FiPdZddlmZddlmZddlZGddZGddZy) zKModule defines the base classes and structures for object tracking in YOLO.) OrderedDict)AnyNc eZdZdZdZdZdZdZy) TrackStateaEnumeration class representing the possible states of an object being tracked. Attributes: New (int): State when the object is newly detected. Tracked (int): State when the object is successfully tracked in subsequent frames. Lost (int): State when the object is no longer tracked. Removed (int): State when the object is removed from tracking. Examples: >>> state = TrackState.New >>> if state == TrackState.New: ... print("Object is newly detected.") rN)__name__ __module__ __qualname____doc__NewTrackedLostRemoved`/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/trackers/basetrack.pyrr s  CG DGrrceZdZdZdZdZedefdZe defdZ de ddfd Z dd Z de d e ddfd Zdd ZddZe ddZy) BaseTrackavBase class for object tracking, providing foundational attributes and methods. Attributes: _count (int): Class-level counter for unique track IDs. track_id (int): Unique identifier for the track. is_activated (bool): Flag indicating whether the track is currently active. state (TrackState): Current state of the track. history (OrderedDict): Ordered history of the track's states. features (list): List of features extracted from the object for tracking. curr_feature (Any): The current feature of the object being tracked. score (float): The confidence score of the tracking. start_frame (int): The frame number where tracking started. frame_id (int): The most recent frame ID processed by the track. time_since_update (int): Frames passed since the last update. location (tuple): The location of the object in the context of multi-camera tracking. Methods: end_frame: Returns the ID of the last frame where the object was tracked. next_id: Increments and returns the next global track ID. activate: Abstract method to activate the track. predict: Abstract method to predict the next state of the track. update: Abstract method to update the track with new data. mark_lost: Marks the track as lost. mark_removed: Marks the track as removed. reset_id: Resets the global track ID counter. Examples: Initialize a new track and mark it as lost: >>> track = BaseTrack() >>> track.mark_lost() >>> print(track.state) # Output: 2 (TrackState.Lost) rcd|_d|_tj|_t |_g|_d|_d|_ d|_ d|_ d|_ tjtjf|_y)zMInitialize a new track with a unique ID and foundational tracking attributes.rFN)track_id is_activatedrrstaterhistoryfeatures curr_featurescore start_frameframe_idtime_since_updatenpinflocationselfs r__init__zBaseTrack.__init__Csf !^^ "}     !"( rreturnc|jS)zDReturn the ID of the most recent frame where the object was tracked.)r r%s r end_framezBaseTrack.end_frameQs}}rcTtxjdz c_tjS)zIIncrement and return the next unique global track ID for object tracking.rr_countrrrnext_idzBaseTrack.next_idVs! ArargsNct)z[Activate the track with provided arguments, initializing necessary attributes for tracking.NotImplementedError)r&r/s ractivatezBaseTrack.activate\!!rct)zRPredict the next state of the track based on the current state and tracking model.r1r%s rpredictzBaseTrack.predict`r4rkwargsct)z`Update the track with new observations and data, modifying its state and attributes accordingly.r1)r&r/r7s rupdatezBaseTrack.updatedr4rc.tj|_y)z@Mark the track as lost by updating its state to TrackState.Lost.N)rrrr%s r mark_lostzBaseTrack.mark_losths __ rc.tj|_y)zEMark the track as removed by setting its state to TrackState.Removed.N)rrrr%s r mark_removedzBaseTrack.mark_removedls'' rcdt_y)z7Reset the global track ID counter to its initial value.rNr,rrrreset_idzBaseTrack.reset_idps  r)r(N)r r r r r-r'propertyintr* staticmethodr.rr3r6r9r;r=r?rrrrrsBF )3 S  "c"d"""C"3"4"%(rr) r collectionsrtypingrnumpyr"rrrrrrFs'Q#*TTr