fixes

Author: avouros

clustering/cross_validation/new/cross_validation.m

@@ -1,7 +1,17 @@
-function [ varargout ] = cross_validation( segmentation_configs, labels_path, folds, clusters, output_path, options, graph)
+function [ varargout ] = cross_validation( segmentation_configs, labels_path, folds, clusters, output_path, options, graph, varargin)
 %CROSS_VALIDATION Summary of this function goes here
 %   Detailed explanation goes here
 
+    WAITBAR = 1;
+    DISPLAY = 1;
+    for i = 1:length(varargin)
+        if isequal(varargin{i},'WAITBAR')
+            WAITBAR = varargin{i+1};
+        elseif isequal(varargin{i},'DISPLAY')
+            DISPLAY = varargin{i+1};
+        end
+    end
+
 %% INITIALIZATION
     min_num = clusters(1);
     max_num = clusters(2);
@@ -48,7 +58,9 @@
 
     idx = 1:classif.nlabels;
     %test_set = ones(1, inst.nlabels);
-    h = waitbar(0,'Loading...','Name','Clustering');
+    if WAITBAR
+        h = waitbar(0,'Loading...','Name','Clustering');
+    end
 
     for i = min_num:step:max_num
         nclusters = i;
@@ -57,10 +69,14 @@
         % i) two-phase clustering (default)   
         fn = fullfile(output_path, sprintf('clustering_%d_%s.mat',nclusters,a));
         if exist(fn ,'file')
-            fprintf('\nData for %d number of clusters (two-phase clustering) found. Loading data...\n', nclusters);
+            if DISPLAY
+                fprintf('\nData for %d number of clusters (two-phase clustering) found. Loading data...\n', nclusters);
+            end
             load(fn);
-        else            
-            fprintf('\nData for %d number of clusters (two-phase clustering) not found. Computing...\n', nclusters);
+        else      
+            if DISPLAY
+                fprintf('\nData for %d number of clusters (two-phase clustering) not found. Computing...\n', nclusters);
+            end
             [res, res1st] = cross_validation_exe( classif, segmentation_configs, idx, folds, labels_path, nclusters, options, features, LABELLING_MAP, CLASSIFICATION_TAGS, segments);
             save(fn, 'res', 'res1st');
         end
@@ -72,17 +88,23 @@
         % ii) clustering using all the constraints (compute coverate) 
         fn = fullfile(output_path, sprintf('clustering_all_%d_%s.mat',nclusters,a));
         if exist(fn ,'file')
-            fprintf('\nData for %d number of clusters (two-phase clustering) found. Loading data...\n', nclusters);
+            if DISPLAY
+                fprintf('\nData for %d number of clusters (two-phase clustering) found. Loading data...\n', nclusters);
+            end
             load(fn);
         else            
-            fprintf('\nData for %d number of clusters (two-phase clustering) not found. Computing...\n', nclusters);
+            if DISPLAY
+                fprintf('\nData for %d number of clusters (two-phase clustering) not found. Computing...\n', nclusters);
+            end
             res = classif.cluster(nclusters);
             save(fn, 'res');
         end        
         res3 = [res3, res];
         covering = [covering, res.coverage(feature_length)];        
 
-        waitbar(i/max_num);        
+        if WAITBAR
+            waitbar(i/max_num);        
+        end
     end
 
     % remap the classes as to not invalidate mixed clusters
@@ -108,7 +130,9 @@
         varargout{5} = res2;
         varargout{6} = res3;
         varargout{7} = covering;
-    end   
-    delete(h);    
+    end  
+    if WAITBAR
+        delete(h);   
+    end
 
 end

demo.m

@@ -318,23 +318,37 @@ function demo(set,user_path,varargin)
 
     %% Labelling Quality
     if LABELLING_QUALITY
-        for i = 1:length(seg_overlap)
-            load(fullfile(project_path,'segmentation',seg_name(i).name));
-            p = strsplit(files(i).name,'.mat');
+        labs = dir(fullfile(project_path,'labels','*.mat'));
+        segs = dir(fullfile(project_path,'segmentation','*.mat'));
+        for i = 1:length(labs)
+            [~,len,ovl,~,~] = split_labels_name(labs(i).name);
+            if isequal(len,'0') || isequal(ovl,'0')
+                continue
+            end  
+            p = strsplit(labs(i).name,'.mat');
             p = p{1};
             output_path = char(fullfile(project_path,'labels',strcat(p,'_check')));
             if ~exist(output_path,'dir')
                 mkdir(output_path);
             end
-            [nc,res1bare,res2bare,res1,res2,res3,covering] = results_clustering_parameters(segmentation_configs,fullfile(project_path,'labels',lab_name(i).name),0,output_path,10,100,10,'WAITBAR', WAITBAR, 'DISPLAY', DISPLAY);
+            for s = 1:length(segs)
+                seg = fullfile(project_path,'segmentation',segs(s).name);
+                [~,~,sl,so] = split_segmentation_name(seg);
+                if isequal(sl,len) && isequal(so,ovl)
+                    load(fullfile(project_path,'segmentation',segs(s).name));
+                    break;
+                end 
+            end
+            mkdir(output_path);            
+            [nc,res1bare,res2bare,res1,res2,res3,covering] = cross_validation(segmentation_configs,fullfile(project_path,'labels',labs(i).name),10,[10,100,10],output_path,'labels',0,'WAITBAR', WAITBAR, 'DISPLAY', DISPLAY);
+            [nc,per_errors1,per_undefined1,coverage,per_errors1_true] = algorithm_statistics(1,1,nc,res1bare,res2bare,res1,res2,res3,covering);
+            data = [nc', per_errors1', per_undefined1', coverage', per_errors1_true'];
+            % export results to CSV file
+            export_num_of_clusters(output_path,data);
             output_path = char(fullfile(project_path,'results',strcat(p,'_cross_validation')));
-            if exist(output_path,'dir');
+            if exist(output_path,'dir')
                 rmdir(output_path,'s');
             end
-            mkdir(output_path);
-            [nc,per_errors1,per_undefined1,coverage] = algorithm_statistics(1,1,nc,res1bare,res2bare,res1,res2,res3,covering);
-            data = [nc', per_errors1', per_undefined1', coverage'];
-            % export results to CSV file
             export_num_of_clusters(output_path,data);
             % generate graphs
             results_clustering_parameters_graphs(output_path,nc,res1bare,res2bare,res1,res2,res3,covering);