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import sys
from pathlib import Path
import torchvision.transforms as tvf
from ..utils.base_model import BaseModel
from hloc import logger
base_path = Path(__file__).parent / "../../third_party"
sys.path.append(str(base_path))
r2d2_path = Path(__file__).parent / "../../third_party/r2d2"
from r2d2.extract import load_network, NonMaxSuppression, extract_multiscale
class R2D2(BaseModel):
default_conf = {
"model_name": "r2d2_WASF_N16.pt",
"max_keypoints": 5000,
"scale_factor": 2**0.25,
"min_size": 256,
"max_size": 1024,
"min_scale": 0,
"max_scale": 1,
"reliability_threshold": 0.7,
"repetability_threshold": 0.7,
}
required_inputs = ["image"]
def _init(self, conf):
model_fn = r2d2_path / "models" / conf["model_name"]
self.norm_rgb = tvf.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
self.net = load_network(model_fn)
self.detector = NonMaxSuppression(
rel_thr=conf["reliability_threshold"],
rep_thr=conf["repetability_threshold"],
)
logger.info(f"Load R2D2 model done.")
def _forward(self, data):
img = data["image"]
img = self.norm_rgb(img)
xys, desc, scores = extract_multiscale(
self.net,
img,
self.detector,
scale_f=self.conf["scale_factor"],
min_size=self.conf["min_size"],
max_size=self.conf["max_size"],
min_scale=self.conf["min_scale"],
max_scale=self.conf["max_scale"],
)
idxs = scores.argsort()[-self.conf["max_keypoints"] or None :]
xy = xys[idxs, :2]
desc = desc[idxs].t()
scores = scores[idxs]
pred = {
"keypoints": xy[None],
"descriptors": desc[None],
"scores": scores[None],
}
return pred
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