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from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import numpy as np | |
from sklearn.utils.linear_assignment_ import linear_assignment | |
import copy | |
from .mot_online.kalman_filter import KalmanFilter | |
from .mot_online.basetrack import BaseTrack, TrackState | |
from .mot_online import matching | |
class STrack(BaseTrack): | |
shared_kalman = KalmanFilter() | |
def __init__(self, tlwh, score): | |
# wait activate | |
self._tlwh = np.asarray(tlwh, dtype=np.float) | |
self.kalman_filter = None | |
self.mean, self.covariance = None, None | |
self.is_activated = False | |
self.score = score | |
self.tracklet_len = 0 | |
def predict(self): | |
mean_state = self.mean.copy() | |
if self.state != TrackState.Tracked: | |
mean_state[7] = 0 | |
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance) | |
def multi_predict(stracks): | |
if len(stracks) > 0: | |
multi_mean = np.asarray([st.mean.copy() for st in stracks]) | |
multi_covariance = np.asarray([st.covariance for st in stracks]) | |
for i, st in enumerate(stracks): | |
if st.state != TrackState.Tracked: | |
multi_mean[i][7] = 0 | |
multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance) | |
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)): | |
stracks[i].mean = mean | |
stracks[i].covariance = cov | |
def activate(self, kalman_filter, frame_id): | |
"""Start a new tracklet""" | |
self.kalman_filter = kalman_filter | |
self.track_id = self.next_id() | |
self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh)) | |
self.tracklet_len = 0 | |
self.state = TrackState.Tracked | |
if frame_id == 1: | |
self.is_activated = True | |
# self.is_activated = True | |
self.frame_id = frame_id | |
self.start_frame = frame_id | |
def re_activate(self, new_track, frame_id, new_id=False): | |
self.mean, self.covariance = self.kalman_filter.update( | |
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh) | |
) | |
self.tracklet_len = 0 | |
self.state = TrackState.Tracked | |
self.is_activated = True | |
self.frame_id = frame_id | |
if new_id: | |
self.track_id = self.next_id() | |
self.score = new_track.score | |
def update(self, new_track, frame_id): | |
""" | |
Update a matched track | |
:type new_track: STrack | |
:type frame_id: int | |
:type update_feature: bool | |
:return: | |
""" | |
self.frame_id = frame_id | |
self.tracklet_len += 1 | |
new_tlwh = new_track.tlwh | |
self.mean, self.covariance = self.kalman_filter.update( | |
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)) | |
self.state = TrackState.Tracked | |
self.is_activated = True | |
self.score = new_track.score | |
# @jit(nopython=True) | |
def tlwh(self): | |
"""Get current position in bounding box format `(top left x, top left y, | |
width, height)`. | |
""" | |
if self.mean is None: | |
return self._tlwh.copy() | |
ret = self.mean[:4].copy() | |
ret[2] *= ret[3] | |
ret[:2] -= ret[2:] / 2 | |
return ret | |
# @jit(nopython=True) | |
def tlbr(self): | |
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e., | |
`(top left, bottom right)`. | |
""" | |
ret = self.tlwh.copy() | |
ret[2:] += ret[:2] | |
return ret | |
# @jit(nopython=True) | |
def tlwh_to_xyah(tlwh): | |
"""Convert bounding box to format `(center x, center y, aspect ratio, | |
height)`, where the aspect ratio is `width / height`. | |
""" | |
ret = np.asarray(tlwh).copy() | |
ret[:2] += ret[2:] / 2 | |
ret[2] /= ret[3] | |
return ret | |
def to_xyah(self): | |
return self.tlwh_to_xyah(self.tlwh) | |
# @jit(nopython=True) | |
def tlbr_to_tlwh(tlbr): | |
ret = np.asarray(tlbr).copy() | |
ret[2:] -= ret[:2] | |
return ret | |
# @jit(nopython=True) | |
def tlwh_to_tlbr(tlwh): | |
ret = np.asarray(tlwh).copy() | |
ret[2:] += ret[:2] | |
return ret | |
def __repr__(self): | |
return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame) | |
class BYTETracker(object): | |
def __init__(self, args, frame_rate=30): | |
self.args = args | |
self.det_thresh = args.new_thresh | |
self.buffer_size = int(frame_rate / 30.0 * args.track_buffer) | |
self.max_time_lost = self.buffer_size | |
self.reset() | |
# below has no effect to final output, just to be compatible to codebase | |
def init_track(self, results): | |
for item in results: | |
if item['score'] > self.opt.new_thresh and item['class'] == 1: | |
self.id_count += 1 | |
item['active'] = 1 | |
item['age'] = 1 | |
item['tracking_id'] = self.id_count | |
if not ('ct' in item): | |
bbox = item['bbox'] | |
item['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2] | |
self.tracks.append(item) | |
def reset(self): | |
self.frame_id = 0 | |
self.kalman_filter = KalmanFilter() | |
self.tracked_stracks = [] # type: list[STrack] | |
self.lost_stracks = [] # type: list[STrack] | |
self.removed_stracks = [] # type: list[STrack] | |
self.tracks = [] | |
# below has no effect to final output, just to be compatible to codebase | |
self.id_count = 0 | |
def step(self, results, public_det=None): | |
self.frame_id += 1 | |
activated_starcks = [] | |
refind_stracks = [] | |
lost_stracks = [] | |
removed_stracks = [] | |
detections = [] | |
detections_second = [] | |
scores = np.array([item['score'] for item in results if item['class'] == 1], np.float32) | |
bboxes = np.vstack([item['bbox'] for item in results if item['class'] == 1]) # N x 4, x1y1x2y2 | |
remain_inds = scores >= self.args.track_thresh | |
dets = bboxes[remain_inds] | |
scores_keep = scores[remain_inds] | |
inds_low = scores > self.args.out_thresh | |
inds_high = scores < self.args.track_thresh | |
inds_second = np.logical_and(inds_low, inds_high) | |
dets_second = bboxes[inds_second] | |
scores_second = scores[inds_second] | |
if len(dets) > 0: | |
'''Detections''' | |
detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for | |
(tlbr, s) in zip(dets, scores_keep)] | |
else: | |
detections = [] | |
''' Add newly detected tracklets to tracked_stracks''' | |
unconfirmed = [] | |
tracked_stracks = [] # type: list[STrack] | |
for track in self.tracked_stracks: | |
if not track.is_activated: | |
unconfirmed.append(track) | |
else: | |
tracked_stracks.append(track) | |
''' Step 2: First association, with Kalman and IOU''' | |
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks) | |
# Predict the current location with KF | |
STrack.multi_predict(strack_pool) | |
dists = matching.iou_distance(strack_pool, detections) | |
#dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections) | |
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=self.args.match_thresh) | |
for itracked, idet in matches: | |
track = strack_pool[itracked] | |
det = detections[idet] | |
if track.state == TrackState.Tracked: | |
track.update(detections[idet], self.frame_id) | |
activated_starcks.append(track) | |
else: | |
track.re_activate(det, self.frame_id, new_id=False) | |
refind_stracks.append(track) | |
''' Step 3: Second association, association the untrack to the low score detections, with IOU''' | |
if len(dets_second) > 0: | |
'''Detections''' | |
detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for | |
(tlbr, s) in zip(dets_second, scores_second)] | |
else: | |
detections_second = [] | |
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] | |
dists = matching.iou_distance(r_tracked_stracks, detections_second) | |
matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5) | |
for itracked, idet in matches: | |
track = r_tracked_stracks[itracked] | |
det = detections_second[idet] | |
if track.state == TrackState.Tracked: | |
track.update(det, self.frame_id) | |
activated_starcks.append(track) | |
else: | |
track.re_activate(det, self.frame_id, new_id=False) | |
refind_stracks.append(track) | |
for it in u_track: | |
track = r_tracked_stracks[it] | |
if not track.state == TrackState.Lost: | |
track.mark_lost() | |
lost_stracks.append(track) | |
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' | |
detections = [detections[i] for i in u_detection] | |
dists = matching.iou_distance(unconfirmed, detections) | |
matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) | |
for itracked, idet in matches: | |
unconfirmed[itracked].update(detections[idet], self.frame_id) | |
activated_starcks.append(unconfirmed[itracked]) | |
for it in u_unconfirmed: | |
track = unconfirmed[it] | |
track.mark_removed() | |
removed_stracks.append(track) | |
""" Step 4: Init new stracks""" | |
for inew in u_detection: | |
track = detections[inew] | |
if track.score < self.det_thresh: | |
continue | |
track.activate(self.kalman_filter, self.frame_id) | |
activated_starcks.append(track) | |
""" Step 5: Update state""" | |
for track in self.lost_stracks: | |
if self.frame_id - track.end_frame > self.max_time_lost: | |
track.mark_removed() | |
removed_stracks.append(track) | |
self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked] | |
self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks) | |
self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks) | |
self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks) | |
self.lost_stracks.extend(lost_stracks) | |
self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks) | |
self.removed_stracks.extend(removed_stracks) | |
self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks) | |
output_stracks = [track for track in self.tracked_stracks if track.is_activated] | |
ret = [] | |
for track in output_stracks: | |
track_dict = {} | |
track_dict['score'] = track.score | |
track_dict['bbox'] = track.tlbr | |
bbox = track_dict['bbox'] | |
track_dict['ct'] = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2] | |
track_dict['active'] = 1 if track.is_activated else 0 | |
track_dict['tracking_id'] = track.track_id | |
track_dict['class'] = 1 | |
ret.append(track_dict) | |
self.tracks = ret | |
return ret | |
def joint_stracks(tlista, tlistb): | |
exists = {} | |
res = [] | |
for t in tlista: | |
exists[t.track_id] = 1 | |
res.append(t) | |
for t in tlistb: | |
tid = t.track_id | |
if not exists.get(tid, 0): | |
exists[tid] = 1 | |
res.append(t) | |
return res | |
def sub_stracks(tlista, tlistb): | |
stracks = {} | |
for t in tlista: | |
stracks[t.track_id] = t | |
for t in tlistb: | |
tid = t.track_id | |
if stracks.get(tid, 0): | |
del stracks[tid] | |
return list(stracks.values()) | |
def remove_duplicate_stracks(stracksa, stracksb): | |
pdist = matching.iou_distance(stracksa, stracksb) | |
pairs = np.where(pdist < 0.15) | |
dupa, dupb = list(), list() | |
for p, q in zip(*pairs): | |
timep = stracksa[p].frame_id - stracksa[p].start_frame | |
timeq = stracksb[q].frame_id - stracksb[q].start_frame | |
if timep > timeq: | |
dupb.append(q) | |
else: | |
dupa.append(p) | |
resa = [t for i, t in enumerate(stracksa) if not i in dupa] | |
resb = [t for i, t in enumerate(stracksb) if not i in dupb] | |
return resa, resb | |
def remove_fp_stracks(stracksa, n_frame=10): | |
remain = [] | |
for t in stracksa: | |
score_5 = t.score_list[-n_frame:] | |
score_5 = np.array(score_5, dtype=np.float32) | |
index = score_5 < 0.45 | |
num = np.sum(index) | |
if num < n_frame: | |
remain.append(t) | |
return remain | |