# 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)
def simulate_tracking(root_folder):
Config.Track.FACE_TRACK_IMAGES_OUT = True
Config.Track.SEND_FIRST_STEP_RECOG_API = False
Config.Track.MIN_MATCH_DISTACE_OUT = True
Config.Track.CURRENT_EXTRACR_TIMER = 5
# Load Face Extractor
face_rec_graph = FaceGraph()
face_rec_graph_coeff = FaceGraph()
face_extractor = FacenetExtractor(
face_rec_graph, model_path=Config.FACENET_DIR)
coeff_extractor = FacenetExtractor(
face_rec_graph_coeff, model_path=Config.COEFF_DIR)
# Create empty KDTreeMatcher
matcher = FaissMatcher()
matcher._match_case = 'TCH'
# Preprocessor
preprocessor = Preprocessor()
# Fake rabbit mq
rabbit_mq = FakeMQ()
# Clean up for
clear_tracking_folder()
if Config.Matcher.CLEAR_SESSION:
clear_session_folder()
# Setup result list
list_of_trackers = TrackersList()
track_results = TrackerResultsDict()
predict_dict = {}
confirmed_ids_dict = {}
sim_detections = gen_images_with_time(root_folder)
for detection in sim_detections:
frame = create_fake_frame(detection)
sleep(0.05)
trackers_return_dict, predict_trackers_dict = \
list_of_trackers.check_delete_trackers(matcher, rabbit_mq)
track_results.update_two_dict(trackers_return_dict)
predict_dict.update(predict_trackers_dict)
confirmed_ids_dict = list_of_trackers.trackers_history.confirm_id(
confirmed_ids_dict)
list_of_trackers.trackers_history.check_time(matcher)
list_of_trackers.update_dlib_trackers(frame)
facial_quality = 1
# Crop face for features extraction
origin_bb = detection[2]
display_face, padded_bbox = CropperUtils.crop_display_face(
frame, origin_bb)
cropped_face = CropperUtils.crop_face(frame, origin_bb)
bbox_str = '_'.join(np.array(origin_bb, dtype=np.unicode).tolist())
# Calculate embedding
preprocessed_image = preprocessor.process(cropped_face)
emb_array, _ = face_extractor.extract_features(preprocessed_image)
_, coeff = coeff_extractor.extract_features(preprocessed_image)
if coeff < 0.15:
img_path = '../data/notenoughcoeff/{}_{}_{}.jpg'.format(
detection, bbox_str, coeff)
cv2.imwrite(img_path, cv2.cvtColor(display_face, cv2.COLOR_BGR2RGB))
facial_quality = -1
else:
with open('../data/coeff_log.txt', 'a') as f:
f.write('{}_{}_{}, coeff: {}\n'.format(bbox_str, detection[0],
padded_bbox, coeff))
matched_fid = list_of_trackers.matching_face_with_trackers(
frame, detection[0], origin_bb, emb_array, facial_quality)
if facial_quality == -1 or coeff < 0.15:
continue
list_of_trackers.update_trackers_list(
matched_fid, time.time(), origin_bb, display_face, emb_array, 0,
'VVT', 1, detection[0], padded_bbox, matcher, rabbit_mq)
list_of_trackers.check_recognize_tracker(matcher, rabbit_mq,
matched_fid)
sleep(6)
list_of_trackers.check_delete_trackers(matcher, rabbit_mq)