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utils.py
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utils.py
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import sys
import os
import cv2
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
from tqdm import tqdm
class Logger(object):
def __init__(self,logfile):
self.terminal = sys.stdout
self.log = open(logfile, "a")
def write(self, message):
self.terminal.write(message) #print to screen
self.log.write(message) #print to logfile
def flush(self):
#this flush method is needed for python 3 compatibility.
#this handles the flush command by doing nothing.
#you might want to specify some extra behavior here.
pass
def Decide_preprocess(blockzxy,config):
# Decide Preprocess of different stride, window wide-center
# S2:9; S3:5; S4:3; S5:2 for suitble upper 100 and time saving
# Decide stride 2 when wc = [],[150,75],[100,70]
config['num_file'] = 9
config['stridezxy'] = [blockzxy[0] // 2, blockzxy[1] // 2, blockzxy[2] // 2]
config['window_wc'] = []
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S2_Wlivertumor/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S2_Wlivertumor/seg"
preprocess(blockzxy, config)
config['window_wc'] = [150, 75]
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S2_W15075/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S2_W15075/seg"
preprocess(blockzxy, config)
config['window_wc'] = [100, 70]
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S2_W10070/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S2_W10070/seg"
preprocess(blockzxy, config)
# Decide stride 4 when wc = [],[150,75],[100,70]
config['num_file'] = 3
config['stridezxy'] = [blockzxy[0] // 4, blockzxy[1] // 4, blockzxy[2] // 4]
config['window_wc'] = []
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S4_Wlivertumor/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S4_Wlivertumor/seg"
preprocess(blockzxy, config)
config['window_wc'] = [150,75]
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S4_W15075/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S4_W15075/seg"
preprocess(blockzxy, config)
config['window_wc'] = [100,70]
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S4_W10070/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S4_W10070/seg"
preprocess(blockzxy, config)
# Decide stride 5 when wc = [],[150,75],[100,70]
config['num_file'] = 2
config['stridezxy'] = [blockzxy[0] // 5, blockzxy[1] // 5, blockzxy[2] // 5]
config['window_wc'] = []
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S5_Wlivertumor/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S5_Wlivertumor/seg"
preprocess(blockzxy, config)
config['window_wc'] = [150, 75]
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S5_W15075/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S5_W15075/seg"
preprocess(blockzxy, config)
config['window_wc'] = [100, 70]
config['savedct_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S5_W10070/ct"
config['savedseg_path'] = "/data/lihuiyu/LiTS/Decide_Preprocessing/S5_W10070/seg"
preprocess(blockzxy, config)
print('Time {:.3f} min'.format((time.time() - start_time) / 60))
print(time.strftime('%Y/%m/%d-%H:%M:%S', time.localtime()))
def histgram_plot_save(train_path, check_path):
# polt gray histgram
num = 131
bins = 100
pbar = tqdm(total=num) # Initialise
for i in range(num):
ct_array = sitk.GetArrayFromImage(sitk.ReadImage(os.path.join(train_path, 'volume-' + str(i) + '.nii')))
seg_array = sitk.GetArrayFromImage(sitk.ReadImage(os.path.join(train_path, 'segmentation-' + str(i) + '.nii')))
seg_bg = seg_array == 0
seg_liver = seg_array >= 1
seg_tumor = seg_array == 2
ct_bg = ct_array * seg_bg
ct_liver = ct_array * seg_liver
ct_tumor = ct_array * seg_tumor
bg_min = ct_bg.min()
bg_max = ct_bg.max()
liver_min = ct_liver.min()
liver_max = ct_liver.max()
tumor_min = ct_tumor.min()
tumor_max = ct_tumor.max()
ct_bg = np.float32(ct_bg)
ct_liver = np.float32(ct_liver)
ct_tumor = np.float32(ct_tumor)
hist_bg = cv2.calcHist([ct_bg.flatten()], [0], None, [bins], [int(bg_min), int(bg_max)]) # shape(100, 1)
hist_liver = cv2.calcHist([ct_liver.flatten()], [0], None, [bins],
[int(liver_min), int(liver_max)]) # shape(100, 1)
hist_tumor = cv2.calcHist([ct_tumor.flatten()], [0], None, [bins],
[int(tumor_min), int(tumor_max)]) # shape(100, 1)
plt.figure()
plt.plot(hist_bg, 'k')
plt.plot(hist_liver, 'r')
plt.plot(hist_tumor, 'g')
plt.legend(('bg', 'liver', 'tumor'), loc='upper right')
plt.title('Tissue Intensity Distribution' + 'volume-' + str(i))
saved_name = os.path.join(check_path, 'volume-' + str(i) + '.png')
plt.savefig(saved_name)
pbar.update(1)
pbar.close()
def get_GrayScaleRange(train_ct_path):
# get the gray scale range of train-liver, train_tumor
num = 131
liver_lower = 0;liver_upper = 0
tumor_lower = 0;tumor_upper = 0
for i in range(num):
ct_array = sitk.GetArrayFromImage(sitk.ReadImage(os.path.join(train_ct_path,'volume-'+str(i)+'.nii')))
seg_array = sitk.GetArrayFromImage(sitk.ReadImage(os.path.join(train_ct_path,'segmentation-'+str(i)+'.nii')))
seg_bg = seg_array==0
seg_liver = seg_array>=1
seg_tumor = seg_array==2
ct_bg = ct_array*seg_bg
ct_liver = ct_array*seg_liver
ct_tumor = ct_array*seg_tumor
liver_min = ct_liver.min()
liver_max = ct_liver.max()
tumor_min = ct_tumor.min()
tumor_max = ct_tumor.max()
print(i,'all:(',ct_array.min(),',',ct_array.max(),')',
'bg:(',ct_bg.min(),',',ct_bg.max(),')',
'liver:(',liver_min,',',liver_max,')',
'tumor:(',tumor_min,',',tumor_max,')',)
liver_lower+=liver_min
liver_upper+=liver_max
tumor_lower += tumor_min
tumor_upper += tumor_max
print('liver:(',liver_lower/num,',',liver_upper/num,')')
print('tumor:(', tumor_lower / num, ',', tumor_upper / num, ')')