def test_check_recognize_tracker_not_mergeable_new_face(self, mock_save):
def fake_img_save(arg1, arg2):
return True
mock_save.side_effect = fake_img_save
matcher = FakeMatcher()
embs = np.eye(12)
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
matcher.build(embs, labels)
# total 12 tracker
zero = np.zeros(12)
trackers = {}
for i in range(12):
vec = zero.copy()
vec[i] = 1
tracker = create_tracker([vec] * 6)
trackers[i] = tracker
vec = np.zeros(12)
vec[1] = 1
vec[2] = 1
vec[3] = 2
vec[4] = 3
new_tracker = create_tracker([vec] * 6)
tracker_list = TrackersList()
tracker_list.trackers = {1: new_tracker}
tracker_list.current_trackers_history.trackers = trackers
tracker_list.current_trackers_history.history_matcher = matcher
tracker_list.current_trackers_history.labels = labels
tracker_list.check_recognize_tracker(matcher, Mock(), 1)
def test_check_recognize_tracker_mergeable(self, mock_save):
def fake_img_save(arg1, arg2):
return True
mock_save.side_effect = fake_img_save
matcher = HistoryMatcher()
# embs = np.eye(12)
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# total 12 tracker
zero = np.zeros(12)
trackers = {}
for i in range(12):
vec = zero.copy()
vec[i] = 1
tracker = create_tracker([vec] * 30)
tracker.name = 'TCH-VVT-' + str(i)
trackers['TCH-VVT-' + str(i)] = tracker
matcher.from_trackers_rebuild_matcher(trackers)
vec = np.zeros(12)
vec[1] = 1
new_tracker = create_tracker([vec] * 30)
tracker_list = TrackersList()
tracker_list.trackers = {1: new_tracker}
tracker_list.current_trackers_history.trackers = trackers
tracker_list.current_trackers_history.history_matcher = matcher
tracker_list.current_trackers_history.labels = labels
result = tracker_list.check_recognize_tracker(matcher, Mock(), 1)
self.assertTrue(result)
self.assertEqual('TCH-VVT-1', tracker_list.trackers[1].name)
def test_check_recognize_tracker_detecting_name(self):
'''
What?
input:
matcher
track_id
rabbit_mq
:return:
'''
matcher = FakeMatcher()
embs = np.eye(12)
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
matcher.build(embs, labels)
# total 12 tracker
zero = np.zeros(12)
trackers = {}
for i in range(12):
vec = zero.copy()
vec[i] = 1
tracker = create_tracker([vec] * 40)
tracker.name = 'NOT_DETECTING'
trackers[i] = tracker
tracker_list = TrackersList()
tracker_list.trackers = trackers
result = tracker_list.check_recognize_tracker(Mock(), Mock(), 0)
self.assertEqual(-1, result)
def test_check_recognize_tracker_not_mergeable_old_face(self, mock_save):
def fake_img_save(arg1, arg2):
return True
mock_save.side_effect = fake_img_save
matcher = FakeMatcher()
embs = np.eye(12)
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
matcher.build(embs, labels)
# total 12 tracker
zero = np.zeros(12)
trackers = {}
for i in range(12):
vec = zero.copy()
vec[i] = 1
tracker = create_tracker([vec] * 30)
tracker.name = 'TCH-VVT-' + str(i)
trackers[i] = tracker
vec = np.zeros(12)
vec[1] = 1
vec[2] = 1
vec[3] = 2
vec[4] = 3
new_tracker = create_tracker([vec] * 30)
history_matcher = HistoryMatcher()
history_matcher.from_trackers_rebuild_matcher(trackers)
tracker_list = TrackersList()
tracker_list.trackers = {1: new_tracker}
tracker_list.current_trackers_history.trackers = trackers
tracker_list.current_trackers_history.history_matcher = history_matcher
tracker_list.current_trackers_history.labels = labels
recognizer_matcher = FakeMatcher()
recog_embs = np.vstack((vec for _ in range(12)))
recognizer_matcher.build(recog_embs, ['13'] * 12)
tracker_list.check_recognize_tracker(recognizer_matcher, Mock(), 1)
def test_check_recognize_tracker_detecting_name_less_element(self):
matcher = FakeMatcher()
embs = np.eye(12)
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
matcher.build(embs, labels)
# total 12 tracker
zero = np.zeros(12)
trackers = {}
for i in range(12):
vec = zero.copy()
vec[i] = 1
tracker = create_tracker([vec] * 2)
trackers[i] = tracker
tracker_list = TrackersList()
tracker_list.trackers = trackers
result = tracker_list.check_recognize_tracker(matcher, Mock(), 1)
self.assertTrue(result)
def test_check_recognize_tracker_detecting_name_bad_track(self):
matcher = FakeMatcher()
embs = np.eye(12)
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
matcher.build(embs, labels)
# total 12 tracker
zero = np.zeros(12)
trackers = {}
for i in range(12):
vec = zero.copy()
vec[i] = 1
tracker = create_tracker([vec] * 40)
trackers[i] = tracker
tracker_list = TrackersList()
tracker_list.trackers = trackers
result = tracker_list.check_recognize_tracker(Mock(), Mock(), 1)
self.assertEqual(-1, result)
self.assertEqual('BAD-TRACK', tracker_list.trackers[1].name)
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)