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import torch
from kornia.feature.adalam import AdalamFilter
from kornia.utils.helpers import get_cuda_device_if_available
from ..utils.base_model import BaseModel
class AdaLAM(BaseModel):
# See https://kornia.readthedocs.io/en/latest/_modules/kornia/feature/adalam/adalam.html.
default_conf = {
"area_ratio": 100,
"search_expansion": 4,
"ransac_iters": 128,
"min_inliers": 6,
"min_confidence": 200,
"orientation_difference_threshold": 30,
"scale_rate_threshold": 1.5,
"detected_scale_rate_threshold": 5,
"refit": True,
"force_seed_mnn": True,
"device": get_cuda_device_if_available(),
}
required_inputs = [
"image0",
"image1",
"descriptors0",
"descriptors1",
"keypoints0",
"keypoints1",
"scales0",
"scales1",
"oris0",
"oris1",
]
def _init(self, conf):
self.adalam = AdalamFilter(conf)
def _forward(self, data):
assert data["keypoints0"].size(0) == 1
if data["keypoints0"].size(1) < 2 or data["keypoints1"].size(1) < 2:
matches = torch.zeros(
(0, 2), dtype=torch.int64, device=data["keypoints0"].device
)
else:
matches = self.adalam.match_and_filter(
data["keypoints0"][0],
data["keypoints1"][0],
data["descriptors0"][0].T,
data["descriptors1"][0].T,
data["image0"].shape[2:],
data["image1"].shape[2:],
data["oris0"][0],
data["oris1"][0],
data["scales0"][0],
data["scales1"][0],
)
matches_new = torch.full(
(data["keypoints0"].size(1),),
-1,
dtype=torch.int64,
device=data["keypoints0"].device,
)
matches_new[matches[:, 0]] = matches[:, 1]
return {
"matches0": matches_new.unsqueeze(0),
"matching_scores0": torch.zeros(matches_new.size(0)).unsqueeze(0),
}
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