| 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) |
| 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 |
| 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 |
|
|
