def train(readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
for i in range(7):
ratioKnns.append(LetterKnnClassifier())
print ratioKnns
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(
cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(
const.getStringFromNumber(c1))
knn.populateData()
knn.trainModel()
for r in ratioKnns:
r.trainModel()
print "Completed training"
print "------------------"
def train(readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
for i in range(7):
ratioKnns.append(LetterKnnClassifier())
print ratioKnns
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(
cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(const.getStringFromNumber(c1))
knn.populateData()
knn.trainModel()
for r in ratioKnns:
r.trainModel()
print "Completed training"
print "------------------"
path = "./training3/"
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
ratioKnns[c2].loadTrainingImage(path+f, c1)
print "LetterClass. "+str(c2)+":"+str(cc.getColorFromNumber(c2))+" trained: "+f+ " as "+str(const.getStringFromNumber(c1))
knn.populateData()
knn.trainModel()
for r in ratioKnns:
r.trainModel()
print "done training"
print len(knn.train)
path = "./training3/"
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(
const.getStringFromNumber(c1)
)
knn.populateData()
knn.trainModel()
for r in ratioKnns:
r.trainModel()
print "done training"