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main_planar.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this toolbox is an addition to the toolbox provided by the authors of
% CEPPnP and OPnP
% we extended it to show the use of MLPnP
% if you use this file it would be neat to cite our paper:
%
% @INPROCEEDINGS {mlpnp2016,
% author = "Urban, S.; Leitloff, J.; Hinz, S.",
% title = "MLPNP - A REAL-TIME MAXIMUM LIKELIHOOD SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM.",
% booktitle = "ISPRS Annals of Photogrammetry, Remote Sensing \& Spatial Information Sciences",
% year = "2016",
% volume = "3",
% pages = "131-138"}
%
% Copyright (C) <2016> <Steffen Urban>
% Steffen Urban email: [email protected]
% Copyright (C) 2016 Steffen Urban
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% 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, write to the Free Software Foundation, Inc.,
% 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 28.06.2016 Steffen Urban
clear; clc;
IniToolbox;
% experimental parameters
nl= [1,2,3,4,5,6,7,8,9,10];
%nl= 5*ones(1,10);
nlsamples = [0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]; %percentatge of samples for each sigma
npts= [10:10:200];
num = 200;
% compared methods
A= zeros(size(npts));
B= zeros(num,1);
% if you want to use UPnP you have to download and install OpenGV
% also if you want to reproduce the runtime shown in the MLPnP paper
% yout have to install the OpenGV fork with MLPnP
name= {'MLPnPWithCov','MLPnP','LHM', 'EPnP','RPnP', 'DLS','PPnP', 'ASPnP','OPnP','EPPnP','CEPPnP'};
f= {@MLPNP_with_COV, @MLPNP_without_COV,@LHM, @EPnP_planar, @RPnP, @robust_dls_pnp, @PPnP, @ASPnP,@OPnP, @EPPnP_planar, @CEPPnP_planar};
marker= { 'x', 'd', 'x', 's', 'd', '^', '*', '<', '>','o','+'};
color= {'g', 'g','c', 'r', [1,0.5,0],'m', [1,0.5,1], 'b', 'r','k',[0,0.5,0.5]};
markerfacecolor= {'g','g','c','g',[1,0.5,0],'m', [1,0.5,1], 'b', 'r','k',[0,0.5,0.5]};
method_list= struct('name', name, 'f', f, 'mean_r', A, 'mean_t', A,...
'med_r', A, 'med_t', A, 'std_r', A, 'std_t', A, 'r', B, 't', B,...
'marker', marker, 'color', color, 'markerfacecolor', markerfacecolor);
meanR = zeros(length(npts),length(method_list)+1);
medianR = zeros(length(npts),length(method_list)+1);
meanT = zeros(length(npts),length(method_list)+1);
medianT = zeros(length(npts),length(method_list)+1);
medianTime = zeros(length(npts),length(method_list)+1);
% experiments
for i= 1:length(npts)
npt= npts(i);
fprintf('npt = %d (num sg = %d ): ', npt, length(nl));
for k= 1:length(method_list)
method_list(k).c = zeros(1,num);
method_list(k).e = zeros(1,num);
method_list(k).r = zeros(1,num);
method_list(k).t = zeros(1,num);
end
%index_fail = [];
index_fail = cell(1,length(name));
for j= 1:num
% camera's parameters
width= 640;
height= 480;
f= 800;
K = [f 0 0
0 f 0
0 0 1];
% generate 3d coordinates in camera space
XXw= [xrand(2,npt,[-2 2]); zeros(1,npt)];
R= rodrigues(randn(3,1));
t= [rand-0.5;rand-0.5;rand*4+4];
Xc= R*XXw+repmat(t,1,npt);
% projection
xx= [Xc(1,:)./Xc(3,:); Xc(2,:)./Xc(3,:)]*f;
randomvals = randn(2,npt);
nnl = round(npt * nlsamples);
nls = zeros(1,npt);
id = 1;
for idnl = 1:length(nl)
sigma = nl(idnl);
nls(id:id+nnl(idnl)-1) = sigma .* ones(1,nnl(idnl));
id = id+nnl(idnl);
end
xxn= xx+randomvals.*[nls;nls];
homx = [xxn/f; ones(1,size(xxn,2))];
v = normc(homx);
Cu = zeros(2,2,npt);
Evv = zeros(3,3,npt);
cov = zeros(9,size(Cu,3));
for id = 1:npt
Cu(:,:,id) = diag([nls(id) nls(id)]);
cov_proj = K\diag([nls(id) nls(id) 0])/K';
J = (eye(3)-(v(:,id)*v(:,id)')/(v(:,id)'*v(:,id)))/norm(homx(:,id));
Evv(:,:,id) = J*cov_proj*J';
cov(:,id) = reshape(Evv(:,:,id),9,1);
end
% pose estimation
for k= 1:length(method_list)
% try
if strcmp(method_list(k).name, 'CEPPnP')
tic;
mXXw = XXw - repmat(mean(XXw,2),1,size(XXw,2));
[R1,t1]= method_list(k).f(mXXw,xxn/f,Cu);
t1 = t1 - R1 * mean(XXw,2);
tcost = toc;
elseif strcmp(method_list(k).name, 'MLPnP') || ...
strcmp(method_list(k).name, 'MLPnPWithCov')
tic;
[R1,t1]= method_list(k).f(XXw,v,cov);
tcost = toc;
else
tic;
[R1,t1]= method_list(k).f(XXw,xxn/f);
tcost = toc;
end
% catch
% disp(['The solver - ',method_list(k).name,' - encounters internal errors!!!']);
% %index_fail = [index_fail, j];
% index_fail{k} = [index_fail{k}, j];
% break;
% end
%no solution
if size(t1,2) < 1
disp(['The solver - ',method_list(k).name,' - returns no solution!!!']);
%index_fail = [index_fail, j];
index_fail{k} = [index_fail{k}, j];
break;
elseif (sum(sum(sum(imag(R1).^2))>0) == size(R1,3) || sum(sum(imag(t1(:,:,1)).^2)>0) == size(t1,2))
index_fail{k} = [index_fail{k}, j];
continue;
end
%choose the solution with smallest error
error = inf;
for jjj = 1:size(R1,3)
tempy = cal_pose_err([R1(:,:,jjj) t1(:,jjj)],[R t]);
if sum(tempy) < error
cost = tcost;
ercorr= mean(sqrt(sum((R1(:,:,jjj) * XXw + t1(:,jjj) * ones(1,npt) - Xc).^2)));
y = tempy;
error = sum(tempy);
end
end
method_list(k).c(j)= cost * 1000;
method_list(k).e(j)= ercorr;
method_list(k).r(j)= y(1);
method_list(k).t(j)= y(2);
end
showpercent(j,num);
end
fprintf('\n');
% save result
for k= 1:length(method_list)
%results without deleting solutions
tmethod_list = method_list(k);
method_list(k).c(index_fail{k}) = [];
method_list(k).e(index_fail{k}) = [];
method_list(k).r(index_fail{k}) = [];
method_list(k).t(index_fail{k}) = [];
% computational cost should be computed in a separated procedure as
% in main_time.m
method_list(k).pfail(i) = 100 * numel(index_fail{k})/num;
method_list(k).mean_c(i)= mean(method_list(k).c);
method_list(k).mean_e(i)= mean(method_list(k).e);
method_list(k).med_c(i)= median(method_list(k).c);
method_list(k).med_e(i)= median(method_list(k).e);
method_list(k).std_c(i)= std(method_list(k).c);
method_list(k).std_e(i)= std(method_list(k).e);
method_list(k).mean_r(i)= mean(method_list(k).r);
method_list(k).mean_t(i)= mean(method_list(k).t);
method_list(k).med_r(i)= median(method_list(k).r);
method_list(k).med_t(i)= median(method_list(k).t);
method_list(k).std_r(i)= std(method_list(k).r);
method_list(k).std_t(i)= std(method_list(k).t);
meanR (i,1) = npts(i);
meanT (i,1) = npts(i);
medianR (i,1) = npts(i);
medianT (i,1) = npts(i);
medianTime(i,1) = npts(i);
meanR(i,k+1) = method_list(k).mean_r(i);
meanT(i,k+1) = method_list(k).mean_t(i);
medianR(i,k+1) = method_list(k).med_r(i);
medianT(i,k+1) = method_list(k).med_t(i);
medianTime(i,k+1) = method_list(k).med_c(i);
%results deleting solutions where not all the methods finds one
tmethod_list.c(unique([index_fail{:}])) = [];
tmethod_list.e(unique([index_fail{:}])) = [];
tmethod_list.r(unique([index_fail{:}])) = [];
tmethod_list.t(unique([index_fail{:}])) = [];
method_list(k).deleted_mean_c(i)= mean(tmethod_list.c);
method_list(k).deleted_mean_e(i)= mean(tmethod_list.e);
method_list(k).deleted_med_c(i)= median(tmethod_list.c);
method_list(k).deleted_med_e(i)= median(tmethod_list.e);
method_list(k).deleted_std_c(i)= std(tmethod_list.c);
method_list(k).deleted_std_e(i)= std(tmethod_list.e);
method_list(k).deleted_mean_r(i)= mean(tmethod_list.r);
method_list(k).deleted_mean_t(i)= mean(tmethod_list.t);
method_list(k).deleted_med_r(i)= median(tmethod_list.r);
method_list(k).deleted_med_t(i)= median(tmethod_list.t);
method_list(k).deleted_std_r(i)= std(tmethod_list.r);
method_list(k).deleted_std_t(i)= std(tmethod_list.t);
end
end
save planar3Dresults method_list npts;
plotPlanar3D;