from pathlib import Path import argparse from ... import extract_features, match_features, triangulation, logger from ... import pairs_from_covisibility, pairs_from_retrieval, localize_sfm TEST_SLICES = [2, 3, 4, 5, 6, 13, 14, 15, 16, 17, 18, 19, 20, 21] def generate_query_list(dataset, path, slice_): cameras = {} with open(dataset / "intrinsics.txt", "r") as f: for line in f.readlines(): if line[0] == "#" or line == "\n": continue data = line.split() cameras[data[0]] = data[1:] assert len(cameras) == 2 queries = dataset / f"{slice_}/test-images-{slice_}.txt" with open(queries, "r") as f: queries = [q.rstrip("\n") for q in f.readlines()] out = [[q] + cameras[q.split("_")[2]] for q in queries] with open(path, "w") as f: f.write("\n".join(map(" ".join, out))) def run_slice(slice_, root, outputs, num_covis, num_loc): dataset = root / slice_ ref_images = dataset / "database" query_images = dataset / "query" sift_sfm = dataset / "sparse" outputs = outputs / slice_ outputs.mkdir(exist_ok=True, parents=True) query_list = dataset / "queries_with_intrinsics.txt" sfm_pairs = outputs / f"pairs-db-covis{num_covis}.txt" loc_pairs = outputs / f"pairs-query-netvlad{num_loc}.txt" ref_sfm = outputs / "sfm_superpoint+superglue" results = outputs / f"CMU_hloc_superpoint+superglue_netvlad{num_loc}.txt" # pick one of the configurations for extraction and matching retrieval_conf = extract_features.confs["netvlad"] feature_conf = extract_features.confs["superpoint_aachen"] matcher_conf = match_features.confs["superglue"] pairs_from_covisibility.main(sift_sfm, sfm_pairs, num_matched=num_covis) features = extract_features.main(feature_conf, ref_images, outputs, as_half=True) sfm_matches = match_features.main( matcher_conf, sfm_pairs, feature_conf["output"], outputs ) triangulation.main(ref_sfm, sift_sfm, ref_images, sfm_pairs, features, sfm_matches) generate_query_list(root, query_list, slice_) global_descriptors = extract_features.main(retrieval_conf, ref_images, outputs) global_descriptors = extract_features.main(retrieval_conf, query_images, outputs) pairs_from_retrieval.main( global_descriptors, loc_pairs, num_loc, query_list=query_list, db_model=ref_sfm ) features = extract_features.main(feature_conf, query_images, outputs, as_half=True) loc_matches = match_features.main( matcher_conf, loc_pairs, feature_conf["output"], outputs ) localize_sfm.main( ref_sfm, dataset / "queries/*_time_queries_with_intrinsics.txt", loc_pairs, features, loc_matches, results, ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--slices", type=str, default="*", help="a single number, an interval (e.g. 2-6), " "or a Python-style list or int (e.g. [2, 3, 4]", ) parser.add_argument( "--dataset", type=Path, default="datasets/cmu_extended", help="Path to the dataset, default: %(default)s", ) parser.add_argument( "--outputs", type=Path, default="outputs/aachen_extended", help="Path to the output directory, default: %(default)s", ) parser.add_argument( "--num_covis", type=int, default=20, help="Number of image pairs for SfM, default: %(default)s", ) parser.add_argument( "--num_loc", type=int, default=10, help="Number of image pairs for loc, default: %(default)s", ) args = parser.parse_args() if args.slice == "*": slices = TEST_SLICES if "-" in args.slices: min_, max_ = args.slices.split("-") slices = list(range(int(min_), int(max_) + 1)) else: slices = eval(args.slices) if isinstance(slices, int): slices = [slices] for slice_ in slices: logger.info("Working on slice %s.", slice_) run_slice( f"slice{slice_}", args.dataset, args.outputs, args.num_covis, args.num_loc )