TensorFlow 1 version
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View source on GitHub
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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, dtype=None
)
The data will be looped over (in batches).
Arguments | |
|---|---|
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
|
height_shift_range
|
Float, 1-D array-like or int
(-height_shift_range, +height_shift_range)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).
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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].
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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:
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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".
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validation_split
|
Float. Fraction of images reserved for validation (strictly between 0 and 1). |
dtype
|
Dtype to use for the generated arrays. |
Examples:
Example of using .flow(x, y):
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_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)
# 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),
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_generator(
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.
Arguments
x: 3D tensor, single image.
transform_parameters: Dictionary with string - parameter pairs
describing the transformation.
Currently, the following parameters
from the dictionary are used:
- `'theta'`: Float. Rotation angle in degrees.
- `'tx'`: Float. Shift in the x direction.
- `'ty'`: Float. Shift in the y direction.
- `'shear'`: Float. Shear angle in degrees.
- `'zx'`: Float. Zoom in the x direction.
- `'zy'`: Float. Zoom in the y direction.
- `'flip_horizontal'`: Boolean. Horizontal flip.
- `'flip_vertical'`: Boolean. Vertical flip.
- `'channel_shift_intensity'`: Float. Channel shift intensity.
- `'brightness'`: Float. Brightness shift intensity.
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.
Arguments
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', subset=None
)
Takes data & label arrays, generates batches of augmented data.
| Arguments | |
|---|---|
x
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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
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Boolean (default: True). |
sample_weight
|
Sample weights. |
seed
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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"
(only relevant if save_to_dir is set). Default: "png".
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subset
|
Subset of data ("training" or "validation") if
validation_split is set in ImageDataGenerator.
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| 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.
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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.
| Arguments | |
|---|---|
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.
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directory
|
string, path to the directory to read images from. If None,
data in x_col column should be absolute paths.
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x_col
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string, column in dataframe that contains the filenames (or
absolute paths if directory is None).
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y_col
|
string or list, column/s in dataframe that has the target data.
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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.
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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.
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class_mode
|
one of "binary", "categorical", "input", "multi_output",
"raw", sparse" or None. Default: "categorical".
Mode for yielding the targets:
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