基于MATLAB的图像处理的课程设计(车牌识别系统)

来源：用户分享时间：2025/5/22 10:30:31 本文由

loading 分享下载这篇文档手机版

说明：文章内容仅供预览，部分内容可能不全，需要完整文档或者需要复制内容，请下载word后使用。下载word有问题请添加微信号:xxxxxxx或QQ：xxxxxx 处理（尽可能给您提供完整文档），感谢您的支持与谅解。

p1=str2num(p{1});

y=imresize(handles.img,p1,'bilinear'); %最近邻插值法放大

imshow(y);

------------------------------------------------

function

shuangsanci_Callback(hObject, eventdata, handles)

%双三次放大

axes(handles.axes2);

prompt={'输入放大倍数:'}; defans={'2'};

p=inputdlg(prompt,'输入放大倍数',1,defans);

p1=str2num(p{1});

y=imresize(handles.img,p1,'bicubic');

imshow(y);

-------------------------------------------------------

function

suoxiao_Callback(hObject, eventdata, handles)

%邻近插值缩小

axes(handles.axes2);

prompt={'输入放大倍数:'}; defans={'0.2'};

p=inputdlg(prompt,'输入放大倍数',1,defans);

p1=str2num(p{1});

y=imresize(handles.img,p1,

'nearest'); %最近邻插值法缩小

imshow(y);

------------------------------------------------------

function

shuangxian_Callback(hObject, eventdata, handles)

%双线性缩小

axes(handles.axes2);

prompt={'输入放大倍数:'}; defans={'0.2'};

p=inputdlg(prompt,'输入放大倍数',1,defans);

p1=str2num(p{1});

y=imresize(handles.img,p1,'bilinear'); %最近邻插值法缩小

imshow(y);

--------------------------------------------------------

function

shuangsancisuox_Callback(hObject, eventdata, handles)

%双三次缩小

axes(handles.axes2);

prompt={'输入放大倍数:'}; defans={'0.2'};

p=inputdlg(prompt,'输入放大倍数',1,defans);

p1=str2num(p{1});

y=imresize(handles.img,p1,'bicubic');

imshow(y);

------------------------------------------------------

function

xuanzhuan_Callback(hObject, eventdata, handles)

-------------------------------------------------------

function

updown_Callback(hObject, eventdata, handles)

%上下翻转

axes(handles.axes2); x=(handles.img);

if isrgb(handles.img) for k=1:3

y(:,:,k)=flipud(x(:,:,k));%上下翻转函数

end

imshow(y); else

x=(handles.img); y=flipud(x); imshow(y); end

---------------------------------------------------------

function

leftright_Callback(hObject, eventdata, handles)

%左右翻转

axes(handles.axes2); if isrgb(handles.img) x=(handles.img); for k=1:3

y(:,:,k)=fliplr(x(:,:,k));%左右翻转函数

end

imshow(y); else

x=(handles.img); y=fliplr(x); imshow(y); end

------------------------------------------------------

function

zeft90_Callback(hObject, eventdata, handles)

%左转90度

axes(handles.axes2); x=(handles.img);

y=imrotate(x,90); imshow(y);

-------------------------------------------------------

function

right90_Callback(hObject, eventdata, handles)

%右转90度

axes(handles.axes2); x=(handles.img);

y=imrotate(x,-90); imshow(y);

----------------------------

--------------------------

function

other_Callback(hObject, eventdata, handles)

%任意角度旋转

axes(handles.axes2); prompt={'输入参数1:'}; defans={'30'};

p=inputdlg(prompt,'输入参数',1,defans);

p1=str2num(p{1});

y=imrotate(handles.img,p1); imshow(y);

-----------------------------------------------------

function gs_Callback(hObject, eventdata, handles)

%加入高斯噪声

axes(handles.axes2);

prompt={'输入参数1:','输入参数2'};

defans={'0','0.02'};

p=inputdlg(prompt,'输入参数',1,defans);

p1=str2num(p{1});

p2=str2num(p{2}); y=imnoise(handles.img,'gaussian',p1,p2); imshow(y);

handles.noise_img=y; guidata(hObject,handles); -------------------------------------------------------

function jy_Callback(hObject, eventdata, handles)

%加入椒盐噪声

prompt={'输入参数1:'}; %对话框的设置，用户输入的是字符串

defans={'0.02'}; %缺省值 p=inputdlg(prompt,'输入参数',1,defans);

p1=str2num(p{1}); %字符串转化为数值

axes(handles.axes2);

x=(handles.img); y=imnoise(x,'salt & pepper',p1);

imshow(y);

handles.noise_img=y;

guidata(hObject,handles); --------------------------------------------------------

function cx_Callback(hObject, eventdata, handles)

%加入乘性噪声

axes(handles.axes2); prompt={'输入参数1:'}; defans={'0.02'};

p=inputdlg(prompt,'输入参数',1,defans);

p1=str2num(p{1});

y=imnoise(handles.img,'speckle',p1);

imshow(y);

handles.noise_img=y;

guidata(hObject,handles); -----------------------------------------------------

function

zhifangtutongji_Callback(hObject, eventdata, handles)

---------------------------------------------------------

function red_Callback(hObject, eventdata, handles)

%R直方图

set(handles.axes2,'HandleVisibility','ON');

axes(handles.axes2);

x=imhist(handles.img(:,:,1)); %直方图统计

x1=x(1:10:256); horz=1:10:256; bar(horz,x1);

set(handles.axes2,'xtick',0:50:255);

----------------------------

-----------------------------

function

gray_Callback(hObject, eventdata, handles)

%G直方图

set(handles.axes2,'HandleVisibility','ON');

axes(handles.axes2); if isrgb(handles.img)

x=imhist(handles.img(:,:,2)); %直方图统计

x1=x(1:10:256); horz=1:10:256; bar(horz,x1);

set(handles.axes2,'xtick',0:50:255);

else

msgbox('这是灰度图像','旋转失败');

end

------------------------------------------------------

function

blue_Callback(hObject, eventdata, handles)

%B直方图

set(handles.axes2,'HandleVisibility','ON');

axes(handles.axes2); if isrgb(handles.img)

x=imhist(handles.img(:,:,3)); %直方图统计

x1=x(1:10:256); horz=1:10:256; bar(horz,x1);

%axis([0 255 0 150000]); set(handles.axes2,'xtick',0:50:255);

%set(handles.axes2,'ytick',0:2000:15000);

else

msgbox('这是灰度图像','旋转失败');

end

---------------------------------------------------------

function

junheng_Callback(hObject, eventdata, handles)

%直方图均衡

set(handles.axes2,'HandleVisibility','ON');

axes(handles.axes2); if isrgb(handles.img)

a=histeq(handles.img(:,:,1));

b=histeq(handles.img(:,:,2));

c=histeq(handles.img(:,:,3));

k(:,:,1)=a; k(:,:,2)=b; k(:,:,3)=c; imshow(k); else

h=histeq(handles.img); %直方图均衡

imshow(h); end

--------------------------------------------------------

%频谱分析

-----------------------------------------------------

function

pinpu_Callback(hObject, eventdata, handles)

--------------------------------------------------------

function

pinputu_Callback(hObject, eventdata, handles)

%显示频谱图

axes(handles.axes2); x=(handles.img); if isrgb(x)

m=fft2(x(:,:,1));

y=fftshift(m);

imshow(log(abs(y)),[]); else

m=fft2(x);

y=fftshift(m);

imshow(log(abs(y)),[]); end

--------------------------------------------------------

function

frequency_Callback(hObject, eventdata, handles)

%低通滤波器

axes(handles.axes2); x=(handles.img); if isrgb(x)

msgbox('这是彩色图像,不能通过低通滤波器','失败');

else

y1=imnoise(x,'salt & pepper'); % 叠加椒盐噪声

f=double(y1); % 数据类型转换，MATLAB不支持图像的无符号整型的计算

g=fft2(f); % 傅立叶变换

g=fftshift(g); % 转换数据矩阵

[M,N]=size(g);

nn=2; % 二阶巴特沃斯(Butterworth)低通滤波器

d0=10; %截止频率为10 m=fix(M/2); n=fix(N/2); for i=1:M

for j=1:N

d=sqrt((i-m)^2+(j-n)^2);

h=1/(1+0.414*(d/d0)^(2*nn)); % 计算低通滤波器传递函数

result(i,j)=h*g(i,j);

end end

搜索更多关于：基于MATLAB的图像处理的课程设计(车牌识别系统) 的文档

基于MATLAB的图像处理的课程设计(车牌识别系统).doc 将本文的Word文档下载到电脑，方便复制、编辑、收藏和打印

下载这篇word文档

本文链接：https://www.diyifanwen.net/c3c5y65yty57l7tx29ybm0wacw0f2i000g9b_4.html（转载请注明文章来源）