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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# 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
        )

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	#
	# 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
	)