2 * folded-ctf is an implementation of the folded hierarchy of
3 * classifiers for object detection, developed by Francois Fleuret
6 * Copyright (c) 2008 Idiap Research Institute, http://www.idiap.ch/
7 * Written by Francois Fleuret <francois.fleuret@idiap.ch>
9 * This file is part of folded-ctf.
11 * folded-ctf is free software: you can redistribute it and/or modify
12 * it under the terms of the GNU General Public License version 3 as
13 * published by the Free Software Foundation.
15 * folded-ctf is distributed in the hope that it will be useful, but
16 * WITHOUT ANY WARRANTY; without even the implied warranty of
17 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18 * General Public License for more details.
20 * You should have received a copy of the GNU General Public License
21 * along with folded-ctf. If not, see <http://www.gnu.org/licenses/>.
25 #include "pose_cell_hierarchy.h"
28 PoseCellHierarchy::PoseCellHierarchy() {
32 PoseCellHierarchy::PoseCellHierarchy(LabelledImagePool *train_pool) {
33 _nb_levels = global.nb_levels;
34 _min_head_radius = global.min_head_radius;
35 _max_head_radius = global.max_head_radius;
36 _root_cell_nb_xy_per_radius = global.root_cell_nb_xy_per_radius;
39 int nb_total_targets = 0;
40 for(int i = 0; i < train_pool->nb_images(); i++) {
41 image = train_pool->grab_image(i);
42 // We are going to symmetrize
43 nb_total_targets += 2 * image->nb_targets();
44 train_pool->release_image(i);
47 RelativeBellyPoseCell targets[nb_total_targets];
50 for(int i = 0; i < train_pool->nb_images(); i++) {
51 image = train_pool->grab_image(i);
54 add_root_cells(image, &cell_set);
56 for(int t = 0; t < image->nb_targets(); t++) {
57 Pose pose = *image->get_target_pose(t);
60 cell_set.get_containing_cell(&pose)->get_centroid(&coarse);
62 targets[u]._belly_xc.set((pose._belly_xc - coarse._head_xc) / coarse._head_radius);
63 targets[u]._belly_yc.set((pose._belly_yc - coarse._head_yc) / coarse._head_radius);
66 pose.horizontal_flip(image->width());
68 cell_set.get_containing_cell(&pose)->get_centroid(&coarse);
70 targets[u]._belly_xc.set((pose._belly_xc - coarse._head_xc) / coarse._head_radius);
71 targets[u]._belly_yc.set((pose._belly_yc - coarse._head_yc) / coarse._head_radius);
75 train_pool->release_image(i);
78 scalar_t fattening = 1.1;
80 Interval belly_rxc, belly_ryc;
82 belly_rxc.set(&targets[0]._belly_xc);
83 belly_ryc.set(&targets[0]._belly_yc);
85 for(int t = 0; t < nb_total_targets; t++) {
86 belly_rxc.swallow(&targets[t]._belly_xc);
87 belly_ryc.swallow(&targets[t]._belly_yc);
90 belly_rxc.min *= fattening;
91 belly_rxc.max *= fattening;
92 belly_ryc.min *= fattening;
93 belly_ryc.max *= fattening;
95 scalar_t belly_rxc_min = belly_resolution * floor(belly_rxc.min / belly_resolution);
96 int nb_belly_rxc = int(ceil((belly_rxc.max - belly_rxc_min) / belly_resolution));
98 scalar_t belly_ryc_min = belly_resolution * floor(belly_ryc.min / belly_resolution);
99 int nb_belly_ryc = int(ceil((belly_ryc.max - belly_ryc_min) / belly_resolution));
101 int used[nb_belly_rxc * nb_belly_rxc];
103 for(int k = 0; k < nb_belly_rxc * nb_belly_ryc; k++) {
107 // An ugly way to compute the convexe enveloppe
109 for(scalar_t alpha = 0; alpha < M_PI * 2; alpha += (2 * M_PI) / 100) {
110 scalar_t vx = cos(alpha), vy = sin(alpha);
113 for(int t = 0; t < nb_total_targets; t++) {
114 rho = min(rho, vx * targets[t]._belly_xc.middle() + vy * targets[t]._belly_yc.middle());
119 for(int j = 0; j < nb_belly_ryc; j++) {
120 for(int i = 0; i < nb_belly_rxc; i++) {
122 vx * (scalar_t(i + 0) * belly_resolution + belly_rxc_min) +
123 vy * (scalar_t(j + 0) * belly_resolution + belly_ryc_min) < rho
125 vx * (scalar_t(i + 1) * belly_resolution + belly_rxc_min) +
126 vy * (scalar_t(j + 0) * belly_resolution + belly_ryc_min) < rho
128 vx * (scalar_t(i + 0) * belly_resolution + belly_rxc_min) +
129 vy * (scalar_t(j + 1) * belly_resolution + belly_ryc_min) < rho
131 vx * (scalar_t(i + 1) * belly_resolution + belly_rxc_min) +
132 vy * (scalar_t(j + 1) * belly_resolution + belly_ryc_min) < rho
134 used[i + j * nb_belly_rxc] = 0;
141 for(int j = 0; j < nb_belly_ryc; j++) {
142 for(int i = 0; i < nb_belly_rxc; i++) {
143 if(used[i + nb_belly_rxc * j]) {
149 _belly_cells = new RelativeBellyPoseCell[_nb_belly_cells];
152 for(int j = 0; j < nb_belly_ryc; j++) {
153 for(int i = 0; i < nb_belly_rxc; i++) {
155 if(used[i + nb_belly_rxc * j]) {
157 RelativeBellyPoseCell mother;
159 scalar_t x = scalar_t(i) * belly_resolution + belly_rxc_min;
160 scalar_t y = scalar_t(j) * belly_resolution + belly_ryc_min;
162 mother._belly_xc.set(x, x + belly_resolution);
163 mother._belly_yc.set(y, y + belly_resolution);
165 _belly_cells[k++] = mother;
171 PoseCellHierarchy::~PoseCellHierarchy() {
172 delete[] _belly_cells;
175 int PoseCellHierarchy::nb_levels() {
179 void PoseCellHierarchy::get_containing_cell(Image *image, int level,
180 Pose *pose, PoseCell *result_cell) {
181 PoseCellSet cell_set;
183 for(int l = 0; l < level + 1; l++) {
184 cell_set.erase_content();
186 add_root_cells(image, &cell_set);
188 add_subcells(l, result_cell, &cell_set);
191 *result_cell = *(cell_set.get_containing_cell(pose));
195 void PoseCellHierarchy::add_root_cells(Image *image, PoseCellSet *cell_set) {
197 const int nb_scales = int((log(_max_head_radius) - log(_min_head_radius)) / log(2) *
198 global.nb_scales_per_power_of_two);
200 scalar_t alpha = log(_min_head_radius);
201 scalar_t beta = log(2) / scalar_t(global.nb_scales_per_power_of_two);
203 for(int s = 0; s < nb_scales; s++) {
204 scalar_t cell_xy_size = exp(alpha + scalar_t(s) * beta) / global.root_cell_nb_xy_per_radius;
206 cell._head_radius.min = exp(alpha + scalar_t(s) * beta);
207 cell._head_radius.max = exp(alpha + scalar_t(s+1) * beta);
208 cell._head_tilt.min = -M_PI;
209 cell._head_tilt.max = M_PI;
210 for(scalar_t y = 0; y < image->height(); y += cell_xy_size)
211 for(scalar_t x = 0; x < image->width(); x += cell_xy_size) {
212 cell._head_xc.min = x;
213 cell._head_xc.max = x + cell_xy_size;
214 cell._head_yc.min = y;
215 cell._head_yc.max = y + cell_xy_size;
216 cell._belly_xc.min = cell._head_xc.min - pseudo_infty;
217 cell._belly_xc.max = cell._head_xc.max + pseudo_infty;
218 cell._belly_yc.min = cell._head_yc.min - pseudo_infty;
219 cell._belly_yc.max = cell._head_yc.max + pseudo_infty;
220 cell_set->add_cell(&cell);
225 void PoseCellHierarchy::add_subcells(int level, PoseCell *root,
226 PoseCellSet *cell_set) {
232 // Here we split the belly-center coordinate cell part
233 PoseCell cell = *root;
234 scalar_t r = sqrt(cell._head_radius.min * cell._head_radius.max);
235 scalar_t x = (cell._head_xc.min + cell._head_xc.max) / 2.0;
236 scalar_t y = (cell._head_yc.min + cell._head_yc.max) / 2.0;
237 for(int k = 0; k < _nb_belly_cells; k++) {
238 cell._belly_xc.min = (_belly_cells[k]._belly_xc.min * r) + x;
239 cell._belly_xc.max = (_belly_cells[k]._belly_xc.max * r) + x;
240 cell._belly_yc.min = (_belly_cells[k]._belly_yc.min * r) + y;
241 cell._belly_yc.max = (_belly_cells[k]._belly_yc.max * r) + y;
242 cell_set->add_cell(&cell);
249 cerr << "Inconsistent level in PoseCellHierarchy::add_subcells" << endl;
257 int PoseCellHierarchy::nb_incompatible_poses(LabelledImagePool *pool) {
258 PoseCell target_cell;
259 PoseCellSet cell_set;
260 LabelledImage *image;
264 for(int i = 0; i < pool->nb_images(); i++) {
265 image = pool->grab_image(i);
267 for(int t = 0; t < image->nb_targets(); t++) {
268 cell_set.erase_content();
270 int error_level = -1;
272 for(int l = 0; error_level < 0 && l < _nb_levels; l++) {
273 cell_set.erase_content();
276 add_root_cells(image, &cell_set);
278 add_subcells(l, &target_cell, &cell_set);
281 int nb_compliant = 0;
283 for(int c = 0; c < cell_set.nb_cells(); c++) {
284 if(cell_set.get_cell(c)->contains(image->get_target_pose(t))) {
285 target_cell = *(cell_set.get_cell(c));
290 if(nb_compliant != 1) {
295 if(error_level >= 0) {
300 pool->release_image(i);
306 void PoseCellHierarchy::write(ostream *os) {
307 write_var(os, &_min_head_radius);
308 write_var(os, &_max_head_radius);
309 write_var(os, &_root_cell_nb_xy_per_radius);
310 write_var(os, &_nb_belly_cells);
311 for(int k = 0; k < _nb_belly_cells; k++)
312 write_var(os, &_belly_cells[k]);
315 void PoseCellHierarchy::read(istream *is) {
316 delete[] _belly_cells;
317 read_var(is, &_min_head_radius);
318 read_var(is, &_max_head_radius);
319 read_var(is, &_root_cell_nb_xy_per_radius);
320 read_var(is, &_nb_belly_cells);
321 delete[] _belly_cells;
322 _belly_cells = new RelativeBellyPoseCell[_nb_belly_cells];
323 for(int k = 0; k < _nb_belly_cells; k++) {
324 read_var(is, &_belly_cells[k]);