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)