upload dataset

src/classification/cnn_keras.py

@@ -234,13 +234,17 @@ class CNNKeras(Classifier):
                 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 " +
-                             self.weight_path + str(e))
+                             str(e))
 
         output_classification = self.model.predict_generator(
-            classify_generator, classify_generator.samples, verbose=2)
+            classify_generator)
 
         one_hot_output = np.argmax(output_classification, axis=1)
 

src/pynovisao.py

@@ -843,7 +843,7 @@ class Act(object):
                 if idx_segment % 10 == 0:
                     self.tk.write_log("Saving %d of %d", (idx_segment+1), qtd_superpixel)
 
-                segment, size_segment, idx_segment, run_time = self.segmenter.get_segment(idx_segment = idx_segment)
+                segment, size_segment, xml,idx_segment, run_time = self.segmenter.get_segment(idx_segment = idx_segment)
                 filepath = File.save_class_image(segment, self.dataset, self.classes[idx_class]["name"].value, self._image_name, idx_segment)
                 if filepath:
                     self.tk.append_log("\nSegment saved in %s", filepath)

src/segmentation/skimage_segmenter.py

@@ -178,7 +178,7 @@ class SkimageSegmenter(object):
         if SkimageSegmenter.FORCE_OPT == False:
             colored_image = cv2.addWeighted(self._original_image, 0.7, class_color, 0.3, 0)
         else:
-            colored_image = cv2.addWeighted(image, 0.7, class_color, 0.3, 0)
+            colored_image = cv2.addWeighted(image, 0.1, class_color, 0.9, 0)
         colored_image = cv2.bitwise_and(colored_image, colored_image, mask=mask_segment)
         
         # Create a new image keeping the painting only in pixels inside of segments