def test():
model = SiameseComparator()()
model.load_weights(model_path.get() + '/siamese-mars-small128.h5')
model.summary()
feature_vector = FeatureVector()
session_runner = SessionRunner()
extractor = MarsExtractorAPI('mars_api', True)
ip = extractor.get_in_pipe()
op = extractor.get_out_pipe()
extractor.use_session_runner(session_runner)
session_runner.start()
extractor.run()
image_files = []
for id in range(1, 5):
image_files.append(
glob.glob(input_path.get() + '/patches/{}/*.jpg'.format(id)))
print(len(image_files))
patch0 = [
cv2.imread(image_files[0][randint(0, len(image_files[0]))])
for _ in range(10)
]
# patch0_1 = [cv2.imread(image_files[0][randint(0, len(image_files[0]))]) for _ in range(10)]
patch1 = [
cv2.imread(image_files[1][randint(0, len(image_files[1]))])
for _ in range(10)
]
patch2 = [
cv2.imread(image_files[2][randint(0, len(image_files[2]))])
for _ in range(10)
]
patch3 = [
cv2.imread(image_files[3][randint(0, len(image_files[3]))])
for _ in range(10)
]
#patch_pair = [_ for _ in itertools.combinations_with_replacement([patch0[0], patch1[0], patch2[0], patch3[0]], 2)]
f_vec0 = np.array([extract_features(patch, ip, op)[0] for patch in patch0])
# f_vec0_1 = np.array(extract_features(patch0_1, ip, op))
f_vec1 = np.array([extract_features(patch, ip, op)[0] for patch in patch1])
f_vec2 = np.array([extract_features(patch, ip, op)[0] for patch in patch2])
f_vec3 = np.array([extract_features(patch, ip, op)[0] for patch in patch3])
#print(f_vec1)
output = model.predict(
[np.expand_dims(f_vec1, 0),
np.expand_dims(f_vec3, 0)])
print(output)
cap = cv2.VideoCapture(-1)
# cap = cv2.VideoCapture(videos_path.get()+'/Hitman Agent 47 - car chase scene HD.mp4')
session_runner = SessionRunner()
while True:
ret, image = cap.read()
if ret:
break
detection = AgeDetection()
detector_ip = detection.get_in_pipe()
detector_op = detection.get_out_pipe()
detection.use_session_runner(session_runner)
detection.use_threading()
session_runner.start()
detection.run()
tracker = Sort()
frame_no = 0
def read_video():
# start = time.time()
while True:
ret, image = cap.read()
if not ret:
continue
detector_ip.push(Inference(image.copy()))
def train():
feature_vector = FeatureVector()
session_runner = SessionRunner()
extractor = MarsExtractorAPI('mars_api', True)
ip = extractor.get_in_pipe()
op = extractor.get_out_pipe()
extractor.use_session_runner(session_runner)
session_runner.start()
extractor.run()
for id in range(1, 5):
image_files = glob.glob(
'/home/allahbaksh/Tailgating_detection/SecureIt/data/obj_tracking/outputs/patches/{}/*.jpg'
.format(id))
for image_file in image_files:
patch = cv2.imread(image_file)
f_vec = extract_features(patch, ip, op)
# print(f_vec.shape)
# print(f_vec[])
# break
feature_vector.add_vector(id, f_vec[0])
# for x in range(200):
# feature_vector.add_vector(randint(0, 30), [randint(0, 128) for _ in range(128)])
samples = create_samples(feature_vector.get_vector_dict())
print(count_0)
print(count_1)
# print(feature_vector.get_vector_dict())
model = SiameseComparator()()
sklearn.utils.shuffle(samples)
# print()
# print(samples[1])
# print(len(samples))
train_samples, val_samples = train_test_split(samples, test_size=0.2)
train_generator = generator(train_samples, batch_size=16)
validation_generator = generator(val_samples, batch_size=16)
epoch = 10
saved_weights_name = 'model.h5'
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=3,
mode='min',
verbose=1)
checkpoint = ModelCheckpoint(saved_weights_name,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
tensorboard = TensorBoard(log_dir=os.path.expanduser('~/logs/'),
histogram_freq=0,
write_graph=True,
write_images=False)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['mae', 'acc'])
history = model.fit_generator(
generator=train_generator,
steps_per_epoch=len(train_samples),
epochs=epoch,
verbose=1,
validation_data=validation_generator,
nb_val_samples=len(val_samples),
callbacks=[early_stop, checkpoint, tensorboard])