Towards-Realtime-MOT/tracker/multitracker.py

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2019-09-27 05:37:47 +00:00
import numpy as np
from numba import jit
from collections import deque
import itertools
import os
import os.path as osp
import time
import torch
from utils.utils import *
from utils.log import logger
from utils.kalman_filter import KalmanFilter
from models import *
from tracker import matching
from .basetrack import BaseTrack, TrackState
class STrack(BaseTrack):
def __init__(self, tlwh, score, temp_feat, buffer_size=30):
# 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
self.smooth_feat = None
self.update_features(temp_feat)
self.features = deque([], maxlen=buffer_size)
self.alpha = 0.9
def update_features(self, feat):
self.curr_feat = feat
if self.smooth_feat is None:
self.smooth_feat = feat
else:
self.smooth_feat = self.alpha *self.smooth_feat + (1-self.alpha) * feat
self.features.append(feat)
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
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 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
#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.update_features(new_track.curr_feat)
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()
def update(self, new_track, frame_id, update_feature=True):
"""
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
if update_feature:
self.update_features(new_track.curr_feat)
@property
@jit
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
@jit
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
@jit
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)
@staticmethod
@jit
def tlbr_to_tlwh(tlbr):
ret = np.asarray(tlbr).copy()
ret[2:] -= ret[:2]
return ret
@staticmethod
@jit
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)
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class JDETracker(object):
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def __init__(self, opt, frame_rate=30):
self.opt = opt
self.model = Darknet(opt.cfg, opt.img_size, nID=14455)
# load_darknet_weights(self.model, opt.weights)
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self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False)
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self.model.cuda().eval()
self.tracked_stracks = [] # type: list[STrack]
self.lost_stracks = [] # type: list[STrack]
self.removed_stracks = [] # type: list[STrack]
self.frame_id = 0
self.det_thresh = opt.conf_thres
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
self.max_time_lost = self.buffer_size
self.kalman_filter = KalmanFilter()
def update(self, im_blob, img0):
self.frame_id += 1
activated_starcks = []
refind_stracks = []
lost_stracks = []
removed_stracks = []
t1 = time.time()
''' Step 1: Network forward, get detections & embeddings'''
with torch.no_grad():
pred = self.model(im_blob)
pred = pred[pred[:, :, 4] > self.opt.conf_thres]
if len(pred) > 0:
dets = non_max_suppression(pred.unsqueeze(0), self.opt.conf_thres, self.opt.nms_thres)[0].cpu()
scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
'''Detections'''
detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for
(tlbrs, f) in zip(dets[:, :5], dets[:, -self.model.emb_dim:])]
else:
detections = []
t2 = time.time()
# print('Forward: {} s'.format(t2-t1))
''' 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 embedding'''
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
# Predict the current location with KF
for strack in strack_pool:
strack.predict()
dists = matching.embedding_distance(strack_pool, detections)
dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)
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, with IOU'''
detections = [detections[i] for i in u_detection]
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)
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)
for itracked, idet in matches:
track = r_tracked_stracks[itracked]
det = detections[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)
t4 = time.time()
# print('Ramained match {} s'.format(t4-t3))
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 = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]
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)
# get scores of lost tracks
output_stracks = [track for track in self.tracked_stracks if track.is_activated]
logger.debug('===========Frame {}=========='.format(self.frame_id))
logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
t5 = time.time()
# print('Final {} s'.format(t5-t4))
return output_stracks
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