image.py

"""Utilities for real-time data augmentation on image data.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import re
from six.moves import range
import os
import threading
import warnings
import multiprocessing.pool
from keras_preprocessing import get_keras_submodule

try:
    IteratorType = get_keras_submodule('utils').Sequence
except ImportError:
    IteratorType = object

try:
    from PIL import ImageEnhance
    from PIL import Image as pil_image
except ImportError:
    pil_image = None
    ImageEnhance = None

try:
    import scipy
    # scipy.linalg cannot be accessed until explicitly imported
    from scipy import linalg
    # scipy.ndimage cannot be accessed until explicitly imported
    from scipy import ndimage
except ImportError:
    scipy = None

if pil_image is not None:
    _PIL_INTERPOLATION_METHODS = {
        'nearest': pil_image.NEAREST,
        'bilinear': pil_image.BILINEAR,
        'bicubic': pil_image.BICUBIC,
    }
    # These methods were only introduced in version 3.4.0 (2016).
    if hasattr(pil_image, 'HAMMING'):
        _PIL_INTERPOLATION_METHODS['hamming'] = pil_image.HAMMING
    if hasattr(pil_image, 'BOX'):
        _PIL_INTERPOLATION_METHODS['box'] = pil_image.BOX
    # This method is new in version 1.1.3 (2013).
    if hasattr(pil_image, 'LANCZOS'):
        _PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS


def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0,
                    fill_mode='nearest', cval=0.):
    """Performs a random rotation of a Numpy image tensor.

    # Arguments
        x: Input tensor. Must be 3D.
        rg: Rotation range, in degrees.
        row_axis: Index of axis for rows in the input tensor.
        col_axis: Index of axis for columns in the input tensor.
        channel_axis: Index of axis for channels in the input tensor.
        fill_mode: Points outside the boundaries of the input
            are filled according to the given mode
            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
        cval: Value used for points outside the boundaries
            of the input if `mode='constant'`.

    # Returns
        Rotated Numpy image tensor.
    """
    theta = np.random.uniform(-rg, rg)
    x = apply_affine_transform(x, theta=theta, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval)
    return x


def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0,
                 fill_mode='nearest', cval=0.):
    """Performs a random spatial shift of a Numpy image tensor.

    # Arguments
        x: Input tensor. Must be 3D.
        wrg: Width shift range, as a float fraction of the width.
        hrg: Height shift range, as a float fraction of the height.
        row_axis: Index of axis for rows in the input tensor.
        col_axis: Index of axis for columns in the input tensor.
        channel_axis: Index of axis for channels in the input tensor.
        fill_mode: Points outside the boundaries of the input
            are filled according to the given mode
            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
        cval: Value used for points outside the boundaries
            of the input if `mode='constant'`.

    # Returns
        Shifted Numpy image tensor.
    """
    h, w = x.shape[row_axis], x.shape[col_axis]
    tx = np.random.uniform(-hrg, hrg) * h
    ty = np.random.uniform(-wrg, wrg) * w
    x = apply_affine_transform(x, tx=tx, ty=ty, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval)
    return x


def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0,
                 fill_mode='nearest', cval=0.):
    """Performs a random spatial shear of a Numpy image tensor.

    # Arguments
        x: Input tensor. Must be 3D.
        intensity: Transformation intensity in degrees.
        row_axis: Index of axis for rows in the input tensor.
        col_axis: Index of axis for columns in the input tensor.
        channel_axis: Index of axis for channels in the input tensor.
        fill_mode: Points outside the boundaries of the input
            are filled according to the given mode
            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
        cval: Value used for points outside the boundaries
            of the input if `mode='constant'`.

    # Returns
        Sheared Numpy image tensor.
    """
    shear = np.random.uniform(-intensity, intensity)
    x = apply_affine_transform(x, shear=shear, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval)
    return x


def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,
                fill_mode='nearest', cval=0.):
    """Performs a random spatial zoom of a Numpy image tensor.

    # Arguments
        x: Input tensor. Must be 3D.
        zoom_range: Tuple of floats; zoom range for width and height.
        row_axis: Index of axis for rows in the input tensor.
        col_axis: Index of axis for columns in the input tensor.
        channel_axis: Index of axis for channels in the input tensor.
        fill_mode: Points outside the boundaries of the input
            are filled according to the given mode
            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
        cval: Value used for points outside the boundaries
            of the input if `mode='constant'`.

    # Returns
        Zoomed Numpy image tensor.

    # Raises
        ValueError: if `zoom_range` isn't a tuple.
    """
    if len(zoom_range) != 2:
        raise ValueError('`zoom_range` should be a tuple or list of two'
                         ' floats. Received: %s' % (zoom_range,))

    if zoom_range[0] == 1 and zoom_range[1] == 1:
        zx, zy = 1, 1
    else:
        zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
    x = apply_affine_transform(x, zx=zx, zy=zy, channel_axis=channel_axis,
                               fill_mode=fill_mode, cval=cval)
    return x


def apply_channel_shift(x, intensity, channel_axis=0):
    """Performs a channel shift.

    # Arguments
        x: Input tensor. Must be 3D.
        intensity: Transformation intensity.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.

    """
    x = np.rollaxis(x, channel_axis, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [
        np.clip(x_channel + intensity,
                min_x,
                max_x)
        for x_channel in x]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_axis + 1)
    return x


def random_channel_shift(x, intensity_range, channel_axis=0):
    """Performs a random channel shift.

    # Arguments
        x: Input tensor. Must be 3D.
        intensity_range: Transformation intensity.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.
    """
    intensity = np.random.uniform(-intensity_range, intensity_range)
    return apply_channel_shift(x, intensity, channel_axis=channel_axis)


def apply_brightness_shift(x, brightness):
    """Performs a brightness shift.

    # Arguments
        x: Input tensor. Must be 3D.
        brightness: Float. The new brightness value.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.

    # Raises
        ValueError if `brightness_range` isn't a tuple.
    """
    if ImageEnhance is None:
        raise ImportError('Using brightness shifts requires PIL. '
                          'Install PIL or Pillow.')
    x = array_to_img(x)
    x = imgenhancer_Brightness = ImageEnhance.Brightness(x)
    x = imgenhancer_Brightness.enhance(brightness)
    x = img_to_array(x)
    return x


def random_brightness(x, brightness_range):
    """Performs a random brightness shift.

    # Arguments
        x: Input tensor. Must be 3D.
        brightness_range: Tuple of floats; brightness range.
        channel_axis: Index of axis for channels in the input tensor.

    # Returns
        Numpy image tensor.

    # Raises
        ValueError if `brightness_range` isn't a tuple.
    """
    if len(brightness_range) != 2:
        raise ValueError(
            '`brightness_range should be tuple or list of two floats. '
            'Received: %s' % (brightness_range,))

    u = np.random.uniform(brightness_range[0], brightness_range[1])
    return apply_brightness_shift(x, u)


def transform_matrix_offset_center(matrix, x, y):
    o_x = float(x) / 2 + 0.5
    o_y = float(y) / 2 + 0.5
    offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])
    reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])
    transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)
    return transform_matrix


def apply_affine_transform(x, theta=0, tx=0, ty=0, shear=0, zx=1, zy=1,
                           row_axis=0, col_axis=1, channel_axis=2,
                           fill_mode='nearest', cval=0.):
    """Applies an affine transformation specified by the parameters given.

    # Arguments
        x: 2D numpy array, single image.
        theta: Rotation angle in degrees.
        tx: Width shift.
        ty: Heigh shift.
        shear: Shear angle in degrees.
        zx: Zoom in x direction.
        zy: Zoom in y direction
        row_axis: Index of axis for rows in the input image.
        col_axis: Index of axis for columns in the input image.
        channel_axis: Index of axis for channels in the input image.
        fill_mode: Points outside the boundaries of the input
            are filled according to the given mode
            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).
        cval: Value used for points outside the boundaries
            of the input if `mode='constant'`.

    # Returns
        The transformed version of the input.
    """
    if scipy is None:
        raise ImportError('Image transformations require SciPy. '
                          'Install SciPy.')
    transform_matrix = None
    if theta != 0:
        theta = np.deg2rad(theta)
        rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
                                    [np.sin(theta), np.cos(theta), 0],
                                    [0, 0, 1]])
        transform_matrix = rotation_matrix

    if tx != 0 or ty != 0:
        shift_matrix = np.array([[1, 0, tx],
                                 [0, 1, ty],
                                 [0, 0, 1]])
        if transform_matrix is None:
            transform_matrix = shift_matrix
        else:
            transform_matrix = np.dot(transform_matrix, shift_matrix)

    if shear != 0:
        shear = np.deg2rad(shear)
        shear_matrix = np.array([[1, -np.sin(shear), 0],
                                 [0, np.cos(shear), 0],
                                 [0, 0, 1]])
        if transform_matrix is None:
            transform_matrix = shear_matrix
        else:
            transform_matrix = np.dot(transform_matrix, shear_matrix)

    if zx != 1 or zy != 1:
        zoom_matrix = np.array([[zx, 0, 0],
                                [0, zy, 0],
                                [0, 0, 1]])
        if transform_matrix is None:
            transform_matrix = zoom_matrix
        else:
            transform_matrix = np.dot(transform_matrix, zoom_matrix)

    if transform_matrix is not None:
        h, w = x.shape[row_axis], x.shape[col_axis]
        transform_matrix = transform_matrix_offset_center(
            transform_matrix, h, w)
        x = np.rollaxis(x, channel_axis, 0)
        final_affine_matrix = transform_matrix[:2, :2]
        final_offset = transform_matrix[:2, 2]

        channel_images = [scipy.ndimage.interpolation.affine_transform(
            x_channel,
            final_affine_matrix,
            final_offset,
            order=1,
            mode=fill_mode,
            cval=cval) for x_channel in x]
        x = np.stack(channel_images, axis=0)
        x = np.rollaxis(x, 0, channel_axis + 1)
    return x


def flip_axis(x, axis):
    x = np.asarray(x).swapaxes(axis, 0)
    x = x[::-1, ...]
    x = x.swapaxes(0, axis)
    return x


def array_to_img(x, data_format='channels_last', scale=True, dtype='float32'):
    """Converts a 3D Numpy array to a PIL Image instance.

    # Arguments
        x: Input Numpy array.
        data_format: Image data format.
            either "channels_first" or "channels_last".
        scale: Whether to rescale image values
            to be within `[0, 255]`.
        dtype: Dtype to use.

    # Returns
        A PIL Image instance.

    # Raises
        ImportError: if PIL is not available.
        ValueError: if invalid `x` or `data_format` is passed.
    """
    if pil_image is None:
        raise ImportError('Could not import PIL.Image. '
                          'The use of `array_to_img` requires PIL.')
    x = np.asarray(x, dtype=dtype)
    if x.ndim != 3:
        raise ValueError('Expected image array to have rank 3 (single image). '
                         'Got array with shape: %s' % (x.shape,))

    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Invalid data_format: %s' % data_format)

    # Original Numpy array x has format (height, width, channel)
    # or (channel, height, width)
    # but target PIL image has format (width, height, channel)
    if data_format == 'channels_first':
        x = x.transpose(1, 2, 0)
    if scale:
        x = x + max(-np.min(x), 0)
        x_max = np.max(x)
        if x_max != 0:
            x /= x_max
        x *= 255
    if x.shape[2] == 4:
        # RGBA
        return pil_image.fromarray(x.astype('uint8'), 'RGBA')
    elif x.shape[2] == 3:
        # RGB
        return pil_image.fromarray(x.astype('uint8'), 'RGB')
    elif x.shape[2] == 1:
        # grayscale
        return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')
    else:
        raise ValueError('Unsupported channel number: %s' % (x.shape[2],))


def img_to_array(img, data_format='channels_last', dtype='float32'):
    """Converts a PIL Image instance to a Numpy array.

    # Arguments
        img: PIL Image instance.
        data_format: Image data format,
            either "channels_first" or "channels_last".
        dtype: Dtype to use for the returned array.

    # Returns
        A 3D Numpy array.

    # Raises
        ValueError: if invalid `img` or `data_format` is passed.
    """
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format: %s' % data_format)
    # Numpy array x has format (height, width, channel)
    # or (channel, height, width)
    # but original PIL image has format (width, height, channel)
    x = np.asarray(img, dtype=dtype)
    if len(x.shape) == 3:
        if data_format == 'channels_first':
            x = x.transpose(2, 0, 1)
    elif len(x.shape) == 2:
        if data_format == 'channels_first':
            x = x.reshape((1, x.shape[0], x.shape[1]))
        else:
            x = x.reshape((x.shape[0], x.shape[1], 1))
    else:
        raise ValueError('Unsupported image shape: %s' % (x.shape,))
    return x


def save_img(path,
             x,
             data_format='channels_last',
             file_format=None,
             scale=True,
             **kwargs):
    """Saves an image stored as a Numpy array to a path or file object.

    # Arguments
        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()`.
    """
    img = array_to_img(x, data_format=data_format, scale=scale)
    if img.mode == 'RGBA' and (file_format == 'jpg' or file_format == 'jpeg'):
        warnings.warn('The JPG format does not support '
                      'RGBA images, converting to RGB.')
        img = img.convert('RGB')
    img.save(path, format=file_format, **kwargs)


def load_img(path, grayscale=False, color_mode='rgb', target_size=None,
             interpolation='nearest'):
    """Loads an image into PIL format.

    # Arguments
        path: Path to image file.
        color_mode: One of "grayscale", "rbg", "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.
    """
    if grayscale is True:
        warnings.warn('grayscale is deprecated. Please use '
                      'color_mode = "grayscale"')
        color_mode = 'grayscale'
    if pil_image is None:
        raise ImportError('Could not import PIL.Image. '
                          'The use of `array_to_img` requires PIL.')
    img = pil_image.open(path)
    if color_mode == 'grayscale':
        if img.mode != 'L':
            img = img.convert('L')
    elif color_mode == 'rgba':
        if img.mode != 'RGBA':
            img = img.convert('RGBA')
    elif color_mode == 'rgb':
        if img.mode != 'RGB':
            img = img.convert('RGB')
    else:
        raise ValueError('color_mode must be "grayscale", "rbg", or "rgba"')
    if target_size is not None:
        width_height_tuple = (target_size[1], target_size[0])
        if img.size != width_height_tuple:
            if interpolation not in _PIL_INTERPOLATION_METHODS:
                raise ValueError(
                    'Invalid interpolation method {} specified. Supported '
                    'methods are {}'.format(
                        interpolation,
                        ", ".join(_PIL_INTERPOLATION_METHODS.keys())))
            resample = _PIL_INTERPOLATION_METHODS[interpolation]
            img = img.resize(width_height_tuple, resample)
    return img


def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'):
    return [os.path.join(root, f)
            for root, _, files in os.walk(directory) for f in files
            if re.match(r'([\w]+\.(?:' + ext + '))', f.lower())]


class ImageDataGenerator(object):
    """Generate batches of tensor image data with real-time data augmentation.
     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 implied 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)`:

    ```python
    (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_generator(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)`:

    ```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_generator(
            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_generator(
        train_generator,
        steps_per_epoch=2000,
        epochs=50)
    ```

    Example of using ```.flow_from_dataframe(dataframe, directory,
                                            x_col, y_col,
                                            has_ext)```:

    ```python

    train_df = pandas.read_csv("./train.csv")
    valid_df = pandas.read_csv("./valid.csv")

    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_dataframe(
            dataframe=train_df,
            directory='data/train',
            x_col="filename",
            y_col="class",
            has_ext=True,
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')

    validation_generator = test_datagen.flow_from_dataframe(
            dataframe=valid_df,
            directory='data/validation',
            x_col="filename",
            y_col="class",
            has_ext=True,
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')

    model.fit_generator(
            train_generator,
            steps_per_epoch=2000,
            epochs=50,
            validation_data=validation_generator,
            validation_steps=800)
    ```
    """

    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='channels_last',
                 validation_split=0.0,
                 dtype='float32'):
        self.featurewise_center = featurewise_center
        self.samplewise_center = samplewise_center
        self.featurewise_std_normalization = featurewise_std_normalization
        self.samplewise_std_normalization = samplewise_std_normalization
        self.zca_whitening = zca_whitening
        self.zca_epsilon = zca_epsilon
        self.rotation_range = rotation_range
        self.width_shift_range = width_shift_range
        self.height_shift_range = height_shift_range
        self.brightness_range = brightness_range
        self.shear_range = shear_range
        self.zoom_range = zoom_range
        self.channel_shift_range = channel_shift_range
        self.fill_mode = fill_mode
        self.cval = cval
        self.horizontal_flip = horizontal_flip
        self.vertical_flip = vertical_flip
        self.rescale = rescale
        self.preprocessing_function = preprocessing_function
        self.dtype = dtype

        if data_format not in {'channels_last', 'channels_first'}:
            raise ValueError(
                '`data_format` should be `"channels_last"` '
                '(channel after row and column) or '
                '`"channels_first"` (channel before row and column). '
                'Received: %s' % data_format)
        self.data_format = data_format
        if data_format == 'channels_first':
            self.channel_axis = 1
            self.row_axis = 2
            self.col_axis = 3
        if data_format == 'channels_last':
            self.channel_axis = 3
            self.row_axis = 1
            self.col_axis = 2
        if validation_split and not 0 < validation_split < 1:
            raise ValueError(
                '`validation_split` must be strictly between 0 and 1. '
                ' Received: %s' % validation_split)
        self._validation_split = validation_split

        self.mean = None
        self.std = None
        self.principal_components = None

        if np.isscalar(zoom_range):
            self.zoom_range = [1 - zoom_range, 1 + zoom_range]
        elif len(zoom_range) == 2:
            self.zoom_range = [zoom_range[0], zoom_range[1]]
        else:
            raise ValueError('`zoom_range` should be a float or '
                             'a tuple or list of two floats. '
                             'Received: %s' % (zoom_range,))
        if zca_whitening:
            if not featurewise_center:
                self.featurewise_center = True
                warnings.warn('This ImageDataGenerator specifies '
                              '`zca_whitening`, which overrides '
                              'setting of `featurewise_center`.')
            if featurewise_std_normalization:
                self.featurewise_std_normalization = False
                warnings.warn('This ImageDataGenerator specifies '
                              '`zca_whitening` '
                              'which overrides setting of'
                              '`featurewise_std_normalization`.')
        if featurewise_std_normalization:
            if not featurewise_center:
                self.featurewise_center = True
                warnings.warn('This ImageDataGenerator specifies '
                              '`featurewise_std_normalization`, '
                              'which overrides setting of '
                              '`featurewise_center`.')
        if samplewise_std_normalization:
            if not samplewise_center:
                self.samplewise_center = True
                warnings.warn('This ImageDataGenerator specifies '
                              '`samplewise_std_normalization`, '
                              'which overrides setting of '
                              '`samplewise_center`.')

    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.

        # 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.
        """
        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.

        # Arguments
            directory: 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)`,
                default: `(256, 256)`.
                The dimensions to which all images found will be resized.
            color_mode: One of "grayscale", "rbg", "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_generator()`,
                  `model.evaluate_generator()`, etc.).
                  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)
            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"
                (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,
                            x_col="filename", y_col="class", has_ext=True,
                            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'):
        """Takes the dataframe and the path to a directory
         and generates batches of augmented/normalized data.

        # A simple tutorial can be found at: http://bit.ly/keras_flow_from_dataframe

        # Arguments
                dataframe: Pandas dataframe containing the filenames of the
                           images in a column and classes in another or column/s
                           that can be fed as raw target data.
                directory: string, path to the target directory that contains all
                           the images mapped in the dataframe.
                x_col: string, column in the dataframe that contains
                       the filenames of the target images.
                y_col: string or list of strings,columns in
                       the dataframe that will be the target data.
                has_ext: bool, True if filenames in dataframe[x_col]
                        has filename extensions,else False.
                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", "rbg". 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: 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 "categorical", "binary", "sparse",
                  "input", "other" 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).
                - `"other"` will be numpy array of y_col data
                 - None, no labels are returned (the generator will only
                         yield batches of image data, which is useful to use
                 `model.predict_generator()`, `model.evaluate_generator()`, etc.).
                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"
                (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 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.
        """

        return DataFrameIterator(dataframe, directory, self,
                                 x_col=x_col, y_col=y_col, has_ext=has_ext,
                                 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)

    def flow_video_from_dataframe(self, dataframe, directory,
                            x_col="filename", y_col="class", has_ext=True,
                            target_size=(256, 256), color_mode='rgb',
                            classes=None, path_classes=None, class_mode='categorical',
                            batch_size=32, nb_frames=1, shuffle=True, seed=None, skip=0,
                            save_to_dir=None,
                            save_prefix='',
                            save_format='png',
                            subset=None,
                            interpolation='nearest'):
        """Takes the dataframe and the path to a directory
         and generates batches of augmented/normalized data.

        # A simple tutorial can be found at: http://bit.ly/keras_flow_from_dataframe

        # Arguments
                dataframe: Pandas dataframe containing the filenames of the
                           images in a column and classes in another or column/s
                           that can be fed as raw target data.
                directory: string, path to the target directory that contains all
                           the images mapped in the dataframe.
                x_col: string, column in the dataframe that contains
                       the filenames of the target images.
                y_col: string or list of strings,columns in
                       the dataframe that will be the target data.
                has_ext: bool, True if filenames in dataframe[x_col]
                        has filename extensions,else False.
                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", "rbg". 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: 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 "categorical", "binary", "sparse",
                  "input", "other" 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).
                - `"other"` will be numpy array of y_col data
                 - None, no labels are returned (the generator will only
                         yield batches of image data, which is useful to use
                 `model.predict_generator()`, `model.evaluate_generator()`, etc.).
                batch_size: size of the batches of data (default: 32).
                nb_frames: Integer, number of frames to use for each video.
                shuffle: whether to shuffle the data (default: True)
                seed: optional random seed for shuffling and transformations.
                skip: Integer, number of frames to skip between each frame of the data video folder
                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"
                (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 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.
        """
        return DataFrameIterator(dataframe, directory, self,
                                 x_col=x_col, y_col=y_col, has_ext=has_ext,
                                 target_size=target_size, color_mode=color_mode,
                                 classes=classes, path_classes=path_classes, class_mode=class_mode,
                                 data_format=self.data_format,
                                 batch_size=batch_size, nb_frames=nb_frames, shuffle=shuffle, seed=seed, skip=skip,
                                 save_to_dir=save_to_dir,
                                 save_prefix=save_prefix,
                                 save_format=save_format,
                                 subset=subset,
                                 interpolation=interpolation)

    def standardize(self, x):
        """Applies the normalization configuration to a batch of inputs.

        # Arguments
            x: Batch of inputs to be normalized.

        # Returns
            The inputs, normalized.
        """
        if self.preprocessing_function:
            x = self.preprocessing_function(x)
        if self.rescale:
            x *= self.rescale
        if self.samplewise_center:
            x -= np.mean(x, keepdims=True)
        if self.samplewise_std_normalization:
            x /= (np.std(x, keepdims=True) + 1e-6)

        if self.featurewise_center:
            if self.mean is not None:
                x -= self.mean
            else:
                warnings.warn('This ImageDataGenerator specifies '
                              '`featurewise_center`, but it hasn\'t '
                              'been fit on any training data. Fit it '
                              'first by calling `.fit(numpy_data)`.')
        if self.featurewise_std_normalization:
            if self.std is not None:
                x /= (self.std + 1e-6)
            else:
                warnings.warn('This ImageDataGenerator specifies '
                              '`featurewise_std_normalization`, '
                              'but it hasn\'t '
                              'been fit on any training data. Fit it '
                              'first by calling `.fit(numpy_data)`.')
        if self.zca_whitening:
            if self.principal_components is not None:
                flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
                whitex = np.dot(flatx, self.principal_components)
                x = np.reshape(whitex, x.shape)
            else:
                warnings.warn('This ImageDataGenerator specifies '
                              '`zca_whitening`, but it hasn\'t '
                              'been fit on any training data. Fit it '
                              'first by calling `.fit(numpy_data)`.')
        return x

    def get_random_transform(self, img_shape, seed=None):
        """Generates random parameters for a transformation.

        # Arguments
            seed: Random seed.
            img_shape: Tuple of integers.
                Shape of the image that is transformed.

        # Returns
            A dictionary containing randomly chosen parameters describing the
            transformation.
        """
        img_row_axis = self.row_axis - 1
        img_col_axis = self.col_axis - 1

        if seed is not None:
            np.random.seed(seed)

        if self.rotation_range:
            theta = np.random.uniform(
                -self.rotation_range,
                self.rotation_range)
        else:
            theta = 0

        if self.height_shift_range:
            try:  # 1-D array-like or int
                tx = np.random.choice(self.height_shift_range)
                tx *= np.random.choice([-1, 1])
            except ValueError:  # floating point
                tx = np.random.uniform(-self.height_shift_range,
                                       self.height_shift_range)
            if np.max(self.height_shift_range) < 1:
                tx *= img_shape[img_row_axis]
        else:
            tx = 0

        if self.width_shift_range:
            try:  # 1-D array-like or int
                ty = np.random.choice(self.width_shift_range)
                ty *= np.random.choice([-1, 1])
            except ValueError:  # floating point
                ty = np.random.uniform(-self.width_shift_range,
                                       self.width_shift_range)
            if np.max(self.width_shift_range) < 1:
                ty *= img_shape[img_col_axis]
        else:
            ty = 0

        if self.shear_range:
            shear = np.random.uniform(
                -self.shear_range,
                self.shear_range)
        else:
            shear = 0

        if self.zoom_range[0] == 1 and self.zoom_range[1] == 1:
            zx, zy = 1, 1
        else:
            zx, zy = np.random.uniform(
                self.zoom_range[0],
                self.zoom_range[1],
                2)

        flip_horizontal = (np.random.random() < 0.5) * self.horizontal_flip
        flip_vertical = (np.random.random() < 0.5) * self.vertical_flip

        channel_shift_intensity = None
        if self.channel_shift_range != 0:
            channel_shift_intensity = np.random.uniform(-self.channel_shift_range,
                                                        self.channel_shift_range)

        brightness = None
        if self.brightness_range is not None:
            if len(self.brightness_range) != 2:
                raise ValueError(
                    '`brightness_range should be tuple or list of two floats. '
                    'Received: %s' % (self.brightness_range,))
            brightness = np.random.uniform(self.brightness_range[0],
                                           self.brightness_range[1])

        transform_parameters = {'theta': theta,
                                'tx': tx,
                                'ty': ty,
                                'shear': shear,
                                'zx': zx,
                                'zy': zy,
                                'flip_horizontal': flip_horizontal,
                                'flip_vertical': flip_vertical,
                                'channel_shift_intensity': channel_shift_intensity,
                                'brightness': brightness}

        return transform_parameters

    def apply_transform(self, 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_intencity'`: Float. Channel shift intensity.
                - `'brightness'`: Float. Brightness shift intensity.

        # Returns
            A transformed version of the input (same shape).
        """
        # x is a single image, so it doesn't have image number at index 0
        img_row_axis = self.row_axis - 1
        img_col_axis = self.col_axis - 1
        img_channel_axis = self.channel_axis - 1

        x = apply_affine_transform(x, transform_parameters.get('theta', 0),
                                   transform_parameters.get('tx', 0),
                                   transform_parameters.get('ty', 0),
                                   transform_parameters.get('shear', 0),
                                   transform_parameters.get('zx', 1),
                                   transform_parameters.get('zy', 1),
                                   row_axis=img_row_axis,
                                   col_axis=img_col_axis,
                                   channel_axis=img_channel_axis,
                                   fill_mode=self.fill_mode,
                                   cval=self.cval)

        if transform_parameters.get('channel_shift_intensity') is not None:
            x = apply_channel_shift(x,
                                    transform_parameters['channel_shift_intensity'],
                                    img_channel_axis)

        if transform_parameters.get('flip_horizontal', False):
            x = flip_axis(x, img_col_axis)

        if transform_parameters.get('flip_vertical', False):
            x = flip_axis(x, img_row_axis)

        if transform_parameters.get('brightness') is not None:
            x = apply_brightness_shift(x, transform_parameters['brightness'])

        return x

    def random_transform(self, x, seed=None):
        """Applies a random transformation to an image.

        # Arguments
            x: 3D tensor, single image.
            seed: Random seed.

        # Returns
            A randomly transformed version of the input (same shape).
        """
        params = self.get_random_transform(x.shape, seed)
        return self.apply_transform(x, params)

    def fit(self, 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.

        # 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.
       """
        x = np.asarray(x, dtype=self.dtype)
        if x.ndim != 4:
            raise ValueError('Input to `.fit()` should have rank 4. '
                             'Got array with shape: ' + str(x.shape))
        if x.shape[self.channel_axis] not in {1, 3, 4}:
            warnings.warn(
                'Expected input to be images (as Numpy array) '
                'following the data format convention "' +
                self.data_format + '" (channels on axis ' +
                str(self.channel_axis) + '), i.e. expected '
                'either 1, 3 or 4 channels on axis ' +
                str(self.channel_axis) + '. '
                'However, it was passed an array with shape ' +
                str(x.shape) + ' (' + str(x.shape[self.channel_axis]) +
                ' channels).')

        if seed is not None:
            np.random.seed(seed)

        x = np.copy(x)
        if augment:
            ax = np.zeros(
                tuple([rounds * x.shape[0]] + list(x.shape)[1:]),
                dtype=self.dtype)
            for r in range(rounds):
                for i in range(x.shape[0]):
                    ax[i + r * x.shape[0]] = self.random_transform(x[i])
            x = ax

        if self.featurewise_center:
            self.mean = np.mean(x, axis=(0, self.row_axis, self.col_axis))
            broadcast_shape = [1, 1, 1]
            broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis]
            self.mean = np.reshape(self.mean, broadcast_shape)
            x -= self.mean

        if self.featurewise_std_normalization:
            self.std = np.std(x, axis=(0, self.row_axis, self.col_axis))
            broadcast_shape = [1, 1, 1]
            broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis]
            self.std = np.reshape(self.std, broadcast_shape)
            x /= (self.std + 1e-6)

        if self.zca_whitening:
            if scipy is None:
                raise ImportError('Using zca_whitening requires SciPy. '
                                  'Install SciPy.')
            flat_x = np.reshape(
                x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
            sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0]
            u, s, _ = scipy.linalg.svd(sigma)
            s_inv = 1. / np.sqrt(s[np.newaxis] + self.zca_epsilon)
            self.principal_components = (u * s_inv).dot(u.T)


class Iterator(IteratorType):
    """Base class for image data iterators.

    Every `Iterator` must implement the `_get_batches_of_transformed_samples`
    method.

    # Arguments
        n: Integer, total number of samples in the dataset to loop over.
        batch_size: Integer, size of a batch.
        shuffle: Boolean, whether to shuffle the data between epochs.
        seed: Random seeding for data shuffling.
    """

    def __init__(self, n, batch_size, shuffle, seed):
        self.n = n
        self.batch_size = batch_size
        self.seed = seed
        self.shuffle = shuffle
        self.batch_index = 0
        self.total_batches_seen = 0
        self.lock = threading.Lock()
        self.index_array = None
        self.index_generator = self._flow_index()

    def _set_index_array(self):
        self.index_array = np.arange(self.n)
        if self.shuffle:
            self.index_array = np.random.permutation(self.n)

    def __getitem__(self, idx):
        if idx >= len(self):
            raise ValueError('Asked to retrieve element {idx}, '
                             'but the Sequence '
                             'has length {length}'.format(idx=idx,
                                                          length=len(self)))
        if self.seed is not None:
            np.random.seed(self.seed + self.total_batches_seen)
        self.total_batches_seen += 1
        if self.index_array is None:
            self._set_index_array()
        index_array = self.index_array[self.batch_size * idx:
                                       self.batch_size * (idx + 1)]
        return self._get_batches_of_transformed_samples(index_array)

    def common_init(self, image_data_generator,
                    target_size,
                    color_mode,
                    data_format,
                    save_to_dir,
                    save_prefix,
                    save_format,
                    subset,
                    interpolation):
        self.image_data_generator = image_data_generator
        self.target_size = tuple(target_size)
        if color_mode not in {'rgb', 'rgba', 'grayscale'}:
            raise ValueError('Invalid color mode:', color_mode,
                             '; expected "rgb", "rgba", or "grayscale".')
        self.color_mode = color_mode
        self.data_format = data_format
        if self.color_mode == 'rgba':
            if self.data_format == 'channels_last':
                self.image_shape = self.target_size + (4,)
            else:
                self.image_shape = (4,) + self.target_size
        elif self.color_mode == 'rgb':
            if self.data_format == 'channels_last':
                self.image_shape = self.target_size + (3,)
            else:
                self.image_shape = (3,) + self.target_size
        else:
            if self.data_format == 'channels_last':
                self.image_shape = self.target_size + (1,)
            else:
                self.image_shape = (1,) + self.target_size
        self.save_to_dir = save_to_dir
        self.save_prefix = save_prefix
        self.save_format = save_format
        self.interpolation = interpolation
        if subset is not None:
            validation_split = self.image_data_generator._validation_split
            if subset == 'validation':
                split = (0, validation_split)
            elif subset == 'training':
                split = (validation_split, 1)
            else:
                raise ValueError(
                    'Invalid subset name: %s;'
                    'expected "training" or "validation"' % (subset,))
        else:
            split = None
        self.split = split
        self.subset = subset

    def __len__(self):
        return (self.n + self.batch_size - 1) // self.batch_size  # round up

    def on_epoch_end(self):
        self._set_index_array()

    def reset(self):
        self.batch_index = 0

    def _flow_index(self):
        # Ensure self.batch_index is 0.
        self.reset()
        while 1:
            if self.seed is not None:
                np.random.seed(self.seed + self.total_batches_seen)
            if self.batch_index == 0:
                self._set_index_array()

            current_index = (self.batch_index * self.batch_size) % self.n
            if self.n > current_index + self.batch_size:
                self.batch_index += 1
            else:
                self.batch_index = 0
            self.total_batches_seen += 1
            yield self.index_array[current_index:
                                   current_index + self.batch_size]

    def __iter__(self):
        # Needed if we want to do something like:
        # for x, y in data_gen.flow(...):
        return self

    def __next__(self, *args, **kwargs):
        return self.next(*args, **kwargs)

    def _get_batches_of_transformed_samples(self, index_array):
        """Gets a batch of transformed samples.

        # Arguments
            index_array: Array of sample indices to include in batch.

        # Returns
            A batch of transformed samples.
        """
        raise NotImplementedError


class NumpyArrayIterator(Iterator):
    """Iterator yielding data from a Numpy array.

    # Arguments
        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='channels_last',
                 save_to_dir=None, save_prefix='', save_format='png',
                 subset=None, dtype='float32'):
        self.dtype = dtype
        if (type(x) is tuple) or (type(x) is list):
            if type(x[1]) is not list:
                x_misc = [np.asarray(x[1])]
            else:
                x_misc = [np.asarray(xx) for xx in x[1]]
            x = x[0]
            for xx in x_misc:
                if len(x) != len(xx):
                    raise ValueError(
                        'All of the arrays in `x` '
                        'should have the same length. '
                        'Found a pair with: len(x[0]) = %s, len(x[?]) = %s' %
                        (len(x), len(xx)))
        else:
            x_misc = []

        if y is not None and len(x) != len(y):
            raise ValueError('`x` (images tensor) and `y` (labels) '
                             'should have the same length. '
                             'Found: x.shape = %s, y.shape = %s' %
                             (np.asarray(x).shape, np.asarray(y).shape))
        if sample_weight is not None and len(x) != len(sample_weight):
            raise ValueError('`x` (images tensor) and `sample_weight` '
                             'should have the same length. '
                             'Found: x.shape = %s, sample_weight.shape = %s' %
                             (np.asarray(x).shape, np.asarray(sample_weight).shape))
        if subset is not None:
            if subset not in {'training', 'validation'}:
                raise ValueError('Invalid subset name:', subset,
                                 '; expected "training" or "validation".')
            split_idx = int(len(x) * image_data_generator._validation_split)

            if not np.array_equal(
                    np.unique(y[:split_idx]),
                    np.unique(y[split_idx:])):
                raise ValueError('Training and validation subsets '
                                 'have different number of classes after '
                                 'the split. If your numpy arrays are '
                                 'sorted by the label, you might want '
                                 'to shuffle them.')

            if subset == 'validation':
                x = x[:split_idx]
                x_misc = [np.asarray(xx[:split_idx]) for xx in x_misc]
                if y is not None:
                    y = y[:split_idx]
            else:
                x = x[split_idx:]
                x_misc = [np.asarray(xx[split_idx:]) for xx in x_misc]
                if y is not None:
                    y = y[split_idx:]

        self.x = np.asarray(x, dtype=self.dtype)
        self.x_misc = x_misc
        if self.x.ndim != 4:
            raise ValueError('Input data in `NumpyArrayIterator` '
                             'should have rank 4. You passed an array '
                             'with shape', self.x.shape)
        channels_axis = 3 if data_format == 'channels_last' else 1
        if self.x.shape[channels_axis] not in {1, 3, 4}:
            warnings.warn('NumpyArrayIterator is set to use the '
                          'data format convention "' + data_format + '" '
                          '(channels on axis ' + str(channels_axis) +
                          '), i.e. expected either 1, 3, or 4 '
                          'channels on axis ' + str(channels_axis) + '. '
                          'However, it was passed an array with shape ' +
                          str(self.x.shape) + ' (' +
                          str(self.x.shape[channels_axis]) + ' channels).')
        if y is not None:
            self.y = np.asarray(y)
        else:
            self.y = None
        if sample_weight is not None:
            self.sample_weight = np.asarray(sample_weight)
        else:
            self.sample_weight = None
        self.image_data_generator = image_data_generator
        self.data_format = data_format
        self.save_to_dir = save_to_dir
        self.save_prefix = save_prefix
        self.save_format = save_format
        super(NumpyArrayIterator, self).__init__(x.shape[0],
                                                 batch_size,
                                                 shuffle,
                                                 seed)

    def _get_batches_of_transformed_samples(self, index_array):
        batch_x = np.zeros(tuple([len(index_array)] + list(self.x.shape)[1:]),
                           dtype=self.dtype)
        for i, j in enumerate(index_array):
            x = self.x[j]
            params = self.image_data_generator.get_random_transform(x.shape)
            x = self.image_data_generator.apply_transform(
                x.astype(self.dtype), params)
            x = self.image_data_generator.standardize(x)
            batch_x[i] = x

        if self.save_to_dir:
            for i, j in enumerate(index_array):
                img = array_to_img(batch_x[i], self.data_format, scale=True)
                fname = '{prefix}_{index}_{hash}.{format}'.format(
                    prefix=self.save_prefix,
                    index=j,
                    hash=np.random.randint(1e4),
                    format=self.save_format)
                img.save(os.path.join(self.save_to_dir, fname))
        batch_x_miscs = [xx[index_array] for xx in self.x_misc]
        output = (batch_x if batch_x_miscs == []
                  else [batch_x] + batch_x_miscs,)
        if self.y is None:
            return output[0]
        output += (self.y[index_array],)
        if self.sample_weight is not None:
            output += (self.sample_weight[index_array],)
        return output

    def next(self):
        """For python 2.x.

        # Returns
            The next batch.
        """
        # Keeps under lock only the mechanism which advances
        # the indexing of each batch.
        with self.lock:
            index_array = next(self.index_generator)
        # The transformation of images is not under thread lock
        # so it can be done in parallel
        return self._get_batches_of_transformed_samples(index_array)


def _iter_valid_files(directory, white_list_formats, follow_links):
    """Iterates on files with extension in `white_list_formats` contained in `directory`.

    # Arguments
        directory: Absolute path to the directory
            containing files to be counted
        white_list_formats: Set of strings containing allowed extensions for
            the files to be counted.
        follow_links: Boolean.

    # Yields
        Tuple of (root, filename) with extension in `white_list_formats`.
    """
    def _recursive_list(subpath):
        return sorted(os.walk(subpath, followlinks=follow_links),
                      key=lambda x: x[0])

    for root, _, files in _recursive_list(directory):
        for fname in sorted(files):
            for extension in white_list_formats:
                if fname.lower().endswith('.tiff'):
                    warnings.warn('Using \'.tiff\' files with multiple bands '
                                  'will cause distortion. '
                                  'Please verify your output.')
                if fname.lower().endswith('.' + extension):
                    yield root, fname


def _list_valid_filenames_in_directory(directory, white_list_formats, split,
                                       class_indices, follow_links, df=False):
    """Lists paths of files in `subdir` with extensions in `white_list_formats`.

    # Arguments
        directory: absolute path to a directory containing the files to list.
            The directory name is used as class label
            and must be a key of `class_indices`.
        white_list_formats: set of strings containing allowed extensions for
            the files to be counted.
        split: tuple of floats (e.g. `(0.2, 0.6)`) to only take into
            account a certain fraction of files in each directory.
            E.g.: `segment=(0.6, 1.0)` would only account for last 40 percent
            of images in each directory.
        class_indices: dictionary mapping a class name to its index.
        follow_links: boolean.
        df: boolean

    # Returns
        classes: a list of class indices(returns only if `df=False`)
        filenames: if `df=False`,returns the path of valid files in `directory`,
            relative from `directory`'s parent (e.g., if `directory` is
            "dataset/class1", the filenames will be
            `["class1/file1.jpg", "class1/file2.jpg", ...]`).
            if `df=True`, returns only the filenames that are found inside the
             provided directory (e.g., if `directory` is
            "dataset/", the filenames will be
            `["file1.jpg", "file2.jpg", ...]`).
    """
    dirname = os.path.basename(directory)
    if split:
        num_files = len(list(
            _iter_valid_files(directory, white_list_formats, follow_links)))
        start, stop = int(split[0] * num_files), int(split[1] * num_files)
        valid_files = list(
            _iter_valid_files(
                directory, white_list_formats, follow_links))[start: stop]
    else:
        valid_files = _iter_valid_files(
            directory, white_list_formats, follow_links)
    if df:
        filenames = []
        for root, fname in valid_files:
            filenames.append(os.path.basename(fname))
        return filenames
    classes = []
    filenames = []
    for root, fname in valid_files:
        classes.append(class_indices[dirname])
        absolute_path = os.path.join(root, fname)
        relative_path = os.path.join(
            dirname, os.path.relpath(absolute_path, directory))
        filenames.append(relative_path)

    return classes, filenames


class DirectoryIterator(Iterator):
    """Iterator capable of reading images from a directory on disk.

    # Arguments
        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='channels_last',
                 save_to_dir=None, save_prefix='', save_format='png',
                 follow_links=False,
                 subset=None,
                 interpolation='nearest',
                 dtype='float32'):
        super(DirectoryIterator, self).common_init(image_data_generator,
                                                   target_size,
                                                   color_mode,
                                                   data_format,
                                                   save_to_dir,
                                                   save_prefix,
                                                   save_format,
                                                   subset,
                                                   interpolation)
        self.directory = directory
        self.classes = classes
        if class_mode not in {'categorical', 'binary', 'sparse',
                              'input', None}:
            raise ValueError('Invalid class_mode:', class_mode,
                             '; expected one of "categorical", '
                             '"binary", "sparse", "input"'
                             ' or None.')
        self.class_mode = class_mode
        self.dtype = dtype
        white_list_formats = {'png', 'jpg', 'jpeg', 'bmp',
                              'ppm', 'tif', 'tiff'}
        # First, count the number of samples and classes.
        self.samples = 0

        if not classes:
            classes = []
            for subdir in sorted(os.listdir(directory)):
                if os.path.isdir(os.path.join(directory, subdir)):
                    classes.append(subdir)
        self.num_classes = len(classes)
        self.class_indices = dict(zip(classes, range(len(classes))))

        pool = multiprocessing.pool.ThreadPool()

        # Second, build an index of the images
        # in the different class subfolders.
        results = []
        self.filenames = []
        i = 0
        for dirpath in (os.path.join(directory, subdir) for subdir in classes):
            results.append(
                pool.apply_async(_list_valid_filenames_in_directory,
                                 (dirpath, white_list_formats, self.split,
                                  self.class_indices, follow_links)))
        classes_list = []
        for res in results:
            classes, filenames = res.get()
            classes_list.append(classes)
            self.filenames += filenames
        self.samples = len(self.filenames)
        self.classes = np.zeros((self.samples,), dtype='int32')
        for classes in classes_list:
            self.classes[i:i + len(classes)] = classes
            i += len(classes)

        print('Found %d images belonging to %d classes.' %
              (self.samples, self.num_classes))
        pool.close()
        pool.join()
        super(DirectoryIterator, self).__init__(self.samples,
                                                batch_size,
                                                shuffle,
                                                seed)

    def _get_batches_of_transformed_samples(self, index_array):
        batch_x = np.zeros(
            (len(index_array),) + self.image_shape,
            dtype=self.dtype)
        # build batch of image data
        for i, j in enumerate(index_array):
            fname = self.filenames[j]
            img = load_img(os.path.join(self.directory, fname),
                           color_mode=self.color_mode,
                           target_size=self.target_size,
                           interpolation=self.interpolation)
            x = img_to_array(img, data_format=self.data_format)
            # Pillow images should be closed after `load_img`,
            # but not PIL images.
            if hasattr(img, 'close'):
                img.close()
            params = self.image_data_generator.get_random_transform(x.shape)
            x = self.image_data_generator.apply_transform(x, params)
            x = self.image_data_generator.standardize(x)
            batch_x[i] = x
        # optionally save augmented images to disk for debugging purposes
        if self.save_to_dir:
            for i, j in enumerate(index_array):
                img = array_to_img(batch_x[i], self.data_format, scale=True)
                fname = '{prefix}_{index}_{hash}.{format}'.format(
                    prefix=self.save_prefix,
                    index=j,
                    hash=np.random.randint(1e7),
                    format=self.save_format)
                img.save(os.path.join(self.save_to_dir, fname))
        # build batch of labels
        if self.class_mode == 'input':
            batch_y = batch_x.copy()
        elif self.class_mode == 'sparse':
            batch_y = self.classes[index_array]
        elif self.class_mode == 'binary':
            batch_y = self.classes[index_array].astype(self.dtype)
        elif self.class_mode == 'categorical':
            batch_y = np.zeros(
                (len(batch_x), self.num_classes),
                dtype=self.dtype)
            for i, label in enumerate(self.classes[index_array]):
                batch_y[i, label] = 1.
        else:
            return batch_x
        return batch_x, batch_y

    def next(self):
        """For python 2.x.

        # Returns
            The next batch.
        """
        with self.lock:
            index_array = next(self.index_generator)
        # The transformation of images is not under thread lock
        # so it can be done in parallel
        return self._get_batches_of_transformed_samples(index_array)


class DataFrameIterator(Iterator):
    """Iterator capable of reading images from a directory on disk
        through a dataframe.

    # Arguments
        dataframe: Pandas dataframe containing the filenames of the
                   images in a column and classes in another or column/s
                   that can be fed as raw target data.
        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.
            if used with dataframe,this will be the directory to under which
            all the images are present.
        image_data_generator: Instance of `ImageDataGenerator`
            to use for random transformations and normalization.
        x_col: Column in dataframe that contains all the filenames.
        y_col: Column/s in dataframe that has the target data.
        has_ext: bool, Whether the filenames in x_col has extensions or not.
        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
            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),
            `"other"`: targets are the data(numpy array) of y_col data
            `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.
        skip: Integer, number of frames to skip between each frame of the data video folder
        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.
    """

    def __init__(self, dataframe, directory, image_data_generator,
                 x_col="filenames", y_col="class", has_ext=True,
                 target_size=(256, 256), color_mode='rgb',
                 classes=None, path_classes=None, class_mode='categorical',
                 batch_size=32, nb_frames=None, shuffle=True, seed=None, skip=0,
                 data_format=None,
                 save_to_dir=None, save_prefix='', save_format='png',
                 follow_links=False,
                 subset=None,
                 interpolation='nearest',
                 dtype='float32'):
        super(DataFrameIterator, self).common_init(image_data_generator,
                                                   target_size,
                                                   color_mode,
                                                   data_format,
                                                   save_to_dir,
                                                   save_prefix,
                                                   save_format,
                                                   subset,
                                                   interpolation)
        try:
            import pandas as pd
        except ImportError:
            raise ImportError('Install pandas to use flow_from_dataframe.')
        if type(x_col) != str:
            raise ValueError("x_col must be a string.")
        if type(has_ext) != bool:
            raise ValueError("has_ext must be either True if filenames in"
                             " x_col has extensions,else False.")
        
        self.df = dataframe.drop_duplicates(x_col)
        self.df[x_col] = self.df[x_col].astype(str)

        self.classes_df = pd.read_csv(path_classes, names=['label'])
        #Extracting list of classes from the dataframe
        self.classes = [str(cl[0]) for cl in self.classes_df.values]
        self.nb_classes = len(self.classes)
        self.classes_indices = dict(zip(self.classes, range(self.nb_classes)))

        self.x_col = x_col
        self.y_col = y_col
        self.directory = directory

        if class_mode not in {'categorical', 'binary', 'sparse',
                              'input', 'other', None}:
            raise ValueError('Invalid class_mode:', class_mode,
                             '; expected one of "categorical", '
                             '"binary", "sparse", "input"'
                             '"other" or None.')
        self.class_mode = class_mode
        self.nb_frames = nb_frames
        self.skip = skip
        self.dtype = dtype

        filenames=dataframe[x_col].tolist()
        self.filenames = list(map(str, filenames))


        self.samples = len(self.filenames)
        if self.nb_classes > 0:
            print('Found %d video folders belonging to %d classes.' %
                  (self.samples, self.nb_classes))
        else:
            print('Found %d video folders.' % self.samples)

        super(DataFrameIterator, self).__init__(self.samples,
                                                batch_size,
                                                shuffle,
                                                seed)

    def _get_batches_of_transformed_samples(self, index_array):
        batch_x = np.zeros(
            (len(index_array), self.nb_frames) + self.image_shape,
            dtype=self.dtype)
        # build batch of image data
        for i, j in enumerate(index_array):
            fname = self.filenames[j]
            folder = os.path.join(self.directory, fname)
            files = sorted(os.listdir(folder))
            #TODO: At the moment the number of image sample per video is given as parameter and 
            #TODO: if there are not enough frames, the remaining space will stay empty ( full of 0 )
            for idx in range(min(len(files)//(1+self.skip),self.nb_frames)):
                path_img = os.path.join(folder, files[idx*(1+self.skip)])
                img = load_img(path_img,
                               color_mode=self.color_mode,
                               target_size=self.target_size,
                               interpolation=self.interpolation)
                x = img_to_array(img, data_format=self.data_format)
                # Pillow images should be closed after `load_img`,
                # but not PIL images.
                if hasattr(img, 'close'):
                    img.close()
                params = self.image_data_generator.get_random_transform(x.shape)
                x = self.image_data_generator.apply_transform(x, params)
                x = self.image_data_generator.standardize(x)
                batch_x[i,idx] = x/255
        # optionally save augmented images to disk for debugging purposes
        if self.save_to_dir:
            for i, j in enumerate(index_array):
                for idx in range(self.nb_frames):
                    img = array_to_img(batch_x[i,idx], self.data_format, scale=True)
                    fname = '{prefix}_{index}_{hash}.{format}'.format(
                        prefix=self.save_prefix,
                        index=j,
                        hash=np.random.randint(1e7),
                        format=self.save_format)
                    img.save(os.path.join(self.save_to_dir, self.filenames[j], fname))

        # build batch of labels
        if self.class_mode == 'input':
            batch_y = batch_x.copy()
        elif self.class_mode == 'sparse':
            batch_y = self.classes[index_array]
        elif self.class_mode == 'binary':
            batch_y = self.classes[index_array].astype(self.dtype)
        elif self.class_mode == 'categorical':
            batch_y = np.zeros(
                (len(batch_x), self.nb_classes),
                dtype=self.dtype)

            for i, label in enumerate(self.df.iloc[index_array][self.y_col].values):
                batch_y[i, self.classes_indices[label]] = 1

        elif self.class_mode == 'other':
            batch_y = self.data[index_array]
        else:
            return batch_x
        return batch_x, batch_y

    def next(self):
        """For python 2.x.

        # Returns
            The next batch.
        """
        with self.lock:
            index_array = next(self.index_generator)
        # The transformation of images is not under thread lock
        # so it can be done in parallel
        return self._get_batches_of_transformed_samples(index_array)