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1
2 ///////////////////////////////////////////////////////////////////////////
3 // This program is free software: you can redistribute it and/or modify  //
4 // it under the terms of the version 3 of the GNU General Public License //
5 // as published by the Free Software Foundation.                         //
6 //                                                                       //
7 // This program is distributed in the hope that it will be useful, but   //
8 // WITHOUT ANY WARRANTY; without even the implied warranty of            //
9 // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU      //
10 // General Public License for more details.                              //
11 //                                                                       //
12 // You should have received a copy of the GNU General Public License     //
13 // along with this program. If not, see <http://www.gnu.org/licenses/>.  //
14 //                                                                       //
15 // Written by Francois Fleuret                                           //
16 // (C) Idiap Research Institute                                          //
17 //                                                                       //
18 // Contact <francois.fleuret@idiap.ch> for comments & bug reports        //
19 ///////////////////////////////////////////////////////////////////////////
20
21 #include <string.h>
22
23 #include "global.h"
24
25 Global global;
26
27 Global::Global() {
28   log_stream = 0;
29 }
30
31 Global::~Global() {
32   delete log_stream;
33 }
34
35 void Global::init_parser(ParamParser *parser) {
36   // The nice level of the process
37   parser->add_association("niceness", "15", false);
38
39   // Seed to initialize the random generator
40   parser->add_association("random-seed", "0", false);
41
42   // Should the pictures be b&w
43   parser->add_association("pictures-for-article", "no", false);
44
45   // The name of the image pool to use
46   parser->add_association("pool-name", "", false);
47   // The name of the test image pool to use
48   parser->add_association("test-pool-name", "", false);
49   // From where to load or where to save the detector
50   parser->add_association("detector-name", "default.det", false);
51   // Where to put the generated files
52   parser->add_association("result-path", "/tmp/", false);
53
54   // What kind of loss for the boosting
55   parser->add_association("loss-type", "exponential", false);
56
57   // How many images to produce/process
58   parser->add_association("nb-images", "-1", false);
59   // What is the number of the feature to show in the images
60   parser->add_association("material-feature-nb", "-1", false);
61
62   // What is the maximum tree depth
63   parser->add_association("tree-depth-max", "1", false);
64   // What is the proportion of negative cells we actually use during training
65   parser->add_association("proportion-negative-cells-for-training", "0.025", false);
66   // How many negative samples to sub-sample for boosting every classifier
67   parser->add_association("nb-negative-samples-per-positive", "10", false);
68   // How many features we will look at for boosting optimization
69   parser->add_association("nb-features-for-boosting-optimization", "10000", false);
70   // Do we allow head-belly registration
71   parser->add_association("force-head-belly-independence", "no", false);
72   // How many weak-learners in every classifier
73   parser->add_association("nb-weak-learners-per-classifier", "100", false);
74   // How many classifiers per level
75   parser->add_association("nb-classifiers-per-level", "25", false);
76   // How many levels
77   parser->add_association("nb-levels", "2", false);
78
79   // Proportion of images from the pool to use for training
80   parser->add_association("proportion-for-train", "0.5", false);
81   // Proportion of images from the pool to use for validation
82   parser->add_association("proportion-for-validation", "0.25", false);
83   // Proportion of images from the pool to use for test (negative
84   // means everything else)
85   parser->add_association("proportion-for-test", "0.25", false);
86   // During training, should we write the ROC curve estimated on the
87   // validation set (which cost a bit of computation)
88   parser->add_association("write-validation-rocs", "no", false);
89
90   // Should we write down the PNGs for the results of the parsing
91   parser->add_association("write-parse-images", "no", false);
92
93   // Should we write down the PNGs for the tags
94   parser->add_association("write-tag-images", "no", false);
95
96   // What is the wanted true overall positive rate
97   parser->add_association("wanted-true-positive-rate", "0.75", false);
98   // How many rates to try for the sequence of tests
99   parser->add_association("nb-wanted-true-positive-rates", "10", false);
100
101   // What is the minimum radius of the heads to detect. This is used
102   // as the reference size.
103   parser->add_association("min-head-radius", "25", false);
104   // What is the maximum size of the heads to detect.
105   parser->add_association("max-head-radius", "200", false);
106   // How many translation cell for one scale when generating the "top
107   // level" cells for an image.
108   parser->add_association("root-cell-nb-xy-per-radius", "5", false);
109
110   // What is the minimum size of the windows
111   parser->add_association("pi-feature-window-min-size", "0.1", false);
112
113   // How many scales between two powers of two for the multi-scale
114   // images
115   parser->add_association("nb-scales-per-power-of-two", "5", false);
116
117   // Should we display a progress bar for lengthy operations
118   parser->add_association("progress-bar", "yes", false);
119 }
120
121 void Global::read_parser(ParamParser *parser) {
122   niceness = parser->get_association_int("niceness");
123   random_seed = parser->get_association_int("random-seed");
124   pictures_for_article = parser->get_association_bool("pictures-for-article");
125
126   strncpy(pool_name, parser->get_association("pool-name"), buffer_size);
127   strncpy(test_pool_name, parser->get_association("test-pool-name"), buffer_size);
128   strncpy(detector_name, parser->get_association("detector-name"), buffer_size);
129   strncpy(result_path, parser->get_association("result-path"), buffer_size);
130
131   char buffer[buffer_size];
132   sprintf(buffer, "%s/log", result_path);
133   log_stream = new ofstream(buffer);
134
135   char *l = parser->get_association("loss-type");
136   if(strcmp(l, "exponential") == 0)
137     loss_type = LOSS_EXPONENTIAL;
138   else if(strcmp(l, "ev-regularized") == 0)
139     loss_type = LOSS_EV_REGULARIZED;
140   else if(strcmp(l, "hinge") == 0)
141     loss_type = LOSS_HINGE;
142   else if(strcmp(l, "logistic") == 0)
143     loss_type = LOSS_LOGISTIC;
144   else {
145     cerr << "Unknown loss type." << endl;
146     exit(1);
147   }
148
149   nb_images = parser->get_association_int("nb-images");
150   material_feature_nb = parser->get_association_int("material-feature-nb");
151   tree_depth_max = parser->get_association_int("tree-depth-max");
152   nb_weak_learners_per_classifier = parser->get_association_int("nb-weak-learners-per-classifier");
153   nb_classifiers_per_level = parser->get_association_int("nb-classifiers-per-level");
154   nb_levels = parser->get_association_int("nb-levels");
155   proportion_negative_cells_for_training = parser->get_association_scalar("proportion-negative-cells-for-training");
156   nb_negative_samples_per_positive = parser->get_association_int("nb-negative-samples-per-positive");
157   nb_features_for_boosting_optimization = parser->get_association_int("nb-features-for-boosting-optimization");
158   force_head_belly_independence = parser->get_association_bool("force-head-belly-independence");
159   proportion_for_train = parser->get_association_scalar("proportion-for-train");
160   proportion_for_validation = parser->get_association_scalar("proportion-for-validation");
161   proportion_for_test = parser->get_association_scalar("proportion-for-test");
162   write_validation_rocs = parser->get_association_bool("write-validation-rocs");
163   write_parse_images = parser->get_association_bool("write-parse-images");
164   write_tag_images = parser->get_association_bool("write-tag-images");
165   wanted_true_positive_rate = parser->get_association_scalar("wanted-true-positive-rate");
166   nb_wanted_true_positive_rates = parser->get_association_int("nb-wanted-true-positive-rates");
167
168   min_head_radius = parser->get_association_scalar("min-head-radius");
169   max_head_radius = parser->get_association_scalar("max-head-radius");
170   root_cell_nb_xy_per_radius = parser->get_association_int("root-cell-nb-xy-per-radius");
171
172   pi_feature_window_min_size = parser->get_association_scalar("pi-feature-window-min-size");
173
174   nb_scales_per_power_of_two = parser->get_association_int("nb-scales-per-power-of-two");
175
176   bar.set_visible(parser->get_association_bool("progress-bar"));
177 }