108 lines
3.3 KiB
Matlab
108 lines
3.3 KiB
Matlab
clear
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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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if(exist('D:/Datasets/DISFA/Videos_LeftCamera/', 'file'))
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DISFA_dir = 'D:/Datasets/DISFA/Videos_LeftCamera/';
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elseif(exist('E:/Datasets/DISFA/Videos_LeftCamera/', 'file'))
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DISFA_dir = 'E:/Datasets/DISFA/Videos_LeftCamera/';
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elseif(exist('/multicomp/datasets/face_datasets/DISFA/Videos_LeftCamera/', 'file'))
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DISFA_dir = '/multicomp/datasets/face_datasets/DISFA/Videos_LeftCamera/';
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elseif(exist('/media/tadas/2EBEA130BEA0F20F/datasets/DISFA/', 'file'))
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DISFA_dir = '/media/tadas/2EBEA130BEA0F20F/datasets/DISFA/Videos_LeftCamera/';
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else
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fprintf('Cannot find DIFA location\n');
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end
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videos = dir([DISFA_dir, '*.avi']);
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output = 'out_DISFA/';
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%%
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% Do it in parrallel for speed (replace the parfor with for if no parallel
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% toolbox is available)
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parfor v = 1:numel(videos)
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vid_file = [DISFA_dir, videos(v).name];
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command = sprintf('%s -f "%s" -out_dir "%s" -aus ', executable, vid_file, output);
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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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%% Now evaluate the predictions
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% Note that DISFA was used in training, this is not meant for experimental
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% results but rather to show how to do AU prediction and how to interpret
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% the results
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Label_dir = [DISFA_dir, '/../ActionUnit_Labels/'];
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prediction_dir = 'out_DISFA/';
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label_folders = dir([Label_dir, 'SN*']);
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AUs_disfa = [1,2,4,5,6,9,12,15,17,20,25,26];
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labels_all = [];
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label_ids = [];
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for i=1:numel(label_folders)
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labels = [];
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for au = AUs_disfa
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in_file = sprintf('%s/%s/%s_au%d.txt', Label_dir, label_folders(i).name, label_folders(i).name, au);
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A = dlmread(in_file, ',');
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labels = cat(2, labels, A(:,2));
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end
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labels_all = cat(1, labels_all, labels);
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user_id = str2num(label_folders(i).name(3:end));
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label_ids = cat(1, label_ids, repmat(user_id, size(labels,1),1));
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end
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preds_files = dir([prediction_dir, '*SN*.csv']);
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tab = readtable([prediction_dir, preds_files(1).name]);
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column_names = tab.Properties.VariableNames;
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aus_pred_int = [];
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au_inds_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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au_inds_in_file = cat(1, au_inds_in_file, c);
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end
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end
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inds_au = zeros(numel(AUs_disfa),1);
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for ind=1:numel(AUs_disfa)
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inds_au(ind) = au_inds_in_file(aus_pred_int==AUs_disfa(ind));
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end
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preds_all = zeros(size(labels_all,1), numel(AUs_disfa));
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for i=1:numel(preds_files)
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preds = dlmread([prediction_dir, preds_files(i).name], ',', 1, 0);
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%preds = preds(:,5:5+numel(aus_pred_int)-1);
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user_id = str2num(preds_files(i).name(end - 11:end-9));
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rel_ids = label_ids == user_id;
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preds_all(rel_ids,:) = preds(:,inds_au);
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end
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%% now do the actual evaluation that the collection has been done
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f = fopen('results/DISFA_valid_res.txt', 'w');
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au_res = zeros(1, numel(AUs_disfa));
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for au = 1:numel(AUs_disfa)
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[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_au_prediction_results( preds_all(:,au), labels_all(:,au));
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fprintf(f, 'AU%d results - corr %.3f, rms %.3f, ccc - %.3f\n', AUs_disfa(au), corrs, rms, ccc);
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au_res(au) = ccc;
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end
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fclose(f); |