| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145 | // Ceres Solver - A fast non-linear least squares minimizer// Copyright 2015 Google Inc. All rights reserved.// http://ceres-solver.org///// 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 Google Inc. 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 THE COPYRIGHT OWNER 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.//// Author: strandmark@google.com (Petter Strandmark)//// Class for loading the data required for descibing a Fields of Experts (FoE)// model. The Fields of Experts regularization consists of terms of the type////   alpha * log(1 + (1/2)*sum(F .* X)^2),//// where F is a d-by-d image patch and alpha is a constant. This is implemented// by a FieldsOfExpertsSum object which represents the dot product between the// image patches and a FieldsOfExpertsLoss which implements the log(1 + (1/2)s)// part.//// [1] S. Roth and M.J. Black. "Fields of Experts." International Journal of//     Computer Vision, 82(2):205--229, 2009.#ifndef CERES_EXAMPLES_FIELDS_OF_EXPERTS_H_#define CERES_EXAMPLES_FIELDS_OF_EXPERTS_H_#include <iostream>#include <vector>#include "ceres/loss_function.h"#include "ceres/cost_function.h"#include "ceres/sized_cost_function.h"#include "pgm_image.h"namespace ceres {namespace examples {// One sum in the FoE regularizer. This is a dot product between a filter and an// image patch. It simply calculates the dot product between the filter// coefficients given in the constructor and the scalar parameters passed to it.class FieldsOfExpertsCost : public ceres::CostFunction { public:  explicit FieldsOfExpertsCost(const std::vector<double>& filter);  // The number of scalar parameters passed to Evaluate must equal the number of  // filter coefficients passed to the constructor.  virtual bool Evaluate(double const* const* parameters,                        double* residuals,                        double** jacobians) const; private:  const std::vector<double>& filter_;};// The loss function used to build the correct regularization. See above.////   f(x) = alpha_i * log(1 + (1/2)s)//class FieldsOfExpertsLoss : public ceres::LossFunction { public:  explicit FieldsOfExpertsLoss(double alpha) : alpha_(alpha) { }  virtual void Evaluate(double, double*) const; private:  const double alpha_;};// This class loads a set of filters and coefficients from file. Then the users// obtains the correct loss and cost functions through NewCostFunction and// NewLossFunction.class FieldsOfExperts { public:  // Creates an empty object with size() == 0.  FieldsOfExperts();  // Attempts to load filters from a file. If unsuccessful it returns false and  // sets size() == 0.  bool LoadFromFile(const std::string& filename);  // Side length of a square filter in this FoE. They are all of the same size.  int Size() const {    return size_;  }  // Total number of pixels the filter covers.  int NumVariables() const {    return size_ * size_;  }  // Number of filters used by the FoE.  int NumFilters() const {    return num_filters_;  }  // Creates a new cost function. The caller is responsible for deallocating the  // memory. alpha_index specifies which filter is used in the cost function.  ceres::CostFunction* NewCostFunction(int alpha_index) const;  // Creates a new loss function. The caller is responsible for deallocating the  // memory. alpha_index specifies which filter this loss function is for.  ceres::LossFunction* NewLossFunction(int alpha_index) const;  // Gets the delta pixel indices for all pixels in a patch.  const std::vector<int>& GetXDeltaIndices() const {    return x_delta_indices_;  }  const std::vector<int>& GetYDeltaIndices() const {    return y_delta_indices_;  } private:  // The side length of a square filter.  int size_;  // The number of different filters used.  int num_filters_;  // Pixel offsets for all variables.  std::vector<int> x_delta_indices_, y_delta_indices_;  // The coefficients in front of each term.  std::vector<double> alpha_;  // The filters used for the dot product with image patches.  std::vector<std::vector<double> > filters_;};}  // namespace examples}  // namespace ceres#endif  // CERES_EXAMPLES_FIELDS_OF_EXPERTS_H_
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