306 lines
11 KiB
Python
306 lines
11 KiB
Python
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import numpy as np
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import cv2
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import sklearn.metrics as skm
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from scipy.signal import convolve2d
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import math
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from skimage.metrics import structural_similarity as ssim
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def image_read_cv2(path, mode='RGB'):
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img_BGR = cv2.imread(path).astype('float32')
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assert mode == 'RGB' or mode == 'GRAY' or mode == 'YCrCb', 'mode error'
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if mode == 'RGB':
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img = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2RGB)
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elif mode == 'GRAY':
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img = np.round(cv2.cvtColor(img_BGR, cv2.COLOR_BGR2GRAY))
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elif mode == 'YCrCb':
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img = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YCrCb)
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return img
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class Evaluator():
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@classmethod
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def input_check(cls, imgF, imgA=None, imgB=None):
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if imgA is None:
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assert type(imgF) == np.ndarray, 'type error'
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assert len(imgF.shape) == 2, 'dimension error'
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else:
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assert type(imgF) == type(imgA) == type(imgB) == np.ndarray, 'type error'
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assert imgF.shape == imgA.shape == imgB.shape, 'shape error'
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assert len(imgF.shape) == 2, 'dimension error'
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@classmethod
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def EN(cls, img): # entropy
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cls.input_check(img)
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a = np.uint8(np.round(img)).flatten()
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h = np.bincount(a) / a.shape[0]
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return -sum(h * np.log2(h + (h == 0)))
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@classmethod
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def SD(cls, img):
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cls.input_check(img)
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return np.std(img)
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@classmethod
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def SF(cls, img):
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cls.input_check(img)
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return np.sqrt(np.mean((img[:, 1:] - img[:, :-1]) ** 2) + np.mean((img[1:, :] - img[:-1, :]) ** 2))
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@classmethod
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def AG(cls, img): # Average gradient
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cls.input_check(img)
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Gx, Gy = np.zeros_like(img), np.zeros_like(img)
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Gx[:, 0] = img[:, 1] - img[:, 0]
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Gx[:, -1] = img[:, -1] - img[:, -2]
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Gx[:, 1:-1] = (img[:, 2:] - img[:, :-2]) / 2
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Gy[0, :] = img[1, :] - img[0, :]
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Gy[-1, :] = img[-1, :] - img[-2, :]
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Gy[1:-1, :] = (img[2:, :] - img[:-2, :]) / 2
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return np.mean(np.sqrt((Gx ** 2 + Gy ** 2) / 2))
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@classmethod
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def MI(cls, image_F, image_A, image_B):
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cls.input_check(image_F, image_A, image_B)
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return skm.mutual_info_score(image_F.flatten(), image_A.flatten()) + skm.mutual_info_score(image_F.flatten(),
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image_B.flatten())
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@classmethod
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def MSE(cls, image_F, image_A, image_B): # MSE
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cls.input_check(image_F, image_A, image_B)
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return (np.mean((image_A - image_F) ** 2) + np.mean((image_B - image_F) ** 2)) / 2
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@classmethod
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def CC(cls, image_F, image_A, image_B):
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cls.input_check(image_F, image_A, image_B)
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rAF = np.sum((image_A - np.mean(image_A)) * (image_F - np.mean(image_F))) / np.sqrt(
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(np.sum((image_A - np.mean(image_A)) ** 2)) * (np.sum((image_F - np.mean(image_F)) ** 2)))
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rBF = np.sum((image_B - np.mean(image_B)) * (image_F - np.mean(image_F))) / np.sqrt(
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(np.sum((image_B - np.mean(image_B)) ** 2)) * (np.sum((image_F - np.mean(image_F)) ** 2)))
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return (rAF + rBF) / 2
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@classmethod
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def PSNR(cls, image_F, image_A, image_B):
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cls.input_check(image_F, image_A, image_B)
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return 10 * np.log10(np.max(image_F) ** 2 / cls.MSE(image_F, image_A, image_B))
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@classmethod
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def SCD(cls, image_F, image_A, image_B): # The sum of the correlations of differences
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cls.input_check(image_F, image_A, image_B)
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imgF_A = image_F - image_A
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imgF_B = image_F - image_B
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corr1 = np.sum((image_A - np.mean(image_A)) * (imgF_B - np.mean(imgF_B))) / np.sqrt(
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(np.sum((image_A - np.mean(image_A)) ** 2)) * (np.sum((imgF_B - np.mean(imgF_B)) ** 2)))
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corr2 = np.sum((image_B - np.mean(image_B)) * (imgF_A - np.mean(imgF_A))) / np.sqrt(
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(np.sum((image_B - np.mean(image_B)) ** 2)) * (np.sum((imgF_A - np.mean(imgF_A)) ** 2)))
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return corr1 + corr2
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@classmethod
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def VIFF(cls, image_F, image_A, image_B):
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cls.input_check(image_F, image_A, image_B)
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return cls.compare_viff(image_A, image_F)+cls.compare_viff(image_B, image_F)
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@classmethod
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def compare_viff(cls,ref, dist): # viff of a pair of pictures
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sigma_nsq = 2
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eps = 1e-10
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num = 0.0
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den = 0.0
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for scale in range(1, 5):
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N = 2 ** (4 - scale + 1) + 1
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sd = N / 5.0
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# Create a Gaussian kernel as MATLAB's
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m, n = [(ss - 1.) / 2. for ss in (N, N)]
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y, x = np.ogrid[-m:m + 1, -n:n + 1]
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h = np.exp(-(x * x + y * y) / (2. * sd * sd))
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h[h < np.finfo(h.dtype).eps * h.max()] = 0
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sumh = h.sum()
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if sumh != 0:
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win = h / sumh
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if scale > 1:
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ref = convolve2d(ref, np.rot90(win, 2), mode='valid')
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dist = convolve2d(dist, np.rot90(win, 2), mode='valid')
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ref = ref[::2, ::2]
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dist = dist[::2, ::2]
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mu1 = convolve2d(ref, np.rot90(win, 2), mode='valid')
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mu2 = convolve2d(dist, np.rot90(win, 2), mode='valid')
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mu1_sq = mu1 * mu1
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mu2_sq = mu2 * mu2
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mu1_mu2 = mu1 * mu2
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sigma1_sq = convolve2d(ref * ref, np.rot90(win, 2), mode='valid') - mu1_sq
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sigma2_sq = convolve2d(dist * dist, np.rot90(win, 2), mode='valid') - mu2_sq
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sigma12 = convolve2d(ref * dist, np.rot90(win, 2), mode='valid') - mu1_mu2
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sigma1_sq[sigma1_sq < 0] = 0
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sigma2_sq[sigma2_sq < 0] = 0
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g = sigma12 / (sigma1_sq + eps)
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sv_sq = sigma2_sq - g * sigma12
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g[sigma1_sq < eps] = 0
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sv_sq[sigma1_sq < eps] = sigma2_sq[sigma1_sq < eps]
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sigma1_sq[sigma1_sq < eps] = 0
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g[sigma2_sq < eps] = 0
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sv_sq[sigma2_sq < eps] = 0
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sv_sq[g < 0] = sigma2_sq[g < 0]
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g[g < 0] = 0
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sv_sq[sv_sq <= eps] = eps
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num += np.sum(np.log10(1 + g * g * sigma1_sq / (sv_sq + sigma_nsq)))
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den += np.sum(np.log10(1 + sigma1_sq / sigma_nsq))
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vifp = num / den
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if np.isnan(vifp):
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return 1.0
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else:
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return vifp
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@classmethod
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def Qabf(cls, image_F, image_A, image_B):
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cls.input_check(image_F, image_A, image_B)
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gA, aA = cls.Qabf_getArray(image_A)
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gB, aB = cls.Qabf_getArray(image_B)
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gF, aF = cls.Qabf_getArray(image_F)
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QAF = cls.Qabf_getQabf(aA, gA, aF, gF)
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QBF = cls.Qabf_getQabf(aB, gB, aF, gF)
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# 计算QABF
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deno = np.sum(gA + gB)
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nume = np.sum(np.multiply(QAF, gA) + np.multiply(QBF, gB))
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return nume / deno
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@classmethod
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def Qabf_getArray(cls,img):
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# Sobel Operator Sobel
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h1 = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]).astype(np.float32)
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h2 = np.array([[0, 1, 2], [-1, 0, 1], [-2, -1, 0]]).astype(np.float32)
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h3 = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).astype(np.float32)
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SAx = convolve2d(img, h3, mode='same')
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SAy = convolve2d(img, h1, mode='same')
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gA = np.sqrt(np.multiply(SAx, SAx) + np.multiply(SAy, SAy))
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aA = np.zeros_like(img)
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aA[SAx == 0] = math.pi / 2
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aA[SAx != 0]=np.arctan(SAy[SAx != 0] / SAx[SAx != 0])
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return gA, aA
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@classmethod
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def Qabf_getQabf(cls,aA, gA, aF, gF):
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L = 1
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Tg = 0.9994
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kg = -15
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Dg = 0.5
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Ta = 0.9879
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ka = -22
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Da = 0.8
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GAF,AAF,QgAF,QaAF,QAF = np.zeros_like(aA),np.zeros_like(aA),np.zeros_like(aA),np.zeros_like(aA),np.zeros_like(aA)
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GAF[gA>gF]=gF[gA>gF]/gA[gA>gF]
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GAF[gA == gF] = gF[gA == gF]
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GAF[gA <gF] = gA[gA<gF]/gF[gA<gF]
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AAF = 1 - np.abs(aA - aF) / (math.pi / 2)
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QgAF = Tg / (1 + np.exp(kg * (GAF - Dg)))
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QaAF = Ta / (1 + np.exp(ka * (AAF - Da)))
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QAF = QgAF* QaAF
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return QAF
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@classmethod
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def SSIM(cls, image_F, image_A, image_B):
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cls.input_check(image_F, image_A, image_B)
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return ssim(image_F,image_A)+ssim(image_F,image_B)
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def VIFF(image_F, image_A, image_B):
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refA=image_A
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refB=image_B
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dist=image_F
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sigma_nsq = 2
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eps = 1e-10
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numA = 0.0
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denA = 0.0
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numB = 0.0
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denB = 0.0
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for scale in range(1, 5):
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N = 2 ** (4 - scale + 1) + 1
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sd = N / 5.0
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# Create a Gaussian kernel as MATLAB's
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m, n = [(ss - 1.) / 2. for ss in (N, N)]
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y, x = np.ogrid[-m:m + 1, -n:n + 1]
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h = np.exp(-(x * x + y * y) / (2. * sd * sd))
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h[h < np.finfo(h.dtype).eps * h.max()] = 0
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sumh = h.sum()
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if sumh != 0:
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win = h / sumh
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if scale > 1:
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refA = convolve2d(refA, np.rot90(win, 2), mode='valid')
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refB = convolve2d(refB, np.rot90(win, 2), mode='valid')
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dist = convolve2d(dist, np.rot90(win, 2), mode='valid')
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refA = refA[::2, ::2]
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refB = refB[::2, ::2]
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dist = dist[::2, ::2]
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mu1A = convolve2d(refA, np.rot90(win, 2), mode='valid')
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mu1B = convolve2d(refB, np.rot90(win, 2), mode='valid')
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mu2 = convolve2d(dist, np.rot90(win, 2), mode='valid')
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mu1_sq_A = mu1A * mu1A
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mu1_sq_B = mu1B * mu1B
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mu2_sq = mu2 * mu2
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mu1A_mu2 = mu1A * mu2
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mu1B_mu2 = mu1B * mu2
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sigma1A_sq = convolve2d(refA * refA, np.rot90(win, 2), mode='valid') - mu1_sq_A
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sigma1B_sq = convolve2d(refB * refB, np.rot90(win, 2), mode='valid') - mu1_sq_B
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sigma2_sq = convolve2d(dist * dist, np.rot90(win, 2), mode='valid') - mu2_sq
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sigma12_A = convolve2d(refA * dist, np.rot90(win, 2), mode='valid') - mu1A_mu2
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sigma12_B = convolve2d(refB * dist, np.rot90(win, 2), mode='valid') - mu1B_mu2
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sigma1A_sq[sigma1A_sq < 0] = 0
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sigma1B_sq[sigma1B_sq < 0] = 0
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sigma2_sq[sigma2_sq < 0] = 0
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gA = sigma12_A / (sigma1A_sq + eps)
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gB = sigma12_B / (sigma1B_sq + eps)
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sv_sq_A = sigma2_sq - gA * sigma12_A
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sv_sq_B = sigma2_sq - gB * sigma12_B
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gA[sigma1A_sq < eps] = 0
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gB[sigma1B_sq < eps] = 0
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sv_sq_A[sigma1A_sq < eps] = sigma2_sq[sigma1A_sq < eps]
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sv_sq_B[sigma1B_sq < eps] = sigma2_sq[sigma1B_sq < eps]
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sigma1A_sq[sigma1A_sq < eps] = 0
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sigma1B_sq[sigma1B_sq < eps] = 0
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gA[sigma2_sq < eps] = 0
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gB[sigma2_sq < eps] = 0
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sv_sq_A[sigma2_sq < eps] = 0
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sv_sq_B[sigma2_sq < eps] = 0
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sv_sq_A[gA < 0] = sigma2_sq[gA < 0]
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sv_sq_B[gB < 0] = sigma2_sq[gB < 0]
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gA[gA < 0] = 0
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gB[gB < 0] = 0
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sv_sq_A[sv_sq_A <= eps] = eps
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sv_sq_B[sv_sq_B <= eps] = eps
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numA += np.sum(np.log10(1 + gA * gA * sigma1A_sq / (sv_sq_A + sigma_nsq)))
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numB += np.sum(np.log10(1 + gB * gB * sigma1B_sq / (sv_sq_B + sigma_nsq)))
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denA += np.sum(np.log10(1 + sigma1A_sq / sigma_nsq))
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denB += np.sum(np.log10(1 + sigma1B_sq / sigma_nsq))
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||
|
|
|
||
|
|
vifpA = numA / denA
|
||
|
|
vifpB =numB / denB
|
||
|
|
|
||
|
|
if np.isnan(vifpA):
|
||
|
|
vifpA=1
|
||
|
|
if np.isnan(vifpB):
|
||
|
|
vifpB = 1
|
||
|
|
return vifpA+vifpB
|