1%% Reset everything 2 3clear all; 4clc; 5close all; 6addpath('helpers'); 7 8%% Configure the benchmark 9 10% noncentral case 11cam_number = 4; 12% Getting 10 points, and testing all algorithms with the respective number of points 13pt_number = 50; 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 = { 'seventeenpt'; 'rel_nonlin_noncentral'; 'rel_nonlin_noncentral' }; 21% This defines the number of points used for every algorithm 22indices = { [1:1:17]; [1:1:17]; [1:1:50] }; 23% The name of the algorithms in the final plots 24names = { '17pt'; 'nonlin. opt. (17pts)'; 'nonlin. opt. (50pts)' }; 25 26% The maximum noise to analyze 27max_noise = 5.0; 28% The step in between different noise levels 29noise_step = 0.1; 30 31%% Run the benchmark 32 33%prepare the overall result arrays 34number_noise_levels = max_noise / noise_step + 1; 35num_algorithms = size(algorithms,1); 36mean_rotation_errors = zeros(num_algorithms,number_noise_levels); 37median_rotation_errors = zeros(num_algorithms,number_noise_levels); 38mean_position_errors = zeros(num_algorithms,number_noise_levels); 39median_position_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 position_errors = zeros(num_algorithms,iterations); 51 52 counter = 0; 53 54 for i=1:iterations 55 56 % generate experiment 57 [v1,v2,t,R] = create2D2DExperiment(pt_number,cam_number,noise,outlier_fraction); 58 [t_perturbed,R_perturbed] = perturb(t,R,0.01); 59 T_perturbed = [R_perturbed,t_perturbed]; 60 T_gt = [R,t]; 61 62 for a=1:num_algorithms 63 Out = opengv(algorithms{a},indices{a},v1,v2,T_perturbed); 64 [position_error, rotation_error] = evaluateTransformationError( T_gt, Out ); 65 position_errors(a,i) = position_error; 66 rotation_errors(a,i) = rotation_error; 67 end 68 69 counter = counter + 1; 70 if counter == 100 71 counter = 0; 72 display(['Iteration ' num2str(i) ' of ' num2str(iterations) '(noise level ' num2str(noise) ')']); 73 end 74 end 75 76 %Now compute the mean and median value of the error for each algorithm 77 for a=1:num_algorithms 78 mean_rotation_errors(a,n) = mean(rotation_errors(a,:)); 79 median_rotation_errors(a,n) = median(rotation_errors(a,:)); 80 mean_position_errors(a,n) = mean(position_errors(a,:)); 81 median_position_errors(a,n) = median(position_errors(a,:)); 82 end 83 84end 85 86%% Plot the results 87 88figure(1) 89plot(noise_levels,mean_rotation_errors,'LineWidth',2) 90legend(names,'Location','NorthWest') 91xlabel('noise level [pix]') 92ylabel('mean rot. error [rad]') 93grid on 94 95figure(2) 96plot(noise_levels,median_rotation_errors,'LineWidth',2) 97legend(names,'Location','NorthWest') 98xlabel('noise level [pix]') 99ylabel('median rot. error [rad]') 100grid on 101 102figure(3) 103plot(noise_levels,mean_position_errors,'LineWidth',2) 104legend(names,'Location','NorthWest') 105xlabel('noise level [pix]') 106ylabel('mean pos. error [m]') 107grid on 108 109figure(4) 110plot(noise_levels,median_position_errors,'LineWidth',2) 111legend(names,'Location','NorthWest') 112xlabel('noise level [pix]') 113ylabel('median pos. error [m]') 114grid on