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137 lines
4.6 KiB
137 lines
4.6 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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from collections import deque
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import numpy as np
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from ..utils import matching
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from ..utils.gmc import GMC
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from ..utils.kalman_filter import KalmanFilterXYWH
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from .basetrack import TrackState
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from .byte_tracker import BYTETracker, STrack
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class BOTrack(STrack):
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shared_kalman = KalmanFilterXYWH()
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def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
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super().__init__(tlwh, score, cls)
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self.smooth_feat = None
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self.curr_feat = None
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if feat is not None:
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self.update_features(feat)
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self.features = deque([], maxlen=feat_history)
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self.alpha = 0.9
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def update_features(self, feat):
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feat /= np.linalg.norm(feat)
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self.curr_feat = feat
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if self.smooth_feat is None:
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self.smooth_feat = feat
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else:
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self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat
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self.features.append(feat)
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self.smooth_feat /= np.linalg.norm(self.smooth_feat)
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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[6] = 0
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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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def re_activate(self, new_track, frame_id, new_id=False):
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if new_track.curr_feat is not None:
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self.update_features(new_track.curr_feat)
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super().re_activate(new_track, frame_id, new_id)
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def update(self, new_track, frame_id):
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if new_track.curr_feat is not None:
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self.update_features(new_track.curr_feat)
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super().update(new_track, frame_id)
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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[2:] / 2
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return ret
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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][6] = 0
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multi_mean[i][7] = 0
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multi_mean, multi_covariance = BOTrack.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 convert_coords(self, tlwh):
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return self.tlwh_to_xywh(tlwh)
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@staticmethod
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def tlwh_to_xywh(tlwh):
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"""Convert bounding box to format `(center x, center y, width,
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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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return ret
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class BOTSORT(BYTETracker):
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def __init__(self, args, frame_rate=30):
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super().__init__(args, frame_rate)
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# ReID module
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self.proximity_thresh = args.proximity_thresh
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self.appearance_thresh = args.appearance_thresh
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if args.with_reid:
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# haven't supported BoT-SORT(reid) yet
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self.encoder = None
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# self.gmc = GMC(method=args.cmc_method, verbose=[args.name, args.ablation])
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self.gmc = GMC(method=args.cmc_method)
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def get_kalmanfilter(self):
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return KalmanFilterXYWH()
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def init_track(self, dets, scores, cls, img=None):
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if len(dets) == 0:
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return []
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if self.args.with_reid and self.encoder is not None:
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features_keep = self.encoder.inference(img, dets)
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return [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)] # detections
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else:
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return [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] # 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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dists_mask = (dists > self.proximity_thresh)
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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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if self.args.with_reid and self.encoder is not None:
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emb_dists = matching.embedding_distance(tracks, detections) / 2.0
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emb_dists[emb_dists > self.appearance_thresh] = 1.0
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emb_dists[dists_mask] = 1.0
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dists = np.minimum(dists, emb_dists)
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return dists
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def multi_predict(self, tracks):
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BOTrack.multi_predict(tracks)
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