correct += 1.0
else:
wrong += 1.0
print "Correctness:" + str(100*correct/(correct+wrong)) + "%"
print "Wrong number:" + str(wrong)
return
else:
pred = []
for sli in slices:
if cf == "SVM":
pred.append(SVM_Predict(clf,sli[0])[0])
else:
pred.append(KNN_Predict(knn,sli[0])[0])
if __name__ == '__main__':
#im = Image.open(os.curdir + os.sep + "43192.png")
LoadData()
#clf = SVC()
#knn = KNN()
#cf = raw_input("Choose SVM or KNN as classifier:")
im = Image.open("Test.png")
#ans = Segment.shotgun(im,True)
Segment.shotgun(im,False)
Correctness("SVM",False) # (cf,True,ans)
''' Main Process
'''
import os
from PIL import Image
import imtools
import Denoise,Segment,Recognition
if __name__ == '__main__':
isRecog = True
s_dir = os.curdir + os.sep + "Sample"
img_list = imtools.get_imlist(s_dir)
Recognition.LoadData()
cf = raw_input("Choose SVM or KNN as classifier:")
for pic in img_list:
pre = Image.open(pic)
pre.show()
pre = Denoise.refine(pre)
seg = Segment.shotgun(pre,True)
Recognition.Correctness(cf,isRecog,seg) # (cf,True,ans)
