s495728/Detection-of-plant-diseases
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feature/load-dataset #2

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s495733 merged 7 commits from feature/load-dataset into main 2024-05-05 19:42:12 +02:00
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1 changed files with 20 additions and 21 deletions
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dataset/dataset.py
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@ -16,6 +16,7 @@ class Dataset:
`shuffle_buffer_size (int)`: Size of the buffer for shuffling the dataset. `shuffle_buffer_size (int)`: Size of the buffer for shuffling the dataset.
`batch_size (int)`: Batch size for the dataset. `batch_size (int)`: Batch size for the dataset.
''' '''
def __init__(self, def __init__(self,
data_dir: Path, data_dir: Path,
seed: int = 42, seed: int = 42,
@ -25,10 +26,11 @@ class Dataset:
self.data_dir = data_dir self.data_dir = data_dir
self.seed = seed self.seed = seed
self.repeat = repeat self.repeat = repeat
self.shuffle_buffer_size = shuffle_buffer_size
self.batch_size = batch_size self.batch_size = batch_size
self.dataset = self._load_dataset()\ self.dataset = self._load_dataset()\
.shuffle(shuffle_buffer_size, seed=self.seed)\ .shuffle(self.shuffle_buffer_size, seed=self.seed)\
.repeat(self.repeat)\ .repeat(self.repeat)\
.prefetch(tf.data.experimental.AUTOTUNE) .prefetch(tf.data.experimental.AUTOTUNE)
@ -40,27 +42,24 @@ class Dataset:
pass pass
else: else:
dataset = dataset.map( dataset = dataset.map(
_preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE) self._preprocess, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset 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): def _get_image(self, image_path):
path = tf.strings.split(image_path, os.path.sep)[-2] img = tf.io.read_file(image_path)
plant = tf.strings.split(path, '___')[0] img = tf.io.decode_jpeg(img, channels=3) / 255
disease = tf.strings.split(path, '___')[1] return tf.cast(img, dtype=tf.float32, name=None)
return tf.cast(plant, dtype=tf.string, name=None), tf.cast(disease, dtype=tf.string, name=None)
def _preprocess(self, image_path):
labels = self._get_labels(image_path)
image = self._get_image(image_path)
def _get_image(image_path): # returns X, Y1, Y2
img = tf.io.read_file(image_path) return image, labels
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