bp神经网络的车牌识别bp神经网络的车牌识别

close all; clear all; THREAD_TX=0;%分割修正参数 上 THREAD_BX=-8;%-8;%分割修正参数上 THREAD_LY=0;%分割修正参数 左 THREAD_RY=0;%分割修正参数 右 %训练神经网络 net=BP_Set(); %提供了五个测试图片 [fn,pn,fi]=uigetfile('image\*.jpg','选择图片'); I=imread([pn fn]);%输入原始图像 figure(1),imshow(I);title('原图') I1=rgb2gray(I); gao=imnoise(I1,'salt'); %加高斯噪声 zhong = medfilt2(gao);%中值滤波 figure(2),subplot(2,2,1),imshow(I1);title('灰度图'); figure(2),subplot(2,2,2),imhist(I1);title('灰度图直方图'); figure(2),subplot(2,2,3),imshow(gao);title('加入椒盐噪声图像'); figure(2),subplot(2,2,4),imshow(zhong );title('中值滤波去噪图像'); I2=edge(I1,'sobel',0.15,'both'); figure(3),imshow(I2);title('robert算子边缘检测') se=[1;1;1]; I3=imerode(I2,se); figure(4),imshow(I3);title('腐蚀后图像'); se=strel('rectangle',[25,25]); I4=imclose(I3,se); %figure(5),imshow(I4);title('平滑图像的轮廓'); I5=bwareaopen(I4,1000); %figure(6),imshow(I5);title('从对象中移除小对象'); [y,x,z]=size(I5); myI=double(I5); tic Blue_y=zeros(y,1); for i=1:y for j=1:x if(myI(i,j,1)==1) Blue_y(i,1)= Blue_y(i,1)+1;%像素点统计 end end end [temp MaxY]=max(Blue_y);%Y方向车牌区域确定 PY1=MaxY; while ((Blue_y(PY1,1)>=5)&&(PY1>1)) PY1=PY1-1; end PY2=MaxY; while ((Blue_y(PY2,1)>=5)&&(PY2<y)) PY2=PY2+1; end IY=I(PY1:PY2,:,:); %%%%%% X方向 %%%%%%%%% Blue_x=zeros(1,x);%进一步确定x方向的车牌区域 for j=1:x for i=PY1:PY2 if(myI(i,j,1)==1) Blue_x(1,j)= Blue_x(1,j)+1; end end end PX1=1; while ((Blue_x(1,PX1)<6)&&(PX1<x)) PX1=PX1+1; end PX2=x; while ((Blue_x(1,PX2)<6)&&(PX2>PX1)) PX2=PX2-1; end PX1=PX1-4;%对车牌区域的校正 PX2=PX2+2; dw=I(PY1+THREAD_TX:PY2+THREAD_BX,PX1+THREAD_LY:PX2+THREAD_RY,:);%car2.jpg dw=I(PY1:PY2,PX1+6:PX2-8,:); t=toc; figure(7),subplot(1,2,1),imshow(IY),title('行方向合理区域'); figure(7),subplot(1,2,2),imshow(dw),title('定位剪切后的彩色车牌图像') imwrite(dw,'cp.jpg'); %-------写入车牌----------------------------------------------------------- b=rgb2gray(dw); %imwrite(b,'1.车牌灰度图像.jpg'); figure(8);subplot(3,2,1),imshow(b),title('1.车牌灰度图像') g_max=double(max(max(b))); g_min=double(min(min(b))); T=round(g_max-(g_max-g_min)/3); % T 为二值化的阈值 [m,n]=size(b); d=(double(b)>=T); % d:二值图像 %imwrite(d,'2.车牌二值图像.jpg'); figure(8);subplot(3,2,2),imshow(d),title('2.车牌二值图像') figure(8),subplot(3,2,3),imshow(d),title('3.均值滤波前') % 滤波 h=fspecial('average',3); d=im2bw(round(filter2(h,d))); %imwrite(d,'4.均值滤波后.jpg'); figure(8),subplot(3,2,4),imshow(d),title('4.均值滤波后') % 某些图像进行操作 % 膨胀或腐蚀 % se=strel('square',3); % 使用一个3X3的正方形结果元素对象对创建的图像进行膨胀 % 'line'/'diamond'/'ball'... se=eye(2); % eye(n) returns the n-by-n identity matrix 单位矩阵 [m,n]=size(d); if bwarea(d)/m/n>=0.365 d=imerode(d,se); elseif bwarea(d)/m/n<=0.235 d=imdilate(d,se); end %imwrite(d,'5.膨胀或腐蚀处理后.jpg'); figure(8),subplot(3,2,5),imshow(d),title('5.膨胀或腐蚀处理后') imwrite(d,'cp_BIN.jpg'); [dy,dx]=size(d); I8=double(d); Xn=zeros(1,dx); for j=1:dx for i=1:dy if(I8(i,j)==1) Xn(1,j)= Xn(1,j)+1; end end end figure; plot(Xn),title('车牌列像素点累计'),xlabel('列值'),ylabel('像素和'); % 寻找连续有文字的块，若长度大于某阈值，则认为该块有两个字符组成，需要分割 d=qiege(d); [m,n]=size(d); figure,subplot(2,1,1),imshow(d),title(n) k1=1;k2=1;s=sum(d);j=1; while j~=n while s(j)==0 j=j+1; end k1=j; while s(j)~=0 && j<=n-1 j=j+1; end k2=j-1; if k2-k1>=round(n/6.5) [val,num]=min(sum(d(:,[k1+5:k2-5]))); d(:,k1+num+5)=0; % 分割 end end % 再切割 d=qiege(d); % 切割出 7 个字符 y1=10;y2=0.26;flag=0;word1=[]; while flag==0 [m,n]=size(d); left=1;wide=0; while sum(d(:,wide+1))~=0 wide=wide+1; end if wide<y1 % 认为是左侧干扰 d(:,[1:wide])=0; d=qiege(d); else temp=qiege(imcrop(d,[1 1 wide m])); [m,n]=size(temp); all=sum(sum(temp)); two_thirds=sum(sum(temp([round(m/3):2*round(m/3)],:))); if two_thirds/all>y2 flag=1;word1=temp; % WORD 1 end d(:,[1:wide])=0;d=qiege(d); end end % 分割出第二个字符 [word2,d]=getword(d); % 分割出第三个字符 [word3,d]=getword(d); % 分割出第四个字符 [word4,d]=getword(d); % 分割出第五个字符 [word5,d]=getword(d); % 分割出第六个字符 [word6,d]=getword(d); % 分割出第七个字符 [word7,d]=getword(d); subplot(5,7,1),imshow(word1),title('1'); subplot(5,7,2),imshow(word2),title('2'); subplot(5,7,3),imshow(word3),title('3'); subplot(5,7,4),imshow(word4),title('4'); subplot(5,7,5),imshow(word5),title('5'); subplot(5,7,6),imshow(word6),title('6'); subplot(5,7,7),imshow(word7),title('7'); [m,n]=size(word1); % 归一化大小为 40*20 word1=imresize(word1,[40 20]); word2=imresize(word2,[40 20]); word3=imresize(word3,[40 20]); word4=imresize(word4,[40 20]); word5=imresize(word5,[40 20]); word6=imresize(word6,[40 20]); word7=imresize(word7,[40 20]); subplot(5,7,15),imshow(word1),title('1'); subplot(5,7,16),imshow(word2),title('2'); subplot(5,7,17),imshow(word3),title('3'); subplot(5,7,18),imshow(word4),title('4'); subplot(5,7,19),imshow(word5),title('5'); subplot(5,7,20),imshow(word6),title('6'); subplot(5,7,21),imshow(word7),title('7'); imwrite(word1,'1.jpg'); imwrite(word2,'2.jpg'); imwrite(word3,'3.jpg'); imwrite(word4,'4.jpg'); imwrite(word5,'5.jpg'); imwrite(word6,'6.jpg'); imwrite(word7,'7.jpg'); S=Recognize(net); figure; S=strcat('识别出的车牌号为:',S); imshow(I),title(S); disp(S);