OpenCV+Python识别车牌和字符分割的实现

# -*- coding: utf-8 -*-"""@email:cuiran2001@163.com@author: cuiran"""import cv2import numpy as npfrom PIL import Imageimport os.pathfrom skimage import io,datadef stretch(img): ''' 图像拉伸函数 ''' maxi=float(img.max()) mini=float(img.min()) for i in range(img.shape[0]):  for j in range(img.shape[1]):   img[i,j]=(255/(maxi-mini)*img[i,j]-(255*mini)/(maxi-mini)) return imgdef dobinaryzation(img): ''' 二值化处理函数 ''' maxi=float(img.max()) mini=float(img.min()) x=maxi-((maxi-mini)/2) #二值化,返回阈值ret 和 二值化操作后的图像thresh ret,thresh=cv2.threshold(img,x,255,cv2.THRESH_BINARY) #返回二值化后的黑白图像 return threshdef find_rectangle(contour): ''' 寻找矩形轮廓 ''' y,x=[],[] for p in contour:  y.append(p[0][0])  x.append(p[0][1]) return [min(y),min(x),max(y),max(x)]def locate_license(img,afterimg): ''' 定位车牌号 ''' img,contours,hierarchy=cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) #找出最大的三个区域 block=[] for c in contours:  #找出轮廓的左上点和右下点，由此计算它的面积和长度比  r=find_rectangle(c)  a=(r[2]-r[0])*(r[3]-r[1]) #面积  s=(r[2]-r[0])*(r[3]-r[1]) #长度比  block.append([r,a,s]) #选出面积最大的3个区域 block=sorted(block,key=lambda b: b[1])[-3:] #使用颜色识别判断找出最像车牌的区域 maxweight,maxindex=0,-1 for i in range(len(block)):  b=afterimg[block[i][0][1]:block[i][0][3],block[i][0][0]:block[i][0][2]]  #BGR转HSV  hsv=cv2.cvtColor(b,cv2.COLOR_BGR2HSV)  #蓝色车牌的范围  lower=np.array([100,50,50])  upper=np.array([140,255,255])  #根据阈值构建掩膜  mask=cv2.inRange(hsv,lower,upper)  #统计权值  w1=0  for m in mask:   w1+=m/255  w2=0  for n in w1:   w2+=n  #选出最大权值的区域  if w2>maxweight:   maxindex=i   maxweight=w2 return block[maxindex][0]def find_license(img): ''' 预处理函数 ''' m=400*img.shape[0]/img.shape[1] #压缩图像 img=cv2.resize(img,(400,int(m)),interpolation=cv2.INTER_CUBIC) #BGR转换为灰度图像 gray_img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #灰度拉伸 stretchedimg=stretch(gray_img) '''进行开运算，用来去除噪声''' r=16 h=w=r*2+1 kernel=np.zeros((h,w),np.uint8) cv2.circle(kernel,(r,r),r,1,-1) #开运算 openingimg=cv2.morphologyEx(stretchedimg,cv2.MORPH_OPEN,kernel) #获取差分图，两幅图像做差 cv2.absdiff('图像1','图像2') strtimg=cv2.absdiff(stretchedimg,openingimg) #图像二值化 binaryimg=dobinaryzation(strtimg) #canny边缘检测 canny=cv2.Canny(binaryimg,binaryimg.shape[0],binaryimg.shape[1]) '''消除小的区域，保留大块的区域，从而定位车牌''' #进行闭运算 kernel=np.ones((5,19),np.uint8) closingimg=cv2.morphologyEx(canny,cv2.MORPH_CLOSE,kernel) #进行开运算 openingimg=cv2.morphologyEx(closingimg,cv2.MORPH_OPEN,kernel) #再次进行开运算 kernel=np.ones((11,5),np.uint8) openingimg=cv2.morphologyEx(openingimg,cv2.MORPH_OPEN,kernel) #消除小区域，定位车牌位置 rect=locate_license(openingimg,img) return rect,imgdef cut_license(afterimg,rect): ''' 图像分割函数 ''' #转换为宽度和高度 rect[2]=rect[2]-rect[0] rect[3]=rect[3]-rect[1] rect_copy=tuple(rect.copy()) rect=[0,0,0,0] #创建掩膜 mask=np.zeros(afterimg.shape[:2],np.uint8) #创建背景模型 大小只能为13*5，行数只能为1，单通道浮点型 bgdModel=np.zeros((1,65),np.float64) #创建前景模型 fgdModel=np.zeros((1,65),np.float64) #分割图像 cv2.grabCut(afterimg,mask,rect_copy,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT) mask2=np.where((mask==2)|(mask==0),0,1).astype('uint8') img_show=afterimg*mask2[:,:,np.newaxis] return img_showdef deal_license(licenseimg): ''' 车牌图片二值化 ''' #车牌变为灰度图像 gray_img=cv2.cvtColor(licenseimg,cv2.COLOR_BGR2GRAY) #均值滤波 去除噪声 kernel=np.ones((3,3),np.float32)/9 gray_img=cv2.filter2D(gray_img,-1,kernel) #二值化处理 ret,thresh=cv2.threshold(gray_img,120,255,cv2.THRESH_BINARY) return threshdef find_end(start,arg,black,white,width,black_max,white_max): end=start+1 for m in range(start+1,width-1):  if (black[m] if arg else white[m])>(0.98*black_max if arg else 0.98*white_max):   end=m   break return endif __name__=='__main__': img=cv2.imread('test_images/car001.jpg',cv2.IMREAD_COLOR) #预处理图像 rect,afterimg=find_license(img) #框出车牌号 cv2.rectangle(afterimg,(rect[0],rect[1]),(rect[2],rect[3]),(0,255,0),2) cv2.imshow('afterimg',afterimg) #分割车牌与背景 cutimg=cut_license(afterimg,rect) cv2.imshow('cutimg',cutimg) #二值化生成黑白图 thresh=deal_license(cutimg) cv2.imshow('thresh',thresh) cv2.waitKey(0) #分割字符 ''' 判断底色和字色 ''' #记录黑白像素总和 white=[] black=[] height=thresh.shape[0] #263 width=thresh.shape[1] #400 #print('height',height) #print('width',width) white_max=0 black_max=0 #计算每一列的黑白像素总和 for i in range(width):  line_white=0  line_black=0  for j in range(height):   if thresh[j][i]==255:    line_white+=1   if thresh[j][i]==0:    line_black+=1  white_max=max(white_max,line_white)  black_max=max(black_max,line_black)  white.append(line_white)  black.append(line_black)  print('white',white)  print('black',black) #arg为true表示黑底白字，False为白底黑字 arg=True if black_max<white_max:  arg=False n=1 start=1 end=2 s_width=28 s_height=28 while n<width-2:  n+=1  #判断是白底黑字还是黑底白字 0.05参数对应上面的0.95 可作调整  if(white[n] if arg else black[n])>(0.02*white_max if arg else 0.02*black_max):   start=n   end=find_end(start,arg,black,white,width,black_max,white_max)   n=end   if end-start>5:    cj=thresh[1:height,start:end]    # new_image = cj.resize((s_width,s_height),Image.BILINEAR)    # cj=cj.reshape(28, 28)    print("result/%s.jpg" % (n))    #保存分割的图片 by cayden    # cj.save("result/%s.jpg" % (n))    infile="result/%s.jpg" % (n)    io.imsave(infile,cj)    # im = Image.open(infile)    # out=im.resize((s_width,s_height),Image.BILINEAR)    # out.save(infile)    cv2.imshow('cutlicense',cj)    cv2.waitKey(0) cv2.waitKey(0) cv2.destroyAllWindows()