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// Copyright (c) 2021, Viktor Larsson
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// * Neither the name of the copyright holder nor the
// names of its contributors may be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE
// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
// ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef POSELIB_RANSAC_IMPL_H_
#define POSELIB_RANSAC_IMPL_H_
#include "PoseLib/types.h"
#include <vector>
namespace poselib {
struct RansacState {
size_t best_minimal_inlier_count = 0;
double best_minimal_msac_score = std::numeric_limits<double>::max();
size_t dynamic_max_iter = 100000;
double log_prob_missing_model = std::log(1.0 - 0.9999);
};
template <typename Solver, typename Model = CameraPose>
void score_models(const Solver &estimator, const std::vector<Model> &models, const RansacOptions &opt,
RansacState &state, RansacStats &stats, Model *best_model) {
// Find best model among candidates
int best_model_ind = -1;
size_t inlier_count = 0;
for (size_t i = 0; i < models.size(); ++i) {
double score_msac = estimator.score_model(models[i], &inlier_count);
bool more_inliers = inlier_count > state.best_minimal_inlier_count;
bool better_score = score_msac < state.best_minimal_msac_score;
if (more_inliers || better_score) {
if (more_inliers) {
state.best_minimal_inlier_count = inlier_count;
}
if (better_score) {
state.best_minimal_msac_score = score_msac;
}
best_model_ind = i;
// check if we should update best model already
if (score_msac < stats.model_score) {
stats.model_score = score_msac;
*best_model = models[i];
stats.num_inliers = inlier_count;
}
}
}
if (best_model_ind == -1)
return;
// Refinement
Model refined_model = models[best_model_ind];
estimator.refine_model(&refined_model);
stats.refinements++;
double refined_msac_score = estimator.score_model(refined_model, &inlier_count);
if (refined_msac_score < stats.model_score) {
stats.model_score = refined_msac_score;
stats.num_inliers = inlier_count;
*best_model = refined_model;
}
// update number of iterations
stats.inlier_ratio = static_cast<double>(stats.num_inliers) / static_cast<double>(estimator.num_data);
if (stats.inlier_ratio >= 0.9999) {
// this is to avoid log(prob_outlier) = -inf below
state.dynamic_max_iter = opt.min_iterations;
} else if (stats.inlier_ratio <= 0.0001) {
// this is to avoid log(prob_outlier) = 0 below
state.dynamic_max_iter = opt.max_iterations;
} else {
const double prob_outlier = 1.0 - std::pow(stats.inlier_ratio, estimator.sample_sz);
state.dynamic_max_iter =
std::ceil(state.log_prob_missing_model / std::log(prob_outlier) * opt.dyn_num_trials_mult);
}
}
// Templated LO-RANSAC implementation (inspired by RansacLib from Torsten Sattler)
template <typename Solver, typename Model = CameraPose>
RansacStats ransac(Solver &estimator, const RansacOptions &opt, Model *best_model) {
RansacStats stats;
if (estimator.num_data < estimator.sample_sz) {
return stats;
}
// Score/Inliers for best model found so far
stats.num_inliers = 0;
stats.model_score = std::numeric_limits<double>::max();
// best inl/score for minimal model, used to decide when to LO
RansacState state;
state.dynamic_max_iter = opt.max_iterations;
state.log_prob_missing_model = std::log(1.0 - opt.success_prob);
// Score initial model if it was supplied
if (opt.score_initial_model) {
score_models(estimator, {*best_model}, opt, state, stats, best_model);
}
size_t inlier_count = 0;
std::vector<Model> models;
for (stats.iterations = 0; stats.iterations < opt.max_iterations; stats.iterations++) {
if (stats.iterations > opt.min_iterations && stats.iterations > state.dynamic_max_iter) {
break;
}
models.clear();
estimator.generate_models(&models);
score_models(estimator, models, opt, state, stats, best_model);
}
// Final refinement
Model refined_model = *best_model;
estimator.refine_model(&refined_model);
stats.refinements++;
double refined_msac_score = estimator.score_model(refined_model, &inlier_count);
if (refined_msac_score < stats.model_score) {
*best_model = refined_model;
stats.num_inliers = inlier_count;
}
return stats;
}
} // namespace poselib
#endif
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