dataset/dataset.py

@@ -31,12 +31,12 @@ class Dataset:
         self.shuffle_buffer_size = shuffle_buffer_size
         self.batch_size = batch_size
 
-        self.dataset = self._load_dataset()\
+        self.dataset = self.__load_dataset()\
             .shuffle(self.shuffle_buffer_size, seed=self.seed)\
             .repeat(self.repeat)\
             .prefetch(tf.data.experimental.AUTOTUNE)
 
-    def _load_dataset(self) -> tf.data.Dataset:
+    def __load_dataset(self) -> tf.data.Dataset:
         # check if path has 'test' word in it
         dataset = tf.data.Dataset.list_files(str(self.data_dir / '*/*'))
         if 'test' in str(self.data_dir).lower():
@@ -44,11 +44,11 @@ class Dataset:
             pass
         else:
             dataset = dataset.map(
-                self._preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+                self.__preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
 
         return dataset
 
-    def _get_labels(self, image_path):
+    def __get_labels(self, image_path):
         path = tf.strings.split(image_path, os.path.sep)[-2]
         plant = tf.strings.split(path, '___')[0]
         disease = tf.strings.split(path, '___')[1]
@@ -58,14 +58,14 @@ class Dataset:
 
         return tf.cast(one_hot_plant, dtype=tf.uint8, name=None), tf.cast(one_hot_disease, dtype=tf.uint8, name=None)
 
-    def _get_image(self, image_path):
+    def __get_image(self, image_path):
         img = tf.io.read_file(image_path)
         img = tf.io.decode_jpeg(img, channels=3)
         return tf.cast(img, dtype=tf.float32, name=None) / 255.
 
-    def _preprocess(self, image_path):
-        labels = self._get_labels(image_path)
-        image = self._get_image(image_path)
+    def __preprocess(self, image_path):
+        labels = self.__get_labels(image_path)
+        image = self.__get_image(image_path)
 
         # returns X, Y1, Y2
         return image, labels[0], labels[1]