50 lines
1.3 KiB
Matlab
50 lines
1.3 KiB
Matlab
% Change to your downloaded location
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clear
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addpath('C:\liblinear\matlab')
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addpath('../training_code/');
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addpath('../utilities/');
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addpath('../../data extraction/');
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DISFA_aus = [1, 2, 4, 5, 6, 9, 12, 15, 17, 20, 25, 26];
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au = DISFA_aus(1);
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op = cd('../DISFA/');
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rest_aus = setdiff(DISFA_aus, au);
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shared_defs;
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% need to split the rest
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[~, ~, test_samples, test_labels, raw_test, PC, means, scaling, vid_ids, success] = Prepare_HOG_AU_data_generic([], users, au, rest_aus, hog_data_dir);
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test_samples = sparse(test_samples);
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%%
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root = [hog_data_dir, '/../'];
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for i=1:numel(users)
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input_train_label_files{i} = [root, '/ActionUnit_Labels/', users{i}, '/', users{i}];
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end
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labels_gt_test = [];
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for a=1:numel(DISFA_aus)
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labels_gt_test = cat(2, labels_gt_test, extract_au_labels(input_train_label_files, DISFA_aus(a)));
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end
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cd(op);
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%%
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for a=1:4%numel(DISFA_aus)
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name = sprintf('mat_models/AU_%d_static_intensity.mat', DISFA_aus(a));
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load(name);
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svr_test = @svr_test_linear;
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model.eval_ids = ones(size(labels_gt_test,1),1);
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model.vid_ids = vid_ids;
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model.success = success;
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[~, predictions_all] = svr_test(labels_gt_test(:,a), test_samples, model);
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[ ~, ~, ~, ccc, ~, ~ ] = evaluate_regression_results( predictions_all, labels_gt_test(:,a));
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fprintf('AU%d, CCC - %.3f\n', DISFA_aus(a), ccc);
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end
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