bzr branch
http://suren.me/webbzr/normxcorr/trunk
31
by Suren A. Chilingaryan
CUDAfication of real-time module |
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function [validx, validy, displx, disply]=RTCorrCode(grid_x,grid_y,Firstimagename,ImageFolder) |
1
by Suren A. Chilingaryan
Initial import |
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% Real time Correlation Code
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%
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% Written by Chris
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RTselection = menu(sprintf('End processing by end.txt or by last image?'),... |
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'Stop with end.txt','Stop with image check','Exit'); |
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if RTselection==1 |
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end
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if RTselection==2 |
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end
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if RTselection==3 |
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return
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end
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% Filename
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if exist('Firstimagename')==0 |
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[Firstimagename ImageFolder]=uigetfile('*.tif','Open First Image'); |
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end
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
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if ~isempty(Firstimagename) |
1
by Suren A. Chilingaryan
Initial import |
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% Get the number of image name
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letters=isletter(Firstimagename); |
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Pointposition=findstr(Firstimagename,'.'); |
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Firstimagenamesize=size(Firstimagename); |
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counter=Pointposition-1; |
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counterpos=1; |
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letterstest=0; |
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while letterstest==0 |
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letterstest=letters(counter); |
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if letterstest==1 |
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break
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end
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Numberpos(counterpos)=counter; |
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counter=counter-1; |
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counterpos=counterpos+1; |
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if counter==0 |
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break
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end
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end
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Filename_first = Firstimagename(1:min(Numberpos)-1); |
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Firstfilenumber=Firstimagename(min(Numberpos):max(Numberpos)); |
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Lastname_first = Firstimagename(max(Numberpos)+1:Firstimagenamesize(1,2)); |
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Firstfilenumbersize=size(Firstfilenumber); |
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onemore=10^(Firstfilenumbersize(1,2)); |
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filenamelist(1,:)=Firstimagename; |
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h=figure; |
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if exist('grid_x')==0 |
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fpstest=1; |
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Filelist=[Firstimagename;Firstimagename]; |
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while fpstest==1 |
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[grid_x,grid_y]=grid_generator(Firstimagename,ImageFolder); |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
60 |
[processingtime]=fpstestfunc(grid_x,grid_y,Filelist,ImageFolder); |
1
by Suren A. Chilingaryan
Initial import |
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fpstest = menu(sprintf(['Processing the selected grid will allow ' , num2str(1/processingtime),' frames per second' ]),'Try again','Use the grid'); |
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if fpstest==1 |
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clear grid_x; clear grid_y; |
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end
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end
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end
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Firstfilenumber=str2num(Firstfilenumber); |
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u=1+onemore+Firstfilenumber; |
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ustr=num2str(u); |
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filenamelist(2,:)=[Filename_first ustr(2:Firstfilenumbersize(1,2)+1) Lastname_first]; |
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numberofimages=2; |
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counter=1; |
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input_points_x=grid_x; |
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input_points_y=grid_y; |
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base_points_x=grid_x; |
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base_points_y=grid_y; |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
80 |
base = uint8(mean(double(imread([ImageFolder, filenamelist(1,:)])),3)); % read in the base image ( which is always image number one. You might want to change that to improve correlation results in case the light conditions are changing during the experiment |
1
by Suren A. Chilingaryan
Initial import |
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numberofmarkers=max(size(grid_x))*min(size(grid_x)); |
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validx(:,1)=reshape(grid_x,[],1); |
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displx=zeros(numberofmarkers,1); |
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validy(:,1)=reshape(grid_y,[],1); |
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disply=zeros(numberofmarkers,1); |
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tic
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
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while exist([ImageFolder, 'end.txt'],'file') ==0; |
1
by Suren A. Chilingaryan
Initial import |
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pause(0.01); |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
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if exist([ImageFolder, filenamelist((counter+1),:)],'file') ==2; |
1
by Suren A. Chilingaryan
Initial import |
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warning(['# Processed Images: ', num2str(numberofimages-1),'; # markers:',num2str(numberofmarkers), '; Processing Image: ',filenamelist(counter+1,:)]) % plot a title onto the image |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
94 |
input = mean(double(imread([ImageFolder, filenamelist((counter+1),:)])),3); % read in the image which has to be correlated |
1
by Suren A. Chilingaryan
Initial import |
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input_points_for(:,1)=reshape(input_points_x,[],1); % we reshape the input points to one row of values since this is the shape cpcorr will accept |
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input_points_for(:,2)=reshape(input_points_y,[],1); |
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base_points_for(:,1)=reshape(base_points_x,[],1); |
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base_points_for(:,2)=reshape(base_points_y,[],1); |
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input_correl(:,:)=cpcorr(input_points_for, base_points_for, input, base); % here we go and give all the markers and images to process to cpcorr.m which ic a function provided by the matlab image processing toolbox |
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input_correl_x=input_correl(:,1); % the results we get from cpcorr for the x-direction |
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input_correl_y=input_correl(:,2); % the results we get from cpcorr for the y-direction |
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validx(:,counter+1)=input_correl_x; % lets save the data |
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savelinex=input_correl_x'; |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
106 |
dlmwrite([ImageFolder, 'resultsimcorrx.txt'], savelinex , 'delimiter', '\t', '-append'); % Here we save the result from each image; if you are desperately want to run this function with e.g. matlab 6.5 then you should comment this line out. If you do that the data will be saved at the end of the correlation step - good luck ;-) |
1
by Suren A. Chilingaryan
Initial import |
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validy(:,counter+1)=input_correl_y; |
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saveliney=input_correl_y'; |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
110 |
dlmwrite([ImageFolder, 'resultsimcorry.txt'], saveliney , 'delimiter', '\t', '-append'); |
1
by Suren A. Chilingaryan
Initial import |
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base_points_x=grid_x; |
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base_points_y=grid_y; |
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input_points_x=input_correl_x; |
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input_points_y=input_correl_y; |
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subplot(2,2,1) |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
118 |
imshow([ImageFolder, filenamelist(counter+1,:)]) % update image |
1
by Suren A. Chilingaryan
Initial import |
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hold on |
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plot(grid_x,grid_y,'g+') % plot start position of raster |
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plot(input_correl_x,input_correl_y,'r+') % plot actual postition of raster |
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hold off |
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drawnow
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displx(:,counter+1)=validx(:,counter+1)-validx(:,1); |
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disply(:,counter+1)=validy(:,counter+1)-validy(:,1); |
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subplot(2,2,2) |
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xdata=validx(:,counter+1); |
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ydata=displx(:,counter+1); |
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if counter==1 |
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x(1)=0 |
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x(2)=0 |
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end
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[x,resnormx,residual,exitflagx,output] = lsqcurvefit(@linearfit, [x(1) x(2)], xdata, ydata); |
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plot(xdata,ydata,'.'); |
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hold on; |
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ydatafit=x(1)*xdata+x(2); |
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plot(xdata,ydatafit,'r'); |
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hold off |
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xlabel('x-pos [pixel]') |
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ylabel('x-displ [pixel]') |
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title('x displ. versus x pos. in [pixel]') |
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slopex(counter,:)=[i x(1) x(2)]; |
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subplot(2,2,4) |
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xdata=validy(:,counter+1); |
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ydata=disply(:,counter+1); |
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if counter==1 |
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y(1)=0 |
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y(2)=0 |
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end
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[y,resnormx,residual,exitflagx,output] = lsqcurvefit(@linearfit, [y(1) y(2)], xdata, ydata); |
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plot(xdata,ydata,'.g'); |
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hold on; |
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ydatafit=y(1)*xdata+y(2); |
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plot(xdata,ydatafit,'r'); |
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hold off |
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xlabel('y-pos [pixel]') |
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ylabel('y-displ [pixel]') |
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title('y displ. versus y pos. in [pixel]') |
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slopey(counter,:)=[i y(1) y(2)]; |
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subplot(2,2,3) |
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plot(slopex(:,2),'-b') |
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hold on |
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plot(slopey(:,2),'-g') |
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hold off |
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xlabel('Image # [ ]') |
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ylabel('x- and y-strain [ ]') |
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title('Strain in x and y direction versus Image #') |
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counter=counter+1; |
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u=1+u; |
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ustr=num2str(u); |
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filenamelist(counter+1,:)=[Filename_first ustr(2:Firstfilenumbersize(1,2)+1) Lastname_first]; |
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[numberofmarkers numberofimages]=size(validx); |
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if RTselection==2 |
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
183 |
if exist([ImageFolder, filenamelist((counter+1),:)],'file') ==0; |
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save ([ImageFolder, 'validx.dat'], 'validx', '-ascii', '-tabs'); |
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save ([ImageFolder, 'validy.dat'], 'validy', '-ascii', '-tabs'); |
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1
by Suren A. Chilingaryan
Initial import |
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warning('Last image detected, RTCorrCode stopped') |
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return
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end
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end
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subplot(2,2,1),title(['# Processed Images: ', num2str(numberofimages-1),'; fps: ', num2str((numberofimages-1)/toc),'; # markers:',num2str(numberofmarkers), '; Waiting for Image: ',filenamelist(counter+1,:)]) % plot a title onto the image |
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end
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end
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
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save ([ImageFolder, 'validx.dat'], 'validx', '-ascii', '-tabs') |
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save ([ImageFolder, 'validy.dat'], 'validy', '-ascii', '-tabs') |
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1
by Suren A. Chilingaryan
Initial import |
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msgboxwicon=msgbox('end.txt file detected, RTCorrCode stopped','Processing stopped!') |
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warning('end.txt file detected, RTCorrCode stopped') |
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end
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%----------------------------------
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%
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
206 |
function [processingtime]=fpstestfunc(grid_x,grid_y,filenamelist, ImageFolder) |
1
by Suren A. Chilingaryan
Initial import |
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tic; |
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input_points_x=grid_x; |
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base_points_x=grid_x; |
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input_points_y=grid_y; |
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base_points_y=grid_y; |
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% [row,col]=size(base_points_x); % this will determine the number of rasterpoints we have to run through
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% [r,c]=size(filenamelist); % this will determine the number of images we have to loop through
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31
by Suren A. Chilingaryan
CUDAfication of real-time module |
218 |
base = uint8(mean(double(imread([ImageFolder, filenamelist(1,:)])),3)); % read in the base image ( which is always image number one. You might want to change that to improve correlation results in case the light conditions are changing during the experiment |
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input = uint8(mean(double(imread([ImageFolder, filenamelist(2,:)])),3)); % read in the image which has to be correlated |
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1
by Suren A. Chilingaryan
Initial import |
220 |
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input_points_for(:,1)=reshape(input_points_x,[],1); % we reshape the input points to one row of values since this is the shape cpcorr will accept |
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input_points_for(:,2)=reshape(input_points_y,[],1); |
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base_points_for(:,1)=reshape(base_points_x,[],1); |
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base_points_for(:,2)=reshape(base_points_y,[],1); |
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input_correl(:,:)=cpcorr(input_points_for, base_points_for, input, base); % here we go and give all the markers and images to process to cpcorr.m which ic a function provided by the matlab image processing toolbox |
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input_correl_x=input_correl(:,1); % the results we get from cpcorr for the x-direction |
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input_correl_y=input_correl(:,2); % the results we get from cpcorr for the y-direction |
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processingtime=toc; |