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from pathlib import Path
from pprint import pformat
import argparse
from ... import extract_features, match_features, triangulation
from ... import pairs_from_covisibility, pairs_from_retrieval, localize_sfm
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset",
type=Path,
default="datasets/aachen_v1.1",
help="Path to the dataset, default: %(default)s",
)
parser.add_argument(
"--outputs",
type=Path,
default="outputs/aachen_v1.1",
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/"
sift_sfm = dataset / "3D-models/aachen_v_1_1"
outputs = args.outputs # where everything will be saved
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-v1.1_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_max"]
matcher_conf = match_features.confs["superglue"]
features = extract_features.main(feature_conf, images, outputs)
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
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