DISFA adaptation.

matlab_version/AU_training/experiments/BP4D/Read_geom_files.m

@@ -23,7 +23,7 @@ function [geom_data, valid_ids] = Read_geom_files(users, hog_data_dir)
                 if(i == 1)
                     tab = readtable(geom_file);
                     column_names = tab.Properties.VariableNames; 
-                    valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'valid'));
+                    valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'success'));
                     shape_inds = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'p_'));
                 end
         

matlab_version/AU_training/experiments/BP4D/Read_geom_files_dynamic.m

@@ -25,7 +25,7 @@ function [geom_data, valid_ids] = Read_geom_files_dynamic(users, hog_data_dir)
                 if(i == 1)
                     tab = readtable(geom_file);
                     column_names = tab.Properties.VariableNames; 
-                    valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'valid'));
+                    valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'success'));
                     shape_inds = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'p_'));
                 end
         

matlab_version/AU_training/experiments/DISFA/Prepare_HOG_AU_data_generic.m

@@ -1,13 +1,11 @@
 function [data_train, labels_train, data_test, labels_test, raw_test, PC, means_norm, stds_norm, vid_ids_test, success_test] = ...
-    Prepare_HOG_AU_data_generic(train_users, test_users, au_train, rest_aus, hog_data_dir)
+    Prepare_HOG_AU_data_generic(train_users, test_users, au_train, rest_aus,  root, features_dir)
 
 %% This should be a separate function?
 
 input_train_label_files = cell(numel(train_users),1);
 input_test_label_files = cell(numel(test_users),1);
 
-root = [hog_data_dir, '/../'];
-    
 % This is for loading the labels
 for i=1:numel(train_users)   
     input_train_label_files{i} = [root, '/ActionUnit_Labels/', train_users{i}, '/', train_users{i}];
@@ -19,12 +17,12 @@ for i=1:numel(test_users)
 end
 
 % First extracting the labels
-[train_geom_data] = Read_geom_files(train_users, hog_data_dir);
-[test_geom_data] = Read_geom_files(test_users, hog_data_dir);
+[train_geom_data] = Read_geom_files(train_users, features_dir);
+[test_geom_data] = Read_geom_files(test_users, features_dir);
 
 % Reading in the HOG data
-[train_data, tracked_inds_hog, vid_ids_train] = Read_HOG_files(train_users, hog_data_dir);
-[test_data, success_test, vid_ids_test] = Read_HOG_files(test_users, hog_data_dir);
+[train_data, tracked_inds_hog, vid_ids_train] = Read_HOG_files(train_users, features_dir);
+[test_data, success_test, vid_ids_test] = Read_HOG_files(test_users, features_dir);
 
 train_data = cat(2, train_data, train_geom_data);
 raw_test = cat(2, test_data, test_geom_data);

matlab_version/AU_training/experiments/DISFA/Prepare_HOG_AU_data_generic_dynamic.m

@@ -1,13 +1,11 @@
 function [data_train, labels_train, data_test, labels_test, raw_test, PC, means_norm, stds_norm, vid_ids_test, success_test] = ...
-    Prepare_HOG_AU_data_generic_dynamic(train_users, test_users, au_train, rest_aus, hog_data_dir)
+    Prepare_HOG_AU_data_generic_dynamic(train_users, test_users, au_train, rest_aus, root, features_dir)
 
 %% This should be a separate function?
 
 input_train_label_files = cell(numel(train_users),1);
 input_test_label_files = cell(numel(test_users),1);
 
-root = [hog_data_dir, '/../'];
-    
 % This is for loading the labels
 for i=1:numel(train_users)   
     input_train_label_files{i} = [root, '/ActionUnit_Labels/', train_users{i}, '/', train_users{i}];
@@ -19,12 +17,12 @@ for i=1:numel(test_users)
 end
 
 % First extracting the labels
-[train_geom_data] = Read_geom_files_dynamic(train_users, hog_data_dir);
-[test_geom_data] = Read_geom_files_dynamic(test_users, hog_data_dir);
+[train_geom_data] = Read_geom_files_dynamic(train_users, features_dir);
+[test_geom_data] = Read_geom_files_dynamic(test_users, features_dir);
 
 % Reading in the HOG data
-[train_data, tracked_inds_hog, vid_ids_train] = Read_HOG_files_dynamic(train_users, hog_data_dir);
-[test_data, success_test, vid_ids_test] = Read_HOG_files_dynamic(test_users, hog_data_dir);
+[train_data, tracked_inds_hog, vid_ids_train] = Read_HOG_files_dynamic(train_users, features_dir);
+[test_data, success_test, vid_ids_test] = Read_HOG_files_dynamic(test_users, features_dir);
 
 train_data = cat(2, train_data, train_geom_data);
 raw_test = cat(2, test_data, test_geom_data);

matlab_version/AU_training/experiments/DISFA/Read_geom_files.m

@@ -6,14 +6,23 @@ function [geom_data] = Read_geom_files(users, hog_data_dir)
     
     for i=1:numel(users)
         
-        geom_file = [hog_data_dir, '/../model_params/LeftVideo' users{i} '_comp.txt'];        
+        geom_file = [hog_data_dir, 'LeftVideo' users{i} '_comp.csv'];        
         
-        res = dlmread(geom_file, ',', 1, 0);        
-        valid = logical(res(:,4));
-        
-        %res_rot = res(:,6:8);
-        
-        res = res(:,11:end);   
+        if(i == 1)
+            tab = readtable(geom_file);
+            column_names = tab.Properties.VariableNames; 
+            valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'success'));
+            shape_inds = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'p_'));
+        end
+                
+        res = dlmread(geom_file, ',', 1, 0);    
+
+        % Check the confidence of detection
+        valid = logical(res(:, valid_ind));              
+        res = res(:, shape_inds);
+                
+        % Do not consider global parameters
+        res = res(:, 7:end);
         
         actual_locs = res * V';
         res = cat(2, actual_locs, res);

matlab_version/AU_training/experiments/DISFA/Read_geom_files_dynamic.m

@@ -6,15 +6,24 @@ function [geom_data] = Read_geom_files_dynamic(users, hog_data_dir)
     
     for i=1:numel(users)
         
-        geom_file = [hog_data_dir, '/../model_params/LeftVideo' users{i} '_comp.txt'];        
-        
-        res = dlmread(geom_file, ',', 1, 0);        
-        valid = logical(res(:,4));
-        
-        %res_rot = res(:,6:8);
-        
-        res = res(:,11:end);   
+        geom_file = [hog_data_dir, 'LeftVideo' users{i} '_comp.csv'];        
         
+        if(i == 1)
+            tab = readtable(geom_file);
+            column_names = tab.Properties.VariableNames; 
+            valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'success'));
+            shape_inds = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'p_'));
+        end
+                
+        res = dlmread(geom_file, ',', 1, 0);    
+
+        % Check the confidence of detection
+        valid = logical(res(:, valid_ind));        
+        res = res(:, shape_inds);
+                
+        % Do not consider global parameters
+        res = res(:, 7:end);
+                
         actual_locs = res * V';
         res = cat(2, actual_locs, res);
        

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVM_train_dyn.m

@@ -30,7 +30,7 @@ for a=1:numel(aus)
     [users_train, users_valid] = get_balanced_fold(DISFA_dir, users, au, 1/3, 1);
 
     % need to split the rest
-    [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic_dynamic(users_train, users_valid, au, rest_aus, hog_data_dir);
+    [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic_dynamic(users_train, users_valid, au, rest_aus, DISFA_dir, hog_data_dir);
 
     train_labels(train_labels > 1) = 1;
     valid_labels(valid_labels > 1) = 1;

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVM_train_stat.m

@@ -30,7 +30,7 @@ for a=1:numel(aus)
     [users_train, users_valid] = get_balanced_fold(DISFA_dir, users, au, 1/3, 1);
 
     % need to split the rest
-    [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic(users_train, users_valid, au, rest_aus, hog_data_dir);
+    [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic(users_train, users_valid, au, rest_aus, DISFA_dir, hog_data_dir);
 
     train_labels(train_labels > 1) = 1;
     valid_labels(valid_labels > 1) = 1;

matlab_version/AU_training/experiments/DISFA/Script_HOG_SVR_train_dyn_5_fold_shift.m

@@ -36,7 +36,7 @@ for a=1:numel(aus)
         rest_aus = setdiff(all_aus, au);        
 
         % load the training and testing data for the current fold
-        [~, ~, test_samples, test_labels, ~, ~, ~, ~, test_ids, test_success] = Prepare_HOG_AU_data_generic_dynamic({}, test_folds{t}, au, rest_aus, hog_data_dir);
+        [~, ~, test_samples, test_labels, ~, ~, ~, ~, test_ids, test_success] = Prepare_HOG_AU_data_generic_dynamic({}, test_folds{t}, au, rest_aus, DISFA_dir, hog_data_dir);
 
         % create the training and validation data
         users_train = setdiff(users, unique(test_ids));        
@@ -44,7 +44,7 @@ for a=1:numel(aus)
         [users_train, users_valid] = get_balanced_fold(DISFA_dir, users_train, au, 1/4, 1);
         
         % need to split the rest
-        [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic_dynamic(users_train, users_valid, au, rest_aus, hog_data_dir);
+        [train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic_dynamic(users_train, users_valid, au, rest_aus, DISFA_dir, hog_data_dir);
 
         train_samples = sparse(train_samples);
         valid_samples = sparse(valid_samples);

matlab_version/AU_training/experiments/DISFA/shared_defs.m

@@ -24,7 +24,7 @@ else
     fprintf('DISFA location not found (or not defined)\n'); 
 end
 
-hog_data_dir = [DISFA_dir, '/hog_aligned_rigid/'];
+hog_data_dir = 'E:\datasets\face_datasets_processed\disfa/';
 
 users = {'SN001';
          'SN002';