Lp.zip_车牌定位

clc; clear; close all; %蓝色：红色分量0到80 绿色分量20到105 蓝色分量70到170 %蓝色/红色>1 蓝色/绿色>1 红色/绿色<1 %白色标准差小于15 三种颜色均大于100 [fn,pn,fi]=uigetfile('*.*','选择图片');%fn为返回的文件名；pn为返回的文件的路径名；fi为选择的文件类型 I=imread([pn fn]);%figure,imshow(I);title('原始图像');%显示原始图像 tic G=fspecial('gaussian',5); I=imfilter(I,G,'same'); Ib=I; imshow(I); I2=rgb2hsv(I);%I(:,:,1)值从 0 到 1，对应于颜色在颜色圈上的位置。随着色调从 0 增加到 1 %颜色从红色过渡到橙色、黄色、绿色、青色、蓝色、品红色，最后又回到红色。 %I(:,:,2)色调的量或距离中性色的量。0 表示中性色，1 表示最大饱和度。 %I(:,:,3)特定颜色的红色、绿色和蓝色分量的最大明度 [a,b]=find((I2(:,:,1)<0.75)&(I2(:,:,1)>0.59)&(I2(:,:,2)>0.5)&I2(:,:,3)>0.2);%0.56-0.78 % I(a,b,:)=0; cz=size(a); for i=1:cz(1) I(a(i),b(i),:)=0; end Ic=Ib-I; figure,imshow(Ic); % [m,n,~]=size(I2); % z=[];z2=[]; % for i=1:m % for j=1:n % if I2(i,j,1)<0.75&&I2(i,j,1)>0.59&&I2(i,j,2)>0.4&&I2(i,j,3)>0.2 % z=[z,i];z2=[z2,j]; % end % end % end % cz=size(z); % for i=1:cz(2) % I(z(i),z2(i),:)=0; % end % Ic=Ib-I; % figure,imshow(Ic); I3=rgb2gray(Ic); figure,imshow(I3); I3=imadjust(I3,[0.05,0.1],[0,1]); figure,imshow(I3); se=strel('square',18); I3=imclose(I3,se); I4=im2bw(I3); figure,imshow(I4); % I42=imclearborder(I4,8); % figure('name','ceshi'),imshow(I42); [lm,on]=bwlabel(I4,8); status=regionprops(lm,'area','image'); for i=1:on if status(i).Area>=100&&size(status(i).Image,1)>5&&size(status(i).Image,2)>5 lmx=lm; lmx(lmx~=i)=0; [r,c]=find(lmx==i); %找出矩阵中非零元素所在行和列，并存在r，c中 [rectx,recty,area,perimeter,ckb] = minboundzsj(c,r,'a'); if ckb>3&&ckb<5 rect=[rectx,recty]; if recty(2)==recty(1)||rectx(2)==rectx(1) degree=0; else if norm(rect(2,:)-rect(1,:))>=norm(rect(3,:)-rect(2,:)) degree=-rad2deg(atan((recty(2)-recty(1))/(rectx(2)-rectx(1)))); else degree=-(rad2deg(atan((recty(2)-recty(1))/(rectx(2)-rectx(1))))-90); end end figure,imshow(Ib); hold on % scatter(rectx(3),recty(3)) % scatter(rectx(1),recty(1)) % scatter(rectx(1),1); % scatter(1356.6,1) line(rectx,recty); end end end bw=imrotate(Ib,-degree,'bilinear','loose'); figure,imshow(bw);title('倾斜校正'); [om, on, o] = size(I); new_m = ceil(abs(om*cosd(degree)) + abs(on*sind(degree)));%ceil朝无穷大方向取整 new_n = ceil(abs(on*cosd(degree)) + abs(om*sind(degree))); m1 = [1 0 0; 0 -1 0; -0.5*on 0.5*om 1]; m2 = [cosd(degree) -sind(degree) 0; sind(degree) cosd(degree) 0; 0 0 1]; m3 = [1 0 0; 0 -1 0; 0.5*new_n 0.5*new_m 1]; rect=[rect,ones(5,1)]; xrect=rect*m1*m2*m3; figure,imshow(bw); hold on line(xrect(:,1),xrect(:,2)); xrect2=eval(vpa(xrect,6)); xrectx=unique(xrect2(:,1)); xrecty=unique(xrect2(:,2)); width=abs(xrectx(2)-xrectx(1)); height=abs(xrecty(2)-xrecty(1)); Icp=imcrop(bw,[min(xrect2(:,1)) min(xrect2(:,2)) width height]); figure,imshow(Icp); % figure,imshow(bw); % hold on % scatter(min(xrect2(:,1)),max(xrect2(:,2))) % [om, on, ~] = size(I); % new_m = ceil(abs(om*cosd(degree)) + abs(on*sind(degree)));%ceil朝无穷大方向取整 % new_n = ceil(abs(on*cosd(degree)) + abs(om*sind(degree))); % rect=[rect,ones(5,1)]; % rm1 = [1 0 0; 0 -1 0; -0.5*new_n 0.5*new_m 1]; % rm2 = [cosd(degree) sind(degree) 0; -sind(degree) cosd(degree) 0; 0 0 1]; % rm3 = [1 0 0; 0 -1 0; 0.5*on 0.5*om 1]; % for i = 1:new_n % for j = 1:new_m % % rotated image's coordinates to no-rotation image's coordinates % old_coordinate = [i j 1]*rm1*rm2*rm3; % col = round(old_coordinate(1)); % row = round(old_coordinate(2)); % % prevent border overflow % if row < 1 || col < 1 || row > om || col > on % new_img_nnp(j, i) = 0; % new_img_lp(j, i) = 0; % else % % nearest neighbor interpolation % new_img_nnp(j, i, 1) = I(row, col, 1); % new_img_nnp(j, i, 2) = I(row, col, 2); % new_img_nnp(j, i, 3) = I(row, col, 3); % % % bilinear interpolation % % left = floor(col); % % right = ceil(col); % % top = floor(row); % % bottom = ceil(row); % % % % a = col - left; % % b = row - top; % % new_img_lp(j, i, 1) = (1-a)*(1-b)*I(top, left, 1) + a*(1-b)*I(top, right, 1) + ... % % (1-a)*b*I(bottom, left, 1) + a*b*I(bottom, right, 1); % % new_img_lp(j, i, 2) = (1-a)*(1-b)*I(top, left, 2) + a*(1-b)*I(top, right, 2) + ... % % (1-a)*b*I(bottom, left, 2) + a*b*I(bottom, right, 2); % % new_img_lp(j, i, 3) = (1-a)*(1-b)*I(top, left, 3) + a*(1-b)*I(top, right, 3) + ... % % (1-a)*b*I(bottom, left, 2) + a*b*I(bottom, right, 3); % end % end % end % figure, imshow(new_img_nnp/255), title('nearest neighbor interpolation'); % % figure, imshow(new_img_lp/255), title('bilinear interpolation'); % csx=[2 4 3 1 2];csy=[1 3 4 2 1]; % figure,line(csx,csy) % status=regionprops(lm,'area','boundingbox','image'); % figure,imshow(I4); % xlabel('最小外接矩形'); % fhtjz=[]; % for i=1:n % mianji=status(i).BoundingBox(3)*status(i).BoundingBox(4); % if mianji>=100 % % rectangle('position',status(i).BoundingBox,'edgecolor','r'); % fhtjz=[fhtjz,i]; % end % end % cx=size(fhtjz,2); % i=1; % for i=1:cx % Icl=im2double(status(i).Image); % Icl2=padarray(Icl,[round(size(Icl,1)/2) round(size(Icl,2)/2)],'both'); % [r,c]=find(Icl2==1); %找出矩阵中非零元素所在行和列，并存在r，c中 % [rectx,recty,area,perimeter] = minboundzsj(c,r,'a'); % figure,imshow(Icl2); % hold on % line(rectx,recty); % end % status=regionprops(l,'BoundingBox');%返回一个长度4的数组，分别表示矩形的左上角点坐标和矩形的长宽 % figure,imshow(I4); % xlabel('最小外接矩形'); % for i=1:n % rectangle('position',status(i).BoundingBox,'edgecolor','r'); % end % fkcbl=[]; % for i=1:n % kcbl(i)=status(i).BoundingBox(3)/status(i).BoundingBox(4);%宽长比例 % if kcbl(i)>3&&kcbl(i)<4 % fkcbl=[fkcbl,i]; % end % end % cx=size(fkcbl,2); % figure,imshow(I4); % xlabel('合适的最小外接矩形'); % for i=1:cx % rectangle('position',status(fkcbl(i)).BoundingBox,'edgecolor','r'); % end % [r,cv]=find(I4==1); %找出矩阵中非零元素所在行和列，并存在r，cv中 % [rectx,recty,area,perimeter] = minboundzsj(cv,r,'a'); % figure,imshow(I4); % hold on % line(rectx,recty); % I4=edge(I3,'sobel','horizontal'); % BW = edge(I,'sobel',thresh,direction) %根据所指定的敏感度阈值thresh，在所指定的方向direction上，用Sobel算子进行边缘检测。 % Direction可取的字符串值为horizontal(水平方向)、vertical(垂直方向)或both(两个方向) % figure,imshow(I4); toc