Python计算图片SSIM和PSNR

分两种情况：

1. 在网络训练过程中计算Output和Groundtruth之间的SSIM，作为损失函数；

2. 直接计算两张图片之间的SSIM。

情况1

https://github.com/congyucn/pytorch-ssim

可直接使用上述代码。

情况2

参考上述代码，可将上述代码更改如下：

def ssim(img1,img2):
    img1 = torch.from_numpy(np.rollaxis(img1, 2)).float().unsqueeze(0)/255.0
    img2 = torch.from_numpy(np.rollaxis(img2, 2)).float().unsqueeze(0)/255.0   
    img1 = Variable( img1,  requires_grad=False)    # torch.Size([256, 256, 3])
    img2 = Variable( img2, requires_grad = False)
    ssim_value = pytorch_ssim.ssim(img1, img2).item()
    return ssim_value

而计算psnr的代码见：https://blog.csdn.net/qazwsxrx/article/details/104550550

总体而言，计算两张图片的psnr和ssim的代码如下所示：

import numpy 
import numpy as np
import math
import cv2
import torch
import pytorch_ssim
from torch.autograd import Variable

original = cv2.imread("1.png")      # numpy.adarray
contrast = cv2.imread("2.png",1)

def psnr(img1, img2):
    mse = numpy.mean( (img1 - img2) ** 2 )
    if mse == 0:
        return 100
    PIXEL_MAX = 255.0
    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))

def ssim(img1,img2):
    img1 = torch.from_numpy(np.rollaxis(img1, 2)).float().unsqueeze(0)/255.0
    img2 = torch.from_numpy(np.rollaxis(img2, 2)).float().unsqueeze(0)/255.0   
    img1 = Variable( img1,  requires_grad=False)    # torch.Size([256, 256, 3])
    img2 = Variable( img2, requires_grad = False)
    ssim_value = pytorch_ssim.ssim(img1, img2).item()
    return ssim_value

psnrValue = psnr(original,contrast)
ssimValue = ssim(original,contrast)
print(psnrValue)
print(ssimValue)