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"""Module defines the base classes and structures for object tracking in YOLO."""
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from collections import OrderedDict
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import numpy as np
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class TrackState:
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"""
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Enumeration class representing the possible states of an object being tracked.
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Attributes:
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New (int): State when the object is newly detected.
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Tracked (int): State when the object is successfully tracked in subsequent frames.
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Lost (int): State when the object is no longer tracked.
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Removed (int): State when the object is removed from tracking.
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Examples:
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>>> state = TrackState.New
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>>> if state == TrackState.New:
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>>> print("Object is newly detected.")
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"""
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New = 0
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Tracked = 1
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Lost = 2
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Removed = 3
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class BaseTrack:
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"""
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Base class for object tracking, providing foundational attributes and methods.
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Attributes:
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_count (int): Class-level counter for unique track IDs.
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track_id (int): Unique identifier for the track.
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is_activated (bool): Flag indicating whether the track is currently active.
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state (TrackState): Current state of the track.
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history (OrderedDict): Ordered history of the track's states.
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features (List): List of features extracted from the object for tracking.
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curr_feature (Any): The current feature of the object being tracked.
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score (float): The confidence score of the tracking.
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start_frame (int): The frame number where tracking started.
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frame_id (int): The most recent frame ID processed by the track.
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time_since_update (int): Frames passed since the last update.
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location (Tuple): The location of the object in the context of multi-camera tracking.
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Methods:
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end_frame: Returns the ID of the last frame where the object was tracked.
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next_id: Increments and returns the next global track ID.
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activate: Abstract method to activate the track.
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predict: Abstract method to predict the next state of the track.
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update: Abstract method to update the track with new data.
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mark_lost: Marks the track as lost.
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mark_removed: Marks the track as removed.
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reset_id: Resets the global track ID counter.
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Examples:
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Initialize a new track and mark it as lost:
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>>> track = BaseTrack()
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>>> track.mark_lost()
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>>> print(track.state) # Output: 2 (TrackState.Lost)
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"""
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_count = 0
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def __init__(self):
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"""
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Initializes a new track with a unique ID and foundational tracking attributes.
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Examples:
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Initialize a new track
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>>> track = BaseTrack()
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>>> print(track.track_id)
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0
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"""
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self.track_id = 0
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self.is_activated = False
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self.state = TrackState.New
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self.history = OrderedDict()
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self.features = []
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self.curr_feature = None
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self.score = 0
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self.start_frame = 0
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self.frame_id = 0
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self.time_since_update = 0
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self.location = (np.inf, np.inf)
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@property
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def end_frame(self):
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"""Returns the ID of the most recent frame where the object was tracked."""
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return self.frame_id
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@staticmethod
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def next_id():
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"""Increment and return the next unique global track ID for object tracking."""
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BaseTrack._count += 1
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return BaseTrack._count
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def activate(self, *args):
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"""Activates the track with provided arguments, initializing necessary attributes for tracking."""
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raise NotImplementedError
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def predict(self):
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"""Predicts the next state of the track based on the current state and tracking model."""
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raise NotImplementedError
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def update(self, *args, **kwargs):
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"""Updates the track with new observations and data, modifying its state and attributes accordingly."""
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raise NotImplementedError
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def mark_lost(self):
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"""Marks the track as lost by updating its state to TrackState.Lost."""
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self.state = TrackState.Lost
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def mark_removed(self):
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"""Marks the track as removed by setting its state to TrackState.Removed."""
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self.state = TrackState.Removed
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@staticmethod
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def reset_id():
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"""Reset the global track ID counter to its initial value."""
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BaseTrack._count = 0
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