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dataset.py
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import os
from tqdm import tqdm
import numpy as np
import random
import nibabel as nib
from torch.utils import data
from scipy import ndimage as nd
from utils.resample import downsample
def norm_01(img):
img = img.astype(np.float32)
img = (img - img.min()) / (img.max() - img.min())
return img
def determine_axis(orientation, direction='axial'):
if direction == 'sagittal':
return orientation.index('R') if 'R' in orientation else orientation.index('L')
elif direction == 'coronal':
return orientation.index('A') if 'A' in orientation else orientation.index('P')
elif direction == 'axial':
return orientation.index('S') if 'S' in orientation else orientation.index('I')
def read_img(in_path, direction='axial', simulate_lr=True):
img_list = []
axis_list = []
filenames = os.listdir(in_path)
for f in tqdm(filenames):
img = nib.load(os.path.join(in_path, f))
orientation = nib.aff2axcodes(img.affine)
axis = determine_axis(orientation, direction=direction)
img_vol = np.array(img.dataobj)
img_vol = norm_01(img_vol)
if simulate_lr:
img_vol = downsample(img_vol, current_res=1, downsample_res=4, axis=axis)
img_list.append(img_vol)
axis_list.append(axis)
return img_list, axis_list
# -----------------------
# Training data
# -----------------------
class ImgTrain(data.Dataset):
def __init__(self, in_path, sample_size, thick_direction, simulate_lr=True, is_train=True, stage=1):
self.sample_size = sample_size
self.patch, self.axis = read_img(in_path=in_path, direction=thick_direction, simulate_lr=simulate_lr)
self.is_train = is_train
self.stage = stage
def __len__(self):
return len(self.patch)
def __getitem__(self, item):
subject_img = self.patch[item]
thick_axis = self.axis[item]
# randomly choice a slice to resample
if self.is_train and self.stage == 1:
down_axis = random.choice(np.delete(np.arange(3), thick_axis))
else:
down_axis = thick_axis
# randomly choice a slice along the down_axis
slice_idx_0 = random.randint(0, subject_img.shape[down_axis] - 3)
# get the 2D slice
slice_img_0 = np.take(subject_img, slice_idx_0, axis=down_axis)
slice_idx_1 = slice_idx_0 + 1 # the median slice
slice_img_1 = np.take(subject_img, slice_idx_1, axis=down_axis)
slice_idx_2 = slice_idx_0 + 2 # the next slice
slice_img_2 = np.take(subject_img, slice_idx_2, axis=down_axis)
# zero_pad to 256
if slice_img_0.shape[0] < self.sample_size:
slice_img_0 = np.pad(slice_img_0, ((0, self.sample_size - slice_img_0.shape[0]), (0, 0)), 'constant', constant_values=0)
slice_img_1 = np.pad(slice_img_1, ((0, self.sample_size - slice_img_1.shape[0]), (0, 0)), 'constant', constant_values=0)
slice_img_2 = np.pad(slice_img_2, ((0, self.sample_size - slice_img_2.shape[0]), (0, 0)), 'constant', constant_values=0)
if slice_img_0.shape[1] < self.sample_size:
slice_img_0 = np.pad(slice_img_0, ((0, 0), (0, self.sample_size - slice_img_0.shape[1])), 'constant', constant_values=0)
slice_img_1 = np.pad(slice_img_1, ((0, 0), (0, self.sample_size - slice_img_1.shape[1])), 'constant', constant_values=0)
slice_img_2 = np.pad(slice_img_2, ((0, 0), (0, self.sample_size - slice_img_2.shape[1])), 'constant', constant_values=0)
# random crop
h, w = slice_img_0.shape
x = random.randint(0, h - self.sample_size)
y = random.randint(0, w - self.sample_size)
slice_img_0 = slice_img_0[np.newaxis, x:x + self.sample_size, y:y + self.sample_size]
slice_img_1 = slice_img_1[np.newaxis, x:x + self.sample_size, y:y + self.sample_size]
slice_img_2 = slice_img_2[np.newaxis, x:x + self.sample_size, y:y + self.sample_size]
return slice_img_0, slice_img_1, slice_img_2
def loader_train(in_path, batch_size, thick_direction, sample_size, is_train, stage=1):
"""
:param in_path_hr: the path of HR patches
:param batch_size: N in Equ. 3
:param sample_size: K in Equ. 3
:param is_train:
:return:
"""
return data.DataLoader(
dataset=ImgTrain(in_path=in_path, sample_size=sample_size, thick_direction=thick_direction, simulate_lr=is_train, is_train=is_train, stage=stage),
batch_size=batch_size,
shuffle=is_train,
drop_last=is_train
)
# -----------------------
# Testing data
# -----------------------
class ImgTest(data.Dataset):
def __init__(self, in_path, scale, direction='axial', simulate_lr=True):
self.direction = direction
self.scale = scale
self.simulate_lr = simulate_lr
self.data = self.read_img(in_path)
print(f'loading data {in_path} with shape: {self.data.shape}')
def read_img(self, in_path):
self.img = nib.load(in_path)
orientation = nib.aff2axcodes(self.img.affine)
self.axis = determine_axis(orientation, direction=self.direction)
img_vol = np.array(self.img.dataobj)
img_vol = norm_01(img_vol)
if self.simulate_lr:
img_vol = downsample(img_vol, current_res=1, downsample_res=self.scale, axis=self.axis)
return img_vol
def __len__(self):
return self.data.shape[self.axis] - 1
def __getitem__(self, item):
# randomly choice a slice along the down_axis
slice_idx_0 = item
# get the 2D slice
slice_img_0 = np.take(self.data, slice_idx_0, axis=self.axis)
slice_idx_1 = slice_idx_0 + 1 # the median slice
slice_img_1 = np.take(self.data, slice_idx_1, axis=self.axis)
return slice_img_0, slice_img_1
def loader_test(dataset):
return data.DataLoader(
dataset=dataset,
batch_size=1,
shuffle=False
)