tensorflow/python/keras/preprocessing/image_dataset.py - platform/external/tensorflow - Git at Google

 # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
 """Keras image dataset loading utilities."""
 # pylint: disable=g-classes-have-attributes

 import numpy as np

 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.keras.layers.preprocessing import image_preprocessing
 from tensorflow.python.keras.preprocessing import dataset_utils
 from tensorflow.python.keras.preprocessing import image as keras_image_ops
 from tensorflow.python.ops import image_ops
 from tensorflow.python.ops import io_ops
 from tensorflow.python.util.tf_export import keras_export


 ALLOWLIST_FORMATS = ('.bmp', '.gif', '.jpeg', '.jpg', '.png')


 @keras_export('keras.utils.image_dataset_from_directory',
               'keras.preprocessing.image_dataset_from_directory',
               v1=[])
 def image_dataset_from_directory(directory,
                                  labels='inferred',
                                  label_mode='int',
                                  class_names=None,
                                  color_mode='rgb',
                                  batch_size=32,
                                  image_size=(256, 256),
                                  shuffle=True,
                                  seed=None,
                                  validation_split=None,
                                  subset=None,
                                  interpolation='bilinear',
                                  follow_links=False,
                                  crop_to_aspect_ratio=False,
                                  **kwargs):
   """Generates a `tf.data.Dataset` from image files in a directory.

   If your directory structure is:

   ```
   main_directory/
   ...class_a/
   ......a_image_1.jpg
   ......a_image_2.jpg
   ...class_b/
   ......b_image_1.jpg
   ......b_image_2.jpg
   ```

   Then calling `image_dataset_from_directory(main_directory, labels='inferred')`
   will return a `tf.data.Dataset` that yields batches of images from
   the subdirectories `class_a` and `class_b`, together with labels
   0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).

   Supported image formats: jpeg, png, bmp, gif.
   Animated gifs are truncated to the first frame.

   Args:
     directory: Directory where the data is located.
         If `labels` is "inferred", it should contain
         subdirectories, each containing images for a class.
         Otherwise, the directory structure is ignored.
     labels: Either "inferred"
         (labels are generated from the directory structure),
         None (no labels),
         or a list/tuple of integer labels of the same size as the number of
         image files found in the directory. Labels should be sorted according
         to the alphanumeric order of the image file paths
         (obtained via `os.walk(directory)` in Python).
     label_mode:
         - 'int': means that the labels are encoded as integers
             (e.g. for `sparse_categorical_crossentropy` loss).
         - 'categorical' means that the labels are
             encoded as a categorical vector
             (e.g. for `categorical_crossentropy` loss).
         - 'binary' means that the labels (there can be only 2)
             are encoded as `float32` scalars with values 0 or 1
             (e.g. for `binary_crossentropy`).
         - None (no labels).
     class_names: Only valid if "labels" is "inferred". This is the explict
         list of class names (must match names of subdirectories). Used
         to control the order of the classes
         (otherwise alphanumerical order is used).
     color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
         Whether the images will be converted to
         have 1, 3, or 4 channels.
     batch_size: Size of the batches of data. Default: 32.
     image_size: Size to resize images to after they are read from disk.
         Defaults to `(256, 256)`.
         Since the pipeline processes batches of images that must all have
         the same size, this must be provided.
     shuffle: Whether to shuffle the data. Default: True.
         If set to False, sorts the data in alphanumeric order.
     seed: Optional random seed for shuffling and transformations.
     validation_split: Optional float between 0 and 1,
         fraction of data to reserve for validation.
     subset: One of "training" or "validation".
         Only used if `validation_split` is set.
     interpolation: String, the interpolation method used when resizing images.
       Defaults to `bilinear`. Supports `bilinear`, `nearest`, `bicubic`,
       `area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.
     follow_links: Whether to visits subdirectories pointed to by symlinks.
         Defaults to False.
     crop_to_aspect_ratio: If True, resize the images without aspect
       ratio distortion. When the original aspect ratio differs from the target
       aspect ratio, the output image will be cropped so as to return the largest
       possible window in the image (of size `image_size`) that matches
       the target aspect ratio. By default (`crop_to_aspect_ratio=False`),
       aspect ratio may not be preserved.
     **kwargs: Legacy keyword arguments.

   Returns:
     A `tf.data.Dataset` object.
       - If `label_mode` is None, it yields `float32` tensors of shape
         `(batch_size, image_size[0], image_size[1], num_channels)`,
         encoding images (see below for rules regarding `num_channels`).
       - Otherwise, it yields a tuple `(images, labels)`, where `images`
         has shape `(batch_size, image_size[0], image_size[1], num_channels)`,
         and `labels` follows the format described below.

   Rules regarding labels format:
     - if `label_mode` is `int`, the labels are an `int32` tensor of shape
       `(batch_size,)`.
     - if `label_mode` is `binary`, the labels are a `float32` tensor of
       1s and 0s of shape `(batch_size, 1)`.
     - if `label_mode` is `categorial`, the labels are a `float32` tensor
       of shape `(batch_size, num_classes)`, representing a one-hot
       encoding of the class index.

   Rules regarding number of channels in the yielded images:
     - if `color_mode` is `grayscale`,
       there's 1 channel in the image tensors.
     - if `color_mode` is `rgb`,
       there are 3 channel in the image tensors.
     - if `color_mode` is `rgba`,
       there are 4 channel in the image tensors.
   """
   if 'smart_resize' in kwargs:
     crop_to_aspect_ratio = kwargs.pop('smart_resize')
   if kwargs:
     raise TypeError(f'Unknown keywords argument(s): {tuple(kwargs.keys())}')
   if labels not in ('inferred', None):
     if not isinstance(labels, (list, tuple)):
       raise ValueError(
           '`labels` argument should be a list/tuple of integer labels, of '
           'the same size as the number of image files in the target '
           'directory. If you wish to infer the labels from the subdirectory '
           'names in the target directory, pass `labels="inferred"`. '
           'If you wish to get a dataset that only contains images '
           '(no labels), pass `label_mode=None`.')
     if class_names:
       raise ValueError('You can only pass `class_names` if the labels are '
                        'inferred from the subdirectory names in the target '
                        'directory (`labels="inferred"`).')
   if label_mode not in {'int', 'categorical', 'binary', None}:
     raise ValueError(
         '`label_mode` argument must be one of "int", "categorical", "binary", '
         'or None. Received: %s' % (label_mode,))
   if labels is None or label_mode is None:
     labels = None
     label_mode = None
   if color_mode == 'rgb':
     num_channels = 3
   elif color_mode == 'rgba':
     num_channels = 4
   elif color_mode == 'grayscale':
     num_channels = 1
   else:
     raise ValueError(
         '`color_mode` must be one of {"rbg", "rgba", "grayscale"}. '
         'Received: %s' % (color_mode,))
   interpolation = image_preprocessing.get_interpolation(interpolation)
   dataset_utils.check_validation_split_arg(
       validation_split, subset, shuffle, seed)

   if seed is None:
     seed = np.random.randint(1e6)
   image_paths, labels, class_names = dataset_utils.index_directory(
       directory,
       labels,
       formats=ALLOWLIST_FORMATS,
       class_names=class_names,
       shuffle=shuffle,
       seed=seed,
       follow_links=follow_links)

   if label_mode == 'binary' and len(class_names) != 2:
     raise ValueError(
         'When passing `label_mode="binary", there must exactly 2 classes. '
         'Found the following classes: %s' % (class_names,))

   image_paths, labels = dataset_utils.get_training_or_validation_split(
       image_paths, labels, validation_split, subset)
   if not image_paths:
     raise ValueError('No images found.')

   dataset = paths_and_labels_to_dataset(
       image_paths=image_paths,
       image_size=image_size,
       num_channels=num_channels,
       labels=labels,
       label_mode=label_mode,
       num_classes=len(class_names),
       interpolation=interpolation,
       crop_to_aspect_ratio=crop_to_aspect_ratio)
   if shuffle:
     # Shuffle locally at each iteration
     dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
   dataset = dataset.batch(batch_size)
   # Users may need to reference `class_names`.
   dataset.class_names = class_names
   # Include file paths for images as attribute.
   dataset.file_paths = image_paths
   return dataset


 def paths_and_labels_to_dataset(image_paths,
                                 image_size,
                                 num_channels,
                                 labels,
                                 label_mode,
                                 num_classes,
                                 interpolation,
                                 crop_to_aspect_ratio=False):
   """Constructs a dataset of images and labels."""
   # TODO(fchollet): consider making num_parallel_calls settable
   path_ds = dataset_ops.Dataset.from_tensor_slices(image_paths)
   args = (image_size, num_channels, interpolation, crop_to_aspect_ratio)
   img_ds = path_ds.map(
       lambda x: load_image(x, *args))
   if label_mode:
     label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes)
     img_ds = dataset_ops.Dataset.zip((img_ds, label_ds))
   return img_ds


 def load_image(path, image_size, num_channels, interpolation,
                crop_to_aspect_ratio=False):
   """Load an image from a path and resize it."""
   img = io_ops.read_file(path)
   img = image_ops.decode_image(
       img, channels=num_channels, expand_animations=False)
   if crop_to_aspect_ratio:
     img = keras_image_ops.smart_resize(img, image_size,
                                        interpolation=interpolation)
   else:
     img = image_ops.resize_images_v2(img, image_size, method=interpolation)
   img.set_shape((image_size[0], image_size[1], num_channels))
   return img
	# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# ==============================================================================
	"""Keras image dataset loading utilities."""
	# pylint: disable=g-classes-have-attributes

	import numpy as np

	from tensorflow.python.data.ops import dataset_ops
	from tensorflow.python.keras.layers.preprocessing import image_preprocessing
	from tensorflow.python.keras.preprocessing import dataset_utils
	from tensorflow.python.keras.preprocessing import image as keras_image_ops
	from tensorflow.python.ops import image_ops
	from tensorflow.python.ops import io_ops
	from tensorflow.python.util.tf_export import keras_export


	ALLOWLIST_FORMATS = ('.bmp', '.gif', '.jpeg', '.jpg', '.png')


	@keras_export('keras.utils.image_dataset_from_directory',
	'keras.preprocessing.image_dataset_from_directory',
	v1=[])
	def image_dataset_from_directory(directory,
	labels='inferred',
	label_mode='int',
	class_names=None,
	color_mode='rgb',
	batch_size=32,
	image_size=(256, 256),
	shuffle=True,
	seed=None,
	validation_split=None,
	subset=None,
	interpolation='bilinear',
	follow_links=False,
	crop_to_aspect_ratio=False,
	**kwargs):
	"""Generates a `tf.data.Dataset` from image files in a directory.

	If your directory structure is:

	```
	main_directory/
	...class_a/
	......a_image_1.jpg
	......a_image_2.jpg
	...class_b/
	......b_image_1.jpg
	......b_image_2.jpg
	```

	Then calling `image_dataset_from_directory(main_directory, labels='inferred')`
	will return a `tf.data.Dataset` that yields batches of images from
	the subdirectories `class_a` and `class_b`, together with labels
	0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).

	Supported image formats: jpeg, png, bmp, gif.
	Animated gifs are truncated to the first frame.

	Args:
	directory: Directory where the data is located.
	If `labels` is "inferred", it should contain
	subdirectories, each containing images for a class.
	Otherwise, the directory structure is ignored.
	labels: Either "inferred"
	(labels are generated from the directory structure),
	None (no labels),
	or a list/tuple of integer labels of the same size as the number of
	image files found in the directory. Labels should be sorted according
	to the alphanumeric order of the image file paths
	(obtained via `os.walk(directory)` in Python).
	label_mode:
	- 'int': means that the labels are encoded as integers
	(e.g. for `sparse_categorical_crossentropy` loss).
	- 'categorical' means that the labels are
	encoded as a categorical vector
	(e.g. for `categorical_crossentropy` loss).
	- 'binary' means that the labels (there can be only 2)
	are encoded as `float32` scalars with values 0 or 1
	(e.g. for `binary_crossentropy`).
	- None (no labels).
	class_names: Only valid if "labels" is "inferred". This is the explict
	list of class names (must match names of subdirectories). Used
	to control the order of the classes
	(otherwise alphanumerical order is used).
	color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
	Whether the images will be converted to
	have 1, 3, or 4 channels.
	batch_size: Size of the batches of data. Default: 32.
	image_size: Size to resize images to after they are read from disk.
	Defaults to `(256, 256)`.
	Since the pipeline processes batches of images that must all have
	the same size, this must be provided.
	shuffle: Whether to shuffle the data. Default: True.
	If set to False, sorts the data in alphanumeric order.
	seed: Optional random seed for shuffling and transformations.
	validation_split: Optional float between 0 and 1,
	fraction of data to reserve for validation.
	subset: One of "training" or "validation".
	Only used if `validation_split` is set.
	interpolation: String, the interpolation method used when resizing images.
	Defaults to `bilinear`. Supports `bilinear`, `nearest`, `bicubic`,
	`area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.
	follow_links: Whether to visits subdirectories pointed to by symlinks.
	Defaults to False.
	crop_to_aspect_ratio: If True, resize the images without aspect
	ratio distortion. When the original aspect ratio differs from the target
	aspect ratio, the output image will be cropped so as to return the largest
	possible window in the image (of size `image_size`) that matches
	the target aspect ratio. By default (`crop_to_aspect_ratio=False`),
	aspect ratio may not be preserved.
	**kwargs: Legacy keyword arguments.

	Returns:
	A `tf.data.Dataset` object.
	- If `label_mode` is None, it yields `float32` tensors of shape
	`(batch_size, image_size[0], image_size[1], num_channels)`,
	encoding images (see below for rules regarding `num_channels`).
	- Otherwise, it yields a tuple `(images, labels)`, where `images`
	has shape `(batch_size, image_size[0], image_size[1], num_channels)`,
	and `labels` follows the format described below.

	Rules regarding labels format:
	- if `label_mode` is `int`, the labels are an `int32` tensor of shape
	`(batch_size,)`.
	- if `label_mode` is `binary`, the labels are a `float32` tensor of
	1s and 0s of shape `(batch_size, 1)`.
	- if `label_mode` is `categorial`, the labels are a `float32` tensor
	of shape `(batch_size, num_classes)`, representing a one-hot
	encoding of the class index.

	Rules regarding number of channels in the yielded images:
	- if `color_mode` is `grayscale`,
	there's 1 channel in the image tensors.
	- if `color_mode` is `rgb`,
	there are 3 channel in the image tensors.
	- if `color_mode` is `rgba`,
	there are 4 channel in the image tensors.
	"""
	if 'smart_resize' in kwargs:
	crop_to_aspect_ratio = kwargs.pop('smart_resize')
	if kwargs:
	raise TypeError(f'Unknown keywords argument(s): {tuple(kwargs.keys())}')
	if labels not in ('inferred', None):
	if not isinstance(labels, (list, tuple)):
	raise ValueError(
	'`labels` argument should be a list/tuple of integer labels, of '
	'the same size as the number of image files in the target '
	'directory. If you wish to infer the labels from the subdirectory '
	'names in the target directory, pass `labels="inferred"`. '
	'If you wish to get a dataset that only contains images '
	'(no labels), pass `label_mode=None`.')
	if class_names:
	raise ValueError('You can only pass `class_names` if the labels are '
	'inferred from the subdirectory names in the target '
	'directory (`labels="inferred"`).')
	if label_mode not in {'int', 'categorical', 'binary', None}:
	raise ValueError(
	'`label_mode` argument must be one of "int", "categorical", "binary", '
	'or None. Received: %s' % (label_mode,))
	if labels is None or label_mode is None:
	labels = None
	label_mode = None
	if color_mode == 'rgb':
	num_channels = 3
	elif color_mode == 'rgba':
	num_channels = 4
	elif color_mode == 'grayscale':
	num_channels = 1
	else:
	raise ValueError(
	'`color_mode` must be one of {"rbg", "rgba", "grayscale"}. '
	'Received: %s' % (color_mode,))
	interpolation = image_preprocessing.get_interpolation(interpolation)
	dataset_utils.check_validation_split_arg(
	validation_split, subset, shuffle, seed)

	if seed is None:
	seed = np.random.randint(1e6)
	image_paths, labels, class_names = dataset_utils.index_directory(
	directory,
	labels,
	formats=ALLOWLIST_FORMATS,
	class_names=class_names,
	shuffle=shuffle,
	seed=seed,
	follow_links=follow_links)

	if label_mode == 'binary' and len(class_names) != 2:
	raise ValueError(
	'When passing `label_mode="binary", there must exactly 2 classes. '
	'Found the following classes: %s' % (class_names,))

	image_paths, labels = dataset_utils.get_training_or_validation_split(
	image_paths, labels, validation_split, subset)
	if not image_paths:
	raise ValueError('No images found.')

	dataset = paths_and_labels_to_dataset(
	image_paths=image_paths,
	image_size=image_size,
	num_channels=num_channels,
	labels=labels,
	label_mode=label_mode,
	num_classes=len(class_names),
	interpolation=interpolation,
	crop_to_aspect_ratio=crop_to_aspect_ratio)
	if shuffle:
	# Shuffle locally at each iteration
	dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
	dataset = dataset.batch(batch_size)
	# Users may need to reference `class_names`.
	dataset.class_names = class_names
	# Include file paths for images as attribute.
	dataset.file_paths = image_paths
	return dataset


	def paths_and_labels_to_dataset(image_paths,
	image_size,
	num_channels,
	labels,
	label_mode,
	num_classes,
	interpolation,
	crop_to_aspect_ratio=False):
	"""Constructs a dataset of images and labels."""
	# TODO(fchollet): consider making num_parallel_calls settable
	path_ds = dataset_ops.Dataset.from_tensor_slices(image_paths)
	args = (image_size, num_channels, interpolation, crop_to_aspect_ratio)
	img_ds = path_ds.map(
	lambda x: load_image(x, *args))
	if label_mode:
	label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes)
	img_ds = dataset_ops.Dataset.zip((img_ds, label_ds))
	return img_ds


	def load_image(path, image_size, num_channels, interpolation,
	crop_to_aspect_ratio=False):
	"""Load an image from a path and resize it."""
	img = io_ops.read_file(path)
	img = image_ops.decode_image(
	img, channels=num_channels, expand_animations=False)
	if crop_to_aspect_ratio:
	img = keras_image_ops.smart_resize(img, image_size,
	interpolation=interpolation)
	else:
	img = image_ops.resize_images_v2(img, image_size, method=interpolation)
	img.set_shape((image_size[0], image_size[1], num_channels))
	return img