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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@@ -0,0 +1,402 @@
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label: 0
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display_name: "background"
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label: 1
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display_name: "accordion"
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label: 2
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display_name: "airplane"
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label: 3
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display_name: "ant"
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label: 4
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display_name: "antelope"
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label: 5
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display_name: "apple"
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label: 6
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display_name: "armadillo"
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label: 7
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display_name: "artichoke"
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label: 8
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display_name: "axe"
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label: 9
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display_name: "baby_bed"
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label: 10
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display_name: "backpack"
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label: 11
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display_name: "bagel"
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label: 12
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display_name: "balance_beam"
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label: 13
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display_name: "banana"
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label: 14
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display_name: "band_aid"
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label: 15
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display_name: "banjo"
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label: 16
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display_name: "baseball"
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label: 17
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display_name: "basketball"
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label: 18
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display_name: "bathing_cap"
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label: 19
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display_name: "beaker"
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label: 20
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display_name: "bear"
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label: 21
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display_name: "bee"
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label: 22
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display_name: "bell_pepper"
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label: 23
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display_name: "bench"
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label: 24
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display_name: "bicycle"
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label: 25
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display_name: "binder"
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label: 26
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display_name: "bird"
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label: 27
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display_name: "bookshelf"
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label: 28
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display_name: "bow_tie"
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label: 29
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display_name: "bow"
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label: 30
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display_name: "bowl"
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label: 31
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display_name: "brassiere"
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label: 32
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display_name: "burrito"
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label: 33
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display_name: "bus"
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label: 34
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display_name: "butterfly"
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label: 35
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display_name: "camel"
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label: 36
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display_name: "can_opener"
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label: 37
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display_name: "car"
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label: 38
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display_name: "cart"
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label: 39
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display_name: "cattle"
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label: 40
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display_name: "cello"
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label: 41
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display_name: "centipede"
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label: 42
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display_name: "chain_saw"
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label: 43
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display_name: "chair"
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label: 44
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display_name: "chime"
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label: 45
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display_name: "cocktail_shaker"
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label: 46
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display_name: "coffee_maker"
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label: 47
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display_name: "computer_keyboard"
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label: 48
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display_name: "computer_mouse"
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label: 49
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display_name: "corkscrew"
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label: 50
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display_name: "cream"
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label: 51
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display_name: "croquet_ball"
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label: 52
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display_name: "crutch"
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label: 53
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display_name: "cucumber"
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label: 54
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display_name: "cup_or_mug"
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label: 55
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display_name: "diaper"
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label: 56
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display_name: "digital_clock"
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label: 57
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display_name: "dishwasher"
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label: 58
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display_name: "dog"
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label: 59
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display_name: "domestic_cat"
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label: 60
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display_name: "dragonfly"
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label: 61
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display_name: "drum"
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label: 62
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display_name: "dumbbell"
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label: 63
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display_name: "electric_fan"
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label: 64
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display_name: "elephant"
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label: 65
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display_name: "face_powder"
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label: 66
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display_name: "fig"
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label: 67
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display_name: "filing_cabinet"
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label: 68
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display_name: "flower_
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label: 69
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display_name: "fl
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label: 70
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display_name: "fox"
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label: 71
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display_name: "french_h
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label: 72
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display_name: "frog"
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label: 73
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display_name: "frying_
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label: 74
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display_name: "giant_pa
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label: 75
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display_name: "goldfish"
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label: 76
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display_name: "golf_ball"
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label: 77
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display_name: "golfcart"
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label: 78
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display_name: "guacamole"
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label: 79
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display_name: "guitar"
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label: 80
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display_name: "hair_dryer"
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label: 81
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display_name: "hair_spray"
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label: 82
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display_name: "hamburger"
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label: 83
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display_name: "hammer"
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label: 84
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display_name: "hamster"
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label: 85
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display_name: "harmonica"
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label: 86
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display_name: "harp"
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label: 87
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display_name: "hat_with_a_wide_brim"
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label: 88
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display_name: "head_cabbage"
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label: 89
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display_name: "helmet"
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label: 90
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display_name: "hippopotamus"
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label: 91
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display_name: "horizontal_bar"
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label: 92
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display_name: "horse"
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label: 93
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display_name: "hotdog"
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label: 94
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display_name: "iPod"
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label: 95
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display_name: "isopod"
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label: 96
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display_name: "jellyfish"
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label: 97
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display_name: "koala_bear"
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label: 98
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display_name: "ladle"
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label: 99
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display_name: "ladybug"
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label: 100
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display_name: "lamp"
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label: 101
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display_name: "laptop"
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label: 102
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display_name: "lemon"
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label: 103
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display_name: "lion"
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label: 104
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display_name: "lipstick"
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label: 105
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display_name: "lizard"
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label: 106
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display_name: "lobster"
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label: 107
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display_name: "maillot"
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label: 108
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display_name: "maraca"
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label: 109
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display_name: "microphone"
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label: 110
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display_name: "microwave"
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label: 111
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display_name: "milk_can"
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label: 112
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display_name: "miniskirt"
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||||
label: 113
|
||||
display_name: "monkey"
|
||||
label: 114
|
||||
display_name: "motorcycle"
|
||||
label: 115
|
||||
display_name: "mushroom"
|
||||
label: 116
|
||||
display_name: "nail"
|
||||
label: 117
|
||||
display_name: "neck_brace"
|
||||
label: 118
|
||||
display_name: "oboe"
|
||||
label: 119
|
||||
display_name: "orange"
|
||||
label: 120
|
||||
display_name: "otter"
|
||||
label: 121
|
||||
display_name: "pencil_box"
|
||||
label: 122
|
||||
display_name: "pencil_sharpener"
|
||||
label: 123
|
||||
display_name: "perfume"
|
||||
label: 124
|
||||
display_name: "person"
|
||||
label: 125
|
||||
display_name: "piano"
|
||||
label: 126
|
||||
display_name: "pineapple"
|
||||
label: 127
|
||||
display_name: "ping-pong_ball"
|
||||
label: 128
|
||||
display_name: "pitcher"
|
||||
label: 129
|
||||
display_name: "pizza"
|
||||
label: 130
|
||||
display_name: "plastic_bag"
|
||||
label: 131
|
||||
display_name: "plate_rack"
|
||||
label: 132
|
||||
display_name: "pomegranate"
|
||||
label: 133
|
||||
display_name: "popsicle"
|
||||
label: 134
|
||||
display_name: "porcupine"
|
||||
label: 135
|
||||
display_name: "power_drill"
|
||||
label: 136
|
||||
display_name: "pretzel"
|
||||
label: 137
|
||||
display_name: "printer"
|
||||
label: 138
|
||||
display_name: "puck"
|
||||
label: 139
|
||||
display_name: "punching_bag"
|
||||
label: 140
|
||||
display_name: "purse"
|
||||
label: 141
|
||||
display_name: "rabbit"
|
||||
label: 142
|
||||
display_name: "racket"
|
||||
label: 143
|
||||
display_name: "ray"
|
||||
label: 144
|
||||
display_name: "red_panda"
|
||||
label: 145
|
||||
display_name: "refrigerator"
|
||||
label: 146
|
||||
display_name: "remote_control"
|
||||
label: 147
|
||||
display_name: "rubber_eraser"
|
||||
label: 148
|
||||
display_name: "rugby_ball"
|
||||
label: 149
|
||||
display_name: "ruler"
|
||||
label: 150
|
||||
display_name: "salt_or_pepper_shaker"
|
||||
label: 151
|
||||
display_name: "saxophone"
|
||||
label: 152
|
||||
display_name: "scorpion"
|
||||
label: 153
|
||||
display_name: "screwdriver"
|
||||
label: 154
|
||||
display_name: "seal"
|
||||
label: 155
|
||||
display_name: "sheep"
|
||||
label: 156
|
||||
display_name: "ski"
|
||||
label: 157
|
||||
display_name: "skunk"
|
||||
label: 158
|
||||
display_name: "snail"
|
||||
label: 159
|
||||
display_name: "snake"
|
||||
label: 160
|
||||
display_name: "snowmobile"
|
||||
label: 161
|
||||
display_name: "snowplow"
|
||||
label: 162
|
||||
display_name: "soap_dispenser"
|
||||
label: 163
|
||||
display_name: "soccer_ball"
|
||||
label: 164
|
||||
display_name: "sofa"
|
||||
label: 165
|
||||
display_name: "spatula"
|
||||
label: 166
|
||||
display_name: "squirrel"
|
||||
label: 167
|
||||
display_name: "starfish"
|
||||
label: 168
|
||||
display_name: "stethoscope"
|
||||
label: 169
|
||||
display_name: "stove"
|
||||
label: 170
|
||||
display_name: "strainer"
|
||||
label: 171
|
||||
display_name: "strawberry"
|
||||
label: 172
|
||||
display_name: "stretcher"
|
||||
label: 173
|
||||
display_name: "sunglasses"
|
||||
label: 174
|
||||
display_name: "swimming_trunks"
|
||||
label: 175
|
||||
display_name: "swine"
|
||||
label: 176
|
||||
display_name: "syringe"
|
||||
label: 177
|
||||
display_name: "table"
|
||||
label: 178
|
||||
display_name: "tape_player"
|
||||
label: 179
|
||||
display_name: "tennis_ball"
|
||||
label: 180
|
||||
display_name: "tick"
|
||||
label: 181
|
||||
display_name: "tie"
|
||||
label: 182
|
||||
display_name: "tiger"
|
||||
label: 183
|
||||
display_name: "toaster"
|
||||
label: 184
|
||||
display_name: "traffic_light"
|
||||
label: 185
|
||||
display_name: "train"
|
||||
label: 186
|
||||
display_name: "trombone"
|
||||
label: 187
|
||||
display_name: "trumpet"
|
||||
label: 188
|
||||
display_name: "turtle"
|
||||
label: 189
|
||||
display_name: "tv_or_monitor"
|
||||
label: 190
|
||||
display_name: "unicycle"
|
||||
label: 191
|
||||
display_name: "vacuum"
|
||||
label: 192
|
||||
display_name: "violin"
|
||||
label: 193
|
||||
display_name: "volleyball"
|
||||
label: 194
|
||||
display_name: "waffle_iron"
|
||||
label: 195
|
||||
display_name: "washer"
|
||||
label: 196
|
||||
display_name: "water_bottle"
|
||||
label: 197
|
||||
display_name: "watercraft"
|
||||
label: 198
|
||||
display_name: "whale"
|
||||
label: 199
|
||||
display_name: "wine_bottle"
|
||||
label: 200
|
||||
display_name: "zebra"
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 200 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 191 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 45 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 41 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 117 KiB |
Reference in New Issue
Block a user