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FunctionTest.py
121 lines (94 loc) · 3.29 KB
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FunctionTest.py
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# -*- coding: utf-8 -*-
import numpy as np
import cv2
from sklearn import svm
import os
import PlateLocater
import PlateJudger
import CharsSegmenter
import CharsIndentifier
kPredictSize = 10
# 测试车牌定位 和 车牌判别
def test_plate_locate():
PlateLocater.m_debug = False
rootdir = "resources/easy_test"
print 'test_plate_locate'
for parent,dirnames,filenames in os.walk(rootdir):
index_file = 0
print filenames
for filename in filenames:
cv2.destroyAllWindows()
index_file += 1
imgPlate = cv2.imread(os.path.join(parent,filename),cv2.IMREAD_COLOR)
print '文件名:',filename.split('.')[0]
cv2.imshow("原图",imgPlate)
Result = PlateLocater.fuzzyLocate(imgPlate)
print '候选车牌数量：',len(Result)
index_loc = 0
for img in Result:
index_loc += 1
cv2.imshow("候选车牌"+str(index_loc),img)
cv2.waitKey(0)
resultVec = PlateJudger.platesJudge(Result)
print 'SVM筛选后的车牌数量：',len(resultVec)
index_loc = 0
for img in resultVec:
index_loc += 1
cv2.imshow("SVM-"+str(index_loc),img)
cv2.waitKey(0)
if index_file >20:
break
# 测试字符分割
# chars_segment.cpp 79L
def test_chars_segment():
print "test_chars_segment"
img_plate = cv2.imread("resources/image/chars_segment.jpg",cv2.IMREAD_COLOR)
cv2.imshow("test",img_plate)
# print cv2.countNonZero(img_plate[:,:,0])
# print img_plate.shape[0],img_plate.shape[1],img_plate.shape[1]*img_plate.shape[0]
# cv2.waitKey(0)
# cv2.destroyAllWindows()
segmented = CharsSegmenter.charsSegment(img_plate)
index_char = 0
for char in segmented:
index_char += 1
cv2.imshow("chars_segment"+str(index_char), char)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 测试字符识别
# chars.hpp 30L
def test_chars_identify():
print "test_chars_identify"
img_plate = cv2.imread("resources/image/chars_identify.jpg",cv2.IMREAD_COLOR)
# 车牌分割
segmented = CharsSegmenter.charsSegment(img_plate)
licence = ''
CharsIndentifier.initModel()
for index, char in enumerate(segmented):
print 'char:'
print char
# licence += CharsIndentifier.identify(char)
char_feature = CharsIndentifier.features(char,kPredictSize)
if index == 0:
char_text = CharsIndentifier.identifyChinese(char_feature)
else:
char_text = CharsIndentifier.identifyDigitLetter(char_feature)
licence += char_text
print "识别结果:"
print licence
def test_ann_train():
print 'test_ann_train'
import ANNtrain
ANNtrain.chinese_data_preprocess()
ANNtrain.train_chinese_model()
def test_digit_letter_train():
print 'test_digit_letter_train'
import ANNtrain
ANNtrain.digit_letter_data_preprocess()
ANNtrain.train_digit_letter_model()
if __name__ == '__main__':
# test_plate_locate()
# test_chars_segment()
# test_ann_train()
# test_digit_letter_train()
test_chars_identify()