def save2txt(path, savepath='labels.txt'): print(os.path.exists(path)) with open(savepath, 'w') as t: imgfiles = glob.iglob(os.path.join(path, '**/*.tif'), recursive=True) for imgfile in imgfiles: imgname = os.path.split(imgfile)[-1] label = 0 if 'OK' in imgfile else 1 # label = 0 if imgfile.split('/')[-2] == 'OK' else 1 t.write(imgname + '\t' + str(label) + '\n') t.close()

这是一个Python函数，名为save2txt，它有两个参数：path和savepath。path是一个文件夹路径，savepath是一个可选参数，表示保存标签的文件路径，默认为'labels.txt'。

函数的作用是遍历path文件夹下所有的.tif文件，并将它们的标签保存到savepath文件中。如果.tif文件名中包含'OK'，则标签为0，否则标签为1。

相关问题

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。

import os import numpy as np import nibabel as nib import imageio import cv2 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] # 三个位置表示三个不同角度的切片 if silce_seg.max() == 0: continue else: silce_seg = (silce_seg - silce_seg.min()) / (silce_seg.max() - silce_seg.min()) * 255 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 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 # 将切片信息保存为png格式 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) 替换掉代码中的cv2模块，实现相同功能

可以使用PIL库替换掉cv2模块，实现相同的功能。具体替换方法如下：

1.导入PIL库

from PIL import Image

2.将cv2.threshold替换为PIL.ImageOps.autocontrast

silce_seg = (silce_seg - silce_seg.min()) / (silce_seg.max() - silce_seg.min()) * 255
silce_seg = Image.fromarray(silce_seg.astype('uint8'))
silce_seg = ImageOps.autocontrast(silce_seg, cutoff=1)
silce_seg = np.array(silce_seg)

3.将cv2.imwrite替换为PIL.Image.save

imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), silce_seg)
imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), silce_vol)

替换后的代码如下：

import os 
import numpy as np 
import nibabel as nib 
import imageio 
from PIL import Image, ImageOps

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]  # 三个位置表示三个不同角度的切片         
        if silce_seg.max() == 0:             
            continue         
        else:             
            silce_seg = (silce_seg - silce_seg.min()) / (silce_seg.max() - silce_seg.min()) * 255             
            silce_seg = Image.fromarray(silce_seg.astype('uint8'))
            silce_seg = ImageOps.autocontrast(silce_seg, cutoff=1)
            silce_seg = np.array(silce_seg)

            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                  

                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         
        # 将切片信息保存为png格式     
    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)