Spaces:
Sleeping
Sleeping
File size: 1,562 Bytes
aa46ae9 8320ccc aa46ae9 8320ccc aa46ae9 8320ccc aa46ae9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import torch
from hloc import logger
from ..utils.base_model import BaseModel
class XFeatDense(BaseModel):
default_conf = {
"keypoint_threshold": 0.005,
"max_keypoints": 8000,
}
required_inputs = [
"image0",
"image1",
]
def _init(self, conf):
self.net = torch.hub.load(
"verlab/accelerated_features",
"XFeat",
pretrained=True,
top_k=self.conf["max_keypoints"],
)
logger.info("Load XFeat(dense) model done.")
def _forward(self, data):
# Compute coarse feats
out0 = self.net.detectAndComputeDense(
data["image0"], top_k=self.conf["max_keypoints"]
)
out1 = self.net.detectAndComputeDense(
data["image1"], top_k=self.conf["max_keypoints"]
)
# Match batches of pairs
idxs_list = self.net.batch_match(
out0["descriptors"], out1["descriptors"]
)
B = len(data["image0"])
# Refine coarse matches
# this part is harder to batch, currently iterate
matches = []
for b in range(B):
matches.append(
self.net.refine_matches(
out0, out1, matches=idxs_list, batch_idx=b
)
)
# we use results from one batch
matches = matches[0]
pred = {
"keypoints0": matches[:, :2],
"keypoints1": matches[:, 2:],
"mconf": torch.ones_like(matches[:, 0]),
}
return pred
|