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"""This script is to load 3D face model for Deep3DFaceRecon_pytorch
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"""
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import numpy as np
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from PIL import Image
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from scipy.io import loadmat, savemat
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from array import array
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import os.path as osp
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def LoadExpBasis(bfm_folder='BFM'):
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n_vertex = 53215
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Expbin = open(osp.join(bfm_folder, 'Exp_Pca.bin'), 'rb')
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exp_dim = array('i')
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exp_dim.fromfile(Expbin, 1)
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expMU = array('f')
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expPC = array('f')
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expMU.fromfile(Expbin, 3*n_vertex)
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expPC.fromfile(Expbin, 3*exp_dim[0]*n_vertex)
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Expbin.close()
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expPC = np.array(expPC)
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expPC = np.reshape(expPC, [exp_dim[0], -1])
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expPC = np.transpose(expPC)
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expEV = np.loadtxt(osp.join(bfm_folder, 'std_exp.txt'))
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return expPC, expEV
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def transferBFM09(bfm_folder='BFM'):
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print('Transfer BFM09 to BFM_model_front......')
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original_BFM = loadmat(osp.join(bfm_folder, '01_MorphableModel.mat'))
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shapePC = original_BFM['shapePC']
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shapeEV = original_BFM['shapeEV']
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shapeMU = original_BFM['shapeMU']
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texPC = original_BFM['texPC']
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texEV = original_BFM['texEV']
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texMU = original_BFM['texMU']
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expPC, expEV = LoadExpBasis(bfm_folder)
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idBase = shapePC*np.reshape(shapeEV, [-1, 199])
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idBase = idBase/1e5
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idBase = idBase[:, :80]
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exBase = expPC*np.reshape(expEV, [-1, 79])
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exBase = exBase/1e5
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exBase = exBase[:, :64]
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texBase = texPC*np.reshape(texEV, [-1, 199])
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texBase = texBase[:, :80]
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index_exp = loadmat(osp.join(bfm_folder, 'BFM_front_idx.mat'))
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index_exp = index_exp['idx'].astype(np.int32) - 1
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index_shape = loadmat(osp.join(bfm_folder, 'BFM_exp_idx.mat'))
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index_shape = index_shape['trimIndex'].astype(
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np.int32) - 1
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index_shape = index_shape[index_exp]
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idBase = np.reshape(idBase, [-1, 3, 80])
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idBase = idBase[index_shape, :, :]
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idBase = np.reshape(idBase, [-1, 80])
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texBase = np.reshape(texBase, [-1, 3, 80])
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texBase = texBase[index_shape, :, :]
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texBase = np.reshape(texBase, [-1, 80])
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exBase = np.reshape(exBase, [-1, 3, 64])
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exBase = exBase[index_exp, :, :]
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exBase = np.reshape(exBase, [-1, 64])
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meanshape = np.reshape(shapeMU, [-1, 3])/1e5
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meanshape = meanshape[index_shape, :]
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meanshape = np.reshape(meanshape, [1, -1])
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meantex = np.reshape(texMU, [-1, 3])
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meantex = meantex[index_shape, :]
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meantex = np.reshape(meantex, [1, -1])
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other_info = loadmat(osp.join(bfm_folder, 'facemodel_info.mat'))
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frontmask2_idx = other_info['frontmask2_idx']
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skinmask = other_info['skinmask']
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keypoints = other_info['keypoints']
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point_buf = other_info['point_buf']
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tri = other_info['tri']
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tri_mask2 = other_info['tri_mask2']
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savemat(osp.join(bfm_folder, 'BFM_model_front.mat'), {'meanshape': meanshape, 'meantex': meantex, 'idBase': idBase, 'exBase': exBase, 'texBase': texBase,
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'tri': tri, 'point_buf': point_buf, 'tri_mask2': tri_mask2, 'keypoints': keypoints, 'frontmask2_idx': frontmask2_idx, 'skinmask': skinmask})
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def load_lm3d(bfm_folder):
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Lm3D = loadmat(osp.join(bfm_folder, 'similarity_Lm3D_all.mat'))
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Lm3D = Lm3D['lm']
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lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
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Lm3D = np.stack([Lm3D[lm_idx[0], :], np.mean(Lm3D[lm_idx[[1, 2]], :], 0), np.mean(
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Lm3D[lm_idx[[3, 4]], :], 0), Lm3D[lm_idx[5], :], Lm3D[lm_idx[6], :]], axis=0)
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Lm3D = Lm3D[[1, 2, 0, 3, 4], :]
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return Lm3D
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if __name__ == '__main__':
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transferBFM09() |