基于颜色的图像分割算法MATLAB代码

% load image (colorful fabric acquired with frame grabber and camera) rgb = imread('UnitedArabEmirates_Dubai_05_006.jpg'); % Matrox frame grabber + Pulnix camera % imshow(rgb) % explore color content using image viewer % imview(rgb) % smooth image (reduce noise/color variation) rgb = imfilter(rgb,ones(3,3)/9); % imview(rgb) % view image and RGB layers (nonuniform illumination) % figure(1), set(1,'position',[99 79 826 589]) % subplot(2,2,1), subimage(rgb), title('fabric image'), axis off % subplot(2,2,2), map=gray(256); map(:,2:3)=0; subimage(rgb(:,:,1),map), title('red layer'), axis off % subplot(2,2,3), map=gray(256); map(:,[1 3])=0; subimage(rgb(:,:,2),map), title('green layer'), axis off % subplot(2,2,4), map=gray(256); map(:,1:2)=0; subimage(rgb(:,:,3),map), title('blue layer'), axis off % RGB histograms (poor separability) % figure(1), set(1,'position',[452 68 560 420]) % figure(2), set(2,'position',[16 269 560 420]) c='rgb'; for i=1:3 n=hist(reshape(double(rgb(:,:,i)),[size(rgb,1)*size(rgb,2) 1]),0.5:256); line(0:255,n,'color',c(i)) end axis tight, xlim([0 255]), box on xlabel intensity, ylabel population, title histograms % convert image to L*a*b* color space (transform) cform = makecform('srgb2lab'); lab = applycform(rgb,cform); % view components (note illumination free) % figure(1), figure(2) % subplot(2,2,1), subimage(rgb), title('fabric image'), axis off % subplot(2,2,2), subimage(lab(:,:,1)), title('L* layer'), axis off % subplot(2,2,3), map=gray(256); map(:,3)=0; map(:,2)=map(end:-1:1,2); subimage(lab(:,:,2),map), title('a* layer'), axis off % subplot(2,2,4), map=gray(256); map(:,3)=map(end:-1:1,3); subimage(lab(:,:,3),map), title('b* layer'), axis off figure; mask=mask_generator(rgb); bw=repmat(logical(sum(mask,3)),[1 1 3]); im=rgb; im(~bw)=nan; figure(3), imshow(im) % color markers (region average values of L*, a* and b*) l=lab(:,:,1); a=lab(:,:,2); b=lab(:,:,3); for i = 1:size(mask,3) cMark(i,1) = mean2(l(mask(:,:,i))); cMark(i,2) = mean2(a(mask(:,:,i))); cMark(i,3) = mean2(b(mask(:,:,i))); end % classify every pixel using nearest neighbor rule distance = zeros([size(rgb,1) size(rgb,2) size(mask,3)]); for i=1:size(mask,3) dx=double(a)-cMark(i,2); dy=double(b)-cMark(i,3); distance(:,:,i) = sqrt(dx.^2 + dy.^2); end cLabels=0:(size(mask,3)-1); [value,index]=min(distance,[],3); label=cLabels(index); % display segmented image cMap=double(applycform(uint8(cMark),makecform('lab2srgb')))/255; figure('position',[9 344 1005 334]) subplot(121), subimage(rgb), title('Original Image'), axis off, subplot(122), subimage(label+1,cMap), title('Segmented Image'), axis off %---------------------------------------------------------------------- % Step 4. Perform statistical analysis on distinct objects %---------------------------------------------------------------------- % compute histograms & statistics % bins=0.5:20; pop=zeros(length(bins),5); % for i=1:5 %loop thru each color % mask = (label==i); %segment distinct regions % L = bwlabel(mask); %label region % stats = regionprops(L,'Area'); %extract areas of each % A=[stats.Area]; r=sqrt(A/pi); %convert areas to radii % pop(:,i) = hist(r,bins)'; %generate histogram % mu(i)=mean(r); sigma(i)=std(r); %first order stats % end % display histograms for each color % figure('position',[303 65 461 241]), h=plot(bins,pop); % for i=1:5, set(h(i),'color',cMap(i+1,:),'linewidth',2), end % xlabel radius, ylabel pop, title histogram % set(gca,'position',[0.13 0.2 0.775 0.65],'color',cMap(1,:)), shg % zoom Y axis to see scarce, large contributions better % ylim([0 50]) % display first order stats for each color % [dummy,order]=sort(mu); % for index=1:5 % i=order(index); % text(10,10+7*index,sprintf('\\mu=%.1f, \\sigma=%.1f',mu(i),sigma(i)),... % 'color',cMap(i+1,:),'fontsize',15) % end