do not reload enhancement model in dir_in mode, simplify

src/eynollah/eynollah.py

@@ -363,10 +363,11 @@ class Eynollah:
 
     def predict_enhancement(self, img):
         self.logger.debug("enter predict_enhancement")
-        model_enhancement, session_enhancement = self.start_new_session_and_model(self.model_dir_of_enhancement)
+        if not self.dir_in:
+            self.model_enhancement, _ = self.start_new_session_and_model(self.model_dir_of_enhancement)
 
-        img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
+        img_height_model = self.model_enhancement.layers[len(self.model_enhancement.layers) - 1].output_shape[1]
-        img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
+        img_width_model = self.model_enhancement.layers[len(self.model_enhancement.layers) - 1].output_shape[2]
         if img.shape[0] < img_height_model:
             img = cv2.resize(img, (img.shape[1], img_width_model), interpolation=cv2.INTER_NEAREST)
 
@@ -409,9 +410,8 @@ class Eynollah:
                     index_y_u = img_h
                     index_y_d = img_h - img_height_model
 
-                img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
+                img_patch = img[np.newaxis, index_y_d:index_y_u, index_x_d:index_x_u, :]
-                label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
+                label_p_pred = self.model_enhancement.predict(img_patch, verbose=0)
-                                                         verbose=0)
 
                 seg = label_p_pred[0, :, :, :]
                 seg = seg * 255