车牌识别系统MATLAB源代码完整.doc

clc; clear all; close all;filename, pathname, filterindex = uigetfile(*.jpg;*.tif;*.png;*.gif,All Image Files;. *.*,All Files , 选择待处理图像, . images01.jpg);file = fullfile(pathname, filename);%文件路径和文件名创建合成完整文件名id = Get_Id(file);%得到file中的所有对象Img = imread(file);%根据路径和文件名读取图片到ImgPlate, bw, Loc = Pre_Process(Img); % 车牌区域预处理result = Plate_Process(Plate, id); % 车牌区域二值化处理% 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bw = Segmation(result);words = Main_Process(bw); % 主流程处理Write_Mask(words, id); % 写出到模板库str = Pattern_Recognition(words); % 识别function id = Get_Id(file)% 获取图像id信息% 输入参数：% file图像路径% 输出参数：% id图像id信息info = imfinfo(file);FS = 422227 354169 293184 235413 214202 . 130938 490061 120297 98686 137193 . 80558 46208 69947 58110 62115 . 59072 52168 60457 53979 50223;id = find(FS = info.FileSize);if isempty(id) warndlg(未建立该图像模板库，可能运行出错！, 警告); id = 1;endfunction R = Cubic_Spline(P)% 三次样条插值% 输入参数：% P节点矩阵% 输出参数：% R样条节点矩阵% 计算相邻插值点之间的弦长chordlen = sqrt(sum(diff(P, , 1).2,2);% 将弦长参数归一化到0,1上chordlen = chordlen/sum(chordlen);% 计算每个插值节点处的累加弦长，作为给点处的参数cumarc = 0; cumsum(chordlen);x = cumarc;N = size(P, 1);R = ;% 以下部分为一元三次样条插值的程序，对于空间三维数据，以同样的累加% 弦长作为参数，对x，y，z分量做三次一元样条插值得到的结果便是对三维数据% 做三次样条插值for k = 1 : size(P, 2) y = P(:, k); m = zeros(1, N); M = zeros(1, N); n = m; d = m; A = eye(N); A = 2*A; m(1) = 1; n(N) = 1; m(N) = 1; n(1) = 1; for i = 2:N-1 m(i) = (x(i+1)-x(i)/(x(i+1)-x(i-1); n(i) = 1 - m(i); d(i) = 6*(y(i+1) - y(i)/(x(i+1) - x(i) - (y(i) - y(i-1)/(x(i) - x(i-1)/(x(i+1)-x(i-1); end for j = 1 : N-1 A(j, j+1) = m(j); A(j+1, j) = n(j+1); end p = A(2:N-1, 2:N-1); q = d(2:N-1); Q = inv(p)*q; M = zeros(1,N); M(1,1) = 0; M(1,N) = 0; M(1,2:N-1)= Q; S = ; temp = ; for i = 1:N-1 % 对每一个分量计算出来的插值曲线进行采样，以便将其画出。 s = 50;% 采样点个数 z = linspace(x(i),x(i+1),s); h = x(i+1)-x(i); for j = 1:length(z) S(j) = M(i)*(x(i+1)-z(j)3)/(6*h)+M(i+1)*(z(j)-x(i)3)/(6*h)+(y(i)-M(i)*(h2)/6)*(x(i+1)-z(j)/h)+(y(i+1)-M(i+1)*h2/6)*(z(j)-x(i)/h); end temp = temp S; end R(:, k) = temp; endfunction mask = Get_PointSplineMask(Img, Ptn)% 获取封闭有序节点的蒙板图像% Img图像矩阵% Ptn封闭有序节点% 输出参数：% mask蒙板图像if ndims(Img) = 3 I = rgb2gray(Img);else I = Img;endmask = zeros(size(I);Ptn = Cubic_Spline(Ptn); % 样条插值for i = 1 : size(Ptn, 1)-1 pt1 = Ptn(i, :); % 线段起点 pt2 = Ptn(i+1, :); % 线段终点 x1 = pt1(1); y1 = pt1(2); x2 = pt2(1); y2 = pt2(2); % 直线段参数 A = (y1 - y2)/(x1*y2 - x2*y1); B = (-x1 + x2)/(x1*y2 - x2*y1); % 直线段取点 xk = linspace(min(x1, x2), max(x1, x2); if B = 0 yk = linspace(min(y1, y2), max(y1, y2); else yk = (-1 - A*xk)/B; end % 赋值操作 for j = 1 : length(xk) if isnan(round(yk(j) & isnan(round(xk(j) & . isinf(round(yk(j) & isinf(round(xk(j) & . round(yk(j) 0 & round(xk(j) 0 mask(round(yk(j), round(xk(j) = 1; end endendmask = logical(mask); % 类型转换mask = bwmorph(mask, bridge); % 桥接操作mask = imfill(mask, hole); % 补洞操作function Im = Image_Rotate(Img, num, flag)% 旋转校正函数% 输入函数：% Img图像矩阵% num图像序号% flag显示图像窗口% 输出函数：% Im结果图像if nargin 3 flag = 0;endrole = 6 0 0 -13 5 10 0 10 0 -5 2 -1 2 2 0 -5 -2 0 6 2;Im = imrotate(Img, role(num), bilinear);if flag figure(2); subplot(1, 2, 1); imshow(Img); title(原图像); subplot(1, 2, 2); imshow(Im); title(旋转图像);endfunction words = Main_Process(bw, flag_display)% 主流程处理，分割字符并获取% 输入参数：% bw车牌区域图像% flag_display显示图像标记% 输出参数：% words车牌字符数据if nargin 2 flag_display = 1;endm, n = size(bw);k1 = 1;k2 = 1;s = sum(bw); % 列积分投影j = 1; % 列游标while j = n % 寻找车牌图像左侧边界 while s(j) = 0 & j = n-1 j = j + 1; end k1 = j-1; % 车牌图像左侧边界 % 寻找车牌图像右侧边界 while s(j) = 0 & j Tol val, num = min(sum(bw(:, k1+5:k2-5); bw(:, k1+num+5)=0; % 抹去该字符 endend% 再切割bw = Segmation(bw);% 切割出 7 个字符m, n = size(bw);wideTol = round(n/20); % 区域宽度最小约束rateTol = 0.25; % 中心区域比值约束flag = 0;word1 = ;while flag = 0 m, n = size(bw); left = 1; wide = 0; % 找到空隙位置 while sum(bw(:,wide+1) = 0 wide = wide+1; end if wide rateTol flag = 1; word1 = temp; %提取WORD 1 end bw(:, 1:wide) = 0; % 抹去已处理的区域 bw = Segmation(bw); endend% 分割出第二个字符word2, bw = Word_Segmation(bw);% 分割出第三个字符word3, bw = Word_Segmation(bw);% 分割出第四个字符word4, bw = Word_Segmation(bw);% 分割出第五个字符word5, bw = Word_Segmation(bw);% 分割出第六个字符word6, bw = Word_Segmation(bw);% 分割出第七个字符word7, bw = Word_Segmation(bw);wid = size(word1, 2) size(word2, 2) size(word3, 2) . size(word4, 2) size(word5, 2) size(word6, 2) size(word7, 2);maxwid, indmax = max(wid);maxwid = maxwid + 10;wordi = word1;wordi = zeros(size(wordi, 1), round(maxwid-size(word1, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word1, 2)/2);word1 = wordi;wordi = word2;wordi = zeros(size(wordi, 1), round(maxwid-size(word2, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word2, 2)/2);word2 = wordi;wordi = word3;wordi = zeros(size(wordi, 1), round(maxwid-size(word3, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word3, 2)/2);word3 = wordi;wordi = word4;wordi = zeros(size(wordi, 1), round(maxwid-size(word4, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word4, 2)/2);word4 = wordi;wordi = word5;wordi = zeros(size(wordi, 1), round(maxwid-size(word5, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word5, 2)/2);word5 = wordi;wordi = word6;wordi = zeros(size(wordi, 1), round(maxwid-size(word6, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word6, 2)/2);word6 = wordi;wordi = word7;wordi = zeros(size(wordi, 1), round(maxwid-size(word7, 2)/2) wordi zeros(size(wordi, 1), round(maxwid-size(word7, 2)/2);word7 = wordi;% figure(1);% subplot(2, 7, 1); imshow(word1); title(字符1);% subplot(2, 7, 2); imshow(word2); title(字符2);% subplot(2, 7, 3); imshow(word3); title(字符3);% subplot(2, 7, 4); imshow(word4); title(字符4);% subplot(2, 7, 5); imshow(word5); title(字符5);% subplot(2, 7, 6); imshow(word6); title(字符6);% subplot(2, 7, 7); imshow(word7); title(字符7);% 切割出的字符归一化大小为 40*20,此处演示word11 = imresize(word1, 40 20);word21 = imresize(word2, 40 20);word31 = imresize(word3, 40 20);word41 = imresize(word4, 40 20);word51 = imresize(word5, 40 20);word61 = imresize(word6, 40 20);word71 = imresize(word7, 40 20);% subplot(2, 7, 8); imshow(word11); title(字符1);% subplot(2, 7, 9); imshow(word21); title(字符2);% subplot(2, 7, 10); imshow(word31); title(字符3);% subplot(2, 7, 11); imshow(word41); title(字符4);% subplot(2, 7, 12); imshow(word51); title(字符5);% subplot(2, 7, 13); imshow(word61); title(字符6);% subplot(2, 7, 14); imshow(word71); title(字符7);% 赋值操作words.word1 = word11;words.word2 = word21;words.word3 = word31;words.word4 = word41;words.word5 = word51;words.word6 = word61;words.word7 = word71;if flag_display figure; subplot(2, 7, 1); imshow(word1); title(字符1, FontWeight, Bold); subplot(2, 7, 2); imshow(word2); title(字符2, FontWeight, Bold); subplot(2, 7, 3); imshow(word3); title(字符3, FontWeight, Bold); subplot(2, 7, 4); imshow(word4); title(字符4, FontWeight, Bold); subplot(2, 7, 5); imshow(word5); title(字符5, FontWeight, Bold); subplot(2, 7, 6); imshow(word6); title(字符6, FontWeight, Bold); subplot(2, 7, 7); imshow(word7); title(字符7, FontWeight, Bold); subplot(2, 7, 8); imshow(word11); title(字符1, FontWeight, Bold); subplot(2, 7, 9); imshow(word21); title(字符2, FontWeight, Bold); subplot(2, 7, 10); imshow(word31); title(字符3, FontWeight, Bold); subplot(2, 7, 11); imshow(word41); title(字符4, FontWeight, Bold); subplot(2, 7, 12); imshow(word51); title(字符5, FontWeight, Bold); subplot(2, 7, 13); imshow(word61); title(字符6, FontWeight, Bold); subplot(2, 7, 14); imshow(word71); title(字符7, FontWeight, Bold);endfunction mask = Mask_Process(Img, id)% 图像蒙版处理函数% 输入参数：% Img图像矩阵% id图像序号% 输出参数：% mask模板图像% 如果已经存在模板图像则不再取模板filename = sprintf(maskmask%d.jpg, id);if exist(filename, file) mask = imread(filename); if isa(mask, logical) mask = im2bw(mask); end sz = size(Img); if isequal(sz(1:2), size(mask) mask = imresize(mask, sz(1:2); mask = logical(mask); end return;endI = Img;Ptn = ;figure;subplot(1, 3, 1); imshow(I);title(取点_左键取点，右键退出, Color, r, . FontWeight, Bold);hold on;set(gcf, units, normalized, position, 0 0 1 1);x, y, button = ginput(1); % 点击while button = 1 plot(x, y, r+, LineWidth, 2); % 绘制节点 Ptn = Ptn; x y; % 存储节点 x, y, button = ginput(1); % 点击endif size(Ptn, 1) 2 return;endPtn = Ptn; Ptn(1, :);plot(Ptn(:, 1), Ptn(:, 2), ro-, LineWidth, 2, MarkerFaceColor, k);title(原图像, Color, k, . FontWeight, Bold);mask = Get_PointSplineMask(I, Ptn); % 获取蒙板图像subplot(1, 3, 2); imshow(mask);title(蒙板图像, Color, k, . FontWeight, Bold);if ndims(I) = 3 I1 = I .* uint8(cat(3, mask, mask, mask);else if isequal(size(I), size(mask) mask = imresize(mask, size(I); mask = logical(mask); end I1 = I .* mask;endsubplot(1, 3, 3); imshow(I1);title(蒙板分割结果, Color, k, . FontWeight, Bold);imwrite(mask, filename);function str = Pattern_Recognition(words)% 车牌字符识别% 输入参数：% words车牌字符图像% 输出参数：% str识别结果pattern = ;dirpath = ./标准库/;%读取所有样本文件files = ls(dirpath *.jpg);for t = 1 : length(files) imagedata = imread(dirpath, files(t,:); %读取样本文件 imagedata = im2bw(imagedata, 0.5); pattern(t).feature = imagedata; end % 字符库列表chepaiword=. 沪 C E N 2 6 7 浙 B T 2 0 0 8 . 皖 N 6 9 3 3 5 皖 A M 3 7 6 7 . 沪 B 9 1 2 1 3 沪 F S 2 6 9 5 . 吉 A E 3 1 2 7 沪 B B 3 3 9 3 . 沪 J B 7 7 7 8 京 H Z 9 1 3 9 . 晋 K C 3 8 4 1 苏 D V 0 0 0 1 . 苏 A V 8 8 8 8 浙 B 8 8 8 8 8 . 沪 F Q 1 2 7 7 苏 E D 0 0 0 9 . 沪 E C 1 2 8 8 苏 G B 0 5 7 8 . 沪 B B 2 0 2 9 沪 D 5 4 2 2 6; for m = 1 : 7; for n = 1 : length(files); switch m case 1 distance(n)=sum(sum(abs(words.word1-pattern(n).feature); case 2 distance(n)=sum(sum(abs(words.word2-pattern(n).feature); case 3 distance(n)=sum(sum(abs(words.word3-pattern(n).feature); case 4 distance(n)=sum(sum(abs(words.word4-pattern(n).feature); case 5 distance(n)=sum(sum(abs(words.word5-pattern(n).feature); case 6 distance(n)=sum(sum(abs(words.word6-pattern(n).feature); case 7 distance(n)=sum(sum(abs(words.word7-pattern(n).feature); end end yvalue,xnumber=min(distance); filename = files(xnumber, :); pathstr, name, ext = fileparts(filename); result(m) = chepaiword(str2num(name);endstr = 识别结果为： result;msgbox(str, 车牌识别, modal); str = result;function result = Plate_Process(plate, id, flag)% 车牌区域处理% 输入参数：% plate车牌区域% id车牌序号% flag显示图像标记% 输出参数：% result结果图像if nargin 3 flag = 1;endif ndims(plate) = 3 plate1 = rgb2gray(plate);% figure(1)% imshow(plate1)else plate1 = plate;endIm = Image_Rotate(plate1, id);th = 0.85 0.50 0.85 0.70 0.30 . 0.40 0.65 0.45 0.45 0.65 . 0.51 0.80 0.40 0.75 0.60 . 0.55 0.40 0.40 0.76 0.72;bw = im2bw(Im, th(id); % 车牌区域二值化h = fspecial(average, 2); % 均值滤波模板bw1 = imfilter(bw, h, replicate); % 均值滤波% figure(1)% imshow(bw1)mask = Mask_Process(bw1, id); % 模板处理bw2 = bw1 .* mask; % 模板滤波result = bw2;if flag figure; subplot(2, 2, 1); imshow(plate); title(车牌区域图像, FontWeight, Bold); subplot(2, 2, 2); imshow(Im); title(车牌区域校正图像, FontWeight, Bold); subplot(2, 2, 3); imshow(bw1); title(车牌区域二值图像, FontWeight, Bold); subplot(2, 2, 4); imshow(bw2); title(滤波二值图像, FontWeight, Bold);endfunction Plate, bw, Loc = Pre_Process(Img, parm, flag)% 车牌图像预处理，提取车牌区域% 输入参数：% Img图像矩阵% parm参数向量% flag是否显示处理结果% 输出参数：% Plate分割结果if nargin 3 flag = 1;endif nargin 900 parm = 0.35 0.9 90 0.35 0.7 90 2; end if size(Img, 2) 700 & size(Img, 2) 500 & size(Img, 2) 700 parm = 0.5 0.54 50 0.6 0.7 50 3; end if size(Img, 2) 800 rate = 800/y; I = imresize(I, rate);endy, x, z = size(I); % y方向对应行、x方向对应列、z方向对应深度myI = double(I); % 数据类型转换bw1 = zeros(y, x);bw2 = zeros(y, x);Blue_y = zeros(y, 1);% 对每一个像素进行分析，统计满足条件的像素所在的行对应的个数for i = 1 : y for j = 1 : x rij = myI(i, j, 1)/(myI(i, j, 3)+eps); gij = myI(i, j, 2)/(myI(i, j, 3)+eps); bij = myI(i, j, 3); % 蓝色RGB的灰度范围 if (rij parm(1) & gij parm(3) . | (gij parm(1) & rij parm(3) Blue_y(i, 1) = Blue_y(i, 1) + 1; % 蓝色象素点统计 bw1(i, j) = 1; end endend% Y方向车牌区域确定temp, MaxY = max(Blue_y);Th = parm(7);% 向上追溯，直到车牌区域上边界PY1 = MaxY;while (Blue_y(PY1,1)Th) & (PY11) PY1 = PY1 - 1;end% 向下追溯，直到车牌区域下边界PY2 = MaxY;while (Blue_y(PY2,1)Th) & (PY2y) PY2 = PY2 + 1;end% 对车牌区域的修正PY1 = PY1 - 2;PY2 = PY2 + 2;if PY1 y PY2 = y;end% 得到车牌区域IY = I(PY1:PY2, :, :);% X 方向 % 进一步确定X方向的车牌区域Blue_x = zeros(1,x);for j = 1:x for i = PY1:PY2 rij = myI(i, j, 1)/(myI(i, j, 3)+eps); gij = myI(i, j, 2)/(myI(i, j, 3)+eps); bij = myI(i, j, 3); % 蓝色RGB的灰度范围 if (rij parm(4) & gij parm(6) . | (gij parm(4) & rij parm(6) Blue_x(1,j) = Blue_x(1,j) + 1; % 蓝色象素点统计 bw2(i, j) = 1; end endend% 想右追溯，直到找到车牌区域左边界PX1 = 1;while (Blue_x(1,PX1)Th) & (PX1x) PX1 = PX1 + 1;end% 想左追溯，直到找到车牌区域右边界PX2 = x;while (Blue_x(1,PX2)PX1) PX2 = PX2 - 1;end% 对车牌区域的修正PX1 = PX1 - 2;PX2 = PX2 + 2;if PX1 x PX2 = x;end% 得到车牌区域IX = I(:, PX1:PX2, :);% 分割车牌区域Plate = I(PY1:PY2, PX1:PX2, :);Loc.row = PY1 PY2;Loc.col = PX1 PX2;bw = bw1 + bw2;bw = logical(bw);bw(1:PY1, :) = 0;bw(PY2:end, :) = 0;bw(:, 1:PX1) = 0;bw(:, PX2:end) = 0;if flag figure; subplot(2, 2, 3); imshow(IY); title(行过滤结果, FontWeight, Bold); subplot(2, 2, 1); imshow(IX); title(列过滤结果, FontWeight, Bold); subplot(2, 2, 2); imshow(I); title(原图像, FontWeight, Bold); subplot(2, 2, 4); imshow(Plate); title(分割结果, FontWeight, Bold);endfunction e = Segmation(d)% 车牌图像切割% 输入参数：% d车牌图像% 输出参数：% e切割后的车牌图像m, n = size(d);% 初始化参数top = 1; % 矩形框上参数bottom = m; % 矩形框下参数left = 1; % 矩形框左参数right = n; % 矩形框右参数 % 寻找矩形框上参数while sum(d(top,:)=0 & top=1 bottom = bottom - 1;end% 寻找矩形框左参数while sum(d(:,left)=0 & left=1 right = right - 1;enddd = right - left; % 矩形框长hh = bottom - top