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339 lines
12 KiB
339 lines
12 KiB
import numpy as np
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from ..utils import matching
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from ..utils.kalman_filter import KalmanFilterXYAH
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from .basetrack import BaseTrack, TrackState
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class STrack(BaseTrack):
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shared_kalman = KalmanFilterXYAH()
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def __init__(self, tlwh, score, cls):
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# wait activate
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self._tlwh = np.asarray(self.tlbr_to_tlwh(tlwh[:-1]), 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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self.idx = tlwh[-1]
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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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return
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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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@staticmethod
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def multi_gmc(stracks, H=np.eye(2, 3)):
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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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R = H[:2, :2]
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R8x8 = np.kron(np.eye(4, dtype=float), R)
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t = H[:2, 2]
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for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
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mean = R8x8.dot(mean)
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mean[:2] += t
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cov = R8x8.dot(cov).dot(R8x8.transpose())
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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.convert_coords(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.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(self.mean, self.covariance,
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self.convert_coords(new_track.tlwh))
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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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self.idx = new_track.idx
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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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new_tlwh = new_track.tlwh
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self.mean, self.covariance = self.kalman_filter.update(self.mean, self.covariance,
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self.convert_coords(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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self.cls = new_track.cls
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self.idx = new_track.idx
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def convert_coords(self, tlwh):
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return self.tlwh_to_xyah(tlwh)
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@property
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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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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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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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@staticmethod
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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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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 f"OT_{self.track_id}_({self.start_frame}-{self.end_frame})"
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class BYTETracker:
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def __init__(self, args, 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.args = args
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self.max_time_lost = int(frame_rate / 30.0 * args.track_buffer)
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self.kalman_filter = self.get_kalmanfilter()
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def update(self, results, img=None):
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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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scores = results.conf
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bboxes = results.xyxy
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# add index
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bboxes = np.concatenate([bboxes, np.arange(len(bboxes)).reshape(-1, 1)], axis=-1)
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cls = results.cls
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remain_inds = scores > self.args.track_high_thresh
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inds_low = scores > self.args.track_low_thresh
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inds_high = scores < self.args.track_high_thresh
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inds_second = np.logical_and(inds_low, inds_high)
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dets_second = bboxes[inds_second]
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dets = bboxes[remain_inds]
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scores_keep = scores[remain_inds]
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scores_second = scores[inds_second]
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cls_keep = cls[remain_inds]
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cls_second = cls[inds_second]
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detections = self.init_track(dets, scores_keep, cls_keep, img)
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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 = self.joint_stracks(tracked_stracks, self.lost_stracks)
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# Predict the current location with KF
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self.multi_predict(strack_pool)
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if hasattr(self, "gmc"):
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warp = self.gmc.apply(img, dets)
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STrack.multi_gmc(strack_pool, warp)
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STrack.multi_gmc(unconfirmed, warp)
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dists = self.get_dists(strack_pool, detections)
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matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.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(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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""" 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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detections_second = self.init_track(dets_second, scores_second, cls_second, img)
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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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# TODO
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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 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 = self.get_dists(unconfirmed, 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.args.new_track_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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self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
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self.tracked_stracks = self.joint_stracks(self.tracked_stracks, activated_starcks)
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self.tracked_stracks = self.joint_stracks(self.tracked_stracks, refind_stracks)
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self.lost_stracks = self.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 = self.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 = self.remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
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output = [
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track.tlbr.tolist() + [track.track_id, track.score, track.cls, track.idx] for track in self.tracked_stracks
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if track.is_activated]
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return np.asarray(output, dtype=np.float32)
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def get_kalmanfilter(self):
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return KalmanFilterXYAH()
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def init_track(self, dets, scores, cls, img=None):
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return [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] if len(dets) else [] # detections
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def get_dists(self, tracks, detections):
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dists = matching.iou_distance(tracks, detections)
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# TODO: mot20
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# if not self.args.mot20:
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dists = matching.fuse_score(dists, detections)
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return dists
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def multi_predict(self, tracks):
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STrack.multi_predict(tracks)
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@staticmethod
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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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@staticmethod
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def sub_stracks(tlista, tlistb):
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stracks = {t.track_id: t for t in tlista}
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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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@staticmethod
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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 = [], []
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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 i not in dupa]
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resb = [t for i, t in enumerate(stracksb) if i not in dupb]
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return resa, resb
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