--- /dev/null
+
+///////////////////////////////////////////////////////////////////////////
+// This program is free software: you can redistribute it and/or modify //
+// it under the terms of the version 3 of the GNU General Public License //
+// as published by the Free Software Foundation. //
+// //
+// This program is distributed in the hope that it will be useful, but //
+// WITHOUT ANY WARRANTY; without even the implied warranty of //
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU //
+// General Public License for more details. //
+// //
+// You should have received a copy of the GNU General Public License //
+// along with this program. If not, see <http://www.gnu.org/licenses/>. //
+// //
+// Written by Francois Fleuret, (C) IDIAP //
+// Contact <francois.fleuret@idiap.ch> for comments & bug reports //
+///////////////////////////////////////////////////////////////////////////
+
+#include <string.h>
+
+#include "global.h"
+
+Global global;
+
+Global::Global() {
+ log_stream = 0;
+}
+
+Global::~Global() {
+ delete log_stream;
+}
+
+void Global::init_parser(ParamParser *parser) {
+ // The nice level of the process
+ parser->add_association("niceness", "5", false);
+
+ // Seed to initialize the random generator
+ parser->add_association("random-seed", "0", false);
+
+ // Should the pictures be b&w
+ parser->add_association("pictures-for-article", "no", false);
+
+ // The name of the image pool to use
+ parser->add_association("pool-name", "", false);
+ // The name of the test image pool to use
+ parser->add_association("test-pool-name", "", false);
+ // From where to load or where to save the detector
+ parser->add_association("detector-name", "default.det", false);
+ // Where to put the generated files
+ parser->add_association("result-path", "/tmp/", false);
+
+ // What kind of loss for the boosting
+ parser->add_association("loss-type", "exponential", false);
+
+ // How many images to produce/process
+ parser->add_association("nb-images", "-1", false);
+
+ // What is the maximum tree depth
+ parser->add_association("tree-depth-max", "1", false);
+ // What is the proportion of negative cells we actually use during training
+ parser->add_association("proportion-negative-cells-for-training", "0.025", false);
+ // How many negative samples to sub-sample for boosting every classifier
+ parser->add_association("nb-negative-samples-per-positive", "10", false);
+ // How many features we will look at for boosting optimization
+ parser->add_association("nb-features-for-boosting-optimization", "10000", false);
+ // Do we allow head-belly registration
+ parser->add_association("force-head-belly-independence", "no", false);
+ // How many weak-learners in every classifier
+ parser->add_association("nb-weak-learners-per-classifier", "10", false);
+ // How many classifiers per level
+ parser->add_association("nb-classifiers-per-level", "25", false);
+ // How many levels
+ parser->add_association("nb-levels", "1", false);
+
+ // Proportion of images from the pool to use for training
+ parser->add_association("proportion-for-train", "0.5", false);
+ // Proportion of images from the pool to use for validation
+ parser->add_association("proportion-for-validation", "0.25", false);
+ // Proportion of images from the pool to use for test (negative
+ // means everything else)
+ parser->add_association("proportion-for-test", "0.25", false);
+ // During training, should we write the ROC curve estimated on the
+ // validation set (which cost a bit of computation)
+ parser->add_association("write-validation-rocs", "no", false);
+
+ // Should we write down the PNGs for the results of the parsing
+ parser->add_association("write-parse-images", "no", false);
+
+ // Should we write down the PNGs for the tags
+ parser->add_association("write-tag-images", "no", false);
+
+ // What is the wanted true overall positive rate
+ parser->add_association("wanted-true-positive-rate", "0.5", false);
+ // How many rates to try for the sequence of tests
+ parser->add_association("nb-wanted-true-positive-rates", "10", false);
+
+ // What is the minimum radius of the heads to detect. This is used
+ // as the reference size.
+ parser->add_association("min-head-radius", "25", false);
+ // What is the maximum size of the heads to detect.
+ parser->add_association("max-head-radius", "200", false);
+ // How many translation cell for one scale when generating the "top
+ // level" cells for an image.
+ parser->add_association("root-cell-nb-xy-per-scale", "5", false);
+
+ // What is the minimum size of the windows
+ parser->add_association("pi-feature-window-min-size", "0.1", false);
+
+ // How many scales between two powers of two for the multi-scale
+ // images
+ parser->add_association("nb-scales-per-power-of-two", "5", false);
+
+ // Should we display a progress bar for lengthy operations
+ parser->add_association("progress-bar", "yes", false);
+}
+
+void Global::read_parser(ParamParser *parser) {
+ niceness = parser->get_association_int("niceness");
+ random_seed = parser->get_association_int("random-seed");
+ pictures_for_article = parser->get_association_bool("pictures-for-article");
+
+ strncpy(pool_name, parser->get_association("pool-name"), buffer_size);
+ strncpy(test_pool_name, parser->get_association("test-pool-name"), buffer_size);
+ strncpy(detector_name, parser->get_association("detector-name"), buffer_size);
+ strncpy(result_path, parser->get_association("result-path"), buffer_size);
+
+ char buffer[buffer_size];
+ sprintf(buffer, "%s/log", result_path);
+ log_stream = new ofstream(buffer);
+
+ char *l = parser->get_association("loss-type");
+ if(strcmp(l, "exponential") == 0)
+ loss_type = LOSS_EXPONENTIAL;
+ else if(strcmp(l, "ev-regularized") == 0)
+ loss_type = LOSS_EV_REGULARIZED;
+ else if(strcmp(l, "hinge") == 0)
+ loss_type = LOSS_HINGE;
+ else if(strcmp(l, "logistic") == 0)
+ loss_type = LOSS_LOGISTIC;
+ else {
+ cerr << "Unknown loss type." << endl;
+ exit(1);
+ }
+
+ nb_images = parser->get_association_int("nb-images");
+ tree_depth_max = parser->get_association_int("tree-depth-max");
+ nb_weak_learners_per_classifier = parser->get_association_int("nb-weak-learners-per-classifier");
+ nb_classifiers_per_level = parser->get_association_int("nb-classifiers-per-level");
+ nb_levels = parser->get_association_int("nb-levels");
+ proportion_negative_cells_for_training = parser->get_association_scalar("proportion-negative-cells-for-training");
+ nb_negative_samples_per_positive = parser->get_association_int("nb-negative-samples-per-positive");
+ nb_features_for_boosting_optimization = parser->get_association_int("nb-features-for-boosting-optimization");
+ force_head_belly_independence = parser->get_association_bool("force-head-belly-independence");
+ proportion_for_train = parser->get_association_scalar("proportion-for-train");
+ proportion_for_validation = parser->get_association_scalar("proportion-for-validation");
+ proportion_for_test = parser->get_association_scalar("proportion-for-test");
+ write_validation_rocs = parser->get_association_bool("write-validation-rocs");
+ write_parse_images = parser->get_association_bool("write-parse-images");
+ write_tag_images = parser->get_association_bool("write-tag-images");
+ wanted_true_positive_rate = parser->get_association_scalar("wanted-true-positive-rate");
+ nb_wanted_true_positive_rates = parser->get_association_int("nb-wanted-true-positive-rates");
+
+ min_head_radius = parser->get_association_scalar("min-head-radius");
+ max_head_radius = parser->get_association_scalar("max-head-radius");
+ root_cell_nb_xy_per_scale = parser->get_association_int("root-cell-nb-xy-per-scale");
+
+ pi_feature_window_min_size = parser->get_association_scalar("pi-feature-window-min-size");
+
+ nb_scales_per_power_of_two = parser->get_association_int("nb-scales-per-power-of-two");
+
+ bar.set_visible(parser->get_association_bool("progress-bar"));
+}
projects / folded-ctf.git / blobdiff