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temp_scripts.py
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import numpy as np
from scipy.spatial.distance import euclidean
import os, sys, time, csv, subprocess
from dltk.core.io.preprocessing import normalise_zero_one, resize_image_with_crop_or_pad
import dltk
import h5py
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import h5py
import sklearn
from sklearn.neighbors import KDTree
import nibabel as nib
from multiprocessing import Pool, Process
def flatten_IBIS():
for root, dirs, files in os.walk('/data1/data'):
for file in files:
if '.mnc' in file and 'IBIS' in file:
orig_filename = file.split('//')[-1]
filename = file.split('//')[-1][4:]
print(filename)
print('/data1/data/IBIS/' + filename)
os.rename(os.path.join(root, orig_filename), '/data1/data/IBIS/' + filename)
def check_hdf5():
f = h5py.File('/data1/data/deepqc/deepqc.hdf5', 'r')
images = f['MRI']
for i, image in enumerate(images):
plt.imshow(image[:, :, 100])
plt.savefig('/data1/data/deepqc/test/' + str(i) + '.png')
def generate_iseg_images():
iseg_dir = 'E:/iseg2017/'
t1 = nib.load(iseg_dir + 'training/subject-1-T1.img').get_data()
t2 = nib.load(iseg_dir + 'training/subject-1-T2.img').get_data()
labels = nib.load(iseg_dir + 'training/subject-1-label.img').get_data()
print(t1.shape, labels.shape)
plt.imshow(t1[:, :, t1.shape[2]//2+10, 0].T, cmap='gray')
plt.axis('off')
plt.savefig(iseg_dir + 'example_t1.png', bbox_inches='tight')
plt.imshow(t2[:, :, t2.shape[2]//2+10, 0].T, cmap='gray')
plt.axis('off')
plt.savefig(iseg_dir + 'example_t2.png', bbox_inches='tight')
plt.imshow(labels[:, :, labels.shape[2]//2+10, 0].T)
plt.axis('off')
plt.savefig(iseg_dir + 'example_labels.png', bbox_inches='tight')
#IBIS 3 timepoints
ibis_dir = 'E:/iseg2017/IBIS/103430/'
t1_06 = nib.load(iseg_dir + 'IBIS/103430/V06/deface/ibis_103430_V06_t1w.mnc').get_data()
t1_12 = nib.load(ibis_dir + 'V12/deface/deface_103430_V12_t1w.mnc').get_data()
t1_24= nib.load(ibis_dir + 'V24/deface/deface_103430_V24_t1w.mnc').get_data()
labels1 = nib.load(iseg_dir + 'IBIS/103430_V06_label.nii.gz').get_data()
labels2 = nib.load(iseg_dir + 'IBIS/107524_V06_label.nii.gz').get_data()
print(t1_06.shape)
print(t1_12.shape)
plt.imshow(t1_06[:, t1_06.shape[1]//2-10, :].T, cmap='gray')
plt.axis('off')
plt.savefig(iseg_dir + 'ibis_v06_example.png')
plt.imshow(t1_12[:, t1_12.shape[1]//2, :].T, cmap='gray')
plt.axis('off')
plt.savefig(iseg_dir + 'ibis_v12_example.png')
plt.imshow(t1_24[:, t1_24.shape[1]//2, :].T, cmap='gray', origin='lower')
plt.axis('off')
plt.savefig(iseg_dir + 'ibis_v24_example.png')
plt.imshow(labels1[:, :, labels1.shape[2]//2].T, origin='lower')
plt.axis('off')
plt.savefig(iseg_dir + 'ibis_v06_example_labels.png', bbox_inches='tight')
plt.imshow(labels2[:, :, labels2.shape[2]//2+10].T, origin='lower')
plt.axis('off')
plt.savefig(iseg_dir + 'ibis_v24_example_labels.png', bbox_inches='tight')
if __name__ == "__main__":
# check_hdf5()
generate_iseg_images()