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