forked from PulseFocusPlatform/PulseFocusPlatform
145 lines
5.2 KiB
Python
145 lines
5.2 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is borrow from https://github.com/nwojke/deep_sort/blob/master/deep_sort/track.py
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"""
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from ppdet.core.workspace import register, serializable
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__all__ = ['TrackState', 'Track']
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class TrackState(object):
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"""
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Enumeration type for the single target track state. Newly created tracks are
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classified as `tentative` until enough evidence has been collected. Then,
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the track state is changed to `confirmed`. Tracks that are no longer alive
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are classified as `deleted` to mark them for removal from the set of active
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tracks.
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"""
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Tentative = 1
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Confirmed = 2
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Deleted = 3
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@register
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@serializable
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class Track(object):
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"""
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A single target track with state space `(x, y, a, h)` and associated
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velocities, where `(x, y)` is the center of the bounding box, `a` is the
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aspect ratio and `h` is the height.
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Args:
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mean (ndarray): Mean vector of the initial state distribution.
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covariance (ndarray): Covariance matrix of the initial state distribution.
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track_id (int): A unique track identifier.
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n_init (int): Number of consecutive detections before the track is confirmed.
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The track state is set to `Deleted` if a miss occurs within the first
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`n_init` frames.
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max_age (int): The maximum number of consecutive misses before the track
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state is set to `Deleted`.
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feature (Optional[ndarray]): Feature vector of the detection this track
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originates from. If not None, this feature is added to the `features` cache.
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Attributes:
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hits (int): Total number of measurement updates.
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age (int): Total number of frames since first occurance.
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time_since_update (int): Total number of frames since last measurement
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update.
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state (TrackState): The current track state.
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features (List[ndarray]): A cache of features. On each measurement update,
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the associated feature vector is added to this list.
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"""
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def __init__(self,
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mean,
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covariance,
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track_id,
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n_init,
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max_age,
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feature=None):
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self.mean = mean
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self.covariance = covariance
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self.track_id = track_id
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self.hits = 1
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self.age = 1
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self.time_since_update = 0
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self.state = TrackState.Tentative
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self.features = []
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if feature is not None:
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self.features.append(feature)
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self._n_init = n_init
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self._max_age = max_age
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def to_tlwh(self):
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"""Get position in format `(top left x, top left y, width, height)`."""
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ret = self.mean[:4].copy()
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ret[2] *= ret[3]
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ret[:2] -= ret[2:] / 2
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return ret
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def to_tlbr(self):
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"""Get position in bounding box format `(min x, miny, max x, max y)`."""
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ret = self.to_tlwh()
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ret[2:] = ret[:2] + ret[2:]
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return ret
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def predict(self, kalman_filter):
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"""
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Propagate the state distribution to the current time step using a Kalman
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filter prediction step.
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"""
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self.mean, self.covariance = kalman_filter.predict(self.mean,
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self.covariance)
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self.age += 1
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self.time_since_update += 1
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def update(self, kalman_filter, detection):
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"""
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Perform Kalman filter measurement update step and update the associated
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detection feature cache.
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"""
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self.mean, self.covariance = kalman_filter.update(self.mean,
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self.covariance,
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detection.to_xyah())
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self.features.append(detection.feature)
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self.hits += 1
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self.time_since_update = 0
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if self.state == TrackState.Tentative and self.hits >= self._n_init:
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self.state = TrackState.Confirmed
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def mark_missed(self):
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"""Mark this track as missed (no association at the current time step).
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"""
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if self.state == TrackState.Tentative:
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self.state = TrackState.Deleted
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elif self.time_since_update > self._max_age:
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self.state = TrackState.Deleted
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def is_tentative(self):
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"""Returns True if this track is tentative (unconfirmed)."""
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return self.state == TrackState.Tentative
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def is_confirmed(self):
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"""Returns True if this track is confirmed."""
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return self.state == TrackState.Confirmed
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def is_deleted(self):
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"""Returns True if this track is dead and should be deleted."""
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return self.state == TrackState.Deleted
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