CV_Assignment.zip_Haar特征提取_adaboost haar_assignment_haar adaboos

%Simple (partial) implementation of the Adaboost %Computer Vision (EL3105) %% load test data load my_viola_24x24; %X -images, y- {face = 1, nonface = -1} %% Diaplay training data %find all the faces indF = find(y==1); % Vectorized version % M = X(:,:,indF); % M = reshape(M,24,24,1,length(indF)); % Non-vectorized version tic M=uint8(zeros(24,24,1,length(indF))); ii=1; for kk=indF, M(:,:,1,ii)=X(:,:,kk); ii=ii+1; end toc figure, montage(M); %disply all faces in a single image indnF = find(y==-1); % Vectorized version % M = X(:,:,indnF); % M = reshape(M,24,24,1,length(indnF)); % Non-vectorized version M=uint8(zeros(24,24,1,length(indnF))); ii=1; for kk=indnF, M(:,:,1,ii)=X(:,:,kk); ii=ii+1; end figure, montage(M); %disply all non-faces in a single image %% Calculate Haar features L=15; d1=3; d2=3; HFs=DefHaarFeature_example(L,d1,d2); %features definition L=9; d1=2; d2=2; HFs1=DefHaarFeature_example(L,d1,d2); HFs = [HFs,HFs1]; % D=9; l1=2; l2=2; % HFs2=DefHaarFeature_example_V(D,l1,l2); % HFs = [HFs,HFs2]; %tic F = zeros(size(HFs,2),size(X,3)); %allocate memery for the computed features for kk=1:size(X,3), XX = double(X(:,:,kk)); XX = (XX-mean2(XX))/std2(XX); %image intensity and contrast correction C = cumsum(cumsum(XX)')'; %integral image calculation F(:,kk) = HaarFeature(C,HFs); %calculation of the Haar features for image kk end %toc %% Weak classifier f=F(82,:); %for example select feature number 60 W=ones(1,size(X,3)); %initial weights: iPos = find(y==1); %indexes to face images iNeg = find(y==-1); %indexes to non-face images W(iPos) = 1/length(iPos); %for faces W(iNeg) = 1/length(iNeg); %for non-faces tic TH = -Inf*ones(size(F,1),1); E = Inf*ones(size(F,1),1); P = zeros(size(F,1),1); for kk = 1:size(F,1), [th,e,p] = WeakLearner(F(kk,:),W,y); %find the weak-classifier TH(kk) = th; E(kk) = e; P(kk) = p; end toc % tic % [TH1,E1] = WeakLearner_MultiFeature(F,W,y); % toc [e,ith] = min(E); f=F(ith,:); %display the best Haar pattern figure; for kk=1:24, subplot(4,6,kk); imshow(X(:,:,kk+20),[]); hold on; hd = HFs(:,ith); plot([hd(4)+1,hd(5);... hd(4)+1,hd(5);... hd(4)+1,hd(4)+1;... hd(5),hd(5)],... [hd(1)+1,hd(1)+1;... hd(3),hd(3);... hd(1)+1,hd(3);... hd(1)+1,hd(3)],'r-'); plot([hd(4)+1,hd(5)],[hd(2)+0.5,hd(2)+0.5],'g-'); end %disply results for the estimated weak-classifier Pos = f(iPos); Neg = f(iNeg); [PP,bP]=hist(Pos,30); [NN,bN]=hist(Neg,30); figure, plot(bP,PP/length(iPos),'-g'); hold on plot(bN,NN/length(iNeg),'-r'); plot([TH(ith),TH(ith)],[0,max(NN)/length(iNeg)],'-k'); %% Adaboost T=5; [FS,TH,P,A]=CV_adaboost(HFs,F,y,T); %Dispaly selected features figure; for kk=1:16, subplot(4,4,kk); imshow(X(:,:,kk+20),[]); hold on; dispHaarFeature(FS); end