Spaces:
Running
Running
File size: 2,334 Bytes
8320ccc 9223079 8320ccc 9223079 8320ccc 49a0323 8320ccc 9223079 3c77caa 9223079 3c77caa 9223079 8320ccc 9223079 8320ccc 9223079 3c77caa e15a186 3c77caa 9223079 |
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 60 61 62 63 64 65 66 67 68 69 70 71 |
import subprocess
import sys
from pathlib import Path
import torch
from PIL import Image
from .. import logger
from ..utils.base_model import BaseModel
sys.path.append(str(Path(__file__).parent / "../../third_party"))
from DKM.dkm import DKMv3_outdoor
dkm_path = Path(__file__).parent / "../../third_party/DKM"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class DKMv3(BaseModel):
default_conf = {
"model_name": "DKMv3_outdoor.pth",
"match_threshold": 0.2,
"checkpoint_dir": dkm_path / "pretrained",
"max_keypoints": -1,
}
required_inputs = [
"image0",
"image1",
]
# Models exported using
dkm_models = {
"DKMv3_outdoor.pth": "https://github.com/Parskatt/storage/releases/download/dkmv3/DKMv3_outdoor.pth",
"DKMv3_indoor.pth": "https://github.com/Parskatt/storage/releases/download/dkmv3/DKMv3_indoor.pth",
}
def _init(self, conf):
model_path = dkm_path / "pretrained" / conf["model_name"]
# Download the model.
if not model_path.exists():
model_path.parent.mkdir(exist_ok=True)
link = self.dkm_models[conf["model_name"]]
cmd = ["wget", "--quiet", link, "-O", str(model_path)]
logger.info(f"Downloading the DKMv3 model with `{cmd}`.")
subprocess.run(cmd, check=True)
self.net = DKMv3_outdoor(path_to_weights=str(model_path), device=device)
logger.info("Loading DKMv3 model done")
def _forward(self, data):
img0 = data["image0"].cpu().numpy().squeeze() * 255
img1 = data["image1"].cpu().numpy().squeeze() * 255
img0 = img0.transpose(1, 2, 0)
img1 = img1.transpose(1, 2, 0)
img0 = Image.fromarray(img0.astype("uint8"))
img1 = Image.fromarray(img1.astype("uint8"))
W_A, H_A = img0.size
W_B, H_B = img1.size
warp, certainty = self.net.match(img0, img1, device=device)
matches, certainty = self.net.sample(
warp, certainty, num=self.conf["max_keypoints"]
)
kpts1, kpts2 = self.net.to_pixel_coordinates(
matches, H_A, W_A, H_B, W_B
)
pred = {
"keypoints0": kpts1,
"keypoints1": kpts2,
"mconf": certainty,
}
return pred
|