import numpy as np
from torchvision import transforms
from PIL import Image
import numbers
import torch

class ResizeImage():
    def __init__(self, size):
      if isinstance(size, int):
        self.size = (int(size), int(size))
      else:
        self.size = size
    def __call__(self, img):
      th, tw = self.size
      return img.resize((th, tw))


class Normalize(object):
    """Normalize an tensor image with mean and standard deviation.
    Given mean: (R, G, B),
    will normalize each channel of the torch.*Tensor, i.e.
    channel = channel - mean
    Args:
        mean (sequence): Sequence of means for R, G, B channels respecitvely.
    """

    def __init__(self, mean=None, meanfile=None):
        if mean:
            self.mean = mean
        else:
            arr = np.load(meanfile)
            self.mean = torch.from_numpy(arr.astype('float32')/255.0)[[2,1,0],:,:]

    def __call__(self, tensor):
        """
        Args:
            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
        Returns:
            Tensor: Normalized image.
        """
        # TODO: make efficient
        for t, m in zip(tensor, self.mean):
            t.sub_(m)
        return tensor



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

    def __init__(self, size, start_x, start_y):
        if isinstance(size, int):
            self.size = (int(size), int(size))
        else:
            self.size = size
        self.start_x = start_x
        self.start_y = start_y

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be cropped.
        Returns:
            PIL.Image: Cropped image.
        """
        th, tw = self.size
        return img.crop((self.start_x, self.start_y, self.start_x + tw, self.start_y + th))


class ForceFlip(object):
    """Horizontally flip the given PIL.Image randomly with a probability of 0.5."""

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be flipped.
        Returns:
            PIL.Image: Randomly flipped image.
        """
        return img.transpose(Image.FLIP_LEFT_RIGHT)

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.
        """
        w, h = (img.shape[1], img.shape[2])
        th, tw = self.size
        w_off = int((w - tw) / 2.)
        h_off = int((h - th) / 2.)
        img = img[:, h_off:h_off+th, w_off:w_off+tw]
        return img


def image_train(resize_size=256, crop_size=224):
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                   std=[0.229, 0.224, 0.225])
  return  transforms.Compose([
        ResizeImage(resize_size),
        transforms.RandomResizedCrop(crop_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize
    ])

def image_test(resize_size=256, crop_size=224):
  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                   std=[0.229, 0.224, 0.225])
  start_center = (resize_size - crop_size - 1) / 2
  return transforms.Compose([
    ResizeImage(resize_size),
    PlaceCrop(crop_size, start_center, start_center),
    transforms.ToTensor(),
    normalize
  ])

def image_test_10crop(resize_size=256, crop_size=224):
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                   std=[0.229, 0.224, 0.225])
    start_first = 0
    start_center = (resize_size - crop_size - 1) / 2
    start_last = resize_size - crop_size - 1
    data_transforms = [
        transforms.Compose([
        ResizeImage(resize_size),ForceFlip(),
        PlaceCrop(crop_size, start_first, start_first),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),ForceFlip(),
        PlaceCrop(crop_size, start_last, start_last),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),ForceFlip(),
        PlaceCrop(crop_size, start_last, start_first),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),ForceFlip(),
        PlaceCrop(crop_size, start_first, start_last),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),ForceFlip(),
        PlaceCrop(crop_size, start_center, start_center),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),
        PlaceCrop(crop_size, start_first, start_first),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),
        PlaceCrop(crop_size, start_last, start_last),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),
        PlaceCrop(crop_size, start_last, start_first),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),
        PlaceCrop(crop_size, start_first, start_last),
        transforms.ToTensor(),
        normalize
        ]),
        transforms.Compose([
        ResizeImage(resize_size),
        PlaceCrop(crop_size, start_center, start_center),
        transforms.ToTensor(),
        normalize
        ])
    ]
    return data_transforms
