66 lines
1.7 KiB
Matlab
66 lines
1.7 KiB
Matlab
%% Reset everything
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clear all;
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clc;
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close all;
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addpath('helpers');
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%% Configure the benchmark
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% central case -> only one camera
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cam_number = 1;
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% let's only get 6 points, and generate new ones in each iteration
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pt_number = 50;
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% noise test, so no outliers
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outlier_fraction = 0.0;
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% repeat 1000 iterations
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iterations = 1000;
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% The algorithms we want to test
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algorithms = { 'p2p'; 'p3p_kneip'; 'p3p_gao'; 'epnp'; 'upnp' };
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% This defines the number of points used for every algorithm
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indices = { [1, 2]; [1, 2, 3]; [1, 2, 3]; [1:1:50]; [1:1:50] };
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% The name of the algorithms on the plots
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names = { 'P2P'; 'P3P (Kneip)'; 'P3P (Gao)'; 'EPnP (50pts)'; 'UPnP (50pts)'};
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% The noise in this experiment
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noise = 1.0;
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%% Run the benchmark
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%prepare the overall result array
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num_algorithms = size(algorithms,1);
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execution_times = zeros(num_algorithms,iterations);
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counter = 0;
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for i=1:iterations
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% generate experiment
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[points,v,t,R] = create2D3DExperiment(pt_number,cam_number,noise,outlier_fraction);
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[t_perturbed,R_perturbed] = perturb(t,R,0.01);
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T_perturbed = [R_perturbed,t_perturbed];
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% run all algorithms
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for a=1:num_algorithms
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tic;
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T = opengv_donotuse(algorithms{a},indices{a},points,v,T_perturbed);
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execution_times(a,i) = toc/20.0;
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end
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counter = counter + 1;
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if counter == 100
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counter = 0;
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display(['Iteration ' num2str(i) ' of ' num2str(iterations)]);
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end
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end
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%% Plot the results
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bins = [0.000001:0.000001:0.00001];
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hist(execution_times',bins)
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legend(names,'Location','NorthWest')
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xlabel('execution times [s]')
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grid on
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mean(execution_times')
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