import cv2
import random
import numbers
from collections.abc import Iterable

import data.transforms.functional_cv as F

_cv_interpolation_to_str = {
    cv2.INTER_NEAREST: 'cv2.INTER_NEAREST',
    cv2.INTER_LINEAR: 'INTER_LINEAR',
    cv2.INTER_AREA: 'INTER_AREA',
    cv2.INTER_CUBIC: 'INTER_CUBIC',
    cv2.INTER_LANCZOS4: 'INTER_LANCZOS4',
}


class Compose(object):
    """Composes several transforms together.
    Args:
        transforms (list of ``Transform`` objects): list of transforms to compose.
    Example:
        >>> transforms.Compose([
        >>>     transforms.CenterCrop(10),
        >>>     transforms.ToTensor(),
        >>> ])
    """

    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, img):
        for t in self.transforms:
            img = t(img)
        return img

    def __repr__(self):
        format_string = self.__class__.__name__ + '('
        for t in self.transforms:
            format_string += '\n'
            format_string += '    {0}'.format(t)
        format_string += '\n)'
        return format_string


class Lambda(object):
    """Apply a user-defined lambda as a transform.

    Args:
        lambd (function): Lambda/function to be used for transform.
    """

    def __init__(self, lambd):
        assert callable(lambd), repr(type(lambd).__name__) + " object is not callable"
        self.lambd = lambd

    def __call__(self, img):
        return self.lambd(img)

    def __repr__(self):
        return self.__class__.__name__ + '()'


class ToTensor(object):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
    Converts a PIL Image or numpy.ndarray (H x W x C) in the range
    [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
    if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
    or if the numpy.ndarray has dtype = np.uint8
    In the other cases, tensors are returned without scaling.
    """

    def __call__(self, pic):
        """
        Args:
            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
        Returns:
            Tensor: Converted image.
        """
        return F.to_tensor(pic)

    def __repr__(self):
        return self.__class__.__name__ + '()'


class Normalize(object):
    """Normalize a tensor image with mean and standard deviation.
    Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
    will normalize each channel of the input ``torch.*Tensor`` i.e.
    ``input[channel] = (input[channel] - mean[channel]) / std[channel]``
    .. note::
        This transform acts out of place, i.e., it does not mutates the input tensor.
    Args:
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channel.
    """

    def __init__(self, mean, std, inplace=False):
        self.mean = mean
        self.std = std
        self.inplace = inplace

    def __call__(self, tensor):
        """
        Args:
            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        Returns:
            Tensor: Normalized Tensor image.
        """
        return F.normalize(tensor, self.mean, self.std)

    def __repr__(self):
        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)


class Resize(object):
    """Resize the input PIL Image to the given size.
    Args:
        size (sequence or int): Desired output size. If size is a sequence like
            (h, w), output size will be matched to this. If size is an int,
            smaller edge of the image will be matched to this number.
            i.e, if height > width, then image will be rescaled to
            (size * height / width, size)
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``
    """

    def __init__(self, size, interpolation=cv2.INTER_LINEAR):
        assert isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2)
        self.size = size
        self.interpolation = interpolation

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be scaled.
        Returns:
            PIL Image: Rescaled image.
        """
        return F.resize(img, self.size, self.interpolation)

    def __repr__(self):
        interpolate_str = _cv_interpolation_to_str[self.interpolation]
        return self.__class__.__name__ + '(size={0}, interpolation={1})'.format(self.size, interpolate_str)


class CenterCrop(object):
    """Crops the given PIL Image at the center.
    Args:
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made.
    """

    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be cropped.
        Returns:
            PIL Image: Cropped image.
        """
        return F.center_crop(img, self.size)

    def __repr__(self):
        return self.__class__.__name__ + '(size={0})'.format(self.size)


class ColorJitter(object):
    """Randomly change the brightness, contrast and saturation of an image.
    Args:
        brightness (float or tuple of float (min, max)): How much to jitter brightness.
            brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
            or the given [min, max]. Should be non negative numbers.
        contrast (float or tuple of float (min, max)): How much to jitter contrast.
            contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
            or the given [min, max]. Should be non negative numbers.
        saturation (float or tuple of float (min, max)): How much to jitter saturation.
            saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
            or the given [min, max]. Should be non negative numbers.
        hue (float or tuple of float (min, max)): How much to jitter hue.
            hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
            Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
    """

    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
        self.brightness = self._check_input(brightness, 'brightness')
        self.contrast = self._check_input(contrast, 'contrast')
        self.saturation = self._check_input(saturation, 'saturation')
        self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
                                     clip_first_on_zero=False)

    def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
        if isinstance(value, numbers.Number):
            if value < 0:
                raise ValueError("If {} is a single number, it must be non negative.".format(name))
            value = [center - value, center + value]
            if clip_first_on_zero:
                value[0] = max(value[0], 0)
        elif isinstance(value, (tuple, list)) and len(value) == 2:
            if not bound[0] <= value[0] <= value[1] <= bound[1]:
                raise ValueError("{} values should be between {}".format(name, bound))
        else:
            raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name))

        # if value is 0 or (1., 1.) for brightness/contrast/saturation
        # or (0., 0.) for hue, do nothing
        if value[0] == value[1] == center:
            value = None
        return value

    @staticmethod
    def get_params(brightness, contrast, saturation, hue):
        """Get a randomized transform to be applied on image.
        Arguments are same as that of __init__.
        Returns:
            Transform which randomly adjusts brightness, contrast and
            saturation in a random order.
        """
        transforms = []

        if brightness is not None:
            brightness_factor = random.uniform(brightness[0], brightness[1])
            transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

        if contrast is not None:
            contrast_factor = random.uniform(contrast[0], contrast[1])
            transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

        if saturation is not None:
            saturation_factor = random.uniform(saturation[0], saturation[1])
            transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

        if hue is not None:
            hue_factor = random.uniform(hue[0], hue[1])
            transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))

        random.shuffle(transforms)
        transform = Compose(transforms)

        return transform

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Input image.
        Returns:
            PIL Image: Color jittered image.
        """
        transform = self.get_params(self.brightness, self.contrast,
                                    self.saturation, self.hue)
        return transform(img)

    def __repr__(self):
        format_string = self.__class__.__name__ + '('
        format_string += 'brightness={0}'.format(self.brightness)
        format_string += ', contrast={0}'.format(self.contrast)
        format_string += ', saturation={0}'.format(self.saturation)
        format_string += ', hue={0})'.format(self.hue)
        return format_string
