sbb_binarize: Load each model only once

sbb_binarize/sbb_binarize.py

@@ -31,6 +31,12 @@ class SbbBinarizer:
 
         self.start_new_session()
 
+        self.model_files = glob('%s/*.h5' % self.model_dir)
+
+        self.models = []
+        for model_file in self.model_files:
+            self.models.append(self.load_model(model_file))
+
     def start_new_session(self):
         config = tf.ConfigProto()
         config.gpu_options.allow_growth = True
@@ -48,8 +54,8 @@ class SbbBinarizer:
         n_classes = model.layers[len(model.layers)-1].output_shape[3]
         return model, model_height, model_width, n_classes
 
-    def predict(self, model_name, img, use_patches):
-        model, model_height, model_width, n_classes = self.load_model(model_name)
+    def predict(self, model_in, img, use_patches):
+        model, model_height, model_width, n_classes = model_in
 
         if use_patches:
 
@@ -196,12 +202,11 @@ class SbbBinarizer:
             raise ValueError("Must pass either a opencv2 image or an image_path")
         if image_path is not None:
             image = cv2.imread(image_path)
-        list_of_model_files = glob('%s/*.h5' % self.model_dir)
         img_last = 0
-        for n, model_in in enumerate(list_of_model_files):
-            self.log.info('Predicting with model %s [%s/%s]' % (model_in, n + 1, len(list_of_model_files)))
+        for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
+            self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
 
-            res = self.predict(model_in, image, use_patches)
+            res = self.predict(model, image, use_patches)
 
             img_fin = np.zeros((res.shape[0], res.shape[1], 3))
             res[:, :][res[:, :] == 0] = 2