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import os |
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import random |
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import h5py |
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import numpy as np |
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import torch |
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from scipy import ndimage |
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from scipy.ndimage.interpolation import zoom |
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from torch.utils.data import Dataset |
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def random_rot_flip(image, label): |
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k = np.random.randint(0, 4) |
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image = np.rot90(image, k) |
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label = np.rot90(label, k) |
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axis = np.random.randint(0, 2) |
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image = np.flip(image, axis=axis).copy() |
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label = np.flip(label, axis=axis).copy() |
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return image, label |
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def random_rotate(image, label): |
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angle = np.random.randint(-20, 20) |
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image = ndimage.rotate(image, angle, order=0, reshape=False) |
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label = ndimage.rotate(label, angle, order=0, reshape=False) |
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return image, label |
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class RandomGenerator(object): |
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def __init__(self, output_size): |
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self.output_size = output_size |
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def __call__(self, sample): |
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image, label = sample['image'], sample['label'] |
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if random.random() > 0.5: |
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image, label = random_rot_flip(image, label) |
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elif random.random() > 0.5: |
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image, label = random_rotate(image, label) |
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x, y = image.shape |
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if x != self.output_size[0] or y != self.output_size[1]: |
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image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=3) |
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label = zoom(label, (self.output_size[0] / x, self.output_size[1] / y), order=0) |
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image = torch.from_numpy(image.astype(np.float32)).unsqueeze(0) |
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label = torch.from_numpy(label.astype(np.float32)) |
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sample = {'image': image, 'label': label.long()} |
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return sample |
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class Synapse_dataset(Dataset): |
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def __init__(self, base_dir, list_dir, split, transform=None): |
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self.transform = transform |
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self.split = split |
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self.sample_list = open(os.path.join(list_dir, self.split+'.txt')).readlines() |
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self.data_dir = base_dir |
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def __len__(self): |
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return len(self.sample_list) |
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def __getitem__(self, idx): |
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if self.split == "train": |
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slice_name = self.sample_list[idx].strip('\n') |
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data_path = os.path.join(self.data_dir, slice_name+'.npz') |
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data = np.load(data_path) |
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image, label = data['image'], data['label'] |
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else: |
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vol_name = self.sample_list[idx].strip('\n') |
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filepath = self.data_dir + "/{}.npy.h5".format(vol_name) |
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data = h5py.File(filepath) |
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image, label = data['image'][:], data['label'][:] |
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sample = {'image': image, 'label': label} |
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if self.transform: |
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sample = self.transform(sample) |
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sample['case_name'] = self.sample_list[idx].strip('\n') |
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return sample |
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