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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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"""Enumeration of possible object tracking states.""" |
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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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"""Base class for object tracking, handling basic track attributes and operations.""" |
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_count = 0 |
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track_id = 0 |
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is_activated = False |
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state = TrackState.New |
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history = OrderedDict() |
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features = [] |
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curr_feature = None |
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score = 0 |
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start_frame = 0 |
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frame_id = 0 |
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time_since_update = 0 |
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location = (np.inf, np.inf) |
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@property |
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def end_frame(self): |
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"""Return the last frame ID of the track.""" |
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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 global track ID counter.""" |
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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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"""Activate the track with the provided arguments.""" |
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raise NotImplementedError |
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def predict(self): |
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"""Predict the next state of the track.""" |
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raise NotImplementedError |
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def update(self, *args, **kwargs): |
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"""Update the track with new observations.""" |
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raise NotImplementedError |
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def mark_lost(self): |
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"""Mark the track as lost.""" |
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self.state = TrackState.Lost |
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def mark_removed(self): |
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"""Mark the track as 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.""" |
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BaseTrack._count = 0 |
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