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%% Reset everything
clear all;
clc;
close all;
addpath('helpers');
%% Configure the benchmark
% central case -> only one camera
cam_number = 1;
% Getting 10 points, and testing all algorithms with the respective number of points
pt_number = 10;
% noise test, so no outliers
outlier_fraction = 0.0;
% repeat 1000 iterations
iterations = 1000;
% The algorithms we want to test
algorithms = { 'twopt';'fivept_stewenius'; 'fivept_nister'; 'fivept_kneip'; 'sevenpt'; 'eightpt'; 'eigensolver' };
% This defines the number of points used for every algorithm
indices = { [1,2]; [1, 2, 3, 4, 5]; [1, 2, 3, 4, 5]; [1, 2, 3, 4, 5]; [1, 2, 3, 4, 5, 6, 7]; [1, 2, 3, 4, 5, 6, 7, 8]; [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] };
% The name of the algorithms in the final plots
names = { '2pt';'5pt (Stewenius)'; '5pt (Nister)'; '5pt (Kneip)'; '7pt'; '8pt'; 'eigensolver' };
% 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
[v1,v2,t,R] = create2D2DExperiment(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
Out = opengv_donotuse(algorithms{a},indices{a},v1,v2,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)]);
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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