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Example_gal.cpp
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1 
23 #include <iostream>
24 #include <tuple>
25 #include <vector>
44 #include "utils.h"
46 
47 using namespace std;
48 using namespace ModelFitting;
49 
50 // This example demonstrates how to use the DataVsModelResiduals to perform
51 // minimization over an observed image and a FrameModel. The real parameters
52 // are:
53 // - I0 : 12.
54 // - X : 128
55 // - Y : 128
56 // - X_SCALE : 0.83
57 // - Y_SCALE : 0.25
58 // - ROT_ANGLE : 2.3
59 
60 int main(int argc, char **argv) {
61  std::string engine_impl("levmar");
62  if (argc > 1) {
63  engine_impl = argv[1];
64  }
65 
66  // We read the image from the aux dir. Note that we will use a cv:Mat type,
67  // so the ModelFitting/Image/OpenCvMatImageTraits.h must be included.
68  cv::Mat image;
69  double pixel_scale {};
70  auto image_path = Elements::pathSearchInEnvVariable("gal.fits", "ELEMENTS_AUX_PATH");
71  tie(image, pixel_scale) = readImage(image_path[0].string());
72  size_t image_cols = image.cols;
73  size_t image_rows = image.rows;
74 
75  //
76  // Model creation
77  //
78  // The frame model we will use will contain a single extended model, with a
79  // single exponential component.
80 
81  // First we define the parameters of the exponential. We are going to minimize
82  // only the I0, so it is the only EngineParameter. For the engine parameters
83  // we need to use a coordinate converter. The options are:
84  // - NeutralConverter : Does no conversion
85  // - NormalizedConverter : Normalizes the parameter so the engine value is 1
86  // for a specific world value
87  // - SigmoidConverter : Converts the parameter using the sigmoid function
88  // - ExpSigmoidConverter : Converts the parameter using the exponential sigmoid function
89  auto i0 = std::make_shared<EngineParameter>(50000., make_unique<ExpSigmoidConverter>(1, 1000000.));
90  auto n = std::make_shared<ManualParameter>(1.);
91  auto k = std::make_shared<ManualParameter>(1.);
92 
93  // We create the component list of the extended model with the single exponential
94  auto reg_man = make_unique<OnlySmooth>();
95  auto exp = make_unique<SersicModelComponent>(move(reg_man), i0, n, k);
96  vector<unique_ptr<ModelComponent>> component_list {};
97  component_list.emplace_back(move(exp));
98 
99  // We create the extended model. All of its parameters will be optimized by
100  // the minimization engine.
101  auto x = std::make_shared<EngineParameter>(120, make_unique<NormalizedConverter>(1500.));
102  auto y = std::make_shared<EngineParameter>(140, make_unique<NormalizedConverter>(1500.));
103  auto x_scale = std::make_shared<EngineParameter>(1.0, make_unique<SigmoidConverter>(0, 10.));
104  auto y_scale = std::make_shared<EngineParameter>(1.0, make_unique<SigmoidConverter>(0, 10.));
105  auto rot_angle = std::make_shared<EngineParameter>(20.0 * M_PI/180.0, make_unique<SigmoidConverter>(0, 2*M_PI));
106 
107  // The size of the extended model (??? from the detection step ???)
108  double width = 128;
109  double height = 128;
110 
111  // We create the extended model list with a single model
112  vector<TransformedModel> extended_models {};
113  extended_models.emplace_back(std::move(component_list), x_scale, y_scale,
114  rot_angle, width, height, x, y);
115 
116  // We add a constant background
117  auto back = std::make_shared<EngineParameter>(100., make_unique<ExpSigmoidConverter>(1, 1000000.));
118  vector<ConstantModel> constant_models {};
119  constant_models.emplace_back(back);
120 
121  // We read the PSF from the file
122  auto psf_path = Elements::pathSearchInEnvVariable("psf_gal.fits", "ELEMENTS_AUX_PATH");
123  auto psf = readPsf(psf_path[0].string());
124 
125  // Finally we create the FrameModel with same pixel scale and size as the
126  // input image
127  FrameModel<OpenCvPsf, cv::Mat> frame_model {
128  pixel_scale, image_cols, image_rows, move(constant_models), {},
129  move(extended_models), move(psf)
130  };
131 
132  writeToFits(frame_model.getImage(), "example3b.fits");
133 
134  //
135  // Minimization
136  //
137 
138  // First we need to specify which parameters are optimized by the engine
139  EngineParameterManager manager {};
140  manager.registerParameter(i0);
141  manager.registerParameter(x);
142  manager.registerParameter(y);
143  manager.registerParameter(x_scale);
144  manager.registerParameter(y_scale);
145  manager.registerParameter(rot_angle);
146  manager.registerParameter(back);
147 
148  // Now we need to create the DataVsModelResiduals. We will set all the weights
149  // as ones and we will use the LogChiSquareComparator.
150  // Note that because we use cv::Mat as input we have to include the file
151  // ModelFitting/Engine/OpenCvDataVsModelInputTraits.h
152  cv::Mat weight = cv::Mat::ones(image.rows, image.cols, CV_64F);
153  auto data_vs_model = createDataVsModelResiduals(std::move(image), std::move(frame_model),
154  std::move(weight), LogChiSquareComparator{});
155 
156  // We create a residual estimator and we add our block provider
157  ResidualEstimator res_estimator {};
158  res_estimator.registerBlockProvider(move(data_vs_model));
159 
160  // We print the parameters before the minimization for comparison
161  cout << "I0 = " << i0->getValue() << '\n';
162  cout << "X = " << x->getValue() << '\n';
163  cout << "Y = " << y->getValue() << '\n';
164  cout << "X_SCALE = " << x_scale->getValue() << '\n';
165  cout << "Y_SCALE = " << y_scale->getValue() << '\n';
166  cout << "angle = " << rot_angle->getValue() << '\n';
167  cout << "Background = " << back->getValue() << '\n';
168 
169  // Finally we create a levmar engine and we solve the problem
170  auto engine = LeastSquareEngineManager::create(engine_impl);
171  auto t1 = chrono::steady_clock::now();
172  auto solution = engine->solveProblem(manager, res_estimator);
173  auto t2 = chrono::steady_clock::now();
174 
175  // We print the results
176  cout << "\nTime of fitting: " << chrono::duration <double, milli> (t2-t1).count() << " ms" << endl;
177  cout << "\n";
178 
179  cout << "I0 = " << i0->getValue() << '\n';
180  cout << "X = " << x->getValue() << '\n';
181  cout << "Y = " << y->getValue() << '\n';
182  cout << "X_SCALE = " << x_scale->getValue() << '\n';
183  cout << "Y_SCALE = " << y_scale->getValue() << '\n';
184  cout << "angle = " << rot_angle->getValue() << '\n';
185  cout << "Background = " << back->getValue() << '\n';
186 
187  printLevmarInfo(boost::any_cast<array<double,10>>(solution.underlying_framework_info));
188 
189  // We create the component list of the extended model with the single exponential
190  reg_man = make_unique<OnlySmooth>();
191  exp = make_unique<SersicModelComponent>(move(reg_man), i0, n, k);
192  component_list.clear();
193  component_list.emplace_back(move(exp));
194  extended_models.clear();
195  extended_models.emplace_back(move(component_list),x_scale, y_scale,
196  rot_angle, width, height, x, y);
197  constant_models.clear();
198  constant_models.emplace_back(back);
199  FrameModel<OpenCvPsf, cv::Mat> frame_model_after {
200  pixel_scale, image_cols, image_rows, move(constant_models), {},
201  move(extended_models), readPsf(psf_path[0].string())
202  };
203  writeToFits(frame_model_after.getImage(), "example3b2.fits");
204 
205 }
std::pair< cv::Mat, double > readImage(const std::string &filename)
Definition: utils.h:77
T tie(T...args)
T exp(T...args)
int main()
Definition: Example1.cpp:47
std::shared_ptr< DependentParameter< std::shared_ptr< EngineParameter > > > x
T endl(T...args)
ModelFitting::OpenCvPsf readPsf(const std::string &filename)
Definition: utils.h:53
std::shared_ptr< DependentParameter< std::shared_ptr< EngineParameter > > > y
STL class.
void registerBlockProvider(std::unique_ptr< ResidualBlockProvider > provider)
Registers a ResidualBlockProvider to the ResidualEstimator.
void registerParameter(std::shared_ptr< EngineParameter > parameter)
Registers an EngineParameter to the EngineParameterManager.
T move(T...args)
T count(T...args)
void writeToFits(const cv::Mat &image, const std::string &filename)
Definition: utils.h:40
STL class.
ELEMENTS_API std::vector< boost::filesystem::path > pathSearchInEnvVariable(const std::string &file_name, const std::string &path_like_env_variable, SearchType search_type=SearchType::Recursive)
STL class.
Class responsible for managing the parameters the least square engine minimizes.
std::unique_ptr< DataVsModelResiduals< typename std::remove_reference< DataType >::type, typename std::remove_reference< ModelType >::type, typename std::remove_reference< WeightType >::type, typename std::remove_reference< Comparator >::type > > createDataVsModelResiduals(DataType &&data, ModelType &&model, WeightType &&weight, Comparator &&comparator)
void printLevmarInfo(std::array< double, 10 > info)
Definition: utils.h:118
Provides to the LeastSquareEngine the residual values.
const double pixel_scale
Definition: TestImage.cpp:72
Data vs model comparator which computes a modified residual.
T emplace_back(T...args)