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A10G
| """This script is to generate skin attention mask for Deep3DFaceRecon_pytorch | |
| """ | |
| import math | |
| import numpy as np | |
| import os | |
| import cv2 | |
| class GMM: | |
| def __init__(self, dim, num, w, mu, cov, cov_det, cov_inv): | |
| self.dim = dim # feature dimension | |
| self.num = num # number of Gaussian components | |
| self.w = w # weights of Gaussian components (a list of scalars) | |
| self.mu= mu # mean of Gaussian components (a list of 1xdim vectors) | |
| self.cov = cov # covariance matrix of Gaussian components (a list of dimxdim matrices) | |
| self.cov_det = cov_det # pre-computed determinet of covariance matrices (a list of scalars) | |
| self.cov_inv = cov_inv # pre-computed inverse covariance matrices (a list of dimxdim matrices) | |
| self.factor = [0]*num | |
| for i in range(self.num): | |
| self.factor[i] = (2*math.pi)**(self.dim/2) * self.cov_det[i]**0.5 | |
| def likelihood(self, data): | |
| assert(data.shape[1] == self.dim) | |
| N = data.shape[0] | |
| lh = np.zeros(N) | |
| for i in range(self.num): | |
| data_ = data - self.mu[i] | |
| tmp = np.matmul(data_,self.cov_inv[i]) * data_ | |
| tmp = np.sum(tmp,axis=1) | |
| power = -0.5 * tmp | |
| p = np.array([math.exp(power[j]) for j in range(N)]) | |
| p = p/self.factor[i] | |
| lh += p*self.w[i] | |
| return lh | |
| def _rgb2ycbcr(rgb): | |
| m = np.array([[65.481, 128.553, 24.966], | |
| [-37.797, -74.203, 112], | |
| [112, -93.786, -18.214]]) | |
| shape = rgb.shape | |
| rgb = rgb.reshape((shape[0] * shape[1], 3)) | |
| ycbcr = np.dot(rgb, m.transpose() / 255.) | |
| ycbcr[:, 0] += 16. | |
| ycbcr[:, 1:] += 128. | |
| return ycbcr.reshape(shape) | |
| def _bgr2ycbcr(bgr): | |
| rgb = bgr[..., ::-1] | |
| return _rgb2ycbcr(rgb) | |
| gmm_skin_w = [0.24063933, 0.16365987, 0.26034665, 0.33535415] | |
| gmm_skin_mu = [np.array([113.71862, 103.39613, 164.08226]), | |
| np.array([150.19858, 105.18467, 155.51428]), | |
| np.array([183.92976, 107.62468, 152.71820]), | |
| np.array([114.90524, 113.59782, 151.38217])] | |
| gmm_skin_cov_det = [5692842.5, 5851930.5, 2329131., 1585971.] | |
| gmm_skin_cov_inv = [np.array([[0.0019472069, 0.0020450759, -0.00060243998],[0.0020450759, 0.017700525, 0.0051420014],[-0.00060243998, 0.0051420014, 0.0081308950]]), | |
| np.array([[0.0027110141, 0.0011036990, 0.0023122299],[0.0011036990, 0.010707724, 0.010742856],[0.0023122299, 0.010742856, 0.017481629]]), | |
| np.array([[0.0048026871, 0.00022935172, 0.0077668377],[0.00022935172, 0.011729696, 0.0081661865],[0.0077668377, 0.0081661865, 0.025374353]]), | |
| np.array([[0.0011989699, 0.0022453172, -0.0010748957],[0.0022453172, 0.047758564, 0.020332102],[-0.0010748957, 0.020332102, 0.024502251]])] | |
| gmm_skin = GMM(3, 4, gmm_skin_w, gmm_skin_mu, [], gmm_skin_cov_det, gmm_skin_cov_inv) | |
| gmm_nonskin_w = [0.12791070, 0.31130761, 0.34245777, 0.21832393] | |
| gmm_nonskin_mu = [np.array([99.200851, 112.07533, 140.20602]), | |
| np.array([110.91392, 125.52969, 130.19237]), | |
| np.array([129.75864, 129.96107, 126.96808]), | |
| np.array([112.29587, 128.85121, 129.05431])] | |
| gmm_nonskin_cov_det = [458703648., 6466488., 90611376., 133097.63] | |
| gmm_nonskin_cov_inv = [np.array([[0.00085371657, 0.00071197288, 0.00023958916],[0.00071197288, 0.0025935620, 0.00076557708],[0.00023958916, 0.00076557708, 0.0015042332]]), | |
| np.array([[0.00024650150, 0.00045542428, 0.00015019422],[0.00045542428, 0.026412144, 0.018419769],[0.00015019422, 0.018419769, 0.037497383]]), | |
| np.array([[0.00037054974, 0.00038146760, 0.00040408765],[0.00038146760, 0.0085505722, 0.0079136286],[0.00040408765, 0.0079136286, 0.010982352]]), | |
| np.array([[0.00013709733, 0.00051228428, 0.00012777430],[0.00051228428, 0.28237113, 0.10528370],[0.00012777430, 0.10528370, 0.23468947]])] | |
| gmm_nonskin = GMM(3, 4, gmm_nonskin_w, gmm_nonskin_mu, [], gmm_nonskin_cov_det, gmm_nonskin_cov_inv) | |
| prior_skin = 0.8 | |
| prior_nonskin = 1 - prior_skin | |
| # calculate skin attention mask | |
| def skinmask(imbgr): | |
| im = _bgr2ycbcr(imbgr) | |
| data = im.reshape((-1,3)) | |
| lh_skin = gmm_skin.likelihood(data) | |
| lh_nonskin = gmm_nonskin.likelihood(data) | |
| tmp1 = prior_skin * lh_skin | |
| tmp2 = prior_nonskin * lh_nonskin | |
| post_skin = tmp1 / (tmp1+tmp2) # posterior probability | |
| post_skin = post_skin.reshape((im.shape[0],im.shape[1])) | |
| post_skin = np.round(post_skin*255) | |
| post_skin = post_skin.astype(np.uint8) | |
| post_skin = np.tile(np.expand_dims(post_skin,2),[1,1,3]) # reshape to H*W*3 | |
| return post_skin | |
| def get_skin_mask(img_path): | |
| print('generating skin masks......') | |
| names = [i for i in sorted(os.listdir( | |
| img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i] | |
| save_path = os.path.join(img_path, 'mask') | |
| if not os.path.isdir(save_path): | |
| os.makedirs(save_path) | |
| for i in range(0, len(names)): | |
| name = names[i] | |
| print('%05d' % (i), ' ', name) | |
| full_image_name = os.path.join(img_path, name) | |
| img = cv2.imread(full_image_name).astype(np.float32) | |
| skin_img = skinmask(img) | |
| cv2.imwrite(os.path.join(save_path, name), skin_img.astype(np.uint8)) | |