add StrongSORT Tacker
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Pongsatorn Kanjanasantisak
parent
ffc2e99678
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b7d8b3266f
93 changed files with 20230 additions and 6 deletions
348
feeder/trackers/bytetrack/byte_tracker.py
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348
feeder/trackers/bytetrack/byte_tracker.py
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import numpy as np
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from ultralytics.yolo.utils.ops import xywh2xyxy, xyxy2xywh
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from trackers.bytetrack.kalman_filter import KalmanFilter
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from trackers.bytetrack import matching
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from trackers.bytetrack.basetrack import BaseTrack, TrackState
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class STrack(BaseTrack):
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shared_kalman = KalmanFilter()
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def __init__(self, tlwh, score, cls):
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# wait activate
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self._tlwh = np.asarray(tlwh, dtype=np.float32)
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self.kalman_filter = None
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self.mean, self.covariance = None, None
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self.is_activated = False
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self.score = score
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self.tracklet_len = 0
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self.cls = cls
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def predict(self):
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mean_state = self.mean.copy()
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if self.state != TrackState.Tracked:
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mean_state[7] = 0
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self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
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@staticmethod
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def multi_predict(stracks):
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if len(stracks) > 0:
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multi_mean = np.asarray([st.mean.copy() for st in stracks])
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multi_covariance = np.asarray([st.covariance for st in stracks])
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for i, st in enumerate(stracks):
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if st.state != TrackState.Tracked:
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multi_mean[i][7] = 0
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multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
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for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
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stracks[i].mean = mean
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stracks[i].covariance = cov
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def activate(self, kalman_filter, frame_id):
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"""Start a new tracklet"""
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self.kalman_filter = kalman_filter
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self.track_id = self.next_id()
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self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
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self.tracklet_len = 0
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self.state = TrackState.Tracked
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if frame_id == 1:
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self.is_activated = True
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# self.is_activated = True
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self.frame_id = frame_id
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self.start_frame = frame_id
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def re_activate(self, new_track, frame_id, new_id=False):
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self.mean, self.covariance = self.kalman_filter.update(
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self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
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)
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self.tracklet_len = 0
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self.state = TrackState.Tracked
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self.is_activated = True
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self.frame_id = frame_id
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if new_id:
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self.track_id = self.next_id()
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self.score = new_track.score
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self.cls = new_track.cls
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def update(self, new_track, frame_id):
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"""
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Update a matched track
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:type new_track: STrack
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:type frame_id: int
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:type update_feature: bool
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:return:
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"""
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self.frame_id = frame_id
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self.tracklet_len += 1
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# self.cls = cls
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new_tlwh = new_track.tlwh
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self.mean, self.covariance = self.kalman_filter.update(
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self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
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self.state = TrackState.Tracked
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self.is_activated = True
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self.score = new_track.score
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@property
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# @jit(nopython=True)
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def tlwh(self):
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"""Get current position in bounding box format `(top left x, top left y,
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width, height)`.
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"""
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if self.mean is None:
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return self._tlwh.copy()
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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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@property
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# @jit(nopython=True)
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def tlbr(self):
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"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
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`(top left, bottom right)`.
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"""
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ret = self.tlwh.copy()
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ret[2:] += ret[:2]
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return ret
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@staticmethod
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# @jit(nopython=True)
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def tlwh_to_xyah(tlwh):
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"""Convert bounding box to format `(center x, center y, aspect ratio,
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height)`, where the aspect ratio is `width / height`.
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"""
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ret = np.asarray(tlwh).copy()
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ret[:2] += ret[2:] / 2
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ret[2] /= ret[3]
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return ret
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def to_xyah(self):
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return self.tlwh_to_xyah(self.tlwh)
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@staticmethod
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# @jit(nopython=True)
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def tlbr_to_tlwh(tlbr):
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ret = np.asarray(tlbr).copy()
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ret[2:] -= ret[:2]
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return ret
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@staticmethod
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# @jit(nopython=True)
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def tlwh_to_tlbr(tlwh):
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ret = np.asarray(tlwh).copy()
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ret[2:] += ret[:2]
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return ret
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def __repr__(self):
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return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
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class BYTETracker(object):
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def __init__(self, track_thresh=0.45, match_thresh=0.8, track_buffer=25, frame_rate=30):
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self.tracked_stracks = [] # type: list[STrack]
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self.lost_stracks = [] # type: list[STrack]
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self.removed_stracks = [] # type: list[STrack]
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self.frame_id = 0
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self.track_buffer=track_buffer
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self.track_thresh = track_thresh
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self.match_thresh = match_thresh
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self.det_thresh = track_thresh + 0.1
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self.buffer_size = int(frame_rate / 30.0 * track_buffer)
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self.max_time_lost = self.buffer_size
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self.kalman_filter = KalmanFilter()
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def update(self, dets, _):
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self.frame_id += 1
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activated_starcks = []
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refind_stracks = []
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lost_stracks = []
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removed_stracks = []
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xyxys = dets[:, 0:4]
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xywh = xyxy2xywh(xyxys.numpy())
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confs = dets[:, 4]
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clss = dets[:, 5]
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classes = clss.numpy()
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xyxys = xyxys.numpy()
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confs = confs.numpy()
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remain_inds = confs > self.track_thresh
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inds_low = confs > 0.1
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inds_high = confs < self.track_thresh
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inds_second = np.logical_and(inds_low, inds_high)
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dets_second = xywh[inds_second]
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dets = xywh[remain_inds]
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scores_keep = confs[remain_inds]
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scores_second = confs[inds_second]
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clss_keep = classes[remain_inds]
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clss_second = classes[inds_second]
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if len(dets) > 0:
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'''Detections'''
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detections = [STrack(xyxy, s, c) for
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(xyxy, s, c) in zip(dets, scores_keep, clss_keep)]
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else:
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detections = []
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''' Add newly detected tracklets to tracked_stracks'''
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unconfirmed = []
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tracked_stracks = [] # type: list[STrack]
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for track in self.tracked_stracks:
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if not track.is_activated:
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unconfirmed.append(track)
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else:
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tracked_stracks.append(track)
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''' Step 2: First association, with high score detection boxes'''
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strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
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# Predict the current location with KF
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STrack.multi_predict(strack_pool)
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dists = matching.iou_distance(strack_pool, detections)
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#if not self.args.mot20:
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dists = matching.fuse_score(dists, detections)
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matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.match_thresh)
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for itracked, idet in matches:
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track = strack_pool[itracked]
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det = detections[idet]
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if track.state == TrackState.Tracked:
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track.update(detections[idet], self.frame_id)
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activated_starcks.append(track)
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else:
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track.re_activate(det, self.frame_id, new_id=False)
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refind_stracks.append(track)
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''' Step 3: Second association, with low score detection boxes'''
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# association the untrack to the low score detections
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if len(dets_second) > 0:
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'''Detections'''
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detections_second = [STrack(xywh, s, c) for (xywh, s, c) in zip(dets_second, scores_second, clss_second)]
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else:
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detections_second = []
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r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
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dists = matching.iou_distance(r_tracked_stracks, detections_second)
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matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
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for itracked, idet in matches:
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track = r_tracked_stracks[itracked]
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det = detections_second[idet]
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if track.state == TrackState.Tracked:
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track.update(det, self.frame_id)
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activated_starcks.append(track)
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else:
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track.re_activate(det, self.frame_id, new_id=False)
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refind_stracks.append(track)
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for it in u_track:
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track = r_tracked_stracks[it]
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if not track.state == TrackState.Lost:
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track.mark_lost()
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lost_stracks.append(track)
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'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
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detections = [detections[i] for i in u_detection]
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dists = matching.iou_distance(unconfirmed, detections)
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#if not self.args.mot20:
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dists = matching.fuse_score(dists, detections)
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matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
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for itracked, idet in matches:
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unconfirmed[itracked].update(detections[idet], self.frame_id)
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activated_starcks.append(unconfirmed[itracked])
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for it in u_unconfirmed:
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track = unconfirmed[it]
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track.mark_removed()
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removed_stracks.append(track)
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""" Step 4: Init new stracks"""
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for inew in u_detection:
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track = detections[inew]
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if track.score < self.det_thresh:
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continue
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track.activate(self.kalman_filter, self.frame_id)
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activated_starcks.append(track)
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""" Step 5: Update state"""
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for track in self.lost_stracks:
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if self.frame_id - track.end_frame > self.max_time_lost:
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track.mark_removed()
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removed_stracks.append(track)
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# print('Ramained match {} s'.format(t4-t3))
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self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
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self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
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self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
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self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
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self.lost_stracks.extend(lost_stracks)
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self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
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self.removed_stracks.extend(removed_stracks)
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self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
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# get scores of lost tracks
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output_stracks = [track for track in self.tracked_stracks if track.is_activated]
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outputs = []
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for t in output_stracks:
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output= []
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tlwh = t.tlwh
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tid = t.track_id
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tlwh = np.expand_dims(tlwh, axis=0)
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xyxy = xywh2xyxy(tlwh)
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xyxy = np.squeeze(xyxy, axis=0)
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output.extend(xyxy)
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output.append(tid)
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output.append(t.cls)
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output.append(t.score)
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outputs.append(output)
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return outputs
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#track_id, class_id, conf
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def joint_stracks(tlista, tlistb):
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exists = {}
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res = []
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for t in tlista:
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exists[t.track_id] = 1
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res.append(t)
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for t in tlistb:
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tid = t.track_id
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if not exists.get(tid, 0):
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exists[tid] = 1
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res.append(t)
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return res
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def sub_stracks(tlista, tlistb):
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stracks = {}
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for t in tlista:
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stracks[t.track_id] = t
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for t in tlistb:
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tid = t.track_id
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if stracks.get(tid, 0):
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del stracks[tid]
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return list(stracks.values())
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def remove_duplicate_stracks(stracksa, stracksb):
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pdist = matching.iou_distance(stracksa, stracksb)
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pairs = np.where(pdist < 0.15)
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dupa, dupb = list(), list()
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for p, q in zip(*pairs):
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timep = stracksa[p].frame_id - stracksa[p].start_frame
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timeq = stracksb[q].frame_id - stracksb[q].start_frame
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if timep > timeq:
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dupb.append(q)
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else:
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dupa.append(p)
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resa = [t for i, t in enumerate(stracksa) if not i in dupa]
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resb = [t for i, t in enumerate(stracksb) if not i in dupb]
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return resa, resb
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