def __init__()

in face_decoder.py [0:0]

• 13 lines of code
• 7 McCabe index (conditional complexity)

	def __init__(self,model_path = './BFM/BFM_model_front.mat'):
		model = loadmat(model_path)
		self.meanshape = tf.constant(model['meanshape']) # mean face shape. [3*N,1]
		self.idBase = tf.constant(model['idBase']) # identity basis. [3*N,80]
		self.exBase = tf.constant(model['exBase'].astype(np.float32)) # expression basis. [3*N,64]
		self.meantex = tf.constant(model['meantex']) # mean face texture. [3*N,1] (0-255)
		self.texBase = tf.constant(model['texBase']) # texture basis. [3*N,80]
		self.point_buf = tf.constant(model['point_buf']) # face indices for each vertex that lies in. starts from 1. [N,8]
		self.face_buf = tf.constant(model['tri']) # vertex indices for each face. starts from 1. [F,3]
		self.front_mask_render = tf.squeeze(tf.constant(model['frontmask2_idx'])) # vertex indices for small face region to compute photometric error. starts from 1.
		self.mask_face_buf = tf.constant(model['tri_mask2']) # vertex indices for each face from small face region. starts from 1. [f,3]
		self.skin_mask = tf.squeeze(tf.constant(model['skinmask'])) # vertex indices for pre-defined skin region to compute reflectance loss
		self.keypoints = tf.squeeze(tf.constant(model['keypoints'])) # vertex indices for 68 landmarks. starts from 1. [68,1]