menu item place changed

src/classification/cnn_keras.py

@@ -524,7 +524,7 @@ class CNNKeras(Classifier):
         return train_generator, validation_generator, test_generator
 
     def single_classify(self, image_path, directory, extractors, dict_classes):
-        from matplotlib.pylab im imread
+        from matplotlib.pylab import imread
         image = imread(image_path)
         predict = self.model.predict([image])[0]
         return dict_classes[predict]
\ No newline at end of file

src/main.py

@@ -89,11 +89,12 @@ if __name__ == "__main__":
     tk.add_command("Load h5 weight (only for CNNs)", act.open_weight)
     tk.add_command("Execute", act.run_classifier, 'C')
     tk.add_command("Execute folder", act.run_classifier_folder)
-    tk.add_command("Execute Graphical Confusion Matrix", act.run_grafic_confusion_matrix)
     
     tk.add_menu("Experimenter")
     tk.add_check_button("Ground Truth", act.toggle_ground_truth, default_state = False)
     tk.add_separator()
+    tk.add_command("Execute Graphical Confusion Matrix", act.run_grafic_confusion_matrix)
+    tk.add_separator()
     tk.add_command("Cross Validation", act.cross_validation, 'X')
     tk.add_command("Experimenter All", act.experimenter_all, 'p')
 

src/pynovisao.py

-#!/usr/bin/python
-# -*- coding: utf-8 -*-
 #
 """
     This file must contain the implementation code for all actions of pynovisao.
@@ -465,7 +463,7 @@ class Act(object):
         def process_config():
             """Update the current classifier."""
             new_config = self.tk.get_config_and_destroy()
-
+            
             self.classifier = [new_config[classifier].meta for classifier in new_config
                                 if new_config[classifier].value == True ][0]()
 
@@ -946,8 +944,14 @@ class Act(object):
 
     def run_grafic_confusion_matrix(self):
         '''
-
+        Generate a a graphical confusion matrix where images are classified and according to classification go to the wrong or right folder.
         '''
+        from classification import WekaClassifiers
+
+        if not isinstance(self.classifier, WekaClassifiers):
+            message='Only available to Weka Classifiers.'
+            IException(message)
+
         
         if not self.has_trained:
             message='Dataset Must Be Trained.'