-
Notifications
You must be signed in to change notification settings - Fork 44
/
load_mats.py
114 lines (87 loc) · 4.28 KB
/
load_mats.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
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='asset/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, 160470*199
shapeEV = original_BFM['shapeEV'] # corresponding eigen value, 199*1
shapeMU = original_BFM['shapeMU'] # mean face, 160470*1
texPC = original_BFM['texPC'] # texture basis, 160470*199
texEV = original_BFM['texEV'] # eigen value, 199*1
texMU = original_BFM['texMU'] # mean texture, 160470*1
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