text: import random
  
  import numpy as np
  import torch
  from PIL import Image, ImageOps, ImageFilter
  
  
  class Normalize:
      """Normalize a tensor image with mean and standard deviation.
      Args:
          mean (tuple): means for each channel.
          std (tuple): standard deviations for each channel.
      """
  
      def __init__(self, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)):
          self.mean = mean
          self.std = std
  
      def __call__(self, sample):
          img = sample["image"]
          mask = sample["label"]
          img = np.array(img).astype(np.float32)
          mask = np.array(mask).astype(np.float32)
          img /= 255.0
          img -= self.mean
          img /= self.std
  
          return {"image": img, "label": mask}
  
  
  class ToTensor:
      """Convert ndarrays in sample to Tensors."""
  
      def __call__(self, sample):
          # swap color axis because
          # numpy image: H x W x C
          # torch image: C X H X W
          img = sample["image"]
          mask = sample["label"]
          img = np.array(img).astype(np.float32).transpose((2, 0, 1))
          mask = np.array(mask).astype(np.float32)
  
          img = torch.from_numpy(img).float()
          mask = torch.from_numpy(mask).float()
  
          return {"image": img, "label": mask}
  
  
  class RandomHorizontalFlip:
      def __call__(self, sample):
          img = sample["image"]
          mask = sample["label"]
          if random.random() < 0.5:
              img = img.transpose(Image.FLIP_LEFT_RIGHT)
              mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
  
          return {"image": img, "label": mask}
  
  
  class RandomGaussianBlur:
      def __call__(self, sample):
          img = sample["image"]
          mask = sample["label"]
          if random.random() < 0.5:
              img = img.filter(ImageFilter.GaussianBlur(radius=random.random()))
  
          return {"image": img, "label": mask}
  
  
  class RandomScaleCrop:
      def __init__(self, base_size, crop_size, fill=255):
          self.base_size = base_size
          self.crop_size = crop_size
          self.fill = fill
  
      def __call__(self, sample):
          img = sample["image"]
          mask = sample["label"]
          # random scale (short edge)
          short_size = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0))
          w, h = img.size
          if h > w:
              ow = short_size
              oh = int(1.0 * h * ow / w)
          else:
              oh = short_size
              ow = int(1.0 * w * oh / h)
          img = img.resize((ow, oh), Image.BILINEAR)
          mask = mask.resize((ow, oh), Image.NEAREST)
          # pad crop
          if short_size < self.crop_size:
              padh = self.crop_size - oh if oh < self.crop_size else 0
              padw = self.crop_size - ow if ow < self.crop_size else 0
              img = ImageOps.expand(img, border=(0, 0, padw, padh), fill=0)
              mask = ImageOps.expand(mask, border=(0, 0, padw, padh), fill=self.fill)
          # random crop crop_size
          w, h = img.size
          x1 = random.randint(0, w - self.crop_size)
          y1 = random.randint(0, h - self.crop_size)
          img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
          mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size))
  
          return {"image": img, "label": mask}
  
  
  class FixScale:
      def __init__(self, crop_size):
          self.crop_size = crop_size
  
      def __call__(self, sample):
          img = sample["image"]
          mask = sample["label"]
          w, h = img.size
          if w > h:
              oh = self.crop_size
              ow = int(1.0 * w * oh / h)
          else:
              ow = self.crop_size
              oh = int(1.0 * h * ow / w)
          img = img.resize((ow, oh), Image.BILINEAR)
          mask = mask.resize((ow, oh), Image.NEAREST)
  
          return {"image": img, "label": mask}
  