def __init__(self, regressor_checkpoint, smpl_dir, device = torch.device('cuda') , use_smplx = False):
#For image transform
transform_list = [ transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.normalize_transform = transforms.Compose(transform_list)
#Load Hand network
self.opt = TestOptions().parse([])
#Default options
self.opt.single_branch = True
self.opt.main_encoder = "resnet50"
# self.opt.data_root = "/home/hjoo/dropbox/hand_yu/data/"
self.opt.model_root = "./extra_data"
self.opt.smplx_model_file = os.path.join(smpl_dir,'SMPLX_NEUTRAL.pkl')
self.opt.batchSize = 1
self.opt.phase = "test"
self.opt.nThreads = 0
self.opt.which_epoch = -1
self.opt.checkpoint_path = regressor_checkpoint
self.opt.serial_batches = True # no shuffle
self.opt.no_flip = True # no flip
self.opt.process_rank = -1
# self.opt.which_epoch = str(epoch)
self.model_regressor = H3DWModel(self.opt)
# if there is no specified checkpoint, then skip
assert self.model_regressor.success_load, "Specificed checkpoints does not exists: {}".format(self.opt.checkpoint_path)
self.model_regressor.eval()
def __init__(self, body_regressor_checkpoint, hand_regressor_checkpoint, smpl_dir, device=torch.device('cuda'), use_smplx=True):
super().__init__('BodyMocap')
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print("Loading Body Pose Estimator")
self.__load_body_estimator()
self.visualizer = Visualizer('opengl')
self.frame_id = 0 #count frames
parser = argparse.ArgumentParser()
parser.add_argument("--rot90", default=False, type= bool, help="clockwise rotate 90 degrees")
#parser.add_argument("--camera_topic", default="/logi_c922_2/image_rect_color", help="choose a topic as input image")
parser.add_argument("--body_only", default=False, type= bool, help="detect only body and save its result")
parser.add_argument("--result_path", default="/home/student/result/", help="choose a topic as input image")
parser.add_argument("--save_result", default=False, help="save result or not")
args = parser.parse_args()
self.rot90 = args.rot90
#self.camera_topic = args.camera_topic
self.body_only = args.body_only
self.result_path = args.result_path
self.save_result = args.save_result
self.load = [0,0]
self.angle_leg = 0
self.angle_trunk = 0
self.start = 0
self.angles = np.empty((1,20),dtype = float)
self.body_side = np.empty((25,3),dtype = float)
# Load parametric model (SMPLX or SMPL)
if use_smplx:
smplModelPath = smpl_dir + '/SMPLX_NEUTRAL.pkl'
self.smpl = SMPLX(smpl_dir,
batch_size=1,
num_betas = 10,
use_pca = False,
create_transl=False).to(self.device)
self.use_smplx = True
else:
smplModelPath = smpl_dir + '/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl'
self.smpl = SMPL(smplModelPath, batch_size=1, create_transl=False).to(self.device)
self.use_smplx = False
#Load pre-trained neural network
SMPL_MEAN_PARAMS = '/home/student/frankmocap/extra_data/body_module/data_from_spin/smpl_mean_params.npz'
self.model_regressor = hmr(SMPL_MEAN_PARAMS).to(self.device)
body_checkpoint = torch.load(body_regressor_checkpoint)
self.model_regressor.load_state_dict(body_checkpoint['model'], strict=False)
self.model_regressor.eval()
#hand module init
transform_list = [ transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.normalize_transform = transforms.Compose(transform_list)
#Load Hand network
self.opt = TestOptions().parse([])
#Default options
self.opt.single_branch = True
self.opt.main_encoder = "resnet50"
# self.opt.data_root = "/home/hjoo/dropbox/hand_yu/data/"
self.opt.model_root = "/home/student/frankmocap/extra_data"
self.opt.smplx_model_file = os.path.join(smpl_dir,'SMPLX_NEUTRAL.pkl')
self.opt.batchSize = 1
self.opt.phase = "test"
self.opt.nThreads = 0
self.opt.which_epoch = -1
self.opt.checkpoint_path = hand_regressor_checkpoint
self.opt.serial_batches = True # no shuffle
self.opt.no_flip = True # no flip
self.opt.process_rank = -1
# self.opt.which_epoch = str(epoch)
self.hand_model_regressor = H3DWModel(self.opt)
# if there is no specified checkpoint, then skip
assert self.hand_model_regressor.success_load, "Specificed checkpoints does not exists: {}".format(self.opt.checkpoint_path)
self.hand_model_regressor.eval()
self.hand_bbox_detector = HandBboxDetector('third_view', self.device)
#subscriber and publisher initialization
#input subscriber
self.br = CvBridge()
self.subscription_img = self.create_subscription(Image, '/side_img', self.callback_side,10)
self.subscription_img = self.create_subscription(Image, '/front_img', self.callback_front,10)
#output publisher
self.publisher_pose = self.create_publisher(Image,'/pose',10) #images with keypoints annotation
#self.publisher_keypoints = self.create_publisher(Float32MultiArray,'/keypoints',10) #keypoints coordinates
self.publisher_risk = self.create_publisher(Int64,'/risk',10) #risk level
self.publisher_angles = self.create_publisher(Float32MultiArray,'/angles',10)