def dirPicTest():
clf = joblib.load('model/svm.pkl')
print type(clf)
print clf
platePath = 'resource/general_test'
for root, dirs, files in os.walk( platePath ):
print len(files)
for file in files:
path = os.path.join( root, file )
print 'loading....',path
imgPlate = cv2.imread(path,cv2.IMREAD_COLOR)
PlateLocater.m_debug = False
Result = PlateLocater.fuzzyLocate(imgPlate)
for plate in Result:
feature = PlateJudger.getHistogramFeatures(plate)
res = clf.predict(feature)
flag = (res==1)
print flag[0]
if flag[0]:
cv2.imshow('test',plate)
cv2.waitKey(0)
cv2.destroyAllWindows()
return None
def CV_dirPicTest():
model = CV_trainModel()
platePath = 'resource/general_test'
for root, dirs, files in os.walk( platePath ):
print len(files)
for file in files:
path = os.path.join( root, file )
print 'loading....',path
imgPlate = cv2.imread(path,cv2.IMREAD_COLOR)
PlateLocater.m_debug = False
Result = PlateLocater.fuzzyLocate(imgPlate)
for plate in Result:
feature = PlateJudger.getHistogramFeatures(plate)
print feature.dtype
feature = np.float32(feature)
res = model.predict(feature)
a = (res==1)
print a[0]
if a[0]:
cv2.imshow('test',plate)
cv2.waitKey(0)
cv2.destroyAllWindows()
def features(inMat, sizeData):
# 抠取中间区域
center_rect = getCenterRect(inMat)
x, y, w, h = center_rect
roi = inMat[y:y + h, x:x + w]
# low data feature
low_data = cv2.resize(roi, (sizeData, sizeData))
VERTICAL = 0
HORIZONTAL = 1
# Histogram feature
vhist = PlateJudger.ProjectedHistogram(low_data, VERTICAL)
hhist = PlateJudger.ProjectedHistogram(low_data, HORIZONTAL)
# print vhist.shape
# print hhist.shape
# print low_data
numCols = vhist.shape[1] + hhist.shape[
1] + low_data.shape[0] * low_data.shape[1]
outMat = np.zeros((1, numCols))
# feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
index = 0
for i in range(0, vhist.shape[1]):
outMat[0, index] = vhist[0, i]
index += 1
# print outMat
for i in range(0, hhist.shape[1]):
outMat[0, index] = hhist[0, i]
index += 1
for i in range(0, low_data.shape[0]):
for j in range(0, low_data.shape[0]):
outMat[0, index] = low_data[i, j]
index += 1
return outMat
def CV_PicTest():
model = CV_trainModel()
imgPlate = cv2.imread('plate_judge.jpg',cv2.IMREAD_COLOR)
PlateLocater.m_debug = False
Result = PlateLocater.fuzzyLocate(imgPlate)
for plate in Result:
cv2.imshow('test',plate)
feature = PlateJudger.getHistogramFeatures(plate)
feature = np.float32(feature)
res = model.predict(feature)
print res
cv2.waitKey(0)
cv2.destroyAllWindows()
def plateRecognize(inMat):
PlateLocater.m_debug = False
CharsIndentifier.m_debug = False
Result = PlateLocater.fuzzyLocate(inMat)
if m_debug:
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)
cv2.destroyAllWindows()
resultVec = PlateJudger.platesJudge(Result)
if m_debug:
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.imwrite("debug/SVM-"+str(index_loc)+".jpg", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
lisences = list()
for img_plate in resultVec:
segmented = CharsSegmenter.charsSegment(img_plate)
if m_debug:
index_char = 0
for char in segmented:
index_char += 1
cv2.imshow("chars_segment"+str(index_char), char)
cv2.imwrite("debug/segmented-"+str(index_char)+".jpg", char)
cv2.waitKey(0)
cv2.destroyAllWindows()
lisence = CharsIndentifier.identifyPlate(segmented)
# print lisence
lisences.append(lisence)
return lisences
def loadNegativeTrainData(platePath): traindata = np.ones((1,172)) label = np.array([0]) isFirst = True for root, dirs, files in os.walk( platePath ): print len(files) for file in files: path = os.path.join( root, file ) print 'loading....',path imgPlate = cv2.imread(path,cv2.IMREAD_COLOR) feature = PlateJudger.getHistogramFeatures(imgPlate) if isFirst: traindata = feature isFirst = False else: traindata = np.vstack([traindata,feature]) label = np.hstack([label,0]) return traindata,label
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
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
