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%% Reset everything
clear all;
clc;
close all;
addpath('helpers');
%% Configure the benchmark
% central case -> only one camera
cam_number = 4;
% let's only get 6 points, and generate new ones in each iteration
pt_number = 50;
% noise test, so no outliers
outlier_fraction = 0.0;
% repeat 1000 iterations
iterations = 1000;
% The algorithms we want to test
algorithms = { 'gp3p'; 'gpnp'; 'upnp'};
% This defines the number of points used for every algorithm
indices = { [1, 2, 3]; [1:1:50]; [1:1:50] };
% The name of the algorithms on the plots
names = { 'GP3P'; 'GPnP (50pts)'; 'UPnP (50pts)' };
% The noise in this experiment
noise = 1.0;
%% Run the benchmark
%prepare the overall result arrays
num_algorithms = size(algorithms,1);
execution_times = zeros(num_algorithms,iterations);
counter = 0;
for i=1:iterations
% generate experiment
[points,v,t,R] = create2D3DExperiment(pt_number,cam_number,noise,outlier_fraction);
[t_perturbed,R_perturbed] = perturb(t,R,0.01);
T_perturbed = [R_perturbed,t_perturbed];
for a=1:num_algorithms
tic
T = opengv_donotuse(algorithms{a},indices{a},points,v,T_perturbed);
execution_times(a,i) = toc/20.0;
end
counter = counter + 1;
if counter == 100
counter = 0;
display(['Iteration ' num2str(i) ' of ' num2str(iterations) '(noise level ' num2str(noise) ')']);
end
end
%% Plot the results
hist(execution_times')
legend(names,'Location','NorthWest')
xlabel('execution times [s]')
grid on
mean(execution_times')
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