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| import os | |
| import sys | |
| import torch | |
| from pathlib import Path | |
| import torchvision.transforms as tfm | |
| import torch.nn.functional as F | |
| import urllib.request | |
| import numpy as np | |
| from ..utils.base_model import BaseModel | |
| from .. import logger | |
| duster_path = Path(__file__).parent / "../../third_party/dust3r" | |
| sys.path.append(str(duster_path)) | |
| from dust3r.inference import inference | |
| from dust3r.model import load_model, AsymmetricCroCo3DStereo | |
| from dust3r.image_pairs import make_pairs | |
| from dust3r.cloud_opt import global_aligner, GlobalAlignerMode | |
| from dust3r.utils.geometry import find_reciprocal_matches, xy_grid | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class Duster(BaseModel): | |
| default_conf = { | |
| "name": "Duster3r", | |
| "model_path": duster_path / "model_weights/duster_vit_large.pth", | |
| "max_keypoints": 3000, | |
| "vit_patch_size": 16, | |
| } | |
| def _init(self, conf): | |
| self.normalize = tfm.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) | |
| self.model_path = self.conf["model_path"] | |
| self.download_weights() | |
| # self.net = load_model(self.model_path, device) | |
| self.net = AsymmetricCroCo3DStereo.from_pretrained( | |
| self.model_path | |
| # "naver/DUSt3R_ViTLarge_BaseDecoder_512_dpt" | |
| ).to(device) | |
| logger.info(f"Loaded Dust3r model") | |
| def download_weights(self): | |
| url = "https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth" | |
| self.model_path.parent.mkdir(parents=True, exist_ok=True) | |
| if not os.path.isfile(self.model_path): | |
| logger.info("Downloading Duster(ViT large)... (takes a while)") | |
| urllib.request.urlretrieve(url, self.model_path) | |
| def preprocess(self, img): | |
| # the super-class already makes sure that img0,img1 have | |
| # same resolution and that h == w | |
| _, h, _ = img.shape | |
| imsize = h | |
| if not ((h % self.vit_patch_size) == 0): | |
| imsize = int( | |
| self.vit_patch_size * round(h / self.vit_patch_size, 0) | |
| ) | |
| img = tfm.functional.resize(img, imsize, antialias=True) | |
| _, new_h, new_w = img.shape | |
| if not ((new_w % self.vit_patch_size) == 0): | |
| safe_w = int( | |
| self.vit_patch_size * round(new_w / self.vit_patch_size, 0) | |
| ) | |
| img = tfm.functional.resize(img, (new_h, safe_w), antialias=True) | |
| img = self.normalize(img).unsqueeze(0) | |
| return img | |
| def _forward(self, data): | |
| img0, img1 = data["image0"], data["image1"] | |
| mean = torch.tensor([0.5, 0.5, 0.5]).to(device) | |
| std = torch.tensor([0.5, 0.5, 0.5]).to(device) | |
| img0 = (img0 - mean.view(1, 3, 1, 1)) / std.view(1, 3, 1, 1) | |
| img1 = (img1 - mean.view(1, 3, 1, 1)) / std.view(1, 3, 1, 1) | |
| images = [ | |
| {"img": img0, "idx": 0, "instance": 0}, | |
| {"img": img1, "idx": 1, "instance": 1}, | |
| ] | |
| pairs = make_pairs( | |
| images, scene_graph="complete", prefilter=None, symmetrize=True | |
| ) | |
| output = inference(pairs, self.net, device, batch_size=1) | |
| scene = global_aligner( | |
| output, device=device, mode=GlobalAlignerMode.PairViewer | |
| ) | |
| # retrieve useful values from scene: | |
| imgs = scene.imgs | |
| confidence_masks = scene.get_masks() | |
| pts3d = scene.get_pts3d() | |
| pts2d_list, pts3d_list = [], [] | |
| for i in range(2): | |
| conf_i = confidence_masks[i].cpu().numpy() | |
| pts2d_list.append( | |
| xy_grid(*imgs[i].shape[:2][::-1])[conf_i] | |
| ) # imgs[i].shape[:2] = (H, W) | |
| pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i]) | |
| if len(pts3d_list[1]) == 0: | |
| pred = { | |
| "keypoints0": torch.zeros([0, 2]), | |
| "keypoints1": torch.zeros([0, 2]), | |
| } | |
| logger.warning(f"Matched {0} points") | |
| else: | |
| reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches( | |
| *pts3d_list | |
| ) | |
| logger.info(f"Found {num_matches} matches") | |
| mkpts1 = pts2d_list[1][reciprocal_in_P2] | |
| mkpts0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2] | |
| top_k = self.conf["max_keypoints"] | |
| if top_k is not None and len(mkpts0) > top_k: | |
| keep = np.round(np.linspace(0, len(mkpts0) - 1, top_k)).astype( | |
| int | |
| ) | |
| mkpts0 = mkpts0[keep] | |
| mkpts1 = mkpts1[keep] | |
| pred = { | |
| "keypoints0": torch.from_numpy(mkpts0), | |
| "keypoints1": torch.from_numpy(mkpts1), | |
| } | |
| return pred | |