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import argparse
import glob
from pathlib import Path
from ... import (
extract_features,
localize_sfm,
match_features,
pairs_from_covisibility,
pairs_from_retrieval,
triangulation,
)
from . import colmap_from_nvm
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 = glob.glob((image_dir / "**/*.jpg").as_posix(), recursive=True)
queries = [
Path(q).relative_to(image_dir.parents[0]).as_posix() for q in sorted(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)))
def run(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,
)
if __name__ == "__main__":
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()
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