Buckets:
| import os | |
| import random | |
| import h5py | |
| import numpy as np | |
| import torch | |
| from scipy import ndimage | |
| from scipy.ndimage.interpolation import zoom | |
| from torch.utils.data import Dataset | |
| def random_rot_flip(image, label): | |
| k = np.random.randint(0, 4) | |
| image = np.rot90(image, k) | |
| label = np.rot90(label, k) | |
| axis = np.random.randint(0, 2) | |
| image = np.flip(image, axis=axis).copy() | |
| label = np.flip(label, axis=axis).copy() | |
| return image, label | |
| def random_rotate(image, label): | |
| angle = np.random.randint(-20, 20) | |
| image = ndimage.rotate(image, angle, order=0, reshape=False) | |
| label = ndimage.rotate(label, angle, order=0, reshape=False) | |
| return image, label | |
| class RandomGenerator(object): | |
| def __init__(self, output_size): | |
| self.output_size = output_size | |
| def __call__(self, sample): | |
| image, label = sample['image'], sample['label'] | |
| if random.random() > 0.5: | |
| image, label = random_rot_flip(image, label) | |
| elif random.random() > 0.5: | |
| image, label = random_rotate(image, label) | |
| x, y = image.shape | |
| if x != self.output_size[0] or y != self.output_size[1]: | |
| image = zoom(image, (self.output_size[0] / x, self.output_size[1] / y), order=3) # why not 3? | |
| label = zoom(label, (self.output_size[0] / x, self.output_size[1] / y), order=0) | |
| image = torch.from_numpy(image.astype(np.float32)).unsqueeze(0) | |
| label = torch.from_numpy(label.astype(np.float32)) | |
| sample = {'image': image, 'label': label.long()} | |
| return sample | |
| class Synapse_dataset(Dataset): | |
| def __init__(self, base_dir, list_dir, split, transform=None): | |
| self.transform = transform # using transform in torch! | |
| self.split = split | |
| self.sample_list = open(os.path.join(list_dir, self.split+'.txt')).readlines() | |
| self.data_dir = base_dir | |
| def __len__(self): | |
| return len(self.sample_list) | |
| def __getitem__(self, idx): | |
| if self.split == "train": | |
| slice_name = self.sample_list[idx].strip('\n') | |
| data_path = os.path.join(self.data_dir, slice_name+'.npz') | |
| data = np.load(data_path) | |
| image, label = data['image'], data['label'] | |
| else: | |
| vol_name = self.sample_list[idx].strip('\n') | |
| filepath = self.data_dir + "/{}.npy.h5".format(vol_name) | |
| data = h5py.File(filepath) | |
| image, label = data['image'][:], data['label'][:] | |
| sample = {'image': image, 'label': label} | |
| if self.transform: | |
| sample = self.transform(sample) | |
| sample['case_name'] = self.sample_list[idx].strip('\n') | |
| return sample | |
Xet Storage Details
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