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loader.py
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import os
import numpy as np
from scipy.io import loadmat, savemat
import cv2
from array import array
#######################################################################################
# Auxiliary functions for loading data
# Partially adapted/modified from https://github.com/microsoft/Deep3DFaceReconstruction
#######################################################################################
def load_data(im_path, txt_path):
return cv2.imread(im_path), np.loadtxt(txt_path)
def load_center3d(path='BFM/center.mat'):
return loadmat(path)['head_center'].reshape([1, 1, 3])
def load_lm3d(nums=5):
Lm3D = loadmat('BFM/similarity_Lm3D_all.mat')
Lm3D = Lm3D['lm']
# calculate 5 facial landmarks using 68 landmarks
if nums == 5:
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],:]
elif nums == 10:
lm_idx = np.array([1, 2, 16, 17, 31, 34, 37, 46, 49, 55]) - 1
Lm3D = Lm3D[lm_idx, :]
else:
return None
return Lm3D
def load_mask(path):
mask = loadmat(path)['mask']
return mask
def load_boundary_ind(path='BFM/border_index.mat'):
return loadmat(path)['border_index'].squeeze(1)
def read_facemodel(facemodel_pth='BFM/BFM_model_front.mat'):
if not os.path.isfile(facemodel_pth):
transferBFM09(os.path.dirname(facemodel_pth))
facemodel = BFM(facemodel_pth)
return facemodel
# define facemodel for reconstruction
class BFM():
def __init__(self, model_path):
model = loadmat(model_path)
self.meanshape = model['meanshape'] # mean face shape
self.idBase = model['idBase'] # identity basis
self.exBase = model['exBase'] # expression basis
self.meantex = model['meantex'] # mean face texture
self.texBase = model['texBase'] # texture basis
self.point_buf = model['point_buf'] # adjacent face index for each vertex, starts from 1 (only used for calculating face normal)
self.tri = model['tri'] # vertex index for each triangle face, starts from 1
self.keypoints = np.squeeze(model['keypoints']).astype(np.int32) - 1 # 68 face landmark index, starts from 0
# load expression basis
def LoadExpBasis():
n_vertex = 53215
Expbin = open('BFM/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)
expPC = np.array(expPC)
expPC = np.reshape(expPC,[exp_dim[0],-1])
expPC = np.transpose(expPC)
expEV = np.loadtxt('BFM/std_exp.txt')
return expPC,expEV
# transfer original BFM09 to our face model
def transferBFM09(path):
original_BFM = loadmat(os.path.join(path, '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 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 align face landmarks which contains only 35709 vertex.
# original BFM09 contains 53490 vertex, and expression basis provided by JuYong contains 53215 vertex.
# thus we select corresponding vertex to get our face model.
index_exp = loadmat(os.path.join(path,'BFM_front_idx.mat'))
index_exp = index_exp['idx'].astype(np.int32) - 1 #starts from 0 (to 53215)
index_shape = loadmat(os.path.join(path,'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(os.path.join(path,'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(os.path.join(path,'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})