View source on GitHub |
Generate batches of tensor image data with real-time data augmentation.
tf.keras.preprocessing.image.ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
zca_epsilon=1e-06,
rotation_range=0,
width_shift_range=0.0,
height_shift_range=0.0,
brightness_range=None,
shear_range=0.0,
zoom_range=0.0,
channel_shift_range=0.0,
fill_mode='nearest',
cval=0.0,
horizontal_flip=False,
vertical_flip=False,
rescale=None,
preprocessing_function=None,
data_format=None,
validation_split=0.0,
interpolation_order=1,
dtype=None
)
The data will be looped over (in batches).
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)
:
(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)
:
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.
# 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)
Methods
apply_transform
apply_transform(
x, transform_parameters
)
Applies a transformation to an image according to given parameters.
Args | |
---|---|
x
|
3D tensor, single image. |
transform_parameters
|
Dictionary with string - parameter pairs
describing the transformation.
Currently, the following parameters
from the dictionary are used:
|
Returns | |
---|---|
A transformed version of the input (same shape). |
fit
fit(
x, augment=False, rounds=1, seed=None
)
Fits the data generator to some sample data.
This computes the internal data stats related to the data-dependent transformations, based on an array of sample data.
Only required if featurewise_center
or
featurewise_std_normalization
or zca_whitening
are set to True.
When rescale
is set to a value, rescaling is applied to
sample data before computing the internal data stats.
Args | |
---|---|
x
|
Sample data. Should have rank 4. In case of grayscale data, the channels axis 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. |
augment
|
Boolean (default: False). Whether to fit on randomly augmented samples. |
rounds
|
Int (default: 1).
If using data augmentation (augment=True ),
this is how many augmentation passes over the data to use.
|
seed
|
Int (default: None). Random seed. |
flow
flow(
x,
y=None,
batch_size=32,
shuffle=True,
sample_weight=None,
seed=None,
save_to_dir=None,
save_prefix='',
save_format='png',
ignore_class_split=False,
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".
|
ignore_class_split
|
Boolean (default: False), ignore difference in number of classes in labels across train and validation split (useful for non-classification tasks) |
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".
|
flow_from_dataframe
flow_from_dataframe(
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.
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 :
|
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.
|
flow_from_directory
flow_from_directory(
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',
keep_aspect_ratio=False
)
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 for more details. |
target_size
|
Tuple of integers (height, width) . The dimensions to
which all images found will be resized. Defaults to (256,256) .
|
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.
Determines the type of label arrays that are returned:
|
batch_size
|
Size of the batches of data. Defaults to 32 .
|
shuffle
|
Whether to shuffle the data If False , sorts the
data in alphanumeric order. Defaults to 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).
Defaults to "png".
|
follow_links
|
Whether to follow symlinks inside
class subdirectories. Defaults to 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. Defaults to "nearest" .
|
keep_aspect_ratio
|
Boolean, whether to resize images to a target size without aspect ratio distortion. The image is cropped in the center with target aspect ratio before resizing. |
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.
|
get_random_transform
get_random_transform(
img_shape, seed=None
)
Generates random parameters for a transformation.
Args | |
---|---|
img_shape
|
Tuple of integers. Shape of the image that is transformed. |
seed
|
Random seed. |
Returns | |
---|---|
A dictionary containing randomly chosen parameters describing the transformation. |
random_transform
random_transform(
x, seed=None
)
Applies a random transformation to an image.
Args | |
---|---|
x
|
3D tensor, single image. |
seed
|
Random seed. |
Returns | |
---|---|
A randomly transformed version of the input (same shape). |
standardize
standardize(
x
)
Applies the normalization configuration in-place to a batch of inputs.
x
is changed in-place since the function is mainly used internally
to standardize images and feed them to your network. If a copy of x
would be created instead it would have a significant performance cost.
If you want to apply this method without changing the input in-place
you can call the method creating a copy before:
standardize(np.copy(x))
Args | |
---|---|
x
|
Batch of inputs to be normalized. |
Returns | |
---|---|
The inputs, normalized. |