import argparse from typing import Union, Optional, Dict, List, Tuple from pathlib import Path import pprint from queue import Queue from threading import Thread from functools import partial from tqdm import tqdm import h5py import torch from . import matchers, logger from .utils.base_model import dynamic_load from .utils.parsers import names_to_pair, names_to_pair_old, parse_retrieval import numpy as np """ A set of standard configurations that can be directly selected from the command line using their name. Each is a dictionary with the following entries: - output: the name of the match file that will be generated. - model: the model configuration, as passed to a feature matcher. """ confs = { "superglue": { "output": "matches-superglue", "model": { "name": "superglue", "weights": "outdoor", "sinkhorn_iterations": 50, "match_threshold": 0.2, }, "preprocessing": { "grayscale": True, "resize_max": 1024, "dfactor": 8, "force_resize": False, }, }, "superglue-fast": { "output": "matches-superglue-it5", "model": { "name": "superglue", "weights": "outdoor", "sinkhorn_iterations": 5, "match_threshold": 0.2, }, }, "superpoint-lightglue": { "output": "matches-lightglue", "model": { "name": "lightglue", "match_threshold": 0.2, "width_confidence": 0.99, # for point pruning "depth_confidence": 0.95, # for early stopping, "features": "superpoint", "model_name": "superpoint_lightglue.pth", }, "preprocessing": { "grayscale": True, "resize_max": 1024, "dfactor": 8, "force_resize": False, }, }, "disk-lightglue": { "output": "matches-lightglue", "model": { "name": "lightglue", "match_threshold": 0.2, "width_confidence": 0.99, # for point pruning "depth_confidence": 0.95, # for early stopping, "features": "disk", "model_name": "disk_lightglue.pth", }, "preprocessing": { "grayscale": True, "resize_max": 1024, "dfactor": 8, "force_resize": False, }, }, "sgmnet": { "output": "matches-sgmnet", "model": { "name": "sgmnet", "seed_top_k": [256, 256], "seed_radius_coe": 0.01, "net_channels": 128, "layer_num": 9, "head": 4, "seedlayer": [0, 6], "use_mc_seeding": True, "use_score_encoding": False, "conf_bar": [1.11, 0.1], "sink_iter": [10, 100], "detach_iter": 1000000, "match_threshold": 0.2, }, "preprocessing": { "grayscale": True, "resize_max": 1024, "dfactor": 8, "force_resize": False, }, }, "NN-superpoint": { "output": "matches-NN-mutual-dist.7", "model": { "name": "nearest_neighbor", "do_mutual_check": True, "distance_threshold": 0.7, "match_threshold": 0.2, }, }, "NN-ratio": { "output": "matches-NN-mutual-ratio.8", "model": { "name": "nearest_neighbor", "do_mutual_check": True, "ratio_threshold": 0.8, "match_threshold": 0.2, }, }, "NN-mutual": { "output": "matches-NN-mutual", "model": { "name": "nearest_neighbor", "do_mutual_check": True, "match_threshold": 0.2, }, }, "Dual-Softmax": { "output": "matches-Dual-Softmax", "model": { "name": "dual_softmax", "match_threshold": 0.01, "inv_temperature": 20, }, }, "adalam": { "output": "matches-adalam", "model": { "name": "adalam", "match_threshold": 0.2, }, }, } class WorkQueue: def __init__(self, work_fn, num_threads=1): self.queue = Queue(num_threads) self.threads = [ Thread(target=self.thread_fn, args=(work_fn,)) for _ in range(num_threads) ] for thread in self.threads: thread.start() def join(self): for thread in self.threads: self.queue.put(None) for thread in self.threads: thread.join() def thread_fn(self, work_fn): item = self.queue.get() while item is not None: work_fn(item) item = self.queue.get() def put(self, data): self.queue.put(data) class FeaturePairsDataset(torch.utils.data.Dataset): def __init__(self, pairs, feature_path_q, feature_path_r): self.pairs = pairs self.feature_path_q = feature_path_q self.feature_path_r = feature_path_r def __getitem__(self, idx): name0, name1 = self.pairs[idx] data = {} with h5py.File(self.feature_path_q, "r") as fd: grp = fd[name0] for k, v in grp.items(): data[k + "0"] = torch.from_numpy(v.__array__()).float() # some matchers might expect an image but only use its size data["image0"] = torch.empty((1,) + tuple(grp["image_size"])[::-1]) with h5py.File(self.feature_path_r, "r") as fd: grp = fd[name1] for k, v in grp.items(): data[k + "1"] = torch.from_numpy(v.__array__()).float() data["image1"] = torch.empty((1,) + tuple(grp["image_size"])[::-1]) return data def __len__(self): return len(self.pairs) def writer_fn(inp, match_path): pair, pred = inp with h5py.File(str(match_path), "a", libver="latest") as fd: if pair in fd: del fd[pair] grp = fd.create_group(pair) matches = pred["matches0"][0].cpu().short().numpy() grp.create_dataset("matches0", data=matches) if "matching_scores0" in pred: scores = pred["matching_scores0"][0].cpu().half().numpy() grp.create_dataset("matching_scores0", data=scores) def main( conf: Dict, pairs: Path, features: Union[Path, str], export_dir: Optional[Path] = None, matches: Optional[Path] = None, features_ref: Optional[Path] = None, overwrite: bool = False, ) -> Path: if isinstance(features, Path) or Path(features).exists(): features_q = features if matches is None: raise ValueError( "Either provide both features and matches as Path" " or both as names." ) else: if export_dir is None: raise ValueError( "Provide an export_dir if features is not" f" a file path: {features}." ) features_q = Path(export_dir, features + ".h5") if matches is None: matches = Path( export_dir, f'{features}_{conf["output"]}_{pairs.stem}.h5' ) if features_ref is None: features_ref = features_q match_from_paths(conf, pairs, matches, features_q, features_ref, overwrite) return matches def find_unique_new_pairs(pairs_all: List[Tuple[str]], match_path: Path = None): """Avoid to recompute duplicates to save time.""" pairs = set() for i, j in pairs_all: if (j, i) not in pairs: pairs.add((i, j)) pairs = list(pairs) if match_path is not None and match_path.exists(): with h5py.File(str(match_path), "r", libver="latest") as fd: pairs_filtered = [] for i, j in pairs: if ( names_to_pair(i, j) in fd or names_to_pair(j, i) in fd or names_to_pair_old(i, j) in fd or names_to_pair_old(j, i) in fd ): continue pairs_filtered.append((i, j)) return pairs_filtered return pairs @torch.no_grad() def match_from_paths( conf: Dict, pairs_path: Path, match_path: Path, feature_path_q: Path, feature_path_ref: Path, overwrite: bool = False, ) -> Path: logger.info( "Matching local features with configuration:" f"\n{pprint.pformat(conf)}" ) if not feature_path_q.exists(): raise FileNotFoundError(f"Query feature file {feature_path_q}.") if not feature_path_ref.exists(): raise FileNotFoundError(f"Reference feature file {feature_path_ref}.") match_path.parent.mkdir(exist_ok=True, parents=True) assert pairs_path.exists(), pairs_path pairs = parse_retrieval(pairs_path) pairs = [(q, r) for q, rs in pairs.items() for r in rs] pairs = find_unique_new_pairs(pairs, None if overwrite else match_path) if len(pairs) == 0: logger.info("Skipping the matching.") return device = "cuda" if torch.cuda.is_available() else "cpu" Model = dynamic_load(matchers, conf["model"]["name"]) model = Model(conf["model"]).eval().to(device) dataset = FeaturePairsDataset(pairs, feature_path_q, feature_path_ref) loader = torch.utils.data.DataLoader( dataset, num_workers=5, batch_size=1, shuffle=False, pin_memory=True ) writer_queue = WorkQueue(partial(writer_fn, match_path=match_path), 5) for idx, data in enumerate(tqdm(loader, smoothing=0.1)): data = { k: v if k.startswith("image") else v.to(device, non_blocking=True) for k, v in data.items() } pred = model(data) pair = names_to_pair(*pairs[idx]) writer_queue.put((pair, pred)) writer_queue.join() logger.info("Finished exporting matches.") def scale_keypoints(kpts, scale): if np.any(scale != 1.0): kpts *= kpts.new_tensor(scale) return kpts @torch.no_grad() def match_images(model, feat0, feat1): # forward pass to match keypoints desc0 = feat0["descriptors"][0] desc1 = feat1["descriptors"][0] if len(desc0.shape) == 2: desc0 = desc0.unsqueeze(0) if len(desc1.shape) == 2: desc1 = desc1.unsqueeze(0) if isinstance(feat0["keypoints"], list): feat0["keypoints"] = feat0["keypoints"][0][None] if isinstance(feat1["keypoints"], list): feat1["keypoints"] = feat1["keypoints"][0][None] pred = model( { "image0": feat0["image"], "keypoints0": feat0["keypoints"], "scores0": feat0["scores"][0].unsqueeze(0), "descriptors0": desc0, "image1": feat1["image"], "keypoints1": feat1["keypoints"], "scores1": feat1["scores"][0].unsqueeze(0), "descriptors1": desc1, } ) pred = { k: v.cpu().detach()[0] if isinstance(v, torch.Tensor) else v for k, v in pred.items() } kpts0, kpts1 = ( feat0["keypoints"][0].cpu().numpy(), feat1["keypoints"][0].cpu().numpy(), ) matches, confid = pred["matches0"], pred["matching_scores0"] # Keep the matching keypoints. valid = matches > -1 mkpts0 = kpts0[valid] mkpts1 = kpts1[matches[valid]] mconfid = confid[valid] # rescale the keypoints to their original size s0 = feat0["original_size"] / feat0["size"] s1 = feat1["original_size"] / feat1["size"] kpts0_origin = scale_keypoints(torch.from_numpy(kpts0 + 0.5), s0) - 0.5 kpts1_origin = scale_keypoints(torch.from_numpy(kpts1 + 0.5), s1) - 0.5 mkpts0_origin = scale_keypoints(torch.from_numpy(mkpts0 + 0.5), s0) - 0.5 mkpts1_origin = scale_keypoints(torch.from_numpy(mkpts1 + 0.5), s1) - 0.5 ret = { "image0_orig": feat0["image_orig"], "image1_orig": feat1["image_orig"], "keypoints0": kpts0, "keypoints1": kpts1, "keypoints0_orig": kpts0_origin.numpy(), "keypoints1_orig": kpts1_origin.numpy(), "mkeypoints0": mkpts0, "mkeypoints1": mkpts1, "mkeypoints0_orig": mkpts0_origin.numpy(), "mkeypoints1_orig": mkpts1_origin.numpy(), "mconf": mconfid, } del feat0, feat1, desc0, desc1, kpts0, kpts1, kpts0_origin, kpts1_origin torch.cuda.empty_cache() return ret if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pairs", type=Path, required=True) parser.add_argument("--export_dir", type=Path) parser.add_argument( "--features", type=str, default="feats-superpoint-n4096-r1024" ) parser.add_argument("--matches", type=Path) parser.add_argument( "--conf", type=str, default="superglue", choices=list(confs.keys()) ) args = parser.parse_args() main(confs[args.conf], args.pairs, args.features, args.export_dir)