119 lines
3.7 KiB
Matlab
119 lines
3.7 KiB
Matlab
clear
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curr_dir = cd('.');
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% Replace this with your downloaded 300-W train data
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if(exist([getenv('USERPROFILE') '/Dropbox/AAM/eye_clm/mpii_data/'], 'file'))
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database_root = [getenv('USERPROFILE') '/Dropbox/AAM/eye_clm/mpii_data/'];
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elseif(exist('D:\Dropbox/Dropbox/AAM/eye_clm/mpii_data/', 'file'))
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database_root = 'D:\Dropbox/Dropbox/AAM/eye_clm/mpii_data/';
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elseif(exist('F:\Dropbox/AAM/eye_clm/mpii_data/', 'file'))
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database_root = 'F:\Dropbox/AAM/eye_clm/mpii_data/';
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elseif(exist('/multicomp/datasets/mpii_gaze/mpii_data/', 'file'))
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database_root = '/multicomp/datasets/mpii_gaze/mpii_data/';
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elseif(exist('/media/tadas/5E08AE0D08ADE3ED/Dropbox/AAM/eye_clm/mpii_data/', 'file'))
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database_root = '/media/tadas/5E08AE0D08ADE3ED/Dropbox/AAM/eye_clm/mpii_data/';
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else
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fprintf('MPII gaze dataset not found\n');
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end
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output_loc = './gaze_estimates_MPII/';
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if(~exist(output_loc, 'dir'))
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mkdir(output_loc);
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end
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output = './mpii_out/';
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%% Perform actual gaze predictions
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if(isunix)
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executable = '"../../build/bin/FaceLandmarkImg"';
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else
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executable = '"../../x64/Release/FaceLandmarkImg.exe"';
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end
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command = sprintf('%s -fx 1028 -fy 1028 ', executable);
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p_dirs = dir([database_root, 'p*']);
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parfor p=1:numel(p_dirs)
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tic
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input_loc = ['-gaze -fdir "', [database_root, p_dirs(p).name], '" '];
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out_img_loc = ['-out_dir "', [output, p_dirs(p).name], '" '];
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command_c = cat(2, command, input_loc, out_img_loc);
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if(isunix)
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unix(command_c, '-echo');
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else
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dos(command_c);
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end
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end
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%%
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% Extract the results
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predictions_l = zeros(750, 3);
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predictions_r = zeros(750, 3);
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gt_l = zeros(750, 3);
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gt_r = zeros(750, 3);
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angle_err_l = zeros(750,1);
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angle_err_r = zeros(750,1);
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p_dirs = dir([database_root, 'p*']);
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curr = 1;
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for p=1:numel(p_dirs)
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load([database_root, p_dirs(p).name, '/Data.mat']);
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for i=1:size(filenames, 1)
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fname = sprintf('%s/%s/%d_%d_%d_%d_%d_%d_%d.csv', output, p_dirs(p).name,...
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filenames(i,1), filenames(i,2), filenames(i,3), filenames(i,4),...
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filenames(i,5), filenames(i,6), filenames(i,7));
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if(p==1 && i==1)
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% First read in the column names
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tab = readtable(fname);
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column_names = tab.Properties.VariableNames;
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gaze_0_ids = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'gaze_0_'));
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gaze_1_ids = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'gaze_1_'));
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end
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if(exist(fname, 'file'))
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all_params = dlmread(fname, ',', 1, 0);
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else
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all_params = [];
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end
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% If there was a face detected
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if(size(all_params,1)>0)
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predictions_r(curr,:) = all_params(1,gaze_0_ids);
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predictions_l(curr,:) = all_params(1,gaze_1_ids);
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else
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predictions_r(curr,:) = [0,0,-1];
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predictions_l(curr,:) = [0,0,-1];
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end
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head_rot = headpose(i,1:3);
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gt_r(curr,:) = data.right.gaze(i,:)';
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gt_r(curr,:) = gt_r(curr,:) / norm(gt_r(curr,:));
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gt_l(curr,:) = data.left.gaze(i,:)';
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gt_l(curr,:) = gt_l(curr,:) / norm(gt_l(curr,:));
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angle_err_l(curr) = acos(predictions_l(curr,:) * gt_l(curr,:)') * 180/pi;
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angle_err_r(curr) = acos(predictions_r(curr,:) * gt_r(curr,:)') * 180/pi;
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curr = curr + 1;
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end
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end
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all_errors = cat(1, angle_err_l, angle_err_r);
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mean_error = mean(all_errors);
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median_error = median(all_errors);
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save('mpii_1500_errs.mat', 'all_errors', 'mean_error', 'median_error');
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f = fopen('mpii_1500_errs.txt', 'w');
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fprintf(f, 'Mean error, median error\n');
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fprintf(f, '%.3f, %.3f\n', mean_error, median_error);
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fclose(f); |