364 lines
13 KiB
Python
364 lines
13 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from sklearn.utils.linear_assignment_ import linear_assignment
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import copy
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from .mot_online.kalman_filter import KalmanFilter
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from .mot_online.basetrack import BaseTrack, TrackState
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from .mot_online import matching
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class STrack(BaseTrack):
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shared_kalman = KalmanFilter()
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def __init__(self, tlwh, score):
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# wait activate
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self._tlwh = np.asarray(tlwh, dtype=np.float)
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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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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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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(
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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, args, frame_rate=30):
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self.args = args
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self.det_thresh = args.new_thresh
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self.buffer_size = int(frame_rate / 30.0 * args.track_buffer)
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self.max_time_lost = self.buffer_size
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self.reset()
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# below has no effect to final output, just to be compatible to codebase
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def init_track(self, results):
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for item in results:
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if item['score'] > self.opt.new_thresh and item['class'] == 1:
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self.id_count += 1
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item['active'] = 1
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item['age'] = 1
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item['tracking_id'] = self.id_count
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if not ('ct' in item):
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bbox = item['bbox']
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item['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
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self.tracks.append(item)
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def reset(self):
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self.frame_id = 0
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self.kalman_filter = KalmanFilter()
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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.tracks = []
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# below has no effect to final output, just to be compatible to codebase
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self.id_count = 0
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def step(self, results, public_det=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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detections = []
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detections_second = []
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scores = np.array([item['score'] for item in results if item['class'] == 1], np.float32)
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bboxes = np.vstack([item['bbox'] for item in results if item['class'] == 1]) # N x 4, x1y1x2y2
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remain_inds = scores >= self.args.track_thresh
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dets = bboxes[remain_inds]
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scores_keep = scores[remain_inds]
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inds_low = scores > self.args.out_thresh
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inds_high = scores < self.args.track_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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scores_second = scores[inds_second]
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if len(dets) > 0:
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'''Detections'''
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detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
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(tlbr, s) in zip(dets, scores_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 Kalman and IOU'''
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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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#dists = matching.fuse_motion(self.kalman_filter, 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(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, association the untrack to the low score detections， with IOU'''
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if len(dets_second) > 0:
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'''Detections'''
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detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
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(tlbr, s) in zip(dets_second, scores_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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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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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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output_stracks = [track for track in self.tracked_stracks if track.is_activated]
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ret = []
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for track in output_stracks:
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track_dict = {}
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track_dict['score'] = track.score
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track_dict['bbox'] = track.tlbr
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bbox = track_dict['bbox']
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track_dict['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
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track_dict['active'] = 1 if track.is_activated else 0
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track_dict['tracking_id'] = track.track_id
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track_dict['class'] = 1
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ret.append(track_dict)
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self.tracks = ret
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return ret
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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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def remove_fp_stracks(stracksa, n_frame=10):
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remain = []
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for t in stracksa:
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score_5 = t.score_list[-n_frame:]
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score_5 = np.array(score_5, dtype=np.float32)
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index = score_5 < 0.45
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num = np.sum(index)
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if num < n_frame:
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remain.append(t)
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return remain
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