78 lines
2.1 KiB
Matlab
78 lines
2.1 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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% set maximum and minimum number of points per cam
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pt_number_per_cam = 50;
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% set maximum and minimum number of outliers
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min_outlier_fraction = 0.1;
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max_outlier_fraction = 0.2;
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% repeat 10000 iterations
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iterations = 1000;
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% The name of the algorithms in the final plots
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names = { 'Homogeneous'; 'Vanilla' };
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% The noise in this experiment
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noise = 0.5;
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%% Run the benchmark
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%prepare the overall result arrays
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ransac_iterations = zeros(2,iterations);
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%Run the RANSAC with homogeneous sampling
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counter = 0;
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for i=1:iterations
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%generate random outlier fraction
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outlier_fraction = rand() * (max_outlier_fraction - min_outlier_fraction) + min_outlier_fraction;
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% generate experiment
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[v1,v2,cam_offsets,t,R] = createMulti2D2DExperiment(pt_number_per_cam,cam_number,noise,outlier_fraction);
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Out = opengv_experimental1( v1{1,1}, v1{2,1}, v1{3,1}, v1{4,1}, v2{1,1}, v2{2,1}, v2{3,1}, v2{4,1}, cam_offsets, 2 );
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ransac_iterations(1,i) = Out(1,5);
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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(['Homogeneous sampling: Iteration ' num2str(i) ' of ' num2str(iterations)]);
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end
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end
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%Run the RANSAC with vanilla sampling
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counter = 0;
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for i=1:iterations
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%generate random outlier fraction
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outlier_fraction = rand() * (max_outlier_fraction - min_outlier_fraction) + min_outlier_fraction;
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% generate experiment
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[v1,v2,t,R] = create2D2DExperiment(pt_number_per_cam*cam_number,cam_number,noise,outlier_fraction);
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Out = opengv_experimental2( v1, v2, 2 );
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ransac_iterations(2,i) = Out(1,5);
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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(['Vanilla sampling: 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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figure(1)
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hist(ransac_iterations')
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[Y,X] = hist(ransac_iterations')
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legend(names,'Location','NorthEast')
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xlabel('number of iterations')
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grid on |