def __init__(self,
initial_matcher: Optional[LocalFeature] = None,
fast_matcher: Optional[nn.Module] = None,
ransac: Optional[nn.Module] = None,
minimum_inliers_num: int = 30) -> None:
super().__init__()
self.initial_matcher = initial_matcher or (LocalFeatureMatcher(
GFTTAffNetHardNet(3000), DescriptorMatcher('smnn', 0.95)))
self.fast_matcher = fast_matcher or LoFTR('outdoor')
self.ransac = ransac or RANSAC('homography',
inl_th=5.0,
batch_size=4096,
max_iter=10,
max_lo_iters=10)
self.minimum_inliers_num = minimum_inliers_num
# placeholders
self.target: torch.Tensor
self.target_initial_representation: Dict[str, torch.Tensor] = {}
self.target_fast_representation: Dict[str, torch.Tensor] = {}
self.previous_homography: Optional[torch.Tensor] = None
self.reset_tracking()
def test_pretrained_indoor_smoke(self, device, dtype):
loftr = LoFTR('indoor').to(device, dtype)
assert loftr is not None
def test_pretrained_indoor_smoke(self, device):
if device == torch.device('cpu'):
loftr = LoFTR('indoor').to(device)