/
dataset.py
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/
dataset.py
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import numpy as np
import nibabel as nib
import math
import os
from skimage.util import view_as_windows
class Train_dataset(object):
def __init__(self, batch_size, overlapping=1):
self.batch_size = batch_size
self.data_path = '/imatge/isanchez/projects/neuro/ADNI-Screening-1.5T'
self.subject_list = os.listdir(self.data_path)
self.subject_list = np.delete(self.subject_list, 120)
self.heigth_patch = 112 # 128
self.width_patch = 112 # 128
self.depth_patch = 76 # 92
self.margin = 16
self.overlapping = overlapping
self.num_patches = (math.ceil((224 / (self.heigth_patch)) / (self.overlapping))) * (
math.ceil((224 / (self.width_patch)) / (self.overlapping))) * (
math.ceil((152 / (self.depth_patch)) / (self.overlapping)))
def mask(self, iteration):
subject_batch = self.subject_list[iteration * self.batch_size:self.batch_size + (iteration * self.batch_size)]
subjects_true = np.empty([self.batch_size, 256, 256, 184])
i = 0
for subject in subject_batch:
if subject != 'ADNI_SCREENING_CLINICAL_FILE_08_02_17.csv':
filename = os.path.join(self.data_path, subject)
filename = os.path.join(filename, 'T1_brain_extractedBrainExtractionMask.nii.gz')
proxy = nib.load(filename)
data = np.array(proxy.dataobj)
paddwidthr = int((256 - proxy.shape[0]) / 2)
paddheightr = int((256 - proxy.shape[1]) / 2)
paddepthr = int((184 - proxy.shape[2]) / 2)
if (paddwidthr * 2 + proxy.shape[0]) != 256:
paddwidthl = paddwidthr + 1
else:
paddwidthl = paddwidthr
if (paddheightr * 2 + proxy.shape[1]) != 256:
paddheightl = paddheightr + 1
else:
paddheightl = paddheightr
if (paddepthr * 2 + proxy.shape[2]) != 184:
paddepthl = paddepthr + 1
else:
paddepthl = paddepthr
data_padded = np.pad(data,
[(paddwidthl, paddwidthr), (paddheightl, paddheightr), (paddepthl, paddepthr)],
'constant', constant_values=0)
subjects_true[i] = data_padded
i = i + 1
mask = np.empty(
[self.batch_size * self.num_patches, self.width_patch + self.margin, self.heigth_patch + self.margin,
self.depth_patch + self.margin, 1])
i = 0
for subject in subjects_true:
patch = view_as_windows(subject, window_shape=(
(self.width_patch + self.margin), (self.heigth_patch + self.margin), (self.depth_patch + self.margin)),
step=(self.width_patch - self.margin, self.heigth_patch - self.margin,
self.depth_patch - self.margin))
for d in range(patch.shape[0]):
for v in range(patch.shape[1]):
for h in range(patch.shape[2]):
p = patch[d, v, h, :]
p = p[:, np.newaxis]
p = p.transpose((0, 2, 3, 1))
mask[i] = p
i = i + 1
return mask
def patches_true(self, iteration):
subjects_true = self.data_true(iteration)
patches_true = np.empty(
[self.batch_size * self.num_patches, self.width_patch + self.margin, self.heigth_patch + self.margin,
self.depth_patch + self.margin, 1])
i = 0
for subject in subjects_true:
patch = view_as_windows(subject, window_shape=(
(self.width_patch + self.margin), (self.heigth_patch + self.margin), (self.depth_patch + self.margin)),
step=(self.width_patch - self.margin, self.heigth_patch - self.margin,
self.depth_patch - self.margin))
for d in range(patch.shape[0]):
for v in range(patch.shape[1]):
for h in range(patch.shape[2]):
p = patch[d, v, h, :]
p = p[:, np.newaxis]
p = p.transpose((0, 2, 3, 1))
patches_true[i] = p
i = i + 1
return patches_true
def data_true(self, iteration):
subject_batch = self.subject_list[iteration * self.batch_size:self.batch_size + (iteration * self.batch_size)]
subjects = np.empty([self.batch_size, 224, 224, 152])
i = 0
for subject in subject_batch:
if subject != 'ADNI_SCREENING_CLINICAL_FILE_08_02_17.csv':
filename = os.path.join(self.data_path, subject)
filename = os.path.join(filename, 'T1_brain_extractedBrainExtractionBrain.nii.gz')
proxy = nib.load(filename)
data = np.array(proxy.dataobj)
paddwidthr = int((256 - proxy.shape[0]) / 2)
paddheightr = int((256 - proxy.shape[1]) / 2)
paddepthr = int((184 - proxy.shape[2]) / 2)
if (paddwidthr * 2 + proxy.shape[0]) != 256:
paddwidthl = paddwidthr + 1
else:
paddwidthl = paddwidthr
if (paddheightr * 2 + proxy.shape[1]) != 256:
paddheightl = paddheightr + 1
else:
paddheightl = paddheightr
if (paddepthr * 2 + proxy.shape[2]) != 184:
paddepthl = paddepthr + 1
else:
paddepthl = paddepthr
data_padded = np.pad(data,
[(paddwidthl, paddwidthr), (paddheightl, paddheightr), (paddepthl, paddepthr)],
'constant', constant_values=0)
subjects[i] = data_padded[16:240, 16:240, 16:168] # remove background
i = i + 1
return subjects