写一个计算image_norm, compressed_image的MSE的代码,在jupyter notebooks上运行
时间: 2024-02-16 14:05:29 浏览: 34
好的,以下是计算`original_image`和`compressed_image`的MSE的代码:
```python
from PIL import Image
import numpy as np
# 加载原始图像和压缩后的图像
original_image = Image.open('0.jpg')
compressed_image = Image.open('1.jpg')
# 将图像转换为numpy数组
original_array = np.array(original_image)
compressed_array = np.array(compressed_image)
# 计算两个图像的差值
diff = original_array - compressed_array
# 计算MSE
mse = np.mean(np.square(diff))
# 计算image_norm
image_norm = np.linalg.norm(original_array)
print('原始图像的范数为:', image_norm)
print('压缩后的图像的MSE为:', mse)
```
在Jupyter Notebook上运行时,可以将代码复制粘贴到一个代码单元格中,然后按下Shift + Enter键来执行代码。请确保你已经将`'0.jpg'`和`'1.jpg'`替换成了你实际使用的图像文件路径。
相关推荐
![pptx](https://img-home.csdnimg.cn/images/20210720083543.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![docx](https://img-home.csdnimg.cn/images/20210720083331.png)