113 lines
3.7 KiB
Matlab
113 lines
3.7 KiB
Matlab
clear;
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face_processed_dir = 'E:\datasets\face_datasets_processed';
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%% CK+
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hog_dir = [face_processed_dir, '/ck+/'];
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hog_files = dir([hog_dir '*.hog']);
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[appearance_data, valid_inds, vid_ids_train] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data = appearance_data(valid_inds,:);
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vid_ids_train = vid_ids_train(valid_inds,:);
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%% Bosphorus
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hog_dir = [face_processed_dir, '/bosph/'];
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hog_files = dir([hog_dir '*.hog']);
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% Remove non-frontal
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frontal = true(size(hog_files));
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for i = 1:numel(frontal)
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if(~isempty(strfind(hog_files(i).name, 'YR')) || ~isempty(strfind(hog_files(i).name, 'PR'))|| ~isempty(strfind(hog_files(i).name, 'CR')))
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frontal(i) = false;
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end
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end
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hog_files = hog_files(frontal);
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[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
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vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
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appearance_data = cat(1,appearance_data, appearance_data_tmp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
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%% FERA2011
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hog_dir = [face_processed_dir, '/fera2011/'];
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hog_files = dir([hog_dir '*.hog']);
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[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
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vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
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appearance_data = cat(1,appearance_data, appearance_data_tmp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
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%% UNBC
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hog_dir = [face_processed_dir, '/unbc/'];
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hog_files = dir([hog_dir '*.hog']);
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[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
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vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
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appearance_data = cat(1,appearance_data, appearance_data_tmp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
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%% DISFA
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hog_dir = [face_processed_dir, '/disfa/'];
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hog_files = dir([hog_dir '*.hog']);
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[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
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vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
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appearance_data = cat(1,appearance_data, appearance_data_tmp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
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%% BP4D train
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hog_dir = [face_processed_dir, '/bp4d/train/'];
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hog_files = dir([hog_dir '*.hog']);
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[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
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vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
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appearance_data = cat(1,appearance_data, appearance_data_tmp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
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%% SEMAINE train
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hog_dir = [face_processed_dir, '/semaine/train/'];
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hog_files = dir([hog_dir '*.hog']);
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[appearance_data_tmp, valid_inds_tmp, vid_ids_train_tmp] = Read_HOG_files_small(hog_files, hog_dir);
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appearance_data_tmp = appearance_data_tmp(valid_inds_tmp,:);
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vid_ids_train_tmp = vid_ids_train_tmp(valid_inds_tmp,:);
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appearance_data = cat(1,appearance_data, appearance_data_tmp);
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vid_ids_train = cat(1,vid_ids_train, vid_ids_train_tmp);
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%%
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means_norm = mean(appearance_data);
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stds_norm = std(appearance_data);
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normed_data = bsxfun(@times, bsxfun(@plus, appearance_data, -means_norm), 1./stds_norm);
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[PC, score, eigen_vals] = princomp(normed_data, 'econ');
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% Keep 95 percent of variability
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total_sum = sum(eigen_vals);
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count = numel(eigen_vals);
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for i=1:numel(eigen_vals)
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if ((sum(eigen_vals(1:i)) / total_sum) >= 0.95)
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count = i;
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break;
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end
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end
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PC = PC(:,1:count);
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save('generic_face_rigid.mat', 'PC', 'means_norm', 'stds_norm');
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