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dcm2pic.py
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dcm2pic.py
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import cv2
import pydicom
import os
import numpy as np
#------------------------------------------------------------------------------------
# 遍历训练集或者测试集,讲所有dcm格式影像按照命名顺序拼接组成一个numpy的3维数组,
# 然后保存为npy格式。之所以用命名顺序不使用dicom读取切片DCM文件坐标信息是因为经
# 过数据分析发现部分样本的坐标信息存在问题,不能正确的表示切片的顺序。
#------------------------------------------------------------------------------------
files_dir = '/data/data_for_ai/test/'
files_dir_save = '/data/data_for_ai/train_temp/'
files_dir_save_npy = '/data/data_for_ai/test_temp_npy/'
if not os.path.exists(files_dir_save):
os.makedirs(files_dir_save)
# if not os.path.exists(files_dir_save_npy):
# os.makedirs(files_dir_save_npy)
list_sample_names = os.listdir(files_dir)
for i in range(len(list_sample_names)):
# if not os.path.exists(files_dir_save + list_sample_names[i]):
# os.makedirs(files_dir_save + list_sample_names[i])
sample_single_dir = files_dir + list_sample_names[i] + '/'
list_pics = os.listdir(sample_single_dir)
temp_cts = []
pic_single_name_half = list_pics[0][:-9]
for j in range(len(list_pics)):
temp_pic_name_ID = str(j+1)
if len(temp_pic_name_ID) == 3:
temp_pic_name_ID = '00' + temp_pic_name_ID
if len(temp_pic_name_ID) == 2:
temp_pic_name_ID = '000' + temp_pic_name_ID
if len(temp_pic_name_ID) == 1:
temp_pic_name_ID = '0000' + temp_pic_name_ID
if len(list_pics[0]) < 14:
try:
temp_ct = pydicom.read_file(sample_single_dir + 'image-' + str(j+1) + '.dcm')
except Exception:
print('Error')
else:
try:
temp_ct = pydicom.read_file(sample_single_dir + pic_single_name_half + temp_pic_name_ID + '.dcm')
except Exception:
print('Error')
temp_cts.append(temp_ct)
# print('ok')
image_cts_np = np.zeros((len(temp_cts), 512, 512),dtype=np.int16)
# print(temp_cts[0].ImageOrientationPatient[5])
# if temp_cts[0].ImageOrientationPatient[5] != 0:
# print(list_sample_names[i])
for k in range(len(temp_cts)):
# print(temp_cts[k].PatientPosition)
# print(temp_cts[k].SliceThickness)
temp_image = temp_cts[k].pixel_array
temp_image.astype(np.int16)
temp_image[temp_image < 0] = 0
temp_image[temp_image > 4096] = 0
intercept = temp_cts[k].RescaleIntercept
slope = temp_cts[k].RescaleSlope
if slope != 1:
temp_image = slope * temp_image.astype(np.float64)
temp_image = temp_image.astype(np.int16)
temp_image = temp_image + intercept
image_cts_np[k,:,:] = temp_image
# cv2.imshow('temp', temp_image)
# cv2.waitKey()
# cv2.imwrite(files_dir_save + list_sample_names[i] + '/' + str(k) + '.png', temp_image)
np.save(files_dir_save_npy + list_sample_names[i] + '.npy', image_cts_np)
print(i, 'ok')