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DATID-3D / pose_estimation /util /load_mats.py

f12ab4c about 1 year ago

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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, 3exp_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