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| import subprocess | |
| import sys | |
| from pathlib import Path | |
| from hloc import logger | |
| from ..utils.base_model import BaseModel | |
| darkfeat_path = Path(__file__).parent / "../../third_party/DarkFeat" | |
| sys.path.append(str(darkfeat_path)) | |
| from darkfeat import DarkFeat as DarkFeat_ | |
| class DarkFeat(BaseModel): | |
| default_conf = { | |
| "model_name": "DarkFeat.pth", | |
| "max_keypoints": 1000, | |
| "detection_threshold": 0.5, | |
| "sub_pixel": False, | |
| } | |
| weight_urls = { | |
| "DarkFeat.pth": "https://drive.google.com/uc?id=1Thl6m8NcmQ7zSAF-1_xaFs3F4H8UU6HX&confirm=t", | |
| } | |
| proxy = "http://localhost:1080" | |
| required_inputs = ["image"] | |
| def _init(self, conf): | |
| model_path = darkfeat_path / "checkpoints" / conf["model_name"] | |
| link = self.weight_urls[conf["model_name"]] | |
| if not model_path.exists(): | |
| model_path.parent.mkdir(exist_ok=True) | |
| cmd_wo_proxy = ["gdown", link, "-O", str(model_path)] | |
| cmd = ["gdown", link, "-O", str(model_path), "--proxy", self.proxy] | |
| logger.info( | |
| f"Downloading the DarkFeat model with `{cmd_wo_proxy}`." | |
| ) | |
| try: | |
| subprocess.run(cmd_wo_proxy, check=True) | |
| except subprocess.CalledProcessError as e: | |
| logger.info(f"Downloading the model failed `{e}`.") | |
| logger.info(f"Downloading the DarkFeat model with `{cmd}`.") | |
| try: | |
| subprocess.run(cmd, check=True) | |
| except subprocess.CalledProcessError as e: | |
| logger.error("Failed to download the DarkFeat model.") | |
| raise e | |
| self.net = DarkFeat_(model_path) | |
| logger.info("Load DarkFeat model done.") | |
| def _forward(self, data): | |
| pred = self.net({"image": data["image"]}) | |
| keypoints = pred["keypoints"] | |
| descriptors = pred["descriptors"] | |
| scores = pred["scores"] | |
| idxs = scores.argsort()[-self.conf["max_keypoints"] or None :] | |
| keypoints = keypoints[idxs, :2] | |
| descriptors = descriptors[:, idxs] | |
| scores = scores[idxs] | |
| return { | |
| "keypoints": keypoints[None], # 1 x N x 2 | |
| "scores": scores[None], # 1 x N | |
| "descriptors": descriptors[None], # 1 x 128 x N | |
| } | |