def setUp(self): settings.init() settings.manga_format = 'png' settings.destination_path = os.path.join('.', 'tests', 'test_download') if not os.path.exists(settings.destination_path): os.makedirs(settings.destination_path)
def main(): settings.init() Thread(target=run_web_server).start() Thread(target=run_registration_server).start() #start threads for each of the clients that are in the settings file #for id, port in settings.ports_map.items(): #print(id, port) Thread(target=run_cam_socket, kwargs=dict(p=8002)).start()
def main(arguments): settings.init(arguments) browser = settings.browser for anime in settings.animes: download_anime(browser, anime) browser.dispose()
def main (): computed_feature_vectors = {} # used to avoid recomputation of SIFT features try: settings.init() settings.check_settings() settings.print_settings() except NameError as ne: print('NameError: {0}'.format(ne)) attributes = read_file(settings.filepaths['attributes']) images = read_file(settings.filepaths['images']) votes_tmp = read_file(settings.filepaths['votes']) votes = split_entries(votes_tmp, ' ') # check if min-max-scaler was already instantiated # if so, open and use it # otherwise, geneate min-max-scaler pickle file and use it try: used_scaler = scaler.open_if_exists() print('[IRS] Using existing scaler pickle file') except IOError as ioe: print('[IRS] Scaler pickle file does not exist') print('[IRS] Hence it will now be created (may take some time)') used_scaler = scaler.create(images, computed_feature_vectors) # check if k-means clustering was already done # if so, open and use it # otherwise, generate k-means clustering pickle file and use it try: used_kmeanspp = kmeanspp.open_if_exists() print('[IRS] Using existing k-means++ pickle file') except IOError as ioe: print('[IRS] k-means++ pickle file does not exist') print('[IRS] Hence this will now be created (may take some time)') used_kmeanspp = kmeanspp.create(images, computed_feature_vectors, used_scaler) # ai_dict, aic_dict = map_images_to_attributes(attributes, images, votes) # generate splits and train classifiers classifiers = bow.learn_and_evaluate(attributes, ai_dict, aic_dict, 'symmetric', './', computed_feature_vectors, used_scaler, used_kmeanspp ) print('Trained classifiers for {} attributes (10 each; total: {})'.format( len(classifiers), get_total_classifier_count(classifiers) )) # recognize scenes scene_rec.learn_and_evaluate(used_scaler, used_kmeanspp, classifiers)
def main(): settings.init() settings.config_file = DEFAULT_CONFIG_FILE arguments = get_arguments(sys.argv[1:]) if arguments.verbose: settings.logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s :: %(levelname)s :: %(module)s :: %(lineno)s :: %(funcName)s :: %(message)s') stream_handler = logging.StreamHandler() stream_handler.setFormatter(formatter) if arguments.verbose == 0: settings.logger.setLevel(logging.NOTSET) elif arguments.verbose == 1: settings.logger.setLevel(logging.DEBUG) elif arguments.verbose == 2: settings.logger.setLevel(logging.INFO) elif arguments.verbose == 3: settings.logger.setLevel(logging.WARNING) elif arguments.verbose == 4: settings.logger.setLevel(logging.ERROR) elif arguments.verbose == 5: settings.logger.setLevel(logging.CRITICAL) settings.logger.addHandler(stream_handler) if arguments.config_file: settings.config_file = arguments.config_file if arguments.destination_path: settings.destination_path = arguments.destination_path if arguments.format: settings.manga_format = arguments.format if arguments.remove: remove = arguments.remove config = get_config(settings.config_file) mangas = [] if config['mangas'] is not None: mangas = config['mangas'] if settings.destination_path is None: if config['destinationPath'] is not None: settings.destination_path = config['destinationPath'] else: settings.destination_path = DEFAULT_DESTINATION_PATH if settings.manga_format is None: if config['mangaFormat'] is not None: settings.manga_format = config['mangaFormat'] else: settings.manga_format = DEFAULT_MANGA_FORMAT settings.logger.debug('mangas : %s', mangas) settings.logger.debug('settings : %s', settings) settings.logger.debug('settings.manga_format : %s', settings.manga_format) settings.logger.debug('remove : %s', remove) scraper = cfscrape.create_scraper() for manga in mangas: download_manga(scraper, manga)
def onSettingsChanged( self ): settings.init() settings.readSettings()
from __future__ import unicode_literals import discord from discord.ext import commands import sys import settings.settings as settings from cogs.music_cog import Music from cogs.random_cog import Random from cogs.my_cog import MyCog settings.init(sys.argv[1:]) intents = discord.Intents.default() intents.members = True bot = commands.Bot( command_prefix=commands.when_mentioned_or("!"), description="MyDiscordBot", intents=intents, ) @bot.command() async def rekt(context): if str(context.author) == "Harkame#2009": await context.send("Tchao") await bot.logout() else:
def mamba(): # Setup all shared global utilities in settings module settings.init()
def onSettingsChanged(self): settings.init() settings.readSettings()