-
-///////////////////////////////////////////////////////////////////////////
-// 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 //
-///////////////////////////////////////////////////////////////////////////
+/*
+ * folded-ctf is an implementation of the folded hierarchy of
+ * classifiers for object detection, developed by Francois Fleuret
+ * and Donald Geman.
+ *
+ * Copyright (c) 2008 Idiap Research Institute, http://www.idiap.ch/
+ * Written by Francois Fleuret <francois.fleuret@idiap.ch>
+ *
+ * This file is part of folded-ctf.
+ *
+ * folded-ctf is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License version 3 as
+ * published by the Free Software Foundation.
+ *
+ * folded-ctf 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 folded-ctf. If not, see <http://www.gnu.org/licenses/>.
+ *
+ */
#include <string.h>
void Global::init_parser(ParamParser *parser) {
// The nice level of the process
- parser->add_association("niceness", "5", false);
+ parser->add_association("niceness", "15", false);
// Seed to initialize the random generator
parser->add_association("random-seed", "0", false);
// How many images to produce/process
parser->add_association("nb-images", "-1", false);
+ // What is the number of the feature to show in the images
+ parser->add_association("material-feature-nb", "-1", false);
// What is the maximum tree depth
parser->add_association("tree-depth-max", "1", 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);
+ parser->add_association("nb-weak-learners-per-classifier", "100", 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);
+ parser->add_association("nb-levels", "2", false);
// Proportion of images from the pool to use for training
parser->add_association("proportion-for-train", "0.5", false);
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);
+ parser->add_association("wanted-true-positive-rate", "0.75", false);
// How many rates to try for the sequence of tests
parser->add_association("nb-wanted-true-positive-rates", "10", false);
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
+ // How many translation cell per radius when generating the "top
// level" cells for an image.
- parser->add_association("root-cell-nb-xy-per-scale", "5", false);
+ parser->add_association("root-cell-nb-xy-per-radius", "5", false);
// What is the minimum size of the windows
parser->add_association("pi-feature-window-min-size", "0.1", false);
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)
}
nb_images = parser->get_association_int("nb-images");
+ material_feature_nb = parser->get_association_int("material-feature-nb");
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");
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");
+ root_cell_nb_xy_per_radius = parser->get_association_int("root-cell-nb-xy-per-radius");
pi_feature_window_min_size = parser->get_association_scalar("pi-feature-window-min-size");