add threads, open_weight function added and classifier not found message changed

src/pynovisao.py

@@ -25,10 +25,11 @@ from util.config import Config
 from util.file_utils import File as f
 from util.utils import TimeUtils
 
+
 class Act(object):
     """Store all actions of Pynovisao."""
 
-    def __init__(self, tk, args):
+    def __init__(self, tk, thread, args):
         """Constructor.
 
         Parameters
@@ -59,9 +60,12 @@ class Act(object):
         self._init_dataset(args["dataset"])
         self._init_classes(args["classes"], args["colors"])
         
+        self.thread = thread
+
         self._dataset_generator = True
         self._ground_truth = False
         self._gt_segments = None
+        self.weight_path = None
 
     
     def _init_dataset(self, directory):
@@ -152,6 +156,11 @@ class Act(object):
             self._gt_segments = None
 
         
+
+    def open_weight(self):
+        """Open a new weight."""
+        self.weight_path = self.tk.utils.ask_weight_name()
+        
     def restore_image(self):
         """Refresh the image and clean the segmentation.
         """
@@ -424,7 +433,7 @@ class Act(object):
             The user must install the required dependencies to classifiers.
         """
         if self.classifier is None:
-            raise IException("You must install python-weka-wrapper")
+            raise IException("Classifier not found!")
         
         title = "Choosing a classifier"
         self.tk.write_log(title)
@@ -452,7 +461,7 @@ class Act(object):
             The user must install the required dependencies to classifiers.
         """
         if self.classifier is None:
-            raise IException("You must install python-weka-wrapper")
+            raise IException("Classifier not found!")
         
         title = "Configuring %s" % self.classifier.get_name()
         self.tk.write_log(title)
@@ -482,7 +491,7 @@ class Act(object):
             If there's no image opened.
         """
         if self.classifier is None:
-            raise IException("You must install python-weka-wrapper")
+            raise IException("Classifier not found!")
         
         if self._const_image is None:
             raise IException("Image not found")
@@ -502,20 +511,6 @@ class Act(object):
             list_segments = self.segmenter.get_list_segments()
             self._gt_segments = [None]*(max(list_segments)+1)
         
-        # Train the classifier ( this program does not perform the training of ConvNets ).
-        if self.classifier.must_train():
-            self.tk.append_log("Creating training data... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
-            
-            fextractor = FeatureExtractor(self.extractors)
-            output_file, run_time = fextractor.extract_all(self.dataset, "training", overwrite = False)
-        
-            self.tk.append_log("Training classifier... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
-            
-            self.classifier.train(self.dataset, "training")
-        
-        self._image = self._const_image
-
-        
         #  New and optimized classification
         tmp = ".tmp"
         f.remove_dir(f.make_path(self.dataset, tmp))
@@ -530,9 +525,8 @@ class Act(object):
             len_segments[idx_segment] = size_segment
             
         # Perform the feature extraction of all segments in image ( not applied to ConvNets ).
-        if self.classifier.must_train():
-            self.tk.append_log("Running extractors on test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
-            
+        if self.classifier.must_extract_features():
+            self.tk.append_log("Running extractors on test images... (%0.3f seconds)", (TimeUtils.get_time() - start_time))            
             output_file, _ = fextractor.extract_all(self.dataset, "test", dirs=[tmp])
                 
         self.tk.append_log("Running classifier on test data... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
@@ -570,7 +564,28 @@ class Act(object):
             
         self.tk.append_log("\nClassification finished")
         self.tk.append_log("Time elapsed: %0.3f seconds", (end_time - start_time))
+
+    def run_training(self):
+        start_time = TimeUtils.get_time()
+        
+        if self._const_image is None:
+            raise IException("Image not found")
+        
+        if self.classifier.must_train():
+            
+            if self.classifier.must_extract_features():
+                self.tk.append_log("Creating training data... (%0.3f seconds)", (TimeUtils.get_time() - start_time))
+                fextractor = FeatureExtractor(self.extractors)
+                output_file, run_time = fextractor.extract_all(self.dataset, "training", overwrite = False)
+        
+            self.tk.append_log("Training classifier...")
+            
+            self.thread.start_new_thread(self.classifier.train, (self.dataset, "training"))
+
+            self.tk.append_log("DONE (%0.3f seconds)",  (TimeUtils.get_time() - start_time))
         
+        self._image = self._const_image
+
     
     def _show_ground_truth(self, list_segments, len_segments, labels, start_time):
         """Paint only wrong classified segments and show ground truth confusion matrix.
@@ -633,7 +648,7 @@ class Act(object):
             The user must install the required dependencies to classifiers.
         """
         if self.classifier is None:
-            raise IException("You must install python-weka-wrapper")
+            raise IException("Classifier not found!")
         
         if self.classifier.must_train():
             self.tk.write_log("Creating training data...")
@@ -658,7 +673,7 @@ class Act(object):
             The user must install the required dependencies to classifiers.
         """
         if self.classifier is None:
-            raise IException("You must install python-weka-wrapper")
+            raise IException("Classifier not found!")
         
         if self.tk.ask_ok_cancel("Experimenter All", "This may take several minutes to complete. Are you sure?"):
             if self.classifier.must_train():