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]。
| 评估指标 | 缩写 |
|---|---|
| 信息熵 | EN |
| 空间频率 | SF |
| 标准差 | SD |
| 峰值信噪比 | PSNR |
| 均方误差 | MSE |
| 互信息 | MI |
| 视觉保真度 | VIF |
| 平均梯度 | AG |
| 相关系数 | CC |
| 差异相关和 | SCD |
| 基于梯度的融合性能 | Qabf |
| 结构相似度测量 | SSIM |
| 多尺度结构相似度测量 | MS-SSIM |
| 基于噪声评估的融合性能 | Nabf |
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 Imagefrom Metric import *from time import timeimport warningswarnings.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_SSIMif __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 npfrom PIL import Imagefrom Metric import *from natsort import natsortedfrom tqdm import tqdmimport osimport statisticsimport warningsfrom openpyxl import Workbook, load_workbookfrom openpyxl.utils import get_column_letterwarnings.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_SSIMif __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 npfrom PIL import Imagefrom Metric import *from natsort import natsortedfrom tqdm import tqdmimport osimport statisticsimport warningsfrom openpyxl import Workbook, load_workbookfrom openpyxl.utils import get_column_letterwarnings.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_SSIMif __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 npfrom scipy.signal import convolve2dfrom Qabf import get_Qabffrom Nabf import get_Nabfimport mathfrom ssim import ssim, ms_ssimdef 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 entropydef 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 SFdef 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 SDdef 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 PSNRdef 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 MSEdef 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 hdef 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 vifpdef VIF_function(A, B, F): VIF = vifp_mscale(A, F) + vifp_mscale(B, F) return VIFdef 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 CCdef 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 rdef SCD_function(A, B, F): r = corr2(F - B, A) + corr2(F - A, B) return rdef 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.shapecount = hang * lieN = gray_levelh = 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]] + 1h = h / np.sum(h)im1_marg = np.sum(h, axis=0)im2_marg = np.sum(h, axis=1)H_x = 0H_y = 0for 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 = 0for 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_yreturn MIdef MI_function(A, B, F, gray_level=256):MIA = Hab(A, F, gray_level)MIB = Hab(B, F, gray_level)MI_results = MIA + MIBreturn MI_resultsdef AG_function(image):width = image.shape[1]width = width - 1height = image.shape[0]height = height - 1tmp = 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 AGdef 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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