├── Metric_Python ├── .idea │ ├── .gitignore │ ├── .name │ ├── Metric_Python.iml │ ├── deployment.xml │ ├── inspectionProfiles │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── misc.xml │ └── modules.xml ├── FMI.py ├── Metric.py ├── Nabf.py ├── Qabf.py ├── __pycache__ │ ├── Metric.cpython-37.pyc │ ├── Metric.cpython-38.pyc │ ├── Metric.cpython-39.pyc │ ├── Nabf.cpython-37.pyc │ ├── Nabf.cpython-38.pyc │ ├── Nabf.cpython-39.pyc │ ├── Qabf.cpython-37.pyc │ ├── Qabf.cpython-38.pyc │ ├── Qabf.cpython-39.pyc │ ├── Qabf2.cpython-37.pyc │ ├── ssim.cpython-37.pyc │ ├── ssim.cpython-38.pyc │ ├── ssim_torch.cpython-37.pyc │ ├── ssim_torch.cpython-39.pyc │ └── vif_utils.cpython-37.pyc ├── eval_multi_method.py ├── eval_one_image.py ├── eval_one_method.py ├── ssim.py └── write.py ├── README.md ├── Results └── TNO │ ├── DIDFuse │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── DRF │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── FusionGAN │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── GAN-FM │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── GTF │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── IFCNN │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── Our │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── PMGI │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 43.png │ ├── 44.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ ├── U2Fusion │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png │ └── UMF-CMGR │ ├── 1.png │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.png │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 35.png │ ├── 36.png │ ├── 37.png │ ├── 38.png │ ├── 39.png │ ├── 4.png │ ├── 40.png │ ├── 41.png │ ├── 42.png │ ├── 5.png │ ├── 6.png │ ├── 7.png │ ├── 8.png │ └── 9.png └── datasets └── TNO ├── ir ├── 1.png ├── 10.png ├── 11.png ├── 12.png ├── 13.png ├── 14.png ├── 15.png ├── 16.png ├── 17.png ├── 18.png ├── 19.png ├── 2.png ├── 20.png ├── 21.png ├── 22.png ├── 23.png ├── 24.png ├── 25.png ├── 26.png ├── 27.png ├── 28.png ├── 29.png ├── 3.png ├── 30.png ├── 31.png ├── 32.png ├── 33.png ├── 34.png ├── 35.png ├── 36.png ├── 37.png ├── 38.png ├── 39.png ├── 4.png ├── 40.png ├── 41.png ├── 42.png ├── 5.png ├── 6.png ├── 7.png ├── 8.png └── 9.png └── vi ├── 1.png ├── 10.png ├── 11.png ├── 12.png ├── 13.png ├── 14.png ├── 15.png ├── 16.png ├── 17.png ├── 18.png ├── 19.png ├── 2.png ├── 20.png ├── 21.png ├── 22.png ├── 23.png ├── 24.png ├── 25.png ├── 26.png ├── 27.png ├── 28.png ├── 29.png ├── 3.png ├── 30.png ├── 31.png ├── 32.png ├── 33.png ├── 34.png ├── 35.png ├── 36.png ├── 37.png ├── 38.png ├── 39.png ├── 4.png ├── 40.png ├── 41.png ├── 42.png ├── 5.png ├── 6.png ├── 7.png ├── 8.png └── 9.png /Metric_Python/.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | # 数据源本地存储已忽略文件 5 | /dataSources/ 6 | /dataSources.local.xml 7 | # 基于编辑器的 HTTP 客户端请求 8 | /httpRequests/ 9 | -------------------------------------------------------------------------------- /Metric_Python/.idea/.name: -------------------------------------------------------------------------------- 1 | Metric.py -------------------------------------------------------------------------------- /Metric_Python/.idea/Metric_Python.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /Metric_Python/.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | -------------------------------------------------------------------------------- /Metric_Python/.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 26 | -------------------------------------------------------------------------------- /Metric_Python/.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /Metric_Python/.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /Metric_Python/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /Metric_Python/FMI.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.fftpack import dctn 3 | from scipy.signal import convolve2d 4 | from scipy.ndimage import sobel, generic_gradient_magnitude 5 | 6 | 7 | def analysis_fmi(ima, imb, imf, feature, w): 8 | ima = np.double(ima) 9 | imb = np.double(imb) 10 | imf = np.double(imf) 11 | 12 | # Feature Extraction 13 | if feature == 'none': # Raw pixels (no feature extraction) 14 | aFeature = ima 15 | bFeature = imb 16 | fFeature = imf 17 | elif feature == 'gradient': # Gradient 18 | aFeature = generic_gradient_magnitude(ima, sobel) 19 | bFeature = generic_gradient_magnitude(imb, sobel) 20 | fFeature = generic_gradient_magnitude(imf, sobel) 21 | elif feature == 'edge': # Edge 22 | aFeature = np.double(sobel(ima) > w) 23 | bFeature = np.double(sobel(imb) > w) 24 | fFeature = np.double(sobel(imf) > w) 25 | elif feature == 'dct': # DCT 26 | aFeature = dctn(ima, type=2, norm='ortho') 27 | bFeature = dctn(imb, type=2, norm='ortho') 28 | fFeature = dctn(imf, type=2, norm='ortho') 29 | elif feature == 'wavelet': # Discrete Meyer wavelet 30 | raise NotImplementedError('Wavelet feature extraction not yet implemented in Python!') 31 | else: 32 | raise ValueError( 33 | "Please specify a feature extraction method among 'gradient', 'edge', 'dct', 'wavelet', or 'none' (raw pixels)!") 34 | 35 | m, n = aFeature.shape 36 | w = w // 2 37 | fmi_map = np.ones((m - 2 * w, n - 2 * w)) 38 | -------------------------------------------------------------------------------- /Metric_Python/Metric.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.signal import convolve2d 3 | from Qabf import get_Qabf 4 | from Nabf import get_Nabf 5 | import math 6 | from ssim import ssim, ms_ssim 7 | 8 | 9 | def EN_function(image_array): 10 | # 计算图像的直方图 11 | histogram, bins = np.histogram(image_array, bins=256, range=(0, 255)) 12 | # 将直方图归一化 13 | histogram = histogram / float(np.sum(histogram)) 14 | # 计算熵 15 | entropy = -np.sum(histogram * np.log2(histogram + 1e-7)) 16 | return entropy 17 | 18 | 19 | def SF_function(image): 20 | image_array = np.array(image) 21 | RF = np.diff(image_array, axis=0) 22 | RF1 = np.sqrt(np.mean(np.mean(RF ** 2))) 23 | CF = np.diff(image_array, axis=1) 24 | CF1 = np.sqrt(np.mean(np.mean(CF ** 2))) 25 | SF = np.sqrt(RF1 ** 2 + CF1 ** 2) 26 | return SF 27 | 28 | 29 | def SD_function(image_array): 30 | m, n = image_array.shape 31 | u = np.mean(image_array) 32 | SD = np.sqrt(np.sum(np.sum((image_array - u) ** 2)) / (m * n)) 33 | return SD 34 | 35 | 36 | def PSNR_function(A, B, F): 37 | A = A / 255.0 38 | B = B / 255.0 39 | F = F / 255.0 40 | m, n = F.shape 41 | MSE_AF = np.sum(np.sum((F - A) ** 2)) / (m * n) 42 | MSE_BF = np.sum(np.sum((F - B) ** 2)) / (m * n) 43 | MSE = 0.5 * MSE_AF + 0.5 * MSE_BF 44 | PSNR = 20 * np.log10(255 / np.sqrt(MSE)) 45 | return PSNR 46 | 47 | 48 | def MSE_function(A, B, F): 49 | A = A / 255.0 50 | B = B / 255.0 51 | F = F / 255.0 52 | m, n = F.shape 53 | MSE_AF = np.sum(np.sum((F - A) ** 2)) / (m * n) 54 | MSE_BF = np.sum(np.sum((F - B) ** 2)) / (m * n) 55 | MSE = 0.5 * MSE_AF + 0.5 * MSE_BF 56 | return MSE 57 | 58 | 59 | def fspecial_gaussian(shape, sigma): 60 | """ 61 | 2D gaussian mask - should give the same result as MATLAB's fspecial('gaussian',...) 62 | """ 63 | m, n = [(ss - 1.) / 2. for ss in shape] 64 | y, x = np.ogrid[-m:m + 1, -n:n + 1] 65 | h = np.exp(-(x * x + y * y) / (2. * sigma * sigma)) 66 | h[h < np.finfo(h.dtype).eps * h.max()] = 0 67 | sumh = h.sum() 68 | if sumh != 0: 69 | h /= sumh 70 | return h 71 | 72 | 73 | def vifp_mscale(ref, dist): 74 | sigma_nsq = 2 75 | num = 0 76 | den = 0 77 | for scale in range(1, 5): 78 | N = 2 ** (4 - scale + 1) + 1 79 | win = fspecial_gaussian((N, N), N / 5) 80 | 81 | if scale > 1: 82 | ref = convolve2d(ref, win, mode='valid') 83 | dist = convolve2d(dist, win, mode='valid') 84 | ref = ref[::2, ::2] 85 | dist = dist[::2, ::2] 86 | 87 | mu1 = convolve2d(ref, win, mode='valid') 88 | mu2 = convolve2d(dist, win, mode='valid') 89 | mu1_sq = mu1 * mu1 90 | mu2_sq = mu2 * mu2 91 | mu1_mu2 = mu1 * mu2 92 | sigma1_sq = convolve2d(ref * ref, win, mode='valid') - mu1_sq 93 | sigma2_sq = convolve2d(dist * dist, win, mode='valid') - mu2_sq 94 | sigma12 = convolve2d(ref * dist, win, mode='valid') - mu1_mu2 95 | sigma1_sq[sigma1_sq < 0] = 0 96 | sigma2_sq[sigma2_sq < 0] = 0 97 | 98 | g = sigma12 / (sigma1_sq + 1e-10) 99 | sv_sq = sigma2_sq - g * sigma12 100 | 101 | g[sigma1_sq < 1e-10] = 0 102 | sv_sq[sigma1_sq < 1e-10] = sigma2_sq[sigma1_sq < 1e-10] 103 | sigma1_sq[sigma1_sq < 1e-10] = 0 104 | 105 | g[sigma2_sq < 1e-10] = 0 106 | sv_sq[sigma2_sq < 1e-10] = 0 107 | 108 | sv_sq[g < 0] = sigma2_sq[g < 0] 109 | g[g < 0] = 0 110 | sv_sq[sv_sq <= 1e-10] = 1e-10 111 | 112 | num += np.sum(np.log10(1 + g ** 2 * sigma1_sq / (sv_sq + sigma_nsq))) 113 | den += np.sum(np.log10(1 + sigma1_sq / sigma_nsq)) 114 | vifp = num / den 115 | return vifp 116 | 117 | 118 | def VIF_function(A, B, F): 119 | VIF = vifp_mscale(A, F) + vifp_mscale(B, F) 120 | return VIF 121 | 122 | 123 | def CC_function(A, B, F): 124 | rAF = np.sum((A - np.mean(A)) * (F - np.mean(F))) / np.sqrt( 125 | np.sum((A - np.mean(A)) ** 2) * np.sum((F - np.mean(F)) ** 2)) 126 | rBF = np.sum((B - np.mean(B)) * (F - np.mean(F))) / np.sqrt( 127 | np.sum((B - np.mean(B)) ** 2) * np.sum((F - np.mean(F)) ** 2)) 128 | CC = np.mean([rAF, rBF]) 129 | return CC 130 | 131 | 132 | def corr2(a, b): 133 | a = a - np.mean(a) 134 | b = b - np.mean(b) 135 | r = np.sum(a * b) / np.sqrt(np.sum(a * a) * np.sum(b * b)) 136 | return r 137 | 138 | 139 | def SCD_function(A, B, F): 140 | r = corr2(F - B, A) + corr2(F - A, B) 141 | return r 142 | 143 | 144 | def Qabf_function(A, B, F): 145 | return get_Qabf(A, B, F) 146 | 147 | 148 | def Nabf_function(A, B, F): 149 | return Nabf_function(A, B, F) 150 | 151 | 152 | def Hab(im1, im2, gray_level): 153 | hang, lie = im1.shape 154 | count = hang * lie 155 | N = gray_level 156 | h = np.zeros((N, N)) 157 | for i in range(hang): 158 | for j in range(lie): 159 | h[im1[i, j], im2[i, j]] = h[im1[i, j], im2[i, j]] + 1 160 | h = h / np.sum(h) 161 | im1_marg = np.sum(h, axis=0) 162 | im2_marg = np.sum(h, axis=1) 163 | H_x = 0 164 | H_y = 0 165 | for i in range(N): 166 | if im1_marg[i] != 0: 167 | H_x = H_x + im1_marg[i] * math.log2(im1_marg[i]) 168 | for i in range(N): 169 | if im2_marg[i] != 0: 170 | H_y = H_y + im2_marg[i] * math.log2(im2_marg[i]) 171 | H_xy = 0 172 | for i in range(N): 173 | for j in range(N): 174 | if h[i, j] != 0: 175 | H_xy = H_xy + h[i, j] * math.log2(h[i, j]) 176 | MI = H_xy - H_x - H_y 177 | return MI 178 | 179 | 180 | def MI_function(A, B, F, gray_level=256): 181 | MIA = Hab(A, F, gray_level) 182 | MIB = Hab(B, F, gray_level) 183 | MI_results = MIA + MIB 184 | return MI_results 185 | 186 | 187 | def MI_function2(A, B, C, F, gray_level=256): 188 | MIA = Hab(A, F, gray_level) 189 | MIB = Hab(B, F, gray_level) 190 | MIC = Hab(C, F, gray_level) 191 | MI_results = MIA + MIB + MIC 192 | return MI_results 193 | 194 | 195 | def AG_function(image): 196 | width = image.shape[0] 197 | width = width - 1 198 | height = image.shape[1] 199 | height = height - 1 200 | tmp = 0.0 201 | [grady, gradx] = np.gradient(image) 202 | s = np.sqrt((np.square(gradx) + np.square(grady)) / 2) 203 | AG = np.sum(np.sum(s)) / (width * height) 204 | return AG 205 | 206 | 207 | def SSIM_function(A, B, F): 208 | ssim_A = ssim(A, F) 209 | ssim_B = ssim(B, F) 210 | SSIM = ssim_B/2 + ssim_A/2 211 | return SSIM.item() 212 | 213 | 214 | def MS_SSIM_function(A, B, F): 215 | ssim_A = ms_ssim(A, F) 216 | ssim_B = ms_ssim(B, F) 217 | MS_SSIM = ssim_B/2 + ssim_A/2 218 | return MS_SSIM.item() 219 | 220 | 221 | def Nabf_function(A, B, F): 222 | Nabf = get_Nabf(A, B, F) 223 | return Nabf 224 | -------------------------------------------------------------------------------- /Metric_Python/Nabf.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.signal import convolve2d 3 | import math 4 | 5 | def sobel_fn(x): 6 | # Sobel operators 7 | vtemp = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) / 8 8 | htemp = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) / 8 9 | 10 | a, b = htemp.shape 11 | x_ext = per_extn_im_fn(x, a) 12 | p, q = x_ext.shape 13 | gv = np.zeros((p - 2, q - 2)) 14 | gh = np.zeros((p - 2, q - 2)) 15 | gv = convolve2d(x_ext, vtemp, mode='valid') 16 | gh = convolve2d(x_ext, htemp, mode='valid') 17 | # for ii in range(1, p - 1): 18 | # for jj in range(1, q - 1): 19 | # gv[ii - 1, jj - 1] = np.sum(x_ext[ii - 1:ii + 2, jj - 1:jj + 2] * vtemp) 20 | # gh[ii - 1, jj - 1] = np.sum(x_ext[ii - 1:ii + 2, jj - 1:jj + 2] * htemp) 21 | 22 | return gv, gh 23 | 24 | 25 | def per_extn_im_fn(x, wsize): 26 | """ 27 | Periodic extension of the given image in 4 directions. 28 | 29 | xout_ext = per_extn_im_fn(x, wsize) 30 | 31 | Periodic extension by (wsize-1)/2 on all 4 sides. 32 | wsize should be odd. 33 | 34 | Example: 35 | Y = per_extn_im_fn(X, 5); % Periodically extends 2 rows and 2 columns in all sides. 36 | """ 37 | 38 | hwsize = (wsize - 1) // 2 # Half window size excluding centre pixel. 39 | 40 | p, q = x.shape 41 | xout_ext = np.zeros((p + wsize - 1, q + wsize - 1)) 42 | xout_ext[hwsize: p + hwsize, hwsize: q + hwsize] = x 43 | 44 | # Row-wise periodic extension. 45 | if wsize - 1 == hwsize + 1: 46 | xout_ext[0: hwsize, :] = xout_ext[2, :].reshape(1, -1) 47 | xout_ext[p + hwsize: p + wsize - 1, :] = xout_ext[-3, :].reshape(1, -1) 48 | 49 | # Column-wise periodic extension. 50 | xout_ext[:, 0: hwsize] = xout_ext[:, 2].reshape(-1, 1) 51 | xout_ext[:, q + hwsize: q + wsize - 1] = xout_ext[:, -3].reshape(-1, 1) 52 | 53 | return xout_ext 54 | 55 | def get_Nabf(I1, I2, f): 56 | # Parameters for Petrovic Metrics Computation. 57 | Td=2 58 | wt_min=0.001 59 | P=1 60 | Lg=1.5 61 | Nrg=0.9999 62 | kg=19 63 | sigmag=0.5 64 | Nra=0.9995 65 | ka=22 66 | sigmaa=0.5 67 | 68 | xrcw = f.astype(np.float64) 69 | x1 = I1.astype(np.float64) 70 | x2 = I2.astype(np.float64) 71 | 72 | # Edge Strength & Orientation. 73 | gvA,ghA=sobel_fn(x1) 74 | gA=np.sqrt(ghA**2+gvA**2) 75 | 76 | gvB,ghB=sobel_fn(x2) 77 | gB=np.sqrt(ghB**2+gvB**2) 78 | 79 | gvF,ghF=sobel_fn(xrcw) 80 | gF=np.sqrt(ghF**2+gvF**2) 81 | 82 | # Relative Edge Strength & Orientation. 83 | gAF=np.zeros(gA.shape) 84 | gBF=np.zeros(gB.shape) 85 | aA=np.zeros(ghA.shape) 86 | aB=np.zeros(ghB.shape) 87 | aF=np.zeros(ghF.shape) 88 | p,q=xrcw.shape 89 | maskAF1 = (gA == 0) | (gF == 0) 90 | maskAF2 = (gA > gF) 91 | gAF[~maskAF1] = np.where(maskAF2, gF / gA, gA / gF)[~maskAF1] 92 | maskBF1 = (gB == 0) | (gF == 0) 93 | maskBF2 = (gB > gF) 94 | gBF[~maskBF1] = np.where(maskBF2, gF / gB, gB / gF)[~maskBF1] 95 | aA = np.where((gvA == 0) & (ghA == 0), 0, np.arctan(gvA / ghA)) 96 | aB = np.where((gvB == 0) & (ghB == 0), 0, np.arctan(gvB / ghB)) 97 | aF = np.where((gvF == 0) & (ghF == 0), 0, np.arctan(gvF / ghF)) 98 | 99 | aAF=np.abs(np.abs(aA-aF)-np.pi/2)*2/np.pi 100 | aBF=np.abs(np.abs(aB-aF)-np.pi/2)*2/np.pi 101 | 102 | QgAF = Nrg / (1 + np.exp(-kg * (gAF - sigmag))) 103 | QaAF = Nra / (1 + np.exp(-ka * (aAF - sigmaa))) 104 | QAF = np.sqrt(QgAF * QaAF) 105 | QgBF = Nrg / (1 + np.exp(-kg * (gBF - sigmag))) 106 | QaBF = Nra / (1 + np.exp(-ka * (aBF - sigmaa))) 107 | QBF = np.sqrt(QgBF * QaBF) 108 | 109 | wtA = wt_min * np.ones((p, q)) 110 | wtB = wt_min * np.ones((p, q)) 111 | cA = np.ones((p, q)) 112 | cB = np.ones((p, q)) 113 | wtA = np.where(gA >= Td, cA * gA ** Lg, 0) 114 | wtB = np.where(gB >= Td, cB * gB ** Lg, 0) 115 | 116 | wt_sum = np.sum(wtA + wtB) 117 | QAF_wtsum = np.sum(QAF * wtA) / wt_sum # Information Contributions of A. 118 | QBF_wtsum = np.sum(QBF * wtB) / wt_sum # Information Contributions of B. 119 | QABF = QAF_wtsum + QBF_wtsum # QABF=sum(sum(QAF.*wtA+QBF.*wtB))/wt_sum -> Total Fusion Performance. 120 | 121 | 122 | Qdelta = np.abs(QAF - QBF) 123 | QCinfo = (QAF + QBF - Qdelta) / 2 124 | QdeltaAF = QAF - QCinfo 125 | QdeltaBF = QBF - QCinfo 126 | QdeltaAF_wtsum = np.sum(QdeltaAF * wtA) / wt_sum 127 | QdeltaBF_wtsum = np.sum(QdeltaBF * wtB) / wt_sum 128 | QdeltaABF = QdeltaAF_wtsum + QdeltaBF_wtsum # Total Fusion Gain. 129 | QCinfo_wtsum = np.sum(QCinfo * (wtA + wtB)) / wt_sum 130 | QABF11 = QdeltaABF + QCinfo_wtsum # Total Fusion Performance. 131 | 132 | rr = np.zeros((p, q)) 133 | rr = np.where(gF <= np.minimum(gA, gB), 1, 0) 134 | 135 | 136 | LABF = np.sum(rr * ((1 - QAF) * wtA + (1 - QBF) * wtB)) / wt_sum 137 | 138 | na1 = np.where((gF > gA) & (gF > gB), 2 - QAF - QBF, 0) 139 | NABF1 = np.sum(na1 * (wtA + wtB)) / wt_sum 140 | 141 | # Fusion Artifacts (NABF) changed by B. K. Shreyamsha Kumar. 142 | 143 | na = np.where((gF > gA) & (gF > gB), 1, 0) 144 | NABF = np.sum(na * ((1 - QAF) * wtA + (1 - QBF) * wtB)) / wt_sum 145 | return NABF -------------------------------------------------------------------------------- /Metric_Python/Qabf.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import math 3 | from scipy.signal import convolve2d 4 | 5 | 6 | def sobel_fn(x): 7 | # Sobel operators 8 | vtemp = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) / 8 9 | htemp = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) / 8 10 | 11 | a, b = htemp.shape 12 | x_ext = per_extn_im_fn(x, a) 13 | p, q = x_ext.shape 14 | gv = np.zeros((p - 2, q - 2)) 15 | gh = np.zeros((p - 2, q - 2)) 16 | gv = convolve2d(x_ext, vtemp, mode='valid') 17 | gh = convolve2d(x_ext, htemp, mode='valid') 18 | # for ii in range(1, p - 1): 19 | # for jj in range(1, q - 1): 20 | # gv[ii - 1, jj - 1] = np.sum(x_ext[ii - 1:ii + 2, jj - 1:jj + 2] * vtemp) 21 | # gh[ii - 1, jj - 1] = np.sum(x_ext[ii - 1:ii + 2, jj - 1:jj + 2] * htemp) 22 | 23 | return gv, gh 24 | 25 | 26 | def per_extn_im_fn(x, wsize): 27 | """ 28 | Periodic extension of the given image in 4 directions. 29 | 30 | xout_ext = per_extn_im_fn(x, wsize) 31 | 32 | Periodic extension by (wsize-1)/2 on all 4 sides. 33 | wsize should be odd. 34 | 35 | Example: 36 | Y = per_extn_im_fn(X, 5); % Periodically extends 2 rows and 2 columns in all sides. 37 | """ 38 | 39 | hwsize = (wsize - 1) // 2 # Half window size excluding centre pixel. 40 | 41 | p, q = x.shape 42 | xout_ext = np.zeros((p + wsize - 1, q + wsize - 1)) 43 | xout_ext[hwsize: p + hwsize, hwsize: q + hwsize] = x 44 | 45 | # Row-wise periodic extension. 46 | if wsize - 1 == hwsize + 1: 47 | xout_ext[0: hwsize, :] = xout_ext[2, :].reshape(1, -1) 48 | xout_ext[p + hwsize: p + wsize - 1, :] = xout_ext[-3, :].reshape(1, -1) 49 | 50 | # Column-wise periodic extension. 51 | xout_ext[:, 0: hwsize] = xout_ext[:, 2].reshape(-1, 1) 52 | xout_ext[:, q + hwsize: q + wsize - 1] = xout_ext[:, -3].reshape(-1, 1) 53 | 54 | return xout_ext 55 | 56 | def get_Qabf(pA, pB, pF): 57 | L = 1 58 | Tg = 0.9994 59 | kg = -15 60 | Dg = 0.5; 61 | Ta = 0.9879 62 | ka = -22 63 | Da = 0.8 64 | 65 | # Sobel Operator Sobel算子 66 | h1 = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]).astype(np.float32) 67 | h2 = np.array([[0, 1, 2], [-1, 0, 1], [-2, -1, 0]]).astype(np.float32) 68 | h3 = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).astype(np.float32) 69 | 70 | # if y is the response to h1 and x is the response to h3;then the intensity is sqrt(x^2+y^2) and is arctan(y/x); 71 | # 如果y对应h1，x对应h2，则强度为sqrt(x^2+y^2)，方向为arctan(y/x) 72 | 73 | strA = pA 74 | strB = pB 75 | strF = pF 76 | 77 | # 数组旋转180度 78 | def flip180(arr): 79 | return np.flip(arr) 80 | 81 | # 相当于matlab的Conv2 82 | def convolution(k, data): 83 | k = flip180(k) 84 | data = np.pad(data, ((1, 1), (1, 1)), 'constant', constant_values=(0, 0)) 85 | img_new = convolve2d(data, k, mode='valid') 86 | return img_new 87 | 88 | def getArray(img): 89 | SAx = convolution(h3, img) 90 | SAy = convolution(h1, img) 91 | gA = np.sqrt(np.multiply(SAx, SAx) + np.multiply(SAy, SAy)) 92 | n, m = img.shape 93 | aA = np.zeros((n, m)) 94 | zero_mask = SAx == 0 95 | aA[~zero_mask] = np.arctan(SAy[~zero_mask] / SAx[~zero_mask]) 96 | aA[zero_mask] = np.pi / 2 97 | # for i in range(n): 98 | # for j in range(m): 99 | # if (SAx[i, j] == 0): 100 | # aA[i, j] = math.pi / 2 101 | # else: 102 | # aA[i, j] = math.atan(SAy[i, j] / SAx[i, j]) 103 | return gA, aA 104 | 105 | # 对strB和strF进行相同的操作 106 | gA, aA = getArray(strA) 107 | gB, aB = getArray(strB) 108 | gF, aF = getArray(strF) 109 | 110 | # the relative strength and orientation value of GAF,GBF and AAF,ABF; 111 | def getQabf(aA, gA, aF, gF): 112 | mask = (gA > gF) 113 | GAF = np.where(mask, gF / gA, np.where(gA == gF, gF, gA / gF)) 114 | 115 | AAF = 1 - np.abs(aA - aF) / (math.pi / 2) 116 | 117 | QgAF = Tg / (1 + np.exp(kg * (GAF - Dg))) 118 | QaAF = Ta / (1 + np.exp(ka * (AAF - Da))) 119 | 120 | QAF = QgAF * QaAF 121 | return QAF 122 | 123 | QAF = getQabf(aA, gA, aF, gF) 124 | QBF = getQabf(aB, gB, aF, gF) 125 | 126 | # 计算QABF 127 | deno = np.sum(gA + gB) 128 | nume = np.sum(np.multiply(QAF, gA) + np.multiply(QBF, gB)) 129 | output = nume / deno 130 | return output -------------------------------------------------------------------------------- /Metric_Python/__pycache__/Metric.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Z-zachary/Image-evalution/9c2fa2a191ff41dd3ec1b803741b23370c95f30b/Metric_Python/__pycache__/Metric.cpython-37.pyc -------------------------------------------------------------------------------- /Metric_Python/__pycache__/Metric.cpython-38.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /Metric_Python/eval_multi_method.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from PIL import Image 3 | from Metric import * 4 | from natsort import natsorted 5 | from tqdm import tqdm 6 | import os 7 | import warnings 8 | from openpyxl import Workbook, load_workbook 9 | from openpyxl.utils import get_column_letter 10 | 11 | warnings.filterwarnings("ignore") 12 | 13 | 14 | def write_excel(excel_name, worksheet_name, column_index=0, data=None): 15 | try: 16 | workbook = load_workbook(excel_name) 17 | except FileNotFoundError: 18 | # 文件不存在，创建新的 Workbook 19 | workbook = Workbook() 20 | 21 | # 获取或创建一个工作表 22 | if worksheet_name in workbook.sheetnames: 23 | worksheet = workbook[worksheet_name] 24 | else: 25 | worksheet = workbook.create_sheet(title=worksheet_name) 26 | 27 | # 在指定列中插入数据 28 | column = get_column_letter(column_index + 1) 29 | for i, value in enumerate(data): 30 | cell = worksheet[column + str(i + 1)] 31 | cell.value = value 32 | 33 | # 保存文件 34 | workbook.save(excel_name) 35 | 36 | 37 | def evaluation_one(ir_name, vi_name, f_name): 38 | f_img = Image.open(f_name).convert('L') 39 | ir_img = Image.open(ir_name).convert('L') 40 | vi_img = Image.open(vi_name).convert('L') 41 | 42 | f_img_int = np.array(f_img).astype(np.int32) 43 | f_img_double = np.array(f_img).astype(np.float32) 44 | 45 | ir_img_int = np.array(ir_img).astype(np.int32) 46 | ir_img_double = np.array(ir_img).astype(np.float32) 47 | 48 | vi_img_int = np.array(vi_img).astype(np.int32) 49 | vi_img_double = np.array(vi_img).astype(np.float32) 50 | 51 | EN = EN_function(f_img_int) 52 | MI = MI_function(ir_img_int, vi_img_int, f_img_int, gray_level=256) 53 | 54 | SF = SF_function(f_img_double) 55 | SD = SD_function(f_img_double) 56 | AG = AG_function(f_img_double) 57 | PSNR = PSNR_function(ir_img_double, vi_img_double, f_img_double) 58 | MSE = MSE_function(ir_img_double, vi_img_double, f_img_double) 59 | VIF = VIF_function(ir_img_double, vi_img_double, f_img_double) 60 | CC = CC_function(ir_img_double, vi_img_double, f_img_double) 61 | SCD = SCD_function(ir_img_double, vi_img_double, f_img_double) 62 | Qabf = Qabf_function(ir_img_double, vi_img_double, f_img_double) 63 | Nabf = Nabf_function(ir_img_double, vi_img_double, f_img_double) 64 | SSIM = SSIM_function(ir_img_double, vi_img_double, f_img_double) 65 | MS_SSIM = MS_SSIM_function(ir_img_double, vi_img_double, f_img_double) 66 | return EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM 67 | 68 | 69 | if __name__ == '__main__': 70 | with_mean = True 71 | dataroot = r'../datasets' 72 | results_root = '../Results' 73 | dataset = 'TNO' 74 | ir_dir = os.path.join(dataroot, dataset, 'ir') 75 | vi_dir = os.path.join(dataroot, dataset, 'vi') 76 | f_dir = os.path.join(results_root, dataset) 77 | save_dir = '../Metric' 78 | os.makedirs(save_dir, exist_ok=True) 79 | sum_dir = '../datasets/TNO/sum/' 80 | 81 | metric_save_name = os.path.join(save_dir, 'metric_{}.xlsx'.format(dataset)) 82 | filelist = natsorted(os.listdir(ir_dir)) 83 | 84 | # Method_list = ['Our', 'GTF', 'DIDFuse', 'PMGI', 'FusionGAN', 'UMF-CMGR', 'DRF', 85 | # 'U2Fusion', 'IFCNN', 'GAN-FM'] 86 | Method_list = ['Our'] 87 | for i, Method in enumerate(Method_list): 88 | EN_list = [] 89 | MI_list = [] 90 | SF_list = [] 91 | AG_list = [] 92 | SD_list = [] 93 | CC_list = [] 94 | SCD_list = [] 95 | VIF_list = [] 96 | MSE_list = [] 97 | PSNR_list = [] 98 | Qabf_list = [] 99 | Nabf_list = [] 100 | SSIM_list = [] 101 | MS_SSIM_list = [] 102 | filename_list = [''] 103 | sub_f_dir = os.path.join(f_dir, Method) 104 | eval_bar = tqdm(filelist) 105 | for _, item in enumerate(eval_bar): 106 | ir_name = os.path.join(ir_dir, item) 107 | vi_name = os.path.join(vi_dir, item) 108 | f_name = os.path.join(sub_f_dir, item) 109 | EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM = evaluation_one(ir_name, vi_name, 110 | f_name) 111 | EN_list.append(EN) 112 | MI_list.append(MI) 113 | SF_list.append(SF) 114 | AG_list.append(AG) 115 | SD_list.append(SD) 116 | CC_list.append(CC) 117 | SCD_list.append(SCD) 118 | VIF_list.append(VIF) 119 | MSE_list.append(MSE) 120 | PSNR_list.append(PSNR) 121 | Qabf_list.append(Qabf) 122 | Nabf_list.append(Nabf) 123 | SSIM_list.append(SSIM) 124 | MS_SSIM_list.append(MS_SSIM) 125 | filename_list.append(item) 126 | eval_bar.set_description("{} | {}".format(Method, item)) 127 | if with_mean: 128 | # 添加均值 129 | EN_list.append(np.mean(EN_list)) 130 | MI_list.append(np.mean(MI_list)) 131 | SF_list.append(np.mean(SF_list)) 132 | AG_list.append(np.mean(AG_list)) 133 | SD_list.append(np.mean(SD_list)) 134 | CC_list.append(np.mean(CC_list)) 135 | SCD_list.append(np.mean(SCD_list)) 136 | VIF_list.append(np.mean(VIF_list)) 137 | MSE_list.append(np.mean(MSE_list)) 138 | PSNR_list.append(np.mean(PSNR_list)) 139 | Qabf_list.append(np.mean(Qabf_list)) 140 | Nabf_list.append(np.mean(Nabf_list)) 141 | SSIM_list.append(np.mean(SSIM_list)) 142 | MS_SSIM_list.append(np.mean(MS_SSIM_list)) 143 | filename_list.append('mean') 144 | 145 | ## 添加标准差 146 | EN_list.append(np.std(EN_list)) 147 | MI_list.append(np.std(MI_list)) 148 | SF_list.append(np.std(SF_list)) 149 | AG_list.append(np.std(AG_list)) 150 | SD_list.append(np.std(SD_list)) 151 | CC_list.append(np.std(CC_list[:-1])) 152 | SCD_list.append(np.std(SCD_list)) 153 | VIF_list.append(np.std(VIF_list)) 154 | MSE_list.append(np.std(MSE_list)) 155 | PSNR_list.append(np.std(PSNR_list)) 156 | Qabf_list.append(np.std(Qabf_list)) 157 | Nabf_list.append(np.std(Nabf_list)) 158 | SSIM_list.append(np.std(SSIM_list)) 159 | MS_SSIM_list.append(np.std(MS_SSIM_list)) 160 | filename_list.append('std') 161 | 162 | ## 保留三位小数 163 | EN_list = [round(x, 3) for x in EN_list] 164 | MI_list = [round(x, 3) for x in MI_list] 165 | SF_list = [round(x, 3) for x in SF_list] 166 | AG_list = [round(x, 3) for x in AG_list] 167 | SD_list = [round(x, 3) for x in SD_list] 168 | CC_list = [round(x, 3) for x in CC_list] 169 | SCD_list = [round(x, 3) for x in SCD_list] 170 | VIF_list = [round(x, 3) for x in VIF_list] 171 | MSE_list = [round(x, 3) for x in MSE_list] 172 | PSNR_list = [round(x, 3) for x in PSNR_list] 173 | Qabf_list = [round(x, 3) for x in Qabf_list] 174 | Nabf_list = [round(x, 3) for x in Nabf_list] 175 | SSIM_list = [round(x, 3) for x in SSIM_list] 176 | MS_SSIM_list = [round(x, 3) for x in MS_SSIM_list] 177 | 178 | EN_list.insert(0, '{}'.format(Method)) 179 | MI_list.insert(0, '{}'.format(Method)) 180 | SF_list.insert(0, '{}'.format(Method)) 181 | AG_list.insert(0, '{}'.format(Method)) 182 | SD_list.insert(0, '{}'.format(Method)) 183 | CC_list.insert(0, '{}'.format(Method)) 184 | SCD_list.insert(0, '{}'.format(Method)) 185 | VIF_list.insert(0, '{}'.format(Method)) 186 | MSE_list.insert(0, '{}'.format(Method)) 187 | PSNR_list.insert(0, '{}'.format(Method)) 188 | Qabf_list.insert(0, '{}'.format(Method)) 189 | Nabf_list.insert(0, '{}'.format(Method)) 190 | SSIM_list.insert(0, '{}'.format(Method)) 191 | MS_SSIM_list.insert(0, '{}'.format(Method)) 192 | if i == 0: 193 | write_excel(metric_save_name, 'EN', 0, filename_list) 194 | write_excel(metric_save_name, "MI", 0, filename_list) 195 | write_excel(metric_save_name, "SF", 0, filename_list) 196 | write_excel(metric_save_name, "AG", 0, filename_list) 197 | write_excel(metric_save_name, "SD", 0, filename_list) 198 | write_excel(metric_save_name, "CC", 0, filename_list) 199 | write_excel(metric_save_name, "SCD", 0, filename_list) 200 | write_excel(metric_save_name, "VIF", 0, filename_list) 201 | write_excel(metric_save_name, "MSE", 0, filename_list) 202 | write_excel(metric_save_name, "PSNR", 0, filename_list) 203 | write_excel(metric_save_name, "Qabf", 0, filename_list) 204 | write_excel(metric_save_name, "Nabf", 0, filename_list) 205 | write_excel(metric_save_name, "SSIM", 0, filename_list) 206 | write_excel(metric_save_name, "MS_SSIM", 0, filename_list) 207 | write_excel(metric_save_name, 'EN', i + 1, EN_list) 208 | write_excel(metric_save_name, 'MI', i + 1, MI_list) 209 | write_excel(metric_save_name, 'SF', i + 1, SF_list) 210 | write_excel(metric_save_name, 'AG', i + 1, AG_list) 211 | write_excel(metric_save_name, 'SD', i + 1, SD_list) 212 | write_excel(metric_save_name, 'CC', i + 1, CC_list) 213 | write_excel(metric_save_name, 'SCD', i + 1, SCD_list) 214 | write_excel(metric_save_name, 'VIF', i + 1, VIF_list) 215 | write_excel(metric_save_name, 'MSE', i + 1, MSE_list) 216 | write_excel(metric_save_name, 'PSNR', i + 1, PSNR_list) 217 | write_excel(metric_save_name, 'Qabf', i + 1, Qabf_list) 218 | write_excel(metric_save_name, 'Nabf', i + 1, Nabf_list) 219 | write_excel(metric_save_name, 'SSIM', i + 1, SSIM_list) 220 | write_excel(metric_save_name, 'MS_SSIM', i + 1, MS_SSIM_list) 221 | -------------------------------------------------------------------------------- /Metric_Python/eval_one_image.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | from Metric import * 3 | from time import time 4 | import warnings 5 | 6 | warnings.filterwarnings("ignore") 7 | 8 | 9 | def evaluation_one(ir_name, vi_name, f_name): 10 | f_img = Image.open(f_name).convert('L') 11 | ir_img = Image.open(ir_name).convert('L') 12 | vi_img = Image.open(vi_name).convert('L') 13 | f_img_int = np.array(f_img).astype(np.int32) 14 | 15 | f_img_double = np.array(f_img).astype(np.float32) 16 | ir_img_int = np.array(ir_img).astype(np.int32) 17 | ir_img_double = np.array(ir_img).astype(np.float32) 18 | 19 | vi_img_int = np.array(vi_img).astype(np.int32) 20 | vi_img_double = np.array(vi_img).astype(np.float32) 21 | 22 | EN = EN_function(f_img_int) 23 | MI = MI_function(ir_img_int, vi_img_int, f_img_int, gray_level=256) 24 | 25 | SF = SF_function(f_img_double) 26 | SD = SD_function(f_img_double) 27 | AG = AG_function(f_img_double) 28 | PSNR = PSNR_function(ir_img_double, vi_img_double, f_img_double) 29 | MSE = MSE_function(ir_img_double, vi_img_double, f_img_double) 30 | VIF = VIF_function(ir_img_double, vi_img_double, f_img_double) 31 | CC = CC_function(ir_img_double, vi_img_double, f_img_double) 32 | SCD = SCD_function(ir_img_double, vi_img_double, f_img_double) 33 | Qabf = Qabf_function(ir_img_double, vi_img_double, f_img_double) 34 | Nabf = Nabf_function(ir_img_double, vi_img_double, f_img_double) 35 | SSIM = SSIM_function(ir_img_double, vi_img_double, f_img_double) 36 | MS_SSIM = MS_SSIM_function(ir_img_double, vi_img_double, f_img_double) 37 | return EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM 38 | 39 | 40 | if __name__ == '__main__': 41 | f_name = r'E:\Desktop\metric\Test\Results\TNO\GTF\01.png' 42 | ir_name = r'E:\Desktop\metric\Test\datasets\TNO\ir\01.png' 43 | vi_name = r'E:\Desktop\metric\Test\datasets\TNO\vi\01.png' 44 | EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM = evaluation_one(ir_name, vi_name, f_name) 45 | print('EN:', round(EN, 4)) 46 | print('MI:', round(MI, 4)) 47 | print('SF:', round(SF, 4)) 48 | print('AG:', round(AG, 4)) 49 | print('SD:', round(SD, 4)) 50 | print('CC:', round(CC, 4)) 51 | print('SCD:', round(SCD, 4)) 52 | print('VIF:', round(VIF, 4)) 53 | print('MSE:', round(MSE, 4)) 54 | print('PSNR:', round(PSNR, 4)) 55 | print('Qabf:', round(Qabf, 4)) 56 | print('Nabf:', round(Nabf, 4)) 57 | print('SSIM:', round(SSIM, 4)) 58 | print('MS_SSIM:', round(MS_SSIM, 4)) 59 | -------------------------------------------------------------------------------- /Metric_Python/eval_one_method.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from PIL import Image 3 | from Metric import * 4 | from natsort import natsorted 5 | from tqdm import tqdm 6 | import os 7 | import statistics 8 | import warnings 9 | from openpyxl import Workbook, load_workbook 10 | from openpyxl.utils import get_column_letter 11 | 12 | warnings.filterwarnings("ignore") 13 | 14 | 15 | def write_excel(excel_name='metric.xlsx', worksheet_name='VIF', column_index=0, data=None): 16 | try: 17 | workbook = load_workbook(excel_name) 18 | except FileNotFoundError: 19 | # 文件不存在，创建新的 Workbook 20 | workbook = Workbook() 21 | 22 | # 获取或创建一个工作表 23 | if worksheet_name in workbook.sheetnames: 24 | worksheet = workbook[worksheet_name] 25 | else: 26 | worksheet = workbook.create_sheet(title=worksheet_name) 27 | 28 | # 在指定列中插入数据 29 | column = get_column_letter(column_index + 1) 30 | for i, value in enumerate(data): 31 | cell = worksheet[column + str(i + 1)] 32 | cell.value = value 33 | 34 | # 保存文件 35 | workbook.save(excel_name) 36 | 37 | 38 | def evaluation_one(ir_name, vi_name, f_name): 39 | f_img = Image.open(f_name).convert('L') 40 | ir_img = Image.open(ir_name).convert('L') 41 | vi_img = Image.open(vi_name).convert('L') 42 | 43 | f_img_int = np.array(f_img).astype(np.int32) 44 | f_img_double = np.array(f_img).astype(np.float32) 45 | 46 | ir_img_int = np.array(ir_img).astype(np.int32) 47 | ir_img_double = np.array(ir_img).astype(np.float32) 48 | 49 | vi_img_int = np.array(vi_img).astype(np.int32) 50 | vi_img_double = np.array(vi_img).astype(np.float32) 51 | 52 | EN = EN_function(f_img_int) 53 | MI = MI_function(ir_img_int, vi_img_int, f_img_int, gray_level=256) 54 | 55 | SF = SF_function(f_img_double) 56 | SD = SD_function(f_img_double) 57 | AG = AG_function(f_img_double) 58 | PSNR = PSNR_function(ir_img_double, vi_img_double, f_img_double) 59 | MSE = MSE_function(ir_img_double, vi_img_double, f_img_double) 60 | VIF = VIF_function(ir_img_double, vi_img_double, f_img_double) 61 | CC = CC_function(ir_img_double, vi_img_double, f_img_double) 62 | SCD = SCD_function(ir_img_double, vi_img_double, f_img_double) 63 | Qabf = Qabf_function(ir_img_double, vi_img_double, f_img_double) 64 | Nabf = Nabf_function(ir_img_double, vi_img_double, f_img_double) 65 | SSIM = SSIM_function(ir_img_double, vi_img_double, f_img_double) 66 | MS_SSIM = MS_SSIM_function(ir_img_double, vi_img_double, f_img_double) 67 | return EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM 68 | 69 | 70 | if __name__ == '__main__': 71 | with_mean = True 72 | EN_list = [] 73 | MI_list = [] 74 | SF_list = [] 75 | AG_list = [] 76 | SD_list = [] 77 | CC_list = [] 78 | SCD_list = [] 79 | VIF_list = [] 80 | MSE_list = [] 81 | PSNR_list = [] 82 | Qabf_list = [] 83 | Nabf_list = [] 84 | SSIM_list = [] 85 | MS_SSIM_list = [] 86 | filename_list = [''] 87 | dataset_name = 'test_imgs' 88 | ir_dir = os.path.join('..\datasets', dataset_name, 'ir') 89 | vi_dir = os.path.join('..\datasets', dataset_name, 'vi') 90 | Method = 'SeAFusion' 91 | f_dir = os.path.join('..\Results', dataset_name, Method) 92 | save_dir = '..\Metric' 93 | os.makedirs(save_dir, exist_ok=True) 94 | metric_save_name = os.path.join(save_dir, 'metric_{}_{}.xlsx'.format(dataset_name, Method)) 95 | filelist = natsorted(os.listdir(ir_dir)) 96 | eval_bar = tqdm(filelist) 97 | for _, item in enumerate(eval_bar): 98 | ir_name = os.path.join(ir_dir, item) 99 | vi_name = os.path.join(vi_dir, item) 100 | f_name = os.path.join(f_dir, item) 101 | EN, MI, SF, AG, SD, CC, SCD, VIF, MSE, PSNR, Qabf, Nabf, SSIM, MS_SSIM = evaluation_one(ir_name, vi_name, 102 | f_name) 103 | EN_list.append(EN) 104 | MI_list.append(MI) 105 | SF_list.append(SF) 106 | AG_list.append(AG) 107 | SD_list.append(SD) 108 | CC_list.append(CC) 109 | SCD_list.append(SCD) 110 | VIF_list.append(VIF) 111 | MSE_list.append(MSE) 112 | PSNR_list.append(PSNR) 113 | Qabf_list.append(Qabf) 114 | Nabf_list.append(Nabf) 115 | SSIM_list.append(SSIM) 116 | MS_SSIM_list.append(MS_SSIM) 117 | filename_list.append(item) 118 | eval_bar.set_description("{} | {}".format(Method, item)) 119 | if with_mean: 120 | # 添加均值 121 | EN_list.append(np.mean(EN_list)) 122 | MI_list.append(np.mean(MI_list)) 123 | SF_list.append(np.mean(SF_list)) 124 | AG_list.append(np.mean(AG_list)) 125 | SD_list.append(np.mean(SD_list)) 126 | CC_list.append(np.mean(CC_list)) 127 | SCD_list.append(np.mean(SCD_list)) 128 | VIF_list.append(np.mean(VIF_list)) 129 | MSE_list.append(np.mean(MSE_list)) 130 | PSNR_list.append(np.mean(PSNR_list)) 131 | Qabf_list.append(np.mean(Qabf_list)) 132 | Nabf_list.append(np.mean(Nabf_list)) 133 | SSIM_list.append(np.mean(SSIM_list)) 134 | MS_SSIM_list.append(np.mean(MS_SSIM_list)) 135 | filename_list.append('mean') 136 | 137 | ## 添加标准差 138 | EN_list.append(np.std(EN_list)) 139 | MI_list.append(np.std(MI_list)) 140 | SF_list.append(np.std(SF_list)) 141 | AG_list.append(np.std(AG_list)) 142 | SD_list.append(np.std(SD_list)) 143 | CC_list.append(np.std(CC_list[:-1])) 144 | SCD_list.append(np.std(SCD_list)) 145 | VIF_list.append(np.std(VIF_list)) 146 | MSE_list.append(np.std(MSE_list)) 147 | PSNR_list.append(np.std(PSNR_list)) 148 | Qabf_list.append(np.std(Qabf_list)) 149 | Nabf_list.append(np.std(Nabf_list)) 150 | SSIM_list.append(np.std(SSIM_list)) 151 | MS_SSIM_list.append(np.std(MS_SSIM_list)) 152 | filename_list.append('std') 153 | 154 | ## 保留三位小数 155 | EN_list = [round(x, 3) for x in EN_list] 156 | MI_list = [round(x, 3) for x in MI_list] 157 | SF_list = [round(x, 3) for x in SF_list] 158 | AG_list = [round(x, 3) for x in AG_list] 159 | SD_list = [round(x, 3) for x in SD_list] 160 | CC_list = [round(x, 3) for x in CC_list] 161 | SCD_list = [round(x, 3) for x in SCD_list] 162 | VIF_list = [round(x, 3) for x in VIF_list] 163 | MSE_list = [round(x, 3) for x in MSE_list] 164 | PSNR_list = [round(x, 3) for x in PSNR_list] 165 | Qabf_list = [round(x, 3) for x in Qabf_list] 166 | Nabf_list = [round(x, 3) for x in Nabf_list] 167 | SSIM_list = [round(x, 3) for x in SSIM_list] 168 | MS_SSIM_list = [round(x, 3) for x in MS_SSIM_list] 169 | 170 | EN_list.insert(0, '{}'.format(Method)) 171 | MI_list.insert(0, '{}'.format(Method)) 172 | SF_list.insert(0, '{}'.format(Method)) 173 | AG_list.insert(0, '{}'.format(Method)) 174 | SD_list.insert(0, '{}'.format(Method)) 175 | CC_list.insert(0, '{}'.format(Method)) 176 | SCD_list.insert(0, '{}'.format(Method)) 177 | VIF_list.insert(0, '{}'.format(Method)) 178 | MSE_list.insert(0, '{}'.format(Method)) 179 | PSNR_list.insert(0, '{}'.format(Method)) 180 | Qabf_list.insert(0, '{}'.format(Method)) 181 | Nabf_list.insert(0, '{}'.format(Method)) 182 | SSIM_list.insert(0, '{}'.format(Method)) 183 | MS_SSIM_list.insert(0, '{}'.format(Method)) 184 | write_excel(metric_save_name, 'EN', 0, filename_list) 185 | write_excel(metric_save_name, "MI", 0, filename_list) 186 | write_excel(metric_save_name, "SF", 0, filename_list) 187 | write_excel(metric_save_name, "AG", 0, filename_list) 188 | write_excel(metric_save_name, "SD", 0, filename_list) 189 | write_excel(metric_save_name, "CC", 0, filename_list) 190 | write_excel(metric_save_name, "SCD", 0, filename_list) 191 | write_excel(metric_save_name, "VIF", 0, filename_list) 192 | write_excel(metric_save_name, "MSE", 0, filename_list) 193 | write_excel(metric_save_name, "PSNR", 0, filename_list) 194 | write_excel(metric_save_name, "Qabf", 0, filename_list) 195 | write_excel(metric_save_name, "Nabf", 0, filename_list) 196 | write_excel(metric_save_name, "SSIM", 0, filename_list) 197 | write_excel(metric_save_name, "MS_SSIM", 0, filename_list) 198 | write_excel(metric_save_name, 'EN', 1, EN_list) 199 | write_excel(metric_save_name, 'MI', 1, MI_list) 200 | write_excel(metric_save_name, 'SF', 1, SF_list) 201 | write_excel(metric_save_name, 'AG', 1, AG_list) 202 | write_excel(metric_save_name, 'SD', 1, SD_list) 203 | write_excel(metric_save_name, 'CC', 1, CC_list) 204 | write_excel(metric_save_name, 'SCD', 1, SCD_list) 205 | write_excel(metric_save_name, 'VIF', 1, VIF_list) 206 | write_excel(metric_save_name, 'MSE', 1, MSE_list) 207 | write_excel(metric_save_name, 'PSNR', 1, PSNR_list) 208 | write_excel(metric_save_name, 'Qabf', 1, Qabf_list) 209 | write_excel(metric_save_name, 'Nabf', 1, Nabf_list) 210 | write_excel(metric_save_name, 'SSIM', 1, SSIM_list) 211 | write_excel(metric_save_name, 'MS_SSIM', 1, MS_SSIM_list) 212 | -------------------------------------------------------------------------------- /Metric_Python/ssim.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | import torchvision.transforms.functional as TF 7 | import numpy as np 8 | 9 | 10 | def _fspecial_gauss_1d(size, sigma): 11 | r"""Create 1-D gauss kernel 12 | Args: 13 | size (int): the size of gauss kernel 14 | sigma (float): sigma of normal distribution 15 | Returns: 16 | torch.Tensor: 1D kernel (1 x 1 x size) 17 | """ 18 | coords = torch.arange(size, dtype=torch.float32) 19 | coords -= size // 2 20 | 21 | g = torch.exp(-(coords ** 2) / (2 * sigma ** 2)) 22 | g /= g.sum() 23 | 24 | return g.unsqueeze(0).unsqueeze(0) 25 | 26 | 27 | def gaussian_filter(input, win): 28 | r""" Blur input with 1-D kernel 29 | Args: 30 | input (torch.Tensor): a batch of tensors to be blurred 31 | window (torch.Tensor): 1-D gauss kernel 32 | Returns: 33 | torch.Tensor: blurred tensors 34 | """ 35 | assert all([ws == 1 for ws in win.shape[1:-1]]), win.shape 36 | if len(input.shape) == 4: 37 | conv = F.conv2d 38 | elif len(input.shape) == 5: 39 | conv = F.conv3d 40 | else: 41 | raise NotImplementedError(input.shape) 42 | 43 | C = input.shape[1] 44 | out = input 45 | for i, s in enumerate(input.shape[2:]): 46 | if s >= win.shape[-1]: 47 | perms = list(range(win.ndim)) 48 | perms[2 + i] = perms[-1] 49 | perms[-1] = 2 + i 50 | out = conv(out, weight=win.permute(perms), stride=1, padding=0, groups=C) 51 | else: 52 | warnings.warn( 53 | f"Skipping Gaussian Smoothing at dimension 2+{i} for input: {input.shape} and win size: {win.shape[-1]}" 54 | ) 55 | 56 | return out 57 | 58 | 59 | def _ssim(X, Y, data_range, win, K=(0.01, 0.03)): 60 | K1, K2 = K 61 | # batch, channel, [depth,] height, width = X.shape 62 | compensation = 1.0 63 | 64 | C1 = (K1 * data_range) ** 2 65 | C2 = (K2 * data_range) ** 2 66 | 67 | win = win.type_as(X) 68 | 69 | mu1 = gaussian_filter(X, win) 70 | mu2 = gaussian_filter(Y, win) 71 | 72 | mu1_sq = mu1.pow(2) 73 | mu2_sq = mu2.pow(2) 74 | mu1_mu2 = mu1 * mu2 75 | 76 | sigma1_sq = compensation * (gaussian_filter(X * X, win) - mu1_sq) 77 | sigma2_sq = compensation * (gaussian_filter(Y * Y, win) - mu2_sq) 78 | sigma12 = compensation * (gaussian_filter(X * Y, win) - mu1_mu2) 79 | 80 | cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2) # set alpha=beta=gamma=1 81 | ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map 82 | 83 | ssim_per_channel = torch.flatten(ssim_map, 2).mean(-1) 84 | cs = torch.flatten(cs_map, 2).mean(-1) 85 | return ssim_per_channel, cs 86 | 87 | 88 | def ssim(X, 89 | Y, 90 | data_range=255, 91 | size_average=True, 92 | win_size=11, 93 | win_sigma=1.5, 94 | win=None, 95 | K=(0.01, 0.03), 96 | nonnegative_ssim=False): 97 | # 输出的是灰度图像，其shape是[H, W] 98 | # 需要扩展为 [B, C, H, W] 99 | X = TF.to_tensor(X).unsqueeze(0).unsqueeze(0) * 255.0 100 | Y = TF.to_tensor(Y).unsqueeze(0).unsqueeze(0) * 255.0 101 | print(X.shape,Y.shape) 102 | if not X.shape == Y.shape: 103 | raise ValueError("Input images should have the same dimensions.") 104 | 105 | for d in range(len(X.shape) - 1, 1, -1): 106 | X = torch.squeeze(X, dim=d) 107 | Y = torch.squeeze(Y, dim=d) 108 | 109 | if len(X.shape) not in (4, 5): 110 | raise ValueError(f"Input images should be 4-d or 5-d tensors, but got {X.shape}") 111 | 112 | if not X.dtype == Y.dtype: 113 | raise ValueError("Input images should have the same dtype.") 114 | 115 | if win is not None: # set win_size 116 | win_size = win.shape[-1] 117 | 118 | if not (win_size % 2 == 1): 119 | raise ValueError("Window size should be odd.") 120 | 121 | if win is None: 122 | win = _fspecial_gauss_1d(win_size, win_sigma) 123 | win = win.repeat([X.shape[1]] + [1] * (len(X.shape) - 1)) 124 | 125 | ssim_per_channel, _ = _ssim(X, Y, data_range=data_range, win=win, K=K) 126 | if nonnegative_ssim: 127 | ssim_per_channel = F.relu(ssim_per_channel) 128 | 129 | if size_average: 130 | return ssim_per_channel.mean() 131 | else: 132 | return ssim_per_channel.mean(dim=1) 133 | 134 | 135 | def ms_ssim( 136 | X, 137 | Y, 138 | data_range=255, 139 | size_average=True, 140 | win_size=11, 141 | win_sigma=1.5, 142 | win=None, 143 | weights=None, 144 | K=(0.01, 0.03) 145 | ): 146 | # 输出的是灰度图像，其shape是[H, W] 147 | # 需要扩展为 [B, C, H, W] 148 | X = TF.to_tensor(X).unsqueeze(0).unsqueeze(0) * 255.0 149 | Y = TF.to_tensor(Y).unsqueeze(0).unsqueeze(0) * 255.0 150 | if not X.shape == Y.shape: 151 | raise ValueError("Input images should have the same dimensions.") 152 | 153 | for d in range(len(X.shape) - 1, 1, -1): 154 | X = X.squeeze(dim=d) 155 | Y = Y.squeeze(dim=d) 156 | 157 | if not X.dtype == Y.dtype: 158 | raise ValueError("Input images should have the same dtype.") 159 | 160 | if len(X.shape) == 4: 161 | avg_pool = F.avg_pool2d 162 | elif len(X.shape) == 5: 163 | avg_pool = F.avg_pool3d 164 | else: 165 | raise ValueError(f"Input images should be 4-d or 5-d tensors, but got {X.shape}") 166 | 167 | if win is not None: # set win_size 168 | win_size = win.shape[-1] 169 | 170 | if not (win_size % 2 == 1): 171 | raise ValueError("Window size should be odd.") 172 | 173 | smaller_side = min(X.shape[-2:]) 174 | assert smaller_side > (win_size - 1) * ( 175 | 2 ** 4 176 | ), "Image size should be larger than %d due to the 4 downsamplings in ms-ssim" % ((win_size - 1) * (2 ** 4)) 177 | 178 | if weights is None: 179 | weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333] 180 | weights = torch.tensor(weights, dtype=X.dtype) 181 | 182 | if win is None: 183 | win = _fspecial_gauss_1d(win_size, win_sigma) 184 | win = win.repeat([X.shape[1]] + [1] * (len(X.shape) - 1)) 185 | 186 | levels = weights.shape[0] 187 | mcs = [] 188 | for i in range(levels): 189 | ssim_per_channel, cs = _ssim(X, Y, win=win, data_range=data_range, K=K) 190 | 191 | if i < levels - 1: 192 | mcs.append(F.relu(cs)) 193 | padding = [s % 2 for s in X.shape[2:]] 194 | X = avg_pool(X, kernel_size=2, padding=padding) 195 | Y = avg_pool(Y, kernel_size=2, padding=padding) 196 | 197 | ssim_per_channel = F.relu(ssim_per_channel) # (batch, channel) 198 | mcs_and_ssim = torch.stack(mcs + [ssim_per_channel], dim=0) # (level, batch, channel) 199 | ms_ssim_val = torch.prod(mcs_and_ssim ** weights.reshape((-1, 1, 1)), dim=0) 200 | 201 | if size_average: 202 | return ms_ssim_val.mean() 203 | else: 204 | return ms_ssim_val.mean(dim=1) 205 | 206 | 207 | class SSIM(nn.Module): 208 | def __init__( 209 | self, 210 | data_range=255, 211 | size_average=True, 212 | win_size=11, 213 | win_sigma=1.5, 214 | channel=3, 215 | spatial_dims=2, 216 | K=(0.01, 0.03), 217 | nonnegative_ssim=False, 218 | ): 219 | super(SSIM, self).__init__() 220 | self.win_size = win_size 221 | self.win = _fspecial_gauss_1d(win_size, win_sigma).tile([channel, 1] + [1] * spatial_dims) 222 | self.size_average = size_average 223 | self.data_range = data_range 224 | self.K = K 225 | self.nonnegative_ssim = nonnegative_ssim 226 | 227 | def forward(self, X, Y): 228 | return ssim( 229 | X, 230 | Y, 231 | data_range=self.data_range, 232 | size_average=self.size_average, 233 | win=self.win, 234 | K=self.K, 235 | nonnegative_ssim=self.nonnegative_ssim, 236 | ).item() 237 | 238 | 239 | class MS_SSIM(nn.Module): 240 | def __init__( 241 | self, 242 | data_range=255, 243 | size_average=True, 244 | win_size=11, 245 | win_sigma=1.5, 246 | channel=3, 247 | spatial_dims=2, 248 | weights=None, 249 | K=(0.01, 0.03), 250 | ): 251 | super(MS_SSIM, self).__init__() 252 | self.win_size = win_size 253 | self.win = _fspecial_gauss_1d(win_size, win_sigma).tile([channel, 1] + [1] * spatial_dims) 254 | self.size_average = size_average 255 | self.data_range = data_range 256 | self.weights = weights 257 | self.K = K 258 | 259 | def forward(self, X, Y): 260 | return ms_ssim( 261 | X, 262 | Y, 263 | data_range=self.data_range, 264 | size_average=self.size_average, 265 | win=self.win, 266 | weights=self.weights, 267 | K=self.K, 268 | ).item() 269 | -------------------------------------------------------------------------------- /Metric_Python/write.py: -------------------------------------------------------------------------------- 1 | Metric_list = ['EN', 'MI', 'SF', 'AG', 'SD', 'CC', 'SCD', 'VIF', 'MSE', 'PSNR', 'Qabf', 'Nabf', 'SSIM', 'MS_SSIM'] 2 | for metric in Metric_list: 3 | print('print(\'{}:\', round({}, 4))'.format(metric, metric)) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Image-evalution 2 | Python implementation of image fusion evaluation metrics 3 | 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