129 lines
4.1 KiB
Matlab
129 lines
4.1 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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% noncentral case
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cam_number = 4;
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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 5000 iterations per noise level
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iterations = 5000;
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% The algorithms we want to test
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algorithms = { 'gp3p'; 'gpnp'; 'gpnp'; 'abs_nonlin_noncentral'; 'abs_nonlin_noncentral'; 'upnp'; 'upnp' };
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% This defines the number of points used for every algorithm
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indices = { [1, 2, 3]; [1:1:10]; [1:1:50]; [1:1:10]; [1:1:50]; [1:1:10]; [1:1:50] };
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% The name of the algorithms on the plots
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names = { 'GP3P'; 'GPnP (10pts)'; 'GPnP (50pts)'; 'nonlin. opt. (10pts)'; 'nonlin. opt. (50pts)'; 'UPnP (10pts)'; 'UPnP (50pts)' };
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% The maximum noise to analyze
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max_noise = 5.0;
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% The step in between different noise levels
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noise_step = 0.1;
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%% Run the benchmark
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%prepare the overall result arrays
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number_noise_levels = max_noise / noise_step + 1;
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num_algorithms = size(algorithms,1);
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mean_position_errors = zeros(num_algorithms,number_noise_levels);
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mean_rotation_errors = zeros(num_algorithms,number_noise_levels);
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median_position_errors = zeros(num_algorithms,number_noise_levels);
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median_rotation_errors = zeros(num_algorithms,number_noise_levels);
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noise_levels = zeros(1,number_noise_levels);
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%Run the experiment
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for n=1:number_noise_levels
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noise = (n - 1) * noise_step;
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noise_levels(1,n) = noise;
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display(['Analyzing noise level: ' num2str(noise)])
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position_errors = zeros(num_algorithms,iterations);
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rotation_errors = zeros(num_algorithms,iterations);
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counter = 0;
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validIterations = 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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T_gt = [R,t];
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% run all algorithms
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allValid = 1;
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for a=1:num_algorithms
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T = opengv(algorithms{a},indices{a},points,v,T_perturbed);
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[position_error, rotation_error] = evaluateTransformationError( T_gt, T );
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if( position_error > 100 )
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allValid = 0;
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break;
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else
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position_errors(a,validIterations+1) = position_error;
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rotation_errors(a,validIterations+1) = rotation_error;
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end
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end
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if allValid == 1
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validIterations = validIterations +1;
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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) '(noise level ' num2str(noise) ')']);
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end
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end
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%Now compute the mean and median value of the error for each algorithm
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for a=1:num_algorithms
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mean_position_errors(a,n) = mean(position_errors(a,1:validIterations));
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median_position_errors(a,n) = median(position_errors(a,1:validIterations));
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mean_rotation_errors(a,n) = mean(rotation_errors(a,1:validIterations));
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median_rotation_errors(a,n) = median(rotation_errors(a,1:validIterations));
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end
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end
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%% Plot the results
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figure(1)
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plot(noise_levels',mean_rotation_errors','LineWidth',2)
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legend(names,'Location','NorthWest')
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xlabel('noise level [pix]')
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ylabel('mean rot. error [rad]')
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grid on
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figure(2)
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plot(noise_levels',median_rotation_errors','LineWidth',2)
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legend(names,'Location','NorthWest')
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xlabel('noise level [pix]')
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ylabel('median rot. error [rad]')
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grid on
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figure(3)
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plot(noise_levels',mean_position_errors','LineWidth',2)
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legend(names,'Location','NorthWest')
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xlabel('noise level [pix]')
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ylabel('mean pos. error [m]')
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grid on
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figure(4)
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plot(noise_levels',median_position_errors','LineWidth',2)
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legend(names,'Location','NorthWest')
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xlabel('noise level [pix]')
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ylabel('median pos. error [m]')
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grid on |