import numbers
import random
import math
import warnings
import numpy as np
from PIL import Image, ImageEnhance, ImageOps


class ShearX(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
            Image.BICUBIC, fillcolor=self.fillcolor)


class ShearY(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
            Image.BICUBIC, fillcolor=self.fillcolor)


class TranslateX(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, 0, magnitude * x.size[0] * random.choice([-1, 1]), 0, 1, 0),
            fillcolor=self.fillcolor)


class TranslateY(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * x.size[1] * random.choice([-1, 1])),
            fillcolor=self.fillcolor)


class Rotate(object):
    # from https://stackoverflow.com/questions/
    # 5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
    def __call__(self, x, magnitude):
        rot = x.convert("RGBA").rotate(magnitude * random.choice([-1, 1]))
        return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(x.mode)


class Color(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Color(x).enhance(1 + magnitude * random.choice([-1, 1]))


class Posterize(object):
    def __call__(self, x, magnitude):
        return ImageOps.posterize(x, magnitude)


class Solarize(object):
    def __call__(self, x, magnitude):
        return ImageOps.solarize(x, magnitude)


class Contrast(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Contrast(x).enhance(1 + magnitude * random.choice([-1, 1]))


class Sharpness(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Sharpness(x).enhance(1 + magnitude * random.choice([-1, 1]))


class Brightness(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Brightness(x).enhance(1 + magnitude * random.choice([-1, 1]))


class AutoContrast(object):
    def __call__(self, x, magnitude):
        return ImageOps.autocontrast(x)


class Equalize(object):
    def __call__(self, x, magnitude):
        return ImageOps.equalize(x)


class Invert(object):
    def __call__(self, x, magnitude):
        return ImageOps.invert(x)


class ImageNetPolicy(object):
    """ Randomly choose one of the best 24 Sub-policies on ImageNet.
        Example:
        >>> policy = ImageNetPolicy()
        >>> transformed = policy(image)
        Example as a PyTorch Transform:
        >>> transform = transforms.Compose([
        >>>     transforms.Resize(256),
        >>>     ImageNetPolicy(),
        >>>     transforms.ToTensor()])
    """
    def __init__(self, fillcolor=(128, 128, 128)):
        self.policies = [
            SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
            SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),

            SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
            SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
            SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
            SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
            SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),

            SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
            SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
            SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),

            SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
            SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
            SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
            SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
            SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),

            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
        ]

    def __call__(self, img):
        policy_idx = random.randint(0, len(self.policies) - 1)
        return self.policies[policy_idx](img)

    def __repr__(self):
        return "AutoAugment ImageNet Policy"

class SubPolicy(object):
    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
        ranges = {
            "shearX": np.linspace(0, 0.3, 10),
            "shearY": np.linspace(0, 0.3, 10),
            "translateX": np.linspace(0, 150 / 331, 10),
            "translateY": np.linspace(0, 150 / 331, 10),
            "rotate": np.linspace(0, 30, 10),
            "color": np.linspace(0.0, 0.9, 10),
            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
            "solarize": np.linspace(256, 0, 10),
            "contrast": np.linspace(0.0, 0.9, 10),
            "sharpness": np.linspace(0.0, 0.9, 10),
            "brightness": np.linspace(0.0, 0.9, 10),
            "autocontrast": [0] * 10,
            "equalize": [0] * 10,
            "invert": [0] * 10
        }

        func = {
            "shearX": ShearX(fillcolor=fillcolor),
            "shearY": ShearY(fillcolor=fillcolor),
            "translateX": TranslateX(fillcolor=fillcolor),
            "translateY": TranslateY(fillcolor=fillcolor),
            "rotate": Rotate(),
            "color": Color(),
            "posterize": Posterize(),
            "solarize": Solarize(),
            "contrast": Contrast(),
            "sharpness": Sharpness(),
            "brightness": Brightness(),
            "autocontrast": AutoContrast(),
            "equalize": Equalize(),
            "invert": Invert()
        }

        self.p1 = p1
        self.operation1 = func[operation1]
        self.magnitude1 = ranges[operation1][magnitude_idx1]
        self.p2 = p2
        self.operation2 = func[operation2]
        self.magnitude2 = ranges[operation2][magnitude_idx2]

    def __call__(self, img):
        if random.random() < self.p1:
            img = self.operation1(img, self.magnitude1)
        if random.random() < self.p2:
            img = self.operation2(img, self.magnitude2)
        return img

def crop(img, i, j, h, w):
    """Crop the given PIL Image.

    Args:
        img (PIL Image): Image to be cropped.
        i (int): i in (i,j) i.e coordinates of the upper left corner.
        j (int): j in (i,j) i.e coordinates of the upper left corner.
        h (int): Height of the cropped image.
        w (int): Width of the cropped image.

    Returns:
        PIL Image: Cropped image.
    """
    return img.crop((j, i, j + w, i + h))

def resize(img, size, interpolation=Image.BILINEAR):
    r"""Resize the input PIL Image to the given size.

    Args:
        img (PIL Image): Image to be resized.
        size (sequence or int): Desired output size. If size is a sequence like
            (h, w), the output size will be matched to this. If size is an int,
            the smaller edge of the image will be matched to this number maintaing
            the aspect ratio. i.e, if height > width, then image will be rescaled to
            :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``

    Returns:
        PIL Image: Resized image.
    """
    if isinstance(size, int):
        w, h = img.size
        if (w <= h and w == size) or (h <= w and h == size):
            return img
        if w < h:
            ow = size
            oh = int(size * h / w)
            return img.resize((ow, oh), interpolation)
        else:
            oh = size
            ow = int(size * w / h)
            return img.resize((ow, oh), interpolation)
    else:
        return img.resize(size[::-1], interpolation)

def center_crop(img, output_size):
    if isinstance(output_size, numbers.Number):
        output_size = (int(output_size), int(output_size))
    w, h = img.size
    th, tw = output_size
    i = int(round((h - th) / 2.))
    j = int(round((w - tw) / 2.))
    return crop(img, i, j, th, tw)

def resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR):
    """Crop the given PIL Image and resize it to desired size.

    Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.

    Args:
        img (PIL Image): Image to be cropped.
        i (int): i in (i,j) i.e coordinates of the upper left corner
        j (int): j in (i,j) i.e coordinates of the upper left corner
        h (int): Height of the cropped image.
        w (int): Width of the cropped image.
        size (sequence or int): Desired output size. Same semantics as ``resize``.
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``.
    Returns:
        PIL Image: Cropped image.
    """
    img = crop(img, i, j, h, w)
    img = resize(img, size, interpolation)
    return img

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=Image.BILINEAR):
        self.size           = size
        self.interpolation  = interpolation

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be scaled.

        Returns:
            PIL Image: Rescaled image.
        """
        return resize(img, self.size, self.interpolation)
    
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):
        self.size = size

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be cropped.

        Returns:
            PIL Image: Cropped image.
        """
        return center_crop(img, self.size)

class RandomResizedCrop(object):
    """Crop the given PIL Image to random size and aspect ratio.

    A crop of random size (default: of 0.08 to 1.0) of the original size and a random
    aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
    is finally resized to given size.
    This is popularly used to train the Inception networks.

    Args:
        size: expected output size of each edge
        scale: range of size of the origin size cropped
        ratio: range of aspect ratio of the origin aspect ratio cropped
        interpolation: Default: PIL.Image.BILINEAR
    """

    def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):
        self.size = size
        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
            warnings.warn("range should be of kind (min, max)")

        self.interpolation = interpolation
        self.scale = scale
        self.ratio = ratio

    @staticmethod
    def get_params(img, scale, ratio):
        """Get parameters for ``crop`` for a random sized crop.

        Args:
            img (PIL Image): Image to be cropped.
            scale (tuple): range of size of the origin size cropped
            ratio (tuple): range of aspect ratio of the origin aspect ratio cropped

        Returns:
            tuple: params (i, j, h, w) to be passed to ``crop`` for a random
                sized crop.
        """
        area = img.size[0] * img.size[1]

        for attempt in range(10):
            target_area = random.uniform(*scale) * area
            log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
            aspect_ratio = math.exp(random.uniform(*log_ratio))

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if w <= img.size[0] and h <= img.size[1]:
                i = random.randint(0, img.size[1] - h)
                j = random.randint(0, img.size[0] - w)
                return i, j, h, w

        # Fallback to central crop
        in_ratio = img.size[0] / img.size[1]
        if (in_ratio < min(ratio)):
            w = img.size[0]
            h = int(round(w / min(ratio)))
        elif (in_ratio > max(ratio)):
            h = img.size[1]
            w = int(round(h * max(ratio)))
        else:  # whole image
            w = img.size[0]
            h = img.size[1]
        i = (img.size[1] - h) // 2
        j = (img.size[0] - w) // 2
        return i, j, h, w

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be cropped and resized.

        Returns:
            PIL Image: Randomly cropped and resized image.
        """
        i, j, h, w = self.get_params(img, self.scale, self.ratio)
        return resized_crop(img, i, j, h, w, self.size, self.interpolation)
import numbers
import random
import math
import warnings
import numpy as np
from PIL import Image, ImageEnhance, ImageOps


class ShearX(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
            Image.BICUBIC, fillcolor=self.fillcolor)


class ShearY(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
            Image.BICUBIC, fillcolor=self.fillcolor)


class TranslateX(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, 0, magnitude * x.size[0] * random.choice([-1, 1]), 0, 1, 0),
            fillcolor=self.fillcolor)


class TranslateY(object):
    def __init__(self, fillcolor=(128, 128, 128)):
        self.fillcolor = fillcolor

    def __call__(self, x, magnitude):
        return x.transform(
            x.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * x.size[1] * random.choice([-1, 1])),
            fillcolor=self.fillcolor)


class Rotate(object):
    # from https://stackoverflow.com/questions/
    # 5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
    def __call__(self, x, magnitude):
        rot = x.convert("RGBA").rotate(magnitude * random.choice([-1, 1]))
        return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(x.mode)


class Color(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Color(x).enhance(1 + magnitude * random.choice([-1, 1]))


class Posterize(object):
    def __call__(self, x, magnitude):
        return ImageOps.posterize(x, magnitude)


class Solarize(object):
    def __call__(self, x, magnitude):
        return ImageOps.solarize(x, magnitude)


class Contrast(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Contrast(x).enhance(1 + magnitude * random.choice([-1, 1]))


class Sharpness(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Sharpness(x).enhance(1 + magnitude * random.choice([-1, 1]))


class Brightness(object):
    def __call__(self, x, magnitude):
        return ImageEnhance.Brightness(x).enhance(1 + magnitude * random.choice([-1, 1]))


class AutoContrast(object):
    def __call__(self, x, magnitude):
        return ImageOps.autocontrast(x)


class Equalize(object):
    def __call__(self, x, magnitude):
        return ImageOps.equalize(x)


class Invert(object):
    def __call__(self, x, magnitude):
        return ImageOps.invert(x)


class ImageNetPolicy(object):
    """ Randomly choose one of the best 24 Sub-policies on ImageNet.
        Example:
        >>> policy = ImageNetPolicy()
        >>> transformed = policy(image)
        Example as a PyTorch Transform:
        >>> transform = transforms.Compose([
        >>>     transforms.Resize(256),
        >>>     ImageNetPolicy(),
        >>>     transforms.ToTensor()])
    """
    def __init__(self, fillcolor=(128, 128, 128)):
        self.policies = [
            SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
            SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),

            SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
            SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
            SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
            SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
            SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),

            SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
            SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
            SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),

            SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
            SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
            SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
            SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
            SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),

            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
        ]

    def __call__(self, img):
        policy_idx = random.randint(0, len(self.policies) - 1)
        return self.policies[policy_idx](img)

    def __repr__(self):
        return "AutoAugment ImageNet Policy"

class SubPolicy(object):
    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
        ranges = {
            "shearX": np.linspace(0, 0.3, 10),
            "shearY": np.linspace(0, 0.3, 10),
            "translateX": np.linspace(0, 150 / 331, 10),
            "translateY": np.linspace(0, 150 / 331, 10),
            "rotate": np.linspace(0, 30, 10),
            "color": np.linspace(0.0, 0.9, 10),
            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
            "solarize": np.linspace(256, 0, 10),
            "contrast": np.linspace(0.0, 0.9, 10),
            "sharpness": np.linspace(0.0, 0.9, 10),
            "brightness": np.linspace(0.0, 0.9, 10),
            "autocontrast": [0] * 10,
            "equalize": [0] * 10,
            "invert": [0] * 10
        }

        func = {
            "shearX": ShearX(fillcolor=fillcolor),
            "shearY": ShearY(fillcolor=fillcolor),
            "translateX": TranslateX(fillcolor=fillcolor),
            "translateY": TranslateY(fillcolor=fillcolor),
            "rotate": Rotate(),
            "color": Color(),
            "posterize": Posterize(),
            "solarize": Solarize(),
            "contrast": Contrast(),
            "sharpness": Sharpness(),
            "brightness": Brightness(),
            "autocontrast": AutoContrast(),
            "equalize": Equalize(),
            "invert": Invert()
        }

        self.p1 = p1
        self.operation1 = func[operation1]
        self.magnitude1 = ranges[operation1][magnitude_idx1]
        self.p2 = p2
        self.operation2 = func[operation2]
        self.magnitude2 = ranges[operation2][magnitude_idx2]

    def __call__(self, img):
        if random.random() < self.p1:
            img = self.operation1(img, self.magnitude1)
        if random.random() < self.p2:
            img = self.operation2(img, self.magnitude2)
        return img

def crop(img, i, j, h, w):
    """Crop the given PIL Image.

    Args:
        img (PIL Image): Image to be cropped.
        i (int): i in (i,j) i.e coordinates of the upper left corner.
        j (int): j in (i,j) i.e coordinates of the upper left corner.
        h (int): Height of the cropped image.
        w (int): Width of the cropped image.

    Returns:
        PIL Image: Cropped image.
    """
    return img.crop((j, i, j + w, i + h))

def resize(img, size, interpolation=Image.BILINEAR):
    r"""Resize the input PIL Image to the given size.

    Args:
        img (PIL Image): Image to be resized.
        size (sequence or int): Desired output size. If size is a sequence like
            (h, w), the output size will be matched to this. If size is an int,
            the smaller edge of the image will be matched to this number maintaing
            the aspect ratio. i.e, if height > width, then image will be rescaled to
            :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``

    Returns:
        PIL Image: Resized image.
    """
    if isinstance(size, int):
        w, h = img.size
        if (w <= h and w == size) or (h <= w and h == size):
            return img
        if w < h:
            ow = size
            oh = int(size * h / w)
            return img.resize((ow, oh), interpolation)
        else:
            oh = size
            ow = int(size * w / h)
            return img.resize((ow, oh), interpolation)
    else:
        return img.resize(size[::-1], interpolation)

def center_crop(img, output_size):
    if isinstance(output_size, numbers.Number):
        output_size = (int(output_size), int(output_size))
    w, h = img.size
    th, tw = output_size
    i = int(round((h - th) / 2.))
    j = int(round((w - tw) / 2.))
    return crop(img, i, j, th, tw)

def resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR):
    """Crop the given PIL Image and resize it to desired size.

    Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.

    Args:
        img (PIL Image): Image to be cropped.
        i (int): i in (i,j) i.e coordinates of the upper left corner
        j (int): j in (i,j) i.e coordinates of the upper left corner
        h (int): Height of the cropped image.
        w (int): Width of the cropped image.
        size (sequence or int): Desired output size. Same semantics as ``resize``.
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``.
    Returns:
        PIL Image: Cropped image.
    """
    img = crop(img, i, j, h, w)
    img = resize(img, size, interpolation)
    return img

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=Image.BILINEAR):
        self.size           = size
        self.interpolation  = interpolation

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be scaled.

        Returns:
            PIL Image: Rescaled image.
        """
        return resize(img, self.size, self.interpolation)
    
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):
        self.size = size

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be cropped.

        Returns:
            PIL Image: Cropped image.
        """
        return center_crop(img, self.size)

class RandomResizedCrop(object):
    """Crop the given PIL Image to random size and aspect ratio.

    A crop of random size (default: of 0.08 to 1.0) of the original size and a random
    aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
    is finally resized to given size.
    This is popularly used to train the Inception networks.

    Args:
        size: expected output size of each edge
        scale: range of size of the origin size cropped
        ratio: range of aspect ratio of the origin aspect ratio cropped
        interpolation: Default: PIL.Image.BILINEAR
    """

    def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=Image.BILINEAR):
        self.size = size
        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
            warnings.warn("range should be of kind (min, max)")

        self.interpolation = interpolation
        self.scale = scale
        self.ratio = ratio

    @staticmethod
    def get_params(img, scale, ratio):
        """Get parameters for ``crop`` for a random sized crop.

        Args:
            img (PIL Image): Image to be cropped.
            scale (tuple): range of size of the origin size cropped
            ratio (tuple): range of aspect ratio of the origin aspect ratio cropped

        Returns:
            tuple: params (i, j, h, w) to be passed to ``crop`` for a random
                sized crop.
        """
        area = img.size[0] * img.size[1]

        for attempt in range(10):
            target_area = random.uniform(*scale) * area
            log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
            aspect_ratio = math.exp(random.uniform(*log_ratio))

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if w <= img.size[0] and h <= img.size[1]:
                i = random.randint(0, img.size[1] - h)
                j = random.randint(0, img.size[0] - w)
                return i, j, h, w

        # Fallback to central crop
        in_ratio = img.size[0] / img.size[1]
        if (in_ratio < min(ratio)):
            w = img.size[0]
            h = int(round(w / min(ratio)))
        elif (in_ratio > max(ratio)):
            h = img.size[1]
            w = int(round(h * max(ratio)))
        else:  # whole image
            w = img.size[0]
            h = img.size[1]
        i = (img.size[1] - h) // 2
        j = (img.size[0] - w) // 2
        return i, j, h, w

    def __call__(self, img):
        """
        Args:
            img (PIL Image): Image to be cropped and resized.

        Returns:
            PIL Image: Randomly cropped and resized image.
        """
        i, j, h, w = self.get_params(img, self.scale, self.ratio)
        return resized_crop(img, i, j, h, w, self.size, self.interpolation)
# from https://stackoverflow.com/questions/ # 5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand Randomly choose one of the best 24 Sub-policies on ImageNet.         Example:         >>> policy = ImageNetPolicy()         >>> transformed = policy(image)         Example as a PyTorch Transform:         >>> transform = transforms.Compose([         >>>     transforms.Resize(256),         >>>     ImageNetPolicy(),         >>>     transforms.ToTensor()]) Crop the given PIL Image.      Args:         img (PIL Image): Image to be cropped.         i (int): i in (i,j) i.e coordinates of the upper left corner.         j (int): j in (i,j) i.e coordinates of the upper left corner.         h (int): Height of the cropped image.         w (int): Width of the cropped image.      Returns:         PIL Image: Cropped image. Resize the input PIL Image to the given size.      Args:         img (PIL Image): Image to be resized.         size (sequence or int): Desired output size. If size is a sequence like             (h, w), the output size will be matched to this. If size is an int,             the smaller edge of the image will be matched to this number maintaing             the aspect ratio. i.e, if height > width, then image will be rescaled to             :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`         interpolation (int, optional): Desired interpolation. Default is             ``PIL.Image.BILINEAR``      Returns:         PIL Image: Resized image. Crop the given PIL Image and resize it to desired size.      Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.      Args:         img (PIL Image): Image to be cropped.         i (int): i in (i,j) i.e coordinates of the upper left corner         j (int): j in (i,j) i.e coordinates of the upper left corner         h (int): Height of the cropped image.         w (int): Width of the cropped image.         size (sequence or int): Desired output size. Same semantics as ``resize``.         interpolation (int, optional): Desired interpolation. Default is             ``PIL.Image.BILINEAR``.     Returns:         PIL Image: Cropped image. 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`` Args:             img (PIL Image): Image to be scaled.          Returns:             PIL Image: Rescaled image. 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. Args:             img (PIL Image): Image to be cropped.          Returns:             PIL Image: Cropped image. Crop the given PIL Image to random size and aspect ratio.      A crop of random size (default: of 0.08 to 1.0) of the original size and a random     aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop     is finally resized to given size.     This is popularly used to train the Inception networks.      Args:         size: expected output size of each edge         scale: range of size of the origin size cropped         ratio: range of aspect ratio of the origin aspect ratio cropped         interpolation: Default: PIL.Image.BILINEAR Get parameters for ``crop`` for a random sized crop.          Args:             img (PIL Image): Image to be cropped.             scale (tuple): range of size of the origin size cropped             ratio (tuple): range of aspect ratio of the origin aspect ratio cropped          Returns:             tuple: params (i, j, h, w) to be passed to ``crop`` for a random                 sized crop. # Fallback to central crop # whole image Args:             img (PIL Image): Image to be cropped and resized.          Returns:             PIL Image: Randomly cropped and resized image.