def Compute_norm()

in face_decoder.py [0:0]

• 17 lines of code
• 4 McCabe index (conditional complexity)

	def Compute_norm(self,face_shape,facemodel):
		shape = face_shape
		face_id = facemodel.face_buf
		point_id = facemodel.point_buf

		# face_id and point_id index starts from 1
		face_id = tf.cast(face_id - 1,tf.int32)
		point_id = tf.cast(point_id - 1,tf.int32)

		#compute normal for each face
		v1 = tf.gather(shape,face_id[:,0], axis = 1)
		v2 = tf.gather(shape,face_id[:,1], axis = 1)
		v3 = tf.gather(shape,face_id[:,2], axis = 1)
		e1 = v1 - v2
		e2 = v2 - v3
		face_norm = tf.cross(e1,e2)

		face_norm = tf.nn.l2_normalize(face_norm, dim = 2) # normalized face_norm first
		face_norm = tf.concat([face_norm,tf.zeros([tf.shape(face_shape)[0],1,3])], axis = 1)

		#compute normal for each vertex using one-ring neighborhood
		v_norm = tf.reduce_sum(tf.gather(face_norm, point_id, axis = 1), axis = 2)
		v_norm = tf.nn.l2_normalize(v_norm, dim = 2)
		
		return v_norm