tf.keras.preprocessing.image.ImageDataGenerator

TensorFlow 1 version View source on GitHub

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

The data will be looped over (in batches).

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

    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

    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

    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

    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 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 ba