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// 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 describing 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/cost_function.h"
#include "ceres/loss_function.h"
#include "ceres/sized_cost_function.h"
#include "pgm_image.h"
namespace ceres::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.
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const override;
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) {}
void Evaluate(double, double*) const override;
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 ceres::examples
#endif // CERES_EXAMPLES_FIELDS_OF_EXPERTS_H_