| // 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: |
| // |
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| // 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 |
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| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| // dgossow@google.com (David Gossow) |
| |
| #ifndef CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_ |
| #define CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_ |
| |
| #include <numeric> |
| #include <vector> |
| |
| #include "ceres/dynamic_cost_function.h" |
| #include "ceres/internal/fixed_array.h" |
| #include "ceres/internal/port.h" |
| #include "ceres/internal/scoped_ptr.h" |
| |
| namespace ceres { |
| |
| // DynamicCostFunctionToFunctor allows users to use CostFunction |
| // objects in templated functors which are to be used for automatic |
| // differentiation. It works similar to CostFunctionToFunctor, with the |
| // difference that it allows you to wrap a cost function with dynamic numbers |
| // of parameters and residuals. |
| // |
| // For example, let us assume that |
| // |
| // class IntrinsicProjection : public CostFunction { |
| // public: |
| // IntrinsicProjection(const double* observation); |
| // virtual bool Evaluate(double const* const* parameters, |
| // double* residuals, |
| // double** jacobians) const; |
| // }; |
| // |
| // is a cost function that implements the projection of a point in its |
| // local coordinate system onto its image plane and subtracts it from |
| // the observed point projection. It can compute its residual and |
| // either via analytic or numerical differentiation can compute its |
| // jacobians. The intrinsics are passed in as parameters[0] and the point as |
| // parameters[1]. |
| // |
| // Now we would like to compose the action of this CostFunction with |
| // the action of camera extrinsics, i.e., rotation and |
| // translation. Say we have a templated function |
| // |
| // template<typename T> |
| // void RotateAndTranslatePoint(double const* const* parameters, |
| // double* residuals); |
| // |
| // Then we can now do the following, |
| // |
| // struct CameraProjection { |
| // CameraProjection(const double* observation) |
| // : intrinsic_projection_.(new IntrinsicProjection(observation)) { |
| // } |
| // template <typename T> |
| // bool operator()(T const* const* parameters, |
| // T* residual) const { |
| // const T* rotation = parameters[0]; |
| // const T* translation = parameters[1]; |
| // const T* intrinsics = parameters[2]; |
| // const T* point = parameters[3]; |
| // T transformed_point[3]; |
| // RotateAndTranslatePoint(rotation, translation, point, transformed_point); |
| // |
| // // Note that we call intrinsic_projection_, just like it was |
| // // any other templated functor. |
| // const T* projection_parameters[2]; |
| // projection_parameters[0] = intrinsics; |
| // projection_parameters[1] = transformed_point; |
| // return intrinsic_projection_(projection_parameters, residual); |
| // } |
| // |
| // private: |
| // DynamicCostFunctionToFunctor intrinsic_projection_; |
| // }; |
| class DynamicCostFunctionToFunctor { |
| public: |
| // Takes ownership of cost_function. |
| explicit DynamicCostFunctionToFunctor(CostFunction* cost_function) |
| : cost_function_(cost_function) { |
| CHECK_NOTNULL(cost_function); |
| } |
| |
| bool operator()(double const* const* parameters, double* residuals) const { |
| return cost_function_->Evaluate(parameters, residuals, NULL); |
| } |
| |
| template <typename JetT> |
| bool operator()(JetT const* const* inputs, JetT* output) const { |
| const std::vector<int32>& parameter_block_sizes = |
| cost_function_->parameter_block_sizes(); |
| const int num_parameter_blocks = |
| static_cast<int>(parameter_block_sizes.size()); |
| const int num_residuals = cost_function_->num_residuals(); |
| const int num_parameters = std::accumulate(parameter_block_sizes.begin(), |
| parameter_block_sizes.end(), 0); |
| |
| internal::FixedArray<double> parameters(num_parameters); |
| internal::FixedArray<double*> parameter_blocks(num_parameter_blocks); |
| internal::FixedArray<double> jacobians(num_residuals * num_parameters); |
| internal::FixedArray<double*> jacobian_blocks(num_parameter_blocks); |
| internal::FixedArray<double> residuals(num_residuals); |
| |
| // Build a set of arrays to get the residuals and jacobians from |
| // the CostFunction wrapped by this functor. |
| double* parameter_ptr = parameters.get(); |
| double* jacobian_ptr = jacobians.get(); |
| for (int i = 0; i < num_parameter_blocks; ++i) { |
| parameter_blocks[i] = parameter_ptr; |
| jacobian_blocks[i] = jacobian_ptr; |
| for (int j = 0; j < parameter_block_sizes[i]; ++j) { |
| *parameter_ptr++ = inputs[i][j].a; |
| } |
| jacobian_ptr += num_residuals * parameter_block_sizes[i]; |
| } |
| |
| if (!cost_function_->Evaluate(parameter_blocks.get(), |
| residuals.get(), |
| jacobian_blocks.get())) { |
| return false; |
| } |
| |
| // Now that we have the incoming Jets, which are carrying the |
| // partial derivatives of each of the inputs w.r.t to some other |
| // underlying parameters. The derivative of the outputs of the |
| // cost function w.r.t to the same underlying parameters can now |
| // be computed by applying the chain rule. |
| // |
| // d output[i] d output[i] d input[j] |
| // -------------- = sum_j ----------- * ------------ |
| // d parameter[k] d input[j] d parameter[k] |
| // |
| // d input[j] |
| // -------------- = inputs[j], so |
| // d parameter[k] |
| // |
| // outputJet[i] = sum_k jacobian[i][k] * inputJet[k] |
| // |
| // The following loop, iterates over the residuals, computing one |
| // output jet at a time. |
| for (int i = 0; i < num_residuals; ++i) { |
| output[i].a = residuals[i]; |
| output[i].v.setZero(); |
| |
| for (int j = 0; j < num_parameter_blocks; ++j) { |
| const int32 block_size = parameter_block_sizes[j]; |
| for (int k = 0; k < parameter_block_sizes[j]; ++k) { |
| output[i].v += |
| jacobian_blocks[j][i * block_size + k] * inputs[j][k].v; |
| } |
| } |
| } |
| |
| return true; |
| } |
| |
| private: |
| internal::scoped_ptr<CostFunction> cost_function_; |
| }; |
| |
| } // namespace ceres |
| |
| #endif // CERES_PUBLIC_DYNAMIC_COST_FUNCTION_TO_FUNCTOR_H_ |