free of charge, to any person obtaining a copy // of this software and associated documentation files (the "Software"), to // deal in the Software without restriction, including without limitation the // rights to use, copy, modify, merge, publish, distribute, sublicense, and/or // sell copies of the Software, and to permit persons to whom the Software is // furnished to do so, subject to the following conditions: // // The above copyright notice and this permission notice shall be included in // all copies or substantial portions of the Software. // // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING // FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS // IN THE SOFTWARE. #include "libmv/multiview/fundamental_kernel.h" #include "libmv/multiview/robust_estimation.h" #include "libmv/multiview/robust_fundamental.h" #include "libmv/numeric/numeric.h" namespace libmv { // TODO(keir): This interface is a bit ugly; consider fixing it. double FundamentalFromCorrespondences8PointRobust(const Mat &x1, const Mat &x2, double max_error, Mat3 *F, vector<int> *inliers, double outliers_probability) { // The threshold is on the sum of the squared errors in the two images. // Actually, Sampson's approximation of this error. double threshold = 2 * Square(max_error); double best_score = HUGE_VAL; typedef fundamental::kernel::NormalizedEightPointKernel Kernel; Kernel kernel(x1, x2); *F = Estimate(kernel, MLEScorer<Kernel>(threshold), inliers, &best_score, outliers_probability); if (best_score == HUGE_VAL) return HUGE_VAL; else return std::sqrt(best_score / 2.0); } double FundamentalFromCorrespondences7PointRobust(const Mat &x1, const Mat &x2, double max_error, Mat3 * F, vector<int> *inliers, double outliers_probability) { // The threshold is on the sum of the squared errors in the two images. // Actually, Sampson's approximation of this error. double threshold = 2 * Square(max_error); double best_score = HUGE_VAL; typedef fundamental::kernel::NormalizedSevenPointKernel Kernel; Kernel kernel(x1, x2); *F = Estimate(kernel, MLEScorer<Kernel>(threshold), inliers, &best_score, outliers_probability); if (best_score == HUGE_VAL) return HUGE_VAL; else return std::sqrt(best_score / 2.0); } } // namespace libmv