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github
Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master
seperate_eval.m
.m
Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/seperate_eval.m
4,612
utf_8
cdf6ca65b7064dec5676eb185cf0d937
function [test_acc] = seperate_eval(name) addpath([pwd '\lppTSVM']); %%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz" datapath = 'provide the path of your dataset'; train = load ([datapath name '\' name '_train_R.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. index_tune = importdata ([datapath name '\conxuntos.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. test_eval = load ([datapath name '\' name '_test_R.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. %%% Checking whether any index valu is zero or not if zero then increase all index by 1 if length(find(index_tune == 0))>0 index_tune = index_tune + 1; end %%% Remove NaN and store in cell for k=1:size(index_tune,1) index_sep{k}=index_tune(k,~isnan(index_tune(k,:))); end %%% To Evaluate test_data = test_eval(:,2:end-1); test_label = test_eval(:,end); test_lab = test_label; %%% Just replica for further modifying the class label %%% To Tune dataX=train(:,2:end-1); dataY=train(:,end); dataYY = dataY; %%% Just replica for further modifying the class label %%%%%% Normalization start % do normalization for each feature mean_X=mean(dataX,1); dataX=dataX-repmat(mean_X,size(dataX,1),1); norm_X=sum(dataX.^2,1); norm_X=sqrt(norm_X); norm_eval = norm_X; %%% Just save fornormalizing the evaluation data norm_X=repmat(norm_X,size(dataX,1),1); dataX=dataX./norm_X; %%%% Normalize the evaluation data norm_ev = repmat(norm_eval,size(test_data,1),1); test_data=test_data./norm_ev; %%%% End of normalization of evaluation data %%%% End of Normalization %%%% %%%% Modifying the class label as per TBSVM and chcking whether binaryvclass data or not unique_classes = unique(dataYY); if (numel(unique(unique_classes))>2) error('Data belongs to multi-class, please provide binary class data'); else dataY(dataYY==unique_classes(1),:)=1; dataY(dataYY==unique_classes(2),:)=-1; %%% For valuation on test data test_label(test_lab==unique_classes(1),:)=1; test_label(test_lab==unique_classes(2),:)=-1; end try %%% Seperation of data %%% To Tune trainX=dataX(index_sep{1},:); trainY=dataY(index_sep{1},:); testX=dataX(index_sep{2},:); testY=dataY(index_sep{2},:); %%% If dataset needs in TWSVM/TBSVM format % DataTrain.A = trainX(trainY==1,:); % DataTrain.B = trainX(trainY==-1,:); DataTrain = [trainX trainY]; test = [testX testY]; c1 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; % c5 = scale_range_rbf(dataX); c5 = [2^-10,2^-9,2^-8,2^-7,2^-6,2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5,2^6,2^7,2^8,2^9,2^10]; MAX_acc = 0; Resultall = []; count = 0; for i=1:length(c1) for m=1:length(c5) count = count +1 %%%% Just displaying the number of iteration c=c1(i); kern_para = c5(m); Predict_Y =lpp_TSVM(DataTrain,test,kern_para,c); test_accuracy=length(find(Predict_Y==testY))/numel(testY); %%%% Save only optimal parameter with testing accuracy if test_accuracy>MAX_acc; % paramater tuning: we prefer the parameter which lead to better accuracy on the test data. MAX_acc=test_accuracy; OptPara.c=c; OptPara.kernPara = kern_para; OptPara.kerntype = 'rbf'; end % %%% Save all results % currResult=[FunPara.c1 FunPara.c2 FunPara.c3 FunPara.c4 FunPara.kerfPara.pars test_accuracy]; % Resultall = [Resultall; currResult]; clear Predict_Y; end end %%%% Training and valuation with optimal parameter value clear DataTrain test; % DataTrain.A = dataX(dataY==1,:); % DataTrain.B = dataX(dataY==-1,:); DataTrain = [dataX dataY]; test = [test_data test_label]; Predict_Y =lpp_TSVM(DataTrain,test,OptPara.kernPara,OptPara.c); test_acc = length(find(Predict_Y==test_label))/numel(test_label) OptPara.test_acc = test_acc*100; filename = ['Res_' name '.mat']; save (filename, 'OptPara'); catch disp('error in the code'); keyboard end end
github
Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master
lpp_TSVM.m
.m
Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/lppTSVM/lpp_TSVM.m
6,608
utf_8
1c7b0a1631142a93c62f585405c02249
% ___________________________________________________________________ % % This is twin SVM Linear programming problem method for nonlinear case. % The twin SVM formulation is considered and % its penalty form in its dual is formulated in 1-norm and solved % using Newton method % date : Dec 20, 2011 %_____________________________________________________________________ % The formulation is very sensitive to the parameter values. For sinc % function the following values of the parameters, in general, give % better results when 200 samples for traiing and 800 samples for testing % are considered. function [classifier err] = lpp_TSVM(C,test_data,mew,nu) [no_input,no_col] = size(C); % x1 = train(:,1:no_col-1); obs = C(:,no_col); %Observed values A = zeros(1,no_col-1); B = zeros(1,no_col-1); for i = 1:no_input if(obs(i) == 1) A = [A;C(i,1:no_col-1)]; else B = [B;C(i,1:no_col-1)]; end; end; [rowA,n] = size(A); A = A(2:rowA,:); [rowB,n] = size(B); B = B(2:rowB,:) ; alpha = 1.0; % It works when alpha = 1.0 % neta = 10^-5; ep = 0.1; % penality parameter % tol = 0.01; itmax = 100; beta = 10^-3; % m = no_input % n = no_col -1 % m2 = 2 * m [m1,n] = size(A); e1 = ones(m1,1); [m2,n] = size(B); e2 = ones(m2,1); m= m1 + m2; I = speye(m); C = [A ; B]; % [m,n] = size(C); % C = C(:,1:no_col-1); K=zeros(m1,m); for i=1:m1 for j=1:m nom = norm( A(i,:) - C(j,:) ); K(i,j) = exp( -mew * nom * nom ); end end G = [K e1]; size(G); GT = G'; K=zeros(m2,m); for i=1:m2 for j=1:m nom = norm( B(i,:) - C(j,:) ); K(i,j) = exp( -mew * nom * nom ); end end H = [K e2]; size(H); HT = H'; % y = y1; em1 = m+1; e = ones(em1,1); iter = 0; u1 = ones(m2,1); v1 = ones(m1,1); delphi= 1; % delphi_2 = ones(m,1); % delta = zeros(m,1); % i = 0; while( iter < itmax && norm (delphi) > tol ) iter = iter + 1; del11 = max( -HT * u1 + GT * v1 - neta * e, 0 ); del12 = max( HT * u1 - GT * v1 - neta * e, 0 ); del13 = max( -v1 - e1, 0 ); del14 = max( v1 - e1, 0 ); del15 = max( u1 - nu * e2, 0 ); delu1 = max( -u1, 0 ); initial_train_time=tic; delphi_1 = -ep * e2 + H * ( - del11 + del12 ) + del15 - alpha * delu1; delphi_2 = - del13 + del14 + G * ( del11 - del12 ); xx = diag(sign(del11)) + diag(sign(del12)); H12 = - H * ( xx ) * GT ; H21 = - G * ( xx ) * HT; H11 = H * xx * HT + diag(sign(del15)) + alpha * diag(sign(delu1)); H22 = G * xx * GT + diag(sign(del13)) + diag(sign(del14)) ; hessian = [ [H11 H12]; [H21 H22] ]; delphi = [delphi_1;delphi_2] ; delta = ( hessian + beta * I ) \ delphi ; u1 = u1 - delta(1:m2); v1 = v1 - delta (m2+1:m); norm(delta); norm(delphi); end iter; del11 = max( -HT * u1 + GT * v1 - neta * e, 0 ); del12 = max( HT * u1 - GT * v1 - neta * e, 0 ); w1 = ( del11 - del12 ); % _______________________________________________________________________ iter = 0; u2 = ones(m1,1); v2 = ones(m2,1); delphi= 1; % delphi_2 = ones(m,1); % delta = zeros(m2,1); % i = 0; while( iter < itmax && norm (delphi) > tol ) iter = iter + 1; del11 = max( GT * u2 + HT * v2- neta * e, 0 ); del12 = max( - GT * u2 - HT * v2 - neta * e, 0 ); del13 = max( - v2 - e2, 0 ); del14 = max( v2 - e2, 0 ); del15 = max( u2 - nu * e1, 0 ); delu2 = max( -u2, 0 ); delphi_1 = - ep * e1 + G * (del11 - del12) + del15 - alpha * delu2; delphi_2 = - del13 + del14 + H * ( del11 - del12 ); xx = diag(sign(del11)) + diag(sign(del12)); H12 = G * (xx) * HT ; H21 = H * (xx) * GT; H11 = G * xx * GT + diag(sign(del15)) + alpha * diag(sign(delu2)); H22 = diag(sign(del13)) + diag(sign(del14)) + H * xx * HT ; hessian = [ [H11 H12]; [H21 H22] ]; delphi = [delphi_1;delphi_2] ; delta = ( hessian + beta * I ) \ delphi ; u2 = u2 - delta(1:m1); v2 = v2 - delta (m1+1:m); norm(delta); norm(delphi); end iter; del11 = max( GT * u2 + HT *v2 - neta * e, 0 ); del12 = max( - GT * u2 - HT * v2 - neta * e, 0 ); w2 = ( del11 - del12 ); time = toc(initial_train_time); %% ------------------ Testing Part ------------- %% [no_test,no_col] = size(test_data); Ker_row =zeros(no_test,no_input); for i=1:no_test for j=1:no_input nom = norm( test_data(i,1:no_col-1) - C(j,:) ); Ker_row(i,j) = exp( -mew * nom * nom ); end end K = [Ker_row ones(no_test,1)]; size(K); y1 = K * w1 / norm(w1); y2 = K * w2 / norm(w2); for i = 1 : no_test if abs(y1(i)) < abs(y2(i)) classifier(i) = 1; else classifier(i) = -1; end; end; x1 =[]; x2 =[]; for i=1:no_test if classifier(i) == 1 x1 = [x1; test_data(i,1:no_col-1)]; else x2 = [x2; test_data(i,1:no_col-1)]; end end %----------------------------- err = 0.; classifier = classifier'; obs = test_data(:,no_col); %[test_size,n] = size(classifier); for i = 1:no_test if(classifier(i) ~= obs(i)) err = err+1; end end test_data1 =[]; test_data2 =[]; for i=1:no_test if obs(i)== 1 test_data1 = [test_data1; test_data(i,1:no_col-1)]; else test_data2 = [test_data2; test_data(i,1:no_col-1)]; end end %% To check the sparseness count=0; index=0; for i= 1:size(w1,1) if(w1(i)>10^-2 && w2(i)>10^-2) count=count+1; index(count)=i; end end % count = 0; % for i=1:size(w1,1) % if w1(i) ~=0 & w2(i) ~=0 % count = count+1 % end % end % count % % count1 = 0; % for i=1:size(w1,1) % if w1(i) ~=0 % count1 = count1+1; % end % end % % count2 = 0; % for i=1:size(w2,1) % if w2(i) ~=0 % count2 = count2+1; % end % end
github
Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master
normalize.m
.m
Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/lppTSVM/normalize.m
620
utf_8
b69935e172ec81c7e859090cf1994fed
% ----------------------------------------------------------------------- % Time series problem whose input series is given as A matrix, with N % attributes i.e. A(M:N). Here we normalize the data column-wise so that % the mean of the series is zero with standard deviation equals to one. % Input is the two-demensional matrix A(.) and the output is the % two-dimensional matrix c(.) % ----------------------------------------------------------------------- function c = normalize(A) [m,n] = size(A); e = ones(m,1); sd = std(A); c = A - e*mean(A); c =imdivide(c,e*sd);
github
Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master
kfold_eval.m
.m
Analysis-of-Twin-SVM-on-44-binary-datasets-master/Improved_LSTWSVM/kfold_eval.m
5,220
utf_8
b5c083945e6ab761c2c080813b9ea530
function [mean_acc] = seperate_eval(name) addpath([pwd '\improved_LSTWSVM']) %%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz" datapath = 'provide the path of your dataset'; tot_data = load([datapath name '\' name '_R.dat']); index_tune = importdata ([datapath name '\conxuntos.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. %%% Checking whether any index valu is zero or not if zero then increase all index by 1 if length(find(index_tune == 0))>0 index_tune = index_tune + 1; end %%% Remove NaN and store in cell for k=1:size(index_tune,1) index_sep{k}=index_tune(k,~isnan(index_tune(k,:))); end %%% Removing first i.e. indexing column and seperate data and classes data=tot_data(:,2:end); dataX=data(:,1:end-1); dataY=data(:,end); dataYY = dataY; %%% Just replica for further modifying the class label %%%%%% Normalization start % do normalization for each feature mean_X=mean(dataX,1); dataX=dataX-repmat(mean_X,size(dataX,1),1); norm_X=sum(dataX.^2,1); norm_X=sqrt(norm_X); norm_eval = norm_X; %%% Just save fornormalizing the evaluation data norm_X=repmat(norm_X,size(dataX,1),1); dataX=dataX./norm_X; %%%%%% End of Normalization %%%% Modifying the class label as per TBSVM and chcking whether binaryvclass data or not unique_classes = unique(dataYY); if (numel(unique(unique_classes))>2) error('Data belongs to multi-class, please provide binary class data'); else dataY(dataYY==unique_classes(1),:)=1; dataY(dataYY==unique_classes(2),:)=-1; end %%% Seperation of data %%% To Tune trainX=dataX(index_sep{1},:); trainY=dataY(index_sep{1},:); testX=dataX(index_sep{2},:); testY=dataY(index_sep{2},:); %%% If dataset needs in TWSVM/TBSVM format % DataTrain.A = trainX(trainY==1,:); % DataTrain.B = trainX(trainY==-1,:); DataTrain = [trainX trainY]; test = [testX testY]; c1 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; c2 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; c3 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; %%% Eps1 c4 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; %%% Eps2 % c5 = scale_range_rbf(dataX); c5 = [2^-10,2^-9,2^-8,2^-7,2^-6,2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5,2^6,2^7,2^8,2^9,2^10]; MAX_acc = 0; Resultall = []; count = 0; for i=1:length(c1) % for j=1:length(c2) for k=1:length(c3) % for l=1:length(c4) for m=1:length(c5) count = count +1 %%%% Just displaying the number of iteration FunPara.c1=c1(i); % FunPara.c2=c2(j); FunPara.c2=c1(i); FunPara.c3=c3(k); % FunPara.c4=c4(l); FunPara.c4=c3(k); FunPara.kerfPara.type = 'rbf'; FunPara.kerfPara.pars = c5(m); Predict_Y =Improved_LSTWSVM(test,DataTrain,FunPara); test_accuracy=length(find(Predict_Y'==testY))/numel(testY); %%%% Save only optimal parameter with testing accuracy if test_accuracy>MAX_acc; % paramater tuning: we prefer the parameter which lead to better accuracy on the test data. MAX_acc=test_accuracy; OptPara.c1=FunPara.c1; OptPara.c2=FunPara.c2; OptPara.c3=FunPara.c3; OptPara.c4=FunPara.c4; OptPara.kerfPara.type = FunPara.kerfPara.type; OptPara.kerfPara.pars = FunPara.kerfPara.pars; end clear Predict_Y; end % end end % end end %%%% Training and evaluation with optimal parameter value clear DataTrain trainX trainY testX testY test; %%%for datasets where training-testing partition is not available, performance vealuation is based on cross-validation. fold_index = importdata([datapath name '\conxuntos_kfold.dat']); %%% Checking whether any index valu is zero or not if zero then increase all index by 1 if length(find(fold_index == 0))>0 fold_index = fold_index + 1; end for k=1:size(fold_index,1) index{k,1}=fold_index(k,~isnan(fold_index(k,:))); end for f=1:4 trainX=dataX(index{2*f-1},:); trainY=dataY(index{2*f-1},:); testX=dataX(index{2*f},:); testY=dataY(index{2*f},:); % DataTrain.A = trainX(trainY==1,:); % DataTrain.B = trainX(trainY==-1,:); DataTrain = [trainX trainY]; test = [testX testY]; Predict_Y =Improved_LSTWSVM(test,DataTrain,OptPara); test_acc(f)=length(find(Predict_Y'==testY))/numel(testY); clear Predict_Y DataTrain trainX trainY testX testY; end mean_acc = mean(test_acc) OptPara.test_acc = mean_acc*100; filename = ['Res_' name '.mat']; save (filename, 'OptPara'); end
github
Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master
seperate_eval.m
.m
Analysis-of-Twin-SVM-on-44-binary-datasets-master/Improved_LSTWSVM/seperate_eval.m
5,149
utf_8
a244531f9bbb7bfef82856e1b20c71eb
function [test_acc] = seperate_eval(name) addpath([pwd '\improved_LSTWSVM']) %%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz" datapath = 'provide the path of your dataset'; train = load ([datapath name '\' name '_train_R.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. index_tune = importdata ([datapath name '\conxuntos.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. test_eval = load ([datapath name '\' name '_test_R.dat']);% for datasets where training-testing partition is available, paramter tuning is based on this file. %%% Checking whether any index valu is zero or not if zero then increase all index by 1 if length(find(index_tune == 0))>0 index_tune = index_tune + 1; end %%% Remove NaN and store in cell for k=1:size(index_tune,1) index_sep{k}=index_tune(k,~isnan(index_tune(k,:))); end %%% To Evaluate test_data = test_eval(:,2:end-1); test_label = test_eval(:,end); test_lab = test_label; %%% Just replica for further modifying the class label %%% To Tune dataX=train(:,2:end-1); dataY=train(:,end); dataYY = dataY; %%% Just replica for further modifying the class label %%%%%% Normalization start % do normalization for each feature mean_X=mean(dataX,1); dataX=dataX-repmat(mean_X,size(dataX,1),1); norm_X=sum(dataX.^2,1); norm_X=sqrt(norm_X); norm_eval = norm_X; %%% Just save fornormalizing the evaluation data norm_X=repmat(norm_X,size(dataX,1),1); dataX=dataX./norm_X; %%%% Normalize the evaluation data norm_ev = repmat(norm_eval,size(test_data,1),1); test_data=test_data./norm_ev; %%%% End of normalization of evaluation data %%%% End of Normalization %%%% %%%% Modifying the class label as per TBSVM and chcking whether binaryvclass data or not unique_classes = unique(dataYY); if (numel(unique(unique_classes))>2) error('Data belongs to multi-class, please provide binary class data'); else dataY(dataYY==unique_classes(1),:)=1; dataY(dataYY==unique_classes(2),:)=-1; %%% For valuation on test data test_label(test_lab==unique_classes(1),:)=1; test_label(test_lab==unique_classes(2),:)=-1; end %%% Seperation of data %%% To Tune trainX=dataX(index_sep{1},:); trainY=dataY(index_sep{1},:); testX=dataX(index_sep{2},:); testY=dataY(index_sep{2},:); %%% If dataset needs in TWSVM/TBSVM format % DataTrain.A = trainX(trainY==1,:); % DataTrain.B = trainX(trainY==-1,:); DataTrain = [trainX trainY]; test = [testX testY]; c1 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; c2 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; c3 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; %%% Eps1 c4 = [2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5]; %%% Eps2 % c5 = scale_range_rbf(dataX); c5 = [2^-10,2^-9,2^-8,2^-7,2^-6,2^-5,2^-4,2^-3,2^-2,2^-1,2^0,2^1,2^2,2^3,2^4,2^5,2^6,2^7,2^8,2^9,2^10]; MAX_acc = 0; Resultall = []; count = 0; for i=1:length(c1) % for j=1:length(c2) for k=1:length(c3) % for l=1:length(c4) for m=1:length(c5) count = count +1 %%%% Just displaying the number of iteration FunPara.c1=c1(i); % FunPara.c2=c2(j); FunPara.c2=c1(i); FunPara.c3=c3(k); % FunPara.c4=c4(l); FunPara.c4=c3(k); FunPara.kerfPara.type = 'rbf'; FunPara.kerfPara.pars = c5(m); Predict_Y =Improved_LSTWSVM(test,DataTrain,FunPara); test_accuracy=length(find(Predict_Y'==testY))/numel(testY); %%%% Save only optimal parameter with testing accuracy if test_accuracy>MAX_acc; % paramater tuning: we prefer the parameter which lead to better accuracy on the test data. MAX_acc=test_accuracy; OptPara.c1=FunPara.c1; OptPara.c2=FunPara.c2; OptPara.c3=FunPara.c3; OptPara.c4=FunPara.c4; OptPara.kerfPara.type = FunPara.kerfPara.type; OptPara.kerfPara.pars = FunPara.kerfPara.pars; end clear Predict_Y; end % end end % end end %%%% Training and valuation with optimal parameter value clear DataTrain test; % DataTrain.A = dataX(dataY==1,:); % DataTrain.B = dataX(dataY==-1,:); DataTrain = [dataX dataY]; test = [test_data test_label]; Predict_Y =Improved_LSTWSVM(test,DataTrain,OptPara); test_acc = length(find(Predict_Y'==test_label))/numel(test_label) OptPara.test_acc = test_acc*100; filename = ['Res_' name '.mat']; save (filename, 'OptPara'); end
github
MouradGridach/Machine-Learning-Stanford-master
submit.m
.m
Machine-Learning-Stanford-master/ex4/submit.m
17,129
utf_8
7e97c75d2b70d978e93fbdb7dfa9d95b
function submit(partId, webSubmit) %SUBMIT Submit your code and output to the ml-class servers % SUBMIT() will connect to the ml-class server and submit your solution fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ... homework_id()); if ~exist('partId', 'var') || isempty(partId) partId = promptPart(); end if ~exist('webSubmit', 'var') || isempty(webSubmit) webSubmit = 0; % submit directly by default end % Check valid partId partNames = validParts(); if ~isValidPartId(partId) fprintf('!! Invalid homework part selected.\n'); fprintf('!! Expected an integer from 1 to %d.\n', numel(partNames) + 1); fprintf('!! Submission Cancelled\n'); return end if ~exist('ml_login_data.mat','file') [login password] = loginPrompt(); save('ml_login_data.mat','login','password'); else load('ml_login_data.mat'); [login password] = quickLogin(login, password); save('ml_login_data.mat','login','password'); end if isempty(login) fprintf('!! Submission Cancelled\n'); return end fprintf('\n== Connecting to ml-class ... '); if exist('OCTAVE_VERSION') fflush(stdout); end % Setup submit list if partId == numel(partNames) + 1 submitParts = 1:numel(partNames); else submitParts = [partId]; end for s = 1:numel(submitParts) thisPartId = submitParts(s); if (~webSubmit) % submit directly to server [login, ch, signature, auxstring] = getChallenge(login, thisPartId); if isempty(login) || isempty(ch) || isempty(signature) % Some error occured, error string in first return element. fprintf('\n!! Error: %s\n\n', login); return end % Attempt Submission with Challenge ch_resp = challengeResponse(login, password, ch); [result, str] = submitSolution(login, ch_resp, thisPartId, ... output(thisPartId, auxstring), source(thisPartId), signature); partName = partNames{thisPartId}; fprintf('\n== [ml-class] Submitted Assignment %s - Part %d - %s\n', ... homework_id(), thisPartId, partName); fprintf('== %s\n', strtrim(str)); if exist('OCTAVE_VERSION') fflush(stdout); end else [result] = submitSolutionWeb(login, thisPartId, output(thisPartId), ... source(thisPartId)); result = base64encode(result); fprintf('\nSave as submission file [submit_ex%s_part%d.txt (enter to accept default)]:', ... homework_id(), thisPartId); saveAsFile = input('', 's'); if (isempty(saveAsFile)) saveAsFile = sprintf('submit_ex%s_part%d.txt', homework_id(), thisPartId); end fid = fopen(saveAsFile, 'w'); if (fid) fwrite(fid, result); fclose(fid); fprintf('\nSaved your solutions to %s.\n\n', saveAsFile); fprintf(['You can now submit your solutions through the web \n' ... 'form in the programming exercises. Select the corresponding \n' ... 'programming exercise to access the form.\n']); else fprintf('Unable to save to %s\n\n', saveAsFile); fprintf(['You can create a submission file by saving the \n' ... 'following text in a file: (press enter to continue)\n\n']); pause; fprintf(result); end end end end % ================== CONFIGURABLES FOR EACH HOMEWORK ================== function id = homework_id() id = '4'; end function [partNames] = validParts() partNames = { 'Feedforward and Cost Function', ... 'Regularized Cost Function', ... 'Sigmoid Gradient', ... 'Neural Network Gradient (Backpropagation)' ... 'Regularized Gradient' ... }; end function srcs = sources() % Separated by part srcs = { { 'nnCostFunction.m' }, ... { 'nnCostFunction.m' }, ... { 'sigmoidGradient.m' }, ... { 'nnCostFunction.m' }, ... { 'nnCostFunction.m' } }; end function out = output(partId, auxstring) % Random Test Cases X = reshape(3 * sin(1:1:30), 3, 10); Xm = reshape(sin(1:32), 16, 2) / 5; ym = 1 + mod(1:16,4)'; t1 = sin(reshape(1:2:24, 4, 3)); t2 = cos(reshape(1:2:40, 4, 5)); t = [t1(:) ; t2(:)]; if partId == 1 [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0); out = sprintf('%0.5f ', J); elseif partId == 2 [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5); out = sprintf('%0.5f ', J); elseif partId == 3 out = sprintf('%0.5f ', sigmoidGradient(X)); elseif partId == 4 [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0); out = sprintf('%0.5f ', J); out = [out sprintf('%0.5f ', grad)]; elseif partId == 5 [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5); out = sprintf('%0.5f ', J); out = [out sprintf('%0.5f ', grad)]; end end % ====================== SERVER CONFIGURATION =========================== % ***************** REMOVE -staging WHEN YOU DEPLOY ********************* function url = site_url() url = 'http://class.coursera.org/ml-003'; end function url = challenge_url() url = [site_url() '/assignment/challenge']; end function url = submit_url() url = [site_url() '/assignment/submit']; end % ========================= CHALLENGE HELPERS ========================= function src = source(partId) src = ''; src_files = sources(); if partId <= numel(src_files) flist = src_files{partId}; for i = 1:numel(flist) fid = fopen(flist{i}); if (fid == -1) error('Error opening %s (is it missing?)', flist{i}); end line = fgets(fid); while ischar(line) src = [src line]; line = fgets(fid); end fclose(fid); src = [src '||||||||']; end end end function ret = isValidPartId(partId) partNames = validParts(); ret = (~isempty(partId)) && (partId >= 1) && (partId <= numel(partNames) + 1); end function partId = promptPart() fprintf('== Select which part(s) to submit:\n'); partNames = validParts(); srcFiles = sources(); for i = 1:numel(partNames) fprintf('== %d) %s [', i, partNames{i}); fprintf(' %s ', srcFiles{i}{:}); fprintf(']\n'); end fprintf('== %d) All of the above \n==\nEnter your choice [1-%d]: ', ... numel(partNames) + 1, numel(partNames) + 1); selPart = input('', 's'); partId = str2num(selPart); if ~isValidPartId(partId) partId = -1; end end function [email,ch,signature,auxstring] = getChallenge(email, part) str = urlread(challenge_url(), 'post', {'email_address', email, 'assignment_part_sid', [homework_id() '-' num2str(part)], 'response_encoding', 'delim'}); str = strtrim(str); r = struct; while(numel(str) > 0) [f, str] = strtok (str, '|'); [v, str] = strtok (str, '|'); r = setfield(r, f, v); end email = getfield(r, 'email_address'); ch = getfield(r, 'challenge_key'); signature = getfield(r, 'state'); auxstring = getfield(r, 'challenge_aux_data'); end function [result, str] = submitSolutionWeb(email, part, output, source) result = ['{"assignment_part_sid":"' base64encode([homework_id() '-' num2str(part)], '') '",' ... '"email_address":"' base64encode(email, '') '",' ... '"submission":"' base64encode(output, '') '",' ... '"submission_aux":"' base64encode(source, '') '"' ... '}']; str = 'Web-submission'; end function [result, str] = submitSolution(email, ch_resp, part, output, ... source, signature) params = {'assignment_part_sid', [homework_id() '-' num2str(part)], ... 'email_address', email, ... 'submission', base64encode(output, ''), ... 'submission_aux', base64encode(source, ''), ... 'challenge_response', ch_resp, ... 'state', signature}; str = urlread(submit_url(), 'post', params); % Parse str to read for success / failure result = 0; end % =========================== LOGIN HELPERS =========================== function [login password] = loginPrompt() % Prompt for password [login password] = basicPrompt(); if isempty(login) || isempty(password) login = []; password = []; end end function [login password] = basicPrompt() login = input('Login (Email address): ', 's'); password = input('Password: ', 's'); end function [login password] = quickLogin(login,password) disp(['You are currently logged in as ' login '.']); cont_token = input('Is this you? (y/n - type n to reenter password)','s'); if(isempty(cont_token) || cont_token(1)=='Y'||cont_token(1)=='y') return; else [login password] = loginPrompt(); end end function [str] = challengeResponse(email, passwd, challenge) str = sha1([challenge passwd]); end % =============================== SHA-1 ================================ function hash = sha1(str) % Initialize variables h0 = uint32(1732584193); h1 = uint32(4023233417); h2 = uint32(2562383102); h3 = uint32(271733878); h4 = uint32(3285377520); % Convert to word array strlen = numel(str); % Break string into chars and append the bit 1 to the message mC = [double(str) 128]; mC = [mC zeros(1, 4-mod(numel(mC), 4), 'uint8')]; numB = strlen * 8; if exist('idivide') numC = idivide(uint32(numB + 65), 512, 'ceil'); else numC = ceil(double(numB + 65)/512); end numW = numC * 16; mW = zeros(numW, 1, 'uint32'); idx = 1; for i = 1:4:strlen + 1 mW(idx) = bitor(bitor(bitor( ... bitshift(uint32(mC(i)), 24), ... bitshift(uint32(mC(i+1)), 16)), ... bitshift(uint32(mC(i+2)), 8)), ... uint32(mC(i+3))); idx = idx + 1; end % Append length of message mW(numW - 1) = uint32(bitshift(uint64(numB), -32)); mW(numW) = uint32(bitshift(bitshift(uint64(numB), 32), -32)); % Process the message in successive 512-bit chs for cId = 1 : double(numC) cSt = (cId - 1) * 16 + 1; cEnd = cId * 16; ch = mW(cSt : cEnd); % Extend the sixteen 32-bit words into eighty 32-bit words for j = 17 : 80 ch(j) = ch(j - 3); ch(j) = bitxor(ch(j), ch(j - 8)); ch(j) = bitxor(ch(j), ch(j - 14)); ch(j) = bitxor(ch(j), ch(j - 16)); ch(j) = bitrotate(ch(j), 1); end % Initialize hash value for this ch a = h0; b = h1; c = h2; d = h3; e = h4; % Main loop for i = 1 : 80 if(i >= 1 && i <= 20) f = bitor(bitand(b, c), bitand(bitcmp(b), d)); k = uint32(1518500249); elseif(i >= 21 && i <= 40) f = bitxor(bitxor(b, c), d); k = uint32(1859775393); elseif(i >= 41 && i <= 60) f = bitor(bitor(bitand(b, c), bitand(b, d)), bitand(c, d)); k = uint32(2400959708); elseif(i >= 61 && i <= 80) f = bitxor(bitxor(b, c), d); k = uint32(3395469782); end t = bitrotate(a, 5); t = bitadd(t, f); t = bitadd(t, e); t = bitadd(t, k); t = bitadd(t, ch(i)); e = d; d = c; c = bitrotate(b, 30); b = a; a = t; end h0 = bitadd(h0, a); h1 = bitadd(h1, b); h2 = bitadd(h2, c); h3 = bitadd(h3, d); h4 = bitadd(h4, e); end hash = reshape(dec2hex(double([h0 h1 h2 h3 h4]), 8)', [1 40]); hash = lower(hash); end function ret = bitadd(iA, iB) ret = double(iA) + double(iB); ret = bitset(ret, 33, 0); ret = uint32(ret); end function ret = bitrotate(iA, places) t = bitshift(iA, places - 32); ret = bitshift(iA, places); ret = bitor(ret, t); end % =========================== Base64 Encoder ============================ % Thanks to Peter John Acklam % function y = base64encode(x, eol) %BASE64ENCODE Perform base64 encoding on a string. % % BASE64ENCODE(STR, EOL) encode the given string STR. EOL is the line ending % sequence to use; it is optional and defaults to '\n' (ASCII decimal 10). % The returned encoded string is broken into lines of no more than 76 % characters each, and each line will end with EOL unless it is empty. Let % EOL be empty if you do not want the encoded string broken into lines. % % STR and EOL don't have to be strings (i.e., char arrays). The only % requirement is that they are vectors containing values in the range 0-255. % % This function may be used to encode strings into the Base64 encoding % specified in RFC 2045 - MIME (Multipurpose Internet Mail Extensions). The % Base64 encoding is designed to represent arbitrary sequences of octets in a % form that need not be humanly readable. A 65-character subset % ([A-Za-z0-9+/=]) of US-ASCII is used, enabling 6 bits to be represented per % printable character. % % Examples % -------- % % If you want to encode a large file, you should encode it in chunks that are % a multiple of 57 bytes. This ensures that the base64 lines line up and % that you do not end up with padding in the middle. 57 bytes of data fills % one complete base64 line (76 == 57*4/3): % % If ifid and ofid are two file identifiers opened for reading and writing, % respectively, then you can base64 encode the data with % % while ~feof(ifid) % fwrite(ofid, base64encode(fread(ifid, 60*57))); % end % % or, if you have enough memory, % % fwrite(ofid, base64encode(fread(ifid))); % % See also BASE64DECODE. % Author: Peter John Acklam % Time-stamp: 2004-02-03 21:36:56 +0100 % E-mail: [email protected] % URL: http://home.online.no/~pjacklam if isnumeric(x) x = num2str(x); end % make sure we have the EOL value if nargin < 2 eol = sprintf('\n'); else if sum(size(eol) > 1) > 1 error('EOL must be a vector.'); end if any(eol(:) > 255) error('EOL can not contain values larger than 255.'); end end if sum(size(x) > 1) > 1 error('STR must be a vector.'); end x = uint8(x); eol = uint8(eol); ndbytes = length(x); % number of decoded bytes nchunks = ceil(ndbytes / 3); % number of chunks/groups nebytes = 4 * nchunks; % number of encoded bytes % add padding if necessary, to make the length of x a multiple of 3 if rem(ndbytes, 3) x(end+1 : 3*nchunks) = 0; end x = reshape(x, [3, nchunks]); % reshape the data y = repmat(uint8(0), 4, nchunks); % for the encoded data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Split up every 3 bytes into 4 pieces % % aaaaaabb bbbbcccc ccdddddd % % to form % % 00aaaaaa 00bbbbbb 00cccccc 00dddddd % y(1,:) = bitshift(x(1,:), -2); % 6 highest bits of x(1,:) y(2,:) = bitshift(bitand(x(1,:), 3), 4); % 2 lowest bits of x(1,:) y(2,:) = bitor(y(2,:), bitshift(x(2,:), -4)); % 4 highest bits of x(2,:) y(3,:) = bitshift(bitand(x(2,:), 15), 2); % 4 lowest bits of x(2,:) y(3,:) = bitor(y(3,:), bitshift(x(3,:), -6)); % 2 highest bits of x(3,:) y(4,:) = bitand(x(3,:), 63); % 6 lowest bits of x(3,:) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Now perform the following mapping % % 0 - 25 -> A-Z % 26 - 51 -> a-z % 52 - 61 -> 0-9 % 62 -> + % 63 -> / % % We could use a mapping vector like % % ['A':'Z', 'a':'z', '0':'9', '+/'] % % but that would require an index vector of class double. % z = repmat(uint8(0), size(y)); i = y <= 25; z(i) = 'A' + double(y(i)); i = 26 <= y & y <= 51; z(i) = 'a' - 26 + double(y(i)); i = 52 <= y & y <= 61; z(i) = '0' - 52 + double(y(i)); i = y == 62; z(i) = '+'; i = y == 63; z(i) = '/'; y = z; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Add padding if necessary. % npbytes = 3 * nchunks - ndbytes; % number of padding bytes if npbytes y(end-npbytes+1 : end) = '='; % '=' is used for padding end if isempty(eol) % reshape to a row vector y = reshape(y, [1, nebytes]); else nlines = ceil(nebytes / 76); % number of lines neolbytes = length(eol); % number of bytes in eol string % pad data so it becomes a multiple of 76 elements y = [y(:) ; zeros(76 * nlines - numel(y), 1)]; y(nebytes + 1 : 76 * nlines) = 0; y = reshape(y, 76, nlines); % insert eol strings eol = eol(:); y(end + 1 : end + neolbytes, :) = eol(:, ones(1, nlines)); % remove padding, but keep the last eol string m = nebytes + neolbytes * (nlines - 1); n = (76+neolbytes)*nlines - neolbytes; y(m+1 : n) = ''; % extract and reshape to row vector y = reshape(y, 1, m+neolbytes); end % output is a character array y = char(y); end
github
MouradGridach/Machine-Learning-Stanford-master
submitWeb.m
.m
Machine-Learning-Stanford-master/ex4/submitWeb.m
827
utf_8
bfb2fa08cac9d8d797e3071d3fdd7ca1
% submitWeb Creates files from your code and output for web submission. % % If the submit function does not work for you, use the web-submission mechanism. % Call this function to produce a file for the part you wish to submit. Then, % submit the file to the class servers using the "Web Submission" button on the % Programming Exercises page on the course website. % % You should call this function without arguments (submitWeb), to receive % an interactive prompt for submission; optionally you can call it with the partID % if you so wish. Make sure your working directory is set to the directory % containing the submitWeb.m file and your assignment files. function submitWeb(partId) if ~exist('partId', 'var') || isempty(partId) partId = []; end submit(partId, 1); end
github
MouradGridach/Machine-Learning-Stanford-master
submit.m
.m
Machine-Learning-Stanford-master/ex3/Logistic Regression/submit.m
17,041
utf_8
f68fe1ee499ec01df18037b089673b0c
function submit(partId, webSubmit) %SUBMIT Submit your code and output to the ml-class servers % SUBMIT() will connect to the ml-class server and submit your solution fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ... homework_id()); if ~exist('partId', 'var') || isempty(partId) partId = promptPart(); end if ~exist('webSubmit', 'var') || isempty(webSubmit) webSubmit = 0; % submit directly by default end % Check valid partId partNames = validParts(); if ~isValidPartId(partId) fprintf('!! Invalid homework part selected.\n'); fprintf('!! Expected an integer from 1 to %d.\n', numel(partNames) + 1); fprintf('!! Submission Cancelled\n'); return end if ~exist('ml_login_data.mat','file') [login password] = loginPrompt(); save('ml_login_data.mat','login','password'); else load('ml_login_data.mat'); [login password] = quickLogin(login, password); save('ml_login_data.mat','login','password'); end if isempty(login) fprintf('!! Submission Cancelled\n'); return end fprintf('\n== Connecting to ml-class ... '); if exist('OCTAVE_VERSION') fflush(stdout); end % Setup submit list if partId == numel(partNames) + 1 submitParts = 1:numel(partNames); else submitParts = [partId]; end for s = 1:numel(submitParts) thisPartId = submitParts(s); if (~webSubmit) % submit directly to server [login, ch, signature, auxstring] = getChallenge(login, thisPartId); if isempty(login) || isempty(ch) || isempty(signature) % Some error occured, error string in first return element. fprintf('\n!! Error: %s\n\n', login); return end % Attempt Submission with Challenge ch_resp = challengeResponse(login, password, ch); [result, str] = submitSolution(login, ch_resp, thisPartId, ... output(thisPartId, auxstring), source(thisPartId), signature); partName = partNames{thisPartId}; fprintf('\n== [ml-class] Submitted Assignment %s - Part %d - %s\n', ... homework_id(), thisPartId, partName); fprintf('== %s\n', strtrim(str)); if exist('OCTAVE_VERSION') fflush(stdout); end else [result] = submitSolutionWeb(login, thisPartId, output(thisPartId), ... source(thisPartId)); result = base64encode(result); fprintf('\nSave as submission file [submit_ex%s_part%d.txt (enter to accept default)]:', ... homework_id(), thisPartId); saveAsFile = input('', 's'); if (isempty(saveAsFile)) saveAsFile = sprintf('submit_ex%s_part%d.txt', homework_id(), thisPartId); end fid = fopen(saveAsFile, 'w'); if (fid) fwrite(fid, result); fclose(fid); fprintf('\nSaved your solutions to %s.\n\n', saveAsFile); fprintf(['You can now submit your solutions through the web \n' ... 'form in the programming exercises. Select the corresponding \n' ... 'programming exercise to access the form.\n']); else fprintf('Unable to save to %s\n\n', saveAsFile); fprintf(['You can create a submission file by saving the \n' ... 'following text in a file: (press enter to continue)\n\n']); pause; fprintf(result); end end end end % ================== CONFIGURABLES FOR EACH HOMEWORK ================== function id = homework_id() id = '3'; end function [partNames] = validParts() partNames = { 'Vectorized Logistic Regression ', ... 'One-vs-all classifier training', ... 'One-vs-all classifier prediction', ... 'Neural network prediction function' ... }; end function srcs = sources() % Separated by part srcs = { { 'lrCostFunction.m' }, ... { 'oneVsAll.m' }, ... { 'predictOneVsAll.m' }, ... { 'predict.m' } }; end function out = output(partId, auxdata) % Random Test Cases X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))']; y = sin(X(:,1) + X(:,2)) > 0; Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ... 1 1 ; 1 2 ; 2 1 ; 2 2 ; ... -1 1 ; -1 2 ; -2 1 ; -2 2 ; ... 1 -1 ; 1 -2 ; -2 -1 ; -2 -2 ]; ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]'; t1 = sin(reshape(1:2:24, 4, 3)); t2 = cos(reshape(1:2:40, 4, 5)); if partId == 1 [J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1); out = sprintf('%0.5f ', J); out = [out sprintf('%0.5f ', grad)]; elseif partId == 2 out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1)); elseif partId == 3 out = sprintf('%0.5f ', predictOneVsAll(t1, Xm)); elseif partId == 4 out = sprintf('%0.5f ', predict(t1, t2, Xm)); end end % ====================== SERVER CONFIGURATION =========================== % ***************** REMOVE -staging WHEN YOU DEPLOY ********************* function url = site_url() url = 'http://class.coursera.org/ml-003'; end function url = challenge_url() url = [site_url() '/assignment/challenge']; end function url = submit_url() url = [site_url() '/assignment/submit']; end % ========================= CHALLENGE HELPERS ========================= function src = source(partId) src = ''; src_files = sources(); if partId <= numel(src_files) flist = src_files{partId}; for i = 1:numel(flist) fid = fopen(flist{i}); if (fid == -1) error('Error opening %s (is it missing?)', flist{i}); end line = fgets(fid); while ischar(line) src = [src line]; line = fgets(fid); end fclose(fid); src = [src '||||||||']; end end end function ret = isValidPartId(partId) partNames = validParts(); ret = (~isempty(partId)) && (partId >= 1) && (partId <= numel(partNames) + 1); end function partId = promptPart() fprintf('== Select which part(s) to submit:\n'); partNames = validParts(); srcFiles = sources(); for i = 1:numel(partNames) fprintf('== %d) %s [', i, partNames{i}); fprintf(' %s ', srcFiles{i}{:}); fprintf(']\n'); end fprintf('== %d) All of the above \n==\nEnter your choice [1-%d]: ', ... numel(partNames) + 1, numel(partNames) + 1); selPart = input('', 's'); partId = str2num(selPart); if ~isValidPartId(partId) partId = -1; end end function [email,ch,signature,auxstring] = getChallenge(email, part) str = urlread(challenge_url(), 'post', {'email_address', email, 'assignment_part_sid', [homework_id() '-' num2str(part)], 'response_encoding', 'delim'}); str = strtrim(str); r = struct; while(numel(str) > 0) [f, str] = strtok (str, '|'); [v, str] = strtok (str, '|'); r = setfield(r, f, v); end email = getfield(r, 'email_address'); ch = getfield(r, 'challenge_key'); signature = getfield(r, 'state'); auxstring = getfield(r, 'challenge_aux_data'); end function [result, str] = submitSolutionWeb(email, part, output, source) result = ['{"assignment_part_sid":"' base64encode([homework_id() '-' num2str(part)], '') '",' ... '"email_address":"' base64encode(email, '') '",' ... '"submission":"' base64encode(output, '') '",' ... '"submission_aux":"' base64encode(source, '') '"' ... '}']; str = 'Web-submission'; end function [result, str] = submitSolution(email, ch_resp, part, output, ... source, signature) params = {'assignment_part_sid', [homework_id() '-' num2str(part)], ... 'email_address', email, ... 'submission', base64encode(output, ''), ... 'submission_aux', base64encode(source, ''), ... 'challenge_response', ch_resp, ... 'state', signature}; str = urlread(submit_url(), 'post', params); % Parse str to read for success / failure result = 0; end % =========================== LOGIN HELPERS =========================== function [login password] = loginPrompt() % Prompt for password [login password] = basicPrompt(); if isempty(login) || isempty(password) login = []; password = []; end end function [login password] = basicPrompt() login = input('Login (Email address): ', 's'); password = input('Password: ', 's'); end function [login password] = quickLogin(login,password) disp(['You are currently logged in as ' login '.']); cont_token = input('Is this you? (y/n - type n to reenter password)','s'); if(isempty(cont_token) || cont_token(1)=='Y'||cont_token(1)=='y') return; else [login password] = loginPrompt(); end end function [str] = challengeResponse(email, passwd, challenge) str = sha1([challenge passwd]); end % =============================== SHA-1 ================================ function hash = sha1(str) % Initialize variables h0 = uint32(1732584193); h1 = uint32(4023233417); h2 = uint32(2562383102); h3 = uint32(271733878); h4 = uint32(3285377520); % Convert to word array strlen = numel(str); % Break string into chars and append the bit 1 to the message mC = [double(str) 128]; mC = [mC zeros(1, 4-mod(numel(mC), 4), 'uint8')]; numB = strlen * 8; if exist('idivide') numC = idivide(uint32(numB + 65), 512, 'ceil'); else numC = ceil(double(numB + 65)/512); end numW = numC * 16; mW = zeros(numW, 1, 'uint32'); idx = 1; for i = 1:4:strlen + 1 mW(idx) = bitor(bitor(bitor( ... bitshift(uint32(mC(i)), 24), ... bitshift(uint32(mC(i+1)), 16)), ... bitshift(uint32(mC(i+2)), 8)), ... uint32(mC(i+3))); idx = idx + 1; end % Append length of message mW(numW - 1) = uint32(bitshift(uint64(numB), -32)); mW(numW) = uint32(bitshift(bitshift(uint64(numB), 32), -32)); % Process the message in successive 512-bit chs for cId = 1 : double(numC) cSt = (cId - 1) * 16 + 1; cEnd = cId * 16; ch = mW(cSt : cEnd); % Extend the sixteen 32-bit words into eighty 32-bit words for j = 17 : 80 ch(j) = ch(j - 3); ch(j) = bitxor(ch(j), ch(j - 8)); ch(j) = bitxor(ch(j), ch(j - 14)); ch(j) = bitxor(ch(j), ch(j - 16)); ch(j) = bitrotate(ch(j), 1); end % Initialize hash value for this ch a = h0; b = h1; c = h2; d = h3; e = h4; % Main loop for i = 1 : 80 if(i >= 1 && i <= 20) f = bitor(bitand(b, c), bitand(bitcmp(b), d)); k = uint32(1518500249); elseif(i >= 21 && i <= 40) f = bitxor(bitxor(b, c), d); k = uint32(1859775393); elseif(i >= 41 && i <= 60) f = bitor(bitor(bitand(b, c), bitand(b, d)), bitand(c, d)); k = uint32(2400959708); elseif(i >= 61 && i <= 80) f = bitxor(bitxor(b, c), d); k = uint32(3395469782); end t = bitrotate(a, 5); t = bitadd(t, f); t = bitadd(t, e); t = bitadd(t, k); t = bitadd(t, ch(i)); e = d; d = c; c = bitrotate(b, 30); b = a; a = t; end h0 = bitadd(h0, a); h1 = bitadd(h1, b); h2 = bitadd(h2, c); h3 = bitadd(h3, d); h4 = bitadd(h4, e); end hash = reshape(dec2hex(double([h0 h1 h2 h3 h4]), 8)', [1 40]); hash = lower(hash); end function ret = bitadd(iA, iB) ret = double(iA) + double(iB); ret = bitset(ret, 33, 0); ret = uint32(ret); end function ret = bitrotate(iA, places) t = bitshift(iA, places - 32); ret = bitshift(iA, places); ret = bitor(ret, t); end % =========================== Base64 Encoder ============================ % Thanks to Peter John Acklam % function y = base64encode(x, eol) %BASE64ENCODE Perform base64 encoding on a string. % % BASE64ENCODE(STR, EOL) encode the given string STR. EOL is the line ending % sequence to use; it is optional and defaults to '\n' (ASCII decimal 10). % The returned encoded string is broken into lines of no more than 76 % characters each, and each line will end with EOL unless it is empty. Let % EOL be empty if you do not want the encoded string broken into lines. % % STR and EOL don't have to be strings (i.e., char arrays). The only % requirement is that they are vectors containing values in the range 0-255. % % This function may be used to encode strings into the Base64 encoding % specified in RFC 2045 - MIME (Multipurpose Internet Mail Extensions). The % Base64 encoding is designed to represent arbitrary sequences of octets in a % form that need not be humanly readable. A 65-character subset % ([A-Za-z0-9+/=]) of US-ASCII is used, enabling 6 bits to be represented per % printable character. % % Examples % -------- % % If you want to encode a large file, you should encode it in chunks that are % a multiple of 57 bytes. This ensures that the base64 lines line up and % that you do not end up with padding in the middle. 57 bytes of data fills % one complete base64 line (76 == 57*4/3): % % If ifid and ofid are two file identifiers opened for reading and writing, % respectively, then you can base64 encode the data with % % while ~feof(ifid) % fwrite(ofid, base64encode(fread(ifid, 60*57))); % end % % or, if you have enough memory, % % fwrite(ofid, base64encode(fread(ifid))); % % See also BASE64DECODE. % Author: Peter John Acklam % Time-stamp: 2004-02-03 21:36:56 +0100 % E-mail: [email protected] % URL: http://home.online.no/~pjacklam if isnumeric(x) x = num2str(x); end % make sure we have the EOL value if nargin < 2 eol = sprintf('\n'); else if sum(size(eol) > 1) > 1 error('EOL must be a vector.'); end if any(eol(:) > 255) error('EOL can not contain values larger than 255.'); end end if sum(size(x) > 1) > 1 error('STR must be a vector.'); end x = uint8(x); eol = uint8(eol); ndbytes = length(x); % number of decoded bytes nchunks = ceil(ndbytes / 3); % number of chunks/groups nebytes = 4 * nchunks; % number of encoded bytes % add padding if necessary, to make the length of x a multiple of 3 if rem(ndbytes, 3) x(end+1 : 3*nchunks) = 0; end x = reshape(x, [3, nchunks]); % reshape the data y = repmat(uint8(0), 4, nchunks); % for the encoded data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Split up every 3 bytes into 4 pieces % % aaaaaabb bbbbcccc ccdddddd % % to form % % 00aaaaaa 00bbbbbb 00cccccc 00dddddd % y(1,:) = bitshift(x(1,:), -2); % 6 highest bits of x(1,:) y(2,:) = bitshift(bitand(x(1,:), 3), 4); % 2 lowest bits of x(1,:) y(2,:) = bitor(y(2,:), bitshift(x(2,:), -4)); % 4 highest bits of x(2,:) y(3,:) = bitshift(bitand(x(2,:), 15), 2); % 4 lowest bits of x(2,:) y(3,:) = bitor(y(3,:), bitshift(x(3,:), -6)); % 2 highest bits of x(3,:) y(4,:) = bitand(x(3,:), 63); % 6 lowest bits of x(3,:) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Now perform the following mapping % % 0 - 25 -> A-Z % 26 - 51 -> a-z % 52 - 61 -> 0-9 % 62 -> + % 63 -> / % % We could use a mapping vector like % % ['A':'Z', 'a':'z', '0':'9', '+/'] % % but that would require an index vector of class double. % z = repmat(uint8(0), size(y)); i = y <= 25; z(i) = 'A' + double(y(i)); i = 26 <= y & y <= 51; z(i) = 'a' - 26 + double(y(i)); i = 52 <= y & y <= 61; z(i) = '0' - 52 + double(y(i)); i = y == 62; z(i) = '+'; i = y == 63; z(i) = '/'; y = z; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Add padding if necessary. % npbytes = 3 * nchunks - ndbytes; % number of padding bytes if npbytes y(end-npbytes+1 : end) = '='; % '=' is used for padding end if isempty(eol) % reshape to a row vector y = reshape(y, [1, nebytes]); else nlines = ceil(nebytes / 76); % number of lines neolbytes = length(eol); % number of bytes in eol string % pad data so it becomes a multiple of 76 elements y = [y(:) ; zeros(76 * nlines - numel(y), 1)]; y(nebytes + 1 : 76 * nlines) = 0; y = reshape(y, 76, nlines); % insert eol strings eol = eol(:); y(end + 1 : end + neolbytes, :) = eol(:, ones(1, nlines)); % remove padding, but keep the last eol string m = nebytes + neolbytes * (nlines - 1); n = (76+neolbytes)*nlines - neolbytes; y(m+1 : n) = ''; % extract and reshape to row vector y = reshape(y, 1, m+neolbytes); end % output is a character array y = char(y); end
github
MouradGridach/Machine-Learning-Stanford-master
submitWeb.m
.m
Machine-Learning-Stanford-master/ex3/Logistic Regression/submitWeb.m
827
utf_8
bfb2fa08cac9d8d797e3071d3fdd7ca1
% submitWeb Creates files from your code and output for web submission. % % If the submit function does not work for you, use the web-submission mechanism. % Call this function to produce a file for the part you wish to submit. Then, % submit the file to the class servers using the "Web Submission" button on the % Programming Exercises page on the course website. % % You should call this function without arguments (submitWeb), to receive % an interactive prompt for submission; optionally you can call it with the partID % if you so wish. Make sure your working directory is set to the directory % containing the submitWeb.m file and your assignment files. function submitWeb(partId) if ~exist('partId', 'var') || isempty(partId) partId = []; end submit(partId, 1); end
github
MouradGridach/Machine-Learning-Stanford-master
submit.m
.m
Machine-Learning-Stanford-master/ex1/submit.m
17,317
utf_8
d91bc52d795ebec0d8a1667cd9810fea
function submit(partId, webSubmit) %SUBMIT Submit your code and output to the ml-class servers % SUBMIT() will connect to the ml-class server and submit your solution fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ... homework_id()); if ~exist('partId', 'var') || isempty(partId) partId = promptPart(); end if ~exist('webSubmit', 'var') || isempty(webSubmit) webSubmit = 0; % submit directly by default end % Check valid partId partNames = validParts(); if ~isValidPartId(partId) fprintf('!! Invalid homework part selected.\n'); fprintf('!! Expected an integer from 1 to %d.\n', numel(partNames) + 1); fprintf('!! Submission Cancelled\n'); return end if ~exist('ml_login_data.mat','file') [login password] = loginPrompt(); save('ml_login_data.mat','login','password'); else load('ml_login_data.mat'); [login password] = quickLogin(login, password); save('ml_login_data.mat','login','password'); end if isempty(login) fprintf('!! Submission Cancelled\n'); return end fprintf('\n== Connecting to ml-class ... '); if exist('OCTAVE_VERSION') fflush(stdout); end % Setup submit list if partId == numel(partNames) + 1 submitParts = 1:numel(partNames); else submitParts = [partId]; end for s = 1:numel(submitParts) thisPartId = submitParts(s); if (~webSubmit) % submit directly to server [login, ch, signature, auxstring] = getChallenge(login, thisPartId); if isempty(login) || isempty(ch) || isempty(signature) % Some error occured, error string in first return element. fprintf('\n!! Error: %s\n\n', login); return end % Attempt Submission with Challenge ch_resp = challengeResponse(login, password, ch); [result, str] = submitSolution(login, ch_resp, thisPartId, ... output(thisPartId, auxstring), source(thisPartId), signature); partName = partNames{thisPartId}; fprintf('\n== [ml-class] Submitted Assignment %s - Part %d - %s\n', ... homework_id(), thisPartId, partName); fprintf('== %s\n', strtrim(str)); if exist('OCTAVE_VERSION') fflush(stdout); end else [result] = submitSolutionWeb(login, thisPartId, output(thisPartId), ... source(thisPartId)); result = base64encode(result); fprintf('\nSave as submission file [submit_ex%s_part%d.txt (enter to accept default)]:', ... homework_id(), thisPartId); saveAsFile = input('', 's'); if (isempty(saveAsFile)) saveAsFile = sprintf('submit_ex%s_part%d.txt', homework_id(), thisPartId); end fid = fopen(saveAsFile, 'w'); if (fid) fwrite(fid, result); fclose(fid); fprintf('\nSaved your solutions to %s.\n\n', saveAsFile); fprintf(['You can now submit your solutions through the web \n' ... 'form in the programming exercises. Select the corresponding \n' ... 'programming exercise to access the form.\n']); else fprintf('Unable to save to %s\n\n', saveAsFile); fprintf(['You can create a submission file by saving the \n' ... 'following text in a file: (press enter to continue)\n\n']); pause; fprintf(result); end end end end % ================== CONFIGURABLES FOR EACH HOMEWORK ================== function id = homework_id() id = '1'; end function [partNames] = validParts() partNames = { 'Warm up exercise ', ... 'Computing Cost (for one variable)', ... 'Gradient Descent (for one variable)', ... 'Feature Normalization', ... 'Computing Cost (for multiple variables)', ... 'Gradient Descent (for multiple variables)', ... 'Normal Equations'}; end function srcs = sources() % Separated by part srcs = { { 'warmUpExercise.m' }, ... { 'computeCost.m' }, ... { 'gradientDescent.m' }, ... { 'featureNormalize.m' }, ... { 'computeCostMulti.m' }, ... { 'gradientDescentMulti.m' }, ... { 'normalEqn.m' }, ... }; end function out = output(partId, auxstring) % Random Test Cases X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))']; Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2)); X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25]; Y2 = Y1.^0.5 + Y1; if partId == 1 out = sprintf('%0.5f ', warmUpExercise()); elseif partId == 2 out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]')); elseif partId == 3 out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10)); elseif partId == 4 out = sprintf('%0.5f ', featureNormalize(X2(:,2:4))); elseif partId == 5 out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]')); elseif partId == 6 out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10)); elseif partId == 7 out = sprintf('%0.5f ', normalEqn(X2, Y2)); end end % ====================== SERVER CONFIGURATION =========================== % ***************** REMOVE -staging WHEN YOU DEPLOY ********************* function url = site_url() url = 'http://class.coursera.org/ml-006'; end function url = challenge_url() url = [site_url() '/assignment/challenge']; end function url = submit_url() url = [site_url() '/assignment/submit']; end % ========================= CHALLENGE HELPERS ========================= function src = source(partId) src = ''; src_files = sources(); if partId <= numel(src_files) flist = src_files{partId}; for i = 1:numel(flist) fid = fopen(flist{i}); if (fid == -1) error('Error opening %s (is it missing?)', flist{i}); end line = fgets(fid); while ischar(line) src = [src line]; line = fgets(fid); end fclose(fid); src = [src '||||||||']; end end end function ret = isValidPartId(partId) partNames = validParts(); ret = (~isempty(partId)) && (partId >= 1) && (partId <= numel(partNames) + 1); end function partId = promptPart() fprintf('== Select which part(s) to submit:\n'); partNames = validParts(); srcFiles = sources(); for i = 1:numel(partNames) fprintf('== %d) %s [', i, partNames{i}); fprintf(' %s ', srcFiles{i}{:}); fprintf(']\n'); end fprintf('== %d) All of the above \n==\nEnter your choice [1-%d]: ', ... numel(partNames) + 1, numel(partNames) + 1); selPart = input('', 's'); partId = str2num(selPart); if ~isValidPartId(partId) partId = -1; end end function [email,ch,signature,auxstring] = getChallenge(email, part) str = urlread(challenge_url(), 'post', {'email_address', email, 'assignment_part_sid', [homework_id() '-' num2str(part)], 'response_encoding', 'delim'}); str = strtrim(str); r = struct; while(numel(str) > 0) [f, str] = strtok (str, '|'); [v, str] = strtok (str, '|'); r = setfield(r, f, v); end email = getfield(r, 'email_address'); ch = getfield(r, 'challenge_key'); signature = getfield(r, 'state'); auxstring = getfield(r, 'challenge_aux_data'); end function [result, str] = submitSolutionWeb(email, part, output, source) result = ['{"assignment_part_sid":"' base64encode([homework_id() '-' num2str(part)], '') '",' ... '"email_address":"' base64encode(email, '') '",' ... '"submission":"' base64encode(output, '') '",' ... '"submission_aux":"' base64encode(source, '') '"' ... '}']; str = 'Web-submission'; end function [result, str] = submitSolution(email, ch_resp, part, output, ... source, signature) params = {'assignment_part_sid', [homework_id() '-' num2str(part)], ... 'email_address', email, ... 'submission', base64encode(output, ''), ... 'submission_aux', base64encode(source, ''), ... 'challenge_response', ch_resp, ... 'state', signature}; str = urlread(submit_url(), 'post', params); % Parse str to read for success / failure result = 0; end % =========================== LOGIN HELPERS =========================== function [login password] = loginPrompt() % Prompt for password [login password] = basicPrompt(); if isempty(login) || isempty(password) login = []; password = []; end end function [login password] = basicPrompt() login = input('Login (Email address): ', 's'); password = input('Password: ', 's'); end function [login password] = quickLogin(login,password) disp(['You are currently logged in as ' login '.']); cont_token = input('Is this you? (y/n - type n to reenter password)','s'); if(isempty(cont_token) || cont_token(1)=='Y'||cont_token(1)=='y') return; else [login password] = loginPrompt(); end end function [str] = challengeResponse(email, passwd, challenge) str = sha1([challenge passwd]); end % =============================== SHA-1 ================================ function hash = sha1(str) % Initialize variables h0 = uint32(1732584193); h1 = uint32(4023233417); h2 = uint32(2562383102); h3 = uint32(271733878); h4 = uint32(3285377520); % Convert to word array strlen = numel(str); % Break string into chars and append the bit 1 to the message mC = [double(str) 128]; mC = [mC zeros(1, 4-mod(numel(mC), 4), 'uint8')]; numB = strlen * 8; if exist('idivide') numC = idivide(uint32(numB + 65), 512, 'ceil'); else numC = ceil(double(numB + 65)/512); end numW = numC * 16; mW = zeros(numW, 1, 'uint32'); idx = 1; for i = 1:4:strlen + 1 mW(idx) = bitor(bitor(bitor( ... bitshift(uint32(mC(i)), 24), ... bitshift(uint32(mC(i+1)), 16)), ... bitshift(uint32(mC(i+2)), 8)), ... uint32(mC(i+3))); idx = idx + 1; end % Append length of message mW(numW - 1) = uint32(bitshift(uint64(numB), -32)); mW(numW) = uint32(bitshift(bitshift(uint64(numB), 32), -32)); % Process the message in successive 512-bit chs for cId = 1 : double(numC) cSt = (cId - 1) * 16 + 1; cEnd = cId * 16; ch = mW(cSt : cEnd); % Extend the sixteen 32-bit words into eighty 32-bit words for j = 17 : 80 ch(j) = ch(j - 3); ch(j) = bitxor(ch(j), ch(j - 8)); ch(j) = bitxor(ch(j), ch(j - 14)); ch(j) = bitxor(ch(j), ch(j - 16)); ch(j) = bitrotate(ch(j), 1); end % Initialize hash value for this ch a = h0; b = h1; c = h2; d = h3; e = h4; % Main loop for i = 1 : 80 if(i >= 1 && i <= 20) f = bitor(bitand(b, c), bitand(bitcmp(b), d)); k = uint32(1518500249); elseif(i >= 21 && i <= 40) f = bitxor(bitxor(b, c), d); k = uint32(1859775393); elseif(i >= 41 && i <= 60) f = bitor(bitor(bitand(b, c), bitand(b, d)), bitand(c, d)); k = uint32(2400959708); elseif(i >= 61 && i <= 80) f = bitxor(bitxor(b, c), d); k = uint32(3395469782); end t = bitrotate(a, 5); t = bitadd(t, f); t = bitadd(t, e); t = bitadd(t, k); t = bitadd(t, ch(i)); e = d; d = c; c = bitrotate(b, 30); b = a; a = t; end h0 = bitadd(h0, a); h1 = bitadd(h1, b); h2 = bitadd(h2, c); h3 = bitadd(h3, d); h4 = bitadd(h4, e); end hash = reshape(dec2hex(double([h0 h1 h2 h3 h4]), 8)', [1 40]); hash = lower(hash); end function ret = bitadd(iA, iB) ret = double(iA) + double(iB); ret = bitset(ret, 33, 0); ret = uint32(ret); end function ret = bitrotate(iA, places) t = bitshift(iA, places - 32); ret = bitshift(iA, places); ret = bitor(ret, t); end % =========================== Base64 Encoder ============================ % Thanks to Peter John Acklam % function y = base64encode(x, eol) %BASE64ENCODE Perform base64 encoding on a string. % % BASE64ENCODE(STR, EOL) encode the given string STR. EOL is the line ending % sequence to use; it is optional and defaults to '\n' (ASCII decimal 10). % The returned encoded string is broken into lines of no more than 76 % characters each, and each line will end with EOL unless it is empty. Let % EOL be empty if you do not want the encoded string broken into lines. % % STR and EOL don't have to be strings (i.e., char arrays). The only % requirement is that they are vectors containing values in the range 0-255. % % This function may be used to encode strings into the Base64 encoding % specified in RFC 2045 - MIME (Multipurpose Internet Mail Extensions). The % Base64 encoding is designed to represent arbitrary sequences of octets in a % form that need not be humanly readable. A 65-character subset % ([A-Za-z0-9+/=]) of US-ASCII is used, enabling 6 bits to be represented per % printable character. % % Examples % -------- % % If you want to encode a large file, you should encode it in chunks that are % a multiple of 57 bytes. This ensures that the base64 lines line up and % that you do not end up with padding in the middle. 57 bytes of data fills % one complete base64 line (76 == 57*4/3): % % If ifid and ofid are two file identifiers opened for reading and writing, % respectively, then you can base64 encode the data with % % while ~feof(ifid) % fwrite(ofid, base64encode(fread(ifid, 60*57))); % end % % or, if you have enough memory, % % fwrite(ofid, base64encode(fread(ifid))); % % See also BASE64DECODE. % Author: Peter John Acklam % Time-stamp: 2004-02-03 21:36:56 +0100 % E-mail: [email protected] % URL: http://home.online.no/~pjacklam if isnumeric(x) x = num2str(x); end % make sure we have the EOL value if nargin < 2 eol = sprintf('\n'); else if sum(size(eol) > 1) > 1 error('EOL must be a vector.'); end if any(eol(:) > 255) error('EOL can not contain values larger than 255.'); end end if sum(size(x) > 1) > 1 error('STR must be a vector.'); end x = uint8(x); eol = uint8(eol); ndbytes = length(x); % number of decoded bytes nchunks = ceil(ndbytes / 3); % number of chunks/groups nebytes = 4 * nchunks; % number of encoded bytes % add padding if necessary, to make the length of x a multiple of 3 if rem(ndbytes, 3) x(end+1 : 3*nchunks) = 0; end x = reshape(x, [3, nchunks]); % reshape the data y = repmat(uint8(0), 4, nchunks); % for the encoded data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Split up every 3 bytes into 4 pieces % % aaaaaabb bbbbcccc ccdddddd % % to form % % 00aaaaaa 00bbbbbb 00cccccc 00dddddd % y(1,:) = bitshift(x(1,:), -2); % 6 highest bits of x(1,:) y(2,:) = bitshift(bitand(x(1,:), 3), 4); % 2 lowest bits of x(1,:) y(2,:) = bitor(y(2,:), bitshift(x(2,:), -4)); % 4 highest bits of x(2,:) y(3,:) = bitshift(bitand(x(2,:), 15), 2); % 4 lowest bits of x(2,:) y(3,:) = bitor(y(3,:), bitshift(x(3,:), -6)); % 2 highest bits of x(3,:) y(4,:) = bitand(x(3,:), 63); % 6 lowest bits of x(3,:) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Now perform the following mapping % % 0 - 25 -> A-Z % 26 - 51 -> a-z % 52 - 61 -> 0-9 % 62 -> + % 63 -> / % % We could use a mapping vector like % % ['A':'Z', 'a':'z', '0':'9', '+/'] % % but that would require an index vector of class double. % z = repmat(uint8(0), size(y)); i = y <= 25; z(i) = 'A' + double(y(i)); i = 26 <= y & y <= 51; z(i) = 'a' - 26 + double(y(i)); i = 52 <= y & y <= 61; z(i) = '0' - 52 + double(y(i)); i = y == 62; z(i) = '+'; i = y == 63; z(i) = '/'; y = z; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Add padding if necessary. % npbytes = 3 * nchunks - ndbytes; % number of padding bytes if npbytes y(end-npbytes+1 : end) = '='; % '=' is used for padding end if isempty(eol) % reshape to a row vector y = reshape(y, [1, nebytes]); else nlines = ceil(nebytes / 76); % number of lines neolbytes = length(eol); % number of bytes in eol string % pad data so it becomes a multiple of 76 elements y = [y(:) ; zeros(76 * nlines - numel(y), 1)]; y(nebytes + 1 : 76 * nlines) = 0; y = reshape(y, 76, nlines); % insert eol strings eol = eol(:); y(end + 1 : end + neolbytes, :) = eol(:, ones(1, nlines)); % remove padding, but keep the last eol string m = nebytes + neolbytes * (nlines - 1); n = (76+neolbytes)*nlines - neolbytes; y(m+1 : n) = ''; % extract and reshape to row vector y = reshape(y, 1, m+neolbytes); end % output is a character array y = char(y); end
github
MouradGridach/Machine-Learning-Stanford-master
submitWeb.m
.m
Machine-Learning-Stanford-master/ex1/submitWeb.m
827
utf_8
bfb2fa08cac9d8d797e3071d3fdd7ca1
% submitWeb Creates files from your code and output for web submission. % % If the submit function does not work for you, use the web-submission mechanism. % Call this function to produce a file for the part you wish to submit. Then, % submit the file to the class servers using the "Web Submission" button on the % Programming Exercises page on the course website. % % You should call this function without arguments (submitWeb), to receive % an interactive prompt for submission; optionally you can call it with the partID % if you so wish. Make sure your working directory is set to the directory % containing the submitWeb.m file and your assignment files. function submitWeb(partId) if ~exist('partId', 'var') || isempty(partId) partId = []; end submit(partId, 1); end
github
xiaohuige1/udn_extend-master
fast_rcnn_get_minibatch.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_get_minibatch.m
6,754
utf_8
78226ab5c4a79dab0d13aac70792d9b6
function [im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_blob] = fast_rcnn_get_minibatch(conf, image_roidb) % [im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_blob] ... % = fast_rcnn_get_minibatch(conf, image_roidb) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- num_images = length(image_roidb); % Infer number of classes from the number of columns in gt_overlaps num_classes = size(image_roidb(1).overlap, 2); % Sample random scales to use for each image in this batch random_scale_inds = randi(length(conf.scales), num_images, 1); assert(mod(conf.batch_size, num_images) == 0, ... sprintf('num_images %d must divide BATCH_SIZE %d', num_images, conf.batch_size)); rois_per_image = conf.batch_size / num_images; fg_rois_per_image = round(rois_per_image * conf.fg_fraction); % Get the input image blob [im_blob, im_scales] = get_image_blob(conf, image_roidb, random_scale_inds); % build the region of interest and label blobs rois_blob = zeros(0, 5, 'single'); labels_blob = zeros(0, 1, 'single'); bbox_targets_blob = zeros(0, 4 * (num_classes+1), 'single'); bbox_loss_blob = zeros(size(bbox_targets_blob), 'single'); for i = 1:num_images [labels, ~, im_rois, bbox_targets, bbox_loss] = ... sample_rois(conf, image_roidb(i), fg_rois_per_image, rois_per_image); % Add to ROIs blob feat_rois = fast_rcnn_map_im_rois_to_feat_rois(conf, im_rois, im_scales(i)); batch_ind = i * ones(size(feat_rois, 1), 1); rois_blob_this_image = [batch_ind, feat_rois]; rois_blob = [rois_blob; rois_blob_this_image]; % Add to labels, bbox targets, and bbox loss blobs labels_blob = [labels_blob; labels]; bbox_targets_blob = [bbox_targets_blob; bbox_targets]; bbox_loss_blob = [bbox_loss_blob; bbox_loss]; end % permute data into caffe c++ memory, thus [num, channels, height, width] im_blob = im_blob(:, :, [3, 2, 1], :); % from rgb to brg im_blob = single(permute(im_blob, [2, 1, 3, 4])); rois_blob = rois_blob - 1; % to c's index (start from 0) rois_blob = single(permute(rois_blob, [3, 4, 2, 1])); labels_blob = single(permute(labels_blob, [3, 4, 2, 1])); bbox_targets_blob = single(permute(bbox_targets_blob, [3, 4, 2, 1])); bbox_loss_blob = single(permute(bbox_loss_blob, [3, 4, 2, 1])); assert(~isempty(im_blob)); assert(~isempty(rois_blob)); assert(~isempty(labels_blob)); assert(~isempty(bbox_targets_blob)); assert(~isempty(bbox_loss_blob)); end %% Build an input blob from the images in the roidb at the specified scales. function [im_blob, im_scales] = get_image_blob(conf, images, random_scale_inds) num_images = length(images); processed_ims = cell(num_images, 1); im_scales = nan(num_images, 1); for i = 1:num_images im = imread(images(i).image_path); target_size = conf.scales(random_scale_inds(i)); [im, im_scale] = prep_im_for_blob(im, conf.image_means, target_size, conf.max_size); im_scales(i) = im_scale; processed_ims{i} = im; end im_blob = im_list_to_blob(processed_ims); end %% Generate a random sample of ROIs comprising foreground and background examples. function [labels, overlaps, rois, bbox_targets, bbox_loss_weights] = ... sample_rois(conf, image_roidb, fg_rois_per_image, rois_per_image) [overlaps, labels] = max(image_roidb(1).overlap, [], 2); gt_ignores = image_roidb.ignores; labels(find(gt_ignores==1)) = 0; overlaps(find(gt_ignores==1)) = -1; % labels = image_roidb(1).max_classes; % overlaps = image_roidb(1).max_overlaps; rois = image_roidb(1).boxes; % Select foreground ROIs as those with >= FG_THRESH overlap fg_inds = find(overlaps >= conf.fg_thresh); % Guard against the case when an image has fewer than fg_rois_per_image % foreground ROIs fg_rois_per_this_image = min(fg_rois_per_image, length(fg_inds)); % Sample foreground regions without replacement if ~isempty(fg_inds) fg_inds = fg_inds(randperm(length(fg_inds), fg_rois_per_this_image)); end % Select background ROIs as those within [BG_THRESH_LO, BG_THRESH_HI) bg_inds = find(overlaps < conf.bg_thresh_hi & overlaps >= conf.bg_thresh_lo); % Compute number of background ROIs to take from this image (guarding % against there being fewer than desired) bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image; bg_rois_per_this_image = min(bg_rois_per_this_image, length(bg_inds)); % Sample foreground regions without replacement if ~isempty(bg_inds) bg_inds = bg_inds(randperm(length(bg_inds), bg_rois_per_this_image)); end % The indices that we're selecting (both fg and bg) keep_inds = [fg_inds; bg_inds]; % Select sampled values from various arrays labels = labels(keep_inds); % Clamp labels for the background ROIs to 0 labels((fg_rois_per_this_image+1):end) = 0; overlaps = overlaps(keep_inds); rois = rois(keep_inds, :); assert(all(labels == image_roidb.bbox_targets(keep_inds, 1))); % Infer number of classes from the number of columns in gt_overlaps num_classes = size(image_roidb(1).overlap, 2); [bbox_targets, bbox_loss_weights] = get_bbox_regression_labels(conf, ... image_roidb.bbox_targets(keep_inds, :), num_classes); end function [bbox_targets, bbox_loss_weights] = get_bbox_regression_labels(conf, bbox_target_data, num_classes) %% Bounding-box regression targets are stored in a compact form in the roidb. % This function expands those targets into the 4-of-4*(num_classes+1) representation used % by the network (i.e. only one class has non-zero targets). % The loss weights are similarly expanded. % Return (N, (num_classes+1) * 4, 1, 1) blob of regression targets % Return (N, (num_classes+1 * 4, 1, 1) blob of loss weights clss = bbox_target_data(:, 1); bbox_targets = zeros(length(clss), 4 * (num_classes+1), 'single'); bbox_loss_weights = zeros(size(bbox_targets), 'single'); inds = find(clss > 0); for i = 1:length(inds) ind = inds(i); cls = clss(ind); targets_inds = (1+cls*4):((cls+1)*4); bbox_targets(ind, targets_inds) = bbox_target_data(ind, 2:end); bbox_loss_weights(ind, targets_inds) = 1; end end
github
xiaohuige1/udn_extend-master
fast_rcnn_conv_feat_detect.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_conv_feat_detect.m
4,211
utf_8
7757435a0286baaedd67b1aa30c1f523
function [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, caffe_net, im, conv_feat_blob, boxes, max_rois_num_in_gpu) % [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, caffe_net, im, conv_feat_blob, boxes, max_rois_num_in_gpu) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- [rois_blob, ~] = get_blobs(conf, im, boxes); % permute data into caffe c++ memory, thus [num, channels, height, width] rois_blob = rois_blob - 1; % to c's index (start from 0) rois_blob = permute(rois_blob, [3, 4, 2, 1]); rois_blob = single(rois_blob); % set conv feature map as 'data' caffe_net.blobs('data').copy_data_from(conv_feat_blob); total_rois = size(rois_blob, 4); total_scores = cell(ceil(total_rois / max_rois_num_in_gpu), 1); total_box_deltas = cell(ceil(total_rois / max_rois_num_in_gpu), 1); for i = 1:ceil(total_rois / max_rois_num_in_gpu) sub_ind_start = 1 + (i-1) * max_rois_num_in_gpu; sub_ind_end = min(total_rois, i * max_rois_num_in_gpu); sub_rois_blob = rois_blob(:, :, :, sub_ind_start:sub_ind_end); % only set rois blob here net_inputs = {[], sub_rois_blob}; % Reshape net's input blobs caffe_net.reshape_as_input(net_inputs); output_blobs = caffe_net.forward(net_inputs); if conf.test_binary % simulate binary logistic regression scores = caffe_net.blobs('cls_score').get_data(); scores = squeeze(scores)'; % Return scores as fg - bg scores = bsxfun(@minus, scores, scores(:, 1)); else % use softmax estimated probabilities scores = output_blobs{2}; scores = squeeze(scores)'; end % Apply bounding-box regression deltas box_deltas = output_blobs{1}; box_deltas = squeeze(box_deltas)'; total_scores{i} = scores; total_box_deltas{i} = box_deltas; end scores = cell2mat(total_scores); box_deltas = cell2mat(total_box_deltas); pred_boxes = fast_rcnn_bbox_transform_inv(boxes, box_deltas); pred_boxes = clip_boxes(pred_boxes, size(im, 2), size(im, 1)); % remove scores and boxes for back-ground pred_boxes = pred_boxes(:, 5:end); scores = scores(:, 2:end); end function [rois_blob, im_scale_factors] = get_blobs(conf, im, rois) im_scale_factors = get_image_blob_scales(conf, im); rois_blob = get_rois_blob(conf, rois, im_scale_factors); end function im_scales = get_image_blob_scales(conf, im) im_scales = arrayfun(@(x) prep_im_for_blob_size(size(im), x, conf.test_max_size), conf.test_scales, 'UniformOutput', false); im_scales = cell2mat(im_scales); end function [rois_blob] = get_rois_blob(conf, im_rois, im_scale_factors) [feat_rois, levels] = map_im_rois_to_feat_rois(conf, im_rois, im_scale_factors); rois_blob = single([levels, feat_rois]); end function [feat_rois, levels] = map_im_rois_to_feat_rois(conf, im_rois, scales) im_rois = single(im_rois); if length(scales) > 1 widths = im_rois(:, 3) - im_rois(:, 1) + 1; heights = im_rois(:, 4) - im_rois(:, 2) + 1; areas = widths .* heights; scaled_areas = bsxfun(@times, areas(:), scales(:)'.^2); levels = max(abs(scaled_areas - 224.^2), 2); else levels = ones(size(im_rois, 1), 1); end feat_rois = round(bsxfun(@times, im_rois-1, scales(levels))) + 1; end function boxes = clip_boxes(boxes, im_width, im_height) % x1 >= 1 & <= im_width boxes(:, 1:4:end) = max(min(boxes(:, 1:4:end), im_width), 1); % y1 >= 1 & <= im_height boxes(:, 2:4:end) = max(min(boxes(:, 2:4:end), im_height), 1); % x2 >= 1 & <= im_width boxes(:, 3:4:end) = max(min(boxes(:, 3:4:end), im_width), 1); % y2 >= 1 & <= im_height boxes(:, 4:4:end) = max(min(boxes(:, 4:4:end), im_height), 1); end
github
xiaohuige1/udn_extend-master
fast_rcnn_im_detect_our.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_im_detect_our.m
4,807
utf_8
dcac6458c08d769832bb3ca5be673b93
function [pred_boxes, scores] = fast_rcnn_im_detect_our(conf, caffe_net, im, boxes, max_rois_num_in_gpu) % [pred_boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, boxes, max_rois_num_in_gpu) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- [im_blob, rois_blob, ~] = get_blobs(conf, im, boxes); % When mapping from image ROIs to feature map ROIs, there's some aliasing % (some distinct image ROIs get mapped to the same feature ROI). % Here, we identify duplicate feature ROIs, so we only compute features % on the unique subset. [~, index, inv_index] = unique(rois_blob, 'rows'); rois_blob = rois_blob(index, :); boxes = boxes(index, :); % permute data into caffe c++ memory, thus [num, channels, height, width] im_blob = im_blob(:, :, [3, 2, 1], :); % from rgb to brg im_blob = permute(im_blob, [2, 1, 3, 4]); im_blob = single(im_blob); rois_blob = rois_blob - 1; % to c's index (start from 0) rois_blob = permute(rois_blob, [3, 4, 2, 1]); rois_blob = single(rois_blob); total_rois = size(rois_blob, 4); total_scores = cell(ceil(total_rois / max_rois_num_in_gpu), 1); total_box_deltas = cell(ceil(total_rois / max_rois_num_in_gpu), 1); for i = 1:ceil(total_rois / max_rois_num_in_gpu) sub_ind_start = 1 + (i-1) * max_rois_num_in_gpu; sub_ind_end = min(total_rois, i * max_rois_num_in_gpu); sub_rois_blob = rois_blob(:, :, :, sub_ind_start:sub_ind_end); net_inputs = {im_blob, sub_rois_blob}; % Reshape net's input blobs caffe_net.reshape_as_input(net_inputs); output_blobs = caffe_net.forward(net_inputs); if conf.test_binary % simulate binary logistic regression scores = caffe_net.blobs('cls_score').get_data(); scores = squeeze(scores)'; % Return scores as fg - bg scores = bsxfun(@minus, scores, scores(:, 1)); else % use softmax estimated probabilities scores = output_blobs{1}; scores = squeeze(scores)'; end % Apply bounding-box regression deltas % box_deltas = output_blobs{1}; % box_deltas = squeeze(box_deltas)'; total_scores{i} = scores; % total_box_deltas{i} = box_deltas; end scores = cell2mat(total_scores); % box_deltas = cell2mat(total_box_deltas); % pred_boxes = boxes; %fast_rcnn_bbox_transform_inv(boxes, box_deltas); % pred_boxes = clip_boxes(pred_boxes, size(im, 2), size(im, 1)); % Map scores and predictions back to the original set of boxes scores = scores(inv_index, :); pred_boxes = pred_boxes(inv_index, :); % remove scores and boxes for back-ground % pred_boxes = pred_boxes(:, 5:end); scores = scores(:, 2:end); end function [data_blob, rois_blob, im_scale_factors] = get_blobs(conf, im, rois) [data_blob, im_scale_factors] = get_image_blob(conf, im); rois_blob = get_rois_blob(conf, rois, im_scale_factors); end function [blob, im_scales] = get_image_blob(conf, im) [ims, im_scales] = arrayfun(@(x) prep_im_for_blob(im, conf.image_means, x, conf.test_max_size), conf.test_scales, 'UniformOutput', false); im_scales = cell2mat(im_scales); blob = im_list_to_blob(ims); end function [rois_blob] = get_rois_blob(conf, im_rois, im_scale_factors) [feat_rois, levels] = map_im_rois_to_feat_rois(conf, im_rois, im_scale_factors); rois_blob = single([levels, feat_rois]); end function [feat_rois, levels] = map_im_rois_to_feat_rois(conf, im_rois, scales) im_rois = single(im_rois); if length(scales) > 1 widths = im_rois(:, 3) - im_rois(:, 1) + 1; heights = im_rois(:, 4) - im_rois(:, 2) + 1; areas = widths .* heights; scaled_areas = bsxfun(@times, areas(:), scales(:)'.^2); [~, levels] = min(abs(scaled_areas - 224.^2), [], 2); else levels = ones(size(im_rois, 1), 1); end feat_rois = round(bsxfun(@times, im_rois-1, scales(levels))) + 1; end function boxes = clip_boxes(boxes, im_width, im_height) % x1 >= 1 & <= im_width boxes(:, 1:4:end) = max(min(boxes(:, 1:4:end), im_width), 1); % y1 >= 1 & <= im_height boxes(:, 2:4:end) = max(min(boxes(:, 2:4:end), im_height), 1); % x2 >= 1 & <= im_width boxes(:, 3:4:end) = max(min(boxes(:, 3:4:end), im_width), 1); % y2 >= 1 & <= im_height boxes(:, 4:4:end) = max(min(boxes(:, 4:4:end), im_height), 1); end
github
xiaohuige1/udn_extend-master
fast_rcnn_train.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_train.m
9,725
utf_8
c003ebd57a0c1417b4bbd979092a88b2
function save_model_path = fast_rcnn_train(conf, imdb_train, roidb_train, varargin) % save_model_path = fast_rcnn_train(conf, imdb_train, roidb_train, varargin) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- %% inputs ip = inputParser; ip.addRequired('conf', @isstruct); ip.addRequired('imdb_train', @iscell); ip.addRequired('roidb_train', @iscell); ip.addParamValue('do_val', false, @isscalar); ip.addParamValue('imdb_val', struct(), @isstruct); ip.addParamValue('roidb_val', struct(), @isstruct); ip.addParamValue('val_iters', 500, @isscalar); ip.addParamValue('val_interval', 2000, @isscalar); ip.addParamValue('snapshot_interval',... 10000, @isscalar); ip.addParamValue('solver_def_file', fullfile(pwd, 'models', 'Zeiler_conv5', 'solver.prototxt'), ... @isstr); ip.addParamValue('net_file', fullfile(pwd, 'models', 'Zeiler_conv5', 'Zeiler_conv5'), ... @isstr); ip.addParamValue('cache_name', 'Zeiler_conv5', ... @isstr); ip.parse(conf, imdb_train, roidb_train, varargin{:}); opts = ip.Results; %% try to find trained model imdbs_name = cell2mat(cellfun(@(x) x.name, imdb_train, 'UniformOutput', false)); cache_dir = fullfile(pwd, 'output', 'fast_rcnn_cachedir', opts.cache_name, imdbs_name); save_model_path = fullfile(cache_dir, 'final'); if exist(save_model_path, 'file') return; end %% init % init caffe solver mkdir_if_missing(cache_dir); caffe_log_file_base = fullfile(cache_dir, 'caffe_log'); caffe.init_log(caffe_log_file_base); caffe_solver = caffe.Solver(opts.solver_def_file); caffe_solver.net.copy_from(opts.net_file); % init log timestamp = datestr(datevec(now()), 'yyyymmdd_HHMMSS'); mkdir_if_missing(fullfile(cache_dir, 'log')); log_file = fullfile(cache_dir, 'log', ['train_', timestamp, '.txt']); diary(log_file); % set random seed prev_rng = seed_rand(conf.rng_seed); caffe.set_random_seed(conf.rng_seed); % set gpu/cpu if conf.use_gpu caffe.set_mode_gpu(); else caffe.set_mode_cpu(); end disp('conf:'); disp(conf); disp('opts:'); disp(opts); %% making tran/val data fprintf('Preparing training data...'); [image_roidb_train, bbox_means, bbox_stds]... = fast_rcnn_prepare_image_roidb(conf, opts.imdb_train, opts.roidb_train); fprintf('Done.\n'); %% try to train/val with images which have maximum size potentially, to validate whether the gpu memory is enough % num_classes = size(image_roidb_train(1).overlap, 2); % check_gpu_memory(conf, caffe_solver, num_classes, opts.do_val); %% training shuffled_inds = []; train_results = []; val_results = []; iter_ = caffe_solver.iter(); max_iter = caffe_solver.max_iter(); while (iter_ < max_iter) caffe_solver.net.set_phase('train'); % generate minibatch training data [shuffled_inds, sub_db_inds] = generate_random_minibatch(shuffled_inds, image_roidb_train, conf.ims_per_batch); [im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_weights_blob] = ... fast_rcnn_get_minibatch(conf, image_roidb_train(sub_db_inds)); net_inputs = {im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_weights_blob}; caffe_solver.net.reshape_as_input(net_inputs); % one iter SGD update caffe_solver.net.set_input_data(net_inputs); caffe_solver.step(1); rst = caffe_solver.net.get_output(); train_results = parse_rst(train_results, rst); % do valdiation per val_interval iterations if ~mod(iter_, opts.val_interval) show_state(iter_, train_results, val_results); train_results = []; val_results = []; diary; diary; % flush diary end % snapshot if ~mod(iter_, opts.snapshot_interval) snapshot(caffe_solver, bbox_means, bbox_stds, cache_dir, sprintf('iter_%d', iter_)); end iter_ = caffe_solver.iter(); end % final snapshot snapshot(caffe_solver, bbox_means, bbox_stds, cache_dir, sprintf('iter_%d', iter_)); save_model_path = snapshot(caffe_solver, bbox_means, bbox_stds, cache_dir, 'final'); diary off; caffe.reset_all(); rng(prev_rng); end function [shuffled_inds, sub_inds] = generate_random_minibatch(shuffled_inds, image_roidb_train, ims_per_batch) % shuffle training data per batch if isempty(shuffled_inds) % make sure each minibatch, only has horizontal images or vertical % images, to save gpu memory hori_image_inds = arrayfun(@(x) x.im_size(2) >= x.im_size(1), image_roidb_train, 'UniformOutput', true); vert_image_inds = ~hori_image_inds; hori_image_inds = find(hori_image_inds); vert_image_inds = find(vert_image_inds); % random perm lim = floor(length(hori_image_inds) / ims_per_batch) * ims_per_batch; hori_image_inds = hori_image_inds(randperm(length(hori_image_inds), lim)); lim = floor(length(vert_image_inds) / ims_per_batch) * ims_per_batch; vert_image_inds = vert_image_inds(randperm(length(vert_image_inds), lim)); % combine sample for each ims_per_batch hori_image_inds = reshape(hori_image_inds, ims_per_batch, []); vert_image_inds = reshape(vert_image_inds, ims_per_batch, []); shuffled_inds = [hori_image_inds, vert_image_inds]; shuffled_inds = shuffled_inds(:, randperm(size(shuffled_inds, 2))); shuffled_inds = num2cell(shuffled_inds, 1); end if nargout > 1 % generate minibatch training data sub_inds = shuffled_inds{1}; assert(length(sub_inds) == ims_per_batch); shuffled_inds(1) = []; end end function check_gpu_memory(conf, caffe_solver, num_classes, do_val) %% try to train/val with images which have maximum size potentially, to validate whether the gpu memory is enough % generate pseudo training data with max size im_blob = single(zeros(max(conf.scales), conf.max_size, 3, conf.ims_per_batch)); rois_blob = single(repmat([0; 0; 0; max(conf.scales)-1; conf.max_size-1], 1, conf.batch_size)); rois_blob = permute(rois_blob, [3, 4, 1, 2]); labels_blob = single(ones(conf.batch_size, 1)); labels_blob = permute(labels_blob, [3, 4, 2, 1]); bbox_targets_blob = zeros(4 * (num_classes+1), conf.batch_size, 'single'); bbox_targets_blob = single(permute(bbox_targets_blob, [3, 4, 1, 2])); bbox_loss_weights_blob = bbox_targets_blob; net_inputs = {im_blob, rois_blob, labels_blob, bbox_targets_blob, bbox_loss_weights_blob}; % Reshape net's input blobs caffe_solver.net.reshape_as_input(net_inputs); % one iter SGD update caffe_solver.net.set_input_data(net_inputs); caffe_solver.step(1); if do_val % use the same net with train to save memory caffe_solver.net.set_phase('test'); caffe_solver.net.forward(net_inputs); caffe_solver.net.set_phase('train'); end end function model_path = snapshot(caffe_solver, bbox_means, bbox_stds, cache_dir, file_name) bbox_stds_flatten = reshape(bbox_stds', [], 1); bbox_means_flatten = reshape(bbox_means', [], 1); % merge bbox_means, bbox_stds into the model bbox_pred_layer_name = 'bbox_pred'; weights = caffe_solver.net.params(bbox_pred_layer_name, 1).get_data(); biase = caffe_solver.net.params(bbox_pred_layer_name, 2).get_data(); weights_back = weights; biase_back = biase; weights = ... bsxfun(@times, weights, bbox_stds_flatten'); % weights = weights * stds; biase = ... biase .* bbox_stds_flatten + bbox_means_flatten; % bias = bias * stds + means; caffe_solver.net.set_params_data(bbox_pred_layer_name, 1, weights); caffe_solver.net.set_params_data(bbox_pred_layer_name, 2, biase); model_path = fullfile(cache_dir, file_name); caffe_solver.net.save(model_path); fprintf('Saved as %s\n', model_path); % restore net to original state caffe_solver.net.set_params_data(bbox_pred_layer_name, 1, weights_back); caffe_solver.net.set_params_data(bbox_pred_layer_name, 2, biase_back); end function show_state(iter, train_results, val_results) fprintf('\n------------------------- Iteration %d -------------------------\n', iter); fprintf('Training : error %.3g, loss (cls %.3g, reg %.3g)\n', ... 1 - mean(train_results.accuarcy.data), ... mean(train_results.loss_cls.data), ... mean(train_results.loss_bbox.data)); if exist('val_results', 'var') && ~isempty(val_results) fprintf('Testing : error %.3g, loss (cls %.3g, reg %.3g)\n', ... 1 - mean(val_results.accuarcy.data), ... mean(val_results.loss_cls.data), ... mean(val_results.loss_bbox.data)); end end
github
xiaohuige1/udn_extend-master
fast_rcnn_im_detect.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_im_detect.m
4,781
utf_8
76b56954f7f1f2d32f89b7d0a00e8338
function [pred_boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, boxes, max_rois_num_in_gpu) % [pred_boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, boxes, max_rois_num_in_gpu) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- [im_blob, rois_blob, ~] = get_blobs(conf, im, boxes); % When mapping from image ROIs to feature map ROIs, there's some aliasing % (some distinct image ROIs get mapped to the same feature ROI). % Here, we identify duplicate feature ROIs, so we only compute features % on the unique subset. [~, index, inv_index] = unique(rois_blob, 'rows'); rois_blob = rois_blob(index, :); boxes = boxes(index, :); % permute data into caffe c++ memory, thus [num, channels, height, width] im_blob = im_blob(:, :, [3, 2, 1], :); % from rgb to brg im_blob = permute(im_blob, [2, 1, 3, 4]); im_blob = single(im_blob); rois_blob = rois_blob - 1; % to c's index (start from 0) rois_blob = permute(rois_blob, [3, 4, 2, 1]); rois_blob = single(rois_blob); total_rois = size(rois_blob, 4); total_scores = cell(ceil(total_rois / max_rois_num_in_gpu), 1); total_box_deltas = cell(ceil(total_rois / max_rois_num_in_gpu), 1); for i = 1:ceil(total_rois / max_rois_num_in_gpu) sub_ind_start = 1 + (i-1) * max_rois_num_in_gpu; sub_ind_end = min(total_rois, i * max_rois_num_in_gpu); sub_rois_blob = rois_blob(:, :, :, sub_ind_start:sub_ind_end); net_inputs = {im_blob, sub_rois_blob}; % Reshape net's input blobs caffe_net.reshape_as_input(net_inputs); output_blobs = caffe_net.forward(net_inputs); if conf.test_binary % simulate binary logistic regression scores = caffe_net.blobs('cls_score').get_data(); scores = squeeze(scores)'; % Return scores as fg - bg scores = bsxfun(@minus, scores, scores(:, 1)); else % use softmax estimated probabilities scores = output_blobs{2}; scores = squeeze(scores)'; end % Apply bounding-box regression deltas box_deltas = output_blobs{1}; box_deltas = squeeze(box_deltas)'; total_scores{i} = scores; total_box_deltas{i} = box_deltas; end scores = cell2mat(total_scores); box_deltas = cell2mat(total_box_deltas); pred_boxes = fast_rcnn_bbox_transform_inv(boxes, box_deltas); pred_boxes = clip_boxes(pred_boxes, size(im, 2), size(im, 1)); % Map scores and predictions back to the original set of boxes scores = scores(inv_index, :); pred_boxes = pred_boxes(inv_index, :); % remove scores and boxes for back-ground pred_boxes = pred_boxes(:, 5:end); scores = scores(:, 2:end); end function [data_blob, rois_blob, im_scale_factors] = get_blobs(conf, im, rois) [data_blob, im_scale_factors] = get_image_blob(conf, im); rois_blob = get_rois_blob(conf, rois, im_scale_factors); end function [blob, im_scales] = get_image_blob(conf, im) [ims, im_scales] = arrayfun(@(x) prep_im_for_blob(im, conf.image_means, x, conf.test_max_size), conf.test_scales, 'UniformOutput', false); im_scales = cell2mat(im_scales); blob = im_list_to_blob(ims); end function [rois_blob] = get_rois_blob(conf, im_rois, im_scale_factors) [feat_rois, levels] = map_im_rois_to_feat_rois(conf, im_rois, im_scale_factors); rois_blob = single([levels, feat_rois]); end function [feat_rois, levels] = map_im_rois_to_feat_rois(conf, im_rois, scales) im_rois = single(im_rois); if length(scales) > 1 widths = im_rois(:, 3) - im_rois(:, 1) + 1; heights = im_rois(:, 4) - im_rois(:, 2) + 1; areas = widths .* heights; scaled_areas = bsxfun(@times, areas(:), scales(:)'.^2); [~, levels] = min(abs(scaled_areas - 224.^2), [], 2); else levels = ones(size(im_rois, 1), 1); end feat_rois = round(bsxfun(@times, im_rois-1, scales(levels))) + 1; end function boxes = clip_boxes(boxes, im_width, im_height) % x1 >= 1 & <= im_width boxes(:, 1:4:end) = max(min(boxes(:, 1:4:end), im_width), 1); % y1 >= 1 & <= im_height boxes(:, 2:4:end) = max(min(boxes(:, 2:4:end), im_height), 1); % x2 >= 1 & <= im_width boxes(:, 3:4:end) = max(min(boxes(:, 3:4:end), im_width), 1); % y2 >= 1 & <= im_height boxes(:, 4:4:end) = max(min(boxes(:, 4:4:end), im_height), 1); end
github
xiaohuige1/udn_extend-master
fast_rcnn_test.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_test.m
8,455
utf_8
1b4a7dc5b5a0d67d5458497cdc47242d
function mAP = fast_rcnn_test(conf, imdb, roidb, varargin) % mAP = fast_rcnn_test(conf, imdb, roidb, varargin) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- %% inputs ip = inputParser; ip.addRequired('conf', @isstruct); ip.addRequired('imdb', @isstruct); ip.addRequired('roidb', @isstruct); ip.addParamValue('net_def_file', '', @isstr); ip.addParamValue('net_file', '', @isstr); ip.addParamValue('cache_name', '', @isstr); ip.addParamValue('suffix', '', @isstr); ip.addParamValue('ignore_cache', false, @islogical); ip.parse(conf, imdb, roidb, varargin{:}); opts = ip.Results; %% set cache dir cache_dir = fullfile(pwd, 'output', 'fast_rcnn_cachedir', opts.cache_name, imdb.name); mkdir_if_missing(cache_dir); %% init log timestamp = datestr(datevec(now()), 'yyyymmdd_HHMMSS'); mkdir_if_missing(fullfile(cache_dir, 'log')); log_file = fullfile(cache_dir, 'log', ['test_', timestamp, '.txt']); diary(log_file); num_images = length(imdb.image_ids); num_classes = imdb.num_classes; try aboxes = cell(num_classes, 1); if opts.ignore_cache throw(''); end for i = 1:num_classes load(fullfile(cache_dir, [imdb.classes{i} '_boxes_' imdb.name opts.suffix])); aboxes{i} = boxes; end catch %% testing % init caffe net caffe_log_file_base = fullfile(cache_dir, 'caffe_log'); caffe.init_log(caffe_log_file_base); caffe_net = caffe.Net(opts.net_def_file, 'test'); caffe_net.copy_from(opts.net_file); % set random seed prev_rng = seed_rand(conf.rng_seed); caffe.set_random_seed(conf.rng_seed); % set gpu/cpu if conf.use_gpu caffe.set_mode_gpu(); else caffe.set_mode_cpu(); end % determine the maximum number of rois in testing max_rois_num_in_gpu = check_gpu_memory(conf, caffe_net); disp('opts:'); disp(opts); disp('conf:'); disp(conf); %heuristic: keep an average of 40 detections per class per images prior to NMS max_per_set = 40 * num_images; % heuristic: keep at most 100 detection per class per image prior to NMS max_per_image = 100; % detection thresold for each class (this is adaptively set based on the max_per_set constraint) thresh = -inf * ones(num_classes, 1); % top_scores will hold one minheap of scores per class (used to enforce the max_per_set constraint) top_scores = cell(num_classes, 1); % all detections are collected into: % all_boxes[cls][image] = N x 5 array of detections in % (x1, y1, x2, y2, score) aboxes = cell(num_classes, 1); box_inds = cell(num_classes, 1); for i = 1:num_classes aboxes{i} = cell(length(imdb.image_ids), 1); box_inds{i} = cell(length(imdb.image_ids), 1); end count = 0; t_start = tic; for i = 1:num_images count = count + 1; fprintf('%s: test (%s) %d/%d ', procid(), imdb.name, count, num_images); th = tic; d = roidb.rois(i); im = imread(imdb.image_at(i)); [boxes, scores] = fast_rcnn_im_detect(conf, caffe_net, im, d.boxes, max_rois_num_in_gpu); for j = 1:num_classes inds = find(~d.gt & scores(:, j) > thresh(j)); if ~isempty(inds) [~, ord] = sort(scores(inds, j), 'descend'); ord = ord(1:min(length(ord), max_per_image)); inds = inds(ord); cls_boxes = boxes(inds, (1+(j-1)*4):((j)*4)); cls_scores = scores(inds, j); aboxes{j}{i} = [aboxes{j}{i}; cat(2, single(cls_boxes), single(cls_scores))]; box_inds{j}{i} = [box_inds{j}{i}; inds]; else aboxes{j}{i} = [aboxes{j}{i}; zeros(0, 5, 'single')]; box_inds{j}{i} = box_inds{j}{i}; end end fprintf(' time: %.3fs\n', toc(th)); if mod(count, 1000) == 0 for j = 1:num_classes [aboxes{j}, box_inds{j}, thresh(j)] = ... keep_top_k(aboxes{j}, box_inds{j}, i, max_per_set, thresh(j)); end disp(thresh); end end for j = 1:num_classes [aboxes{j}, box_inds{j}, thresh(j)] = ... keep_top_k(aboxes{j}, box_inds{j}, i, max_per_set, thresh(j)); end disp(thresh); for i = 1:num_classes top_scores{i} = sort(top_scores{i}, 'descend'); if (length(top_scores{i}) > max_per_set) thresh(i) = top_scores{i}(max_per_set); end % go back through and prune out detections below the found threshold for j = 1:length(imdb.image_ids) if ~isempty(aboxes{i}{j}) I = find(aboxes{i}{j}(:,end) < thresh(i)); aboxes{i}{j}(I,:) = []; box_inds{i}{j}(I,:) = []; end end save_file = fullfile(cache_dir, [imdb.classes{i} '_boxes_' imdb.name opts.suffix]); boxes = aboxes{i}; inds = box_inds{i}; save(save_file, 'boxes', 'inds'); clear boxes inds; end fprintf('test all images in %f seconds.\n', toc(t_start)); caffe.reset_all(); rng(prev_rng); end % ------------------------------------------------------------------------ % Peform AP evaluation % ------------------------------------------------------------------------ if isequal(imdb.eval_func, @imdb_eval_voc) for model_ind = 1:num_classes cls = imdb.classes{model_ind}; res(model_ind) = imdb.eval_func(cls, aboxes{model_ind}, imdb, opts.cache_name, opts.suffix); end else % ilsvrc res = imdb.eval_func(aboxes, imdb, opts.cache_name, opts.suffix); end if ~isempty(res) fprintf('\n~~~~~~~~~~~~~~~~~~~~\n'); fprintf('Results:\n'); aps = [res(:).ap]' * 100; disp(aps); disp(mean(aps)); fprintf('~~~~~~~~~~~~~~~~~~~~\n'); mAP = mean(aps); else mAP = nan; end diary off; end function max_rois_num = check_gpu_memory(conf, caffe_net) %% try to determine the maximum number of rois max_rois_num = 0; for rois_num = 500:500:5000 % generate pseudo testing data with max size im_blob = single(zeros(conf.max_size, conf.max_size, 3, 1)); rois_blob = single(repmat([0; 0; 0; conf.max_size-1; conf.max_size-1], 1, rois_num)); rois_blob = permute(rois_blob, [3, 4, 1, 2]); net_inputs = {im_blob, rois_blob}; % Reshape net's input blobs caffe_net.reshape_as_input(net_inputs); caffe_net.forward(net_inputs); gpuInfo = gpuDevice(); max_rois_num = rois_num; if gpuInfo.FreeMemory < 2 * 10^9 % 2GB for safety break; end end end % ------------------------------------------------------------------------ function [boxes, box_inds, thresh] = keep_top_k(boxes, box_inds, end_at, top_k, thresh) % ------------------------------------------------------------------------ % Keep top K X = cat(1, boxes{1:end_at}); if isempty(X) return; end scores = sort(X(:,end), 'descend'); thresh = scores(min(length(scores), top_k)); for image_index = 1:end_at if ~isempty(boxes{image_index}) bbox = boxes{image_index}; keep = find(bbox(:,end) >= thresh); boxes{image_index} = bbox(keep,:); box_inds{image_index} = box_inds{image_index}(keep); end end end
github
xiaohuige1/udn_extend-master
fast_rcnn_prepare_image_roidb.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_prepare_image_roidb.m
6,244
utf_8
c1d573a68d0365fd02ff3a204ccf9a3b
function [image_roidb, bbox_means, bbox_stds] = fast_rcnn_prepare_image_roidb(conf, imdbs, roidbs, bbox_means, bbox_stds) % [image_roidb, bbox_means, bbox_stds] = fast_rcnn_prepare_image_roidb(conf, imdbs, roidbs, cache_img, bbox_means, bbox_stds) % Gather useful information from imdb and roidb % pre-calculate mean (bbox_means) and std (bbox_stds) of the regression % term for normalization % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- if ~exist('bbox_means', 'var') bbox_means = []; bbox_stds = []; end if ~iscell(imdbs) imdbs = {imdbs}; roidbs = {roidbs}; end imdbs = imdbs(:); roidbs = roidbs(:); image_roidb = ... cellfun(@(x, y) ... // @(imdbs, roidbs) arrayfun(@(z) ... //@([1:length(x.image_ids)]) struct('image_path', x.image_at(z), 'image_id', x.image_ids{z}, 'im_size', x.sizes(z, :), 'imdb_name', x.name, 'ignores', y.rois(z).ignores, ... 'overlap', y.rois(z).overlap, 'boxes', y.rois(z).boxes, 'class', y.rois(z).class, 'image', [], 'bbox_targets', []), ... [1:length(x.image_ids)]', 'UniformOutput', true),... imdbs, roidbs, 'UniformOutput', false); image_roidb = cat(1, image_roidb{:}); % enhance roidb to contain bounding-box regression targets [image_roidb, bbox_means, bbox_stds] = append_bbox_regression_targets(conf, image_roidb, bbox_means, bbox_stds); end function [image_roidb, means, stds] = append_bbox_regression_targets(conf, image_roidb, means, stds) % means and stds -- (k+1) * 4, include background class num_images = length(image_roidb); % Infer number of classes from the number of columns in gt_overlaps num_classes = size(image_roidb(1).overlap, 2); valid_imgs = true(num_images, 1); for i = 1:num_images rois = image_roidb(i).boxes; % try [image_roidb(i).bbox_targets, valid_imgs(i)] = ... compute_targets(conf, rois, image_roidb(i).overlap, image_roidb(i).ignores); % catch % debug = 1; % end end if ~all(valid_imgs) image_roidb = image_roidb(valid_imgs); num_images = length(image_roidb); fprintf('Warning: fast_rcnn_prepare_image_roidb: filter out %d images, which contains zero valid samples\n', sum(~valid_imgs)); end if ~(exist('means', 'var') && ~isempty(means) && exist('stds', 'var') && ~isempty(stds)) % Compute values needed for means and stds % var(x) = E(x^2) - E(x)^2 class_counts = zeros(num_classes, 1) + eps; sums = zeros(num_classes, 4); squared_sums = zeros(num_classes, 4); for i = 1:num_images targets = image_roidb(i).bbox_targets; for cls = 1:num_classes cls_inds = find(targets(:, 1) == cls); if ~isempty(cls_inds) class_counts(cls) = class_counts(cls) + length(cls_inds); sums(cls, :) = sums(cls, :) + sum(targets(cls_inds, 2:end), 1); squared_sums(cls, :) = squared_sums(cls, :) + sum(targets(cls_inds, 2:end).^2, 1); end end end means = bsxfun(@rdivide, sums, class_counts); stds = (bsxfun(@minus, bsxfun(@rdivide, squared_sums, class_counts), means.^2)).^0.5; % add background class means = [0, 0, 0, 0; means]; stds = [0, 0, 0, 0; stds]; end % Normalize targets for i = 1:num_images targets = image_roidb(i).bbox_targets; for cls = 1:num_classes cls_inds = find(targets(:, 1) == cls); if ~isempty(cls_inds) image_roidb(i).bbox_targets(cls_inds, 2:end) = ... bsxfun(@minus, image_roidb(i).bbox_targets(cls_inds, 2:end), means(cls+1, :)); image_roidb(i).bbox_targets(cls_inds, 2:end) = ... bsxfun(@rdivide, image_roidb(i).bbox_targets(cls_inds, 2:end), stds(cls+1, :)); end end end end function [bbox_targets, is_valid] = compute_targets(conf, rois, overlap, gt_ignores) overlap = full(overlap); [max_overlaps, max_labels] = max(overlap, [], 2); % ensure ROIs are floats rois = single(rois); bbox_targets = zeros(size(rois, 1), 5, 'single'); % Indices of ground-truth ROIs gt_inds_full = find(max_overlaps == 1); if ~isempty(gt_inds_full) gt_inds = gt_inds_full & ~gt_ignores; max_overlaps(find(gt_inds == 0)) = -1; gt_inds = find(gt_inds == 1); % gt_inds(find(gt_inds == 0)) = []; else gt_inds = []; end if ~isempty(gt_inds) % Indices of examples for which we try to make predictions ex_inds = find(max_overlaps >= conf.bbox_thresh); % Get IoU overlap between each ex ROI and gt ROI ex_gt_overlaps = boxoverlap(rois(ex_inds, :), rois(gt_inds, :)); % try assert(all(abs(max(ex_gt_overlaps, [], 2) - max_overlaps(ex_inds)) < 10^-4)); % catch % debug = 1; % end % Find which gt ROI each ex ROI has max overlap with: % this will be the ex ROI's gt target [~, gt_assignment] = max(ex_gt_overlaps, [], 2); gt_rois = rois(gt_inds(gt_assignment), :); ex_rois = rois(ex_inds, :); [regression_label] = fast_rcnn_bbox_transform(ex_rois, gt_rois); bbox_targets(ex_inds, :) = [max_labels(ex_inds), regression_label]; end % Select foreground ROIs as those with >= fg_thresh overlap is_fg = max_overlaps >= conf.fg_thresh; % Select background ROIs as those within [bg_thresh_lo, bg_thresh_hi) is_bg = max_overlaps < conf.bg_thresh_hi & max_overlaps >= conf.bg_thresh_lo; % check if there is any fg or bg sample. If no, filter out this image is_valid = true; if ~any(is_fg | is_bg) is_valid = false; end end
github
xiaohuige1/udn_extend-master
fast_rcnn_generate_sliding_windows.m
.m
udn_extend-master/functions/fast_rcnn/fast_rcnn_generate_sliding_windows.m
1,729
utf_8
a788da565d8e7d1810407473c3135094
function roidb = fast_rcnn_generate_sliding_windows(conf, imdb, roidb, roipool_in_size) % [pred_boxes, scores] = fast_rcnn_conv_feat_detect(conf, im, conv_feat, boxes, max_rois_num_in_gpu, net_idx) % -------------------------------------------------------- % Fast R-CNN % Reimplementation based on Python Fast R-CNN (https://github.com/rbgirshick/fast-rcnn) % Copyright (c) 2015, Shaoqing Ren % Licensed under The MIT License [see LICENSE for details] % -------------------------------------------------------- regions.images = imdb.image_ids; im_sizes = imdb.sizes; regions.boxes = cellfun(@(x) generate_sliding_windows_one_image(conf, x, roipool_in_size), num2cell(im_sizes, 2), 'UniformOutput', false); roidb = roidb_from_proposal(imdb, roidb, regions); end function boxes = generate_sliding_windows_one_image(conf, im_size, roipool_in_size) im_scale = prep_im_for_blob_size(im_size, conf.scales, conf.max_size); im_size = round(im_size * im_scale); x1 = 1:conf.feat_stride:im_size(2); y1 = 1:conf.feat_stride:im_size(1); [x1, y1] = meshgrid(x1, y1); x1 = x1(:); y1 = y1(:); x2 = x1 + roipool_in_size * conf.feat_stride - 1; y2 = y1 + roipool_in_size * conf.feat_stride - 1; boxes = [x1, y1, x2, y2]; boxes = filter_boxes(im_size, boxes); boxes = bsxfun(@times, boxes-1, 1/im_scale) + 1; end function boxes = filter_boxes(im_size, boxes) valid_ind = boxes(:, 1) >= 1 & boxes(:, 1) <= im_size(2) & ... boxes(:, 2) >= 1 & boxes(:, 2) <= im_size(1) & ... boxes(:, 3) >= 1 & boxes(:, 3) <= im_size(2) & ... boxes(:, 4) >= 1 & boxes(:, 4) <= im_size(1); boxes = boxes(valid_ind, :); end
github
xiaohuige1/udn_extend-master
showboxes.m
.m
udn_extend-master/utils/showboxes.m
2,624
utf_8
be6b3bca7e6364f27e7ac8d3f76a3628
function showboxes(im, boxes, legends, color_conf) % Draw bounding boxes on top of an image. % showboxes(im, boxes) % % ------------------------------------------------------- fix_width = 800; if isa(im, 'gpuArray') im = gather(im); end imsz = size(im); scale = fix_width / imsz(2); im = imresize(im, scale); if size(boxes{1}, 2) >= 5 boxes = cellfun(@(x) [x(:, 1:4) * scale, x(:, 5)], boxes, 'UniformOutput', false); else boxes = cellfun(@(x) x(:, 1:4) * scale, boxes, 'UniformOutput', false); end if ~exist('color_conf', 'var') color_conf = 'default'; end image(im); axis image; axis off; set(gcf, 'Color', 'white'); valid_boxes = cellfun(@(x) ~isempty(x), boxes, 'UniformOutput', true); valid_boxes_num = sum(valid_boxes); if valid_boxes_num > 0 switch color_conf case 'default' colors_candidate = colormap('hsv'); colors_candidate = colors_candidate(1:(floor(size(colors_candidate, 1)/valid_boxes_num)):end, :); colors_candidate = mat2cell(colors_candidate, ones(size(colors_candidate, 1), 1))'; colors = cell(size(valid_boxes)); colors(valid_boxes) = colors_candidate(1:sum(valid_boxes)); case 'voc' colors_candidate = colormap('hsv'); colors_candidate = colors_candidate(1:(floor(size(colors_candidate, 1)/20)):end, :); colors_candidate = mat2cell(colors_candidate, ones(size(colors_candidate, 1), 1))'; colors = colors_candidate; end for i = 1:length(boxes) if isempty(boxes{i}) continue; end for j = 1:size(boxes{i}) box = boxes{i}(j, 1:4); if size(boxes{i}, 2) >= 5 score = boxes{i}(j, end); linewidth = 2 + min(max(score, 0), 1) * 2; rectangle('Position', RectLTRB2LTWH(box), 'LineWidth', linewidth, 'EdgeColor', colors{i}); label = sprintf('%s : %.3f', legends{i}, score); text(double(box(1))+2, double(box(2)), label, 'BackgroundColor', 'w'); else linewidth = 2; rectangle('Position', RectLTRB2LTWH(box), 'LineWidth', linewidth, 'EdgeColor', colors{i}); label = sprintf('%s(%d)', legends{i}, i); text(double(box(1))+2, double(box(2)), label, 'BackgroundColor', 'w'); end end end end end function [ rectsLTWH ] = RectLTRB2LTWH( rectsLTRB ) %rects (l, t, r, b) to (l, t, w, h) rectsLTWH = [rectsLTRB(:, 1), rectsLTRB(:, 2), rectsLTRB(:, 3)-rectsLTRB(:,1)+1, rectsLTRB(:,4)-rectsLTRB(2)+1]; end
github
xiaohuige1/udn_extend-master
classification_demo.m
.m
udn_extend-master/external/caffe_new/matlab/demo/classification_demo.m
5,412
utf_8
8f46deabe6cde287c4759f3bc8b7f819
function [scores, maxlabel] = classification_demo(im, use_gpu) % [scores, maxlabel] = classification_demo(im, use_gpu) % % Image classification demo using BVLC CaffeNet. % % IMPORTANT: before you run this demo, you should download BVLC CaffeNet % from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html) % % **************************************************************************** % For detailed documentation and usage on Caffe's Matlab interface, please % refer to Caffe Interface Tutorial at % http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab % **************************************************************************** % % input % im color image as uint8 HxWx3 % use_gpu 1 to use the GPU, 0 to use the CPU % % output % scores 1000-dimensional ILSVRC score vector % maxlabel the label of the highest score % % You may need to do the following before you start matlab: % $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64 % $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 % Or the equivalent based on where things are installed on your system % % Usage: % im = imread('../../examples/images/cat.jpg'); % scores = classification_demo(im, 1); % [score, class] = max(scores); % Five things to be aware of: % caffe uses row-major order % matlab uses column-major order % caffe uses BGR color channel order % matlab uses RGB color channel order % images need to have the data mean subtracted % Data coming in from matlab needs to be in the order % [width, height, channels, images] % where width is the fastest dimension. % Here is the rough matlab for putting image data into the correct % format in W x H x C with BGR channels: % % permute channels from RGB to BGR % im_data = im(:, :, [3, 2, 1]); % % flip width and height to make width the fastest dimension % im_data = permute(im_data, [2, 1, 3]); % % convert from uint8 to single % im_data = single(im_data); % % reshape to a fixed size (e.g., 227x227). % im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % % subtract mean_data (already in W x H x C with BGR channels) % im_data = im_data - mean_data; % If you have multiple images, cat them with cat(4, ...) % Add caffe/matlab to you Matlab search PATH to use matcaffe if exist('../+caffe', 'dir') addpath('..'); else error('Please run this demo from caffe/matlab/demo'); end % Set caffe mode if exist('use_gpu', 'var') && use_gpu caffe.set_mode_gpu(); gpu_id = 0; % we will use the first gpu in this demo caffe.set_device(gpu_id); else caffe.set_mode_cpu(); end % Initialize the network using BVLC CaffeNet for image classification % Weights (parameter) file needs to be downloaded from Model Zoo. model_dir = '../../models/bvlc_reference_caffenet/'; net_model = [model_dir 'deploy.prototxt']; net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel']; phase = 'test'; % run with phase test (so that dropout isn't applied) if ~exist(net_weights, 'file') error('Please download CaffeNet from Model Zoo before you run this demo'); end % Initialize a network net = caffe.Net(net_model, net_weights, phase); if nargin < 1 % For demo purposes we will use the cat image fprintf('using caffe/examples/images/cat.jpg as input image\n'); im = imread('../../examples/images/cat.jpg'); end % prepare oversampled input % input_data is Height x Width x Channel x Num tic; input_data = {prepare_image(im)}; toc; % do forward pass to get scores % scores are now Channels x Num, where Channels == 1000 tic; % The net forward function. It takes in a cell array of N-D arrays % (where N == 4 here) containing data of input blob(s) and outputs a cell % array containing data from output blob(s) scores = net.forward(input_data); toc; scores = scores{1}; scores = mean(scores, 2); % take average scores over 10 crops [~, maxlabel] = max(scores); % call caffe.reset_all() to reset caffe caffe.reset_all(); % ------------------------------------------------------------------------ function crops_data = prepare_image(im) % ------------------------------------------------------------------------ % caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that % is already in W x H x C with BGR channels d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat'); mean_data = d.mean_data; IMAGE_DIM = 256; CROPPED_DIM = 227; % Convert an image returned by Matlab's imread to im_data in caffe's data % format: W x H x C with BGR channels im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR im_data = permute(im_data, [2, 1, 3]); % flip width and height im_data = single(im_data); % convert from uint8 to single im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR) % oversample (4 corners, center, and their x-axis flips) crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); indices = [0 IMAGE_DIM-CROPPED_DIM] + 1; n = 1; for i = indices for j = indices crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :); crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n); n = n + 1; end end center = floor(indices(2) / 2) + 1; crops_data(:,:,:,5) = ... im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:); crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
github
xiaohuige1/udn_extend-master
script_rpn_rcnn_new.m
.m
udn_extend-master/experiments/script_rpn_rcnn_new.m
4,036
utf_8
bcd6a8c3655d0587b6d7c9eca263c332
function [train_box train_box_rcnn train_box_rpn]= script_rpn_rcnn_new() clc; clear mex; clear is_valid_handle; % to clear init_key run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup')); %% -------------------- CONFIG -------------------- opts.caffe_version = 'caffe'; opts.gpu_id = auto_select_gpu; active_caffe_mex(opts.gpu_id, opts.caffe_version); % opts.nms_overlap_thres = 0.7; opts.after_nms_topN = 300; opts.use_gpu = true; opts.test_scales = 800; %% -------------------- INIT_MODEL -------------------- model_dir_rcnn = fullfile(pwd, 'def_model/final_nohnm.caffemodel'); rcnn_model_net = fullfile(pwd, 'def_model/test_fastrcnn.prototxt'); proposal_detection_model = load_proposal_detection_model(); proposal_detection_model.detection_net_def ... = rcnn_model_net; proposal_detection_model.detection_net ... = model_dir_rcnn; proposal_detection_model.conf_proposal.test_scales = opts.test_scales; proposal_detection_model.conf_detection.test_scales = opts.test_scales; if opts.use_gpu proposal_detection_model.conf_detection.image_means = gpuArray(proposal_detection_model.conf_detection.image_means); end caffe.init_log(fullfile(pwd, 'caffe_log')); % proposal net fast_rcnn_net = caffe.Net(proposal_detection_model.detection_net_def, 'test'); fast_rcnn_net.copy_from(proposal_detection_model.detection_net); % set gpu/cpu if opts.use_gpu caffe.set_mode_gpu(); else caffe.set_mode_cpu(); end %% -------------------- WARM UP -------------------- load('imdb/cache/imdb_caltech_test.mat') rpn = load('rpn_0_2.mat'); train_box ={}; train_box_rcnn = {}; running_time = []; for j = 1:length(rpn.dt_boxes) img_name = fullfile(imdb.image_dir, [imdb.image_ids{j} '.jpg']); im = imread(img_name); fprintf('%d: %s\n',j, img_name); if opts.use_gpu im = gpuArray(im); end aboxes = rpn.dt_boxes{j}; aboxes(:,3) = aboxes(:,1)+aboxes(:,3); aboxes(:,4) = aboxes(:,2)+aboxes(:,4); % test detection th = tic(); if 0%proposal_detection_model.is_share_feature [boxes, scores] = fast_rcnn_conv_feat_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ... rpn_net.blobs(proposal_detection_model.last_shared_output_blob_name), ... aboxes(:, 1:4), opts.after_nms_topN); else [boxes, scores] = fast_rcnn_im_detect_our(proposal_detection_model.conf_detection, fast_rcnn_net, im, ... aboxes(:, 1:4), 5); end t_detection = toc(th); % visualize classes = proposal_detection_model.classes; boxes_cell = cell(length(classes), 1); thres = 0; for i = 1:length(boxes_cell) if(isempty(boxes)) continue; end try boxes_cell{i} = [boxes(:, (1+(i-1)*4):(i*4)), scores(:, i)]; train_box_rcnn{j} = boxes_cell{i}; catch pause end boxes_cell{i} = boxes_cell{i}(nms(boxes_cell{i}, 0.3), :); I = boxes_cell{i}(:, 5) >= thres; boxes_cell{i} = boxes_cell{i}(I, :); end train_box{j} = boxes_cell; end save('test_rst', 'train_box_rcnn', 'train_box'); caffe.reset_all(); % clear mex; end function proposal_detection_model = load_proposal_detection_model() ld = load(fullfile(pwd, 'final/model_setting.mat')); proposal_detection_model = ld.proposal_detection_model; clear ld; end function aboxes = boxes_filter(aboxes, per_nms_topN, nms_overlap_thres, after_nms_topN, use_gpu) % to speed up nms if per_nms_topN > 0 aboxes = aboxes(1:min(length(aboxes), per_nms_topN), :); end % do nms if nms_overlap_thres > 0 && nms_overlap_thres < 1 aboxes = aboxes(nms(aboxes, nms_overlap_thres, use_gpu), :); end if after_nms_topN > 0 aboxes = aboxes(1:min(length(aboxes), after_nms_topN), :); end end
github
qboticslabs/Autoware-master
velCapture.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velCapture.m
1,047
utf_8
994f8bb886b3e3ebef61b89d3c9c00a9
% Function to capture catvehicle velocity and plotting live graph function velCapture(ROS_IP, roboname) %If number of argument is not two, flag message and exit. if nargin < 2 disp('Uage: velocityProfiler(192.168.0.32, catvehicle)'); return; end close all; %rosshutdown; modelname = strcat('/',roboname); %Connect to ROS master master_uri= strcat('http://',ROS_IP); master_uri = strcat(master_uri,':11311'); %rosinit(master_uri); %get handle for /catvehicle/vel topic for subscribing to the data speedsub = rossubscriber(strcat(modelname,'/vel')); dt = datestr(now,'mmmm-dd-yyyy-HH-MM-SS'); sprintf('Velocity capture starts at %s',dt) t = 0:0.05:50; output = zeros(length(t),1); figure; grid on; title('Velocity [m/s]'); for i = 1:length(t) speedata = receive(speedsub,10); output(i) = speedata.Linear.X; plot([max(i-1,1),i], output([max(i-1,1),i]),'b-'); hold on; drawnow; end dt = datestr(now,'mmmm-dd-yyyy-HH-MM-SS'); file = strcat(dt,'.mat'); save(file, 'output'); grid on; title('Velocity [m/s]'); end
github
qboticslabs/Autoware-master
profileByMatrix.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/profileByMatrix.m
1,840
utf_8
f81181e46cb60adc3466c4779083ce0d
%Implementation of follower algorithm %Developed by Rahul Kumar Bhadani <[email protected]> %ROS_IP = IP Address of ROS Master %lead = name of the model of leader AV Car %follower = name of the model of follower car function profileByMatrix(ROS_IP, roboname, vel_input, time_input, tire_angle) %If number of argument is not two, flag message and exit. if nargin < 4 sprintf('Uage: velocityProfiler(192.168.0.32, catvehicle, velmatfile, timematfile)'); return; end if nargin < 5 tire_angle = 0.0; end rosshutdown; close all; modelname = strcat('/',roboname); %Connect to ROS master master_uri= strcat('http://',ROS_IP); master_uri = strcat(master_uri,':11311'); rosinit(master_uri); %get handle for /catvehicle/cmd_vel topic for publishing the data velpub = rospublisher(strcat(modelname,'/cmd_vel'),rostype.geometry_msgs_Twist); %get handle for /catvehicle/vel topic for subscribing to the data speedsub = rossubscriber(strcat(modelname,'/vel')); vmat = load(vel_input); tmat = load(time_input); t = tmat.t; %Velocity profile input = vmat.Vel; %Velocity profile will be sine %input = abs(2*sin(t)); %Variable to store output velocity output = zeros(length(t),1); %handle for rosmessage object for velpub topic velMsgs = rosmessage(velpub); for i=1:length(t) velMsgs.Linear.X = input(i); velMsgs.Angular.Z = tire_angle; %Publish on the topic /catvehicle/cmd_vel send(velpub, velMsgs); %Read from the topic /catvehicle/speed speedata = receive(speedsub,10); output(i) = speedata.Linear.X; pause(0.1); if i == 3000 break; end end %Plot the input and output velocity profile [n, p] = size(output); T = 1:n; plot(T, input'); hold on; plot(T, output); title('Original Data'); legend('Input function', 'Output response'); grid on; save input.mat input output
github
qboticslabs/Autoware-master
velocityProfiler.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/velocityProfiler.m
1,758
utf_8
d12bb47043d421e21fc4fd4fa8ce4b02
%Matlab scripto to publish velocity on /catvehicle/cmd_vel topic and %subscribe to catvehicle/speed topic %Developed by Rahul Kumar Bhadani <[email protected]> %ROS_IP = IP Address of ROS Master %roboname = name of the model function velocityProfiler(ROS_IP, roboname, tire_angle) %If number of argument is not two, flag message and exit. if nargin < 2 sprintf('Uage: velocityProfiler(192.168.0.32, catvehicle)'); return; end if nargin < 3 tire_angle = 0.0; end rosshutdown; close all; modelname = strcat('/',roboname); %Connect to ROS master master_uri= strcat('http://',ROS_IP); master_uri = strcat(master_uri,':11311'); rosinit(master_uri); %get handle for /catvehicle/cmd_vel topic for publishing the data velpub = rospublisher(strcat(modelname,'/cmd_vel'),rostype.geometry_msgs_Twist); %get handle for /catvehicle/vel topic for subscribing to the data speedsub = rossubscriber(strcat(modelname,'/vel')); %Discretize timestamp t = 0:0.01:150; v1 = 3; v2 = 6; v3 = 0; %Velocity profile input = v1.*(t<50) + v2.*(t>=50).*(t<100) + v3.*(t>= 100); %Velocity profile will be sine %input = abs(2*sin(t)); %Variable to store output velocity output = zeros(length(t),1); %handle for rosmessage object for velpub topic velMsgs = rosmessage(velpub); for i=1:length(t) velMsgs.Linear.X = input(i); velMsgs.Angular.Z = tire_angle; %Publish on the topic /catvehicle/cmd_vel send(velpub, velMsgs); %Read from the topic /catvehicle/speed speedata = receive(speedsub,10); output(i) = speedata.Linear.X; end %Plot the input and output velocity profile [n, p] = size(output); T = 1:n; plot(T, input'); hold on; plot(T, output); title('Original Data'); legend('Input function', 'Output response'); grid on;
github
qboticslabs/Autoware-master
follower_profile.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/matlab files/follower_profile.m
2,381
utf_8
ea7d00e67f5e7709a2cd7287216af4af
%Implementation of follower algorithm %Developed by Rahul Kumar Bhadani <[email protected]> %ROS_IP = IP Address of ROS Master %lead = name of the model of leader AV Car %follower = name of the model of follower car function follower_profile(ROS_IP, lead, follower) %If number of argument is not two, flag message and exit. if nargin < 2 sprintf('Usage: velocityProfiler(192.168.0.32, catvehicle)'); return; end rosshutdown; close all; modelname1 = strcat('/',lead); modelname2 = strcat('/',follower); %Connect to ROS master master_uri= strcat('http://',ROS_IP); master_uri = strcat(master_uri,':11311'); rosinit(master_uri); %get handle for cmd_vel topic for publishing the data velpub1 = rospublisher(strcat(modelname1,'/cmd_vel'),rostype.geometry_msgs_Twist); velpub2 = rospublisher(strcat(modelname2,'/cmd_vel'),rostype.geometry_msgs_Twist); %get handle for speed topic for subscribing to the data speedsub1 = rossubscriber(strcat(modelname1,'/vel')); speedsub2 = rossubscriber(strcat(modelname2,'/vel')); %get handle for /DistanceEstimator distanceEstimaterSub = rossubscriber('/DistanceEstimator/dist'); %Discretize timestamp t = 0:0.05:150; v1 = 3; v2 = 6; v3 = 0; %Velocity profile input = v1.*(t<50) + v2.*(t>=50).*(t<100) + v3.*(t>= 100); %Velocity profile will be sine %input = abs(2*sin(t)); %Variable to store output velocity output1 = zeros(length(t),1); output2 = zeros(length(t),1); %handle for rosmessage object for velpub topic velMsgs1 = rosmessage(velpub1); velMsgs2 = rosmessage(velpub2); for i=1:length(t) velMsgs1.Linear.X = input(i); velMsgs1.Angular.Z = 0.0; %Publish on the topic /catvehicle/cmd_vel send(velpub1, velMsgs1); %Read from the topic /catvehicle/speed speedata1 = receive(speedsub1,10); distance = receive(distanceEstimaterSub,10); x = distance.Data; %Follower control rule velMsgs2.Linear.X = (1/30.*x + 2/3).*speedata1.Linear.X; velMsgs2.Angular.Z = 0.0; send(velpub2, velMsgs2); speedata2 = receive(speedsub2,10); output1(i) = speedata1.Linear.X; output2(i) = speedata2.Linear.X; end %Plot the input and output velocity profile [n, p] = size(output1); T = 1:n; plot(T, input'); hold on; plot(T, output1); plot(T, output2); title('Original Data'); legend('Input function', 'Output response of lead','Output response of follower'); grid on;
github
qboticslabs/Autoware-master
plotDisvout.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDisvout.m
241
utf_8
241fa676f6444e00a60b8c69a5e19efd
% Author: Jonathan Sprinkle % plots the distance outputs from a data file function plotData( timeseries ) % this timeseries is what we have figure hold on plot(timeseries.Data); plot(timeseries.uVelOut); legend({'Distance','VelOut'}); end
github
qboticslabs/Autoware-master
plotData.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotData.m
350
utf_8
17951edcd31fa9c02deeb1c49a2e0d7b
% Author: Jonathan Sprinkle % plots the distance outputs from a data file function plotData( timeseries ) % this timeseries is what we have figure hold on plot(timeseries.dist); plot(timeseries.velConverted); plot(timeseries.vdot); plot(timeseries.vout); plot(timeseries.uTireAngle); legend({'dist','velConverted','vdot','vout','uTireAngle'}); end
github
qboticslabs/Autoware-master
plotDistances.m
.m
Autoware-master/ros/src/system/gazebo/catvehicle/simulink/plotDistances.m
288
utf_8
c0779b1561faff69c733c5ec8a3a9ac7
% Author: Jonathan Sprinkle % plots the distance outputs from a data file function plotDistances load distances.mat % this timeseries is what we have figure hold on plot(DistanceEstimator.Data__signal_1_); plot(DistanceEstimator.Data__signal_2_); legend({'Distance','Angle (rad)'}); end
github
LucienVen/gd-master
Select.m
.m
gd-master/algorithm_demo/GA/Select.m
256
utf_8
179fd71a71f4daeb87894c1540de8ea6
%% 选择操作 %输入 %Chrom 种群 %FitnV 适应度值 %GGAP:代沟 %输出 %SelCh 被选择的个体 function SelCh=Select(Chrom,FitnV,GGAP) NIND=size(Chrom,1); NSel=max(floor(NIND*GGAP+.5),2); ChrIx=Sus(FitnV,NSel); SelCh=Chrom(ChrIx,:);
github
LucienVen/gd-master
PathLength.m
.m
gd-master/algorithm_demo/GA/PathLength.m
331
utf_8
13ebb52c35fa4850fcf2511f98294da0
%% 计算各个体的路径长度 % 输入: % D 两两城市之间的距离 % Chrom 个体的轨迹 function len=PathLength(D,Chrom) [row,col]=size(D); NIND=size(Chrom,1); len=zeros(NIND,1); for i=1:NIND p=[Chrom(i,:) Chrom(i,1)]; i1=p(1:end-1); i2=p(2:end); len(i,1)=sum(D((i1-1)*col+i2)); end
github
LucienVen/gd-master
InitPop.m
.m
gd-master/algorithm_demo/GA/InitPop.m
290
utf_8
6b17fdfc122469cbc5d1bfb4736c2100
%% 初始化种群 %输入: % NIND:种群大小 % N: 个体染色体长度(这里为城市的个数) %输出: %初始种群 function Chrom=InitPop(NIND,N) Chrom=zeros(NIND,N);%用于存储种群 for i=1:NIND Chrom(i,:)=randperm(N);%随机生成初始种群 end
github
LucienVen/gd-master
Sus.m
.m
gd-master/algorithm_demo/GA/Sus.m
985
utf_8
6df9c400c837d331ed8072eab4de995e
% 输入: %FitnV 个体的适应度值 %Nsel 被选择个体的数目 % 输出: %NewChrIx 被选择个体的索引号 function NewChrIx = Sus(FitnV,Nsel) [Nind,ans] = size(FitnV); cumfit = cumsum(FitnV); % 平均适应度*个体行号=适应度在每行的平均比例 trials = cumfit(Nind) / Nsel * (rand + (0:Nsel-1)'); % 适应度真实累加向量,拓展为Nind*Nsel维矩阵 Mf = cumfit(:, ones(1, Nsel)); % 适应度平均比例向量,拓展为Nsel*Nind维矩阵,转置后能与Mf进行比较操作 Mt = trials(:, ones(1, Nind))'; % 寻找适应度高于该种群每行应有的适应度平均比例的个体 % Mt<Mf:从上向下开始逼近该行最佳个体 % z<=mt:从下向上开始逼近该行最佳个体 % &:确定最佳个体所在行 % 获取其行号 [NewChrIx, ans] = find(Mt < Mf & [ zeros(1, Nsel); Mf(1:Nind-1, :) ] <= Mt); % 生成随机序列 [ans, shuf] = sort(rand(Nsel, 1)); % 随机化 NewChrIx = NewChrIx(shuf);
github
LucienVen/gd-master
Distanse.m
.m
gd-master/algorithm_demo/GA/Distanse.m
303
utf_8
6596609d0bd418a504c5658780a7f85c
%% 计算两两城市之间的距离 %输入 a 各城市的位置坐标 %输出 D 两两城市之间的距离 function D=Distanse(a) row=size(a,1); D=zeros(row,row); for i=1:row for j=i+1:row D(i,j)=((a(i,1)-a(j,1))^2+(a(i,2)-a(j,2))^2)^0.5; D(j,i)=D(i,j); end end
github
LucienVen/gd-master
OutputPath.m
.m
gd-master/algorithm_demo/GA/OutputPath.m
186
UNKNOWN
ae0e96e563d28fa8e74cc505593c99cd
%% ���·������ %���룺R ·�� function p=OutputPath(R) R=[R,R(1)]; N=length(R); p=num2str(R(1)); for i=2:N p=[p,'->',num2str(R(i))]; end disp(p)
github
LucienVen/gd-master
Recombin.m
.m
gd-master/algorithm_demo/GA/Recombin.m
1,484
utf_8
3e1af8d1876ce772ba45578149206262
%% 交叉操作 % 输入 %SelCh 被选择的个体 %Pc 交叉概率 %输出: % SelCh 交叉后的个体 function SelCh=Recombin(SelCh,Pc) NSel=size(SelCh,1); for i=1:2:NSel-mod(NSel,2) if Pc>=rand %交叉概率Pc [SelCh(i,:),SelCh(i+1,:)]=intercross(SelCh(i,:),SelCh(i+1,:)); end end %输入: %a和b为两个待交叉的个体 %输出: %a和b为交叉后得到的两个个体 function [a,b]=intercross(a,b) L=length(a); r1=randsrc(1,1,[1:L]); r2=randsrc(1,1,[1:L]); if r1~=r2 a0=a;b0=b; s=min([r1,r2]); e=max([r1,r2]); for i=s:e a1=a;b1=b; a(i)=b0(i); b(i)=a0(i); x=find(a==a(i)); y=find(b==b(i)); i1=x(x~=i); i2=y(y~=i); if ~isempty(i1) a(i1)=a1(i); end if ~isempty(i2) b(i2)=b1(i); end end end % % %交叉算法采用部分匹配交叉%交叉算法采用部分匹配交叉 % function [a,b]=intercross(a,b) % L=length(a); % r1=ceil(rand*L); % r2=ceil(rand*L); % r1=4;r2=7; % if r1~=r2 % s=min([r1,r2]); % e=max([r1,r2]); % a1=a;b1=b; % a(s:e)=b1(s:e); % b(s:e)=a1(s:e); % for i=[setdiff(1:L,s:e)] % [tf, loc] = ismember(a(i),a(s:e)); % if tf % a(i)=a1(loc+s-1); % end % [tf, loc]=ismember(b(i),b(s:e)); % if tf % b(i)=b1(loc+s-1); % end % end % end
github
LucienVen/gd-master
Reins.m
.m
gd-master/algorithm_demo/GA/Reins.m
338
utf_8
12d9f6b6178ac2b153a1fba9781b143c
%% 重插入子代的新种群 %输入: %Chrom 父代的种群 %SelCh 子代种群 %ObjV 父代适应度 %输出 % Chrom 组合父代与子代后得到的新种群 function Chrom=Reins(Chrom,SelCh,ObjV) NIND=size(Chrom,1); NSel=size(SelCh,1); [TobjV,index]=sort(ObjV); Chrom=[Chrom(index(1:NIND-NSel),:);SelCh];
github
LucienVen/gd-master
Reverse.m
.m
gd-master/algorithm_demo/GA/Reverse.m
574
utf_8
9bc395d8caadb1712d5f089b36a7fc01
%% 进化逆转函数 %输入 %SelCh 被选择的个体 %D 个城市的距离矩阵 %输出 %SelCh 进化逆转后的个体 function SelCh=Reverse(SelCh,D) [row,col]=size(SelCh); ObjV=PathLength(D,SelCh); %计算路径长度 SelCh1=SelCh; for i=1:row r1=randsrc(1,1,[1:col]); r2=randsrc(1,1,[1:col]); mininverse=min([r1 r2]); maxinverse=max([r1 r2]); SelCh1(i,mininverse:maxinverse)=SelCh1(i,maxinverse:-1:mininverse); end ObjV1=PathLength(D,SelCh1); %计算路径长度 index=ObjV1<ObjV; SelCh(index,:)=SelCh1(index,:);
github
LucienVen/gd-master
DrawPath.m
.m
gd-master/algorithm_demo/GA/DrawPath.m
637
utf_8
b3eab77cf2d7da9806543d7d85ff2f93
%% 画路径函数 %输入 % Chrom 待画路径 % X 各城市坐标位置 function DrawPath(Chrom,X) R=[Chrom(1,:) Chrom(1,1)]; %一个随机解(个体) figure; hold on plot(X(:,1),X(:,2),'o','color',[0.5,0.5,0.5]) plot(X(Chrom(1,1),1),X(Chrom(1,1),2),'rv','MarkerSize',20) for i=1:size(X,1) text(X(i,1)+0.05,X(i,2)+0.05,num2str(i),'color',[1,0,0]); end A=X(R,:); row=size(A,1); for i=2:row %坐标转换 [arrowx,arrowy] = dsxy2figxy(gca,A(i-1:i,1),A(i-1:i,2)); annotation('textarrow',arrowx,arrowy,'HeadWidth',8,'color',[0,0,1]); end hold off xlabel('X') ylabel('Y') title('Track') box on
github
LucienVen/gd-master
Mutate.m
.m
gd-master/algorithm_demo/GA/Mutate.m
289
utf_8
b34c58d441f4ceffd3fdbf23eda39b62
%% 变异操作 %输入: %SelCh 被选择的个体 %Pm 变异概率 %输出: % SelCh 变异后的个体 function SelCh=Mutate(SelCh,Pm) [NSel,L]=size(SelCh); for i=1:NSel if Pm>=rand R=randperm(L); SelCh(i,R(1:2))=SelCh(i,R(2:-1:1)); end end
github
LucienVen/gd-master
Fitness.m
.m
gd-master/algorithm_demo/GA/Fitness.m
153
utf_8
feee0abaf4bf254c9cbad9e0e456053d
%% 适配值函数 %输入: %个体的长度(TSP的距离) %输出: %个体的适应度值 function FitnV=Fitness(len) FitnV=1./len;
github
BII-wushuang/FLLIT-master
intersections.m
.m
FLLIT-master/src/intersections.m
11,443
utf_8
ea1423b06fc1ebab4dbd147edf8c0a07
function [x0,y0,iout,jout] = intersections(x1,y1,x2,y2,robust) %INTERSECTIONS Intersections of curves. % Computes the (x,y) locations where two curves intersect. The curves % can be broken with NaNs or have vertical segments. % % Example: % [X0,Y0] = intersections(X1,Y1,X2,Y2,ROBUST); % % where X1 and Y1 are equal-length vectors of at least two points and % represent curve 1. Similarly, X2 and Y2 represent curve 2. % X0 and Y0 are column vectors containing the points at which the two % curves intersect. % % ROBUST (optional) set to 1 or true means to use a slight variation of the % algorithm that might return duplicates of some intersection points, and % then remove those duplicates. The default is true, but since the % algorithm is slightly slower you can set it to false if you know that % your curves don't intersect at any segment boundaries. Also, the robust % version properly handles parallel and overlapping segments. % % The algorithm can return two additional vectors that indicate which % segment pairs contain intersections and where they are: % % [X0,Y0,I,J] = intersections(X1,Y1,X2,Y2,ROBUST); % % For each element of the vector I, I(k) = (segment number of (X1,Y1)) + % (how far along this segment the intersection is). For example, if I(k) = % 45.25 then the intersection lies a quarter of the way between the line % segment connecting (X1(45),Y1(45)) and (X1(46),Y1(46)). Similarly for % the vector J and the segments in (X2,Y2). % % You can also get intersections of a curve with itself. Simply pass in % only one curve, i.e., % % [X0,Y0] = intersections(X1,Y1,ROBUST); % % where, as before, ROBUST is optional. % Version: 2.0, 25 May 2017 % Author: Douglas M. Schwarz % Email: dmschwarz=ieee*org, dmschwarz=urgrad*rochester*edu % Real_email = regexprep(Email,{'=','*'},{'@','.'}) % Theory of operation: % % Given two line segments, L1 and L2, % % L1 endpoints: (x1(1),y1(1)) and (x1(2),y1(2)) % L2 endpoints: (x2(1),y2(1)) and (x2(2),y2(2)) % % we can write four equations with four unknowns and then solve them. The % four unknowns are t1, t2, x0 and y0, where (x0,y0) is the intersection of % L1 and L2, t1 is the distance from the starting point of L1 to the % intersection relative to the length of L1 and t2 is the distance from the % starting point of L2 to the intersection relative to the length of L2. % % So, the four equations are % % (x1(2) - x1(1))*t1 = x0 - x1(1) % (x2(2) - x2(1))*t2 = x0 - x2(1) % (y1(2) - y1(1))*t1 = y0 - y1(1) % (y2(2) - y2(1))*t2 = y0 - y2(1) % % Rearranging and writing in matrix form, % % [x1(2)-x1(1) 0 -1 0; [t1; [-x1(1); % 0 x2(2)-x2(1) -1 0; * t2; = -x2(1); % y1(2)-y1(1) 0 0 -1; x0; -y1(1); % 0 y2(2)-y2(1) 0 -1] y0] -y2(1)] % % Let's call that A*T = B. We can solve for T with T = A\B. % % Once we have our solution we just have to look at t1 and t2 to determine % whether L1 and L2 intersect. If 0 <= t1 < 1 and 0 <= t2 < 1 then the two % line segments cross and we can include (x0,y0) in the output. % % In principle, we have to perform this computation on every pair of line % segments in the input data. This can be quite a large number of pairs so % we will reduce it by doing a simple preliminary check to eliminate line % segment pairs that could not possibly cross. The check is to look at the % smallest enclosing rectangles (with sides parallel to the axes) for each % line segment pair and see if they overlap. If they do then we have to % compute t1 and t2 (via the A\B computation) to see if the line segments % cross, but if they don't then the line segments cannot cross. In a % typical application, this technique will eliminate most of the potential % line segment pairs. % Input checks. if verLessThan('matlab','7.13') error(nargchk(2,5,nargin)) %#ok<NCHKN> else narginchk(2,5) end % Adjustments based on number of arguments. switch nargin case 2 robust = true; x2 = x1; y2 = y1; self_intersect = true; case 3 robust = x2; x2 = x1; y2 = y1; self_intersect = true; case 4 robust = true; self_intersect = false; case 5 self_intersect = false; end % x1 and y1 must be vectors with same number of points (at least 2). if sum(size(x1) > 1) ~= 1 || sum(size(y1) > 1) ~= 1 || ... length(x1) ~= length(y1) error('X1 and Y1 must be equal-length vectors of at least 2 points.') end % x2 and y2 must be vectors with same number of points (at least 2). if sum(size(x2) > 1) ~= 1 || sum(size(y2) > 1) ~= 1 || ... length(x2) ~= length(y2) error('X2 and Y2 must be equal-length vectors of at least 2 points.') end % Force all inputs to be column vectors. x1 = x1(:); y1 = y1(:); x2 = x2(:); y2 = y2(:); % Compute number of line segments in each curve and some differences we'll % need later. n1 = length(x1) - 1; n2 = length(x2) - 1; xy1 = [x1 y1]; xy2 = [x2 y2]; dxy1 = diff(xy1); dxy2 = diff(xy2); % Determine the combinations of i and j where the rectangle enclosing the % i'th line segment of curve 1 overlaps with the rectangle enclosing the % j'th line segment of curve 2. % Original method that works in old MATLAB versions, but is slower than % using binary singleton expansion (explicit or implicit). % [i,j] = find( ... % repmat(mvmin(x1),1,n2) <= repmat(mvmax(x2).',n1,1) & ... % repmat(mvmax(x1),1,n2) >= repmat(mvmin(x2).',n1,1) & ... % repmat(mvmin(y1),1,n2) <= repmat(mvmax(y2).',n1,1) & ... % repmat(mvmax(y1),1,n2) >= repmat(mvmin(y2).',n1,1)); % Select an algorithm based on MATLAB version and number of line % segments in each curve. We want to avoid forming large matrices for % large numbers of line segments. If the matrices are not too large, % choose the best method available for the MATLAB version. if n1 > 1000 || n2 > 1000 || verLessThan('matlab','7.4') % Determine which curve has the most line segments. if n1 >= n2 % Curve 1 has more segments, loop over segments of curve 2. ijc = cell(1,n2); min_x1 = mvmin(x1); max_x1 = mvmax(x1); min_y1 = mvmin(y1); max_y1 = mvmax(y1); for k = 1:n2 k1 = k + 1; ijc{k} = find( ... min_x1 <= max(x2(k),x2(k1)) & max_x1 >= min(x2(k),x2(k1)) & ... min_y1 <= max(y2(k),y2(k1)) & max_y1 >= min(y2(k),y2(k1))); ijc{k}(:,2) = k; end ij = vertcat(ijc{:}); i = ij(:,1); j = ij(:,2); else % Curve 2 has more segments, loop over segments of curve 1. ijc = cell(1,n1); min_x2 = mvmin(x2); max_x2 = mvmax(x2); min_y2 = mvmin(y2); max_y2 = mvmax(y2); for k = 1:n1 k1 = k + 1; ijc{k}(:,2) = find( ... min_x2 <= max(x1(k),x1(k1)) & max_x2 >= min(x1(k),x1(k1)) & ... min_y2 <= max(y1(k),y1(k1)) & max_y2 >= min(y1(k),y1(k1))); ijc{k}(:,1) = k; end ij = vertcat(ijc{:}); i = ij(:,1); j = ij(:,2); end elseif verLessThan('matlab','9.1') % Use bsxfun. [i,j] = find( ... bsxfun(@le,mvmin(x1),mvmax(x2).') & ... bsxfun(@ge,mvmax(x1),mvmin(x2).') & ... bsxfun(@le,mvmin(y1),mvmax(y2).') & ... bsxfun(@ge,mvmax(y1),mvmin(y2).')); else % Use implicit expansion. [i,j] = find( ... mvmin(x1) <= mvmax(x2).' & mvmax(x1) >= mvmin(x2).' & ... mvmin(y1) <= mvmax(y2).' & mvmax(y1) >= mvmin(y2).'); end % Find segments pairs which have at least one vertex = NaN and remove them. % This line is a fast way of finding such segment pairs. We take % advantage of the fact that NaNs propagate through calculations, in % particular subtraction (in the calculation of dxy1 and dxy2, which we % need anyway) and addition. % At the same time we can remove redundant combinations of i and j in the % case of finding intersections of a line with itself. if self_intersect remove = isnan(sum(dxy1(i,:) + dxy2(j,:),2)) | j <= i + 1; else remove = isnan(sum(dxy1(i,:) + dxy2(j,:),2)); end i(remove) = []; j(remove) = []; % Initialize matrices. We'll put the T's and B's in matrices and use them % one column at a time. AA is a 3-D extension of A where we'll use one % plane at a time. n = length(i); T = zeros(4,n); AA = zeros(4,4,n); AA([1 2],3,:) = -1; AA([3 4],4,:) = -1; AA([1 3],1,:) = dxy1(i,:).'; AA([2 4],2,:) = dxy2(j,:).'; B = -[x1(i) x2(j) y1(i) y2(j)].'; % Loop through possibilities. Trap singularity warning and then use % lastwarn to see if that plane of AA is near singular. Process any such % segment pairs to determine if they are colinear (overlap) or merely % parallel. That test consists of checking to see if one of the endpoints % of the curve 2 segment lies on the curve 1 segment. This is done by % checking the cross product % % (x1(2),y1(2)) - (x1(1),y1(1)) x (x2(2),y2(2)) - (x1(1),y1(1)). % % If this is close to zero then the segments overlap. % If the robust option is false then we assume no two segment pairs are % parallel and just go ahead and do the computation. If A is ever singular % a warning will appear. This is faster and obviously you should use it % only when you know you will never have overlapping or parallel segment % pairs. if robust overlap = false(n,1); warning_state = warning('off','MATLAB:singularMatrix'); % Use try-catch to guarantee original warning state is restored. try lastwarn('') for k = 1:n T(:,k) = AA(:,:,k)\B(:,k); [unused,last_warn] = lastwarn; %#ok<ASGLU> lastwarn('') if strcmp(last_warn,'MATLAB:singularMatrix') % Force in_range(k) to be false. T(1,k) = NaN; % Determine if these segments overlap or are just parallel. overlap(k) = rcond([dxy1(i(k),:);xy2(j(k),:) - xy1(i(k),:)]) < eps; end end warning(warning_state) catch err warning(warning_state) rethrow(err) end % Find where t1 and t2 are between 0 and 1 and return the corresponding % x0 and y0 values. in_range = (T(1,:) >= 0 & T(2,:) >= 0 & T(1,:) <= 1 & T(2,:) <= 1).'; % For overlapping segment pairs the algorithm will return an % intersection point that is at the center of the overlapping region. if any(overlap) ia = i(overlap); ja = j(overlap); % set x0 and y0 to middle of overlapping region. T(3,overlap) = (max(min(x1(ia),x1(ia+1)),min(x2(ja),x2(ja+1))) + ... min(max(x1(ia),x1(ia+1)),max(x2(ja),x2(ja+1)))).'/2; T(4,overlap) = (max(min(y1(ia),y1(ia+1)),min(y2(ja),y2(ja+1))) + ... min(max(y1(ia),y1(ia+1)),max(y2(ja),y2(ja+1)))).'/2; selected = in_range | overlap; else selected = in_range; end xy0 = T(3:4,selected).'; % Remove duplicate intersection points. [xy0,index] = unique(xy0,'rows'); x0 = xy0(:,1); y0 = xy0(:,2); % Compute how far along each line segment the intersections are. if nargout > 2 sel_index = find(selected); sel = sel_index(index); iout = i(sel) + T(1,sel).'; jout = j(sel) + T(2,sel).'; end else % non-robust option for k = 1:n [L,U] = lu(AA(:,:,k)); T(:,k) = U\(L\B(:,k)); end % Find where t1 and t2 are between 0 and 1 and return the corresponding % x0 and y0 values. in_range = (T(1,:) >= 0 & T(2,:) >= 0 & T(1,:) < 1 & T(2,:) < 1).'; x0 = T(3,in_range).'; y0 = T(4,in_range).'; % Compute how far along each line segment the intersections are. if nargout > 2 iout = i(in_range) + T(1,in_range).'; jout = j(in_range) + T(2,in_range).'; end end % Plot the results (useful for debugging). % plot(x1,y1,x2,y2,x0,y0,'ok'); function y = mvmin(x) % Faster implementation of movmin(x,k) when k = 1. y = min(x(1:end-1),x(2:end)); function y = mvmax(x) % Faster implementation of movmax(x,k) when k = 1. y = max(x(1:end-1),x(2:end));
github
BII-wushuang/FLLIT-master
DataProcessing_New.m
.m
FLLIT-master/src/DataProcessing_New.m
29,995
utf_8
7ca0ff2ca117284f58bb761b4646e067
% Processes the raw data to extract relevant parameters function DataProcessing_New(fps,data_dir,scale, bodylength) if (nargin < 1) fps = 2000; scale=11; % dimension of arena is 11mm data_dir = uigetdir('./Results/Tracking/'); bodylength = 2.88; end bodylength = bodylength * 512 / scale; try addpath('./Export-Fig'); catch end pixel_mm = 11/512; % 1 pixel corresponds to how many mm frame_ms = 1000/fps; % 1 frame corresponds to how many ms pos_bs = strfind(data_dir,'Results'); sub_dir = data_dir(pos_bs(end)+length('Results/Tracking/'):length(data_dir)); output_dir = ['./Results/Tracking/' sub_dir '/']; seg_dir = ['./Results/SegmentedImages/' sub_dir '/']; load([output_dir 'trajectory.mat']); load([output_dir 'norm_trajectory.mat']); load([output_dir 'CoM.mat']); %trajectory(:,:,2) = -trajectory(:,:,2); norm_trajectory(:,:,2) = -norm_trajectory(:,:,2); nlegs = size(trajectory,2); if (nlegs == 6) legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1]; else legs_id = {'L1', 'L2', 'L3', 'L4', 'R1', 'R2', 'R3', 'R4'}; colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 1 0 1; 0 1 1; 1 0.5 0]; end start = zeros(nlegs,1); missing = zeros(nlegs,1); for j = 1:nlegs if (isempty(find(trajectory(:,j,1),1))) missing(j) = 1; continue; end start(j) = find(trajectory(:,j,1),1); end startframe = max(start); endframe = length(CoM); nframes = endframe - startframe + 1; dist = zeros(endframe,1); x = zeros(endframe, nlegs); y = zeros(endframe, nlegs); nx = zeros(endframe, nlegs); ny = zeros(endframe, nlegs); % body length try img = imread([seg_dir 'roi_' num2str(1) '.png']); body_length = zeros(nframes,1); for i = startframe : endframe img = imread([seg_dir 'roi_' num2str(i) '.png']); img_norm = imtranslate(img, [255 - CoM(i,1), 255 - CoM(i,2)]); img_norm = imrotate(img_norm, CoM(i,3)); img_norm = imcrop(img_norm, [size(img_norm,1)/2-150 size(img_norm,2)/2-150 300 300]); [Y,X] = find(img_norm); body_length(i-startframe+1) = max(Y(find(X==150)))-min(Y(find(X==150))); end fileID = fopen([output_dir 'bodylength.xlsx'],'w'); fprintf(fileID, '%s\t\n', 'Mean body length (mm)'); fprintf(fileID, '%d\n\n', mean(body_length)*scale/512); fprintf(fileID, '%s\t%s\t\n', 'Frame #', 'Length (mm)'); fclose(fileID); dlmwrite([output_dir 'bodylength.xlsx'],[(startframe:endframe)', body_length*scale/512],'delimiter','\t','-append'); catch end for j =1:nlegs if(missing(j)) continue; end nonzero = find(trajectory(:,j,1)); for i = startframe : endframe if (trajectory(i,j,1) ~= 0) x(i,j) = trajectory(i,j,1); y(i,j) = trajectory(i,j,2); nx(i,j) = norm_trajectory(i,j,1); ny(i,j) = norm_trajectory(i,j,2); else previdx = nonzero(find(nonzero<i, 1)); nextidx = nonzero(find(nonzero>i, 1)); if (~isempty(nextidx)) x(i,j) = trajectory(previdx,j,1) + (i-previdx)/(nextidx-previdx) * (trajectory(nextidx,j,1) - trajectory(previdx,j,1)); y(i,j) = trajectory(previdx,j,2) + (i-previdx)/(nextidx-previdx) * (trajectory(nextidx,j,2) - trajectory(previdx,j,2)); nx(i,j) = norm_trajectory(previdx,j,1) + (i-previdx)/(nextidx-previdx) * (norm_trajectory(nextidx,j,1) - norm_trajectory(previdx,j,1)); ny(i,j) = norm_trajectory(previdx,j,2) + (i-previdx)/(nextidx-previdx) * (norm_trajectory(nextidx,j,2) - norm_trajectory(previdx,j,2)); else x(i,j) = x(i-1,j); y(i,j) = y(i-1,j); nx(i,j) = nx(i-1,j); ny(i,j) = ny(i-1,j); end end end end for i = startframe : endframe if (i == startframe) dist(i) = 0; else dist(i) = dist(i-1) + (CoM(i,1) - CoM(i-1,1))*sind(CoM(i-1,3)) + (CoM(i,2) - CoM(i-1,2))*-cosd(CoM(i-1,3)); end end %% Locate turning points %Finding the turning points: first use the RDP algorithm to reduce number %of points to describe the 2D trajectory, if the turning angle at a reduced point %is larger than a threshold (set to 50 deg), this point %will be identified as a turning point. reducedpoints = DouglasPeucker([CoM(startframe:endframe,1), CoM(startframe:endframe,2)],10,0); turn_angle = zeros(length(reducedpoints)-2,1); for i = 1 : length(reducedpoints) - 2 vec_1 = [reducedpoints(i+2,1)-reducedpoints(i+1,1), reducedpoints(i+2,2)-reducedpoints(i+1,2)]; vec_2 = [reducedpoints(i+1,1)-reducedpoints(i,1), reducedpoints(i+1,2)-reducedpoints(i,2)]; turn_angle(i) = acosd(dot(vec_1,vec_2) / (norm(vec_1)*norm(vec_2))); end turn_thres = 50; turn_points_idx = find(turn_angle>turn_thres); turn_points = zeros(length(turn_points_idx),2); for i = 1 : length(turn_points_idx) turn_points(i,:) = [reducedpoints(turn_points_idx(i)+1,1), reducedpoints(turn_points_idx(i)+1,2)]; turn_labels{i} = ['Turn' num2str(i) ' = (' num2str(round(turn_points(i,1))) ', ' num2str(round(turn_points(i,2))) ')']; end try clf(1); catch end figure(1); scatter(CoM(startframe : endframe,1),CoM(startframe : endframe,2),'*'); title('Trajectory'); axis([0 512 0 512]); axis square set(gca,'Ydir','reverse') % if (~isempty(turn_points_idx)) % labelpoints(turn_points(:,1)', turn_points(:,2)', turn_labels, 'Center'); % end export_fig([output_dir 'BodyTrajectory.pdf'], '-pdf','-transparent'); %% Obtaining the CoM speed i = startframe : endframe; Distance = dist(startframe : endframe); skip = min(nframes/2,floor(50/frame_ms)); B(1) = 0; B(startframe+1:endframe) = diff(dist(startframe:endframe)); f = fit((startframe + skip / 2: skip: endframe - skip/2)', B(startframe + skip / 2: skip: endframe - skip/2)', 'smoothingspline', 'SmoothingParam', 0.8); %Speedhist = hist(11000/512*f(i),(-20:0.5:50)); Speed = [[1:nframes]'*frame_ms, f(i)*pixel_mm*1000/frame_ms]; fileID = fopen([output_dir 'BodyVelocity.xlsx'],'w'); fprintf(fileID,'%s \t %s', 'Time(ms)', 'Velocity (mm/s)'); fprintf(fileID,'\n'); fclose(fileID); dlmwrite([output_dir 'BodyVelocity.xlsx'],Speed,'delimiter','\t','-append'); clf(1); figure(1); hold on Speedplot = plot(Speed(:,1),Speed(:,2)); plot(zeros(nframes,1),'--','color',[0 0 0]); hold off title('Body Velocity'); axis([1 nframes*frame_ms -30 60]); Speedplot.Parent.XLabel.String = 'Time(ms)'; Speedplot.Parent.YLabel.String = 'Velocity (mm/s)'; export_fig([output_dir 'BodyVelocity.pdf'], '-pdf','-transparent'); % Speedhistplot = plot(Speedhist); % title('Histogram of Drosophila Velocity'); % Speedhistplot.Parent.XLabel.String = 'Velocity'; % Speedhistplot.Parent.YLabel.String = '# Occurence'; % export_fig([output_dir 'HistogramVelocity.pdf'], '-pdf','-transparent'); %% Leg Speed and Gait legspd = zeros(nframes,nlegs); nlegspd = zeros(nframes,nlegs); legsy = zeros(nframes,nlegs); gait = zeros(nframes,nlegs); ngait = zeros(nframes,nlegs); foot_dragging = zeros(nframes,nlegs); i = startframe : endframe; clf(1); figure(1); for j = 1:nlegs % Speed in arena-centered frame of reference displacement(startframe: endframe)=sqrt(x(startframe: endframe,j).^2+y(startframe: endframe,j).^2); for k = startframe:endframe if (k==startframe) spd(k,j) = 0; else spd(k,j) = sqrt((x(k,j)-x(k-1,j))^2+(y(k,j)-y(k-1,j))^2); end end f = fit((startframe: 10: endframe)', spd(startframe: 10: endframe,j), 'smoothingspline', 'SmoothingParam', 1); legspd(:, j) = abs(f(i))*pixel_mm*1000/frame_ms; % Vertical leg trajectories in body-centred frame of reference f = fit((startframe: 10: endframe)', ny(startframe: 10: endframe,j), 'smoothingspline', 'SmoothingParam', 0.95); legsy(:, j) = f(i); hold on subplot(2, round(nlegs/2), j); plot(legsy(:,j)); title(legs_id{j}); axis([1 nframes -150 150]); hold off % Gait coordination skip = floor(10 / frame_ms); g = fit((startframe: skip: endframe)', x(startframe: skip: endframe,j), 'smoothingspline', 'SmoothingParam', 1); h = fit((startframe: skip: endframe)', y(startframe: skip: endframe,j), 'smoothingspline', 'SmoothingParam', 1); gait_idx = find(abs(gradient(g(i)))>0.3*frame_ms | abs(gradient(h(i)))>0.3*frame_ms); % gait_idx = find(legspd(:,j)>mean(legspd(:,j))/3); gait(gait_idx,j) = 1; ng = fit((startframe: skip: endframe)', nx(startframe: skip: endframe,j), 'smoothingspline', 'SmoothingParam', 1); nh = fit((startframe: skip: endframe)', ny(startframe: skip: endframe,j), 'smoothingspline', 'SmoothingParam', 1); ngait_idx = find(abs(gradient(ng(i)))>0.2*frame_ms | abs(gradient(nh(i)))>0.2*frame_ms); ngait(ngait_idx,j) = 1; thres = 15/frame_ms; cc = bwconncomp(gait(:,j)); for k = 1 : cc.NumObjects if cellfun(@length,cc.PixelIdxList(k))<thres gait(cc.PixelIdxList{k},j) = 0; end end cc = bwconncomp(~gait(:,j)); for k = 1 : cc.NumObjects if cellfun(@length,cc.PixelIdxList(k))<thres gait(cc.PixelIdxList{k},j) = 1; end end cc = bwconncomp(ngait(:,j)); for k = 1 : cc.NumObjects if cellfun(@length,cc.PixelIdxList(k))<thres ngait(cc.PixelIdxList{k},j) = 0; end end cc = bwconncomp(~ngait(:,j)); for k = 1 : cc.NumObjects if cellfun(@length,cc.PixelIdxList(k))<thres ngait(cc.PixelIdxList{k},j) = 1; end end % Foot dragging is identified as an event where the leg is moving with % respect to the arena yet relatively stationary with respect to the % body. The episode must last a minimum of 20ms. foot_dragging(:,j) = ~ngait(:,j).*gait(:,j); cc = bwconncomp(foot_dragging(:,j)); for k = 1 : cc.NumObjects if cellfun(@length,cc.PixelIdxList(k))<20/frame_ms foot_dragging(cc.PixelIdxList{k},j) = 0; end end clear connComp; end mtit('Vertical leg trajectories in body-centred frame of reference','xoff',0,'yoff',.04); export_fig([output_dir 'LegVerticalTrajectories.pdf'], '-pdf','-transparent'); % legspd = legspd.*gait; fileID = fopen([output_dir 'LegSpeed.xlsx'],'w'); fprintf(fileID,'%s \t %s \t', 'Time(ms)', legs_id{:}); fprintf(fileID,'\n'); fclose(fileID); dlmwrite([output_dir 'LegSpeed.xlsx'],[[1:nframes]'*frame_ms, legspd],'delimiter','\t','-append'); % fileID = fopen([output_dir 'FootDragging.xlsx'],'w'); % fprintf(fileID,'%s \t %s \t %s \n', 'Leg', 'StartFrame', 'EndFrame'); % for j = 1 : nlegs % cc = bwconncomp(foot_dragging(:,j)); % if(cc.NumObjects>0) % fprintf(fileID,'%s', legs_id{j}); % for k = 1 : cc.NumObjects % fprintf(fileID,'\t %d \t %d \n', min(cc.PixelIdxList{k}), max(cc.PixelIdxList{k})); % end % end % end % fclose(fileID); clf(1); figure(1); colormap('jet'); clims = [0 200]; Gaitplot = imagesc([1 nframes*frame_ms], [1 nlegs], abs(legspd)', clims); colorbar; title('Gait and Speed'); Gaitplot.Parent.YTickLabel = legs_id; Gaitplot.Parent.XLabel.String = 'Time(ms)'; export_fig([output_dir 'Gait.pdf'], '-pdf','-transparent'); %% Gait index if (nlegs ==6) gaitindex = zeros(nframes,1); for i = 1:nframes if isequal(gait(i,:),[1 0 1 0 1 0]) || isequal(gait(i,:),[0 1 0 1 0 1]) gaitindex(i) = 1; continue; elseif isequal(gait(i,:),[1 0 0 0 1 0]) || isequal(gait(i,:),[0 1 0 1 0 0 0]) || isequal(gait(i,:),[1 0 0 0 0 1]) || isequal(gait(i,:),[0 0 1 1 0 0]) || isequal(gait(i,:),[0 1 0 0 0 1]) || isequal(gait(i,:),[0 0 1 0 1 0]) gaitindex(i) = -1; end end % Gait index averaged over a moving window of 80ms gaitindex = movmean(gaitindex,80/frame_ms); fileID = fopen([output_dir 'GaitIndex.xlsx'],'w'); fprintf(fileID,'%s \t %s', 'Time(ms)', 'Gait Index'); fprintf(fileID,'\n'); fclose(fileID); dlmwrite([output_dir 'GaitIndex.xlsx'],[[1:nframes]'*frame_ms, gaitindex],'delimiter','\t','-append'); clf(1); figure(1); GaitIndexplot = plot([1:nframes]'*frame_ms, gaitindex); title('Gait Index'); axis([0 nframes*frame_ms -1 1]); GaitIndexplot.Parent.YLabel.String = 'Gait Index'; GaitIndexplot.Parent.XLabel.String = 'Time(ms)'; export_fig([output_dir 'GaitIndex.pdf'], '-pdf','-transparent'); end %% Stride Parameters fileID = fopen([output_dir 'StrideParameters.xlsx'],'w'); fclose(fileID); landing_std = zeros(nlegs,2); takeoff_std = zeros(nlegs,2); landing_mean = zeros(nlegs,2); takeoff_mean = zeros(nlegs,2); for j = 1:nlegs if(missing(j)) continue; end cc = bwconncomp(gait(:,j)); nstrides(j) = cc.NumObjects; if(nstrides(j)==0) fileID = fopen([output_dir 'StrideParameters.xlsx'],'a'); fprintf(fileID,'%s',legs_id{j}); fprintf(fileID,'\n'); fprintf(fileID,'%s \t', 'Stride #', 'Duration (ms)', 'Period (ms)', 'Displacement (mm)', 'Path Covered (mm)', 'Take-off time (ms)', 'Landing time (ms)', 'AEP x (mm)', 'AEP y (mm)', 'PEP x (mm)', 'PEP y (mm)', 'Amplitude (mm)', 'Stance linearity (mm)', 'Stretch (mm)'); fprintf(fileID,'\n'); fclose(fileID); continue; end for k = 1 : nstrides(j) stride_start(j,k) = min(cc.PixelIdxList{k}); stride_duration(j,k) = max(cc.PixelIdxList{k}) - min(cc.PixelIdxList{k}); stride_end(j,k) = max(cc.PixelIdxList{k}); stride_dist(j,k) = sqrt((y(startframe-1 + stride_start(j,k)+stride_duration(j,k),j) - y(startframe-1 + stride_start(j,k),j))^2+(x(startframe-1 + stride_start(j,k)+stride_duration(j,k),j) - x(startframe-1 + stride_start(j,k),j))^2); path_length(j,k) = sum(spd(startframe-1 + stride_start(j,k):startframe-1 + stride_start(j,k)+stride_duration(j,k),j)); landing(j,k,:) = [nx(startframe-1 + stride_start(j,k)+stride_duration(j,k),j), ny(startframe-1 + stride_start(j,k)+stride_duration(j,k),j)]; takeoff(j,k,:) = [nx(startframe-1 + stride_start(j,k),j), ny(startframe-1 + stride_start(j,k),j)]; Xinter= interp1(startframe-1 + stride_start(j,k):min(20,stride_duration(j,k)-1):startframe-1 + stride_start(j,k) + stride_duration(j,k),nx(startframe-1 + stride_start(j,k):min(20,stride_duration(j,k)-1):startframe-1 + stride_start(j,k)+stride_duration(j,k),j),startframe-1+stride_start(j,k):startframe-1+stride_start(j,k)+stride_duration(j,k),'spline'); Yinter= interp1(startframe-1 + stride_start(j,k):min(20,stride_duration(j,k)-1):startframe-1 + stride_start(j,k) + stride_duration(j,k),ny(startframe-1 + stride_start(j,k):min(20,stride_duration(j,k)-1):startframe-1 + stride_start(j,k)+stride_duration(j,k),j),startframe-1+stride_start(j,k):startframe-1+stride_start(j,k)+stride_duration(j,k),'spline'); stride_amplitude(j,k) = ny(startframe-1 + stride_start(j,k)+stride_duration(j,k),j) - ny(startframe-1 + stride_start(j,k),j); stride_regularity(j,k) = mean(sqrt(sum(abs([nx(startframe-1+stride_start(j,k):startframe-1+stride_start(j,k)+stride_duration(j,k),j), ny(startframe-1+stride_start(j,k):startframe-1+stride_start(j,k)+stride_duration(j,k),j)] - [Xinter' Yinter']).^2,2))); stretch(j,k) = mean(sqrt(sum(nx(startframe-1+stride_start(j,k)+round(stride_duration(j,k)/2),j)^2+(ny(startframe-1+stride_start(j,k)+round(stride_duration(j,k)/2),j)-0.157*bodylength)^2))); end for k = 1 : nstrides(j)-1 stride_period(j,k) = stride_start(j,k+1) - stride_start(j,k); end if (nstrides(j)>1) mean_stride_period(j) = sum(stride_period(j,:))/(nstrides(j)-1); else stride_period(j,1) = nan; mean_stride_period(j) = nan; end stride_period(j,nstrides(j)) = nan; landing_std(j,:) = squeeze(std(landing(j,1 : nstrides(j),:)))'; takeoff_std(j,:) = squeeze(std(takeoff(j,1 : nstrides(j),:)))'; landing_mean(j,:) = squeeze(sum(landing(j,:,:)))'/nstrides(j); takeoff_mean(j,:) = squeeze(sum(takeoff(j,:,:)))'/nstrides(j); l_std(j) = 0; t_std(j) = 0; for k = 1: nstrides(j) l_std(j) = l_std(j) + sqrt((landing(j,k,1)-landing_mean(j,1))^2 + (landing(j,k,2)-landing_mean(j,2))^2); t_std(j) = t_std(j) + sqrt((takeoff(j,k,1)-takeoff_mean(j,1))^2 + (takeoff(j,k,2)-takeoff_mean(j,2))^2); end l_std(j) = l_std(j) / nstrides(j); t_std(j) = t_std(j) / nstrides(j); total_path_length(j) = sum(path_length(j,:)); mov_percentage(j) = sum(stride_duration(j,:))/nframes; fileID = fopen([output_dir 'StrideParameters.xlsx'],'a'); fprintf(fileID,'%s',legs_id{j}); fprintf(fileID,'\n'); fprintf(fileID,'%s \t', 'Stride #', 'Duration (ms)', 'Period (ms)', 'Displacement (mm)', 'Path Covered (mm)', 'Take-off time (ms)', 'Landing time (ms)', 'AEP x (mm)', 'AEP y (mm)', 'PEP x (mm)', 'PEP y (mm)', 'Amplitude (mm)', 'Stance linearity (mm)', 'Stretch (mm)'); fprintf(fileID,'\n'); fclose(fileID); dlmwrite([output_dir 'StrideParameters.xlsx'], [[1:nstrides(j)]' stride_duration(j,1:nstrides(j))'*frame_ms stride_period(j,1:nstrides(j))'*frame_ms stride_dist(j,1:nstrides(j))'*scale/512 path_length(j,1:nstrides(j))'*scale/512 stride_start(j,1:nstrides(j))'*frame_ms stride_end(j,1:nstrides(j))'*frame_ms landing(j,1:nstrides(j),1)'*scale/512 landing(j,1:nstrides(j),2)'*scale/512 takeoff(j,1:nstrides(j),1)'*scale/512 takeoff(j,1:nstrides(j),2)'*scale/512 stride_amplitude(j,1:nstrides(j))'*scale/512 stride_regularity(j,1:nstrides(j))'*scale/512 stretch(j,1:nstrides(j))'*scale/512],'delimiter','\t','-append'); fileID = fopen([output_dir 'StrideParameters.xlsx'],'a'); fprintf(fileID,'\n'); fclose(fileID); dlmwrite([output_dir 'StrideParameters.xlsx'], ['______________'],'delimiter','\t','-append'); end %% Path area covered % Returns the area of the minimum convex hull required to cover the entire % path covered by each leg in the body-centered frame of reference area = zeros(nlegs,1); area_over_path = zeros(nlegs,1); pathpca = zeros(nlegs,2,2); centre = zeros(nlegs, 2); fileID = fopen([output_dir 'LegParameters.xlsx'],'w'); % fprintf(fileID,'%s \t', 'Leg', 'Movement %', 'Mean Stride Period (ms)', 'Total Path Covered (mm)', 'Mean Landing x (mm)', 'Mean Landing y (mm)', 'Mean Take-off x (mm)', 'Mean Take-off y (mm)', 'Std Landing x (mm)', 'Std Landing y (mm)', 'Std Take-off x (mm)', 'Std Take-off y (mm)', 'Domain Area (mm^2)', 'Domain Area / Path (mm)', 'Domain Length (mm)', 'Domain Width (mm)', 'Footprint/PCA deviation (deg)'); fprintf(fileID,'%s \t', 'Leg', 'Movement %', 'Mean Stride Period (ms)', 'Total Path Covered (mm)', 'Mean AEP x (mm)', 'Mean AEP y (mm)', 'Mean PEP x (mm)', 'Mean PEP y (mm)', 'Std AEP (mm)', 'Std PEP (mm)', 'Domain Area (mm^2)', 'Domain Area / Path (mm)', 'Domain Length (mm)', 'Domain Width (mm)', 'Footprint/PCA deviation (deg)'); fprintf(fileID,'\n'); clf(1); figure(1); for j = 1:nlegs if(missing(j) || nstrides(j)==0) continue; end [convex{j},area(j)] = convhull(nx(startframe : endframe,j),ny(startframe : endframe,j)); area_over_path(j) = area(j)/total_path_length(j); centre(j,:) = mean([nx(startframe : endframe,j),ny(startframe : endframe,j)]); [domain_length(j), domain_width(j)] = projectpca([nx(startframe : endframe,j),ny(startframe : endframe,j)]); bounding = boundary(nx(startframe : endframe,j),ny(startframe : endframe,j)); bounding = mod(bounding,nframes)+((mod(bounding,nframes)==0)*nframes)+startframe-1; hold on legplot(j) = scatter(nx(startframe : endframe,j),ny(startframe : endframe,j),3,[colors(j,1) colors(j,2) colors(j,3)],'filled'); landingplot(j) = scatter(landing_mean(j,1),landing_mean(j,2),50,[0 0 0],'filled'); takeoffplot(j) = scatter(takeoff_mean(j,1),takeoff_mean(j,2),50,[0 0 0],'d','filled'); landingplot(j) = scatter(landing_mean(j,1),landing_mean(j,2),30,[colors(j,1) colors(j,2) colors(j,3)],'filled'); takeoffplot(j) = scatter(takeoff_mean(j,1),takeoff_mean(j,2),30,[colors(j,1) colors(j,2) colors(j,3)],'d','filled'); % plot(nx(bounding,j),ny(bounding,j)); k = -100:100; stride_vec = [landing_mean(j,1)-takeoff_mean(j,1) landing_mean(j,2)-takeoff_mean(j,2)]; stride_vec = stride_vec/norm(stride_vec); footprint_dev(j) = acos(stride_vec(1)*pathpca(j,1,1) + stride_vec(2)*pathpca(j,1,2))*180/pi; hold off fprintf(fileID,'%s',legs_id{j}); fprintf(fileID,'\t %0.2f', [100*mov_percentage(j) mean_stride_period(j)*frame_ms total_path_length(j)*scale/512 landing_mean(j,1)*scale/512 landing_mean(j,2)*scale/512 takeoff_mean(j,1)*scale/512 takeoff_mean(j,2)*scale/512 landing_std(j,1)*scale/512 landing_std(j,2)*scale/512 takeoff_std(j,1)*scale/512 takeoff_std(j,2)*scale/512 area(j)*scale/512*scale/512 area_over_path(j)*scale/512 domain_length(j)*scale/512 domain_width(j)*scale/512 footprint_dev(j)]); % fprintf(fileID,'\t %0.3f', [100*mov_percentage(j) mean_stride_period(j)*frame_ms total_path_length(j)*scale/512 landing_mean(j,1)*scale/512 landing_mean(j,2)*scale/512 takeoff_mean(j,1)*scale/512 takeoff_mean(j,2)*scale/512 l_std(j)*scale/512 t_std(j)*scale/512 area(j)*scale/512*scale/512 area_over_path(j)*scale/512 domain_length(j)*scale/512 domain_width(j)*scale/512 footprint_dev(j)]); fprintf(fileID,'\n'); end fclose(fileID); axis([-150 150 -150 150]); axis square title('Leg trajectories in body-centered frame of reference'); export_fig([output_dir 'LegDomain.pdf'], '-pdf','-transparent'); fileID = fopen([output_dir 'LegDomainOverlap.xlsx'],'w'); for j = 1:nlegs if(missing(j) || nstrides(j)==0) continue; end for k = j+1:nlegs if(missing(k) || nstrides(k)==0) continue; end P1.x = nx(convex{j}+startframe-1,j); P1.y = ny(convex{j}+startframe-1,j); P1.hole = 0; P2.x = nx(convex{k}+startframe-1,k); P2.y = ny(convex{k}+startframe-1,k); P2.hole = 0; if(isempty(intersections(P1.x,P1.y,P2.x,P2.y))) overlap = 0; else % Returns the intesersection polygon between the convex polygons for leg j and k C = PolygonClip(P1,P2,1); overlap = polyarea(C.x,C.y) * (scale/512)^2; end fprintf(fileID,'%s \t %0.3f \n', ['Overlap ' legs_id{j} '&' legs_id{k}], overlap); end end fclose(fileID); %% Estimate leg lengths % imroi = imread([seg_dir 'roi_' num2str(1) '.png']); % imsize = size(imroi); % leglengths = zeros(endframe-startframe+1,nlegs); % % for i = startframe:endframe; % imroi = imread([seg_dir 'roi_' num2str(i) '.png']); % imseg = imread([seg_dir 'img_' num2str(i) '.png']); % imlegs = imseg(:,:,1)==255; % imbody = imroi-imlegs; % legs = bwconncomp(imlegs); % idxlegs = zeros(nlegs,2); % idxconncomp = zeros(nlegs,1); % for b = 1 : legs.NumObjects % imglegs{b} = zeros(imsize); % imglegs{b}(legs.PixelIdxList{b})=1; % end % for j = 1:nlegs % overallMinDistance = inf; % for b = 1 : legs.NumObjects % [legY, legX] = ind2sub(imsize, legs.PixelIdxList{b}); % distances = sqrt((legX - x(i,j)).^2 + (legY - y(i,j)).^2); % [minDistance, indexOfMin] = min(distances); % if minDistance < overallMinDistance % overallMinDistance = minDistance; % idxlegs(j,:) = [legX(indexOfMin), legY(indexOfMin)]; % idxconncomp(j) = b; % end % end % D = bwdistgeodesic(imbody+imglegs{idxconncomp(j)}>0,[idxlegs(j,1)], [idxlegs(j,2)],'quasi-euclidean'); % leglengths(i-startframe+1,j) = D(round(CoM(i,2)+0.2*body_length),round(CoM(i,1))); % end % % % geodesic distance between middle legs % D = bwdistgeodesic(imbody+imglegs{idxconncomp(2)}+imglegs{idxconncomp(5)}>0,[idxlegs(2,1)], [idxlegs(2,2)],'quasi-euclidean'); % middlelegs_dist(i-startframe+1) = D(idxlegs(5,2),idxlegs(5,1)); % % horizontal spread distance across middle legs % if (norm_trajectory(i-startframe+1,2,1)~=0 && norm_trajectory(i-startframe+1,5,1)~=0) % middlelegs_spread(i-startframe+1) = abs(norm_trajectory(i-startframe+1,2,1) -norm_trajectory(i-startframe+1,5,1)); % else % middlelegs_spread(i-startframe+1) = nan; % end % end % leglengths2 = leglengths; % leglengths2(find(isnan(leglengths))) = 0; % leglengths2(find(isinf(leglengths))) = 0; % fileID = fopen([output_dir 'Leglengths.xlsx'],'w'); % for j = 1: nlegs % fprintf(fileID,'\t%s',legs_id{j}); % end % fprintf(fileID,'\n'); % fprintf(fileID, '%s\t', 'Mean (mm)'); % fclose(fileID); % mean_ll = zeros (1, nlegs); % std_ll = zeros (1, nlegs); % for j = 1 : nlegs % A = leglengths2(:,j); % mean_ll(j) = mean(A(A>0)); % std_ll(j) = std(A(A>0)); % end % dlmwrite([output_dir 'Leglengths.xlsx'],mean_ll*scale/512,'delimiter','\t','-append'); % fileID = fopen([output_dir 'Leglengths.xlsx'],'a'); % fprintf(fileID,'\n'); % fprintf(fileID, '%s\t', 'Std (mm)'); % fclose(fileID); % dlmwrite([output_dir 'Leglengths.xlsx'],std_ll*scale/512,'delimiter','\t','-append'); % fileID = fopen([output_dir 'Leglengths.xlsx'],'a'); % fprintf(fileID,'\n'); % fprintf(fileID,'Frame #\n'); % fclose(fileID); % dlmwrite([output_dir 'Leglengths.xlsx'],[(startframe:endframe)', leglengths*scale/512],'delimiter','\t','-append'); % % middlelegsAEP = sqrt((landing_mean(2,1)-landing_mean(5,1))^2+(landing_mean(2,2)-landing_mean(5,2))^2); % middlelegsPEP = sqrt((takeoff_mean(2,1)-takeoff_mean(5,1))^2+(takeoff_mean(2,2)-takeoff_mean(5,2))^2); % % middlelegs_dist2 = middlelegs_dist; % middlelegs_spread2 = middlelegs_spread; % middlelegs_dist2(find(isnan(middlelegs_dist))) = 0; % middlelegs_dist2(find(isinf(middlelegs_dist))) = 0; % middlelegs_spread2(find(isnan(middlelegs_spread))) = 0; % middlelegs_spread2(find(isnan(middlelegs_spread))) = 0; % % fileID = fopen([output_dir 'MiddleLegsSpread.xlsx'],'w'); % fprintf(fileID, '%s\t%s\t%s\t%s\t\n', 'Mean Shortest Path Distance (mm)', 'Mean Horizontal Spread (mm)', 'Mean AEP Distance (mm)', 'Mean PEP Distance (mm)'); % fprintf(fileID, '%0.3f\t', mean(middlelegs_dist2(middlelegs_dist2>0)) * scale/512, mean(middlelegs_spread2(middlelegs_spread2>0)) * scale / 512, middlelegsAEP * scale/512, middlelegsPEP * scale/512); % fprintf(fileID,'\n\n'); % fprintf(fileID, '%s\t', 'Frame #', 'Shortest Path Distance (mm)', 'Horizontal Spread (mm)'); % fprintf(fileID,'\n'); % fclose(fileID); % dlmwrite([output_dir 'MiddleLegsSpread.xlsx'],[(startframe:endframe)', (middlelegs_dist*scale/512)', (middlelegs_spread*scale/512)'],'delimiter','\t','-append'); middlelegsAEP = sqrt((landing_mean(2,1)-landing_mean(5,1))^2+(landing_mean(2,2)-landing_mean(5,2))^2); middlelegsPEP = sqrt((takeoff_mean(2,1)-takeoff_mean(5,1))^2+(takeoff_mean(2,2)-takeoff_mean(5,2))^2); stancewidth = (middlelegsAEP + middlelegsPEP)/2; fileID = fopen([output_dir 'StanceWidth.xlsx'],'w'); fprintf(fileID, '%s\t%s\t%s\n', 'Mean AEP Distance (mm)', 'Mean PEP Distance (mm)', 'Stance Width (mm)'); fprintf(fileID, '%0.3f\t', middlelegsAEP * scale/512, middlelegsPEP * scale/512, stancewidth * scale / 512); fclose(fileID); %% Estimate angle subtended by leg from vertical axis % legangles = zeros(endframe-startframe+1,nlegs); % % for i = startframe:endframe % for j = 1:3 % if (nx(i,j)==0) % legangles(i-startframe+1,j) = nan; % else % legangles(i-startframe+1,j) = -mod(-atan2d(nx(i,j),ny(i,j)-0.157*bodylength)+360,360); % end % end % for j = 4:6 % if (nx(i,j)==0) % legangles(i-startframe+1,j) = nan; % else % legangles(i-startframe+1,j) = mod(atan2d(nx(i,j),ny(i,j)-0.157*bodylength)+360,360); % end % end % end % % if ~exist([output_dir 'LegAngles'], 'dir') % mkdir([output_dir 'LegAngles']); % end % % fileID = fopen([output_dir 'LegAngles/LegAngles.xlsx'],'w'); % fprintf(fileID,'%s \t %s \t', 'Time(ms)', legs_id{:}); % fprintf(fileID,'\n'); % fclose(fileID); % dlmwrite([output_dir 'LegAngles/LegAngles.xlsx'],[[1:nframes]'*frame_ms, legangles],'delimiter','\t','-append', 'precision','%.1f'); % % PEP_angles = cell(6,1); % AEP_angles = cell(6,1); % angle_variations = cell(6,1); % fileID = fopen([output_dir 'LegAngles/StrideVariation.xlsx'],'w'); % for j = 1:size(stride_start,1) % fprintf(fileID,'%s \n', legs_id{j}); % fprintf(fileID,'%s \t', 'Stride #', 'PEP Angle', 'AEP Angle', 'Angle Swiped'); % fprintf(fileID,'\n'); % for i = 1:size(stride_start,2) % if (stride_start(j,i)==0) % continue; % end % PEP_angles{j}(i) = legangles(stride_start(j,i),j); % AEP_angles{j}(i) = legangles(stride_end(j,i),j); % angle_variations{j}(i) = abs(legangles(stride_end(j,i),j)-legangles(stride_start(j,i),j)); % fprintf(fileID,'%d \t', i, PEP_angles{j}(i), AEP_angles{j}(i), angle_variations{j}(i)); % fprintf(fileID,'\n'); % end % end % fprintf(fileID,'\n'); % fprintf(fileID,'\n'); % % fprintf(fileID,'%s \t', 'Leg', 'Mean PEP Angle', 'Std PEP Angle', 'Mean AEP Angle', 'Std AEP Angle', 'Mean Angle Swiped', 'Std Angle Swiped'); % fprintf(fileID,'\n'); % for j = 1:nlegs % fprintf(fileID,'%s \t %d \t', legs_id{j}, mean(PEP_angles{j}), std(PEP_angles{j}), mean(AEP_angles{j}), std(AEP_angles{j}), mean(angle_variations{j}), std(angle_variations{j})); % fprintf(fileID,'\n'); % end % fclose(fileID); %% Project onto PCA eigenvectors to compute the domain length/width function [domain_length, domain_width] = projectpca(points) m = mean(points); v = pca(points); n = size(points,1); p_v1 = zeros(n,1); p_v2 = zeros(n,1); for i = 1 : n p_v1(i) = projectaxis(points(i,:), v(:,1), m); p_v2(i) = projectaxis(points(i,:), v(:,2), m); end domain_length = max(p_v1) - min(p_v1); domain_width = max(p_v2) - min(p_v2); function p_proj = projectaxis(p, v, m, axis) p_proj = p*v-m*v;
github
BII-wushuang/FLLIT-master
Segmentation.m
.m
FLLIT-master/src/Segmentation.m
8,894
utf_8
89de5a8eba5bbe2e49b22b847761cc93
%Classifier for leg segmentation function Segmentation (data_dir,score_thres,foreground_thres,load_wl) %% locate the image folders and the output folders if (nargin < 1) load_wl = 1; score_thres = 0.65; foreground_thres = 0.1; data_dir = uigetdir('./Data'); addpath(genpath('./KernelBoost-v0.1/')); end clear weak_learners; %addpath(genpath('./KernelBoost-v0.1/')); %fprintf('Processing the folder: %s \n',data_dir); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = [pwd '/Data' sub_dir '/']; output_dir = [pwd '/Results/SegmentedImages' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end img_list = load_img_list(data_dir); I = imread([data_dir img_list(1).name]); %% Call the background if(~exist([data_dir 'Background/Background.png'])) [~,~,ref_img,~] = video2background(data_dir, sub_dir); if(~exist([data_dir 'Background'])) mkdir([data_dir 'Background']) end imwrite(uint8(ref_img),[data_dir 'Background/Background.png'], 'png'); else ref_img = double(imread([data_dir 'Background/Background.png'])); end imshow(uint8(ref_img)); pause(2); for i = 1 : length(img_list) I = imread([data_dir img_list(i).name]); I = double(I); roi_img = (max(ref_img - I(:,:,1),0) ./ ref_img) > foreground_thres; roi_img = bwareaopen(roi_img, 200); img_output = repmat(I/255,[1 1 3]); img_output(:,:,3) = img_output(:,:,3) + roi_img; imshow(img_output); pause(0.01); imwrite(roi_img,[output_dir 'roi_' num2str(i) '.png'],'png'); end %% Another section ref_img = imread([data_dir 'Background/Background.png']); ref_img = double(ref_img); ref_img = padarray(ref_img,[20 20],'replicate'); params = setup_config_L('Drive'); %params = setup_lists(params); %params = setup_directories_L(params); params.border_skip_size = 20; params.pos_samples_no = 30000; params.neg_samples_no = 30000; params.sample_size = [41 41]'; if (load_wl) fprintf('Please select the trained classifier to be used.\n'); [wl_file, wl_dir] = uigetfile([pwd '/Results/Classifiers/','*.mat']); load([wl_dir wl_file]); else if(~exist([pwd '/Results/Classifiers' sub_dir '_Classifier.mat'])) sample_ratio = 20; tot_idx.pos = 0; tot_idx.neg = 0; data = []; % Collect the positive and negative samples for i_img = 1 : floor(length(img_list) / sample_ratio) I = imread([data_dir img_list(i_img * sample_ratio).name]); I = double(I); I = padarray(I,[20 20],'replicate'); [pos_img,neg_img_body,neg_img_bkg] = leg_segment(I,ref_img,foreground_thres); show_img_output = repmat(I/255,[1 1 3]); show_img_output(:,:,1) = show_img_output(:,:,1) + pos_img; show_img_output(:,:,3) = show_img_output(:,:,3) + neg_img_body; imshow(show_img_output,[]); data.train.imgs.X{i_img,1} = I / 255; data.train.imgs.X{i_img,2} = (ref_img - I) / 255; gt_img = zeros(size(I)); gt_img(pos_img > 0) = 1; gt_img(neg_img_body > 0) = -1; gt_img(neg_img_bkg > 0) = -1; data.train.gts.X{i_img} = gt_img; % ------------------------------ border_img = zeros(size(I)); border_img(1:params.border_skip_size,:) = 1; border_img(:,1:params.border_skip_size) = 1; border_img((end-params.border_skip_size):end,:) = 1; border_img(:,(end-params.border_skip_size):end) = 1; pos_sampling = find((pos_img) & (~border_img) & (~neg_img_body)); neg_body_sampling = find(neg_img_body & (~border_img) & (~pos_img)); neg_bkg_sampling = find(neg_img_bkg & (~border_img)); % sample counts imgs_no = floor(length(img_list) / sample_ratio); npos_img = ceil(params.pos_samples_no / imgs_no); nneg_img = ceil(params.neg_samples_no / imgs_no); % getting a random subset? neg_sampling = neg_body_sampling(randi(length(neg_body_sampling),[nneg_img,1])); neg_sampling = [neg_sampling; neg_bkg_sampling(randi(length(neg_bkg_sampling),[nneg_img,1]))]; npos = length(pos_sampling); nneg = length(neg_sampling); good_sampling_idx{i_img}.pos = pos_sampling(randi(npos,[max(npos_img,nneg_img),1])); good_sampling_idx{i_img}.neg = neg_sampling(randi(nneg,[max(npos_img,nneg_img),1])); good_sampling_idx{i_img}.pos_val = pos_img(good_sampling_idx{i_img}.pos); good_sampling_idx{i_img}.neg_val = pos_img(good_sampling_idx{i_img}.neg); tot_idx.pos = tot_idx.pos+length(good_sampling_idx{i_img}.pos); tot_idx.neg = tot_idx.neg+length(good_sampling_idx{i_img}.neg); end data.train.imgs.idxs = 1:2; data.train.imgs.sub_ch_no = 2; samples_idx = []; for i_img = 1 : floor(length(img_list) / sample_ratio) I = imread([data_dir img_list(i_img * sample_ratio).name]); I = padarray(I,[20 20],'replicate'); samp_idx = [good_sampling_idx{i_img}.pos ; good_sampling_idx{i_img}.neg]; samp_idx_2D = zeros(length(samp_idx),2); [samp_idx_2D(:,1),samp_idx_2D(:,2)] = ind2sub(size(I),samp_idx); labels = [good_sampling_idx{i_img}.pos_val ; good_sampling_idx{i_img}.neg_val]; labels = double(labels); labels(labels < 0.5) = -1; samples_idx_img = zeros(length(samp_idx),4); samples_idx_img(:,1) = i_img; samples_idx_img(:,2:3) = samp_idx_2D; samples_idx_img(:,4) = labels; samples_idx = cat(1,samples_idx,samples_idx_img); end samples_idx = samples_idx(1 : (params.pos_samples_no + params.neg_samples_no),:); params.pos_samples_no = sum(samples_idx(:,end)==1); params.neg_samples_no = sum(samples_idx(:,end)==-1); params.T1_size = round((params.pos_samples_no+params.neg_samples_no)/3); params.T2_size = round((params.pos_samples_no+params.neg_samples_no)/3); params.pos_to_sample_no = params.T1_size; params.neg_to_sample_no = params.T1_size; params.wl_no = 100; fprintf('Training the classifier over a random subset of the images.\n'); % Train the classifier here weak_learners = train_boost_general(params,data,samples_idx); sub_dir_pos = strfind(sub_dir,'/'); if(~exist(['./Results/Classifiers/' sub_dir(1:sub_dir_pos(end))])) mkdir(['./Results/Classifiers/' sub_dir(1:sub_dir_pos(end))]); end save ([pwd '/Results/Classifiers/' sub_dir '_Classifier.mat'],'weak_learners'); else load([pwd '/Results/Classifiers/' sub_dir '_Classifier.mat']); end end fprintf('Training completed, now applying the classifier to all images.\n'); fprintf('Output directory: %s\n', output_dir); % Applying the classifier sample_ratio = 1; sec_no = 100; for i_sec = 1 : ceil(length(img_list) / sample_ratio / sec_no) X = []; if (i_sec < ceil(length(img_list) / sample_ratio / sec_no)) imgs_sec = 1 + (i_sec - 1) * sec_no : sample_ratio :(i_sec) * sec_no; else imgs_sec = 1 + (i_sec - 1) * sec_no : sample_ratio : length(img_list); end for i_img = 1 : length(imgs_sec) I = imread([data_dir img_list(imgs_sec(i_img)).name]); I = double(I); I = padarray(I,[20 20],'replicate'); %neg_img{i_img} = leg_segment_clean(I,roi_img); bkg_sub = (ref_img - I); I(:,:,2) = bkg_sub; I = I / 255; X{i_img,1} = I(:,:,1); X{i_img,2} = I(:,:,2); roi_img = imread([output_dir 'roi_' num2str(imgs_sec(i_img)) '.png']); roi_images{i_img} = padarray(roi_img,[20 20],'replicate'); end score_images = batch_evaluate_boost_images(X,params,weak_learners,roi_images); for i_img = 1 : length(imgs_sec) I = imread([data_dir img_list(imgs_sec(i_img)).name]); I = double(I)/255; I = padarray(I,[20 20],'replicate'); score_img = score_images{i_img}; est_leg = score_img > score_thres; %est_leg = bwdist(est_leg)<3 .*roi_images{i_img}; %est_leg = est_leg & ~ neg_img{i_img} & roi_images{i_img}; est_leg = filter_small_comp(est_leg,15); show_img_output = repmat(I,[1 1 3]); show_img_output(:,:,1) = show_img_output(:,:,1) + est_leg; imshow(show_img_output); imwrite(imcrop(show_img_output,[21 21 size(I,2)-41 size(I,1)-41]),[output_dir 'img_' num2str(imgs_sec(i_img)) '.png'],'png'); end end
github
BII-wushuang/FLLIT-master
FLLIT.m
.m
FLLIT-master/src/FLLIT.m
94,500
utf_8
d0a100d59fd1e1a11f9e0a06ec94cee3
% FLLIT program: GUI incorporating the program workflow % This GUI is created with the MATLAB GUIDE feature. % The pushbuttons call functions to perform individual tasks such as % segmentation, tracking or data processing. function varargout = FLLIT(varargin) % FLLIT MATLAB code for FLLIT.fig % FLLIT, by itself, creates a new FLLIT or raises the existing % singleton*. % % H = FLLIT returns the handle to a new FLLIT or the handle to % the existing singleton*. % % FLLIT('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in FLLIT.M with the given input arguments. % % FLLIT('Property','Value',...) creates a new FLLIT or raises % the existing singleton*. Starting from the left, property value pairs are % applied to the GUI before FLLIT_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to FLLIT_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help FLLIT % Last Modified by GUIDE v2.5 04-Nov-2021 00:00:59 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @FLLIT_OpeningFcn, ... 'gui_OutputFcn', @FLLIT_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT %% --- Executes just before FLLIT is made visible. function FLLIT_OpeningFcn(hObject, eventdata, handles, varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin Command line arguments to FLLIT (see VARARGIN) % Addpath try addpath(genpath('./KernelBoost-v0.1/')); catch end % Choose default Command line output for FLLIT handles.output = hObject; % Update handles structure guidata(hObject, handles); % 'gcf' refers to 'get current figure' and in this context all the updated % data are feed into the 'hMainGui'. The 'hMainGui' can then provide the % data as global variables to the other functions that are called in this GUI setappdata(0, 'hMainGui', gcf); S.fh = handles.figMain; % Fix the central figure axis in pixel coordinates S.ax = gca; set(S.ax,'unit','pix','position',[(handles.figMain.Position(3)-512)/2 handles.figMain.Position(4)-512 512 512]); S.XLM = get(S.ax,'xlim'); S.YLM = get(S.ax,'ylim'); S.AXP = get(S.ax,'pos'); S.DFX = diff(S.XLM); S.DFY = diff(S.YLM); S.tx = handles.Position_Text; % The fh_wbmcfn function captures motion of the mouse cursor and the cursor % location is displayed in the GUI set(S.fh,'windowbuttonmotionfcn',{@fh_wbmfcn,S}) % The clicker function captures double clicks of the mouse and is used for % annotating / labelling of the leg tip locations set(handles.figMain, 'WindowButtonDownFcn', {@clicker,handles}); initialize_gui(hObject, handles, false); %% --- Outputs from this function are returned to the Command line. function varargout = FLLIT_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Get default Command line output from handles structure varargout{1} = handles.output; %% -------------------------------------------------------------------- function initialize_gui(fig_handle, handles, isreset) % Update handles structure guidata(handles.figMain, handles); %% --- Executes on button press in Adjust_Prediction. function Adjust_Prediction_Callback(hObject, eventdata, handles) % hObject handle to Adjust_Prediction (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % First, obtain the global variables stored in 'hMainGui': named the % current frame in the tracking progess as well as currently stored % trajectory data of the legs hMainGui = getappdata(0, 'hMainGui'); i = getappdata(hMainGui, 'current_frame'); trajectory = getappdata(hMainGui, 'trajectory'); nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end if (isempty(i)) % If current frame is empty, it means that tracking has not been run handles.Text_Display.String = 'Adjustments are to correct tracking errors, please run tracking first.'; elseif (strcmp(hObject.String, 'Adjust Prediction')) % Clicking on the adjust prediction button will result in itself being % displayed as the 'Exit without Saving' button. Once the adjust % prediction mode is activated, double clicking on the image will be % registered and return the tip location of the selected leg. hObject.String = 'Exit without saving'; % A new button 'Save and Exit' will also appear. The handle to this % 'Save and Exit' button is handles.Annotation. handles.Annotation.Enable = 'On'; handles.Annotation.Visible = 'On'; handles.Annotation.String = 'Save and Exit'; handles.ConsoleUpdate.Enable = 'On'; handles.ConsoleUpdate.Visible = 'On'; handles.prevframe.Enable = 'Off'; handles.prevframe.Visible = 'Off'; handles.nextframe.Enable = 'Off'; handles.nextframe.Visible = 'Off'; if (~strcmp(handles.Tracking.String, 'Initial')) handles.Tracking.String = 'Resume'; end set(handles.Text_Display,'String',['Entering adjustment mode: select a leg and double click on figure to assign tip location.']); elseif (strcmp(hObject.String, 'Exit without saving')) % if user decides to exit without saving, all user input on the current frame will be % discarded hObject.String = 'Adjust Prediction'; handles.Annotation.String = 'Annotate'; handles.Annotation.Enable = 'Off'; handles.Annotation.Visible = 'Off'; handles.ConsoleUpdate.Enable = 'Off'; handles.ConsoleUpdate.Visible = 'Off'; handles.prevframe.Enable = 'On'; handles.prevframe.Visible = 'On'; handles.nextframe.Enable = 'On'; handles.nextframe.Visible = 'On'; handles.Tracking_Text.String = ''; for kk = 1:nLegs if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; for j = 1: 2 : length(handles.figMain.CurrentAxes.Children)-2 if(strcmp(handles.figMain.CurrentAxes.Children(j).Type,'text') && strcmp(handles.figMain.CurrentAxes.Children(j).String,legs_id{kk})) delete(handles.figMain.CurrentAxes.Children(j:j+1)); end end end end for i = 1 : nLegs x = str2num(handles.Tracking_Text.String(i,9:11)); y = str2num(handles.Tracking_Text.String(i,13:15)); for j = 1: 2 : length(handles.figMain.CurrentAxes.Children)-2 if(strcmp(handles.figMain.CurrentAxes.Children(j).Type,'text') && strcmp(handles.figMain.CurrentAxes.Children(j).String,legs_id{i})) delete(handles.figMain.CurrentAxes.Children(j:j+1)); if(x~=0) hold on scatter(x,y,'w'); text(x,y,legs_id{i},'Color',[colors(i,1) colors(i,2) colors(i,3)],'FontSize',14); hold off end end end end end handles.ManualInitiate.Value = 0; %% --- Executes on button press in ConsoleUpdate. function ConsoleUpdate_Callback(hObject, eventdata, handles) % hObject handle to ConsoleUpdate (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end for i = 1 : nLegs x = str2num(handles.Tracking_Text.String(i,9:11)); y = str2num(handles.Tracking_Text.String(i,13:15)); for j = 1 : length(handles.figMain.CurrentAxes.Children)-2 if(strcmp(handles.figMain.CurrentAxes.Children(j).Type,'text') && strcmp(handles.figMain.CurrentAxes.Children(j).String,legs_id{i})) delete(handles.figMain.CurrentAxes.Children(j:j+1)); hold on scatter(x,y,'w'); text(x,y,legs_id{i},'Color',[colors(i,1) colors(i,2) colors(i,3)],'FontSize',14); hold off end end end %% --- Executes on button press in Annotation. function Annotation_Callback(hObject, eventdata, handles) % hObject handle to Annotation (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % This allows the user input to relabel the legs on the current frame. This % will then update the data in the trajectory as well as the normalised % trajectory. hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); seg_dir = ['./Results/SegmentedImages' sub_dir '/']; output_dir = ['./Results/Tracking/' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end CoM = getappdata(hMainGui, 'CoM'); trajectory = getappdata(hMainGui, 'trajectory'); norm_trajectory = getappdata(hMainGui, 'norm_trajectory'); current_frame = getappdata(hMainGui,'current_frame'); nLegs = 8 - handles.L4.Value - handles.R4.Value; trajectory(current_frame,2,1) = str2num(handles.Tracking_Text.String(2,9:11)); trajectory(current_frame,2,2) = str2num(handles.Tracking_Text.String(2,13:15)); norm_trajectory(current_frame,2,1) = cosd(CoM(current_frame,3))*((trajectory(current_frame,2,1)-CoM(current_frame,1))) + sind(CoM(current_frame,3))*((trajectory(current_frame,2,2)-CoM(current_frame,2))); if(strcmp(handles.Tracking.String, 'Initial') && norm_trajectory(current_frame,2,1)>0) CoM(current_frame,3) = mod(CoM(current_frame,3)+180,360); setappdata(hMainGui,'CoM',CoM); % find body_length i = current_frame; img = imread([seg_dir 'roi_' num2str(i) '.png']); img_norm = imtranslate(img, [255 - CoM(i,1), 255 - CoM(i,2)]); img_norm = imrotate(img_norm, CoM(i,3)); img_norm = imcrop(img_norm, [size(img_norm,1)/2-150 size(img_norm,2)/2-150 300 300]); [Y,X] = find(img_norm); bodylength = max(Y(find(X==150)))-min(Y(find(X==150))); setappdata(hMainGui, 'bodylength', bodylength); handles.BLText.Visible = 'On'; handles.BLText.Visible = 'On'; handles.BodyLength.String = num2str(round(bodylength)); ResetImageSize_Callback(hObject, eventdata, handles); end for i = 1: nLegs if (~isempty(str2num(handles.Tracking_Text.String(i,9:11)))) trajectory(current_frame,i,1) = str2num(handles.Tracking_Text.String(i,9:11)); trajectory(current_frame,i,2) = str2num(handles.Tracking_Text.String(i,13:15)); norm_trajectory(current_frame,i,1) = cosd(CoM(current_frame,3))*((trajectory(current_frame,i,1)-CoM(current_frame,1))) + sind(CoM(current_frame,3))*((trajectory(current_frame,i,2)-CoM(current_frame,2))); norm_trajectory(current_frame,i,2) = -sind(CoM(current_frame,3))*((trajectory(current_frame,i,1)-CoM(current_frame,1))) + cosd(CoM(current_frame,3))*((trajectory(current_frame,i,2)-CoM(current_frame,2))); else trajectory(current_frame,i,:) = 0; norm_trajectory(current_frame,i,:) = 0; end end setappdata(hMainGui,'trajectory',trajectory); setappdata(hMainGui,'norm_trajectory',norm_trajectory); save([output_dir 'CoM.mat'],'CoM'); save([output_dir 'trajectory.mat'],'trajectory'); save([output_dir 'norm_trajectory.mat'],'norm_trajectory'); csvwrite([output_dir 'CoM.csv'],CoM); csvwrite([output_dir 'trajectory.csv'],trajectory); csvwrite([output_dir 'norm_trajectory.csv'],norm_trajectory); hObject.String = 'Annotate'; hObject.Enable = 'Off'; hObject.Visible = 'Off'; handles.ConsoleUpdate.Enable = 'Off'; handles.ConsoleUpdate.Visible = 'Off'; handles.prevframe.Enable = 'On'; handles.prevframe.Visible = 'On'; handles.nextframe.Enable = 'On'; handles.nextframe.Visible = 'On'; handles.Adjust_Prediction.String = 'Adjust Prediction'; handles.Tracking.String = 'Resume'; %% --- Executes on button press in prevframe. function prevframe_Callback(hObject, eventdata, handles) % hObject handle to prevframe (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); bodylength = getappdata(hMainGui, 'bodylength'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); seg_dir = ['./Results/SegmentedImages' sub_dir '/']; data_dir = ['./Data' sub_dir '/']; img_list = load_img_list(data_dir); setappdata(hMainGui,'current_frame', getappdata(hMainGui, 'current_frame') - 1); i = getappdata(hMainGui, 'current_frame'); handles.text.String = ['Current frame: ' num2str(i)]; handles.GoToDropdown.Value = i; display = handles.ChooseDisplay.Value; switch display case 1 handles.GoToDropdown.String = 1:length(img_list); imshow([data_dir img_list(i).name]); case 2 roi_list = dir([seg_dir 'roi_*.png']); handles.GoToDropdown.String = 1:length(roi_list); imshow([seg_dir 'roi_' num2str(i) '.png']); case 3 seg_list = dir([seg_dir 'img_*.png']); handles.GoToDropdown.String = 1:length(seg_list); imshow([seg_dir 'img_' num2str(i) '.png']); case 4 handles.Tracking.String = 'Resume'; CoM = getappdata(hMainGui, 'CoM'); trajectory = getappdata(hMainGui, 'trajectory'); nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end if(~isempty(trajectory)) handles.text.String = ['Current frame: ' num2str(i)]; handles.Tracking_Text.String = ''; imshow([data_dir img_list(i).name]); hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if(eval(['handles.' legs_id{kk} '.Value'])) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; continue; end if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end end end %% --- Executes on button press in nextframe. function nextframe_Callback(hObject, eventdata, handles) % hObject handle to nextframe (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); bodylength = getappdata(hMainGui, 'bodylength'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); seg_dir = ['./Results/SegmentedImages' sub_dir '/']; data_dir = ['./Data' sub_dir '/']; img_list = load_img_list(data_dir); setappdata(hMainGui,'current_frame', getappdata(hMainGui, 'current_frame') + 1); i = getappdata(hMainGui, 'current_frame'); handles.text.String = ['Current frame: ' num2str(i)]; handles.GoToDropdown.Value = i; display = handles.ChooseDisplay.Value; switch display case 1 handles.GoToDropdown.String = 1:length(img_list); imshow([data_dir img_list(i).name]); case 2 roi_list = dir([seg_dir 'roi_*.png']); handles.GoToDropdown.String = 1:length(roi_list); imshow([seg_dir 'roi_' num2str(i) '.png']); case 3 seg_list = dir([seg_dir 'img_*.png']); handles.GoToDropdown.String = 1:length(seg_list); imshow([seg_dir 'img_' num2str(i) '.png']); case 4 CoM = getappdata(hMainGui, 'CoM'); trajectory = getappdata(hMainGui, 'trajectory'); norm_trajectory = getappdata(hMainGui, 'norm_trajectory'); se = strel('disk',4); skip = 1; max_distance = 20; nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end if (handles.ViewerMode.Value) if(~isempty(trajectory)) handles.text.String = ['Current frame: ' num2str(i)]; handles.Tracking_Text.String = ''; imshow([data_dir img_list(i).name]); hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if(eval(['handles.' legs_id{kk} '.Value'])) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; continue; end if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end end else handles.Tracking.String = 'Resume'; Im = (imread([seg_dir 'img_' num2str(i) '.png'])); Imroi = imread([seg_dir 'roi_' num2str(i) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; if (nLegs>6) Imwork = Imwork - (bwdist(imerode(Imroi,strel('disk',6)))<15); Imwork = bwareaopen(Imwork>0,5); else Imwork = Imwork - (bwdist(imerode(Imroi,se))<10); Imwork = bwareaopen(Imwork>0,3); end [locY,locX] = find(imerode(Imroi,se) > 0); [centre_of_mass,theta] = findCoM(locX,locY); if (abs(theta - CoM(i-skip,3)) > 90 && abs(theta - CoM(i-skip,3)) < 270) theta = mod(theta + 180,360); end CoM(i, :) = [centre_of_mass, theta]; clear raw_tips; clear raw_normtips; [raw_tips, raw_normtips] = findtips(Imwork, Imroi, locX, locY, centre_of_mass, theta); x_t1 = raw_normtips; x_t0 = reshape(norm_trajectory(i-1,:),[nLegs 2]); target_indices = hungarianlinker(x_t0, x_t1, max_distance); for kk = 1:length(target_indices) if (target_indices(kk) > 0) norm_trajectory(i, kk, :) = raw_normtips(target_indices(kk),:); trajectory(i, kk, :) = raw_tips(target_indices(kk),:); else norm_trajectory(i, kk, :) = 0; trajectory(i, kk, :) = 0; end end leftoveridx = find(hungarianlinker(raw_normtips, x_t0, max_distance)==-1); x_t1 = raw_normtips(leftoveridx,:); x_t2 = raw_tips(leftoveridx,:); last_seen_tips = reshape(norm_trajectory(i,:),[nLegs 2]); unassigned_idx = find(last_seen_tips(:,1) == 0); for kk = 1:length(unassigned_idx) if(eval(['handles.' legs_id{unassigned_idx(kk)} '.Value'])) continue; end idx = find(norm_trajectory(1:i,unassigned_idx(kk),1),1,'last'); last_seen_tips(unassigned_idx(kk),:) = reshape(norm_trajectory(idx,unassigned_idx(kk),:), [1 2]); end if (~isempty(unassigned_idx) && ~isempty(x_t1)) C = hungarianlinker(last_seen_tips(unassigned_idx,:), x_t1, 1.25*max_distance); for l = 1:length(C) if (C(l) ~= -1) norm_trajectory(i,unassigned_idx(l), :) = x_t1(C(l), :); trajectory(i,unassigned_idx(l), :) = x_t2(C(l), :); end end end handles.Tracking_Text.String = ''; oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if (eval(['handles.' legs_id{kk} '.Value'])) trajectory(i,kk,1) = 0; trajectory(i,kk,2) = 0; norm_trajectory(i,kk,1) = 0; norm_trajectory(i,kk,2) = 0; handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; continue; end if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end setappdata(hMainGui, 'CoM' , CoM); setappdata(hMainGui, 'trajectory' , trajectory); setappdata(hMainGui, 'norm_trajectory' , norm_trajectory); handles.GoToDropdown.String = 1:find(trajectory,1,'last'); end end %% --- Executes on button press in Select_Folder. function Select_Folder_Callback(hObject, eventdata, handles) % hObject handle to Select_Folder (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); if(~isempty(getappdata(hMainGui, 'trajectory'))) choice = questdlg('Save tracking data?', ... 'FLLIT', ... 'Yes', 'No', 'Cancel', 'Cancel'); % Handle response switch choice case 'Yes' data_dir = getappdata(hMainGui, 'data_dir'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); output_dir = ['./Results/SegmentedImages' sub_dir '/']; trajectory = getappdata(hMainGui, 'trajectory'); norm_trajectory = getappdata(hMainGui, 'norm_trajectory'); CoM = getappdata(hMainGui, 'CoM'); save([output_dir 'CoM.mat'],'CoM'); save([output_dir 'trajectory.mat'],'trajectory'); save([output_dir 'norm_trajectory.mat'],'norm_trajectory'); csvwrite([output_dir 'CoM.csv'],CoM); csvwrite([output_dir 'trajectory.csv'],trajectory); csvwrite([output_dir 'norm_trajectory.csv'],norm_trajectory); setappdata(hMainGui, 'trajectory', []); case 'No' setappdata(hMainGui, 'trajectory', []); case 'Cancel' return; end end handles.BLText.Visible = 'Off'; handles.BodyLength.Visible = 'Off'; handles.BodyLength.String = '0'; handles.ViewBackground.Enable = 'Off'; handles.Foreground.Enable = 'Off'; handles.Segmentation.Enable = 'Off'; handles.Tracking.Enable = 'Off'; handles.Adjust_Prediction.Enable = 'Off'; handles.Video.Enable = 'Off'; handles.DataProcessing.Enable = 'Off'; handles.prevframe.Enable = 'Off'; handles.nextframe.Enable = 'Off'; handles.GoToFrame.Visible = 'Off'; handles.GoToDropdown.Visible = 'Off'; handles.ChooseDisplay.Visible = 'Off'; handles.Tracking_Text.String = ''; data_dir = uigetdir(['./Data']); pos_bs = strfind(data_dir,'Data'); if(~isempty(pos_bs)) sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); setappdata(hMainGui, 'data_dir', data_dir); set(handles.Text_Display, 'String', data_dir); else return; end data_dir = ['./Data' sub_dir '/']; handles.Progress.String = ''; handles.Tracking.String = 'Tracking'; img_list = load_img_list(data_dir); if(~isempty(img_list)) handles.ViewBackground.Enable = 'On'; handles.Foreground.Enable = 'On'; handles.Segmentation.Enable = 'On'; handles.Tracking.Enable = 'On'; handles.Adjust_Prediction.Enable = 'On'; handles.Video.Enable = 'On'; handles.DataProcessing.Enable = 'On'; handles.prevframe.Enable = 'On'; handles.nextframe.Enable = 'On'; handles.GoToFrame.Visible = 'On'; handles.GoToDropdown.Visible = 'On'; handles.ChooseDisplay.Visible = 'On'; handles.GoToDropdown.String = 1:length(img_list); handles.GoToDropdown.Value = 1; handles.ChooseDisplay.String = {'Original'}; handles.ChooseDisplay.Value = 1; handles.text.String = 'Current frame: 1'; setappdata(hMainGui,'current_frame',1); handles.Tracking_Text.String = ''; oriIm = im2double(imread([data_dir img_list(1).name])); imshow(oriIm); else handles.ViewBackground.Enable = 'Off'; handles.Foreground.Enable = 'Off'; handles.Segmentation.Enable = 'Off'; handles.Tracking.Enable = 'Off'; handles.Video.Enable = 'Off'; handles.Adjust_Prediction.Enable = 'Off'; handles.DataProcessing.Enable = 'Off'; handles.prevframe.Enable = 'Off'; handles.nextframe.Enable = 'Off'; handles.GoToFrame.Visible = 'Off'; handles.GoToDropdown.Visible = 'Off'; handles.ChooseDisplay.Visible = 'Off'; end seg_dir = ['./Results/SegmentedImages' sub_dir '/']; seg_list = dir([seg_dir 'img_*.png']); if(~isempty(seg_list)) handles.ChooseDisplay.String{2} = 'ROI'; handles.ChooseDisplay.String{3} = 'Segmented'; handles.ChooseDisplay.Value = 3; segIm = im2double(imread([seg_dir seg_list(1).name])); imshow(segIm); end output_dir = ['./Results/Tracking' sub_dir '/']; if(exist([output_dir 'trajectory.mat'])) handles.ChooseDisplay.String{4} = 'Tracking'; handles.ChooseDisplay.Value = 4; handles.Progress.String = 'Tracking Complete.'; set(handles.Text_Display, 'String', 'Tracking already completed for this folder'); load([output_dir 'trajectory.mat']); load([output_dir 'norm_trajectory.mat']); load([output_dir 'CoM.mat']); handles.ViewerMode.Value = 1; handles.ViewTracking.Visible = 'On'; handles.ViewTracking.Enable = 'On'; startframe = find(trajectory, 1, 'first'); endframe = length(CoM); % find body_length i = startframe; try img = imread([seg_dir 'roi_' num2str(i) '.png']); img_norm = imtranslate(img, [255 - CoM(i,1), 255 - CoM(i,2)]); img_norm = imrotate(img_norm, CoM(i,3)); img_norm = imcrop(img_norm, [size(img_norm,1)/2-150 size(img_norm,2)/2-150 300 300]); [Y,X] = find(img_norm); bodylength = max(Y(find(X==150)))-min(Y(find(X==150))); catch bodylength = 130; end setappdata(hMainGui, 'bodylength', bodylength); handles.BLText.Visible = 'On'; handles.BodyLength.Visible = 'On'; handles.BodyLength.String = num2str(round(bodylength)); ResetImageSize_Callback(hObject, eventdata, handles); setappdata(hMainGui, 'start_frame',startframe); setappdata(hMainGui,'current_frame',startframe); setappdata(hMainGui, 'trajectory', trajectory); setappdata(hMainGui, 'norm_trajectory', norm_trajectory); setappdata(hMainGui, 'CoM', CoM); handles.VideoStart.Visible = 'On'; handles.VideoEnd.Visible = 'On'; handles.VideoStartFrame.Visible = 'On'; handles.VideoEndFrame.Visible = 'On'; handles.VideoStartFrame.String = num2str(startframe); handles.VideoEndFrame.String = num2str(endframe); nLegs = size(trajectory,2); if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end i = startframe; handles.text.String = ['Current frame: ' num2str(i)]; handles.GoToDropdown.Value = i; handles.Tracking_Text.String = ''; oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:size(trajectory,2) if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end else setappdata(hMainGui, 'bodylength', 0); setappdata(hMainGui, 'trajectory', []); setappdata(hMainGui, 'norm_trajectory', []); setappdata(hMainGui, 'CoM', []); handles.ViewerMode.Value = 0; handles.ViewTracking.Visible = 'Off'; handles.ViewTracking.Enable = 'Off'; handles.VideoStart.Visible = 'Off'; handles.VideoEnd.Visible = 'Off'; handles.VideoStartFrame.Visible = 'Off'; handles.VideoEndFrame.Visible = 'Off'; end %% --- Executes on button press in Segmentation. function Segmentation_Callback(hObject, eventdata, handles) % hObject handle to Segmentation (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); set(handles.Text_Display, 'String', 'Starting segmentation.'); pause(1); handles.axes1 = gcf; pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = ['./Data' sub_dir '/']; output_dir = ['./Results/SegmentedImages' sub_dir '/']; if (~exist([output_dir '/roi_1.png'])) pause(1); Foreground_Callback(hObject, eventdata, handles); end handles.ChooseDisplay.Value = 3; ref_img = imread([data_dir 'Background/Background.png']); ref_img = double(ref_img); ref_img = padarray(ref_img,[20 20],'replicate'); img_list = load_img_list(data_dir); params = setup_config_L('Drive'); %params = setup_lists(params); %params = setup_directories_L(params); params.border_skip_size = 20; params.pos_samples_no = 30000; params.neg_samples_no = 30000; params.sample_size = [41 41]'; load_wl = handles.LoadClassifier.Value; foreground_thres = handles.ForegroundSlider.Value; score_thres = handles.SegmentationSlider.Value; if (load_wl) set(handles.Text_Display, 'String','Please select the trained classifier to be used.'); [wl_file, wl_dir] = uigetfile(['./Results/Classifiers/','*.mat']); load([wl_dir wl_file]); else if(~exist(['./Results/Classifiers' sub_dir '_Classifier.mat'])) set(handles.Text_Display, 'String','Training the classifer over a random subset of the images.'); sample_ratio = 20; tot_idx.pos = 0; tot_idx.neg = 0; data = []; % Collect the positive and negative samples for i_img = 1 : floor(length(img_list) / sample_ratio) I = imread([data_dir img_list(i_img * sample_ratio).name]); I = double(I); I = padarray(I,[20 20],'replicate'); [pos_img,neg_img_body,neg_img_bkg] = leg_segment(I,ref_img,foreground_thres); show_img_output = repmat(I/255,[1 1 3]); show_img_output(:,:,1) = show_img_output(:,:,1) + pos_img; show_img_output(:,:,3) = show_img_output(:,:,3) + neg_img_body; imshow(show_img_output,[]); pause(0.1); data.train.imgs.X{i_img,1} = I / 255; data.train.imgs.X{i_img,2} = (ref_img - I) / 255; gt_img = zeros(size(I)); gt_img(pos_img > 0) = 1; gt_img(neg_img_body > 0) = -1; gt_img(neg_img_bkg > 0) = -1; data.train.gts.X{i_img} = gt_img; % ------------------------------ border_img = zeros(size(I)); border_img(1:params.border_skip_size,:) = 1; border_img(:,1:params.border_skip_size) = 1; border_img((end-params.border_skip_size):end,:) = 1; border_img(:,(end-params.border_skip_size):end) = 1; pos_sampling = find((pos_img) & (~border_img) & (~neg_img_body)); neg_body_sampling = find(neg_img_body & (~border_img) & (~pos_img)); neg_bkg_sampling = find(neg_img_bkg & (~border_img)); % sample counts imgs_no = floor(length(img_list) / sample_ratio); npos_img = ceil(params.pos_samples_no / imgs_no); nneg_img = ceil(params.neg_samples_no / imgs_no); % getting a random subset? neg_sampling = neg_body_sampling(randi(length(neg_body_sampling),[nneg_img,1])); neg_sampling = [neg_sampling; neg_bkg_sampling(randi(length(neg_bkg_sampling),[nneg_img,1]))]; npos = length(pos_sampling); nneg = length(neg_sampling); good_sampling_idx{i_img}.pos = pos_sampling(randi(npos,[ceil(npos_img),1])); good_sampling_idx{i_img}.neg = neg_sampling(randi(nneg,[ceil(nneg_img),1])); good_sampling_idx{i_img}.pos_val = pos_img(good_sampling_idx{i_img}.pos); good_sampling_idx{i_img}.neg_val = pos_img(good_sampling_idx{i_img}.neg); tot_idx.pos = tot_idx.pos+length(good_sampling_idx{i_img}.pos); tot_idx.neg = tot_idx.neg+length(good_sampling_idx{i_img}.neg); end data.train.imgs.idxs = 1:2; data.train.imgs.sub_ch_no = 2; samples_idx = []; % Preparing the samples for i_img = 1 : floor(length(img_list) / sample_ratio) I = imread([data_dir img_list(i_img * sample_ratio).name]); I = padarray(I,[20 20],'replicate'); samp_idx = [good_sampling_idx{i_img}.pos ; good_sampling_idx{i_img}.neg]; samp_idx_2D = zeros(length(samp_idx),2); [samp_idx_2D(:,1),samp_idx_2D(:,2)] = ind2sub(size(I),samp_idx); labels = [good_sampling_idx{i_img}.pos_val ; good_sampling_idx{i_img}.neg_val]; labels = double(labels); labels(labels < 0.5) = -1; samples_idx_img = zeros(length(samp_idx),4); samples_idx_img(:,1) = i_img; samples_idx_img(:,2:3) = samp_idx_2D; samples_idx_img(:,4) = labels; samples_idx = cat(1,samples_idx,samples_idx_img); end samples_idx = samples_idx(1 : (params.pos_samples_no + params.neg_samples_no),:); params.pos_samples_no = sum(samples_idx(:,end)==1); params.neg_samples_no = sum(samples_idx(:,end)==-1); params.T1_size = round((params.pos_samples_no+params.neg_samples_no)/3); params.T2_size = round((params.pos_samples_no+params.neg_samples_no)/3); params.pos_to_sample_no = params.T1_size; params.neg_to_sample_no = params.T1_size; params.wl_no = 100; % Train the classifier here weak_learners = train_boost_general(params,data,samples_idx); sub_dir_pos = strfind(sub_dir,'/'); if(~exist(['./Results/Classifiers/' sub_dir(1:sub_dir_pos(end))])) mkdir(['./Results/Classifiers/' sub_dir(1:sub_dir_pos(end))]); end save (['./Results/Classifiers/' sub_dir '_Classifier.mat'],'weak_learners'); else load(['./Results/Classifiers/' sub_dir '_Classifier.mat']); end end set(handles.Text_Display, 'String','Training completed, now applying the classifier to all images.'); fprintf('Output directory: %s\n', output_dir); % Applying the classifier sample_ratio = 1; sec_no = 10; for i_sec = 1 : ceil(length(img_list) / sample_ratio / sec_no) X = []; if (i_sec < ceil(length(img_list) / sample_ratio / sec_no)) imgs_sec = 1 + (i_sec - 1) * sec_no : sample_ratio :(i_sec) * sec_no; else imgs_sec = 1 + (i_sec - 1) * sec_no : sample_ratio : length(img_list); end for i_img = 1 : length(imgs_sec) I = imread([data_dir img_list(imgs_sec(i_img)).name]); I = double(I); I = padarray(I,[20 20],'replicate'); %neg_img{i_img} = leg_segment_clean(I,roi_img); bkg_sub = (ref_img - I); I(:,:,2) = bkg_sub; I = I / 255; X{i_img,1} = I(:,:,1); X{i_img,2} = I(:,:,2); roi_img = imread([output_dir 'roi_' num2str(imgs_sec(i_img)) '.png']); roi_images{i_img} = padarray(roi_img,[20 20],'replicate'); end score_images = batch_evaluate_boost_images(X,params,weak_learners,roi_images); for i_img = 1 : length(imgs_sec) I = imread([data_dir img_list(imgs_sec(i_img)).name]); I = double(I)/255; I = padarray(I,[20 20],'replicate'); score_img = score_images{i_img}; est_leg = score_img > score_thres; %est_leg = bwdist(est_leg)<3 .*roi_images{i_img}; %est_leg = est_leg & ~ neg_img{i_img} & roi_images{i_img}; est_leg = filter_small_comp(est_leg,5); show_img_output = repmat(I,[1 1 3]); show_img_output(:,:,1) = show_img_output(:,:,1) + est_leg; imwrite(imcrop(show_img_output,[21 21 size(I,2)-41 size(I,1)-41]),[output_dir 'img_' num2str(imgs_sec(i_img)) '.png'],'png'); if (i_img == 1) imshow(imcrop(show_img_output,[21 21 size(I,2)-41 size(I,1)-41])); handles.GoToDropdown.Value = (i_sec-1)*10 + i_img; handles.Progress.String = ['Segmentation Progress: ' num2str(min(i_sec*10,length(img_list))) '/' num2str(length(img_list))]; pause(0.1); end end end handles.Text_Display.String = 'Segmentation complete, proceeding with tracking.'; pause(5); Tracking_Callback(hObject, eventdata, handles); %% --- Executes on button press in Tracking. function Tracking_Callback(hObject, eventdata, handles) % hObject handle to Tracking (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); if(isempty(getappdata(hMainGui, 'trajectory'))) setappdata(hMainGui, 'CoM', []); setappdata(hMainGui, 'trajectory', []); setappdata(hMainGui, 'norm_trajectory', []); setappdata(hMainGui, 'start_frame', []); end bodylength = getappdata(hMainGui, 'bodylength'); CoM = getappdata(hMainGui, 'CoM'); trajectory = getappdata(hMainGui, 'trajectory'); norm_trajectory = getappdata(hMainGui, 'norm_trajectory'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); seg_dir = ['./Results/SegmentedImages' sub_dir '/']; data_dir = ['./Data' sub_dir '/']; img_list = load_img_list(data_dir); output_dir = ['./Results/Tracking/' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end if(length(handles.ChooseDisplay.String)<4) handles.ChooseDisplay.String{4} = 'Tracking'; end removed_legs = handles.L1.Value+handles.L2.Value+handles.L3.Value+handles.L4.Value+handles.R1.Value+handles.R2.Value+handles.R3.Value+handles.R4.Value; nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end se = strel('disk',4); max_distance = 20; if (isempty(seg_dir)) set(handles.Text_Display, 'String', 'Please ensure that segmentation has been completed first.'); else set(handles.Text_Display, 'String', 'Performing legs tracking.'); pause(1); handles.axes1 = gcf; drawnow end_frame = length(dir([seg_dir 'img_*.png'])); handles.ChooseDisplay.Value = 4; %Manually initiate tracking by labeling leg tips on chosen frame if(get(handles.ManualInitiate,'Value')) setappdata(hMainGui, 'CoM', []); setappdata(hMainGui, 'trajectory', []); setappdata(hMainGui, 'norm_trajectory', []); i = handles.GoToDropdown.Value; Imroi = imread([seg_dir 'roi_' num2str(i) '.png']); handles.Tracking_Text.String = ''; setappdata(hMainGui, 'current_frame', i); setappdata(hMainGui, 'start_frame', i); handles.GoToDropdown.String = i:i; Imroi = imerode(Imroi,se); [locY,locX] = find(Imroi > 0); %Centre_of_Mass [centre_of_mass,theta] = findCoM(locX,locY); CoM(i, :) = [centre_of_mass, theta]; setappdata(hMainGui, 'CoM' , CoM); oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); handles.Tracking.String = 'Initial'; % find body_length img = imread([seg_dir 'roi_' num2str(i) '.png']); img_norm = imtranslate(img, [255 - CoM(i,1), 255 - CoM(i,2)]); img_norm = imrotate(img_norm, CoM(i,3)); img_norm = imcrop(img_norm, [size(img_norm,1)/2-150 size(img_norm,2)/2-150 300 300]); [Y,X] = find(img_norm); bodylength = max(Y(find(X==150)))-min(Y(find(X==150))); setappdata(hMainGui, 'bodylength', bodylength); handles.BLText.Visible = 'On'; handles.BodyLength.Visible = 'On'; handles.BodyLength.String = num2str(round(bodylength)); ResetImageSize_Callback(hObject, eventdata, handles); hold on scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); hold off %Automatically initiate tracking elseif(isempty(getappdata(hMainGui, 'start_frame'))); for i = 1: end_frame clear connComp; % Load necessary images Im = imread([seg_dir 'img_' num2str(i) '.png']); Imroi = imread([seg_dir 'roi_' num2str(i) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; connComp = bwconncomp(Imwork); %Find a frame with correct number of legs (each leg component %must contain at least 35 pixels) if (connComp.NumObjects == 8 - removed_legs && min(cellfun(@length,connComp.PixelIdxList)) > 35) handles.text.String = ['Current frame: ' num2str(i)]; Imroi = imerode(Imroi,se); [locY,locX] = find(Imroi > 0); %Centre_of_Mass [centre_of_mass,theta] = findCoM(locX,locY); locX_norm = locX - centre_of_mass(1); locY_norm = locY - centre_of_mass(2); points = [cosd(theta) sind(theta); -sind(theta) cosd(theta)]* [locX_norm'; locY_norm']; %Attempt to ensure that the head is in the positive %y-direction by comparing which side has more number of %pixel points if (length(find(points(2,:) > 0)) < length(find(points(2,:) < 0))) theta = mod(theta + 180,360); end for cc = 1 : 8 - removed_legs legpixels = connComp.PixelIdxList{cc}; imleg = zeros(size(Imwork)); imleg(legpixels) = 1; [skr,~] = skeleton(imleg); skr_start = 3; [~,exy,~] = anaskel(bwmorph(skr > skr_start,'skel',Inf)); imgX = exy(1,:) - centre_of_mass(1); imgY = exy(2,:) - centre_of_mass(2); normpoints = [cosd(theta) sind(theta); -sind(theta) cosd(theta)]* [imgX; imgY]; [~,tipidx]=max(min(pdist2(points',normpoints','euclidean'))); for j = 1:2 raw_normtips(cc,j) = normpoints(j,tipidx); raw_tips(cc,j) = exy(j,tipidx); end end leftlegs_idx = find(raw_normtips(:,1)<0); if(length(leftlegs_idx)~=4 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.L4.Value) continue; end rightlegs_idx = find(raw_normtips(:,1)>0); if(length(rightlegs_idx)~=4 - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.R4.Value) continue; end [~,left_sort] = sort(raw_normtips(leftlegs_idx,2)); leg_counter = 0; for kk = 1 : nLegs/2 if (eval(['handles.L' num2str(kk) '.Value'])) leg_counter = leg_counter+1; continue; end trajectory(i, kk, :) = raw_tips(leftlegs_idx(left_sort(kk-leg_counter)),:); norm_trajectory(i, kk, :) = raw_normtips(leftlegs_idx(left_sort(kk-leg_counter)),:); end [~,right_sort] = sort(raw_normtips(rightlegs_idx,2)); leg_counter = 0; for kk = 1 :nLegs/2 if (eval(['handles.R' num2str(kk) '.Value'])) leg_counter = leg_counter+1; continue; end trajectory(i, kk+nLegs/2, :) = raw_tips(rightlegs_idx(right_sort(kk-leg_counter)),:); norm_trajectory(i, kk+nLegs/2, :) = raw_normtips(rightlegs_idx(right_sort(kk-leg_counter)),:); end CoM(i, :) = [centre_of_mass, theta]; handles.Tracking_Text.String = ''; setappdata(hMainGui, 'start_frame', i); setappdata(hMainGui, 'current_frame', i); oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); hold on; % find body_length img = imread([seg_dir 'roi_' num2str(i) '.png']); img_norm = imtranslate(img, [255 - CoM(i,1), 255 - CoM(i,2)]); img_norm = imrotate(img_norm, CoM(i,3)); img_norm = imcrop(img_norm, [size(img_norm,1)/2-150 size(img_norm,2)/2-150 300 300]); [Y,X] = find(img_norm); bodylength = max(Y(find(X==150)))-min(Y(find(X==150))); setappdata(hMainGui, 'bodylength', bodylength); handles.BLText.Visible = 'On'; handles.BodyLength.Visible = 'On'; handles.BodyLength.String = num2str(round(bodylength)); ResetImageSize_Callback(hObject, eventdata, handles); scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if (eval(['handles.' legs_id{kk} '.Value'])) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; end hold off; drawnow break else continue; end end end startframe = getappdata(hMainGui, 'start_frame'); if (~get(handles.ManualInitiate,'Value') && startframe>1) for k = startframe-1 : -1 : 1 Im = (imread([seg_dir 'img_' num2str(k) '.png'])); Imroi = imread([seg_dir 'roi_' num2str(k) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; if (nLegs>6) Imwork = Imwork - (bwdist(imerode(Imroi,strel('disk',6)))<15); Imwork = bwareaopen(Imwork>0,5); else Imwork = Imwork - (bwdist(imerode(Imroi,se))<10); Imwork = bwareaopen(Imwork>0,3); end [locY,locX] = find(imerode(Imroi,se) > 0); [centre_of_mass,theta] = findCoM(locX,locY); if (abs(theta - CoM(k+1,3)) > 90 && abs(theta - CoM(k+1,3)) < 270) theta = mod(theta + 180,360); end CoM(k, :) = [centre_of_mass, theta]; clear raw_tips; clear raw_normtips; [raw_tips, raw_normtips] = findtips(Imwork, Imroi, locX, locY, centre_of_mass, theta); x_t1 = raw_normtips; x_t0 = reshape(norm_trajectory(k+1,:),[nLegs 2]); target_indices = hungarianlinker(x_t0, x_t1, max_distance); for kk = 1:length(target_indices) if (target_indices(kk) > 0) norm_trajectory(k, kk, :) = raw_normtips(target_indices(kk),:); trajectory(k, kk, :) = raw_tips(target_indices(kk),:); else norm_trajectory(k, kk, :) = 0; trajectory(k, kk, :) = 0; end end leftoveridx = find(hungarianlinker(raw_normtips, x_t0, max_distance)==-1); x_t1 = raw_normtips(leftoveridx,:); x_t2 = raw_tips(leftoveridx,:); last_seen_tips = reshape(norm_trajectory(k,:),[nLegs 2]); unassigned_idx = find(last_seen_tips(:,1) == 0); for kk = 1:length(unassigned_idx) if(eval(['handles.' legs_id{unassigned_idx(kk)} '.Value'])) continue; end idx = find(norm_trajectory(1:startframe,unassigned_idx(kk),1),1,'last'); last_seen_tips(unassigned_idx(kk),:) = reshape(norm_trajectory(idx,unassigned_idx(kk),:), [1 2]); end if (~isempty(unassigned_idx) && ~isempty(x_t1)) C = hungarianlinker(last_seen_tips(unassigned_idx,:), x_t1, 1.25*max_distance); for l = 1:length(C) if (C(l) ~= -1) norm_trajectory(k,unassigned_idx(l), :) = x_t1(C(l), :); trajectory(k,unassigned_idx(l), :) = x_t2(C(l), :); end end end end setappdata(hMainGui, 'start_frame', 1); end if (strcmp(handles.Tracking.String, 'Tracking') || strcmp(handles.Tracking.String, 'Resume')) handles.Tracking.String = 'Pause'; else if (~strcmp(handles.Tracking.String, 'Initial')) handles.Tracking.String = 'Resume'; end end skip = 1; while (strcmp(handles.Tracking.String, 'Pause')) setappdata(hMainGui, 'current_frame', getappdata(hMainGui, 'current_frame') + skip); i = getappdata(hMainGui, 'current_frame'); if (i>end_frame) setappdata(hMainGui, 'current_frame', end_frame); break; end handles.Progress.String = ['Tracking Progress: ' num2str(i) '/' num2str(length(img_list))]; handles.text.String = ['Current frame: ' num2str(i)]; % Load necessary images Im = (imread([seg_dir 'img_' num2str(i) '.png'])); Imroi = imread([seg_dir 'roi_' num2str(i) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; [locY,locX] = find(imerode(Imroi,se) > 0); if (nLegs>6) Imwork = Imwork - (bwdist(imerode(Imroi,strel('disk',6)))<15); Imwork = bwareaopen(Imwork>0,5); else Imwork = Imwork - (bwdist(imerode(Imroi,se))<10); Imwork = bwareaopen(Imwork>0,3); end %Centre_of_Mass [centre_of_mass,theta] = findCoM(locX,locY); if (abs(theta - CoM(i-skip,3)) > 90 && abs(theta - CoM(i-skip,3)) < 270) theta = mod(theta + 180,360); end CoM(i, :) = [centre_of_mass, theta]; clear raw_tips; clear raw_normtips; [raw_tips, raw_normtips] = findtips(Imwork, Imroi, locX, locY, centre_of_mass, theta); x_t1 = raw_normtips; x_t0 = reshape(norm_trajectory(i-1,:),[nLegs 2]); target_indices = hungarianlinker(x_t0, x_t1, max_distance); for kk = 1:length(target_indices) if (target_indices(kk) > 0) norm_trajectory(i, kk, :) = raw_normtips(target_indices(kk),:); trajectory(i, kk, :) = raw_tips(target_indices(kk),:); else norm_trajectory(i, kk, :) = 0; trajectory(i, kk, :) = 0; end end leftoveridx = find(hungarianlinker(raw_normtips, x_t0, max_distance)==-1); x_t1 = raw_normtips(leftoveridx,:); x_t2 = raw_tips(leftoveridx,:); last_seen_tips = reshape(norm_trajectory(i,:),[nLegs 2]); unassigned_idx = find(last_seen_tips(:,1) == 0); for kk = 1:length(unassigned_idx) if(eval(['handles.' legs_id{unassigned_idx(kk)} '.Value'])) continue; end idx = find(norm_trajectory(1:i,unassigned_idx(kk),1),1,'last'); last_seen_tips(unassigned_idx(kk),:) = reshape(norm_trajectory(idx,unassigned_idx(kk),:), [1 2]); end if (~isempty(unassigned_idx) && ~isempty(x_t1)) C = hungarianlinker(last_seen_tips(unassigned_idx,:), x_t1, 1.25*max_distance); for l = 1:length(C) if (C(l) ~= -1) norm_trajectory(i,unassigned_idx(l), :) = x_t1(C(l), :); trajectory(i,unassigned_idx(l), :) = x_t2(C(l), :); end end end handles.GoToDropdown.Value = i; handles.Tracking_Text.String = ''; oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if (eval(['handles.' legs_id{kk} '.Value'])) trajectory(i,kk,1) = 0; trajectory(i,kk,2) = 0; norm_trajectory(i,kk,1) = 0; norm_trajectory(i,kk,2) = 0; handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; continue; end if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end setappdata(hMainGui, 'CoM' , CoM); setappdata(hMainGui, 'trajectory' , trajectory); setappdata(hMainGui, 'norm_trajectory' , norm_trajectory); handles.GoToDropdown.String = 1:find(CoM(:,1),1,'last'); end if (getappdata(hMainGui, 'current_frame') == end_frame) save([output_dir 'CoM.mat'],'CoM'); save([output_dir 'trajectory.mat'],'trajectory'); save([output_dir 'norm_trajectory.mat'],'norm_trajectory'); csvwrite([output_dir 'CoM.csv'],CoM); csvwrite([output_dir 'trajectory.csv'],trajectory); csvwrite([output_dir 'norm_trajectory.csv'],norm_trajectory); handles.Tracking.String = 'Complete'; handles.Text_Display.String = 'Tracking Complete'; handles.VideoStart.Visible = 'On'; handles.VideoEnd.Visible = 'On'; handles.VideoStartFrame.Visible = 'On'; handles.VideoEndFrame.Visible = 'On'; handles.VideoStartFrame.String = num2str(getappdata(hMainGui, 'start_frame')); handles.VideoEndFrame.String = num2str(end_frame); end end %% --- Mouse cursor location in axis function fh_wbmfcn(varargin) % WindowButtonMotionFcn for the figure. S = varargin{3}; % Get the structure. F = get(S.fh,'currentpoint'); % The current point w.r.t the figure. % Figure out of the current point is over the axes or not -> logicals. tf1 = (S.fh.Position(3)-512)/2 <= F(1) && F(1) <= (S.fh.Position(3)+512)/2; tf2 = (S.fh.Position(4)-512) <= F(2) && F(2) <= S.fh.Position(4); if tf1 && tf2 % Calculate the current point w.r.t. the axes. Cx = round(F(1) - (S.fh.Position(3)-512)/2); Cy = round(S.fh.Position(4) - F(2)); % The mouse location is displayed at handles.Position_Text. set(S.tx,'str',num2str([Cx,Cy],'%3.0f %3.0f')) end %% --- Double click to label tip position of a leg function clicker(h,~,handles) hMainGui = getappdata(0, 'hMainGui'); nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; switch get(get(handles.Labelling_Panel, 'SelectedObject'), 'Tag'); case 'Leg_L1', id = 1; case 'Leg_L2', id = 2; case 'Leg_L3', id = 3; case 'Leg_L4', id = 4; case 'Leg_R1', id = 5; case 'Leg_R2', id = 6; case 'Leg_R3', id = 7; case 'Leg_R4', id = 8; end else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; switch get(get(handles.Labelling_Panel, 'SelectedObject'), 'Tag'); case 'Leg_L1', id = 1; case 'Leg_L2', id = 2; case 'Leg_L3', id = 3; case 'Leg_R1', id = 4; case 'Leg_R2', id = 5; case 'Leg_R3', id = 6; end end F = get(h, 'currentpoint'); if (strcmp(get(h, 'selectiontype'),'open') && (handles.figMain.Position(3)-512)/2 <= F(1) && F(1) <= (handles.figMain.Position(3)+512)/2 && (handles.figMain.Position(4)-512) <= F(2) && F(2) <= handles.figMain.Position(4)) Cx = round(F(1) - (handles.figMain.Position(3)-512)/2); Cy = round(handles.figMain.Position(4) - F(2)); if (strcmp(handles.Annotation.String,'Save and Exit')) handles.Tracking_Text.String(id,:) = sprintf('Leg %2s: %3d %3d', legs_id{id}, Cx, Cy); for i = 1: length(handles.figMain.CurrentAxes.Children)-2 if(strcmp(handles.figMain.CurrentAxes.Children(i).Type,'text') && strcmp(handles.figMain.CurrentAxes.Children(i).String,legs_id{id})) delete(handles.figMain.CurrentAxes.Children(i:i+1)); end end hold on scatter(Cx,Cy,'w'); text(Cx,Cy,legs_id{id},'Color',[colors(id,1) colors(id,2) colors(id,3)],'FontSize',14); hold off end end function update_display(hObject, eventdata, handles) nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end %% --- Executes when selected object is changed in Labelling_Panel. function Labelling_Panel_SelectionChangedFcn(hObject, eventdata, handles) % hObject handle to the selected object in Labelling_Panel % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) if (strcmp(handles.Tracking.String,'Resume')) switch get(get(handles.Labelling_Panel, 'SelectedObject'), 'Tag'); case 'Leg_L1', set(handles.Text_Display,'String','Selecting leg L1'); case 'Leg_L2', set(handles.Text_Display,'String','Selecting leg L2'); case 'Leg_L3', set(handles.Text_Display,'String','Selecting leg L3'); case 'Leg_R1', set(handles.Text_Display,'String','Selecting leg R1'); case 'Leg_R2', set(handles.Text_Display,'String','Selecting leg R2'); case 'Leg_R3', set(handles.Text_Display,'String','Selecting leg R3'); case 'Leg_L4', set(handles.Text_Display,'String','Selecting leg L4'); case 'Leg_R4', set(handles.Text_Display,'String','Selecting leg R4'); end end %% --- Executes on button press in Video. function Video_Callback(hObject, eventdata, handles) % hObject handle to Video (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); bodylength = getappdata(hMainGui, 'bodylength'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); tracking_dir = ['./Results/Tracking' sub_dir '/']; if (exist(['./Results/Tracking' sub_dir '/trajectory.mat'])) Video_base(data_dir,30,str2num(handles.Video_Step.String),str2num(handles.VideoStartFrame.String),str2num(handles.VideoEndFrame.String), bodylength); else handles.Text_Display.String = 'Please ensure that tracking has been completed first.'; end %% --- Executes on selection change in GoToDropdown. function GoToDropdown_Callback(hObject, eventdata, handles) % hObject handle to GoToDropdown (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: contents = cellstr(get(hObject,'String')) returns GoToDropdown contents as cell array % contents{get(hObject,'Value')} returns selected item from GoToDropdown hMainGui = getappdata(0, 'hMainGui'); i = handles.GoToDropdown.Value; setappdata(hMainGui,'current_frame',i); handles.text.String = ['Current frame: ' num2str(i)]; data_dir = getappdata(hMainGui, 'data_dir'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = ['./Data' sub_dir '/']; img_list = load_img_list(data_dir); oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); display = handles.ChooseDisplay.Value; switch display case 1 handles.GoToDropdown.String = 1:length(img_list); imshow(oriIm); case 2 seg_dir = ['./Results/SegmentedImages' sub_dir '/']; roi_list = dir([seg_dir 'roi_*.png']); handles.GoToDropdown.String = 1:length(roi_list); imshow([seg_dir 'roi_' num2str(i) '.png']); case 3 seg_dir = ['./Results/SegmentedImages' sub_dir '/']; seg_list = dir([seg_dir 'img_*.png']); handles.GoToDropdown.String = 1:length(seg_list); imshow([seg_dir 'img_' num2str(i) '.png']); case 4 trajectory = getappdata(hMainGui, 'trajectory'); CoM = getappdata(hMainGui, 'CoM'); bodylength = getappdata(hMainGui, 'bodylength'); startframe = 1; handles.Tracking_Text.String = ''; i = handles.GoToDropdown.Value + startframe - 1; setappdata(hMainGui,'current_frame',i); handles.text.String = ['Current frame: ' num2str(i)]; oriIm = im2double(imread([data_dir img_list(i).name])); imshow(oriIm); handles.GoToDropdown.String = 1:length(trajectory); nLegs = size(trajectory,2); if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if(eval(['handles.' legs_id{kk} '.Value'])) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; continue; end if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end end %% --- Executes on button press in BatchProcess. function BatchProcess_Callback(hObject, eventdata, handles) % hObject handle to BatchProcess (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); prompt = {'Input 0 for segmentation only or 1 to include tracking'}; dlg_title = 'Options for batch process'; num_lines = 1; defaultans = {'0'}; answer = inputdlg(prompt,dlg_title,num_lines,defaultans); if(isempty(answer)) return; end answer = ~(answer{1}=='0'); data_dir = uipickfiles; try if(data_dir==0) return; end catch end if(isempty(data_dir)) return; end for i = 1 : length(data_dir) ProcessFolder(data_dir{i}, answer, handles); end function ProcessFolder(data_dir, answer, handles) score_thres = str2num(handles.SegmentationEdit.String); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); if(exist(data_dir) && isempty(dir([data_dir '/*.tif'])) && isempty(dir([data_dir '/*.bmp']))) dir_fn = dir(data_dir); for i_dir = 1:length(dir_fn)-2 if (dir_fn(i_dir+2).isdir) ProcessFolder([data_dir '/' dir_fn(i_dir+2).name], answer, handles); pause(1); end end else data_dir = ['./Data' sub_dir]; handles.Text_Display.String = ['Performing segmentation for the folder: ' data_dir]; fprintf('Performing segmentation for the folder: %s.\n', data_dir); pause(1); try Segmentation(data_dir,score_thres,0); catch MExc fprintf('An error occured during segmentation for the folder: %s.\n', data_dir); disp('Execution will continue.'); disp(MExc.message); end handles.Text_Display.String = ['Completed segmentation for the folder: ' data_dir]; fprintf('Completed segmentation for the folder: %s.\n', data_dir); pause(1) if (answer) handles.Text_Display.String = ['Performing tracking for the folder: ' data_dir]; fprintf('Performing tracking for the folder: %s.\n', data_dir); pause(1); try Tracking_Base(data_dir); catch MExc fprintf('An error occured during tracking for the folder: %s.\n', data_dir); disp('Execution will continue.'); disp(MExc.message); end handles.Text_Display.String = ['Completed tracking for the folder: ' data_dir]; fprintf('Completed tracking for the folder: %s.\n', data_dir); pause(1); end end %% --- Change display mode function ChooseDisplay_Callback(hObject, eventdata, handles) % hObject handle to ChooseDisplay (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: contents = cellstr(get(hObject,'String')) returns ChooseDisplay contents as cell array % contents{get(hObject,'Value')} returns selected item from ChooseDisplay GoToDropdown_Callback(hObject, eventdata, handles); %% --- Change start frame of make video function VideoStartFrame_Callback(hObject, eventdata, handles) % hObject handle to VideoStartFrame (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); if (str2num(hObject.String) < 1) hObject.String = '1'; end %% --- Change end frame of make video function VideoEndFrame_Callback(hObject, eventdata, handles) % hObject handle to VideoEndFrame (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); CoM = getappdata(hMainGui, 'CoM'); if (str2num(hObject.String) > length(CoM)) hObject.String = num2str(length(CoM)); end if (str2num(hObject.String) < str2num(handles.VideoStartFrame.String)) hObject.String = num2str(str2num(handles.VideoStartFrame.String)+str2num(handles.Video_Step.String)); end %% --- Change segmentation threshold via slider function SegmentationSlider_Callback(hObject, eventdata, handles) % hObject handle to SegmentationSlider (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'Value') returns position of slider % get(hObject,'Min') and get(hObject,'Max') to determine range of slider handles.SegmentationEdit.String = num2str(handles.SegmentationSlider.Value); %% --- Change segmentation threshold via numerical input function SegmentationEdit_Callback(hObject, eventdata, handles) % hObject handle to SegmentationEdit (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of SegmentationEdit as text % str2double(get(hObject,'String')) returns contents of SegmentationEdit as a double handles.SegmentationSlider.Value = str2num(handles.SegmentationEdit.String); %% --- Change foreground threshold via slider function ForegroundSlider_Callback(hObject, eventdata, handles) % hObject handle to SegmentationSlider (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'Value') returns position of slider % get(hObject,'Min') and get(hObject,'Max') to determine range of slider handles.ForegroundEdit.String = num2str(handles.ForegroundSlider.Value); %% --- Change foreground threshold via numerical input function ForegroundEdit_Callback(hObject, eventdata, handles) % hObject handle to SegmentationEdit (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of SegmentationEdit as text % str2double(get(hObject,'String')) returns contents of SegmentationEdit as a double handles.ForegroundSlider.Value = str2num(handles.ForegroundEdit.String); %% --- Executes when figMain is resized. function figMain_SizeChangedFcn(hObject, eventdata, handles) % hObject handle to figMain (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) S.fh = handles.figMain; % Fix the central figure axis in pixel coordinates S.ax = gca; set(S.ax,'unit','pix','position',[(handles.figMain.Position(3)-512)/2 handles.figMain.Position(4)-512 512 512]); %% --- Executes on button press in ViewerMode. function ViewerMode_Callback(hObject, eventdata, handles) % hObject handle to ViewerMode (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hint: get(hObject,'Value') returns toggle state of ViewerMode if (handles.ViewerMode.Value) handles.ViewTracking.Visible = 'On'; handles.ViewTracking.Enable = 'On'; else handles.ViewTracking.Visible = 'Off'; handles.ViewTracking.Enable = 'Off'; end %% --- Executes on button press in ViewTracking. function ViewTracking_Callback(hObject, eventdata, handles) % hObject handle to ViewTracking (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); if(isempty(getappdata(hMainGui, 'trajectory'))) setappdata(hMainGui, 'CoM', []); setappdata(hMainGui, 'trajectory', []); setappdata(hMainGui, 'norm_trajectory', []); setappdata(hMainGui, 'start_frame', []); end bodylength = getappdata(hMainGui, 'bodylength'); CoM = getappdata(hMainGui, 'CoM'); trajectory = getappdata(hMainGui, 'trajectory'); norm_trajectory = getappdata(hMainGui, 'norm_trajectory'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); seg_dir = ['./Results/SegmentedImages' sub_dir '/']; data_dir = ['./Data' sub_dir '/']; img_list = load_img_list(data_dir); output_dir = ['./Results/Tracking/' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end if(length(handles.ChooseDisplay.String)<4) handles.ChooseDisplay.String{4} = 'Tracking'; end nLegs = 8 - handles.L4.Value - handles.R4.Value; if (nLegs > 6) colors = [1 0 0; 0 1 0; 0 0 1; 1 0 0.5; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3','L4', 'R1', 'R2', 'R3','R4'}; else colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0 0.5; 1 0.5 0]; legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; end end_frame = length(dir([seg_dir 'img_*.png'])); if (strcmp(handles.ViewTracking.String, 'View Tracking')) handles.ViewTracking.String = 'Pause Viewing'; else handles.ViewTracking.String = 'View Tracking'; end while (strcmp(handles.ViewTracking.String, 'Pause Viewing')) setappdata(hMainGui, 'current_frame', getappdata(hMainGui, 'current_frame') + 1); i = getappdata(hMainGui, 'current_frame'); if (i>end_frame) setappdata(hMainGui, 'current_frame', end_frame); break; end if(~isempty(trajectory)) handles.text.String = ['Current frame: ' num2str(i)]; handles.GoToDropdown.Value = i; handles.Tracking_Text.String = ''; imshow([data_dir img_list(i).name]); hold on; scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); for kk = 1:nLegs if(eval(['handles.' legs_id{kk} '.Value'])) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end if(trajectory(i,kk,1)~=0) handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: %3d %3d', legs_id{kk}, trajectory(i,kk,1), trajectory(i,kk,2))]; scatter(trajectory(i,kk,1),trajectory(i,kk,2),'w'); text(trajectory(i,kk,1),trajectory(i,kk,2),legs_id{kk},'Color',[colors(kk,1) colors(kk,2) colors(kk,3)],'FontSize',14); else handles.Tracking_Text.String = [handles.Tracking_Text.String; sprintf('Leg %2s: ', legs_id{kk})]; end end hold off; drawnow % Update Display nLegs = 8 - handles.L1.Value - handles.L2.Value - handles.L3.Value - handles.R1.Value - handles.R2.Value - handles.R3.Value - handles.L4.Value - handles.R4.Value; if(length(str2num(handles.Tracking_Text.String(:,9:11))) < nLegs) set(handles.Missing, 'BackgroundColor', [1 0 0]); else set(handles.Missing, 'BackgroundColor', [0 1 0]); end end end %% --- Executes on button press in DataProcess. function DataProcess_Callback(hObject, eventdata, handles) % hObject handle to DataProcess (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); if(isempty(getappdata(hMainGui, 'data_dir'))) set(handles.Text_Display, 'String', 'Analysing tracked data.'); pause(0.5); tracking_dir = uigetdir('./Results/Tracking/'); defaultfps = {'1000'}; fps = inputdlg({'Recording FPS for dataset?'},'Input Recording FPS',1,defaultfps); fps = str2num(fps{:}); defaultscale = {'11.0'}; scale = inputdlg({'Image Size (mm)?'},'Image Size Input',1,defaultscale); scale = str2double(scale{:}); defaultbodylength = {'2.90'}; bodylength = inputdlg({'Body length (mm)?'},'Input Body Length',1,defaultbodylength); bodylength = str2double(bodylength{:}); DataProcessing_New(fps, tracking_dir, scale, bodylength); set(handles.Text_Display, 'String', 'Data Processing complete.'); else data_dir = getappdata(hMainGui, 'data_dir'); defaultfps = {'1000'}; fps = inputdlg({'Recording FPS for dataset?'},'Input Recording FPS',1,defaultfps); fps = str2num(fps{:}); defaultscale = {'11.0'}; scale = inputdlg({'Image Size (mm)?'},'Image Size Input',1,defaultscale); scale = str2double(scale{:}); defaultbodylength = {'2.90'}; bodylength = inputdlg({'Body length (mm)?'},'Input Body Length',1,defaultbodylength); bodylength = str2double(bodylength{:}); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); tracking_dir = ['./Results/Tracking' sub_dir '/']; if(~exist([tracking_dir 'CoM.mat'])) handles.Text_Display.String = 'Please ensure that tracking has been completed first.'; else set(handles.Text_Display, 'String', 'Analysing tracked data.'); pause(0.5); DataProcessing_New(fps, tracking_dir, scale, bodylength); set(handles.Text_Display, 'String', 'Data Processing complete.'); end end %% --- Executes on button press in BatchDataProcess. function BatchDataProcess_Callback(hObject, eventdata, handles) % hObject handle to BatchDataProcess (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) data_dir = uipickfiles('filterspec',[pwd '/Results/Tracking']); defaultfps = {'1000'}; fps = inputdlg({'Recording FPS for dataset?'},'Input Recording FPS',1,defaultfps); fps = str2num(fps{:}); defaultscale = {'11.0'}; scale = inputdlg({'Image Size (mm)?'},'Image Size Input',1,defaultscale); scale = str2double(scale{:}); defaultbodylength = {'2.90'}; bodylength = inputdlg({'Body length (mm)?'},'Input Body Length',1,defaultbodylength); bodylength = str2double(bodylength{:}); try if(data_dir==0) return; end catch end if(isempty(data_dir)) return; end for i = 1 : length(data_dir) AnalyseFolder(data_dir{i}, fps, scale, bodylength); end function AnalyseFolder(data_dir, fps, scale, bodylength) pos_bs = strfind(data_dir,'Results'); sub_dir = data_dir(pos_bs(end)+length('Results/Tracking/'):length(data_dir)); output_dir = ['./Results/Tracking/' sub_dir '/']; if(~exist([output_dir 'trajectory.mat'])) dir_fn = dir(output_dir); for i_dir = 1:length(dir_fn)-2 AnalyseFolder([output_dir '/' dir_fn(i_dir+2).name], fps, scale, bodylength); pause(1); end else try DataProcessing_New(fps, output_dir, scale, bodylength); catch MExc fprintf('An error occured during data processing for the folder: %s.\n', data_dir); disp('Execution will continue.'); disp(MExc.message); end end %% --- Executes on key press with focus on figMain or any of its controls. function figMain_WindowKeyPressFcn(hObject, eventdata, handles) % hObject handle to figMain (see GCBO) % eventdata structure with the following fields (see MATLAB.UI.FIGURE) % Key: name of the key that was pressed, in lower case % Character: character interpretation of the key(s) that was pressed % Modifier: name(s) of the modifier key(s) (i.e., control, shift) pressed % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); trajectory = getappdata(hMainGui, 'trajectory'); last = max(length(trajectory),1); switch eventdata.Key case '1', handles.Leg_L1.Value = 1; case '2', handles.Leg_L2.Value = 1; case '3', handles.Leg_L3.Value = 1; case '4', handles.Leg_R1.Value = 1; case '5', handles.Leg_R2.Value = 1; case '6', handles.Leg_R3.Value = 1; case '7', handles.Leg_L4.Value = 1; case '8', handles.Leg_R4.Value = 1; case 'home', handles.GoToDropdown.Value = 1; GoToDropdown_Callback(hObject, eventdata, handles); case 'end', handles.GoToDropdown.Value = last; GoToDropdown_Callback(hObject, eventdata, handles); case 'uparrow', handles.ChooseDisplay.Value = max(handles.ChooseDisplay.Value-1,1); ChooseDisplay_Callback(hObject, eventdata, handles); case 'downarrow', handles.ChooseDisplay.Value = min(handles.ChooseDisplay.Value+1, length(handles.ChooseDisplay.String)); ChooseDisplay_Callback(hObject, eventdata, handles); end if(strcmp(handles.nextframe.Enable,'on')) switch eventdata.Key case 'leftarrow', try prevframe_Callback(hObject, eventdata, handles); catch end case 'rightarrow', try nextframe_Callback(hObject, eventdata, handles); catch end end end % --- Executes on slider movement. function ImageSizeSlider_Callback(hObject, eventdata, handles) % hObject handle to ImageSizeSlider (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'Value') returns position of slider % get(hObject,'Min') and get(hObject,'Max') to determine range of slider handles.ImageSize.String = num2str(hObject.Value); function ImageSize_Callback(hObject, eventdata, handles) % hObject handle to ImageSize (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of ImageSize as text % str2double(get(hObject,'String')) returns contents of ImageSize as a double handles.ImageSizeSlider.Value = str2num(hObject.String); % --- Executes on button press in ResetImageSize. function ResetImageSize_Callback(hObject, eventdata, handles) % hObject handle to ResetImageSize (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) bodylength = str2num(handles.BodyLength.String); if(bodylength>0) handles.ImageSizeSlider.Value = round(128/bodylength*11*2)/2; handles.ImageSize.String = num2str(round(128/bodylength*11*2)/2); else handles.ImageSizeSlider.Value = 11; handles.ImageSize.String = num2str(11); end %% --- Executes when user attempts to close figMain. function figMain_CloseRequestFcn(hObject, eventdata, handles) % hObject handle to figMain (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Construct a questdlg with three options hMainGui = getappdata(0, 'hMainGui'); choice = questdlg('Are you sure?', ... 'FLLIT', ... 'Save and Exit', 'Exit without Saving', 'Cancel', 'Cancel'); % Handle response switch choice case 'Save and Exit' if(~isempty(getappdata(hMainGui, 'trajectory'))) data_dir = getappdata(hMainGui, 'data_dir'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); output_dir = ['./Results/SegmentedImages' sub_dir '/']; trajectory = getappdata(hMainGui, 'trajectory'); norm_trajectory = getappdata(hMainGui, 'norm_trajectory'); CoM = getappdata(hMainGui, 'CoM'); save([output_dir 'CoM.mat'],'CoM'); save([output_dir 'trajectory.mat'],'trajectory'); save([output_dir 'norm_trajectory.mat'],'norm_trajectory'); csvwrite([output_dir 'CoM.csv'],CoM); csvwrite([output_dir 'trajectory.csv'],trajectory); csvwrite([output_dir 'norm_trajectory.csv'],norm_trajectory); display('Saving... Exiting...'); else display('Nothing to save! Exiting...'); end T = timerfind; if (~isempty(T)) stop(T); delete(T); end clear T delete(hObject); case 'Exit without Saving' display('Exiting...'); T = timerfind; if (~isempty(T)) stop(T); delete(T); end clear T delete(hObject); case 'Cancel' return; end % --- Executes on button press in Foreground. function Foreground_Callback(hObject, eventdata, handles) % hObject handle to Foreground (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); set(handles.Text_Display, 'String', 'Starting foreground extraction.'); pause(1); handles.axes1 = gcf; pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = ['./Data' sub_dir '/']; output_dir = ['./Results/SegmentedImages' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end img_list = load_img_list(data_dir); I = imread([data_dir img_list(1).name]); if(~exist([data_dir 'Background/Background.png']) || handles.OverrideBackground.Value==1) [~,~,ref_img,~] = video2background(data_dir, sub_dir); if(~exist([data_dir 'Background'])) mkdir([data_dir 'Background']) end imwrite(uint8(ref_img),[data_dir 'Background/Background.png'], 'png'); else ref_img = double(imread([data_dir 'Background/Background.png'])); end imshow(uint8(ref_img)); set(handles.Text_Display, 'String', 'This is the background averaged over the dataset.'); pause(2); if(length(handles.ChooseDisplay.String)<2) handles.ChooseDisplay.String{2} = 'ROI'; end handles.ChooseDisplay.Value = 2; foreground_thres = handles.ForegroundSlider.Value; set(handles.Text_Display, 'String', 'Extracting the region of interest.'); for i = 1 : length(img_list) I = imread([data_dir img_list(i).name]); I = double(I); roi_img = (max(ref_img - I(:,:,1),0) ./ ref_img) > foreground_thres; roi_img = bwareaopen(roi_img, 200); handles.GoToDropdown.Value = i; img_output = repmat(I/255,[1 1 3]); img_output(:,:,3) = img_output(:,:,3) + roi_img; imshow(img_output); pause(0.01); imwrite(roi_img,[output_dir 'roi_' num2str(i) '.png'],'png'); end % --- Executes on button press in ViewBackground. function ViewBackground_Callback(hObject, eventdata, handles) % hObject handle to ViewBackground (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) hMainGui = getappdata(0, 'hMainGui'); data_dir = getappdata(hMainGui, 'data_dir'); pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = ['./Data' sub_dir '/']; if (~exist([data_dir 'Background/Background.png'])) set(handles.Text_Display, 'String', 'No valid Background/Background.png found.'); else ref_img = imread([data_dir 'Background/Background.png']); imshow(ref_img) set(handles.Text_Display, 'String', 'Loaded Background/Background.png as background'); end
github
BII-wushuang/FLLIT-master
connectify.m
.m
FLLIT-master/src/connectify.m
2,905
utf_8
31614bbf2f02d96c33f38684ed9b3eb9
function Image = connectify(img, imroi, imseg) %CONNECTIFY Find the disconnected components and group them leg-wise. % Convert the pixel level segmentation result to an object level. se1 = strel('disk',5); se2 = strel('disk',0); % Morphological operations to estimate the body of the fly imbody = imerode(imroi,se1); imbody = imdilate(imbody,se1); % %figure, imshow(imfuse(imroi,imbody)); if (0) imsk_old = bwmorph(img,'skel',Inf)*255; imsk_new = bwmorph(skeleton(img) > 10, 'skel', Inf) * 255; montage_image(:,:,1,1) = imsk_old; montage_image(:,:,1,2) = imsk_new; figure, montage(montage_image); end connComp = bwconncomp(img); nRegions = connComp.NumObjects; % Get information on the segmentation's centroid, medial axis with its % end points and junctions. This will then be used to % identify whether regions must be connected or not! for cc = 1:nRegions legpixels = connComp.PixelIdxList{cc}; [I,J] = ind2sub(size(img),legpixels); connComp.centroid{cc} = [mean(I) mean(J)]; imleg = zeros(size(img)); imleg(legpixels) = 1; [skr,rad] = skeleton(imleg); imsk = bwmorph(skr > 10,'skel',Inf); % Skeleton analysis: Get the end points. [dmap,exy,jxy] = anaskel(imsk); connComp.exy{cc} = exy; connComp.jxy{cc} = jxy; connComp.medialAxis{cc} = find(imsk > 0); end distThresh = 10; %17.5 PT1 = []; PT2 = []; for i = 1:nRegions for j = i+1:nRegions exyI = connComp.exy{i}; exyJ = connComp.exy{j}; [p1_,p2_] = checkToConnect(exyI,exyJ,distThresh); PT1 = [PT1 p1_]; PT2 = [PT2 p2_]; end end Image = img; npoints = size(PT1,2); for i = 1:npoints x = [PT1(1,i) PT2(1,i)]; y = [PT1(2,i) PT2(2,i)]; Image = func_DrawLine(Image,y(1),x(1),y(2),x(2),1); %X = PT1(1,i):PT2(1,i); %Y = round(interp1(x,y,X)); %ind = sub2ind(size(Image),Y,X); %Image(ind) = 255; end end function [pt1,pt2] = checkToConnect(exyI,exyJ,distThresh) pt1 = []; pt2 = []; nendpoints_i = length(exyI(1,:)); for k = 1:nendpoints_i A = bsxfun(@minus,exyJ,exyI(:,k)); distances = sqrt(sum(A.^2)); idx = find(distances < distThresh); for i = 1:length(idx) % exyJ(:,idx(i)) and exyI(:,k) are close endpoints of some two medial % axis. Now must enforce directionality constraint probableLeg1 = exyI(:,k) - exyI(:,~(k-1)+1); probableLeg1 = probableLeg1./norm(probableLeg1); %probableLeg2 = exyJ(:,~(idx(i)-1)+1) - exyJ(:,idx(i)); probableJoin = exyJ(:,idx(i)) - exyI(:,k); probableJoin = - (probableJoin./norm(probableJoin)); theta = acosd(probableLeg1' * probableJoin); if (theta > 120) %|| (theta < 30) % If this also holds, then mark this point pt1 = [pt1 exyI(:,k)]; pt2 = [pt2 exyJ(:,idx(i))]; end end end end
github
BII-wushuang/FLLIT-master
Segmentation_Base.m
.m
FLLIT-master/src/Segmentation_Base.m
1,141
utf_8
ad553607debd1a8254b7acf918d2c9e1
%Classifier for leg segmentation function Segmentation_Base (data_dir) if(nargin<1) data_dir = uigetdir('./Data'); end pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = ['./Data' sub_dir '/']; output_dir = ['./Results/SegmentedImages' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end [thres, ~,~,ref_img,~] = video2background(data_dir, sub_dir); img_list = load_img_list(data_dir); for i_img = 1 : length(img_list) I = imread([data_dir img_list(i_img).name]); I = double(I); I = padarray(I,[20 20],'replicate'); roi_img = (max(ref_img - I(:,:,1),0) ./ ref_img) > thres; [pos_img,~,~] = leg_segment(I,ref_img,thres); %pos_img = (filter_small_comp(pos_img,15) - imroi)>0.1; show_img_output = repmat(double(I)/255,[1 1 3]); show_img_output(:,:,1) = show_img_output(:,:,1) + pos_img; imwrite(imcrop(show_img_output,[21 21 size(I,2)-41 size(I,1)-41]),[output_dir 'img_' num2str(i_img) '.png'],'png'); imwrite(imcrop(roi_img,[21 21 size(I,2)-41 size(I,1)-41]),[output_dir 'roi_' num2str(i_img) '.png'],'png'); end end
github
BII-wushuang/FLLIT-master
clean_skeleton.m
.m
FLLIT-master/src/clean_skeleton.m
1,995
utf_8
a56e0b53522559dc4596367baa437d66
% A function to clean skeletons. % function [bw_body,bw_junction,img]=clean_skeleton(seg) %thin process img=bwmorph(seg,'skel','Inf'); %img=bwmorph(img,'spur'); % count the neighbours of the skeletons neighbour_count=imfilter(uint8(img),ones(3)); bw_body=neighbour_count<=3 & img; bw_junction=neighbour_count>3 & img; bw_ends=neighbour_count<=2 & img; %find the terminal segments bw_ends=imreconstruct(bw_ends,bw_body); CC = bwconncomp(bw_ends); numPixels = cellfun(@numel,CC.PixelIdxList); [idx]=find(numPixels<3); while ~isempty(idx) spur_length=2; % Remove the terminal segments if they are too short bw_body(bw_ends & ~bwareaopen(bw_ends, spur_length)) = false; img=(bw_body+bw_junction)>0; % Thin the binary image img = bwmorph(img, 'skel', Inf); %img=bwmorph(img,'spur'); neighbour_count = imfilter(uint8(img), ones(3)); bw_body = neighbour_count <=3 & img; bw_junction = neighbour_count >3 & img; bw_ends = neighbour_count <=2 & img; % Find the terminal segments - i.e. those containing end points bw_ends = imreconstruct(bw_ends, bw_body); CC = bwconncomp(bw_ends); numPixels = cellfun(@numel,CC.PixelIdxList); [idx]=find(numPixels<2); end img=(bw_body+bw_junction)>0; img_no_soma=img; %new_img_thin_wo_nu=bwmorph(new_img_thin_wo_nu,'spur'); neighbour_count = imfilter(uint8(img_no_soma), ones(3)); bw_body = neighbour_count <=3 & img_no_soma; %figure,imshow(bw_body) bw_junction = neighbour_count >3 & img_no_soma; % This is for removing small segments in between two junctions. bw_ends = neighbour_count <=2 & img_no_soma; % Find the terminal segments - i.e. those containing end points bw_ends = imreconstruct(bw_ends, bw_body); bw_non_terminal=bw_body.*~(bw_ends); % non terminals CC = bwconncomp(bw_non_terminal); numPixels = cellfun(@numel,CC.PixelIdxList); [idx]=find(numPixels<2); for jj=1:length(idx) bw_junction(CC.PixelIdxList{idx(jj)})=true; bw_body(CC.PixelIdxList{idx(jj)})=false; end end
github
BII-wushuang/FLLIT-master
uipickfiles.m
.m
FLLIT-master/src/uipickfiles.m
48,144
utf_8
a2c259c89144d8e1e2ce065bad663669
function out = uipickfiles(varargin) %uipickfiles: GUI program to select files and/or folders. % % Syntax: % files = uipickfiles('PropertyName',PropertyValue,...) % % The current folder can be changed by operating in the file navigator: % double-clicking on a folder in the list or pressing Enter to move further % down the tree, using the popup menu, clicking the up arrow button or % pressing Backspace to move up the tree, typing a path in the box to move % to any folder or right-clicking (control-click on Mac) on the path box to % revisit a previously-visited folder. These folders are listed in order % of when they were last visited (most recent at the top) and the list is % saved between calls to uipickfiles. The list can be cleared or its % maximum length changed with the items at the bottom of the menu. % (Windows only: To go to a UNC-named resource you will have to type the % UNC name in the path box, but all such visited resources will be % remembered and listed along with the mapped drives.) The items in the % file navigator can be sorted by name, modification date or size by % clicking on the headers, though neither date nor size are displayed. All % folders have zero size. % % Files can be added to the list by double-clicking or selecting files % (non-contiguous selections are possible with the control key) and % pressing the Add button. Control-F will select all the files listed in % the navigator while control-A will select everything (Command instead of % Control on the Mac). Since double-clicking a folder will open it, % folders can be added only by selecting them and pressing the Add button. % Files/folders in the list can be removed or re-ordered. Recall button % will insert into the Selected Files list whatever files were returned the % last time uipickfiles was run. When finished, a press of the Done button % will return the full paths to the selected items in a cell array, % structure array or character array. If the Cancel button or the escape % key is pressed then zero is returned. % % The figure can be moved and resized in the usual way and this position is % saved and used for subsequent calls to uipickfiles. The default position % can be restored by double-clicking in a vacant region of the figure. % % The following optional property/value pairs can be specified as arguments % to control the indicated behavior: % % Property Value % ---------- ---------------------------------------------------------- % FilterSpec String to specify starting folder and/or file filter. % Ex: 'C:\bin' will start up in that folder. '*.txt' % will list only files ending in '.txt'. 'c:\bin\*.txt' will % do both. Default is to start up in the current folder and % list all files. Can be changed with the GUI. % % REFilter String containing a regular expression used to filter the % file list. Ex: '\.m$|\.mat$' will list files ending in % '.m' and '.mat'. Default is empty string. Can be used % with FilterSpec and both filters are applied. Can be % changed with the GUI. % % REDirs Logical flag indicating whether to apply the regular % expression filter to folder names. Default is false which % means that all folders are listed. Can be changed with the % GUI. % % Type Two-column cell array where the first column contains file % filters and the second column contains descriptions. If % this property is specified an additional popup menu will % appear below the File Filter and selecting an item will put % that item into the File Filter. By default, the first item % will be entered into the File Filter. For example, % { '*.m', 'M-files' ; % '*.mat', 'MAT-files' }. % Can also be a cell vector of file filter strings in which % case the descriptions will be the same as the file filters % themselves. % Must be a cell array even if there is only one entry. % % Prompt String containing a prompt appearing in the title bar of % the figure. Default is 'Select files'. % % NumFiles Scalar or vector specifying number of files that must be % selected. A scalar specifies an exact value; a two-element % vector can be used to specify a range, [min max]. The % function will not return unless the specified number of % files have been chosen. Default is [] which accepts any % number of files. % % Append Cell array of strings, structure array or char array % containing a previously returned output from uipickfiles. % Used to start up program with some entries in the Selected % Files list. Any included files that no longer exist will % not appear. Default is empty cell array, {}. % % Output String specifying the data type of the output: 'cell', % 'struct' or 'char'. Specifying 'cell' produces a cell % array of strings, the strings containing the full paths of % the chosen files. 'Struct' returns a structure array like % the result of the dir function except that the 'name' field % contains a full path instead of just the file name. 'Char' % returns a character array of the full paths. This is most % useful when you have just one file and want it in a string % instead of a cell array containing just one string. The % default is 'cell'. % % All properties and values are case-insensitive and need only be % unambiguous. For example, % % files = uipickfiles('num',1,'out','ch') % % is valid usage. % Version: 1.15, 2 March 2012 % Author: Douglas M. Schwarz % Email: dmschwarz=ieee*org, dmschwarz=urgrad*rochester*edu % Real_email = regexprep(Email,{'=','*'},{'@','.'}) % Copyright (c) 2007, Douglas M. Schwarz % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright notice, this % list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright notice, % this list of conditions and the following disclaimer in the documentation % and/or other materials provided with the distribution % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE % DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE % FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL % DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR % SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER % CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, % OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE % OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. % Define properties and set default values. prop.filterspec = [pwd '/Data']; prop.refilter = ''; prop.redirs = false; prop.type = {}; prop.prompt = 'Select data folder'; prop.numfiles = []; prop.append = []; prop.output = 'cell'; % Process inputs and set prop fields. prop = parsepropval(prop,varargin{:}); % Validate FilterSpec property. if isempty(prop.filterspec) prop.filterspec = '*'; end if ~ischar(prop.filterspec) error('FilterSpec property must contain a string.') end % Validate REFilter property. if ~ischar(prop.refilter) error('REFilter property must contain a string.') end % Validate REDirs property. if ~isscalar(prop.redirs) error('REDirs property must contain a scalar.') end % Validate Type property. if isempty(prop.type) elseif iscellstr(prop.type) && isscalar(prop.type) prop.type = repmat(prop.type(:),1,2); elseif iscellstr(prop.type) && size(prop.type,2) == 2 else error(['Type property must be empty or a cellstr vector or ',... 'a 2-column cellstr matrix.']) end % Validate Prompt property. if ~ischar(prop.prompt) error('Prompt property must contain a string.') end % Validate NumFiles property. if numel(prop.numfiles) > 2 || any(prop.numfiles < 0) error('NumFiles must be empty, a scalar or two-element vector.') end prop.numfiles = unique(prop.numfiles); if isequal(prop.numfiles,1) numstr = 'Select exactly 1 data folder.'; elseif length(prop.numfiles) == 1 numstr = sprintf('Select exactly %d items.',prop.numfiles); else numstr = sprintf('Select %d to %d items.',prop.numfiles); end % Validate Append property and initialize pick data. if isstruct(prop.append) && isfield(prop.append,'name') prop.append = {prop.append.name}; elseif ischar(prop.append) prop.append = cellstr(prop.append); end if isempty(prop.append) file_picks = {}; full_file_picks = {}; dir_picks = dir(' '); % Create empty directory structure. elseif iscellstr(prop.append) && isvector(prop.append) num_items = length(prop.append); file_picks = cell(1,num_items); full_file_picks = cell(1,num_items); dir_fn = fieldnames(dir(' ')); dir_picks = repmat(cell2struct(cell(length(dir_fn),1),dir_fn(:)),... num_items,1); for item = 1:num_items if exist(prop.append{item},'dir') && ... ~any(strcmp(full_file_picks,prop.append{item})) full_file_picks{item} = prop.append{item}; [unused,fn,ext] = fileparts(prop.append{item}); file_picks{item} = [fn,ext]; temp = dir(fullfile(prop.append{item},'..')); if ispc || ismac thisdir = strcmpi({temp.name},[fn,ext]); else thisdir = strcmp({temp.name},[fn,ext]); end dir_picks(item) = temp(thisdir); dir_picks(item).name = prop.append{item}; elseif exist(prop.append{item},'file') && ... ~any(strcmp(full_file_picks,prop.append{item})) full_file_picks{item} = prop.append{item}; [unused,fn,ext] = fileparts(prop.append{item}); file_picks{item} = [fn,ext]; dir_picks(item) = dir(prop.append{item}); dir_picks(item).name = prop.append{item}; else continue end end % Remove items which no longer exist. missing = cellfun(@isempty,full_file_picks); full_file_picks(missing) = []; file_picks(missing) = []; dir_picks(missing) = []; else error('Append must be a cell, struct or char array.') end % Validate Output property. legal_outputs = {'cell','struct','char'}; out_idx = find(strncmpi(prop.output,legal_outputs,length(prop.output))); if length(out_idx) == 1 prop.output = legal_outputs{out_idx}; else error(['Value of ''Output'' property, ''%s'', is illegal or '... 'ambiguous.'],prop.output) end % Set style preference for display of folders. % 1 => folder icon before and filesep after % 2 => bullet before and filesep after % 3 => filesep after only folder_style_pref = 1; fsdata = set_folder_style(folder_style_pref); % Initialize file lists. if exist(prop.filterspec,'dir') current_dir = prop.filterspec; filter = '*'; else [current_dir,f,e] = fileparts(prop.filterspec); filter = [f,e]; end if isempty(current_dir) current_dir = pwd; end if isempty(filter) filter = '*'; end re_filter = prop.refilter; full_filter = fullfile(current_dir,filter); network_volumes = {}; [path_cell,new_network_vol] = path2cell(current_dir); if exist(new_network_vol,'dir') network_volumes = unique([network_volumes,{new_network_vol}]); end fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,[1 0 0])); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); % Initialize some data. show_full_path = false; nodupes = true; % Get history preferences and set history. history = getpref('uipickfiles','history',... struct('name',current_dir,'time',now)); default_history_size = 15; history_size = getpref('uipickfiles','history_size',default_history_size); history = update_history(history,current_dir,now,history_size); % Get figure position preference and create figure. gray = get(0,'DefaultUIControlBackgroundColor'); if ispref('uipickfiles','figure_position'); fig_pos = getpref('uipickfiles','figure_position'); fig = figure('Position',fig_pos,... 'Color',gray,... 'MenuBar','none',... 'WindowStyle','modal',... 'Resize','on',... 'NumberTitle','off',... 'Name',prop.prompt,... 'IntegerHandle','off',... 'CloseRequestFcn',@cancel,... 'ButtonDownFcn',@reset_figure_size,... 'KeyPressFcn',@keypressmisc,... 'Visible','off'); else fig_pos = [0 0 740 494]; fig = figure('Position',fig_pos,... 'Color',gray,... 'MenuBar','none',... 'WindowStyle','modal',... 'Resize','on',... 'NumberTitle','off',... 'Name',prop.prompt,... 'IntegerHandle','off',... 'CloseRequestFcn',@cancel,... 'CreateFcn',{@movegui,'center'},... 'ButtonDownFcn',@reset_figure_size,... 'KeyPressFcn',@keypressmisc,... 'Visible','off'); end % Set system-dependent items. if ismac set(fig,'DefaultUIControlFontName','Lucida Grande') set(fig,'DefaultUIControlFontSize',9) sort_ctrl_size = 8; mod_key = 'command'; action = 'Control-click'; elseif ispc set(fig,'DefaultUIControlFontName','Tahoma') set(fig,'DefaultUIControlFontSize',8) sort_ctrl_size = 7; mod_key = 'control'; action = 'Right-click'; else sort_ctrl_size = get(fig,'DefaultUIControlFontSize') - 1; mod_key = 'control'; action = 'Right-click'; end % Create uicontrols. frame1 = uicontrol('Style','frame',... 'Position',[255 260 110 70]); frame2 = uicontrol('Style','frame',... 'Position',[275 135 110 100]); navlist = uicontrol('Style','listbox',... 'Position',[10 10 250 320],... 'String',filenames,... 'Value',[],... 'BackgroundColor','w',... 'Callback',@clicknav,... 'KeyPressFcn',@keypressnav,... 'Max',2); tri_up = repmat([1 1 1 1 0 1 1 1 1;1 1 1 0 0 0 1 1 1;1 1 0 0 0 0 0 1 1;... 1 0 0 0 0 0 0 0 1],[1 1 3]); tri_up(tri_up == 1) = NaN; tri_down = tri_up(end:-1:1,:,:); tri_null = NaN(4,9,3); tri_icon = {tri_down,tri_null,tri_up}; sort_state = [1 0 0]; last_sort_state = [1 1 1]; sort_cb = zeros(1,3); sort_cb(1) = uicontrol('Style','checkbox',... 'Position',[15 331 70 15],... 'String','Name',... 'FontSize',sort_ctrl_size,... 'Value',sort_state(1),... 'CData',tri_icon{sort_state(1)+2},... 'KeyPressFcn',@keypressmisc,... 'Callback',{@sort_type,1}); sort_cb(2) = uicontrol('Style','checkbox',... 'Position',[85 331 70 15],... 'String','Date',... 'FontSize',sort_ctrl_size,... 'Value',sort_state(2),... 'CData',tri_icon{sort_state(2)+2},... 'KeyPressFcn',@keypressmisc,... 'Callback',{@sort_type,2}); sort_cb(3) = uicontrol('Style','checkbox',... 'Position',[155 331 70 15],... 'String','Size',... 'FontSize',sort_ctrl_size,... 'Value',sort_state(3),... 'CData',tri_icon{sort_state(3)+2},... 'KeyPressFcn',@keypressmisc,... 'Callback',{@sort_type,3}); pickslist = uicontrol('Style','listbox',... 'Position',[380 10 350 320],... 'String',file_picks,... 'BackgroundColor','w',... 'Callback',@clickpicks,... 'KeyPressFcn',@keypresslist,... 'Max',2,... 'Value',[]); openbut = uicontrol('Style','pushbutton',... 'Position',[270 300 80 20],... 'String','Open',... 'Enable','off',... 'KeyPressFcn',@keypressmisc,... 'Callback',@open); arrow = [ ... ' 1 '; ' 10 '; ' 10 '; '000000000000'; ' 10 '; ' 10 '; ' 1 ']; cmap = NaN(128,3); cmap(double('10'),:) = [0.5 0.5 0.5;0 0 0]; arrow_im = NaN(7,76,3); arrow_im(:,45:56,:) = ind2rgb(double(arrow),cmap); addbut = uicontrol('Style','pushbutton',... 'Position',[270 270 80 20],... 'String','Add ',... 'Enable','off',... 'CData',arrow_im,... 'KeyPressFcn',@keypressmisc,... 'Callback',@add); removebut = uicontrol('Style','pushbutton',... 'Position',[290 205 80 20],... 'String','Remove',... 'Enable','off',... 'KeyPressFcn',@keypressmisc,... 'Callback',@remove); moveupbut = uicontrol('Style','pushbutton',... 'Position',[290 175 80 20],... 'String','Move Up',... 'Enable','off',... 'KeyPressFcn',@keypressmisc,... 'Callback',@moveup); movedownbut = uicontrol('Style','pushbutton',... 'Position',[290 145 80 20],... 'String','Move Down',... 'Enable','off',... 'KeyPressFcn',@keypressmisc,... 'Callback',@movedown); dir_popup = uicontrol('Style','popupmenu',... 'Position',[10 350 225 20],... 'BackgroundColor','w',... 'String',path_cell,... 'Value',length(path_cell),... 'KeyPressFcn',@keypressmisc,... 'Callback',@dirpopup); uparrow = [ ... ' 0 '; ' 000 '; '00000 '; ' 0 '; ' 0 '; ' 0 '; ' 000000']; cmap = NaN(128,3); cmap(double('0'),:) = [0 0 0]; uparrow_im = ind2rgb(double(uparrow),cmap); up_dir_but = uicontrol('Style','pushbutton',... 'Position',[240 350 20 20],... 'CData',uparrow_im,... 'KeyPressFcn',@keypressmisc,... 'Callback',@dir_up_one,... 'ToolTip','Go to parent folder'); if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end hist_cm = uicontextmenu; pathbox = uicontrol('Style','edit',... 'Position',[10 375 250 26],... 'BackgroundColor','w',... 'String',current_dir,... 'HorizontalAlignment','left',... 'TooltipString',[action,' to display folder history'],... 'KeyPressFcn',@keypressmisc,... 'Callback',@change_path,... 'UIContextMenu',hist_cm); label1 = uicontrol('Style','text',... 'Position',[10 401 250 16],... 'String','Current Folder',... 'HorizontalAlignment','center',... 'TooltipString',[action,' to display folder history'],... 'UIContextMenu',hist_cm); hist_menus = []; make_history_cm() label2 = uicontrol('Style','text',... 'Position',[10 440+36 80 17],... 'String','File Filter',... 'HorizontalAlignment','left'); label3 = uicontrol('Style','text',... 'Position',[100 440+36 160 17],... 'String','Reg. Exp. Filter',... 'HorizontalAlignment','left'); showallfiles = uicontrol('Style','checkbox',... 'Position',[270 420+32 110 20],... 'String','Show All Files',... 'Value',0,... 'HorizontalAlignment','left',... 'KeyPressFcn',@keypressmisc,... 'Callback',@togglefilter); refilterdirs = uicontrol('Style','checkbox',... 'Position',[270 420+10 100 20],... 'String','RE Filter Dirs',... 'Value',prop.redirs,... 'HorizontalAlignment','left',... 'KeyPressFcn',@keypressmisc,... 'Callback',@toggle_refiltdirs); filter_ed = uicontrol('Style','edit',... 'Position',[10 420+30 80 26],... 'BackgroundColor','w',... 'String',filter,... 'HorizontalAlignment','left',... 'KeyPressFcn',@keypressmisc,... 'Callback',@setfilspec); refilter_ed = uicontrol('Style','edit',... 'Position',[100 420+30 160 26],... 'BackgroundColor','w',... 'String',re_filter,... 'HorizontalAlignment','left',... 'KeyPressFcn',@keypressmisc,... 'Callback',@setrefilter); type_value = 1; type_popup = uicontrol('Style','popupmenu',... 'Position',[10 422 250 20],... 'String','',... 'BackgroundColor','w',... 'Value',type_value,... 'KeyPressFcn',@keypressmisc,... 'Callback',@filter_type_callback,... 'Visible','off'); if ~isempty(prop.type) set(filter_ed,'String',prop.type{type_value,1}) setfilspec() set(type_popup,'String',prop.type(:,2),'Visible','on') end viewfullpath = uicontrol('Style','checkbox',... 'Position',[380 335 230 20],... 'String','Show full paths',... 'Value',show_full_path,... 'HorizontalAlignment','left',... 'KeyPressFcn',@keypressmisc,... 'Callback',@showfullpath); remove_dupes = uicontrol('Style','checkbox',... 'Position',[380 360 280 20],... 'String','Remove duplicates (as per full path)',... 'Value',nodupes,... 'HorizontalAlignment','left',... 'KeyPressFcn',@keypressmisc,... 'Callback',@removedupes); recall_button = uicontrol('Style','pushbutton',... 'Position',[665 335 65 20],... 'String','Recall',... 'KeyPressFcn',@keypressmisc,... 'Callback',@recall,... 'ToolTip','Add previously selected items'); label4 = uicontrol('Style','text',... 'Position',[380 405 350 20],... 'String','Selected data folders',... 'HorizontalAlignment','center'); done_button = uicontrol('Style','pushbutton',... 'Position',[280 80 80 30],... 'String','Done',... 'KeyPressFcn',@keypressmisc,... 'Callback',@done); cancel_button = uicontrol('Style','pushbutton',... 'Position',[280 30 80 30],... 'String','Cancel',... 'KeyPressFcn',@keypressmisc,... 'Callback',@cancel); % If necessary, add warning about number of items to be selected. num_files_warn = uicontrol('Style','text',... 'Position',[380 385 350 16],... 'String',numstr,... 'ForegroundColor',[0.8 0 0],... 'HorizontalAlignment','center',... 'Visible','off'); if ~isempty(prop.numfiles) set(num_files_warn,'Visible','on') end resize() % Make figure visible and hide handle. set(fig,'HandleVisibility','off',... 'Visible','on',... 'ResizeFcn',@resize) % Wait until figure is closed. uiwait(fig) % Compute desired output. switch prop.output case 'cell' out = full_file_picks; case 'struct' out = dir_picks(:); case 'char' out = char(full_file_picks); case 'cancel' out = 0; end % Update history preference. setpref('uipickfiles','history',history) if ~isempty(full_file_picks) && ~strcmp(prop.output,'cancel') setpref('uipickfiles','full_file_picks',full_file_picks) end % Update figure position preference. setpref('uipickfiles','figure_position',fig_pos) % ----------------- Callback nested functions ---------------- function add(varargin) values = get(navlist,'Value'); for i = 1:length(values) dir_pick = fdir(values(i)); pick = dir_pick.name; pick_full = fullfile(current_dir,pick); dir_pick.name = pick_full; if ~nodupes || ~any(strcmp(full_file_picks,pick_full)) file_picks{end + 1} = pick; %#ok<AGROW> full_file_picks{end + 1} = pick_full; %#ok<AGROW> dir_picks(end + 1) = dir_pick; %#ok<AGROW> end end if show_full_path set(pickslist,'String',full_file_picks,'Value',[]); else set(pickslist,'String',file_picks,'Value',[]); end set([removebut,moveupbut,movedownbut],'Enable','off'); end function remove(varargin) values = get(pickslist,'Value'); file_picks(values) = []; full_file_picks(values) = []; dir_picks(values) = []; top = get(pickslist,'ListboxTop'); num_above_top = sum(values < top); top = top - num_above_top; num_picks = length(file_picks); new_value = min(min(values) - num_above_top,num_picks); if num_picks == 0 new_value = []; set([removebut,moveupbut,movedownbut],'Enable','off') end if show_full_path set(pickslist,'String',full_file_picks,'Value',new_value,... 'ListboxTop',top) else set(pickslist,'String',file_picks,'Value',new_value,... 'ListboxTop',top) end end function open(varargin) values = get(navlist,'Value'); if fdir(values).isdir set(fig,'pointer','watch') drawnow % Convert 'My Documents' to 'Documents' when necessary. if ispc && strcmp(fdir(values).name,'My Documents') if isempty(dir(fullfile(current_dir,fdir(values).name))) values = find(strcmp({fdir.name},'Documents')); end end current_dir = fullfile(current_dir,fdir(values).name); history = update_history(history,current_dir,now,history_size); make_history_cm() full_filter = fullfile(current_dir,filter); path_cell = path2cell(current_dir); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(dir_popup,'String',path_cell,'Value',length(path_cell)) if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end set(pathbox,'String',current_dir) set(navlist,'ListboxTop',1,'Value',[],'String',filenames) set(addbut,'Enable','off') set(openbut,'Enable','off') set(fig,'pointer','arrow') end end function clicknav(varargin) value = get(navlist,'Value'); nval = length(value); dbl_click_fcn = @add; switch nval case 0 set([addbut,openbut],'Enable','off') case 1 set(addbut,'Enable','on'); if fdir(value).isdir set(openbut,'Enable','on') dbl_click_fcn = @open; else set(openbut,'Enable','off') end otherwise set(addbut,'Enable','on') set(openbut,'Enable','off') end if strcmp(get(fig,'SelectionType'),'open') dbl_click_fcn(); end end function keypressmisc(h,evt) %#ok<INUSL> if strcmp(evt.Key,'escape') && isequal(evt.Modifier,cell(1,0)) % Escape key means Cancel. cancel() end end function keypressnav(h,evt) %#ok<INUSL> if length(path_cell) > 1 && strcmp(evt.Key,'backspace') && ... isequal(evt.Modifier,cell(1,0)) % Backspace means go to parent folder. dir_up_one() elseif strcmp(evt.Key,'f') && isequal(evt.Modifier,{mod_key}) % Control-F (Command-F on Mac) means select all files. value = find(~[fdir.isdir]); set(navlist,'Value',value) elseif strcmp(evt.Key,'rightarrow') && ... isequal(evt.Modifier,cell(1,0)) % Right arrow key means select the file. add() elseif strcmp(evt.Key,'escape') && isequal(evt.Modifier,cell(1,0)) % Escape key means Cancel. cancel() end end function keypresslist(h,evt) %#ok<INUSL> if strcmp(evt.Key,'backspace') && isequal(evt.Modifier,cell(1,0)) % Backspace means remove item from list. remove() elseif strcmp(evt.Key,'escape') && isequal(evt.Modifier,cell(1,0)) % Escape key means Cancel. cancel() end end function clickpicks(varargin) value = get(pickslist,'Value'); if isempty(value) set([removebut,moveupbut,movedownbut],'Enable','off') else set(removebut,'Enable','on') if min(value) == 1 set(moveupbut,'Enable','off') else set(moveupbut,'Enable','on') end if max(value) == length(file_picks) set(movedownbut,'Enable','off') else set(movedownbut,'Enable','on') end end if strcmp(get(fig,'SelectionType'),'open') remove(); end end function recall(varargin) if ispref('uipickfiles','full_file_picks') ffp = getpref('uipickfiles','full_file_picks'); else ffp = {}; end for i = 1:length(ffp) if exist(ffp{i},'dir') && ... (~nodupes || ~any(strcmp(full_file_picks,ffp{i}))) full_file_picks{end + 1} = ffp{i}; %#ok<AGROW> [unused,fn,ext] = fileparts(ffp{i}); file_picks{end + 1} = [fn,ext]; %#ok<AGROW> temp = dir(fullfile(ffp{i},'..')); if ispc || ismac thisdir = strcmpi({temp.name},[fn,ext]); else thisdir = strcmp({temp.name},[fn,ext]); end dir_picks(end + 1) = temp(thisdir); %#ok<AGROW> dir_picks(end).name = ffp{i}; elseif exist(ffp{i},'file') && ... (~nodupes || ~any(strcmp(full_file_picks,ffp{i}))) full_file_picks{end + 1} = ffp{i}; %#ok<AGROW> [unused,fn,ext] = fileparts(ffp{i}); file_picks{end + 1} = [fn,ext]; %#ok<AGROW> dir_picks(end + 1) = dir(ffp{i}); %#ok<AGROW> dir_picks(end).name = ffp{i}; end end if show_full_path set(pickslist,'String',full_file_picks,'Value',[]); else set(pickslist,'String',file_picks,'Value',[]); end set([removebut,moveupbut,movedownbut],'Enable','off'); end function sort_type(h,evt,cb) %#ok<INUSL> if sort_state(cb) sort_state(cb) = -sort_state(cb); last_sort_state(cb) = sort_state(cb); else sort_state = zeros(1,3); sort_state(cb) = last_sort_state(cb); end set(sort_cb,{'CData'},tri_icon(sort_state + 2)') fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(dir_popup,'String',path_cell,'Value',length(path_cell)) if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end set(pathbox,'String',current_dir) set(navlist,'String',filenames,'Value',[]) set(addbut,'Enable','off') set(openbut,'Enable','off') set(fig,'pointer','arrow') end function dirpopup(varargin) value = get(dir_popup,'Value'); container = path_cell{min(value + 1,length(path_cell))}; path_cell = path_cell(1:value); set(fig,'pointer','watch') drawnow if ispc && value == 1 current_dir = ''; full_filter = filter; drives = getdrives(network_volumes); num_drives = length(drives); temp = tempname; mkdir(temp) dir_temp = dir(temp); rmdir(temp) fdir = repmat(dir_temp(1),num_drives,1); [fdir.name] = deal(drives{:}); else current_dir = cell2path(path_cell); history = update_history(history,current_dir,now,history_size); make_history_cm() full_filter = fullfile(current_dir,filter); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); end filenames = {fdir.name}'; selected = find(strcmp(filenames,container)); filenames = annotate_file_names(filenames,fdir,fsdata); set(dir_popup,'String',path_cell,'Value',length(path_cell)) if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end set(pathbox,'String',current_dir) set(navlist,'String',filenames,'Value',selected) set(addbut,'Enable','off') set(fig,'pointer','arrow') end function dir_up_one(varargin) value = length(path_cell) - 1; container = path_cell{value + 1}; path_cell = path_cell(1:value); set(fig,'pointer','watch') drawnow if ispc && value == 1 current_dir = ''; full_filter = filter; drives = getdrives(network_volumes); num_drives = length(drives); temp = tempname; mkdir(temp) dir_temp = dir(temp); rmdir(temp) fdir = repmat(dir_temp(1),num_drives,1); [fdir.name] = deal(drives{:}); else current_dir = cell2path(path_cell); history = update_history(history,current_dir,now,history_size); make_history_cm() full_filter = fullfile(current_dir,filter); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); end filenames = {fdir.name}'; selected = find(strcmp(filenames,container)); filenames = annotate_file_names(filenames,fdir,fsdata); set(dir_popup,'String',path_cell,'Value',length(path_cell)) if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end set(pathbox,'String',current_dir) set(navlist,'String',filenames,'Value',selected) set(addbut,'Enable','off') set(fig,'pointer','arrow') end function change_path(varargin) set(fig,'pointer','watch') drawnow proposed_path = get(pathbox,'String'); % Process any folders named '..'. proposed_path_cell = path2cell(proposed_path); ddots = strcmp(proposed_path_cell,'..'); ddots(find(ddots) - 1) = true; proposed_path_cell(ddots) = []; proposed_path = cell2path(proposed_path_cell); % Check for existance of folder. if ~exist(proposed_path,'dir') set(fig,'pointer','arrow') uiwait(errordlg(['Folder "',proposed_path,... '" does not exist.'],'','modal')) return end current_dir = proposed_path; history = update_history(history,current_dir,now,history_size); make_history_cm() full_filter = fullfile(current_dir,filter); [path_cell,new_network_vol] = path2cell(current_dir); if exist(new_network_vol,'dir') network_volumes = unique([network_volumes,{new_network_vol}]); end fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(dir_popup,'String',path_cell,'Value',length(path_cell)) if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end set(pathbox,'String',current_dir) set(navlist,'String',filenames,'Value',[]) set(addbut,'Enable','off') set(openbut,'Enable','off') set(fig,'pointer','arrow') end function showfullpath(varargin) show_full_path = get(viewfullpath,'Value'); if show_full_path set(pickslist,'String',full_file_picks) else set(pickslist,'String',file_picks) end end function removedupes(varargin) nodupes = get(remove_dupes,'Value'); if nodupes num_picks = length(full_file_picks); [unused,rev_order] = unique(full_file_picks(end:-1:1)); %#ok<SETNU> order = sort(num_picks + 1 - rev_order); full_file_picks = full_file_picks(order); file_picks = file_picks(order); dir_picks = dir_picks(order); if show_full_path set(pickslist,'String',full_file_picks,'Value',[]) else set(pickslist,'String',file_picks,'Value',[]) end set([removebut,moveupbut,movedownbut],'Enable','off') end end function moveup(varargin) value = get(pickslist,'Value'); set(removebut,'Enable','on') n = length(file_picks); omega = 1:n; index = zeros(1,n); index(value - 1) = omega(value); index(setdiff(omega,value - 1)) = omega(setdiff(omega,value)); file_picks = file_picks(index); full_file_picks = full_file_picks(index); dir_picks = dir_picks(index); value = value - 1; if show_full_path set(pickslist,'String',full_file_picks,'Value',value) else set(pickslist,'String',file_picks,'Value',value) end if min(value) == 1 set(moveupbut,'Enable','off') end set(movedownbut,'Enable','on') end function movedown(varargin) value = get(pickslist,'Value'); set(removebut,'Enable','on') n = length(file_picks); omega = 1:n; index = zeros(1,n); index(value + 1) = omega(value); index(setdiff(omega,value + 1)) = omega(setdiff(omega,value)); file_picks = file_picks(index); full_file_picks = full_file_picks(index); dir_picks = dir_picks(index); value = value + 1; if show_full_path set(pickslist,'String',full_file_picks,'Value',value) else set(pickslist,'String',file_picks,'Value',value) end if max(value) == n set(movedownbut,'Enable','off') end set(moveupbut,'Enable','on') end function togglefilter(varargin) set(fig,'pointer','watch') drawnow value = get(showallfiles,'Value'); if value filter = '*'; re_filter = ''; set([filter_ed,refilter_ed],'Enable','off') else filter = get(filter_ed,'String'); re_filter = get(refilter_ed,'String'); set([filter_ed,refilter_ed],'Enable','on') end full_filter = fullfile(current_dir,filter); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(navlist,'String',filenames,'Value',[]) set(addbut,'Enable','off') set(fig,'pointer','arrow') end function toggle_refiltdirs(varargin) set(fig,'pointer','watch') drawnow value = get(refilterdirs,'Value'); prop.redirs = value; full_filter = fullfile(current_dir,filter); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(navlist,'String',filenames,'Value',[]) set(addbut,'Enable','off') set(fig,'pointer','arrow') end function setfilspec(varargin) set(fig,'pointer','watch') drawnow filter = get(filter_ed,'String'); if isempty(filter) filter = '*'; set(filter_ed,'String',filter) end % Process file spec if a subdirectory was included. [p,f,e] = fileparts(filter); if ~isempty(p) newpath = fullfile(current_dir,p,''); set(pathbox,'String',newpath) filter = [f,e]; if isempty(filter) filter = '*'; end set(filter_ed,'String',filter) change_path(); end full_filter = fullfile(current_dir,filter); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(navlist,'String',filenames,'Value',[]) set(addbut,'Enable','off') set(fig,'pointer','arrow') end function setrefilter(varargin) set(fig,'pointer','watch') drawnow re_filter = get(refilter_ed,'String'); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(navlist,'String',filenames,'Value',[]) set(addbut,'Enable','off') set(fig,'pointer','arrow') end function filter_type_callback(varargin) type_value = get(type_popup,'Value'); set(filter_ed,'String',prop.type{type_value,1}) setfilspec() end function done(varargin) % Optional shortcut: click on a file and press 'Done'. % if isempty(full_file_picks) && strcmp(get(addbut,'Enable'),'on') % add(); % end numfiles = length(full_file_picks); if ~isempty(prop.numfiles) if numfiles < prop.numfiles(1) msg = {'Too few items selected.',numstr}; uiwait(errordlg(msg,'','modal')) return elseif numfiles > prop.numfiles(end) msg = {'Too many items selected.',numstr}; uiwait(errordlg(msg,'','modal')) return end end fig_pos = get(fig,'Position'); delete(fig) end function cancel(varargin) prop.output = 'cancel'; fig_pos = get(fig,'Position'); delete(fig) end function history_cb(varargin) set(fig,'pointer','watch') drawnow current_dir = history(varargin{3}).name; history = update_history(history,current_dir,now,history_size); make_history_cm() full_filter = fullfile(current_dir,filter); path_cell = path2cell(current_dir); fdir = filtered_dir(full_filter,re_filter,prop.redirs,... @(x)file_sort(x,sort_state)); filenames = {fdir.name}'; filenames = annotate_file_names(filenames,fdir,fsdata); set(dir_popup,'String',path_cell,'Value',length(path_cell)) if length(path_cell) > 1 set(up_dir_but','Enable','on') else set(up_dir_but','Enable','off') end set(pathbox,'String',current_dir) set(navlist,'ListboxTop',1,'Value',[],'String',filenames) set(addbut,'Enable','off') set(openbut,'Enable','off') set(fig,'pointer','arrow') end function clear_history(varargin) history = update_history(history(1),'',[],history_size); make_history_cm() end function set_history_size(varargin) result_cell = inputdlg('Number of Recent Folders:','',1,... {sprintf('%g',history_size)}); if isempty(result_cell) return end result = sscanf(result_cell{1},'%f'); if isempty(result) || result < 1 return end history_size = result; history = update_history(history,'',[],history_size); make_history_cm() setpref('uipickfiles','history_size',history_size) end function resize(varargin) % Get current figure size. P = 'Position'; pos = get(fig,P); w = pos(3); % figure width in pixels h = pos(4); % figure height in pixels % Enforce minimum figure size. w = max(w,564); h = max(h,443); if any(pos(3:4) < [w h]) pos(3:4) = [w h]; set(fig,P,pos) end % Change positions of all uicontrols based on the current figure % width and height. navw_pckw = round([1 1;-350 250]\[w-140;0]); navw = navw_pckw(1); pckw = navw_pckw(2); navp = [10 10 navw h-174]; pckp = [w-10-pckw 10 pckw h-174]; set(navlist,P,navp) set(pickslist,P,pckp) set(frame1,P,[navw+5 h-234 110 70]) set(openbut,P,[navw+20 h-194 80 20]) set(addbut,P,[navw+20 h-224 80 20]) frame2y = round((h-234 + 110 - 100)/2); set(frame2,P,[w-pckw-115 frame2y 110 100]) set(removebut,P,[w-pckw-100 frame2y+70 80 20]) set(moveupbut,P,[w-pckw-100 frame2y+40 80 20]) set(movedownbut,P,[w-pckw-100 frame2y+10 80 20]) set(done_button,P,[navw+30 80 80 30]) set(cancel_button,P,[navw+30 30 80 30]) set(sort_cb(1),P,[15 h-163 70 15]) set(sort_cb(2),P,[85 h-163 70 15]) set(sort_cb(3),P,[155 h-163 70 15]) set(dir_popup,P,[10 h-144 navw-25 20]) set(up_dir_but,P,[navw-10 h-144 20 20]) set(pathbox,P,[10 h-119 navw 26]) set(label1,P,[10 h-93 navw 16]) set(viewfullpath,P,[pckp(1) h-159 230 20]) set(remove_dupes,P,[pckp(1) h-134 280 20]) set(recall_button,P,[w-75 h-159 65 20]) set(label4,P,[w-10-pckw h-89 pckw 20]) set(num_files_warn,P,[w-10-pckw h-109 pckw 16]) set(label2,P,[10 h-18 80 17]) set(label3,P,[100 h-18 160 17]) set(showallfiles,P,[270 h-42 110 20]) set(refilterdirs,P,[270 h-64 100 20]) set(filter_ed,P,[10 h-44 80 26]) set(refilter_ed,P,[100 h-44 160 26]) set(type_popup,P,[10 h-72 250 20]) end function reset_figure_size(varargin) if strcmp(get(fig,'SelectionType'),'open') root_units = get(0,'units'); screen_size = get(0,'ScreenSize'); set(0,'Units',root_units) hw = [740 494]; pos = [round((screen_size(3:4) - hw - [0 26])/2),hw]; set(fig,'Position',pos) resize() end end % ------------------ Other nested functions ------------------ function make_history_cm % Make context menu for history. if ~isempty(hist_menus) delete(hist_menus) end num_hist = length(history); hist_menus = zeros(1,num_hist+2); for i = 1:num_hist hist_menus(i) = uimenu(hist_cm,'Label',history(i).name,... 'Callback',{@history_cb,i}); end hist_menus(num_hist+1) = uimenu(hist_cm,... 'Label','Clear Menu',... 'Separator','on',... 'Callback',@clear_history); hist_menus(num_hist+2) = uimenu(hist_cm,'Label',... sprintf('Set Number of Recent Folders (%d) ...',history_size),... 'Callback',@set_history_size); end end % -------------------- Subfunctions -------------------- function [c,network_vol] = path2cell(p) % Turns a path string into a cell array of path elements. if ispc p = strrep(p,'/','\'); c1 = regexp(p,'(^\\\\[^\\]+\\[^\\]+)|(^[A-Za-z]+:)|[^\\]+','match'); vol = c1{1}; c = [{'My Computer'};c1(:)]; if strncmp(vol,'\\',2) network_vol = vol; else network_vol = ''; end else c = textscan(p,'%s','delimiter','/'); c = [{filesep};c{1}(2:end)]; network_vol = ''; end end % -------------------- function p = cell2path(c) % Turns a cell array of path elements into a path string. if ispc p = fullfile(c{2:end},''); else p = fullfile(c{:},''); end end % -------------------- function d = filtered_dir(full_filter,re_filter,filter_both,sort_fcn) % Like dir, but applies filters and sorting. p = fileparts(full_filter); if isempty(p) && full_filter(1) == '/' p = '/'; end if exist(full_filter,'dir') dfiles = dir(' '); else dfiles = dir(full_filter); end if ~isempty(dfiles) dfiles([dfiles.isdir]) = []; end ddir = dir(p); ddir = ddir([ddir.isdir]); [unused,index0] = sort(lower({ddir.name})); %#ok<ASGLU> ddir = ddir(index0); ddir(strcmp({ddir.name},'.') | strcmp({ddir.name},'..')) = []; % Additional regular expression filter. if nargin > 1 && ~isempty(re_filter) if ispc || ismac no_match = cellfun('isempty',regexpi({dfiles.name},re_filter)); else no_match = cellfun('isempty',regexp({dfiles.name},re_filter)); end dfiles(no_match) = []; end if filter_both if nargin > 1 && ~isempty(re_filter) if ispc || ismac no_match = cellfun('isempty',regexpi({ddir.name},re_filter)); else no_match = cellfun('isempty',regexp({ddir.name},re_filter)); end ddir(no_match) = []; end end % Set navigator style: % 1 => list all folders before all files, case-insensitive sorting % 2 => mix files and folders, case-insensitive sorting % 3 => list all folders before all files, case-sensitive sorting nav_style = 1; switch nav_style case 1 [unused,index1] = sort_fcn(dfiles); %#ok<ASGLU> [unused,index2] = sort_fcn(ddir); %#ok<ASGLU> d = [ddir(index2);dfiles(index1)]; case 2 d = [dfiles;ddir]; [unused,index] = sort(lower({d.name})); %#ok<ASGLU> d = d(index); case 3 [unused,index1] = sort({dfiles.name}); %#ok<ASGLU> [unused,index2] = sort({ddir.name}); %#ok<ASGLU> d = [ddir(index2);dfiles(index1)]; end end % -------------------- function [files_sorted,index] = file_sort(files,sort_state) switch find(sort_state) case 1 [files_sorted,index] = sort(lower({files.name})); if sort_state(1) < 0 files_sorted = files_sorted(end:-1:1); index = index(end:-1:1); end case 2 if sort_state(2) > 0 [files_sorted,index] = sort([files.datenum]); else [files_sorted,index] = sort([files.datenum],'descend'); end case 3 if sort_state(3) > 0 [files_sorted,index] = sort([files.bytes]); else [files_sorted,index] = sort([files.bytes],'descend'); end end end % -------------------- function drives = getdrives(other_drives) % Returns a cell array of drive names on Windows. letters = char('A':'Z'); num_letters = length(letters); drives = cell(1,num_letters); for i = 1:num_letters if exist([letters(i),':\'],'dir'); drives{i} = [letters(i),':']; end end drives(cellfun('isempty',drives)) = []; if nargin > 0 && iscellstr(other_drives) drives = [drives,unique(other_drives)]; end end % -------------------- function filenames = annotate_file_names(filenames,dir_listing,fsdata) % Adds a trailing filesep character to folder names and, optionally, % prepends a folder icon or bullet symbol. for i = 1:length(filenames) if dir_listing(i).isdir filenames{i} = sprintf('%s%s%s%s',fsdata.pre,filenames{i},... fsdata.filesep,fsdata.post); end end end % -------------------- function history = update_history(history,current_dir,time,history_size) if ~isempty(current_dir) % Insert or move current_dir to the top of the history. % If current_dir already appears in the history list, delete it. match = strcmp({history.name},current_dir); history(match) = []; % Prepend history with (current_dir,time). history = [struct('name',current_dir,'time',time),history]; end % Trim history to keep at most <history_size> newest entries. history = history(1:min(history_size,end)); end % -------------------- function success = generate_folder_icon(icon_path) % Black = 1, manila color = 2, transparent = 3. im = [ ... 3 3 3 1 1 1 1 3 3 3 3 3; 3 3 1 2 2 2 2 1 3 3 3 3; 3 1 1 1 1 1 1 1 1 1 1 3; 1 2 2 2 2 2 2 2 2 2 2 1; 1 2 2 2 2 2 2 2 2 2 2 1; 1 2 2 2 2 2 2 2 2 2 2 1; 1 2 2 2 2 2 2 2 2 2 2 1; 1 2 2 2 2 2 2 2 2 2 2 1; 1 2 2 2 2 2 2 2 2 2 2 1; 1 1 1 1 1 1 1 1 1 1 1 1]; cmap = [0 0 0;255 220 130;255 255 255]/255; fid = fopen(icon_path,'w'); if fid > 0 fclose(fid); imwrite(im,cmap,icon_path,'Transparency',[1 1 0]) end success = exist(icon_path,'file'); end % -------------------- function fsdata = set_folder_style(folder_style_pref) % Set style to preference. fsdata.style = folder_style_pref; % If style = 1, check to make sure icon image file exists. If it doesn't, % try to create it. If that fails set style = 2. if fsdata.style == 1 icon_path = fullfile(prefdir,'uipickfiles_folder_icon.png'); if ~exist(icon_path,'file') success = generate_folder_icon(icon_path); if ~success fsdata.style = 2; end end end % Set pre and post fields. if fsdata.style == 1 icon_url = ['file://localhost/',... strrep(strrep(icon_path,':','|'),'\','/')]; fsdata.pre = sprintf('<html><img src="%s">&nbsp;',icon_url); fsdata.post = '</html>'; elseif fsdata.style == 2 fsdata.pre = '<html><b>&#8226;</b>&nbsp;'; fsdata.post = '</html>'; elseif fsdata.style == 3 fsdata.pre = ''; fsdata.post = ''; end fsdata.filesep = filesep; end % -------------------- function prop = parsepropval(prop,varargin) % Parse property/value pairs and return a structure. properties = fieldnames(prop); arg_index = 1; while arg_index <= length(varargin) arg = varargin{arg_index}; if ischar(arg) prop_index = match_property(arg,properties); prop.(properties{prop_index}) = varargin{arg_index + 1}; arg_index = arg_index + 2; elseif isstruct(arg) arg_fn = fieldnames(arg); for i = 1:length(arg_fn) prop_index = match_property(arg_fn{i},properties); prop.(properties{prop_index}) = arg.(arg_fn{i}); end arg_index = arg_index + 1; else error(['Properties must be specified by property/value pairs',... ' or structures.']) end end end % -------------------- function prop_index = match_property(arg,properties) % Utility function for parsepropval. prop_index = find(strcmpi(arg,properties)); if isempty(prop_index) prop_index = find(strncmpi(arg,properties,length(arg))); end if length(prop_index) ~= 1 error('Property ''%s'' does not exist or is ambiguous.',arg) end end
github
BII-wushuang/FLLIT-master
Tracking_Base.m
.m
FLLIT-master/src/Tracking_Base.m
8,496
utf_8
72f608bee2e36e1c5b5872785e7da70c
% A very simple tracker based on the hungarian linker method % Outputs tip positions and identities of individual legs function Tracking_Base (data_dir) nLegs = 6; %preassume there are 6 legs %% Section 1: locate image folder and create output folder if (nargin < 1) data_dir = uigetdir('./Data'); end pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); seg_dir = ['./Results/SegmentedImages' sub_dir '/']; data_dir = ['./Data' sub_dir '/']; img_list = load_img_list(data_dir); output_dir = ['./Results/Tracking' sub_dir '/']; if(~exist(output_dir)) mkdir(output_dir); end %% End of section 1 %% Section 2: se = strel('disk',4); % maximal movement of legs in between frames max_distance = 20; trajectory = []; norm_trajectory = []; CoM = []; end_frame = length(dir([seg_dir '*.png']))/2; for i = 1: end_frame clear connComp; % Load necessary images Im = (imread([seg_dir 'img_' num2str(i) '.png'])); Imroi = imread([seg_dir 'roi_' num2str(i) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; connComp = bwconncomp(Imwork); if (connComp.NumObjects == 6 && min(cellfun(@length,connComp.PixelIdxList)) > 35) Imroi = imerode(Imroi,se); [locY,locX] = find(Imroi > 0); [centre_of_mass,theta] = findCoM(locX,locY); locX = locX - centre_of_mass(1); locY = locY - centre_of_mass(2); points = [cosd(theta) sind(theta); -sind(theta) cosd(theta)]* [locX'; locY']; %Attempt to ensure that the head is in the positive %y-direction by comparing which side has more number of %pixel points if (length(find(points(2,:) > 0))< length(find(points(2,:) < 0))) theta = mod(theta + 180,360); end for cc = 1:nLegs legpixels = connComp.PixelIdxList{cc}; imleg = zeros(size(Imwork)); imleg(legpixels) = 1; [skr,~] = skeleton(imleg); skr_start = 3; [~,exy,~] = anaskel(bwmorph(skr > skr_start,'skel',Inf)); % while (length(exy)>2) % skr_start = skr_start+1; % [~,exy,~] = anaskel(bwmorph(skr > skr_start,'skel',Inf)); % end imgX = exy(1,:) - centre_of_mass(1); imgY = exy(2,:) - centre_of_mass(2); normpoints = [cosd(theta) sind(theta); -sind(theta) cosd(theta)]* [imgX; imgY]; [~,tipidx]=max(min(pdist2(points',normpoints','euclidean'))); for j = 1:2 raw_normtips(cc,j) = normpoints(j,tipidx); raw_tips(cc,j) = exy(j,tipidx); end end leftlegs_idx = find(raw_normtips(:,1)<0); if(length(leftlegs_idx)~=3) continue; end rightlegs_idx = find(raw_normtips(:,1)>=0); [~,left_sort] = sort(raw_normtips(leftlegs_idx,2)); leg_counter = 0; for kk = 1 :3 trajectory(i, kk, :) = raw_tips(leftlegs_idx(left_sort(kk-leg_counter)),:); norm_trajectory(i, kk, :) = raw_normtips(leftlegs_idx(left_sort(kk-leg_counter)),:); end leg_counter = 0; [~,right_sort] = sort(raw_normtips(rightlegs_idx,2)); for kk = 1 :3 trajectory(i, kk+3, :) = raw_tips(rightlegs_idx(right_sort(kk-leg_counter)),:); norm_trajectory(i, kk+3, :) = raw_normtips(rightlegs_idx(right_sort(kk-leg_counter)),:); end CoM(i, :) = [centre_of_mass, theta]; start_frame = i; fprintf('Tracking automatically initiated on frame %d for dataset %s\n', i, sub_dir); for k = i-1 : -1 : 1 Im = (imread([seg_dir 'img_' num2str(k) '.png'])); Imroi = imread([seg_dir 'roi_' num2str(k) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; Imwork = Imwork - (bwdist(imerode(Imroi,se))<10); [locY,locX] = find(imerode(Imroi,se) > 0); %Centre_of_Mass [centre_of_mass,theta] = findCoM(locX,locY); if (abs(theta - CoM(k+1,3)) > 90 && abs(theta - CoM(k+1,3)) < 270) theta = mod(theta + 180,360); end CoM(k, :) = [centre_of_mass, theta]; [raw_tips, raw_normtips] = findtips(Imwork, Imroi, locX, locY, centre_of_mass, theta); x_t1 = raw_normtips; x_t0 = reshape(norm_trajectory(k+1,:),[nLegs 2]); target_indices = hungarianlinker(x_t0, x_t1, max_distance); for kk = 1:length(target_indices) if (target_indices(kk) > 0) norm_trajectory(k, kk, :) = raw_normtips(target_indices(kk),:); trajectory(k, kk, :) = raw_tips(target_indices(kk),:); else norm_trajectory(k, kk, :) = 0; trajectory(k, kk, :) = 0; end end leftoveridx = find(hungarianlinker(raw_normtips, x_t0, max_distance)==-1); x_t1 = raw_normtips(leftoveridx,:); x_t2 = raw_tips(leftoveridx,:); last_seen_tips = reshape(norm_trajectory(k,:),[nLegs 2]); unassigned_idx = find(last_seen_tips(:,1) == 0); for kk = 1:length(unassigned_idx) idx = find(norm_trajectory(1:i,unassigned_idx(kk),1),1,'last'); last_seen_tips(unassigned_idx(kk),:) = reshape(norm_trajectory(idx,unassigned_idx(kk),:), [1 2]); end if (~isempty(unassigned_idx) && ~isempty(x_t1)) C = hungarianlinker(last_seen_tips(unassigned_idx,:), x_t1, 1.25*max_distance); for l = 1:length(C) if (C(l) ~= -1) norm_trajectory(k,unassigned_idx(l), :) = x_t1(C(l), :); trajectory(k,unassigned_idx(l), :) = x_t2(C(l), :); end end end end break else continue; end end skip = 1; for i = start_frame+1 : skip : end_frame if (mod(i,50) == 1) fprintf('Tracking progress: frame %d for dataset %s\n', i, sub_dir); end % Load necessary images Im = (imread([seg_dir 'img_' num2str(i) '.png'])); Imroi = imread([seg_dir 'roi_' num2str(i) '.png']); % Fix image/segmentation irregularities Imwork = double(Im(:,:,1) == 255) .* Imroi; Imwork = Imwork - (bwdist(imerode(Imroi,se))<10); [locY,locX] = find(imerode(Imroi,se) > 0); %Centre_of_Mass [centre_of_mass,theta] = findCoM(locX,locY); if (abs(theta - CoM(i-1,3)) > 90 && abs(theta - CoM(i-1,3)) < 270) theta = mod(theta + 180,360); end CoM(i, :) = [centre_of_mass, theta]; [raw_tips, raw_normtips] = findtips(Imwork, Imroi, locX, locY, centre_of_mass, theta); x_t1 = raw_normtips; x_t0 = reshape(norm_trajectory(i-1,:),[nLegs 2]); target_indices = hungarianlinker(x_t0, x_t1, max_distance); for kk = 1:length(target_indices) if (target_indices(kk) > 0) norm_trajectory(i, kk, :) = raw_normtips(target_indices(kk),:); trajectory(i, kk, :) = raw_tips(target_indices(kk),:); end end leftoveridx = find(hungarianlinker(raw_normtips, x_t0, max_distance)==-1); x_t1 = raw_normtips(leftoveridx,:); x_t2 = raw_tips(leftoveridx,:); last_seen_tips = reshape(norm_trajectory(i,:),[6 2]); unassigned_idx = find(last_seen_tips(:,1) == 0); for kk = 1:length(unassigned_idx) idx = find(norm_trajectory(:,unassigned_idx(kk),1),1,'last'); last_seen_tips(unassigned_idx(kk),:) = reshape(norm_trajectory(idx,unassigned_idx(kk),:), [1 2]); end if (~isempty(unassigned_idx) && ~isempty(x_t1)) C = hungarianlinker(last_seen_tips(unassigned_idx,:), x_t1, max_distance); for l = 1:length(C) if (C(l) ~= -1) norm_trajectory(i,unassigned_idx(l), :) = x_t1(C(l), :); trajectory(i,unassigned_idx(l), :) = x_t2(C(l), :); end end end end save([output_dir 'CoM.mat'],'CoM'); save([output_dir 'trajectory.mat'],'trajectory'); save([output_dir 'norm_trajectory.mat'],'norm_trajectory'); csvwrite([output_dir 'CoM.csv'],CoM); csvwrite([output_dir 'trajectory.csv'],trajectory); csvwrite([output_dir 'norm_trajectory.csv'],norm_trajectory); end
github
BII-wushuang/FLLIT-master
findmissing.m
.m
FLLIT-master/src/findmissing.m
1,352
utf_8
cd60672044a321bbf585c163a0b5d667
function findmissing() data_dir = uipickfiles('FilterSpec',[pwd '/Results/Tracking']); fileID = fopen('missing_tips.csv','w'); fprintf(fileID,'%s \t %s \t %s \t %s \t %s \t %s \t %s \t %s', 'Dataset', '#frames', 'L1', 'L2', 'L3', 'L4', 'L5', 'L6'); fprintf(fileID,'\n'); fclose(fileID); for i = 1 : length(data_dir) ProcessFolder(data_dir{i}); end end function ProcessFolder(data_dir) pos_bs = strfind(data_dir,'Tracking'); sub_dir = data_dir(pos_bs(end)+length('Tracking'):length(data_dir)); if(exist(data_dir) && isempty(dir([data_dir '/trajectory.mat'])) ) dir_fn = dir(data_dir); for i_dir = 1:length(dir_fn)-2 ProcessFolder([data_dir '/' dir_fn(i_dir+2).name]); pause(1); end else data_dir = ['./Results/Tracking' sub_dir]; data_name_pos = strfind(data_dir,'/'); data_name = data_dir(data_name_pos(end)+1:end); load([data_dir '/trajectory.mat']); output{1} = data_name; output{2} = length(trajectory); for j =1:6 output{j+2} = length(find(trajectory(:,j,1)==0)); end fileID = fopen('missing_tips.csv','a'); formatSpec = '%s \t %d \t %d \t %d \t %d \t %d \t %d \t %d \t %d'; fprintf(fileID,formatSpec,output{:}); fprintf(fileID,'\n'); fclose(fileID); end end
github
BII-wushuang/FLLIT-master
Video_base.m
.m
FLLIT-master/src/Video_base.m
6,558
utf_8
bc69c4af23e5170754e8fcda84c9abc9
%Plot the trajectory of the fly's legs function Video(data_dir,fps,skip,startframe,endframe,bodylength) pos_bs = strfind(data_dir,'Data'); sub_dir = data_dir(pos_bs(end)+length('Data'):length(data_dir)); data_dir = [pwd '/Data' sub_dir '/']; if(~isempty(dir([data_dir '*.tif']))) img_list = dir([data_dir '*.tif']); else img_list = dir([data_dir '*.bmp']); end load(['./Results/Tracking/' sub_dir '/trajectory.mat']); load(['./Results/Tracking/' sub_dir '/norm_trajectory.mat']); load(['./Results/Tracking/' sub_dir '/CoM.mat']); nlegs = size(trajectory,2); if (nlegs == 6) legs_id = {'L1', 'L2', 'L3', 'R1', 'R2', 'R3'}; colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1]; else legs_id = {'L1', 'L2', 'L3', 'L4', 'R1', 'R2', 'R3', 'R4'}; trajectory = trajectory(:,[1 2 3 7 4 5 6 8],:); norm_trajectory = norm_trajectory(:,[1 2 3 7 4 5 6 8],:); colors = [1 0 0; 0 1 0; 0 0 1; 1 1 0; 0 1 1; 1 0 1; 1 0.5 0; 1 0 0.5]; end start = zeros(nlegs,1); for j = 1:nlegs; start(j) = find(trajectory(:,j,1),1); end startf = max(start); endf = length(CoM); for j =1:size(trajectory,2) nonzero = find(norm_trajectory(:,j,1)); for i = startf : endf if (trajectory(i,j,1) ~= 0) x(i,j) = trajectory(i,j,1); y(i,j) = trajectory(i,j,2); nx(i,j) = norm_trajectory(i,j,1); ny(i,j) = norm_trajectory(i,j,2); else previdx = nonzero(find(nonzero<i, 1)); nextidx = nonzero(find(nonzero>i, 1)); if(isempty(previdx)) x(i,j) = trajectory(nextidx,j,1); y(i,j) = trajectory(nextidx,j,2); nx(i,j) = norm_trajectory(nextidx,j,1); ny(i,j) = norm_trajectory(nextidx,j,2); elseif (~isempty(nextidx)) x(i,j) = trajectory(previdx,j,1) + (i-previdx)/(nextidx-previdx) * (trajectory(nextidx,j,1) - trajectory(previdx,j,1)); y(i,j) = trajectory(previdx,j,2) + (i-previdx)/(nextidx-previdx) * (trajectory(nextidx,j,2) - trajectory(previdx,j,2)); nx(i,j) = norm_trajectory(previdx,j,1) + (i-previdx)/(nextidx-previdx) * (norm_trajectory(nextidx,j,1) - norm_trajectory(previdx,j,1)); ny(i,j) = norm_trajectory(previdx,j,2) + (i-previdx)/(nextidx-previdx) * (norm_trajectory(nextidx,j,2) - norm_trajectory(previdx,j,2)); else x(i,j) = x(i-1,j); y(i,j) = y(i-1,j); nx(i,j) = nx(i-1,j); ny(i,j) = ny(i-1,j); end end end end for i = startf : endf if (i == startf) dist(i) = 0; else dist(i) = dist(i-1) + (CoM(i,1) - CoM(i-1,1))*sind(CoM(i-1,3)) + (CoM(i,2) - CoM(i-1,2))*-cosd(CoM(i-1,3)); end end if(isempty(strfind(sub_dir, '/'))) pos_bs = strfind(sub_dir, '\'); else pos_bs = strfind(sub_dir, '/'); end Vtitle = sub_dir(pos_bs(end)+1:end); fig = figure('doublebuffer','off','Visible','off'); set(fig,'Units','pixels','Position',[1 1 1920 1080]); set(0,'CurrentFigure',fig); f = waitbar(0,'0.00%','Name','Processing Video...',... 'CreateCancelBtn','setappdata(gcbf,''canceling'',1)'); setappdata(f,'canceling',0); counter = 0; ori_dir = pwd; cd(['./Results/Tracking' sub_dir]); mkdir('video_tmp'); for i = startframe : skip : endframe % Check for clicked Cancel button if getappdata(f,'canceling') break end counter = counter+1; if mod(i,50) == 0 fprintf('Processing frame %d\n',i); end waitbar(i/endframe,f,sprintf('%2.2f%%', i/endframe*100)) t = startframe : skip : i; nt = t; set(0,'CurrentFigure',fig); img = imread([data_dir img_list(i).name]); hold on subplot(4,4,[2:3 6:7]) imshow(img); axis square hold off img_norm = imtranslate(img, [255 - CoM(i,1), 255 - CoM(i,2)]); img_norm = imrotate(img_norm, CoM(i,3)); img_norm = imcrop(img_norm, [size(img_norm,1)/2-150 size(img_norm,2)/2-150 300 300]); hold on subplot(4,4,[10:11 14:15]) imshow(img_norm); axis square hold off hold on % Superposed trajectory with fly subplot(4,4,[2:3 6:7]) for j = 1: size(trajectory,2) p(j) = plot (x(t,j), y(t,j)); end scatter(CoM(i,1)+sind(CoM(i,3))*.157*bodylength,CoM(i,2)-cosd(CoM(i,3))*.157*bodylength,'w*'); title(Vtitle,'Interpreter', 'none'); axis([0 512 0 512]); axis square set(gca,'Ydir','reverse') hold on % Superposed trajectory with fly in normalised plane subplot(4,4,[10:11 14:15]) scatter(150,150-0.157*bodylength,'w*'); for j = 1: size(trajectory,2) np(j) = plot (150+nx(t,j), 150+ny(t,j)); end title('Leg motion in normalised plane'); axis square % CoM trajectory in x-y plane subplot(4,4,[1 5]) plot (CoM(t,1), 512-CoM(t,2)); title('CoM trajectory in x-y plane'); axis([0 512 0 512]); axis square % Forward displacement of fly subplot(4,4,[4 8]) plot(nt, dist(t)); title('Forward displacement (direction it is heading)'); xlabel('time (ms)'); ylabel('displacement (pixels)'); axis square % Vertical displacement of legs subplot(4,4,[9 13]) npy = plot (nt, -ny(t,1:size(trajectory,2))); title('Vertical displacement of legs'); xlabel('time (ms)'); ylabel('displacement (pixels)'); axis square set(gca,'Color',[0.5 0.5 0.5]); % Lateral displacement of legs subplot(4,4,[12 16]) npx = plot (nt, nx(t,1: size(trajectory,2))); title('Lateral displacement of legs'); xlabel('time (ms)'); ylabel('displacement (pixels)'); axis square set(gca,'Color',[0.5 0.5 0.5]); hold off for j = 1 : size(trajectory,2) p(j).Color = [colors(j,1) colors(j,2) colors(j,3)]; p(j).LineStyle = '--'; np(j).Color = [colors(j,1) colors(j,2) colors(j,3)]; np(j).LineStyle = '--'; npx(j).Color = [colors(j,1) colors(j,2) colors(j,3)]; npy(j).Color = [colors(j,1) colors(j,2) colors(j,3)]; end % drawnow img = getframe(fig); imwrite(img.cdata, sprintf('video_tmp/%04d.png',counter)); end [status,cmdout] = system(['ffmpeg -i video_tmp/%04d.png -r ' num2str(fps) ' -y Video.mp4']); if (status~=0) fid = fopen('VideoErrMsg.txt','w'); fprintf(fid,'%s\n',cmdout); fclose(fid); else rmdir('video_tmp', 's'); end cd(ori_dir); delete(f) % myVideo = VideoWriter(['./Results/Tracking' sub_dir '/Video.mp4'], 'MPEG-4');
github
BII-wushuang/FLLIT-master
munkres.m
.m
FLLIT-master/src/munkres.m
8,302
utf_8
05adcef4b6504b76b07ca6f59e869d20
function [assignment,cost] = munkres(costMat) % MUNKRES Munkres (Hungarian) Algorithm for Linear Assignment Problem. % % [ASSIGN,COST] = munkres(COSTMAT) returns the optimal column indices, % ASSIGN assigned to each row and the minimum COST based on the assignment % problem represented by the COSTMAT, where the (i,j)th element represents the cost to assign the jth % job to the ith worker. % % Partial assignment: This code can identify a partial assignment is a full % assignment is not feasible. For a partial assignment, there are some % zero elements in the returning assignment vector, which indicate % un-assigned tasks. The cost returned only contains the cost of partially % assigned tasks. % This is vectorized implementation of the algorithm. It is the fastest % among all Matlab implementations of the algorithm. % Examples % Example 1: a 5 x 5 example %{ [assignment,cost] = munkres(magic(5)); disp(assignment); % 3 2 1 5 4 disp(cost); %15 %} % Example 2: 400 x 400 random data %{ n=400; A=rand(n); tic [a,b]=munkres(A); toc % about 2 seconds %} % Example 3: rectangular assignment with inf costs %{ A=rand(10,7); A(A>0.7)=Inf; [a,b]=munkres(A); %} % Example 4: an example of partial assignment %{ A = [1 3 Inf; Inf Inf 5; Inf Inf 0.5]; [a,b]=munkres(A) %} % a = [1 0 3] % b = 1.5 % Reference: % "Munkres' Assignment Algorithm, Modified for Rectangular Matrices", % http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html % version 2.3 by Yi Cao at Cranfield University on 11th September 2011 % Copyright (c) 2009, Yi Cao % All rights reserved. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are met: % % * Redistributions of source code must retain the above copyright notice, this % list of conditions and the following disclaimer. % % * Redistributions in binary form must reproduce the above copyright notice, % this list of conditions and the following disclaimer in the documentation % and/or other materials provided with the distribution % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE % DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE % FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL % DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR % SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER % CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, % OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE % OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. assignment = zeros(1,size(costMat,1)); cost = 0; validMat = costMat == costMat & costMat < Inf; bigM = 10^(ceil(log10(sum(costMat(validMat))))+1); costMat(~validMat) = bigM; % costMat(costMat~=costMat)=Inf; % validMat = costMat<Inf; validCol = any(validMat,1); validRow = any(validMat,2); nRows = sum(validRow); nCols = sum(validCol); n = max(nRows,nCols); if ~n return end maxv=10*max(costMat(validMat)); dMat = zeros(n) + maxv; dMat(1:nRows,1:nCols) = costMat(validRow,validCol); %************************************************* % Munkres' Assignment Algorithm starts here %************************************************* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % STEP 1: Subtract the row minimum from each row. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% minR = min(dMat,[],2); minC = min(bsxfun(@minus, dMat, minR)); %************************************************************************** % STEP 2: Find a zero of dMat. If there are no starred zeros in its % column or row start the zero. Repeat for each zero %************************************************************************** zP = dMat == bsxfun(@plus, minC, minR); starZ = zeros(n,1); while any(zP(:)) [r,c]=find(zP,1); starZ(r)=c; zP(r,:)=false; zP(:,c)=false; end while 1 %************************************************************************** % STEP 3: Cover each column with a starred zero. If all the columns are % covered then the matching is maximum %************************************************************************** if all(starZ>0) break end coverColumn = false(1,n); coverColumn(starZ(starZ>0))=true; coverRow = false(n,1); primeZ = zeros(n,1); [rIdx, cIdx] = find(dMat(~coverRow,~coverColumn)==bsxfun(@plus,minR(~coverRow),minC(~coverColumn))); while 1 %************************************************************************** % STEP 4: Find a noncovered zero and prime it. If there is no starred % zero in the row containing this primed zero, Go to Step 5. % Otherwise, cover this row and uncover the column containing % the starred zero. Continue in this manner until there are no % uncovered zeros left. Save the smallest uncovered value and % Go to Step 6. %************************************************************************** cR = find(~coverRow); cC = find(~coverColumn); rIdx = cR(rIdx); cIdx = cC(cIdx); Step = 6; while ~isempty(cIdx) uZr = rIdx(1); uZc = cIdx(1); primeZ(uZr) = uZc; stz = starZ(uZr); if ~stz Step = 5; break; end coverRow(uZr) = true; coverColumn(stz) = false; z = rIdx==uZr; rIdx(z) = []; cIdx(z) = []; cR = find(~coverRow); z = dMat(~coverRow,stz) == minR(~coverRow) + minC(stz); rIdx = [rIdx(:);cR(z)]; cIdx = [cIdx(:);stz(ones(sum(z),1))]; end if Step == 6 % ************************************************************************* % STEP 6: Add the minimum uncovered value to every element of each covered % row, and subtract it from every element of each uncovered column. % Return to Step 4 without altering any stars, primes, or covered lines. %************************************************************************** [minval,rIdx,cIdx]=outerplus(dMat(~coverRow,~coverColumn),minR(~coverRow),minC(~coverColumn)); minC(~coverColumn) = minC(~coverColumn) + minval; minR(coverRow) = minR(coverRow) - minval; else break end end %************************************************************************** % STEP 5: % Construct a series of alternating primed and starred zeros as % follows: % Let Z0 represent the uncovered primed zero found in Step 4. % Let Z1 denote the starred zero in the column of Z0 (if any). % Let Z2 denote the primed zero in the row of Z1 (there will always % be one). Continue until the series terminates at a primed zero % that has no starred zero in its column. Unstar each starred % zero of the series, star each primed zero of the series, erase % all primes and uncover every line in the matrix. Return to Step 3. %************************************************************************** rowZ1 = find(starZ==uZc); starZ(uZr)=uZc; while rowZ1>0 starZ(rowZ1)=0; uZc = primeZ(rowZ1); uZr = rowZ1; rowZ1 = find(starZ==uZc); starZ(uZr)=uZc; end end % Cost of assignment rowIdx = find(validRow); colIdx = find(validCol); starZ = starZ(1:nRows); vIdx = starZ <= nCols; assignment(rowIdx(vIdx)) = colIdx(starZ(vIdx)); pass = assignment(assignment>0); pass(~diag(validMat(assignment>0,pass))) = 0; assignment(assignment>0) = pass; cost = trace(costMat(assignment>0,assignment(assignment>0))); function [minval,rIdx,cIdx]=outerplus(M,x,y) ny=size(M,2); minval=inf; for c=1:ny M(:,c)=M(:,c)-(x+y(c)); minval = min(minval,min(M(:,c))); end [rIdx,cIdx]=find(M==minval);
github
BII-wushuang/FLLIT-master
pdftops.m
.m
FLLIT-master/src/Export-Fig/pdftops.m
5,994
utf_8
24eb803667c83c8a28424c979311652b
function varargout = pdftops(cmd) %PDFTOPS Calls a local pdftops executable with the input command % % Example: % [status result] = pdftops(cmd) % % Attempts to locate a pdftops executable, finally asking the user to % specify the directory pdftops was installed into. The resulting path is % stored for future reference. % % Once found, the executable is called with the input command string. % % This function requires that you have pdftops (from the Xpdf package) % installed on your system. You can download this from: % http://www.foolabs.com/xpdf % % IN: % cmd - Command string to be passed into pdftops (e.g. '-help'). % % OUT: % status - 0 iff command ran without problem. % result - Output from pdftops. % Copyright: Oliver Woodford, 2009-2010 % Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS. % Thanks to Christoph Hertel for pointing out a bug in check_xpdf_path under linux. % 23/01/2014 - Add full path to pdftops.txt in warning. % 27/05/2015 - Fixed alert in case of missing pdftops; fixed code indentation % 02/05/2016 - Added possible error explanation suggested by Michael Pacer (issue #137) % 02/05/2016 - Search additional possible paths suggested by Jonas Stein (issue #147) % 03/05/2016 - Display the specific error message if pdftops fails for some reason (issue #148) % Call pdftops [varargout{1:nargout}] = system([xpdf_command(xpdf_path()) cmd]); end function path_ = xpdf_path % Return a valid path % Start with the currently set path path_ = user_string('pdftops'); % Check the path works if check_xpdf_path(path_) return end % Check whether the binary is on the path if ispc bin = 'pdftops.exe'; else bin = 'pdftops'; end if check_store_xpdf_path(bin) path_ = bin; return end % Search the obvious places if ispc paths = {'C:\Program Files\xpdf\pdftops.exe', 'C:\Program Files (x86)\xpdf\pdftops.exe'}; else paths = {'/usr/bin/pdftops', '/usr/local/bin/pdftops'}; end for a = 1:numel(paths) path_ = paths{a}; if check_store_xpdf_path(path_) return end end % Ask the user to enter the path errMsg1 = 'Pdftops not found. Please locate the program, or install xpdf-tools from '; url1 = 'http://foolabs.com/xpdf'; fprintf(2, '%s\n', [errMsg1 '<a href="matlab:web(''-browser'',''' url1 ''');">' url1 '</a>']); errMsg1 = [errMsg1 url1]; %if strncmp(computer,'MAC',3) % Is a Mac % % Give separate warning as the MacOS uigetdir dialogue box doesn't have a title % uiwait(warndlg(errMsg1)) %end % Provide an alternative possible explanation as per issue #137 errMsg2 = 'If you have pdftops installed, perhaps Matlab is shaddowing it as described in '; url2 = 'https://github.com/altmany/export_fig/issues/137'; fprintf(2, '%s\n', [errMsg2 '<a href="matlab:web(''-browser'',''' url2 ''');">issue #137</a>']); errMsg2 = [errMsg2 url1]; state = 0; while 1 if state option1 = 'Install pdftops'; else option1 = 'Issue #137'; end answer = questdlg({errMsg1,'',errMsg2},'Pdftops error',option1,'Locate pdftops','Cancel','Cancel'); drawnow; % prevent a Matlab hang: http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem switch answer case 'Install pdftops' web('-browser',url1); case 'Issue #137' web('-browser',url2); state = 1; case 'Locate pdftops' base = uigetdir('/', errMsg1); if isequal(base, 0) % User hit cancel or closed window break end base = [base filesep]; %#ok<AGROW> bin_dir = {'', ['bin' filesep], ['lib' filesep]}; for a = 1:numel(bin_dir) path_ = [base bin_dir{a} bin]; if exist(path_, 'file') == 2 break end end if check_store_xpdf_path(path_) return end otherwise % User hit Cancel or closed window break end end error('pdftops executable not found.'); end function good = check_store_xpdf_path(path_) % Check the path is valid good = check_xpdf_path(path_); if ~good return end % Update the current default path to the path found if ~user_string('pdftops', path_) warning('Path to pdftops executable could not be saved. Enter it manually in %s.', fullfile(fileparts(which('user_string.m')), '.ignore', 'pdftops.txt')); return end end function good = check_xpdf_path(path_) % Check the path is valid [good, message] = system([xpdf_command(path_) '-h']); %#ok<ASGLU> % system returns good = 1 even when the command runs % Look for something distinct in the help text good = ~isempty(strfind(message, 'PostScript')); % Display the error message if the pdftops executable exists but fails for some reason if ~good && exist(path_,'file') % file exists but generates an error fprintf('Error running %s:\n', path_); fprintf(2,'%s\n\n',message); end end function cmd = xpdf_command(path_) % Initialize any required system calls before calling ghostscript % TODO: in Unix/Mac, find a way to determine whether to use "export" (bash) or "setenv" (csh/tcsh) shell_cmd = ''; if isunix % Avoids an error on Linux with outdated MATLAB lib files % R20XXa/bin/glnxa64/libtiff.so.X % R20XXa/sys/os/glnxa64/libstdc++.so.X shell_cmd = 'export LD_LIBRARY_PATH=""; '; end if ismac shell_cmd = 'export DYLD_LIBRARY_PATH=""; '; end % Construct the command string cmd = sprintf('%s"%s" ', shell_cmd, path_); end
github
BII-wushuang/FLLIT-master
crop_borders.m
.m
FLLIT-master/src/Export-Fig/crop_borders.m
4,976
utf_8
c814ff486afb188464069b51e4b5ed8a
function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts) %CROP_BORDERS Crop the borders of an image or stack of images % % [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding]) % %IN: % A - HxWxCxN stack of images. % bcol - Cx1 background colour vector. % padding - scalar indicating how much padding to have in relation to % the cropped-image-size (0<=padding<=1). Default: 0 % crop_amounts - 4-element vector of crop amounts: [top,right,bottom,left] % where NaN/Inf indicate auto-cropping, 0 means no cropping, % and any other value mean cropping in pixel amounts. % %OUT: % B - JxKxCxN cropped stack of images. % vA - coordinates in A that contain the cropped image % vB - coordinates in B where the cropped version of A is placed % bb_rel - relative bounding box (used for eps-cropping) %{ % 06/03/15: Improved image cropping thanks to Oscar Hartogensis % 08/06/15: Fixed issue #76: case of transparent figure bgcolor % 21/02/16: Enabled specifying non-automated crop amounts % 04/04/16: Fix per Luiz Carvalho for old Matlab releases % 23/10/16: Fixed issue #175: there used to be a 1px minimal padding in case of crop, now removed %} if nargin < 3 padding = 0; end if nargin < 4 crop_amounts = nan(1,4); % =auto-cropping end crop_amounts(end+1:4) = NaN; % fill missing values with NaN [h, w, c, n] = size(A); if isempty(bcol) % case of transparent bgcolor bcol = A(ceil(end/2),1,:,1); end if isscalar(bcol) bcol = bcol(ones(c, 1)); end % Crop margin from left if ~isfinite(crop_amounts(4)) bail = false; for l = 1:w for a = 1:c if ~all(col(A(:,l,a,:)) == bcol(a)) bail = true; break; end end if bail break; end end else l = 1 + abs(crop_amounts(4)); end % Crop margin from right if ~isfinite(crop_amounts(2)) bcol = A(ceil(end/2),w,:,1); bail = false; for r = w:-1:l for a = 1:c if ~all(col(A(:,r,a,:)) == bcol(a)) bail = true; break; end end if bail break; end end else r = w - abs(crop_amounts(2)); end % Crop margin from top if ~isfinite(crop_amounts(1)) bcol = A(1,ceil(end/2),:,1); bail = false; for t = 1:h for a = 1:c if ~all(col(A(t,:,a,:)) == bcol(a)) bail = true; break; end end if bail break; end end else t = 1 + abs(crop_amounts(1)); end % Crop margin from bottom bcol = A(h,ceil(end/2),:,1); if ~isfinite(crop_amounts(3)) bail = false; for b = h:-1:t for a = 1:c if ~all(col(A(b,:,a,:)) == bcol(a)) bail = true; break; end end if bail break; end end else b = h - abs(crop_amounts(3)); end if padding == 0 % no padding % Issue #175: there used to be a 1px minimal padding in case of crop, now removed %{ if ~isequal([t b l r], [1 h 1 w]) % Check if we're actually croppping padding = 1; % Leave one boundary pixel to avoid bleeding on resize bcol(:) = nan; % make the 1px padding transparent end %} elseif abs(padding) < 1 % pad value is a relative fraction of image size padding = sign(padding)*round(mean([b-t r-l])*abs(padding)); % ADJUST PADDING else % pad value is in units of 1/72" points padding = round(padding); % fix cases of non-integer pad value end if padding > 0 % extra padding % Create an empty image, containing the background color, that has the % cropped image size plus the padded border B = repmat(bcol,[(b-t)+1+padding*2,(r-l)+1+padding*2,1,n]); % Fix per Luiz Carvalho % vA - coordinates in A that contain the cropped image vA = [t b l r]; % vB - coordinates in B where the cropped version of A will be placed vB = [padding+1, (b-t)+1+padding, padding+1, (r-l)+1+padding]; % Place the original image in the empty image B(vB(1):vB(2), vB(3):vB(4), :, :) = A(vA(1):vA(2), vA(3):vA(4), :, :); A = B; else % extra cropping vA = [t-padding b+padding l-padding r+padding]; A = A(vA(1):vA(2), vA(3):vA(4), :, :); vB = [NaN NaN NaN NaN]; end % For EPS cropping, determine the relative BoundingBox - bb_rel bb_rel = [l-1 h-b-1 r+1 h-t+1]./[w h w h]; end function A = col(A) A = A(:); end
github
BII-wushuang/FLLIT-master
isolate_axes.m
.m
FLLIT-master/src/Export-Fig/isolate_axes.m
4,721
utf_8
253cd7b7d8fc7cb00d0cc55926f32de5
function fh = isolate_axes(ah, vis) %ISOLATE_AXES Isolate the specified axes in a figure on their own % % Examples: % fh = isolate_axes(ah) % fh = isolate_axes(ah, vis) % % This function will create a new figure containing the axes/uipanels % specified, and also their associated legends and colorbars. The objects % specified must all be in the same figure, but they will generally only be % a subset of the objects in the figure. % % IN: % ah - An array of axes and uipanel handles, which must come from the % same figure. % vis - A boolean indicating whether the new figure should be visible. % Default: false. % % OUT: % fh - The handle of the created figure. % Copyright (C) Oliver Woodford 2011-2013 % Thank you to Rosella Blatt for reporting a bug to do with axes in GUIs % 16/03/12: Moved copyfig to its own function. Thanks to Bob Fratantonio % for pointing out that the function is also used in export_fig.m % 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it % 08/10/13: Bug fix to allchildren suggested by Will Grant (many thanks!) % 05/12/13: Bug fix to axes having different units. Thanks to Remington Reid for reporting % 21/04/15: Bug fix for exporting uipanels with legend/colorbar on HG1 (reported by Alvaro % on FEX page as a comment on 24-Apr-2014); standardized indentation & help section % 22/04/15: Bug fix: legends and colorbars were not exported when exporting axes handle in HG2 % Make sure we have an array of handles if ~all(ishandle(ah)) error('ah must be an array of handles'); end % Check that the handles are all for axes or uipanels, and are all in the same figure fh = ancestor(ah(1), 'figure'); nAx = numel(ah); for a = 1:nAx if ~ismember(get(ah(a), 'Type'), {'axes', 'uipanel'}) error('All handles must be axes or uipanel handles.'); end if ~isequal(ancestor(ah(a), 'figure'), fh) error('Axes must all come from the same figure.'); end end % Tag the objects so we can find them in the copy old_tag = get(ah, 'Tag'); if nAx == 1 old_tag = {old_tag}; end set(ah, 'Tag', 'ObjectToCopy'); % Create a new figure exactly the same as the old one fh = copyfig(fh); %copyobj(fh, 0); if nargin < 2 || ~vis set(fh, 'Visible', 'off'); end % Reset the object tags for a = 1:nAx set(ah(a), 'Tag', old_tag{a}); end % Find the objects to save ah = findall(fh, 'Tag', 'ObjectToCopy'); if numel(ah) ~= nAx close(fh); error('Incorrect number of objects found.'); end % Set the axes tags to what they should be for a = 1:nAx set(ah(a), 'Tag', old_tag{a}); end % Keep any legends and colorbars which overlap the subplots % Note: in HG1 these are axes objects; in HG2 they are separate objects, therefore we % don't test for the type, only the tag (hopefully nobody but Matlab uses them!) lh = findall(fh, 'Tag', 'legend', '-or', 'Tag', 'Colorbar'); nLeg = numel(lh); if nLeg > 0 set([ah(:); lh(:)], 'Units', 'normalized'); try ax_pos = get(ah, 'OuterPosition'); % axes and figures have the OuterPosition property catch ax_pos = get(ah, 'Position'); % uipanels only have Position, not OuterPosition end if nAx > 1 ax_pos = cell2mat(ax_pos(:)); end ax_pos(:,3:4) = ax_pos(:,3:4) + ax_pos(:,1:2); try leg_pos = get(lh, 'OuterPosition'); catch leg_pos = get(lh, 'Position'); % No OuterPosition in HG2, only in HG1 end if nLeg > 1; leg_pos = cell2mat(leg_pos); end leg_pos(:,3:4) = leg_pos(:,3:4) + leg_pos(:,1:2); ax_pos = shiftdim(ax_pos, -1); % Overlap test M = bsxfun(@lt, leg_pos(:,1), ax_pos(:,:,3)) & ... bsxfun(@lt, leg_pos(:,2), ax_pos(:,:,4)) & ... bsxfun(@gt, leg_pos(:,3), ax_pos(:,:,1)) & ... bsxfun(@gt, leg_pos(:,4), ax_pos(:,:,2)); ah = [ah; lh(any(M, 2))]; end % Get all the objects in the figure axs = findall(fh); % Delete everything except for the input objects and associated items delete(axs(~ismember(axs, [ah; allchildren(ah); allancestors(ah)]))); end function ah = allchildren(ah) ah = findall(ah); if iscell(ah) ah = cell2mat(ah); end ah = ah(:); end function ph = allancestors(ah) ph = []; for a = 1:numel(ah) h = get(ah(a), 'parent'); while h ~= 0 ph = [ph; h]; h = get(h, 'parent'); end end end
github
BII-wushuang/FLLIT-master
im2gif.m
.m
FLLIT-master/src/Export-Fig/im2gif.m
6,048
utf_8
5a7437140f8d013158a195de1e372737
%IM2GIF Convert a multiframe image to an animated GIF file % % Examples: % im2gif infile % im2gif infile outfile % im2gif(A, outfile) % im2gif(..., '-nocrop') % im2gif(..., '-nodither') % im2gif(..., '-ncolors', n) % im2gif(..., '-loops', n) % im2gif(..., '-delay', n) % % This function converts a multiframe image to an animated GIF. % % To create an animation from a series of figures, export to a multiframe % TIFF file using export_fig, then convert to a GIF, as follows: % % for a = 2 .^ (3:6) % peaks(a); % export_fig test.tif -nocrop -append % end % im2gif('test.tif', '-delay', 0.5); % %IN: % infile - string containing the name of the input image. % outfile - string containing the name of the output image (must have the % .gif extension). Default: infile, with .gif extension. % A - HxWxCxN array of input images, stacked along fourth dimension, to % be converted to gif. % -nocrop - option indicating that the borders of the output are not to % be cropped. % -nodither - option indicating that dithering is not to be used when % converting the image. % -ncolors - option pair, the value of which indicates the maximum number % of colors the GIF can have. This can also be a quantization % tolerance, between 0 and 1. Default/maximum: 256. % -loops - option pair, the value of which gives the number of times the % animation is to be looped. Default: 65535. % -delay - option pair, the value of which gives the time, in seconds, % between frames. Default: 1/15. % Copyright (C) Oliver Woodford 2011 function im2gif(A, varargin) % Parse the input arguments [A, options] = parse_args(A, varargin{:}); if options.crop ~= 0 % Crop A = crop_borders(A, A(ceil(end/2),1,:,1)); end % Convert to indexed image [h, w, c, n] = size(A); A = reshape(permute(A, [1 2 4 3]), h, w*n, c); map = unique(reshape(A, h*w*n, c), 'rows'); if size(map, 1) > 256 dither_str = {'dither', 'nodither'}; dither_str = dither_str{1+(options.dither==0)}; if options.ncolors <= 1 [B, map] = rgb2ind(A, options.ncolors, dither_str); if size(map, 1) > 256 [B, map] = rgb2ind(A, 256, dither_str); end else [B, map] = rgb2ind(A, min(round(options.ncolors), 256), dither_str); end else if max(map(:)) > 1 map = double(map) / 255; A = double(A) / 255; end B = rgb2ind(im2double(A), map); end B = reshape(B, h, w, 1, n); % Bug fix to rgb2ind map(B(1)+1,:) = im2double(A(1,1,:)); % Save as a gif imwrite(B, map, options.outfile, 'LoopCount', round(options.loops(1)), 'DelayTime', options.delay); end %% Parse the input arguments function [A, options] = parse_args(A, varargin) % Set the defaults options = struct('outfile', '', ... 'dither', true, ... 'crop', true, ... 'ncolors', 256, ... 'loops', 65535, ... 'delay', 1/15); % Go through the arguments a = 0; n = numel(varargin); while a < n a = a + 1; if ischar(varargin{a}) && ~isempty(varargin{a}) if varargin{a}(1) == '-' opt = lower(varargin{a}(2:end)); switch opt case 'nocrop' options.crop = false; case 'nodither' options.dither = false; otherwise if ~isfield(options, opt) error('Option %s not recognized', varargin{a}); end a = a + 1; if ischar(varargin{a}) && ~ischar(options.(opt)) options.(opt) = str2double(varargin{a}); else options.(opt) = varargin{a}; end end else options.outfile = varargin{a}; end end end if isempty(options.outfile) if ~ischar(A) error('No output filename given.'); end % Generate the output filename from the input filename [path, outfile] = fileparts(A); options.outfile = fullfile(path, [outfile '.gif']); end if ischar(A) % Read in the image A = imread_rgb(A); end end %% Read image to uint8 rgb array function [A, alpha] = imread_rgb(name) % Get file info info = imfinfo(name); % Special case formats switch lower(info(1).Format) case 'gif' [A, map] = imread(name, 'frames', 'all'); if ~isempty(map) map = uint8(map * 256 - 0.5); % Convert to uint8 for storage A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0 A = permute(A, [1 2 5 4 3]); end case {'tif', 'tiff'} A = cell(numel(info), 1); for a = 1:numel(A) [A{a}, map] = imread(name, 'Index', a, 'Info', info); if ~isempty(map) map = uint8(map * 256 - 0.5); % Convert to uint8 for storage A{a} = reshape(map(uint32(A{a})+1,:), [size(A) size(map, 2)]); % Assume indexed from 0 end if size(A{a}, 3) == 4 % TIFF in CMYK colourspace - convert to RGB if isfloat(A{a}) A{a} = A{a} * 255; else A{a} = single(A{a}); end A{a} = 255 - A{a}; A{a}(:,:,4) = A{a}(:,:,4) / 255; A{a} = uint8(A(:,:,1:3) .* A{a}(:,:,[4 4 4])); end end A = cat(4, A{:}); otherwise [A, map, alpha] = imread(name); A = A(:,:,:,1); % Keep only first frame of multi-frame files if ~isempty(map) map = uint8(map * 256 - 0.5); % Convert to uint8 for storage A = reshape(map(uint32(A)+1,:), [size(A) size(map, 2)]); % Assume indexed from 0 elseif size(A, 3) == 4 % Assume 4th channel is an alpha matte alpha = A(:,:,4); A = A(:,:,1:3); end end end
github
BII-wushuang/FLLIT-master
read_write_entire_textfile.m
.m
FLLIT-master/src/Export-Fig/read_write_entire_textfile.m
924
utf_8
779e56972f5d9778c40dee98ddbd677e
%READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory % % Read or write an entire text file to/from memory, without leaving the % file open if an error occurs. % % Reading: % fstrm = read_write_entire_textfile(fname) % Writing: % read_write_entire_textfile(fname, fstrm) % %IN: % fname - Pathname of text file to be read in. % fstrm - String to be written to the file, including carriage returns. % %OUT: % fstrm - String read from the file. If an fstrm input is given the % output is the same as that input. function fstrm = read_write_entire_textfile(fname, fstrm) modes = {'rt', 'wt'}; writing = nargin > 1; fh = fopen(fname, modes{1+writing}); if fh == -1 error('Unable to open file %s.', fname); end try if writing fwrite(fh, fstrm, 'char*1'); else fstrm = fread(fh, '*char')'; end catch ex fclose(fh); rethrow(ex); end fclose(fh); end
github
BII-wushuang/FLLIT-master
pdf2eps.m
.m
FLLIT-master/src/Export-Fig/pdf2eps.m
1,471
utf_8
a1f41f0c7713c73886a2323e53ed982b
%PDF2EPS Convert a pdf file to eps format using pdftops % % Examples: % pdf2eps source dest % % This function converts a pdf file to eps format. % % This function requires that you have pdftops, from the Xpdf suite of % functions, installed on your system. This can be downloaded from: % http://www.foolabs.com/xpdf % %IN: % source - filename of the source pdf file to convert. The filename is % assumed to already have the extension ".pdf". % dest - filename of the destination eps file. The filename is assumed to % already have the extension ".eps". % Copyright (C) Oliver Woodford 2009-2010 % Thanks to Aldebaro Klautau for reporting a bug when saving to % non-existant directories. function pdf2eps(source, dest) % Construct the options string for pdftops options = ['-q -paper match -eps -level2 "' source '" "' dest '"']; % Convert to eps using pdftops [status, message] = pdftops(options); % Check for error if status % Report error if isempty(message) error('Unable to generate eps. Check destination directory is writable.'); else error(message); end end % Fix the DSC error created by pdftops fid = fopen(dest, 'r+'); if fid == -1 % Cannot open the file return end fgetl(fid); % Get the first line str = fgetl(fid); % Get the second line if strcmp(str(1:min(13, end)), '% Produced by') fseek(fid, -numel(str)-1, 'cof'); fwrite(fid, '%'); % Turn ' ' into '%' end fclose(fid); end
github
BII-wushuang/FLLIT-master
print2array.m
.m
FLLIT-master/src/Export-Fig/print2array.m
10,117
utf_8
826905ad12ce0de461386980b4aae89b
function [A, bcol] = print2array(fig, res, renderer, gs_options) %PRINT2ARRAY Exports a figure to an image array % % Examples: % A = print2array % A = print2array(figure_handle) % A = print2array(figure_handle, resolution) % A = print2array(figure_handle, resolution, renderer) % A = print2array(figure_handle, resolution, renderer, gs_options) % [A bcol] = print2array(...) % % This function outputs a bitmap image of the given figure, at the desired % resolution. % % If renderer is '-painters' then ghostcript needs to be installed. This % can be downloaded from: http://www.ghostscript.com % % IN: % figure_handle - The handle of the figure to be exported. Default: gcf. % resolution - Resolution of the output, as a factor of screen % resolution. Default: 1. % renderer - string containing the renderer paramater to be passed to % print. Default: '-opengl'. % gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If % multiple options are needed, enclose in call array: {'-a','-b'} % % OUT: % A - MxNx3 uint8 image of the figure. % bcol - 1x3 uint8 vector of the background color % Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015- %{ % 05/09/11: Set EraseModes to normal when using opengl or zbuffer % renderers. Thanks to Pawel Kocieniewski for reporting the issue. % 21/09/11: Bug fix: unit8 -> uint8! Thanks to Tobias Lamour for reporting it. % 14/11/11: Bug fix: stop using hardcopy(), as it interfered with figure size % and erasemode settings. Makes it a bit slower, but more reliable. % Thanks to Phil Trinh and Meelis Lootus for reporting the issues. % 09/12/11: Pass font path to ghostscript. % 27/01/12: Bug fix affecting painters rendering tall figures. Thanks to % Ken Campbell for reporting it. % 03/04/12: Bug fix to median input. Thanks to Andy Matthews for reporting it. % 26/10/12: Set PaperOrientation to portrait. Thanks to Michael Watts for % reporting the issue. % 26/02/15: If temp dir is not writable, use the current folder for temp % EPS/TIF files (Javier Paredes) % 27/02/15: Display suggested workarounds to internal print() error (issue #16) % 28/02/15: Enable users to specify optional ghostscript options (issue #36) % 10/03/15: Fixed minor warning reported by Paul Soderlind; fixed code indentation % 28/05/15: Fixed issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() func) % 07/07/15: Fixed issue #83: use numeric handles in HG1 % 11/12/16: Fixed cropping issue reported by Harry D. %} % Generate default input arguments, if needed if nargin < 2 res = 1; if nargin < 1 fig = gcf; end end % Warn if output is large old_mode = get(fig, 'Units'); set(fig, 'Units', 'pixels'); px = get(fig, 'Position'); set(fig, 'Units', old_mode); npx = prod(px(3:4)*res)/1e6; if npx > 30 % 30M pixels or larger! warning('MATLAB:LargeImage', 'print2array generating a %.1fM pixel image. This could be slow and might also cause memory problems.', npx); end % Retrieve the background colour bcol = get(fig, 'Color'); % Set the resolution parameter res_str = ['-r' num2str(ceil(get(0, 'ScreenPixelsPerInch')*res))]; % Generate temporary file name tmp_nam = [tempname '.tif']; try % Ensure that the temp dir is writable (Javier Paredes 26/2/15) fid = fopen(tmp_nam,'w'); fwrite(fid,1); fclose(fid); delete(tmp_nam); % cleanup isTempDirOk = true; catch % Temp dir is not writable, so use the current folder [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU> fpath = pwd; tmp_nam = fullfile(fpath,[fname fext]); isTempDirOk = false; end % Enable users to specify optional ghostscript options (issue #36) if nargin > 3 && ~isempty(gs_options) if iscell(gs_options) gs_options = sprintf(' %s',gs_options{:}); elseif ~ischar(gs_options) error('gs_options input argument must be a string or cell-array of strings'); else gs_options = [' ' gs_options]; end else gs_options = ''; end if nargin > 2 && strcmp(renderer, '-painters') % First try to print directly to tif file try % Print the file into a temporary TIF file and read it into array A [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam); if err, rethrow(ex); end catch % error - try to print to EPS and then using Ghostscript to TIF % Print to eps file if isTempDirOk tmp_eps = [tempname '.eps']; else tmp_eps = fullfile(fpath,[fname '.eps']); end print2eps(tmp_eps, fig, 0, renderer, '-loose'); try % Initialize the command to export to tiff using ghostscript cmd_str = ['-dEPSCrop -q -dNOPAUSE -dBATCH ' res_str ' -sDEVICE=tiff24nc']; % Set the font path fp = font_path(); if ~isempty(fp) cmd_str = [cmd_str ' -sFONTPATH="' fp '"']; end % Add the filenames cmd_str = [cmd_str ' -sOutputFile="' tmp_nam '" "' tmp_eps '"' gs_options]; % Execute the ghostscript command ghostscript(cmd_str); catch me % Delete the intermediate file delete(tmp_eps); rethrow(me); end % Delete the intermediate file delete(tmp_eps); % Read in the generated bitmap A = imread(tmp_nam); % Delete the temporary bitmap file delete(tmp_nam); end % Set border pixels to the correct colour if isequal(bcol, 'none') bcol = []; elseif isequal(bcol, [1 1 1]) bcol = uint8([255 255 255]); else for l = 1:size(A, 2) if ~all(reshape(A(:,l,:) == 255, [], 1)) break; end end for r = size(A, 2):-1:l if ~all(reshape(A(:,r,:) == 255, [], 1)) break; end end for t = 1:size(A, 1) if ~all(reshape(A(t,:,:) == 255, [], 1)) break; end end for b = size(A, 1):-1:t if ~all(reshape(A(b,:,:) == 255, [], 1)) break; end end bcol = uint8(median(single([reshape(A(:,[l r],:), [], size(A, 3)); reshape(A([t b],:,:), [], size(A, 3))]), 1)); for c = 1:size(A, 3) A(:,[1:l-1, r+1:end],c) = bcol(c); A([1:t-1, b+1:end],:,c) = bcol(c); end end else if nargin < 3 renderer = '-opengl'; end % Print the file into a temporary TIF file and read it into array A [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam); % Throw any error that occurred if err % Display suggested workarounds to internal print() error (issue #16) fprintf(2, 'An error occured with Matlab''s builtin print function.\nTry setting the figure Renderer to ''painters'' or use opengl(''software'').\n\n'); rethrow(ex); end % Set the background color if isequal(bcol, 'none') bcol = []; else bcol = bcol * 255; if isequal(bcol, round(bcol)) bcol = uint8(bcol); else bcol = squeeze(A(1,1,:)); end end end % Check the output size is correct if isequal(res, round(res)) px = round([px([4 3])*res 3]); % round() to avoid an indexing warning below if ~isequal(size(A), px) % Correct the output size A = A(1:min(end,px(1)),1:min(end,px(2)),:); end end end % Function to create a TIF image of the figure and read it into an array function [A, err, ex] = read_tif_img(fig, res_str, renderer, tmp_nam) err = false; ex = []; % Temporarily set the paper size old_pos_mode = get(fig, 'PaperPositionMode'); old_orientation = get(fig, 'PaperOrientation'); set(fig, 'PaperPositionMode','auto', 'PaperOrientation','portrait'); try % Workaround for issue #69: patches with LineWidth==0.75 appear wide (internal bug in Matlab's print() function) fp = []; % in case we get an error below fp = findall(fig, 'Type','patch', 'LineWidth',0.75); set(fp, 'LineWidth',0.5); % Fix issue #83: use numeric handles in HG1 if ~using_hg2(fig), fig = double(fig); end % Print to tiff file print(fig, renderer, res_str, '-dtiff', tmp_nam); % Read in the printed file A = imread(tmp_nam); % Delete the temporary file delete(tmp_nam); catch ex err = true; end set(fp, 'LineWidth',0.75); % restore original figure appearance % Reset the paper size set(fig, 'PaperPositionMode',old_pos_mode, 'PaperOrientation',old_orientation); end % Function to return (and create, where necessary) the font path function fp = font_path() fp = user_string('gs_font_path'); if ~isempty(fp) return end % Create the path % Start with the default path fp = getenv('GS_FONTPATH'); % Add on the typical directories for a given OS if ispc if ~isempty(fp) fp = [fp ';']; end fp = [fp getenv('WINDIR') filesep 'Fonts']; else if ~isempty(fp) fp = [fp ':']; end fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype']; end user_string('gs_font_path', fp); end
github
BII-wushuang/FLLIT-master
append_pdfs.m
.m
FLLIT-master/src/Export-Fig/append_pdfs.m
2,678
utf_8
949c7c4ec3f5af6ff23099f17b1dfd79
%APPEND_PDFS Appends/concatenates multiple PDF files % % Example: % append_pdfs(output, input1, input2, ...) % append_pdfs(output, input_list{:}) % append_pdfs test.pdf temp1.pdf temp2.pdf % % This function appends multiple PDF files to an existing PDF file, or % concatenates them into a PDF file if the output file doesn't yet exist. % % This function requires that you have ghostscript installed on your % system. Ghostscript can be downloaded from: http://www.ghostscript.com % % IN: % output - string of output file name (including the extension, .pdf). % If it exists it is appended to; if not, it is created. % input1 - string of an input file name (including the extension, .pdf). % All input files are appended in order. % input_list - cell array list of input file name strings. All input % files are appended in order. % Copyright: Oliver Woodford, 2011 % Thanks to Reinhard Knoll for pointing out that appending multiple pdfs in % one go is much faster than appending them one at a time. % Thanks to Michael Teo for reporting the issue of a too long command line. % Issue resolved on 5/5/2011, by passing gs a command file. % Thanks to Martin Wittmann for pointing out the quality issue when % appending multiple bitmaps. % Issue resolved (to best of my ability) 1/6/2011, using the prepress % setting % 26/02/15: If temp dir is not writable, use the output folder for temp % files when appending (Javier Paredes); sanity check of inputs function append_pdfs(varargin) if nargin < 2, return; end % sanity check % Are we appending or creating a new file append = exist(varargin{1}, 'file') == 2; output = [tempname '.pdf']; try % Ensure that the temp dir is writable (Javier Paredes 26/2/15) fid = fopen(output,'w'); fwrite(fid,1); fclose(fid); delete(output); isTempDirOk = true; catch % Temp dir is not writable, so use the output folder [dummy,fname,fext] = fileparts(output); %#ok<ASGLU> fpath = fileparts(varargin{1}); output = fullfile(fpath,[fname fext]); isTempDirOk = false; end if ~append output = varargin{1}; varargin = varargin(2:end); end % Create the command file if isTempDirOk cmdfile = [tempname '.txt']; else cmdfile = fullfile(fpath,[fname '.txt']); end fh = fopen(cmdfile, 'w'); fprintf(fh, '-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile="%s" -f', output); fprintf(fh, ' "%s"', varargin{:}); fclose(fh); % Call ghostscript ghostscript(['@"' cmdfile '"']); % Delete the command file delete(cmdfile); % Rename the file if needed if append movefile(output, varargin{1}); end end
github
BII-wushuang/FLLIT-master
using_hg2.m
.m
FLLIT-master/src/Export-Fig/using_hg2.m
1,064
utf_8
a1883d15c4304cd0ac406c117e3047ea
%USING_HG2 Determine if the HG2 graphics engine is used % % tf = using_hg2(fig) % %IN: % fig - handle to the figure in question. % %OUT: % tf - boolean indicating whether the HG2 graphics engine is being used % (true) or not (false). % 19/06/2015 - Suppress warning in R2015b; cache result for improved performance % 06/06/2016 - Fixed issue #156 (bad return value in R2016b) function tf = using_hg2(fig) persistent tf_cached if isempty(tf_cached) try if nargin < 1, fig = figure('visible','off'); end oldWarn = warning('off','MATLAB:graphicsversion:GraphicsVersionRemoval'); try % This generates a [supressed] warning in R2015b: tf = ~graphicsversion(fig, 'handlegraphics'); catch tf = ~verLessThan('matlab','8.4'); % =R2014b end warning(oldWarn); catch tf = false; end if nargin < 1, delete(fig); end tf_cached = tf; else tf = tf_cached; end end
github
BII-wushuang/FLLIT-master
eps2pdf.m
.m
FLLIT-master/src/Export-Fig/eps2pdf.m
8,602
utf_8
a52a68e75e8696267fb74733d396a237
function eps2pdf(source, dest, crop, append, gray, quality, gs_options) %EPS2PDF Convert an eps file to pdf format using ghostscript % % Examples: % eps2pdf source dest % eps2pdf(source, dest, crop) % eps2pdf(source, dest, crop, append) % eps2pdf(source, dest, crop, append, gray) % eps2pdf(source, dest, crop, append, gray, quality) % eps2pdf(source, dest, crop, append, gray, quality, gs_options) % % This function converts an eps file to pdf format. The output can be % optionally cropped and also converted to grayscale. If the output pdf % file already exists then the eps file can optionally be appended as a new % page on the end of the eps file. The level of bitmap compression can also % optionally be set. % % This function requires that you have ghostscript installed on your % system. Ghostscript can be downloaded from: http://www.ghostscript.com % % Inputs: % source - filename of the source eps file to convert. The filename is % assumed to already have the extension ".eps". % dest - filename of the destination pdf file. The filename is assumed % to already have the extension ".pdf". % crop - boolean indicating whether to crop the borders off the pdf. % Default: true. % append - boolean indicating whether the eps should be appended to the % end of the pdf as a new page (if the pdf exists already). % Default: false. % gray - boolean indicating whether the output pdf should be grayscale % or not. Default: false. % quality - scalar indicating the level of image bitmap quality to % output. A larger value gives a higher quality. quality > 100 % gives lossless output. Default: ghostscript prepress default. % gs_options - optional ghostscript options (e.g.: '-dNoOutputFonts'). If % multiple options are needed, enclose in call array: {'-a','-b'} % Copyright (C) Oliver Woodford 2009-2014, Yair Altman 2015- % Suggestion of appending pdf files provided by Matt C at: % http://www.mathworks.com/matlabcentral/fileexchange/23629 % Thank you to Fabio Viola for pointing out compression artifacts, leading % to the quality setting. % Thank you to Scott for pointing out the subsampling of very small images, % which was fixed for lossless compression settings. % 9/12/2011 Pass font path to ghostscript. % 26/02/15: If temp dir is not writable, use the dest folder for temp % destination files (Javier Paredes) % 28/02/15: Enable users to specify optional ghostscript options (issue #36) % 01/03/15: Upon GS error, retry without the -sFONTPATH= option (this might solve % some /findfont errors according to James Rankin, FEX Comment 23/01/15) % 23/06/15: Added extra debug info in case of ghostscript error; code indentation % 04/10/15: Suggest a workaround for issue #41 (missing font path; thanks Mariia Fedotenkova) % 22/02/16: Bug fix from latest release of this file (workaround for issue #41) % 20/03/17: Added informational message in case of GS croak (issue #186) % Intialise the options string for ghostscript options = ['-q -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile="' dest '"']; % Set crop option if nargin < 3 || crop options = [options ' -dEPSCrop']; end % Set the font path fp = font_path(); if ~isempty(fp) options = [options ' -sFONTPATH="' fp '"']; end % Set the grayscale option if nargin > 4 && gray options = [options ' -sColorConversionStrategy=Gray -dProcessColorModel=/DeviceGray']; end % Set the bitmap quality if nargin > 5 && ~isempty(quality) options = [options ' -dAutoFilterColorImages=false -dAutoFilterGrayImages=false']; if quality > 100 options = [options ' -dColorImageFilter=/FlateEncode -dGrayImageFilter=/FlateEncode -c ".setpdfwrite << /ColorImageDownsampleThreshold 10 /GrayImageDownsampleThreshold 10 >> setdistillerparams"']; else options = [options ' -dColorImageFilter=/DCTEncode -dGrayImageFilter=/DCTEncode']; v = 1 + (quality < 80); quality = 1 - quality / 100; s = sprintf('<< /QFactor %.2f /Blend 1 /HSample [%d 1 1 %d] /VSample [%d 1 1 %d] >>', quality, v, v, v, v); options = sprintf('%s -c ".setpdfwrite << /ColorImageDict %s /GrayImageDict %s >> setdistillerparams"', options, s, s); end end % Enable users to specify optional ghostscript options (issue #36) if nargin > 6 && ~isempty(gs_options) if iscell(gs_options) gs_options = sprintf(' %s',gs_options{:}); elseif ~ischar(gs_options) error('gs_options input argument must be a string or cell-array of strings'); else gs_options = [' ' gs_options]; end options = [options gs_options]; end % Check if the output file exists if nargin > 3 && append && exist(dest, 'file') == 2 % File exists - append current figure to the end tmp_nam = tempname; try % Ensure that the temp dir is writable (Javier Paredes 26/2/15) fid = fopen(tmp_nam,'w'); fwrite(fid,1); fclose(fid); delete(tmp_nam); catch % Temp dir is not writable, so use the dest folder [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU> fpath = fileparts(dest); tmp_nam = fullfile(fpath,[fname fext]); end % Copy the file copyfile(dest, tmp_nam); % Add the output file names options = [options ' -f "' tmp_nam '" "' source '"']; try % Convert to pdf using ghostscript [status, message] = ghostscript(options); catch me % Delete the intermediate file delete(tmp_nam); rethrow(me); end % Delete the intermediate file delete(tmp_nam); else % File doesn't exist or should be over-written % Add the output file names options = [options ' -f "' source '"']; % Convert to pdf using ghostscript [status, message] = ghostscript(options); end % Check for error if status % Retry without the -sFONTPATH= option (this might solve some GS % /findfont errors according to James Rankin, FEX Comment 23/01/15) orig_options = options; if ~isempty(fp) options = regexprep(options, ' -sFONTPATH=[^ ]+ ',' '); status = ghostscript(options); if ~status, return; end % hurray! (no error) end % Report error if isempty(message) error('Unable to generate pdf. Check destination directory is writable.'); elseif ~isempty(strfind(message,'/typecheck in /findfont')) % Suggest a workaround for issue #41 (missing font path) font_name = strtrim(regexprep(message,'.*Operand stack:\s*(.*)\s*Execution.*','$1')); fprintf(2, 'Ghostscript error: could not find the following font(s): %s\n', font_name); fpath = fileparts(mfilename('fullpath')); gs_fonts_file = fullfile(fpath, '.ignore', 'gs_font_path.txt'); fprintf(2, ' try to add the font''s folder to your %s file\n\n', gs_fonts_file); error('export_fig error'); else fprintf(2, '\nGhostscript error: perhaps %s is open by another application\n', dest); if ~isempty(gs_options) fprintf(2, ' or maybe the%s option(s) are not accepted by your GS version\n', gs_options); end fprintf(2, ' or maybe you have another gs executable in your system''s path\n'); fprintf(2, 'Ghostscript options: %s\n\n', orig_options); error(message); end end end % Function to return (and create, where necessary) the font path function fp = font_path() fp = user_string('gs_font_path'); if ~isempty(fp) return end % Create the path % Start with the default path fp = getenv('GS_FONTPATH'); % Add on the typical directories for a given OS if ispc if ~isempty(fp) fp = [fp ';']; end fp = [fp getenv('WINDIR') filesep 'Fonts']; else if ~isempty(fp) fp = [fp ':']; end fp = [fp '/usr/share/fonts:/usr/local/share/fonts:/usr/share/fonts/X11:/usr/local/share/fonts/X11:/usr/share/fonts/truetype:/usr/local/share/fonts/truetype']; end user_string('gs_font_path', fp); end
github
BII-wushuang/FLLIT-master
export_fig.m
.m
FLLIT-master/src/Export-Fig/export_fig.m
63,939
utf_8
d501f71f10a1918328c0e9d450cd1ed3
function [imageData, alpha] = export_fig(varargin) %EXPORT_FIG Exports figures in a publication-quality format % % Examples: % imageData = export_fig % [imageData, alpha] = export_fig % export_fig filename % export_fig filename -format1 -format2 % export_fig ... -nocrop % export_fig ... -c[<val>,<val>,<val>,<val>] % export_fig ... -transparent % export_fig ... -native % export_fig ... -m<val> % export_fig ... -r<val> % export_fig ... -a<val> % export_fig ... -q<val> % export_fig ... -p<val> % export_fig ... -d<gs_option> % export_fig ... -depsc % export_fig ... -<renderer> % export_fig ... -<colorspace> % export_fig ... -append % export_fig ... -bookmark % export_fig ... -clipboard % export_fig ... -update % export_fig ... -nofontswap % export_fig ... -font_space <char> % export_fig ... -linecaps % export_fig ... -noinvert % export_fig(..., handle) % % This function saves a figure or single axes to one or more vector and/or % bitmap file formats, and/or outputs a rasterized version to the workspace, % with the following properties: % - Figure/axes reproduced as it appears on screen % - Cropped borders (optional) % - Embedded fonts (vector formats) % - Improved line and grid line styles % - Anti-aliased graphics (bitmap formats) % - Render images at native resolution (optional for bitmap formats) % - Transparent background supported (pdf, eps, png, tiff) % - Semi-transparent patch objects supported (png, tiff) % - RGB, CMYK or grayscale output (CMYK only with pdf, eps, tiff) % - Variable image compression, including lossless (pdf, eps, jpg) % - Optional rounded line-caps (pdf, eps) % - Optionally append to file (pdf, tiff) % - Vector formats: pdf, eps % - Bitmap formats: png, tiff, jpg, bmp, export to workspace % % This function is especially suited to exporting figures for use in % publications and presentations, because of the high quality and % portability of media produced. % % Note that the background color and figure dimensions are reproduced % (the latter approximately, and ignoring cropping & magnification) in the % output file. For transparent background (and semi-transparent patch % objects), use the -transparent option or set the figure 'Color' property % to 'none'. To make axes transparent set the axes 'Color' property to % 'none'. PDF, EPS, TIF & PNG are the only formats that support a transparent % background; only TIF & PNG formats support transparency of patch objects. % % The choice of renderer (opengl, zbuffer or painters) has a large impact % on the quality of output. The default value (opengl for bitmaps, painters % for vector formats) generally gives good results, but if you aren't % satisfied then try another renderer. Notes: 1) For vector formats (EPS, % PDF), only painters generates vector graphics. 2) For bitmaps, only % opengl can render transparent patch objects correctly. 3) For bitmaps, % only painters will correctly scale line dash and dot lengths when % magnifying or anti-aliasing. 4) Fonts may be substitued with Courier when % using painters. % % When exporting to vector format (PDF & EPS) and bitmap format using the % painters renderer, this function requires that ghostscript is installed % on your system. You can download this from: % http://www.ghostscript.com % When exporting to eps it additionally requires pdftops, from the Xpdf % suite of functions. You can download this from: % http://www.foolabs.com/xpdf % % Inputs: % filename - string containing the name (optionally including full or % relative path) of the file the figure is to be saved as. If % a path is not specified, the figure is saved in the current % directory. If no name and no output arguments are specified, % the default name, 'export_fig_out', is used. If neither a % file extension nor a format are specified, a ".png" is added % and the figure saved in that format. % -format1, -format2, etc. - strings containing the extensions of the % file formats the figure is to be saved as. % Valid options are: '-pdf', '-eps', '-png', % '-tif', '-jpg' and '-bmp'. All combinations % of formats are valid. % -nocrop - option indicating that the borders of the output are not to % be cropped. % -c[<val>,<val>,<val>,<val>] - option indicating crop amounts. Must be % a 4-element vector of numeric values: [top,right,bottom,left] % where NaN/Inf indicate auto-cropping, 0 means no cropping, % and any other value mean cropping in pixel amounts. % -transparent - option indicating that the figure background is to be % made transparent (png, pdf, tif and eps output only). % -m<val> - option where val indicates the factor to magnify the % on-screen figure pixel dimensions by when generating bitmap % outputs (does not affect vector formats). Default: '-m1'. % -r<val> - option val indicates the resolution (in pixels per inch) to % export bitmap and vector outputs at, keeping the dimensions % of the on-screen figure. Default: '-r864' (for vector output % only). Note that the -m option overides the -r option for % bitmap outputs only. % -native - option indicating that the output resolution (when outputting % a bitmap format) should be such that the vertical resolution % of the first suitable image found in the figure is at the % native resolution of that image. To specify a particular % image to use, give it the tag 'export_fig_native'. Notes: % This overrides any value set with the -m and -r options. It % also assumes that the image is displayed front-to-parallel % with the screen. The output resolution is approximate and % should not be relied upon. Anti-aliasing can have adverse % effects on image quality (disable with the -a1 option). % -a1, -a2, -a3, -a4 - option indicating the amount of anti-aliasing to % use for bitmap outputs. '-a1' means no anti- % aliasing; '-a4' is the maximum amount (default). % -<renderer> - option to force a particular renderer (painters, opengl or % zbuffer). Default value: opengl for bitmap formats or % figures with patches and/or transparent annotations; % painters for vector formats without patches/transparencies. % -<colorspace> - option indicating which colorspace color figures should % be saved in: RGB (default), CMYK or gray. CMYK is only % supported in pdf, eps and tiff output. % -q<val> - option to vary bitmap image quality (in pdf, eps and jpg % files only). Larger val, in the range 0-100, gives higher % quality/lower compression. val > 100 gives lossless % compression. Default: '-q95' for jpg, ghostscript prepress % default for pdf & eps. Note: lossless compression can % sometimes give a smaller file size than the default lossy % compression, depending on the type of images. % -p<val> - option to pad a border of width val to exported files, where % val is either a relative size with respect to cropped image % size (i.e. p=0.01 adds a 1% border). For EPS & PDF formats, % val can also be integer in units of 1/72" points (abs(val)>1). % val can be positive (padding) or negative (extra cropping). % If used, the -nocrop flag will be ignored, i.e. the image will % always be cropped and then padded. Default: 0 (i.e. no padding). % -append - option indicating that if the file (pdfs only) already % exists, the figure is to be appended as a new page, instead % of being overwritten (default). % -bookmark - option to indicate that a bookmark with the name of the % figure is to be created in the output file (pdf only). % -clipboard - option to save output as an image on the system clipboard. % Note: background transparency is not preserved in clipboard % -d<gs_option> - option to indicate a ghostscript setting. For example, % -dMaxBitmap=0 or -dNoOutputFonts (Ghostscript 9.15+). % -depsc - option to use EPS level-3 rather than the default level-2 print % device. This solves some bugs with Matlab's default -depsc2 device % such as discolored subplot lines on images (vector formats only). % -update - option to download and install the latest version of export_fig % -nofontswap - option to avoid font swapping. Font swapping is automatically % done in vector formats (only): 11 standard Matlab fonts are % replaced by the original figure fonts. This option prevents this. % -font_space <char> - option to set a spacer character for font-names that % contain spaces, used by EPS/PDF. Default: '' % -linecaps - option to create rounded line-caps (vector formats only). % -noinvert - option to avoid setting figure's InvertHardcopy property to % 'off' during output (this solves some problems of empty outputs). % handle - The handle of the figure, axes or uipanels (can be an array of % handles, but the objects must be in the same figure) to be % saved. Default: gcf. % % Outputs: % imageData - MxNxC uint8 image array of the exported image. % alpha - MxN single array of alphamatte values in the range [0,1], % for the case when the background is transparent. % % Some helpful examples and tips can be found at: % https://github.com/altmany/export_fig % % See also PRINT, SAVEAS, ScreenCapture (on the Matlab File Exchange) %{ % Copyright (C) Oliver Woodford 2008-2014, Yair Altman 2015- % The idea of using ghostscript is inspired by Peder Axensten's SAVEFIG % (fex id: 10889) which is itself inspired by EPS2PDF (fex id: 5782). % The idea for using pdftops came from the MATLAB newsgroup (id: 168171). % The idea of editing the EPS file to change line styles comes from Jiro % Doke's FIXPSLINESTYLE (fex id: 17928). % The idea of changing dash length with line width came from comments on % fex id: 5743, but the implementation is mine :) % The idea of anti-aliasing bitmaps came from Anders Brun's MYAA (fex id: % 20979). % The idea of appending figures in pdfs came from Matt C in comments on the % FEX (id: 23629) % Thanks to Roland Martin for pointing out the colour MATLAB % bug/feature with colorbar axes and transparent backgrounds. % Thanks also to Andrew Matthews for describing a bug to do with the figure % size changing in -nodisplay mode. I couldn't reproduce it, but included a % fix anyway. % Thanks to Tammy Threadgill for reporting a bug where an axes is not % isolated from gui objects. %} %{ % 23/02/12: Ensure that axes limits don't change during printing % 14/03/12: Fix bug in fixing the axes limits (thanks to Tobias Lamour for reporting it). % 02/05/12: Incorporate patch of Petr Nechaev (many thanks), enabling bookmarking of figures in pdf files. % 09/05/12: Incorporate patch of Arcelia Arrieta (many thanks), to keep tick marks fixed. % 12/12/12: Add support for isolating uipanels. Thanks to michael for suggesting it. % 25/09/13: Add support for changing resolution in vector formats. Thanks to Jan Jaap Meijer for suggesting it. % 07/05/14: Add support for '~' at start of path. Thanks to Sally Warner for suggesting it. % 24/02/15: Fix Matlab R2014b bug (issue #34): plot markers are not displayed when ZLimMode='manual' % 25/02/15: Fix issue #4 (using HG2 on R2014a and earlier) % 25/02/15: Fix issue #21 (bold TeX axes labels/titles in R2014b) % 26/02/15: If temp dir is not writable, use the user-specified folder for temporary EPS/PDF files (Javier Paredes) % 27/02/15: Modified repository URL from github.com/ojwoodford to /altmany % Indented main function % Added top-level try-catch block to display useful workarounds % 28/02/15: Enable users to specify optional ghostscript options (issue #36) % 06/03/15: Improved image padding & cropping thanks to Oscar Hartogensis % 26/03/15: Fixed issue #49 (bug with transparent grayscale images); fixed out-of-memory issue % 26/03/15: Fixed issue #42: non-normalized annotations on HG1 % 26/03/15: Fixed issue #46: Ghostscript crash if figure units <> pixels % 27/03/15: Fixed issue #39: bad export of transparent annotations/patches % 28/03/15: Fixed issue #50: error on some Matlab versions with the fix for issue #42 % 29/03/15: Fixed issue #33: bugs in Matlab's print() function with -cmyk % 29/03/15: Improved processing of input args (accept space between param name & value, related to issue #51) % 30/03/15: When exporting *.fig files, then saveas *.fig if figure is open, otherwise export the specified fig file % 30/03/15: Fixed edge case bug introduced yesterday (commit #ae1755bd2e11dc4e99b95a7681f6e211b3fa9358) % 09/04/15: Consolidated header comment sections; initialize output vars only if requested (nargout>0) % 14/04/15: Workaround for issue #45: lines in image subplots are exported in invalid color % 15/04/15: Fixed edge-case in parsing input parameters; fixed help section to show the -depsc option (issue #45) % 21/04/15: Bug fix: Ghostscript croaks on % chars in output PDF file (reported by Sven on FEX page, 15-Jul-2014) % 22/04/15: Bug fix: Pdftops croaks on relative paths (reported by Tintin Milou on FEX page, 19-Jan-2015) % 04/05/15: Merged fix #63 (Kevin Mattheus Moerman): prevent tick-label changes during export % 07/05/15: Partial fix for issue #65: PDF export used painters rather than opengl renderer (thanks Nguyenr) % 08/05/15: Fixed issue #65: bad PDF append since commit #e9f3cdf 21/04/15 (thanks Robert Nguyen) % 12/05/15: Fixed issue #67: exponent labels cropped in export, since fix #63 (04/05/15) % 28/05/15: Fixed issue #69: set non-bold label font only if the string contains symbols (\beta etc.), followup to issue #21 % 29/05/15: Added informative error message in case user requested SVG output (issue #72) % 09/06/15: Fixed issue #58: -transparent removed anti-aliasing when exporting to PNG % 19/06/15: Added -update option to download and install the latest version of export_fig % 07/07/15: Added -nofontswap option to avoid font-swapping in EPS/PDF % 16/07/15: Fixed problem with anti-aliasing on old Matlab releases % 11/09/15: Fixed issue #103: magnification must never become negative; also fixed reported error msg in parsing input params % 26/09/15: Alert if trying to export transparent patches/areas to non-PNG outputs (issue #108) % 04/10/15: Do not suggest workarounds for certain errors that have already been handled previously % 01/11/15: Fixed issue #112: use same renderer in print2eps as export_fig (thanks to Jesús Pestana Puerta) % 10/11/15: Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX % 19/11/15: Fixed clipboard export in R2015b (thanks to Dan K via FEX) % 21/02/16: Added -c option for indicating specific crop amounts (idea by Cedric Noordam on FEX) % 08/05/16: Added message about possible error reason when groot.Units~=pixels (issue #149) % 17/05/16: Fixed case of image YData containing more than 2 elements (issue #151) % 08/08/16: Enabled exporting transparency to TIF, in addition to PNG/PDF (issue #168) % 11/12/16: Added alert in case of error creating output PDF/EPS file (issue #179) % 13/12/16: Minor fix to the commit for issue #179 from 2 days ago % 22/03/17: Fixed issue #187: only set manual ticks when no exponent is present % 09/04/17: Added -linecaps option (idea by Baron Finer, issue #192) % 15/09/17: Fixed issue #205: incorrect tick-labels when Ticks number don't match the TickLabels number % 15/09/17: Fixed issue #210: initialize alpha map to ones instead of zeros when -transparent is not used % 18/09/17: Added -font_space option to replace font-name spaces in EPS/PDF (workaround for issue #194) % 18/09/17: Added -noinvert option to solve some export problems with some graphic cards (workaround for issue #197) % 08/11/17: Fixed issue #220: exponent is removed in HG1 when TickMode is 'manual' (internal Matlab bug) % 08/11/17: Fixed issue #221: alert if the requested folder does not exist %} if nargout [imageData, alpha] = deal([]); end hadError = false; displaySuggestedWorkarounds = true; % Ensure the figure is rendered correctly _now_ so that properties like axes limits are up-to-date drawnow; pause(0.05); % this solves timing issues with Java Swing's EDT (http://undocumentedmatlab.com/blog/solving-a-matlab-hang-problem) % Parse the input arguments fig = get(0, 'CurrentFigure'); [fig, options] = parse_args(nargout, fig, varargin{:}); % Ensure that we have a figure handle if isequal(fig,-1) return; % silent bail-out elseif isempty(fig) error('No figure found'); end % Isolate the subplot, if it is one cls = all(ismember(get(fig, 'Type'), {'axes', 'uipanel'})); if cls % Given handles of one or more axes, so isolate them from the rest fig = isolate_axes(fig); else % Check we have a figure if ~isequal(get(fig, 'Type'), 'figure') error('Handle must be that of a figure, axes or uipanel'); end % Get the old InvertHardcopy mode old_mode = get(fig, 'InvertHardcopy'); end % Hack the font units where necessary (due to a font rendering bug in print?). % This may not work perfectly in all cases. % Also it can change the figure layout if reverted, so use a copy. magnify = options.magnify * options.aa_factor; if isbitmap(options) && magnify ~= 1 fontu = findall(fig, 'FontUnits', 'normalized'); if ~isempty(fontu) % Some normalized font units found if ~cls fig = copyfig(fig); set(fig, 'Visible', 'off'); fontu = findall(fig, 'FontUnits', 'normalized'); cls = true; end set(fontu, 'FontUnits', 'points'); end end try % MATLAB "feature": axes limits and tick marks can change when printing Hlims = findall(fig, 'Type', 'axes'); if ~cls % Record the old axes limit and tick modes Xlims = make_cell(get(Hlims, 'XLimMode')); Ylims = make_cell(get(Hlims, 'YLimMode')); Zlims = make_cell(get(Hlims, 'ZLimMode')); Xtick = make_cell(get(Hlims, 'XTickMode')); Ytick = make_cell(get(Hlims, 'YTickMode')); Ztick = make_cell(get(Hlims, 'ZTickMode')); Xlabel = make_cell(get(Hlims, 'XTickLabelMode')); Ylabel = make_cell(get(Hlims, 'YTickLabelMode')); Zlabel = make_cell(get(Hlims, 'ZTickLabelMode')); end % Set all axes limit and tick modes to manual, so the limits and ticks can't change % Fix Matlab R2014b bug (issue #34): plot markers are not displayed when ZLimMode='manual' set(Hlims, 'XLimMode', 'manual', 'YLimMode', 'manual'); set_tick_mode(Hlims, 'X'); set_tick_mode(Hlims, 'Y'); if ~using_hg2(fig) set(Hlims,'ZLimMode', 'manual'); set_tick_mode(Hlims, 'Z'); end catch % ignore - fix issue #4 (using HG2 on R2014a and earlier) end % Fix issue #21 (bold TeX axes labels/titles in R2014b when exporting to EPS/PDF) try if using_hg2(fig) && isvector(options) % Set the FontWeight of axes labels/titles to 'normal' % Fix issue #69: set non-bold font only if the string contains symbols (\beta etc.) texLabels = findall(fig, 'type','text', 'FontWeight','bold'); symbolIdx = ~cellfun('isempty',strfind({texLabels.String},'\')); set(texLabels(symbolIdx), 'FontWeight','normal'); end catch % ignore end % Fix issue #42: non-normalized annotations on HG1 (internal Matlab bug) annotationHandles = []; try if ~using_hg2(fig) annotationHandles = findall(fig,'Type','hggroup','-and','-property','Units','-and','-not','Units','norm'); try % suggested by Jesús Pestana Puerta (jespestana) 30/9/2015 originalUnits = get(annotationHandles,'Units'); set(annotationHandles,'Units','norm'); catch end end catch % should never happen, but ignore in any case - issue #50 end % Fix issue #46: Ghostscript crash if figure units <> pixels oldFigUnits = get(fig,'Units'); set(fig,'Units','pixels'); % Set to print exactly what is there if options.invert_hardcopy set(fig, 'InvertHardcopy', 'off'); end % Set the renderer switch options.renderer case 1 renderer = '-opengl'; case 2 renderer = '-zbuffer'; case 3 renderer = '-painters'; otherwise renderer = '-opengl'; % Default for bitmaps end % Handle transparent patches hasTransparency = ~isempty(findall(fig,'-property','FaceAlpha','-and','-not','FaceAlpha',1)); hasPatches = ~isempty(findall(fig,'type','patch')); if hasTransparency % Alert if trying to export transparent patches/areas to non-supported outputs (issue #108) % http://www.mathworks.com/matlabcentral/answers/265265-can-export_fig-or-else-draw-vector-graphics-with-transparent-surfaces % TODO - use transparency when exporting to PDF by not passing via print2eps msg = 'export_fig currently supports transparent patches/areas only in PNG output. '; if options.pdf warning('export_fig:transparency', '%s\nTo export transparent patches/areas to PDF, use the print command:\n print(gcf, ''-dpdf'', ''%s.pdf'');', msg, options.name); elseif ~options.png && ~options.tif % issue #168 warning('export_fig:transparency', '%s\nTo export the transparency correctly, try using the ScreenCapture utility on the Matlab File Exchange: http://bit.ly/1QFrBip', msg); end end try % Do the bitmap formats first if isbitmap(options) if abs(options.bb_padding) > 1 displaySuggestedWorkarounds = false; error('For bitmap output (png,jpg,tif,bmp) the padding value (-p) must be between -1<p<1') end % Get the background colour if options.transparent && (options.png || options.alpha) % Get out an alpha channel % MATLAB "feature": black colorbar axes can change to white and vice versa! hCB = findall(fig, 'Type','axes', 'Tag','Colorbar'); if isempty(hCB) yCol = []; xCol = []; else yCol = get(hCB, 'YColor'); xCol = get(hCB, 'XColor'); if iscell(yCol) yCol = cell2mat(yCol); xCol = cell2mat(xCol); end yCol = sum(yCol, 2); xCol = sum(xCol, 2); end % MATLAB "feature": apparently figure size can change when changing % colour in -nodisplay mode pos = get(fig, 'Position'); % Set the background colour to black, and set size in case it was % changed internally tcol = get(fig, 'Color'); set(fig, 'Color', 'k', 'Position', pos); % Correct the colorbar axes colours set(hCB(yCol==0), 'YColor', [0 0 0]); set(hCB(xCol==0), 'XColor', [0 0 0]); % The following code might cause out-of-memory errors try % Print large version to array B = print2array(fig, magnify, renderer); % Downscale the image B = downsize(single(B), options.aa_factor); catch % This is more conservative in memory, but kills transparency (issue #58) B = single(print2array(fig, magnify/options.aa_factor, renderer)); end % Set background to white (and set size) set(fig, 'Color', 'w', 'Position', pos); % Correct the colorbar axes colours set(hCB(yCol==3), 'YColor', [1 1 1]); set(hCB(xCol==3), 'XColor', [1 1 1]); % The following code might cause out-of-memory errors try % Print large version to array A = print2array(fig, magnify, renderer); % Downscale the image A = downsize(single(A), options.aa_factor); catch % This is more conservative in memory, but kills transparency (issue #58) A = single(print2array(fig, magnify/options.aa_factor, renderer)); end % Set the background colour (and size) back to normal set(fig, 'Color', tcol, 'Position', pos); % Compute the alpha map alpha = round(sum(B - A, 3)) / (255 * 3) + 1; A = alpha; A(A==0) = 1; A = B ./ A(:,:,[1 1 1]); clear B % Convert to greyscale if options.colourspace == 2 A = rgb2grey(A); end A = uint8(A); % Crop the background if options.crop %[alpha, v] = crop_borders(alpha, 0, 1, options.crop_amounts); %A = A(v(1):v(2),v(3):v(4),:); [alpha, vA, vB] = crop_borders(alpha, 0, options.bb_padding, options.crop_amounts); if ~any(isnan(vB)) % positive padding B = repmat(uint8(zeros(1,1,size(A,3))),size(alpha)); B(vB(1):vB(2), vB(3):vB(4), :) = A(vA(1):vA(2), vA(3):vA(4), :); % ADDED BY OH A = B; else % negative padding A = A(vA(1):vA(2), vA(3):vA(4), :); end end if options.png % Compute the resolution res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3; % Save the png imwrite(A, [options.name '.png'], 'Alpha', double(alpha), 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res); % Clear the png bit options.png = false; end % Return only one channel for greyscale if isbitmap(options) A = check_greyscale(A); end if options.alpha % Store the image imageData = A; % Clear the alpha bit options.alpha = false; end % Get the non-alpha image if isbitmap(options) alph = alpha(:,:,ones(1, size(A, 3))); A = uint8(single(A) .* alph + 255 * (1 - alph)); clear alph end if options.im % Store the new image imageData = A; end else % Print large version to array if options.transparent % MATLAB "feature": apparently figure size can change when changing % colour in -nodisplay mode pos = get(fig, 'Position'); tcol = get(fig, 'Color'); set(fig, 'Color', 'w', 'Position', pos); A = print2array(fig, magnify, renderer); set(fig, 'Color', tcol, 'Position', pos); tcol = 255; else [A, tcol] = print2array(fig, magnify, renderer); end % Crop the background if options.crop A = crop_borders(A, tcol, options.bb_padding, options.crop_amounts); end % Downscale the image A = downsize(A, options.aa_factor); if options.colourspace == 2 % Convert to greyscale A = rgb2grey(A); else % Return only one channel for greyscale A = check_greyscale(A); end % Outputs if options.im imageData = A; end if options.alpha imageData = A; alpha = ones(size(A, 1), size(A, 2), 'single'); end end % Save the images if options.png res = options.magnify * get(0, 'ScreenPixelsPerInch') / 25.4e-3; imwrite(A, [options.name '.png'], 'ResolutionUnit', 'meter', 'XResolution', res, 'YResolution', res); end if options.bmp imwrite(A, [options.name '.bmp']); end % Save jpeg with given quality if options.jpg quality = options.quality; if isempty(quality) quality = 95; end if quality > 100 imwrite(A, [options.name '.jpg'], 'Mode', 'lossless'); else imwrite(A, [options.name '.jpg'], 'Quality', quality); end end % Save tif images in cmyk if wanted (and possible) if options.tif if options.colourspace == 1 && size(A, 3) == 3 A = double(255 - A); K = min(A, [], 3); K_ = 255 ./ max(255 - K, 1); C = (A(:,:,1) - K) .* K_; M = (A(:,:,2) - K) .* K_; Y = (A(:,:,3) - K) .* K_; A = uint8(cat(3, C, M, Y, K)); clear C M Y K K_ end append_mode = {'overwrite', 'append'}; imwrite(A, [options.name '.tif'], 'Resolution', options.magnify*get(0, 'ScreenPixelsPerInch'), 'WriteMode', append_mode{options.append+1}); end end % Now do the vector formats if isvector(options) % Set the default renderer to painters if ~options.renderer if hasTransparency || hasPatches % This is *MUCH* slower, but more accurate for patches and transparent annotations (issue #39) renderer = '-opengl'; else renderer = '-painters'; end end options.rendererStr = renderer; % fix for issue #112 % Generate some filenames tmp_nam = [tempname '.eps']; try % Ensure that the temp dir is writable (Javier Paredes 30/1/15) fid = fopen(tmp_nam,'w'); fwrite(fid,1); fclose(fid); delete(tmp_nam); isTempDirOk = true; catch % Temp dir is not writable, so use the user-specified folder [dummy,fname,fext] = fileparts(tmp_nam); %#ok<ASGLU> fpath = fileparts(options.name); tmp_nam = fullfile(fpath,[fname fext]); isTempDirOk = false; end if isTempDirOk pdf_nam_tmp = [tempname '.pdf']; else pdf_nam_tmp = fullfile(fpath,[fname '.pdf']); end if options.pdf pdf_nam = [options.name '.pdf']; try copyfile(pdf_nam, pdf_nam_tmp, 'f'); catch, end % fix for issue #65 else pdf_nam = pdf_nam_tmp; end % Generate the options for print p2eArgs = {renderer, sprintf('-r%d', options.resolution)}; if options.colourspace == 1 % CMYK % Issue #33: due to internal bugs in Matlab's print() function, we can't use its -cmyk option %p2eArgs{end+1} = '-cmyk'; end if ~options.crop % Issue #56: due to internal bugs in Matlab's print() function, we can't use its internal cropping mechanism, % therefore we always use '-loose' (in print2eps.m) and do our own cropping (in crop_borders) %p2eArgs{end+1} = '-loose'; end if any(strcmpi(varargin,'-depsc')) % Issue #45: lines in image subplots are exported in invalid color. % The workaround is to use the -depsc parameter instead of the default -depsc2 p2eArgs{end+1} = '-depsc'; end try % Generate an eps print2eps(tmp_nam, fig, options, p2eArgs{:}); % Remove the background, if desired if options.transparent && ~isequal(get(fig, 'Color'), 'none') eps_remove_background(tmp_nam, 1 + using_hg2(fig)); end % Fix colorspace to CMYK, if requested (workaround for issue #33) if options.colourspace == 1 % CMYK % Issue #33: due to internal bugs in Matlab's print() function, we can't use its -cmyk option change_rgb_to_cmyk(tmp_nam); end % Add a bookmark to the PDF if desired if options.bookmark fig_nam = get(fig, 'Name'); if isempty(fig_nam) warning('export_fig:EmptyBookmark', 'Bookmark requested for figure with no name. Bookmark will be empty.'); end add_bookmark(tmp_nam, fig_nam); end % Generate a pdf eps2pdf(tmp_nam, pdf_nam_tmp, 1, options.append, options.colourspace==2, options.quality, options.gs_options); % Ghostscript croaks on % chars in the output PDF file, so use tempname and then rename the file try % Rename the file (except if it is already the same) % Abbie K's comment on the commit for issue #179 (#commitcomment-20173476) if ~isequal(pdf_nam_tmp, pdf_nam) movefile(pdf_nam_tmp, pdf_nam, 'f'); end catch % Alert in case of error creating output PDF/EPS file (issue #179) if exist(pdf_nam_tmp, 'file') error(['Could not create ' pdf_nam ' - perhaps the folder does not exist, or you do not have write permissions']); else error('Could not generate the intermediary EPS file.'); end end catch ex % Delete the eps delete(tmp_nam); rethrow(ex); end % Delete the eps delete(tmp_nam); if options.eps || options.linecaps try % Generate an eps from the pdf % since pdftops can't handle relative paths (e.g., '..\'), use a temp file eps_nam_tmp = strrep(pdf_nam_tmp,'.pdf','.eps'); pdf2eps(pdf_nam, eps_nam_tmp); % Issue #192: enable rounded line-caps if options.linecaps fstrm = read_write_entire_textfile(eps_nam_tmp); fstrm = regexprep(fstrm, '[02] J', '1 J'); read_write_entire_textfile(eps_nam_tmp, fstrm); if options.pdf eps2pdf(eps_nam_tmp, pdf_nam, 1, options.append, options.colourspace==2, options.quality, options.gs_options); end end if options.eps movefile(eps_nam_tmp, [options.name '.eps'], 'f'); else % if options.pdf try delete(eps_nam_tmp); catch, end end catch ex if ~options.pdf % Delete the pdf delete(pdf_nam); end try delete(eps_nam_tmp); catch, end rethrow(ex); end if ~options.pdf % Delete the pdf delete(pdf_nam); end end end % Revert the figure or close it (if requested) if cls || options.closeFig % Close the created figure close(fig); else % Reset the hardcopy mode set(fig, 'InvertHardcopy', old_mode); % Reset the axes limit and tick modes for a = 1:numel(Hlims) try set(Hlims(a), 'XLimMode', Xlims{a}, 'YLimMode', Ylims{a}, 'ZLimMode', Zlims{a},... 'XTickMode', Xtick{a}, 'YTickMode', Ytick{a}, 'ZTickMode', Ztick{a},... 'XTickLabelMode', Xlabel{a}, 'YTickLabelMode', Ylabel{a}, 'ZTickLabelMode', Zlabel{a}); catch % ignore - fix issue #4 (using HG2 on R2014a and earlier) end end % Revert the tex-labels font weights try set(texLabels, 'FontWeight','bold'); catch, end % Revert annotation units for handleIdx = 1 : numel(annotationHandles) try oldUnits = originalUnits{handleIdx}; catch oldUnits = originalUnits; end try set(annotationHandles(handleIdx),'Units',oldUnits); catch, end end % Revert figure units set(fig,'Units',oldFigUnits); end % Output to clipboard (if requested) if options.clipboard % Delete the output file if unchanged from the default name ('export_fig_out.png') if strcmpi(options.name,'export_fig_out') try fileInfo = dir('export_fig_out.png'); if ~isempty(fileInfo) timediff = now - fileInfo.datenum; ONE_SEC = 1/24/60/60; if timediff < ONE_SEC delete('export_fig_out.png'); end end catch % never mind... end end % Save the image in the system clipboard % credit: Jiro Doke's IMCLIPBOARD: http://www.mathworks.com/matlabcentral/fileexchange/28708-imclipboard try error(javachk('awt', 'export_fig -clipboard output')); catch warning('export_fig -clipboard output failed: requires Java to work'); return; end try % Import necessary Java classes import java.awt.Toolkit import java.awt.image.BufferedImage import java.awt.datatransfer.DataFlavor % Get System Clipboard object (java.awt.Toolkit) cb = Toolkit.getDefaultToolkit.getSystemClipboard(); % Add java class (ImageSelection) to the path if ~exist('ImageSelection', 'class') javaaddpath(fileparts(which(mfilename)), '-end'); end % Get image size ht = size(imageData, 1); wd = size(imageData, 2); % Convert to Blue-Green-Red format try imageData2 = imageData(:, :, [3 2 1]); catch % Probably gray-scaled image (2D, without the 3rd [RGB] dimension) imageData2 = imageData(:, :, [1 1 1]); end % Convert to 3xWxH format imageData2 = permute(imageData2, [3, 2, 1]); % Append Alpha data (unused - transparency is not supported in clipboard copy) alphaData2 = uint8(permute(255*alpha,[3,2,1])); %=255*ones(1,wd,ht,'uint8') imageData2 = cat(1, imageData2, alphaData2); % Create image buffer imBuffer = BufferedImage(wd, ht, BufferedImage.TYPE_INT_RGB); imBuffer.setRGB(0, 0, wd, ht, typecast(imageData2(:), 'int32'), 0, wd); % Create ImageSelection object from the image buffer imSelection = ImageSelection(imBuffer); % Set clipboard content to the image cb.setContents(imSelection, []); catch warning('export_fig -clipboard output failed: %s', lasterr); %#ok<LERR> end end % Don't output the data to console unless requested if ~nargout clear imageData alpha end catch err % Display possible workarounds before the error message if displaySuggestedWorkarounds && ~strcmpi(err.message,'export_fig error') if ~hadError, fprintf(2, 'export_fig error. '); end fprintf(2, 'Please ensure:\n'); fprintf(2, ' that you are using the <a href="https://github.com/altmany/export_fig/archive/master.zip">latest version</a> of export_fig\n'); if ismac fprintf(2, ' and that you have <a href="http://pages.uoregon.edu/koch">Ghostscript</a> installed\n'); else fprintf(2, ' and that you have <a href="http://www.ghostscript.com">Ghostscript</a> installed\n'); end try if options.eps fprintf(2, ' and that you have <a href="http://www.foolabs.com/xpdf">pdftops</a> installed\n'); end catch % ignore - probably an error in parse_args end fprintf(2, ' and that you do not have <a href="matlab:which export_fig -all">multiple versions</a> of export_fig installed by mistake\n'); fprintf(2, ' and that you did not made a mistake in the <a href="matlab:help export_fig">expected input arguments</a>\n'); try % Alert per issue #149 if ~strncmpi(get(0,'Units'),'pixel',5) fprintf(2, ' or try to set groot''s Units property back to its default value of ''pixels'' (<a href="matlab:web(''https://github.com/altmany/export_fig/issues/149'',''-browser'');">details</a>)\n'); end catch % ignore - maybe an old MAtlab release end fprintf(2, '\nIf the problem persists, then please <a href="https://github.com/altmany/export_fig/issues">report a new issue</a>.\n\n'); end rethrow(err) end end function options = default_options() % Default options used by export_fig options = struct(... 'name', 'export_fig_out', ... 'crop', true, ... 'crop_amounts', nan(1,4), ... % auto-crop all 4 image sides 'transparent', false, ... 'renderer', 0, ... % 0: default, 1: OpenGL, 2: ZBuffer, 3: Painters 'pdf', false, ... 'eps', false, ... 'png', false, ... 'tif', false, ... 'jpg', false, ... 'bmp', false, ... 'clipboard', false, ... 'colourspace', 0, ... % 0: RGB/gray, 1: CMYK, 2: gray 'append', false, ... 'im', false, ... 'alpha', false, ... 'aa_factor', 0, ... 'bb_padding', 0, ... 'magnify', [], ... 'resolution', [], ... 'bookmark', false, ... 'closeFig', false, ... 'quality', [], ... 'update', false, ... 'fontswap', true, ... 'font_space', '', ... 'linecaps', false, ... 'invert_hardcopy', true, ... 'gs_options', {{}}); end function [fig, options] = parse_args(nout, fig, varargin) % Parse the input arguments % Set the defaults native = false; % Set resolution to native of an image options = default_options(); options.im = (nout == 1); % user requested imageData output options.alpha = (nout == 2); % user requested alpha output % Go through the other arguments skipNext = false; for a = 1:nargin-2 if skipNext skipNext = false; continue; end if all(ishandle(varargin{a})) fig = varargin{a}; elseif ischar(varargin{a}) && ~isempty(varargin{a}) if varargin{a}(1) == '-' switch lower(varargin{a}(2:end)) case 'nocrop' options.crop = false; options.crop_amounts = [0,0,0,0]; case {'trans', 'transparent'} options.transparent = true; case 'opengl' options.renderer = 1; case 'zbuffer' options.renderer = 2; case 'painters' options.renderer = 3; case 'pdf' options.pdf = true; case 'eps' options.eps = true; case 'png' options.png = true; case {'tif', 'tiff'} options.tif = true; case {'jpg', 'jpeg'} options.jpg = true; case 'bmp' options.bmp = true; case 'rgb' options.colourspace = 0; case 'cmyk' options.colourspace = 1; case {'gray', 'grey'} options.colourspace = 2; case {'a1', 'a2', 'a3', 'a4'} options.aa_factor = str2double(varargin{a}(3)); case 'append' options.append = true; case 'bookmark' options.bookmark = true; case 'native' native = true; case 'clipboard' options.clipboard = true; options.im = true; options.alpha = true; case 'svg' msg = ['SVG output is not supported by export_fig. Use one of the following alternatives:\n' ... ' 1. saveas(gcf,''filename.svg'')\n' ... ' 2. plot2svg utility: http://github.com/jschwizer99/plot2svg\n' ... ' 3. export_fig to EPS/PDF, then convert to SVG using generic (non-Matlab) tools\n']; error(sprintf(msg)); %#ok<SPERR> case 'update' % Download the latest version of export_fig into the export_fig folder try zipFileName = 'https://github.com/altmany/export_fig/archive/master.zip'; folderName = fileparts(which(mfilename('fullpath'))); targetFileName = fullfile(folderName, datestr(now,'yyyy-mm-dd.zip')); urlwrite(zipFileName,targetFileName); catch error('Could not download %s into %s\n',zipFileName,targetFileName); end % Unzip the downloaded zip file in the export_fig folder try unzip(targetFileName,folderName); catch error('Could not unzip %s\n',targetFileName); end case 'nofontswap' options.fontswap = false; case 'font_space' options.font_space = varargin{a+1}; skipNext = true; case 'linecaps' options.linecaps = true; case 'noinvert' options.invert_hardcopy = false; otherwise try wasError = false; if strcmpi(varargin{a}(1:2),'-d') varargin{a}(2) = 'd'; % ensure lowercase 'd' options.gs_options{end+1} = varargin{a}; elseif strcmpi(varargin{a}(1:2),'-c') if numel(varargin{a})==2 skipNext = true; vals = str2num(varargin{a+1}); %#ok<ST2NM> else vals = str2num(varargin{a}(3:end)); %#ok<ST2NM> end if numel(vals)~=4 wasError = true; error('option -c cannot be parsed: must be a 4-element numeric vector'); end options.crop_amounts = vals; options.crop = true; else % scalar parameter value val = str2double(regexp(varargin{a}, '(?<=-(m|M|r|R|q|Q|p|P))-?\d*.?\d+', 'match')); if isempty(val) || isnan(val) % Issue #51: improved processing of input args (accept space between param name & value) val = str2double(varargin{a+1}); if isscalar(val) && ~isnan(val) skipNext = true; end end if ~isscalar(val) || isnan(val) wasError = true; error('option %s is not recognised or cannot be parsed', varargin{a}); end switch lower(varargin{a}(2)) case 'm' % Magnification may never be negative if val <= 0 wasError = true; error('Bad magnification value: %g (must be positive)', val); end options.magnify = val; case 'r' options.resolution = val; case 'q' options.quality = max(val, 0); case 'p' options.bb_padding = val; end end catch err % We might have reached here by raising an intentional error if wasError % intentional raise rethrow(err) else % unintentional error(['Unrecognized export_fig input option: ''' varargin{a} '''']); end end end else [p, options.name, ext] = fileparts(varargin{a}); if ~isempty(p) % Issue #221: alert if the requested folder does not exist if ~exist(p,'dir'), error(['Folder ' p ' does not exist!']); end options.name = [p filesep options.name]; end switch lower(ext) case {'.tif', '.tiff'} options.tif = true; case {'.jpg', '.jpeg'} options.jpg = true; case '.png' options.png = true; case '.bmp' options.bmp = true; case '.eps' options.eps = true; case '.pdf' options.pdf = true; case '.fig' % If no open figure, then load the specified .fig file and continue if isempty(fig) fig = openfig(varargin{a},'invisible'); varargin{a} = fig; options.closeFig = true; else % save the current figure as the specified .fig file and exit saveas(fig(1),varargin{a}); fig = -1; return end case '.svg' msg = ['SVG output is not supported by export_fig. Use one of the following alternatives:\n' ... ' 1. saveas(gcf,''filename.svg'')\n' ... ' 2. plot2svg utility: http://github.com/jschwizer99/plot2svg\n' ... ' 3. export_fig to EPS/PDF, then convert to SVG using generic (non-Matlab) tools\n']; error(sprintf(msg)); %#ok<SPERR> otherwise options.name = varargin{a}; end end end end % Quick bail-out if no figure found if isempty(fig), return; end % Do border padding with repsect to a cropped image if options.bb_padding options.crop = true; end % Set default anti-aliasing now we know the renderer if options.aa_factor == 0 try isAA = strcmp(get(ancestor(fig, 'figure'), 'GraphicsSmoothing'), 'on'); catch, isAA = false; end options.aa_factor = 1 + 2 * (~(using_hg2(fig) && isAA) | (options.renderer == 3)); end % Convert user dir '~' to full path if numel(options.name) > 2 && options.name(1) == '~' && (options.name(2) == '/' || options.name(2) == '\') options.name = fullfile(char(java.lang.System.getProperty('user.home')), options.name(2:end)); end % Compute the magnification and resolution if isempty(options.magnify) if isempty(options.resolution) options.magnify = 1; options.resolution = 864; else options.magnify = options.resolution ./ get(0, 'ScreenPixelsPerInch'); end elseif isempty(options.resolution) options.resolution = 864; end % Set the default format if ~isvector(options) && ~isbitmap(options) options.png = true; end % Check whether transparent background is wanted (old way) if isequal(get(ancestor(fig(1), 'figure'), 'Color'), 'none') options.transparent = true; end % If requested, set the resolution to the native vertical resolution of the % first suitable image found if native && isbitmap(options) % Find a suitable image list = findall(fig, 'Type','image', 'Tag','export_fig_native'); if isempty(list) list = findall(fig, 'Type','image', 'Visible','on'); end for hIm = list(:)' % Check height is >= 2 height = size(get(hIm, 'CData'), 1); if height < 2 continue end % Account for the image filling only part of the axes, or vice versa yl = get(hIm, 'YData'); if isscalar(yl) yl = [yl(1)-0.5 yl(1)+height+0.5]; else yl = [min(yl), max(yl)]; % fix issue #151 (case of yl containing more than 2 elements) if ~diff(yl) continue end yl = yl + [-0.5 0.5] * (diff(yl) / (height - 1)); end hAx = get(hIm, 'Parent'); yl2 = get(hAx, 'YLim'); % Find the pixel height of the axes oldUnits = get(hAx, 'Units'); set(hAx, 'Units', 'pixels'); pos = get(hAx, 'Position'); set(hAx, 'Units', oldUnits); if ~pos(4) continue end % Found a suitable image % Account for stretch-to-fill being disabled pbar = get(hAx, 'PlotBoxAspectRatio'); pos = min(pos(4), pbar(2)*pos(3)/pbar(1)); % Set the magnification to give native resolution options.magnify = abs((height * diff(yl2)) / (pos * diff(yl))); % magnification must never be negative: issue #103 break end end end function A = downsize(A, factor) % Downsample an image if factor == 1 % Nothing to do return end try % Faster, but requires image processing toolbox A = imresize(A, 1/factor, 'bilinear'); catch % No image processing toolbox - resize manually % Lowpass filter - use Gaussian as is separable, so faster % Compute the 1d Gaussian filter filt = (-factor-1:factor+1) / (factor * 0.6); filt = exp(-filt .* filt); % Normalize the filter filt = single(filt / sum(filt)); % Filter the image padding = floor(numel(filt) / 2); for a = 1:size(A, 3) A(:,:,a) = conv2(filt, filt', single(A([ones(1, padding) 1:end repmat(end, 1, padding)],[ones(1, padding) 1:end repmat(end, 1, padding)],a)), 'valid'); end % Subsample A = A(1+floor(mod(end-1, factor)/2):factor:end,1+floor(mod(end-1, factor)/2):factor:end,:); end end function A = rgb2grey(A) A = cast(reshape(reshape(single(A), [], 3) * single([0.299; 0.587; 0.114]), size(A, 1), size(A, 2)), class(A)); % #ok<ZEROLIKE> end function A = check_greyscale(A) % Check if the image is greyscale if size(A, 3) == 3 && ... all(reshape(A(:,:,1) == A(:,:,2), [], 1)) && ... all(reshape(A(:,:,2) == A(:,:,3), [], 1)) A = A(:,:,1); % Save only one channel for 8-bit output end end function eps_remove_background(fname, count) % Remove the background of an eps file % Open the file fh = fopen(fname, 'r+'); if fh == -1 error('Not able to open file %s.', fname); end % Read the file line by line while count % Get the next line l = fgets(fh); if isequal(l, -1) break; % Quit, no rectangle found end % Check if the line contains the background rectangle if isequal(regexp(l, ' *0 +0 +\d+ +\d+ +r[fe] *[\n\r]+', 'start'), 1) % Set the line to whitespace and quit l(1:regexp(l, '[\n\r]', 'start', 'once')-1) = ' '; fseek(fh, -numel(l), 0); fprintf(fh, l); % Reduce the count count = count - 1; end end % Close the file fclose(fh); end function b = isvector(options) b = options.pdf || options.eps; end function b = isbitmap(options) b = options.png || options.tif || options.jpg || options.bmp || options.im || options.alpha; end % Helper function function A = make_cell(A) if ~iscell(A) A = {A}; end end function add_bookmark(fname, bookmark_text) % Adds a bookmark to the temporary EPS file after %%EndPageSetup % Read in the file fh = fopen(fname, 'r'); if fh == -1 error('File %s not found.', fname); end try fstrm = fread(fh, '*char')'; catch ex fclose(fh); rethrow(ex); end fclose(fh); % Include standard pdfmark prolog to maximize compatibility fstrm = strrep(fstrm, '%%BeginProlog', sprintf('%%%%BeginProlog\n/pdfmark where {pop} {userdict /pdfmark /cleartomark load put} ifelse')); % Add page bookmark fstrm = strrep(fstrm, '%%EndPageSetup', sprintf('%%%%EndPageSetup\n[ /Title (%s) /OUT pdfmark',bookmark_text)); % Write out the updated file fh = fopen(fname, 'w'); if fh == -1 error('Unable to open %s for writing.', fname); end try fwrite(fh, fstrm, 'char*1'); catch ex fclose(fh); rethrow(ex); end fclose(fh); end function set_tick_mode(Hlims, ax) % Set the tick mode of linear axes to manual % Leave log axes alone as these are tricky M = get(Hlims, [ax 'Scale']); if ~iscell(M) M = {M}; end %idx = cellfun(@(c) strcmp(c, 'linear'), M); idx = find(strcmp(M,'linear')); %set(Hlims(idx), [ax 'TickMode'], 'manual'); % issue #187 %set(Hlims(idx), [ax 'TickLabelMode'], 'manual'); % this hides exponent label in HG2! for idx2 = 1 : numel(idx) try % Fix for issue #187 - only set manual ticks when no exponent is present hAxes = Hlims(idx(idx2)); props = {[ax 'TickMode'],'manual', [ax 'TickLabelMode'],'manual'}; tickVals = get(hAxes,[ax 'Tick']); tickStrs = get(hAxes,[ax 'TickLabel']); if isempty(strtrim(hAxes.([ax 'Ruler']).SecondaryLabel.String)) % Fix for issue #205 - only set manual ticks when the Ticks number match the TickLabels number if numel(tickVals) == numel(tickStrs) set(hAxes, props{:}); % no exponent and matching ticks, so update both ticks and tick labels to manual end end catch % probably HG1 % Fix for issue #220 - exponent is removed in HG1 when TickMode is 'manual' (internal Matlab bug) if isequal(tickVals, str2num(tickStrs)') %#ok<ST2NM> set(hAxes, props{:}); % revert back to old behavior end end end end function change_rgb_to_cmyk(fname) % convert RGB => CMYK within an EPS file % Do post-processing on the eps file try % Read the EPS file into memory fstrm = read_write_entire_textfile(fname); % Replace all gray-scale colors fstrm = regexprep(fstrm, '\n([\d.]+) +GC\n', '\n0 0 0 ${num2str(1-str2num($1))} CC\n'); % Replace all RGB colors fstrm = regexprep(fstrm, '\n[0.]+ +[0.]+ +[0.]+ +RC\n', '\n0 0 0 1 CC\n'); % pure black fstrm = regexprep(fstrm, '\n([\d.]+) +([\d.]+) +([\d.]+) +RC\n', '\n${sprintf(''%.4g '',[1-[str2num($1),str2num($2),str2num($3)]/max([str2num($1),str2num($2),str2num($3)]),1-max([str2num($1),str2num($2),str2num($3)])])} CC\n'); % Overwrite the file with the modified contents read_write_entire_textfile(fname, fstrm); catch % never mind - leave as is... end end
github
BII-wushuang/FLLIT-master
ghostscript.m
.m
FLLIT-master/src/Export-Fig/ghostscript.m
7,706
utf_8
92dbafb8d4fb243cae8716c6ecb0bbe5
function varargout = ghostscript(cmd) %GHOSTSCRIPT Calls a local GhostScript executable with the input command % % Example: % [status result] = ghostscript(cmd) % % Attempts to locate a ghostscript executable, finally asking the user to % specify the directory ghostcript was installed into. The resulting path % is stored for future reference. % % Once found, the executable is called with the input command string. % % This function requires that you have Ghostscript installed on your % system. You can download this from: http://www.ghostscript.com % % IN: % cmd - Command string to be passed into ghostscript. % % OUT: % status - 0 iff command ran without problem. % result - Output from ghostscript. % Copyright: Oliver Woodford, 2009-2015, Yair Altman 2015- %{ % Thanks to Jonas Dorn for the fix for the title of the uigetdir window on Mac OS. % Thanks to Nathan Childress for the fix to default location on 64-bit Windows systems. % 27/04/11 - Find 64-bit Ghostscript on Windows. Thanks to Paul Durack and % Shaun Kline for pointing out the issue % 04/05/11 - Thanks to David Chorlian for pointing out an alternative % location for gs on linux. % 12/12/12 - Add extra executable name on Windows. Thanks to Ratish % Punnoose for highlighting the issue. % 28/06/13 - Fix error using GS 9.07 in Linux. Many thanks to Jannick % Steinbring for proposing the fix. % 24/10/13 - Fix error using GS 9.07 in Linux. Many thanks to Johannes % for the fix. % 23/01/14 - Add full path to ghostscript.txt in warning. Thanks to Koen % Vermeer for raising the issue. % 27/02/15 - If Ghostscript croaks, display suggested workarounds % 30/03/15 - Improved performance by caching status of GS path check, if ok % 14/05/15 - Clarified warning message in case GS path could not be saved % 29/05/15 - Avoid cryptic error in case the ghostscipt path cannot be saved (issue #74) % 10/11/15 - Custom GS installation webpage for MacOS. Thanks to Andy Hueni via FEX %} try % Call ghostscript [varargout{1:nargout}] = system([gs_command(gs_path()) cmd]); catch err % Display possible workarounds for Ghostscript croaks url1 = 'https://github.com/altmany/export_fig/issues/12#issuecomment-61467998'; % issue #12 url2 = 'https://github.com/altmany/export_fig/issues/20#issuecomment-63826270'; % issue #20 hg2_str = ''; if using_hg2, hg2_str = ' or Matlab R2014a'; end fprintf(2, 'Ghostscript error. Rolling back to GS 9.10%s may possibly solve this:\n * <a href="%s">%s</a> ',hg2_str,url1,url1); if using_hg2 fprintf(2, '(GS 9.10)\n * <a href="%s">%s</a> (R2014a)',url2,url2); end fprintf('\n\n'); if ismac || isunix url3 = 'https://github.com/altmany/export_fig/issues/27'; % issue #27 fprintf(2, 'Alternatively, this may possibly be due to a font path issue:\n * <a href="%s">%s</a>\n\n',url3,url3); % issue #20 fpath = which(mfilename); if isempty(fpath), fpath = [mfilename('fullpath') '.m']; end fprintf(2, 'Alternatively, if you are using csh, modify shell_cmd from "export..." to "setenv ..."\nat the bottom of <a href="matlab:opentoline(''%s'',174)">%s</a>\n\n',fpath,fpath); end rethrow(err); end end function path_ = gs_path % Return a valid path % Start with the currently set path path_ = user_string('ghostscript'); % Check the path works if check_gs_path(path_) return end % Check whether the binary is on the path if ispc bin = {'gswin32c.exe', 'gswin64c.exe', 'gs'}; else bin = {'gs'}; end for a = 1:numel(bin) path_ = bin{a}; if check_store_gs_path(path_) return end end % Search the obvious places if ispc default_location = 'C:\Program Files\gs\'; dir_list = dir(default_location); if isempty(dir_list) default_location = 'C:\Program Files (x86)\gs\'; % Possible location on 64-bit systems dir_list = dir(default_location); end executable = {'\bin\gswin32c.exe', '\bin\gswin64c.exe'}; ver_num = 0; % If there are multiple versions, use the newest for a = 1:numel(dir_list) ver_num2 = sscanf(dir_list(a).name, 'gs%g'); if ~isempty(ver_num2) && ver_num2 > ver_num for b = 1:numel(executable) path2 = [default_location dir_list(a).name executable{b}]; if exist(path2, 'file') == 2 path_ = path2; ver_num = ver_num2; end end end end if check_store_gs_path(path_) return end else executable = {'/usr/bin/gs', '/usr/local/bin/gs'}; for a = 1:numel(executable) path_ = executable{a}; if check_store_gs_path(path_) return end end end % Ask the user to enter the path while true if strncmp(computer, 'MAC', 3) % Is a Mac % Give separate warning as the uigetdir dialogue box doesn't have a % title uiwait(warndlg('Ghostscript not found. Please locate the program.')) end base = uigetdir('/', 'Ghostcript not found. Please locate the program.'); if isequal(base, 0) % User hit cancel or closed window break; end base = [base filesep]; %#ok<AGROW> bin_dir = {'', ['bin' filesep], ['lib' filesep]}; for a = 1:numel(bin_dir) for b = 1:numel(bin) path_ = [base bin_dir{a} bin{b}]; if exist(path_, 'file') == 2 if check_store_gs_path(path_) return end end end end end if ismac error('Ghostscript not found. Have you installed it (http://pages.uoregon.edu/koch)?'); else error('Ghostscript not found. Have you installed it from www.ghostscript.com?'); end end function good = check_store_gs_path(path_) % Check the path is valid good = check_gs_path(path_); if ~good return end % Update the current default path to the path found if ~user_string('ghostscript', path_) filename = fullfile(fileparts(which('user_string.m')), '.ignore', 'ghostscript.txt'); warning('Path to ghostscript installation could not be saved in %s (perhaps a permissions issue). You can manually create this file and set its contents to %s, to improve performance in future invocations (this warning is safe to ignore).', filename, path_); return end end function good = check_gs_path(path_) persistent isOk if isempty(path_) isOk = false; elseif ~isequal(isOk,true) % Check whether the path is valid [status, message] = system([gs_command(path_) '-h']); %#ok<ASGLU> isOk = status == 0; end good = isOk; end function cmd = gs_command(path_) % Initialize any required system calls before calling ghostscript % TODO: in Unix/Mac, find a way to determine whether to use "export" (bash) or "setenv" (csh/tcsh) shell_cmd = ''; if isunix shell_cmd = 'export LD_LIBRARY_PATH=""; '; % Avoids an error on Linux with GS 9.07 end if ismac shell_cmd = 'export DYLD_LIBRARY_PATH=""; '; % Avoids an error on Mac with GS 9.07 end % Construct the command string cmd = sprintf('%s"%s" ', shell_cmd, path_); end
github
BII-wushuang/FLLIT-master
fix_lines.m
.m
FLLIT-master/src/Export-Fig/fix_lines.m
6,290
utf_8
8437006b104957762090e3d875688cb6
%FIX_LINES Improves the line style of eps files generated by print % % Examples: % fix_lines fname % fix_lines fname fname2 % fstrm_out = fixlines(fstrm_in) % % This function improves the style of lines in eps files generated by % MATLAB's print function, making them more similar to those seen on % screen. Grid lines are also changed from a dashed style to a dotted % style, for greater differentiation from dashed lines. % % The function also places embedded fonts after the postscript header, in % versions of MATLAB which place the fonts first (R2006b and earlier), in % order to allow programs such as Ghostscript to find the bounding box % information. % %IN: % fname - Name or path of source eps file. % fname2 - Name or path of destination eps file. Default: same as fname. % fstrm_in - File contents of a MATLAB-generated eps file. % %OUT: % fstrm_out - Contents of the eps file with line styles fixed. % Copyright: (C) Oliver Woodford, 2008-2014 % The idea of editing the EPS file to change line styles comes from Jiro % Doke's FIXPSLINESTYLE (fex id: 17928) % The idea of changing dash length with line width came from comments on % fex id: 5743, but the implementation is mine :) % Thank you to Sylvain Favrot for bringing the embedded font/bounding box % interaction in older versions of MATLAB to my attention. % Thank you to D Ko for bringing an error with eps files with tiff previews % to my attention. % Thank you to Laurence K for suggesting the check to see if the file was % opened. % 01/03/15: Issue #20: warn users if using this function in HG2 (R2014b+) % 27/03/15: Fixed out of memory issue with enormous EPS files (generated by print() with OpenGL renderer), related to issue #39 function fstrm = fix_lines(fstrm, fname2) % Issue #20: warn users if using this function in HG2 (R2014b+) if using_hg2 warning('export_fig:hg2','The fix_lines function should not be used in this Matlab version.'); end if nargout == 0 || nargin > 1 if nargin < 2 % Overwrite the input file fname2 = fstrm; end % Read in the file fstrm = read_write_entire_textfile(fstrm); end % Move any embedded fonts after the postscript header if strcmp(fstrm(1:15), '%!PS-AdobeFont-') % Find the start and end of the header ind = regexp(fstrm, '[\n\r]%!PS-Adobe-'); [ind2, ind2] = regexp(fstrm, '[\n\r]%%EndComments[\n\r]+'); % Put the header first if ~isempty(ind) && ~isempty(ind2) && ind(1) < ind2(1) fstrm = fstrm([ind(1)+1:ind2(1) 1:ind(1) ind2(1)+1:end]); end end % Make sure all line width commands come before the line style definitions, % so that dash lengths can be based on the correct widths % Find all line style sections ind = [regexp(fstrm, '[\n\r]SO[\n\r]'),... % This needs to be here even though it doesn't have dots/dashes! regexp(fstrm, '[\n\r]DO[\n\r]'),... regexp(fstrm, '[\n\r]DA[\n\r]'),... regexp(fstrm, '[\n\r]DD[\n\r]')]; ind = sort(ind); % Find line width commands [ind2, ind3] = regexp(fstrm, '[\n\r]\d* w[\n\r]'); % Go through each line style section and swap with any line width commands % near by b = 1; m = numel(ind); n = numel(ind2); for a = 1:m % Go forwards width commands until we pass the current line style while b <= n && ind2(b) < ind(a) b = b + 1; end if b > n % No more width commands break; end % Check we haven't gone past another line style (including SO!) if a < m && ind2(b) > ind(a+1) continue; end % Are the commands close enough to be confident we can swap them? if (ind2(b) - ind(a)) > 8 continue; end % Move the line style command below the line width command fstrm(ind(a)+1:ind3(b)) = [fstrm(ind(a)+4:ind3(b)) fstrm(ind(a)+1:ind(a)+3)]; b = b + 1; end % Find any grid line definitions and change to GR format % Find the DO sections again as they may have moved ind = int32(regexp(fstrm, '[\n\r]DO[\n\r]')); if ~isempty(ind) % Find all occurrences of what are believed to be axes and grid lines ind2 = int32(regexp(fstrm, '[\n\r] *\d* *\d* *mt *\d* *\d* *L[\n\r]')); if ~isempty(ind2) % Now see which DO sections come just before axes and grid lines ind2 = repmat(ind2', [1 numel(ind)]) - repmat(ind, [numel(ind2) 1]); ind2 = any(ind2 > 0 & ind2 < 12); % 12 chars seems about right ind = ind(ind2); % Change any regions we believe to be grid lines to GR fstrm(ind+1) = 'G'; fstrm(ind+2) = 'R'; end end % Define the new styles, including the new GR format % Dot and dash lengths have two parts: a constant amount plus a line width % variable amount. The constant amount comes after dpi2point, and the % variable amount comes after currentlinewidth. If you want to change % dot/dash lengths for a one particular line style only, edit the numbers % in the /DO (dotted lines), /DA (dashed lines), /DD (dot dash lines) and % /GR (grid lines) lines for the style you want to change. new_style = {'/dom { dpi2point 1 currentlinewidth 0.08 mul add mul mul } bdef',... % Dot length macro based on line width '/dam { dpi2point 2 currentlinewidth 0.04 mul add mul mul } bdef',... % Dash length macro based on line width '/SO { [] 0 setdash 0 setlinecap } bdef',... % Solid lines '/DO { [1 dom 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dotted lines '/DA { [4 dam 1.5 dam] 0 setdash 0 setlinecap } bdef',... % Dashed lines '/DD { [1 dom 1.2 dom 4 dam 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dot dash lines '/GR { [0 dpi2point mul 4 dpi2point mul] 0 setdash 1 setlinecap } bdef'}; % Grid lines - dot spacing remains constant % Construct the output % This is the original (memory-intensive) code: %first_sec = strfind(fstrm, '% line types:'); % Isolate line style definition section %[second_sec, remaining] = strtok(fstrm(first_sec+1:end), '/'); %[remaining, remaining] = strtok(remaining, '%'); %fstrm = [fstrm(1:first_sec) second_sec sprintf('%s\r', new_style{:}) remaining]; fstrm = regexprep(fstrm,'(% line types:.+?)/.+?%',['$1',sprintf('%s\r',new_style{:}),'%']); % Write the output file if nargout == 0 || nargin > 1 read_write_entire_textfile(fname2, fstrm); end end
github
BII-wushuang/FLLIT-master
train_boost_context_v4.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v4.m
26,156
utf_8
517c845afa1d5078668d16631157513e
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context_v3(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; weak_learners_ctx1(params.wl_no).alpha = 0; weak_learners_ctx2(params.wl_no).alpha = 0; debug_flag = 1; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; W_ctx2 = W; R_ctx2 = R; train_scores = zeros(params.wl_no,3); train_scores_ctx1 = zeros(params.wl_no,3); train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; ftrs_prev_ctx = []; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. %save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; if(~isempty(ctx_ftrs2_w{i_w - 1,i_img})) ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; end ctx_ftrs1_w{i_w - 1,i_img} = []; ctx_ftrs2_w{i_w - 1,i_img} = []; end for i_prev = 1 : length(ftrs_prev_ctx) ctx_loc_all = [ctx_loc_all, ftrs_prev_ctx{i_prev}]; end ctx_glb = ctx_glb_all(T2_idx,:); ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end fprintf(' Training regression tree on learned features combined with context 1 2...\n'); t_tr = tic; reg_tree_l = LDARegStumpTrain(single([features,... ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... /sum(W_ctx2(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... weak_learners_ctx2(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); ctx2_list = weak_learners_ctx2(i_w).ctx_list; ctx2_list(ctx2_list < ncf_g + 1) = []; ctx2_list = ctx2_list - ncf_g; tmp_kmc = []; tmp_ltree = []; for i_km = 1 : length(ctx2_list) tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); end weak_learners_ctx2(i_w).kmc = tmp_kmc; weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; weak_learners_ctx2(i_w).ctx2_list = ctx2_list; ctx_combine = [ctx_glb_all, ctx_loc_all]; for i_ctx = 1 : length(weak_learners_ctx2(i_w).ctx_list) ctx2_list_1 = weak_learners_ctx2(i_w).ctx_list; ftrs_prev_ctx{end + 1} = ctx_combine(:,ctx2_list_1(i_ctx)); end clear ctx_combine; end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); ctx_glb_all = [ctx_glb_all, ctx_loc_all]; clear ctx_loc_all; features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... weak_learners_ctx2(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx2(i_w).kernels(idxs),... weak_learners_ctx2(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx2 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx2 = LDARegStumpPredict(... weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 features_ctx2 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); fprintf(' Finding alpha 2 through line search...\n'); t_alp = tic; alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); weak_learners_ctx1(i_w).alpha = alpha_ctx1; alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; W_ctx2 = compute_wi(current_response_ctx2); R_ctx2 = compute_ri(current_response_ctx2); weak_learners_ctx2(i_w).alpha = alpha_ctx2; end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); train_scores_ctx2(i_w,1) = 100*MR; train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); train_scores_ctx2(i_w,3) = alpha_ctx2; end save('train_scores_sav_v3.mat','train_scores','train_scores_ctx1','train_scores_ctx2'); if(i_w > 0) % collect th colour value of the sample data samp_rgb = zeros(size(samples_idx,1),3); for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); I = reshape(I,[],3); samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); end wltree = weak_learners(i_w).reg_tree; [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); lf_hist = histc(samp_lf,1:length(wltree)); lf_hist = lf_hist(1:length(wltree)); lf_hist = lf_hist / sum(lf_hist); [lfn,lf_id] = sort(lf_hist,'descend'); lf_id(lfn < 0.3) = []; lfn(lfn < 0.3) = []; % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % for i_n = 1 : length(wltree) bkg_ftrs2{i_n} = []; %bkg_idxs{i_n} = []; end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); if(debug_flag) if(mod(i_img,7) == 1) leaf_img_prev{i_img} = leaf_image; end end t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; ctx_c2 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); if(length(n_idx) / n_samp_img > 0.2) I_2D = reshape(I,[],3); n_idx = downsample(n_idx,200); bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end weak_learners_ctx1(i_w + 1).ctx_c1 = ctx_c1; weak_learners_ctx1(i_w + 1).ctx_c1 = ctx_c1; n_km = 30; ctx_c2 = []; clear ftrs_kmc_c2; kmc_ctx_c2 = []; tleaf_ctx_c2 = []; for i_n = 1 : length(wltree) if(~isempty(bkg_ftrs2{i_n})) tStart = tic; [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); t1 = toc(tStart); fprintf('Clustering the context ftrs 2 took %d seconds', t1); ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; else ftrs_kmc{i_w,i_n} = {}; end end weak_learners_ctx2(i_w + 1).ctx_c2 = ctx_c2; n_ftrs2 = length(ctx_c2); for i_img = 1 : size(data.train.(ch).X,1) ctx_ftrs2_w{i_w,i_img} = []; tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); ftrs2_img = zeros(size(leaf_image)); samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); for i_n = 1 : length(wltree) if(~isempty(ftrs_kmc{i_w,i_n})) ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); end end for i_ctx = 1 : length(ctx_c2) tStart = tic; lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs2(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_admm_lat_fix.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_admm_lat_fix.m
4,714
utf_8
eda4fdfab5dc7b3892e6ad9bd1a84b50
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % includes the latent label % discard the kernel features and adopts the new admm features % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_admm] = train_admm_lat_fix(params,data,samples_idx) samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); X = data.train.imgs.X(:,data.train.imgs.idxs); features_admm = collect_admm_ftrs(X,samples_idx); features_admm1 = collect_admm_ftrs1(X,samples_idx); features_admm = [features_admm,features_admm1]; T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); i_ch = 1; ch = 'imgs'; sub_ch_no = data.train.imgs.sub_ch_no; fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),1 : sub_ch_no,samples_idx(T1_idx,1:3),R(T1_idx),W(T1_idx),i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); end for i_s = 1 : sub_ch_no if(isfield(params,'n_kb')) kernels{i_ch}{i_s} = kernels{i_ch}{i_s}(1:params.n_kb); kernel_params{i_ch}{i_s} = kernel_params{i_ch}{i_s}(1:params.n_kb); end end for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; [weak_learners_admm(i_w).kernels,weak_learners_admm(i_w).kernel_params,... weak_learners_admm(i_w).reg_tree, weak_learners_admm(i_w).ctx_list]... = train_kernel_gb_ctx(X,kernels,kernel_params,params,features_admm,... samples_idx,W_ctx,R_ctx); cached_responses_ctx = evaluate_weak_learners_ctx(X,params,features_admm,samples_idx,weak_learners_admm(i_w)); weak_learners_admm(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_admm(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree, weak_learners(i_w).ctx_list]... = train_kernel_gb_ctx(X,kernels,kernel_params,params,[],... samples_idx,W,R); cached_responses = evaluate_weak_learners_ctx(X,params,[],samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_admm','weak_learners'); save([params.codename '_train_scores_sav.mat'],'train_scores_ctx','train_scores'); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_ctx_ac.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_ac.m
5,940
utf_8
b79ed0d183f4ecd4875b7cac7501df6c
% use the mask distance as well as the main branch distance % eavluate the effect of auto context % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boost_ctx_ac(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% if(i_w > 1) % in this step, apply the auto context feature learning X_ac = X; for i_img = 1 : size(X,1) X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; end [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; W_ac = compute_wi(current_response_ac); R_ac = compute_ri(current_response_ac); train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); else for i_img = 1 : size(X,1) ac_ftrs{i_img} = zeros(size(X{i_img,1})); end end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% if(i_w > 1) % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_boost_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); end %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning [ctx_glb_all,ac_ftrs] = collect_global_ctx(X,data.train.masks,... params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_general_v2.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_general_v2.m
6,136
utf_8
1010bed3cb1e38df54bdffd912ad6ee4
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_general_v2(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); train_scores = zeros(params.wl_no,3); tmp_sav_fn = sprintf('weak_learners_%s.mat',date); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree] = remove_useless_filters(reg_tree,kernels,kernel_params); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; save(tmp_sav_fn,'train_scores','weak_learners'); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_context_HRF.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_HRF.m
29,371
utf_8
4bc94478de61653c0df23f8a47a4c38d
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx1] = train_boost_context_HRF(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); % weak_learners(params.wl_no).alpha = 0; % % weak_learners_ctx1(params.wl_no).alpha = 0; % % weak_learners_ctx2(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; % current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; % W_ctx2 = W; % % R_ctx2 = R; %train_scores = zeros(params.wl_no,3); %train_scores_ctx1 = zeros(params.wl_no,3); %train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > 7) = 7; wgt_img = wgt_img/ 7; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. % save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); % ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); % save('tmp_ftrs.mat'); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; % if(isempty(ctx_ftrs2_w{i_w - 1,i_img})) % % save('empty_flag_sav.mat', ctx_ftrs2_w); % % %ctx_loc_all(samples_idx(:,1) == i_img,:) = ones(sum(samples_idx(:,1) == i_img),n_km) * 5000; % % else % ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; % end ctx_ftrs1_w{i_w - 1,i_img} = []; % ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); % ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end % fprintf(' Training regression tree on learned features combined with context 1 2...\n'); % % t_tr = tic; % % if(isfield(params,'ctx2_tree_depth')) % % tree_d_l = params.ctx2_tree_depth; % % else % % tree_d_l = params.tree_depth; % % end % % reg_tree_l = LDARegStumpTrain(single([features,... % ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... % /sum(W_ctx2(T2_idx)),uint32(tree_d_l)); % % time_tr = toc(t_tr); % % fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); % [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... % weak_learners_ctx2(i_w).ctx_list]... % = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); % % ctx2_list = weak_learners_ctx2(i_w).ctx_list; % % ctx2_list(ctx2_list < ncf_g + 1) = []; % % ctx2_list = ctx2_list - ncf_g; % % tmp_kmc = []; % % tmp_ltree = []; % % for i_km = 1 : length(ctx2_list) % % tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); % % tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); % % end % % weak_learners_ctx2(i_w).kmc = tmp_kmc; % % weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; % weak_learners_ctx2(i_w).ctx2_list = ctx2_list; save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); % ctx_glb_all = [ctx_glb_all, ctx_loc_all]; % clear ctx_loc_all; % features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; % % % t_ev = tic; % fprintf(' Evaluating the learned kernels on the whole training set...\n'); % features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); % for i_ch = 1:params.ch_no % ch = params.ch_list{i_ch}; % sub_ch_no = data.train.(ch).sub_ch_no; % % X = data.train.(ch).X(:,data.train.(ch).idxs); % for i_s = 1:sub_ch_no % idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... % weak_learners_ctx2(i_w).kernel_params)); % if (~isempty(idxs)) % features(:,idxs) = mexEvaluateKernels(X(:,i_s),... % samples_idx(:,1:3),params.sample_size,... % weak_learners_ctx2(i_w).kernels(idxs),... % weak_learners_ctx2(i_w).kernel_params(idxs)); % end % end % end % ev_time = toc(t_ev); % fprintf(' Evaluation completed in %f seconds\n',ev_time); % % fprintf(' Performing ctx2 prediction on the whole training set...\n'); % % t_pr = tic; % cached_responses_ctx2 = LDARegStumpPredict(... % weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); % time_pr = toc(t_pr); % fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); % fprintf(' Finding alpha 2 through line search...\n'); % t_alp = tic; % alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); % time_alp = toc(t_alp); % fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; % alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; % % current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; % % W_ctx2 = compute_wi(current_response_ctx2); % % W_ctx2 = W_ctx2 .* wgt_samp; % % R_ctx2 = compute_ri(current_response_ctx2); % % weak_learners_ctx2(i_w).alpha = alpha_ctx2; save([params.codename '_weak_learners_sav.mat'],'weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; % MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); % % fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); % % train_scores_ctx2(i_w,1) = 100*MR; % % train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); % % train_scores_ctx2(i_w,3) = alpha_ctx2; % % MR = sum(((current_response_ctx2>0)~=(labels>0)) .* wgt_samp)/... % (length(labels) * mean(wgt_samp)); % % train_scores_ctx2w(i_w,1) = 100*MR; save([params.codename '_train_scores_sav_w.mat'],'train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save([params.codename '_MR_sav.mat'],'MR_img','MR_img_ctx1'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data % samp_rgb = zeros(size(samples_idx,1),3); % % for i_img = 1 : size(data.train.(ch).X,1) % % X = data.train.(ch).X(i_img,data.train.(ch).idxs); % % clear I; % % for i_b = 1 : 3 % % I(:,:,i_b) = X{i_b}; % % end % % % samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); % % posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); % % I = reshape(I,[],3); % % samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); % % end wltree = weak_learners(i_w).reg_tree; % [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); % % lf_hist = histc(samp_lf,1:length(wltree)); % % lf_hist = lf_hist(1:length(wltree)); % % lf_hist = lf_hist / sum(lf_hist); % % [lfn,lf_id] = sort(lf_hist,'descend'); % % lf_id(lfn < 0.3) = []; % % lfn(lfn < 0.3) = []; % % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % % for i_n = 1 : length(wltree) % % bkg_ftrs2{i_n} = []; % % % % %bkg_idxs{i_n} = []; % % end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); % if(length(n_idx) / n_samp_img > 0.2) % % I_2D = reshape(I,[],3); % % n_idx = downsample(n_idx,200); % % bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; % % % end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues % ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > 0.5; known_mb = filter_small_comp(known_mb,50); known_mb = (dist_ctx_map > 10) .* known_mb; dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end % n_km = 30; % % ctx_c2 = []; % % clear ftrs_kmc_c2; % % kmc_ctx_c2 = []; % % tleaf_ctx_c2 = []; % % for i_n = 1 : length(wltree) % % if(~isempty(bkg_ftrs2{i_n})) % % tStart = tic; % % [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); % % t1 = toc(tStart); % % fprintf('Clustering the context ftrs 2 took %d seconds', t1); % % % ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); % % ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; % % kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; % % tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; % % else % % ftrs_kmc{i_w,i_n} = {}; % % end % % end % % n_ftrs2 = length(ctx_c2); % % % for i_img = 1 : size(data.train.(ch).X,1) % % ctx_ftrs2_w{i_w,i_img} = []; % % X = data.train.(ch).X(i_img,data.train.(ch).idxs); % % tStart = tic; % % % to avoid the computational burden, now apply the method only % % on some sample points % % samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); % % [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); % % t1 = toc(tStart); % % fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % % ftrs2_img = zeros(size(leaf_image)); % % samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); % % samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); % % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); % % for i_n = 1 : length(wltree) % % if(~isempty(ftrs_kmc{i_w,i_n})) % % ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); % % ftrs2_img = (i_n * 100 + ftrs2_img_tmp); % % % ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); % % % end % % end % for i_ctx = 1 : length(ctx_c2) % % tStart = tic; % % lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); % % if(sum(lf_ctx(:))) % % dist_ctx_map = bwdist(lf_ctx); % % ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); % % else % % ctx_ftrs2(:,i_ctx) = 5000; % % end % % t1 = toc(tStart); % % fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); % % end % ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; % ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; % end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_context_v3.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v3.m
25,557
utf_8
5aa5932a1ada7fb8cf18470eb03d4606
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context_v3(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; weak_learners_ctx1(params.wl_no).alpha = 0; weak_learners_ctx2(params.wl_no).alpha = 0; debug_flag = 1; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; W_ctx2 = W; R_ctx2 = R; train_scores = zeros(params.wl_no,3); train_scores_ctx1 = zeros(params.wl_no,3); train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. %save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; if(~isempty(ctx_ftrs2_w{i_w - 1,i_img})) ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; end ctx_ftrs1_w{i_w - 1,i_img} = []; ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end fprintf(' Training regression tree on learned features combined with context 1 2...\n'); t_tr = tic; reg_tree_l = LDARegStumpTrain(single([features,... ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... /sum(W_ctx2(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... weak_learners_ctx2(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); ctx2_list = weak_learners_ctx2(i_w).ctx_list; ctx2_list(ctx2_list < ncf_g + 1) = []; ctx2_list = ctx2_list - ncf_g; tmp_kmc = []; tmp_ltree = []; for i_km = 1 : length(ctx2_list) tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); end weak_learners_ctx2(i_w).kmc = tmp_kmc; weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; weak_learners_ctx2(i_w).ctx2_list = ctx2_list; end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); ctx_glb_all = [ctx_glb_all, ctx_loc_all]; clear ctx_loc_all; features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... weak_learners_ctx2(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx2(i_w).kernels(idxs),... weak_learners_ctx2(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx2 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx2 = LDARegStumpPredict(... weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 features_ctx2 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); fprintf(' Finding alpha 2 through line search...\n'); t_alp = tic; alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); weak_learners_ctx1(i_w).alpha = alpha_ctx1; alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; W_ctx2 = compute_wi(current_response_ctx2); R_ctx2 = compute_ri(current_response_ctx2); weak_learners_ctx2(i_w).alpha = alpha_ctx2; end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); train_scores_ctx2(i_w,1) = 100*MR; train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); train_scores_ctx2(i_w,3) = alpha_ctx2; end save('train_scores_sav_v3.mat','train_scores','train_scores_ctx1','train_scores_ctx2'); if(i_w > 0) % collect th colour value of the sample data samp_rgb = zeros(size(samples_idx,1),3); for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); I = reshape(I,[],3); samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); end wltree = weak_learners(i_w).reg_tree; [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); lf_hist = histc(samp_lf,1:length(wltree)); lf_hist = lf_hist(1:length(wltree)); lf_hist = lf_hist / sum(lf_hist); [lfn,lf_id] = sort(lf_hist,'descend'); lf_id(lfn < 0.3) = []; lfn(lfn < 0.3) = []; % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % for i_n = 1 : length(wltree) bkg_ftrs2{i_n} = []; %bkg_idxs{i_n} = []; end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); if(debug_flag) if(mod(i_img,7) == 1) leaf_img_prev{i_img} = leaf_image; end end t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; ctx_c2 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); if(length(n_idx) / n_samp_img > 0.2) I_2D = reshape(I,[],3); n_idx = downsample(n_idx,200); bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end weak_learners_ctx1(i_w + 1).ctx_c1 = ctx_c1; weak_learners_ctx2(i_w + 1).ctx_c1 = ctx_c1; n_km = 30; ctx_c2 = []; clear ftrs_kmc_c2; kmc_ctx_c2 = []; tleaf_ctx_c2 = []; for i_n = 1 : length(wltree) if(~isempty(bkg_ftrs2{i_n})) tStart = tic; [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); t1 = toc(tStart); fprintf('Clustering the context ftrs 2 took %d seconds', t1); ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; else ftrs_kmc{i_w,i_n} = {}; end end weak_learners_ctx2(i_w + 1).ctx_c2 = ctx_c2; n_ftrs2 = length(ctx_c2); for i_img = 1 : size(data.train.(ch).X,1) ctx_ftrs2_w{i_w,i_img} = []; tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); ftrs2_img = zeros(size(leaf_image)); samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); for i_n = 1 : length(wltree) if(~isempty(ftrs_kmc{i_w,i_n})) ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); end end for i_ctx = 1 : length(ctx_c2) tStart = tic; lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs2(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_ctx_latent_img_debug.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_latent_img_debug.m
6,410
utf_8
8229269b926bdeb54873ea52c86d242a
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boost_ctx_latent_img_debug(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% if(i_w > 1) % in this step, apply the auto context feature learning X_ac = X; for i_img = 1 : size(X,1) X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; end [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; W_ac = compute_wi(current_response_ac); R_ac = compute_ri(current_response_ac); train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); else for i_img = 1 : size(X,1) ac_ftrs{i_img} = zeros(size(X{i_img,1})); end end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% if(i_w > 1) % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_boost_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); % now the context feature is a subset of the full context features weak_learners_ctx(i_w).ctx_lf_list = ctx_lf_list; % this version simply ignores the ctx_list assigned by the previous % method % weak_learners_ctx(i_w).ctx_list = ctx_list; weak_learners_ctx(i_w).ctx_latent = ctx_list_latent; cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); end %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning [ctx_glb_all,ac_ftrs,ctx_lf_list,ctx_list_latent,sub_region_img] = collect_global_ctx_latent(X,data.train.gts,data.train.masks,... params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_admm_ctx_fix.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_admm_ctx_fix.m
5,704
utf_8
6693d498880a21f4f3d8fd90e3046e81
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % includes the latent label % discard the kernel features and adopts the new admm features % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_admm] = train_admm_ctx_fix(params,data,samples_idx) samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); X = data.train.imgs.X(:,data.train.imgs.idxs); features_admm = collect_admm_ftrs(X,samples_idx); features_admm1 = collect_admm_ftrs1(X,samples_idx); features_admm = [features_admm,features_admm1]; ctx_y = collect_label_ctx(data.train.gts.X,samples_idx); T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); i_ch = 1; ch = 'imgs'; sub_ch_no = data.train.imgs.sub_ch_no; fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),1 : sub_ch_no,samples_idx(T1_idx,1:3),R(T1_idx),W(T1_idx),i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); end for i_s = 1 : sub_ch_no if(isfield(params,'n_kb')) kernels{i_ch}{i_s} = kernels{i_ch}{i_s}(1:params.n_kb); kernel_params{i_ch}{i_s} = kernel_params{i_ch}{i_s}(1:params.n_kb); end end for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; [weak_learners_admm(i_w).kernels,weak_learners_admm(i_w).kernel_params,... weak_learners_admm(i_w).reg_tree, weak_learners_admm(i_w).ctx_list]... = train_kernel_gb_ctx(X,kernels,kernel_params,params,features_admm,... samples_idx,W_ctx,R_ctx); [cached_responses_ctx,lf_resp_ctx] = evaluate_weak_learners_ctx_leaf_node(X,params,features_admm,samples_idx,weak_learners_admm(i_w)); wltree = weak_learners_admm(i_w).reg_tree; for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) resp_n = (lf_resp_ctx == i_n); n_resp_lf(i_n) = sum(resp_n); leaf_struct_stack = ctx_y(resp_n,:,:); wltree(i_n).n_resp = n_resp_lf(i_n); if(n_resp_lf(i_n) > 0) m_patch_leaf = mean(leaf_struct_stack,1); wltree(i_n).struct_y = reshape(m_patch_leaf,21,21); end end end weak_learners_admm(i_w).reg_tree = wltree; % leaf_img = reshape(leaf_struct_stack(85474,:,:),21,21); % % m_patch_leaf = mean(leaf_struct_stack,1); % % m_patch_leaf_img = reshape(m_patch_leaf,21,21); % weak_learners_admm(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_admm(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree, weak_learners(i_w).ctx_list]... = train_kernel_gb_ctx(X,kernels,kernel_params,params,[],... samples_idx,W,R); cached_responses = evaluate_weak_learners_ctx(X,params,[],samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_admm','weak_learners'); save([params.codename '_train_scores_sav.mat'],'train_scores_ctx','train_scores'); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_latent_img_v2.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_latent_img_v2.m
5,988
utf_8
6d2642ea978b7da9056495d29ecf656f
% use the mask distance as well as the main branch distance % eavluate the effect of auto context % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boost_latent_img_v2(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. % introduce the concept of latent images samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% if(i_w > 1) % in this step, apply the auto context feature learning X_ac = X; for i_img = 1 : size(X,1) X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; end [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; W_ac = compute_wi(current_response_ac); R_ac = compute_ri(current_response_ac); train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); else for i_img = 1 : size(X,1) ac_ftrs{i_img} = zeros(size(X{i_img,1})); end end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% if(i_w > 1) % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_boost_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); end %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning [ctx_glb_all,ac_ftrs] = collect_global_ctx(X,data.train.masks,... params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_ctxplus_lftrs.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctxplus_lftrs.m
24,119
utf_8
aee065fc88ff5c243e5a950b83f26c06
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx1] = train_boost_ctxplus_lftrs(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; W_ctx1 = W; R_ctx1 = R; n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); tol_z_wgt = params.tol_z_wgt; fig_thres = params.fig_thres; lf_thrs = params.lf_thrs; for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > tol_z_wgt) = tol_z_wgt; wgt_img = wgt_img/ tol_z_wgt; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); % if(i_w > 1) % % % new leaf features % % features_lf = cell(sub_ch_no,1); % kernels_lf = cell(sub_ch_no,1); % kernel_params_lf = cell(sub_ch_no,1); % % % end % for i_ch = 1:params.ch_no i_ch = 1; ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels... (kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); % % features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),... % params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); % t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); % learn the leaf specific features designed which is adaptive to % the specfic subset of samples if(i_w > 1) % leaf_samples = zeros(size(samples_idx,1),1); % % for i_img = 1 : size(data.train.(ch).X,1) % % leaf_samples(samples_idx(:,1) == i_img,1) = lf_samp_idx{i_img}; % % end % % leaf_samples = leaf_samples(wr_idxs); % % % hist_leaf_sp = histc(leaf_samples,1:max(leaf_samples)); % % n_samp = size(wr_idxs,1); % % leaf_idx = find(hist_leaf_sp); % % current_response_prec = current_response; % % current_response_prec(labels < 0) = -current_response(labels < 0); % % current_response_prec = current_response_prec(wr_idxs); % % n_leaf = length(leaf_idx); % % leaf_prec = zeros(n_leaf,1); % % leaf_h = zeros(n_leaf,1); % % W1 = compute_wi(current_response); % % W_wr = W1(wr_idxs); % % for ilf = 1 : length(leaf_idx) % % lf = leaf_idx(ilf); % % leaf_prec(ilf) = mean(current_response_prec(leaf_samples == lf) > 0); % % leaf_h(ilf) = hist_leaf_sp(lf); % % leaf_err(ilf) = mean(W_wr(leaf_samples == lf)); % % end % % [~,lfi] = sort(leaf_h); % % leaf_prec(lfi); % % leaf_err(lfi); % only collect the leaf features for the large enough samples leaf_samples = zeros(size(samples_idx,1),1); for i_img = 1 : size(data.train.(ch).X,1) leaf_samples(samples_idx(:,1) == i_img,1) = lf_samp_idx{i_img}; end leaf_samples = leaf_samples(wr_idxs); hist_leaf_sp = histc(leaf_samples,1:max(leaf_samples)); leaf_idx = find((hist_leaf_sp / params.T1_size) > lf_thrs); n_subf(i_w) = length(leaf_idx); save('tmp_n_subf_sav.mat','n_subf'); kernels_lf1 = cell(length(leaf_idx),1); kernel_params_lf1 = cell(length(leaf_idx),1); for ilf = 1 : length(leaf_idx) features_lf{i_ch} = cell(sub_ch_no,1); kernels_lf{i_ch} = cell(sub_ch_no,1); kernel_params_lf{i_ch} = cell(sub_ch_no,1); sp_lf_idx = leaf_samples == (leaf_idx(ilf)); params1 = params; params1.rand_samples_no = 1000; for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning the leaf features on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels_lf{i_ch}{i_s},kernel_params_lf{i_ch}{i_s}] = ... mexMultipleSmoothRegression(params1,params.(ch),X(:,i_s),... X_idxs,s_T1(sp_lf_idx,:),wr_responses(sp_lf_idx),wr_weights(sp_lf_idx),i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels_lf{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the leaf filters learned on the subchannel\n'); features_lf{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T1(sp_lf_idx,1:3),... params.sample_size,kernels_lf{i_ch}{i_s},kernel_params_lf{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end fprintf(' Merging leaf features and kernels...\n'); % kernels_lf{1} = kernels_lf; [kernels_lf,kernel_params_lf,features_lf] = merge_features_kernels... (kernels_lf,kernel_params_lf,features_lf); fprintf(' Start reducing %d leaf features on subchannel \n',size(features_lf,2)); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; R_ctx1_wr = R_ctx1(wr_idxs); W_ctx1_wr = W_ctx1(wr_idxs); reg_tree_lf = LDARegStumpTrain(single(features_lf... ),R_ctx1_wr(sp_lf_idx),W_ctx1_wr(sp_lf_idx)... /sum(W_ctx1_wr(sp_lf_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless leaf kernels...\n'); [kernels_lf1{ilf},kernel_params_lf1{ilf}]... = remove_useless_filters(reg_tree_lf,kernels_lf,kernel_params_lf); fprintf(' %d leaf features is reduced to %d \n',... size(features_lf,2),length(kernels_lf1{ilf})); clear features_lf kernel_params_lf kernel_params_lf; end % from this step, the context features and leaf features are added for individual image. nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; ctx_ftrs1_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); fprintf(' Combine the %d leaf features\n',length(leaf_idx)); features_lf = cell(length(leaf_idx),1); for ilf = 1 : length(leaf_idx) t_ev = tic; fprintf(' Evaluating the leaf filters %d of %d\n',ilf,length(leaf_idx)); ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),kernel_params_lf1{ilf})); if (~isempty(idxs)) features_lf{ilf}(:,idxs) = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),... params.sample_size,kernels_lf1{ilf}(idxs),... kernel_params_lf1{ilf}(idxs)); end end ev_time = toc(t_ev); fprintf(' Done! (took %f seconds)\n',ev_time); end features_lf1 = []; kernels_lf2 = []; kernel_params_lf2 = []; for ilf = 1 : length(leaf_idx) features_lf1 = [features_lf1, features_lf{ilf}]; kernels_lf2 = [kernels_lf2; kernels_lf1{ilf}]; kernel_params_lf2 = [kernel_params_lf2; kernel_params_lf1{ilf}]; end fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... features_lf1,ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx_lftrs(reg_tree_g,kernels,kernel_params,.... kernels_lf2,kernel_params_lf2); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; save([params.codename '_weak_learners_sav.mat'],'weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; save([params.codename '_train_scores_sav_w.mat'],'train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save([params.codename '_MR_sav.mat'],'MR_img','MR_img_ctx1'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data wltree = weak_learners(i_w).reg_tree; for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); end end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % record the leaf node each sample is assigned to lf_samp_idx_img = leaf_image(samp_idx_img); % set the pixels on the context as irrelevant to avoid the overfitting issues mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > fig_thres; known_mb = (bwdist(~mask_img) > 10) .* known_mb; if(sum(known_mb(:))) dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,end + 1) = 5000; end ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; lf_samp_idx{i_img} = lf_samp_idx_img; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_RF_ctx.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_RF_ctx.m
4,195
utf_8
def6c9c60931fb18d7d9eb07038a6cca
% use the mask distance as well as the main branch distance % evaluate the effect of auto context % takes the random forest framework % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx] = train_RF_ctx(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% end % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning ctx_glb_all = collect_global_ctx(X,data.train.masks,... params,weak_learners(params.wl_no),samples_idx); W_ctx = W_ctx .* wgt_samp; for i_w = 1 : params.wl_no_ctx fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_rf_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); current_response_ctx = current_response_ctx + cached_responses_ctx; train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx / i_w,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx'); %%%% complete training the global context feature weak learners wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_context.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context.m
23,755
utf_8
e372e8bdd37a924891c53b0a9c01edb2
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; weak_learners_ctx1(params.wl_no).alpha = 0; weak_learners_ctx2(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; W_ctx2 = W; R_ctx2 = R; train_scores = zeros(params.wl_no,3); train_scores_ctx1 = zeros(params.wl_no,3); train_scores_ctx2 = zeros(params.wl_no,3); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 11) % from this step, the context featuers are added for individual image. n_km = 30; %save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); ctx_loc_all = zeros(size(samples_idx,1),n_km); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; ctx_ftrs1_w{i_w - 1,i_img} = []; ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end fprintf(' Training regression tree on learned features combined with context 1 2...\n'); t_tr = tic; reg_tree_l = LDARegStumpTrain(single([features,... ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... /sum(W_ctx2(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 11) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... weak_learners_ctx2(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); ctx2_list = weak_learners_ctx2(i_w).ctx_list; ctx2_list(ctx2_list < ncf_g + 1) = []; ctx2_list = ctx2_list - ncf_g; tmp_kmc = []; tmp_ltree = []; for i_km = 1 : length(ctx2_list) tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); end weak_learners_ctx2(i_w).kmc = tmp_kmc; weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; weak_learners_ctx2(i_w).ctx2_list = ctx2_list; end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 11) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); ctx_glb_all = [ctx_glb_all, ctx_loc_all]; clear ctx_loc_all; features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... weak_learners_ctx2(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx2(i_w).kernels(idxs),... weak_learners_ctx2(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx2 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx2 = LDARegStumpPredict(... weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 features_ctx2 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 11) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); fprintf(' Finding alpha 2 through line search...\n'); t_alp = tic; alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; if(i_w > 11) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); weak_learners_ctx1(i_w).alpha = alpha_ctx1; alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; W_ctx2 = compute_wi(current_response_ctx2); R_ctx2 = compute_ri(current_response_ctx2); weak_learners_ctx2(i_w).alpha = alpha_ctx2; end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); if(i_w > 11) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); train_scores_ctx2(i_w,1) = 100*MR; train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); train_scores_ctx2(i_w,3) = alpha_ctx2; end train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; save('train_scores_sav.mat','train_scores','train_scores_ctx1','train_scores_ctx2'); if(i_w > 10) % collect th colour value of the sample data samp_rgb = zeros(size(samples_idx,1),3); for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); I = reshape(I,[],3); samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); end wltree = weak_learners(i_w).reg_tree; [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); lf_hist = histc(samp_lf,1:length(wltree)); lf_hist = lf_hist(1:length(wltree)); lf_hist = lf_hist / sum(lf_hist); [lfn,lf_id] = sort(lf_hist,'descend'); lf_id(lfn < 0.3) = []; lfn(lfn < 0.3) = []; n_km = 30; % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % for i_n = 1 : length(wltree) bkg_ftrs2{i_n} = []; %bkg_idxs{i_n} = []; end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; [score_image,leaf_image] = predict_img_wl(X,params,weak_learners(i_w)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; ctx_c2 = []; for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); if(length(n_idx) / img_pg > 0.2) I_2D = reshape(I,[],3); n_idx = downsample(n_idx,1000); bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end end % set the pixels on the context as irrelevant to avoid the overfitting issues ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end n_km = 30; ctx_c2 = []; clear ftrs_kmc_c2; kmc_ctx_c2 = []; tleaf_ctx_c2 = []; for i_n = 1 : length(wltree) if(~isempty(bkg_ftrs2{i_n})) tStart = tic; [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); t1 = toc(tStart); fprintf('Clustering the context ftrs 2 took %d seconds', t1); ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; else ftrs_kmc{i_w,i_n} = {}; end end for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); [~,leaf_image] = predict_img_wl(X,params,weak_learners(i_w)); ftrs2_img = zeros(size(leaf_image)); samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); for i_n = 1 : length(wltree) if(~isempty(ftrs_kmc{i_w,i_n})) ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); end end for i_ctx = 1 : length(ctx_c2) lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs2(:,i_ctx) = 5000; end end ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_context_v8.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v8.m
16,247
utf_8
db83495a2d7559523cf4feda14816fd3
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx1] = train_boost_context_v8(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; W_ctx1 = W; R_ctx1 = R; n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > 7) = 7; wgt_img = wgt_img/ 7; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; ctx_ftrs1_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; save([params.codename '_weak_learners_sav.mat'],'weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; save([params.codename '_train_scores_sav_w.mat'],'train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save([params.codename '_MR_sav.mat'],'MR_img','MR_img_ctx1'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data wltree = weak_learners(i_w).reg_tree; for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); end end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > 0.5; known_mb = filter_small_comp(known_mb,50); known_mb = (dist_ctx_map > 10) .* known_mb; dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_context_v6.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v6.m
28,277
utf_8
af53953ab4065ff89b4a847d878f0501
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context_v6(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); % weak_learners(params.wl_no).alpha = 0; % % weak_learners_ctx1(params.wl_no).alpha = 0; % % weak_learners_ctx2(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; W_ctx2 = W; R_ctx2 = R; %train_scores = zeros(params.wl_no,3); %train_scores_ctx1 = zeros(params.wl_no,3); %train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > 7) = 7; wgt_img = wgt_img/ 7; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. %save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); % save('tmp_ftrs.mat'); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; if(isempty(ctx_ftrs2_w{i_w - 1,i_img})) save('empty_flag_sav.mat', ctx_ftrs2_w); %ctx_loc_all(samples_idx(:,1) == i_img,:) = ones(sum(samples_idx(:,1) == i_img),n_km) * 5000; else ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; end ctx_ftrs1_w{i_w - 1,i_img} = []; ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end fprintf(' Training regression tree on learned features combined with context 1 2...\n'); t_tr = tic; if(isfield(params,'ctx2_tree_depth')) tree_d_l = params.ctx2_tree_depth; else tree_d_l = params.tree_depth; end reg_tree_l = LDARegStumpTrain(single([features,... ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... /sum(W_ctx2(T2_idx)),uint32(tree_d_l)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... weak_learners_ctx2(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); ctx2_list = weak_learners_ctx2(i_w).ctx_list; ctx2_list(ctx2_list < ncf_g + 1) = []; ctx2_list = ctx2_list - ncf_g; tmp_kmc = []; tmp_ltree = []; for i_km = 1 : length(ctx2_list) tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); end weak_learners_ctx2(i_w).kmc = tmp_kmc; weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; weak_learners_ctx2(i_w).ctx2_list = ctx2_list; save('weak_learners_sav_July12.mat','weak_learners_ctx2','weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); ctx_glb_all = [ctx_glb_all, ctx_loc_all]; clear ctx_loc_all; features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... weak_learners_ctx2(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx2(i_w).kernels(idxs),... weak_learners_ctx2(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx2 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx2 = LDARegStumpPredict(... weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 features_ctx2 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); fprintf(' Finding alpha 2 through line search...\n'); t_alp = tic; alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; W_ctx2 = compute_wi(current_response_ctx2); W_ctx2 = W_ctx2 .* wgt_samp; R_ctx2 = compute_ri(current_response_ctx2); weak_learners_ctx2(i_w).alpha = alpha_ctx2; save('weak_learners_sav.mat','weak_learners','weak_learners_ctx1','weak_learners_ctx1') end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); train_scores_ctx2(i_w,1) = 100*MR; train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); train_scores_ctx2(i_w,3) = alpha_ctx2; MR = sum(((current_response_ctx2>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx2w(i_w,1) = 100*MR; save('train_scores_sav_w.mat','train_scores_w','train_scores_ctx1w','train_scores_ctx2w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save('MR_sav.mat','MR_img','MR_img_ctx1'); save('train_scores_sav.mat','train_scores','train_scores_ctx1','train_scores_ctx2'); end if(i_w > 0) % collect th colour value of the sample data samp_rgb = zeros(size(samples_idx,1),3); for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); I = reshape(I,[],3); samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); end wltree = weak_learners(i_w).reg_tree; [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); lf_hist = histc(samp_lf,1:length(wltree)); lf_hist = lf_hist(1:length(wltree)); lf_hist = lf_hist / sum(lf_hist); [lfn,lf_id] = sort(lf_hist,'descend'); lf_id(lfn < 0.3) = []; lfn(lfn < 0.3) = []; % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % for i_n = 1 : length(wltree) bkg_ftrs2{i_n} = []; %bkg_idxs{i_n} = []; end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; ctx_c2 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); if(length(n_idx) / n_samp_img > 0.2) I_2D = reshape(I,[],3); n_idx = downsample(n_idx,200); bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues % ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end n_km = 30; ctx_c2 = []; clear ftrs_kmc_c2; kmc_ctx_c2 = []; tleaf_ctx_c2 = []; for i_n = 1 : length(wltree) if(~isempty(bkg_ftrs2{i_n})) tStart = tic; [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); t1 = toc(tStart); fprintf('Clustering the context ftrs 2 took %d seconds', t1); ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; else ftrs_kmc{i_w,i_n} = {}; end end n_ftrs2 = length(ctx_c2); for i_img = 1 : size(data.train.(ch).X,1) ctx_ftrs2_w{i_w,i_img} = []; X = data.train.(ch).X(i_img,data.train.(ch).idxs); tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); ftrs2_img = zeros(size(leaf_image)); samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); for i_n = 1 : length(wltree) if(~isempty(ftrs_kmc{i_w,i_n})) ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); ftrs2_img = (i_n * 100 + ftrs2_img_tmp); % ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); end end for i_ctx = 1 : length(ctx_c2) tStart = tic; lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs2(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_LTM_validation_3D.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_LTM_validation_3D.m
7,392
utf_8
ed681933bec2d489d679a407dac8bb0f
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,train_scores] = train_LTM_validation_3D(params,fn,ftrs,wgt,samples_idx) % Train a KernelBoost classifier on the given samples % the classifier combine the added discriptor % allows an additional weight, i.e. assign the weight according to the % number of pixels consisted in the seed % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; labels = samples_idx(:,5); samples_idx = samples_idx(:,1:4); samples_idx(:,2:3) = samples_idx(:,2:3) + params.border_size; current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); W = W .* wgt; R = compute_ri(current_response); train_scores = zeros(params.wl_no,4); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = import_3D_data_v2(fn.train.imgs.X(1,:)); % for i_img = 1 : length(data.train.(ch).X) % % X{i_img,1} = sum(data.train.(ch).X{i_img},3); % % end %X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = 1; sub_ch_no = data.train.(ch).sub_ch_no; sub_ch_no = 1; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); % add the histogram discriptor ftrs1 = ftrs(T2_idx,:); features = [features,ftrs1]; fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree... ] = remove_useless_filters_ftrs(reg_tree,kernels,kernel_params); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; sub_ch_no = 1; % X = data.train.(ch).X(:,data.train.(ch).idxs); X = expand_img_v3(data.train.(ch).X,params); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); % idxs = 1; if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); % add the hd feature features = [features,ftrs]; fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); % incorperate the weight W = W .* wgt; R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); WMR = sum(((current_response>0)~=(labels>0)) .* wgt )/sum(wgt); fprintf(' Weighted Misclassif rate: %.2f | Loss: %f\n',100*WMR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; train_scores(i_w,4) = 100 * WMR; wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); figure(4); plot(1:params.wl_no,train_scores(:,4),'r'); legend('WMR'); saveas(gcf,fullfile(params.results_dir,'Weighted_MR_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_ctx_ac_v2.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_ac_v2.m
6,845
utf_8
074895ccf168b760b801b029f0a692d2
% use the mask distance as well as the main branch distance % eavluate the effect of auto context % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boost_ctx_ac_v2(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% if(i_w > 1) % in this step, apply the auto context feature learning X_ac = X; for i_img = 1 : size(X,1) X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; end [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; W_ac = compute_wi(current_response_ac); R_ac = compute_ri(current_response_ac); train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); else for i_img = 1 : size(X,1) ac_ftrs{i_img} = zeros(size(X{i_img,1})); end end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% if(i_w > 1) % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_boost_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); % now the context feature is a subset of the full context features weak_learners_ctx(i_w).ctx_lf_list = ctx_lf_list; % this version simply ignores the ctx_list assigned by the previous % method % weak_learners_ctx(i_w).ctx_list = ctx_list; weak_learners_ctx(i_w).ctx_latent = ctx_list_latent; cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); end % collect the succesful new region if(i_w > 1) for i_img = 1 : size(X,1) sub_rg{i_img} = sub_rg{i_img} + sub_region_img{i_img}; end else for i_img = 1 : size(X,1) sub_rg{i_img} = zeros(size(X{i_img,1})); end end % save([params.codename '_sub_region_train.mat'],'sub_rg'); %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning [ctx_glb_all,ac_ftrs,ctx_lf_list,ctx_list_latent,sub_region_img] = collect_global_ctx_adv(X,data.train.gts,data.train.masks,... params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_ctx_debug.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_debug.m
17,266
utf_8
e04fe67438959b8d920c36c60326a167
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx1] = train_boost_ctx_debug(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; W_ctx1 = W; R_ctx1 = R; n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); tol_z_wgt = params.tol_z_wgt; fig_thres = params.fig_thres; if(~isfield(params,'weight_invariance')) params.weight_invariance = 0; end w_invar = params.weight_invariance; for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > tol_z_wgt) = tol_z_wgt; wgt_img = wgt_img/ tol_z_wgt; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; if(w_invar) W_init = W; end % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); if(w_invar) [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,W_init(wr_idxs),i_ch,i_s,ch); else [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); end sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; ctx_ftrs1_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) % only for the sake of debugging the function save('tmp_debug.mat'); features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; save([params.codename '_weak_learners_sav.mat'],'weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; save([params.codename '_train_scores_sav_w.mat'],'train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save([params.codename '_MR_sav.mat'],'MR_img','MR_img_ctx1'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data wltree = weak_learners(i_w).reg_tree; for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); end end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > fig_thres; known_mb = (bwdist(~mask_img) > 10) .* known_mb; if(sum(known_mb(:))) dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,end + 1) = 5000; end ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_ctx_latent_img.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx_latent_img.m
6,404
utf_8
841e2c372107c2f583b349576c351b15
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boost_ctx_latent_img(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% if(i_w > 1) % in this step, apply the auto context feature learning X_ac = X; for i_img = 1 : size(X,1) X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; end [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; W_ac = compute_wi(current_response_ac); R_ac = compute_ri(current_response_ac); train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); else for i_img = 1 : size(X,1) ac_ftrs{i_img} = zeros(size(X{i_img,1})); end end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% if(i_w > 1) % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_boost_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); % now the context feature is a subset of the full context features weak_learners_ctx(i_w).ctx_lf_list = ctx_lf_list; % this version simply ignores the ctx_list assigned by the previous % method % weak_learners_ctx(i_w).ctx_list = ctx_list; weak_learners_ctx(i_w).ctx_latent = ctx_list_latent; cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); end %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning [ctx_glb_all,ac_ftrs,ctx_lf_list,ctx_list_latent,sub_region_img] = collect_global_ctx_latent(X,data.train.gts,data.train.masks,... params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_lat_img_node_dist.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_lat_img_node_dist.m
9,152
utf_8
8b1524b85dff52bd2f964deacefc5bd1
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_boost_lat_img_node_dist(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% % if(i_w > 1) % % % in this step, apply the auto context feature learning % % X_ac = X; % % for i_img = 1 : size(X,1) % % X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; % % end % % [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... % weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); % % cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); % % weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); % % current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; % % W_ac = compute_wi(current_response_ac); % % R_ac = compute_ri(current_response_ac); % % train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); % % else % % for i_img = 1 : size(X,1) % % ac_ftrs{i_img} = zeros(size(X{i_img,1})); % % end % % end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% % %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% % % if(i_w > 1) % % % load the global context features % % % [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... % weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... % = train_kernel_boost_ctx(X,params,ctx_glb_all,... % samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); % % % now the context feature is a subset of the full context features % % weak_learners_ctx(i_w).ctx_lf_list = ctx_lf_list; % % % this version simply ignores the ctx_list assigned by the previous % % method % % % weak_learners_ctx(i_w).ctx_list = ctx_list; % % % weak_learners_ctx(i_w).ctx_latent = ctx_list_latent; % % cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); % % weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); % % current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; % % W_ctx = compute_wi(current_response_ctx); % % R_ctx = compute_ri(current_response_ctx); % % train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); % % % save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); % % save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); % % % end %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning ln_idx_imgs{i_w} = collect_node_dist_latent_img(X,data.train.masks,... params,weak_learners(i_w)); if(i_w > 5) for i_img = 1 : 20 for i_w1 = 1 : i_w lf_label_img{i_w1} = zeros([size(X{i_img,1}) length(ln_idx_imgs{i_w1}{i_img})]); for i_l1 = 1 : length(ln_idx_imgs{i_w1}{i_img}) tmp_lf = zeros(size(X{i_img,1})); tmp_lf(ln_idx_imgs{i_w1}{i_img}{i_l1}) = 1; lf_label_img{i_w1}(:,:,i_l1) = tmp_lf; end end lf_l = []; optd = lf_label_img{i_w1}(:,:,6); for i_w1 = 1 : i_w for i_l1 = 1 : size(lf_label_img{i_w1},3) lf_img1 = lf_label_img{i_w1}(:,:,i_l1); n_l1{i_w1,i_l1} = sum(lf_img1(:)); for i_w2 = 1 : i_w for i_l2 = 1 : size(lf_label_img{i_w2},3) lf_img2 = lf_label_img{i_w2}(:,:,i_l2); n_ol{i_w1,i_l1}(i_w2,i_l2) = sum(lf_img1(:) .* lf_img2(:)); uoa_l{i_w1,i_l1}(i_w2,i_l2) = sum(lf_img1(:) .* lf_img2(:)) ./... sum((lf_img1(:) + lf_img2(:)) > 0); end end end end for i_w1 = 1 : i_w % for i_l1 = 1 : for i_l1 = 1: size(lf_label_img{i_w1},3) ol_lf_l{i_w1}(:,:,i_l1) = optd .* lf_label_img{i_w1}(:,:,i_l1); n_ol{i_w1}(i_l1) = sum(sum(optd .* lf_label_img{i_w1}(:,:,i_l1))); end end end end % [ctx_glb_all,ac_ftrs,ctx_lf_list,ctx_list_latent,sub_region_img] = ... % collect_global_ctx_latent(X,data.train.gts,data.train.masks,... % params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_context_v2.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v2.m
25,632
utf_8
3b1cd934b09cd47ddf2c794aab407783
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context_v2(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); % weak_learners(params.wl_no).alpha = 0; % % weak_learners_ctx1(params.wl_no).alpha = 0; % % weak_learners_ctx2(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; W_ctx2 = W; R_ctx2 = R; train_scores = zeros(params.wl_no,3); train_scores_ctx1 = zeros(params.wl_no,3); train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. %save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); save('tmp_ftrs.mat'); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; if(isempty(ctx_ftrs2_w{i_w - 1,i_img})) save('empty_flag_sav.mat', ctx_ftrs2_w); %ctx_loc_all(samples_idx(:,1) == i_img,:) = ones(sum(samples_idx(:,1) == i_img),n_km) * 5000; else ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; end ctx_ftrs1_w{i_w - 1,i_img} = []; ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end fprintf(' Training regression tree on learned features combined with context 1 2...\n'); t_tr = tic; reg_tree_l = LDARegStumpTrain(single([features,... ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... /sum(W_ctx2(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... weak_learners_ctx2(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); ctx2_list = weak_learners_ctx2(i_w).ctx_list; ctx2_list(ctx2_list < ncf_g + 1) = []; ctx2_list = ctx2_list - ncf_g; tmp_kmc = []; tmp_ltree = []; for i_km = 1 : length(ctx2_list) tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); end weak_learners_ctx2(i_w).kmc = tmp_kmc; weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; weak_learners_ctx2(i_w).ctx2_list = ctx2_list; save('weak_learners_sav.mat','weak_learners_ctx2','weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); ctx_glb_all = [ctx_glb_all, ctx_loc_all]; clear ctx_loc_all; features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... weak_learners_ctx2(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx2(i_w).kernels(idxs),... weak_learners_ctx2(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx2 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx2 = LDARegStumpPredict(... weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 features_ctx2 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); fprintf(' Finding alpha 2 through line search...\n'); t_alp = tic; alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); weak_learners_ctx1(i_w).alpha = alpha_ctx1; alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; W_ctx2 = compute_wi(current_response_ctx2); R_ctx2 = compute_ri(current_response_ctx2); weak_learners_ctx2(i_w).alpha = alpha_ctx2; save('weak_learners_sav.mat','weak_learners','weak_learners_ctx1','weak_learners_ctx1') end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); train_scores_ctx2(i_w,1) = 100*MR; train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); train_scores_ctx2(i_w,3) = alpha_ctx2; end save('train_scores_sav.mat','train_scores','train_scores_ctx1','train_scores_ctx2'); if(i_w > 0) % collect th colour value of the sample data samp_rgb = zeros(size(samples_idx,1),3); for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); I = reshape(I,[],3); samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); end wltree = weak_learners(i_w).reg_tree; [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); lf_hist = histc(samp_lf,1:length(wltree)); lf_hist = lf_hist(1:length(wltree)); lf_hist = lf_hist / sum(lf_hist); [lfn,lf_id] = sort(lf_hist,'descend'); lf_id(lfn < 0.3) = []; lfn(lfn < 0.3) = []; % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % for i_n = 1 : length(wltree) bkg_ftrs2{i_n} = []; %bkg_idxs{i_n} = []; end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; ctx_c2 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); if(length(n_idx) / n_samp_img > 0.2) I_2D = reshape(I,[],3); n_idx = downsample(n_idx,200); bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end n_km = 30; ctx_c2 = []; clear ftrs_kmc_c2; kmc_ctx_c2 = []; tleaf_ctx_c2 = []; for i_n = 1 : length(wltree) if(~isempty(bkg_ftrs2{i_n})) tStart = tic; [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); t1 = toc(tStart); fprintf('Clustering the context ftrs 2 took %d seconds', t1); ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; else ftrs_kmc{i_w,i_n} = {}; end end n_ftrs2 = length(ctx_c2); for i_img = 1 : size(data.train.(ch).X,1) ctx_ftrs2_w{i_w,i_img} = []; tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); ftrs2_img = zeros(size(leaf_image)); samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); for i_n = 1 : length(wltree) if(~isempty(ftrs_kmc{i_w,i_n})) ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); end end for i_ctx = 1 : length(ctx_c2) tStart = tic; lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs2(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_3D_CLRG_combined.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_3D_CLRG_combined.m
7,462
utf_8
645b471aa41ba66468a056e0ed21fb45
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,train_scores] = train_boost_3D_CLRG_combined(params,data,ftrs,wgt,samples_idx,LTClassifier) % Train a KernelBoost classifier on the given samples % the classifier combine the added discriptor % allows an additional weight, i.e. assign the weight according to the % number of pixels consisted in the seed % works on 3D iamge, in accordance with the paper, combine with a CLRG tree % classifier % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; labels = samples_idx(:,5); samples_idx = samples_idx(:,1:4); samples_idx(:,2:3) = samples_idx(:,2:3) + params.border_size; current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); W = W .* wgt; R = compute_ri(current_response); train_scores = zeros(params.wl_no,4); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = expand_img(data.train.(ch).X,params); % for i_img = 1 : length(data.train.(ch).X) % % X{i_img,1} = sum(data.train.(ch).X{i_img},3); % % end %X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = 1; sub_ch_no = data.train.(ch).sub_ch_no; sub_ch_no = 1; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); % add the histogram discriptor ftrs1 = ftrs(T2_idx,:); features = [features,ftrs1]; fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree... ] = remove_useless_filters_ftrs(reg_tree,kernels,kernel_params); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; sub_ch_no = 1; % X = data.train.(ch).X(:,data.train.(ch).idxs); X = expand_img(data.train.(ch).X,params); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); % idxs = 1; if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); % add the hd feature features = [features,ftrs]; fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); % incorperate the weight W = W .* wgt; R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); WMR = sum(((current_response>0)~=(labels>0)) .* wgt )/sum(wgt); fprintf(' Weighted Misclassif rate: %.2f | Loss: %f\n',100*WMR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; train_scores(i_w,4) = 100 * WMR; wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); figure(4); plot(1:params.wl_no,train_scores(:,4),'r'); legend('WMR'); saveas(gcf,fullfile(params.results_dir,'Weighted_MR_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_ctx.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_ctx.m
17,147
utf_8
33c735dea4921d444df3d77daea1f804
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx1] = train_boost_ctx(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; W_ctx1 = W; R_ctx1 = R; n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); tol_z_wgt = params.tol_z_wgt; fig_thres = params.fig_thres; if(~isfield(params,'weight_invariance')) params.weight_invariance = 0; end w_invar = params.weight_invariance; for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > tol_z_wgt) = tol_z_wgt; wgt_img = wgt_img/ tol_z_wgt; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; if(w_invar) W_init = W; end % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); if(w_invar) [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,W_init(wr_idxs),i_ch,i_s,ch); else [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); end sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; ctx_ftrs1_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; save([params.codename '_weak_learners_sav.mat'],'weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; save([params.codename '_train_scores_sav_w.mat'],'train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save([params.codename '_MR_sav.mat'],'MR_img','MR_img_ctx1'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data wltree = weak_learners(i_w).reg_tree; for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); end end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > fig_thres; known_mb = (bwdist(~mask_img) > 10) .* known_mb; if(sum(known_mb(:))) dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,end + 1) = 5000; end ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_weight_3D.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_weight_3D.m
7,356
utf_8
838979308d182708f995a8a848328582
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,train_scores] = train_boost_weight_3D(params,data,ftrs,wgt,samples_idx) % Train a KernelBoost classifier on the given samples % the classifier combine the added discriptor % allows an additional weight, i.e. assign the weight according to the % number of pixels consisted in the seed % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; labels = samples_idx(:,5); samples_idx = samples_idx(:,1:4); samples_idx(:,2:3) = samples_idx(:,2:3) + params.border_size; current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); W = W .* wgt; R = compute_ri(current_response); train_scores = zeros(params.wl_no,4); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = expand_img_v3(data.train.(ch).X,params); % for i_img = 1 : length(data.train.(ch).X) % % X{i_img,1} = sum(data.train.(ch).X{i_img},3); % % end %X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = 1; sub_ch_no = data.train.(ch).sub_ch_no; sub_ch_no = 1; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); % add the histogram discriptor ftrs1 = ftrs(T2_idx,:); features = [features,ftrs1]; fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree... ] = remove_useless_filters_ftrs(reg_tree,kernels,kernel_params); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; sub_ch_no = 1; % X = data.train.(ch).X(:,data.train.(ch).idxs); X = expand_img_v3(data.train.(ch).X,params); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); % idxs = 1; if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); % add the hd feature features = [features,ftrs]; fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); % incorperate the weight W = W .* wgt; R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); WMR = sum(((current_response>0)~=(labels>0)) .* wgt )/sum(wgt); fprintf(' Weighted Misclassif rate: %.2f | Loss: %f\n',100*WMR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; train_scores(i_w,4) = 100 * WMR; wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); figure(4); plot(1:params.wl_no,train_scores(:,4),'r'); legend('WMR'); saveas(gcf,fullfile(params.results_dir,'Weighted_MR_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_weight.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_weight.m
6,870
utf_8
36b46cfd41edf9337ff82b2b50fa1f21
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,train_scores] = train_boost_weight(params,data,ftrs,wgt,samples_idx) % Train a KernelBoost classifier on the given samples % the classifier combine the added discriptor % allows an additional weight, i.e. assign the weight according to the % number of pixels consisted in the seed % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); W = W .* wgt; R = compute_ri(current_response); train_scores = zeros(params.wl_no,4); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); % add the histogram discriptor ftrs1 = ftrs(T2_idx,:); features = [features,ftrs1]; fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree... ] = remove_useless_filters_ftrs(reg_tree,kernels,kernel_params); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); % add the hd feature features = [features,ftrs]; fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); % incorperate the weight W = W .* wgt; R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); WMR = sum(((current_response>0)~=(labels>0)) .* wgt )/sum(wgt); fprintf(' Weighted Misclassif rate: %.2f | Loss: %f\n',100*WMR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; train_scores(i_w,4) = 100 * WMR; wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); figure(4); plot(1:params.wl_no,train_scores(:,4),'r'); legend('WMR'); saveas(gcf,fullfile(params.results_dir,'Weighted_MR_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_admm_lat_3D.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_admm_lat_3D.m
3,970
utf_8
32e6c6e68884c9ef2e69079f488d343b
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % includes the latent label % discard the kernel features and adopts the new admm features % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners_admm] = train_admm_lat_3D(params,fn,samples_idx) samples_no = size(samples_idx,1); labels = samples_idx(:,5); samples_idx = samples_idx(:,1:4); current_response_admm = zeros(samples_no,1); current_response_ac = current_response_admm; current_response_ctx = current_response_admm; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response_admm); R = compute_ri(current_response_admm); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point % wgt_samp = weight_sample_tol_3D(fn.train.gts,params,samples_idx); wgt_samp = ones(size(samples_idx,1),1); % X = data.train.imgs.X(:,data.train.imgs.idxs); [features_admm,ftrs_kernel,ftrs_win] = collect_admm_ftrs_3D(fn.train.imgs,samples_idx); % [features_admm1,ftrs_kernel1,ftrs_win1] = collect_admm_ftrs1_3D(fn.train.imgs,samples_idx); % features_admm = [features_admm,features_admm1]; % ftrs_kernel = [ftrs_kernel;ftrs_kernel1]; % ftrs_win = [ftrs_win;ftrs_win1]; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response_admm); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% % % [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... % weak_learners(i_w).reg_tree] = train_admm_reg(X,params,samples_idx,T1_idx,T2_idx,W,R); % collect the existing kernel boost features % [kernels_kb,kernel_params_kb,features_kb] = train_kernel_features(X,params,samples_idx,T1_idx,T2_idx,W,R); % collect the admm features [weak_learners_admm(i_w).kernels,weak_learners_admm(i_w).kernel_params,... weak_learners_admm(i_w).reg_tree,weak_learners_admm(i_w).admm_list] =... train_admm_reg_3D(params,features_admm,T2_idx,W,R,ftrs_kernel,ftrs_win); fprintf(' Performing prediction on the whole training set...\n'); admm_list = weak_learners_admm(i_w).admm_list; t_pr = tic; cached_responses_admm = LDARegStumpPredict(weak_learners_admm(i_w).reg_tree,... single(features_admm(:,admm_list))); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); weak_learners_admm(i_w).alpha = search_alpha(current_response_admm,cached_responses_admm,labels,params); current_response_admm = current_response_admm + weak_learners_admm(i_w).alpha * cached_responses_admm; W = compute_wi(current_response_admm); R = compute_ri(current_response_admm); train_scores(i_w,:) = weak_learner_scores(current_response_admm,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_admm'); save([params.codename '_train_scores_sav.mat'],'train_scores'); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_admm_lat.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_admm_lat.m
3,783
utf_8
627afc057b202ef998ede2ba0d357279
% use the mask distance as well as the main branch distance % collect the latent label % eavluate the effect of auto context % includes the latent label % discard the kernel features and adopts the new admm features % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners_admm,weak_learners_ctx,weak_learners_ac] = train_admm_lat(params,data,samples_idx) samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response_admm = zeros(samples_no,1); current_response_ac = current_response_admm; current_response_ctx = current_response_admm; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response_admm); R = compute_ri(current_response_admm); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); X = data.train.imgs.X(:,data.train.imgs.idxs); features_admm = collect_admm_ftrs(X,samples_idx); features_admm1 = collect_admm_ftrs1(X,samples_idx); features_admm = [features_admm,features_admm1]; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response_admm); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% % % [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... % weak_learners(i_w).reg_tree] = train_admm_reg(X,params,samples_idx,T1_idx,T2_idx,W,R); % collect the existing kernel boost features % [kernels_kb,kernel_params_kb,features_kb] = train_kernel_features(X,params,samples_idx,T1_idx,T2_idx,W,R); % collect the admm features [weak_learners_admm(i_w).kernels,weak_learners_admm(i_w).kernel_params,... weak_learners_admm(i_w).reg_tree,... weak_learners_admm(i_w).ctx_list] = train_kernel_boost_ctx(X,params,... features_admm,samples_idx,T1_idx,T2_idx,W,R); % weak_learners_admm(i_w).ctx_lf_list = ctx_lf_list; % this version simply ignores the ctx_list assigned by the previous % method % weak_learners_ctx(i_w).ctx_list = ctx_list; cached_responses_admm = evaluate_weak_learners_ctx(X,params,features_admm,samples_idx,weak_learners_admm(i_w)); weak_learners_admm(i_w).alpha = search_alpha(current_response_admm,cached_responses_admm,labels,params); current_response_admm = current_response_admm + weak_learners_admm(i_w).alpha * cached_responses_admm; W = compute_wi(current_response_admm); R = compute_ri(current_response_admm); train_scores(i_w,:) = weak_learner_scores(current_response_admm,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_admm'); save([params.codename '_train_scores_sav.mat'],'train_scores'); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_context_v7.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v7.m
29,256
utf_8
8ebd680d69ea266a4817f7d1d862a812
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context_v7(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); % weak_learners(params.wl_no).alpha = 0; % % weak_learners_ctx1(params.wl_no).alpha = 0; % % weak_learners_ctx2(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; % current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; % W_ctx2 = W; % % R_ctx2 = R; %train_scores = zeros(params.wl_no,3); %train_scores_ctx1 = zeros(params.wl_no,3); %train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > 7) = 7; wgt_img = wgt_img/ 7; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. % save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); % ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); % save('tmp_ftrs.mat'); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; % if(isempty(ctx_ftrs2_w{i_w - 1,i_img})) % % save('empty_flag_sav.mat', ctx_ftrs2_w); % % %ctx_loc_all(samples_idx(:,1) == i_img,:) = ones(sum(samples_idx(:,1) == i_img),n_km) * 5000; % % else % ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; % end ctx_ftrs1_w{i_w - 1,i_img} = []; % ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); % ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end % fprintf(' Training regression tree on learned features combined with context 1 2...\n'); % % t_tr = tic; % % if(isfield(params,'ctx2_tree_depth')) % % tree_d_l = params.ctx2_tree_depth; % % else % % tree_d_l = params.tree_depth; % % end % % reg_tree_l = LDARegStumpTrain(single([features,... % ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... % /sum(W_ctx2(T2_idx)),uint32(tree_d_l)); % % time_tr = toc(t_tr); % % fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); % [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... % weak_learners_ctx2(i_w).ctx_list]... % = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); % % ctx2_list = weak_learners_ctx2(i_w).ctx_list; % % ctx2_list(ctx2_list < ncf_g + 1) = []; % % ctx2_list = ctx2_list - ncf_g; % % tmp_kmc = []; % % tmp_ltree = []; % % for i_km = 1 : length(ctx2_list) % % tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); % % tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); % % end % % weak_learners_ctx2(i_w).kmc = tmp_kmc; % % weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; % weak_learners_ctx2(i_w).ctx2_list = ctx2_list; save('weak_learners_sav.mat','weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); % ctx_glb_all = [ctx_glb_all, ctx_loc_all]; % clear ctx_loc_all; % features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; % % % t_ev = tic; % fprintf(' Evaluating the learned kernels on the whole training set...\n'); % features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); % for i_ch = 1:params.ch_no % ch = params.ch_list{i_ch}; % sub_ch_no = data.train.(ch).sub_ch_no; % % X = data.train.(ch).X(:,data.train.(ch).idxs); % for i_s = 1:sub_ch_no % idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... % weak_learners_ctx2(i_w).kernel_params)); % if (~isempty(idxs)) % features(:,idxs) = mexEvaluateKernels(X(:,i_s),... % samples_idx(:,1:3),params.sample_size,... % weak_learners_ctx2(i_w).kernels(idxs),... % weak_learners_ctx2(i_w).kernel_params(idxs)); % end % end % end % ev_time = toc(t_ev); % fprintf(' Evaluation completed in %f seconds\n',ev_time); % % fprintf(' Performing ctx2 prediction on the whole training set...\n'); % % t_pr = tic; % cached_responses_ctx2 = LDARegStumpPredict(... % weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); % time_pr = toc(t_pr); % fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); % fprintf(' Finding alpha 2 through line search...\n'); % t_alp = tic; % alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); % time_alp = toc(t_alp); % fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; % alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; % % current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; % % W_ctx2 = compute_wi(current_response_ctx2); % % W_ctx2 = W_ctx2 .* wgt_samp; % % R_ctx2 = compute_ri(current_response_ctx2); % % weak_learners_ctx2(i_w).alpha = alpha_ctx2; save('weak_learners_sav.mat','weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; % MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); % % fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); % % train_scores_ctx2(i_w,1) = 100*MR; % % train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); % % train_scores_ctx2(i_w,3) = alpha_ctx2; % % MR = sum(((current_response_ctx2>0)~=(labels>0)) .* wgt_samp)/... % (length(labels) * mean(wgt_samp)); % % train_scores_ctx2w(i_w,1) = 100*MR; save('train_scores_sav_w.mat','train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save('MR_sav.mat','MR_img','MR_img_ctx1'); save('train_scores_sav.mat','train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data % samp_rgb = zeros(size(samples_idx,1),3); % % for i_img = 1 : size(data.train.(ch).X,1) % % X = data.train.(ch).X(i_img,data.train.(ch).idxs); % % clear I; % % for i_b = 1 : 3 % % I(:,:,i_b) = X{i_b}; % % end % % % samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); % % posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); % % I = reshape(I,[],3); % % samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); % % end wltree = weak_learners(i_w).reg_tree; % [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); % % lf_hist = histc(samp_lf,1:length(wltree)); % % lf_hist = lf_hist(1:length(wltree)); % % lf_hist = lf_hist / sum(lf_hist); % % [lfn,lf_id] = sort(lf_hist,'descend'); % % lf_id(lfn < 0.3) = []; % % lfn(lfn < 0.3) = []; % % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % % for i_n = 1 : length(wltree) % % bkg_ftrs2{i_n} = []; % % % % %bkg_idxs{i_n} = []; % % end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); % if(length(n_idx) / n_samp_img > 0.2) % % I_2D = reshape(I,[],3); % % n_idx = downsample(n_idx,200); % % bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; % % % end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues % ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > 0.5; known_mb = filter_small_comp(known_mb,50); known_mb = (dist_ctx_map > 10) .* known_mb; dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end % n_km = 30; % % ctx_c2 = []; % % clear ftrs_kmc_c2; % % kmc_ctx_c2 = []; % % tleaf_ctx_c2 = []; % % for i_n = 1 : length(wltree) % % if(~isempty(bkg_ftrs2{i_n})) % % tStart = tic; % % [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); % % t1 = toc(tStart); % % fprintf('Clustering the context ftrs 2 took %d seconds', t1); % % % ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); % % ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; % % kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; % % tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; % % else % % ftrs_kmc{i_w,i_n} = {}; % % end % % end % % n_ftrs2 = length(ctx_c2); % % % for i_img = 1 : size(data.train.(ch).X,1) % % ctx_ftrs2_w{i_w,i_img} = []; % % X = data.train.(ch).X(i_img,data.train.(ch).idxs); % % tStart = tic; % % % to avoid the computational burden, now apply the method only % % on some sample points % % samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); % % [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); % % t1 = toc(tStart); % % fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % % ftrs2_img = zeros(size(leaf_image)); % % samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); % % samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); % % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); % % for i_n = 1 : length(wltree) % % if(~isempty(ftrs_kmc{i_w,i_n})) % % ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); % % ftrs2_img = (i_n * 100 + ftrs2_img_tmp); % % % ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); % % % end % % end % for i_ctx = 1 : length(ctx_c2) % % tStart = tic; % % lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); % % if(sum(lf_ctx(:))) % % dist_ctx_map = bwdist(lf_ctx); % % ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); % % else % % ctx_ftrs2(:,i_ctx) = 5000; % % end % % t1 = toc(tStart); % % fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); % % end % ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; % ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; % end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_RF_ctx_ac.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_RF_ctx_ac.m
5,974
utf_8
eb15d4fd53fd945070a6be25d7eb9b5a
% use the mask distance as well as the main branch distance % evaluate the effect of auto context % takes the random forest framework % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx,weak_learners_ac] = train_RF_ctx_ac(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ac = current_response; current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% %%%%%% train the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% if(i_w > 1) % in this step, apply the auto context feature learning X_ac = X; for i_img = 1 : size(X,1) X_ac{i_img,size(X,2) + 1} = ac_ftrs{i_img}; end [weak_learners_ac(i_w).kernels,weak_learners_ac(i_w).kernel_params,... weak_learners_ac(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W_ac,R_ac); cached_responses_ac = evaluate_weak_learners(X_ac,params,samples_idx,weak_learners_ac(i_w)); weak_learners_ac(i_w).alpha = search_alpha(current_response_ac,cached_responses_ac,labels,params); current_response_ac = current_response_ac + weak_learners_ac(i_w).alpha * cached_responses_ac; W_ac = compute_wi(current_response_ac); R_ac = compute_ri(current_response_ac); train_scores_ac(i_w,:) = weak_learner_scores(current_response_ac,labels,wgt_samp,compute_loss); else for i_img = 1 : size(X,1) ac_ftrs{i_img} = zeros(size(X{i_img,1})); end end %%%%%% Complete training the base line weak learners augumented by auto context %%%%%%%%%%%%%%%%% %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% if(i_w > 1) % load the global context features [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_boost_ctx(X,params,ctx_glb_all,... samples_idx,T1_idx,T2_idx,W_ctx,R_ctx); cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners','weak_learners_ac'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx','train_scores_ac'); end %%%% complete training the global context feature weak learners % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning [ctx_glb_all,ac_ftrs] = collect_global_ctx(X,data.train.masks,... params,weak_learners(i_w),samples_idx,ac_ftrs); wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_admm_lat_fn.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_admm_lat_fn.m
3,832
utf_8
b55e33e32ebb694abb32b0e2b5b567d1
% use the mask distance as well as the main branch distance % train the model on the 3D training dataset % collect the latent label % eavluate the effect of auto context % includes the latent label % discard the kernel features and adopts the new admm features % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners_admm,weak_learners_ctx,weak_learners_ac] = train_admm_lat_fn(params,data,samples_idx) samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response_admm = zeros(samples_no,1); current_response_ac = current_response_admm; current_response_ctx = current_response_admm; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response_admm); R = compute_ri(current_response_admm); W_ac = W; R_ac = R; W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol_fn(fn.train.gts,params,samples_idx); X = data.train.imgs.X(:,data.train.imgs.idxs); features_admm = collect_admm_ftrs(X,samples_idx); features_admm1 = collect_admm_ftrs1(X,samples_idx); features_admm = [features_admm,features_admm1]; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response_admm); W = W .* wgt_samp; W_ctx = W_ctx .* wgt_samp; W_ac = W_ac .* wgt_samp; %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% % % [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... % weak_learners(i_w).reg_tree] = train_admm_reg(X,params,samples_idx,T1_idx,T2_idx,W,R); % collect the existing kernel boost features % [kernels_kb,kernel_params_kb,features_kb] = train_kernel_features(X,params,samples_idx,T1_idx,T2_idx,W,R); % collect the admm features [weak_learners_admm(i_w).kernels,weak_learners_admm(i_w).kernel_params,... weak_learners_admm(i_w).reg_tree,... weak_learners_admm(i_w).ctx_list] = train_kernel_boost_ctx(X,params,... features_admm,samples_idx,T1_idx,T2_idx,W,R); % weak_learners_admm(i_w).ctx_lf_list = ctx_lf_list; % this version simply ignores the ctx_list assigned by the previous % method % weak_learners_ctx(i_w).ctx_list = ctx_list; cached_responses_admm = evaluate_weak_learners_ctx(X,params,features_admm,samples_idx,weak_learners_admm(i_w)); weak_learners_admm(i_w).alpha = search_alpha(current_response_admm,cached_responses_admm,labels,params); current_response_admm = current_response_admm + weak_learners_admm(i_w).alpha * cached_responses_admm; W = compute_wi(current_response_admm); R = compute_ri(current_response_admm); train_scores(i_w,:) = weak_learner_scores(current_response_admm,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_admm'); save([params.codename '_train_scores_sav.mat'],'train_scores'); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_context_v9.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v9.m
16,448
utf_8
320fda1a607dfc3e1e2024beb5ebcb0f
% use the mask distance as well as the main branch distance % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx1] = train_boost_context_v9(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; W_ctx1 = W; R_ctx1 = R; n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); tol_z_wgt = params.tol_z_wgt; fig_thres = params.fig_thres; for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > tol_z_wgt) = tol_z_wgt; wgt_img = wgt_img/ tol_z_wgt; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; % W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; ctx_ftrs1_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; save([params.codename '_weak_learners_sav.mat'],'weak_learners','weak_learners_ctx1'); end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; save([params.codename '_train_scores_sav_w.mat'],'train_scores_w','train_scores_ctx1w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save([params.codename '_MR_sav.mat'],'MR_img','MR_img_ctx1'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx1'); end if(i_w > 0) % collect th colour value of the sample data wltree = weak_learners(i_w).reg_tree; for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); mask_img = data.train.masks.X{i_img}; [mask_x,mask_y] = find(mask_img > 0); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),[mask_x,mask_y]); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); n_leaf = 0; ctx_c1 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); end end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); known_mb = score_image > fig_thres; known_mb = (bwdist(~mask_img) > 10) .* known_mb; if(sum(known_mb(:))) dist_ctx_map = bwdist(known_mb); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,end + 1) = 5000; end ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_LTM_validation_3D_v2.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_LTM_validation_3D_v2.m
8,398
utf_8
8b676a7e266f354890aae827c06525c1
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function weak_learners = train_LTM_validation_3D_v2(params,features,wgt,samples_idx) % Train a KernelBoost classifier on the given samples % the classifier combine the added discriptor % allows an additional weight, i.e. assign the weight according to the % number of pixels consisted in the seed % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); labels = samples_idx(:,5); labels = (labels + 3) / 2; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); features1 = features(T2_idx,:); features1 = single(features1); fprintf(' Training classification tree on learned features...\n'); t_tr = tic; weak_learners(i_w).tree = forestTrain(features1,labels(T2_idx),'maxDepth',params.wl_depth,'H',2,'dWts',wgt); weak_learners(i_w).alpha = 1 / params.wl_no; time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end % % % % current_response = zeros(samples_no,1); % % [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); % % W = compute_wi(current_response); % % W = W .* wgt; % % % R = compute_ri(current_response); % % train_scores = zeros(params.wl_no,4); % % for i_w = 1:params.wl_no % t_wl = tic; % fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % % % Indexes of the two training subparts % T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); % T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); % [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); % s_T1 = samples_idx(wr_idxs,1:3); % s_T2 = samples_idx(T2_idx,1:3); % % features = cell(params.ch_no,1); % kernels = cell(params.ch_no,1); % kernel_params = cell(params.ch_no,1); % % for i_ch = 1:params.ch_no % ch = params.ch_list{i_ch}; % fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); % % % X = import_3D_data_v2(fn.train.imgs.X(1,:)); % % % % % % % for i_img = 1 : length(data.train.(ch).X) % % % % X{i_img,1} = sum(data.train.(ch).X{i_img},3); % % % % end % % % %X = data.train.(ch).X(:,data.train.(ch).idxs); % X_idxs = 1; % % sub_ch_no = data.train.(ch).sub_ch_no; % % sub_ch_no = 1; % % features{i_ch} = cell(sub_ch_no,1); % kernels{i_ch} = cell(sub_ch_no,1); % kernel_params{i_ch} = cell(sub_ch_no,1); % % % Learn the filters % fprintf(' Learning filters on the sub-channels\n'); % for i_s = 1:sub_ch_no % t_sch = tic; % fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); % [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); % sch_time = toc(t_sch); % fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); % % t_ev = tic; % fprintf(' Evaluating the filters learned on the subchannel\n'); % features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); % ev_time = toc(t_ev); % fprintf(' Evaluation completed in %f seconds\n',ev_time); % end % end % % fprintf(' Merging features and kernels...\n'); % [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); % fprintf(' Done!\n'); % % % add the histogram discriptor % % ftrs1 = ftrs(T2_idx,:); % % features = [features,ftrs1]; % % % fprintf(' Training regression tree on learned features...\n'); % t_tr = tic; % reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % fprintf(' Removing useless kernels...\n'); % [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree... % ] = remove_useless_filters_ftrs(reg_tree,kernels,kernel_params); % % % t_ev = tic; % fprintf(' Evaluating the learned kernels on the whole training set...\n'); % features = zeros(length(labels),length(weak_learners(i_w).kernels)); % for i_ch = 1:params.ch_no % ch = params.ch_list{i_ch}; % sub_ch_no = data.train.(ch).sub_ch_no; % % sub_ch_no = 1; % % % X = data.train.(ch).X(:,data.train.(ch).idxs); % % X = expand_img_v3(data.train.(ch).X,params); % % for i_s = 1:sub_ch_no % idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); % % idxs = 1; % % if (~isempty(idxs)) % features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); % end % end % end % ev_time = toc(t_ev); % fprintf(' Evaluation completed in %f seconds\n',ev_time); % % % % add the hd feature % % % features = [features,ftrs]; % % fprintf(' Performing prediction on the whole training set...\n'); % t_pr = tic; % cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); % time_pr = toc(t_pr); % fprintf(' Prediction finished, took %f seconds\n',time_pr); % clear features; % % fprintf(' Finding alpha through line search...\n'); % t_alp = tic; % alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); % time_alp = toc(t_alp); % fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); % alpha = alpha * params.shrinkage_factor; % % current_response = current_response + alpha*cached_responses; % % W = compute_wi(current_response); % % % incorperate the weight % W = W .* wgt; % % R = compute_ri(current_response); % % weak_learners(i_w).alpha = alpha; % % MR = sum((current_response>0)~=(labels>0))/length(labels); % fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); % % WMR = sum(((current_response>0)~=(labels>0)) .* wgt )/sum(wgt); % fprintf(' Weighted Misclassif rate: %.2f | Loss: %f\n',100*WMR,compute_loss(current_response)); % % train_scores(i_w,1) = 100*MR; % train_scores(i_w,2) = compute_loss(current_response); % train_scores(i_w,3) = alpha; % train_scores(i_w,4) = 100 * WMR; % % % wl_time = toc(t_wl); % fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); % end % % clf; % figure(1); % plot(1:params.wl_no,train_scores(:,1),'b') % legend('MR'); % saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); % figure(2); % plot(1:params.wl_no,train_scores(:,2),'g'); % legend('loss'); % saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); % figure(3); % plot(1:params.wl_no,train_scores(:,3),'r'); % legend('alpha'); % saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); % % figure(4); % plot(1:params.wl_no,train_scores(:,4),'r'); % legend('WMR'); % saveas(gcf,fullfile(params.results_dir,'Weighted_MR_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_boost_context_v5.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_context_v5.m
28,296
utf_8
7ce667f2a31b5dc8ea96e68e951bb4b3
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_context_v5(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); % weak_learners(params.wl_no).alpha = 0; % % weak_learners_ctx1(params.wl_no).alpha = 0; % % weak_learners_ctx2(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); current_response_ctx1 = current_response; current_response_ctx2 = current_response; W_ctx1 = W; R_ctx1 = R; W_ctx2 = W; R_ctx2 = R; %train_scores = zeros(params.wl_no,3); %train_scores_ctx1 = zeros(params.wl_no,3); %train_scores_ctx2 = zeros(params.wl_no,3); n_km = 30; % assign the weight for each individual data point wgt_samp = zeros(size(samples_idx,1),1); for i_img = 1 : length(data.train.gts.X) gt_img = data.train.gts.X{i_img}; dist_gt = bwdist(gt_img); idx_img = (samples_idx(:,1) == i_img); samp_idx_img_2D = samples_idx(idx_img,2:3); samp_idx_img_1D = sub2ind(size(gt_img),samp_idx_img_2D(:,1),samp_idx_img_2D(:,2)); wgt_img = dist_gt(samp_idx_img_1D); wgt_img(wgt_img > 7) = 7; wgt_img = wgt_img/ 7; wgt_img(dist_gt(samp_idx_img_1D) < 0.3) = 1; wgt_samp(idx_img) = wgt_img; end W = W .* wgt_samp; W_ctx1 = W_ctx1 .* wgt_samp; W_ctx2 = W_ctx2 .* wgt_samp; for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); if(i_w > 1) % from this step, the context featuers are added for individual image. %save('tmp_ftrs.mat'); nf = size(features,1); ncf_g = size(ctx_ftrs1,2); ctx_glb_all = zeros(size(samples_idx,1),ncf_g); ctx_loc_all = zeros(size(samples_idx,1),n_ftrs2); % save('tmp_ftrs.mat'); for i_img = 1 : size(data.train.(ch).X,1) ctx_glb_all(samples_idx(:,1) == i_img,:) = ctx_ftrs1_w{i_w - 1,i_img}; if(isempty(ctx_ftrs2_w{i_w - 1,i_img})) save('empty_flag_sav.mat', ctx_ftrs2_w); %ctx_loc_all(samples_idx(:,1) == i_img,:) = ones(sum(samples_idx(:,1) == i_img),n_km) * 5000; else ctx_loc_all(samples_idx(:,1) == i_img,:) = ctx_ftrs2_w{i_w - 1,i_img}; end ctx_ftrs1_w{i_w - 1,i_img} = []; ctx_ftrs2_w{i_w - 1,i_img} = []; end ctx_glb = ctx_glb_all(T2_idx,:); ctx_loc = ctx_loc_all(T2_idx,:); fprintf(' Training regression tree on learned features combined with context 1...\n'); t_tr = tic; if(isfield(params,'ctx1_tree_depth')) tree_d_g = params.ctx1_tree_depth; else tree_d_g = params.tree_depth; end reg_tree_g = LDARegStumpTrain(single([features,... ctx_glb]),R_ctx1(T2_idx),W_ctx1(T2_idx)... /sum(W_ctx1(T2_idx)),uint32(tree_d_g)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); % t_tr = tic; % reg_tree_l = LDARegStumpTrain(single([features,ctx_loc]),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % for i_img = 1 : size(data.train.(ch).X,1) % % T2_img = samples_idx(T2_idx,1) == i_img; % % ctx_loc_img = ctx_loc(T2_img,:); % % ctx_glb_img = ctx_glb(T2_img,:); % % fprintf(' Training regression tree %d on learned features combined with context 1 2...\n',i_img); % % t_tr = tic; % reg_tree_l{i_img} = LDARegStumpTrain(single([features(T2_img,:),... % ctx_glb_img,ctx_loc_img]),R(T2_idx(T2_img)),W(T2_idx(T2_img)) ... % /sum(W(T2_idx((T2_img)))),... % uint32(params.tree_depth)); % time_tr = toc(t_tr); % fprintf(' Done! (took %f seconds)\n',time_tr); % % end fprintf(' Training regression tree on learned features combined with context 1 2...\n'); t_tr = tic; if(isfield(params,'ctx2_tree_depth')) tree_d_l = params.ctx2_tree_depth; else tree_d_l = params.tree_depth; end reg_tree_l = LDARegStumpTrain(single([features,... ctx_glb,ctx_loc]),R_ctx2(T2_idx),W_ctx2(T2_idx) ... /sum(W_ctx2(T2_idx)),uint32(tree_d_l)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); end fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree]... = remove_useless_filters(reg_tree,kernels,kernel_params); if(i_w > 1) [weak_learners_ctx1(i_w).kernels,weak_learners_ctx1(i_w).kernel_params,weak_learners_ctx1(i_w).reg_tree,... weak_learners_ctx1(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_g,kernels,kernel_params); [weak_learners_ctx2(i_w).kernels,weak_learners_ctx2(i_w).kernel_params,weak_learners_ctx2(i_w).reg_tree,... weak_learners_ctx2(i_w).ctx_list]... = remove_useless_filters_ctx(reg_tree_l,kernels,kernel_params); ctx2_list = weak_learners_ctx2(i_w).ctx_list; ctx2_list(ctx2_list < ncf_g + 1) = []; ctx2_list = ctx2_list - ncf_g; tmp_kmc = []; tmp_ltree = []; for i_km = 1 : length(ctx2_list) tmp_kmc(i_km,:) = kmc_ctx_c2(ctx2_list(i_km),:); tmp_ltree(i_km) = tleaf_ctx_c2(ctx2_list(i_km)); end weak_learners_ctx2(i_w).kmc = tmp_kmc; weak_learners_ctx2(i_w).ltree = tmp_ltree; % ctx2_list = ctx2_list - ncf_g; weak_learners_ctx2(i_w).ctx2_list = ctx2_list; save('weak_learners_sav.mat','weak_learners_ctx2','weak_learners_ctx1','weak_learners'); end t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; if(i_w > 1) features_ctx1 = ctx_glb_all(:,weak_learners_ctx1(i_w).ctx_list); ctx_glb_all = [ctx_glb_all, ctx_loc_all]; clear ctx_loc_all; features_ctx2 = ctx_glb_all(:,weak_learners_ctx2(i_w).ctx_list); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx1(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners_ctx1(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx1(i_w).kernels(idxs),weak_learners_ctx1(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx1 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx1 = LDARegStumpPredict(... weak_learners_ctx1(i_w).reg_tree,single([features, features_ctx1])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners_ctx2(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),... weak_learners_ctx2(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),... samples_idx(:,1:3),params.sample_size,... weak_learners_ctx2(i_w).kernels(idxs),... weak_learners_ctx2(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing ctx2 prediction on the whole training set...\n'); t_pr = tic; cached_responses_ctx2 = LDARegStumpPredict(... weak_learners_ctx2(i_w).reg_tree,single([features, features_ctx2])); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; clear features_ctx1 features_ctx2 ctx_glb_all; end %clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); if(i_w > 1) fprintf(' Finding alpha 1 through line search...\n'); t_alp = tic; alpha_ctx1 = mexLineSearch(current_response_ctx1,cached_responses_ctx1,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx1,time_alp); fprintf(' Finding alpha 2 through line search...\n'); t_alp = tic; alpha_ctx2 = mexLineSearch(current_response_ctx2,cached_responses_ctx2,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha_ctx2,time_alp); end alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); W = W .* wgt_samp; weak_learners(i_w).alpha = alpha; if(i_w > 1) alpha_ctx1 = alpha_ctx1 * params.shrinkage_factor; current_response_ctx1 = current_response_ctx1 + alpha_ctx1*cached_responses_ctx1; W_ctx1 = compute_wi(current_response_ctx1); R_ctx1 = compute_ri(current_response_ctx1); W_ctx1 = W_ctx1 .* wgt_samp; weak_learners_ctx1(i_w).alpha = alpha_ctx1; alpha_ctx2 = alpha_ctx2 * params.shrinkage_factor; current_response_ctx2 = current_response_ctx2 + alpha_ctx2*cached_responses_ctx2; W_ctx2 = compute_wi(current_response_ctx2); W_ctx2 = W_ctx2 .* wgt_samp; R_ctx2 = compute_ri(current_response_ctx2); weak_learners_ctx2(i_w).alpha = alpha_ctx2; save('weak_learners_sav.mat','weak_learners','weak_learners_ctx1','weak_learners_ctx1') end MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; MR = sum(((current_response>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_w(i_w,1) = 100*MR; if(i_w > 1) MR = sum((current_response_ctx1>0)~=(labels>0))/length(labels); fprintf(' Ctx1 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx1)); train_scores_ctx1(i_w,1) = 100*MR; train_scores_ctx1(i_w,2) = compute_loss(current_response_ctx1); train_scores_ctx1(i_w,3) = alpha_ctx1; MR = sum(((current_response_ctx1>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx1w(i_w,1) = 100*MR; MR = sum((current_response_ctx2>0)~=(labels>0))/length(labels); fprintf(' Ctx2 Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response_ctx2)); train_scores_ctx2(i_w,1) = 100*MR; train_scores_ctx2(i_w,2) = compute_loss(current_response_ctx2); train_scores_ctx2(i_w,3) = alpha_ctx2; MR = sum(((current_response_ctx2>0)~=(labels>0)) .* wgt_samp)/... (length(labels) * mean(wgt_samp)); train_scores_ctx2w(i_w,1) = 100*MR; save('train_scores_sav_w.mat','train_scores_w','train_scores_ctx1w','train_scores_ctx2w'); for i_img = 1 : length(data.train.gts.X) img_idx = find(samples_idx(:,1) == i_img); MR_img(i_w,i_img) = sum((current_response(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); MR_img_ctx1(i_w,i_img) = sum((current_response_ctx1(img_idx)>0)~=(labels(img_idx)>0))/length(labels(img_idx)); end save('MR_sav.mat','MR_img','MR_img_ctx1'); save('train_scores_sav.mat','train_scores','train_scores_ctx1','train_scores_ctx2'); end if(i_w > 0) % collect th colour value of the sample data samp_rgb = zeros(size(samples_idx,1),3); for i_img = 1 : size(data.train.(ch).X,1) X = data.train.(ch).X(i_img,data.train.(ch).idxs); clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); posi_samp = sub2ind(size(I(:,:,1)),samp_idx_img(:,2),samp_idx_img(:,3)); I = reshape(I,[],3); samp_rgb(samples_idx == i_img,:) = I(posi_samp,:); end wltree = weak_learners(i_w).reg_tree; [~,samp_lf] = predict_idx_wl(data,params,samples_idx,weak_learners(i_w)); lf_hist = histc(samp_lf,1:length(wltree)); lf_hist = lf_hist(1:length(wltree)); lf_hist = lf_hist / sum(lf_hist); [lfn,lf_id] = sort(lf_hist,'descend'); lf_id(lfn < 0.3) = []; lfn(lfn < 0.3) = []; % for i_lf = 1 : length(lf_id) % % [~,ctx_km_c{i_lf}] = kmeans(samp_rgb(samp_lf == lf_id(i_lf),:),n_km,'EmptyAction','singleton'); % % end % for i_n = 1 : length(wltree) bkg_ftrs2{i_n} = []; %bkg_idxs{i_n} = []; end for i_img = 1 : size(data.train.(ch).X,1) ch = params.ch_list{i_ch}; X = data.train.(ch).X(i_img,data.train.(ch).idxs); gt_img = data.train.gts.X{i_img}; clear I; for i_b = 1 : 3 I(:,:,i_b) = X{i_b}; end tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); % get to explore individual region recognised by individual leaf % node img_pg = size(leaf_image,1) * size(leaf_image,2); %context_img = zeros(size(leaf_image)); % cxt_idx = 1; n_leaf = 0; ctx_c1 = []; ctx_c2 = []; n_samp_img = sum(leaf_image(:) > 0); for i_n = 1 : length(wltree) if(wltree(i_n).isLeaf) ctx_c1(end + 1) = i_n; n_leaf = n_leaf + 1; nv = wltree(i_n).value; n_idx = find(leaf_image == i_n); if(length(n_idx) / n_samp_img > 0.2) I_2D = reshape(I,[],3); n_idx = downsample(n_idx,200); bkg_ftrs2{i_n} = [ bkg_ftrs2{i_n}; I_2D(n_idx,:)]; end end end % contx_list{i_w,i_img} = ctx_c2; samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); ctx_ftrs1 = zeros(size(samp_idx_img,1),length(ctx_c1)); % ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); for i_ctx = 1 : length(ctx_c1) tStart = tic; lf_ctx = (leaf_image == ctx_c1(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs1(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs1(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % set the pixels on the context as irrelevant to avoid the overfitting issues ctx_ftrs1(ctx_ftrs1 < 0.1) = 5000; mask_img = data.train.masks.X{i_img}; dist_ctx_map = bwdist(~mask_img); ctx_ftrs1(:,end + 1) = dist_ctx_map(samp_idx_img); ctx_ftrs1_w{i_w,i_img} = ctx_ftrs1; end n_km = 30; ctx_c2 = []; clear ftrs_kmc_c2; kmc_ctx_c2 = []; tleaf_ctx_c2 = []; for i_n = 1 : length(wltree) if(~isempty(bkg_ftrs2{i_n})) tStart = tic; [~,ftrs_kmc_tmp] = kmeans(bkg_ftrs2{i_n},n_km,'EmptyAction','singleton'); t1 = toc(tStart); fprintf('Clustering the context ftrs 2 took %d seconds', t1); ctx_c2(end + 1 : end + n_km) = (100 * i_n) + (1:n_km); ftrs_kmc{i_w,i_n} = ftrs_kmc_tmp; kmc_ctx_c2(end + 1: end + n_km,:) = ftrs_kmc_tmp; tleaf_ctx_c2(end + 1: end + n_km,:) = i_n; else ftrs_kmc{i_w,i_n} = {}; end end n_ftrs2 = length(ctx_c2); for i_img = 1 : size(data.train.(ch).X,1) ctx_ftrs2_w{i_w,i_img} = []; X = data.train.(ch).X(i_img,data.train.(ch).idxs); tStart = tic; % to avoid the computational burden, now apply the method only % on some sample points samp_idx_img = samples_idx(samples_idx(:,1) == i_img,:); [score_image,leaf_image] = predict_img_wl_sample(X,params,weak_learners(i_w),samp_idx_img(:,2:3)); t1 = toc(tStart); fprintf( ' Evaluating the image %d took %d seconds \n', i_img, t1); ftrs2_img = zeros(size(leaf_image)); samp_idx_img = samples_idx(samples_idx(:,1) == i_img,2:3); samp_idx_img = sub2ind(size(leaf_image),samp_idx_img(:,1),samp_idx_img(:,2)); ctx_ftrs2 = zeros(size(samp_idx_img,1),length(ctx_c2)); for i_n = 1 : length(wltree) if(~isempty(ftrs_kmc{i_w,i_n})) ftrs2_img_tmp = ftrs_c2_img(X,ftrs_kmc{i_w,i_n}); ftrs2_img = (i_n * 100 + ftrs2_img_tmp); % ftrs2_img = (i_n * 100 + ftrs2_img_tmp) .* (leaf_image == i_n); end end for i_ctx = 1 : length(ctx_c2) tStart = tic; lf_ctx = (ftrs2_img == ctx_c2(i_ctx)); if(sum(lf_ctx(:))) dist_ctx_map = bwdist(lf_ctx); ctx_ftrs2(:,i_ctx) = dist_ctx_map(samp_idx_img); else ctx_ftrs2(:,i_ctx) = 5000; end t1 = toc(tStart); fprintf( ' Calculating the distance map of %d of the image %d took %d seconds \n', i_ctx, i_img, t1); end % ctx_ftrs2(ctx_ftrs2 < 0.1) = 5000; ctx_ftrs2_w{i_w,i_img} = ctx_ftrs2; % clear ctx_ftrs2; end end wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end
github
BII-wushuang/FLLIT-master
train_GB_ctx.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_GB_ctx.m
5,315
utf_8
614ffbd7be504c723ec2cb4499eaf099
% use the mask distance as well as the main branch distance % evaluate the effect of auto context % takes the gradient boost framework % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners,weak_learners_ctx] = train_GB_ctx(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % specfically reserved for testing the effect of unchanged weight when % extracting the features. samples_no = size(samples_idx,1); labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); current_response_ctx = current_response; [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); params.mex_loss_type = mex_loss_type; W = compute_wi(current_response); R = compute_ri(current_response); W_ctx = W; R_ctx = R; % assign the weight for each individual data point wgt_samp = weight_sample_tol(data.train.gts,params,samples_idx); % load the training images X = data.train.imgs.X(:,data.train.imgs.idxs); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); W = W .* wgt_samp; %%%%%%%%%%%%%%%%% train the base line weak learners%%%%%%%%%%%%%%%%% [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,... weak_learners(i_w).reg_tree] = train_kernel_boost(X,params,samples_idx,T1_idx,T2_idx,W,R); cached_responses = evaluate_weak_learners(X,params,samples_idx,weak_learners(i_w)); weak_learners(i_w).alpha = search_alpha(current_response,cached_responses,labels,params); current_response = current_response + weak_learners(i_w).alpha * cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); train_scores(i_w,:) = weak_learner_scores(current_response,labels,wgt_samp,compute_loss); %%%%%%%%%%%%%%%%%%%% complete training the base line weak learners %% end % collect the global features as well as auto context features, be aware % of the global features, they are only prepared for the next round, so % this part should be behind the context and auto context learning ctx_glb_all = collect_global_ctx(X,data.train.masks,... params,weak_learners(params.wl_no),samples_idx); w_invar = 0; i_ch = 1; ch = 'imgs'; sub_ch_no = size(X,2); X_idxs = 1 : sub_ch_no; kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); s_T1 = samples_idx(T1_idx,1:3); % Learn an identify filters for the whole context gb tree fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); if(w_invar) [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,W_init(wr_idxs),i_ch,i_s,ch); else [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression... (params,params.(ch),X(:,i_s),X_idxs,s_T1,R(T1_idx),W(T1_idx),i_ch,i_s,ch); end sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); end for i_w = 1 : params.wl_no_ctx fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); %%%%%%%%%%%% start training the ctx augmented weak learners%%%%%%%%% % load the global context features W_ctx = W_ctx .* wgt_samp; [weak_learners_ctx(i_w).kernels,weak_learners_ctx(i_w).kernel_params,... weak_learners_ctx(i_w).reg_tree, weak_learners_ctx(i_w).ctx_list]... = train_kernel_gb_ctx(X,kernels,kernel_params,params,ctx_glb_all,... samples_idx,W_ctx,R_ctx); cached_responses_ctx = evaluate_weak_learners_ctx(X,params,ctx_glb_all,samples_idx,weak_learners_ctx(i_w)); weak_learners_ctx(i_w).alpha = search_alpha(current_response_ctx,cached_responses_ctx,labels,params); current_response_ctx = current_response_ctx + weak_learners_ctx(i_w).alpha * cached_responses_ctx; W_ctx = compute_wi(current_response_ctx); R_ctx = compute_ri(current_response_ctx); train_scores_ctx(i_w,:) = weak_learner_scores(current_response_ctx,labels,wgt_samp,compute_loss); save([params.codename '_weak_learners_sav.mat'],'weak_learners_ctx','weak_learners'); save([params.codename '_train_scores_sav.mat'],'train_scores','train_scores_ctx'); %%%% complete training the global context feature weak learners wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end end
github
BII-wushuang/FLLIT-master
train_boost_general.m
.m
FLLIT-master/src/KernelBoost-v0.1/train_boost_general.m
6,090
utf_8
9d6c05c3d19de9ecd4d47fac5840fc11
% % samples_idx(:,1) => sample image no % samples_idx(:,2) => sample row % samples_idx(:,3) => sample column % samples_idx(:,4) => sample label (-1/+1) function [weak_learners] = train_boost_general(params,data,samples_idx) % Train a KernelBoost classifier on the given samples % % authors: Carlos Becker, Roberto Rigamonti, CVLab EPFL % e-mail: name <dot> surname <at> epfl <dot> ch % web: http://cvlab.epfl.ch/ % date: February 2014 samples_no = size(samples_idx,1); weak_learners(params.wl_no).alpha = 0; labels = samples_idx(:,4); samples_idx = samples_idx(:,1:3); current_response = zeros(samples_no,1); [compute_wi,compute_ri,compute_loss,compute_indiv_loss,compute_2nd_deriv,mex_loss_type] = select_fncts(params,labels); W = compute_wi(current_response); R = compute_ri(current_response); train_scores = zeros(params.wl_no,3); for i_w = 1:params.wl_no t_wl = tic; fprintf(' Learning WL %d/%d\n',i_w,params.wl_no); % Indexes of the two training subparts T1_idx = sort(randperm(length(labels),params.T1_size),'ascend'); T2_idx = sort(randperm(length(labels),params.T2_size),'ascend'); [wr_idxs,wr_responses,wr_weights] = compute_wr(params,T1_idx,W,R,compute_indiv_loss,compute_2nd_deriv,labels,current_response); s_T1 = samples_idx(wr_idxs,1:3); s_T2 = samples_idx(T2_idx,1:3); features = cell(params.ch_no,1); kernels = cell(params.ch_no,1); kernel_params = cell(params.ch_no,1); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; fprintf(' Learning channel %s (%d/%d)\n',ch,i_ch,params.ch_no); X = data.train.(ch).X(:,data.train.(ch).idxs); X_idxs = data.train.(ch).idxs; sub_ch_no = data.train.(ch).sub_ch_no; features{i_ch} = cell(sub_ch_no,1); kernels{i_ch} = cell(sub_ch_no,1); kernel_params{i_ch} = cell(sub_ch_no,1); % Learn the filters fprintf(' Learning filters on the sub-channels\n'); for i_s = 1:sub_ch_no t_sch = tic; fprintf(' Learning on subchannel %d/%d of channel %s\n',i_s,sub_ch_no,ch); [kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}] = mexMultipleSmoothRegression(params,params.(ch),X(:,i_s),X_idxs,s_T1,wr_responses,wr_weights,i_ch,i_s,ch); sch_time = toc(t_sch); fprintf(' Completed, learned %d filters in %f seconds\n',length(kernels{i_ch}{i_s}),sch_time); t_ev = tic; fprintf(' Evaluating the filters learned on the subchannel\n'); features{i_ch}{i_s} = mexEvaluateKernels(X(:,i_s),s_T2(:,1:3),params.sample_size,kernels{i_ch}{i_s},kernel_params{i_ch}{i_s}); ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); end end fprintf(' Merging features and kernels...\n'); [kernels,kernel_params,features] = merge_features_kernels(kernels,kernel_params,features); fprintf(' Done!\n'); fprintf(' Training regression tree on learned features...\n'); t_tr = tic; reg_tree = LDARegStumpTrain(single(features),R(T2_idx),W(T2_idx)/sum(W(T2_idx)),uint32(params.tree_depth)); time_tr = toc(t_tr); fprintf(' Done! (took %f seconds)\n',time_tr); fprintf(' Removing useless kernels...\n'); [weak_learners(i_w).kernels,weak_learners(i_w).kernel_params,weak_learners(i_w).reg_tree] = remove_useless_filters(reg_tree,kernels,kernel_params); t_ev = tic; fprintf(' Evaluating the learned kernels on the whole training set...\n'); features = zeros(length(labels),length(weak_learners(i_w).kernels)); for i_ch = 1:params.ch_no ch = params.ch_list{i_ch}; sub_ch_no = data.train.(ch).sub_ch_no; X = data.train.(ch).X(:,data.train.(ch).idxs); for i_s = 1:sub_ch_no idxs = find(cellfun(@(x)(x.ch_no==i_ch && x.sub_ch_no==i_s),weak_learners(i_w).kernel_params)); if (~isempty(idxs)) features(:,idxs) = mexEvaluateKernels(X(:,i_s),samples_idx(:,1:3),params.sample_size,weak_learners(i_w).kernels(idxs),weak_learners(i_w).kernel_params(idxs)); end end end ev_time = toc(t_ev); fprintf(' Evaluation completed in %f seconds\n',ev_time); fprintf(' Performing prediction on the whole training set...\n'); t_pr = tic; cached_responses = LDARegStumpPredict(weak_learners(i_w).reg_tree,single(features)); time_pr = toc(t_pr); fprintf(' Prediction finished, took %f seconds\n',time_pr); clear features; fprintf(' Finding alpha through line search...\n'); t_alp = tic; alpha = mexLineSearch(current_response,cached_responses,labels,mex_loss_type); time_alp = toc(t_alp); fprintf(' Good alpha found (alpha=%f), took %f seconds\n',alpha,time_alp); alpha = alpha * params.shrinkage_factor; current_response = current_response + alpha*cached_responses; W = compute_wi(current_response); R = compute_ri(current_response); weak_learners(i_w).alpha = alpha; MR = sum((current_response>0)~=(labels>0))/length(labels); fprintf(' Misclassif rate: %.2f | Loss: %f\n',100*MR,compute_loss(current_response)); train_scores(i_w,1) = 100*MR; train_scores(i_w,2) = compute_loss(current_response); train_scores(i_w,3) = alpha; wl_time = toc(t_wl); fprintf(' Learning WL %d took %f seconds\n------------------------------------------------\n\n',i_w,wl_time); end if(0) clf; figure(1); plot(1:params.wl_no,train_scores(:,1),'b') legend('MR'); saveas(gcf,fullfile(params.results_dir,'MR_train_scores.jpg'),'jpg'); figure(2); plot(1:params.wl_no,train_scores(:,2),'g'); legend('loss'); saveas(gcf,fullfile(params.results_dir,'LOSS_train_scores.jpg'),'jpg'); figure(3); plot(1:params.wl_no,train_scores(:,3),'r'); legend('alpha'); saveas(gcf,fullfile(params.results_dir,'ALPHA_train_scores.jpg'),'jpg'); end end