tf.keras.preprocessing.image.ImageDataGenerator

TensorFlow 1 version

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Generate batches of tensor image data with real-time data augmentation.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.preprocessing.image.ImageDataGenerator

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 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.

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`

View source

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`	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" (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.

`flow_from_dataframe`

View source

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.
`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`