from pathlib import Path import argparse from . import colmap_from_nvm from ... import extract_features, match_features, triangulation from ... import pairs_from_covisibility, pairs_from_retrieval, localize_sfm CONDITIONS = [ "dawn", "dusk", "night", "night-rain", "overcast-summer", "overcast-winter", "rain", "snow", "sun", ] def generate_query_list(dataset, image_dir, path): h, w = 1024, 1024 intrinsics_filename = "intrinsics/{}_intrinsics.txt" cameras = {} for side in ["left", "right", "rear"]: with open(dataset / intrinsics_filename.format(side), "r") as f: fx = f.readline().split()[1] fy = f.readline().split()[1] cx = f.readline().split()[1] cy = f.readline().split()[1] assert fx == fy params = ["SIMPLE_RADIAL", w, h, fx, cx, cy, 0.0] cameras[side] = [str(p) for p in params] queries = sorted(image_dir.glob("**/*.jpg")) queries = [str(q.relative_to(image_dir.parents[0])) for q in queries] out = [[q] + cameras[Path(q).parent.name] for q in queries] with open(path, "w") as f: f.write("\n".join(map(" ".join, out))) parser = argparse.ArgumentParser() parser.add_argument( "--dataset", type=Path, default="datasets/robotcar", help="Path to the dataset, default: %(default)s", ) parser.add_argument( "--outputs", type=Path, default="outputs/robotcar", 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=20, help="Number of image pairs for loc, default: %(default)s", ) args = parser.parse_args() # Setup the paths dataset = args.dataset images = dataset / "images/" outputs = args.outputs # where everything will be saved outputs.mkdir(exist_ok=True, parents=True) query_list = outputs / "{condition}_queries_with_intrinsics.txt" sift_sfm = outputs / "sfm_sift" reference_sfm = outputs / "sfm_superpoint+superglue" sfm_pairs = outputs / f"pairs-db-covis{args.num_covis}.txt" loc_pairs = outputs / f"pairs-query-netvlad{args.num_loc}.txt" results = outputs / f"RobotCar_hloc_superpoint+superglue_netvlad{args.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"] for condition in CONDITIONS: generate_query_list( dataset, images / condition, str(query_list).format(condition=condition) ) features = extract_features.main(feature_conf, images, outputs, as_half=True) colmap_from_nvm.main( dataset / "3D-models/all-merged/all.nvm", dataset / "3D-models/overcast-reference.db", sift_sfm, ) pairs_from_covisibility.main(sift_sfm, sfm_pairs, num_matched=args.num_covis) sfm_matches = match_features.main( matcher_conf, sfm_pairs, feature_conf["output"], outputs ) triangulation.main(reference_sfm, sift_sfm, images, sfm_pairs, features, sfm_matches) global_descriptors = extract_features.main(retrieval_conf, images, outputs) # TODO: do per location and per camera pairs_from_retrieval.main( global_descriptors, loc_pairs, args.num_loc, query_prefix=CONDITIONS, db_model=reference_sfm, ) loc_matches = match_features.main( matcher_conf, loc_pairs, feature_conf["output"], outputs ) localize_sfm.main( reference_sfm, Path(str(query_list).format(condition="*")), loc_pairs, features, loc_matches, results, covisibility_clustering=False, prepend_camera_name=True, )