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from pathlib import Path
import argparse
from .utils import create_query_list_with_intrinsics, scale_sfm_images, evaluate
from ... import extract_features, match_features, pairs_from_covisibility
from ... import triangulation, localize_sfm, pairs_from_retrieval, logger
SCENES = [
"KingsCollege",
"OldHospital",
"ShopFacade",
"StMarysChurch",
"GreatCourt",
]
def run_scene(images, gt_dir, outputs, results, num_covis, num_loc):
ref_sfm_sift = gt_dir / "model_train"
test_list = gt_dir / "list_query.txt"
outputs.mkdir(exist_ok=True, parents=True)
ref_sfm = outputs / "sfm_superpoint+superglue"
ref_sfm_scaled = outputs / "sfm_sift_scaled"
query_list = outputs / "query_list_with_intrinsics.txt"
sfm_pairs = outputs / f"pairs-db-covis{num_covis}.txt"
loc_pairs = outputs / f"pairs-query-netvlad{num_loc}.txt"
feature_conf = {
"output": "feats-superpoint-n4096-r1024",
"model": {
"name": "superpoint",
"nms_radius": 3,
"max_keypoints": 4096,
},
"preprocessing": {
"grayscale": True,
"resize_max": 1024,
},
}
matcher_conf = match_features.confs["superglue"]
retrieval_conf = extract_features.confs["netvlad"]
create_query_list_with_intrinsics(
gt_dir / "empty_all",
query_list,
test_list,
ext=".txt",
image_dir=images,
)
with open(test_list, "r") as f:
query_seqs = {q.split("/")[0] for q in f.read().rstrip().split("\n")}
global_descriptors = extract_features.main(retrieval_conf, images, outputs)
pairs_from_retrieval.main(
global_descriptors,
loc_pairs,
num_loc,
db_model=ref_sfm_sift,
query_prefix=query_seqs,
)
features = extract_features.main(
feature_conf, images, outputs, as_half=True
)
pairs_from_covisibility.main(ref_sfm_sift, sfm_pairs, num_matched=num_covis)
sfm_matches = match_features.main(
matcher_conf, sfm_pairs, feature_conf["output"], outputs
)
scale_sfm_images(ref_sfm_sift, ref_sfm_scaled, images)
triangulation.main(
ref_sfm, ref_sfm_scaled, images, sfm_pairs, features, sfm_matches
)
loc_matches = match_features.main(
matcher_conf, loc_pairs, feature_conf["output"], outputs
)
localize_sfm.main(
ref_sfm,
query_list,
loc_pairs,
features,
loc_matches,
results,
covisibility_clustering=False,
prepend_camera_name=True,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--scenes", default=SCENES, choices=SCENES, nargs="+")
parser.add_argument("--overwrite", action="store_true")
parser.add_argument(
"--dataset",
type=Path,
default="datasets/cambridge",
help="Path to the dataset, default: %(default)s",
)
parser.add_argument(
"--outputs",
type=Path,
default="outputs/cambridge",
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=10,
help="Number of image pairs for loc, default: %(default)s",
)
args = parser.parse_args()
gt_dirs = args.dataset / "CambridgeLandmarks_Colmap_Retriangulated_1024px"
all_results = {}
for scene in args.scenes:
logger.info(f'Working on scene "{scene}".')
results = args.outputs / scene / "results.txt"
if args.overwrite or not results.exists():
run_scene(
args.dataset / scene,
gt_dirs / scene,
args.outputs / scene,
results,
args.num_covis,
args.num_loc,
)
all_results[scene] = results
for scene in args.scenes:
logger.info(f'Evaluate scene "{scene}".')
evaluate(
gt_dirs / scene / "empty_all",
all_results[scene],
gt_dirs / scene / "list_query.txt",
ext=".txt",
)
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