32、matlab:基于模板匹配的车牌识别

本文主要是介绍32、matlab:基于模板匹配的车牌识别，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1、准备工作

1）准备材料

车牌字符模板和测试的实验车牌

2）车牌字符模板

数字、字母和省份缩写

3）测试车牌

四张测试车牌

2、车牌识别实现(已将其嵌入matlab)

1）打开APP

找到APP

找到我的APP双击点开

2)界面介绍

包括：按钮、视图界面和文本框等功能组件

按钮

包含打开图片、清楚图片、退出APP、关于APP、图片预处理、车牌定位、车牌定位处理、文字分割和识别结果

视图界面

初始车牌、预处理车牌、车牌定位和识别结果等

3、导入车牌、清除车牌、退出和关于APP按钮功能效果

导入车牌

代码实现

            [filename,pathname]=uigetfile({'*.*';'*.png';'*.jpg';'*.bmp'});%获取图片路径if isequal(filename,0)||isequal(pathname,0)%打开失败提示errordlg('图片为空','错误');app.Lamp_1.Color=('1.00,0.00,0.00');elseimg1=strcat(pathname,filename);%打开并显示图片img1=imread(img1);app.P1=img1;imshow(img1,'Parent',app.UIAxes_1);app.Lamp_1.Color=('0.00,1.00,0.00');app.Lamp_2.Color=('0.00,1.00,0.00');app.Lamp_3.Color=('0.00,1.00,0.00');app.Lamp_4.Color=('0.00,1.00,0.00');app.Lamp_5.Color=('0.00,1.00,0.00');app.Lamp_6.Color=('0.00,1.00,0.00');endapp.EditField_2.Value=('等待预处理');app.EditField_3.Value=('等待定位');

工程效果

清除车牌

代码实现

im=imread("纯白.png");%清除显示区imshow(im,'Parent',app.UIAxes_1);imshow(im,'Parent',app.UIAxes_2);imshow(im,'Parent',app.UIAxes_3);imshow(im,'Parent',app.UIAxes_4);imshow(im,'Parent',app.UIAxes_5);imshow(im,'Parent',app.UIAxes_6);imshow(im,'Parent',app.UIAxes_7);imshow(im,'Parent',app.UIAxes_8);imshow(im,'Parent',app.UIAxes_9);imshow(im,'Parent',app.UIAxes_10);imshow(im,'Parent',app.UIAxes_11);app.Lamp_1.Color=('1.00,0.00,0.00');app.Lamp_2.Color=('0.00,1.00,0.00');app.Lamp_3.Color=('0.00,1.00,0.00');app.Lamp_4.Color=('0.00,1.00,0.00');app.Lamp_5.Color=('0.00,1.00,0.00');app.Lamp_6.Color=('0.00,1.00,0.00');app.EditField_2.Value='waiting';app.EditField_3.Value='waiting';app.EditField_1.Value='waiting';

工程效果

退出按钮

代码实现

     choice=questdlg('确定关闭吗？','关闭','YES','NO','NO');%是否关闭程序switch choicecase 'YES'delete('1.jpg','2.jpg','3.jpg','4.jpg','5.jpg','6.jpg','7.jpg');delete(app.UIFigure);return;case 'NO'return;end

工程效果

关于APP

代码实现

 msgbox("这是一个车牌自动识别App","对话框",'help');%软件介绍对话框

工程效果

4、车牌识别过程

图片预处理

代码实现

img2=rgb2gray(app.P1);%灰度处理app.P2=img2;imshow(img2,'Parent',app.UIAxes_2);img3= edge(app.P2, 'roberts', 0.15,'both');%边缘处理app.P3=img3;imshow(img3,'Parent',app.UIAxes_2);app.EditField_2.Value=('完成预处理');app.Lamp_2.Color=('1.00,0.00,0.00');

工程效果

车牌定位

代码实现

            se=[1,1,1]';%腐蚀处理img4 = imerode(app.P3,se);app.P4=img4;imshow(img4,'Parent',app.UIAxes_3);se1=strel('rectangle',[30,30]);%图像平滑img5=imclose(app.P4, se1);app.P5=img5;imshow(img5,'Parent',app.UIAxes_3);img6= bwareaopen(app.P5, 2200);%移除对象app.P6=img6;imshow(img6,'Parent',app.UIAxes_3);app.EditField_3.Value='完成车牌定位';[img_Y1, img_Y2, img_X1, img_X2]=cj(app.P6);img8 =app.P1(img_Y1:img_Y2,img_X1:img_X2,:);%原图裁剪app.P7=img8;imshow(img8,'Parent',app.UIAxes_3);app.EditField_3.Value='完成裁剪处理';app.Lamp_3.Color=('1.00,0.00,0.00');

工程效果

定位图处理

代码实现

img9=rgb2gray(app.P7);%灰度app.P8=img9;imshow(img9,'Parent',app.UIAxes_4);img10=histeq(app.P8);%直方图均衡化app.P9=img10;imshow(img10,'Parent',app.UIAxes_4);img11=im2bw(app.P9,0.76);%二值化app.P10=img11;imshow(img11,'Parent',app.UIAxes_4);img12=medfilt2(app.P10);%中值滤波1img12=medfilt2(img12);%中值滤波2app.P11=img12;imshow(img12,'Parent',app.UIAxes_4);img12=fgt(img12);%裁剪app.P12=img12;imshow(img12,'Parent',app.UIAxes_4);app.Lamp_4.Color=('1.00,0.00,0.00');

工程效果

文字分割

代码实现

img12=app.P12;[m,n] = size(img12);s = sum(img12);j = 1;k1 = 1;k2 = 1;while j ~= n%列定位while s(j) == 0j = j + 1;endk1 = j;%定位列有字while s(j) ~= 0 && j <= n-1j = j + 1;endk2 = j + 1;%定位到列没字if k2 - k1 > round(n / 6.5)%分隔太宽了[val, num] = min(sum(img12(:, [k1+5:k2-5])));img12(:, k1+num+5) = 0;endend[word1,img12]=fgzf(img12);%获取字符[word2,img12]=fgzf(img12);[word3,img12]=fgzf(img12);[word4,img12]=fgzf(img12);[word5,img12]=fgzf(img12);[word6,img12]=fgzf(img12);[word7,img12]=fgzf(img12);word1=imresize(word1,[40 20]);%定大小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]);imshow(word1,'Parent',app.UIAxes_5);imshow(word2,'Parent',app.UIAxes_6);imshow(word3,'Parent',app.UIAxes_7);imshow(word4,'Parent',app.UIAxes_8);imshow(word5,'Parent',app.UIAxes_9);imshow(word6,'Parent',app.UIAxes_10);imshow(word7,'Parent',app.UIAxes_11);imwrite(word1,'1.jpg');%存储下来imwrite(word2,'2.jpg');imwrite(word3,'3.jpg');imwrite(word4,'4.jpg');imwrite(word5,'5.jpg');imwrite(word6,'6.jpg');imwrite(word7,'7.jpg');app.Lamp_5.Color=('1.00,0.00,0.00');

工程效果

识别结果

代码实现

liccode=char(['0':'9' 'A':'Z' '京辽鲁陕苏豫浙贵闽粤沪川藏鄂赣甘桂黑冀津吉蒙宁青琼晋皖湘新渝']);subBw2 = zeros(40, 20);num = 1;   % 车牌位数for i = 1:7ii = int2str(i);    % 将整型数据转换为字符串型数据word = imread([ii,'.jpg']); % 读取之前分割出的字符的图片segBw2 = imresize(word, [40,20], 'nearest');    % 调整图片的大小segBw2 = im2bw(segBw2, 0.5);    % 图像二值化if i == 1   % 字符第一位为汉字，定位汉字所在字段kMin = 37;kMax = 44;elseif i == 2   % 第二位为英文字母，定位字母所在字段kMin = 11;kMax = 36;elseif i >= 3   % 第三位开始就是数字了，定位数字所在字段kMin = 1;kMax = 36;endl = 1;for k = kMin : kMaxfname = strcat("ZFMB\",liccode(k),'.jpg');  % 根据字符库找到图片模板samBw2 = imread(fname); % 读取模板库中的图片samBw2 = im2bw(samBw2, 0.5);% 图像二值化% 将待识别图片与模板图片做差for i1 = 1:40for j1 = 1:20subBw2(i1, j1) = segBw2(i1, j1) - samBw2(i1 ,j1);endendDmax = 0;for i2 = 1:40for j2 = 1:20if subBw2(i2, j2) ~= 0Dmax = Dmax + 1;endendenderror(l) = Dmax;l = l + 1;enderrorMin = min(error);findc = find(error == errorMin);Code(num*2 - 1) = liccode(findc(1) + kMin - 1);Code(num*2) = ' ';num = num + 1;enddelete('1.jpg','2.jpg','3.jpg','4.jpg','5.jpg','6.jpg','7.jpg');app.EditField_1.Value=(Code);app.Lamp_6.Color=('1.00,0.00,0.00');