def __init__(self, g_pool,visualize=True):
super().__init__(g_pool)
self.menu = None
# Initialize CNN pipeline
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Hand Detector
self.hand_detector_cfg = {
'input_size': 225,
'confidence_thresh': 0.9,
'max_num_detection': 1,
'nms_thresh': 0.45,
'weights_path': os.path.join(weights_path,'hand_detector_model.pkl'),
}
self.hand_transform = BaseTransform(self.hand_detector_cfg['input_size'], (117.77, 115.42, 107.29), (72.03, 69.83, 71.43))
self.hand_detector = ssd_lite.build_ssd_lite(self.hand_detector_cfg)
self.hand_detector.load_state_dict(torch.load(self.hand_detector_cfg['weights_path'], map_location=lambda storage, loc: storage))
self.hand_detector.eval().to(self.device)
# Fingertip Detector
self.fingertip_detector_cfg = {
'confidence_thresh': 0.6,
'weights_path': os.path.join(weights_path, "fingertip_detector_model.pkl"),
}
self.fingertip_transform = BaseTransform(64, (121.97, 119.65, 111.42), (67.58, 65.17, 67.72))
self.fingertip_detector = unet.UNet(num_classes=10, in_channels=3, depth=4, start_filts=32, up_mode='transpose')
self.fingertip_detector.load_state_dict(torch.load(self.fingertip_detector_cfg['weights_path'], map_location=lambda storage, loc: storage))
self.fingertip_detector.eval().to(self.device)
self.collect_tips = False
self.visualize = visualize
self.hand_viz = []
self.finger_viz = []
def __init__(self, g_pool, visualize=True):
super().__init__(g_pool)
self.menu = None
# Initialize CNN pipeline
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Hand Detector
self.hand_detector_cfg = {
"input_size": 225,
"confidence_thresh": 0.9,
"max_num_detection": 1,
"nms_thresh": 0.45,
"weights_path": os.path.join(weights_path, "hand_detector_model.pkl"),
}
self.hand_transform = BaseTransform(
self.hand_detector_cfg["input_size"],
(117.77, 115.42, 107.29),
(72.03, 69.83, 71.43),
)
self.hand_detector = ssd_lite.build_ssd_lite(self.hand_detector_cfg)
self.hand_detector.load_state_dict(
torch.load(
self.hand_detector_cfg["weights_path"],
map_location=lambda storage, loc: storage,
)
)
self.hand_detector.eval().to(self.device)
# Fingertip Detector
self.fingertip_detector_cfg = {
"confidence_thresh": 0.6,
"weights_path": os.path.join(weights_path, "fingertip_detector_model.pkl"),
}
self.fingertip_transform = BaseTransform(
64, (121.97, 119.65, 111.42), (67.58, 65.17, 67.72)
)
self.fingertip_detector = unet.UNet(
num_classes=10, in_channels=3, depth=4, start_filts=32, up_mode="transpose"
)
self.fingertip_detector.load_state_dict(
torch.load(
self.fingertip_detector_cfg["weights_path"],
map_location=lambda storage, loc: storage,
)
)
self.fingertip_detector.eval().to(self.device)
self.collect_tips = False
self.visualize = visualize
self.hand_viz = []
self.finger_viz = []