def stream_server_start(self, *args, **kargs):
if self.is_streaming():
return
logging.info('StreamServer start: {}'.format(kargs))
self._stream_server = StreamServer(
kargs, lambda data: self.handler('server', data))
self._stream_server.start()
def __init__(self, target, adapter, size=None):
self.client = BluetoothAsyncSocket()
self.adapter = adapter
self.client.bind((self.adapter.bt_address, 0))
self.target = target
self.target_t = (target, 1)
self.size = size if size else 'QQVGA'
self.streamserver = StreamServer('0.0.0.0', CameraConnection.port)
self.streamserver.create_server_socket()
CameraConnection.port += 1
elif data == 'onpause':
global lastStop
droid.log("GUI has gone onPause")
lastStop = time.time()
elif data == 'onresume':
global lastStop
lastStop = None
elif data.startswith('record_start'):
record_start(data.split('$')[1])
elif data == 'record_stop':
record_stop()
else:
droid.log("Not known event %s" % data)
server = StreamServer('', 10000, droid)
server.create_server_socket()
init = True
droid.toggleBluetoothState(True)
camera = Camera(droid, size="QQVGA", callback=callback, err_callback=error)
connect()
droid.startActivity('de.mjpegsample.MjpegSample')
while running:
server.wait_connection()
try:
if len(droid.bluetoothActiveConnections().result) > 0:
if droid.bluetoothReadReady():
def get_iterators_for_model(model_type,
training_trace_set_paths,
validation_trace_set_paths,
conf,
batch_size=512,
hamming=False,
subtype='custom',
request_id=None):
# Stream samples from other machine?
if conf.online:
stream_server = StreamServer(conf)
batch_size = 32
else:
stream_server = None
batch_size = conf.batch_size
training_iterator = None
validation_iterator = None
if model_type == 'aicorrnet' or model_type == 'aiascad': # TODO unify with one iterator
training_iterator = AICorrSignalIterator(training_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server)
validation_iterator = AICorrSignalIterator(validation_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server)
elif model_type == 'aishacpu':
training_iterator = AISHACPUSignalIterator(training_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server,
hamming=hamming,
subtype=subtype)
validation_iterator = AISHACPUSignalIterator(
validation_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server,
hamming=hamming,
subtype=subtype)
elif model_type == 'aishacc':
training_iterator = AISHACPUSignalIterator(training_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server,
hamming=hamming,
subtype='custom')
validation_iterator = AISHACPUSignalIterator(
validation_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server,
hamming=hamming,
subtype='custom')
elif model_type == 'autoenc':
training_iterator = AutoEncoderSignalIterator(
training_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server)
validation_iterator = AutoEncoderSignalIterator(
validation_trace_set_paths,
conf,
batch_size=batch_size,
request_id=request_id,
stream_server=stream_server)
#elif model_type == 'aiascad': # TODO Remove me
# train_set, attack_set, metadata_set = load_ascad(join(conf.datasets_path, "ASCAD/ASCAD_data/ASCAD_databases/ASCAD.h5"), load_metadata=True)
# metadata_train, metadata_attack = metadata_set
# training_iterator = ASCADSignalIterator(train_set, meta=metadata_train)
# validation_iterator = ASCADSignalIterator(attack_set, meta=metadata_attack)
else:
logger.error("Unknown training procedure %s specified." % model_type)
exit(1)
return training_iterator, validation_iterator
import modelMangler as m
from model import *
from lxml import etree
TITLE = L('Spotify')
ICON = 'icon-default.png'
ART = "concert.jpeg"
ABOUT = "about1_1.png"
VERSION = "0.1.2"
data = DataService()
playback = PlaybackService()
playback.start()
streamserver = StreamServer(playback)
cache = dict()
####################################################################################################
def Start():
HTTP.CacheTime = None
ObjectContainer.art = R(ART)
Dict['LocalAddress']=Network.Address
Dict['PublicAddress']=Network.PublicAddress
Initialize()
Thread.Create(refreshLibraryIndex)
def Initialize():
Log.Debug("Initialize()")
# -*- coding: utf-8 -*-
# Copyright (C) 2008 Simon Pantzare
# See COPYING for details.
import cherrypy
from streamserver import StreamServer
import os.path
from code import interact
# Let us first set up the streaming server and add some media to it.
stream = StreamServer()
media = [("m1.mp3", "Sample_MP3_1"), ("m2.mp3", "Sample_MP3_2"),
("va1.mpg", "Sample_MPEG"),
("vidonly1.mpg", "Sample_MPEG_video_only_1"),
("vidonly2.mpg", "Sample_MPEG_video_only_2")]
for mp3 in range(0, 2):
stream.addMP3(*media[mp3])
stream.addMPEG(*media[2])
for mpg in range(3, len(media)):
stream.addMPEGVideo(*media[mpg])
# We could use a templating language but I think the extra dependencies is not
# worth it in this case.
html = """
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN"
"http://www.w3.org/TR/html4/strict.dtd">
<html lang="en">
<head><meta http-equiv="content-type" content="text/html; charset=utf-8">
<title>Streaming Server Demo</title>
<script src="http://prototypejs.org/assets/2008/1/25/prototype-1.6.0.2.js"
