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)