fix problems in load files and load model with keras

src/classification/cnn_keras.py

@@ -198,12 +198,11 @@ class CNNKeras(Classifier):
         predict_directory=File.make_path(dataset, test_dir)
         CLASS_NAMES = np.loadtxt(self.labels_file.value, str)
         # Create a Keras class
-        # Create a Keras class
         if not os.path.exists(File.make_path(predict_directory, "png")):
             os.makedirs(File.make_path(predict_directory, "png"))
 
         for file in os.listdir(predict_directory):
-            print(File.make_path(predict_directory, file))
+            #print(File.make_path(predict_directory, file))
             if os.path.splitext(file)[-1] == ".tif":
                 try:
                     img = Image.open(File.make_path(predict_directory, file))
@@ -216,7 +215,7 @@ class CNNKeras(Classifier):
             else:
                 print(File.make_path(predict_directory, file))
                 #os.symlink(File.make_path(predict_directory, file),File.make_path(predict_directory, 'png', file))
-               
+        print("Classes load from file examples/labels.txt")       
         print(CLASS_NAMES)
 
         try:
@@ -227,11 +226,7 @@ class CNNKeras(Classifier):
                 self.model = load_model(self.weight_path)
                 #path_classes = self.weight_path.replace(                    "_model.h5", "_classes.npy")
                 print("Load Model H5:"+self.weight_path)
-                #np_load_old = np.load
-                #modify the default parameters of np.load
-                #np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
 
-                #CLASS_NAMES = np.load(path_classes).item().keys()
         except Exception as e:
             raise IException("Can't load the model in " +
                              str(e))
@@ -250,21 +245,11 @@ class CNNKeras(Classifier):
             classify_generator, classify_generator.samples, verbose=2)
 
         one_hot_output=np.argmax(output_classification, axis=1)
-        print(len(one_hot_output))        
-        print(one_hot_output)
-
-
-
-        #output_classification = self.model.predict_generator(classify_generator)
-
-        #one_hot_output = np.argmax(output_classification, axis=1)
 
-        #one_hot_output = one_hot_output.tolist()
         classes=[]
         for index in range(0, len(one_hot_output)):
-            #print(index)
             classes.append(CLASS_NAMES[one_hot_output[index]])
-        #print(len(classes))   
+  
         return classes
 
     def train(self, dataset, training_data, force=False):

src/interface/tk_interface.py

@@ -26,7 +26,7 @@ else:
             self.Menu=Menu
             self.Image=Image
             self.Log=Log
-            self.Pop=Pop
+            self.Popup=Pop
             self.CustomGrid=CustomGrid
             self.SelectDialog=SelectDialog
             self.ChooseOneDialog=ChooseOneDialog
@@ -36,7 +36,7 @@ else:
     tk_local.Menu=tk_menu.Menu
     tk_local.Image=tk_canvas.Image
     tk_local.Log=tk_logger.Log
-    tk_local.Pop=tk_popup.Popup
+    tk_local.Popup=tk_popup.Popup
     tk_local.CustomGrid=tk_customframe.CustomGrid
     tk_local.SelectDialog=tk_customdialog.SelectDialog
     tk_local.ChooseOneDialog=tk_customdialog.ChooseOneDialog

src/pynovisao.py

@@ -558,7 +558,7 @@ class Act(object):
             self._gt_segments = [None]*(max(list_segments)+1)
 
         #  New and optimized classification
-        tmp = "tmp"
+        tmp = ".tmp"
         File.remove_dir(File.make_path(self.dataset, tmp))
 
         self.tk.append_log("Generating test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
@@ -618,7 +618,7 @@ class Act(object):
                 len_classes = sum([len_segments[idx] for idx in idx_segment])
                 popup_info += "%-16s%-16s%0.2f%%\n" % (cl["name"].value, str(len_classes), (len_classes*100.0)/len_total)
 
-           
+            print(popup_info)
             self.tk.refresh_image(self._image)
             self.tk.popup(popup_info)
         else: