dataset/dataset.py

@@ -7,8 +7,8 @@ import tensorflow as tf
 class Dataset:
     ''' Class to load and preprocess the dataset. 
     Loads images and labels from the given directory to tf.data.Dataset.
-    
-    
+
+
     Args:
         `data_dir (Path)`: Path to the dataset directory.
         `seed (int)`: Seed for shuffling the dataset.
@@ -16,6 +16,7 @@ class Dataset:
         `shuffle_buffer_size (int)`: Size of the buffer for shuffling the dataset.
         `batch_size (int)`: Batch size for the dataset.
     '''
+
     def __init__(self,
                  data_dir: Path,
                  seed: int = 42,
@@ -25,10 +26,11 @@ class Dataset:
         self.data_dir = data_dir
         self.seed = seed
         self.repeat = repeat
+        self.shuffle_buffer_size = shuffle_buffer_size
         self.batch_size = batch_size
 
         self.dataset = self._load_dataset()\
-            .shuffle(shuffle_buffer_size, seed=self.seed)\
+            .shuffle(self.shuffle_buffer_size, seed=self.seed)\
             .repeat(self.repeat)\
             .prefetch(tf.data.experimental.AUTOTUNE)
 
@@ -40,27 +42,24 @@ class Dataset:
             pass
         else:
             dataset = dataset.map(
-                _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):
+        path = tf.strings.split(image_path, os.path.sep)[-2]
+        plant = tf.strings.split(path, '___')[0]
+        disease = tf.strings.split(path, '___')[1]
+        return tf.cast(plant, dtype=tf.string, name=None), tf.cast(disease, dtype=tf.string, name=None)
 
-def _get_labels(image_path):
-    path = tf.strings.split(image_path, os.path.sep)[-2]
-    plant = tf.strings.split(path, '___')[0]
-    disease = tf.strings.split(path, '___')[1]
-    return tf.cast(plant, dtype=tf.string, name=None), tf.cast(disease, dtype=tf.string, name=None)
+    def _get_image(self, image_path):
+        img = tf.io.read_file(image_path)
+        img = tf.io.decode_jpeg(img, channels=3) / 255
+        return tf.cast(img, dtype=tf.float32, name=None)
 
+    def _preprocess(self, image_path):
+        labels = self._get_labels(image_path)
+        image = self._get_image(image_path)
 
-def _get_image(image_path):
-    img = tf.io.read_file(image_path)
-    img = tf.io.decode_jpeg(img, channels=3) / 255
-    return tf.cast(img, dtype=tf.float32, name=None)
-
-
-def _preprocess(image_path):
-    labels = _get_labels(image_path)
-    image = _get_image(image_path)
-
-    # returns X, Y1, Y2
-    return image, labels
+        # returns X, Y1, Y2
+        return image, labels