matlab-(含教程)基于形态学处理和模板匹配的交通限速标志检测和速度识别matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); % RGB = imread('70-2.jpg'); imshow(RGB); I = rgb2gray(RGB); figure imshow(I); BW = edge(I,'Canny'); %figure imshow(BW); C= 10; B= 300; [centers, radii, metric] = imfindcircles(I,[C B]); % ottengo il primo cerchio con le caratteristiche definite centersStrong2 = centers(1:1,:); radiiStrong2 = radii(1:1); metricStrong2 = metric(1:1); %disegno i 2 risultati figure viscircles(centersStrong2, radiiStrong2,'EdgeColor','b'); %% Estrazione dell immagine contenete il segnale [nrows, ncols, dim] = size(RGB); % iterate over each pixel and determine if it is outside the circle or not for x=1:ncols for y=1:nrows % includo il cerchio rosso attorno il segnale if (x-centersStrong2(1,1))^2 + (y-centersStrong2(1,2))^2 > 1.5*radiiStrong2^2 RGB(y,x,:) = [255 255 255]; end end end % display the image image(RGB); axis equal D= 1.1; imageCrop = imcrop(RGB, [centersStrong2(1,1)-D*radiiStrong2 ,centersStrong2(1,2)-D*radiiStrong2, centersStrong2(1,1)+D*radiiStrong2 , centersStrong2(1,2)+D*radiiStrong2]); [nrows, ncols, dim] = size(imageCrop); for x=1:ncols for y=1:nrows if imageCrop(y,x,:)<= 225 imageCrop(y,x,:) = [0 0 0]; else imageCrop(y,x,:) = [255 255 255]; end end end I = rgb2gray(imageCrop); searchImg = I; test10 = imread('1.jpg'); test20 = imread('2.jpg'); test30 = imread('3.jpg'); test40 = imread('4.jpg'); test50 = imread('5.jpg'); test60 = imread('6.jpg'); test70 = imread('7.jpg'); test80 = imread('8.jpg'); test90 = imread('9.jpg'); test100 = imread('10.jpg'); test110 = imread('11.jpg'); test120 = imread('12.jpg'); test130 = imread('13.jpg'); testImgRgb1 = rgb2gray(test10); c1 = normxcorr2(searchImg,testImgRgb1); b1 = max(c1(:)); testImgRgb2 = rgb2gray(test20); c2 = normxcorr2(searchImg,testImgRgb2); b2 = max(c2(:)); testImgRgb3 = rgb2gray(test30); c3 = normxcorr2(searchImg,testImgRgb3); b3 = max(c3(:)); testImgRgb4 = rgb2gray(test40); c4 = normxcorr2(searchImg,testImgRgb4); b4 = max(c4(:)); testImgRgb5 = rgb2gray(test50); c5 = normxcorr2(searchImg,testImgRgb5); b5 = max(c5(:)); testImgRgb6 = rgb2gray(test60); c6 = normxcorr2(searchImg,testImgRgb6); b6 = max(c6(:)); testImgRgb7 = rgb2gray(test70); c7 = normxcorr2(searchImg,testImgRgb7); b7 = max(c7(:)); testImgRgb8 = rgb2gray(test80); c8 = normxcorr2(searchImg,testImgRgb8); b8 = max(c8(:)); testImgRgb9 = rgb2gray(test90); c9 = normxcorr2(searchImg,testImgRgb9); b9 = max(c9(:)); testImgRgb10 = rgb2gray(test100); c10 = normxcorr2(searchImg,testImgRgb10); b10 = max(c10(:)); testImgRgb11 = rgb2gray(test110); c11 = normxcorr2(searchImg,testImgRgb11); b11 = max(c11(:)); testImgRgb12 = rgb2gray(test120); c12 = normxcorr2(searchImg,testImgRgb12); b12 = max(c12(:)); testImgRgb13 = rgb2gray(test130); c13 = normxcorr2(searchImg,testImgRgb13); b13 = max(c13(:)); %% matrice dei risultati della correlazione e display del risultato A = [10 b1; 20 b2; 30 b3; 40 b4; 50 b5; 60 b6; 70 b7; 80 b8; 90 b9; 100 b10; 110 b11; 120 b12; 130 b13]; % A(:,2) [M, In] = max(A(:,2)); figure image(imageCrop); axis equal figure if In == 1 imshow('1w.jpg'); figure surf(c1) , shading flat end if In == 2 imshow('2w.jpg'); figure surf(c2) , shading flat end if In == 3 imshow('3w.jpg'); figure surf(c3) , shading flat end if In == 4 imshow('4w.jpg'); figure surf(c4) , shading flat end if In == 5 imshow('5w.jpg'); figure surf(c5) , shading flat end if In == 6 imshow('6w.jpg'); figure surf(c6) , shading flat end if In == 7 imshow('7w.jpg'); figure surf(c7) , shading flat end if In == 8 imshow('8w.jpg'); figure surf(c8) , shading flat end if In == 9 imshow('9w.jpg'); figure surf(c9) , shading flat end if In == 10 imshow('10w.jpg'); figure surf(c10) , shading flat end if In == 11 imshow('11w.jpg'); figure surf(c11) , shading flat end if In == 12 imshow('12w.jpg'); figure surf(c12) , shading flat end if In == 13 imshow('13w.jpg'); figure surf(c13) , shading flat end