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)); end
str = ['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; end
if ndims(plate) == 3
plate1 = rgb2gray(plate); % figure(1)
% imshow(plate1) else
plate1 = plate;
end
Im = 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'); end
function [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; end
if nargin < 2 || isempty(parm) if size(Img, 2) > 900
parm = [0.35 0.9 90 0.35 0.7 90 2]; end
if size(Img, 2) > 700 && size(Img, 2) < 900 parm = [0.6 0.9 90 0.6 0.8 90 0.5]; end
if 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) < 500
parm = [0.8 0.9 150 0.8 0.9 150 3]; end end I = 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 > 800
rate = 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 : 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(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; end end end
% 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 end % 对车牌区域的修正 PY1 = PY1 - 2; PY2 = PY2 + 2; if PY1 < 1 PY1 = 1; end if PY2 > 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(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; end end end % 想右追溯，直到找到车牌区域左边界 PX1 = 1; while (Blue_x(1,PX1) % 想左追溯，直到找到车牌区域右边界 PX2 = x; while (Blue_x(1,PX2) % 对车牌区域的修正 PX1 = PX1 - 2; PX2 = PX2 + 2; if PX1 < 1 PX1 = 1; end if PX2 > 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'); end function 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<=m top = top + 1;