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from pathlib import Path | |
import argparse | |
from ... import extract_features, match_features | |
from ... import pairs_from_poses, triangulation | |
from .utils import get_timestamps, delete_unused_images | |
from .utils import build_empty_colmap_model | |
parser = argparse.ArgumentParser() | |
parser.add_argument( | |
"--dataset", | |
type=Path, | |
default="datasets/4Seasons", | |
help="Path to the dataset, default: %(default)s", | |
) | |
parser.add_argument( | |
"--outputs", | |
type=Path, | |
default="outputs/4Seasons", | |
help="Path to the output directory, default: %(default)s", | |
) | |
args = parser.parse_args() | |
ref_dir = args.dataset / "reference" | |
assert ref_dir.exists(), f"{ref_dir} does not exist" | |
ref_images = ref_dir / "undistorted_images" | |
output_dir = args.outputs | |
output_dir.mkdir(exist_ok=True, parents=True) | |
ref_sfm_empty = output_dir / "sfm_reference_empty" | |
ref_sfm = output_dir / "sfm_superpoint+superglue" | |
num_ref_pairs = 20 | |
ref_pairs = output_dir / f"pairs-db-dist{num_ref_pairs}.txt" | |
fconf = extract_features.confs["superpoint_max"] | |
mconf = match_features.confs["superglue"] | |
# Only reference images that have a pose are used in the pipeline. | |
# To save time in feature extraction, we delete unsused images. | |
delete_unused_images(ref_images, get_timestamps(ref_dir / "poses.txt", 0)) | |
# Build an empty COLMAP model containing only camera and images | |
# from the provided poses and intrinsics. | |
build_empty_colmap_model(ref_dir, ref_sfm_empty) | |
# Match reference images that are spatially close. | |
pairs_from_poses.main(ref_sfm_empty, ref_pairs, num_ref_pairs) | |
# Extract, match, and triangulate the reference SfM model. | |
ffile = extract_features.main(fconf, ref_images, output_dir) | |
mfile = match_features.main(mconf, ref_pairs, fconf["output"], output_dir) | |
triangulation.main(ref_sfm, ref_sfm_empty, ref_images, ref_pairs, ffile, mfile) | |