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import argparse
import pickle
from pathlib import Path
import cv2
import h5py
import numpy as np
import pycolmap
import torch
from scipy.io import loadmat
from tqdm import tqdm
from . import logger
from .utils.parsers import names_to_pair, parse_retrieval
def interpolate_scan(scan, kp):
h, w, c = scan.shape
kp = kp / np.array([[w - 1, h - 1]]) * 2 - 1
assert np.all(kp > -1) and np.all(kp < 1)
scan = torch.from_numpy(scan).permute(2, 0, 1)[None]
kp = torch.from_numpy(kp)[None, None]
grid_sample = torch.nn.functional.grid_sample
# To maximize the number of points that have depth:
# do bilinear interpolation first and then nearest for the remaining points
interp_lin = grid_sample(scan, kp, align_corners=True, mode="bilinear")[
0, :, 0
]
interp_nn = torch.nn.functional.grid_sample(
scan, kp, align_corners=True, mode="nearest"
)[0, :, 0]
interp = torch.where(torch.isnan(interp_lin), interp_nn, interp_lin)
valid = ~torch.any(torch.isnan(interp), 0)
kp3d = interp.T.numpy()
valid = valid.numpy()
return kp3d, valid
def get_scan_pose(dataset_dir, rpath):
split_image_rpath = rpath.split("/")
floor_name = split_image_rpath[-3]
scan_id = split_image_rpath[-2]
image_name = split_image_rpath[-1]
building_name = image_name[:3]
path = Path(
dataset_dir,
"database/alignments",
floor_name,
f"transformations/{building_name}_trans_{scan_id}.txt",
)
with open(path) as f:
raw_lines = f.readlines()
P_after_GICP = np.array(
[
np.fromstring(raw_lines[7], sep=" "),
np.fromstring(raw_lines[8], sep=" "),
np.fromstring(raw_lines[9], sep=" "),
np.fromstring(raw_lines[10], sep=" "),
]
)
return P_after_GICP
def pose_from_cluster(
dataset_dir, q, retrieved, feature_file, match_file, skip=None
):
height, width = cv2.imread(str(dataset_dir / q)).shape[:2]
cx = 0.5 * width
cy = 0.5 * height
focal_length = 4032.0 * 28.0 / 36.0
all_mkpq = []
all_mkpr = []
all_mkp3d = []
all_indices = []
kpq = feature_file[q]["keypoints"].__array__()
num_matches = 0
for i, r in enumerate(retrieved):
kpr = feature_file[r]["keypoints"].__array__()
pair = names_to_pair(q, r)
m = match_file[pair]["matches0"].__array__()
v = m > -1
if skip and (np.count_nonzero(v) < skip):
continue
mkpq, mkpr = kpq[v], kpr[m[v]]
num_matches += len(mkpq)
scan_r = loadmat(Path(dataset_dir, r + ".mat"))["XYZcut"]
mkp3d, valid = interpolate_scan(scan_r, mkpr)
Tr = get_scan_pose(dataset_dir, r)
mkp3d = (Tr[:3, :3] @ mkp3d.T + Tr[:3, -1:]).T
all_mkpq.append(mkpq[valid])
all_mkpr.append(mkpr[valid])
all_mkp3d.append(mkp3d[valid])
all_indices.append(np.full(np.count_nonzero(valid), i))
all_mkpq = np.concatenate(all_mkpq, 0)
all_mkpr = np.concatenate(all_mkpr, 0)
all_mkp3d = np.concatenate(all_mkp3d, 0)
all_indices = np.concatenate(all_indices, 0)
cfg = {
"model": "SIMPLE_PINHOLE",
"width": width,
"height": height,
"params": [focal_length, cx, cy],
}
ret = pycolmap.absolute_pose_estimation(all_mkpq, all_mkp3d, cfg, 48.00)
ret["cfg"] = cfg
return ret, all_mkpq, all_mkpr, all_mkp3d, all_indices, num_matches
def main(dataset_dir, retrieval, features, matches, results, skip_matches=None):
assert retrieval.exists(), retrieval
assert features.exists(), features
assert matches.exists(), matches
retrieval_dict = parse_retrieval(retrieval)
queries = list(retrieval_dict.keys())
feature_file = h5py.File(features, "r", libver="latest")
match_file = h5py.File(matches, "r", libver="latest")
poses = {}
logs = {
"features": features,
"matches": matches,
"retrieval": retrieval,
"loc": {},
}
logger.info("Starting localization...")
for q in tqdm(queries):
db = retrieval_dict[q]
ret, mkpq, mkpr, mkp3d, indices, num_matches = pose_from_cluster(
dataset_dir, q, db, feature_file, match_file, skip_matches
)
poses[q] = (ret["qvec"], ret["tvec"])
logs["loc"][q] = {
"db": db,
"PnP_ret": ret,
"keypoints_query": mkpq,
"keypoints_db": mkpr,
"3d_points": mkp3d,
"indices_db": indices,
"num_matches": num_matches,
}
logger.info(f"Writing poses to {results}...")
with open(results, "w") as f:
for q in queries:
qvec, tvec = poses[q]
qvec = " ".join(map(str, qvec))
tvec = " ".join(map(str, tvec))
name = q.split("/")[-1]
f.write(f"{name} {qvec} {tvec}\n")
logs_path = f"{results}_logs.pkl"
logger.info(f"Writing logs to {logs_path}...")
with open(logs_path, "wb") as f:
pickle.dump(logs, f)
logger.info("Done!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_dir", type=Path, required=True)
parser.add_argument("--retrieval", type=Path, required=True)
parser.add_argument("--features", type=Path, required=True)
parser.add_argument("--matches", type=Path, required=True)
parser.add_argument("--results", type=Path, required=True)
parser.add_argument("--skip_matches", type=int)
args = parser.parse_args()
main(**args.__dict__)
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