41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
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from torch.utils.data import Dataset
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import PIL.Image as Image
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import os
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# def make_dataset(root):
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# # root = "./data/train"
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# imgs = []
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# ori_path = os.path.join(root, "Data")
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# ground_path = os.path.join(root, "Ground")
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# names = os.listdir(ori_path)
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# n = len(names)
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# for i in range(n):
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# img = os.path.join(ori_path, names[i])
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# mask = os.path.join(ground_path, names[i])
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# imgs.append((img, mask))
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# return imgs
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class LiverDataset(Dataset):
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def __init__(self, root, transform=None, target_transform=None):
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imgs = "/home/shared/wy/flask_web/Unet_liver_seg-master/data/val/Data/P2_T1_00018.png"
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self.imgs = imgs
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self.transform = transform
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self.target_transform = target_transform
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def __getitem__(self, index):
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# x_path, y_path = self.imgs[index]
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x_path = self.imgs
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img_x = Image.open(x_path).convert('L')
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# img_y = Image.open(y_path).convert('L')
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if self.transform is not None:
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img_x = self.transform(img_x)
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# if self.target_transform is not None:
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# img_y = self.target_transform(img_y)
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return img_x
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def __len__(self):
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return len(self.imgs)
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