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

from .. import backend
from .. import utils
from ..utils import generic_utils

from keras_preprocessing import image

random_rotation = image.random_rotation
random_shift = image.random_shift
random_shear = image.random_shear
random_zoom = image.random_zoom
apply_channel_shift = image.apply_channel_shift
random_channel_shift = image.random_channel_shift
apply_brightness_shift = image.apply_brightness_shift
random_brightness = image.random_brightness
apply_affine_transform = image.apply_affine_transform
load_img = image.load_img


def array_to_img(x, data_format=None, scale=True, dtype=None):
    if data_format is None:
        data_format = backend.image_data_format()
    if dtype is None:
        dtype = backend.floatx()
    return image.array_to_img(x,
                              data_format=data_format,
                              scale=scale,
                              dtype=dtype)


def img_to_array(img, data_format=None, dtype=None):
    if data_format is None:
        data_format = backend.image_data_format()
    if dtype is None:
        dtype = backend.floatx()
    return image.img_to_array(img, data_format=data_format, dtype=dtype)


def save_img(path,
             x,
             data_format=None,
             file_format=None,
             scale=True, **kwargs):
    if data_format is None:
        data_format = backend.image_data_format()
    return image.save_img(path,
                          x,
                          data_format=data_format,
                          file_format=file_format,
                          scale=scale, **kwargs)


class Iterator(image.Iterator, utils.Sequence):
    pass


class DirectoryIterator(image.DirectoryIterator, Iterator):
    __doc__ = image.DirectoryIterator.__doc__

    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=None,
                 save_to_dir=None,
                 save_prefix='',
                 save_format='png',
                 follow_links=False,
                 subset=None,
                 interpolation='nearest',
                 dtype=None):
        if data_format is None:
            data_format = backend.image_data_format()
        if dtype is None:
            dtype = backend.floatx()
        super(DirectoryIterator, self).__init__(
            directory, image_data_generator,
            target_size=target_size,
            color_mode=color_mode,
            classes=classes,
            class_mode=class_mode,
            batch_size=batch_size,
            shuffle=shuffle,
            seed=seed,
            data_format=data_format,
            save_to_dir=save_to_dir,
            save_prefix=save_prefix,
            save_format=save_format,
            follow_links=follow_links,
            subset=subset,
            interpolation=interpolation,
            dtype=dtype)


class NumpyArrayIterator(image.NumpyArrayIterator, Iterator):
    __doc__ = image.NumpyArrayIterator.__doc__

    def __init__(self, x, y, image_data_generator,
                 batch_size=32,
                 shuffle=False,
                 sample_weight=None,
                 seed=None,
                 data_format=None,
                 save_to_dir=None,
                 save_prefix='',
                 save_format='png',
                 subset=None,
                 dtype=None):
        if data_format is None:
            data_format = backend.image_data_format()
        if dtype is None:
            dtype = backend.floatx()
        super(NumpyArrayIterator, self).__init__(
            x, y, image_data_generator,
            batch_size=batch_size,
            shuffle=shuffle,
            sample_weight=sample_weight,
            seed=seed,
            data_format=data_format,
            save_to_dir=save_to_dir,
            save_prefix=save_prefix,
            save_format=save_format,
            subset=subset,
            dtype=dtype)


class DataFrameIterator(image.DataFrameIterator, Iterator):
    __doc__ = image.DataFrameIterator.__doc__

    def __init__(self,
                 dataframe,
                 directory=None,
                 image_data_generator=None,
                 x_col='filename',
                 y_col='class',
                 weight_col=None,
                 target_size=(256, 256),
                 color_mode='rgb',
                 classes=None,
                 class_mode='categorical',
                 batch_size=32,
                 shuffle=True,
                 seed=None,
                 data_format='channels_last',
                 save_to_dir=None,
                 save_prefix='',
                 save_format='png',
                 subset=None,
                 interpolation='nearest',
                 dtype='float32',
                 validate_filenames=True):
        if data_format is None:
            data_format = backend.image_data_format()
        if dtype is None:
            dtype = backend.floatx()
        super(DataFrameIterator, self).__init__(
            dataframe,
            directory=directory,
            image_data_generator=image_data_generator,
            x_col=x_col,
            y_col=y_col,
            weight_col=weight_col,
            target_size=target_size,
            color_mode=color_mode,
            classes=classes,
            class_mode=class_mode,
            batch_size=batch_size,
            shuffle=shuffle,
            seed=seed,
            data_format=data_format,
            save_to_dir=save_to_dir,
            save_prefix=save_prefix,
            save_format=save_format,
            subset=subset,
            interpolation=interpolation,
            dtype=dtype,
            validate_filenames=validate_filenames)


class ImageDataGenerator(image.ImageDataGenerator):
    __doc__ = image.ImageDataGenerator.__doc__

    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,
                 interpolation_order=1,
                 dtype='float32'):
        if data_format is None:
            data_format = backend.image_data_format()
        if dtype is None:
            dtype = backend.floatx()
        super(ImageDataGenerator, self).__init__(
            featurewise_center=featurewise_center,
            samplewise_center=samplewise_center,
            featurewise_std_normalization=featurewise_std_normalization,
            samplewise_std_normalization=samplewise_std_normalization,
            zca_whitening=zca_whitening,
            zca_epsilon=zca_epsilon,
            rotation_range=rotation_range,
            width_shift_range=width_shift_range,
            height_shift_range=height_shift_range,
            brightness_range=brightness_range,
            shear_range=shear_range,
            zoom_range=zoom_range,
            channel_shift_range=channel_shift_range,
            fill_mode=fill_mode,
            cval=cval,
            horizontal_flip=horizontal_flip,
            vertical_flip=vertical_flip,
            rescale=rescale,
            preprocessing_function=preprocessing_function,
            data_format=data_format,
            validation_split=validation_split,
            interpolation_order=interpolation_order,
            dtype=dtype)

    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: string, 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", "rgb", "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()`).
                  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)
                If set to False, sorts the data in alphanumeric order.
            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=None,
                            x_col="filename",
                            y_col="class",
                            weight_col=None,
                            target_size=(256, 256),
                            color_mode='rgb',
                            classes=None,
                            class_mode='categorical',
                            batch_size=32,
                            shuffle=True,
                            seed=None,
                            save_to_dir=None,
                            save_prefix='',
                            save_format='png',
                            subset=None,
                            interpolation='nearest',
                            validate_filenames=True,
                            **kwargs):
        """Takes the dataframe and the path to a directory
         and generates batches of augmented/normalized data.

        **A simple tutorial can be found **[here](
                                    http://bit.ly/keras_flow_from_dataframe).

        # Arguments
            dataframe: Pandas dataframe containing the filepaths relative to
                `directory` (or absolute paths if `directory` is None) of the
                images in a string column. It should include other column/s
                depending on the `class_mode`:
                - if `class_mode` is `"categorical"` (default value) it must
                    include the `y_col` column with the class/es of each image.
                    Values in column can be string/list/tuple if a single class
                    or list/tuple if multiple classes.
                - if `class_mode` is `"binary"` or `"sparse"` it must include
                    the given `y_col` column with class values as strings.
                - if `class_mode` is `"raw"` or `"multi_output"` it should contain
                the columns specified in `y_col`.
                - if `class_mode` is `"input"` or `None` no extra column is needed.
            directory: string, path to the directory to read images from. If `None`,
                data in `x_col` column should be absolute paths.
            x_col: string, column in `dataframe` that contains the filenames (or
                absolute paths if `directory` is `None`).
            y_col: string or list, column/s in `dataframe` that has the target data.
            weight_col: string, column in `dataframe` that contains the sample
                weights. Default: `None`.
            target_size: tuple of integers `(height, width)`, default: `(256, 256)`.
                The dimensions to which all images found will be resized.
            color_mode: one of "grayscale", "rgb", "rgba". Default: "rgb".
                Whether the images will be converted to have 1 or 3 color channels.
            classes: optional list of classes (e.g. `['dogs', 'cats']`).
                Default: None. If not provided, the list of classes will be
                automatically inferred from the `y_col`,
                which will map to the label indices, will be alphanumeric).
                The dictionary containing the mapping from class names to class
                indices can be obtained via the attribute `class_indices`.
            class_mode: one of "binary", "categorical", "input", "multi_output",
                "raw", sparse" or None. Default: "categorical".
                Mode for yielding the targets:
                - `"binary"`: 1D numpy array of binary labels,
                - `"categorical"`: 2D numpy array of one-hot encoded labels.
                    Supports multi-label output.
                - `"input"`: images identical to input images (mainly used to
                    work with autoencoders),
                - `"multi_output"`: list with the values of the different columns,
                - `"raw"`: numpy array of values in `y_col` column(s),
                - `"sparse"`: 1D numpy array of integer labels,
                - `None`, no targets are returned (the generator will only yield
                    batches of image data, which is useful to use in
                    `model.predict_generator()`).
            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.
            validate_filenames: Boolean, whether to validate image filenames in
                `x_col`. If `True`, invalid images will be ignored. Disabling this
                option can lead to speed-up in the execution of this function.
                Default: `True`.

        # 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,
            weight_col=weight_col,
            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,
            validate_filenames=validate_filenames,
            **kwargs
        )


array_to_img.__doc__ = image.array_to_img.__doc__
img_to_array.__doc__ = image.img_to_array.__doc__
save_img.__doc__ = image.save_img.__doc__
