# Ultralytics YOLO 🚀, AGPL-3.0 license """Module defines the base classes and structures for object tracking in YOLO.""" from collections import OrderedDict import numpy as np class TrackState: """ Enumeration 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.") """ New = 0 Tracked = 1 Lost = 2 Removed = 3 class BaseTrack: """ Base 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) """ _count = 0 def __init__(self): """ Initializes a new track with a unique ID and foundational tracking attributes. Examples: Initialize a new track >>> track = BaseTrack() >>> print(track.track_id) 0 """ self.track_id = 0 self.is_activated = False self.state = TrackState.New self.history = OrderedDict() self.features = [] self.curr_feature = None self.score = 0 self.start_frame = 0 self.frame_id = 0 self.time_since_update = 0 self.location = (np.inf, np.inf) @property def end_frame(self): """Returns the ID of the most recent frame where the object was tracked.""" return self.frame_id @staticmethod def next_id(): """Increment and return the next unique global track ID for object tracking.""" BaseTrack._count += 1 return BaseTrack._count def activate(self, *args): """Activates the track with provided arguments, initializing necessary attributes for tracking.""" raise NotImplementedError def predict(self): """Predicts the next state of the track based on the current state and tracking model.""" raise NotImplementedError def update(self, *args, **kwargs): """Updates the track with new observations and data, modifying its state and attributes accordingly.""" raise NotImplementedError def mark_lost(self): """Marks the track as lost by updating its state to TrackState.Lost.""" self.state = TrackState.Lost def mark_removed(self): """Marks the track as removed by setting its state to TrackState.Removed.""" self.state = TrackState.Removed @staticmethod def reset_id(): """Reset the global track ID counter to its initial value.""" BaseTrack._count = 0