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()
