给出相同功能的代码import os import numpy as np import nibabel as nib import imageio from PIL import Image def read_niifile(niifilepath): # 读取niifile文件 img = nib.load(niifilepath) # 提取niifile文件 img_fdata = img.get_fdata(dtype='float32') return img_fdata def save_fig(niifilepath, savepath, num, name): # 保存为图片 name = name.split('-')[1] filepath_seg = niifilepath + "segmentation\" + "segmentation-" + name filepath_vol = niifilepath + "volume\" + "volume-" + name savepath_seg = savepath + "segmentation\" savepath_vol = savepath + "volume\" if not os.path.exists(savepath_seg): os.makedirs(savepath_seg) if not os.path.exists(savepath_vol): os.makedirs(savepath_vol) fdata_vol = read_niifile(filepath_vol) fdata_seg = read_niifile(filepath_seg) (x, y, z) = fdata_seg.shape total = x * y for k in range(z): silce_seg = fdata_seg[:, :, k] if silce_seg.max() == 0: continue else: silce_seg = (silce_seg - silce_seg.min()) / (silce_seg.max() - silce_seg.min()) * 255 silce_seg = np.uint8(Image.fromarray(silce_seg).convert('L')) silce_seg = cv2.threshold(silce_seg, 1, 255, cv2.THRESH_BINARY)[1] if (np.sum(silce_seg == 255) / total) > 0.015: silce_vol = fdata_vol[:, :, k] silce_vol = (silce_vol - silce_vol.min()) / (silce_vol.max() - silce_vol.min()) * 255 silce_vol = np.uint8(Image.fromarray(silce_vol).convert('L')) imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), silce_seg) imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), silce_vol) num += 1 return num if name == 'main': path = r'C:\Users\Administrator\Desktop\LiTS2017' savepath = r'C:\Users\Administrator\Desktop\2D-LiTS2017' filenames = os.listdir(path + "segmentation") num = 0 for filename in filenames: num = save_fig(path, savepath, num, filename)

这段代码实现了将NIfTI格式的医学图像数据转换为2D图像进行保存的功能。具体来说，它首先读取NIfTI格式的图像文件，并提取图像数据。然后，它将图像数据转换为灰度图像，并通过二值化处理将分割标签提取出来，最后将分割标签和对应的图像保存为PNG格式的文件。

这个代码和你之前给出的代码实现了相同的功能，但是具体实现细节有所不同。例如，它使用了PIL库和imageio库来保存图像文件，而你之前使用的是cv2库。此外，它使用了numpy库和nibabel库来读取和处理NIfTI格式的图像数据，而你之前使用的是SimpleITK库。