my_reconstructed = reconstructed_matrix[chosen].values
my_contribution = contribution_matrix[chosen].values
# Initialize fireworks display for the first input column
diameter_max = 100
diameter_min = 15
myDisplay = fireworks.Display(firework_count=15, dimensions=30, dimension_min=0, dimension_max=1000,
diameter_min=15, diameter_max=100, spark_min=10, spark_max=50, gaussian_spark_count=10,
fitness_function=fitness.fitness_manhattan_similarity_sum, catalog=my_catalog,
mutational_data=my_input, spark_dimension_count=10, dimension_limit=False)
myDisplay.create_fireworks(None)
myDisplay.showtime()
watcher = Watcher(iterations=15, threshold=0.001, starting_iteration=10, fw_display=myDisplay)
watcher.iterate(for_range=range(0, 200), reduction=0.99)
logging.info("Finished iterating. Comparing solutions.")
new_reconstructed = np.dot(np.array(my_catalog), np.array(myDisplay.best_spark.position))
FW_result = np.average(paired_distances(
np.array(new_reconstructed).reshape(-1, 1), np.array(my_input).reshape(-1, 1), metric='manhattan'))
MP_result = np.average(paired_distances(
np.array(my_reconstructed).reshape(-1, 1), np.array(my_input).reshape(-1, 1), metric='manhattan'))
FW_result2 = np.average(paired_distances(
np.array(new_reconstructed).reshape(1, -1), np.array(my_input).reshape(1, -1), metric='cosine'))
MP_resul2 = np.average(paired_distances(
np.array(my_reconstructed).reshape(1, -1), np.array(my_input).reshape(1, -1), metric='cosine'))
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_detector.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(gray, (x, y), (x + w, y + h), (255, 0, 0), 2)
count += 1
dir1 = os.path.join(dir, face_id)
if not os.path.exists(dir1):
os.makedirs(dir1)
# Save the captured image into the datasets folder
cv2.imwrite(dir1 + "/" + str(count) + ".jpg", gray[y:y + h,
x:x + w])
cv2.imshow('frame', gray)
if count >= 10:
print("10 face sample and stop dataset")
break
else:
break
capt.release()
cv2.destroyAllWindows()
encode_faces.encoding()
print("encoded images")
def custom_action():
capture_encode_faces()
watch1 = Watcher(watch_file, custom_action) # also call custom action function
watch1.watch() # start the watch going
watch1.look()
from logging import getLogger, INFO, basicConfig
logger = getLogger(__name__)
format_ = "[%(asctime)s]:%(filename)s:%(lineno)s:%(message)s"
basicConfig(format=format_, level=INFO, filename="c4N4Re.log", filemode='w')
if __name__ == "__main__":
config = ConfigParser()
config.read("config.ini")
if (not config.has_section("login")
or not config.has_option("login", "email")
or not config.has_option("login", "app_pass")):
login = Login(config=config)
try:
login.env_login()
except:
logger.critical(
"Unable to retrieve email or app password values from config file."
)
try:
watcher = Watcher(config=config)
watcher.watch()
except KeyboardInterrupt:
logger.info("Captured KeyboardInterrupt exception. Exiting program")
exit(1)
except Exception as e:
logger.critical(f"Exception -> {e}")
exit(1)
def setUp(self) -> None:
self.mock_api_helper: TwitterApiHandler = MagicMock()
self.mock_os_timer: OsTimer = MagicMock()
self.watcher = Watcher(self.mock_api_helper, self.mock_os_timer)
