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chepai_dingwei.m
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chepai_dingwei.m
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function [PY2,PY1,PX2,PX1]=chepai_dingwei(I)
[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)>85 && myI(i,j,1)<255)&&(myI(i,j,2)>150 &&
% myI(i,j,2)<255)&&(myI(i,j,3)>0 && myI(i,j,3)<50); //黄色
%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)>=0.45)))
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');
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)>=0.45)))
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([myI(i,j,1) myI(i,j,2) myI(i,j,3)],1,2)<=22)&&(myI(i,j,1)>=90)&&(myI(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');
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)))
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([myI(i,j,1) myI(i,j,2) myI(i,j,3)],1,2)<=16)&&(myI(i,j,1)>=90)&&(myI(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,