def Reconstruction_Block()

in face_decoder.py [0:0]

• 25 lines of code
• 6 McCabe index (conditional complexity)

	def Reconstruction_Block(self,coeff,opt):
		#coeff: [batchsize,257] reconstruction coefficients

		id_coeff,ex_coeff,tex_coeff,angles,translation,gamma,camera_scale,f_scale = self.Split_coeff(coeff)
		# [batchsize,N,3] canonical face shape in BFM space
		face_shape = self.Shape_formation_block(id_coeff,ex_coeff,self.facemodel)
		# [batchsize,N,3] vertex texture (in RGB order)
		face_texture = self.Texture_formation_block(tex_coeff,self.facemodel)
		# [batchsize,3,3] rotation matrix for face shape
		rotation = self.Compute_rotation_matrix(angles)
		# [batchsize,N,3] vertex normal
		face_norm = self.Compute_norm(face_shape,self.facemodel)
		norm_r = tf.matmul(face_norm,rotation)

		# do rigid transformation for face shape using predicted rotation and translation
		face_shape_t = self.Rigid_transform_block(face_shape,rotation,translation)
		# compute 2d landmark projections 
		# landmark_p: [batchsize,68,2]	
		face_landmark_t = self.Compute_landmark(face_shape_t,self.facemodel)
		landmark_p = self.Projection_block(face_landmark_t,camera_scale,f_scale)

		# [batchsize,N,3] vertex color (in RGB order)
		face_color = self.Illumination_block(face_texture, norm_r, gamma)

		# reconstruction images and region masks for computing photometric loss		
		render_imgs,img_mask,img_mask_crop = self.Render_block(face_shape_t,norm_r,face_color,camera_scale,f_scale,self.facemodel,opt.batch_size,opt.is_train)

		self.id_coeff = id_coeff
		self.ex_coeff = ex_coeff
		self.tex_coeff = tex_coeff
		self.f_scale = f_scale
		self.gamma = gamma
		self.face_shape = face_shape
		self.face_shape_t = face_shape_t
		self.face_texture = face_texture
		self.face_color = face_color
		self.landmark_p = landmark_p
		self.render_imgs = render_imgs
		self.img_mask = img_mask
		self.img_mask_crop = img_mask_crop