// Ceres Solver - A fast non-linear least squares minimizer

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// 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_