def Render_block()

in face_decoder.py [0:0]

• 62 lines of code
• 5 McCabe index (conditional complexity)

	def Render_block(self,face_shape,face_norm,face_color,camera_scale,f_scale,facemodel,batchsize,is_train=True):
		if is_train and is_windows:
			raise ValueError('Not support training with Windows environment.')

		if is_windows:
			return [],[],[]

		# render reconstruction images 
		n_vex = int(facemodel.idBase.shape[0].value/3)
		fov_y = 2*tf.atan(112./(1015.*f_scale))*180./m.pi
		fov_y = tf.reshape(fov_y,[batchsize])
		# full face region
		face_shape = tf.reshape(face_shape,[batchsize,n_vex,3])
		face_norm = tf.reshape(face_norm,[batchsize,n_vex,3])
		face_color = tf.reshape(face_color,[batchsize,n_vex,3])

		# pre-defined cropped face region
		mask_face_shape = tf.gather(face_shape,tf.cast(facemodel.front_mask_render-1,tf.int32),axis = 1)
		mask_face_norm = tf.gather(face_norm,tf.cast(facemodel.front_mask_render-1,tf.int32),axis = 1)
		mask_face_color = tf.gather(face_color,tf.cast(facemodel.front_mask_render-1,tf.int32),axis = 1)

		# setting cammera settings
		camera_position = tf.constant([[0,0,10.0]])*tf.reshape(camera_scale,[-1,1])
		camera_lookat = tf.constant([0,0,0.0])
		camera_up = tf.constant([0,1.0,0])

		# setting light source position(intensities are set to 0 because we have computed the vertex color)
		light_positions = tf.tile(tf.reshape(tf.constant([0,0,1e5]),[1,1,3]),[batchsize,1,1])
		light_intensities = tf.tile(tf.reshape(tf.constant([0.0,0.0,0.0]),[1,1,3]),[batchsize,1,1])
		ambient_color = tf.tile(tf.reshape(tf.constant([1.0,1,1]),[1,3]),[batchsize,1])

		#using tf_mesh_renderer for rasterization (https://github.com/google/tf_mesh_renderer)
		# img: [batchsize,224,224,3] images in RGB order (0-255)
		# mask:[batchsize,224,224,1] transparency for img ({0,1} value)
		with tf.device('/cpu:0'):
			img_rgba = mesh_renderer.mesh_renderer(face_shape,
				tf.cast(facemodel.face_buf-1,tf.int32),
				face_norm,
				face_color,
				camera_position = camera_position,
				camera_lookat = camera_lookat,
				camera_up = camera_up,
				light_positions = light_positions,
				light_intensities = light_intensities,
				image_width = 224,
				image_height = 224,
				fov_y = fov_y,
				near_clip = 0.01,
				far_clip = 50.0,
				ambient_color = ambient_color)

		img = img_rgba[:,:,:,:3]
		mask = img_rgba[:,:,:,3:]

		img = tf.cast(img[:,:,:,::-1],tf.float32) #transfer RGB to BGR
		mask = tf.cast(mask,tf.float32) # full face region

		if is_train:
			# compute mask for small face region
			with tf.device('/cpu:0'):
				img_crop_rgba = mesh_renderer.mesh_renderer(mask_face_shape,
					tf.cast(facemodel.mask_face_buf-1,tf.int32),
					mask_face_norm,
					mask_face_color,
					camera_position = camera_position,
					camera_lookat = camera_lookat,
					camera_up = camera_up,
					light_positions = light_positions,
					light_intensities = light_intensities,
					image_width = 224,
					image_height = 224,
					fov_y = fov_y,
					near_clip = 0.01,
					far_clip = 50.0,
					ambient_color = ambient_color)

			mask_f = img_crop_rgba[:,:,:,3:]
			mask_f = tf.cast(mask_f,tf.float32) # small face region
			return img,mask,mask_f

		img_rgba = tf.cast(tf.clip_by_value(img_rgba,0,255),tf.float32)

		return img_rgba,mask,mask