66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
import os
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from pathlib import Path
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import tensorflow as tf
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class Dataset:
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''' Class to load and preprocess the dataset.
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Loads images and labels from the given directory to tf.data.Dataset.
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Args:
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`data_dir (Path)`: Path to the dataset directory.
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`seed (int)`: Seed for shuffling the dataset.
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`repeat (int)`: Number of times to repeat the dataset.
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`shuffle_buffer_size (int)`: Size of the buffer for shuffling the dataset.
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`batch_size (int)`: Batch size for the dataset.
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'''
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def __init__(self,
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data_dir: Path,
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seed: int = 42,
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repeat: int = 1,
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shuffle_buffer_size: int = 10_000,
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batch_size: int = 64) -> None:
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self.data_dir = data_dir
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self.seed = seed
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self.repeat = repeat
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self.shuffle_buffer_size = shuffle_buffer_size
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self.batch_size = batch_size
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self.dataset = self._load_dataset()\
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.shuffle(self.shuffle_buffer_size, seed=self.seed)\
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.repeat(self.repeat)\
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.prefetch(tf.data.experimental.AUTOTUNE)
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def _load_dataset(self) -> tf.data.Dataset:
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# check if path has 'test' word in it
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dataset = tf.data.Dataset.list_files(str(self.data_dir / '*/*'))
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if 'test' in str(self.data_dir).lower():
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# file names issue - labels have camel case (regex?) and differs from the train/valid sets
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pass
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else:
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dataset = dataset.map(
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self._preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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return dataset
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def _get_labels(self, image_path):
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path = tf.strings.split(image_path, os.path.sep)[-2]
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plant = tf.strings.split(path, '___')[0]
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disease = tf.strings.split(path, '___')[1]
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return tf.cast(plant, dtype=tf.string, name=None), tf.cast(disease, dtype=tf.string, name=None)
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def _get_image(self, image_path):
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img = tf.io.read_file(image_path)
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img = tf.io.decode_jpeg(img, channels=3) / 255
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return tf.cast(img, dtype=tf.float32, name=None)
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def _preprocess(self, image_path):
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labels = self._get_labels(image_path)
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image = self._get_image(image_path)
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# returns X, Y1, Y2
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return image, labels
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