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  • Committer: Suren A. Chilingaryan
  • Date: 2010-04-23 00:02:19 UTC
  • Revision ID: csa@dside.dyndns.org-20100423000219-xagi3lp4su6g1y8e
Tags: single_gpu
CUDAfication of real-time module

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RTCorrCode_orig.m
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function [validx, validy, displx, disply]=RTCorrCode(grid_x,grid_y,straindir,Firstimagename)
 
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function [validx, validy, displx, disply]=RTCorrCode(grid_x,grid_y,Firstimagename,ImageFolder)
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% Real time Correlation Code
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%
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if exist('Firstimagename')==0
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    [Firstimagename ImageFolder]=uigetfile('*.tif','Open First Image');
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    if Firstimagename~~[]
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        cd(ImageFolder);
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    end
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end
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if Firstimagename~~[]
 
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if ~isempty(Firstimagename)
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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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    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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        [processingtime]=fpstestfunc(grid_x,grid_y,Filelist);
 
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        [processingtime]=fpstestfunc(grid_x,grid_y,Filelist,ImageFolder);
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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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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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base = uint8(mean(double(imread(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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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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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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disply=zeros(numberofmarkers,1);
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tic
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while exist('end.txt','file') ==0;
 
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while exist([ImageFolder, 'end.txt'],'file') ==0;
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    pause(0.01);
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    if exist(filenamelist((counter+1),:),'file') ==2;
 
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    if exist([ImageFolder, filenamelist((counter+1),:)],'file') ==2;
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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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        input = mean(double(imread(filenamelist((counter+1),:))),3);       % read in the image which has to be correlated
 
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        input = mean(double(imread([ImageFolder, filenamelist((counter+1),:)])),3);       % read in the image which has to be correlated
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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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        validx(:,counter+1)=input_correl_x;                                                     % lets save the data
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        savelinex=input_correl_x';
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        dlmwrite('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 ;-)
 
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        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 ;-)
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        validy(:,counter+1)=input_correl_y;
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        saveliney=input_correl_y';
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        dlmwrite('resultsimcorry.txt', saveliney , 'delimiter', '\t', '-append');
 
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        dlmwrite([ImageFolder, 'resultsimcorry.txt'], saveliney , 'delimiter', '\t', '-append');
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        base_points_x=grid_x;
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        base_points_y=grid_y;
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        input_points_y=input_correl_y;
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        subplot(2,2,1)
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        imshow(filenamelist(counter+1,:))                     % update image
 
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        imshow([ImageFolder, filenamelist(counter+1,:)])                     % update image
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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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        [numberofmarkers numberofimages]=size(validx);
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        if RTselection==2
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            if exist(filenamelist((counter+1),:),'file') ==0;
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                save validx.dat validx -ascii -tabs
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                save validy.dat validy -ascii -tabs
 
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            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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                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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end
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save validx.dat validx -ascii -tabs
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save validy.dat validy -ascii -tabs
 
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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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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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function [processingtime]=fpstestfunc(grid_x,grid_y,filenamelist)
 
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function [processingtime]=fpstestfunc(grid_x,grid_y,filenamelist, ImageFolder)
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tic;
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input_points_x=grid_x;
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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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base = uint8(mean(double(imread(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(filenamelist(2,:))),3));       % read in the image which has to be correlated
 
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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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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);