LPR.rar_caitu_fenge(I);_lpr_车牌定位

function [PY2,PY1,PX2,PX1]=caitu_tiqu(I,I_bai) [y,x,z]=size(I); myI=double(I); Y_threshlow=5;%这个数值很重要。决定了提取的彩图的质量 X_firrectify=5;%ganrao transaction %====================== Y 方向============================ Blue_y=zeros(y,1); for i=1:y for j=1:x % if (myI(i,j,1)<=48)&&(myI(i,j,2)>=40)&&(myI(i,j,2)<=90)&&(myI(i,j,3)<=220)&&(myI(i,j,3)>=80) % if((myI(i,j,1)<=48)&&((myI(i,j,2)<=100)&&(myI(i,j,2)>=40))&&((myI(i,j,3)<=200)&&(myI(i,j,3)>=80))) if ((myI(i,j,1)<=0.7)&&(myI(i,j,1)>=0.6)&&((myI(i,j,2)<=1.8)&&(myI(i,j,2)>=0.6))&&((myI(i,j,3)<=1.7)&&(myI(i,j,3)>=0.4))) Blue_y(i,1)= Blue_y(i,1)+1; % 蓝色象素点统计 end end end baisebili=0;changkuanbi=0; k=0; while(~((baisebili>=0.12)&&(baisebili<=0.5)&&(changkuanbi>=0.20)&&(changkuanbi<=0.6)))%gai shan qingxie if (k==0)%第一次进来 [temp MaxY]=max(Blue_y);% Y方向车牌区域确定 temp(最多点数):所有行中，最多的累积像素点 MaxY（最多点所在行）:该行中蓝点最多 if temp<=20%2048*1536 照相 msgbox('车牌定位出错','warning'); wavplay(wavread('车牌定位出错.wav'),22000); pause; end PY1=MaxY;%有最多蓝点的行付给PY1 while ((Blue_y(PY1,1)>=Y_threshlow)&&(PY1>1))%找到图片上边界 PY1=PY1-1; end %PY1：存储车牌上边界值 PY2=MaxY; while ((Blue_y(PY2,1)>=Y_threshlow)&&(PY2<y))%阈值为5 PY2=PY2+1; end PY1, PY2 %==============X 方向=============================== X_threshhigh=(PY2-PY1)/11;%这个数值很重要。决定了提取的彩图的质量,适当提高可抗干扰，但是小图会照成剪裁太多 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)<=48)&&((myI(i,j,2)<=90)&&(myI(i,j,2)>=30))&&((myI(i,j,3)<=160)&&(myI(i,j,3)>=80))) % if((myI(i,j,1)<=65)&&((myI(i,j,2)<=100)&&(myI(i,j,2)>=40))&&((myI(i,j,3)<=160)&&(myI(i,j,3)>=90)))%这里由82修改成90.因为图片20090504809 if ((myI(i,j,1)<=0.7)&&(myI(i,j,1)>=0.6)&&((myI(i,j,2)<=1.8)&&(myI(i,j,2)>=0.6))&&((myI(i,j,3)<=1.7)&&(myI(i,j,3)>=0.4))) Blue_x(1,j)= Blue_x(1,j)+1; end end end [temp MaxX]=max(Blue_x); PX1=MaxX-6*(PY2-PY1); if PX1<=1 PX1=1; end while ((Blue_x(1,PX1)<=X_threshhigh)&&(PX1<x))%阈值 PX1=PX1+1; end %确定出X方向车牌起点 PX2=MaxX+6*(PY2-PY1); if PX2>=x PX2=x; end while ((Blue_x(1,PX2)<=X_threshhigh)&&(PX2>PX1))%阈值 PX2=PX2-1; end%确定出X方向车牌终点 % PX1=1; % while ((Blue_x(1,PX1)<=X_threshhigh)&&(PX1<x))%阈值 % PX1=PX1+1; % end %确定出X方向车牌起点 % PX2=x; % while ((Blue_x(1,PX2)<X_threshhigh)&&(PX2>PX1))%阈值 % PX2=PX2-1; % end%确定出X方向车牌终点 PX1 ,PX2 %========================================================= a=PY2-PY1+1;b=PX2-PX1+1; White=0; for i=PY1:PY2 for j=PX1:PX2 if (std([I_bai(i,j,1) I_bai(i,j,2) I_bai(i,j,3)],1,2)<=22)&&(I_bai(i,j,1)>=90)&&(I_bai(i,j,1)<=255) White= White+1; % 白色象素点统计 end end end baisebili=White/(a*b) changkuanbi=a/b k=k+1 %===========================蓝色区域不是车牌区域================= elseif (k~=0) Blue_y(PY1:PY2,1)=0; [temp MaxY]=max(Blue_y); if temp<=20%2048*1536 照相 msgbox('车牌定位出错','warning'); wavplay(wavread('车牌定位出错.wav'),22000); pause; end PY1=MaxY; while ((Blue_y(PY1,1)>=Y_threshlow)&&(PY1>1))%找到图片上边界 %阈值为5 PY1=PY1-1; end %PY1：存储车牌上边界值 PY2=MaxY; while ((Blue_y(PY2,1)>=Y_threshlow)&&(PY2<y))%阈值为5 PY2=PY2+1; end PY1, PY2 %==============2次寻找X方向=============================== X_threshhigh=(PY2-PY1)/15;%这个数值很重要。决定了提取的彩图的质量,适当提高可抗干扰，但是小图会照成剪裁太多,ganrao 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)<=45)&&((myI(i,j,2)<=90)&&(myI(i,j,2)>=20))&&((myI(i,j,3)<=160)&&(myI(i,j,3)>=80))) if ((myI(i,j,1)<=0.7)&&(myI(i,j,1)>=0.6)&&((myI(i,j,2)<=1.8)&&(myI(i,j,2)>=0.6))&&((myI(i,j,3)<=1.7)&&(myI(i,j,3)>=0.4))) Blue_x(1,j)= Blue_x(1,j)+1; end end end %这里修改成了从中间向两边扩展,这个方法不好。因车牌中间某些位置可能出现断层。 %采用增强型两边往中间收缩。 [temp MaxX]=max(Blue_x); PX1=MaxX-6*(PY2-PY1); if PX1<=1 PX1=1; end while ((Blue_x(1,PX1)<=X_threshhigh)&&(PX1<x))%阈值 PX1=PX1+1; end %确定出X方向车牌起点 PX2=MaxX+6*(PY2-PY1); if PX2>=x PX2=x; end while ((Blue_x(1,PX2)<=X_threshhigh)&&(PX2>PX1))%阈值 PX2=PX2-1; end%确定出X方向车牌终点 % PX1=1; % while ((Blue_x(1,PX1)<=X_threshhigh)&&(PX1<x))%阈值 % PX1=PX1+1; % end % PX2=x; % while ((Blue_x(1,PX2)<X_threshhigh)&&(PX2>PX1))%阈值 % PX2=PX2-1; % end PX1 ,PX2 %========================================================= a=PY2-PY1+1;b=PX2-PX1+1; White=0; for i=PY1:PY2 for j=PX1:PX2 if (std([I_bai(i,j,1) I_bai(i,j,2) I_bai(i,j,3)],1,2)<=16)&&(I_bai(i,j,1)>=90)&&(I_bai(i,j,1)<=255) White= White+1; % 白色象素点统计 end end end baisebili=White/(a*b) changkuanbi=a/b k=k+1 end end %======================================================== Y_firrectify=fix((PY2-PY1)/5);%适当扩大这个值可以正确旋转 PY1=PY1-Y_firrectify;%对车牌区域的修正,向上 PY2=PY2+Y_firrectify;%对车牌区域的修正,向下 % IY=I(PY1:PY2,:,:);%在Y方向对图片截取 PX1=PX1-X_firrectify;% 对车牌区域的修正 PX2=PX2+X_firrectify;% 对车牌区域的修正，