Module keras.preprocessing.image_dataset
Keras image dataset loading utilities.
Expand source code
# 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."""
import tensorflow.compat.v2 as tf
# pylint: disable=g-classes-have-attributes
import numpy as np
from keras.layers.preprocessing import image_preprocessing
from keras.preprocessing import dataset_utils
from keras.preprocessing import image as keras_image_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.prefetch(tf.data.AUTOTUNE).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 = tf.data.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 = tf.data.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 = tf.io.read_file(path)
img = tf.image.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 = tf.image.resize(img, image_size, method=interpolation)
img.set_shape((image_size[0], image_size[1], num_channels))
return img
Functions
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 atf.data.Dataset
that yields batches of images from the subdirectoriesclass_a
andclass_b
, together with labels 0 and 1 (0 corresponding toclass_a
and 1 corresponding toclass_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
. Supportsbilinear
,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. - Iflabel_mode
is None, it yieldsfloat32
tensors of shape(batch_size, image_size[0], image_size[1], num_channels)
, encoding images (see below for rules regardingnum_channels
). - Otherwise, it yields a tuple(images, labels)
, whereimages
has shape(batch_size, image_size[0], image_size[1], num_channels)
, andlabels
follows the format described below. Rules regarding labels format: - iflabel_mode
isint
, the labels are anint32
tensor of shape(batch_size,)
. - iflabel_mode
isbinary
, the labels are afloat32
tensor of 1s and 0s of shape(batch_size, 1)
. - iflabel_mode
iscategorial
, the labels are afloat32
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
isgrayscale
, there's 1 channel in the image tensors. - ifcolor_mode
isrgb
, there are 3 channel in the image tensors. - ifcolor_mode
isrgba
, there are 4 channel in the image tensors.Expand source code
@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.prefetch(tf.data.AUTOTUNE).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 load_image(path, image_size, num_channels, interpolation, crop_to_aspect_ratio=False)
-
Load an image from a path and resize it.
Expand source code
def load_image(path, image_size, num_channels, interpolation, crop_to_aspect_ratio=False): """Load an image from a path and resize it.""" img = tf.io.read_file(path) img = tf.image.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 = tf.image.resize(img, image_size, method=interpolation) img.set_shape((image_size[0], image_size[1], num_channels)) return img
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.
Expand source code
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 = tf.data.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 = tf.data.Dataset.zip((img_ds, label_ds)) return img_ds