最新车牌识别系统MATLAB源代码完整解析

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', ...'images\01.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Img[Plate, 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-1m(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));endfor j = 1 : N-1A(j, j+1) = m(j);A(j+1, j) = n(j+1);endp = 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)*(h^2)/6)*((x(i+1)-z(j))/h)+( y(i+1)-M(i+1)*h^2/6)*((z(j)-x(i))/h);endtemp = [temp S];endR(:, 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) == 3I = rgb2gray(Img);elseI = Img;endmask = zeros(size(I));Ptn = Cubic_Spline(Ptn); % 样条插值for i = 1 : size(Ptn, 1)-1pt1 = 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 == 0yk = linspace(min(y1, y2), max(y1, y2));elseyk = (-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)) > 0mask(round(yk(j)), round(xk(j))) = 1;endendendmask = 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 < 3flag = 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 flagfigure(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 < 2flag_display = 1;end[m, 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-1j = j + 1;endk1 = 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 <= n-1j = j + 1;endk2 = j-1; % 车牌图像右侧边界Tol = round(n/6.5); % 字符区域宽度约束if k2-k1 > 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)) ~= 0wide = wide+1;endif wide < wideTol% 认为是左侧干扰bw(:, 1:wide) = 0; % 抹去干扰区域bw = Segmation(bw);else% 提取字符区域temp = Segmation(imcrop(bw, [1 1 wide m]));[m, n] = size(temp);tall = sum(temp(:)); % 该字符所有像素之和% 该字符图像2/3图像区域像素和two_thirds = sum(sum( temp(round(m/3):2*round(m/3), :) ));rate = two_thirds/tall; % 中间区域像素/全局区域像素if rate > rateTolflag = 1;word1 = temp; %提取WORD 1endbw(:, 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_displayfigure;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('mask\\mask%d.jpg', id);if exist(filename, 'file')mask = imread(filename);if ~isa(mask, 'logical')mask = im2bw(mask);endsz = size(Img);if ~isequal(sz(1:2), size(mask))mask = imresize(mask, sz(1:2));mask = logical(mask);endreturn;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 == 1plot(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) < 2return;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) == 3I1 = I .* uint8(cat(3, mask, mask, mask));elseif ~isequal(size(I), size(mask))mask = imresize(mask, size(I));mask = logical(mask);endI1 = 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 mcase 1distance(n)=sum(sum(abs(words.word1-pattern(n).feature)));case 2distance(n)=sum(sum(abs(words.word2-pattern(n).feature)));case 3distance(n)=sum(sum(abs(words.word3-pattern(n).feature)));case 4distance(n)=sum(sum(abs(words.word4-pattern(n).feature)));case 5distance(n)=sum(sum(abs(words.word5-pattern(n).feature)));case 6distance(n)=sum(sum(abs(words.word6-pattern(n).feature)));case 7distance(n)=sum(sum(abs(words.word7-pattern(n).feature)));endend[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 < 3flag = 1;endif ndims(plate) == 3plate1 = rgb2gray(plate);% figure(1)% imshow(plate1)elseplate1 = 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 flagfigure;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 < 3flag = 1;endif nargin < 2 || isempty(parm)if size(Img, 2) > 900parm = [0.35 0.9 90 0.35 0.7 90 2];endif size(Img, 2) > 700 && size(Img, 2) < 900parm = [0.6 0.9 90 0.6 0.8 90 0.5];endif size(Img, 2) > 500 && size(Img, 2) < 700parm = [0.5 0.54 50 0.6 0.7 50 3];endif size(Img, 2) < 500parm = [0.8 0.9 150 0.8 0.9 150 3];endendI = Img;[y, x, z] = size(I); % y方向对应行、x方向对应列、z方向对应深度% 图像尺寸过大会影响处理效率，所以进行放缩处理if y > 800rate = 800/y;I = imresize(I, rate);end[y, 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 : yfor j = 1 : xrij = 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(2) && bij > parm(3)) ...|| (gij < parm(1) && rij < parm(2) && bij > parm(3)) Blue_y(i, 1) = Blue_y(i, 1) + 1; % 蓝色象素点统计bw1(i, j) = 1;endendend% 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) && (PY1>1))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) && (PY2<y))PY2 = PY2 + 1;% 对车牌区域的修正PY1 = PY1 - 2;PY2 = PY2 + 2;if PY1 < 1PY1 = 1;endif PY2 > yPY2 = 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:xfor i = PY1:PY2rij = 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(5) && bij > parm(6)) ...|| (gij < parm(4) && rij < parm(5) && bij > parm(6)) Blue_x(1,j) = Blue_x(1,j) + 1; % 蓝色象素点统计bw2(i, j) = 1;endendend% 想右追溯，直到找到车牌区域左边界PX1 = 1;while (Blue_x(1,PX1)<Th) && (PX1<x)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)<Th) && (PX2>PX1)PX2 = PX2 - 1;end% 对车牌区域的修正PX1 = PX1 - 2;PX2 = PX2 + 2;if PX1 < 1PX1 = 1;if PX2 > xPX2 = 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 flagfigure;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<=mtop = top + 1;% 寻找矩形框下参数while sum(d(bottom,:))==0 && bottom>=1bottom = bottom - 1;end% 寻找矩形框左参数while sum(d(:,left))==0 && left<=nleft = left + 1;end% 寻找矩形框右参数while sum(d(:,right))==0 && right>=1right = right - 1;enddd = right - left; % 矩形框长hh = bottom - top; % 矩形框宽e = imcrop(d, [left top dd hh]); % 车牌图像矩形框裁剪function [word, result] = Word_Segmation(d)% 提取字符% 输入参数：% d——车牌图像% 输出参数：% word——字符图像% result——处理结果word = [];flag = 0;[m, n] = size(d);wideTol = round(n/20); % 区域宽度最小约束rateTol = 0.25; % 中心区域比值约束while flag == 0[m, n] = size(d);wide = 0;while sum(d(:,wide+1)) ~= 0 && wide <= n-2wide = wide + 1;endtemp = Segmation(imcrop(d, [1 1 wide m]));[m1,n1] = size(temp);if wide<wideTol && n1/m1>rateTold(:, 1:wide) = 0;if sum(sum(d)) ~= 0d = Segmation(d); % 切割出最小范围elseword = [];flag = 1;endelseword = Segmation(imcrop(d, [1 1 wide m]));d(:, 1:wide) = 0;if sum(sum(d)) ~= 0;d = Segmation(d);flag = 1;elsed = [];endendendresult = d;function Write_Mask(words, id)% 写出到模板库% 输入参数：% words——字符图像集合% id——序号imwrite(words.word1, sprintf('模板库\\%d_字符%d.jpg', id, 1)); imwrite(words.word2, sprintf('模板库\\%d_字符%d.jpg', id, 2)); imwrite(words.word3, sprintf('模板库\\%d_字符%d.jpg', id, 3)); imwrite(words.word4, sprintf('模板库\\%d_字符%d.jpg', id, 4));imwrite(words.word5, sprintf('模板库\\%d_字符%d.jpg', id, 5));imwrite(words.word6, sprintf('模板库\\%d_字符%d.jpg', id, 6)); imwrite(words.word7, sprintf('模板库\\%d_字符%d.jpg', id, 7));% imwrite(words.word1, sprintf('模板库\\%d.jpg', (id-1)*7+1)); % imwrite(words.word2, sprintf('模板库\\%d.jpg', (id-1)*7+2)); % imwrite(words.word3, sprintf('模板库\\%d.jpg', (id-1)*7+3));% imwrite(words.word4, sprintf('模板库\\%d.jpg', (id-1)*7+4)); % imwrite(words.word5, sprintf('模板库\\%d.jpg', (id-1)*7+5)); % imwrite(words.word6, sprintf('模板库\\%d.jpg', (id-1)*7+6));% imwrite(words.word7, sprintf('模板库\\%d.jpg', (id-1)*7+7));。