File size: 14,446 Bytes
ab854b9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import numpy as np
from .basetrack import BaseTrack, TrackState
from .utils import matching
from .utils.kalman_filter import KalmanFilterXYAH
class STrack(BaseTrack):
shared_kalman = KalmanFilterXYAH()
def __init__(self, tlwh, score, cls):
"""wait activate."""
self._tlwh = np.asarray(self.tlbr_to_tlwh(tlwh[:-1]), dtype=np.float32)
self.kalman_filter = None
self.mean, self.covariance = None, None
self.is_activated = False
self.score = score
self.tracklet_len = 0
self.cls = cls
self.idx = tlwh[-1]
def predict(self):
"""Predicts mean and covariance using Kalman filter."""
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)
@staticmethod
def multi_predict(stracks):
"""Perform multi-object predictive tracking using Kalman filter for given stracks."""
if len(stracks) <= 0:
return
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
@staticmethod
def multi_gmc(stracks, H=np.eye(2, 3)):
"""Update state tracks positions and covariances using a homography matrix."""
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])
R = H[:2, :2]
R8x8 = np.kron(np.eye(4, dtype=float), R)
t = H[:2, 2]
for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
mean = R8x8.dot(mean)
mean[:2] += t
cov = R8x8.dot(cov).dot(R8x8.transpose())
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.convert_coords(self._tlwh))
self.tracklet_len = 0
self.state = TrackState.Tracked
if frame_id == 1:
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):
"""Reactivates a previously lost track with a new detection."""
self.mean, self.covariance = self.kalman_filter.update(self.mean, self.covariance,
self.convert_coords(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
self.cls = new_track.cls
self.idx = new_track.idx
def update(self, new_track, frame_id):
"""
Update a matched track
:type new_track: STrack
:type frame_id: int
: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.convert_coords(new_tlwh))
self.state = TrackState.Tracked
self.is_activated = True
self.score = new_track.score
self.cls = new_track.cls
self.idx = new_track.idx
def convert_coords(self, tlwh):
"""Convert a bounding box's top-left-width-height format to its x-y-angle-height equivalent."""
return self.tlwh_to_xyah(tlwh)
@property
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
@property
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
@staticmethod
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
@staticmethod
def tlbr_to_tlwh(tlbr):
"""Converts top-left bottom-right format to top-left width height format."""
ret = np.asarray(tlbr).copy()
ret[2:] -= ret[:2]
return ret
@staticmethod
def tlwh_to_tlbr(tlwh):
"""Converts tlwh bounding box format to tlbr format."""
ret = np.asarray(tlwh).copy()
ret[2:] += ret[:2]
return ret
def __repr__(self):
"""Return a string representation of the BYTETracker object with start and end frames and track ID."""
return f'OT_{self.track_id}_({self.start_frame}-{self.end_frame})'
class BYTETracker:
def __init__(self, args, frame_rate=30):
"""Initialize a YOLOv8 object to track objects with given arguments and frame rate."""
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.frame_id = 0
self.args = args
self.max_time_lost = int(frame_rate / 30.0 * args.track_buffer)
self.kalman_filter = self.get_kalmanfilter()
self.reset_id()
def update(self, results, img=None):
"""Updates object tracker with new detections and returns tracked object bounding boxes."""
self.frame_id += 1
activated_stracks = []
refind_stracks = []
lost_stracks = []
removed_stracks = []
scores = results.conf
bboxes = results.xyxy
# Add index
bboxes = np.concatenate([bboxes, np.arange(len(bboxes)).reshape(-1, 1)], axis=-1)
cls = results.cls
remain_inds = scores > self.args.track_high_thresh
inds_low = scores > self.args.track_low_thresh
inds_high = scores < self.args.track_high_thresh
inds_second = np.logical_and(inds_low, inds_high)
dets_second = bboxes[inds_second]
dets = bboxes[remain_inds]
scores_keep = scores[remain_inds]
scores_second = scores[inds_second]
cls_keep = cls[remain_inds]
cls_second = cls[inds_second]
detections = self.init_track(dets, scores_keep, cls_keep, img)
# 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 high score detection boxes
strack_pool = self.joint_stracks(tracked_stracks, self.lost_stracks)
# Predict the current location with KF
self.multi_predict(strack_pool)
if hasattr(self, 'gmc') and img is not None:
warp = self.gmc.apply(img, dets)
STrack.multi_gmc(strack_pool, warp)
STrack.multi_gmc(unconfirmed, warp)
dists = self.get_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(det, self.frame_id)
activated_stracks.append(track)
else:
track.re_activate(det, self.frame_id, new_id=False)
refind_stracks.append(track)
# Step 3: Second association, with low score detection boxes
# association the untrack to the low score detections
detections_second = self.init_track(dets_second, scores_second, cls_second, img)
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
# TODO
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_stracks.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 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 = self.get_dists(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_stracks.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.args.new_track_thresh:
continue
track.activate(self.kalman_filter, self.frame_id)
activated_stracks.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 = self.joint_stracks(self.tracked_stracks, activated_stracks)
self.tracked_stracks = self.joint_stracks(self.tracked_stracks, refind_stracks)
self.lost_stracks = self.sub_stracks(self.lost_stracks, self.tracked_stracks)
self.lost_stracks.extend(lost_stracks)
self.lost_stracks = self.sub_stracks(self.lost_stracks, self.removed_stracks)
self.tracked_stracks, self.lost_stracks = self.remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
self.removed_stracks.extend(removed_stracks)
if len(self.removed_stracks) > 1000:
self.removed_stracks = self.removed_stracks[-999:] # clip remove stracks to 1000 maximum
return np.asarray(
[x.tlbr.tolist() + [x.track_id, x.score, x.cls, x.idx] for x in self.tracked_stracks if x.is_activated],
dtype=np.float32)
def get_kalmanfilter(self):
"""Returns a Kalman filter object for tracking bounding boxes."""
return KalmanFilterXYAH()
def init_track(self, dets, scores, cls, img=None):
"""Initialize object tracking with detections and scores using STrack algorithm."""
return [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] if len(dets) else [] # detections
def get_dists(self, tracks, detections):
"""Calculates the distance between tracks and detections using IOU and fuses scores."""
dists = matching.iou_distance(tracks, detections)
# TODO: mot20
# if not self.args.mot20:
dists = matching.fuse_score(dists, detections)
return dists
def multi_predict(self, tracks):
"""Returns the predicted tracks using the YOLOv8 network."""
STrack.multi_predict(tracks)
def reset_id(self):
"""Resets the ID counter of STrack."""
STrack.reset_id()
@staticmethod
def joint_stracks(tlista, tlistb):
"""Combine two lists of stracks into a single one."""
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
@staticmethod
def sub_stracks(tlista, tlistb):
"""DEPRECATED CODE in https://github.com/ultralytics/ultralytics/pull/1890/
stracks = {t.track_id: t for t in tlista}
for t in tlistb:
tid = t.track_id
if stracks.get(tid, 0):
del stracks[tid]
return list(stracks.values())
"""
track_ids_b = {t.track_id for t in tlistb}
return [t for t in tlista if t.track_id not in track_ids_b]
@staticmethod
def remove_duplicate_stracks(stracksa, stracksb):
"""Remove duplicate stracks with non-maximum IOU distance."""
pdist = matching.iou_distance(stracksa, stracksb)
pairs = np.where(pdist < 0.15)
dupa, dupb = [], []
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 i not in dupa]
resb = [t for i, t in enumerate(stracksb) if i not in dupb]
return resa, resb
|