print folder
trainDirect = folder+"tempTrain/"
trainNP = folder+"tempTrainNP/"
testDirect = folder+"tempTest/"
testNP = folder+"tempTestNP/"
"""Load the train/test split information"""
trainFs, testFs = helperFuncs.getTrainTestSplit(True,folder)
trainL = len(trainFs)
testL = len(testFs)
features,labels = helperFuncs.getTargets(outType) #get the OCR vector for each CID
outsize = len(features[features.keys()[0]]) #this it the size of the target (# of OCRfeatures)
means,stds = helperFuncs.getMeansStds(features)
"""load model"""
model = helperFuncs.loadModel(folder+"wholeModel")
while not isfile(testNP+"Xtest.h5"):
print "sleeping because Test folder empty \r",
time.sleep(1.)
print ""
print "Loading np test arrays"
loadedUp = False
while not loadedUp:
def getSize(folder):
fold = folder[folder[-1].rfind("/")+1:]
fold = fold[:fold.find("_")]
print fold
fold = fold[fold.rfind("/")+1:]
print fold
return fold
with open(sys.argv[1]+"wholeModel.pickle",'rb') as f:
model = cPickle.load(f)
size = int(getSize(sys.argv[1]))
imdim = size
#OCRfeatures, labels = getOCRScaledTargets()
OCRTargets, labels = getOCRTargets()
means,stds = getMeansStds()
if True:
ld = listdir("/home/test/usan/")
images = np.zeros((1,1,imdim,imdim),dtype=np.float)
for x in ld:
print x
try:
CID = x
print "reading in"
image = io.imread("/home/test/usan/"+x,as_grey=True)
image = minusOnes(image)
print "numpying"
image = np.array(image)
#image = convertIt(image)
fold = folder[folder[-1].rfind("/") + 1:]
fold = fold[:fold.find("_")]
print fold
fold = fold[fold.rfind("/") + 1:]
print fold
return fold
with open(sys.argv[1] + "wholeModel.pickle", 'rb') as f:
model = cPickle.load(f)
size = int(getSize(sys.argv[1]))
imdim = size
#OCRfeatures, labels = getOCRScaledTargets()
OCRTargets, labels = getOCRTargets()
means, stds = getMeansStds()
if True:
ld = listdir("/home/test/usan/")
images = np.zeros((1, 1, imdim, imdim), dtype=np.float)
for x in ld:
print x
try:
CID = x
print "reading in"
image = io.imread("/home/test/usan/" + x, as_grey=True)
image = minusOnes(image)
print "numpying"
image = np.array(image)
#image = convertIt(image)
print "resizing"
print folder
trainDirect = folder+"tempTrain/"
trainNP = folder+"tempTrainNP/"
testDirect = folder+"tempTest/"
testNP = folder+"tempTestNP/"
"""Load the train/test split information"""
trainFs, testFs = helperFuncs.getTrainTestSplit(True,folder)
trainL = len(trainFs)
testL = len(testFs)
features,labels = helperFuncs.getTargets(outType) #get the OCR vector for each CID
outsize = len(features[features.keys()[0]]) #this it the size of the target (# of OCRfeatures)
means,stds = helperFuncs.getMeansStds()
"""load model"""
#with open(folder+"bestModel.pickle",'rb') as f:
# model = cPickle.load(f)
model = helperFuncs.loadModel(folder+"wholeModel")
while not isfile(testNP+"Xtest.h5"):
print "sleeping because Test folder empty \r",
time.sleep(1.)
print ""
print "Loading np test arrays"
