Skip to content
Permalink
PiperOrigin-RevId: 389056333
64 contributors

Users who have contributed to this file

@fchollet @qlzh727 @ozabluda @vkk800 @wassname @Dref360 @StefanoCappellini @udibr @StefanoD @RitwikGupta @mthrok @taehoonlee
# Copyright 2015 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.
# ==============================================================================
# pylint: disable=invalid-name
# pylint: disable=g-import-not-at-top
# pylint: disable=g-classes-have-attributes
"""Set of tools for real-time data augmentation on image data."""
import tensorflow.compat.v2 as tf
from keras_preprocessing import image
import numpy as np
try:
from scipy import linalg # pylint: disable=unused-import
from scipy import ndimage # pylint: disable=unused-import
except ImportError:
pass
from keras import backend
from keras.preprocessing.image_dataset import image_dataset_from_directory # pylint: disable=unused-import
from keras.utils import data_utils
from keras.utils import tf_inspect
from tensorflow.python.platform import tf_logging
from tensorflow.python.util.tf_export import keras_export
random_rotation = image.random_rotation
random_shift = image.random_shift
random_shear = image.random_shear
random_zoom = image.random_zoom
apply_channel_shift = image.apply_channel_shift
random_channel_shift = image.random_channel_shift
apply_brightness_shift = image.apply_brightness_shift
random_brightness = image.random_brightness
apply_affine_transform = image.apply_affine_transform
@keras_export('keras.preprocessing.image.smart_resize', v1=[])
def smart_resize(x, size, interpolation='bilinear'):
"""Resize images to a target size without aspect ratio distortion.
TensorFlow image datasets typically yield images that have each a different
size. However, these images need to be batched before they can be
processed by Keras layers. To be batched, images need to share the same height
and width.
You could simply do:
```python
size = (200, 200)
ds = ds.map(lambda img: tf.image.resize(img, size))
```
However, if you do this, you distort the aspect ratio of your images, since
in general they do not all have the same aspect ratio as `size`. This is
fine in many cases, but not always (e.g. for GANs this can be a problem).
Note that passing the argument `preserve_aspect_ratio=True` to `resize`
will preserve the aspect ratio, but at the cost of no longer respecting the
provided target size. Because `tf.image.resize` doesn't crop images,
your output images will still have different sizes.
This calls for:
```python
size = (200, 200)
ds = ds.map(lambda img: smart_resize(img, size))
```
Your output images will actually be `(200, 200)`, and will not be distorted.
Instead, the parts of the image that do not fit within the target size
get cropped out.
The resizing process is:
1. Take the largest centered crop of the image that has the same aspect ratio
as the target size. For instance, if `size=(200, 200)` and the input image has
size `(340, 500)`, we take a crop of `(340, 340)` centered along the width.
2. Resize the cropped image to the target size. In the example above,
we resize the `(340, 340)` crop to `(200, 200)`.
Args:
x: Input image or batch of images (as a tensor or NumPy array).
Must be in format `(height, width, channels)` or
`(batch_size, height, width, channels)`.
size: Tuple of `(height, width)` integer. Target size.
interpolation: String, interpolation to use for resizing.
Defaults to `'bilinear'`. Supports `bilinear`, `nearest`, `bicubic`,
`area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`.
Returns:
Array with shape `(size[0], size[1], channels)`. If the input image was a
NumPy array, the output is a NumPy array, and if it was a TF tensor,
the output is a TF tensor.
"""
if len(size) != 2:
raise ValueError('Expected `size` to be a tuple of 2 integers, '
f'but got: {size}.')
img = tf.convert_to_tensor(x)
if img.shape.rank is not None:
if img.shape.rank < 3 or img.shape.rank > 4:
raise ValueError(
'Expected an image array with shape `(height, width, channels)`, '
'or `(batch_size, height, width, channels)`, but '
f'got input with incorrect rank, of shape {img.shape}.')
shape = tf.shape(img)
height, width = shape[-3], shape[-2]
target_height, target_width = size
if img.shape.rank is not None:
static_num_channels = img.shape[-1]
else:
static_num_channels = None
crop_height = tf.cast(
tf.cast(width * target_height, 'float32') / target_width, 'int32')
crop_width = tf.cast(
tf.cast(height * target_width, 'float32') / target_height, 'int32')
# Set back to input height / width if crop_height / crop_width is not smaller.
crop_height = tf.minimum(height, crop_height)
crop_width = tf.minimum(width, crop_width)
crop_box_hstart = tf.cast(
tf.cast(height - crop_height, 'float32') / 2, 'int32')
crop_box_wstart = tf.cast(
tf.cast(width - crop_width, 'float32') / 2, 'int32')
if img.shape.rank == 4:
crop_box_start = tf.stack([0, crop_box_hstart, crop_box_wstart, 0])
crop_box_size = tf.stack([-1, crop_height, crop_width, -1])
else:
crop_box_start = tf.stack([crop_box_hstart, crop_box_wstart, 0])
crop_box_size = tf.stack([crop_height, crop_width, -1])
img = tf.slice(img, crop_box_start, crop_box_size)
img = tf.image.resize(
images=img,
size=size,
method=interpolation)
# Apparent bug in resize_images_v2 may cause shape to be lost
if img.shape.rank is not None:
if img.shape.rank == 4:
img.set_shape((None, None, None, static_num_channels))
if img.shape.rank == 3:
img.set_shape((None, None, static_num_channels))
if isinstance(x, np.ndarray):
return img.numpy()
return img
@keras_export('keras.utils.array_to_img',
'keras.preprocessing.image.array_to_img')
def array_to_img(x, data_format=None, scale=True, dtype=None):
"""Converts a 3D Numpy array to a PIL Image instance.
Usage:
```python
from PIL import Image
img = np.random.random(size=(100, 100, 3))
pil_img = tf.keras.preprocessing.image.array_to_img(img)
```
Args:
x: Input data, in any form that can be converted to a Numpy array.
data_format: Image data format, can be either "channels_first" or
"channels_last". Defaults to `None`, in which case the global setting
`tf.keras.backend.image_data_format()` is used (unless you changed it,
it defaults to "channels_last").
scale: Whether to rescale the image such that minimum and maximum values
are 0 and 255 respectively. Defaults to `True`.
dtype: Dtype to use. Default to `None`, in which case the global setting
`tf.keras.backend.floatx()` is used (unless you changed it, it defaults
to "float32")
Returns:
A PIL Image instance.
Raises:
ImportError: if PIL is not available.
ValueError: if invalid `x` or `data_format` is passed.
"""
if data_format is None:
data_format = backend.image_data_format()
kwargs = {}
if 'dtype' in tf_inspect.getfullargspec(image.array_to_img)[0]:
if dtype is None:
dtype = backend.floatx()
kwargs['dtype'] = dtype
return image.array_to_img(x, data_format=data_format, scale=scale, **kwargs)
@keras_export('keras.utils.img_to_array',
'keras.preprocessing.image.img_to_array')
def img_to_array(img, data_format=None, dtype=None):
"""Converts a PIL Image instance to a Numpy array.
Usage:
```python
from PIL import Image
img_data = np.random.random(size=(100, 100, 3))
img = tf.keras.preprocessing.image.array_to_img(img_data)
array = tf.keras.preprocessing.image.img_to_array(img)
```
Args:
img: Input PIL Image instance.
data_format: Image data format, can be either "channels_first" or
"channels_last". Defaults to `None`, in which case the global setting
`tf.keras.backend.image_data_format()` is used (unless you changed it,
it defaults to "channels_last").
dtype: Dtype to use. Default to `None`, in which case the global setting
`tf.keras.backend.floatx()` is used (unless you changed it, it defaults
to "float32")
Returns:
A 3D Numpy array.
Raises:
ValueError: if invalid `img` or `data_format` is passed.
"""
if data_format is None:
data_format = backend.image_data_format()
kwargs = {}
if 'dtype' in tf_inspect.getfullargspec(image.img_to_array)[0]:
if dtype is None:
dtype = backend.floatx()
kwargs['dtype'] = dtype
return image.img_to_array(img, data_format=data_format, **kwargs)
@keras_export('keras.utils.save_img',
'keras.preprocessing.image.save_img')
def save_img(path,
x,
data_format=None,
file_format=None,
scale=True,
**kwargs):
"""Saves an image stored as a Numpy array to a path or file object.
Args:
path: Path or file object.
x: Numpy array.
data_format: Image data format,
either "channels_first" or "channels_last".
file_format: Optional file format override. If omitted, the
format to use is determined from the filename extension.
If a file object was used instead of a filename, this
parameter should always be used.
scale: Whether to rescale image values to be within `[0, 255]`.
**kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
"""
if data_format is None:
data_format = backend.image_data_format()
image.save_img(path,
x,
data_format=data_format,
file_format=file_format,
scale=scale, **kwargs)
@keras_export('keras.utils.load_img',
'keras.preprocessing.image.load_img')
def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
interpolation='nearest'):
"""Loads an image into PIL format.
Usage:
```
image = tf.keras.preprocessing.image.load_img(image_path)
input_arr = tf.keras.preprocessing.image.img_to_array(image)
input_arr = np.array([input_arr]) # Convert single image to a batch.
predictions = model.predict(input_arr)
```
Args:
path: Path to image file.
grayscale: DEPRECATED use `color_mode="grayscale"`.
color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
The desired image format.
target_size: Either `None` (default to original size)
or tuple of ints `(img_height, img_width)`.
interpolation: Interpolation method used to resample the image if the
target size is different from that of the loaded image.
Supported methods are "nearest", "bilinear", and "bicubic".
If PIL version 1.1.3 or newer is installed, "lanczos" is also
supported. If PIL version 3.4.0 or newer is installed, "box" and
"hamming" are also supported. By default, "nearest" is used.
Returns:
A PIL Image instance.
Raises:
ImportError: if PIL is not available.
ValueError: if interpolation method is not supported.
"""
return image.load_img(path, grayscale=grayscale, color_mode=color_mode,
target_size=target_size, interpolation=interpolation)
@keras_export('keras.preprocessing.image.Iterator')
class Iterator(image.Iterator, data_utils.Sequence):
pass
@keras_export('keras.preprocessing.image.DirectoryIterator')
class DirectoryIterator(image.DirectoryIterator, Iterator): # pylint: disable=inconsistent-mro
"""Iterator capable of reading images from a directory on disk.
Args:
directory: Path to the directory to read images from.
Each subdirectory in this directory will be
considered to contain images from one class,
or alternatively you could specify class subdirectories
via the `classes` argument.
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
target_size: tuple of integers, dimensions to resize input images to.
color_mode: One of `"rgb"`, `"rgba"`, `"grayscale"`.
Color mode to read images.
classes: Optional list of strings, names of subdirectories
containing images from each class (e.g. `["dogs", "cats"]`).
It will be computed automatically if not set.
class_mode: Mode for yielding the targets:
- `"binary"`: binary targets (if there are only two classes),
- `"categorical"`: categorical targets,
- `"sparse"`: integer targets,
- `"input"`: targets are images identical to input images (mainly
used to work with autoencoders),
- `None`: no targets get yielded (only input images are yielded).
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
being yielded, in a viewable format. This is useful
for visualizing the random transformations being
applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
subset: Subset of data (`"training"` or `"validation"`) if
validation_split is set in ImageDataGenerator.
interpolation: Interpolation method used to resample the image if the
target size is different from that of the loaded image.
Supported methods are "nearest", "bilinear", and "bicubic".
If PIL version 1.1.3 or newer is installed, "lanczos" is also
supported. If PIL version 3.4.0 or newer is installed, "box" and
"hamming" are also supported. By default, "nearest" is used.
dtype: Dtype to use for generated arrays.
"""
def __init__(self, directory, image_data_generator,
target_size=(256, 256),
color_mode='rgb',
classes=None,
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=None,
data_format=None,
save_to_dir=None,
save_prefix='',
save_format='png',
follow_links=False,
subset=None,
interpolation='nearest',
dtype=None):
if data_format is None:
data_format = backend.image_data_format()
kwargs = {}
if 'dtype' in tf_inspect.getfullargspec(
image.ImageDataGenerator.__init__)[0]:
if dtype is None:
dtype = backend.floatx()
kwargs['dtype'] = dtype
super(DirectoryIterator, self).__init__(
directory, image_data_generator,
target_size=target_size,
color_mode=color_mode,
classes=classes,
class_mode=class_mode,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
data_format=data_format,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
follow_links=follow_links,
subset=subset,
interpolation=interpolation,
**kwargs)
@keras_export('keras.preprocessing.image.NumpyArrayIterator')
class NumpyArrayIterator(image.NumpyArrayIterator, Iterator):
"""Iterator yielding data from a Numpy array.
Args:
x: Numpy array of input data or tuple.
If tuple, the second elements is either
another numpy array or a list of numpy arrays,
each of which gets passed
through as an output without any modifications.
y: Numpy array of targets data.
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
sample_weight: Numpy array of sample weights.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
being yielded, in a viewable format. This is useful
for visualizing the random transformations being
applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
subset: Subset of data (`"training"` or `"validation"`) if
validation_split is set in ImageDataGenerator.
dtype: Dtype to use for the generated arrays.
"""
def __init__(self, x, y, image_data_generator,
batch_size=32,
shuffle=False,
sample_weight=None,
seed=None,
data_format=None,
save_to_dir=None,
save_prefix='',
save_format='png',
subset=None,
dtype=None):
if data_format is None:
data_format = backend.image_data_format()
kwargs = {}
if 'dtype' in tf_inspect.getfullargspec(
image.NumpyArrayIterator.__init__)[0]:
if dtype is None:
dtype = backend.floatx()
kwargs['dtype'] = dtype
super(NumpyArrayIterator, self).__init__(
x, y, image_data_generator,
batch_size=batch_size,
shuffle=shuffle,
sample_weight=sample_weight,
seed=seed,
data_format=data_format,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
subset=subset,
**kwargs)
class DataFrameIterator(image.DataFrameIterator, Iterator): # pylint: disable=inconsistent-mro
"""Iterator capable of reading images from a directory on disk as a dataframe.
Args:
dataframe: Pandas dataframe containing the filepaths relative to
`directory` (or absolute paths if `directory` is None) of the images in
a string column. It should include other column/s depending on the
`class_mode`:
- if `class_mode` is `"categorical"` (default value) it must include
the `y_col` column with the class/es of each image. Values in
column can be string/list/tuple if a single class or list/tuple if
multiple classes.
- if `class_mode` is `"binary"` or `"sparse"` it must include the
given `y_col` column with class values as strings.
- if `class_mode` is `"raw"` or `"multi_output"` it should contain the
columns specified in `y_col`.
- if `class_mode` is `"input"` or `None` no extra column is needed.
directory: string, path to the directory to read images from. If `None`,
data in `x_col` column should be absolute paths.
image_data_generator: Instance of `ImageDataGenerator` to use for random
transformations and normalization. If None, no transformations and
normalizations are made.
x_col: string, column in `dataframe` that contains the filenames (or
absolute paths if `directory` is `None`).
y_col: string or list, column/s in `dataframe` that has the target data.
weight_col: string, column in `dataframe` that contains the sample
weights. Default: `None`.
target_size: tuple of integers, dimensions to resize input images to.
color_mode: One of `"rgb"`, `"rgba"`, `"grayscale"`. Color mode to read
images.
classes: Optional list of strings, classes to use (e.g. `["dogs",
"cats"]`). If None, all classes in `y_col` will be used.
class_mode: one of "binary", "categorical", "input", "multi_output",
"raw", "sparse" or None. Default: "categorical".
Mode for yielding the targets:
- `"binary"`: 1D numpy array of binary labels,
- `"categorical"`: 2D numpy array of one-hot encoded labels. Supports
multi-label output.
- `"input"`: images identical to input images (mainly used to work
with autoencoders),
- `"multi_output"`: list with the values of the different columns,
- `"raw"`: numpy array of values in `y_col` column(s),
- `"sparse"`: 1D numpy array of integer labels,
- `None`, no targets are returned (the generator will only yield
batches of image data, which is useful to use in `model.predict()`).
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures being yielded,
in a viewable format. This is useful for visualizing the random
transformations being applied, for debugging purposes.
save_prefix: String prefix to use for saving sample images (if
`save_to_dir` is set).
save_format: Format to use for saving sample images (if `save_to_dir` is
set).
subset: Subset of data (`"training"` or `"validation"`) if
validation_split is set in ImageDataGenerator.
interpolation: Interpolation method used to resample the image if the
target size is different from that of the loaded image. Supported
methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3
or newer is installed, "lanczos" is also supported. If PIL version 3.4.0
or newer is installed, "box" and "hamming" are also supported. By
default, "nearest" is used.
dtype: Dtype to use for the generated arrays.
validate_filenames: Boolean, whether to validate image filenames in
`x_col`. If `True`, invalid images will be ignored. Disabling this
option
can lead to speed-up in the instantiation of this class. Default: `True`.
"""
def __init__(
self,
dataframe,
directory=None,
image_data_generator=None,
x_col='filename',
y_col='class',
weight_col=None,
target_size=(256, 256),
color_mode='rgb',
classes=None,
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=None,
data_format='channels_last',
save_to_dir=None,
save_prefix='',
save_format='png',
subset=None,
interpolation='nearest',
dtype='float32',
validate_filenames=True):
super(DataFrameIterator, self).__init__(
dataframe=dataframe,
directory=directory,
image_data_generator=image_data_generator,
x_col=x_col,
y_col=y_col,
weight_col=weight_col,
target_size=target_size,
color_mode=color_mode,
classes=classes,
class_mode=class_mode,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
data_format=data_format,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
subset=subset,
interpolation=interpolation,
dtype=dtype,
validate_filenames=validate_filenames
)
@keras_export('keras.preprocessing.image.ImageDataGenerator')
class ImageDataGenerator(image.ImageDataGenerator):
"""Generate batches of tensor image data with real-time data augmentation.
The data will be looped over (in batches).
Args:
featurewise_center: Boolean.
Set input mean to 0 over the dataset, feature-wise.
samplewise_center: Boolean. Set each sample mean to 0.
featurewise_std_normalization: Boolean.
Divide inputs by std of the dataset, feature-wise.
samplewise_std_normalization: Boolean. Divide each input by its std.
zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
zca_whitening: Boolean. Apply ZCA whitening.
rotation_range: Int. Degree range for random rotations.
width_shift_range: Float, 1-D array-like or int
- float: fraction of total width, if < 1, or pixels if >= 1.
- 1-D array-like: random elements from the array.
- int: integer number of pixels from interval
`(-width_shift_range, +width_shift_range)`
- With `width_shift_range=2` possible values
are integers `[-1, 0, +1]`,
same as with `width_shift_range=[-1, 0, +1]`,
while with `width_shift_range=1.0` possible values are floats
in the interval [-1.0, +1.0).
height_shift_range: Float, 1-D array-like or int
- float: fraction of total height, if < 1, or pixels if >= 1.
- 1-D array-like: random elements from the array.
- int: integer number of pixels from interval
`(-height_shift_range, +height_shift_range)`
- With `height_shift_range=2` possible values
are integers `[-1, 0, +1]`,
same as with `height_shift_range=[-1, 0, +1]`,
while with `height_shift_range=1.0` possible values are floats
in the interval [-1.0, +1.0).
brightness_range: Tuple or list of two floats. Range for picking
a brightness shift value from.
shear_range: Float. Shear Intensity
(Shear angle in counter-clockwise direction in degrees)
zoom_range: Float or [lower, upper]. Range for random zoom.
If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.
channel_shift_range: Float. Range for random channel shifts.
fill_mode: One of {"constant", "nearest", "reflect" or "wrap"}.
Default is 'nearest'.
Points outside the boundaries of the input are filled
according to the given mode:
- 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)
- 'nearest': aaaaaaaa|abcd|dddddddd
- 'reflect': abcddcba|abcd|dcbaabcd
- 'wrap': abcdabcd|abcd|abcdabcd
cval: Float or Int.
Value used for points outside the boundaries
when `fill_mode = "constant"`.
horizontal_flip: Boolean. Randomly flip inputs horizontally.
vertical_flip: Boolean. Randomly flip inputs vertically.
rescale: rescaling factor. Defaults to None.
If None or 0, no rescaling is applied,
otherwise we multiply the data by the value provided
(after applying all other transformations).
preprocessing_function: function that will be applied on each input.
The function will run after the image is resized and augmented.
The function should take one argument:
one image (Numpy tensor with rank 3),
and should output a Numpy tensor with the same shape.
data_format: Image data format,
either "channels_first" or "channels_last".
"channels_last" mode means that the images should have shape
`(samples, height, width, channels)`,
"channels_first" mode means that the images should have shape
`(samples, channels, height, width)`.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
validation_split: Float. Fraction of images reserved for validation
(strictly between 0 and 1).
dtype: Dtype to use for the generated arrays.
Raises:
ValueError: If the value of the argument, `data_format` is other than
`"channels_last"` or `"channels_first"`.
ValueError: If the value of the argument, `validation_split` > 1
or `validation_split` < 0.
Examples:
Example of using `.flow(x, y)`:
```python
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = utils.to_categorical(y_train, num_classes)
y_test = utils.to_categorical(y_test, num_classes)
datagen = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
validation_split=0.2)
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(x_train)
# fits the model on batches with real-time data augmentation:
model.fit(datagen.flow(x_train, y_train, batch_size=32,
subset='training'),
validation_data=datagen.flow(x_train, y_train,
batch_size=8, subset='validation'),
steps_per_epoch=len(x_train) / 32, epochs=epochs)
# here's a more "manual" example
for e in range(epochs):
print('Epoch', e)
batches = 0
for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
model.fit(x_batch, y_batch)
batches += 1
if batches >= len(x_train) / 32:
# we need to break the loop by hand because
# the generator loops indefinitely
break
```
Example of using `.flow_from_directory(directory)`:
```python
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'data/train',
target_size=(150, 150),
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
'data/validation',
target_size=(150, 150),
batch_size=32,
class_mode='binary')
model.fit(
train_generator,
steps_per_epoch=2000,
epochs=50,
validation_data=validation_generator,
validation_steps=800)
```
Example of transforming images and masks together.
```python
# we create two instances with the same arguments
data_gen_args = dict(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=90,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.2)
image_datagen = ImageDataGenerator(**data_gen_args)
mask_datagen = ImageDataGenerator(**data_gen_args)
# Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
image_datagen.fit(images, augment=True, seed=seed)
mask_datagen.fit(masks, augment=True, seed=seed)
image_generator = image_datagen.flow_from_directory(
'data/images',
class_mode=None,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
'data/masks',
class_mode=None,
seed=seed)
# combine generators into one which yields image and masks
train_generator = zip(image_generator, mask_generator)
model.fit(
train_generator,
steps_per_epoch=2000,
epochs=50)
```
"""
def __init__(self,
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-6,
rotation_range=0,
width_shift_range=0.,
height_shift_range=0.,
brightness_range=None,
shear_range=0.,
zoom_range=0.,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=False,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None,
validation_split=0.0,
dtype=None):
if data_format is None:
data_format = backend.image_data_format()
kwargs = {}
if 'dtype' in tf_inspect.getfullargspec(
image.ImageDataGenerator.__init__)[0]:
if dtype is None:
dtype = backend.floatx()
kwargs['dtype'] = dtype
super(ImageDataGenerator, self).__init__(
featurewise_center=featurewise_center,
samplewise_center=samplewise_center,
featurewise_std_normalization=featurewise_std_normalization,
samplewise_std_normalization=samplewise_std_normalization,
zca_whitening=zca_whitening,
zca_epsilon=zca_epsilon,
rotation_range=rotation_range,
width_shift_range=width_shift_range,
height_shift_range=height_shift_range,
brightness_range=brightness_range,
shear_range=shear_range,
zoom_range=zoom_range,
channel_shift_range=channel_shift_range,
fill_mode=fill_mode,
cval=cval,
horizontal_flip=horizontal_flip,
vertical_flip=vertical_flip,
rescale=rescale,
preprocessing_function=preprocessing_function,
data_format=data_format,
validation_split=validation_split,
**kwargs)
def flow(self,
x,
y=None,
batch_size=32,
shuffle=True,
sample_weight=None,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
subset=None):
"""Takes data & label arrays, generates batches of augmented data.
Args:
x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first
element should contain the images and the second element another numpy
array or a list of numpy arrays that gets passed to the output without
any modifications. Can be used to feed the model miscellaneous data
along with the images. In case of grayscale data, the channels axis of
the image array should have value 1, in case of RGB data, it should
have value 3, and in case of RGBA data, it should have value 4.
y: Labels.
batch_size: Int (default: 32).
shuffle: Boolean (default: True).
sample_weight: Sample weights.
seed: Int (default: None).
save_to_dir: None or str (default: None). This allows you to optionally
specify a directory to which to save the augmented pictures being
generated (useful for visualizing what you are doing).
save_prefix: Str (default: `''`). Prefix to use for filenames of saved
pictures (only relevant if `save_to_dir` is set).
save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
"tif", "jpg"
(only relevant if `save_to_dir` is set). Default: "png".
subset: Subset of data (`"training"` or `"validation"`) if
`validation_split` is set in `ImageDataGenerator`.
Returns:
An `Iterator` yielding tuples of `(x, y)`
where `x` is a numpy array of image data
(in the case of a single image input) or a list
of numpy arrays (in the case with
additional inputs) and `y` is a numpy array
of corresponding labels. If 'sample_weight' is not None,
the yielded tuples are of the form `(x, y, sample_weight)`.
If `y` is None, only the numpy array `x` is returned.
Raises:
ValueError: If the Value of the argument, `subset` is other than
"training" or "validation".
"""
return NumpyArrayIterator(
x,
y,
self,
batch_size=batch_size,
shuffle=shuffle,
sample_weight=sample_weight,
seed=seed,
data_format=self.data_format,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
subset=subset)
def flow_from_directory(self,
directory,
target_size=(256, 256),
color_mode='rgb',
classes=None,
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
follow_links=False,
subset=None,
interpolation='nearest'):
"""Takes the path to a directory & generates batches of augmented data.
Args:
directory: string, path to the target directory. It should contain one
subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside
each of the subdirectories directory tree will be included in the
generator. See [this script](
https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
for more details.
target_size: Tuple of integers `(height, width)`, defaults to `(256,
256)`. The dimensions to which all images found will be resized.
color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb". Whether
the images will be converted to have 1, 3, or 4 channels.
classes: Optional list of class subdirectories
(e.g. `['dogs', 'cats']`). Default: None. If not provided, the list
of classes will be automatically inferred from the subdirectory
names/structure under `directory`, where each subdirectory will be
treated as a different class (and the order of the classes, which
will map to the label indices, will be alphanumeric). The
dictionary containing the mapping from class names to class
indices can be obtained via the attribute `class_indices`.
class_mode: One of "categorical", "binary", "sparse",
"input", or None. Default: "categorical".
Determines the type of label arrays that are returned:
- "categorical" will be 2D one-hot encoded labels,
- "binary" will be 1D binary labels,
- "sparse" will be 1D integer labels,
- "input" will be images identical to input images (mainly used to
work with autoencoders).
- If None, no labels are returned (the generator will only yield
batches of image data, which is useful to use with
`model.predict()`).
Please note that in case of class_mode None, the data still needs to
reside in a subdirectory of `directory` for it to work correctly.
batch_size: Size of the batches of data (default: 32).
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.
save_to_dir: None or str (default: None). This allows you to optionally
specify a directory to which to save the augmented pictures being
generated (useful for visualizing what you are doing).
save_prefix: Str. Prefix to use for filenames of saved pictures (only
relevant if `save_to_dir` is set).
save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
"tif", "jpg"
(only relevant if `save_to_dir` is set). Default: "png".
follow_links: Whether to follow symlinks inside
class subdirectories (default: False).
subset: Subset of data (`"training"` or `"validation"`) if
`validation_split` is set in `ImageDataGenerator`.
interpolation: Interpolation method used to resample the image if the
target size is different from that of the loaded image. Supported
methods are `"nearest"`, `"bilinear"`, and `"bicubic"`. If PIL version
1.1.3 or newer is installed, `"lanczos"` is also supported. If PIL
version 3.4.0 or newer is installed, `"box"` and `"hamming"` are also
supported. By default, `"nearest"` is used.
Returns:
A `DirectoryIterator` yielding tuples of `(x, y)`
where `x` is a numpy array containing a batch
of images with shape `(batch_size, *target_size, channels)`
and `y` is a numpy array of corresponding labels.
"""
return DirectoryIterator(
directory,
self,
target_size=target_size,
color_mode=color_mode,
classes=classes,
class_mode=class_mode,
data_format=self.data_format,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
follow_links=follow_links,
subset=subset,
interpolation=interpolation)
def flow_from_dataframe(self,
dataframe,
directory=None,
x_col='filename',
y_col='class',
weight_col=None,
target_size=(256, 256),
color_mode='rgb',
classes=None,
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
subset=None,
interpolation='nearest',
validate_filenames=True,
**kwargs):
"""Takes the dataframe and the path to a directory + generates batches.
The generated batches contain augmented/normalized data.
**A simple tutorial can be found **[here](
http://bit.ly/keras_flow_from_dataframe).
Args:
dataframe: Pandas dataframe containing the filepaths relative to
`directory` (or absolute paths if `directory` is None) of the images
in a string column. It should include other column/s
depending on the `class_mode`:
- if `class_mode` is `"categorical"` (default value) it must include
the `y_col` column with the class/es of each image. Values in
column can be string/list/tuple if a single class or list/tuple if
multiple classes.
- if `class_mode` is `"binary"` or `"sparse"` it must include the
given `y_col` column with class values as strings.
- if `class_mode` is `"raw"` or `"multi_output"` it should contain
the columns specified in `y_col`.
- if `class_mode` is `"input"` or `None` no extra column is needed.
directory: string, path to the directory to read images from. If `None`,
data in `x_col` column should be absolute paths.
x_col: string, column in `dataframe` that contains the filenames (or
absolute paths if `directory` is `None`).
y_col: string or list, column/s in `dataframe` that has the target data.
weight_col: string, column in `dataframe` that contains the sample
weights. Default: `None`.
target_size: tuple of integers `(height, width)`, default: `(256, 256)`.
The dimensions to which all images found will be resized.
color_mode: one of "grayscale", "rgb", "rgba". Default: "rgb". Whether
the images will be converted to have 1 or 3 color channels.
classes: optional list of classes (e.g. `['dogs', 'cats']`). Default is
None. If not provided, the list of classes will be automatically
inferred from the `y_col`, which will map to the label indices, will
be alphanumeric). The dictionary containing the mapping from class
names to class indices can be obtained via the attribute
`class_indices`.
class_mode: one of "binary", "categorical", "input", "multi_output",
"raw", sparse" or None. Default: "categorical".
Mode for yielding the targets:
- `"binary"`: 1D numpy array of binary labels,
- `"categorical"`: 2D numpy array of one-hot encoded labels.
Supports multi-label output.
- `"input"`: images identical to input images (mainly used to work
with autoencoders),
- `"multi_output"`: list with the values of the different columns,
- `"raw"`: numpy array of values in `y_col` column(s),
- `"sparse"`: 1D numpy array of integer labels,
- `None`, no targets are returned (the generator will only yield
batches of image data, which is useful to use in
`model.predict()`).
batch_size: size of the batches of data (default: 32).
shuffle: whether to shuffle the data (default: True)
seed: optional random seed for shuffling and transformations.
save_to_dir: None or str (default: None). This allows you to optionally
specify a directory to which to save the augmented pictures being
generated (useful for visualizing what you are doing).
save_prefix: str. Prefix to use for filenames of saved pictures (only
relevant if `save_to_dir` is set).
save_format: one of "png", "jpeg", "bmp", "pdf", "ppm", "gif",
"tif", "jpg"
(only relevant if `save_to_dir` is set). Default: "png".
subset: Subset of data (`"training"` or `"validation"`) if
`validation_split` is set in `ImageDataGenerator`.
interpolation: Interpolation method used to resample the image if the
target size is different from that of the loaded image. Supported
methods are `"nearest"`, `"bilinear"`, and `"bicubic"`. If PIL version
1.1.3 or newer is installed, `"lanczos"` is also supported. If PIL
version 3.4.0 or newer is installed, `"box"` and `"hamming"` are also
supported. By default, `"nearest"` is used.
validate_filenames: Boolean, whether to validate image filenames in
`x_col`. If `True`, invalid images will be ignored. Disabling this
option can lead to speed-up in the execution of this function.
Defaults to `True`.
**kwargs: legacy arguments for raising deprecation warnings.
Returns:
A `DataFrameIterator` yielding tuples of `(x, y)`
where `x` is a numpy array containing a batch
of images with shape `(batch_size, *target_size, channels)`
and `y` is a numpy array of corresponding labels.
"""
if 'has_ext' in kwargs:
tf_logging.warning(
'has_ext is deprecated, filenames in the dataframe have '
'to match the exact filenames in disk.', DeprecationWarning)
if 'sort' in kwargs:
tf_logging.warning(
'sort is deprecated, batches will be created in the'
'same order than the filenames provided if shuffle'
'is set to False.', DeprecationWarning)
if class_mode == 'other':
tf_logging.warning(
'`class_mode` "other" is deprecated, please use '
'`class_mode` "raw".', DeprecationWarning)
class_mode = 'raw'
if 'drop_duplicates' in kwargs:
tf_logging.warning(
'drop_duplicates is deprecated, you can drop duplicates '
'by using the pandas.DataFrame.drop_duplicates method.',
DeprecationWarning)
return DataFrameIterator(
dataframe,
directory,
self,
x_col=x_col,
y_col=y_col,
weight_col=weight_col,
target_size=target_size,
color_mode=color_mode,
classes=classes,
class_mode=class_mode,
data_format=self.data_format,
batch_size=batch_size,
shuffle=shuffle,
seed=seed,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format,
subset=subset,
interpolation=interpolation,
validate_filenames=validate_filenames)
keras_export('keras.preprocessing.image.random_rotation', allow_multiple_exports=True)(random_rotation)
keras_export('keras.preprocessing.image.random_shift', allow_multiple_exports=True)(random_shift)
keras_export('keras.preprocessing.image.random_shear', allow_multiple_exports=True)(random_shear)
keras_export('keras.preprocessing.image.random_zoom', allow_multiple_exports=True)(random_zoom)
keras_export(
'keras.preprocessing.image.apply_channel_shift', allow_multiple_exports=True)(apply_channel_shift)
keras_export(
'keras.preprocessing.image.random_channel_shift', allow_multiple_exports=True)(random_channel_shift)
keras_export(
'keras.preprocessing.image.apply_brightness_shift', allow_multiple_exports=True)(apply_brightness_shift)
keras_export('keras.preprocessing.image.random_brightness', allow_multiple_exports=True)(random_brightness)
keras_export(
'keras.preprocessing.image.apply_affine_transform', allow_multiple_exports=True)(apply_affine_transform)