def preprocess_video(youtube, start, end, name):
try:
os.makedirs("temp")
except FileExistsError:
# directory already exists
pass
ydl_opts = {
'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
'outtmpl': 'temp/video.mp4'
}
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
ydl.download([youtube])
start = get_time_string(int(start))
end = get_time_string(int(end))
print("Cutting video from {} to {}".format(start, end))
call([
'ffmpeg', '-ss', start, '-i', 'temp/video.mp4', '-to', end, '-c',
'copy', 'temp/' + name + '.mp4'
])
try:
os.makedirs("frames")
except FileExistsError:
# directory already exists
pass
call([
'ffmpeg', '-i', 'temp/' + name + '.mp4', '-r', '25/1',
'frames/output%04d.png'
])
def craw_marqueen(self):
content = xcurl.xcurl(self.marqueen_url).replace("\r", "").replace("\n", "")
if content == "":
xlog.LOG.WARN("get marqueen_url content error")
return
buy_content = utils.reg(config.marqueen_content_str, content)
if buy_content == "":
xlog.LOG.WARN("get marquee content error")
return
buy_list = utils.reg1(config.marqueen_item_str, buy_content)
write_string = ""
today = utils.get_time_string(int(time.time()))
craw_day = today
if self.date != "":
craw_day = self.date
uniq_list = list()
for item in buy_list:
idx = item.find("用户")
if idx == -1:
xlog.LOG.ERROR("find user error")
continue
user_phone = item[idx - 11:idx]
column_list = utils.reg1(config.item_column_str, item)
if len(column_list) < 3:
xlog.LOG.ERROR("column_list length error")
continue
order_time = column_list[0]
if order_time.find(craw_day) == -1:
print order_time, craw_day
xlog.LOG.ERROR("find order_time error")
continue
goods_num = column_list[2].split(" ")[-1]
idx1 = item.find(config.name_str)
idx2 = item.find("<i>x")
goods_name = item[idx1 + len(config.name_str):idx2].strip()
goods_price = 0
if goods_name in self.goods_dict.keys():
goods_price = float(self.goods_dict[goods_name])
goods_total_price = float(goods_num) * float(goods_price)
print user_phone, order_time, goods_name, goods_num, goods_total_price
line = user_phone + " " + order_time + " " + goods_name + " " + goods_num + " " + str(goods_total_price)
if line in self.file_column:
print "column exist"
continue
write_string += line + "\n"
print write_string
if write_string != "":
header = ""
if len(self.file_column) == 0:
header = "用户 订单时间 商品 个数 总积分\n"
write_string = header + write_string
self.write_file(write_string)
async def _command_list(self, message):
"""List your reminders"""
reminders = self.get_reminders(message.author.id)
log.info(reminders)
if not reminders:
msg = "I don't have any reminder for you!"
else:
msg = 'Here are your current reminders:\n'
for reminder in reminders.values():
in_time = reminder['at_time'] - int(datetime.now().timestamp())
msg += '`%s` "%s" in %s\n' % (reminder['uid'], reminder['message'], get_time_string(in_time))
await self.bot.client.send_message(message.author, msg)
async def _played_command(self, message):
"""show your game time"""
msg = ''
played = self.get(message.author.id)
if played:
msg += "As far as i'm aware, you played:\n"
for game, time in played.items():
msg += '`%s : %s`\n' % (game, get_time_string(time))
else:
msg = "I don't remember you playing anything :("
await self.bot.client.send_message(message.channel, msg)
def __init__(self, date):
file_path = "../files/"
file_name = utils.get_time_string(int(time.time()))
self.date = date
if self.date != "":
file_name = self.date
self.files = file_path + file_name + "滚雪球商城抓取结果"
self.goods_file = file_path + "滚雪球商品列表"
self.marqueen_url = "https://shop.gxq168.com/index/login"
self.file_column = list()
self.load_file()
self.goods_dict = dict()
self.load_goods_file()
async def _stats(self, message):
"""show the bot's general stats"""
users = 0
for s in self.client.servers:
users += len(s.members)
msg = 'General informations:\n'
msg += '`Admin :` <@%s>\n' % self.conf['admin_id']
msg += '`Uptime : %s`\n' % get_time_string(
(datetime.now() - self._start_time).total_seconds())
msg += '`Users in touch : %s in %s servers`\n' % (
users, len(self.client.servers))
msg += '`Commands answered : %d`\n' % self._commands
msg += '`Users playing : %d`\n' % len(self.timecounter.playing)
await self.client.send_message(message.channel, msg)
def post(self):
"""Returns all storage files in a zip file.
"""
data = request.get_json()
if 'key' not in data:
return jsonify({'error': 'wrong request'}), 400
if current_app.config['SECRET_KEY'] != data['key']:
return jsonify({'error': 'invalid key'}), 401
# python's zipfile library to zip a folder.
# Does not save the zipped folder.
filename = "storage_{0}.zip".format(get_time_string())
zip_file = _zip_storage_folder()
return send_file(zip_file,
attachment_filename=filename,
as_attachment=True)
def post(self):
if 'file' not in request.files:
response = jsonify({'error': 'file not uploaded'})
return response, 400
posted_file = request.files['file']
filename_mid = posted_file.filename
if filename_mid[-4:] != '.mid':
response = jsonify({'error': 'a non midi file was uploaded'})
return response, 400
filename_mid = secure_filename(filename_mid)
file_collection = FileCollection.generate_file_collection(
filename_mid, current_app.config['MIDISTORE_PATH'],
current_app.config['PARALLEL_RESULTS_PATH'],
current_app.config['CHORD_RESULTS_PATH'])
# save midifile
posted_file.save(os.path.join(file_collection.midi_path))
if not generate_results(file_collection.midi_path,
file_collection.parallels_path,
file_collection.chords_path):
return jsonify({'error': 'an invalid midi file was uploaded'}), 400
if not _db_commit(file_collection):
return jsonify({'error': 'an internal server error occured.'}), 500
try:
zip_file = _zip_results(file_collection)
filename = "results_{}.zip".format(get_time_string())
return send_file(zip_file,
attachment_filename=filename,
as_attachment=True)
except FileNotFoundError:
return jsonify({'error':
'file is not properly zipped in server.'}), 500
gen_iter_array = [] # create array for recording epochs of gen_freq updates
# ------------------------------------------------------------------------------------------------------------------
# set up save path
# ------------------------------------------------------------------------------------------------------------------
# Save initial flow image
if not os.path.exists(os.path.dirname('CTLodopab/experimentsLodopab/')):
print("created CTLodopab/experimentsLodopab/")
print("os.getcwd()", os.getcwd())
os.makedirs(os.path.dirname('CTLodopab/experimentsLodopab/'))
else:
print("experimentsLodopab already exists")
save_path = './CTLodopab/experimentsLodopab/' + 'lodopabCT_CNN_' + utils.get_time_string() + \
'_lr{:.0e}'.format(learning_rate) +\
'_genFreq{:d}'.format(gen_freq) + '_nTrain{:d}'.format(n_train) + '_batchSize{:d}'.format(batch_size) + \
'_optim' + optim_string + '_etaFreq{:d}'.format(eta_freq) + '_gamma_const{:.0e}'.format(gamma_constant) +\
'_etaTol{:.0e}'.format(eta_tol) + '_noise_level{:.0e}'.format(noise_level) +\
'_mu{:.0e}'.format(mu) + '_gp_lam{:d}'.format(gp_lam) +\
'_distMan_lam{:.0e}'.format(dist_man_lam) + '_lr_decay_iter{:d}'.format(lr_decay_iter) + \
'doAugment{:d}'.format(do_augment) + '_architecture' + architecture +\
'_stepType' + step_type + '_tvlam001/'
os.makedirs(save_path, exist_ok=True)
# max and min values for plotting
vmin = min(u0_gen_full_train[0, :].view(-1))
vmax = max(u0_gen_full_train[0, :].view(-1))
vmin_val = min(u_true_full_val[ind_val, :].view(-1))
def __init__(self):
file_path = "../files/"
self.file_name = utils.get_time_string(int(time.time()))
self.files = file_path + self.file_name + "滚雪球商城抓取结果"
self.stat_file = file_path + "滚雪球商城抓取结果汇总"
# model.compile(optimizer='rmsprop', loss=vae_loss)
# model.fit(inputs=[action_data, target_data], epochs=opts.epochs, batch_size=opts.batch_size,
# callbacks=[tbCallBack, BestLossCallBack(model, opts.save_path, opts.save_period)], shuffle=True)
else:
print('There is no proper model for option: ' + opts.model)
return
callback_list = [
tbCallBack,
BestLossCallBack(model, opts.save_path, opts.save_period)
]
# BestLossCallBack(decoder, opts.save_path, opts.save_period, 'decoder')
# training
model.compile(optimizer='rmsprop', loss='mse')
model.fit(action_data,
target_data,
epochs=opts.epochs,
batch_size=opts.batch_size,
callbacks=callback_list,
shuffle=True)
if __name__ == "__main__":
options.timestamp = get_time_string()
train_network(options)
# ()()
# ('') HAANJU.YOO
import tensorflow as tf
import utils
MODEL_PATH = './models/cityscapes/dense_net.py'
#MODEL_PATH = './models/cityscapes/dense_net_multiplexer.py'
#MODEL_PATH = './models/cityscapes/dense_net_concat_all.py'
#MODEL_PATH = './models/cityscapes/dense_net_jitter.py'
#MODEL_PATH = './models/cityscapes/dense_net_fix_bn.py'
#MODEL_PATH = './models/cityscapes/dense_net_dilated.py'
#IMG_WIDTH, IMG_HEIGHT = 2048, 1024
IMG_WIDTH, IMG_HEIGHT = 1024, 448
# IMG_WIDTH, IMG_HEIGHT = 768, 320
SAVE_DIR = os.path.join('/home/kivan/datasets/results/tmp/cityscapes',
utils.get_time_string())
# SPP has 90K
#tf.app.flags.DEFINE_integer('num_iters', 40000, '')
# tf.app.flags.DEFINE_integer('num_iters', 30000, '')
#tf.app.flags.DEFINE_integer('num_iters', 20000, '')
#tf.app.flags.DEFINE_integer('num_iters', 60000, '')
tf.app.flags.DEFINE_integer('max_num_epochs', 30, 'Number of epochs to run.')
###tf.app.flags.DEFINE_integer('num_iters', 20000, '')
tf.app.flags.DEFINE_string('optimizer', 'adam', '')
tf.app.flags.DEFINE_float('decay_power', 1.5, '')
tf.app.flags.DEFINE_float('initial_learning_rate', 5e-4, '')
tf.app.flags.DEFINE_float('end_learning_rate', 5e-5, '')
gen_iter_array = [] # create array for recording epochs of gen_freq updates
# ------------------------------------------------------------------------------------------------------------------
# set up save path
# ------------------------------------------------------------------------------------------------------------------
# Save initial flow image
if not os.path.exists(os.path.dirname('CTEllipse/experimentsEllipse/')):
print("created experimentsEllipse/")
print("os.getcwd()", os.getcwd())
os.makedirs(os.path.dirname('CTEllipse/experimentsEllipse/'))
else:
print("experimentsEllipse already exists")
save_path = './CTEllipse/experimentsEllipse/' + 'ellipseCT_' + utils.get_time_string() + \
'_lr{:.0e}'.format(learning_rate) +\
'_genFreq{:d}'.format(gen_freq) + '_nTrain{:d}'.format(n_train) + '_batchSize{:d}'.format(batch_size) + \
'_optim' + optim_string + '_etaFreq{:d}'.format(eta_freq) + '_gamma_const{:.0e}'.format(gamma_constant) +\
'_etaTol{:.0e}'.format(eta_tol) + '_noise_level{:.0e}'.format(noise_level) +\
'_mu{:.0e}'.format(mu) + '_gp_lam{:d}'.format(gp_lam) +\
'_distMan_lam{:.0e}'.format(dist_man_lam) + '_lr_decay_iter{:d}'.format(lr_decay_iter) + \
'doAugment{:d}'.format(do_augment) + '_architecture' + architecture +\
'_stepType' + step_type + '_tvlam001/'
os.makedirs(save_path, exist_ok=True)
# max and min values for plotting
vmin = min(u0_gen_full_train[0, :].view(-1))
vmax = max(u0_gen_full_train[0, :].view(-1))
vmin_val = min(u_true_full_val[ind_val, :].view(-1))