2024年4月14日发(作者：)
ssim psnr lpips指标代码
下面是SSIM、PSNR和LPIPS指标的代码示例（使用Python
和OpenCV库）：
SSIM指标：
```python
import cv2
import numpy as np
def ssim(img1, img2):
# Convert images to grayscale
img1 = or(img1, _BGR2GRAY)
img2 = or(img2, _BGR2GRAY)
# Calculate SSIM index
ssim_index = eSSIM(img1, img2)
return ssim_index
# Load two images
image1 = ('')
image2 = ('')
# Calculate SSIM index
ssim_index = ssim(image1, image2)
print("SSIM index: ", ssim_index)
```
PSNR指标：
```python
import cv2
import numpy as np
def psnr(img1, img2):
# Convert images to grayscale
img1 = or(img1, _BGR2GRAY)
img2 = or(img2, _BGR2GRAY)
# Calculate MSE (Mean Squared Error)
mse = ((img1 - img2) ** 2)
# Calculate PSNR (Peak Signal to Noise Ratio)
psnr = 10 * 10((255 ** 2) / mse)
return psnr
# Load two images
image1 = ('')
image2 = ('')
# Calculate PSNR
psnr_value = psnr(image1, image2)
print("PSNR: ", psnr_value)
```
LPIPS指标（需要安装lpips库）：
```python
import cv2
import lpips
def lpips_distance(img1, img2):
# Convert images to RGB and normalize to range [-1, 1]
img1 = or(img1, _BGR2RGB) / 255.0 *
2 - 1
img2 = or(img2, _BGR2RGB) / 255.0 *
2 - 1
# Load pretrained LPIPS model
loss_fn = (net='alex')
# Calculate distance using LPIPS model
distance = loss_d((img1),
(img2)).item()
return distance
# Load two images
image1 = ('')
image2 = ('')
# Calculate LPIPS distance
distance = lpips_distance(image1, image2)
print("LPIPS distance: ", distance)
```
请记得替换其中的''和''为你实际使用的图
像路径。同时，对于LPIPS指标，你需要先安装lpips库。
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