X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pom.git;a=blobdiff_plain;f=pom_solver.cc;fp=pom_solver.cc;h=76cc9a5cc3f1b0bb928bee4bf1784bd799f180e1;hp=0000000000000000000000000000000000000000;hb=97a7e68f234cc09807d2d55f550e2516be0e9093;hpb=48c9926a2ed03737a3b024a85cda348caebf4cfe
diff --git a/pom_solver.cc b/pom_solver.cc
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+
+//////////////////////////////////////////////////////////////////////////////////
+// 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 . //
+// //
+// Written by Francois Fleuret //
+// (C) Ecole Polytechnique Federale de Lausanne //
+// Contact for comments & bug reports //
+//////////////////////////////////////////////////////////////////////////////////
+
+#include
+#include
+#include
+
+using namespace std;
+
+#include "pom_solver.h"
+#include "global.h"
+
+//////////////////////////////////////////////////////////////////////
+
+POMSolver::POMSolver(Room *room) : neg(room->view_width(), room->view_height()),
+ neg_view(room->view_width(), room->view_height()),
+ ii_neg(room->view_width(), room->view_height()),
+ ii_neg_view(room->view_width(), room->view_height()) {
+ global_difference.set(global_mu_image_density, global_sigma_image_density);
+}
+
+//////////////////////////////////////////////////////////////////////
+
+void POMSolver::compute_average_images(int camera,
+ Room *room,
+ Vector *proba_absence) {
+ neg.fill(1.0);
+
+ for(int n = 0; n < room->nb_positions(); n++) if((*proba_absence)[n] <= global_proba_ignored) {
+ Rectangle *r = room->avatar(camera, n);
+ if(r->visible)
+ neg.multiply_subarray(r->xmin, r->ymin, r->xmax + 1, r->ymax + 1, (*proba_absence)[n]);
+ }
+}
+
+//////////////////////////////////////////////////////////////////////
+
+void POMSolver::add_log_ratio(int camera,
+ Room *room,
+ ProbaView *view,
+ Vector *proba_absence,
+ Vector *sum) {
+
+ // Computes the average on the complete picture
+
+ compute_average_images(camera, room, proba_absence);
+
+ double s = ii_neg.compute_sum(&neg);
+ double sv = ii_neg_view.compute_sum(&neg, view);
+
+ scalar_t noise_proba = 0.01; // 1% of the scene can remain unexplained
+ scalar_t average_surface = room->view_width() * room->view_height() * (1 + noise_proba) - s;
+ scalar_t average_diff = average_surface + sv;
+
+ // Cycles throw all positions and adds the log likelihood ratio to
+ // the total sum for each
+
+ for(int i = 0; i < room->nb_positions(); i++) {
+ Rectangle *r = room->avatar(camera, i);
+ if(r->visible) {
+ scalar_t lambda = 1 - 1/(*proba_absence)[i];
+
+ scalar_t integral_neg = ii_neg.integral(r->xmin, r->ymin, r->xmax + 1, r->ymax + 1);
+ scalar_t average_surface_givpre = average_surface + integral_neg;
+ scalar_t average_surface_givabs = average_surface + lambda * integral_neg;
+
+ scalar_t integral_neg_view = ii_neg_view.integral(r->xmin, r->ymin, r->xmax + 1, r->ymax + 1);
+ scalar_t average_diff_givpre = average_diff + integral_neg - 2 * integral_neg_view;
+ scalar_t average_diff_givabs = average_diff + lambda * (integral_neg - 2 * integral_neg_view);
+
+ scalar_t log_mu0 = global_difference.log_proba(average_diff_givabs / average_surface_givabs);
+ scalar_t log_mu1 = global_difference.log_proba(average_diff_givpre / average_surface_givpre);
+
+ (*sum)[i] += log_mu1 - log_mu0;
+
+ }
+ }
+}
+
+void POMSolver::solve(Room *room,
+ Vector *prior,
+ Vector *views,
+ Vector *proba_presence,
+ int nb_frame,
+ char *convergence_file_format) {
+
+ Vector log_prior_ratio(prior->length());
+
+ Vector sum(room->nb_positions());
+ Vector proba_absence(room->nb_positions());
+
+ for(int i = 0; i < room->nb_positions(); i++) {
+ log_prior_ratio[i] = log((*prior)[i]/(1 - (*prior)[i]));
+ proba_absence[i] = 1 - (*prior)[i];
+ }
+
+ int nb_stab = 0;
+
+ for(int it = 0; (nb_stab < global_nb_stable_error_for_convergence) && (it < global_max_nb_solver_iterations); it++) {
+
+ sum.clear();
+ for(int c = 0; c < room->nb_cameras(); c++)
+ add_log_ratio(c, room, (*views)[c], &proba_absence, &sum);
+
+ scalar_t e = 0;
+ for(int i = 0; i < room->nb_positions(); i++) {
+ scalar_t np = global_smoothing_coefficient * proba_absence[i] + (1 - global_smoothing_coefficient) / (1 + exp(log_prior_ratio[i] + sum[i]));
+ if(abs(proba_absence[i] - np) > e) e = abs(proba_absence[i] - np);
+ proba_absence[i] = np;
+ }
+
+ if(e < global_error_max) nb_stab++; else nb_stab = 0;
+
+ if(convergence_file_format) {
+ char buffer[buffer_size];
+ for(int p = 0; p < room->nb_positions(); p++) (*proba_presence)[p] = 1 - proba_absence[p];
+ for(int c = 0; c < room->nb_cameras(); c++) {
+ pomsprintf(buffer, buffer_size, convergence_file_format, c, nb_frame, it);
+ cout << "Saving " << buffer << "\n"; cout.flush();
+ room->save_stochastic_view(buffer, c, (*views)[c], proba_presence);
+ }
+ }
+
+ }
+
+ for(int p = 0; p < room->nb_positions(); p++) (*proba_presence)[p] = 1 - proba_absence[p];
+}