dataset/consts.py

@@ -0,0 +1,40 @@
+PLANT_CLASSES = [
+    "Tomato",
+    "Potato",
+    "Corn_(maize)",
+    "Apple",
+    "Blueberry",
+    "Soybean",
+    "Cherry_(including_sour)",
+    "Squash",
+    "Strawberry",
+    "Pepper,_bell",
+    "Peach",
+    "Grape",
+    "Orange",
+    "Raspberry",
+]
+
+DISEASE_CLASSES = [
+    "healthy",
+    "Northern_Leaf_Blight",
+    "Tomato_mosaic_virus",
+    "Early_blight",
+    "Leaf_scorch",
+    "Tomato_Yellow_Leaf_Curl_Virus",
+    "Cedar_apple_rust",
+    "Late_blight",
+    "Spider_mites Two-spotted_spider_mite",
+    "Black_rot",
+    "Bacterial_spot",
+    "Apple_scab",
+    "Powdery_mildew",
+    "Esca_(Black_Measles)",
+    "Haunglongbing_(Citrus_greening)",
+    "Leaf_Mold",
+    "Common_rust_",
+    "Target_Spot",
+    "Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "Septoria_leaf_spot",
+    "Cercospora_leaf_spot Gray_leaf_spot",
+]

dataset/dataset.py

@@ -0,0 +1,75 @@
+import os
+from pathlib import Path
+
+import tensorflow as tf
+
+from .consts import DISEASE_CLASSES, PLANT_CLASSES
+
+
+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.
+        `repeat (int)`: Number of times to repeat the 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,
+                 repeat: int = 1,
+                 shuffle_buffer_size: int = 10_000,
+                 batch_size: int = 64) -> None:
+        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(self.shuffle_buffer_size, seed=self.seed)\
+            .repeat(self.repeat)\
+            .batch(self.batch_size, drop_remainder=True)\
+            .prefetch(tf.data.experimental.AUTOTUNE)
+
+    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():
+            # file names issue - labels have camel case (regex?) and differs from the train/valid sets
+            pass
+        else:
+            dataset = dataset.map(
+                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]
+
+        one_hot_plant = plant == PLANT_CLASSES
+        one_hot_disease = disease == DISEASE_CLASSES
+
+        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):
+        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)
+
+        # returns X, Y1, Y2
+        return image, labels[0], labels[1]
+
+    def __getattr__(self, attr):
+        return getattr(self.dataset, attr)

file_manager/shard_files.py

@@ -0,0 +1,19 @@
+from pathlib import Path
+
+# TODO: split the files into smaller dirs and make list of them
+class FileSharder:
+    def __init__(self,
+                 train_dir: Path = Path('./data/resized_dataset/train'),
+                 valid_dir: Path = Path('./data/resized_dataset/valid'),
+                 test_dir: Path = Path('./data/resized_dataset/test'),
+                 shard_size = 5_000) -> None:
+        self.shard_size = shard_size
+
+        self.train_dir = train_dir
+        self.valid_dir = valid_dir
+        self.test_dir = test_dir
+
+        self.shard()
+
+    def shard(self):
+        pass

requirements.txt

@@ -1,5 +1,4 @@
 tensorflow==2.16.1
-tensorflow-io==0.37.0
 numpy==1.26.4
 opencv-python==4.9.0.80
 wget==3.2

test.py

@@ -0,0 +1,10 @@
+
+from pathlib import Path
+
+from dataset.dataset import Dataset
+
+train_dataset = Dataset(Path('data/resized_dataset/train'))
+valid_dataset = Dataset(Path('data/resized_dataset/valid'))
+
+for i in train_dataset.take(1):
+    print(i)