def load(self, dataflow):
"""
Configure engine according to the given dataflow.
:param dataflow: dataflow object or filename of a dataflow file.
:type dataflow: :py:class:`DataFlow` or string
:return: True on success, False on fail.
"""
if type(dataflow) is str:
df = DataFlow()
if df.load(dataflow):
dataflow = df
if type(dataflow) is DataFlow:
return yc.engine_load(self.ptr, dataflow.ptr) and True or False
raise TypeError(
'dataflow parameter must be a DataFlow object or dataflow filename !'
)
def addFeature(self, definition):
"""
Add a feature defined according the :ref:`feature definition syntax <featplan>`.
:param definition: feature definition
:type definition: string
:rtype: True on success, False on fail.
"""
data = definition.split(':')
if not len(data) == 2:
print 'Syntax error in "%s"' % definition
return False
name, featdef = data
dataflow = DataFlow()
inputNode = dataflow.createInput('audio', self.audio_params)
if featdef.strip():
for s in featdef.split('>'):
s = s.strip()
bb = s.split(' ')
feat = AudioFeatureFactory.get_feature(bb[0])
if not feat:
return False
params = {}
for d in bb[1:]:
if len(d) == 0:
continue
if not '=' in d:
print 'Invalid feature parameter "%s"' % d
return False
dd = d.split('=')
if not len(dd) == 2:
print 'Syntax error in feature parameter "%s"' % d
return False
params[dd[0]] = dd[1]
dataflow.append(feat.get_dataflow(params, self.sample_rate))
fNode = dataflow.finalNodes()[0]
feat_attrs = self.out_attrs.copy()
feat_attrs['yaafedefinition'] = featdef.strip()
outNode = dataflow.createOutput(name, feat_attrs)
dataflow.link(fNode, '', outNode, '')
self.dataflow.merge(dataflow)
return True
def __init__(self, sample_rate=44100, normalize=None, resample=False):
if type(normalize) == int:
normalize = '%i' % normalize
elif type(normalize) == float:
normalize = '%f' % normalize
elif normalize and type(normalize) != str:
normalize = str(normalize)
self.features = {}
self.resample = resample
self.sample_rate = sample_rate
self.audio_params = {
'SampleRate': str(sample_rate),
'Resample': 'yes' if resample else 'no'
}
if normalize:
self.audio_params['RemoveMean'] = 'yes'
self.audio_params['ScaleMax'] = normalize
self.out_attrs = {
'normalize': normalize or '-1',
'version': yaafecore.getYaafeVersion(),
'samplerate': str(sample_rate),
'resample': 'yes' if resample else 'no'
}
self.dataflow = DataFlow()
import logging
from dataflow import DataFlow
from workers import head_url, node_url, newfile_filter, ignore_filter, file_to_db
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
filename='/var/log/homulili/scanner.log',
filemode='w',
)
logger = logging.getLogger(__name__)
master_update_interval = timedelta(minutes=30).total_seconds()
madokami_stage_interval = timedelta(seconds=10).total_seconds()
logger.info('Starting scanner')
df = DataFlow()
x = df.rate_limited_node(target=head_url, interval=master_update_interval)
x = df.rate_limited_node(input=x.out,
target=node_url,
interval=madokami_stage_interval)
x = df.node(input=x.out, target=newfile_filter)
x = df.node(input=x.out, target=ignore_filter)
x = df.node(input=x.out, num_outputs=0, target=file_to_db)
logger.debug('Scanner graph initialized')
df.run()
