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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from loguru import logger
from nymeria.body_motion_provider import create_body_data_provider
from nymeria.handeye import HandEyeSolver
from nymeria.path_provider import SequencePathProvider
from nymeria.recording_data_provider import (
create_recording_data_provider,
RecordingDataProvider,
)
from projectaria_tools.core.mps import ClosedLoopTrajectoryPose
from projectaria_tools.core.sensor_data import TimeDomain
from projectaria_tools.core.sophus import SE3
@dataclass(frozen=True)
class NymeriaDataProviderConfig:
sequence_rootdir: Path
load_head: bool = True
load_observer: bool = True
load_wrist: bool = True
load_body: bool = True
# If true, the filtered semidense points are exported into a npz file at the first loading
view_cached_points: bool = True
# Parameters for filtering semidense points
th_invdep: float = 0.0004
th_dep: float = 0.02
max_point_count: int = 100_000
trajectory_sample_fps: float = 1
# Parameters for solving XSens to Aria world coordinates alignment
handeye_smooth: bool = False
handeye_window: int = 240 * 120
handeye_skip: int = 240 * 5
handeye_stride: int = 2
class NymeriaDataProvider(NymeriaDataProviderConfig):
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
seq_pd = SequencePathProvider(self.sequence_rootdir)
# create data provider for Aria recordings and MPS output
self.recording_head = (
create_recording_data_provider(seq_pd.recording_head)
if self.load_head
else None
)
self.recording_lwrist = (
create_recording_data_provider(seq_pd.recording_lwrist)
if self.load_wrist
else None
)
self.recording_rwrist = (
create_recording_data_provider(seq_pd.recording_rwrist)
if self.load_wrist
else None
)
self.recording_observer = (
create_recording_data_provider(seq_pd.recording_observer)
if self.load_observer
else None
)
# create data provider for body motion
self.body_dp = (
create_body_data_provider(
xdata_npz=seq_pd.body_paths.xsens_processed,
xdata_glb=seq_pd.body_paths.momentum_model,
)
if self.load_body
else None
)
if self.body_dp is None and len(self.get_existing_recordings()) == 0:
raise RuntimeError(
"data provider is empty. "
"Make sure there is at least 1 recording or body motion"
)
# get overlapping timeline
self.timespan_ns: tuple[int, int] = self.__get_timespan_ns()
# compute xsens to aria world alignment
self.__compute_xsens_to_aria_alignment()
def get_existing_recordings(self) -> list[RecordingDataProvider]:
return [
x
for x in [
self.recording_head,
self.recording_observer,
self.recording_lwrist,
self.recording_rwrist,
]
if x is not None
]
def __get_timespan_ns(self, ignore_ns: int = 1e9) -> tuple[int, int]:
"""
\brief Compute overlapping timeline across all loaded data
"""
t_start = 0
t_end = None
if self.body_dp is not None:
t0, t1 = self.body_dp.get_global_timespan_us()
t_start = t0 * 1e3
t_end = t1 * 1e3
for rec in self.get_existing_recordings():
t0, t1 = rec.get_global_timespan_ns()
t_start = t_start if t_start > t0 else t0
t_end = t_end if t_end is None or t_end < t1 else t1
t_start += ignore_ns
t_end -= ignore_ns
assert t_start < t_end, f"invalid time span {t_start= }us, {t_end= }us"
t_start = int(t_start)
t_end = int(t_end)
duration = (t_end - t_start) / 1.0e9
logger.info(f"time span: {t_start= }us {t_end= }us {duration= }s")
return t_start, t_end
def get_synced_rgb_videos(self, t_ns_global: int) -> dict[str, any]:
data = {}
for rec in [self.recording_head, self.recording_observer]:
if rec is None and not rec.has_rgb:
continue
result = rec.get_rgb_image(t_ns_global, time_domain=TimeDomain.TIME_CODE)
if abs(result[-1] / 1e6) > 33: # 33ms
logger.warning(f"time difference for image query: {result[-1]} ms")
data[rec.tag] = result
return data
def get_all_pointclouds(self) -> dict[str, np.ndarray]:
data = {}
for rec in self.get_existing_recordings():
if not rec.has_pointcloud:
continue
if self.view_cached_points:
data[rec.tag] = rec.get_pointcloud_cached(
th_dep=self.th_dep,
th_invdep=self.th_invdep,
max_point_count=self.max_point_count,
)
else:
data[rec.tag] = rec.get_pointcloud(
th_dep=self.th_dep,
th_invdep=self.th_invdep,
max_point_count=self.max_point_count,
)
return data
def get_all_trajectories(self) -> dict[str, np.ndarray]:
data = {}
for rec in self.get_existing_recordings():
if rec.has_vrs and rec.has_pose:
data[rec.tag] = rec.sample_trajectory_world_device(
sample_fps=self.trajectory_sample_fps
)
return data
def get_synced_poses(self, t_ns_global: int) -> dict[str, any]:
data = {}
T_Wd_Hd = None
for rec in self.get_existing_recordings():
if rec is None or not rec.has_pose:
continue
pose: ClosedLoopTrajectoryPose = None
tdiff: int = None
pose, tdiff = rec.get_pose(t_ns_global, time_domain=TimeDomain.TIME_CODE)
if abs(tdiff / 1e6) > 2: # 2ms
logger.warning(f"time difference for pose query {tdiff/1e6} ms")
data[rec.tag] = pose
if rec.tag == "recording_head":
T_Wd_Hd: SE3 = pose.transform_world_device
if (
self.body_dp is not None
and self.recording_head is not None
and T_Wd_Hd is not None
):
T_Wd_Hx = T_Wd_Hd @ self.T_Hd_Hx(t_ns_global)
t_us = t_ns_global / 1e3
skel, skin = self.body_dp.get_posed_skeleton_and_skin(t_us, T_W_Hx=T_Wd_Hx)
data["xsens"] = skel
if skin is not None:
data["momentum"] = skin
return data
def __compute_xsens_to_aria_alignment(self) -> None:
"""
\brief Compute se3 transform from xsens head to aria head
This function will set self.Ts_Hd_Hx and self.t_ns_align
"""
if self.recording_head is None or self.body_dp is None:
self.Ts_Hd_Hx = [SE3.from_matrix(np.eye(4))]
self.t_ns_align = None
return
else:
logger.info("compute alignment from xsens head to aria headset")
assert self.body_dp is not None
assert self.recording_head is not None
# get synchronized trajectory
xsens_traj = self.body_dp.get_T_w_h(self.timespan_ns)
T_Wx_Hx: list[SE3] = xsens_traj[0]
t_ns: list[int] = xsens_traj[-1]
T_Wd_Hd: list[SE3] = []
for t in t_ns:
pose, _ = self.recording_head.get_pose(t, TimeDomain.TIME_CODE)
T_Wd_Hd.append(pose.transform_world_device)
# solve handeye
handeye = HandEyeSolver(
stride=self.handeye_stride,
smooth=self.handeye_smooth,
skip=self.handeye_skip,
window=self.handeye_window,
)
self.Ts_Hd_Hx: list[SE3] = handeye(
T_Wa_A=T_Wd_Hd,
T_Wb_B=T_Wx_Hx,
)
if len(self.Ts_Hd_Hx) > 1:
self.t_ns_align = t_ns[0 :: self.handeye_skip]
else:
self.t_ns_align = None
def T_Hd_Hx(self, t_ns: int) -> SE3:
if self.t_ns_align is None:
return self.Ts_Hd_Hx[0]
if t_ns <= self.t_ns_align[0]:
return self.Ts_Hd_Hx[0]
if t_ns >= self.t_ns_align[-1]:
return self.Ts_Hd_Hx[-1]
idx = np.searchsorted(self.t_ns_align, t_ns)
return self.Ts_Hd_Hx[idx]
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