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| import sys | |
| from pathlib import Path | |
| import subprocess | |
| import torch | |
| from ..utils.base_model import BaseModel | |
| d2net_path = Path(__file__).parent / "../../third_party" | |
| sys.path.append(str(d2net_path)) | |
| from d2net.lib.model_test import D2Net as _D2Net | |
| from d2net.lib.pyramid import process_multiscale | |
| d2net_path = Path(__file__).parent / "../../third_party/d2net" | |
| class D2Net(BaseModel): | |
| default_conf = { | |
| "model_name": "d2_tf.pth", | |
| "checkpoint_dir": d2net_path / "models", | |
| "use_relu": True, | |
| "multiscale": False, | |
| "max_keypoints": 1024, | |
| } | |
| required_inputs = ["image"] | |
| def _init(self, conf): | |
| model_file = conf["checkpoint_dir"] / conf["model_name"] | |
| if not model_file.exists(): | |
| model_file.parent.mkdir(exist_ok=True) | |
| cmd = [ | |
| "wget", | |
| "https://dusmanu.com/files/d2-net/" + conf["model_name"], | |
| "-O", | |
| str(model_file), | |
| ] | |
| subprocess.run(cmd, check=True) | |
| self.net = _D2Net( | |
| model_file=model_file, use_relu=conf["use_relu"], use_cuda=False | |
| ) | |
| def _forward(self, data): | |
| image = data["image"] | |
| image = image.flip(1) # RGB -> BGR | |
| norm = image.new_tensor([103.939, 116.779, 123.68]) | |
| image = image * 255 - norm.view(1, 3, 1, 1) # caffe normalization | |
| if self.conf["multiscale"]: | |
| keypoints, scores, descriptors = process_multiscale(image, self.net) | |
| else: | |
| keypoints, scores, descriptors = process_multiscale( | |
| image, self.net, scales=[1] | |
| ) | |
| keypoints = keypoints[:, [1, 0]] # (x, y) and remove the scale | |
| idxs = scores.argsort()[-self.conf["max_keypoints"] or None :] | |
| keypoints = keypoints[idxs, :2] | |
| descriptors = descriptors[idxs] | |
| scores = scores[idxs] | |
| return { | |
| "keypoints": torch.from_numpy(keypoints)[None], | |
| "scores": torch.from_numpy(scores)[None], | |
| "descriptors": torch.from_numpy(descriptors.T)[None], | |
| } | |