dataset/consts.py

@@ -1,40 +0,0 @@
-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

@@ -1,75 +0,0 @@
-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

@@ -1,19 +0,0 @@
-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

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

test.py

@@ -1,10 +0,0 @@
-
-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)