text: 
  // 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.
  
  // Copyright (c) 2012, 2013 Pierre MOULON.
  
  // This Source Code Form is subject to the terms of the Mozilla Public
  // License, v. 2.0. If a copy of the MPL was not distributed with this
  // file, You can obtain one at http://mozilla.org/MPL/2.0/.
  
  #ifndef I23DSFM_ROBUST_ESTIMATION_SCORE_EVALUATOR_H_
  #define I23DSFM_ROBUST_ESTIMATION_SCORE_EVALUATOR_H_
  
  namespace i23dSFM {
  namespace robust{
  
  using namespace std;
  
  /// Templated Functor class to evaluate a given model over a set of samples.
  template<typename Kernel>
  class ScorerEvaluator {
  public:
    ScorerEvaluator(double threshold) : threshold_(threshold) {}
  
    template <typename T>
    double Score(const Kernel &kernel,
      const typename Kernel::Model &model,
      const std::vector<T> &samples,
      std::vector<T> *inliers) const
    {
      double cost = 0.0;
      for (size_t j = 0; j < samples.size(); ++j) {
        double error = kernel.Error(samples[j], model);
        if (error < threshold_) {
          cost += error;
          inliers->push_back(samples[j]);
        } else {
          cost += threshold_;
        }
      }
      return cost;
    }
  private:
    double threshold_;
  };
  
  } // namespace robust
  } // namespace i23dSFM
  
  #endif // I23DSFM_ROBUST_ESTIMATION_SCORE_EVALUATOR_H_
  