def custom_img_dataset_from_directory(directory,
labels='inferred',
label_mode='categorical',
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):
"generates a tf.data.Dataset from image files in a directory, with brightness label for each image"
WHITELIST_FORMATS = ('.bmp', '.gif', '.jpeg', '.jpg', '.png')
if color_mode == 'rgb':
num_channels = 3
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=WHITELIST_FORMATS,
class_names=class_names,
shuffle=shuffle,
seed=seed,
follow_links=follow_links)
image_paths, labels = dataset_utils.get_training_or_validation_split(
image_paths, labels, validation_split, subset)
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)
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
return dataset
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):
"""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.
Arguments:
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),
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.
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 labels != 'inferred':
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 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)
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)
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
return dataset
def 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):
"""Generates a 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 `from_directory(main_directory, labels='inferred')`
will return a 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.
Arguments:
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),
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.
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 labels != 'inferred':
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 `labels=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 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)
inferred_class_names = []
for subdir in sorted(os.listdir(directory)):
if os.path.isdir(os.path.join(directory, subdir)):
inferred_class_names.append(subdir)
if not class_names:
class_names = inferred_class_names
else:
if set(class_names) != set(inferred_class_names):
raise ValueError(
'The `class_names` passed did not match the '
'names of the subdirectories of the target directory. '
'Expected: %s, but received: %s' %
(inferred_class_names, class_names))
class_indices = dict(zip(class_names, range(len(class_names))))
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,))
# Build an index of the images
# in the different class subfolders.
pool = multiprocessing.pool.ThreadPool()
results = []
filenames = []
for dirpath in (os.path.join(directory, subdir) for subdir in class_names):
results.append(
pool.apply_async(list_labeled_images_in_directory,
(dirpath, class_indices, follow_links)))
labels_list = []
for res in results:
partial_labels, partial_filenames = res.get()
labels_list.append(partial_labels)
filenames += partial_filenames
if labels != 'inferred':
if len(labels) != len(filenames):
raise ValueError('Expected the lengths of `labels` to match the number '
'of images in the target directory. len(labels) is %s '
'while we found %s images in %s.' % (
len(labels), len(filenames), directory))
else:
i = 0
labels = np.zeros((len(filenames),), dtype='int32')
for partial_labels in labels_list:
labels[i:i + len(partial_labels)] = partial_labels
i += len(partial_labels)
print('Found %d images belonging to %d classes.' %
(len(filenames), len(class_names)))
pool.close()
pool.join()
image_paths = [os.path.join(directory, fname) for fname in filenames]
if shuffle:
# Shuffle globally to erase macro-structure
# (the dataset will be further shuffled within a local buffer
# at each iteration)
if seed is None:
seed = np.random.randint(1e6)
rng = np.random.RandomState(seed)
rng.shuffle(image_paths)
rng = np.random.RandomState(seed)
rng.shuffle(labels)
if validation_split:
if not 0 < validation_split < 1:
raise ValueError(
'`validation_split` must be between 0 and 1, received: %s' %
(validation_split,))
num_val_samples = int(validation_split * len(image_paths))
if subset == 'training':
image_paths = image_paths[:-num_val_samples]
labels = labels[:-num_val_samples]
elif subset == 'validation':
image_paths = image_paths[-num_val_samples:]
labels = labels[-num_val_samples:]
else:
raise ValueError('`subset` must be either "training" '
'or "validation", received: %s' % (subset,))
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)
if shuffle:
# Shuffle locally at each iteration
dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
dataset = dataset.batch(batch_size)
return dataset
def image_dataset_from_files(self,
label_mode='int',
color_mode='rgb',
batch_size=32,
image_size=(256, 256),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation='bilinear'):
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 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)
if validation_split:
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 = self.index_files(shuffle=shuffle,
seed=seed,
subset=subset)
if subset == 'training':
print("Remaining pictures", len(image_paths))
else:
print("Excluded pictures", len(image_paths))
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)
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)
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
return dataset