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"""This script is to load 3D face model for Deep3DFaceRecon_pytorch
"""
import numpy as np
from PIL import Image
from scipy.io import loadmat, savemat
from array import array
import os.path as osp
# load expression basis
def LoadExpBasis(bfm_folder='BFM'):
n_vertex = 53215
Expbin = open(osp.join(bfm_folder, 'Exp_Pca.bin'), 'rb')
exp_dim = array('i')
exp_dim.fromfile(Expbin, 1)
expMU = array('f')
expPC = array('f')
expMU.fromfile(Expbin, 3*n_vertex)
expPC.fromfile(Expbin, 3*exp_dim[0]*n_vertex)
Expbin.close()
expPC = np.array(expPC)
expPC = np.reshape(expPC, [exp_dim[0], -1])
expPC = np.transpose(expPC)
expEV = np.loadtxt(osp.join(bfm_folder, 'std_exp.txt'))
return expPC, expEV
# transfer original BFM09 to our face model
def transferBFM09(bfm_folder='BFM'):
print('Transfer BFM09 to BFM_model_front......')
original_BFM = loadmat(osp.join(bfm_folder, '01_MorphableModel.mat'))
shapePC = original_BFM['shapePC'] # shape basis
shapeEV = original_BFM['shapeEV'] # corresponding eigen value
shapeMU = original_BFM['shapeMU'] # mean face
texPC = original_BFM['texPC'] # texture basis
texEV = original_BFM['texEV'] # eigen value
texMU = original_BFM['texMU'] # mean texture
expPC, expEV = LoadExpBasis()
# transfer BFM09 to our face model
idBase = shapePC*np.reshape(shapeEV, [-1, 199])
idBase = idBase/1e5 # unify the scale to decimeter
idBase = idBase[:, :80] # use only first 80 basis
exBase = expPC*np.reshape(expEV, [-1, 79])
exBase = exBase/1e5 # unify the scale to decimeter
exBase = exBase[:, :64] # use only first 64 basis
texBase = texPC*np.reshape(texEV, [-1, 199])
texBase = texBase[:, :80] # use only first 80 basis
# our face model is cropped along face landmarks and contains only 35709 vertex.
# original BFM09 contains 53490 vertex, and expression basis provided by Guo et al. contains 53215 vertex.
# thus we select corresponding vertex to get our face model.
index_exp = loadmat(osp.join(bfm_folder, 'BFM_front_idx.mat'))
index_exp = index_exp['idx'].astype(np.int32) - 1 # starts from 0 (to 53215)
index_shape = loadmat(osp.join(bfm_folder, 'BFM_exp_idx.mat'))
index_shape = index_shape['trimIndex'].astype(
np.int32) - 1 # starts from 0 (to 53490)
index_shape = index_shape[index_exp]
idBase = np.reshape(idBase, [-1, 3, 80])
idBase = idBase[index_shape, :, :]
idBase = np.reshape(idBase, [-1, 80])
texBase = np.reshape(texBase, [-1, 3, 80])
texBase = texBase[index_shape, :, :]
texBase = np.reshape(texBase, [-1, 80])
exBase = np.reshape(exBase, [-1, 3, 64])
exBase = exBase[index_exp, :, :]
exBase = np.reshape(exBase, [-1, 64])
meanshape = np.reshape(shapeMU, [-1, 3])/1e5
meanshape = meanshape[index_shape, :]
meanshape = np.reshape(meanshape, [1, -1])
meantex = np.reshape(texMU, [-1, 3])
meantex = meantex[index_shape, :]
meantex = np.reshape(meantex, [1, -1])
# other info contains triangles, region used for computing photometric loss,
# region used for skin texture regularization, and 68 landmarks index etc.
other_info = loadmat(osp.join(bfm_folder, 'facemodel_info.mat'))
frontmask2_idx = other_info['frontmask2_idx']
skinmask = other_info['skinmask']
keypoints = other_info['keypoints']
point_buf = other_info['point_buf']
tri = other_info['tri']
tri_mask2 = other_info['tri_mask2']
# save our face model
savemat(osp.join(bfm_folder, 'BFM_model_front.mat'), {'meanshape': meanshape, 'meantex': meantex, 'idBase': idBase, 'exBase': exBase, 'texBase': texBase,
'tri': tri, 'point_buf': point_buf, 'tri_mask2': tri_mask2, 'keypoints': keypoints, 'frontmask2_idx': frontmask2_idx, 'skinmask': skinmask})
# load landmarks for standard face, which is used for image preprocessing
def load_lm3d(bfm_folder):
Lm3D = loadmat(osp.join(bfm_folder, 'similarity_Lm3D_all.mat'))
Lm3D = Lm3D['lm']
# calculate 5 facial landmarks using 68 landmarks
lm_idx = np.array([31, 37, 40, 43, 46, 49, 55]) - 1
Lm3D = np.stack([Lm3D[lm_idx[0], :], np.mean(Lm3D[lm_idx[[1, 2]], :], 0), np.mean(
Lm3D[lm_idx[[3, 4]], :], 0), Lm3D[lm_idx[5], :], Lm3D[lm_idx[6], :]], axis=0)
Lm3D = Lm3D[[1, 2, 0, 3, 4], :]
return Lm3D
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