Python 使用相同目录的图像更新目录中的图像

我有一个目录，里面有一组不同大小的图片，让我给你看看图片和它们的大小

from google.colab import drive
drive.mount('/content/drive')
from PIL import Image
import glob
import time
from pylab import *
for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
  print(filename)

from PIL import Image
import glob
import time
from pylab import *
for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
  im=array(Image.open(filename))
  print(im.shape)

该代码的结果是：

/content/drive/My Drive/Colab Notebooks/Cats/cat1.jpg
/content/drive/My Drive/Colab Notebooks/Cats/cat2.jpg
/content/drive/My Drive/Colab Notebooks/Cats/cat3.jpg
/content/drive/My Drive/Colab Notebooks/Cats/cat4.jpg
/content/drive/My Drive/Colab Notebooks/Cats/cat5.jpg
/content/drive/My Drive/Colab Notebooks/Cats/cat6.jpg
/content/drive/My Drive/Colab Notebooks/Cats/cat7.jpg

现在让我们考虑它们的尺寸

from google.colab import drive
drive.mount('/content/drive')
from PIL import Image
import glob
import time
from pylab import *
for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
  print(filename)

from PIL import Image
import glob
import time
from pylab import *
for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
  im=array(Image.open(filename))
  print(im.shape)

该代码的结果是：

(410, 618, 3)
(1200, 1800, 3)
(576, 1024, 3)
(1533, 2300, 3)
(400, 600, 3)
(264, 191, 3)
(194, 259, 3)

当然，我可以使用下面的行将其转换为灰度

im=array(Image.open(filename).convert('L'))

结果:

(410, 618)
(1200, 1800)
(576, 1024)
(1533, 2300)
(400, 600)
(264, 191)
(194, 259)

(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)

---

正如你所看到的，不同的图像有不同的大小，我想要的是以相同的大小重塑所有图像（我知道这一个存在调整大小的功能），我想用相同的图像更新（替换）旧图像，所以我有，我的目录中的所有图像都应该有相同的大小，我该怎么做？请帮助我解决这个问题非常简单，然后我就开始考虑了，下面是我的解决方案

from google.colab import drive
drive.mount('/content/drive')
from PIL import Image
import glob
import time
from pylab import *
#directory_name ='/content/drive/My Drive/Colab Notebooks/Cats/'
for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
  #print(filename[-8:])
  im=(Image.open(filename).convert('L'))
  im=im.resize((100,100))
  #filename=filename[-8:]
  #complete_name =directory_name+filename
  print(complete_name)
  im.save(filename)

测试：

for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
   im=array((Image.open(filename).convert('L')))
   print(im.shape)

结果:

(410, 618)
(1200, 1800)
(576, 1024)
(1533, 2300)
(400, 600)
(264, 191)
(194, 259)

(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)

---

解决方案非常简单，然后我就在考虑，所以这里是我的解决方案

from google.colab import drive
drive.mount('/content/drive')
from PIL import Image
import glob
import time
from pylab import *
#directory_name ='/content/drive/My Drive/Colab Notebooks/Cats/'
for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
  #print(filename[-8:])
  im=(Image.open(filename).convert('L'))
  im=im.resize((100,100))
  #filename=filename[-8:]
  #complete_name =directory_name+filename
  print(complete_name)
  im.save(filename)

测试：

for filename in glob.iglob('/content/drive/My Drive/Colab Notebooks/Cats/*.jpg'):
   im=array((Image.open(filename).convert('L')))
   print(im.shape)

结果:

(410, 618)
(1200, 1800)
(576, 1024)
(1533, 2300)
(400, 600)
(264, 191)
(194, 259)

(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)
(100, 100)

---

在这种情况下，接受你自己的答案是肯定的。在这种情况下，接受你自己的答案是肯定的。