Pytorch--RandomScaleCrop操作

class RandomScaleCrop(object):
   def __init__(self, base_size, crop_size, fill=0):
       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}

https://blog.csdn.net/qq_41847324/article/details/86224628

https://blog.csdn.net/halchan/article/details/98876875