You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
73 lines
2.4 KiB
73 lines
2.4 KiB
import os
|
|
import shutil
|
|
from time import time
|
|
|
|
import numpy as np
|
|
import SimpleITK as sitk
|
|
import nibabel as nib
|
|
import scipy.ndimage as ndimage
|
|
import h5py
|
|
|
|
splits = ['train', 'test']
|
|
# train = True # Set True to process training set and set False for testset
|
|
|
|
for split in splits:
|
|
if (split == 'train'):
|
|
ct_path = './data/synapse/Abdomen/RawData/TrainSet/img' # set your path to your trainset directory
|
|
seg_path = './data/synapse/Abdomen/RawData/TrainSet/label'
|
|
save_path = './data/synapse/train_npz_new/'
|
|
else:
|
|
ct_path = './data/synapse/Abdomen/RawData/TestSet/img' # set your path to your testset directory
|
|
seg_path = './data/synapse/Abdomen/RawData/TestSet/label'
|
|
save_path = './data/synapse/test_vol_h5_new/'
|
|
|
|
if os.path.exists(save_path) is False:
|
|
os.mkdir(save_path)
|
|
|
|
upper = 275
|
|
lower = -125
|
|
|
|
start_time = time()
|
|
|
|
for ct_file in os.listdir(ct_path):
|
|
|
|
ct = nib.load(os.path.join(ct_path, ct_file))
|
|
seg = nib.load(os.path.join(seg_path, ct_file.replace('img', 'label')))
|
|
|
|
# Convert them to numpy format,
|
|
ct_array = ct.get_fdata()
|
|
seg_array = seg.get_fdata()
|
|
|
|
ct_array = np.clip(ct_array, lower, upper)
|
|
|
|
# print([np.min(ct_array), np.max(ct_array)])
|
|
|
|
# normalize each 3D image to [0, 1]
|
|
ct_array = (ct_array - lower) / (upper - lower)
|
|
|
|
# print([np.min(ct_array), np.max(ct_array)])
|
|
|
|
ct_array = np.transpose(ct_array, (2, 0, 1))
|
|
seg_array = np.transpose(seg_array, (2, 0, 1))
|
|
|
|
print('file name:', ct_file)
|
|
print('shape:', ct_array.shape)
|
|
|
|
ct_number = ct_file.split('.')[0]
|
|
if (split == 'test'):
|
|
new_ct_name = ct_number.replace('img', 'case') + '.npy.h5'
|
|
hf = h5py.File(os.path.join(save_path, new_ct_name), 'w')
|
|
hf.create_dataset('image', data=ct_array)
|
|
hf.create_dataset('label', data=seg_array)
|
|
hf.close()
|
|
continue
|
|
|
|
for s_idx in range(ct_array.shape[0]):
|
|
ct_array_s = ct_array[s_idx, :, :]
|
|
seg_array_s = seg_array[s_idx, :, :]
|
|
slice_no = "{:03d}".format(s_idx)
|
|
new_ct_name = ct_number.replace('img', 'case') + '_slice' + slice_no
|
|
np.savez(os.path.join(save_path, new_ct_name), image=ct_array_s, label=seg_array_s)
|
|
|
|
print('already use {:.3f} min'.format((time() - start_time) / 60))
|
|
print('-----------') |