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import os
import numpy as np
import nibabel
from skimage.io import imsave, imread
data_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'data', 'raw')
image_rows = int(512/2)
image_cols = int(512/2) #we will undersample our training 2D images later (for memory and speed)
def create_train_data():
train_data_path = os.path.join(data_path, 'train')
images = os.listdir(train_data_path)
imgs_train=[] #training images
imgsliv_train=[] #training masks (corresponding to liver)
print(('-'*30))
print('Creating training images...')
print(('-'*30))
a=[]
b=[]
for k in range(len(images)):
if k%2==0:
a.append(np.sort(images)[k]) #file names corresponding to training masks
else:
b.append(np.sort(images)[k]) #file names corresponding to training images
for liver,orig in zip(a,b):
imgl=nibabel.load(os.path.join(train_data_path,liver)) #we load 3D training mask
imgo=nibabel.load(os.path.join(train_data_path,orig)) #we load 3D training image
for k in range(imgl.shape[2]):
dimgl=np.array(imgl.get_data()[::2,::2,k]) #axial cuts are made along the z axis with undersampling
dimgo=np.array(imgo.get_data()[::2,::2,k])
if len(np.unique(dimgl))!=1: #we only recover the 2D sections containing the liver
imgsliv_train.append(dimgl)
imgs_train.append(dimgo)
imgs = np.ndarray((len(imgs_train), image_rows, image_cols), dtype=np.uint8)
imgs_mask = np.ndarray((len(imgsliv_train), image_rows, image_cols), dtype=np.uint8)
for index,img in enumerate(imgs_train):
imgs[index,:,:]=img
for index,img in enumerate(imgsliv_train):
imgs_mask[index,:,:]=img
np.save(os.path.join(data_path, '..', 'processed', 'imgs_train.npy'), imgs)
np.save(os.path.join(data_path, '..', 'processed', 'imgsliv_train.npy'), imgs_mask)
print('Saving to .npy files done.')
def load_train_data():
imgs_train = np.load(os.path.join(data_path, '..', 'processed', 'imgs_train.npy'))
imgs_mask_train = np.load(os.path.join(data_path, '..', 'processed', 'imgsliv_train.npy'))
return imgs_train, imgs_mask_train
def create_test_data():
test_data_path = os.path.join(data_path, 'test')
images = os.listdir(test_data_path)
print(('-'*30))
print('Creating testing images...')
print(('-'*30))
imgs_test=[]
imgsliv_test=[]
for image_name in images:
print(image_name)
img=nibabel.load(os.path.join(test_data_path,image_name))
print((img.shape))
for k in range(img.shape[2]):
dimg=np.array(img.get_data()[::2,::2,k])
if 'liver' in image_name:
imgsliv_test.append(dimg)
elif 'orig' in image_name:
imgs_test.append(dimg)
imgst= np.ndarray((len(imgs_test), image_rows, image_cols), dtype=np.uint8)
imgs_maskt= np.ndarray((len(imgsliv_test), image_rows, image_cols), dtype=np.uint8)
for index,img in enumerate(imgs_test):
imgst[index,:,:]=img
for index,img in enumerate(imgsliv_test):
imgs_maskt[index,:,:]=img
np.save(os.path.join(data_path, '..', 'processed', 'imgs_test.npy'), imgst)
np.save(os.path.join(data_path, '..', 'processed', 'imgsliv_test.npy'), imgs_maskt)
print('Saving to .npy files done.')
def load_test_data():
imgst = np.load(os.path.join(data_path, '..', 'processed', 'imgs_test.npy'))
imgs_id = np.load(os.path.join(data_path, '..', 'processed', 'imgsliv_test.npy'))
return [imgst, imgs_id]
if __name__ == '__main__':
create_train_data()
create_test_data()
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