import globimport pydicomimport matplotlib.pyplot as pltimport numpy as npimport osimport multiprocessingimport threadingimport timedef extractDicomFileInfo(filename):"""提取Dicom文件的tag信息input :文件名output:相关信息"""info = {}dcm = pydicom.read_file(filename)info["PatientID"] = dcm.PatientID# 患者IDinfo["PatientName"] = dcm.PatientName # 患者姓名info["PatientBirthData"] = dcm.PatientBirthData # 患者出生日期info["PatientAge"] = dcm.PatientAge # 患者年龄info['PatientSex'] = dcm.PatientSex # 患者性别info['StudyID'] = dcm.StudyID # 检查IDinfo['StudyDate'] = dcm.StudyDate# 检查日期info['StudyTime'] = dcm.StudyTime# 检查时间info['InstitutionName'] = dcm.InstitutionName # 机构名称info['Manufacturer'] = dcm.Manufacturer # 设备制造商info['StudyDescription'] = dcm.StudyDescription # 检查项目描述return infodef saveAsJPGImage(img_array, jpg_path, uid, lens):"""格式转换函数input : 像素矩阵,图片保存路径,uid,lensoutput:"""plt.figure(figsize=(12,6), dpi=80) # 绘制画板arr_temp = np.reshape(img_array, (lens,))max_val = max(arr_temp) # 获取像素极大值min_val = min(arr_temp) # 获取像素极小值img_array = (img_array-min_val)/(max_val-min_val) # 像素值归一化plt.imshow(img_array, cmap=plt.cm.bone)plt.title("UID:{}".format(uid))# print("saving image:{}".format(jpg_path.split('JPG图像/')[1]))plt.savefig(jpg_path)plt.close()def getDicomFile(dicom_dir, jpg_dir):"""获取dicom文件的参数信息input : dicom_dir,jpg_diroutput: args_list"""# 定位绝对路径all_file_list = os.listdir(dicom_dir)# 初始化args_listargs_list = []extension = 'dcm'for f in all_file_list:dcm_dir = os.path.join(dicom_dir, f, 'st01', '*.' + extension)dcm_files = glob.glob(dcm_dir)for i, d in enumerate(dcm_files):dicom_file_path = d# dicom_info = extractDicomFileInfo(dicom_file_path) # 提取tag信息备用dcm_name = os.path.basename(d) # t2640000201al.dcm# jpg_name = jpg_dir + d# print(jpg_name)dcm = pydicom.read_file(dicom_file_path) # 读取dicom文件if "PixelData" not in dcm:print(f"Dataset found with no Pixel Data: {d}")continueif len(dcm.pixel_array.shape) > 2:# 循环提取图片for index in range(int(dcm.pixel_array.shape[0])):# 根据dicom文件的BitAllocation显示1img = np.asarray(dcm.pixel_array[index], dtype='uint16')img_upload_path = os.path.join(jpg_dir, f, dcm_name.split('.d')[0], str(index) + ".png")if not os.path.exists(os.path.join(jpg_dir, f, dcm_name.split('.d')[0])):os.makedirs(os.path.join(jpg_dir, f, dcm_name.split('.d')[0]))# cmap参数是重点，选择plt.cm.bone，胸片显示才会正常plt.imsave(img_upload_path, img, cmap=plt.cm.bone)else:# 单图img = np.asarray(dcm.pixel_array, dtype='uint16')img_upload_path = os.path.join(jpg_dir, "x.png")plt.imsave(img_upload_path, img, cmap=plt.cm.bone)# img_array = dcm.pixel_array# 获取图像像素矩阵# lens = img_array.shape[0]*img_array.shape[1] # 获取像素矩阵大小# uid = dcm.SOPInstanceUID # 获取图像uid# args_tuple = (img_array, jpg_name, uid, lens) # 添加参数tuple# args_list.append(args_tuple)def main():"""main function"""# 存放DICOM文件的文件夹dicom_dir = "D:\papilla_detection\DICOM"# dicom_dir = r'E:\scripts\dicom2png'# 存放转换后的JPG图片的文件夹jpg_dir = "D:\papilla_detection\PNG"# jpg_dir = r'E:\scripts\dicom2png\png'begin_time = time.time() # 开始时间pool = multiprocessing.Pool(processes=5)# 创建进程池args_list = getDicomFile(dicom_dir, jpg_dir) # 获取参数list# pool.starmap(saveAsJPGImage, args_list) # 多进程保存jpg文件# pool.close() # 关闭进程end_time = time.time() # 结束时间print("耗时:{}s".format(round(end_time-begin_time, 4))) # 打印耗时if __name__ == '__main__':main()
