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import sys
from pathlib import Path
from .. import MODEL_REPO_ID, logger
from ..utils.base_model import BaseModel
lightglue_path = Path(__file__).parent / "../../third_party/LightGlue"
sys.path.append(str(lightglue_path))
from lightglue import LightGlue as LG
class LightGlue(BaseModel):
default_conf = {
"match_threshold": 0.2,
"filter_threshold": 0.2,
"width_confidence": 0.99, # for point pruning
"depth_confidence": 0.95, # for early stopping,
"features": "superpoint",
"model_name": "superpoint_lightglue.pth",
"flash": True, # enable FlashAttention if available.
"mp": False, # enable mixed precision
"add_scale_ori": False,
}
required_inputs = [
"image0",
"keypoints0",
"scores0",
"descriptors0",
"image1",
"keypoints1",
"scores1",
"descriptors1",
]
def _init(self, conf):
model_path = self._download_model(
repo_id=MODEL_REPO_ID,
filename="{}/{}".format(
Path(__file__).stem, self.conf["model_name"]
),
)
conf["weights"] = str(model_path)
conf["filter_threshold"] = conf["match_threshold"]
self.net = LG(**conf)
logger.info("Load lightglue model done.")
def _forward(self, data):
input = {}
input["image0"] = {
"image": data["image0"],
"keypoints": data["keypoints0"],
"descriptors": data["descriptors0"].permute(0, 2, 1),
}
if "scales0" in data:
input["image0"] = {**input["image0"], "scales": data["scales0"]}
if "oris0" in data:
input["image0"] = {**input["image0"], "oris": data["oris0"]}
input["image1"] = {
"image": data["image1"],
"keypoints": data["keypoints1"],
"descriptors": data["descriptors1"].permute(0, 2, 1),
}
if "scales1" in data:
input["image1"] = {**input["image1"], "scales": data["scales1"]}
if "oris1" in data:
input["image1"] = {**input["image1"], "oris": data["oris1"]}
return self.net(input)
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