def kalman_predict_out_line(track,line,out_direction):
# print(track.track_id)
# print(line)
# print(out_direction)
# print(track.tlbr)
if box_line_relative(track.tlbr,line)==out_direction:
return 0
predict_num_out=0
prev_mean,prev_cov=track.mean,track.covariance
kal_man=KalmanFilter()
predict_thres=0 if out_direction=='up' else 0
max_long_predict=5 if out_direction=='up' else 2 if track.infer_type() in ['person','motorcycle','biycycle'] else 8
while box_line_relative(mean_to_tlbr(prev_mean),line) !=out_direction:
predict_num_out+=1
cur_mean=prev_mean #of t
mean,cov=kal_man.predict(prev_mean,prev_cov)
if predict_num_out>predict_thres:
new_mean,new_cov=mean,cov
else:
new_mean,new_cov= kal_man.update(prev_mean,prev_cov,mean[:4])
prev_mean,prev_cov=new_mean,new_cov #of t+1
if predict_num_out>=max_long_predict or np.sum(np.abs(cur_mean-mean))==0:
break
# print(mean_to_tlbr(mean))
return predict_num_out
def kalman_predict_out_line(track, line, out_direction, predict_long=None):
# print(track.track_id)
# print(line)
# print(out_direction)
# print(track.tlbr)
if box_line_relative(track.tlbr, line) == out_direction:
return 0
predict_num_out = 0
prev_mean, prev_cov = track.mean, track.covariance
kal_man = KalmanFilter()
predict_thres = 15 if out_direction == 'up' else 0
if predict_long is not None:
max_long_predict = predict_long
else:
max_long_predict = 50 if out_direction == 'up' else 4
while box_line_relative(mean_to_tlbr(prev_mean), line) != out_direction:
predict_num_out += 1
cur_mean = prev_mean #of t
mean, cov = kal_man.predict(prev_mean, prev_cov)
if predict_num_out > predict_thres:
new_mean, new_cov = mean, cov
else:
new_mean, new_cov = kal_man.update(prev_mean, prev_cov, mean[:4])
prev_mean, prev_cov = new_mean, new_cov #of t+1
if predict_num_out >= max_long_predict or np.sum(
np.abs(cur_mean - mean)) == 0:
break
# print(mean_to_tlbr(mean))
return predict_num_out
