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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import numpy as np
import torch
from data_loaders.loader_hand_poses import (
Handedness,
HandPose,
HandPose3dCollection,
load_hand_poses,
)
from projectaria_tools.core.sensor_data import TimeDomain, TimeQueryOptions # @manual
from .pose_utils import lookup_timestamp
@dataclass
class HandPose3dCollectionWithDt:
pose3d_collection: HandPose3dCollection
time_delta_ns: int
class HandDataProviderBase:
def __init__(
self,
) -> None:
self._hand_poses = None
self._sorted_timestamp_ns_list: List[int]
def _init_hand_poses(self, hand_pose_trajectory_filepath: str) -> None:
#self._hand_poses是一个字典，key是timestamp_ns，value是HandPose3dCollection对象，里面包含timestamp_ns和poses字典
# 取某一帧
# frame = hand_poses[1234567890]
# # 取右手
# right_hand = frame.poses[Handedness.Right]
# # 取右手手腕位置
# wrist_se3 = right_hand.wrist_pose
# # 取右手关节角度
# angles = right_hand.joint_angles
self._hand_poses = load_hand_poses(hand_pose_trajectory_filepath)
self._sorted_timestamp_ns_list: List[int] = sorted(self._hand_poses.keys())
@property
def timestamp_ns_list(self) -> List[int]:
return self._sorted_timestamp_ns_list
def get_data_statistics(self) -> Dict[str, Any]:
"""
Returns the stats of the Hand data
"""
assert self._hand_poses is not None
stats = {}
stats["num_frames"] = len(self._sorted_timestamp_ns_list)
stats["num_right_hands"] = sum(
[
1
for it in self._hand_poses.values()
if Handedness.Right in it.poses.keys()
]
)
stats["num_left_hands"] = sum(
[
1
for it in self._hand_poses.values()
if Handedness.Left in it.poses.keys()
]
)
return stats
def get_pose_at_timestamp(
self,
timestamp_ns: int,
time_query_options: TimeQueryOptions,
time_domain: TimeDomain,
acceptable_time_delta: Optional[int] = None,
) -> Optional[HandPose3dCollectionWithDt]:
"""
Return the list of hands available at a given timestamp
"""
if time_domain is not TimeDomain.TIME_CODE:
raise ValueError("Value other than TimeDomain.TIME_CODE not yet supported.")
hand_pose_collection, time_delta_ns = lookup_timestamp(
time_indexed_dict=self._hand_poses,
sorted_timestamp_list=self._sorted_timestamp_ns_list,
query_timestamp=timestamp_ns,
time_query_options=time_query_options,
)
if (
hand_pose_collection is None
or time_delta_ns is None
or (
acceptable_time_delta is not None
and abs(time_delta_ns) > acceptable_time_delta
)
):
return None
else:
return HandPose3dCollectionWithDt(
pose3d_collection=hand_pose_collection, time_delta_ns=time_delta_ns
)
@abstractmethod
def get_hand_mesh_vertices(
self, hand_wrist_data: HandPose
) -> Optional[torch.Tensor]:
"""
Return the hand mesh corresponding to given HandPose
"""
@abstractmethod
def get_hand_mesh_faces_and_normals(
self, hand_wrist_data: HandPose
) -> Optional[List[np.ndarray]]:
"""
Return the hand mesh faces and normals
"""
@abstractmethod
def get_hand_landmarks(self, hand_wrist_data: HandPose) -> Optional[torch.Tensor]:
"""
Return the hand joint landmarks corresponding to given HandPose
See how to map the vertices together to represent a Hand as linked lines using LANDMARK_CONNECTIVITY
"""
@staticmethod
def normalized(
vecs: np.ndarray, axis: int = -1, add_const_to_denom: bool = True
) -> np.ndarray:
"""
Normalize a set of vectors.
Args:
vecs: np.ndarray of shape (..., V).
axis: axis along which to normalize.
add_const_to_denom: if True, add a small constant to the denominator to prevent numerical issues.
Returns:
np.ndarray of the same shape as vecs.
"""
denom = np.linalg.norm(vecs, axis=axis, keepdims=True)
if add_const_to_denom:
denom += 1e-5
return vecs / denom
@staticmethod
def get_triangular_mesh_normals(
vertices: np.ndarray, triangles: np.ndarray
) -> np.ndarray:
"""
Compute the normals of a triangular mesh.
Args:
vertices: np.ndarray of shape (..., V, 3).
triangles: np.ndarray of shape (..., F, 3).
Returns:
normals: np.ndarray of shape (..., F, 3).
"""
norm = np.zeros_like(vertices)
tris = vertices[triangles]
n = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])
n = HandDataProviderBase.normalized(n)
norm[triangles[:, 0]] += n
norm[triangles[:, 1]] += n
norm[triangles[:, 2]] += n
return HandDataProviderBase.normalized(norm)
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