1%% Reset everything 2 3clear all; 4clc; 5close all; 6addpath('helpers'); 7 8%% Configure the benchmark 9 10% central case -> only one camera 11cam_number = 1; 12% Getting 10 points, and testing all algorithms with the respective number of points 13pt_number = 10; 14% noise test, so no outliers 15outlier_fraction = 0.0; 16% repeat 5000 tests per noise level 17iterations = 5000; 18 19% The algorithms we want to test 20algorithms = { 'fivept_stewenius'; 'fivept_nister'; 'fivept_kneip'; 'sevenpt'; 'eightpt'; 'eigensolver'; 'rel_nonlin_central' }; 21% Some parameter that tells us what the result means 22returns = [ 1, 1, 0, 1, 1, 0, 2 ]; % 1means essential matrix(ces) needing decomposition, %0 means rotation matrix(ces), %2 means transformation matrix 23% This defines the number of points used for every algorithm 24indices = { [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]; [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] }; 25% The name of the algorithms in the final plots 26names = { '5pt (Stewenius)'; '5pt (Nister)'; '5pt (Kneip)'; '7pt'; '8pt'; 'eigensolver (10pts)'; 'nonlin. opt. (10pts)' }; 27 28% The maximum noise to analyze 29max_noise = 5.0; 30% The step in between different noise levels 31noise_step = 0.1; 32 33%% Run the benchmark 34 35%prepare the overall result arrays 36number_noise_levels = max_noise / noise_step + 1; 37num_algorithms = size(algorithms,1); 38mean_rotation_errors = zeros(num_algorithms,number_noise_levels); 39median_rotation_errors = zeros(num_algorithms,number_noise_levels); 40noise_levels = zeros(1,number_noise_levels); 41 42%Run the experiment 43for n=1:number_noise_levels 44 45 noise = (n - 1) * noise_step; 46 noise_levels(1,n) = noise; 47 display(['Analyzing noise level: ' num2str(noise)]) 48 49 rotation_errors = zeros(num_algorithms,iterations); 50 51 counter = 0; 52 53 validIterations = 0; 54 55 for i=1:iterations 56 57 % generate experiment 58 [v1,v2,t,R] = create2D2DExperiment(pt_number,cam_number,noise,outlier_fraction); 59 [t_perturbed,R_perturbed] = perturb(t,R,0.01); 60 T_perturbed = [R_perturbed,t_perturbed]; 61 R_gt = R; 62 63 % run all algorithms 64 allValid = 1; 65 66 for a=1:num_algorithms 67 Out = opengv(algorithms{a},indices{a},v1,v2,T_perturbed); 68 69 if ~isempty(Out) 70 71 if returns(1,a) == 1 72 temp = transformEssentials(Out); 73 Out = temp; 74 end 75 if returns(1,a) == 2 76 temp = Out(:,1:3); 77 Out = temp; 78 end 79 80 rotation_errors(a,validIterations+1) = evaluateRotationError( R_gt, Out ); 81 82 else 83 84 allValid = 0; 85 break; 86 87 end 88 end 89 90 if allValid == 1 91 validIterations = validIterations +1; 92 end 93 94 counter = counter + 1; 95 if counter == 100 96 counter = 0; 97 display(['Iteration ' num2str(i) ' of ' num2str(iterations) '(noise level ' num2str(noise) ')']); 98 end 99 end 100 101 %Now compute the mean and median value of the error for each algorithm 102 for a=1:num_algorithms 103 mean_rotation_errors(a,n) = mean(rotation_errors(a,1:validIterations)); 104 median_rotation_errors(a,n) = median(rotation_errors(a,1:validIterations)); 105 end 106 107end 108 109%% Plot the results 110 111figure(1) 112plot(noise_levels,mean_rotation_errors,'LineWidth',2) 113legend(names,'Location','NorthWest') 114xlabel('noise level [pix]') 115ylabel('mean rot. error [rad]') 116grid on 117 118figure(2) 119plot(noise_levels,median_rotation_errors,'LineWidth',2) 120legend(names,'Location','NorthWest') 121xlabel('noise level [pix]') 122ylabel('median rot. error [rad]') 123grid on 124