给我评价两幅图像的ssim以及PSNR的Python代码

以下是给出两幅图像的 SSIM 和 PSNR 的 Python 代码：

import cv2
import numpy as np

def ssim(image1, image2):
    """Calculate the Structural Similarity Index (SSIM) between two images."""
    # Set constants
    C1 = (0.01 * 255) ** 2
    C2 = (0.03 * 255) ** 2

    # Convert the images to grayscale
    img1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
    img2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)

    # Calculate means and variances
    mu1 = cv2.GaussianBlur(img1, (11, 11), 1.5)
    mu2 = cv2.GaussianBlur(img2, (11, 11), 1.5)
    mu1_sq = mu1 ** 2
    mu2_sq = mu2 ** 2
    mu1_mu2 = mu1 * mu2

    sigma1_sq = cv2.GaussianBlur(img1 ** 2, (11, 11), 1.5) - mu1_sq
    sigma2_sq = cv2.GaussianBlur(img2 ** 2, (11, 11), 1.5) - mu2_sq
    sigma12 = cv2.GaussianBlur(img1 * img2, (11, 11), 1.5) - mu1_mu2

    # Calculate SSIM
    num = (2 * mu1_mu2 + C1) * (2 * sigma12 + C2)
    den = (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
    ssim = np.mean(num / den)

    return ssim

def psnr(image1, image2):
    """Calculate the Peak Signal-to-Noise Ratio (PSNR) between two images."""
    mse = np.mean((image1 - image2) ** 2)
    if mse == 0:
        return float('inf')
    else:
        max_pixel = 255.0
        psnr = 20 * np.log10(max_pixel / np.sqrt(mse))
        return psnr

其中，`image1` 和 `image2` 分别为两幅图像的 numpy 数组。使用方法如下：

# Load the two images
image1 = cv2.imread('image1.jpg')
image2 = cv2.imread('image2.jpg')

# Calculate SSIM and PSNR
ssim_value = ssim(image1, image2)
psnr_value = psnr(image1, image2)

# Print the results
print('SSIM:', ssim_value)
print('PSNR:', psnr_value)

请注意，这些度量标准仅作为图像质量的参考，它们并不能完全描述图像的质量。因此，建议在实际应用中结合其他度量标准进行分析。