file_name=f"data_{i}_{j}.xlsx" folder_path=f"C:\\Users\\Administrator\\Desktop\\o\\" 怎么拼接成一个路径
时间: 2024-01-01 17:23:37 浏览: 26
你可以使用Python的字符串拼接操作符"+"来拼接文件名和文件夹路径,如下所示:
```python
file_name = f"data_{i}_{j}.xlsx"
folder_path = f"C:\\Users\\Administrator\\Desktop\\o\\"
path = folder_path + file_name
print(path) # 输出:C:\Users\Administrator\Desktop\o\data_i_j.xlsx
```
这里使用了f-string来格式化字符串,将变量i和j的值插入到文件名中。然后,使用"+"操作符将文件名和文件夹路径拼接在一起,得到完整的路径。最后,使用print函数打印路径。
相关问题
代码import os import numpy as np import nibabel as nib from PIL import Image # 创建保存路径 save_path = 'C:/Users/Administrator/Desktop/2D-LiTS2017' if not os.path.exists(save_path): os.makedirs(save_path) if not os.path.exists(os.path.join(save_path, 'image')): os.makedirs(os.path.join(save_path, 'image')) if not os.path.exists(os.path.join(save_path, 'label')): os.makedirs(os.path.join(save_path, 'label')) # 加载数据集 data_path = 'D:/BaiduNetdiskDownload/LiTS2017' img_path = os.path.join(data_path, 'Training Batch 1') label_path = os.path.join(data_path, 'Training Batch 2') # 转换图像 for file in sorted(os.listdir(img_path)): if file.endswith('.nii'): img_file = os.path.join(img_path, file) img = nib.load(img_file).get_fdata() img = np.transpose(img, (2, 0, 1)) # 转换为z, x, y for i in range(img.shape[0]): img_slice = img[i, :, :] img_slice = (img_slice - np.min(img_slice)) / (np.max(img_slice) - np.min(img_slice)) * 255 # 归一化到0-255 img_slice = img_slice.astype(np.uint8) img_slice = np.stack([img_slice]*3, axis=2) # 转换为三通道图像 img_name = file[:-4] + '' + str(i).zfill(3) + '.png' img_file_save = os.path.join(save_path, 'image', img_name) Image.fromarray(img_slice).save(img_file_save) # 转换标签 for file in sorted(os.listdir(label_path)): if file.endswith('.nii'): label_file = os.path.join(label_path, file) label = nib.load(label_file).get_fdata() label = np.transpose(label, (2, 0, 1)) # 转换为z, x, y for i in range(label.shape[0]): label_slice = label[i, :, :] label_slice[label_slice == 1] = 255 # 肝脏灰度值设为255 label_slice[label_slice == 2] = 128 # 肝脏肿瘤灰度值设为128 label_slice = label_slice.astype(np.uint8) label_name = file[:-4] + '' + str(i).zfill(3) + '.png' label_file_save = os.path.join(save_path, 'label', label_name) Image.fromarray(label_slice).save(label_file_save)出现scaled = scaled.astype(np.promote_types(scaled.dtype, dtype), copy=False) MemoryError错误,怎么修改?给出完整代码
这个错误通常意味着你的计算机没有足够的内存来处理数组。可能需要减少转换图像的尺寸或者使用更高效的算法。以下是一个可能的修改建议:
```python
import os
import numpy as np
import nibabel as nib
from PIL import Image
# 创建保存路径
save_path = 'C:/Users/Administrator/Desktop/2D-LiTS2017'
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(os.path.join(save_path, 'image')):
os.makedirs(os.path.join(save_path, 'image'))
if not os.path.exists(os.path.join(save_path, 'label')):
os.makedirs(os.path.join(save_path, 'label'))
# 加载数据集
data_path = 'D:/BaiduNetdiskDownload/LiTS2017'
img_path = os.path.join(data_path, 'Training Batch 1')
label_path = os.path.join(data_path, 'Training Batch 2')
# 转换图像
for file in sorted(os.listdir(img_path)):
if file.endswith('.nii'):
img_file = os.path.join(img_path, file)
img = nib.load(img_file).get_fdata()
img = np.transpose(img, (2, 0, 1)) # 转换为z, x, y
for i in range(img.shape[0]):
img_slice = img[i, :, :]
img_slice = (img_slice - np.min(img_slice)) / (np.max(img_slice) - np.min(img_slice)) * 255 # 归一化到0-255
img_slice = img_slice.astype(np.uint8)
img_slice = np.stack([img_slice]*3, axis=2) # 转换为三通道图像
#减少图像大小
img_slice = np.array(Image.fromarray(img_slice).resize((512, 512), resample=Image.BICUBIC))
img_name = file[:-4] + '' + str(i).zfill(3) + '.png'
img_file_save = os.path.join(save_path, 'image', img_name)
Image.fromarray(img_slice).save(img_file_save)
# 转换标签
for file in sorted(os.listdir(label_path)):
if file.endswith('.nii'):
label_file = os.path.join(label_path, file)
label = nib.load(label_file).get_fdata()
label = np.transpose(label, (2, 0, 1)) # 转换为z, x, y
for i in range(label.shape[0]):
label_slice = label[i, :, :]
label_slice[label_slice == 1] = 255 # 肝脏灰度值设为255
label_slice[label_slice == 2] = 128 # 肝脏肿瘤灰度值设为128
label_slice = label_slice.astype(np.uint8)
label_name = file[:-4] + '' + str(i).zfill(3) + '.png'
label_file_save = os.path.join(save_path, 'label', label_name)
Image.fromarray(label_slice).save(label_file_save)
```
这里我们使用了 `PIL.Image` 中的 `resize()` 函数来减少图像大小。`resample=Image.BICUBIC` 用于指定插值方法,这里使用了 Bicubic 插值。这个方法可以保留图像的细节,并且可以减少内存使用。你也可以尝试使用其他插值方法。
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) 。用另一段代码实现相同功能
import os
import numpy as np
import nibabel as nib
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]
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(cv2.cvtColor(silce_seg, cv2.COLOR_GRAY2RGB))
silce_seg = cv2.threshold(silce_seg, 1, 255, cv2.THRESH_BINARY)[1]
if (np.sum(silce_seg == [255, 255, 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(cv2.cvtColor(silce_vol, cv2.COLOR_GRAY2RGB))
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(os.path.join(path, "segmentation"))
num = 0
for filename in filenames:
num = save_fig(path, savepath, num, filename)