def __init__(
self,
num_features: int = 500,
mr_size: float = 6.0,
scale_pyr_module: nn.Module = ScalePyramid(3, 1.6, 15),
resp_module: nn.Module = BlobHessian(),
nms_module: nn.Module = ConvSoftArgmax3d((3, 3, 3), (1, 1, 1),
(1, 1, 1),
normalized_coordinates=False,
output_value=True),
ori_module: nn.Module = PassLAF(),
aff_module: nn.Module = PassLAF(),
minima_are_also_good: bool = False,
scale_space_response=False,
):
super(ScaleSpaceDetector, self).__init__()
self.mr_size = mr_size
self.num_features = num_features
self.scale_pyr = scale_pyr_module
self.resp = resp_module
self.nms = nms_module
self.ori = ori_module
self.aff = aff_module
self.minima_are_also_good = minima_are_also_good
# scale_space_response should be True if the response function works on scale space
# like Difference-of-Gaussians
self.scale_space_response = scale_space_response
def __init__(self, criterion, geom_weight=1, level_nb=1):
self.level_nb = level_nb
self.SP = ScalePyramid()
if criterion == "l2":
self.criterion = torch.nn.MSELoss(reduction="none")
elif criterion == "l1":
self.criterion = torch.nn.L1Loss(reduction="none")
elif criterion == "ssim":
self.criterion = klosses.SSIM(reduction="none")
else:
raise ValueError(f"{criterion} not in [l2, l1, ssim]")
self.geom_weight = geom_weight
def __init__(self,
num_features: int = 8000,
upright: bool = False,
device: torch.device = torch.device('cpu')):
detector = ScaleSpaceDetector(
num_features,
resp_module=CornerGFTT(),
nms_module=ConvQuadInterp3d(10, 1e-5),
scale_pyr_module=ScalePyramid(3, 1.6, 32, double_image=False),
ori_module=PassLAF() if upright else LAFOrienter(19),
aff_module=LAFAffNetShapeEstimator(True).eval(),
mr_size=6.0).to(device)
descriptor = LAFDescriptor(None,
patch_size=32,
grayscale_descriptor=True).to(device)
super().__init__(detector, descriptor)
def __init__(self,
num_features: int = 500,
mr_size: float = 6.0,
scale_pyr_module: nn.Module = ScalePyramid(3, 1.6, 10),
resp_module: nn.Module = BlobHessian(),
nms_module: nn.Module = ConvSoftArgmax3d(
(3, 3, 3), (1, 1, 1), (1, 1, 1),
normalized_coordinates=False,
output_value=True),
ori_module: nn.Module = PassLAF(),
aff_module: nn.Module = PassLAF()):
super(ScaleSpaceDetector, self).__init__()
self.mr_size = mr_size
self.num_features = num_features
self.scale_pyr = scale_pyr_module
self.resp = resp_module
self.nms = nms_module
self.ori = ori_module
self.aff = aff_module
return
def __init__(self,
num_features: int = 8000,
upright: bool = False,
rootsift: bool = True,
device: torch.device = torch.device('cpu')):
patch_size: int = 41
detector = ScaleSpaceDetector(
num_features,
resp_module=BlobDoG(),
nms_module=ConvQuadInterp3d(10),
scale_pyr_module=ScalePyramid(3, 1.6, 32, double_image=True),
ori_module=PassLAF() if upright else LAFOrienter(19),
scale_space_response=True,
minima_are_also_good=True,
mr_size=6.0).to(device)
descriptor = LAFDescriptor(SIFTDescriptor(patch_size=patch_size,
rootsift=rootsift),
patch_size=patch_size,
grayscale_descriptor=True).to(device)
super().__init__(detector, descriptor)