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dataset_load.py
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dataset_load.py
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import os
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import h5py
import pickle
dataset='miccai' #dataset to be used: miccai or mrnet or fastmri
mode='train' #train or test
save_path='/home/Co-VeGAN/'
def load_a(path, num):
f = os.listdir(path)
a = len(f)
data = []
#use imgs with more than 10% non-zero values
n_zero_ratio = 0.1
#num is to reduce the number of files loaded
for i in range(len(f)-num):
img = os.path.join(path, f[i])
img_l = nib.load(img)
img_data = img_l.get_fdata()
vol_max = np.max(img_data)
img_data = img_data/vol_max*2
for j in range(img_data.shape[2]):
if (float(np.count_nonzero(img_data[:,:,j]))/np.prod(img_data[:,:,j].shape))>=n_zero_ratio:
img_data[:,:,j] = img_data[:,:,j]-1
img_data_ts = np.rot90(img_data[:,:,j])
data.append(img_data_ts)
data = np.asarray(data)
return data
def load_b(path):
f = os.listdir(path)
data = []
#use imgs with more than 10% non-zero values
n_zero_ratio = 0.1
for i in range(len(f)):
img = os.path.join(path, f[i])
data_new=np.load(img, allow_pickle =True )
data_new=data_new.astype('float32')
for j in range(data_new.shape[0]):
if (float(np.count_nonzero(data_new[j,:,:]))/np.prod(data_new[j,:,:].shape))>=n_zero_ratio:
data_new[j,:,:] = data_new[j,:,:]/127.5-1.0
data.append(data_new[j,:,:])
data = np.asarray(data)
return data
def load_c(path,num,mode):
fl = os.listdir(path)
data = []
#use imgs with more than 10% non-zero values
n_zero_ratio = 0.1
k = 0
for i in range(num):
if mode=='train':
filename = os.path.join(path, fl[i])
else:
filename = os.path.join(path, fl[i+700])
f = h5py.File(filename,'r')
data_new = f['kspace']
data_new = np.asarray(data_new)
for j in range(data_new[0]):
#ifft
knee = np.fft.fftshift(np.fft.ifft2(data_new[j,:,:]))
#crop 320x320 from centre
knee = knee[data_new.shape[1]//2-160:data_new.shape[1]//2+160, data_new.shape[2]//2-160:data_new.shape[2]//2+160]
data.append(knee)
s = k
vol = np.asarray(data)
k = vol.shape[0]
maxr = np.max(np.real(vol[s:k]))
maxi = np.max(np.imag(vol[s:k]))
minr = np.min(np.real(vol[s:k]))
mini = np.min(np.imag(vol[s:k]))
vol[s:k] = vol[s:k]/np.max([maxr,maxi,np.abs(minr),np.abs(mini)])
data = list(vol)
data = np.asarray(data)
return data
def train_data_aug(train_gt,dataset):
if dataset=='miccai':
gt1=train_gt[0:13461,:,:]
gt2a=train_gt[12471:12966,:,:] #overlapping data
gt2b=train_gt[12966:13461,:,:] #overlapping data
gt3a=train_gt[13461:16629,:,:] #non-overlapping data
gt3b=train_gt[16629:19797,:,:] #non-overlapping data
else:
gt1=train_gt[0:8100,:,:]
gt2a=train_gt[7500:7800,:,:] #overlapping data
gt2b=train_gt[7800:8100,:,:] #overlapping data
gt3a=train_gt[8100:10000,:,:] #non-overlapping data
gt3b=train_gt[10000:11900,:,:] #non-overlapping data
gt2=np.vstack((gt2a,gt2b))
gt3=np.vstack((gt3a,gt3b))
gt4 = np.vstack((gt2,gt3))
gt_new=np.vstack((gt1,gt4))
return gt_new
if mode=='train':
if dataset=='miccai':
train_path='/home/Co-VeGAN/training-training/warped-images'
train_gt=load_a(train_path, 1090)
elif dataset=='mrnet':
train_path='/home/Co-VeGAN/train/coronal'
train_gt=load_b(train_path)
elif dataset=='fastmri':
train_path='/home/Co-VeGAN/singlecoil_train'
train_gt=load_c(train_path, 450, 'train')
train_gt_aug=train_data_aug(train_gt,dataset) #created gt for augmented data
with open(os.path.join(save_path,'training_gt_aug.pickle'),'wb') as f:
pickle.dump(train_gt_aug,f,protocol=4)
else:
if dataset=='miccai':
test_path='/home/Co-VeGAN/training-testing/warped-images'
test_data=load_a(test_path, 390)
elif dataset=='mrnet':
test_path='/home/Co-VeGAN/valid/coronal'
test_data=load_b(test_path)
elif dataset=='fastmri':
test_path='/home/Co-VeGAN/singlecoil_train'
test_data=load_c(test_path, 100, 'test')
with open(os.path.join(save_path,'testing_gt.pickle'),'wb') as f:
pickle.dump(test_data,f,protocol=4)