psnr和ssim的代码

PSNR和SSIM是两种用于评估图像质量的指标，其中PSNR是峰值信噪比，SSIM是结构相似性。以下是PSNR和SSIM的Python代码示例：

import cv2
import numpy as np

def psnr(img1, img2):
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float('inf')
    max_pixel = 255.0
    psnr = 20 * np.log10(max_pixel / np.sqrt(mse))
    return psnr

def ssim(img1, img2):
    C1 = 6.5025
    C2 = 58.5225
    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]
    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 np.mean(ssim_map)

其中，PSNR函数计算两幅图像之间的PSNR值，SSIM函数计算两幅图像之间的SSIM值。这两个函数都接收两个参数：img1和img2，表示要进行比较的两幅图像。函数返回值为PSNR或SSIM值。需要注意的是，这两个函数都要求输入的图像形状相同。