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，
程序.zip_300_基于matlab的车牌识别系统_智能车牌识别
版权申诉
5星 · 超过95%的资源 158 浏览量
2022-07-14
09:33:08
上传
评论
收藏 44KB ZIP 举报 
程序.zip （55个子文件） 
程序 
0.BMP 34KB
main.m 6KB 25.BMP 4KB 16.BMP 41KB 15.BMP 44KB 14.BMP 33KB 1.BMP 18KB shibiehanzi.m 645B 10.BMP 46KB 18.BMP 4KB 7.BMP 44KB CPGUI.BMP 51KB 2.BMP 19KB 31.BMP 4KB 26.BMP 76KB 13.BMP 71KB CPSU.BMP 2KB 24.BMP 47KB shibieshuzi.m 378B 20.BMP 46KB touying.m 620B shibiezm_sz.m 2KB CPJING.BMP 2KB SEC_xiuzheng.m 805B 21.BMP 17KB duquszzm.m 3KB 32.BMP 50KB 3.BMP 18KB duchushengyin.m 3KB 11.BMP 67KB 5.BMP 19KB 22.BMP 4KB CPYUE.BMP 2KB 28.BMP 4KB 12.BMP 38KB 17.BMP 4KB 4.BMP 19KB duquhanzi.m 507B 23.BMP 52KB 30.BMP 5KB caitu_fenge.m 6KB 19.BMP 50KB caitu_tiqu.m 7KB 29.BMP 5KB duquzimu.m 2KB 9.BMP 45KB duqushuzi.m 1KB 27.BMP 43KB shibiezimu.m 2KB rando_bianhuan.m 503B shuzifenge.m 1KB cpguizhou.bmp 2KB 33.BMP 32KB 6.BMP 31KB 8.BMP 19KB资源评论
mingzhida2023-03-30资源内容详尽，对我有使用价值，谢谢资源主的分享。
刘良运
- 粉丝: 68
- 资源: 1万+
下载权益
C知道特权
VIP文章
课程特权
VIP享7折，此内容立减3.57元 开通VIP
