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# 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
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