在python中计算ssim的方法（与Matlab结果一致）

如下代码可以计算输入的两张图像的结构相似度（SSIM），结果与matlab计算结果一致

// An highlighted block
import cv2
import numpy as np
def ssim(img1, img2):
 C1 = (0.01 * 255)**2
 C2 = (0.03 * 255)**2
 img1 = img1.astype(np.float64)
 img2 = img2.astype(np.float64)
 kernel = cv2.getGaussianKernel(11, 1.5)
 window = np.outer(kernel, kernel.transpose())
 mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
 mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
 mu1_sq = mu1**2
 mu2_sq = mu2**2
 mu1_mu2 = mu1 * mu2
 sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
 sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
 sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
 ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                             (sigma1_sq + sigma2_sq + C2))
 return ssim_map.mean()
def calculate_ssim(img1, img2):
 '''calculate SSIM
 the same outputs as MATLAB's
 img1, img2: [0, 255]
 '''
 if not img1.shape == img2.shape:
   raise ValueError('Input images must have the same dimensions.')
 if img1.ndim == 2:
   return ssim(img1, img2)
 elif img1.ndim == 3:
   if img1.shape[2] == 3:
     ssims = []
     for i in range(3):
       ssims.append(ssim(img1, img2))
     return np.array(ssims).mean()
   elif img1.shape[2] == 1:
     return ssim(np.squeeze(img1), np.squeeze(img2))
 else:
   raise ValueError('Wrong input image dimensions.')

img1 = cv2.imread("Test2_HR.bmp", 0)
img2 = cv2.imread("Test2_LR2.bmp", 0)
ss = calculate_ssim(img1, img2)
print(ss)

总结

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来源：https://blog.csdn.net/larryli007/article/details/103578420