isimcapr.m

PLS_Toolbox是用于故障检测与诊断方面的matlab工具箱

function newmod = isimcapr(data,model);
%ISIMCAPR Applies IMGSIMCA models to new images
%  This function uses models developed with the IMGSIMCA function
%  to classify areas on a new multivariate image (data). Note that the
%  new image can be of different size (i.e. it can have a different
%  number of pixels than the original multivariate image used to
%  construct the IMGSIMCA model (model)), but it must have the same
%  depth. The output is a structure array (newmod) which contains
%  the new classifications. The fields of the structure are:
%
%          mwaname: name of original multivariate image data
%        modelname: name of the original SIMCA model from IMGSIMCA
%             name: model type, always ISIMCAPR
%             date: date stamp
%             time: time stamp
%             size: size of the image data
%         in_class: array which gives pixels within 95% Q limits for each class
%    closest_class: array which gives closest class for each pixel
%            rqmat: reduced Q values for all classes for all pixels
%          rtsqmat: reduced T^2 values for all classes for all pixels
%
%I/O: newmod = isimcapr(data,model);
%
%See also: IMGPCA, IMGSIMCA, IMGSELCT

%Copyright Eigenvector Research, Inc. 1999-2000
%bmw April 27, 1999
%nbg 11/00 changed help

datadim = size(data);
data = reshape(data,datadim(1)*datadim(2),datadim(3));
[nclass,rtsq,rq] = simcaprd(double(data),model.simcamod,0);
[mr,nr] = size(rtsq);
classclose = ones(datadim(1),datadim(2),nr)*255;
classin = ones(datadim(1),datadim(2),nr+1)*255;
rqmat = zeros(datadim(1),datadim(2),nr);
rtsqmat = rqmat;
for i = 1:nr
  rqmat(:,:,i) = reshape(rq(:,i),datadim(1),datadim(2));
  rtsqmat(:,:,i) = reshape(rtsq(:,i),datadim(1),datadim(2));
end
k = 0;
for i = 1:datadim(2);
  for j = 1:datadim(1);
    k = k+1;
    classclose(j,i,nclass(k)) = 0;
	if ~isempty(find(rq(k,:)<=1))
	  classin(j,i,find(rq(k,:)<=1)) = zeros(size(find(rq(k,:)<=1))); 
    end
	if min(rq(k,:))>1
	  classin(j,i,nr+1) = 0;
	end
  end
end
colormap(hot(256));
for i = 1:nr
  if i == 1
    h1 = figure;
	image(classin(:,:,i)); 
	title('Areas in Class 1 Shown in Black')
    colormap(hot(256));
	h2 = figure;
	image(classclose(:,:,i));
	title('Areas Closest to Class 1 Shown in Black')
	colormap(hot(256))
	h3 = figure;
    imagesc(sqrt(rqmat(:,:,i)));
	title('Q values on Class 1 for Entire Image, 95% Limit = 1')
  else
    figure(h1); image(classin(:,:,i));
	title(sprintf('Areas in Class %g Shown in Black',i));
    figure(h2); image(classclose(:,:,i));
    title(sprintf('Areas Closest to Class %g Shown in Black',i));	
    figure(h3); imagesc(sqrt(rqmat(:,:,i)));
	title(sprintf('Q values on Class %g for Entire Image, 95% Limit = 1',i))
  end
  colormap(hot(256));
  colorbar
  pause
end
figure(h1); image(classin(:,:,nr+1));
title('Areas in Not in Any Class Shown in Black');
% Save the model as a structured array    
newmod = struct('mwaname',inputname(1),'modelname',inputname(2),'name','ISIMCAPR',...
        'date',date,'time',clock,'size',datadim,...
		'in_class',classin,'closest_class',classclose,'rqmat',rqmat,'rtsqmat',rtsqmat);