from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from PIL import Image
import random
import math

import torch
from torchvision.transforms import *

class Random2DTranslation(object):
    """
    With a probability, first increase image size to (1 + 1/8), and then perform random crop.
    Args:
    - height (int): target image height.
    - width (int): target image width.
    - p (float): probability of performing this transformation. Default: 0.5.
    """

    def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
        self.height = height
        self.width = width
        self.p = p
        self.interpolation = interpolation

    def __call__(self, img):
        """
        Args:
        - img (PIL Image): Image to be cropped.
        """
        if random.uniform(0, 1) > self.p:
            return img.resize((self.width, self.height), self.interpolation)

        new_width, new_height = int(round(self.width * 1.125)), int(round(self.height * 1.125))
        resized_img = img.resize((new_width, new_height), self.interpolation)
        x_maxrange = new_width - self.width
        y_maxrange = new_height - self.height
        x1 = int(round(random.uniform(0, x_maxrange)))
        y1 = int(round(random.uniform(0, y_maxrange)))
        croped_img = resized_img.crop((x1, y1, x1 + self.width, y1 + self.height))
        return croped_img


class RandomErasing(object):

    def __init__(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=[0.4914, 0.4822, 0.4465]):
        self.probability = probability
        self.mean = mean
        self.sl = sl
        self.sh = sh
        self.r1 = r1

    def __call__(self, img):

        if random.uniform(0, 1) > self.probability:
            return img

        for attempt in range(100):
            area = img.size()[1] * img.size()[2]

            target_area = random.uniform(self.sl, self.sh) * area
            aspect_ratio = random.uniform(self.r1, 1 / self.r1)

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

            if w < img.size()[2] and h < img.size()[1]:
                x1 = random.randint(0, img.size()[1] - h)
                y1 = random.randint(0, img.size()[2] - w)
                if img.size()[0] == 3:
                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
                else:
                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
                return img

        return img


class ColorAugmentation(object):
    """
    Randomly alter the intensities of RGB channels
    Reference:
    <NAME> al. ImageNet Classification with Deep ConvolutionalNeural Networks. NIPS 2012.
    """

    def __init__(self, p=0.5):
        self.p = p
        self.eig_vec = torch.Tensor([
            [0.4009, 0.7192, -0.5675],
            [-0.8140, -0.0045, -0.5808],
            [0.4203, -0.6948, -0.5836],
        ])
        self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])

    def _check_input(self, tensor):
        assert tensor.dim() == 3 and tensor.size(0) == 3

    def __call__(self, tensor):
        if random.uniform(0, 1) > self.p:
            return tensor
        alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
        quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
        tensor = tensor + quatity.view(3, 1, 1)
        return tensor


def build_transforms(height,
                     width,
                     random_erase=False,  # use random erasing for data augmentation
                     color_jitter=False,  # randomly change the brightness, contrast and saturation
                     color_aug=False,  # randomly alter the intensities of RGB channels
                     **kwargs):
    # use imagenet mean and std as default
    # TODO: compute dataset-specific mean and std
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]
    normalize = Normalize(mean=imagenet_mean, std=imagenet_std)

    # build train transformations
    transform_train = []
    transform_train += [Random2DTranslation(height, width)]
    transform_train += [RandomHorizontalFlip()]
    if color_jitter:
        transform_train += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
    transform_train += [ToTensor()]
    if color_aug:
        transform_train += [ColorAugmentation()]
    transform_train += [normalize]
    if random_erase:
        transform_train += [RandomErasing()]
    transform_train = Compose(transform_train)

    transform_val = []
    transform_val += [Random2DTranslation(height, width)]
    transform_val += [RandomHorizontalFlip()]
    if color_jitter:
        transform_val += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
    transform_val += [ToTensor()]
    if color_aug:
        transform_val += [ColorAugmentation()]
    transform_val += [normalize]
    if random_erase:
        transform_val += [RandomErasing()]
    transform_val = Compose(transform_val)

    # build test transformations
    transform_test = Compose([
        Resize((height, width)),
        ToTensor(),
        normalize,
    ])

    return transform_train, transform_val, transform_test
    #return transform_train, transform_test
    
def train_transforms(height,
                     width,
                     random_erase=False,  # use random erasing for data augmentation
                     color_jitter=False,  # randomly change the brightness, contrast and saturation
                     color_aug=False,  # randomly alter the intensities of RGB channels
                     **kwargs):
    # use imagenet mean and std as default
    # TODO: compute dataset-specific mean and std
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]
    normalize = Normalize(mean=imagenet_mean, std=imagenet_std)

    # build train transformations
    transform_train = []
    transform_train += [Random2DTranslation(height, width)]
    transform_train += [RandomHorizontalFlip()]
    if color_jitter:
        transform_train += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
    transform_train += [ToTensor()]
    if color_aug:
        transform_train += [ColorAugmentation()]
    transform_train += [normalize]
    if random_erase:
        transform_train += [RandomErasing()]
    transform_train = Compose(transform_train)


    return transform_train


def test_transform(height, width):
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]
    normalize = Normalize(mean=imagenet_mean, std=imagenet_std)

    transform_test = Compose([
        Resize((height, width)),
        ToTensor(),
        normalize,
    ])

    return transform_test

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from PIL import Image
import random
import math

import torch
from torchvision.transforms import *

class Random2DTranslation(object):
    """
    With a probability, first increase image size to (1 + 1/8), and then perform random crop.
    Args:
    - height (int): target image height.
    - width (int): target image width.
    - p (float): probability of performing this transformation. Default: 0.5.
    """

    def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
        self.height = height
        self.width = width
        self.p = p
        self.interpolation = interpolation

    def __call__(self, img):
        """
        Args:
        - img (PIL Image): Image to be cropped.
        """
        if random.uniform(0, 1) > self.p:
            return img.resize((self.width, self.height), self.interpolation)

        new_width, new_height = int(round(self.width * 1.125)), int(round(self.height * 1.125))
        resized_img = img.resize((new_width, new_height), self.interpolation)
        x_maxrange = new_width - self.width
        y_maxrange = new_height - self.height
        x1 = int(round(random.uniform(0, x_maxrange)))
        y1 = int(round(random.uniform(0, y_maxrange)))
        croped_img = resized_img.crop((x1, y1, x1 + self.width, y1 + self.height))
        return croped_img


class RandomErasing(object):

    def __init__(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=[0.4914, 0.4822, 0.4465]):
        self.probability = probability
        self.mean = mean
        self.sl = sl
        self.sh = sh
        self.r1 = r1

    def __call__(self, img):

        if random.uniform(0, 1) > self.probability:
            return img

        for attempt in range(100):
            area = img.size()[1] * img.size()[2]

            target_area = random.uniform(self.sl, self.sh) * area
            aspect_ratio = random.uniform(self.r1, 1 / self.r1)

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

            if w < img.size()[2] and h < img.size()[1]:
                x1 = random.randint(0, img.size()[1] - h)
                y1 = random.randint(0, img.size()[2] - w)
                if img.size()[0] == 3:
                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
                else:
                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
                return img

        return img


class ColorAugmentation(object):
    """
    Randomly alter the intensities of RGB channels
    Reference:
    <NAME> al. ImageNet Classification with Deep ConvolutionalNeural Networks. NIPS 2012.
    """

    def __init__(self, p=0.5):
        self.p = p
        self.eig_vec = torch.Tensor([
            [0.4009, 0.7192, -0.5675],
            [-0.8140, -0.0045, -0.5808],
            [0.4203, -0.6948, -0.5836],
        ])
        self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])

    def _check_input(self, tensor):
        assert tensor.dim() == 3 and tensor.size(0) == 3

    def __call__(self, tensor):
        if random.uniform(0, 1) > self.p:
            return tensor
        alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
        quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
        tensor = tensor + quatity.view(3, 1, 1)
        return tensor


def build_transforms(height,
                     width,
                     random_erase=False,  # use random erasing for data augmentation
                     color_jitter=False,  # randomly change the brightness, contrast and saturation
                     color_aug=False,  # randomly alter the intensities of RGB channels
                     **kwargs):
    # use imagenet mean and std as default
    # TODO: compute dataset-specific mean and std
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]
    normalize = Normalize(mean=imagenet_mean, std=imagenet_std)

    # build train transformations
    transform_train = []
    transform_train += [Random2DTranslation(height, width)]
    transform_train += [RandomHorizontalFlip()]
    if color_jitter:
        transform_train += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
    transform_train += [ToTensor()]
    if color_aug:
        transform_train += [ColorAugmentation()]
    transform_train += [normalize]
    if random_erase:
        transform_train += [RandomErasing()]
    transform_train = Compose(transform_train)

    transform_val = []
    transform_val += [Random2DTranslation(height, width)]
    transform_val += [RandomHorizontalFlip()]
    if color_jitter:
        transform_val += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
    transform_val += [ToTensor()]
    if color_aug:
        transform_val += [ColorAugmentation()]
    transform_val += [normalize]
    if random_erase:
        transform_val += [RandomErasing()]
    transform_val = Compose(transform_val)

    # build test transformations
    transform_test = Compose([
        Resize((height, width)),
        ToTensor(),
        normalize,
    ])

    return transform_train, transform_val, transform_test
    #return transform_train, transform_test
    
def train_transforms(height,
                     width,
                     random_erase=False,  # use random erasing for data augmentation
                     color_jitter=False,  # randomly change the brightness, contrast and saturation
                     color_aug=False,  # randomly alter the intensities of RGB channels
                     **kwargs):
    # use imagenet mean and std as default
    # TODO: compute dataset-specific mean and std
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]
    normalize = Normalize(mean=imagenet_mean, std=imagenet_std)

    # build train transformations
    transform_train = []
    transform_train += [Random2DTranslation(height, width)]
    transform_train += [RandomHorizontalFlip()]
    if color_jitter:
        transform_train += [ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)]
    transform_train += [ToTensor()]
    if color_aug:
        transform_train += [ColorAugmentation()]
    transform_train += [normalize]
    if random_erase:
        transform_train += [RandomErasing()]
    transform_train = Compose(transform_train)


    return transform_train


def test_transform(height, width):
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]
    normalize = Normalize(mean=imagenet_mean, std=imagenet_std)

    transform_test = Compose([
        Resize((height, width)),
        ToTensor(),
        normalize,
    ])

    return transform_test

With a probability, first increase image size to (1 + 1/8), and then perform random crop.
     Args:
     - height (int): target image height.
     - width (int): target image width.
     - p (float): probability of performing this transformation. Default: 0.5. Args:
         - img (PIL Image): Image to be cropped. Randomly alter the intensities of RGB channels
     Reference:
     <NAME> al. ImageNet Classification with Deep ConvolutionalNeural Networks. NIPS 2012. # use random erasing for data augmentation # randomly change the brightness, contrast and saturation # randomly alter the intensities of RGB channels # use imagenet mean and std as default # TODO: compute dataset-specific mean and std # build train transformations # build test transformations #return transform_train, transform_test # use random erasing for data augmentation # randomly change the brightness, contrast and saturation # randomly alter the intensities of RGB channels # use imagenet mean and std as default # TODO: compute dataset-specific mean and std # build train transformations