在python中计算ssim的方法（与Matlab结果一致）

Python  /  管理员 发布于 7年前   518

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 blockimport cv2import numpy as npdef 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站的支持！
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