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)