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from pathlib import Path |
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import argparse |
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from . import colmap_from_nvm |
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from ... import extract_features, match_features, triangulation |
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from ... import pairs_from_covisibility, pairs_from_retrieval, localize_sfm |
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CONDITIONS = [ |
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"dawn", |
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"dusk", |
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"night", |
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"night-rain", |
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"overcast-summer", |
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"overcast-winter", |
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"rain", |
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"snow", |
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"sun", |
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] |
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def generate_query_list(dataset, image_dir, path): |
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h, w = 1024, 1024 |
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intrinsics_filename = "intrinsics/{}_intrinsics.txt" |
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cameras = {} |
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for side in ["left", "right", "rear"]: |
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with open(dataset / intrinsics_filename.format(side), "r") as f: |
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fx = f.readline().split()[1] |
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fy = f.readline().split()[1] |
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cx = f.readline().split()[1] |
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cy = f.readline().split()[1] |
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assert fx == fy |
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params = ["SIMPLE_RADIAL", w, h, fx, cx, cy, 0.0] |
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cameras[side] = [str(p) for p in params] |
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queries = sorted(image_dir.glob("**/*.jpg")) |
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queries = [str(q.relative_to(image_dir.parents[0])) for q in queries] |
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out = [[q] + cameras[Path(q).parent.name] for q in queries] |
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with open(path, "w") as f: |
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f.write("\n".join(map(" ".join, out))) |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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"--dataset", |
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type=Path, |
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default="datasets/robotcar", |
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help="Path to the dataset, default: %(default)s", |
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) |
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parser.add_argument( |
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"--outputs", |
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type=Path, |
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default="outputs/robotcar", |
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help="Path to the output directory, default: %(default)s", |
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) |
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parser.add_argument( |
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"--num_covis", |
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type=int, |
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default=20, |
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help="Number of image pairs for SfM, default: %(default)s", |
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) |
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parser.add_argument( |
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"--num_loc", |
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type=int, |
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default=20, |
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help="Number of image pairs for loc, default: %(default)s", |
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) |
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args = parser.parse_args() |
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dataset = args.dataset |
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images = dataset / "images/" |
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outputs = args.outputs |
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outputs.mkdir(exist_ok=True, parents=True) |
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query_list = outputs / "{condition}_queries_with_intrinsics.txt" |
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sift_sfm = outputs / "sfm_sift" |
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reference_sfm = outputs / "sfm_superpoint+superglue" |
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sfm_pairs = outputs / f"pairs-db-covis{args.num_covis}.txt" |
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loc_pairs = outputs / f"pairs-query-netvlad{args.num_loc}.txt" |
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results = outputs / f"RobotCar_hloc_superpoint+superglue_netvlad{args.num_loc}.txt" |
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retrieval_conf = extract_features.confs["netvlad"] |
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feature_conf = extract_features.confs["superpoint_aachen"] |
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matcher_conf = match_features.confs["superglue"] |
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for condition in CONDITIONS: |
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generate_query_list( |
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dataset, images / condition, str(query_list).format(condition=condition) |
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) |
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features = extract_features.main(feature_conf, images, outputs, as_half=True) |
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colmap_from_nvm.main( |
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dataset / "3D-models/all-merged/all.nvm", |
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dataset / "3D-models/overcast-reference.db", |
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sift_sfm, |
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) |
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pairs_from_covisibility.main(sift_sfm, sfm_pairs, num_matched=args.num_covis) |
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sfm_matches = match_features.main( |
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matcher_conf, sfm_pairs, feature_conf["output"], outputs |
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) |
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triangulation.main(reference_sfm, sift_sfm, images, sfm_pairs, features, sfm_matches) |
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global_descriptors = extract_features.main(retrieval_conf, images, outputs) |
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pairs_from_retrieval.main( |
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global_descriptors, |
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loc_pairs, |
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args.num_loc, |
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query_prefix=CONDITIONS, |
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db_model=reference_sfm, |
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) |
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loc_matches = match_features.main( |
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matcher_conf, loc_pairs, feature_conf["output"], outputs |
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) |
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localize_sfm.main( |
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reference_sfm, |
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Path(str(query_list).format(condition="*")), |
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loc_pairs, |
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features, |
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loc_matches, |
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results, |
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covisibility_clustering=False, |
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prepend_camera_name=True, |
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) |
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