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 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
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