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GVP北京智云视图科技有限公司 / HyperLPRC++Apache-2.0

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HyperLRP是一个开源的、基于深度学习高性能中文车牌识别库,由北京智云视图科技有限公司开发,支持PHP、C/C++、Python语言,Windows/Mac/Linux/Android/IOS 平台。 spread retract

http://www.zeusee.com

  • C++ 69.5%
  • Java 19.7%
  • C 4.3%
  • CMake 2.3%
  • Python 2.2%
  • Other 2.0%
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benchmark.py 2.10 KB
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jack_yu_ authored 2018-08-13 00:59 . Update benchmark.py
#coding=utf-8
import os
import numpy as np
import cv2
import json
from hyperlpr import pipline as pp
import sys
from Levenshtein import StringMatcher as sm
reload(sys)
sys.setdefaultencoding("utf-8")
parent= "you folder"
def comparestring(a,b):
g = 0
if len(a) == len(b):
for x,y in zip(a,b):
if x!=y:
g+=1
return g
count = 0 ;
count_p = 0
count_d = 0
count_lev = 0
count_undetected = 0
roi = [470,400,650,580]
for filename in os.listdir(parent):
path = os.path.join(parent,filename)
print path
if path.endswith(".jpg") or path.endswith(".png"):
ics,name = os.path.split(path)
name,ext = name.split(".")
image = cv2.imread(path)
image = image[roi[1]:roi[1]+roi[3],roi[0]:roi[0]+roi[2]]
# cv2.imshow("test",image)
# cv2.waitKey(0)
info,dataset = pp.SimpleRecognizePlate(image)
ext = ext.strip()
name = name.strip()
if len(dataset)==0:
count_undetected +=1
# cv2.imwrite("./cache/bad2/" + name + ".png", image)
for one in dataset:
# p = sm.StringMatcher(seq1=one.encode("utf-8"),seq2=name.encode("utf-8"))
A = one.decode("utf-8")
B = name.decode("utf-8")
print one.decode("utf-8"),"<->",name.decode("utf-8"),"编辑距离:",comparestring(A,B)
if comparestring(A,B)<3:
count_lev+=1
else:
cv2.imwrite("./cache/bad2/"+B+"->"+A+".png",image)
if one.decode("utf-8") == name.decode("utf-8"):
count_p+=1
break
else:
print "error",one.decode("utf-8"), name.decode("utf-8")
count_d+=1
# cv2.imshow("image",image)
# cv2.waitKey(0)
# break
count+=1
print count_p / float(count),"编辑距离[1]:",count_lev/float(count),u"识出",count_p,u"总数",count,u"未识出",count_d,u"未检测出",count_undetected
if count_p+count_d+count_undetected!=count:
print dataset,len(dataset)
# exit(0)

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