图像融合评估指标Python版

图像融合评估指标Python版

这篇博客利用Python把大部分图像融合指标基于图像融合评估指标复现了，从而方便大家更好的使用Python进行指标计算，以及一些I/O 操作。除了几个特征互信息的指标没有成功复现之外，其他指标均可以通过这篇博客提到的Python程序计算得到，其中SSIM和MS_SSIM是基于PyTorch实现的可能无法与原来的程序保持一致，同时使用了一些矩阵运算加速了Nabf和Qabf的计算。但不幸的是在计算VIF时设计大量的卷积运算，而博主在Python中采用cipy.signal.convolve2d来替换MATLAB中的filter函数,导致时间消耗较大，如果你不需要计算VIF可以直接注释掉相关语句 并设置VIF=1即可。

完整demo下载地址：https://download.csdn.net/download/fovever_/87547835

在原来的MATLAB程序中由于没有充分考虑数据类型的影响，在计算SD是会由于uint8数据类型的限制，但是部分数据被截断，在Python中已经解决了这个Bug，同时也在原来的MATLAB版本中修正了这个问题。

在Python版的程序中，只有计算EN和MI是使用的是int型数据，其他指标均使用float型数据。此外除了计算MSE和PSNR时将数据归一化到[0,1]之外，计算其他指标时，数据范围均为[0,255]。

性能评估指标主要分为四类，分别是基于信息论的评估指标，主要包括** EN、MI、PSNR**、基于结构相似性的评估指标，主要包括SSIM、MS_SSIM、MSE、基于图像特征的评估指标， 主要包括SF、SD、AG，基于人类视觉感知的评估指标，主要包括VIF、以及基于源图像与生成图像的评估指标，主要包括CC、SCD、Qabf、Nabf。

接下来是部分程序：

单张图像测试程序: eval_one_image.py

from PIL import Image
from Metric import *
from time import time
import warnings
warnings.filterwarnings("ignore")

def evaluation_one(ir_name, vi_name, f_name):
    f_img = Image.open(f_name).convert('L')
    ir_img = Image.open(ir_name).convert('L')
    vi_img = Image.open(vi_name).convert('L')
    f_img_int = np.array(f_img).astype(np.int32)

    f_img_double = np.array(f_img).astype(np.float32)
    ir_img_int = np.array(ir_img).astype(np.int32)
    ir_img_double = np.array(ir_img).astype(np.float32)

    vi_img_int = np.array(vi_img).astype(np.int32)
    vi_img_double = np.array(vi_img).astype(np.float32)

    EN = EN_function(f_img_int)
    MI = MI_function(ir_img_int, vi_img_int, f_img_int, gray_level=256)

    SF = SF_function(f_img_double)
    SD = SD_function(f_img_double)
    AG = AG_function(f_img_double)
    PSNR = PSNR_function(ir_img_double, vi_img_double, f_img_double)
    MSE = MSE_function(ir_img_double, vi_img_double, f_img_double)
    VIF = VIF_function(ir_img_double, vi_img_double, f_img_double)
    CC = CC_function(ir_img_double, vi_img_double, f_img_double)
    SCD = SCD_function(ir_img_double, vi_img_double, f_img_double)
    Qabf = Qabf_function(ir_img_double, vi_img_double, f_img_double)
    Nabf = Nabf_function(ir_img_double, vi_img_double, f_img_double)
    SSIM = SSIM_function(ir_img_double, vi_img_double, f_img_double)
    MS_SSIM = MS_SSIM_function(ir_img_double, vi_img_double, f_img_double)
    return EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM

if __name__ == '__main__':
    f_name = r'E:\Desktop\metric\Test\Results\TNO\GTF\01.png'
    ir_name = r'E:\Desktop\metric\Test\datasets\TNO\ir\01.png'
    vi_name = r'E:\Desktop\metric\Test\datasets\TNO\vi\01.png'
    EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM = evaluation_one(ir_name, vi_name, f_name)
    print('EN:', round(EN, 4))
    print('MI:', round(MI, 4))
    print('SF:', round(SF, 4))
    print('AG:', round(AG, 4))
    print('SD:', round(SD, 4))
    print('CC:', round(CC, 4))
    print('SCD:', round(SCD, 4))
    print('VIF:', round(VIF, 4))
    print('MSE:', round(MSE, 4))
    print('PSNR:', round(PSNR, 4))
    print('Qabf:', round(Qabf, 4))
    print('Nabf:', round(Nabf, 4))
    print('SSIM:', round(SSIM, 4))
    print('MS_SSIM:', round(MS_SSIM, 4))

测试一个方法中所有图像指标的程序: eval_one_method.py

import numpy as np
from PIL import Image
from Metric import *
from natsort import natsorted
from tqdm import tqdm
import os
import statistics
import warnings
from openpyxl import Workbook, load_workbook
from openpyxl.utils import get_column_letter
warnings.filterwarnings("ignore")

def write_excel(excel_name='metric.xlsx', worksheet_name='VIF', column_index=0, data=None):
    try:
        workbook = load_workbook(excel_name)
    except FileNotFoundError:
    # 文件不存在，创建新的 Workbook
        workbook = Workbook()

    # 获取或创建一个工作表
    if worksheet_name in workbook.sheetnames:
        worksheet = workbook[worksheet_name]
    else:
        worksheet = workbook.create_sheet(title=worksheet_name)

    # 在指定列中插入数据
    column = get_column_letter(column_index + 1)
    for i, value in enumerate(data):
        cell = worksheet[column + str(i+1)]
        cell.value = value
        
    # 保存文件
    workbook.save(excel_name)

def evaluation_one(ir_name, vi_name, f_name):
    f_img = Image.open(f_name).convert('L')
    ir_img = Image.open(ir_name).convert('L')
    vi_img = Image.open(vi_name).convert('L')

    f_img_int = np.array(f_img).astype(np.int32)
    f_img_double = np.array(f_img).astype(np.float32)

    ir_img_int = np.array(ir_img).astype(np.int32)
    ir_img_double = np.array(ir_img).astype(np.float32)

    vi_img_int = np.array(vi_img).astype(np.int32)
    vi_img_double = np.array(vi_img).astype(np.float32)

    EN = EN_function(f_img_int)
    MI = MI_function(ir_img_int, vi_img_int, f_img_int, gray_level=256)

    SF = SF_function(f_img_double)
    SD = SD_function(f_img_double)
    AG = AG_function(f_img_double)
    PSNR = PSNR_function(ir_img_double, vi_img_double, f_img_double)
    MSE = MSE_function(ir_img_double, vi_img_double, f_img_double)
    VIF = VIF_function(ir_img_double, vi_img_double, f_img_double)
    CC = CC_function(ir_img_double, vi_img_double, f_img_double)
    SCD = SCD_function(ir_img_double, vi_img_double, f_img_double)
    Qabf = Qabf_function(ir_img_double, vi_img_double, f_img_double)
    Nabf = Nabf_function(ir_img_double, vi_img_double, f_img_double)
    SSIM = SSIM_function(ir_img_double, vi_img_double, f_img_double)
    MS_SSIM = MS_SSIM_function(ir_img_double, vi_img_double, f_img_double)
    return EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM

if __name__ == '__main__':
    with_mean = True
    EN_list = []
    MI_list = []
    SF_list = []
    AG_list = []
    SD_list = []
    CC_list = []
    SCD_list = []
    VIF_list = []
    MSE_list = []
    PSNR_list = []
    Qabf_list = []
    Nabf_list = []
    SSIM_list = []
    MS_SSIM_list = []
    filename_list = ['']
    dataset_name = 'test_imgs'
    ir_dir = os.path.join('..\datasets', dataset_name, 'ir')
    vi_dir = os.path.join('..\datasets', dataset_name, 'vi')
    Method = 'SeAFusion'
    f_dir = os.path.join('..\Results', dataset_name, Method)
    save_dir = '..\Metric'
    os.makedirs(save_dir, exist_ok=True)
    metric_save_name = os.path.join(save_dir, 'metric_{}_{}.xlsx'.format(dataset_name, Method))
    filelist = natsorted(os.listdir(ir_dir))
    eval_bar = tqdm(filelist)
    for _, item in enumerate(eval_bar):
        ir_name = os.path.join(ir_dir, item)
        vi_name = os.path.join(vi_dir, item)
        f_name = os.path.join(f_dir, item)
        EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM = evaluation_one(ir_name, vi_name, f_name)
        EN_list.append(EN)
        MI_list.append(MI)
        SF_list.append(SF)
        AG_list.append(AG)
        SD_list.append(SD)
        CC_list.append(CC)
        SCD_list.append(SCD)
        VIF_list.append(VIF)
        MSE_list.append(MSE)
        PSNR_list.append(PSNR)
        Qabf_list.append(Qabf)
        Nabf_list.append(Nabf)
        SSIM_list.append(SSIM)
        MS_SSIM_list.append(MS_SSIM)
        filename_list.append(item)
        eval_bar.set_description("{} | {}".format(Method, item))
    if with_mean:
    # 添加均值
        EN_list.append(np.mean(EN_list))
        MI_list.append(np.mean(MI_list))
        SF_list.append(np.mean(SF_list))
        AG_list.append(np.mean(AG_list))
        SD_list.append(np.mean(SD_list))
        CC_list.append(np.mean(CC_list))
        SCD_list.append(np.mean(SCD_list))
        VIF_list.append(np.mean(VIF_list))
        MSE_list.append(np.mean(MSE_list))
        PSNR_list.append(np.mean(PSNR_list))
        Qabf_list.append(np.mean(Qabf_list))
        Nabf_list.append(np.mean(Nabf_list))
        SSIM_list.append(np.mean(SSIM_list))
        MS_SSIM_list.append(np.mean(MS_SSIM_list))
        filename_list.append('mean')

        ## 添加标准差
        EN_list.append(np.std(EN_list))
        MI_list.append(np.std(MI_list))
        SF_list.append(np.std(SF_list))
        AG_list.append(np.std(AG_list))
        SD_list.append(np.std(SD_list))
        CC_list.append(np.std(CC_list[:-1]))
        SCD_list.append(np.std(SCD_list))
        VIF_list.append(np.std(VIF_list))
        MSE_list.append(np.std(MSE_list))
        PSNR_list.append(np.std(PSNR_list))
        Qabf_list.append(np.std(Qabf_list))
        Nabf_list.append(np.std(Nabf_list))
        SSIM_list.append(np.std(SSIM_list))
        MS_SSIM_list.append(np.std(MS_SSIM_list))
        filename_list.append('std')

    ## 保留三位小数
    EN_list = [round(x, 3) for x in EN_list]
    MI_list = [round(x, 3) for x in MI_list]
    SF_list = [round(x, 3) for x in SF_list]
    AG_list = [round(x, 3) for x in AG_list]
    SD_list = [round(x, 3) for x in SD_list]
    CC_list = [round(x, 3) for x in CC_list]
    SCD_list = [round(x, 3) for x in SCD_list]
    VIF_list = [round(x, 3) for x in VIF_list]
    MSE_list = [round(x, 3) for x in MSE_list]
    PSNR_list = [round(x, 3) for x in PSNR_list]
    Qabf_list = [round(x, 3) for x in Qabf_list]
    Nabf_list = [round(x, 3) for x in Nabf_list]
    SSIM_list = [round(x, 3) for x in SSIM_list]
    MS_SSIM_list = [round(x, 3) for x in MS_SSIM_list]

    EN_list.insert(0, '{}'.format(Method))
    MI_list.insert(0, '{}'.format(Method))
    SF_list.insert(0, '{}'.format(Method))
    AG_list.insert(0, '{}'.format(Method))
    SD_list.insert(0, '{}'.format(Method))
    CC_list.insert(0, '{}'.format(Method))
    SCD_list.insert(0, '{}'.format(Method))
    VIF_list.insert(0, '{}'.format(Method))
    MSE_list.insert(0, '{}'.format(Method))
    PSNR_list.insert(0, '{}'.format(Method))
    Qabf_list.insert(0, '{}'.format(Method))
    Nabf_list.insert(0, '{}'.format(Method))
    SSIM_list.insert(0, '{}'.format(Method))
    MS_SSIM_list.insert(0, '{}'.format(Method))
    write_excel(metric_save_name, 'EN', 0, filename_list)
    write_excel(metric_save_name, "MI", 0, filename_list)
    write_excel(metric_save_name, "SF", 0, filename_list)
    write_excel(metric_save_name, "AG", 0, filename_list)
    write_excel(metric_save_name, "SD", 0, filename_list)
    write_excel(metric_save_name, "CC", 0, filename_list)
    write_excel(metric_save_name, "SCD", 0, filename_list)
    write_excel(metric_save_name, "VIF", 0, filename_list)
    write_excel(metric_save_name, "MSE", 0, filename_list)
    write_excel(metric_save_name, "PSNR", 0, filename_list)
    write_excel(metric_save_name, "Qabf", 0, filename_list)
    write_excel(metric_save_name, "Nabf", 0, filename_list)
    write_excel(metric_save_name, "SSIM", 0, filename_list)
    write_excel(metric_save_name, "MS_SSIM", 0, filename_list)
    write_excel(metric_save_name, 'EN', 1, EN_list)
    write_excel(metric_save_name, 'MI', 1, MI_list)
    write_excel(metric_save_name, 'SF', 1, SF_list)
    write_excel(metric_save_name, 'AG', 1, AG_list)
    write_excel(metric_save_name, 'SD', 1, SD_list)
    write_excel(metric_save_name, 'CC', 1, CC_list)
    write_excel(metric_save_name, 'SCD', 1, SCD_list)
    write_excel(metric_save_name, 'VIF', 1, VIF_list)
    write_excel(metric_save_name, 'MSE', 1, MSE_list)
    write_excel(metric_save_name, 'PSNR', 1, PSNR_list)
    write_excel(metric_save_name, 'Qabf', 1, Qabf_list)
    write_excel(metric_save_name, 'Nabf', 1, Nabf_list)
    write_excel(metric_save_name, 'SSIM', 1, SSIM_list)
    write_excel(metric_save_name, 'MS_SSIM', 1, MS_SSIM_list)

测试一个数据集上所有对比算法的指标的程序：eval_multi_method.py

import numpy as np
from PIL import Image
from Metric import *
from natsort import natsorted
from tqdm import tqdm
import os
import statistics
import warnings
from openpyxl import Workbook, load_workbook
from openpyxl.utils import get_column_letter
warnings.filterwarnings("ignore")

def write_excel(excel_name='metric.xlsx', worksheet_name='VIF', column_index=0, data=None):
    try:
        workbook = load_workbook(excel_name)
    except FileNotFoundError:
    # 文件不存在，创建新的 Workbook
        workbook = Workbook()

    # 获取或创建一个工作表
    if worksheet_name in workbook.sheetnames:
        worksheet = workbook[worksheet_name]
    else:
        worksheet = workbook.create_sheet(title=worksheet_name)

    # 在指定列中插入数据
    column = get_column_letter(column_index + 1)
    for i, value in enumerate(data):
        cell = worksheet[column + str(i+1)]
        cell.value = value

    # 保存文件
    workbook.save(excel_name)

def evaluation_one(ir_name, vi_name, f_name):
    f_img = Image.open(f_name).convert('L')
    ir_img = Image.open(ir_name).convert('L')
    vi_img = Image.open(vi_name).convert('L')

    f_img_int = np.array(f_img).astype(np.int32)
    f_img_double = np.array(f_img).astype(np.float32)

    ir_img_int = np.array(ir_img).astype(np.int32)
    ir_img_double = np.array(ir_img).astype(np.float32)

    vi_img_int = np.array(vi_img).astype(np.int32)
    vi_img_double = np.array(vi_img).astype(np.float32)

    EN = EN_function(f_img_int)
    MI = MI_function(ir_img_int, vi_img_int, f_img_int, gray_level=256)

    SF = SF_function(f_img_double)
    SD = SD_function(f_img_double)
    AG = AG_function(f_img_double)
    PSNR = PSNR_function(ir_img_double, vi_img_double, f_img_double)
    MSE = MSE_function(ir_img_double, vi_img_double, f_img_double)
    VIF = VIF_function(ir_img_double, vi_img_double, f_img_double)
    CC = CC_function(ir_img_double, vi_img_double, f_img_double)
    SCD = SCD_function(ir_img_double, vi_img_double, f_img_double)
    Qabf = Qabf_function(ir_img_double, vi_img_double, f_img_double)
    Nabf = Nabf_function(ir_img_double, vi_img_double, f_img_double)
    SSIM = SSIM_function(ir_img_double, vi_img_double, f_img_double)
    MS_SSIM = MS_SSIM_function(ir_img_double, vi_img_double, f_img_double)
    return EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM

if __name__ == '__main__':
    with_mean = True
    dataroot = r'../datasets'
    results_root = '../Results'
    dataset = 'TNO'
    ir_dir = os.path.join(dataroot, dataset, 'ir')
    vi_dir = os.path.join(dataroot, dataset, 'vi')
    f_dir = os.path.join(results_root, dataset)
    save_dir = '../Metric'
    os.makedirs(save_dir, exist_ok=True)

    metric_save_name = os.path.join(save_dir, 'metric_{}.xlsx'.format(dataset))
    filelist = natsorted(os.listdir(ir_dir))

    Method_list = ['GTF', 'DIDFuse', 'RFN-Nest', 'FusionGAN', 'TarDAL', 'UMF-CMGR', 'SeAFusion', 'SwinFusion', 'U2Fusion', 'PSF']
    for i, Method in enumerate(Method_list):
        EN_list = []
        MI_list = []
        SF_list = []
        AG_list = []
        SD_list = []
        CC_list = []
        SCD_list = []
        VIF_list = []
        MSE_list = []
        PSNR_list = []
        Qabf_list = []
        Nabf_list = []
        SSIM_list = []
        MS_SSIM_list = []
        filename_list = ['']
        sub_f_dir = os.path.join(f_dir, Method)
        eval_bar = tqdm(filelist)
        for _, item in enumerate(eval_bar):
            ir_name = os.path.join(ir_dir, item)
            vi_name = os.path.join(vi_dir, item)
            f_name = os.path.join(sub_f_dir, item)
            print(ir_name, vi_name, f_name)
            EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM = evaluation_one(ir_name, vi_name, f_name)
            EN_list.append(EN)
            MI_list.append(MI)
            SF_list.append(SF)
            AG_list.append(AG)
            SD_list.append(SD)
            CC_list.append(CC)
            SCD_list.append(SCD)
            VIF_list.append(VIF)
            MSE_list.append(MSE)
            PSNR_list.append(PSNR)
            Qabf_list.append(Qabf)
            Nabf_list.append(Nabf)
            SSIM_list.append(SSIM)
            MS_SSIM_list.append(MS_SSIM)
            filename_list.append(item)
            eval_bar.set_description("{} | {}".format(Method, item))
        if with_mean:
        # 添加均值
            EN_list.append(np.mean(EN_list))
            MI_list.append(np.mean(MI_list))
            SF_list.append(np.mean(SF_list))
            AG_list.append(np.mean(AG_list))
            SD_list.append(np.mean(SD_list))
            CC_list.append(np.mean(CC_list))
            SCD_list.append(np.mean(SCD_list))
            VIF_list.append(np.mean(VIF_list))
            MSE_list.append(np.mean(MSE_list))
            PSNR_list.append(np.mean(PSNR_list))
            Qabf_list.append(np.mean(Qabf_list))
            Nabf_list.append(np.mean(Nabf_list))
            SSIM_list.append(np.mean(SSIM_list))
            MS_SSIM_list.append(np.mean(MS_SSIM_list))
            filename_list.append('mean')

            ## 添加标准差
            EN_list.append(np.std(EN_list))
            MI_list.append(np.std(MI_list))
            SF_list.append(np.std(SF_list))
            AG_list.append(np.std(AG_list))
            SD_list.append(np.std(SD_list))
            CC_list.append(np.std(CC_list[:-1]))
            SCD_list.append(np.std(SCD_list))
            VIF_list.append(np.std(VIF_list))
            MSE_list.append(np.std(MSE_list))
            PSNR_list.append(np.std(PSNR_list))
            Qabf_list.append(np.std(Qabf_list))
            Nabf_list.append(np.std(Nabf_list))
            SSIM_list.append(np.std(SSIM_list))
            MS_SSIM_list.append(np.std(MS_SSIM_list))
            filename_list.append('std')

        ## 保留三位小数
        EN_list = [round(x, 3) for x in EN_list]
        MI_list = [round(x, 3) for x in MI_list]
        SF_list = [round(x, 3) for x in SF_list]
        AG_list = [round(x, 3) for x in AG_list]
        SD_list = [round(x, 3) for x in SD_list]
        CC_list = [round(x, 3) for x in CC_list]
        SCD_list = [round(x, 3) for x in SCD_list]
        VIF_list = [round(x, 3) for x in VIF_list]
        MSE_list = [round(x, 3) for x in MSE_list]
        PSNR_list = [round(x, 3) for x in PSNR_list]
        Qabf_list = [round(x, 3) for x in Qabf_list]
        Nabf_list = [round(x, 3) for x in Nabf_list]
        SSIM_list = [round(x, 3) for x in SSIM_list]
        MS_SSIM_list = [round(x, 3) for x in MS_SSIM_list]

        EN_list.insert(0, '{}'.format(Method))
        MI_list.insert(0, '{}'.format(Method))
        SF_list.insert(0, '{}'.format(Method))
        AG_list.insert(0, '{}'.format(Method))
        SD_list.insert(0, '{}'.format(Method))
        CC_list.insert(0, '{}'.format(Method))
        SCD_list.insert(0, '{}'.format(Method))
        VIF_list.insert(0, '{}'.format(Method))
        MSE_list.insert(0, '{}'.format(Method))
        PSNR_list.insert(0, '{}'.format(Method))
        Qabf_list.insert(0, '{}'.format(Method))
        Nabf_list.insert(0, '{}'.format(Method))
        SSIM_list.insert(0, '{}'.format(Method))
        MS_SSIM_list.insert(0, '{}'.format(Method))
        if i == 0:
            write_excel(metric_save_name, 'EN', 0, filename_list)
            write_excel(metric_save_name, "MI", 0, filename_list)
            write_excel(metric_save_name, "SF", 0, filename_list)
            write_excel(metric_save_name, "AG", 0, filename_list)
            write_excel(metric_save_name, "SD", 0, filename_list)
            write_excel(metric_save_name, "CC", 0, filename_list)
            write_excel(metric_save_name, "SCD", 0, filename_list)
            write_excel(metric_save_name, "VIF", 0, filename_list)
            write_excel(metric_save_name, "MSE", 0, filename_list)
            write_excel(metric_save_name, "PSNR", 0, filename_list)
            write_excel(metric_save_name, "Qabf", 0, filename_list)
            write_excel(metric_save_name, "Nabf", 0, filename_list)
            write_excel(metric_save_name, "SSIM", 0, filename_list)
            write_excel(metric_save_name, "MS_SSIM", 0, filename_list)
        write_excel(metric_save_name, 'EN', i + 1, EN_list)
        write_excel(metric_save_name, 'MI', i + 1, MI_list)
        write_excel(metric_save_name, 'SF', i + 1, SF_list)
        write_excel(metric_save_name, 'AG', i + 1, AG_list)
        write_excel(metric_save_name, 'SD', i + 1, SD_list)
        write_excel(metric_save_name, 'CC', i + 1, CC_list)
        write_excel(metric_save_name, 'SCD', i + 1, SCD_list)
        write_excel(metric_save_name, 'VIF', i + 1, VIF_list)
        write_excel(metric_save_name, 'MSE', i + 1, MSE_list)
        write_excel(metric_save_name, 'PSNR', i + 1, PSNR_list)
        write_excel(metric_save_name, 'Qabf', i + 1, Qabf_list)
        write_excel(metric_save_name, 'Nabf', i + 1, Nabf_list)
        write_excel(metric_save_name, 'SSIM', i + 1, SSIM_list)
        write_excel(metric_save_name, 'MS_SSIM', i + 1, MS_SSIM_list)

在上述三个程序中均需调用 Metric.py函数：

import numpy as np
from scipy.signal import convolve2d
from Qabf import get_Qabf
from Nabf import get_Nabf
import math
from ssim import ssim, ms_ssim

def EN_function(image_array):
    # 计算图像的直方图
    histogram, bins = np.histogram(image_array, bins=256, range=(0, 255))
    # 将直方图归一化
    histogram = histogram / float(np.sum(histogram))
    # 计算熵
    entropy = -np.sum(histogram * np.log2(histogram + 1e-7))
    return entropy

def SF_function(image):
    image_array = np.array(image)
    RF = np.diff(image_array, axis=0)
    RF1 = np.sqrt(np.mean(np.mean(RF ** 2)))
    CF = np.diff(image_array, axis=1)
    CF1 = np.sqrt(np.mean(np.mean(CF ** 2)))
    SF = np.sqrt(RF1 ** 2 + CF1 ** 2)
    return SF

def SD_function(image_array):
    m, n = image_array.shape
    u = np.mean(image_array)
    SD = np.sqrt(np.sum(np.sum((image_array - u) ** 2)) / (m * n))
    return SD

def PSNR_function(A, B, F):
    A = A / 255.0
    B = B / 255.0
    F = F / 255.0
    m, n = F.shape
    MSE_AF = np.sum(np.sum((F - A)**2))/(m*n)
    MSE_BF = np.sum(np.sum((F - B)**2))/(m*n)
    MSE = 0.5 * MSE_AF + 0.5 * MSE_BF
    PSNR = 20 * np.log10(255/np.sqrt(MSE))
    return PSNR

def MSE_function(A, B, F):
    A = A / 255.0
    B = B / 255.0
    F = F / 255.0
    m, n = F.shape
    MSE_AF = np.sum(np.sum((F - A)**2))/(m*n)
    MSE_BF = np.sum(np.sum((F - B)**2))/(m*n)
    MSE = 0.5 * MSE_AF + 0.5 * MSE_BF
    return MSE

def fspecial_gaussian(shape, sigma):
    """
    2D gaussian mask - should give the same result as MATLAB's fspecial('gaussian',...)
    """
    m, n = [(ss-1.)/2. for ss in shape]
    y, x = np.ogrid[-m:m+1, -n:n+1]
    h = np.exp(-(x*x + y*y) / (2.*sigma*sigma))
    h[h < np.finfo(h.dtype).eps*h.max()] = 0
    sumh = h.sum()
    if sumh != 0:
        h /= sumh
    return h

def vifp_mscale(ref, dist):
    sigma_nsq = 2
    num = 0
    den = 0
    for scale in range(1, 5):
        N = 2**(4-scale+1)+1
        win = fspecial_gaussian((N, N), N/5)

        if scale > 1:
            ref = convolve2d(ref, win, mode='valid')
            dist = convolve2d(dist, win, mode='valid')
            ref = ref[::2, ::2]
            dist = dist[::2, ::2]

        mu1 = convolve2d(ref, win, mode='valid')
        mu2 = convolve2d(dist, win, mode='valid')
        mu1_sq = mu1*mu1
        mu2_sq = mu2*mu2
        mu1_mu2 = mu1*mu2
        sigma1_sq = convolve2d(ref*ref, win, mode='valid') - mu1_sq
        sigma2_sq = convolve2d(dist*dist, win, mode='valid') - mu2_sq
        sigma12 = convolve2d(ref*dist, win, mode='valid') - mu1_mu2
        sigma1_sq[sigma1_sq<0] = 0
        sigma2_sq[sigma2_sq<0] = 0

        g = sigma12 / (sigma1_sq + 1e-10)
        sv_sq = sigma2_sq - g*sigma12

        g[sigma1_sq<1e-10] = 0
        sv_sq[sigma1_sq<1e-10] = sigma2_sq[sigma1_sq<1e-10]
        sigma1_sq[sigma1_sq<1e-10] = 0

        g[sigma2_sq<1e-10] = 0
        sv_sq[sigma2_sq<1e-10] = 0

        sv_sq[g<0] = sigma2_sq[g<0]
        g[g<0] = 0
        sv_sq[sv_sq<=1e-10] = 1e-10

        num += np.sum(np.log10(1+g**2 * sigma1_sq/(sv_sq+sigma_nsq)))
        den += np.sum(np.log10(1+sigma1_sq/sigma_nsq))
    vifp = num/den
    return vifp

def VIF_function(A, B, F):
    VIF = vifp_mscale(A, F) + vifp_mscale(B, F)
    return VIF

def CC_function(A,B,F):
    rAF = np.sum((A - np.mean(A)) * (F - np.mean(F))) / np.sqrt(np.sum((A - np.mean(A)) ** 2) * np.sum((F - np.mean(F)) ** 2))
    rBF = np.sum((B - np.mean(B)) * (F - np.mean(F))) / np.sqrt(np.sum((B - np.mean(B)) ** 2) * np.sum((F - np.mean(F)) ** 2))
    CC = np.mean([rAF, rBF])
    return CC

def corr2(a, b):
    a = a - np.mean(a)
    b = b - np.mean(b)
    r = np.sum(a * b) / np.sqrt(np.sum(a * a) * np.sum(b * b))
    return r

def SCD_function(A, B, F):
    r = corr2(F - B, A) + corr2(F - A, B)
    return r

def Qabf_function(A, B, F):
    return get_Qabf(A, B, F)

def Nabf_function(A, B, F):
    return Nabf_function(A, B, F)


def Hab(im1, im2, gray_level):
	hang, lie = im1.shape
	count = hang * lie
	N = gray_level
	h = np.zeros((N, N))
	for i in range(hang):
		for j in range(lie):
			h[im1[i, j], im2[i, j]] = h[im1[i, j], im2[i, j]] + 1
	h = h / np.sum(h)
	im1_marg = np.sum(h, axis=0)
	im2_marg = np.sum(h, axis=1)
	H_x = 0
	H_y = 0
	for i in range(N):
		if (im1_marg[i] != 0):
			H_x = H_x + im1_marg[i] * math.log2(im1_marg[i])
	for i in range(N):
		if (im2_marg[i] != 0):
			H_x = H_x + im2_marg[i] * math.log2(im2_marg[i])
	H_xy = 0
	for i in range(N):
		for j in range(N):
			if (h[i, j] != 0):
				H_xy = H_xy + h[i, j] * math.log2(h[i, j])
	MI = H_xy - H_x - H_y
	return MI

def MI_function(A, B, F, gray_level=256):
	MIA = Hab(A, F, gray_level)
	MIB = Hab(B, F, gray_level)
	MI_results = MIA + MIB
	return MI_results

def AG_function(image):
	width = image.shape[1]
	width = width - 1
	height = image.shape[0]
	height = height - 1
	tmp = 0.0
	[grady, gradx] = np.gradient(image)
	s = np.sqrt((np.square(gradx) + np.square(grady)) / 2)
	AG = np.sum(np.sum(s)) / (width * height)
	return AG

def SSIM_function(A, B, F):
    ssim_A = ssim(A, F)
    ssim_B = ssim(B, F)
    SSIM = 1 * ssim_A + 1 * ssim_B
    return SSIM.item()

def MS_SSIM_function(A, B, F):
    ssim_A = ms_ssim(A, F)
    ssim_B = ms_ssim(B, F)
    MS_SSIM = 1 * ssim_A + 1 * ssim_B
    return MS_SSIM.item()

def Nabf_function(A, B, F):
    Nabf = get_Nabf(A, B, F)
    return Nabf

完整demo下载地址：https://download.csdn.net/download/fovever_/87547835

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