Spaces:
Running
Running
File size: 3,771 Bytes
9223079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
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,
)
|