基于Matlab实现车牌图像自动识别技术（源码+图像）.rar

% 读取待处理的图像，将其转化为二值图像 I = imread('car.jpg'); I2 = rgb2gray(I); I4 = im2bw(I2, 0.2); % 去除图像中面积过小的、可以肯定不是车牌的区域 bw = bwareaopen(I4, 500); % 为定位车牌，将白色区域膨胀，腐蚀去无关的小物件 se = strel('disk',15); bw = imclose(bw,se); bw = imfill(bw,[1 1]); % 查找连通域边界 [B,L] = bwboundaries(bw,4); imshow(label2rgb(L, @jet, [.5 .5 .5])) hold on % 找出所有连通域中最可能是车牌的那一个 for k = 1:length(B) boundary = B{k}; plot(boundary(:,2),boundary(:,1),'w','LineWidth',2) end % 找到每个连通域的质心 stats = regionprops(L,'Area','Centroid'); % 循环历遍每个连通域的边界 for k = 1:length(B) % 获取一条边界上的所有点 boundary = B{k}; % 计算边界周长 delta_sq = diff(boundary).^2; perimeter = sum(sqrt(sum(delta_sq,2))); % 获取边界所围面积 area = stats(k).Area; % 计算匹配度 metric = 27*area/perimeter^2; % 要显示的匹配度字串 metric_string = sprintf('%2.2f',metric); % 标记出匹配度接近1的连通域 if metric >= 0.9 && metric <= 1.1 centroid = stats(k).Centroid; plot(centroid(1),centroid(2),'ko'); % 提取该连通域所对应在二值图像中的矩形区域 goalboundary = boundary; s = min(goalboundary, [], 1); e = max(goalboundary, [], 1); goal = imcrop(I4,[s(2) s(1) e(2)-s(2) e(1)-s(1)]); end % 显示匹配度字串 text(boundary(1,2)-35,boundary(1,1)+13,... metric_string,'Color','g',... 'FontSize',14,'FontWeight','bold'); end goal = ~goal; goal(256,256) = 0; figure; imshow(goal); w = imread('P.bmp'); w = ~w; C=real(ifft2(fft2(goal).*fft2(rot90(w,2),256,256))); thresh = 240; figure; imshow(C > thresh);