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