def handle_frames(frame):
global tracker_list
global max_age
global min_hits
global track_id_list
detection_results = api.get_person_bbox(frame, thr=0.5)
x_box =[]
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append([(trk.box[0],trk.box[1]),(trk.box[2],trk.box[3])]) #should be changed into the right format instead of the .box format
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(x_box, detection_results, iou_thrd = 0.2)
# Deal with matched detections
if matched.size >0:
for trk_idx, det_idx in matched:
z = detection_results[det_idx]
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk= tracker_list[trk_idx]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
tmp_trk.box =xx
tmp_trk.hits += 1
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets)>0:
for idx in unmatched_dets:
z = detection_results[idx]
x1 = int(z[0][0])
y1 = int(z[0][1])
x2 = int(z[1][0])
y2 = int(z[1][1])
person = frame[y1:y2, x1:x2, :]
identify_name, score = compare.run(person, origin_f, origin_name)
if(identify_name in [ "MJ1", "MJ2", "MJ3", "MJ4", "MJ5"]):
identify_name = "Person_1"
elif(identify_name in ["QY1", "QY2", "QY3", "QY4", "QY5"]):
identify_name = "Person_2"
print("identify name:{}, score:{}".format(identify_name, round(1-score, 2)))
#generate a new tracker for the person
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk = Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
tmp_trk.id = track_id_list.popleft() # assign an ID for the tracker
tmp_trk.personReID_info['personID'] = identify_name #assign the reidentified personID for the tracker
tracker_list.append(tmp_trk)
x_box.append(xx)
# Deal with unmatched tracks
if len(unmatched_trks)>0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
tmp_trk.no_losses += 1
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box =xx
x_box[trk_idx] = xx
# The list of tracks to be annotated and draw the figure
good_tracker_list =[]
for trk in tracker_list:
if ((trk.hits >= min_hits) and (trk.no_losses <=max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
trackerID_str="Unknown Person:"+str(trk.id)
if trk.personReID_info['personID'] == "Unknown":
frame= draw_box_label(frame, x_cv2,personReID_info={'personID':trackerID_str}) # Draw the bounding boxes for unknown person
else:
frame= draw_box_label(frame, x_cv2,personReID_info=trk.personReID_info) # Draw the bounding boxes for re-identified person
#book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses<=max_age]
# #the original codes
# for bbox in detection_results:
# logger.info('coordinates: {} {}. '.
# format(bbox[0], bbox[1]))
# x1 = int(bbox[0][0])
# y1 = int(bbox[0][1])
# x2 = int(bbox[1][0])
# y2 = int(bbox[1][1])
# person = frame[y1:y2, x1:x2, :]
# identify_name, score = compare.run(person, origin_f, origin_name)
# if(identify_name in [ "MJ1", "MJ2", "MJ3", "MJ4", "MJ5"]):
# identify_name = "Person_1"
# elif(identify_name in ["QY1", "QY2", "QY3", "QY4", "QY5"]):
# identify_name = "Person_2"
#
# print("identify name:{}, score:{}".format(identify_name, round(1-score, 2)))
#
# bounding_boxs.append([(x1,y1), (x2,y2), identify_name+' '+str(round(1-score, 2))])
# #img = cam_detection.draw_rectangle(img, (x1,y1,x2,y2), identify_name+' '+str(round((1-score), 2)))
# for obj in bounding_boxs:
# print(obj)
# cv2.putText(frame, obj[2], (obj[0][0], obj[0][1] - 5), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 2)
# frame = cv2.rectangle(frame, obj[0], obj[1], (0, 255, 0), 2)
return frame
def pipeline(img):
'''
Pipeline function for detection and tracking
'''
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
frame_count+=1
img_dim = (img.shape[1], img.shape[0])
z_box = det.get_localization(img) # measurement
if debug:
print('Frame:', frame_count)
x_box =[]
if debug:
for i in range(len(z_box)):
img1= helpers.draw_box_label(img, z_box[i], box_color=(255, 0, 0))
plt.imshow(img1)
plt.show()
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(x_box, z_box, iou_thrd = 0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# Deal with matched detections
if matched.size >0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk= tracker_list[trk_idx]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
tmp_trk.box =xx
tmp_trk.hits += 1
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets)>0:
for idx in unmatched_dets:
z = z_box[idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
tmp_trk.id = track_id_list.popleft() # assign an ID for the tracker
tracker_list.append(tmp_trk)
x_box.append(xx)
# Deal with unmatched tracks
if len(unmatched_trks)>0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
tmp_trk.no_losses += 1
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box =xx
x_box[trk_idx] = xx
# The list of tracks to be annotated
good_tracker_list =[]
for trk in tracker_list:
if ((trk.hits >= min_hits) and (trk.no_losses <=max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
if debug:
print('updated box: ', x_cv2)
print()
img= helpers.draw_box_label(img, x_cv2) # Draw the bounding boxes on the
# images
# Book keeping
deleted_tracks = filter(lambda x: x.no_losses >max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses<=max_age]
if debug:
print('Ending tracker_list: ',len(tracker_list))
print('Ending good tracker_list: ',len(good_tracker_list))
return img
def handle_frames(frame):
global tracker_list
global max_age
global min_hits
global track_id_list
#connect to database
conn = sqlite3.connect('handwash.db', isolation_level=None)
#print("Opened database successfully")
cur = conn.cursor()
#detect hand
try:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
except:
print("Error converting to RGB")
#print(type(frame))
boxes, scores = detector_utils.detect_objects(frame,detection_graph,sess)
# draw bounding boxes on frame
hand_in_sink, hand_in_patient = detector_utils.draw_box_on_image_washhand( \
num_hands_detect, score_thresh, scores, boxes, im_width, \
im_height, frame, sink_loc, patient_loc)
try:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
except:
print("Error converting to BGR")
#detect person
detection_results = api.get_person_bbox(frame, thr=0.50)
x_box =[]
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append([(trk.box[0],trk.box[1]),(trk.box[2],trk.box[3])]) #should be changed into the right format instead of the .box format
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(x_box, detection_results, iou_thrd = 0.2)
# Deal with matched detections
if matched.size >0:
for trk_idx, det_idx in matched:
z = detection_results[det_idx]
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk= tracker_list[trk_idx]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
tmp_trk.box =xx
tmp_trk.hits += 1
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets)>0:
for idx in unmatched_dets:
z = detection_results[idx]
x1 = int(z[0][0])
y1 = int(z[0][1])
x2 = int(z[1][0])
y2 = int(z[1][1])
person = frame[y1:y2, x1:x2, :]
identify_name, score = compare.run(person, origin_f, origin_name)
if(identify_name in [ "QY1", "QY2", "QY3", "QY4", "QY5", "QY6"]):
identify_name = "Doctor"
elif(identify_name in ["YN1", "YN2", "YN3", "YN4", "YN5", "YN6"]):
identify_name = "Nurse"
print("identify name:{}, score:{}".format(identify_name, round(1-score, 2)))
#generate a new tracker for the person
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk = Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
tmp_trk.id = track_id_list.popleft() # assign an ID for the tracker
tmp_trk.personReID_info['personID'] = identify_name #assign the reidentified personID for the tracker
#assign the tracker attribute to new tracker when loose tracking a person but re_id him
if len(unmatched_trks)>0:
for trk_idx in unmatched_trks:
trk_old = tracker_list[trk_idx]
if trk_old.personReID_info['personID'] == identify_name:
tmp_trk.have_washed_hand = trk_old.have_washed_hand
tmp_trk.hand_clean = trk_old.hand_clean
tmp_trk.have_touched_pat = trk_old.have_touched_pat
tmp_trk.violate_rule = trk_old.violate_rule
tracker_list.append(tmp_trk)
x_box.append(xx)
# Deal with unmatched tracks
if len(unmatched_trks)>0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
tmp_trk.no_losses += 1
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx =[xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box =xx
x_box[trk_idx] = xx
# The list of tracks to be annotated and draw the figure
good_tracker_list =[]
for trk in tracker_list:
if ((trk.hits >= min_hits) and (trk.no_losses <=max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
trackerID_str="Unknown Person:"+str(trk.id)
if trk.personReID_info['personID'] == "Unknown":
trk.personReID_info['personID'] = "Unknown Person:"+str(trk.id) # Change the personID for unknown person
frame= draw_box_label(frame, x_cv2, personReID_info=trk.personReID_info) # Draw the bounding boxes for person
#book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
#judge whether the person has washed hand before leaving and add the deleted tracker into the tracke_id_list
for trk in deleted_tracks:
print(trk.box, trk.hits)
if (trk.box[2] >= 640 or trk.box[1]<0) and (trk.hits >= 10):
if trk.have_touched_pat and (not trk.hand_clean):
if trk.violate_rule == 2:
trk.violate_rule = 3
else:
trk.violate_rule = 1
person_tracker_info = "ctime {}, person_ID {}, sub_ID {}".format(int(time.time()), trk.personReID_info['personID'], str(trk.id))
alarm = " washed_hand {},touched_patient {},violate_rule {}".format(str(trk.have_washed_hand),str(trk.have_touched_pat),str(trk.violate_rule))
print(trk.personReID_info['personID']+":"+person_tracker_info+alarm)
info = "insert into HANDEMO (PERSON, CTIME, HLOC, PLOC, HAND, PATIENT, JUDGE) \
values ('{}', {}, '{}', '{}', {}, {}, {})".format(trk.personReID_info['personID'], int(time.time()), '', '', \
int(trk.have_washed_hand), int(trk.have_touched_pat), int(trk.violate_rule))
cur.execute(info)
if trk.violate_rule == 1 or trk.violate_rule == 3:
cmd = "play After.wav"
subprocess.Popen(cmd, shell=True)
if trk.violate_rule == 2:
cmd = "play Before.wav"
subprocess.Popen(cmd, shell=True)
#if trk.violate_rule != 0:
# cmd1 = "play Beep.wav"
# subprocess.Popen(cmd1, shell=True)
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses<=max_age]
#judge whether this guy has washed has hands
#for all detected hand in sink
if len(hand_in_sink):
for w_h_box in hand_in_sink:
for trk in good_tracker_list:
if wash_hand_detector(trk,w_h_box):
person_tracker_info = "ctime {}, person_ID {}, sub_ID {}".format(int(time.time()), trk.personReID_info['personID'] , str(trk.id))
location_info = " hand_location {}, person_location {}".format(str(w_h_box), str(trk.box))
alarm = " washed_hand {},touched_patient {},violate_rule {}".format(str(trk.have_washed_hand),str(trk.have_touched_pat),str(trk.violate_rule))
#alarm = "washed_hand {},touched_patient {},hand_clean {}".format(str(trk.have_washed_hand),str(trk.have_touched_pat),str(trk.hand_clean))
cv2.putText(frame,alarm, (w_h_box[0][0],w_h_box[0][1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255,191,0), 1, cv2.LINE_AA)
print(trk.personReID_info['personID']+":"+person_tracker_info+location_info+alarm)
info = "insert into HANDEMO (PERSON, CTIME, HLOC, PLOC, HAND, PATIENT, JUDGE) \
values ('{}', {}, '{}', '{}', {}, {}, {})".format(trk.personReID_info['personID'], int(time.time()), str(w_h_box), str(trk.box), \
int(trk.have_washed_hand), int(trk.have_touched_pat), int(trk.violate_rule))
cur.execute(info)
if trk.violate_rule == 1 or trk.violate_rule == 3:
cmd = "play After.wav"
subprocess.Popen(cmd, shell=True)
if trk.violate_rule == 2:
cmd = "play Before.wav"
subprocess.Popen(cmd, shell=True)
#if trk.violate_rule != 0:
# cmd1 = "play Beep.wav"
# subprocess.Popen(cmd1, shell=True)
#for all detected hand in patient
if len(hand_in_patient):
for t_p_box in hand_in_patient:
for trk in good_tracker_list:
if touch_patient_detector(trk,t_p_box):
person_tracker_info = "ctime {}, person_ID {}, sub_ID {}".format(int(time.time()), trk.personReID_info['personID'], str(trk.id))
location_info = " hand_location {}, person_location {}".format(str(t_p_box), str(trk.box))
alarm = " washed_hand {},touched_patient {},violate_rule {}".format(str(trk.have_washed_hand),str(trk.have_touched_pat),str(trk.violate_rule))
#alarm = "washed_hand {},touched_patient {},hand_clean {}".format(str(trk.have_washed_hand),str(trk.have_touched_pat),str(trk.hand_clean))
cv2.putText(frame,alarm, (t_p_box[0][0],t_p_box[0][1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0,255,255), 1, cv2.LINE_AA)
print(trk.personReID_info['personID']+":"+person_tracker_info+location_info+alarm)
info = "insert into HANDEMO (PERSON, CTIME, HLOC, PLOC, HAND, PATIENT, JUDGE) \
values ('{}', {}, '{}', '{}', {}, {}, {})".format(trk.personReID_info['personID'], int(time.time()), str(t_p_box), str(trk.box), \
int(trk.have_washed_hand), int(trk.have_touched_pat), int(trk.violate_rule))
cur.execute(info)
if trk.violate_rule == 1 or trk.violate_rule == 3:
cmd = "play After.wav"
subprocess.Popen(cmd, shell=True)
if trk.violate_rule == 2:
cmd = "play Before.wav"
subprocess.Popen(cmd, shell=True)
#if trk.violate_rule != 0:
# cmd1 = "play Beep.wav"
# subprocess.Popen(cmd1, shell=True)
return frame
def tracking_person(detection_results, frame):
global tracker_list
global max_age
global min_hits
global track_id_list
global identity_num
x_box = []
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(
[(trk.box[0], trk.box[1]), (trk.box[2], trk.box[3])]
) #should be changed into the right format instead of the .box format
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(
x_box, detection_results, iou_thrd=0.2)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = detection_results[det_idx]
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
tmp_trk.box = xx
tmp_trk.hits += 1
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets) > 0:
t1 = time.time()
persons = {}
for idx in unmatched_dets:
z = detection_results[idx]
x1 = int(z[0][0])
y1 = int(z[0][1])
x2 = int(z[1][0])
y2 = int(z[1][1])
person = frame[y1:y2, x1:x2, :]
#persons.append(person)
persons[identity_num] = person
identify_name = "Unknown" + str(identity_num)
identity_num += 1
#generate a new tracker for the person
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk = Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
tmp_trk.id = track_id_list.popleft(
) # assign an ID for the tracker
tmp_trk.personReID_info[
'personID'] = identify_name #assign the reidentified personID for the tracker
tracker_list.append(tmp_trk)
x_box.append(xx)
#offload the Re-ID task
arg_send = (persons, )
offload_to_peer(2, next_task_args=persons, client_socket=client_socket)
t2 = time.time()
send_time = t2 - t1
print("send consumes", (t2 - t1))
# Deal with unmatched tracks
if len(unmatched_trks) > 0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
tmp_trk.no_losses += 1
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
x_box[trk_idx] = xx
# The list of tracks to be annotated and draw the figure
good_tracker_list = []
for trk in tracker_list:
if ((trk.hits >= min_hits) and (trk.no_losses <= max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
trackerID_str = "Unknown Person:" + str(trk.id)
if trk.personReID_info['personID'] == "Unknown":
frame = draw_box_label(
frame, x_cv2, personReID_info={
'personID': trackerID_str
}) # Draw the bounding boxes for unknown person
else:
frame = draw_box_label(
frame, x_cv2, personReID_info=trk.personReID_info
) # Draw the bounding boxes for re-identified person
#book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses <= max_age]
return frame, send_time
def server_inference(detection_results, frame):
global tracker_list
global max_age
global min_hits
global track_id_list
x_box = []
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(
[(trk.box[0], trk.box[1]), (trk.box[2], trk.box[3])]
) #should be changed into the right format instead of the .box format
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(
x_box, detection_results, iou_thrd=0.2)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = detection_results[det_idx]
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
tmp_trk.box = xx
tmp_trk.hits += 1
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = detection_results[idx]
x1 = int(z[0][0])
y1 = int(z[0][1])
x2 = int(z[1][0])
y2 = int(z[1][1])
person = frame[y1:y2, x1:x2, :]
identify_name, score = compare.run(person, origin_f, origin_name)
if (identify_name in ["MJ1", "MJ2", "MJ3", "MJ4", "MJ5"]):
identify_name = "Person_1"
elif (identify_name in ["QY1", "QY2", "QY3", "QY4", "QY5"]):
identify_name = "Person_2"
print("identify name:{}, score:{}".format(identify_name,
round(1 - score, 2)))
#generate a new tracker for the person
z = np.expand_dims([n for a in z for n in a], axis=0).T
tmp_trk = Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
tmp_trk.id = track_id_list.popleft(
) # assign an ID for the tracker
tmp_trk.personReID_info[
'personID'] = identify_name #assign the reidentified personID for the tracker
tracker_list.append(tmp_trk)
x_box.append(xx)
# Deal with unmatched tracks
if len(unmatched_trks) > 0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
tmp_trk.no_losses += 1
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
x_box[trk_idx] = xx
# The list of tracks to be annotated and draw the figure
good_tracker_list = []
for trk in tracker_list:
if ((trk.hits >= min_hits) and (trk.no_losses <= max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
trackerID_str = "Unknown Person:" + str(trk.id)
if trk.personReID_info['personID'] == "Unknown":
frame = draw_box_label(
frame, x_cv2, personReID_info={
'personID': trackerID_str
}) # Draw the bounding boxes for unknown person
else:
frame = draw_box_label(
frame, x_cv2, personReID_info=trk.personReID_info
) # Draw the bounding boxes for re-identified person
#book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses <= max_age]
return frame
