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Copy pathtest.py
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90 lines (73 loc) · 3.39 KB
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from utils_new import *
import numpy
import os
sep = os.sep #sep分隔符 //
# img_path = r'./aWITHmask4radiomics/image'
# mask_path = r'./aWITHmask4radiomics/mask'
file_path = r'D:\SSY\data_202003\yuantu'
file = []; original_img = []; voi_img = []; PM_img = []
file = get_filelist_frompath4LNDb(file_path,'nii',None) #获得img_path下格式是nii的所有文件
for i in range(len(file)):
if (i+1) % 3 == 1:
original_img.append(file[i])
elif (i+1) % 3 == 2:
PM_img.append(file[i])
else:
voi_img.append(file[i])
# file2 = [ mask_path+ sep + ((ii.split(sep)[-1]).split('.')[0]) +'_m1_w.nii' for ii in file1] #按照file1文件的对应顺序排file2的顺序
# file2 = []
# file2 = get_filelist_frompath4LNDb(mask_path,'nii',None)
save_path1 = r'./train_data/voi_npy'
save_path2 = r'./train_data/PM_npy'
try:
os.mkdir(save_path1)
os.mkdir(save_path2)#创建文件夹
except:
print('ok')
n = 15
# for i in range(len(original_img)):
# print(i+1,r'/',len(original_img)) #显示运行进程
# patients = wama() #调用类
# patients.appendImageFromNifti('ct', original_img[i]) #读入ct图像
# patients.appendSementicMaskFromNifti('ct', voi_img[i]) #读入mask
#
# scan1 = patients.scan['ct'] #原始ct图像
# mask1 = patients.sementic_mask['ct'] #原始肿瘤mask
#
# scan1 = adjustWindow(scan1, 145, -5) #调整ct的窗宽，窗位
# patients.make_bbox_from_mask('ct') #计算mask区域坐标
# coor = patients.bbox['ct'] #读取mask坐标
# print(coor)
# # if i == 2:
# # image_in_ROI = scan1[(coor[0] - n):(coor[1] + n), (coor[2] - n):(coor[3] + n), (coor[-2]):(coor[-1] + n)]
# # else:
# # image_in_ROI = scan1[(coor[0] - n):(coor[1] + n), (coor[2] - n):(coor[3] + n), (coor[-2] - n):(coor[-1] + n)] # 在c't原图截取包含mask区域
# image_in_ROI = scan1[(coor[0]-n):(coor[1]+n),(coor[2]-n):(coor[3]+n),(coor[-2]-n):(coor[-1]+n)] #在c't原图截取包含mask区域
# mask_in_ROI = mask1[(coor[0]-n):(coor[1]+n),(coor[2]-n):(coor[3]+n),(coor[-2]-n):(coor[-1]+n)] #在mask上截取包含mask区域
#
# # save image
# numpy.save(save_path1+sep+str(i)+'_voi.npy',image_in_ROI)
# # save mask
# # numpy.save(save_path+sep+str(i)+'_msk.npy',mask_in_ROI)
# n = 15
for j in range(len(original_img)):
print(j+1,r'/',len(original_img)) #显示运行进程
patients = wama() #调用类
patients.appendImageFromNifti('ct', original_img[j]) #读入ct图像
patients.appendSementicMaskFromNifti('ct', PM_img[j]) #读入mask
scan1 = patients.scan['ct'] #原始ct图像
mask1 = patients.sementic_mask['ct'] #原始肿瘤mask
scan1 = adjustWindow(scan1, 400, 50) #调整ct的窗宽，窗位
patients.make_bbox_from_mask('ct') #计算mask区域坐标
coor = patients.bbox['ct'] #读取mask坐标
print(coor)
if j == 53:
n = 8
else:
n = 15
image_in_ROI = scan1[(coor[0]-n):(coor[1]+n),(coor[2]-n):(coor[3]+n),(coor[-2]-n):(coor[-1]+n)] #在ct原图截取包含mask区域
mask_in_ROI = mask1[(coor[0]-n):(coor[1]+n),(coor[2]-n):(coor[3]+n),(coor[-2]-n):(coor[-1]+n)] #在mask上截取包含mask区域
# save image
numpy.save(save_path2+sep+str(j)+'_PM.npy',image_in_ROI)
# save mask
# numpy.save(save_path+sep+str(i)+'_msk.npy',mask_in_ROI)