"""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 scipy import linalg

import scipy.ndimage as ndi

from six.moves import range

import os

import threading

import warnings

import multiprocessing.pool

import cv2

from functools import partial

from skimage import data, img_as_float

from skimage import exposure

from . import get_keras_submodule

backend = get_keras_submodule('backend')

keras_utils = get_keras_submodule('utils')

try:

    from PIL import ImageEnhance

    from PIL import Image as pil_image

except ImportError:

    pil_image = None

if pil_image is not None:

    _PIL_INTERPOLATION_METHODS = {

        'nearest': pil_image.NEAREST,

        'bilinear': pil_image.BILINEAR,

        'bicubic': pil_image.BICUBIC,

        'antialias' : pil_image.ANTIALIAS,

    }

    # 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: ', 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.

    """

    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.

    """

    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 = [ndi.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 rgb2gray(rgb):

    r,g,b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]

    gray = 0.2989* r + 0.5870*g + 0.1140*b

    return gray

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=None, scale=True):

    """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]`.

    # 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=backend.floatx())

    if x.ndim != 3:

        raise ValueError('Expected image array to have rank 3 (single image). '

                         'Got array with shape:', x.shape)

    if data_format is None:

        data_format = backend.image_data_format()

    if data_format not in {'channels_first', 'channels_last'}:

        raise ValueError('Invalid data_format:', 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] == 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: ', x.shape[2])

def img_to_array(img, data_format=None):

    """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".

    # Returns

        A 3D Numpy array.

    # Raises

        ValueError: if invalid `img` or `data_format` is passed.

    """

    if data_format is None:

        data_format = backend.image_data_format()

    if data_format not in {'channels_first', 'channels_last'}:

        raise ValueError('Unknown data_format: ', 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=backend.floatx())

    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: ', x.shape)

    return x

def save_img(path,

             x,

             data_format=None,

             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)

    img.save(path, format=file_format, **kwargs)

def load_img(path, grayscale=False, target_size=None,

             interpolation='nearest'):  #nearest

    """Loads an image into PIL format.

    # Arguments

        path: Path to image file.

        grayscale: Boolean, whether to load the image as grayscale.

        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 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 grayscale:

        if img.mode != 'L':

            img = img.convert('L')

    else:

        if img.mode != 'RGB':

            img = img.convert('RGB')

    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).

        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

            (before applying any other transformation).

        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).

    # 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)

    ```

    """

    def __init__(self,

                 contrast_stretching=False,

                 histogram_equalization=False,

                 adaptive_equalization=False,

                 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=None,

                 validation_split=0.0):

        if data_format is None:

            data_format = backend.image_data_format()

        self.contrast_stretching = contrast_stretching

        self.histogram_equalization = histogram_equalization

        self.adaptive_equalization = adaptive_equalization

        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

        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, and in case

                of RGB data, it should have value 3.

            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". Default: "rgb".

                Whether the images will be converted to

                have 1 or 3 color 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 standardize(self, x):

        """Applies the normalization configuration to a batch of inputs.

        # Arguments

            x: Batch of inputs to be normalized.

        # Returns

            The inputs, normalized.

        """

        imagenet_mean = np.array([0.485, 0.456, 0.406])

        imagenet_std  = np.array([0.229, 0.224, 0.225])

        if self.rescale:

            x *= self.rescale

        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) + backend.epsilon())

        #x = (x - imagenet_mean) / imagenet_std

        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 + backend.epsilon())

            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 ')

#        if self.contrast_stretching:

#            if np.random.random() < 0.5:

#                p2, p98 = np.percentile((x),(2,98))

#                x = (exposure.rescale_intensity((x), in_range=(p2, p98)))

     #   if self.adaptive_equalization:

     #       if np.random.random() < 0.5:

     #               x = (exposure.equalize_adapthist((x), clip_limit = 0.03))

     #   if self.histogram_equalization:

     #       if np.random.random() < 0.5:

     #               x = (exposure.equalize_hist((x)))

        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' % 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,

                                'contrast_stretching' : self.contrast_stretching,

                                'adaptive_equalization' : self.adaptive_equalization,

                                'histogram_equalization' : self.histogram_equalization

                                }

        return transform_parameters

    def apply_transform(self, x, transform_parameters):

        """Applies a transformation to an image according to given parameters.

        # Arguments