119 lines
4.2 KiB
Matlab
119 lines
4.2 KiB
Matlab
function [data_train, labels_train, data_test, labels_test, raw_test, PC, means_norm, stds_norm, vid_ids_test, success_test] = ...
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Prepare_HOG_AU_data_generic(train_users, test_users, au_train, rest_aus, root, features_dir)
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%% This should be a separate function?
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input_train_label_files = cell(numel(train_users),1);
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input_test_label_files = cell(numel(test_users),1);
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% This is for loading the labels
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for i=1:numel(train_users)
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input_train_label_files{i} = [root, '/ActionUnit_Labels/', train_users{i}, '/', train_users{i}];
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end
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% This is for loading the labels
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for i=1:numel(test_users)
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input_test_label_files{i} = [root, '/ActionUnit_Labels/', test_users{i}, '/', test_users{i}];
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end
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% First extracting the labels
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[train_geom_data] = Read_geom_files(train_users, features_dir);
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[test_geom_data] = Read_geom_files(test_users, features_dir);
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% Reading in the HOG data
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[train_data, tracked_inds_hog, vid_ids_train] = Read_HOG_files(train_users, features_dir);
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[test_data, success_test, vid_ids_test] = Read_HOG_files(test_users, features_dir);
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train_data = cat(2, train_data, train_geom_data);
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raw_test = cat(2, test_data, test_geom_data);
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% Extracting the labels
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labels_train = extract_au_labels(input_train_label_files, au_train);
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labels_test = extract_au_labels(input_test_label_files, au_train);
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labels_other = zeros(size(labels_train,1), numel(rest_aus));
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% This is used to pick up activity of other AUs for a more 'interesting'
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% data split and not only neutral expressions for negative samples
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if(numel(input_train_label_files) > 0)
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for i=1:numel(rest_aus)
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labels_other(:,i) = extract_au_labels(input_train_label_files, rest_aus(i));
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end
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% can now extract the needed training labels (do not rebalance validation
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% data)
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% make sure the same number of positive and negative samples is taken
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reduced_inds = false(size(labels_train,1),1);
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reduced_inds(labels_train > 0) = true;
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% make sure the same number of positive and negative samples is taken
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pos_count = sum(labels_train > 0);
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neg_count = sum(labels_train == 0);
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% pos_count = pos_count * 8;
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num_other = floor(pos_count / (size(labels_other, 2)));
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inds_all = 1:size(labels_train,1);
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for i=1:size(labels_other, 2)+1
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if(i > size(labels_other, 2))
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% fill the rest with a proportion of neutral
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inds_other = inds_all(sum(labels_other,2)==0 & ~labels_train);
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num_other_i = min(numel(inds_other), pos_count - sum(labels_train(reduced_inds,:)==0));
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else
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% take a proportion of each other AU
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inds_other = inds_all(labels_other(:, i) & ~labels_train);
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num_other_i = min(numel(inds_other), num_other);
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end
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inds_other_to_keep = inds_other(round(linspace(1, numel(inds_other), num_other_i)));
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reduced_inds(inds_other_to_keep) = true;
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end
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% Remove invalid ids based on CLM failing or AU not being labelled
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reduced_inds(~tracked_inds_hog) = false;
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labels_train = labels_train(reduced_inds);
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train_data = train_data(reduced_inds,:);
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end
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geom_size = max(size(train_geom_data,2), size(test_geom_data,2));
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% Peforming zone specific masking
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if(au_train < 8 || au_train == 43 || au_train == 45) % upper face AUs ignore bottom face
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% normalise the data
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pca_file = '../../pca_generation/generic_face_upper.mat';
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load(pca_file);
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elseif(au_train > 9) % lower face AUs ignore upper face and the sides
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% normalise the data
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pca_file = '../../pca_generation/generic_face_lower.mat';
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load(pca_file);
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elseif(au_train == 9) % Central face model
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% normalise the data
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pca_file = '../../pca_generation/generic_face_rigid.mat';
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load(pca_file);
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end
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PC_n = zeros(size(PC)+geom_size);
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PC_n(1:size(PC,1), 1:size(PC,2)) = PC;
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PC_n(size(PC,1)+1:end, size(PC,2)+1:end) = eye(geom_size);
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PC = PC_n;
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means_norm = cat(2, means_norm, zeros(1, geom_size));
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stds_norm = cat(2, stds_norm, ones(1, geom_size));
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data_test = bsxfun(@times, bsxfun(@plus, raw_test, -means_norm), 1./stds_norm);
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data_test = data_test * PC;
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if(numel(train_data > 0))
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data_train = bsxfun(@times, bsxfun(@plus, train_data, -means_norm), 1./stds_norm);
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data_train = data_train * PC;
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else
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data_train = [];
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end
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end |