137 lines
3.6 KiB
Matlab
137 lines
3.6 KiB
Matlab
% Perform static model prediction using images
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clear
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addpath('./helpers');
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find_Bosphorus;
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out_loc = './out_bosph/';
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%%
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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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bosph_dirs = dir([Bosphorus_dir, '/BosphorusDB/BosphorusDB/bs*']);
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%%
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parfor f1=1:numel(bosph_dirs)
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command = executable;
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input_dir = [Bosphorus_dir, '/BosphorusDB/BosphorusDB/', bosph_dirs(f1).name];
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command = cat(2, command, [' -fdir "' input_dir '" -out_dir "' out_loc '"']);
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command = cat(2, command, ' -multi_view 1 -wild -aus ');
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if(isunix)
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unix(command, '-echo')
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else
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dos(command);
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end
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end
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%%
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aus_Bosph = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, 45];
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[ labels_gt, valid_ids, filenames] = extract_Bosphorus_labels(Bosphorus_dir, all_recs, aus_Bosph);
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%% Read the predicted values
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% First read the first file to get the column ids
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tab = readtable([out_loc, filenames{1}, '.csv']);
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column_names = tab.Properties.VariableNames;
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aus_det_id = cellfun(@(x) ~isempty(x) && x==5, strfind(column_names, '_c'));
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aus_det_cell = column_names(aus_det_id);
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aus_det = zeros(size(aus_det_cell));
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for i=1:numel(aus_det)
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aus_det(i) = str2num(aus_det_cell{i}(3:4));
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end
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%%
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labels_pred = zeros(size(labels_gt));
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for i=1:numel(filenames)
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% Will need to read the relevant AUs only
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all_params = dlmread([out_loc, filenames{i}, '.csv'], ',', 1, 0);
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% if multiple faces detected just take the first row
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aus_pred = all_params(1, aus_det_id);
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for k=1:numel(aus_det)
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if(sum(aus_Bosph == aus_det(k))>0)
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labels_pred(i, aus_Bosph == aus_det(k)) = aus_pred(k);
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end
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end
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end
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%%
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f = fopen('results/Bosphorus_res_class.txt', 'w');
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labels_gt_bin = labels_gt;
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labels_gt_bin(labels_gt_bin > 1) = 1;
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f1s_class = zeros(1, numel(aus_Bosph));
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for au = 1:numel(aus_Bosph)
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tp = sum(labels_gt_bin(:,au) == 1 & labels_pred(:, au) == 1);
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fp = sum(labels_gt_bin(:,au) == 0 & labels_pred(:, au) == 1);
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fn = sum(labels_gt_bin(:,au) == 1 & labels_pred(:, au) == 0);
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tn = sum(labels_gt_bin(:,au) == 0 & labels_pred(:, au) == 0);
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precision = tp./(tp+fp);
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recall = tp./(tp+fn);
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f1 = 2 * precision .* recall ./ (precision + recall);
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f1s_class(au) = f1;
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fprintf(f, 'AU%d class, Precision - %.3f, Recall - %.3f, F1 - %.3f\n', aus_Bosph(au), precision, recall, f1);
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end
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fclose(f);
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%% Read the predicted values for intensities
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% First read the first file to get the column ids
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tab = readtable([out_loc, filenames{1}, '.csv']);
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column_names = tab.Properties.VariableNames;
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aus_det_id = cellfun(@(x) ~isempty(x) && x==5, strfind(column_names, '_r'));
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aus_det_cell = column_names(aus_det_id);
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aus_det = zeros(size(aus_det_cell));
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for i=1:numel(aus_det)
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aus_det(i) = str2num(aus_det_cell{i}(3:4));
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end
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%%
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labels_pred = zeros(size(labels_gt));
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for i=1:numel(filenames)
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% Will need to read the relevant AUs only
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all_params = dlmread([out_loc, filenames{i}, '.csv'], ',', 1, 0);
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% if multiple faces detected just take the first row
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aus_pred = all_params(1, aus_det_id);
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for k=1:numel(aus_det)
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if(sum(aus_Bosph == aus_det(k))>0)
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labels_pred(i, aus_Bosph == aus_det(k)) = aus_pred(k);
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end
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end
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end
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%%
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f = fopen('results/Bosphorus_res_int.txt', 'w');
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cccs_reg = zeros(1, numel(aus_Bosph));
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for au = 1:numel(aus_Bosph)
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[ ~, ~, corrs, ccc, rms, ~ ] = evaluate_regression_results( labels_pred(:, au), labels_gt(:, au));
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cccs_reg(au) = ccc;
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fprintf(f, 'AU%d intensity, Corr - %.3f, RMS - %.3f, CCC - %.3f\n', aus_Bosph(au), corrs, rms, ccc);
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end
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fclose(f);
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