FileNames = {Filename_Dst.name}'
时间: 2024-04-22 21:24:13 浏览: 10
根据您提供的代码 `FileNames = {Filename_Dst.name}'`,它是将 `Filename_Dst` 结构体数组中的文件名提取出来并存储在一个单元格数组 `FileNames` 中。
在 MATLAB 中,结构体数组的字段可以通过点操作符 `.` 来访问。每个结构体元素都有一个名为 `name` 的字段,用于存储文件名。
您可以使用以下代码来提取文件名,并将其存储在一个单元格数组中:
```matlab
FileNames = {Filename_Dst.name}';
```
上述代码将提取 `Filename_Dst` 结构体数组中的所有文件名,并将其存储在单元格数组 `FileNames` 中。
希望能对您有所帮助!如果您还有其他问题,请随时提问。
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替换掉import cv2,将代码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= 'E:\\dataset\\LiTS17\\' savepath = 'E:\\dataset\\LiTS17\\2d\\' filenames = os.listdir(path + "segmentation") num = 0 for filename in filenames: num = save_fig(path, savepath, num, filename)改成有相同作用的代码
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 = os.path.join(niifilepath, "segmentation", "segmentation-" + name)
filepath_vol = os.path.join(niifilepath, "volume", "volume-" +name)
savepath_seg = os.path.join(savepath, "segmentation")
savepath_vol = os.path.join(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.astype(np.uint8), 1, 255, cv2.THRESH_BINARY)
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= 'E:\\dataset\\LiTS17\\'
savepath = 'E:\\dataset\\LiTS17\\2d\\'
filenames = os.listdir(os.path.join(path, "segmentation"))
num = 0
for filename in filenames:
num = save_fig(path, savepath, num, filename)
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Pillow模块代替cv2模块来实现相同功能。Pillow模块是Python的一个图像处理库,可以用来读取、处理和保存多格式的图片。我们只需要将cv2.threshold方法替换成Pillow.ImageOps.autocontrast()即可。
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(np.uint8))
silce_seg = ImageOps.autocontrast(silce_seg, cutoff=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 = Image.fromarray(silce_vol.astype(np.uint8))
imageio.imwrite(os.path.join(savepath_seg, '{}.png'.format(num)), np.array(silce_seg))
imageio.imwrite(os.path.join(savepath_vol, '{}.png'.format(num)), np.array(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)