2128589309
- New AU recognition models trained on extra datasets - Bosphorus, UNBC, FERA2011 - Cleaner and clearer separation of static and dynamic AU models - AU training code cleaned up and instructions added - bug fixes with median feature computation - AU prediction correction (smoothing and shifting) with post processing
107 lines
No EOL
2.9 KiB
Matlab
107 lines
No EOL
2.9 KiB
Matlab
clear
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unbc_loc = 'D:/Datasets/UNBC/Images/';
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out_loc = './out_unbc/';
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if(~exist(out_loc, 'dir'))
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mkdir(out_loc);
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end
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%%
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executable = '"../../x64/Release/FeatureExtraction.exe"';
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unbc_dirs = {'042-ll042', '043-jh043', '047-jl047', '048-aa048', '049-bm049',...
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'052-dr052', '059-fn059', '064-ak064', '066-mg066', '080-bn080',...
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'092-ch092', '095-tv095', '096-bg096', '097-gf097', '101-mg101',...
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'103-jk103', '106-nm106', '107-hs107', '108-th108', '109-ib109',...
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'115-jy115', '120-kz120', '121-vw121', '123-jh123', '124-dn124'};
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for f1=1:numel(unbc_dirs)
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if(isdir([unbc_loc, unbc_dirs{f1}]))
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unbc_2_dirs = dir([unbc_loc, unbc_dirs{f1}]);
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unbc_2_dirs = unbc_2_dirs(3:end);
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f1_dir = unbc_dirs{f1};
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command = [executable ' -asvid -q -no2Dfp -no3Dfp -noMparams -noPose -noGaze '];
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for f2=1:numel(unbc_2_dirs)
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f2_dir = unbc_2_dirs(f2).name;
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if(isdir([unbc_loc, unbc_dirs{f1}]))
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curr_vid = [unbc_loc, f1_dir, '/', f2_dir, '/'];
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name = [f1_dir '_' f2_dir];
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output_file = [out_loc name '.au.txt'];
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command = cat(2, command, [' -fdir "' curr_vid '" -of "' output_file '"']);
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end
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end
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dos(command);
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end
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end
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%%
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addpath('./helpers/');
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find_UNBC;
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aus_UNBC = [6, 7, 10, 12, 25, 26];
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[ labels_gt, valid_ids, filenames] = extract_UNBC_labels(UNBC_dir, unbc_dirs, aus_UNBC);
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labels_gt = cat(1, labels_gt{:});
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%% Identifying which column IDs correspond to which AU
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tab = readtable([out_loc, '042-ll042_ll042t1aaaff.au.txt']);
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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_int = [];
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inds_int_in_file = [];
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for c=1:numel(column_names)
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if(strfind(column_names{c}, '_r') > 0)
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aus_pred_int = cat(1, aus_pred_int, int32(str2num(column_names{c}(3:end-2))));
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inds_int_in_file = cat(1, inds_int_in_file, c);
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end
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end
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%%
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inds_au_int = zeros(size(aus_UNBC));
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for ind=1:numel(aus_UNBC)
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if(~isempty(find(aus_pred_int==aus_UNBC(ind), 1)))
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inds_au_int(ind) = find(aus_pred_int==aus_UNBC(ind));
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end
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end
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preds_all_int = [];
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for i=1:numel(filenames)
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fname = dir([out_loc, '/*', filenames{i}, '.au.txt']);
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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_int = preds(:, inds_int_in_file);
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preds_all_int = cat(1, preds_all_int, preds_int);
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end
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%%
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f = fopen('results/UNBC_valid_res_int.txt', 'w');
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for au = 1:numel(aus_UNBC)
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[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_au_prediction_results( preds_all_int(:, inds_au_int(au)), labels_gt(:,au));
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fprintf(f, 'AU%d results - rms %.3f, corr %.3f, ccc - %.3f\n', aus_UNBC(au), rms, corrs, ccc);
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end
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fclose(f); |