118 lines
4.5 KiB
Matlab
118 lines
4.5 KiB
Matlab
function [data_train, labels_train, data_devel, labels_devel, raw_devel, PC, means_norm, stds_norm, devel_ids, devel_success] = ...
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Prepare_HOG_AU_data(train_users, devel_users, au_train, rest_aus, Bosphorus_dir, params_data_dir)
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%%
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addpath(genpath('../data extraction/'));
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% First extracting the labels
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[ labels_train, valid_ids_train, filenames ] = extract_Bosphorus_labels(Bosphorus_dir, train_users, au_train);
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[ labels_other, ~, ~ ] = extract_Bosphorus_labels(Bosphorus_dir, train_users, rest_aus);
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% Reading in the HOG data (of only relevant frames)
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[train_appearance_data, valid_ids_train_hog, vid_ids_train_string] = Read_HOG_files(filenames, params_data_dir);
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[train_geom_data] = Read_geom_files(filenames, params_data_dir);
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% Subsample the data to rebalance it
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if(numel(train_users) > 0)
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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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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(~valid_ids_train) = false;
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reduced_inds(~valid_ids_train_hog) = false;
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labels_other = labels_other(reduced_inds, :);
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labels_train = labels_train(reduced_inds,:);
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train_appearance_data = train_appearance_data(reduced_inds,:);
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train_geom_data = train_geom_data(reduced_inds,:);
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vid_ids_train_string = vid_ids_train_string(reduced_inds,:);
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end
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%% Extract devel data
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% First extracting the labels
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[ labels_devel, valid_ids_devel, filenames_devel ] = extract_Bosphorus_labels(Bosphorus_dir, devel_users, au_train);
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% Reading in the HOG data (of only relevant frames)
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[devel_appearance_data, valid_ids_devel_hog, vid_ids_devel_string] = Read_HOG_files(filenames_devel, params_data_dir);
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devel_success = valid_ids_devel_hog;
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devel_ids = vid_ids_devel_string;
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[devel_geom_data] = Read_geom_files(filenames_devel, params_data_dir);
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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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% Grab all data for validation as want good params for all the data
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raw_devel = cat(2, devel_appearance_data, devel_geom_data);
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devel_appearance_data = bsxfun(@times, bsxfun(@plus, devel_appearance_data, -means_norm), 1./stds_norm);
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data_devel = devel_appearance_data * PC;
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data_devel = cat(2, data_devel, devel_geom_data);
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valid_ids_devel = valid_ids_devel & devel_success;
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data_devel = data_devel(valid_ids_devel,:);
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labels_devel = labels_devel(valid_ids_devel,:);
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raw_devel = raw_devel(valid_ids_devel,:);
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devel_success = devel_success(valid_ids_devel,:);
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devel_ids = devel_ids(valid_ids_devel);
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if(numel(train_users) > 0)
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train_appearance_data = bsxfun(@times, bsxfun(@plus, train_appearance_data, -means_norm), 1./stds_norm);
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data_train = train_appearance_data * PC;
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data_train = cat(2, data_train, train_geom_data);
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else
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data_train = [];
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end
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geom_size = max(size(train_geom_data, 2), size(devel_geom_data, 2));
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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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end |