# read config
if True:
configs = []
with open('../config.txt', 'r') as f:
configs = f.readlines()
configs = [txt_config.strip('\n') for txt_config in configs]
Config.DEMO_FOR = configs[0]
Config.Rabbit.IP_ADDRESS = configs[1]
face_rec_graph = FaceGraph()
face_extractor = FacenetExtractor(face_rec_graph, model_path=Config.FACENET_DIR)
detector = MTCNNDetector(face_rec_graph)
preprocessor = Preprocessor()
matcher = FaissMatcher()
matcher._match_case = 'TCH'
matcher.build(Config.REG_IMAGE_FACE_DICT_FILE)
rb = RabbitMQ()
frame_readers = dict()
register_command = dict() # {session_id: [[register_name, video_path]]}
removed_sessions = Queue()
sent_msg_queue = Queue()
start_time = time.time()
while True:
# if time.time() - start_time >= 10.0:
# try:
# while True:
# rm_id = removed_sessions.get(False)
# frame_readers.pop(rm_id, None)
# sessions.pop(rm_id, None)
def generic_function(cam_url, queue_reader, area, face_extractor_model,
re_source, multi_thread):
'''
This is main function
'''
print("Generic function")
print("Cam URL: {}".format(cam_url))
print("Area: {}".format(area))
# Variables for tracking faces
frame_counter = 0
# Variables holding the correlation trackers and the name per faceid
tracking_dirs = glob.glob(Config.TRACKING_DIR + '/*')
if tracking_dirs == []:
number_of_existing_trackers = 0
else:
lof_int_trackid = [
int(tracking_dir.split('/')[-1]) for tracking_dir in tracking_dirs
]
number_of_existing_trackers = max(lof_int_trackid) + 1
imageid_to_keyid = {}
tracker_manager = TrackerManager(
area,
imageid_to_keyid=imageid_to_keyid,
current_id=number_of_existing_trackers)
if Config.Matcher.CLEAR_SESSION:
clear_session_folder()
global querying_top10_image_ids_queue
# mongodb_faceinfo.remove({})
# reg_dict = PickleUtils.read_pickle(Config.REG_IMAGE_FACE_DICT_FILE)
# if reg_dict is not None:
# for fid in reg_dict:
# mongodb_faceinfo.insert_one({'image_id': fid, 'face_id': reg_dict[fid]})
# print('Saved regdict in mongodb as collection regdict')
matcher = FaissMatcher()
matcher.build(
mongodb_faceinfo, imageid_to_keyid=imageid_to_keyid, use_image_id=True)
svm_matcher = None
if Config.Matcher.CLOSE_SET_SVM:
svm_matcher = SVMMatcher()
svm_matcher.build(mongodb_faceinfo)
track_results = TrackerResultsDict()
if Config.CALC_FPS:
start_time = time.time()
if cam_url is not None:
frame_reader = URLFrameReader(cam_url, scale_factor=1)
elif queue_reader is not None:
frame_reader = RabbitFrameReader(rabbit_mq, queue_reader)
elif args.anno_mode:
frame_reader = URLFrameReader('./nothing.mp4', scale_factor=1)
else:
print('Empty Image Source')
return -1
if not args.anno_mode:
video_out_fps, video_out_w, video_out_h, = frame_reader.get_info()
print(video_out_fps, video_out_w, video_out_h)
bbox = [
int(Config.Frame.ROI_CROP[0] * video_out_w),
int(Config.Frame.ROI_CROP[1] * video_out_h),
int(Config.Frame.ROI_CROP[2] * video_out_w),
int(Config.Frame.ROI_CROP[3] * video_out_h)
]
# bbox = [0, 0, video_out_w, video_out_h]
video_out = None
if Config.Track.TRACKING_VIDEO_OUT:
video_out = VideoHandle(time.time(), video_out_fps, int(video_out_w),
int(video_out_h))
# Turn on querying top 10 from queue
if Config.QUERY_TOP10_MODE:
thread = threading.Thread(target=give_this_id_10_closest_ids)
thread.daemon = True
thread.start()
frame_queue = []
lock = threading.Lock()
if multi_thread:
is_on = [True]
t = threading.Thread(target=(get_frames), args=(frame_queue, frame_reader, re_source, \
cam_url, queue_reader, lock, is_on, ))
t.start()
try:
while True:
ms_msg = rabbit_mq.receive_str(Config.Queues.MERGE)
ms_flag = 'merge'
if ms_msg is None:
ms_msg = rabbit_mq.receive_str(Config.Queues.SPLIT)
ms_flag = 'split'
if ms_msg is not None:
merge_anchor, merge_list = extract_info_from_json(ms_msg)
while merge_list != []:
image_id1 = merge_list.pop()
split_merge_id(ms_flag, image_id1, merge_anchor, matcher,
preprocessor, face_extractor,
tracker_manager, mongodb_dashinfo,
mongodb_faceinfo, mongodb_mslog)
continue
action_msg = rabbit_mq.receive_str(Config.Queues.ACTION)
if action_msg is not None:
return_dict = json.loads(action_msg)
print('Receive: {}'.format(return_dict))
if return_dict['actionType'] == 'getNearest':
lock.acquire()
querying_top10_image_ids_queue.append(return_dict['data'])
lock.release()
continue
if args.anno_mode:
print('Annotation Mode, waiting for new tasks ...')
time.sleep(1)
continue
if multi_thread:
if len(frame_queue) > 0:
lock.acquire()
frame = frame_queue.pop(0)
lock.release()
else:
frame = None
else:
frame = frame_reader.next_frame()
tracker_manager.update_trackers(frame)
#do this before check_and_recognize tracker (sync local matcher vs mongodb)
trackers_return_dict, recognized_results = update_recognition(
self, matcher, svm_matcher, mongodb_faceinfo)
for tracker, matching_result_dict in recognized_results:
handle_results(tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid, \
dump=False)
# trackers_return_dict = tracker_manager.find_and_process_end_track(mongodb_faceinfo)
track_results.merge(trackers_return_dict)
tracker_manager.long_term_history.check_time(
matcher, mongodb_faceinfo)
if frame is None:
print("Waiting for the new image")
# if Config.Track.RECOGNIZE_FULL_TRACK:
# overtime_track_ids = tracker_manager.find_overtime_current_trackers(
# time_last=Config.Track.CURRENT_EXTRACR_TIMER-5,
# find_unrecognized=True
# )
# for overtime_track_id in overtime_track_ids:
# checking_tracker, top_predicted_face_ids, matching_result_dict = \
# tracker_manager.check_and_recognize_tracker(
# matcher,
# overtime_track_id,
# mongodb_faceinfo,
# svm_matcher)
# handle_results(checking_tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid,\
# dump=False)
if re_source and not multi_thread:
print('Trying to connect the stream again ...')
if cam_url is not None:
frame_reader = URLFrameReader(cam_url, scale_factor=1)
elif queue_reader is not None:
frame_reader = RabbitFrameReader(
rabbit_mq, queue_reader)
else:
print('Empty Image Source')
return -1
break
print("Frame ID: %d" % frame_counter)
if "_rotate" in video:
# rotate cw
rotation = int(video.split("_")[-1].split(".")[0])
frame = rotate_image_90(frame, rotation)
if Config.Track.TRACKING_VIDEO_OUT:
video_out.tmp_video_out(frame)
if Config.CALC_FPS:
fps_counter = time.time()
if frame_counter % Config.Frame.FRAME_INTERVAL == 0:
# crop frame
#frame = frame[bbox[1]:bbox[3], bbox[0]:bbox[2],:]
origin_bbs, points = detector.detect_face(frame)
if len(origin_bbs) > 0:
origin_bbs = [origin_bb[:4] for origin_bb in origin_bbs]
display_and_padded_faces = [
CropperUtils.crop_display_face(frame, origin_bb)
for origin_bb in origin_bbs
]
#cropped_faces = [CropperUtils.crop_face(frame, origin_bb) for origin_bb in origin_bbs]
preprocessed_images = [
preprocessor.process(
CropperUtils.crop_face(frame, origin_bb))
for origin_bb in origin_bbs
]
embeddings_array, _ = face_extractor.extract_features_all_at_once(
preprocessed_images)
for i, origin_bb in enumerate(origin_bbs):
display_face, str_padded_bbox = display_and_padded_faces[i]
#cropped_face = CropperUtils.crop_face(frame, origin_bb)
# Calculate embedding
preprocessed_image = preprocessed_images[i]
# preprocessed_image = align_preprocessor.process(frame, points[:,i], aligner, 160)
emb_array = np.asarray([embeddings_array[i]])
face_info = FaceInfo(
#oigin_bb.tolist(),
#emb_array,
frame_counter,
origin_bb,
points[:, i]
#display_face,
#str_padded_bbox,
#points[:,i].tolist()
)
is_good_face = handle_filters(points[:, i], coeff_extractor,
face_info, preprocessed_image)
face_info.is_good = is_good_face
# TODO: refractor matching_detected_face_with_trackers
matched_track_id = tracker_manager.track(face_info)
if not face_info.is_good:
print('BAD FACE')
continue
# Update tracker_manager
tracker_manager.update(matched_track_id, frame, face_info)
checking_tracker = None
# if not Config.Track.RECOGNIZE_FULL_TRACK:
# checking_tracker, top_predicted_face_ids, matching_result_dict = \
# tracker_manager.check_and_recognize_tracker(
# matcher,
# matched_track_id,
# mongodb_faceinfo,
# svm_matcher)
# handle_results(checking_tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid, \
# dump=True)
# if Config.Track.RECOGNIZE_FULL_TRACK:
# overtime_track_ids = tracker_manager.find_overtime_current_trackers(
# time_last=Config.Track.CURRENT_EXTRACR_TIMER-5,
# find_unrecognized=True
# )
# for overtime_track_id in overtime_track_ids:
# checking_tracker, top_predicted_face_ids, matching_result_dict = \
# tracker_manager.check_and_recognize_tracker(
# matcher,
# overtime_track_id,
# mongodb_faceinfo,
# svm_matcher)
# handle_results(checking_tracker, matching_result_dict, imageid_to_keyid = imageid_to_keyid, \
# dump=False)
frame_counter += 1
if Config.CALC_FPS:
print("FPS: %f" % (1 / (time.time() - fps_counter)))
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
Config.Track.CURRENT_EXTRACR_TIMER = 0
trackers_return_dict = tracker_manager.find_and_process_end_track(
mongodb_faceinfo)
Config.Track.HISTORY_CHECK_TIMER = 0
Config.Track.HISTORY_EXTRACT_TIMER = 0
tracker_manager.long_term_history.check_time(matcher, mongodb_faceinfo)
except KeyboardInterrupt:
if multi_thread:
is_on[0] = False
t.join()
print('Keyboard Interrupt !!! Release All !!!')
Config.Track.CURRENT_EXTRACR_TIMER = 0
trackers_return_dict = tracker_manager.find_and_process_end_track(
mongodb_faceinfo)
Config.Track.HISTORY_CHECK_TIMER = 0
Config.Track.HISTORY_EXTRACT_TIMER = 0
tracker_manager.long_term_history.check_time(matcher, mongodb_faceinfo)
if Config.CALC_FPS:
print('Time elapsed: {}'.format(time.time() - start_time))
print('Avg FPS: {}'.format(
(frame_counter + 1) / (time.time() - start_time)))
frame_reader.release()
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
video_out.release()
def generic_function(cam_url, queue_reader, area, re_source, use_frame_queue):
global rabbit_mq
'''
This is main function
'''
print("Generic function")
print("Cam URL: {}".format(cam_url))
print("Area: {}".format(area))
if Config.Matcher.CLEAR_SESSION:
clear_session_folder()
if Config.Mode.CALC_FPS:
start_time = time.time()
if cam_url is not None:
frame_reader = URLFrameReader(cam_url,
scale_factor=1,
re_source=re_source)
elif queue_reader is not None:
frame_reader = RabbitFrameReader(rabbit_mq, queue_reader)
else:
print('Empty Image Source')
return -1
if use_frame_queue:
frame_src = FrameQueue(frame_reader,
max_queue_size=Config.Frame.FRAME_QUEUE_SIZE)
frame_src.start()
else:
frame_src = frame_reader
video_out = None
if Config.Track.TRACKING_VIDEO_OUT:
video_out = VideoHandle(time.time(), video_out_fps, int(video_out_w),
int(video_out_h))
# Variables for tracking faces
frame_counter = 0
# Variables holding the correlation trackers and the name per faceid
tracking_dirs = os.listdir(Config.Dir.TRACKING_DIR)
if tracking_dirs == []:
current_tracker_id = 0
else:
list_of_trackid = [int(tracking_dir) for tracking_dir in tracking_dirs]
current_tracker_id = max(list_of_trackid) + 1
imageid_to_keyid = {}
matcher = FaissMatcher()
matcher.build(database,
imageid_to_keyid=imageid_to_keyid,
use_image_id=True)
tracker_manager = TrackerManager(area,
matcher,
database.mongodb_faceinfo,
imageid_to_keyid=imageid_to_keyid,
current_id=current_tracker_id)
try:
while True:
frame = frame_src.next_frame()
if Config.Mode.CALC_FPS:
fps_counter = time.time()
if frame is None:
print("Waiting for the new image")
tracker_manager.update(rabbit_mq)
time.sleep(1)
continue
# track by kcf
tracker_manager.update_trackers(frame)
tracker_manager.update(rabbit_mq)
origin_bbs, points = TensorflowAdapter.detect_face(frame)
if len(origin_bbs) > 0:
origin_bbs = [origin_bb[:4] for origin_bb in origin_bbs]
embeddings_array = [None] * len(origin_bbs)
tracker_manager.process_new_detections(frame,
origin_bbs,
points,
embeddings_array,
frame_id=frame_counter)
frame_counter += 1
if Config.Mode.CALC_FPS:
print("FPS: %f" % (1 / (time.time() - fps_counter)))
#TODO: this line never run
tracker_manager.update(rabbit_mq)
except KeyboardInterrupt:
if use_frame_queue:
frame_src.stop()
print('Keyboard Interrupt !!! Release All !!!')
tracker_manager.update(rabbit_mq)
frame_src.release()
if Config.CALC_FPS:
print('Time elapsed: {}'.format(time.time() - start_time))
print('Avg FPS: {}'.format(
(frame_counter + 1) / (time.time() - start_time)))
if Config.Track.TRACKING_VIDEO_OUT:
print('Write track video')
video_out.write_track_video(track_results.tracker_results_dict)
video_out.release()
else:
raise Exception('Error')