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import os
import numpy as np
import logging
from pathlib import Path
from ...utils.read_write_model import qvec2rotmat, rotmat2qvec
from ...utils.read_write_model import Image, write_model, Camera
from ...utils.parsers import parse_retrieval
logger = logging.getLogger(__name__)
def get_timestamps(files, idx):
"""Extract timestamps from a pose or relocalization file."""
lines = []
for p in files.parent.glob(files.name):
with open(p) as f:
lines += f.readlines()
timestamps = set()
for line in lines:
line = line.rstrip("\n")
if line[0] == "#" or line == "":
continue
ts = line.replace(",", " ").split()[idx]
timestamps.add(ts)
return timestamps
def delete_unused_images(root, timestamps):
"""Delete all images in root if they are not contained in timestamps."""
images = list(root.glob("**/*.png"))
deleted = 0
for image in images:
ts = image.stem
if ts not in timestamps:
os.remove(image)
deleted += 1
logger.info(f"Deleted {deleted} images in {root}.")
def camera_from_calibration_file(id_, path):
"""Create a COLMAP camera from an MLAD calibration file."""
with open(path, "r") as f:
data = f.readlines()
model, fx, fy, cx, cy = data[0].split()[:5]
width, height = data[1].split()
assert model == "Pinhole"
model_name = "PINHOLE"
params = [float(i) for i in [fx, fy, cx, cy]]
camera = Camera(
id=id_,
model=model_name,
width=int(width),
height=int(height),
params=params,
)
return camera
def parse_poses(path, colmap=False):
"""Parse a list of poses in COLMAP or MLAD quaternion convention."""
poses = []
with open(path) as f:
for line in f.readlines():
line = line.rstrip("\n")
if line[0] == "#" or line == "":
continue
data = line.replace(",", " ").split()
ts, p = data[0], np.array(data[1:], float)
if colmap:
q, t = np.split(p, [4])
else:
t, q = np.split(p, [3])
q = q[[3, 0, 1, 2]] # xyzw to wxyz
R = qvec2rotmat(q)
poses.append((ts, R, t))
return poses
def parse_relocalization(path, has_poses=False):
"""Parse a relocalization file, possibly with poses."""
reloc = []
with open(path) as f:
for line in f.readlines():
line = line.rstrip("\n")
if line[0] == "#" or line == "":
continue
data = line.replace(",", " ").split()
out = data[:2] # ref_ts, q_ts
if has_poses:
assert len(data) == 9
t, q = np.split(np.array(data[2:], float), [3])
q = q[[3, 0, 1, 2]] # xyzw to wxyz
R = qvec2rotmat(q)
out += [R, t]
reloc.append(out)
return reloc
def build_empty_colmap_model(root, sfm_dir):
"""Build a COLMAP model with images and cameras only."""
calibration = "Calibration/undistorted_calib_{}.txt"
cam0 = camera_from_calibration_file(0, root / calibration.format(0))
cam1 = camera_from_calibration_file(1, root / calibration.format(1))
cameras = {0: cam0, 1: cam1}
T_0to1 = np.loadtxt(root / "Calibration/undistorted_calib_stereo.txt")
poses = parse_poses(root / "poses.txt")
images = {}
id_ = 0
for ts, R_cam0_to_w, t_cam0_to_w in poses:
R_w_to_cam0 = R_cam0_to_w.T
t_w_to_cam0 = -(R_w_to_cam0 @ t_cam0_to_w)
R_w_to_cam1 = T_0to1[:3, :3] @ R_w_to_cam0
t_w_to_cam1 = T_0to1[:3, :3] @ t_w_to_cam0 + T_0to1[:3, 3]
for idx, (R_w_to_cam, t_w_to_cam) in enumerate(
zip([R_w_to_cam0, R_w_to_cam1], [t_w_to_cam0, t_w_to_cam1])
):
image = Image(
id=id_,
qvec=rotmat2qvec(R_w_to_cam),
tvec=t_w_to_cam,
camera_id=idx,
name=f"cam{idx}/{ts}.png",
xys=np.zeros((0, 2), float),
point3D_ids=np.full(0, -1, int),
)
images[id_] = image
id_ += 1
sfm_dir.mkdir(exist_ok=True, parents=True)
write_model(cameras, images, {}, path=str(sfm_dir), ext=".bin")
def generate_query_lists(timestamps, seq_dir, out_path):
"""Create a list of query images with intrinsics from timestamps."""
cam0 = camera_from_calibration_file(
0, seq_dir / "Calibration/undistorted_calib_0.txt"
)
intrinsics = [cam0.model, cam0.width, cam0.height] + cam0.params
intrinsics = [str(p) for p in intrinsics]
data = map(lambda ts: " ".join([f"cam0/{ts}.png"] + intrinsics), timestamps)
with open(out_path, "w") as f:
f.write("\n".join(data))
def generate_localization_pairs(sequence, reloc, num, ref_pairs, out_path):
"""Create the matching pairs for the localization.
We simply lookup the corresponding reference frame
and extract its `num` closest frames from the existing pair list.
"""
if "test" in sequence:
# hard pairs will be overwritten by easy ones if available
relocs = [
str(reloc).replace("*", d) for d in ["hard", "moderate", "easy"]
]
else:
relocs = [reloc]
query_to_ref_ts = {}
for reloc in relocs:
with open(reloc, "r") as f:
for line in f.readlines():
line = line.rstrip("\n")
if line[0] == "#" or line == "":
continue
ref_ts, q_ts = line.split()[:2]
query_to_ref_ts[q_ts] = ref_ts
ts_to_name = "cam0/{}.png".format
ref_pairs = parse_retrieval(ref_pairs)
loc_pairs = []
for q_ts, ref_ts in query_to_ref_ts.items():
ref_name = ts_to_name(ref_ts)
selected = [ref_name] + ref_pairs[ref_name][: num - 1]
loc_pairs.extend([" ".join((ts_to_name(q_ts), s)) for s in selected])
with open(out_path, "w") as f:
f.write("\n".join(loc_pairs))
def prepare_submission(results, relocs, poses_path, out_dir):
"""Obtain relative poses from estimated absolute and reference poses."""
gt_poses = parse_poses(poses_path)
all_T_ref0_to_w = {ts: (R, t) for ts, R, t in gt_poses}
pred_poses = parse_poses(results, colmap=True)
all_T_w_to_q0 = {Path(name).stem: (R, t) for name, R, t in pred_poses}
for reloc in relocs.parent.glob(relocs.name):
relative_poses = []
reloc_ts = parse_relocalization(reloc)
for ref_ts, q_ts in reloc_ts:
R_w_to_q0, t_w_to_q0 = all_T_w_to_q0[q_ts]
R_ref0_to_w, t_ref0_to_w = all_T_ref0_to_w[ref_ts]
R_ref0_to_q0 = R_w_to_q0 @ R_ref0_to_w
t_ref0_to_q0 = R_w_to_q0 @ t_ref0_to_w + t_w_to_q0
tvec = t_ref0_to_q0.tolist()
qvec = rotmat2qvec(R_ref0_to_q0)[[1, 2, 3, 0]] # wxyz to xyzw
out = [ref_ts, q_ts] + list(map(str, tvec)) + list(map(str, qvec))
relative_poses.append(" ".join(out))
out_path = out_dir / reloc.name
with open(out_path, "w") as f:
f.write("\n".join(relative_poses))
logger.info(f"Submission file written to {out_path}.")
def evaluate_submission(submission_dir, relocs, ths=[0.1, 0.2, 0.5]):
"""Compute the relocalization recall from predicted and ground truth poses."""
for reloc in relocs.parent.glob(relocs.name):
poses_gt = parse_relocalization(reloc, has_poses=True)
poses_pred = parse_relocalization(
submission_dir / reloc.name, has_poses=True
)
poses_pred = {
(ref_ts, q_ts): (R, t) for ref_ts, q_ts, R, t in poses_pred
}
error = []
for ref_ts, q_ts, R_gt, t_gt in poses_gt:
R, t = poses_pred[(ref_ts, q_ts)]
e = np.linalg.norm(t - t_gt)
error.append(e)
error = np.array(error)
recall = [np.mean(error <= th) for th in ths]
s = f"Relocalization evaluation {submission_dir.name}/{reloc.name}\n"
s += " / ".join([f"{th:>7}m" for th in ths]) + "\n"
s += " / ".join([f"{100*r:>7.3f}%" for r in recall])
logger.info(s)
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