Spaces:
Sleeping
Sleeping
from pathlib import Path | |
from pprint import pformat | |
import argparse | |
from ... import extract_features, match_features | |
from ... import pairs_from_covisibility, pairs_from_retrieval | |
from ... import colmap_from_nvm, triangulation, localize_sfm | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"--dataset", | |
type=Path, | |
default="datasets/aachen", | |
help="Path to the dataset, default: %(default)s", | |
) | |
parser.add_argument( | |
"--outputs", | |
type=Path, | |
default="outputs/aachen", | |
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=50, | |
help="Number of image pairs for loc, default: %(default)s", | |
) | |
args = parser.parse_args() | |
# Setup the paths | |
dataset = args.dataset | |
images = dataset / "images/images_upright/" | |
outputs = args.outputs # where everything will be saved | |
sift_sfm = outputs / "sfm_sift" # from which we extract the reference poses | |
reference_sfm = ( | |
outputs / "sfm_superpoint+superglue" | |
) # the SfM model we will build | |
sfm_pairs = ( | |
outputs / f"pairs-db-covis{args.num_covis}.txt" | |
) # top-k most covisible in SIFT model | |
loc_pairs = ( | |
outputs / f"pairs-query-netvlad{args.num_loc}.txt" | |
) # top-k retrieved by NetVLAD | |
results = ( | |
outputs / f"Aachen_hloc_superpoint+superglue_netvlad{args.num_loc}.txt" | |
) | |
# list the standard configurations available | |
print(f"Configs for feature extractors:\n{pformat(extract_features.confs)}") | |
print(f"Configs for feature matchers:\n{pformat(match_features.confs)}") | |
# 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"] | |
features = extract_features.main(feature_conf, images, outputs) | |
colmap_from_nvm.main( | |
dataset / "3D-models/aachen_cvpr2018_db.nvm", | |
dataset / "3D-models/database_intrinsics.txt", | |
dataset / "aachen.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) | |
pairs_from_retrieval.main( | |
global_descriptors, | |
loc_pairs, | |
args.num_loc, | |
query_prefix="query", | |
db_model=reference_sfm, | |
) | |
loc_matches = match_features.main( | |
matcher_conf, loc_pairs, feature_conf["output"], outputs | |
) | |
localize_sfm.main( | |
reference_sfm, | |
dataset / "queries/*_time_queries_with_intrinsics.txt", | |
loc_pairs, | |
features, | |
loc_matches, | |
results, | |
covisibility_clustering=False, | |
) # not required with SuperPoint+SuperGlue | |