-
-///////////////////////////////////////////////////////////////////////////
-// 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 "pose_cell_hierarchy.h"
#include "gaussian.h"
PoseCellHierarchy::PoseCellHierarchy() {
- _body_cells = 0;
+ _belly_cells = 0;
}
PoseCellHierarchy::PoseCellHierarchy(LabelledImagePool *train_pool) {
_nb_levels = global.nb_levels;
_min_head_radius = global.min_head_radius;
_max_head_radius = global.max_head_radius;
- _root_cell_nb_xy_per_scale = global.root_cell_nb_xy_per_scale;
+ _root_cell_nb_xy_per_radius = global.root_cell_nb_xy_per_radius;
LabelledImage *image;
int nb_total_targets = 0;
train_pool->release_image(i);
}
- RelativeBodyPoseCell targets[nb_total_targets];
+ RelativeBellyPoseCell targets[nb_total_targets];
int u = 0;
for(int i = 0; i < train_pool->nb_images(); i++) {
cell_set.get_containing_cell(&pose)->get_centroid(&coarse);
- targets[u]._body_xc.set((pose._body_xc - coarse._head_xc) / coarse._head_radius);
- targets[u]._body_yc.set((pose._body_yc - coarse._head_yc) / coarse._head_radius);
+ targets[u]._belly_xc.set((pose._belly_xc - coarse._head_xc) / coarse._head_radius);
+ targets[u]._belly_yc.set((pose._belly_yc - coarse._head_yc) / coarse._head_radius);
u++;
pose.horizontal_flip(image->width());
cell_set.get_containing_cell(&pose)->get_centroid(&coarse);
- targets[u]._body_xc.set((pose._body_xc - coarse._head_xc) / coarse._head_radius);
- targets[u]._body_yc.set((pose._body_yc - coarse._head_yc) / coarse._head_radius);
+ targets[u]._belly_xc.set((pose._belly_xc - coarse._head_xc) / coarse._head_radius);
+ targets[u]._belly_yc.set((pose._belly_yc - coarse._head_yc) / coarse._head_radius);
u++;
}
scalar_t fattening = 1.1;
- Interval body_rxc, body_ryc;
+ Interval belly_rxc, belly_ryc;
- body_rxc.set(&targets[0]._body_xc);
- body_ryc.set(&targets[0]._body_yc);
+ belly_rxc.set(&targets[0]._belly_xc);
+ belly_ryc.set(&targets[0]._belly_yc);
for(int t = 0; t < nb_total_targets; t++) {
- body_rxc.swallow(&targets[t]._body_xc);
- body_ryc.swallow(&targets[t]._body_yc);
+ belly_rxc.swallow(&targets[t]._belly_xc);
+ belly_ryc.swallow(&targets[t]._belly_yc);
}
- body_rxc.min *= fattening;
- body_rxc.max *= fattening;
- body_ryc.min *= fattening;
- body_ryc.max *= fattening;
-
- scalar_t body_rxc_min = body_resolution * floor(body_rxc.min / body_resolution);
- int nb_body_rxc = int(ceil((body_rxc.max - body_rxc_min) / body_resolution));
+ belly_rxc.min *= fattening;
+ belly_rxc.max *= fattening;
+ belly_ryc.min *= fattening;
+ belly_ryc.max *= fattening;
- scalar_t body_ryc_min = body_resolution * floor(body_ryc.min / body_resolution);
- int nb_body_ryc = int(ceil((body_ryc.max - body_ryc_min) / body_resolution));
+ scalar_t belly_rxc_min = belly_resolution * floor(belly_rxc.min / belly_resolution);
+ int nb_belly_rxc = int(ceil((belly_rxc.max - belly_rxc_min) / belly_resolution));
- (*global.log_stream) << "body_rxc = " << body_rxc << endl
- << "body_rxc_min = " << body_rxc_min << endl
- << "body_rxc_min + nb_body_rxc * body_resolution = " << body_rxc_min + nb_body_rxc * body_resolution << endl
- << endl
- << "body_ryc = " << body_ryc << endl
- << "body_ryc_min = " << body_ryc_min << endl
- << "body_ryc_min + nb_body_ryc * body_resolution = " << body_ryc_min + nb_body_ryc * body_resolution << endl;
+ scalar_t belly_ryc_min = belly_resolution * floor(belly_ryc.min / belly_resolution);
+ int nb_belly_ryc = int(ceil((belly_ryc.max - belly_ryc_min) / belly_resolution));
- int used[nb_body_rxc * nb_body_rxc];
+ int used[nb_belly_rxc * nb_belly_rxc];
- for(int k = 0; k < nb_body_rxc * nb_body_ryc; k++) {
+ for(int k = 0; k < nb_belly_rxc * nb_belly_ryc; k++) {
used[k] = 1;
}
scalar_t rho = 0;
for(int t = 0; t < nb_total_targets; t++) {
- rho = min(rho, vx * targets[t]._body_xc.middle() + vy * targets[t]._body_yc.middle());
+ rho = min(rho, vx * targets[t]._belly_xc.middle() + vy * targets[t]._belly_yc.middle());
}
rho *= fattening;
- for(int j = 0; j < nb_body_ryc; j++) {
- for(int i = 0; i < nb_body_rxc; i++) {
+ for(int j = 0; j < nb_belly_ryc; j++) {
+ for(int i = 0; i < nb_belly_rxc; i++) {
if(
- vx * (scalar_t(i + 0) * body_resolution + body_rxc_min) +
- vy * (scalar_t(j + 0) * body_resolution + body_ryc_min) < rho
+ vx * (scalar_t(i + 0) * belly_resolution + belly_rxc_min) +
+ vy * (scalar_t(j + 0) * belly_resolution + belly_ryc_min) < rho
&&
- vx * (scalar_t(i + 1) * body_resolution + body_rxc_min) +
- vy * (scalar_t(j + 0) * body_resolution + body_ryc_min) < rho
+ vx * (scalar_t(i + 1) * belly_resolution + belly_rxc_min) +
+ vy * (scalar_t(j + 0) * belly_resolution + belly_ryc_min) < rho
&&
- vx * (scalar_t(i + 0) * body_resolution + body_rxc_min) +
- vy * (scalar_t(j + 1) * body_resolution + body_ryc_min) < rho
+ vx * (scalar_t(i + 0) * belly_resolution + belly_rxc_min) +
+ vy * (scalar_t(j + 1) * belly_resolution + belly_ryc_min) < rho
&&
- vx * (scalar_t(i + 1) * body_resolution + body_rxc_min) +
- vy * (scalar_t(j + 1) * body_resolution + body_ryc_min) < rho
+ vx * (scalar_t(i + 1) * belly_resolution + belly_rxc_min) +
+ vy * (scalar_t(j + 1) * belly_resolution + belly_ryc_min) < rho
) {
- used[i + j * nb_body_rxc] = 0;
+ used[i + j * nb_belly_rxc] = 0;
}
}
}
}
- _nb_body_cells = 0;
- for(int j = 0; j < nb_body_ryc; j++) {
- for(int i = 0; i < nb_body_rxc; i++) {
- if(used[i + nb_body_rxc * j]) {
- _nb_body_cells++;
+ _nb_belly_cells = 0;
+ for(int j = 0; j < nb_belly_ryc; j++) {
+ for(int i = 0; i < nb_belly_rxc; i++) {
+ if(used[i + nb_belly_rxc * j]) {
+ _nb_belly_cells++;
}
}
}
- _body_cells = new RelativeBodyPoseCell[_nb_body_cells];
-
- for(int j = 0; j < nb_body_ryc; j++) {
- for(int i = 0; i < nb_body_rxc; i++) {
- if(used[i + nb_body_rxc * j]) {
- if(sq(scalar_t(i) * body_resolution + body_resolution/2 + body_rxc_min) +
- sq(scalar_t(j) * body_resolution + body_resolution/2 + body_ryc_min) <= 1) {
- (*global.log_stream) << "*";
- } else {
- (*global.log_stream) << "X";
- }
- } else {
- (*global.log_stream) << ".";
- }
- }
- (*global.log_stream) << endl;
- }
+ _belly_cells = new RelativeBellyPoseCell[_nb_belly_cells];
int k = 0;
- for(int j = 0; j < nb_body_ryc; j++) {
- for(int i = 0; i < nb_body_rxc; i++) {
-
- if(used[i + nb_body_rxc * j]) {
-
- RelativeBodyPoseCell mother;
-
- scalar_t x = scalar_t(i) * body_resolution + body_rxc_min;
- scalar_t y = scalar_t(j) * body_resolution + body_ryc_min;
-
- mother._body_xc.set(x, x + body_resolution);
- mother._body_yc.set(y, y + body_resolution);
+ for(int j = 0; j < nb_belly_ryc; j++) {
+ for(int i = 0; i < nb_belly_rxc; i++) {
- // scalar_t dist_min = body_resolution;
- scalar_t dist_min = 1e6;
+ if(used[i + nb_belly_rxc * j]) {
- int nb_got;
+ RelativeBellyPoseCell mother;
- Gaussian dist_body_radius_1, dist_body_radius_2, dist_body_tilt;
+ scalar_t x = scalar_t(i) * belly_resolution + belly_rxc_min;
+ scalar_t y = scalar_t(j) * belly_resolution + belly_ryc_min;
- do {
+ mother._belly_xc.set(x, x + belly_resolution);
+ mother._belly_yc.set(y, y + belly_resolution);
- nb_got = 0;
-
- for(int t = 0; t < nb_total_targets; t++) {
-
- scalar_t dist =
- sqrt(sq(targets[t]._body_xc.middle() - x - body_resolution / 2) +
- sq(targets[t]._body_yc.middle() - y - body_resolution / 2));
-
- if(dist <= dist_min) {
- dist_body_radius_1.add_sample(targets[t]._body_radius_1.middle());
- dist_body_radius_2.add_sample(targets[t]._body_radius_2.middle());
- dist_body_tilt.add_sample(targets[t]._body_tilt.middle());
- nb_got++;
- }
-
- }
-
- dist_min *= 2.0;
- } while(nb_got < min(100, nb_total_targets));
-
- scalar_t zeta = 4;
-
- mother._body_radius_1.set(dist_body_radius_1.expectation() -
- zeta * dist_body_radius_1.standard_deviation(),
- dist_body_radius_1.expectation() +
- zeta * dist_body_radius_1.standard_deviation());
-
- mother._body_radius_2.set(dist_body_radius_2.expectation() -
- zeta * dist_body_radius_2.standard_deviation(),
- dist_body_radius_2.expectation() +
- zeta * dist_body_radius_2.standard_deviation());
-
- mother._body_tilt.set(dist_body_tilt.expectation() -
- zeta * dist_body_tilt.standard_deviation(),
- dist_body_tilt.expectation() +
- zeta * dist_body_tilt.standard_deviation());
-
- _body_cells[k++] = mother;
+ _belly_cells[k++] = mother;
}
}
}
-
- (*global.log_stream) << _nb_body_cells << " body cells." << endl;
}
PoseCellHierarchy::~PoseCellHierarchy() {
- delete[] _body_cells;
+ delete[] _belly_cells;
}
int PoseCellHierarchy::nb_levels() {
scalar_t beta = log(2) / scalar_t(global.nb_scales_per_power_of_two);
for(int s = 0; s < nb_scales; s++) {
- scalar_t cell_xy_size = exp(alpha + scalar_t(s) * beta) / global.root_cell_nb_xy_per_scale;
+ scalar_t cell_xy_size = exp(alpha + scalar_t(s) * beta) / global.root_cell_nb_xy_per_radius;
PoseCell cell;
cell._head_radius.min = exp(alpha + scalar_t(s) * beta);
cell._head_radius.max = exp(alpha + scalar_t(s+1) * beta);
cell._head_xc.max = x + cell_xy_size;
cell._head_yc.min = y;
cell._head_yc.max = y + cell_xy_size;
- cell._body_xc.min = cell._head_xc.min - pseudo_infty;
- cell._body_xc.max = cell._head_xc.max + pseudo_infty;
- cell._body_yc.min = cell._head_yc.min - pseudo_infty;
- cell._body_yc.max = cell._head_yc.max + pseudo_infty;
+ cell._belly_xc.min = cell._head_xc.min - pseudo_infty;
+ cell._belly_xc.max = cell._head_xc.max + pseudo_infty;
+ cell._belly_yc.min = cell._head_yc.min - pseudo_infty;
+ cell._belly_yc.max = cell._head_yc.max + pseudo_infty;
cell_set->add_cell(&cell);
}
}
case 1:
{
- // Here we split the body-center coordinate cell part
+ // Here we split the belly-center coordinate cell part
PoseCell cell = *root;
scalar_t r = sqrt(cell._head_radius.min * cell._head_radius.max);
scalar_t x = (cell._head_xc.min + cell._head_xc.max) / 2.0;
scalar_t y = (cell._head_yc.min + cell._head_yc.max) / 2.0;
- for(int k = 0; k < _nb_body_cells; k++) {
- cell._body_xc.min = (_body_cells[k]._body_xc.min * r) + x;
- cell._body_xc.max = (_body_cells[k]._body_xc.max * r) + x;
- cell._body_yc.min = (_body_cells[k]._body_yc.min * r) + y;
- cell._body_yc.max = (_body_cells[k]._body_yc.max * r) + y;
+ for(int k = 0; k < _nb_belly_cells; k++) {
+ cell._belly_xc.min = (_belly_cells[k]._belly_xc.min * r) + x;
+ cell._belly_xc.max = (_belly_cells[k]._belly_xc.max * r) + x;
+ cell._belly_yc.min = (_belly_cells[k]._belly_yc.min * r) + y;
+ cell._belly_yc.max = (_belly_cells[k]._belly_yc.max * r) + y;
cell_set->add_cell(&cell);
}
}
void PoseCellHierarchy::write(ostream *os) {
write_var(os, &_min_head_radius);
write_var(os, &_max_head_radius);
- write_var(os, &_root_cell_nb_xy_per_scale);
- write_var(os, &_nb_body_cells);
- for(int k = 0; k < _nb_body_cells; k++)
- write_var(os, &_body_cells[k]);
+ write_var(os, &_root_cell_nb_xy_per_radius);
+ write_var(os, &_nb_belly_cells);
+ for(int k = 0; k < _nb_belly_cells; k++)
+ write_var(os, &_belly_cells[k]);
}
void PoseCellHierarchy::read(istream *is) {
- delete[] _body_cells;
+ delete[] _belly_cells;
read_var(is, &_min_head_radius);
read_var(is, &_max_head_radius);
- read_var(is, &_root_cell_nb_xy_per_scale);
- read_var(is, &_nb_body_cells);
- delete[] _body_cells;
- _body_cells = new RelativeBodyPoseCell[_nb_body_cells];
- for(int k = 0; k < _nb_body_cells; k++) {
- read_var(is, &_body_cells[k]);
+ read_var(is, &_root_cell_nb_xy_per_radius);
+ read_var(is, &_nb_belly_cells);
+ delete[] _belly_cells;
+ _belly_cells = new RelativeBellyPoseCell[_nb_belly_cells];
+ for(int k = 0; k < _nb_belly_cells; k++) {
+ read_var(is, &_belly_cells[k]);
}
}
projects / folded-ctf.git / blobdiff