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

以上所述是小编给大家介绍的在python中计算ssim的方法（与Matlab结果一致），希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对python博客网站的支持！
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