def demo()

in demo.py [0:0]

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

def demo():
	# input and output folder
	args = parse_args()

	image_path = 'input'
	save_path = 'output'	
	if not os.path.exists(save_path):
		os.makedirs(save_path)
	img_list = glob.glob(image_path + '/' + '*.png')
	img_list +=glob.glob(image_path + '/' + '*.jpg')

	# read BFM face model
	# transfer original BFM model to our model
	if not os.path.isfile('./BFM/BFM_model_front.mat'):
		transferBFM09()

	# read standard landmarks for preprocessing images
	lm3D = load_lm3d()
	n = 0

	# build reconstruction model
	with tf.Graph().as_default() as graph:
		
		with tf.device('/cpu:0'):
			opt = Option(is_train=False)
		opt.batch_size = 1
		opt.pretrain_weights = args.pretrain_weights
		FaceReconstructor = Face3D()
		images = tf.placeholder(name = 'input_imgs', shape = [opt.batch_size,224,224,3], dtype = tf.float32)

		if args.use_pb and os.path.isfile('network/FaceReconModel.pb'):
			print('Using pre-trained .pb file.')
			graph_def = load_graph('network/FaceReconModel.pb')
			tf.import_graph_def(graph_def,name='resnet',input_map={'input_imgs:0': images})
			# output coefficients of R-Net (dim = 257) 
			coeff = graph.get_tensor_by_name('resnet/coeff:0')
		else:
			print('Using pre-trained .ckpt file: %s'%opt.pretrain_weights)
			import networks
			coeff = networks.R_Net(images,is_training=False)

		# reconstructing faces
		FaceReconstructor.Reconstruction_Block(coeff,opt)
		face_shape = FaceReconstructor.face_shape_t
		face_texture = FaceReconstructor.face_texture
		face_color = FaceReconstructor.face_color
		landmarks_2d = FaceReconstructor.landmark_p
		recon_img = FaceReconstructor.render_imgs
		tri = FaceReconstructor.facemodel.face_buf


		with tf.Session() as sess:
			if not args.use_pb :
				restore_weights(sess,opt)

			print('reconstructing...')
			for file in img_list:
				n += 1
				print(n)
				# load images and corresponding 5 facial landmarks
				img,lm = load_img(file,file.replace('png','txt').replace('jpg','txt'))
				# preprocess input image
				input_img,lm_new,transform_params = align_img(img,lm,lm3D)

				coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\
					face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img})


				# reshape outputs
				input_img = np.squeeze(input_img)
				face_shape_ = np.squeeze(face_shape_, (0))
				face_texture_ = np.squeeze(face_texture_, (0))
				face_color_ = np.squeeze(face_color_, (0))
				landmarks_2d_ = np.squeeze(landmarks_2d_, (0))
				if not is_windows:
					recon_img_ = np.squeeze(recon_img_, (0))

				# save output files
				if not is_windows:
					savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'recon_img':recon_img_,'coeff':coeff_,\
						'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
				save_obj(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','_mesh.obj').replace('.jpg','_mesh.obj')),face_shape_,tri_,np.clip(face_color_,0,255)/255) # 3D reconstruction face (in canonical view)