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| import sys | |
| from pathlib import Path | |
| import subprocess | |
| import torch | |
| from PIL import Image | |
| from ..utils.base_model import BaseModel | |
| from .. import logger | |
| roma_path = Path(__file__).parent / "../../third_party/Roma" | |
| sys.path.append(str(roma_path)) | |
| from roma.models.model_zoo.roma_models import roma_model | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class Roma(BaseModel): | |
| default_conf = { | |
| "name": "two_view_pipeline", | |
| "model_name": "roma_outdoor.pth", | |
| "model_utils_name": "dinov2_vitl14_pretrain.pth", | |
| "max_keypoints": 3000, | |
| } | |
| required_inputs = [ | |
| "image0", | |
| "image1", | |
| ] | |
| weight_urls = { | |
| "roma": { | |
| "roma_outdoor.pth": "https://github.com/Parskatt/storage/releases/download/roma/roma_outdoor.pth", | |
| "roma_indoor.pth": "https://github.com/Parskatt/storage/releases/download/roma/roma_indoor.pth", | |
| }, | |
| "dinov2_vitl14_pretrain.pth": "https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth", | |
| } | |
| # Initialize the line matcher | |
| def _init(self, conf): | |
| model_path = roma_path / "pretrained" / conf["model_name"] | |
| dinov2_weights = roma_path / "pretrained" / conf["model_utils_name"] | |
| # Download the model. | |
| if not model_path.exists(): | |
| model_path.parent.mkdir(exist_ok=True) | |
| link = self.weight_urls["roma"][conf["model_name"]] | |
| cmd = ["wget", link, "-O", str(model_path)] | |
| logger.info(f"Downloading the Roma model with `{cmd}`.") | |
| subprocess.run(cmd, check=True) | |
| if not dinov2_weights.exists(): | |
| dinov2_weights.parent.mkdir(exist_ok=True) | |
| link = self.weight_urls[conf["model_utils_name"]] | |
| cmd = ["wget", link, "-O", str(dinov2_weights)] | |
| logger.info(f"Downloading the dinov2 model with `{cmd}`.") | |
| subprocess.run(cmd, check=True) | |
| logger.info(f"Loading Roma model...") | |
| # load the model | |
| weights = torch.load(model_path, map_location="cpu") | |
| dinov2_weights = torch.load(dinov2_weights, map_location="cpu") | |
| self.net = roma_model( | |
| resolution=(14 * 8 * 6, 14 * 8 * 6), | |
| upsample_preds=False, | |
| weights=weights, | |
| dinov2_weights=dinov2_weights, | |
| device=device, | |
| ) | |
| logger.info(f"Load Roma 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 | |
| # Match | |
| warp, certainty = self.net.match(img0, img1, device=device) | |
| # Sample matches for estimation | |
| 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 | |