import SimpleITK as sitk
import numpy as np
import cv2

def convert_from_dicom_to_jpg(img,low_window,high_window,save_path):
  lungwin = np.array([low_window*1.,high_window*1.])
  newimg = (img-lungwin[0])/(lungwin[1]-lungwin[0])  #归一化
  newimg = (newimg*255).astype('uint8')        #将像素值扩展到[0,255]
  cv2.imwrite(save_path, newimg, [int(cv2.IMWRITE_JPEG_QUALITY), 100])

if __name__ == '__main__':

  # 下面是将对应的dicom格式的图片转成jpg
  dcm_image_path = '/DICOM_image/lung001.dcm'    #读取dicom文件
  output_jpg_path = 'JPG_image/lung001.jpg'
  ds_array = sitk.ReadImage(dcm_image_path)     #读取dicom文件的相关信息
  img_array = sitk.GetArrayFromImage(ds_array)   #获取array
  # SimpleITK读取的图像数据的坐标顺序为zyx，即从多少张切片到单张切片的宽和高，此处我们读取单张，因此img_array的shape
  #类似于 （1，height，width）的形式
  shape = img_array.shape
  img_array = np.reshape(img_array, (shape[1], shape[2])) #获取array中的height和width
  high = np.max(img_array)
  low = np.min(img_array)
  convert_from_dicom_to_jpg(img_array, low, high, output_jpg_path)  #调用函数，转换成jpg文件并保存到对应的路径
  print('FINISHED')