def verifyProxies(self, proxies):
''' Take as input dictionary of {name:proxystring} and verify that
we can communicate with the server (by getting the detector or trainer).
Return a dictionary with 'detector running' or 'trainer running' if running
and 'configured' if not. Also print now so with long config files user
sees output.
'''
res = {}
for key, value in proxies.iteritems():
# add short timeout
value = value + ' -t 100'
try:
detector = easy.getDetector(value)
res[key] = 'detector running'
print("{0} {1}".format(key, res[key]))
continue
except:
pass
try:
trainer = easy.getTrainer(value)
res[key] = 'trainer running'
print("{0} {1}".format(key, res[key]))
continue
except:
pass
try:
trainer = easy.getFileServer(value)
res[key] = 'FileServer running'
print("{0} {1}".format(key, res[key]))
continue
except:
pass
try:
trainer = easy.getCorpusServer(value)
res[key] = 'Corpus running'
print("{0} {1}".format(key, res[key]))
continue
except:
pass
res[key] = 'not running'
print("{0} {1}".format(key, res[key]))
return res
'''
Easy! test for corpus_service.py
Obtain labeled data from a LabelMe server.
See http://new-labelme.csail.mit.edu/Release3.0
matz 6/19/2013
'''
import easy
# The properties file for a LabelMe Corpus contains all pertinent information.
# Take a look at corpus/LabelMeCarsTest.properties and pay particular
# attention to the following properties:
# LMFolders and LMObjectNames
cs = easy.getCorpusServer( "PythonCorpusService:default -p 10021")
corpus = easy.openCorpus( "corpus/LabelMeCircuit.properties", corpusServer=cs )
categories, lablist = easy.getDataSet( corpus, corpusServer=cs,
createMirror=True )
print('Obtained {0} labeled artifact{1} from corpus "{2}":'.format(
len(lablist), ("s","")[len(lablist)==1], corpus.name ));
easy.printCategoryInfo( categories )
# draw the images and their annotations, one image at a time,
# at a given maximum size (width, height)
easy.drawLabelables( lablist, (512, 512) )
print("-----------")
#
# TODO: currently breaks because Caltech101 doesn't get extracted as expected
#categories, lablist = easy.getDataSet( "corpus/CvacCorpusTest" )
# categories, lablist = easy.getDataSet( "corpus/Caltech101.properties", createMirror=False )
#easy.printCategoryInfo( categories )
#runset = easy.createRunSet( categories["car_side"] )
#trainer = easy.getTrainer( "bowTrain:default -p 10103" )
#carSideModel = easy.train( trainer, runset )
#
# Third, a slower walk-through of functionality that digs a bit deeper. All
# following steps are part of that.
# Obtain a set of labeled data from a Corpus,
# print dataset information about this corpus
#
cs = easy.getCorpusServer("CorpusServer:default -p 10011")
#corpus = easy.openCorpus( cs, "corpus/CvacCorpusTest.properties" )
#corpus = easy.openCorpus( cs, "corporate_logos" );
corpus = easy.openCorpus( cs, "trainImg" );
categories, lablist = easy.getDataSet( corpus, corpusServer=cs )
print('Obtained {0} labeled artifact{1} from corpus "{2}":'.format(
len(lablist), ("s","")[len(lablist)==1], corpus.name ));
easy.printCategoryInfo( categories )
#
# add all samples from corpus to a RunSet,
# also obtain a mapping from class ID to label name
#
res = easy.createRunSet( categories )
runset = res['runset']
classmap = res['classmap']