107 lines
2.7 KiB
Matlab
107 lines
2.7 KiB
Matlab
clear
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addpath(genpath('helpers/'));
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find_FERA2011;
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out_loc = './out_fera/';
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%%
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if(isunix)
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executable = '"../../build/bin/FeatureExtraction"';
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else
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executable = '"../../x64/Release/FeatureExtraction.exe"';
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end
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fera_dirs = dir([FERA2011_dir, 'train*']);
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for f1=1:numel(fera_dirs)
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vid_files = dir([FERA2011_dir, fera_dirs(f1).name, '/*.avi']);
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for v=1:numel(vid_files)
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command = [executable ' -aus -au_static '];
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curr_vid = [FERA2011_dir, fera_dirs(f1).name, '/', vid_files(v).name];
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command = cat(2, command, [' -f "' curr_vid '" -out_dir "' out_loc '"']);
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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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end
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%%
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[ labels_gt, valid_ids, filenames] = extract_FERA2011_labels(FERA2011_dir, all_recs, all_aus);
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labels_gt = cat(1, labels_gt{:});
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for i=1:numel(filenames)
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filenames{i} = filenames{i}(1:end-3);
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end
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%% Identifying which column IDs correspond to which AU
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tab = readtable([out_loc, 'train_001.csv']);
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column_names = tab.Properties.VariableNames;
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% As there are both classes and intensities list and evaluate both of them
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aus_pred_class = [];
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inds_class_in_file = [];
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for c=1:numel(column_names)
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if(strfind(column_names{c}, '_c') > 0)
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aus_pred_class = cat(1, aus_pred_class, int32(str2num(column_names{c}(3:end-2))));
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inds_class_in_file = cat(1, inds_class_in_file, c);
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end
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end
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%%
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inds_au_class = zeros(size(all_aus));
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for ind=1:numel(all_aus)
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if(~isempty(find(aus_pred_class==all_aus(ind), 1)))
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inds_au_class(ind) = find(aus_pred_class==all_aus(ind));
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end
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end
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%%
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preds_all_class = [];
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for i=1:numel(filenames)
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fname = dir([out_loc, '/*', filenames{i}, '.csv']);
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fname = fname(1).name;
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preds = dlmread([out_loc '/' fname], ',', 1, 0);
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% Read all of the intensity AUs
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preds_class = preds(:, inds_class_in_file);
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preds_all_class = cat(1, preds_all_class, preds_class);
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end
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%%
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f = fopen('results/FERA2011_res_class.txt', 'w');
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au_res = [];
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for au = 1:numel(all_aus)
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if(inds_au_class(au) ~= 0)
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tp = sum(labels_gt(:,au) == 1 & preds_all_class(:, inds_au_class(au)) == 1);
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fp = sum(labels_gt(:,au) == 0 & preds_all_class(:, inds_au_class(au)) == 1);
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fn = sum(labels_gt(:,au) == 1 & preds_all_class(:, inds_au_class(au)) == 0);
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tn = sum(labels_gt(:,au) == 0 & preds_all_class(:, inds_au_class(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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fprintf(f, 'AU%d class, Precision - %.3f, Recall - %.3f, F1 - %.3f\n', all_aus(au), precision, recall, f1);
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au_res = cat(1, au_res, f1);
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end
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end
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fclose(f); |