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. //
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. //
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/>. //
15 // Written by Francois Fleuret, (C) IDIAP //
16 // Contact <francois.fleuret@idiap.ch> for comments & bug reports //
17 ///////////////////////////////////////////////////////////////////////////
33 void Global::init_parser(ParamParser *parser) {
34 // The nice level of the process
35 parser->add_association("niceness", "5", false);
37 // Seed to initialize the random generator
38 parser->add_association("random-seed", "0", false);
40 // Should the pictures be b&w
41 parser->add_association("pictures-for-article", "no", false);
43 // The name of the image pool to use
44 parser->add_association("pool-name", "", false);
45 // The name of the test image pool to use
46 parser->add_association("test-pool-name", "", false);
47 // From where to load or where to save the detector
48 parser->add_association("detector-name", "default.det", false);
49 // Where to put the generated files
50 parser->add_association("result-path", "/tmp/", false);
52 // What kind of loss for the boosting
53 parser->add_association("loss-type", "exponential", false);
55 // How many images to produce/process
56 parser->add_association("nb-images", "-1", false);
58 // What is the maximum tree depth
59 parser->add_association("tree-depth-max", "1", false);
60 // What is the proportion of negative cells we actually use during training
61 parser->add_association("proportion-negative-cells-for-training", "0.025", false);
62 // How many negative samples to sub-sample for boosting every classifier
63 parser->add_association("nb-negative-samples-per-positive", "10", false);
64 // How many features we will look at for boosting optimization
65 parser->add_association("nb-features-for-boosting-optimization", "10000", false);
66 // Do we allow head-belly registration
67 parser->add_association("force-head-belly-independence", "no", false);
68 // How many weak-learners in every classifier
69 parser->add_association("nb-weak-learners-per-classifier", "10", false);
70 // How many classifiers per level
71 parser->add_association("nb-classifiers-per-level", "25", false);
73 parser->add_association("nb-levels", "1", false);
75 // Proportion of images from the pool to use for training
76 parser->add_association("proportion-for-train", "0.5", false);
77 // Proportion of images from the pool to use for validation
78 parser->add_association("proportion-for-validation", "0.25", false);
79 // Proportion of images from the pool to use for test (negative
80 // means everything else)
81 parser->add_association("proportion-for-test", "0.25", false);
82 // During training, should we write the ROC curve estimated on the
83 // validation set (which cost a bit of computation)
84 parser->add_association("write-validation-rocs", "no", false);
86 // Should we write down the PNGs for the results of the parsing
87 parser->add_association("write-parse-images", "no", false);
89 // Should we write down the PNGs for the tags
90 parser->add_association("write-tag-images", "no", false);
92 // What is the wanted true overall positive rate
93 parser->add_association("wanted-true-positive-rate", "0.5", false);
94 // How many rates to try for the sequence of tests
95 parser->add_association("nb-wanted-true-positive-rates", "10", false);
97 // What is the minimum radius of the heads to detect. This is used
98 // as the reference size.
99 parser->add_association("min-head-radius", "25", false);
100 // What is the maximum size of the heads to detect.
101 parser->add_association("max-head-radius", "200", false);
102 // How many translation cell for one scale when generating the "top
103 // level" cells for an image.
104 parser->add_association("root-cell-nb-xy-per-scale", "5", false);
106 // What is the minimum size of the windows
107 parser->add_association("pi-feature-window-min-size", "0.1", false);
109 // How many scales between two powers of two for the multi-scale
111 parser->add_association("nb-scales-per-power-of-two", "5", false);
113 // Should we display a progress bar for lengthy operations
114 parser->add_association("progress-bar", "yes", false);
117 void Global::read_parser(ParamParser *parser) {
118 niceness = parser->get_association_int("niceness");
119 random_seed = parser->get_association_int("random-seed");
120 pictures_for_article = parser->get_association_bool("pictures-for-article");
122 strncpy(pool_name, parser->get_association("pool-name"), buffer_size);
123 strncpy(test_pool_name, parser->get_association("test-pool-name"), buffer_size);
124 strncpy(detector_name, parser->get_association("detector-name"), buffer_size);
125 strncpy(result_path, parser->get_association("result-path"), buffer_size);
127 char buffer[buffer_size];
128 sprintf(buffer, "%s/log", result_path);
129 log_stream = new ofstream(buffer);
131 char *l = parser->get_association("loss-type");
132 if(strcmp(l, "exponential") == 0)
133 loss_type = LOSS_EXPONENTIAL;
134 else if(strcmp(l, "ev-regularized") == 0)
135 loss_type = LOSS_EV_REGULARIZED;
136 else if(strcmp(l, "hinge") == 0)
137 loss_type = LOSS_HINGE;
138 else if(strcmp(l, "logistic") == 0)
139 loss_type = LOSS_LOGISTIC;
141 cerr << "Unknown loss type." << endl;
145 nb_images = parser->get_association_int("nb-images");
146 tree_depth_max = parser->get_association_int("tree-depth-max");
147 nb_weak_learners_per_classifier = parser->get_association_int("nb-weak-learners-per-classifier");
148 nb_classifiers_per_level = parser->get_association_int("nb-classifiers-per-level");
149 nb_levels = parser->get_association_int("nb-levels");
150 proportion_negative_cells_for_training = parser->get_association_scalar("proportion-negative-cells-for-training");
151 nb_negative_samples_per_positive = parser->get_association_int("nb-negative-samples-per-positive");
152 nb_features_for_boosting_optimization = parser->get_association_int("nb-features-for-boosting-optimization");
153 force_head_belly_independence = parser->get_association_bool("force-head-belly-independence");
154 proportion_for_train = parser->get_association_scalar("proportion-for-train");
155 proportion_for_validation = parser->get_association_scalar("proportion-for-validation");
156 proportion_for_test = parser->get_association_scalar("proportion-for-test");
157 write_validation_rocs = parser->get_association_bool("write-validation-rocs");
158 write_parse_images = parser->get_association_bool("write-parse-images");
159 write_tag_images = parser->get_association_bool("write-tag-images");
160 wanted_true_positive_rate = parser->get_association_scalar("wanted-true-positive-rate");
161 nb_wanted_true_positive_rates = parser->get_association_int("nb-wanted-true-positive-rates");
163 min_head_radius = parser->get_association_scalar("min-head-radius");
164 max_head_radius = parser->get_association_scalar("max-head-radius");
165 root_cell_nb_xy_per_scale = parser->get_association_int("root-cell-nb-xy-per-scale");
167 pi_feature_window_min_size = parser->get_association_scalar("pi-feature-window-min-size");
169 nb_scales_per_power_of_two = parser->get_association_int("nb-scales-per-power-of-two");
171 bar.set_visible(parser->get_association_bool("progress-bar"));