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import argparse
from pathlib import Path
import numpy as np
import scipy.spatial
from . import logger
from .pairs_from_retrieval import pairs_from_score_matrix
from .utils.read_write_model import read_images_binary
DEFAULT_ROT_THRESH = 30 # in degrees
def get_pairwise_distances(images):
ids = np.array(list(images.keys()))
Rs = []
ts = []
for id_ in ids:
image = images[id_]
R = image.qvec2rotmat()
t = image.tvec
Rs.append(R)
ts.append(t)
Rs = np.stack(Rs, 0)
ts = np.stack(ts, 0)
# Invert the poses from world-to-camera to camera-to-world.
Rs = Rs.transpose(0, 2, 1)
ts = -(Rs @ ts[:, :, None])[:, :, 0]
dist = scipy.spatial.distance.squareform(scipy.spatial.distance.pdist(ts))
# Instead of computing the angle between two camera orientations,
# we compute the angle between the principal axes, as two images rotated
# around their principal axis still observe the same scene.
axes = Rs[:, :, -1]
dots = np.einsum("mi,ni->mn", axes, axes, optimize=True)
dR = np.rad2deg(np.arccos(np.clip(dots, -1.0, 1.0)))
return ids, dist, dR
def main(model, output, num_matched, rotation_threshold=DEFAULT_ROT_THRESH):
logger.info("Reading the COLMAP model...")
images = read_images_binary(model / "images.bin")
logger.info(f"Obtaining pairwise distances between {len(images)} images...")
ids, dist, dR = get_pairwise_distances(images)
scores = -dist
invalid = dR >= rotation_threshold
np.fill_diagonal(invalid, True)
pairs = pairs_from_score_matrix(scores, invalid, num_matched)
pairs = [(images[ids[i]].name, images[ids[j]].name) for i, j in pairs]
logger.info(f"Found {len(pairs)} pairs.")
with open(output, "w") as f:
f.write("\n".join(" ".join(p) for p in pairs))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True, type=Path)
parser.add_argument("--output", required=True, type=Path)
parser.add_argument("--num_matched", required=True, type=int)
parser.add_argument(
"--rotation_threshold", default=DEFAULT_ROT_THRESH, type=float
)
args = parser.parse_args()
main(**args.__dict__)
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