基于MATLAB的车牌识别系统设计与实现（毕业论文）

function varargout = chepaishibie(varargin) % 开始初始化代码 - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @chepaishibie_OpeningFcn, ... 'gui_OutputFcn', @chepaishibie_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % 结束初始化代码 pause(1); % 执行之前主要是可见的。 function chepaishibie_OpeningFcn(hObject, eventdata, handles, varargin) axes(handles.axes1); imshow([255]); axes(handles.axes2); imshow([255]); axes(handles.axes3); imshow([255]); axes(handles.axes4); imshow([255]); axes(handles.axes5); imshow([255]); axes(handles.axes6); imshow([255]); axes(handles.axes7); imshow([255]); axes(handles.axes8); imshow([255]); axes(handles.axes9); imshow([255]); axes(handles.axes10); imshow([255]); set(handles.text5,'String',''); % 此函数没有输出参数，见 OutputFcn. % main的选择默认的命令行输出 handles.output = hObject; % 更新handles 结构 guidata(hObject, handles); % UIWAIT 等待用户响应 (see UIRESUME) % uiwait(handles.figure1); % --- 这个函数的输出返回到命令行。 function varargout = chepaishibie_OutputFcn(hObject, eventdata, handles) tic % 获得缺省命令行输出的把手结构 varargout{1} = handles.output; % --- 执行按钮在pushbutton1。 function pushbutton1_Callback(hObject, eventdata, handles) [filename pathname]=uigetfile({'*.jpg';'*.bmp'}, 'File Selector'); I = imread([pathname '\' filename]); handles.I = I; % 更新处理结构 guidata(hObject, handles); axes(handles.axes1); imshow(I);title('原始图片') set(handles.process,'enable','on') % --- 执行过程中按下按钮。 function process_Callback(hObject, eventdata, handles) I = handles.I; I1=rgb2gray(I);;%rgb2gray转换成灰度图 guidata(hObject, handles); axes(handles.axes2); imshow(I1);title('灰度图'); axes(handles.axes3); imhist(I1);title('灰度图直方图'); %继续 pause(2); I2=edge(I1,'roberts',0.15,'both'); guidata(hObject, handles); axes(handles.axes2); imshow(I2);title('robert算子边缘检测'); pause(2); se=[1;1;1]; I3=imerode(I2,se); guidata(hObject, handles); axes(handles.axes3); imshow(I3);title('腐蚀后图像'); %继续 pause(2); se=strel('rectangle',[10,25]);%生成一个矩阵 I4=imclose(I3,se);%闭运算 guidata(hObject, handles); axes(handles.axes2); imshow(I4);title('平滑图像的轮廓'); %继续 pause(2); I5=bwareaopen(I4,2000);%小于2000的对象都被删除 guidata(hObject, handles); axes(handles.axes2); imshow(I5);title('从对象中移除小对象'); %继续 pause(2); [PY2,PY1,PX2,PX1]=chepai_fenge(I5);%调用分割车牌 global threshold; [PY2,PY1,PX2,PX1,threshold]=chepai_xiuzheng(PY2,PY1,PX2,PX1);%调用车牌校正 IY=I(PY1:PY2,:,:); Plate=I5(PY1:PY2,PX1:PX2);%使用caitu_tiqu global dw; dw=Plate; PX1=PX1-1;%对车牌区域的校正 PX2=PX2+1; dw=I(PY1:PY2-8,PX1:PX2,:); axes(handles.axes2); imshow(dw),title('车牌区域的校正'); pause(2); t=tic; guidata(hObject, handles); axes(handles.axes2); imshow(IY),title('水平方向合理区域'); axes(handles.axes3); imshow(dw),title('定位剪切后的彩色车牌图像'); % pause(2); % imwrite(dw,'New number plate.jpg'); % [filename,filepath]=uigetfile('New number plate.jpg','输入一个定位裁剪后的车牌图像'); % jpg=strcat(filepath,filename); a=dw; b=rgb2gray(a);%对定位后的车牌灰度化 g_max=double(max(max(b))); g_min=double(min(min(b))); T=round(g_max-(g_max-g_min)/2); %T 为二值化的阈值 [m,n]=size(b); d=(double(b)>=T); % d:二值图像 pause(1); h=fspecial('average',3); %均值滤波器 d=im2bw(round(filter2(h,d))); se=eye(2); % eye(n) returns the n-by-n identity matrix 单位矩阵 %字符面积与车牌面积之比在(0.235,0.365)之间 [m,n]=size(d); %如果大于0.365则对图像进行腐蚀，如果小于0.235则对图像进行膨胀 if bwarea(d)/m/n>=0.365%计算面积 d=imerode(d,se);%imerode 实现图像腐蚀 d为待处理图像，se是结构元素对象 elseif bwarea(d)/m/n<=0.235 d=imdilate(d,se);%imdilate 图像膨胀 end pause(2); % 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=zifufenge(d); [m,n]=size(d); guidata(hObject, handles); axes(handles.axes3);imshow(d); 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 % 再分割 d=zifufenge(d); % 切割出 7 个字符 y1=10;y2=0.25;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=zifufenge(d); else temp=zifufenge(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; end d(:,[1:wide])=0; d=zifufenge(d); end end % 分割出第二个字符 [word2,d]=getword(d); pause(1); % 分割出第三个字符 [word3,d]=getword(d); pause(1); % 分割出第四个字符 [word4,d]=getword(d); pause(1); % 分割出第五个字符 [word5,d]=getword(d); pause(1); % 分割出第六个字符 [word6,d]=getword(d); pause(1); % 分割出第七个字符 [word7,d]=getword(d); pause(1); guidata(hObject, handles); axes(handles.axes4);imshow(word1),title('1'); guidata(hObject, handles); axes(handles.axes4);imshow(word2),title('2'); guidata(hObject, handles); axes(handles.axes4);imshow(word3),title('3'); guidata(hObject, handles); axes(handles.axes4);imshow(word4),title('4'); guidata(hObject, handles); axes(handles.axes4);imshow(word5),title('5'); guidata(hObject, handles); axes(handles.axes4);imshow(word6),title('6'); guidata(hObject, handles); axes(handles.axes4);imshow(word7),title('7'); [m,n]=size(word1); % 商用系统程序中归一化大小为 40*20,此处演示 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]); guidata(hObject, handles); axes(handles.axes4);imshow(word1),title('1'); guidata(hObject, handles); axes(handles.axes5);imshow(word2),title('2'); guidata(hObject, handles); axes(handles.axes6);imshow(word3),title('3'); guidata(hObject, handles); axes(handles.axes7);imshow(word4),title('4'); guidata(hObject, handles); axes(handles.axes8);imshow(word5),title('5'); guidata(hObject, handles); axes(handles.axes9);imshow(word6),title('6'); guidata(hObject, handles); axes(handles.axes10);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'); liccode=char(['0':'9' 'A':'Z' '桂鲁苏豫京']); %建立自动识别字符代码表 SubBw2=zeros(40,20); %初始值归为0矩阵 l=1; for I=1:7 ii=int2str(I);%将整数转换为字符串 t=imread([ii,'.jpg']); % SegBw2=imresize(t,[40 20],'nearest');%实用最近邻插值法放大图像 SegBw2 = im2bw(t); if l==1 %第一位汉字识别 kmin=37; kmax=41; pause(1); elseif l==2 %第二位 A~Z 字母识别 kmin=11; kmax=36; pause(1); else l>=3 %第三位以后是字母或数字识别 kmin=1; kmax=36; end for k2=kmin:kmax fname=strcat('模板库\',liccode(k2),'.jpg'); %读取字符模板库中的图像 SamBw2 = imread(fname); SamBw2 = im2bw(SamBw2); for i=1:40 for j=1:20 SubBw2(i,j)=SegBw2(i,j)-SamBw2(i,j); end end % 以上相当于两幅图相减得到第三幅图 Dmax=0; for k1=1:40 for l1=1:20 if ( SubBw2(k1,l1) > 0