MSSSIM

import os
import numpy as np
import cv2
import scipy.signal
import pandas as pd
def ssim_index_new(img1,img2,K,win):

    M,N = img1.shape

    img1 = img1.astype(np.float32)
    img2 = img2.astype(np.float32)

    C1 = (K[0]*255)**2
    C2 = (K[1]*255) ** 2
    win = win/np.sum(win)

    mu1 = scipy.signal.convolve2d(img1,win,mode='valid')
    mu2 = scipy.signal.convolve2d(img2,win,mode='valid')
    mu1_sq = np.multiply(mu1,mu1)
    mu2_sq = np.multiply(mu2,mu2)
    mu1_mu2 = np.multiply(mu1,mu2)
    sigma1_sq = scipy.signal.convolve2d(np.multiply(img1,img1),win,mode='valid') - mu1_sq
    sigma2_sq = scipy.signal.convolve2d(np.multiply(img2, img2), win, mode='valid') - mu2_sq
    img12 = np.multiply(img1, img2)
    sigma12 = scipy.signal.convolve2d(np.multiply(img1, img2), win, mode='valid') - mu1_mu2

    if(C1 > 0 and C2>0):
        ssim1 =2*sigma12 + C2
        ssim_map = np.divide(np.multiply((2*mu1_mu2 + C1),(2*sigma12 + C2)),np.multiply((mu1_sq+mu2_sq+C1),(sigma1_sq+sigma2_sq+C2)))
        cs_map = np.divide((2*sigma12 + C2),(sigma1_sq + sigma2_sq + C2))
    else:
        numerator1 = 2*mu1_mu2 + C1
        numerator2 = 2*sigma12 + C2
        denominator1 = mu1_sq + mu2_sq +C1
        denominator2 = sigma1_sq + sigma2_sq +C2

        ssim_map = np.ones(mu1.shape)
        index = np.multiply(denominator1,denominator2)
        #如果index是真，就赋值，是假就原值
        n,m = mu1.shape
        for i in range(n):
            for j in range(m):
                if(index[i][j] > 0):
                    ssim_map[i][j] = numerator1[i][j]*numerator2[i][j]/denominator1[i][j]*denominator2[i][j]
                else:
                    ssim_map[i][j] = ssim_map[i][j]
        for i in range(n):
            for j in range(m):
                if((denominator1[i][j] != 0)and(denominator2[i][j] == 0)):
                    ssim_map[i][j] = numerator1[i][j]/denominator1[i][j]
                else:
                    ssim_map[i][j] = ssim_map[i][j]

        cs_map = np.ones(mu1.shape)
        for i in range(n):
            for j in range(m):
                if(denominator2[i][j] > 0):
                    cs_map[i][j] = numerator2[i][j]/denominator2[i][j]
                else:
                    cs_map[i][j] = cs_map[i][j]


    mssim = np.mean(ssim_map)
    mcs = np.mean(cs_map)

    return mssim,mcs


def msssim(img1,img2):

    K = [0.01,0.03]
    win  = np.multiply(cv2.getGaussianKernel(11, 1.5), (cv2.getGaussianKernel(11, 1.5)).T)  # H.shape == (r, c)
    level = 5
    weight = [0.0448,0.2856,0.3001,0.2363,0.1333]
    method = 'product'

    M,N = img1.shape
    H,W = win.shape

    downsample_filter = np.ones((2,2))/4
    img1 = img1.astype(np.float32)
    img2 = img2.astype(np.float32)

    mssim_array = []
    mcs_array = []

    for i in range(0,level):
        mssim,mcs = ssim_index_new(img1,img2,K,win)
        mssim_array.append(mssim)
        mcs_array.append(mcs)
        filtered_im1 = cv2.filter2D(img1,-1,downsample_filter,anchor = (0,0),borderType=cv2.BORDER_REFLECT)
        filtered_im2 = cv2.filter2D(img2,-1,downsample_filter,anchor = (0,0),borderType=cv2.BORDER_REFLECT)
        img1 = filtered_im1[::2,::2]
        img2 = filtered_im2[::2,::2]

    #print(np.power(mcs_array[:level-1],weight[:level-1]))
    #print(mssim_array[level-1]**weight[level-1])
    overall_mssim = np.prod(np.power(mcs_array[:level-1],weight[:level-1]))*(mssim_array[level-1]**weight[level-1])

    return overall_mssim


# 定义图片文件夹路径
##faceswap FaceShifter-master FaceDancer-main InfoSwap-master BlendFace-master
folder1_path = r'F:\xiangmu\InfoSwap-master\ffhq\B2'
folder2_path = r'F:\xiangmu\InfoSwap-master\ffhq\new2'

# 获取文件夹中的图片文件列表
folder1_files = os.listdir(folder1_path)
folder2_files = os.listdir(folder2_path)


msssim_values = []
# 遍历文件夹中的图片，逐一计算MSSSIM值
for i in range(len(folder1_files)):
    img1 = cv2.imread(os.path.join(folder1_path, folder1_files[i]), cv2.IMREAD_GRAYSCALE)
    img2 = cv2.imread(os.path.join(folder2_path, folder2_files[i]), cv2.IMREAD_GRAYSCALE)
    msssim_value = msssim(img1, img2)
    msssim_values.append(msssim_value)

    print("MSSSIM值{} vs {}: {}".format(folder1_files[i], folder2_files[i], msssim_value))
# 计算平均值
print("平均MSSSIM值:", np.mean(msssim_values))
"""
# 创建DataFrame对象并保存为Excel文件
data = {'Image Name': folder1_files, 'MSSSIM Value': msssim_values}
df = pd.DataFrame(data)
output_path = r'msssim_FaceShifter_ffhq_new1.xlsx'
df.to_excel(output_path, index=False)
"""

大家可以根据这个进行适当修改。