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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.
import csv
import logging
import os
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
from projectaria_tools.core.sensor_data import TimeDomain, TimeQueryOptions # @manual
from projectaria_tools.core.stream_id import StreamId # @manual
from .AlignedBox2d import AlignedBox2d
from .constants import HAND_BOX2D_DATA_CSV_COLUMNS
from .io_utils import float_or_none, is_float
from .loader_poses_utils import check_csv_columns
from .pose_utils import lookup_timestamp
logger = logging.getLogger(__name__)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(filename)s - %(lineno)d - %(levelname)s - %(message)s",
)
@dataclass
class HandBox2d:
box2d: AlignedBox2d
visibility_ratio: float
@dataclass
class HandBox2dCollection:
timestamp_ns: int
box2ds: Dict[int, HandBox2d]
HandBox2dTrajectory = Dict[int, HandBox2dCollection] # trajectory for a single stream
HandBox2dTrajectoryCollection = Dict[
str, HandBox2dTrajectory
] # trajectories for multiple streams
@dataclass
class HandBox2dCollectionWithDt:
box2d_collection: HandBox2dCollection
time_delta_ns: int
#box2ds: Dict[int, HandBox2d]
class HandBox2dProvider:
def __init__(
self, box2d_trajectory_collection: HandBox2dTrajectoryCollection
) -> None:
#真正存数据的，但没有进行排序
self._box2d_trajectory_collection = box2d_trajectory_collection
self._sorted_timestamp_ns_list: Dict[str, List[int]] = {}
for stream_id in self._box2d_trajectory_collection.keys():
self._sorted_timestamp_ns_list[stream_id] = sorted(
self._box2d_trajectory_collection[stream_id].keys()
)
def get_timestamp_ns_list(self, stream_id: StreamId) -> Optional[List[int]]:
return self._sorted_timestamp_ns_list.get(str(stream_id), None)
@property
def stream_ids(self) -> List[StreamId]:
return [StreamId(x) for x in self._box2d_trajectory_collection.keys()]
def get_data_statistics(self) -> Dict[str, Any]:
"""
Returns the stats for Hand 2D bounding boxes
"""
stats = {}
stats["num_frames"] = {
k: len(v) for k, v in self._sorted_timestamp_ns_list.items()
}
stats["stream_ids"] = [str(x) for x in self.stream_ids]
return stats
def get_bbox_at_timestamp(
self,
stream_id: StreamId,
timestamp_ns: int,
time_query_options: TimeQueryOptions,
time_domain: TimeDomain,
) -> Optional[HandBox2dCollectionWithDt]:
"""
Return the list of poses at the given timestamp
"""
if time_domain is not TimeDomain.TIME_CODE:
raise ValueError("Value other than TimeDomain.TIME_CODE not yet supported.")
if stream_id not in self.stream_ids:
raise ValueError(f"Box2d trajectory not available for stream {stream_id}.")
box2d_collection, time_delta_ns = lookup_timestamp(
time_indexed_dict=self._box2d_trajectory_collection[str(stream_id)],
sorted_timestamp_list=self.get_timestamp_ns_list(stream_id=stream_id),
query_timestamp=timestamp_ns,
time_query_options=time_query_options,
)
if box2d_collection is None or time_delta_ns is None:
return None
else:
return HandBox2dCollectionWithDt(
box2d_collection=box2d_collection, time_delta_ns=time_delta_ns
)
def parse_box2ds_from_csv_reader(csv_reader) -> HandBox2dTrajectoryCollection:
box2d_trajectory_collection: HandBox2dTrajectoryCollection = {}
# Read the header row
header = next(csv_reader)
# Ensure we have the desired columns
check_csv_columns(header, HAND_BOX2D_DATA_CSV_COLUMNS)
# Read the rest of the rows in the CSV file
for row in csv_reader:
stream_id = str(StreamId(row[header.index("stream_id")]))
timestamp_ns = int(row[header.index("timestamp[ns]")])
hand_index = int(row[header.index("hand_index")])
visibility_ratio = float_or_none(row[header.index("visibility_ratio[%]")])
if is_float(row[header.index("x_min[pixel]")]):
x_min_px = float(row[header.index("x_min[pixel]")])
x_max_px = float(row[header.index("x_max[pixel]")])
y_min_px = float(row[header.index("y_min[pixel]")])
y_max_px = float(row[header.index("y_max[pixel]")])
box2d = AlignedBox2d(
left=x_min_px, top=y_min_px, right=x_max_px, bottom=y_max_px
)
else:
box2d = None
object_box2d = HandBox2d(
box2d=box2d,
visibility_ratio=visibility_ratio,
)
if stream_id not in box2d_trajectory_collection:
box2d_trajectory_collection[stream_id] = {}
if timestamp_ns not in box2d_trajectory_collection[stream_id]:
box2d_trajectory_collection[stream_id][timestamp_ns] = HandBox2dCollection(
timestamp_ns=timestamp_ns, box2ds={}
)
box2d_trajectory_collection[stream_id][timestamp_ns].box2ds[hand_index] = (
object_box2d
)
return box2d_trajectory_collection
def load_box2d_trajectory_from_csv(filename: str) -> Optional[HandBox2dProvider]:
"""Load Hand 2D bounding box meta data from a CSV file.
Keyword arguments:
filename -- the csv file i.e. sequence_folder + "/box2d_hands.csv"
"""
if not os.path.exists(filename):
logger.warn(f"filename: {filename} does not exist.")
return None
# Open the CSV file for reading
with open(filename, "r") as f:
csv_reader = csv.reader(f)
box2d_trajectory_collection = parse_box2ds_from_csv_reader(
csv_reader=csv_reader
)
return HandBox2dProvider(
box2d_trajectory_collection=box2d_trajectory_collection
)
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