基于matlab设计的车牌识别系统设计.zip

%% clc; clear; close all; [filename filepath] = uigetfile('.jpg', '输入一个需要识别出车牌的图像'); %自动读入图像 file = strcat(filepath, filename); img = imread(file); figure; imshow(img); title('车牌图像'); %% 灰度处理 img1 = rgb2gray(img); % RGB图像转灰度图像 figure; subplot(1, 2, 1); imshow(img1); title('灰度图像'); subplot(1, 2, 2); imhist(img1); title('灰度处理后的灰度直方图'); %% 直方图均衡化 img2 = histeq(img1); %直方图均衡化 figure; subplot(1, 2, 1); imshow(img2); title('灰度图像'); subplot(1, 2, 2); imhist(img2); title('灰度处理后的灰度直方图'); %% 中值滤波 img3 = medfilt2(img2); figure; imshow(img3); title('中值滤波'); %% 边缘提取 img4 = edge(img3, 'sobel', 0.2); figure('name','边缘检测'); imshow(img4); title('sobel算子边缘检测'); %% 图像腐蚀 se=[1;1;1]; img5 = imerode(img4, se); figure('name','图像腐蚀'); imshow(img5); title('图像腐蚀后的图像'); %% 平滑图像，图像膨胀 se = strel('rectangle', [20, 20]); img6 = imclose(img5, se); figure('name','平滑处理'); imshow(img6); title('平滑图像的轮廓'); %% 从图像中删除所有少于1000像素8邻接 img7 = bwareaopen(img6, 1000); figure('name', '移除小对象'); imshow(img7); title('从图像中移除小对象'); %% 切割出图像 [y, x, z] = size(img7); img8 = double(img7); % 转成双精度浮点型 blue_Y = zeros(y, 1); for i = 1:y for j = 1:x if(img8(i, j) == 1) blue_Y(i, 1) = blue_Y(i, 1) + 1; end end end img_Y1 = 1; while (blue_Y(img_Y1) < 5) && (img_Y1 < y) img_Y1 = img_Y1 + 1; end % 找到Y坐标的最大值 img_Y2 = y; while (blue_Y(img_Y2) < 5) && (img_Y2 > img_Y1) img_Y2 = img_Y2 - 1; end % x方向 blue_X = zeros(1, x); for j = 1:x for i = 1:y if(img8(i, j) == 1) % 判断车牌位置区域 blue_X(1, j) = blue_X(1, j) + 1; end end end % 找到x坐标的最小值 img_X1 = 1; while (blue_X(1, img_X1) < 5) && (img_X1 < x) img_X1 = img_X1 + 1; end % 找到x坐标的最小值 img_X2 = x; while (blue_X(1, img_X2) < 5) && (img_X2 > img_X1) img_X2 = img_X2 - 1; end % 对图像进行裁剪 img9 = img(img_Y1:img_Y2, img_X1:img_X2, :); figure('name', '定位剪切图像'); imshow(img9); title('定位剪切后的彩色车牌图像') % 保存提取出来的车牌图像 imwrite(img9, '车牌图像.jpg'); %% 对车牌图像作图像预处理 plate_img = imread('车牌图像.jpg'); % 转换成灰度图像 plate_img1 = rgb2gray(plate_img); % RGB图像转灰度图像 figure; subplot(1, 2, 1); imshow(plate_img1); title('灰度图像'); subplot(1, 2, 2); imhist(plate_img1); title('灰度处理后的灰度直方图'); % 直方图均衡化 plate_img2 = histeq(plate_img1); figure('name', '直方图均衡化'); subplot(1,2,1); imshow(plate_img2); title('直方图均衡化的图像'); subplot(1,2,2); imhist(plate_img2); title('直方图'); % 二值化处理 plate_img3 = im2bw(plate_img2, 0.76); figure('name', '二值化处理'); imshow(plate_img3); title('车牌二值图像'); % 中值滤波 plate_img4 = medfilt2(plate_img3); figure('name', '中值滤波'); imshow(plate_img4); title('中值滤波后的图像'); plate_img5 = my_imsplit(plate_img4); [m, n] = size(plate_img5); s = sum(plate_img5); j = 1; k1 = 1; k2 = 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(plate_img5(:, [k1+5:k2-5]))); plate_img5(:, k1+num+5) = 0; end end y1 = 10; y2 = 0.25; flag = 0; word1 = []; while flag == 0 [m, n] = size(plate_img5); left = 1; width = 0; while sum(plate_img5(:, width+1)) ~= 0 width = width + 1; end if width < y1 plate_img5(:, [1:width]) = 0; plate_img5 = my_imsplit(plate_img5); else temp = my_imsplit(imcrop(plate_img5, [1,1,width,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 plate_img5(:, [1:width]) = 0; plate_img5 = my_imsplit(plate_img5); end end figure; subplot(2,4,1), imshow(plate_img5); % 分割出第二个字符 [word2,plate_img5]=getword(plate_img5); subplot(2,4,2), imshow(plate_img5); % 分割出第三个字符 [word3,plate_img5]=getword(plate_img5); subplot(2,4,3), imshow(plate_img5); % 分割出第四个字符 [word4,plate_img5]=getword(plate_img5); subplot(2,4,4), imshow(plate_img5); % 分割出第五个字符 [word5,plate_img5]=getword(plate_img5); subplot(2,3,4), imshow(plate_img5); % 分割出第六个字符 [word6,plate_img5]=getword(plate_img5); subplot(2,3,5), imshow(plate_img5); % 分割出第七个字符 [word7,plate_img5]=getword(plate_img5); subplot(2,3,6), imshow(plate_img5); figure; subplot(5,7,1),imshow(word1),title('1'); subplot(5,7,2),imshow(word2),title('2'); subplot(5,7,3),imshow(word3),title('3'); subplot(5,7,4),imshow(word4),title('4'); subplot(5,7,5),imshow(word5),title('5'); subplot(5,7,6),imshow(word6),title('6'); subplot(5,7,7),imshow(word7),title('7'); 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]); subplot(5,7,15),imshow(word1),title('11'); subplot(5,7,16),imshow(word2),title('22'); subplot(5,7,17),imshow(word3),title('33'); subplot(5,7,18),imshow(word4),title('44'); subplot(5,7,19),imshow(word5),title('55'); subplot(5,7,20),imshow(word6),title('66'); subplot(5,7,21),imshow(word7),title('77'); imwrite(word1,'1.jpg'); % 创建七位车牌字符图像 imwrite(word2,'2.jpg'); imwrite(word3,'3.jpg'); imwrite(word4,'4.jpg'); imwrite(word5,'5.jpg'); imwrite(word6,'6.jpg'); imwrite(word7,'7.jpg'); %% 进行字符识别 liccode=char(['0':'9' 'A':'Z' '京辽陕苏鲁浙']); subBw2 = zeros(40, 20); num = 1; % 车牌位数 for i = 1:7 ii = int2str(i); word = imread([ii,'.jpg']); segBw2 = imresize(word, [40,20], 'nearest'); segBw2 = im2bw(segBw2, 0.5); if i == 1 kMin = 37; kMax = 42; elseif i == 2 kMin = 11; kMax = 36; elseif i >= 3 kMin = 1; kMax = 36; end l = 1; for k = kMin : kMax fname = strcat('namebook\',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 = 1:40 for j1 = 1:20 subBw2(i1, j1) = segBw2(i1, j1) - samBw2(i1 ,j1); end end % 统计两幅图片不同点的个数，并保存下来 Dmax = 0; for i2 = 1:40 for j2 = 1:20 if subBw2(i2, j2) ~= 0 Dmax = Dmax + 1; end end end error(l) = Dmax; l = l + 1; end % 找到图片差别最少的图像 errorMin = min(error); findc = find(error == errorMin); % error % findc % 根据字库，对应到识别的字符 Code(num*2 - 1) = liccode(findc(1) + kMin - 1); Code(num*2) = ' '; num = num + 1; end % 显示识别结果 disp(Code); msgbox(Code,'识别出的车牌号');