Source code for lib.sedna.core.multi_edge_inference.data_classes

# Copyright 2021 The KubeEdge Authors.
#
# 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 datetime import datetime
from typing import List
from enum import Enum


# Class defining the possible operational modes of the service.
class OP_MODE(Enum):
    DETECTION = "detection"
    TRACKING = "tracking"
    COVID19 = "covid19"
    NOOP = "noop"


# Class defining the output of a ReID service.
class DetTrackResult:
    """
    Base data object exchanged by the MultiEdgeInference components.
    """

    def __init__(
            self,
            frame_index: int = 0,
            bbox: List = None,
            scene=None,
            confidence: List = None,
            detection_time: List = None,
            camera: int = 0,
            bbox_coord: List = [],
            tracking_ids: List = [],
            features: List = [],
            is_target: bool = False,
            ID: List = []):

        # Name of the enduser using the application,
        # used to bound the user to the results
        self.userID = "DEFAULT"
        # Video frame index number
        self.frame_index = frame_index
        # List of bounding boxes containing, for example, pedestrians
        self.bbox = bbox
        # List of tracking IDs, one per bbox
        self.tracklets = tracking_ids
        # Coordinates of each bbox
        self.bbox_coord = bbox_coord
        # Original video frame
        self.scene = scene
        # Confidence value of the detection pass
        self.confidence = confidence
        # When detection was triggered (date)
        self.detection_time = detection_time
        # ID of the camera  where the video stream was acquired
        self.camera = camera
        # List of features extracted for each bbox
        self.features = features
        # Index of the target in the list of features
        self.is_target = is_target
        # List of subjects IDs associated to the list of features.
        # For example ['0002', '0001']. It depends on the ReID gallery.
        self.targetID = ID

        # Image key is used to uniquely identify the video
        # frame associated with this object
        try:
            _time = datetime.strptime(
                self.detection_time, "%a, %d %B %Y %H:%M:%S.%f").timestamp()
            self.image_key = f'{_time}_{self.camera}'
        except Exception as _:
            self.image_key = "0"


class TargetImages:
    def __init__(self, userid, targets=[]) -> None:
        self.userid = userid
        self.targets = targets


class Target:
    def __init__(
            self,
            _userid,
            _features,
            _targetid="0000",
            _tracking_id=None,
            _location=None,
            _frame_index=0) -> None:

        self.userid: str = _userid
        self.features: List = _features
        self.targetid: str = _targetid
        self.tracking_id: str = _tracking_id
        self.location: str = _location
        self.frame_index: int = _frame_index